{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pyFSPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Showcase how to get an SSP template using pyFSPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook will show you how you get an SSP template using the python binding (*pyFSPS*) to Charly Conroy's *FSPS* package.\n",
    "For more information about *pyFSPS* see https://dfm.io/python-fsps/current/ for more information about *FSPS* see https://github.com/cconroy20/fsps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: In order to make this notebook work please first install *pyFSPS* following the installation guide here: https://dfm.io/python-fsps/current/installation/\n",
    "\n",
    "In particular, you will need to set the `SPS_HOME` environment variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since switching between different supported stellar isochrone and spectral libraries in *pyFSPS* requires (re-) installing *pyFSPS* with specific compiler flags **we do not add a dependence onto *pyFSPS*** and leave the installation of it to the user. See here https://dfm.io/python-fsps/current/installation/ for information on the install process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently Rubix supports to run *pyFSPS* to create an SSP template from scratch -- use the config option `source=rerun_from_scratch`. But note, this is mainly a wrapper around the *pyFSPS* `StellarPopulation()` and `get_spectrum()` functions. If you use the `get_ssp_template()` function from *Rubix* to create the ssp template via *pyFSPS* it will only pass default parameters (`add_neb_emission=True, imf_type=2, zmet=None, tage=0.0, peraa=True`) over to pyFSPS.\n",
    "\n",
    "While its possible to pass all relevant function parameters to *pyFSPS* through `rubix.spectra.ssp.fsps_grid.retrieve_ssp_data_from_fsps()` or `.write_fsps_data_to_disk()` we recommend to run *pyFSPS* separate from *Rubix* if you want full control over the process.\n",
    "\n",
    "Since *pyFSPS* runs can take quite some time, this will silently save the ssp template to a *hdf5* file with filename specified under the config entry `filename`. Additionally we support to load pre-existing templates created via *FSPS* using the config option `source=load_from_file` via our standard `HDF5SSPGrid` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-10 17:14:09,206 - rubix - INFO - \n",
      "   ___  __  _____  _____  __\n",
      "  / _ \\/ / / / _ )/  _/ |/_/\n",
      " / , _/ /_/ / _  |/ /_>  <\n",
      "/_/|_|\\____/____/___/_/|_|\n",
      "\n",
      "\n",
      "2025-11-10 17:14:09,206 - rubix - INFO - Rubix version: 0.0.post626+g42b4b7505.d20251110\n",
      "2025-11-10 17:14:09,206 - rubix - INFO - JAX version: 0.7.2\n",
      "2025-11-10 17:14:09,275 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n",
      "2025-11-10 17:14:09,276 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HDF5SSPGrid(age=Array([9.9999997e-05, 1.1220184e-04, 1.2589252e-04, 1.4125378e-04,\n",
       "       1.5848933e-04, 1.7782794e-04, 1.9952621e-04, 2.2387206e-04,\n",
       "       2.5118870e-04, 2.8183832e-04, 3.1622776e-04, 3.5481335e-04,\n",
       "       3.9810708e-04, 4.4668370e-04, 5.0118729e-04, 5.6234130e-04,\n",
       "       6.3095725e-04, 7.0794561e-04, 7.9432840e-04, 8.9125102e-04,\n",
       "       1.0000000e-03, 1.1220183e-03, 1.2589252e-03, 1.4125379e-03,\n",
       "       1.5848933e-03, 1.7782794e-03, 1.9952620e-03, 2.2387207e-03,\n",
       "       2.5118869e-03, 2.8183833e-03, 3.1622776e-03, 3.5481334e-03,\n",
       "       3.9810711e-03, 4.4668368e-03, 5.0118729e-03, 5.6234132e-03,\n",
       "       6.3095726e-03, 7.0794565e-03, 7.9432838e-03, 8.9125102e-03,\n",
       "       9.9999998e-03, 1.1220183e-02, 1.2589254e-02, 1.4125375e-02,\n",
       "       1.5848933e-02, 1.7782794e-02, 1.9952621e-02, 2.2387212e-02,\n",
       "       2.5118863e-02, 2.8183833e-02, 3.1622775e-02, 3.5481334e-02,\n",
       "       3.9810721e-02, 4.4668358e-02, 5.0118729e-02, 5.6234132e-02,\n",
       "       6.3095726e-02, 7.0794582e-02, 7.9432823e-02, 8.9125104e-02,\n",
       "       1.0000000e-01, 1.1220185e-01, 1.2589255e-01, 1.4125374e-01,\n",
       "       1.5848932e-01, 1.7782794e-01, 1.9952624e-01, 2.2387213e-01,\n",
       "       2.5118864e-01, 2.8183830e-01, 3.1622776e-01, 3.5481340e-01,\n",
       "       3.9810717e-01, 4.4668359e-01, 5.0118721e-01, 5.6234133e-01,\n",
       "       6.3095737e-01, 7.0794576e-01, 7.9432821e-01, 8.9125091e-01,\n",
       "       1.0000000e+00, 1.1220185e+00, 1.2589254e+00, 1.4125376e+00,\n",
       "       1.5848932e+00, 1.7782794e+00, 1.9952624e+00, 2.2387211e+00,\n",
       "       2.5118864e+00, 2.8183827e+00, 3.1622777e+00, 3.5481341e+00,\n",
       "       3.9810719e+00, 4.4668355e+00, 5.0118723e+00, 5.6234131e+00,\n",
       "       6.3095737e+00, 7.0794582e+00, 7.9432821e+00, 8.9125090e+00,\n",
       "       1.0000000e+01, 1.1220183e+01, 1.2589254e+01, 1.4125375e+01,\n",
       "       1.5848933e+01, 1.7782795e+01, 1.9952621e+01], dtype=float32), metallicity=Array([4.4904351e-05, 1.4200003e-04, 2.5251572e-04, 4.4904352e-04,\n",
       "       7.9852482e-04, 1.4200003e-03, 2.5251573e-03, 4.4904351e-03,\n",
       "       7.9852482e-03, 1.4199999e-02, 2.5251566e-02, 4.4904340e-02],      dtype=float32), wavelength=Array([8.950e+01, 9.250e+01, 9.450e+01, ..., 9.817e+07, 9.908e+07,\n",
       "       1.000e+08], dtype=float32), flux=Array([[[3.69801944e-25, 1.71711785e-25, 1.01008924e-25, ...,\n",
       "         4.20808249e-11, 4.13591869e-11, 4.06485991e-11],\n",
       "        [2.95627621e-25, 1.37270093e-25, 8.07487082e-26, ...,\n",
       "         3.36403162e-11, 3.30634235e-11, 3.24953640e-11],\n",
       "        [3.62052076e-25, 1.68113235e-25, 9.88920961e-26, ...,\n",
       "         4.11989401e-11, 4.04924289e-11, 3.97967319e-11],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         6.87085782e-21, 6.62186151e-21, 6.38153848e-21],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         6.64786358e-21, 6.40694763e-21, 6.17442546e-21],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         6.16116230e-21, 5.93788412e-21, 5.72238539e-21]],\n",
       "\n",
       "       [[2.47674418e-25, 1.15003767e-25, 6.76506097e-26, ...,\n",
       "         2.81835822e-11, 2.77002657e-11, 2.72243512e-11],\n",
       "        [2.70331983e-25, 1.25524459e-25, 7.38393714e-26, ...,\n",
       "         3.07618514e-11, 3.02343220e-11, 2.97148695e-11],\n",
       "        [3.59155428e-25, 1.66768228e-25, 9.81008994e-26, ...,\n",
       "         4.08693253e-11, 4.01684623e-11, 3.94783338e-11],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         8.89014787e-21, 8.56769041e-21, 8.25660749e-21],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         8.48373481e-21, 8.17602604e-21, 7.87917160e-21],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         6.92724666e-21, 6.67610364e-21, 6.43376153e-21]],\n",
       "\n",
       "       [[2.81423712e-25, 1.30674723e-25, 7.68690040e-26, ...,\n",
       "         3.20240119e-11, 3.14748366e-11, 3.09340713e-11],\n",
       "        [2.62237481e-25, 1.21765895e-25, 7.16284100e-26, ...,\n",
       "         2.98407549e-11, 2.93290184e-11, 2.88251211e-11],\n",
       "        [2.73612140e-25, 1.27047540e-25, 7.47353201e-26, ...,\n",
       "         3.11351084e-11, 3.06011778e-11, 3.00754213e-11],\n",
       "        ...,\n",
       "        [1.32808084e-10, 1.46964954e-10, 1.78483950e-10, ...,\n",
       "         8.73207478e-21, 8.41537447e-21, 8.10981686e-21],\n",
       "        [5.43437206e-09, 5.32875122e-09, 5.96376948e-09, ...,\n",
       "         8.36363771e-21, 8.06031204e-21, 7.76765247e-21],\n",
       "        [9.67010383e-09, 9.52066159e-09, 1.06839435e-08, ...,\n",
       "         7.63764698e-21, 7.36066264e-21, 7.09341060e-21]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[3.35264627e-25, 1.55674908e-25, 9.15752880e-26, ...,\n",
       "         3.81507222e-11, 3.74964816e-11, 3.68522574e-11],\n",
       "        [3.28182234e-25, 1.52386307e-25, 8.96407792e-26, ...,\n",
       "         3.73447974e-11, 3.67043756e-11, 3.60737620e-11],\n",
       "        [3.33899035e-25, 1.55040824e-25, 9.12022862e-26, ...,\n",
       "         3.79953291e-11, 3.73437531e-11, 3.67021517e-11],\n",
       "        ...,\n",
       "        [8.59993882e-11, 1.15849191e-10, 1.53091637e-10, ...,\n",
       "         1.75658879e-20, 1.69284419e-20, 1.63133959e-20],\n",
       "        [4.22778722e-11, 5.95679478e-11, 7.97761926e-11, ...,\n",
       "         1.53844511e-20, 1.48262221e-20, 1.42875434e-20],\n",
       "        [2.23467148e-11, 3.41603967e-11, 4.68701189e-11, ...,\n",
       "         1.51603821e-20, 1.46103038e-20, 1.40795349e-20]],\n",
       "\n",
       "       [[3.28365965e-25, 1.52471627e-25, 8.96909643e-26, ...,\n",
       "         3.73657043e-11, 3.67249252e-11, 3.60939577e-11],\n",
       "        [3.06845691e-25, 1.42479015e-25, 8.38128412e-26, ...,\n",
       "         3.49168507e-11, 3.43180658e-11, 3.37284506e-11],\n",
       "        [3.15609887e-25, 1.46548539e-25, 8.62067320e-26, ...,\n",
       "         3.59141536e-11, 3.52982678e-11, 3.46918119e-11],\n",
       "        ...,\n",
       "        [4.62700885e-11, 7.34858702e-11, 1.02663184e-10, ...,\n",
       "         1.95786429e-20, 1.88677474e-20, 1.81815365e-20],\n",
       "        [5.22229378e-11, 7.36018815e-11, 9.85654544e-11, ...,\n",
       "         1.83188566e-20, 1.76538179e-20, 1.70122553e-20],\n",
       "        [3.85684748e-11, 5.60884464e-11, 7.57659144e-11, ...,\n",
       "         1.75572090e-20, 1.69198550e-20, 1.63050046e-20]],\n",
       "\n",
       "       [[3.02441580e-25, 1.40434041e-25, 8.26098899e-26, ...,\n",
       "         3.44156960e-11, 3.38255049e-11, 3.32443517e-11],\n",
       "        [3.15393394e-25, 1.46448021e-25, 8.61475921e-26, ...,\n",
       "         3.58895171e-11, 3.52740545e-11, 3.46680150e-11],\n",
       "        [3.26780774e-25, 1.51735571e-25, 8.92579844e-26, ...,\n",
       "         3.71853208e-11, 3.65476364e-11, 3.59197151e-11],\n",
       "        ...,\n",
       "        [3.19179711e-22, 1.78760657e-21, 5.73130869e-21, ...,\n",
       "         1.93975841e-20, 1.86948069e-20, 1.80162264e-20],\n",
       "        [6.14501020e-11, 8.57527313e-11, 1.14455764e-10, ...,\n",
       "         2.07055004e-20, 1.99535222e-20, 1.92279652e-20],\n",
       "        [1.24949378e-12, 2.30248633e-12, 3.44144765e-12, ...,\n",
       "         2.00280460e-20, 1.93006602e-20, 1.85987619e-20]]], dtype=float32))"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "from rubix.spectra.ssp.factory import get_ssp_template\n",
    "ssp = get_ssp_template(\"FSPS\")\n",
    "ssp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "config = {\n",
    "      \"name\": \"FSPS (Conroy et al. 2009)\",\n",
    "      # more information on how those models are synthesized: https://github.com/cconroy20/fsps\n",
    "      # and https://dfm.io/python-fsps/current/\n",
    "      \"format\": \"fsps\", # Format of the template\n",
    "      \"source\": \"load_from_file\", # the source can be \"load_from_file\" or \"rerun_from_scratch\"\n",
    "      # \"load_from_file\" is the default and loads the template from a pre-existing file in h5 format specified by \"file_name\"\n",
    "      # if that file is not found, it will automatically run fsps and save the output to disk in h5 format under the \"file_name\" given.\n",
    "      # \"rerun_from_scratch\" # note: this is just meant for the case in which you really want to rerun your template library.\n",
    "      # You should be aware that fsps templates will silently be overwritten by this. Use with caution.\n",
    "      \"file_name\": \"fsps.h5\", # File name of the template, stored in templates directory\n",
    "      # Define the Fields in the template and their units\n",
    "      # This is used to convert them to the required units\n",
    "      \"fields\":{ # Fields in the template and their units\n",
    "        # Name defines the name of the key stored in the hdf5 file\n",
    "        \"age\":{\n",
    "          \"name\": \"age\",\n",
    "          \"units\": \"Gyr\", # Age of the template\n",
    "          \"in_log\": True # If the field is stored in log scale\n",
    "          },\n",
    "        \"metallicity\":{\n",
    "          \"name\": \"metallicity\",\n",
    "          \"units\": \"\", # Metallicity of the template\n",
    "          \"in_log\": True # If the field is stored in log scale\n",
    "          },\n",
    "        \"wavelength\":{\n",
    "          \"name\": \"wavelength\",\n",
    "          \"units\": \"Angstrom\", # Wavelength of the template\n",
    "          \"in_log\": False # If the field is stored in log scale\n",
    "          },\n",
    "        \"flux\":{\n",
    "          \"name\": \"flux\",\n",
    "          \"units\": \"Lsun/Angstrom\", # Luminosity of the template as per pyFSPS documentation\n",
    "          \"in_log\": False # If the field is stored in log scale\n",
    "          }\n",
    "        }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "ssp.age.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "ssp.metallicity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5994,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "ssp.wavelength.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 107, 5994)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "ssp.flux.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "import os\n",
    "from rubix.paths import TEMPLATE_PATH\n",
    "os.path.exists(os.path.join(TEMPLATE_PATH, config['file_name']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's plot some of the spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10000.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6VHl5eZKk5s2bq7S0tOGtg4t33qEHCACA+qvXENjNN9+sBQsW6MCBAzpw4IAWLFigW265RVdffbUkae3atTrnnHMC3daoVqPHh/wDAEC9+V0E/corr+jee+/Vz3/+c5WXl1feid2uSZMm6emnn5Yk9e7dW3/7298C29IoRw0QAACB43cASkpK0quvvqqnn35au3fvllS5CGJSUpJrn4EDBwasgahEDRAAAIFTr4UQpcog1L9//0C2BWfgcHhdpgcIAIB68zsAFRYWatasWVqyZImOHDkih9cns7NXCIFlvIp+iD8AANSf3wHo1ltv1fLlyzVx4kS1b99eFuZdhoRzyMtiqVzsjJWgAQCoP78D0CeffKKPPvpII0eODEZ7UAdn4LFbLSqrMHJQBAQAQL35PQ2+RYsWnOcrDJx5x1rV40b+AeDNInrkAV/5HYAee+wxPfTQQyoqKgpGe1AHZ9GzzWrxuAwAAPzn9xDYnDlztGvXLqWlpalLly6KiYnxuH7jxo0BaxyqOfOOMwABgDfvyRIA6uZ3AHKu9ozQcq8BkugBAgCgIfwOQA8//HCd11VUVDSoMaibw9UDZPW4DABO1AABvvO7Bqg2O3bs0IwZM9SpU6dA3B1qUV0D5HkZAAD4r94BqKioSK+//rouuugi9enTR8uXL9e0adMC2Ta4cfUAVc0CI/8AAFB/fg+BrV69Wn/72980f/58ZWRkaNu2bVq6dKkuuuiiYLQPVZw1QDabxeMyAADwn889QHPmzNF5552nn/70p2rRooVWrFihzZs3y2KxqFWrVsFsI1Td42OnBggAgAbzuQdoxowZmjFjhh599FHZbLZgtgm1cNb8OGfBUwMEAED9+dwD9Nhjj2n+/Pnq2rWrZsyYoS1btgSzXfDicE2DpwcIAICG8jkAPfDAA9qxY4f+3//7f8rJydGwYcM0YMAAGWN08uTJYLYRqh4Cszq7gOgBAgCg3vyeBXbJJZfozTffVE5Oju644w4NHjxYl1xyiUaMGKGnnnoqGG2E5FrftXohxPC1BQCASFfvafDJycn69a9/rTVr1igzM1NDhw7VrFmzAtk2uOFcYADOinUQAZ/5HIBuvPFGvfvuuyooKKhxXb9+/fTMM8/o4MGDAW0cqtUMQOFsDQAAkc3nANSjRw898cQTatOmjcaOHau5c+fWCDzeJ0ZF4Dgclf86AxAnPQRQA28LgM98DkAPPfSQNmzYoJ07d+rKK6/U+++/r+7du2vw4MF69NFHtWnTpiA2E66FEFkJGgCABvO7BqhTp06644479Omnn+ro0aOaMWOGtm/frh/84Afq3Lmz7rzzTn3zzTfBaGtUcw552atWgnYwBgYAQL016GSoycnJuu666/TWW2/p6NGjeu2112Sz2bRq1apAtQ9VnENeVgs1QADqQBE04DO/zwVWF5vNptGjR2v06NGBuku4cfUAMQsMAIAG87sH6PDhw5o4caI6dOggu90um83m8eOPmTNn6oILLlBycrLatm2rq6++Wtu3b/e3SVHBeM0CAwAA9ed3D9DkyZO1f/9+Pfjgg2rfvr0slvp/IC9fvlxTpkzRBRdcoPLycv3ud7/TZZddpq1bt6pZs2b1vt+mqEYNED1AAADUm98BaOXKlfr88881cODABj/4okWLPC6/8cYbatu2rTZs2KCLL764wffflFSfDJUABABAQ/kdgNLT013DMYGWm5srSWrZsmWt15eUlKikpMR1OS8vLyjtaIycLzkLIQIA0HB+1wA988wz+u1vf6u9e/cGtCEOh0NTp07VyJEj1bdv31r3mTlzplJTU10/6enpAW1DY+a9EjQdQAAA1J/fPUDXX3+9ioqK1L17dyUmJtZY/fnEiRP1asiUKVO0ZcsWrVy5ss59HnjgAU2bNs11OS8vL2pCkPGaBRasXjgAAKKB3wHomWeeCXgj7rzzTi1cuFArVqxQp06d6twvLi5OcXFxAX/8SFDdA2T1uAwAAPzndwCaNGlSwB7cGKO77rpLCxYs0LJly9S1a9eA3XdTUx2AnJfD2BgAjRKLZAC+8zsA7d+//4zXZ2Rk+HxfU6ZM0T//+U998MEHSk5OVk5OjiQpNTVVCQkJ/jatSXMVQTMLDEAdeFcAfOd3AOrSpcsZ1/6pqKjw+b7mzp0rSRo1apTH9tdff12TJ0/2t2lNWvUssKouIN7pAACoN78DUGZmpsflsrIyZWZm6qmnntLjjz/u131RyOs7Z48PCyECANBwfgegAQMG1Ng2ZMgQdejQQU8++aSuueaagDQMnrynwVMDBMAbNUCA7xp0Nnh3vXr10rp16wJ1d/DioAYIAICA8bsHyHv1ZWOMsrOz9cgjj6hnz54BaxiquQ8VWp3rAIWrMQAANAF+B6DmzZvXKII2xig9PV3vvPNOwBqGau6dPSyECKAuvCsAvvM7AC1dutTjstVqVZs2bdSjRw/Z7X7fHXzgPtzlqgFyhKs1AABEPr8TyyWXXBKMduAM3Aueq4ug+a4HwBNF0IDv/C6CfvPNN/XRRx+5Lk+fPl3NmzfXiBEjtG/fvoA2DpU8eoAszAIDAKCh/A5ATzzxhGuV5lWrVumFF17Q7Nmz1bp1a917770BbyA8OXuAGO0HAKD+/B4Cy8rKUo8ePSRJ77//vn7605/qV7/6lUaOHFljRWcEhnsPUPVCiOFqDQAAkc/vHqCkpCQdP35ckvS///1Pl156qSQpPj5ep0+fDmzrIIkaIAC+OdNpigB48rsH6NJLL9Wtt96qQYMGaceOHbriiiskSd988406d+4c8AaCGiAAvmF5DMB3fvcAvfjiixo+fLiOHj2qd999V61atZIkbdiwQRMmTAh4AyEZtynvVtYBAgCgweq1EOILL7xQY/u9996rjz/+OCCNgifjVvBcvRBiuFoDAEDkC9i5wPbt26eJEycG6u7ghhogAAACK2ABCMHjHnasnAwVQB0oggZ8RwCKAM6wY7W4B6BwtggAgMhGAIoAzs4ei8Ui1kEEUBcmRwC+87kI+rnnnjvj9QcPHmxwY1A79x4gC0NgAAA0mM8B6Omnnz7rPhkZGQ1qDGpXWw8QAQiAN2qAAN/5HID27NkTzHbgDGrvAQpniwAAiGzUAEUAZ2eP1a0HiPwDAED9+RSAnnvuORUXF/t8py+//LLy8/Pr3Sh4qu4BsrhmgVHsCABA/fkUgO69916/As306dN19OjRejcKnpzDXRZJFmqAAABoMJ9qgIwxGj16tOx230qGOCt8YDl7eyzuNUCOM90CAACciU+J5uGHH/brTq+66iq1bNmyXg1CTc4eIKuVWWAAAARCUAIQAsvUUgMEAADqj1lgEcDVA2ShBggAgEAgAEWA6rBj4VxgAOpE/zDgOwJQBDDuPUBV2+gBAgCg/ghAEcBjHSBWQgRQB94WAN/5HYDOtCBidnZ2gxqD2jkDkI1ZYAAABITfAej888/Xpk2bamx/99131b9//0C0CV5cCyFyLjAAAALC7wA0atQofe9739Of//xnSVJhYaEmT56siRMn6ne/+13AGwipwlFzGnwFCQiAF4qgAd/5fDZ4p5deeknjxo3TrbfeqoULFyo7O1tJSUlau3at+vbtG4w2Rj1TyxAY5wIDAKD+/A5AkjR27Fhdc801mjt3rux2u/773/8SfoLI2dtjsYhp8AAABIDfQ2C7du3S8OHDtXDhQn366aeaPn26fvzjH2v69OkqKysLRhujnjPs2NyHwOgBAgCg3vwOQAMHDlTXrl311Vdf6dJLL9Wf/vQnLV26VO+9956GDh0ajDZGPfdp8LaqMTCGwAAAqD+/A9BLL72kd955R82bN3dtGzFihDIzM3X++ecHsm2o4gpAbjVAFEEDAFB/fgegiRMn1ro9OTlZ8+bNa3CDUFP1LDC5FkIkAKEpcDiMjhWUhLsZAKKQ30XQf//73+u8zmKx1BmQUH/O0S6b1SKbxeKxDYhkt7y5Tku3H9V/bhuuIV1ahrs5AKKI3wHonnvu8bhcVlamoqIixcbGKjExkQAUBM4hMAtF0Ghilm4/Kkl6c9U+AhCAkPJ7COzkyZMePwUFBdq+fbsuvPBCvf3228FoY9TzHAKr3MapMAB4s7ASIuCzgJwMtWfPnpo1a1aN3iEERm3T4B2OMDYIQKPE9yLAdwE7G7zdbtehQ4cCdXdwU9s0eIbAAACoP79rgD788EOPy8YYZWdn64UXXtDIkSMD1jBUq54G774SNAEIAID68jsAXX311R6XLRaL2rRpox/84AeaM2dOoNoFN54nQ63cZkxl+LQw6A8AgN/8DkAOik9CzmMavLU68DiMZCP/AKjC9yHAdwGrAULwVJ8M1eLR48NiiAAA1I9PPUDTpk3z+Q6feuqpejcGtXPW+9gs8uoBIgABAFAfPgWgzMxMn+6MepTgcJ8F5pZ/CEAAANSTTwFo6dKlwW4HzsA50uW+ErTEEBgAAPXlcw3Q7t27ZehxCAvXEJjVewgsXC0CACCy+RyAevbsqaNHj7ouX3/99Tp8+HBQGgVPDo9p8JYa2wEAgH98DkDevT8ff/yxCgsLA94g1OTMOVarZw0Qq0EDAFA/TIOPAO4LIVZOha/cThE0AAD143MA8l6DxrkNwec+Db7yX06ICgBAQ/i8ErQxRpMnT1ZcXJwkqbi4WLfddpuaNWvmsd97770X2BbCYxq8VDkUJoehBwhN0oGTRTpVVBbuZgBo4nwOQJMmTfK4fMMNNwS8Maidew2QJFcdENPg0RRd+GeW3QAQfD4HoNdffz2Y7cAZVNcAVV62cUZ4AAAahCLoCGC8h8BcAShsTQLQCFGVCfiOABQBagyBVf3LEBgAAPVDAIoANYbAqn5hZW4AAOqHABQBjGsavFcRNAEIgBveEQDfEYAigDPoWLxqgBgCAwCgfghAEcCZc2xWzwBEB1DTZ4zhnG/wGUXQgO8IQBHAUUcNED1ATd/EeWs1+qnlKqtg2W8ACCQCUARwrQTtmgXmuR1N18rvjmnPsUJtOZgb7qYAQJNCAIoAzi//NdcBIgBFC867BwCBFdYAtGLFCl155ZXq0KGDLBaL3n///XA2p9FyeM0Cs7mKoMPWJAAAIlpYA1BhYaEGDBigF198MZzNaPSqV4KuvOzsDKAHCACA+vH5XGDBMHbsWI0dOzacTQi5vOIyLf32iC7tk6bEWN9efu9p8M4iaGYHRQ8GwAAgsMIagPxVUlKikpIS1+W8vLwwtqZ+Zvzna32yJUdXDeygZ38+yKfb1DUNnvzTtLmv9N3US4BY1RxAqEVUEfTMmTOVmprq+klPTw93k/z2yZYcSdIHmw75fBvvafCuhRD50GjSOLwAEDwRFYAeeOAB5ebmun6ysrLC3SS/JcX53+nmPQ2eIbDo4H50LQyCwQfMFgR8F1FDYHFxcYqLiwt3MxqkY/MEbT+cL6my29+XN6ya0+ArL1ME3bRxfAEgeCKqB6gpSG+Z4Pr9o83ZPt2mxslQWQk6KrjnH77YwxfUUgG+C2sAKigo0KZNm7Rp0yZJ0p49e7Rp0ybt378/nM0KqpbNYl2//++bwz7dpnoWWOVlG0XQUcFwbm8ACJqwDoGtX79e3//+912Xp02bJkmaNGmS3njjjTC1Krjcv6B9deCUT7epexYYH5BNGYcX/qIGCPBdWAPQqFGjoq7L1r3XZt/xIu04nK9z0pLPfBvXLDDOBRZNomkIjL9kAKFGDVCIeQ9rfLDp4Flv4zB1TINnDKxJc/9bYRYYAAQWASjEnN/qe7er7PVZufPYWW9T5zR4eoCaNA4vAAQPASjEnKFlZI/WkqTNB3NVUl5xxtuUV1Texu5VA8TJUJs2Ai4ABA8BKMScn2kdmicoIcYmh5EOnSo+421Kq5JOrL3ycMXYKgNQOQmoSfNYCLGpj4CR9QCEGAEoxNzreTJaJkqSFmw8cMbblJRXBSCbTZIUY6s8bGXUADVp0VQEDQChRgAKMednmtViUUJsZaB57rPvztibU1ru2QNkdwagcnqAmjTyLQAEDQEoxIxbD9A153d0bd97vLDO2zgDkHPoK6aqFqjcQQBqytxrgJgFBgCBRQAKMVdmsVh04/Aurtlg27Lz67xNWY0aoKoeoAq6CJqyaDq6rHodGMRkwHcEoBBzvtE71/S5oEtLSdLS7UfqvI2zCDrONQRWeeMyiqCbNPdFQqOlBijaFkYFED4EoBBz1i07hzR+UjUM9tHX2Souq306fGkdRdDl9AA1adS4w1/8yQC+IwCFmPMLrrMHaFB6c6WlxKmk3KEN+07WepsaRdBVNy6jBqhJcx8WauodI87n19SfJ4DGgwAUYtVF0JUhxmKxaGjXVpIqF0WsjXcAirE7Z4HxadGkcXgBIGgIQCHmmtnjVtORmlB5Ttq6hsBKvIugmQUWFYzH79GRhqLjWQZPlJSKAQFBAAox93WAnJy1PSW1rOtjjKkxDd7OLLCo4D4NPlqGhiiCBhAqBKAQc9TsAFJcTOVhmLtslzYf8BwGcw85cd4rQTMLrEmLpiwQTc8VQONAAAoxVw2Q2yvvnN4uSVe+sNLV4yN5hhzOBRZdPIbAoiQgRMnTBNAIEIBCrHoWmNsQmN3zMKzefdz1u3sYqjkLjI+Lpsx9OChaaoAAIFQIQCHmqOWrfJzd5nH5f1tzXL87F0G0WS2yVQWf6llg9AA1ZeHu9SkNw99XuJ8zgOhBAAqx2nqA4rx6gJZsO+L69l+9CGL1PjFV42fl9AA1ae5hINTB4O+r9uqcP3yiz749HJLHo4cLQKgRgELM2QPkfmoD7yGw7NxiHTh5WlL1zDBn3Y/EqTCiRThDwUMffCNJuuftTSF9XIIQgFAhAIVYbdPgvXuAJGnj/spVoUvKK9cGioupHiZjFlh0aAwdfKFuAkNgAEKFABRi1StBV29zrwHq3qaZJOmzbytPjpp3ulySlBxvd+1TPQuMT4umzDSCdYBYlyfCsBIi4DP72XdBIFV/q6+9B6htcrx2HS3UB5sOqX+n5urYPEGSlJoQ49rHXlUDxCywpi2aVoImZwUIryPgM3qAQqz2HqDqw9AmOc71+2MLtyrvdJkkzwDELLDoEM4iaNfjhudhASDoCEAh5loJuo51gNwDkFS9JpBHAOJcYFEi/PEj1MGLniAAoUIACrHqIujqbe4zwrwD0AdfHZLkNQRWVQRNDVDT5tEDFK42NIIQBgDBQAAKseohsOrU417K0ybJMwBVVF3pHoCcQ2Z1nT0eTYPnqTCadhAxrn+b9vMMOoqgAZ8RgELMtRK0ew+Q2/XNE2NUG/cAlFQ1Iyy/pDzQzUMj0sQzDwCEFbPAQqy2laAHpjfXkM4t1LlVM9fpLrwNymjh+t05Jb6gpFwOh5G1jtug6YiWLEToayBeP8BnBKAQc9TsAJLdZtV/bh8hSR6nHlj7+9HKPlWsNslx6lA1HV6SkuMqe4OMkYrKKpQUx2FsityHg8K3DlBoH4fPbwChwidniNVWA+RucEZLSVKnFglqmxyvtsnxNfaJj7HKbrWo3GGUX1xGAGqi6A2B3+gMBnzGJ2eIVQ+B1X59amKMvnroMsXF1F2eZbFYlBRv16miMhUUl0upQWgows4zAEVHGmrqxd6htCnrlAamNw93M4BGiyLoEKutCNpbamKM4t3O/VUbZx1QXjGF0E1VoxgCC8/DIgCufvGLcDcBaNQIQCFW28lQ6yOpqg4ov7isgS1CYxVdnSHG7b8AEHwEoBBz9gA1dKje2QOUTw9QVCAYAEBgEYBCzFUD1MCp6+1TK4uj958oamiTEAGipTcoWp4ngPAjAIVYbSdDrY/e7VIkSd/m5De0SWikGkUYCHUbGsNzjmS8foDPCEAhVn3ai4YloN7tkiVJXx84xcyZJsqzCLppH+Mm/vQANEIEoBBzfqg1tAdoSJcWio+xat/xIv3h/S1N/gMyGjWGk6GGGucCAxAqBKAQczgq/23oLLDk+Bj98qJukqS31uzXgx8Qgpoaz5OhhqsN/E1FEo4W4DsCUIg5Q0oD848kadql52jmNf1ksUj/WL1ff/poGyGoiYqWIMKfL4BQIQCFWKDWAZIqV4QePzRDf76mvyRp3so9evPLvQ2+XzQO7mG2ocGguKxC7244oGMFJQ1sFRozvgABviMAhZgjCG9Q112QrrtH95QkPfLfrfrnmv0BfwyEXiCHwP686FvdN/8rXffKqobdUZDx8d0wvH6A7whAIVZ9LrDAnrXwjlHddfXADpKk3y3YrNdW7gno/SP03ENPbcE5c/9Jrdl93Kf7WrQlR5K0+2hhvdsQTM6HoQcDQKgQgELMOQ3eGuBXPj7GpqevH6gr+rWTJD26cKv+vS4rsA+CEDO1/FbJ4TD6yUtf6vq/rtbJwtKz3xO5AgA8EIBCzFUE3eCTYdRksVj0wvjzdcuFXSVJ09/9Wq+u2B3wx0FoeEyD90owFW6Xj/sQgCIFOa1hCLqA7whAIVZdBB2c+7daLfr9Fefq5pGVIejxj7fpTwu3yuHgnTGSeX+wuQ+Jrdx5NHiPG7R7BoDwIgCFmCOA0+DrYrVa9OCPztXvrugtSfrbyj2a+q9NKimvCN6DIuA8iqC9ooh7IHrkv1uVV1x2xvsK5t9bIDh7uOjBABAqBKAQc77BW4L8iWSxWPSri7vrmesHym616MOvDunmN9Yp/ywflGg8PIqgHZ7XeRdF5xZVHtfScoeKyyIv6JJ7AoPXEfAdASjEnB9cgZ4FVperB3XUa5MvULNYm7747rh+/tfVKiotD8ljo2E81gHyus57RLPCYWSM0UWzP9OgRxertNwrMUWIaFnwMViYRQf4jgAUYq4eoBA+5sXntNE7vxquls1i9c2hPPV56FMt3xG8uhEEhvtHmXePj/dlo8oQdDivRKfLKrT/RJHn9fX8XAz5Byqf3wBChAAUYibEPUBO/Tql6lcXd3NdnvTaWj363630BjVinrPAvK6rpYOnwnhWDblr7DVAka64rELz12fpSH5xWNtBfgR8RwAKMYerBij0j33D9zrrwh6tXZdf+2KPrnj2c632cTE9hI93T0yNHiBjVOE2LuYdmCIl/0TqB/jsRdt1/3++1s9ebtwrbc9buUfDZy7RvuP+LYgJNEUEoBBz1jiEIwAlxdn1j1uH6bvHx+rxn/RV+9R47T1epJ//dbXu/demsH97hSdzpoUQ6xgCq2v/+hbdR2ogCbX/ba1caXvf8aKz7Blejy3cquzcYj22cGu4mwKEHQEoxFwrQYdxTMJus2rCsM5aNPVi/WJYhiwWaUHmQY3+y3K9tnKPyisis4C2yTnDEFhFjR4gzwAUaZxPhxreBvLx9SutCNwLnV9cpuzc0wG7PyBUCEAhZkKwDpCvUhNi9MRP+un9O0aqf6dU5ZeU69GFW/Wj51dq7Z4T4W5e1DtTEXRtCyOecQisEfy9Ifh8nUUXyOL2wY/9n4bP/Ew5ufQgI7IQgEIsWCdDbYgB6c214I6ReuIn/dQ8MUbf5uTruldW6Zd/X68N+whC4eJRBO11nXcgqnB4BiDv6xvRn9sZMQ0+ND7feSxg91Va1WO8Yd/JgN0nEAoEoBBzrQQd5nZ4s1kt+sWwDC29b5TGD60cFlu89bCunbtK1879Up9+k8PpNELMowaoRhG0574VDuMxLOY9HFbfc8+FfBY8f2IN8vHmHJ/3PXgqsMNW3qEbaOwIQCHmfIsI9krQ9dWiWaxmXtNPi++9WNcPSVeszaoN+07q1/9vg8Y8vVwvLftOOw/ns+BaiNUY8nLUHBIrd6vrKPcOQI3zz80l0v+aGsPru9/PAuySCFwxHAgke7gbEE2MMW5DYOFty9n0aJusP/+0v+677By9/uVe/WP1Pu0+WqjZi7Zr9qLtapMcpzHnttUdo3oovWViuJvbJHmcCuMsNUAVxnjsU+F17oxG/ufmEqlBqL49bIFU5n2+lLMIdIdupB47RC8CUAi5f2g11h4gb21T4jXjh7015fs99OGmQ1r0TY7W7D6uo/klenttlt7dcFDXDu6kcf3aa2jXloq106kYKB7LGp7hbPBS5ZCXe69PeYV3D1Bk/L1Fas9iY3h5w92ESD12iF4EoBByf3to7D1A3pLi7PrFsAz9YliGissqtH7vSb207Dt9ueu43l67X2+v3a/kOLsu6dVGgzu3UKukOA3p3EIdmieEu+kRy/0DxT3wZJ0o0quf7/bY12GMx7CY9zT5SPlzi9TP0MYwqcH/kNvwF5vQg0hGAAoh9w+xxtBlXl/xMTZd2LO1RvZopVW7juuDTYe05NvDOlZQqoVfZ2vh19mufTu1SNDQri01tEtLDenSUt1aN5M10tJfmNR1YourXvxCJwpLPfZ1ePUA1VgTqJG/5M4P0kgtpG0E+cfvMOJdJ1Yf7ncRoYcOUYwAFEIeQ2BNYKTIYrFoRI/WGtGjtRwOo00HTun/th7W5oO5OlFYqm3ZeTpw8rQOnDyo9zYelCQlx9s1ML25BnRqrh5tk9SldTN1bdVMqYkxYX42jZBXAjpVVKr/fp1dI/xIlT0+7qGnRhF0AJt1qqhUdptVSXGBf/uI1ImGjSD/1LoQpjGmzp4h72HS+ij3s+4IaEwIQCHk/u22MXSZB5LVatH5GS10fkYL17aCknJt3HdSa/ec0Nq9J/T1gVPKLy7X5zuP1ViHxGa1KDHGpkGdW+iiHq01KKO5Mlolqk1SXMTUrwSaw2sI7Dfzv9b/bTtc+74Ozw/AigDVAHnfrKi0XAMfXSyrRdo9c1y97rM2zv8fInU168bwN1pbj44xdfdOBaQHyC3/sIYTIg0BKIQ8eoDC14yQSYqz6+Jz2ujic9pIksoqHNqek6/MrFPaciBXe44Xau+xQh3JL1GFwyi/pFwrdhzVih1HXfcRa7OqTXKc2qbEqW1ynNJS4tU+NUHtU+PVKilWLZvFqm1yvFonxdb5IWSM0YGTp5UYa1OrpLiQPPdAKPc6t1dd4Ueq6gEyge8B8g7qznNdOYxUUl6hOLutnvfsOZXfOSoaqTUljWFUt7bw6DBGVlm0aEu2nvm/nR7XBeKUN+5/cxF66FCH3KIyvbB0pzo0T9B1Q9LVLAg9vuHW9J5RI+b+Damp9QD5IsZmVd+OqerbMdVje1FpuQqKy3WsoFRf7jqmL747ph2HC3Qo97RKKxw6eOr0WRdti7FZZLNaZLVY1CopVhktE5WWHK8yh9GGvSd0qGqZ/gHpzXVZnzR1apEgu9Uqu81SdVurYqwW2W2V2+xWi891WkaV9TcVDqPyCqNyh6PyckXl9nKHQ4mxNnVu1UzGVG2rMK6ZW+UVDtfvVotFQ7q0UHyMzWOI4my1MY4grQTt/cHu/ndbUFyuuKT6ByD3D09b1QNFaAdQ0Gr6HA7jc81cWS2Bxvly3vaPjTWuC0QPEOcNbJqOF5ToZy+v0u5jhZKk9zMP6t+3DW/QF57GiAAUQu7vN1GYf+qUGGtXYqxdbVPi1adDim69qJskqbTcoSP5xTqSX6IjeZX/5uQWKzu3WDm5xTpRWKrjhaU6UViisgqjsqrAUHTitLJOeAYmu9WicofRV1mn9FXWqVA/Rb9cPyRdf/5pf4/6irN9u3YYo7Ly6v1r9gDVdwjM83buH7KFJRVqlVSvu5Xk2WPhfByKoKvtPJyva+d+qV9f0l1Tvt/jrPvXFmjO9HrWtwboRGGpXlm+Sz8b0knNE2Nd2yP00KEWD33wjSv8SNJXB3I1e9F2PfijPmFsVeA1igD04osv6sknn1ROTo4GDBig559/XkOHDg13swLOvXufAHR2sXarOrVIVKcWZ15osaS8QscKSmWqCoGP5Jdo//EiHSsokZHUKy1Z3+vWSvnFZfq/bUe0YsdR5ZeUqayisvel3GG8fnf4XYtS2Wtklc1a2Xtkr+pVslddPllUquxTxbJV9S5V7met2q9ym9Vi0bc5+Xov84Duu/wcr5Obnrk9FQ6jErdw4lwIsbzCofGvrtb2w/l+PR8n786HYrfVgwtKyut1n07uH862iA9Agf8f+k8fbVNecbme/HS7TwHIPQA7nenl9HfhxOp2bdV7Gw/qzVV7tew333dtj9T6rUh2OK9Yq3Ydl91m0YU9WnsE0vr6ZHO2PtqcLZvVog+mjFRObrFu/ft6zVu5Rxf2aK3v924bgJY3DmEPQP/61780bdo0vfzyyxo2bJieeeYZXX755dq+fbvatm06L7Tk2QMUjUNgwRJnt6mj23pDnVs10wVdWtbYLyHW5lrLqLG6du6X2rDvpMb/dbX2uH0De/CDb854u+c/+84jVL+6Yo9OFpZp55ECrdvreZLKRVtyZLVU/g3arNUBzOYW3JyKyxx6P/Og+nZMVVmFQ1/uOu66bs+xQrVJjqu8D4tFFmvlfVpU9W9Ve5y/O6+zWCoDg/sHpnMIrD75J7+4Msy2bFa/N/8ThaVKjLUpPsa37v3SckeNBT+DUQPk3gO4+2iB3vxyr87v3EJXDexY6/5lfvYAeRfK+2pj1UlPi8scHj2CZxpSyz1dpvnrs3Tt+Z3Uop7HCdUKS8r1wtLvNO/zPa6T0SbF2TV1TE9NGtFFMbb6TTM+cLJID36wRZJ0+yXdXSULk0d00Rtf7tV987/SJ/dcpLSU+IA9l3CymDBXHQ4bNkwXXHCBXnjhBUmSw+FQenq67rrrLv32t789423z8vKUmpqq3NxcpaSkKL+4TLmny2p9E3Vuc9bh1HambedLYbxu475X9f2c/b49H8Mot6hMv/jbGknSd4+Plb2ef6RoutbsPq4bX1urklq+zTd1rZNidayg5hR/SerYPKE6RFnkClmyVC4MWVZh1K11M8XarbJ4BK3K4T9r1QbXdlX2WBwrKHXVl3Vplai0lHjXbSyWyv+HHabq/2wjHSss0e6jhWqfGq8ebZPkqOp1XL37hKutl/ZJc/3ufLzqy55JyeM6r/0+2pyt2lw1sIMsqvk+9X/bDquo1PP8Xlf0a6eEGLve3Xigxv10apGgi3q2qfHYZ/PPNftdv3+vW0vXc2+dFKsf9e/gsW+Fo/IULW+53WbyiC6SqhbvNEYOU/kcjNtlhzGSkcflyk1GDkf1MbFaVBXirYq1WRUXU/Wv3Sq5HUer2++Wqidc/bfg9jdVlWS9t3tc9vo7slhqv1+r12PIfX/3fapee0sdbbJYLCqrcOh0aYWKSsv13dECffR1tk4WlUmSzm2fopKyCteQVXrLBF3Rt73apsR7PI7VUvn8bJbK3mar1eJ6/aTK/4/eXLVPR/NL1CstWR/eNdJV81NSXqGfvPiltmbnqXOrRN0wrLPSUuOrX2tVHyPnMZM8j533MSzIz9Pk75/n+vwOh7AGoNLSUiUmJuo///mPrr76atf2SZMm6dSpU/rggw889i8pKVFJSYnrcl5entLT010v4F9X7NITH38bquY3yK4nrnD94QHudh0t0Mqdx1RW4VCb5DgdzitWWUVlgfTmg6fUuVUzbdh7Ul1bN9O3OXlKircro2WijJG+O1Kg4vIKdWudJKPKItql24+4Phh7t0tWszh79QeLs3i7qnDbWZx9qqhUhW4fpqkJMYqxWRUfY9WBk5WBwWa1MOwBhEnnVon6w7g+GnNuWxkjzd+QpT8v2l7rOmH+6Nk2SX+/Zajap3qu4r/7aIHGv7pah/NK6rilfxwlRcp65rqwBqCwDoEdO3ZMFRUVSktL89ielpamb7+tGWRmzpypP/7xj3Xen81a+QYteX7TciVst32dY/aubR7fvjz38fU+PL9FWWpss6iyGHdc//aEH9Spe5skdW/TgOriEHM4jOtkrMb5bV7Ob/aV39SNo+Y257d8WeTxpm21WBRjs6qwpFzJ8XadKiqrvA/nfbm+SVY+tlF1/Yl7j41zf7l963TfbrNY1CzOrvziMh2vGgazWS2u+3YuIuj9rXzrocpvwc7hQ+dQ4tH8EsXYrK7/552vg1Ntvcfu3cS19Tx/cyhXxki926do//FCpSbEKCWhetFQ7/egvOIynSgsldViUYvEWMXara7huS2H8tSnfYo2HzyltsnxatUsts6e7LMVza/Zc1yDMpor3m5TQWm5ThaWKi0lvkavt62qp8EY6bWVezS2Xzu1SY6r6vmwuHpmnL0T7j0n7kOn1qqel+p/q2vGnDMoS8sdlT8VFSotd7gdx+qhwOq/ocr2uR9r979d99u6Lnvctq6/L8/byu1ydQ+I22iD2/27Xy+vdtisFjWLtSkx1q60lHiN7NFKl5zTxjWKYLFI11+QoSsHdNCiLTnauP+k8k6Xe/x/5uyNc3758bjskFolxWpkj9a6emBHJcTWHA7u1iZJS+4bpX+u2acN+yrvv7Si8jV3763yPlbOY2m1uvfIWVRWXKC3z/hXFnxh7QE6dOiQOnbsqC+//FLDhw93bZ8+fbqWL1+uNWvWeOx/th4gAADQ+HmXsIRDWHuAWrduLZvNpsOHPRd4O3z4sNq1a1dj/7i4OMXFRc5CdgAAoHEKaxVubGysBg8erCVLlri2ORwOLVmyxKNHCAAAIJDCPg1+2rRpmjRpkoYMGaKhQ4fqmWeeUWFhoW666aZwNw0AADRRYQ9A119/vY4ePaqHHnpIOTk5GjhwoBYtWlSjMBoAACBQwr4OUEM0hiIqAADgn8bw+c1KfAAAIOoQgAAAQNQhAAEAgKhDAAIAAFGHAAQAAKIOAQgAAEQdAhAAAIg6BCAAABB1CEAAACDqhP1UGA3hXMQ6Ly8vzC0BAAC+cn5uh/NkFBEdgI4fPy5JSk9PD3NLAACAv44fP67U1NSwPHZEB6CWLVtKkvbv3x+2FxCV8vLylJ6erqysLM7L1ghwPBoPjkXjwbFoPHJzc5WRkeH6HA+HiA5AVmtlCVNqaip/zI1ESkoKx6IR4Xg0HhyLxoNj0Xg4P8fD8thhe2QAAIAwIQABAICoE9EBKC4uTg8//LDi4uLC3ZSox7FoXDgejQfHovHgWDQejeFYWEw456ABAACEQUT3AAEAANQHAQgAAEQdAhAAAIg6BCAAABB1IjoAvfjii+rSpYvi4+M1bNgwrV27NtxNimgzZ87UBRdcoOTkZLVt21ZXX321tm/f7rFPcXGxpkyZolatWikpKUnXXnutDh8+7LHP/v37NW7cOCUmJqpt27a6//77VV5e7rHPsmXLdP755ysuLk49evTQG2+8EeynF9FmzZoli8WiqVOnurZxLELn4MGDuuGGG9SqVSslJCSoX79+Wr9+vet6Y4weeughtW/fXgkJCRozZox27tzpcR8nTpzQhAkTlJKSoubNm+uWW25RQUGBxz5ff/21LrroIsXHxys9PV2zZ88OyfOLFBUVFXrwwQfVtWtXJSQkqHv37nrsscc8zifFsQieFStW6Morr1SHDh1ksVj0/vvve1wfytd+/vz56t27t+Lj49WvXz99/PHH/j8hE6HeeecdExsba1577TXzzTffmF/+8pemefPm5vDhw+FuWsS6/PLLzeuvv262bNliNm3aZK644gqTkZFhCgoKXPvcdtttJj093SxZssSsX7/efO973zMjRoxwXV9eXm769u1rxowZYzIzM83HH39sWrdubR544AHXPrt37zaJiYlm2rRpZuvWreb55583NpvNLFq0KKTPN1KsXbvWdOnSxfTv39/cc889ru0ci9A4ceKE6dy5s5k8ebJZs2aN2b17t/n000/Nd99959pn1qxZJjU11bz//vvmq6++Mj/+8Y9N165dzenTp137/PCHPzQDBgwwq1evNp9//rnp0aOHGT9+vOv63Nxck5aWZiZMmGC2bNli3n77bZOQkGBeeeWVkD7fxuzxxx83rVq1MgsXLjR79uwx8+fPN0lJSebZZ5917cOxCJ6PP/7Y/P73vzfvvfeekWQWLFjgcX2oXvsvvvjC2Gw2M3v2bLN161bzhz/8wcTExJjNmzf79XwiNgANHTrUTJkyxXW5oqLCdOjQwcycOTOMrWpajhw5YiSZ5cuXG2OMOXXqlImJiTHz58937bNt2zYjyaxatcoYU/k/iNVqNTk5Oa595s6da1JSUkxJSYkxxpjp06eb8847z+Oxrr/+enP55ZcH+ylFnPz8fNOzZ0+zePFic8kll7gCEMcidGbMmGEuvPDCOq93OBymXbt25sknn3RtO3XqlImLizNvv/22McaYrVu3Gklm3bp1rn0++eQTY7FYzMGDB40xxrz00kumRYsWrmPjfOxevXoF+ilFrHHjxpmbb77ZY9s111xjJkyYYIzhWISSdwAK5Wt/3XXXmXHjxnm0Z9iwYebXv/61X88hIofASktLtWHDBo0ZM8a1zWq1asyYMVq1alUYW9a05ObmSqo+6eyGDRtUVlbm8br37t1bGRkZrtd91apV6tevn9LS0lz7XH755crLy9M333zj2sf9Ppz7cOxqmjJlisaNG1fj9eJYhM6HH36oIUOG6Gc/+5natm2rQYMG6dVXX3Vdv2fPHuXk5Hi8jqmpqRo2bJjHsWjevLmGDBni2mfMmDGyWq1as2aNa5+LL75YsbGxrn0uv/xybd++XSdPngz204wII0aM0JIlS7Rjxw5J0ldffaWVK1dq7NixkjgW4RTK1z5Q71sRGYCOHTumiooKjzd2SUpLS1NOTk6YWtW0OBwOTZ06VSNHjlTfvn0lSTk5OYqNjVXz5s099nV/3XNycmo9Ls7rzrRPXl6eTp8+HYynE5Heeecdbdy4UTNnzqxxHccidHbv3q25c+eqZ8+e+vTTT3X77bfr7rvv1ptvvimp+rU80/tRTk6O2rZt63G93W5Xy5Yt/Tpe0e63v/2tfv7zn6t3796KiYnRoEGDNHXqVE2YMEESxyKcQvna17WPv8cmos8Gj+CZMmWKtmzZopUrV4a7KVEpKytL99xzjxYvXqz4+PhwNyeqORwODRkyRE888YQkadCgQdqyZYtefvllTZo0Kcytiy7//ve/9dZbb+mf//ynzjvvPG3atElTp05Vhw4dOBbwW0T2ALVu3Vo2m63GjJfDhw+rXbt2YWpV03HnnXdq4cKFWrp0qTp16uTa3q5dO5WWlurUqVMe+7u/7u3atav1uDivO9M+KSkpSkhICPTTiUgbNmzQkSNHdP7558tut8tut2v58uV67rnnZLfblZaWxrEIkfbt26tPnz4e284991zt379fUvVreab3o3bt2unIkSMe15eXl+vEiRN+Ha9od//997t6gfr166eJEyfq3nvvdfWScizCJ5SvfV37+HtsIjIAxcbGavDgwVqyZIlrm8Ph0JIlSzR8+PAwtiyyGWN05513asGCBfrss8/UtWtXj+sHDx6smJgYj9d9+/bt2r9/v+t1Hz58uDZv3uzxR7548WKlpKS4PkSGDx/ucR/OfTh21UaPHq3Nmzdr06ZNrp8hQ4ZowoQJrt85FqExcuTIGstB7NixQ507d5Ykde3aVe3atfN4HfPy8rRmzRqPY3Hq1Clt2LDBtc9nn30mh8OhYcOGufZZsWKFysrKXPssXrxYvXr1UosWLYL2/CJJUVGRrFbPjy2bzSaHwyGJYxFOoXztA/a+5VfJdCPyzjvvmLi4OPPGG2+YrVu3ml/96lemefPmHjNe4J/bb7/dpKammmXLlpns7GzXT1FRkWuf2267zWRkZJjPPvvMrF+/3gwfPtwMHz7cdb1z6vVll11mNm3aZBYtWmTatGlT69Tr+++/32zbts28+OKLTL32gfssMGM4FqGydu1aY7fbzeOPP2527txp3nrrLZOYmGj+8Y9/uPaZNWuWad68ufnggw/M119/ba666qpap/8OGjTIrFmzxqxcudL07NnTY/rvqVOnTFpampk4caLZsmWLeeedd0xiYmLUT712N2nSJNOxY0fXNPj33nvPtG7d2kyfPt21D8ciePLz801mZqbJzMw0ksxTTz1lMjMzzb59+4wxoXvtv/jiC2O3281f/vIXs23bNvPwww9H1zR4Y4x5/vnnTUZGhomNjTVDhw41q1evDneTIpqkWn9ef/111z6nT582d9xxh2nRooVJTEw0P/nJT0x2drbH/ezdu9eMHTvWJCQkmNatW5v77rvPlJWVeeyzdOlSM3DgQBMbG2u6devm8RionXcA4liEzn//+1/Tt29fExcXZ3r37m3++te/elzvcDjMgw8+aNLS0kxcXJwZPXq02b59u8c+x48fN+PHjzdJSUkmJSXF3HTTTSY/P99jn6+++spceOGFJi4uznTs2NHMmjUr6M8tkuTl5Zl77rnHZGRkmPj4eNOtWzfz+9//3mPKNMcieJYuXVrrZ8SkSZOMMaF97f/973+bc845x8TGxprzzjvPfPTRR34/H4sxbktoAgAARIGIrAECAABoCAIQAACIOgQgAAAQdQhAAAAg6hCAAABA1CEAAQCAqEMAAgAAUYcABAAAog4BCAAARB0CEAAAiDoEICDK/Pa3v1VcXJx+8Ytf+LT/qFGjZLFYZLFYtGnTpuA2LkJNnjzZ9Rq9//774W4OAB8QgIAo88ADD2jOnDl6++239d133/l0m1/+8pfKzs5W3759PbavWrVKNptN48aNC0ZTz2rUqFGaOnVqWB7b3bPPPqvs7OxwNwOAHwhAQJRJTU3VLbfcIqvVqs2bN/t0m8TERLVr1052u91j+7x583TXXXdpxYoVOnToUDCaGxClpaVBvf/U1FS1a9cuqI8BILAIQEAUKi8vV2JiorZs2VLv+ygoKNC//vUv3X777Ro3bpzeeOONGvuMGjVKd999t6ZPn66WLVuqXbt2euSRR1zX5+fna8KECWrWrJnat2+vp59+ukavzn/+8x/169dPCQkJatWqlcaMGaPCwkJNnjxZy5cv17PPPusaftq7d6/rce+8805NnTpVrVu31uWXXy5JKikp0d133622bdsqPj5eF154odatW+fR3rvuuktTp05VixYtlJaWpldffVWFhYW66aablJycrB49euiTTz6p9+sGoHEgAAFR6A9/+IMKCgoaFID+/e9/q3fv3urVq5duuOEGvfbaazLG1NjvzTffVLNmzbRmzRrNnj1bjz76qBYvXixJmjZtmr744gt9+OGHWrx4sT7//HNt3LjRddvs7GyNHz9eN998s7Zt26Zly5bpmmuukTFGzz77rIYPH+4ansvOzlZ6errH48bGxuqLL77Qyy+/LEmaPn263n33Xb355pvauHGjevToocsvv1wnTpzwuF3r1q21du1a3XXXXbr99tv1s5/9TCNGjNDGjRt12WWXaeLEiSoqKqr3awegETAAosr69etNbGysGTdunOnTp89Z97/kkkvMPffcU2P7iBEjzDPPPGOMMaasrMy0bt3aLF26tMZtL7zwQo9tF1xwgZkxY4bJy8szMTExZv78+a7rTp06ZRITE12Pt2HDBiPJ7N2716+2XXLJJWbQoEEe2woKCkxMTIx56623XNtKS0tNhw4dzOzZs2ttb3l5uWnWrJmZOHGia1t2draRZFatWlXjcSWZBQsW1NpWAI0LPUBAFHE4HPr1r3+tO++8UzfeeKN27typsrIyv+9n+/btWrt2rcaPHy9Jstvtuv766zVv3rwa+/bv39/jcvv27XXkyBHt3r1bZWVlGjp0qOu61NRU9erVy3V5wIABGj16tPr166ef/exnevXVV3Xy5Emf2jh48GCPy7t27VJZWZlGjhzp2hYTE6OhQ4dq27ZttbbXZrOpVatW6tevn2tbWlqaJOnIkSM+tQNA40QAAqLI888/r2PHjunRRx9Vv379VFZWpm+//dbv+5k3b57Ky8vVoUMH2e122e12zZ07V++++65yc3M99o2JifG4bLFY5HA4fHocm82mxYsX65NPPlGfPn30/PPPq1evXtqzZ89Zb9usWTPfn9BZ2uu+zWKxSJLPzwFA40QAAqLEwYMH9eCDD+rFF19Us2bN1LNnT8XFxfldB1ReXq6///3vmjNnjjZt2uT6+eqrr9ShQwe9/fbbPt1Pt27dFBMT41GEnJubqx07dnjsZ7FYNHLkSP3xj39UZmamYmNjtWDBAklSbGysKioqfHq87t27u2qCnMrKyrRu3Tr16dPHp/sA0HTYz74LgKbg7rvv1tixY11r9tjtdp177rl+B6CFCxfq5MmTuuWWW5Samupx3bXXXqt58+bptttuO+v9JCcna9KkSbr//vvVsmVLtW3bVg8//LCsVqurl2XNmjVasmSJLrvsMrVt21Zr1qzR0aNHde6550qSunTpojVr1mjv3r1KSkpSy5YtZbXW/r2uWbNmuv32212Pl5GRodmzZ6uoqEi33HKLX68BgMhHDxAQBRYuXKjPPvtMzz77rMf2fv36+R2A5s2bpzFjxtQIP1JlAFq/fr2+/vprn+7rqaee0vDhw/WjH/1IY8aM0ciRI3XuuecqPj5ekpSSkqIVK1boiiuu0DnnnKM//OEPmjNnjsaOHStJ+s1vfiObzaY+ffqoTZs22r9//xkfb9asWbr22ms1ceJEnX/++fruu+/06aefqkWLFn69BgAin8WYWuatAkCVUaNGaeDAgXrmmWeC/liFhYXq2LGj5syZE5G9MhaLRQsWLNDVV18d7qYAOAt6gACc1UsvvaSkpCSfV472VWZmpt5++23t2rVLGzdu1IQJEyRJV111VUAfJ9huu+02JSUlhbsZAPxADxCAMzp48KBOnz4tScrIyFBsbGzA7jszM1O33nqrtm/frtjYWA0ePFhPPfWUx7TzSHDkyBHl5eVJqpzmX98ZaABChwAEAACiDkNgAAAg6hCAAABA1CEAAQCAqEMAAgAAUYcABAAAog4BCAAARB0CEAAAiDoEIAAAEHUIQAAAIOoQgAAAQNT5/+M9O5pzzGPGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "plt.plot(ssp.wavelength,ssp.flux[0][0])\n",
    "plt.xlabel(r'$\\lambda$ [%s]'%config[\"fields\"][\"wavelength\"][\"units\"])\n",
    "plt.ylabel(r'Flux [%s]'%config[\"fields\"][\"flux\"][\"units\"])\n",
    "#plt.yscale(\"log\")\n",
    "plt.xlim(0,10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10000.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "plt.plot(ssp.wavelength,ssp.flux[-1][-1])\n",
    "plt.xlabel(r'$\\lambda$ [%s]'%config[\"fields\"][\"wavelength\"][\"units\"])\n",
    "plt.ylabel(r'Flux [%s]'%config[\"fields\"][\"flux\"][\"units\"])\n",
    "#plt.yscale(\"log\")\n",
    "plt.xlim(0,10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x71980819b290>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lJWH16tU4duwYQkJCEBUVhdLSUqHM0qVL8fnnn2P37t04ePAgLl++jAcffNDi52AvaDNUQgjpIYJe3YP6a1IAwPmUaBtH03V60maop06dQmhoKJ555hn84x//sLj+rFmzUFNTgy+++EJ4bNKkSRgzZgzS09NbrbN8+XKo1WpRkvToo4+ioqICe/fuBQCEhoZiwoQJSEtLAwDwPI/Bgwdj8eLFeOGFF1BZWYn+/ftjx44deOihhwAAp0+fxogRI5CTk4NJkyaZXJc2QyWEEEK6EGMMtdpam9w602ZQUVGBGTNmICIiAmvWrBEed3Nza/eWkJAglM3JyUFkZKTovFFRUcjJyWnzujeqo9FokJeXJyojkUgQGRkplMnLy4NWqxWVCQoKgp+fX7vXtmc0C4wQQohDqdPVIXRHqE2u/f2c7+Eqd7W4Hs/zmDNnDmQyGbZv3w6Oa161KT8/v926xi0kxcXF8Pb2Fh339vZGcXFxm/XbqlNVVYW6ujqUl5dDr9e3Wub06dPCORQKBby8vCy6tj2jBIgQQgjpZsnJycjJyUFubi7c3d1FxwICAmwUlXOjBIgQQnoKJ1kK2kXmgu/nfG+za1sqIyMD69atg1qtRmBgoMlxNze3duvHxsYK43t8fHxMZl6VlJTAx8enzfpt1fHw8ICLiwukUimkUmm75/Xx8YFGo0FFRYWoFehG17ZnlAARQghxKBzHdagbyhby8/MRHx+PlJQUREVFtVmmPcZdYGFhYcjOzkZiYqLwWFZWFsLCwtqsHxYWZjJV3riOQqHAuHHjkJ2djZiYGACGLrvs7GwsWrQIADBu3DjI5XJkZ2dj5syZAIAzZ87gwoUL7V7bnlECRAghPYSTNAA5jLKyMsTExCAiIgKxsbEmY2WkUin69+9vURfYkiVLMGXKFKSmpiI6OhoZGRk4evQotmzZIpRZsWIFLl26hA8++AAAkJCQgLS0NCxbtgzz5s3D/v37sWvXLqjVaqFOUlIS4uLiMH78eEycOBEbNmxATU0N5s6dCwDw9PREfHw8kpKS0KdPH3h4eGDx4sUICwtrdQaYI6AEiBBCCOkGarUahYWFKCwshK+vr8nxIUOG4Pz58xadMzw8HDt27MDKlSuRnJyMwMBAZGZmYvTo0UKZoqIiXLhwQbjv7+8PtVqNpUuXYuPGjRg0aBC2bt0qapGaNWsWrly5glWrVqG4uBhjxozB3r17RQOj169fD4lEgpkzZ6KhoQFRUVF48803LYrfntA6QA7kw9wLeOvAWWybOwE392+/z5gQ4nxGvLYHdVdpHSBiH2gdINJlVnxyAheu1WLVZz/bOhRCCCHEoVEC5IAadHpbh0AIIYQ4NEqACCGEEOJ0KAEihBBCiNOhBMgBcTTZlRBCCOkUSoAIIYQQ4nQoASKEkJ6CGocJMRslQIQQQghxOpQAEUIIIcTpUALkiKiZmzgBxhjOl9XAgRerJ4TYMUqACCF26ZUvTyFi3QGk7f/d1qE4jCF8MY4oFyFOus/WoRAABw4cAMdxbd6mTp3aofPu3r0bQUFBUKlUCA4ONtnpva1Yxo4dC6VSiYCAAGzbts2kzKZNmzB06FCoVCqEhoYiNzdXdLy+vh4LFy5E37594ebmhpkzZ6KkpKRDz8EeUAJECLFLb39TAABIzfrVxpE4juSG/8NA7hr+Ln/f1qEQGDYuLSoqMrlt3rwZHMdhwYIFFp/zyJEjmD17NuLj43H8+HHExMQgJiYGJ0+ebLNOQUEBoqOjMXXqVOTn5yMxMRHz58/Hvn3NifLOnTuRlJSE1atX49ixYwgJCUFUVBRKS0uFMkuXLsXnn3+O3bt34+DBg7h8+TIefPBBi5+DvaAEiBBCeggZaJsce6JQKODj4yO6lZeX47nnnkNycjIefvhhi8+5ceNGTJ8+Hc8//zxGjBiBNWvWYOzYsUhLS2uzTnp6Ovz9/ZGamooRI0Zg0aJFeOihh7B+/XqhzOuvv44nn3wSc+fOxciRI5Geng5XV1e8++67AIDKykq88847eP3113HXXXdh3LhxeO+993DkyBF89913lr84doASIEII6SGYkwwQZIyBr621ya0zY9IqKiowY8YMREREYM2aNcLjbm5u7d4SEhKEsjk5OYiMjBSdNyoqCjk5OW1e90Z1NBoN8vLyRGUkEgkiIyOFMnl5edBqtaIyQUFB8PPza/fa9kxm6wCIeXi++R+dc/yJI4SQ1rG6OpwZO84m1x5+LA+cq6vF9Xiex5w5cyCTybB9+3ZwXPNf8vz8/Hbrenh4CD8XFxfD29tbdNzb2xvFxcVt1m+rTlVVFerq6lBeXg69Xt9qmdOnTwvnUCgU8PLysuja9owSIAeh5Xlbh0AIsXM0X85+JScnIycnB7m5uXB3dxcdCwgIsFFUzo0SIAeh1dOfNkLIjThH+zDn4oLhx/Jsdm1LZWRkYN26dVCr1QgMDDQ57ubm1m792NhYpKenAwB8fHxMZl6VlJTAx8enzfpt1fHw8ICLiwukUimkUmm75/Xx8YFGo0FFRYWoFehG17ZnlAA5CJ2eWoAIIQSAYRp5B7qhbCE/Px/x8fFISUlBVFRUm2XaY9wFFhYWhuzsbCQmJgqPZWVlISwsrM36YWFhJlPljesoFAqMGzcO2dnZiImJAWDossvOzsaiRYsAAOPGjYNcLkd2djZmzpwJADhz5gwuXLjQ7rXtGSVADsK4BYjWhSOEtIb+NNiXsrIyxMTEICIiArGxsSZjZaRSKfr3729RF9iSJUswZcoUpKamIjo6GhkZGTh69Ci2bNkilFmxYgUuXbqEDz74AACQkJCAtLQ0LFu2DPPmzcP+/fuxa9cuqNVqoU5SUhLi4uIwfvx4TJw4ERs2bEBNTQ3mzp0LAPD09ER8fDySkpLQp08feHh4YPHixQgLC8OkSZM68zLZDCVADkJr1AJE44EIIcT+qdVqFBYWorCwEL6+vibHhwwZgvPnz1t0zvDwcOzYsQMrV65EcnIyAgMDkZmZidGjRwtlioqKcOHCBeG+v78/1Go1li5dio0bN2LQoEHYunWrqEVq1qxZuHLlClatWoXi4mKMGTMGe/fuFQ2MXr9+PSQSCWbOnImGhgZERUXhzTfftCh+e8IxB15nvqqqCp6enqisrBQ1EfZEF67WYsa/P8HIvr+iWnIXPlsUYeuQCOlWQ19o/nZ6PiXahpE4jm/W3IE79D8Z7rxUadtgulB9fT0KCgrg7+8PlUpl63CImdp73+zh85tagByERs/j5fAUuMrrcKioHkCErUMihBBCHBYthOggdDwPV3kdAMDP7WcbR0MIIYQ4NkqAHITOaBC0jpfaMBJCiL1y2PEMhNgAJUAOgjcaqqWnBIgQ0gpn2QqDkK5ACZCDMNoJAzpGCRAhhBDSGTQI2kHwjCHr2l3436UI3DfggK3DIYTYIeoCI8R81ALkIBhjyDgag7IiL2QWT7d1OIQQQohDowTIQRiv1lTXoLBdIIQQQkgPQAmQg9Abrf7M0ThH4gT6uZThkVsy0VtZbutQHAj9cSDEXDQGyEHwfIPwM8dRTz/p+ZaNfwN9XcoxvPdvAGJtHQ4hpIehFiAHwfN1ws+UABFn0NfF0PIz1POijSMhpGMOHDhg2Lm+jdvUqVM7dN7du3cjKCgIKpUKwcHBJju9txXL2LFjoVQqERAQgG3btpmU2bRpE4YOHQqVSoXQ0FDk5uaKjm/ZsgURERHw8PAAx3GoqKjoUPz2ghIgB8GoBYgQQhxKeHg4ioqKTG6bN28Gx3FYsGCBxec8cuQIZs+ejfj4eBw/fhwxMTGIiYnByZMn26xTUFCA6OhoTJ06Ffn5+UhMTMT8+fOxb98+oczOnTuRlJSE1atX49ixYwgJCUFUVBRKS0uFMrW1tZg+fTqSk5MtjtseUQLkIIxbgCQ02ZUQQuyeQqGAj4+P6FZeXo7nnnsOycnJePjhhy0+58aNGzF9+nQ8//zzGDFiBNasWYOxY8ciLS2tzTrp6enw9/dHamoqRowYgUWLFuGhhx7C+vXrhTKvv/46nnzyScydOxcjR45Eeno6XF1d8e677wplEhMT8cILL2DSpEkWx22PKAFyELy+XvhZIuHbKUkIIT0bYwzaBr1Nbox1/AtoRUUFZsyYgYiICKxZs0Z43M3Nrd1bQkKCUDYnJweRkZGi80ZFRSEnJ6fN696ojkajQV5enqiMRCJBZGRku+d1dDQI2kE0ML3wswSUAJGerxTe+C+m4158butQHIhztA7rNDy2LDlok2v/deMUyJWWr8bP8zzmzJkDmUyG7du3gzOazpufn99uXQ8PD+Hn4uJieHt7i457e3ujuLi4zfpt1amqqkJdXR3Ky8uh1+tbLXP69OkbPTWHRQmQg6jRNSc9Eo4SINLz/QN/x1WuP06xUXjE1sEQ0knJycnIyclBbm4u3N3dRccCAgJsFJVzowTIQdTomr/ZcRLn+JZHnNtVrj8A4Dw3zMaREHsjU0jw141TbHZtS2VkZGDdunVQq9UIDAw0Oe7m5tZu/djYWKSnpwMAfHx8UFJSIjpeUlICHx+fNuu3VcfDwwMuLi6QSqWQSqUWn9fRUQLkIK7rjO7QWmeEECfGcVyHuqFsIT8/H/Hx8UhJSUFUVFSbZdpj3AUWFhaG7OxsJCYmCo9lZWUhLCyszfphYWEmU+WN6ygUCowbNw7Z2dmIiYkBYOiyy87OxqJFi9qNzZFRAuQg6nTU7UUIIY6krKwMMTExiIiIQGxsrMk4HalUiv79+1vUBbZkyRJMmTIFqampiI6ORkZGBo4ePYotW7YIZVasWIFLly7hgw8+AAAkJCQgLS0Ny5Ytw7x587B//37s2rULarVaqJOUlIS4uDiMHz8eEydOxIYNG1BTU4O5c+cKZYqLi1FcXIzff/8dAHDixAm4u7vDz88Pffr06dBrZEuUADmIBh1DAPcHoiRH8Qm7w9bhENL96vWQXq6FfpCrrSNxGIyah+2KWq1GYWEhCgsL4evra3J8yJAhOH/+vEXnDA8Px44dO7By5UokJycjMDAQmZmZGD16tFCmqKgIFy5cEO77+/tDrVZj6dKl2LhxIwYNGoStW7eKWqRmzZqFK1euYNWqVSguLsaYMWOwd+9e0cDo9PR0/P3vfxfu33nnnQCA9957D0888YRFz8MecKwzc/psrKqqCp6enqisrBQ1EfZEf8/ah9XfGoaCblPegydW7LZxRIR0L79/7IOkWgd9HwUuLrvb1uE4hK/XTMFUfb7hzkuVNo2lK9XX16OgoAD+/v5QqVS2DoeYqb33zR4+v2kdIAehbZ4FjxH6C51ai4IQRyCpNgx8k17T2DgSx8E5yTR4QroCJUAOQm+09o8OUlD+QwghhHQcJUAOgjcaA62FDDxlQISQFmgMECHms2kC9NZbb+HWW2+Fh4cHPDw8EBYWhq+++sqWIdkt44RHAxl4yn8IIYSQDjNrFtiePXssPvHdd98NFxeXdssMGjQIKSkpCAwMBGMM77//PmbMmIHjx49j1KhRFl+zJzNOgKgFiBBCCOkcsxKgpoWRzMVxHH777TfcfPPN7Za7//77Rff/+c9/4q233sJ3331HCVALxssAaTlavYAQQgjpDLM/SYuLizFgwACzyrbc58Qcer0eu3fvRk1NTZsrWjY0NKChoUG4X1VVZfF1HBXPmjMgDbUAEUIIIZ1i1higuLi4G3ZnGYuNjTV7Xv+JEyfg5uYGpVKJhIQEfPrppxg5cmSrZdeuXQtPT0/hNnjwYLNjcnS80ShoLY0BIoQQQjrFrATovffes6hV56233kK/fv3MKjt8+HDk5+fj+++/x9NPP424uDj88ssvrZZdsWIFKisrhdvFixfNjsnRSZlW+JnGABFCCCGdY/PBJAqFQtgHZdy4cfjhhx+wceNGbN682aSsUqmEUqm0doh2QaarF37WQgZGW4MRQgghHWbxNPj6+nq89tpruO+++zB+/HiMHTtWdOssnudF43yIgZQ3Wg2XAYxWfCWEtEDrANmXAwcOgOO4Nm9Tp07t0Hl3796NoKAgqFQqBAcHm+z03lYsY8eOhVKpREBAALZt22ZSZtOmTRg6dChUKhVCQ0ORm5srOr5lyxZERETAw8MDHMehoqKiQ/HbC4tbgOLj4/Hf//4XDz30ECZOnAiO6/g/uBUrVuDee++Fn58frl+/jh07duDAgQPYt29fh8/ZU8n1zQmQBDyNASKEmKCtMOxLeHg4ioqKTB7fs2cPEhISsGDBAovPeeTIEcyePRtr167Fn/70J+zYsQMxMTE4duyYaENUYwUFBYiOjkZCQgK2b9+O7OxszJ8/H76+vsKGqDt37kRSUhLS09MRGhqKDRs2ICoqCmfOnBEmQNXW1mL69OmYPn06VqxYYXHs9sbiBOiLL77Al19+icmTJ3f64qWlpfjLX/6CoqIieHp64tZbb8W+fftw99208WFLUr65VUzKeBoDRAhxWowx6GzUUyBTKs3+4q9QKODj4yN67NSpU3juueeQnJyMhx9+2OLrb9y4EdOnT8fzzz8PAFizZg2ysrKQlpaG9PT0Vuukp6fD398fqampAIARI0bg8OHDWL9+vZAAvf7663jyyScxd+5coY5arca7776LF154AQCQmJgIwNCa1BNYnADddNNNHZrm3pp33nmnS87jDCR8826ohhYgSoAIIWLO0gWma2jAv+Messm1n3n/I8g7uCN9RUUFZsyYgYiICKxZs0Z43M3Nrd16sbGxQnKTk5ODpKQk0fGoqChkZma2WT8nJweRkZEmdZoSGo1Gg7y8PFGrjkQiQWRkJHJycsx5ag7J4gQoNTUVy5cvR3p6OoYMGdIdMZFWNY96ljIe1NJNCCGOg+d5zJkzBzKZDNu3bxe1IuXn57db13hZmeLiYnh7e4uOe3t7o7i4uM36bdWpqqpCXV0dysvLodfrWy1z+vTpGz01h2VxAjR+/HjU19fj5ptvhqurK+Ryuej4tWvXuiw4YsSoxUdKY4AIIU5MplTimfc/stm1OyI5ORk5OTnIzc016UVpmglNrMviBGj27Nm4dOkSXnnlFXh7e3dqEDSxAC9uAaIuMEKIs+I4rsPdULaQkZGBdevWQa1WIzAw0OS4JV1gPj4+KCkpER0vKSkxGWtkrK06Hh4ecHFxgVQqhVQqtfi8js7iBOjIkSPIyclBSEhId8RD2mK08A+NASLOQAEtQrizOM7o2zFxXPn5+YiPj0dKSoow4Li1Mu0x7gILCwtDdna2MH4HALKystrcQqqpTsup8sZ1FAoFxo0bh+zsbGHvT57nkZ2djUWLFrUbmyOzOAEKCgpCXV1dd8RC2mOU8MigB+U/pKdLkb+NB6WH8Z4uCsAMW4dDiMXKysoQExODiIgIxMbGmozTkUql6N+/v0VdYEuWLMGUKVOQmpqK6OhoZGRk4OjRo9iyZYtQZsWKFbh06RI++OADAEBCQgLS0tKwbNkyzJs3D/v378euXbugVquFOklJSYiLi8P48eMxceJEbNiwATU1NcKsMMAwlqi4uBi///47AMNWVu7u7vDz80OfPn069BrZksUJUEpKCp599ln885//RHBwsMkYIHP3ACOW4YwGQUsYowSI9HgPSg8DAObKaF0w4pjUajUKCwtRWFgIX19fk+NDhgzB+fPnLTpneHg4duzYgZUrVyI5ORmBgYHIzMwUrQFUVFSECxcuCPf9/f2hVquxdOlSbNy4EYMGDcLWrVtFLVKzZs3ClStXsGrVKhQXF2PMmDHYu3evaGB0eno6/v73vwv377zzTgCG7bKeeOIJi56HPeAYs+yjVCIxLB7dcuwPYwwcx0Gv17dWrVtUVVXB09MTlZWVPT7xeuntl/HSJcMaDl9hIkYsysTQfr1sHBUh3eglT6OfK20XhwPZvyYCd+mPG+70oNesvr4eBQUF8Pf3h8qBxv44u/beN3v4/La4Bejrr7/ujjjIjRjlqTQGiBDSGvqrQIj5LE6ApkyZ0h1xkBuhafCEkBugObmEmK9Du8FXVFTgnXfewalTpwAAo0aNwrx58+Dp6XmDmqSjOOMEiPGg73qEEEJIx1m8G/zRo0cxbNgwrF+/HteuXcO1a9fw+uuvY9iwYTh27Fh3xEgAiFaChp5agAghJujPAiHms7gFaOnSpXjggQfw9ttvQyYzVNfpdJg/fz4SExNx6NChLg+SABwzToBoDBAhhBDSGRYnQEePHhUlPwAgk8mwbNkyjB8/vkuDI804XrwOkNHC0IQQQgixkMVdYB4eHqL1BZpcvHixy3aJJ62hFiBCCCGkq1icAM2aNQvx8fHYuXMnLl68iIsXLyIjIwPz58/H7NmzuyNGghaDoMHDwuWbCCGEEGLE4i6wdevWgeM4/OUvf4FOpwMAyOVyPP3000hJSenyAEkjUQKkB8+oD4wQQgjpKIsSIL1ej++++w4vvfQS1q5di7NnzwIAhg0bBldX124JkBhwLbrANJQAEUIIIR1mUReYVCrFPffcg4qKCri6uiI4OBjBwcGU/FhByy4wagEihBD7duDAAXAc1+Zt6tSpHTrv7t27ERQUBJVKheDgYJOd3tuKZezYsVAqlQgICMC2bdtMymzatAlDhw6FSqVCaGgocnNzRcefeuopDBs2DC4uLujfvz9mzJiB06dPd+g52AOLxwCNHj0a586d645YSDtMxgDRNDBCCLFr4eHhKCoqMrlt3rwZHMdhwYIFFp/zyJEjmD17NuLj43H8+HHExMQgJiYGJ0+ebLNOQUEBoqOjMXXqVOTn5yMxMRHz58/Hvn3NGw3v3LkTSUlJWL16NY4dO4aQkBBERUWhtLRUKDNu3Di89957OHXqFPbt2wfGGO655x6r7gHalSzeDHXv3r1YsWIF1qxZg3HjxqFXL/GGnNbc1MweNlOzln9tWILlFdsAAOeYD67GfY0JNw+0bVCEdCfaDNVi2WsiMM0JNkNljIFpbfMlkJNLTDYDt8SpU6cQGhqKZ555Bv/4xz8srj9r1izU1NTgiy++EB6bNGkSxowZg/T09FbrLF++HGq1WpQkPfroo6ioqMDevXsBAKGhoZgwYQLS0tIAADzPY/DgwVi8eDFeeOGFVs/7008/ISQkBL///juGDRtmcrzHbYZ63333AQAeeOAB0S+BLXaDdyYtF0LU8/Q6E0KcE9PyuLzqiE2uPfDlcHAKaYfqVlRUYMaMGYiIiMCaNWuEx93c3NqtFxsbKyQ3OTk5SEpKEh2PiopCZmZmm/VzcnIQGRlpUicxMREAoNFokJeXhxUrVgjHJRIJIiMjkZOT0+o5a2pq8N5778Hf3x+DBw9uN357RbvBO4iWXWB6RgkQIaQl2g7VXvE8jzlz5kAmk2H79u2iBoT8/Px26xq3kBQXF8Pb21t03NvbG8XFxW3Wb6tOVVUV6urqUF5eDr1e32qZlmN83nzzTSxbtgw1NTUYPnw4srKyoFAo2o3fXlmcADVley2bABljuHjxYpcFRlowagGSgIeeBkETQpwUJ5dg4MvhNrt2RyQnJyMnJwe5ubkmiwYHBAR0RWhW8dhjj+Huu+9GUVER1q1bh0ceeQTffvutSReXI+hQAlRUVIQBAwaIHr927Rr8/f2pC6ybGLcAScDodSaEOC2O4zrcDWULGRkZWLduHdRqNQIDA02OW9IF5uPjg5KSEtHxkpIS+Pj4tFm/rToeHh5wcXGBVCqFVCo167yenp7w9PREYGAgJk2ahN69e+PTTz91yIWQLU6Amsb6tFRdXe2QGaCj4EQtQAw6WgmaEELsXn5+PuLj45GSkoKoqKg2y7THuAssLCwM2dnZwvgdAMjKykJYWFib9cPCwkymyhvXUSgUGDduHLKzsxETEwPA0GWXnZ2NRYsWtXlexhgYY2hoaGg3fntldgLUNOiK4zi8+OKLorV/9Ho9vv/+e4wZM6bLAyQGHJjRzzx0NAiaEELsWllZGWJiYhAREYHY2FiTcTpSqRT9+/e3qAtsyZIlmDJlClJTUxEdHY2MjAwcPXoUW7ZsEcqsWLECly5dwgcffAAASEhIQFpaGpYtW4Z58+Zh//792LVrF9RqtVAnKSkJcXFxGD9+PCZOnIgNGzagpqYGc+fOBQCcO3cOO3fuxD333IP+/fvjjz/+QEpKClxcXITJUY7G7ATo+HHD1ErGGE6cOCEa9KRQKBASEoLnnnuu6yMkAABJiy4wnZ7GABFCiD1Tq9UoLCxEYWEhfH19TY4PGTIE58+ft+ic4eHh2LFjB1auXInk5GQEBgYiMzMTo0ePFsoUFRWJNi339/eHWq3G0qVLsXHjRgwaNAhbt24VtUjNmjULV65cwapVq1BcXIwxY8Zg7969wsBolUqFb775Bhs2bEB5eTm8vb1x55134siRIyZDYhyFxesAzZ07Fxs3brSLdXfsYR0Ba9n4ajyW1H4EAChnbjjw4F78OSTYxlER0o1oHSCLZa+Zimn6Y4Y7Peg1a289GWK/7H0dIIuHs7/33nuiYKuqqpCZmenQy2E7AvEgaB56WgmaEEII6TCLE6BHHnlEWCmyrq4O48ePxyOPPILg4GB8/PHHXR4gMRCPAWLQUxcYIYQQ0mEWJ0CHDh3CHXfcAQD49NNPwRhDRUUF/v3vf3doWW9iHuMESAqeZoERQgghnWBxAlRZWYk+ffoAMOwLNnPmTLi6uiI6Ohq//fZblwdIDIwTIAkYtNQFRgghhHSYxQnQ4MGDkZOTg5qaGuzduxf33HMPAKC8vJwGp3WjlrPAaAwQIYQQ0nEWL4SYmJiIxx57DG5ubhgyZAgiIiIAGLrGgoNpVlJ3abkOEG2FQQhpiTrGCTGfxQnQggULMHHiRFy8eBF33303JBJDI9LNN99MY4C6kQTilaCpBYgQ0hJthUqI+SxOgABg/PjxGD9+vOix6OjoLgmItI4z+monAYOOp+96hBBCSEdZnAA1bYnREsdxUKlUCAgIwIwZM4SB0qRrSIwHQXMMPG2FQQhpgb4WEWI+ixOg48eP49ixY9Dr9Rg+fDgA4Ndff4VUKkVQUBDefPNNPPvsszh8+DBGjhzZ5QE7K67FnzY9owSIENISdYIRYi6LZ4HNmDEDkZGRuHz5MvLy8pCXl4c//vgDd999N2bPno1Lly7hzjvvxNKlS7sjXqfFtVj3h9EYIEKICWoDsicHDhwAx3Ft3qZOndqh8+7evRtBQUFQqVQIDg422em9rVjGjh0LpVKJgIAAbNu2zaTMpk2bMHToUKhUKoSGhiI3N7fVczHGcO+994LjOGRmZnboOdgDixOg1157DWvWrBFth+Hp6YmXXnoJr776KlxdXbFq1Srk5eV1aaDOrmULEHWBEUKIfQsPD0dRUZHJbfPmzeA4DgsWLLD4nEeOHMHs2bMRHx+P48ePIyYmBjExMTh58mSbdQoKChAdHY2pU6ciPz8fiYmJmD9/Pvbt2yeU2blzJ5KSkrB69WocO3YMISEhiIqKQmlpqcn5NmzYAI5z/NZGi7vAKisrUVpaatK9deXKFVRVVQEAvLy8oNFouiZCAqCVLjCd1kaREEKIbTHGoNXa5m+gXC43+8NfoVDAx8dH9NipU6fw3HPPITk5GQ8//LDF19+4cSOmT5+O559/HgCwZs0aZGVlIS0tDenp6a3WSU9Ph7+/P1JTUwEAI0aMwOHDh7F+/XphR/jXX38dTz75JObOnSvUUavVePfdd/HCCy8I58rPz0dqaiqOHj3a6g73jsTiBGjGjBmYN28eUlNTMWHCBADADz/8gOeeew4xMTEAgNzcXNxyyy1dGqizk7RMgKgFiBDipLRaLV555RWbXDs5ORkKhaJDdSsqKjBjxgxERERgzZo1wuNubm7t1ouNjRWSm5ycHJPJSFFRUe12ReXk5CAyMtKkTmJiIgBAo9EgLy8PK1asEI5LJBJERkYiJydHeKy2thZz5szBpk2bTBI7R2RxArR582YsXboUjz76KHQ6neEkMhni4uKwfv16AEBQUBC2bt3atZH2JIwBXyQCrv2AaS+aVUXSYuFDRi1AhBDiMHiex5w5cyCTybB9+3ZRK1J+fn67dY2HnBQXF8Pb21t03NvbG8XFxW3Wb6tOVVUV6urqUF5eDr1e32qZ06dPC/eXLl2K8PBwzJgxo914HYXFCZCbmxvefvttrF+/HufOnQNgWATROIMdM2ZMlwXYI5X9BuRtM/xsZgJEs8AIIcRALpcjOTnZZtfuiOTkZOTk5CA3Nxfu7u6iYwEBAV0RWrfas2cP9u/fj+PHj9s6lC7ToYUQAUMidOutt3ZlLM5D32BxlZZdYKAWIEJIC8xJpsFzHNfhbihbyMjIwLp166BWqxEYGGhy3JIuMB8fH5SUlIiOl5SUtNsl1VYdDw8PuLi4QCqVQiqVtnve/fv34+zZs/Dy8hKVmTlzJu644w4cOHCg3edgjyxOgGpqapCSkoLs7GyUlpaCbzEdu6lViJiJMcCMAXUtp8HTLDBCSEstW4qJ7eXn5yM+Ph4pKSnCgOPWyrTHuAssLCwM2dnZwvgdAMjKykJYWFib9cPCwkymyhvXUSgUGDduHLKzs4WxvDzPIzs7G4sWLQIAvPDCC5g/f77oHMHBwVi/fj3uv//+duO3VxYnQPPnz8fBgwfx+OOPw9fXt0dMhbM+o9fM3ASoxR82pqcWIEIIsWdlZWWIiYlBREQEYmNjTcbpSKVS9O/f36IusCVLlmDKlClITU1FdHQ0MjIycPToUWzZskUos2LFCly6dAkffPABACAhIQFpaWlYtmwZ5s2bh/3792PXrl1Qq9VCnaSkJMTFxWH8+PGYOHEiNmzYgJqaGmFWmI+PT6utTH5+fvD397fodbEXFidAX331FdRqNSZPntwd8Tgh876xtewCY3pddwRDCHFgztIF5ijUajUKCwtRWFjY6pTxIUOG4Pz58xadMzw8HDt27MDKlSuRnJyMwMBAZGZmYvTo0UKZoqIiXLhwQbjv7+8PtVqNpUuXYuPGjRg0aBC2bt0qapGaNWsWrly5glWrVqG4uBhjxozB3r17TQZG9yQWJ0C9e/emfb66EjMvATJp2qaVoAkhxK7FxcUhLi6uy8/78MMPt7uGUGurPEdERNxwAPOiRYuELi9zMDM/v+yVxStBr1mzBqtWrUJtbW13xOOEzG0BajENnqcWIEIIIaSjLG4BSk1NxdmzZ+Ht7Y2hQ4eaTAk8duxYlwXnFMzMoCUty9EgaEIIIaTDLE6AmkaIk67SsS4wpqcEiBBCCOkoixOg1atXt3lMTx/KluvgGCCO0RggQgghpKMsHgPUml9//RXLly/HoEGDuuJ0TqaDs8BoJWhCCCGkwzqcANXW1uK9997DHXfcgZEjR+LgwYMmG7QRM5jdAtSixYd37NH3hBBCiC1Z3AX23XffYevWrdi9ezf8/Pxw6tQpfP3117jjjju6I74eqUHHQync6+AgaOoCI4S0QOsAEWI+s1uAUlNTMWrUKDz00EPo3bs3Dh06hBMnToDjOPTt27c7Y+xx/qioa75jZiLTsguMo3WACCEt0FYYhJjP7Bag5cuXY/ny5Xj55ZchlUq7MybnYu40+JZdYNQCRAghhHSY2S1Aa9aswe7du+Hv74/ly5fj5MmT3RmX02BmJjIm0+Dpmx4hhBDSYWYnQCtWrMCvv/6K//u//0NxcTFCQ0MREhICxhjKy8u7M8YezdylxFt2gZm0CBFCnB6NAbIvBw4cAMdxbd6mTp3aofPu3r0bQUFBUKlUCA4ONtnpva1Yxo4dC6VSiYCAgFa3y9i0aROGDh0KlUqF0NBQ5Obmio4XFxfj8ccfh4+PD3r16oWxY8fi448/7tBzsAcWzwKbMmUK3n//fRQXF2PBggUYN24cpkyZgvDwcLz++uvdEWOPY/wnijdzNhfHWk6DpxYgQgixZ+Hh4SgqKjK5bd68GRzHYcGCBRaf88iRI5g9ezbi4+Nx/PhxxMTEICYmpt1emYKCAkRHR2Pq1KnIz89HYmIi5s+fj3379glldu7ciaSkJKxevRrHjh1DSEgIoqKiUFpaKpT5y1/+gjNnzmDPnj04ceIEHnzwQTzyyCM33GPMXnV4Gry7uzueeuopfP/99zh+/DgmTpyIlJSUrozNKZjbldWyxadlQkQIIc6CMQa9vtYmN0u+fCoUCvj4+Ihu5eXleO6555CcnNzuhqZt2bhxI6ZPn47nn38eI0aMwJo1azB27FikpaW1WSc9PR3+/v5ITU3FiBEjsGjRIjz00ENYv369UOb111/Hk08+iblz52LkyJFIT0+Hq6sr3n33XaHMkSNHsHjxYkycOBE333wzVq5cCS8vL+Tl5Vn8POyB2YOg//KXv2DGjBmIioqCm5ub6FhwcDA2bNiA1157rcsD7OmYmS1ALbvAaBA0IcRZ8XwdDhwMtsm1I6acgFTq2qG6FRUVmDFjBiIiIrBmzRrh8ZafqS3FxsYiPT0dAJCTk2Oy5l5UVBQyMzPbrJ+Tk4PIyEiTOomJiQAAjUaDvLw8rFixQjgukUgQGRmJnJwc4bHw8HDs3LkT0dHR8PLywq5du1BfX4+IiIh247dXZidAAQEBeOWVVxAbG4uIiAg88MADeOCBB3DTTTcJZVpujEpujJk5lsdkK4zuCIYQQki34Hkec+bMgUwmw/bt28FxzX/F8/Pz263r4eEh/FxcXAxvb2/RcW9vbxQXF7dZv606VVVVqKurQ3l5OfR6fatlTp8+LdzftWsXZs2ahb59+0Imk8HV1RWffvopAgIC2o3fXpmdAK1atQqrVq3CH3/8gT179iAzMxNLly7FqFGjMGPGDDzwwAMYM2ZMN4bacxj93pvdAiRt2QUG2gqDEOKcJBIXREw5YbNrd0RycjJycnKQm5sLd3d30TFHSSBefPFFVFRU4H//+x/69euHzMxMPPLII/jmm28QHGybFrnOsHgl6EGDBmHBggVYsGABrl+/jq+++gqfffYZ7rrrLri7u+P+++/H008/jVGjRnVHvD0OM3NBw5ZjfmgMECHEWXEc1+FuKFvIyMjAunXroFarERgYaHLcki4wHx8flJSUiI6XlJTAx8enzfpt1fHw8ICLiwukUimkUmm75z179izS0tJw8uRJ4fM9JCQE33zzDTZt2iTE50gsToCMubu745FHHsEjjzwCvV6PAwcOYM+ePcjJyaEEqD1GyYu5XWAmY4BoGjwhhNi9/Px8xMfHIyUlBVFRUW2WaY9xF1hYWBiys7OF8TsAkJWVhbCwsDbrh4WFmUyVN66jUCgwbtw4ZGdnIyYmBoChyy47OxuLFi0CYNj/EzCMDTImlUrBO+jOBJ1KgIxJpVJMmzYN06ZN66pT9mBGyYzZm6G2LEctQIQQYs/KysoQExODiIgIxMbGmozTkUql6N+/v0VdYEuWLMGUKVOQmpqK6OhoZGRk4OjRo9iyZYtQZsWKFbh06RI++OADAEBCQgLS0tKwbNkyzJs3D/v378euXbugVquFOklJSYiLi8P48eMxceJEbNiwATU1NZg7dy4AICgoCAEBAXjqqaewbt069O3bF5mZmcjKysIXX3zRmZfJZiyeBl9SUoLHH38cAwcOhEwmE5rOmm6WWLt2LSZMmAB3d3cMGDAAMTExOHPmjKUhORzxOkDmtgC1HANECCFi9HfBvqjVahQWFuLLL7+Er6+vyW3ChAkWnzM8PBw7duzAli1bEBISgo8++giZmZkYPXq0UKaoqAgXLlwQ7vv7+0OtViMrKwshISFITU3F1q1bRS1Ss2bNwrp167Bq1SqMGTMG+fn52Lt3rzAwWi6X48svv0T//v1x//3349Zbb8UHH3yA999/H/fdd18nXiXbsbgF6IknnsCFCxfw4osvwtfXVzSS3VIHDx7EwoULMWHCBOh0OiQnJ+Oee+7BL7/8gl69enX4vHZP1AVmnpZdYNT+Qwgh9i0uLg5xcXFdft6HH3643TWEWlvlOSIi4oYLFi5atEjo8mpNYGCgQ6/83JLFCdDhw4fxzTffdMmMr71794rub9u2DQMGDEBeXh7uvPPOTp/ffhklQB1dB4hSIEJIC/RXgRDzWZwADR48uNu2YaisrAQA9OnTp9XjDQ0NaGhoEO5XVVV1Sxzdjlk+BqhlFxhNAiOEEEI6zuIxQBs2bMALL7yA8+fPd2kgPM8jMTERkydPFvVlGlu7di08PT2F2+DBg7s0BmvhuObshTdzRWfTLjDq7SeEEEI6yuIWoFmzZqG2thbDhg2Dq6uryerP165d61AgCxcuxMmTJ3H48OE2y6xYsUK0BHhVVZVDJkFcF7QAUVs3IYQQ0nEWJ0AbNmzo8iAWLVqEL774AocOHcKgQYPaLKdUKqFUKrv8+rbEzGwB4hgTTfGgLjBCCCGk4yxOgLpyRDtjDIsXL8ann36KAwcOwN/fv8vObc+Mx1CZPw2eBkETQgghXcXiBMh4bYHW+Pn5mX2uhQsXYseOHfjss8/g7u4uLBLl6ekJF5eO7bfiCJgoebGsC0wPDlIw8IzGABFCCCEdZXECNHTo0HbX/tHrzd+k86233gJgWJ/A2HvvvYcnnnjC0tAch/HUdzOnwTetBK2HFFLoaAg0IYQQ0gkWJ0AtF1LSarU4fvw4Xn/9dfzzn/+06FzdNZ3e3jHRQoiWrQOkgwQKAGYOHSKEEEJIKyxOgEJCQkweGz9+PAYOHIjXXnsNDz74YJcE1pMZb4Bq7jR4qdAF1rTdiHMmj4QQQkhXsHgdoLYMHz4cP/zwQ1edrmdjlq8ELXSBcY1vGeU/hJCWqG/crhw4cAAcx7V5mzp1aofOu3v3bgQFBUGlUiE4ONhkp/e2Yhk7diyUSiUCAgJMtss4dOgQ7r//fgwcOBAcxyEzM7Pd8yUkJIDjuG6ZGW4tFidAVVVVoltlZSVOnz6NlStXIjAwsDti7HmYZYOgGWPCIGhdYwuQxEm7DwkhbaMFUu1LeHg4ioqKTG6bN28Gx3FYsGCBxec8cuQIZs+ejfj4eBw/fhwxMTGIiYnByZMn26xTUFCA6OhoTJ06Ffn5+UhMTMT8+fOxb98+oUxNTQ1CQkKwadOmG8bw6aef4rvvvsPAgQMtjt+eWNwF5uXlZTIImjGGwYMHIyMjo8sC68nE6yDeuAuMseYxQBpODjBAxps/2JwQ4hw4J/lixBhDrZlLiHQ1V4nE7E3AFQoFfHx8RI+dOnUKzz33HJKTk9vd0LQtGzduxPTp0/H8888DANasWYOsrCykpaUhPT291Trp6enw9/dHamoqAGDEiBE4fPgw1q9fL+wIf++99+Lee++94fUvXbqExYsXY9++fYiOjrY4fnticQL09ddfi+5LJBL0798fAQEBkMksPp2Tav6Ha04XGM+YkAA1cIaVt+XQdU9ohBBi52p5HsMOnbDJtc/eGYxeUumNC7aioqICM2bMQEREBNasWSM87ubm1m692NhYIbnJyckR7YgAAFFRUe12WeXk5CAyMtKkTmJiokXx8zyPxx9/HM8//zxGjRplUV17ZHHGMmXKlO6Iw6mIZr+Z8YWNZwDXmDQJCRCjBIgQ0gL1gNktnucxZ84cyGQybN++XdSKlJ+f325dDw8P4efi4mJ4e3uLjnt7ewvr6LWmrTpVVVWoq6sze929f/3rX5DJZHjmmWfMKm/vLE6A3n//ffTr109o+lq2bBm2bNmCkSNH4sMPP8SQIUO6PMgex3gQNLtxV5ZxC5CmMQFSMm33xEYIcVjOMgbIVSLB2TuDbXbtjkhOTkZOTg5yc3Ph7u4uOhYQENAVoXWrvLw8bNy4EceOHTO7C9DeWfxOvvLKK0K2mJOTg7S0NLz66qvo168fli5d2uUB9kSidYDM7LNvmgav4Qw5q4xagAghTorjOPSSSm1y68iHf0ZGBtatW4eMjIxWJwu5ubm1e0tISBDK+vj4oKSkRFS/pKTEZKyRsbbqeHh4mN36880336C0tBR+fn6QyWSQyWQoLCzEs88+i6FDh5p1DntjcQvQxYsXhWw1MzMTDz30EP76179i8uTJJis6k7YYJ0A3HsjHMyZMg6cWIEIIcRz5+fmIj49HSkqKMOC4tTLtMe4CCwsLQ3Z2tmj8TlZWFsLCwtqsHxYWZjJV/kZ1Wnr88cdbHUf0+OOPY+7cuWafx55YnAC5ubnh6tWr8PPzw3//+19hMJZKpUJdXV2XB9gTWdoCpNPzQheYtvEtU1ACRAghdq2srAwxMTGIiIhAbGysyTgdqVQqTCIy15IlSzBlyhSkpqYiOjoaGRkZOHr0KLZs2SKUWbFiBS5duoQPPvgAgGHNnrS0NCxbtgzz5s3D/v37sWvXLqjVaqFOdXU1fv/9d+F+QUEB8vPz0adPH/j5+aFv377o27evKBa5XA4fHx8MHz7cotfFXlicAN19992YP38+brvtNvz666+47777AAA///wzjf8xl1Grjzk9YHrGQ9HYBaZtbAFS0CwwQogJ55gG7yjUajUKCwtRWFgIX19fk+NDhgzB+fPnLTpneHg4duzYgZUrVyI5ORmBgYHIzMzE6NGjhTJFRUWijcv9/f2hVquxdOlSbNy4EYMGDcLWrVtFLVJHjx4VLczY1LgRFxdnsmhiT2FxArRp0yasXLkSFy9exMcffyxkhHl5eXjssce6PMAeySjr4XDjLjCtVgdVUwsQZ5h+qQS1ABFCWuoZg1N7iri4OMTFxXX5eR9++OF21xBqLWGJiIgw2cuz5XFL9+e0NHmzNx1aCDEtLc3k8aVLl5q1HDcRb4DKm9MFxjd3gWmoC4wQQgjptC7bC6ywsBCPP/54V52uZzNeBsiMhRAb9LzQUtTUBUYtQISQlqgDjBDzdVkCRMwnnvl14y4wHa8XWoB0TS1AlAARQlrgKAUixGyUANmChbPAtHodZFxjC5CQANEgaEIIIaSjKAGyAdE0eDM29NPqm1eL1qGpC0zT9YERQgghTsLsQdD//ve/2z1+6dKlTgfjPCxtAWru7tIxw1umpBYgQkgLzrIVBiFdwewEaP369Tcs4+fn16lgnIYo6TFjN3h9c7KjpzFAhJA2UPpDiPnMToAKCgq6Mw7nYpwAmTELTKtv7ibj0dQCRAkQIYQQ0lE0BsgGLN0KgzfqAuOYtFtiIoQQQpyJWQnQv//9b9TX15t90vT0dFy/fr3DQfV8RgmQGV1gGqMuMNb4lknMmD5PCCGEkNaZlQAtXbrUooRm2bJluHLlSoeD6unELUBm7AZvlAA15UtSSoAIIcSuHThwABzHtXkz3nvLErt370ZQUBBUKhWCg4PN2oXhwIEDGDt2LJRKJQICAky2y1i7di0mTJgAd3d3DBgwADExMThz5oyoTEREhMlzSEhI6NBzsAdmjQFijGHatGmQycwbMkS7wt+I8VLQ5uwGb9QC1DgLjBY8I4QQ+xYeHo6ioiKTx/fs2YOEhAQsWLDA4nMeOXIEs2fPxtq1a/GnP/0JO3bsQExMDI4dOybaENVYQUEBoqOjkZCQgO3btyM7Oxvz58+Hr6+vsCHqwYMHsXDhQkyYMAE6nQ7Jycm455578Msvv6BXr17CuZ588km8/PLLwn1XV1eLn4O9MCujWb16tUUnnTFjBvr06dOhgJwBE+0Gb8YYIKY3umeY5yGhBIgQ0oKzTINnjKFOq79xwW7gIpeC48x7nRUKBXx8fESPnTp1Cs899xySk5Pb3dC0LRs3bsT06dPx/PPPAwDWrFmDrKwspKWlIT09vdU66enp8Pf3R2pqKgBgxIgROHz4MNavXy8kQHv37hXV2bZtGwYMGIC8vDzceeedwuOurq4mz8lRdUsCRG6AiTYDu2FxXmfcBWYYBE1jgAghLTlLy3CdVo+Rq/bZ5Nq/vBwFV4XF+4gDACoqKjBjxgxERERgzZo1wuNubm7t1ouNjRWSm5ycHCQlJYmOR0VFITMzs836OTk5iIyMNKmTmJjYZp3KykoAMGnM2L59O/7zn//Ax8cH999/P1588UWHbQXq2LtIOuWaxrhLy7zd4JtwjFqACCHE0fA8jzlz5kAmk2H79u2iVqT8/Px263p4eAg/FxcXw9vbW3Tc29sbxcXFbdZvq05VVRXq6urg4uJiEmtiYiImT54s6labM2cOhgwZgoEDB+Knn37C8uXLcebMGXzyySftxm+vKAGyAT3f3HRrzlYY+satMPSMQ+OWYJCAgTFmdlMsIYT0FC5yKX55Ocpm1+6I5ORk5OTkIDc3F+7u7qJjAQEBXRFal1m4cCFOnjyJw4cPix7/61//KvwcHBwMX19fTJs2DWfPnsWwYcOsHWanUQJkY7w5Y4AaEyYeEnDCGCAePAOklP8QQho5yxggjuM63A1lCxkZGVi3bh3UajUCAwNNjlvSBebj44OSkhLR8ZKSknbH5bRVx8PDw6T1Z9GiRfjiiy9w6NAhDBo0qN24QkNDAQC///47JUDETBYuhKgXEiAOHN/cBabneUgltDAiIYTYq/z8fMTHxyMlJUUYcNxamfYYd4GFhYUhOztbNH4nKysLYWFhbdYPCwszmSrfsg5jDIsXL8ann36KAwcOwN/fv92YjOP29fW9YVl7ZHECVF9fD5VK1eqxoqIih30hrMl4FpjenEHQvGHMEANnGAPEGVqAdIxB0W1REkII6YyysjLExMQgIiICsbGxJuN0pFIp+vfvb1EX2JIlSzBlyhSkpqYiOjoaGRkZOHr0KLZs2SKUWbFiBS5duoQPPvgAAJCQkIC0tDQsW7YM8+bNw/79+7Fr1y6o1WqhzsKFC7Fjxw589tlncHd3F2L19PSEi4sLzp49ix07duC+++5D37598dNPP2Hp0qW48847ceutt3bmZbIZi7fCGDt2bKvZ6scff+ywL4K1Ga/+bE4XWFMLkB4SYQwQBwadngZCE0KIvVKr1SgsLMSXX34JX19fk9uECRMsPmd4eDh27NiBLVu2ICQkBB999BEyMzNFg5WLiopw4cIF4b6/vz/UajWysrIQEhKC1NRUbN26VdQi9dZbb6GyshIRERGiGHfu3AnAMKX/f//7H+655x4EBQXh2WefxcyZM/H555934hWyLYtbgCIiIjBp0iT8/e9/x/Lly1FTU4OFCxdi165d+Oc//9kdMfY4nHHeYsYgaNa4ECIPCSRGs8B0TAdA3g0REkII6ay4uDjExcV1+XkffvjhdtcQarnKM2D47D5+/HibdW40HGPw4ME4ePCg2TE6AosToDfffBPR0dGYP38+vvjiCxQVFcHNzQ25ubltrkJJWjD6RePNmM7ON3aTMRjPAuOh09NaQIQQQkhHdGgQ9L333osHH3wQb731FmQyGT7//HNKfizAjBYxNKcLTKc17AbPcxwkjYOgpRwTZocRQgghxDIWjwE6e/YswsLC8MUXX2Dfvn1YtmwZHnjgASxbtgzaxg9qYj7GbpzENK0DxAuT4A20xpukEkIIIcRsFidAY8aMgb+/P3788Ufcfffd+Mc//oGvv/4an3zyCSZOnNgdMfY8Ro0+PG/GIGhdYwsQJJDom1MgPU8JECGEENIRFidAb775JjIyMuDl5SU8Fh4ejuPHj2Ps2LFdGVuPxRtPfTejC4w1JkCsRQuQjlqACCGEkA6xOAF6/PHHW33c3d0d77zzTqcDcgoWLoSo0ze1AHGQGPWY0Rgg4uh4nqGsusHWYRBCnJDFg6CbFlZqDcdxbSZIpJnxwGe9OS1A+uZ1gIwzVmoBIo4u/v0f8PWZK/goIQzjh/a5cQXSLloZjBDzWZwALVmyRHRfq9WitrYWCoUCrq6ulACZxbgFyJx1gJq6wCSQGP2F01MCRBzc12euAADezymkBIgQYlUWd4GVl5eLbtXV1Thz5gxuv/12fPjhh90RY4/DW9gF1twCxIExGgRNCGmdc2yFSkjXsDgBak1gYCBSUlJMWodI65jR6s9mJUDCXmAScFzznzieWoAIIYSQDumSBAgAZDIZLl++3FWn6+GM9wIzowvMeC8wo694TesDEUIIQGOA7M2BAwfAcVybt6lTp3bovLt370ZQUBBUKhWCg4NNdnpvK5axY8dCqVQiICCg1e0ymqSkpIDjONGO84BhO42WzyEhIaFDz8EeWDwGaM+ePaL7jDEUFRUhLS0NkydP7rLAejJRq48ZLUBgRgshcsYDqKkFiBBC7FV4eDiKiopMHt+zZw8SEhKwYMECi8955MgRzJ49G2vXrsWf/vQn7NixAzExMTh27FibOzIUFBQgOjoaCQkJ2L59O7KzszF//nz4+vqKNkQFgB9++AGbN29uc3PzJ598Ei+//LJw39XV1eLnYC8sToBiYmJE9zmOQ//+/XHXXXchNTW1q+Lq0RizdBC0oQzPScBxEvCMg4Rj0OsoASKEOCHGAG2tba4td4WoKb4dCoUCPj4+osdOnTqF5557DsnJye1uaNqWjRs3Yvr06Xj++ecBAGvWrEFWVhbS0tKQnp7eap309HT4+/sLn9EjRozA4cOHsX79elECVF1djcceewxvv/02/vGPf7R6LldXV5Pn5KgsToB4M3YvJzdg4SDophYgPSTgJMywHhAYdYERh+ciq4NPrxIAvrYOhTgSbS3wykDbXDv5MqDo1aGqFRUVmDFjBiIiIrBmzRrhcTc3t3brxcbGCslNTk4OkpKSRMejoqKQmZnZZv2cnBxERkaa1GnZxbVw4UJER0cjMjKyzQRo+/bt+M9//gMfHx/cf//9ePHFFx22FahDm6GSzjHeDNWcBIjjjXaDBwc9JJCBh45agIiDeynsX+jncg25Fc8BoJXkSc/F8zzmzJkDmUyG7du3iya05Ofnt1vXw8ND+Lm4uBje3t6i497e3iguLm6zflt1qqqqUFdXBxcXF2RkZODYsWP44Ycf2jzPnDlzMGTIEAwcOBA//fQTli9fjjNnzuCTTz5pN357ZVYC1DLbbM/rr7/e4WCchXgIkBldYI1lDC1AhkQIAHQ0DZ44uH4u1wAAA1U5AJ62bTDEcchdDS0xtrp2ByQnJyMnJwe5ublwd3cXHQsICOiKyDrs4sWLWLJkCbKysqBSqdos99e//lX4OTg4GL6+vpg2bRrOnj2LYcOGWSPULmVWAnT8+HGzTsaZ2S/q7JhoFtiNy3PMqAVIwoFvnLynp60wiIPLQhSyEYW/YK+tQyGOhOM63A1lCxkZGVi3bh3UajUCAwNNjlvSBebj44OSkhLR8ZKSknbH5bRVx8PDAy4uLsjLy0NpaaloP0+9Xo9Dhw4hLS0NDQ0NkEqlJucNDQ0FAPz+++89NwH6+uuvuzsO52I8BghmjKlqLN80DZ5vagGidYCIg9vGGb5R7pVPxmIbx9ITMFoK0e7k5+cjPj4eKSkpJjOujMu0x7gLLCwsDNnZ2aLxO1lZWQgLC2uzflhYmMlUeeM606ZNw4kTJ0TH586di6CgICxfvrzV5Mc4bl9fxxzDZ/YYoHPnzsHf359aeboAz2uFn5k5TUAwtPQwjoNUygkJEE+DoImDkxZWQ/pHDerHyG0dSo/A0UpAdqWsrAwxMTGIiIhAbGysyTgdqVSK/v37W9QFtmTJEkyZMgWpqamIjo5GRkYGjh49ii1btghlVqxYgUuXLgl7dyYkJCAtLQ3Lli3DvHnzsH//fuzatQtqtRqAYTPzllPoe/Xqhb59+wqPnz17Fjt27MB9992Hvn374qeffsLSpUtx5513tjll3t6ZvRBiYGAgrly5ItyfNWuWSZMaMY9e3zx905wxQE1dYDwkkEgMK0IDtBUGcXzy05WQVOtw5azS1qEQ0uXUajUKCwvx5ZdfwtfX1+Q2YcIEi88ZHh6OHTt2YMuWLQgJCcFHH32EzMxMUQJTVFSECxcuCPf9/f2hVquRlZWFkJAQpKamYuvWrW22SLVGoVDgf//7H+655x4EBQXh2WefxcyZM/H5559b/BzshdktQC1nK3355ZdYu3ZtlwfkDHijxMW8BMjw2vONY4D0fGMLkI6WJCA9gxn/DAhxOHFxcYiLi+vy8z788MPtriHU2irPERERZo/nBQwrRxsbPHgwDh48aHZ9R9BlW2EQ84kSIDMGMjc1a/OcBBKplAZBE0JaRWOACDGf2QlQ074fLR8jlmPMuAXIjCRG6ALjIJE2/5GjRSkJIYSQjrGoC+yJJ56AUmnoq6+vr0dCQgJ69RJPRXTUBZGsiVnaBQbjBEjaPAiaxgCRHuiP8lpU1GrR+q5GhBDSNcxOgFr2Y8bGxnZ5MM7COOkxbwyQ4f88JI2zwBq7wGgWGOmBbv+XYdmN822vx0YIIZ1mdgL03nvvdWcczsV4HSAzurGMW4CkMmlzF5he2141QuyeFHr0QyVq4X7jwsTpmbV3IrEb9v5+0SBoGxCtBG3WQoiNZTkJZDLDXmCAeckTIfbsA3kKvlctwnjdaVuHQuxY00J8Go3GxpEQS9TWGpZ8kcvtc50v2gzVFkSbgZnfAsTAQSpvHgNEs8CIo5ss/RkA8LBmP4AXbBsMsVsymQyurq64cuUK5HI5JBL67m7PGGOora1FaWkpvLy82lxJ2tYoAbIxs7rAjNYBkipkRl1gNAiaENKsp06D5zgOvr6+KCgoQGFhoa3DIWby8vJqd48yW6MEyAaYhXuBSYzWAVLIZcIgaF5PXWCEkGY9eSsMhUKBwMBA6gZzEHK53G5bfppQAmQLxgmQhdPgpQq50AXGqAWI9ECRfl/Dt1cJUGDrSIi9kUgkUKloeiDpGpQA2YTRtzQLusAYOMhVcqGZ25xVpIljY4yBMUAi6ZldG62ZHfSp4QdKgAgh3YhGkllZTc056PV1wv1pJZ/esI5xF5hMIYeea5oFRglQT/f4O7mY9vpBaHt4dydn9J1gH+7Fe5hvu2AcmvMkyoR0FiVAVnbs+GzRn6g+2iviWWGtMmoBUiiaV4KmhRB7vMO/l6GgrAYnL1XaOhSr+QDx+B+bbuswHFLPHQFESNejLjAr02jKAAwSP6itAxSubdYRWoAggdRFRV1gTsiZ9t2TH70KyXUNYN/jJ+1STx4ETUhXs2kL0KFDh3D//fdj4MCB4DgOmZmZtgzHelr8jdI3VLdbXNgNHhxUKqUwCwzUAkR6jOZ/FNJrDeC09EHeEc6TJhPSeTZNgGpqahASEoJNmzbZMgybq66qaPd4UwLEOA5ShaK5BcicneQJIU6D0kZCzGfTLrB7770X9957ry1DsAvXqyrgeVPbx40XQpQrXZqnwdNu8D2eSloHuVRH3+yJmeg3hRBzOdQYoIaGBjQ0NAj3q6qqbBhN1xm0825gxP3ArP+0elxivBWGQgmekxi+6tFeYD0aYwybpi03/MxPBuBl03gIIaQncahZYGvXroWnp6dwGzx4sK1D6pjW2qlPfd5m8abvdDwkkClUQgvQjWePEUfGGHAWw3ACt0KvOW/rcIgDoL8IhJjPoVqAVqxYgaSkJOF+VVWV4yZBrWEMaGW2j6RxtWjGceDkSpoF5iQYgFX4FwBgpK6u/cI9yF2SYxjEXbF1GA6JOsAIMZ9DJUBKpRJKpdLWYXQKYxza+jP19q5P0bufDx6aFi56XGI0C4yTyoVZYBy1APVoPGOQH78Grk6HkgfcbR2O1byrWGfrEAghTsChEqCegLG2X/InT801lLmrQrTuCydsmMoBElnzIGhKgHo0xgDplXoAwOVr1NpHCCFdyaYJUHV1NX7//XfhfkFBAfLz89GnTx/4+fnZMLLuI+WlAGu/obq2phq93Jq/8TdvhWFIgFhjcsTRIOgejRmN6HCircBIJ9BXIkLMZ9NB0EePHsVtt92G2267DQCQlJSE2267DatWrbJlWN2LlwpjeNpSVXZJdN94M1RwEqMWIEqAejJq4COEkO5j0xagiIgIp+vGyZLciYFof0CrPPctYOhG4b5E2AtMAkhkwmaoHCVAPRrPN//b6PlbYfT052cd9CoSYj6HmgbfE7wnny8kNG3p98s20dd/460wIJFByxnyVimtBN2j6Xkd3FCLvnCGjVCd64sQIcT2KAGyAbO+pTVcF34UFkJsHAOka2y4k1AC1KPpGcNJ1XzkqZ6GUldj63CIA6A0khDzUQJkA01jemrQzpT+6lKT8gwcIJFCxxm2yZYx2gqjJ9MZDXLvW3vRhpEQQkjPQwmQDTR1adVC1WaZ2mvNA6GbxwAB4DghAaIusJ7NOAFqbYFMQkzQrwkhZqMEyCZunABVXflD+LlpJeimBRCbxgDJQAlQT6YXDYLu6f9U6ZO7S1AfGCFm6+l/Ve1SUwtQXTtdYJ7f/QvQGMZ9NA+aNnxINM0Ck1IXWI8mmgVmwzgIIaQnogTIBpo+zGrbSYBcrhcC/13ZWL6xK6SxG6RpELSMusB6NJ2+OcHV19fbMBJroKYLQoh1UQJkA0ILENf+vmb6k5mG8o2DoDnO8P+mFiBKgHo2nm9OgKqvXrVhJMRhUFMhIWajBMgGmhKa+vZmgQHg66sAABIhAWpsAWocBC0HdYH1ZFq+OcHVaLQ2jMR6nG1h1K5Grx4h5qMEyAaavqS1NwYIaE5wmhIgKWtqAWqaBk8tQD0Zr29+fzktJbuEENKVKAGyAaELjLU9C8yYBzMMhtY2dpnpQS1AzkBn1AJ0o/3jegpqAOoc5/gtIaRrUAJkA00JkAYKs8r3a9wKoaExYeKFMUCUAPVkel3z+0sfbIQQ0rUoAbKBpjFAYDd++Rlj6AvDWCAdZ0iAmlqAFJQA9Wh6o1lg4Hp20whnvNgn6TBnaSkkpCtQAmQTjX/szdjNvb6+Dl6coQtMp3cBYNQCRAsh9mgardHAZ+YcH2w8f+N/E6Q9lEISYi5KgKxMwvTCdzROZ5rA8C0+6K5fM+wJpmMScHrD+j+sqQWIxgD1aHqtxtYhWE3Tb705XwoIIaQrUAJkZRLwQnO/K66bHK9usT2GpvB7AEApvKBqzHdY49smZ84xNdpZ6YxbgHjnSHZ5GgVNCLESSoCsTAIGeePYHZXctAWolmuRAB3bAQDYwyZD3vh5qG9KgKgLrEcTJUBOglEXTic5R1cpIV2BEiArk0AvTF9v7U+9pnGbiyY3lx0AAOS5j4SisRGAa3zbFHC+D0hnwtt4FphGZ73uqKaJAcYbwBJCSHeiBMjKJKy5BQiczOQ438ZbUjTABxKttPGe4f8q5jxjRJyRTtf8/lp7ZMwHOedxy8qvsP90iVWvS+lPZ9ErSIi5KAGyMgl4Yfo6a+Xl17fxllx194Kk8RjPyQEAHqjtpiiJPeB1xi181v2nuuqznwEASz7Mt+p19Tx163YOdYERYi5KgKyMAy90gfFSBRbpFomON+3z1ZLGTQpp47c7nhkSIHeuDvU9fpdw52W8EKKtWLs9gbrAOoteP0LMRQmQDTSt4My8b8XHoTF4UfuEcOw719Eog4dJnSDJaUilho4QGd/8La+6sqx7gyU2I24Bsm4nmIusFuMG5EPKWbeblabBE0KshRIgK2PghPV7mNsAMDc5ar2bE54aiQq33fEJKlkv4bFHR/wL/vXnoZAZvt2pdBCO11IC1GPpRFPfrdu1sfC2dxA15hDuvznTqtel3eAJIdZCCZDVccIgaE6qhHtdDRokzXuC8RIJtBI5pEZT3HP7BMPjej1cVIYPQWUdQwXnBgC4XnHFirETa2J6oxYgK2+F8aVbDF4qWo1i75usel29nsYAdQ6NASLEXJQAWRlD8x5ecrkcM348DK2keTaYQq+BL7sEN655bE+t1BWyq3K4eRh2g1fUAxVoSoCsO0uHWA/TG3cHWfeD7eSxm6E4UY7//XaHVa7XtDioM659RAixDUqArEwCXljBWSpXwq2hDv11za04tXDBrB/+J6ozlJ1DVZkP+gwwdJVxOhmuSgw/ayouWClyYm3GY4CsvRWYpNJwbVZk3TFA1AJECLEWSoCszJsvFTYxlckNXV9Nm5sCQB2UqK/zFNW598gxABIMGOoLAOB4DqflfgAA1ZUTQjmtnsflirruDJ9YkfG2ED19heSm/E6rt/3MN0KIc6AEyAaausCkchXkcjn0XPPXe22LrTAAgOkN09573zQQAMBxDKddhwIA+leeEcq9sOUTbHjtReSepXFBPQFvlAxYeQgQJnCn8X/yV3AzLlv1unpKgAghVkIJkJXxHCesAySVK/Dkk08Ku7sDgI65tFpPxiSQuPYGAEg4hlI3w8/+mrPQZK8FfsxAakk8XpW/jXP/3dTNz4JYA+ObxwBZuwtst/Jl3CE9iS2y9Va5XlN+x3TUBUYIsQ7TvRhIt9Jcl0DBawEOkMsVGDBgAJRG6/owXgXA9FuwjEkApTsAQCIBeFVzHcU3KaKyw2ryuyV2YmVGqyJzNuoC88E1q1yn6bdZr6dB0IQQ66AWICu7elRu1AJkmNXlarynl14hKt/QuOpzL60UUBhmfsmkgELe9grQHC0m1yPwRgkQs3YTUCNrJ156Pf3udgajafCEmI0SICtjek4YBC1XGMb7lHH9heOKevEg5noYEiClhgfkhu4xmZSHG2tnCwxG3Qg9Am+cDNimBchaCVDTdXgtbfDbGcbvV/7FCtsFQogDoATIylRoENb4kSkMLUBaiQqT6t/ArfVb0Ke+DsqiQqMahreIVVwEGgdLK1QcvLTX27yGxg72kCKdxzN7SICsi+fpd7erxGz61tYhEGLXKAGyspdl24Sf5Y0JkLtMh2L0RRXc0Edajv4VQ4UyPCeBy8ULGCRpnu3l7sFBVdv2B6KuvQ+R/f8ENk0C6is7/ByIlRgnQDbqApNYbQ+ypo1+qQuMEGIdlABZ2SOyg8LP8sZ1gDxlOmhu64OG0H7w9+uFEWczUF5n2OvrJ4xAyNlyRNwqF+p5D/KCsrrt8euS9rrADr0KXDkF5L7dyWdCupt4Y1DbJAbWHlPC00KInWL8fk3wPmbDSAixf5QAWRlv9E1eoTSMAXKVuoMf4ALmpUTgTTdhwANXUOeiw1e6KSit6Y3IsAPoNdBPqNd/oC9UtXKTczfhzBgDdKWcWoDsHeNtv/ihtQdB8zQNvsvcNLrc1iEQYtcoAbKyUngJPysaB0EPG+AvPOY6YCACeS0GogHTZQfxcK+9kCl5wN1XKOPZzxsSjRvubVjb6jXG8qeB6tJ24ygopQTI3hm3ADnLLunGM9+I5YwT1k9+mG7DSAixf5QAWdnvaN5dW6EwdIEFgEfouZ9x16mjULg3b4Mh6nzwHCT8KHFxg0wvQ/7kCXhJPtfkGi7QADtj242Dow8au8ds1O1lS5QAdR1JFa2pREh7KAGysjLOsInp5/pJUMoNK0D3DRyC2y7+hltK/4DUe3hzYe/Rhv+PngmMerD5cdc+UGk5sF5yZI2KaP1CF79HzdoAXPn0hdaPM5ptY/ecKP9pSvYZ70RPuhsYjwHy54psGAkh9o9WgrYyKccDDDjKD0e01JB/9rklGLPDf0av3n2BvsOA2I8BN2/AJ7j1k/TqD1mDHsvK/4Xf3PxbLwOgV8MV9PrxLSDkXuDmKeKDNN3Y7olbgJwjMdBTAtRlvlY+C2C+rcMgxG5RAmRlcgkP6AF3mRYSSfO3teH3PNpcKCCy/ZMo3CCrq8T1EwEYqqq+8UU/eABlD+5Cv1ujmh+jrga7Jx734xwrQdMsMEKItVAXmJU1rasyQFF3g5Lt4DgouQIwJkVdneeNywPo98kj0JRfEu7X6mjFXbtnnAB1chB0vVaPj/P+QFl1QyeD6h5NiRajBKhTnGOoPCFdgxIgK2tKgBjXuZd+lN8fws8XJL6iY4+NTsHfeieY1Pll72bh5wZaLdoBGM0C6+SZ/rX3NJ7d/SMe2ZzTyTN1Lz1PA3c7g3YCI8R8lABZmaxpanMnE6DhQwcKP/+HfxDlcBPuX+7ljndunY1ZAf8S1amtal4XhKMxQHbvRo0+xy+U4/tzV806V/YvhZg6+Btcq7p048I2IAyCdpLp/t3F2l2WhDgySoCsTGgB6uRLLxkyCSMuX0CfWik0UOCYNFA4dnd+PmZqdkA2sBIRIe8Ij7Pys8LPUpoFZvfEg6DFH2w8z/DnN49g1pbvUF5z4+7MMUMOItd1MiJuy7UoBuuPAaIP8M6g/JEQ81ECZGXC3kpcJxur/e/ErIEfY4EiFUPP/YE6Tikc0jS4QXMwAD8fHIzpnp8gyXcpAGByffPmiFK+nd3kiV0wbg1p+bmmZwx3DP4W0/wP4qoZCdDe0jtRc0aCT4/Z9+J4zIxVzEnbqAWIEPNRAmRlsqY/8J3sAsPAscCAkZDJGB4d9BEaOIXo8CD5ZQzTaLHjwJ9Q62OabMn1dWg4lwO+tqJzcZDuY/RZ1vKDTafX4hv5NOzj78M3Z3654alUV2uwRPox/LX22QXWlOtRF1jn0BggQsxHCZCVSdE1Y4DAccDMd4CASKgUDPWS5gTIjanAOCBYWYAYVoTcE6ZrBYXrfoLyg+mQvDoEFYUnOhcL6RacaBC0ODGo1WqBc3WQFVZjw7dnUVXf/uDhNXgPS+Uf4wvF3yyMwbr0tDwDIcRKKAGysuYuMGnnT+Y90rBoYuTf8VmvyQAMK0wPuNp8bon0Oh7UtL8irNd7t6P8xL7Ox0O6lKgxpEXDiE6vx3/kr2CPYiVGDf0NFY3dYBodj3qtaRIxEacBAG6cfXZ9Nk+Dp4UQCSHWQQmQlQktQF353fr2RLgFuCFI+X9I8F+JQXV7MPi6O4ZoegMAdJI6/NX1+XZP0fvjR6D/e19U/ZDRdXGRTjFu9eE4cQbUoG3A7dKfcaukACU/+qDk+iUwxnDHq/tx28tZ0OgcM5GgLjBCiLVQAmRl0sZp8Fxnu8BaePi2+1AxyQ96f3dMHfQTbqt+A54/nsHNVwxdY72lN154Ucp08FA/1aVxkc5oOxloaGhuyZklPYDzVfXQ8wzD+n6DCUO/wYVrtVaIr+s07wVGXWCdQYOgCTEfJUBWJrQASbr2pZ/ucxO2jfbH98NdwN08BWN9y3H7rTvR648fMfRyL8g0zX8Yr8Edi4KS8XqvR1s9l+bvA1Ca/gDwx9EujZFYiLV5B9crq4Sf/ypTQ1J3DRqdFoevTsaBP+7AtZo/0BWs/YHKHHTPs3qtHruPXkTpddt2MVICRIj5KAGyMikav+FKumAMkBGO4zC9vyeGDBwOPP4pcPtSDFDqcP+te9G7+isotc3T5K/AE0OvluBQ0Cg8OupVaCGORcEaMKD4ILB1GqqOfQzUXuvSWIl5RAOfW3QN1deJ94CTNVxHtUaDR2v+i3jNF/jl6nVrhNj1HPTz+9W9Z/Dip9/j4XT7Xmn7ncMFCFubjcKrNbYOhRCbowTIyppagKRdMQi6LRIpEPkSEPsJFJ7emHFLHoaVfy8c1nFSVF/xwJi8awgoLELU5H+3eSqPPfOAV/2hu5jXffGSG2o5Yqy+VtzFVXHxIuo19XhV/jZWy/8PitqyLroubYZqjl+Kvsag4HIM7H/I1qG06z+H1Rgz+CusVdO/Z0IoAbKyLp0FdiMB04Cnc4Dh0YjwOi08rINM+FlWDdxx+FfM8nsF/6eMau0shnLv3IWL/1lAS81ak2gzVHHXkLZBnABpqqtRa9QqxNWLW4CYgywQwzPH7AI7zbxxPn8Avr0cZutQ2lU50Afq83dD43qsy855vV6LospObO5MiI1QAmRlssYEiJNaIQECgF59gUe3w31q8ywwDgy+dW5w51WG+5wEPrIr+DhkKs7JDRurlkq88J10pOhUg3/fjt83/gnlp75G5YE3wKqvWOc5OK22k01NvbgLg+l1qLrWvC9Y+fnzLc5k3xmQ0NLEO2aCPbzoPM4o4/BkzWe2DqVdN50qwvL6/yD//MAbFzbT+DX/xR3/2ofiSvtcYoGQtshuXIR0JQnjAQ7grNEC1ITj0GvSXGD/CwAMH4ZTalJx+kwIqn2n43fvCvQ+B/Q+V4JMTIduaAOO+IzCJdlA3PVrHv5VmiacKqDiMLDzsOHOgZWomLoWXrc/CUjl1ns+TsNoGnyLIxqNePsLPdOiuqI5AeJ1LbuSOpYAWTtt4h10ENA6pEPKMbwo/w+ATTaL40Zdlp8rVwIA5PV6AI90yTVvDT6JCuaFH84V4v7bhnfJOQmxBmoBsjJhDJDMigkQAKncaK8wiRJBAyWIGZODW7lUjD5eDC+dCwCgCl6oPe+NMd+VIebIcWgH1WHEnZ/ggYD1KGduJuf1+noF9Gu8Uf3rYeoe61bi11avbRAfZXrUVRkNVm+x15ujvDPMQbvA7OX1NTdhDdRfwqWKrum2KvrRG64/1eOb4lNdcj5CrIUSICtrmgUmkVi58c1o89UGhRew/DzwyAe4ddRAPBjyIW75438IKJAg4Jq7UE7LS+FxzAOPHTqMkdfPYuadKQgK/RRvuf4Zl1kfVDFD0iSFHm47olG7ZhAKt82H7srvQIODzkKyI6JZYC0WQtQ1iFuAeB6oq2meGs94cSLB23kXmMBB1wGyhy7GwrIa6HTmfbHqra/C+WsXuuS6h5VL8JlyFbhrpV1yPkKshbrArIjneaMWIMUNSncfudzF0GU1cgYQ9CdITu3BfYdSoS9KxYGLflCVRKOhvw+07hzOK8uhA4OqRI6Ikt/h4vkz/jd6PP7ukoh+daU4+v0cqDjDPlSufDWGnN8NbNoNAKiX9sK121+C76g7wPW7pcun/vd8be+FodOJEyDGdNAYLY7I87oWZ7L9B7Q5mKOM1m7BHl7fX6+ew1j2q1nNQGMk57D9ajkm39x11x9YduNNeQmxJ5QAWRHP64UEyGqDoFuhVLg235FIgVF/BkbGQPrrPkz7diP482/hWKkXCk7fjiDPMWD9lKhw0aFEWoWaShWCc8sQjE/h3qccTwUlo17vil9kQzGr8Esk1WbAlTN0z6j0NRh48HngIHBd6omrN01Dv3F/hlvQXYDStDuNiBnnAlzLPhZdiwSH56HVNBjdF7ekdPQDWmJy4e7RNHZF76ALIdqDsvKr8OUsWLOr+FcAk7ouAE3DjcsQYkcoAbKihoYGIQGS2XDQsEsvd9MHOQ4YPh0YPh2Skp8x/od3MP6nXSiu3Ie8Ej/wVRPg0d8fzNMFF12q0cBpcf1aHwytLIen228YPfBn/DTuZtxVvhlzz3yCp3R7RKd311fC/cInwIVPoIMURW6joB9yOwaMvAOu/pMA1z5WevYOxLgHzOjOxWu1uFQpXgiRMT10RuOCGHPMriRHnQVmD7TnfraofPWlS52+JmPGqTW9d8SxUAJkRXUN9XBtSoAUyhuU7j6Bg28wBdZ7FPCn14GoV+BTcAjRp78AznyJs1cr8XOxH/yrJkDSaxC0A9zwR69aVFV6A5XASFRgTL+v8dMof9ws+wJ9GypQoXIDqwbGXvkZ068dRqT+KPwkVzC4+ifg55+An98EANRxrrjmfguq/e6C7/gH4OF3q9N3mbE2Wl8WbH8Xd5ZfAoxyaMZ46PVG3WKs5Rggxxjux9MYoA5rqKi0qDyv7fy0da1Oh6bOfNqGgzgaSoCsSFNbC7emBEhmg5d+ciLw8yeQhC0wr7xcBdxyj+HGr8ewP37AsNNfAGe/hvZSBvIu+0B59k7ofYZD7y7DdSWP0itS9C/j8RdkA+ChdKkG+tWhbJAnPhtxJ1I0T6J3aSXuLD2KCbUnMY7/FcMkRXBhtbipKh84mQ+cfB11UKHYNRDVXsMh6ReAXgNHoN+QkXDzHuY8iVGLFqCKWg0yfjiN166kI0h+UVyUMehFqyiLE6Cu/GiqqNVAJpXATdn1v8N63jG7wOwhAWItxn0BjS00XOux8V3Q3Vir1cB2oxkJ6RxKgKxIo2mArHEWmFyusn4Ad//dsEVGG38Q2yWRAn6TDDcA8poyTPrtv0D+DlSdysR31/zAqkbCU3Ez4OmBBhcJeDlQzXugvLYG7hcZbkMJJql+B9evFhUBKux1m4BN+odxpbI/+lddQ1jlj7i7NheT+J/Ri6uHf+0JoPYEcBnAT4YwNJDhonQwKl39oPcYCln/YXDzDUD/QYHw8OoHicoDkPaQX2vR28Sw/P038NBlNYKkF1spzMTjfljLBKhrPqBrGrR47J3N4CUcvlq4qEvOCRiNAeK1XXZOa7KHtg9dKy2GOp0ecnnr/x74Lkg2KyrK4dX4s5WGixHSZXrIJ4VjaKivg4xr6gKz0femjiQ/renVDxgzBxgzBx6aWtxz+Thw8Tuwwu9w/cIpXKuqwa9XBqCsZjgGSIaAc/eE3lWOek0fVNVq0HCxFn6cHv7SU1C65IIpedT2l+PDvndhNZ6E6ooOI6+fwy11F3Cz5hL89ZfhjyKoOC2G6QuA6wXA9YPAJQD54tBK0RuX5INQ43IT5AoFdNWlGFRfgGpJLxQPCMPQCTPhffMouPbyhESu6rrXpItxLdZVWln0FgZLW199m2O8qPuoZXdERxMgPeNEW+V+/uN3UF81LKZXVTcPHi6urVc0g1arE3rxmuLTO+heYPbQAsTrTZPH2vpaeMo9kPHDMez45RiMR+Z1RXdjZZnx1HfKgHqSylot/v31/+Dr7o3ZobeiVze0+Npaz3tGdkxT37zwmFJpgxag7qJwBYZOBoZOBncH4AHAgzEMrbgAlJwEik+Av5SH4t9/RsEfGpRW34ybJIHQufWH3sUdvGIAeCkHrYTB7VwdBklOQ+VyHXpPPS717YNfVQNRqXJDOdwhqQYGVFVhYG0ZhtZdxhDtZfjpS+CLa8LsswEoxwBtOaA9IY6TB0YVnwM+3y48pGVS1EKJGs4FdVCilnNBnUSFek4JLSeHlpNDJ5FDx8mg4+TQS5pvvEQOvUQGcBz4xpRDDw6MATxn+L++8XFe4Qovn0AwMOh4w4ePjjHoeT30PA+eZ9CDgZPIEHvPn3FT7z7gjFpxJGAYLGl76xEGBgbjFqCWs8A6hodElABd++k74eczhWcxISi4g2cGrtfVoGnoO88Zxii1nL3mKLorAeJ5BonEvHO3ltDU19fB090Dud9+hNRrmaKV31ouldARVVVXb1yIOJwrVTV4+sOtuOuPoyiXuWDumWD839wFUFp5Ad/uRgmQFdUbDTqUqzr+zdkhcBzQe4jhFhQNCYCBAAbqGoBrBcDV34CyX8GuFaCs8gpKr15F2RUNSi/2hY4LAnPxBS+Xgpdx0Es56KQMGkkdGiR6aGW10PfiUeB1E065DEFNLxUq5a7Q8VJINXr0qtegb20VetdfB6fjwcsluOLmgV4NDRhbcQZ3aH9Cb84wi0rO6eGJWniicXNRQxbTPS6bV+zD39WY/bdPwInGaLSfwnDgwfMSo/stW4A6NghaD4nxWGtojRZb/P3nvE4lQJUV5UICpG+Kz2HHAHW9Q7+eQeae9yC96Ra8NnvejWNoJaGpraoE+nvj9YpNpsvednCmYHFlFVbu+xLzJ4aiprK8Q+cg9m3Vf/6Nt0vS0FtaDTDg4h/ZeHa7FGlxZo4fdRB2kQBt2rQJr732GoqLixESEoI33ngDEydOtHVYXU5Ta9QCpLLdLDCbkimBAUGGGwzDXPo33gAAOg1Q9qshQaq9Cr76Kq5fuYSy0lJcKa3HtUoOtTUu0Ok9oZd6AlJXMLkCvJwDLwWYRAatTIoGiQJ6uTsk4MFVuWB4sRR6KcNJN3d87T8eDVIZwAAJ00Oi5yHjech4HRQ6PZS8BkpeAzmvh5TpoeB1kDE95EwHOdNDxnRQMF3jfR24xqnAksb1lrnG/zfdlzAennwNvPlraBrY0/SByYzucwAGoAKPaPbjy6/ehxsT7/jeHg5MNKhV0vjz9dpqHFk3A1FobdzQjbVs2dAbdbOUl3TsnE2ulze3aDWtVM13W/bpeL7N3IjXa7fjj9P9ANw4AWqtBaiutp0V2Tu47cgb217Bm9e24M1zf8ZQ/9AOnYN0jYvXKrDj2PdQyuT4y/hQ9HHr1elzrsvMwIqSd9Cbq0YJ1xtypsVgrgxPn0vHpv8OxsJ77u+CyO2DzROgnTt3IikpCenp6QgNDcWGDRsQFRWFM2fOYMCAAbYOr0tpjaYpq1Sd/0XtkWQKwGe04QbDl1bPxtuwlmV5HqivAOrKDbf6CkBbB1Z7DfVXLuJqUTGuVdRj0FBf9Ll5NDQ6Ht/uLUDZDwroeTkYJwXjJDB0MMnBcwqAk4BxhvQFHMA4iSFZkRjSmnoO4MDBeC921ur3fyY6KuGkkHLNI14Mw3vEZfScHkM89yJSdwz3ff+M6GwLaj9u92VbUvMf0f3FtTvxzcunEMhfRBTE39I/XhsDPWTQc1LwnBQ8JwGTSMFLZOAlcvASGeY3lnXhNMhIeQia/iPASWUYXH9aOI9r1SlkfvE2pDI5ZDIZZDI5pDI5JDIZ5FI5ZHIFOKkcUonhuFwmh0wih0wugVKuQklxgSguvVYDrgOtEr8WXcK1yjJMCgqxuC4AnDj/Kwb384aXm6dZ5ctrqtG7l3ghT2k3LOB4e71h5P8grgxf5R7GyYPboe3ljeQFL7VanmOmLUD19W3v98VxHUs2p1V8BwWnR2L9R9heO6r5fO28Bmf+KMT7X+3EvAceR4C3b4euS5qVVlXhrXdexrTKHCSy09BBim8PjsaRvuF44a+roOrgGNNDJ/MR9uO/4cddwUWuP07UTUeNXoKpvT7FKK4QFTlrcdT/FowP7Bmb3nKM2XYHy9DQUEyYMAFpaYYdx3mex+DBg7F48WK88MIL7datqqqCp6cnKisr4eHhgW+yP0fBudMAYxCeFmv6sDF8PxY2W2TMcAxccxnDfxr/GfPCt1+ONe9RzTUdZ43FOQBGx5uuywkfchBm8+j4BiTWvAsAuBx/HAMHd+E69MS+6bVAfSXASQzdg5yk1dvrKS9hgv4gwvUnIaVpNYJqpgIPTljPiAODBAxS8MLYryL0AQ9O1MbW1H7FgQndgsaPyaGDV2M6W8gGoELiDsNfgeYaTGidkkABLQL4SyjheqNU0huMGcpMZs2LEB6S3GpSV3yfa3wuHBhnuI4QGzM8Jwl4TOOPt/paqGWThBZDcM2D5W/X/gQPTpzwfCWbiDpOiQe135ic5xL6IkcR3BhXk+Z4DYk5J7xe4Bgk4PFww0Gh9NeSMZjK5wMASpkX/qca32rM0+qPwpurAADsUEaKWj7Bml8nw6vDhHeaYzwkja+epPFxjjHhMR6GLyw8OOg4wyun56TQcxLoG0evGT83Bs7wxabpIQbRJAjhrzcHISLGINxv/uveeL+pKuMg3sWlxTk58WPGZUS/lY3XMvxeGV4NMGb4UsY4yJgWXvpKjNecwnD80eprfRS34EfVLaiTqAyvEsc1v96cRHiVDR3jPCTg0MBL4cpqMLXuKIJwEbVMiU/YbMS+vBHgebz/UiJmcjvhxtXjGAJxxDUEek7R2LXe1LfaytY9jBl+Rw1vstFUQYb6Og1eWL1N+Py2BZsmQBqNBq6urvjoo48QExMjPB4XF4eKigp89tlnovINDQ1oaGhe7baqqgqDBw8WXsAt//oL/lonrmOvrjx9Bv29fWwdBrEz+vpq7Fi1Fhe8ldDIdOAkzQOpJZwe1xVS9GrQoVYJSHUSaDke7rp6KHR6cBxDFaeCG18LF6YTPnBd9HW4ub4I7nwtzikN375l4CFjhi4+WeNNCj3kjf9XMQ0m84YP9TMYhErODVLoIWM8QnAWAPArboK08Y+olBk+tOXQQQIessYPcRkM2780fahTUkdI16hlSnylCgOq/KHj9HB3+xX3NOQKM4076hpzw8eKGDyZnNacHDKG9LUL8UTDLmHvx86qamDwTLlu0wTIpl1gZWVl0Ov18Pb2Fj3u7e2N06dPm5Rfu3Yt/v73v7d5PgYJ6pjC6H7ri7SLH29rIXfTMm2fw/xz85DggPI2zKLkh7RCqnLD46/+s9vOP8X4Ds8DvNYwFoTXg+m14DUaaBpqoa2rQW0vD7CGOvSuLIeHRgN9Qz0UChfU9umPi7//irrK62A6PTRaHfQ6LfQ6Bl7PG246PfSMB9Mx6BkDeB56nkHL66FnhlZUntNDBwmg06BaqgfHcWBSCThOAqlWhwaJDgqtFHVywx9cwzds1tw6wxq/3TIOHKcFx+nFrS6MAy988zZ00LDGr//NLS8AJ2tAL74WgMRwLkPlxv83DyhvehwyHXhIDN99jVoj5NCgmlNBJoqi6dtvU9SGk0oayxi+gTc9JzS2ZghtHeinvwYXfQOK5QPQj7+GKq4X6qFsPAsaWwaEq8CN1cKTvw4dZKiWuELPSRuTZwY/bREKZb64RXMBZVIvVEiat8RpOWheaNgweg0YDH+/dJwMIzXncEHuAx4S9GJ1cOHrcUXau83fOxn0mNWwH3tlE3FN6mF0DSZqyWLCjEpOeC2aWsyMWzOa3semoxLGN7YS6Q3JOOMhbRxPJnofwETrFRm3DDa/V81tPOJ2u6ZGDNM6HMSvIWfUE9AUg7hNsLme0ErCmuNpbpRiovPXcC4ok3mhXNIHt3iPxcwnFope54/+/QrK607DW3cFrnx94xcPBilr/CLC9JDBqMeiKUKOw3WJK87Lb8KAvuPx5Pyl4jeQ45CQ/CY+esMXtTUn4actgitfDzkMYyOBxpmvRi2c4pbUpt9vifB4DacDcLTlr4pV2bQF6PLly7jppptw5MgRhIWFCY8vW7YMBw8exPfffy8qf6MWIEIIIYTYv5ZDWGzBpi1A/fr1g1QqRUlJiejxkpIS+PiYtpAolUoolU46e4oQQgghXcamOyQqFAqMGzcO2dnZwmM8zyM7O1vUIkQIIYQQ0pVsPg0+KSkJcXFxGD9+PCZOnIgNGzagpqYGc+fOtXVohBBCCOmhbJ4AzZo1C1euXMGqVatQXFyMMWPGYO/evSYDowkhhBBCuorN1wHqDHsYREUIIYQQy9jD57dNxwARQgghhNgCJUCEEEIIcTqUABFCCCHE6VACRAghhBCnQwkQIYQQQpwOJUCEEEIIcTqUABFCCCHE6VACRAghhBCnQwkQIYQQQpyOzbfC6IymRayrqqpsHAkhhBBCzNX0uW3LzSgcOgG6evUqAGDw4ME2joQQQgghlrp69So8PT1tcm2HToD69OkDALhw4YLNXkBiUFVVhcGDB+PixYu0L5sdoPfDftB7YT/ovbAflZWV8PPzEz7HbcGhEyCJxDCEydPTk36Z7YSHhwe9F3aE3g/7Qe+F/aD3wn40fY7b5No2uzIhhBBCiI1QAkQIIYQQp+PQCZBSqcTq1auhVCptHYrTo/fCvtD7YT/ovbAf9F7YD3t4LzhmyzlohBBCCCE24NAtQIQQQgghHUEJECGEEEKcDiVAhBBCCHE6lAARQgghxOk4dAK0adMmDB06FCqVCqGhocjNzbV1SA5t7dq1mDBhAtzd3TFgwADExMTgzJkzojL19fVYuHAh+vbtCzc3N8ycORMlJSWiMhcuXEB0dDRcXV0xYMAAPP/889DpdKIyBw4cwNixY6FUKhEQEIBt27Z199NzaCkpKeA4DomJicJj9F5Yz6VLlxAbG4u+ffvCxcUFwcHBOHr0qHCcMYZVq1bB19cXLi4uiIyMxG+//SY6x7Vr1/DYY4/Bw8MDXl5eiI+PR3V1tajMTz/9hDvuuAMqlQqDBw/Gq6++apXn5yj0ej1efPFF+Pv7w8XFBcOGDcOaNWtE+0nRe9F9Dh06hPvvvx8DBw4Ex3HIzMwUHbfma797924EBQVBpVIhODgYX375peVPiDmojIwMplAo2Lvvvst+/vln9uSTTzIvLy9WUlJi69AcVlRUFHvvvffYyZMnWX5+PrvvvvuYn58fq66uFsokJCSwwYMHs+zsbHb06FE2adIkFh4eLhzX6XRs9OjRLDIykh0/fpx9+eWXrF+/fmzFihVCmXPnzjFXV1eWlJTEfvnlF/bGG28wqVTK9u7da9Xn6yhyc3PZ0KFD2a233sqWLFkiPE7vhXVcu3aNDRkyhD3xxBPs+++/Z+fOnWP79u1jv//+u1AmJSWFeXp6sszMTPbjjz+yBx54gPn7+7O6ujqhzPTp01lISAj77rvv2DfffMMCAgLY7NmzheOVlZXM29ubPfbYY+zkyZPsww8/ZC4uLmzz5s1Wfb727J///Cfr27cv++KLL1hBQQHbvXs3c3NzYxs3bhTK0HvRfb788kv2t7/9jX3yyScMAPv0009Fx6312n/77bdMKpWyV199lf3yyy9s5cqVTC6XsxMnTlj0fBw2AZo4cSJbuHChcF+v17OBAweytWvX2jCqnqW0tJQBYAcPHmSMMVZRUcHkcjnbvXu3UObUqVMMAMvJyWGMGf6BSCQSVlxcLJR56623mIeHB2toaGCMMbZs2TI2atQo0bVmzZrFoqKiuvspOZzr16+zwMBAlpWVxaZMmSIkQPReWM/y5cvZ7bff3uZxnueZj48Pe+2114THKioqmFKpZB9++CFjjLFffvmFAWA//PCDUOarr75iHMexS5cuMcYYe/PNN1nv3r2F96bp2sOHD+/qp+SwoqOj2bx580SPPfjgg+yxxx5jjNF7YU0tEyBrvvaPPPIIi46OFsUTGhrKnnrqKYueg0N2gWk0GuTl5SEyMlJ4TCKRIDIyEjk5OTaMrGeprKwE0LzpbF5eHrRareh1DwoKgp+fn/C65+TkIDg4GN7e3kKZqKgoVFVV4eeffxbKGJ+jqQy9d6YWLlyI6Ohok9eL3gvr2bNnD8aPH4+HH34YAwYMwG233Ya3335bOF5QUIDi4mLR6+jp6YnQ0FDRe+Hl5YXx48cLZSIjIyGRSPD9998LZe68804oFAqhTFRUFM6cOYPy8vLufpoOITw8HNnZ2fj1118BAD/++CMOHz6Me++9FwC9F7Zkzde+q/5uOWQCVFZWBr1eL/rDDgDe3t4oLi62UVQ9C8/zSExMxOTJkzF69GgAQHFxMRQKBby8vERljV/34uLiVt+XpmPtlamqqkJdXV13PB2HlJGRgWPHjmHt2rUmx+i9sJ5z587hrbfeQmBgIPbt24enn34azzzzDN5//30Aza9le3+PiouLMWDAANFxmUyGPn36WPR+ObsXXngBjz76KIKCgiCXy3HbbbchMTERjz32GAB6L2zJmq99W2UsfW8cejd40n0WLlyIkydP4vDhw7YOxSldvHgRS5YsQVZWFlQqla3DcWo8z2P8+PF45ZVXAAC33XYbTp48ifT0dMTFxdk4Oueya9cubN++HTt27MCoUaOQn5+PxMREDBw4kN4LYjGHbAHq168fpFKpyYyXkpIS+Pj42CiqnmPRokX44osv8PXXX2PQoEHC4z4+PtBoNKioqBCVN37dfXx8Wn1fmo61V8bDwwMuLi5d/XQcUl5eHkpLSzF27FjIZDLIZDIcPHgQ//73vyGTyeDt7U3vhZX4+vpi5MiRosdGjBiBCxcuAGh+Ldv7e+Tj44PS0lLRcZ1Oh2vXrln0fjm7559/XmgFCg4OxuOPP46lS5cKraT0XtiONV/7tspY+t44ZAKkUCgwbtw4ZGdnC4/xPI/s7GyEhYXZMDLHxhjDokWL8Omnn2L//v3w9/cXHR83bhzkcrnodT9z5gwuXLggvO5hYWE4ceKE6Jc8KysLHh4ewodIWFiY6BxNZei9azZt2jScOHEC+fn5wm38+PF47LHHhJ/pvbCOyZMnmywH8euvv2LIkCEAAH9/f/j4+Ihex6qqKnz//fei96KiogJ5eXlCmf3794PneYSGhgplDh06BK1WK5TJysrC8OHD0bt37257fo6ktrYWEon4Y0sqlYLneQD0XtiSNV/7Lvu7ZdGQaTuSkZHBlEol27ZtG/vll1/YX//6V+bl5SWa8UIs8/TTTzNPT0924MABVlRUJNxqa2uFMgkJCczPz4/t37+fHT16lIWFhbGwsDDheNPU63vuuYfl5+ezvXv3sv79+7c69fr5559np06dYps2baKp12YwngXGGL0X1pKbm8tkMhn75z//yX777Te2fft25urqyv7zn/8IZVJSUpiXlxf77LPP2E8//cRmzJjR6vTf2267jX3//ffs8OHDLDAwUDT9t6Kignl7e7PHH3+cnTx5kmVkZDBXV1enn3ptLC4ujt10003CNPhPPvmE9evXjy1btkwoQ+9F97l+/To7fvw4O378OAPAXn/9dXb8+HFWWFjIGLPea//tt98ymUzG1q1bx06dOsVWr17tXNPgGWPsjTfeYH5+fkyhULCJEyey7777ztYhOTQArd7ee+89oUxdXR1bsGAB6927N3N1dWV//vOfWVFRkeg858+fZ/feey9zcXFh/fr1Y88++yzTarWiMl9//TUbM2YMUygU7OabbxZdg7SuZQJE74X1fP7552z06NFMqVSyoKAgtmXLFtFxnufZiy++yLy9vZlSqWTTpk1jZ86cEZW5evUqmz17NnNzc2MeHh5s7ty57Pr166IyP/74I7v99tuZUqlkN910E0tJSen25+ZIqqqq2JIlS5ifnx9TqVTs5ptvZn/7299EU6bpveg+X3/9daufEXFxcYwx6772u3btYrfccgtTKBRs1KhRTK1WW/x8OMaMltAkhBBCCHECDjkGiBBCCCGkMygBIoQQQojToQSIEEIIIU6HEiBCCCGEOB1KgAghhBDidCgBIoQQQojToQSIEEIIIU6HEiBCCCGEOB1KgAghhBDidCgBIoQQQojToQSIECfzwgsvQKlUYs6cOWaVj4iIAMdx4DgO+fn53Rucg3riiSeE1ygzM9PW4RBCzEAJECFOZsWKFUhNTcWHH36I33//3aw6Tz75JIqKijB69GjR4zk5OZBKpYiOju6OUG8oIiICiYmJNrm2sY0bN6KoqMjWYRBCLEAJECFOxtPTE/Hx8ZBIJDhx4oRZdVxdXeHj4wOZTCZ6/J133sHixYtx6NAhXL58uTvC7RIajaZbz+/p6QkfH59uvQYhpGtRAkSIE9LpdHB1dcXJkyc7fI7q6mrs3LkTTz/9NKKjo7Ft2zaTMhEREXjmmWewbNky9OnTBz4+PnjppZeE49evX8djjz2GXr16wdfXF+vXrzdp1fnoo48QHBwMFxcX9O3bF5GRkaipqcETTzyBgwcPYuPGjUL30/nz54XrLlq0CImJiejXrx+ioqIAAA0NDXjmmWcwYMAAqFQq3H777fjhhx9E8S5evBiJiYno3bs3vL298fbbb6OmpgZz586Fu7s7AgIC8NVXX3X4dSOE2AdKgAhxQitXrkR1dXWnEqBdu3YhKCgIw4cPR2xsLN59910wxkzKvf/+++jVqxe+//57vPrqq3j55ZeRlZUFAEhKSsK3336LPXv2ICsrC9988w2OHTsm1C0qKsLs2bMxb948nDp1CgcOHMCDDz4Ixhg2btyIsLAwoXuuqKgIgwcPFl1XoVDg22+/RXp6OgBg2bJl+Pjjj/H+++/j2LFjCAgIQFRUFK5duyaq169fP+Tm5mLx4sV4+umn8fDDDyM8PBzHjh3DPffcg8cffxy1tbUdfu0IIXaAEUKcytGjR5lCoWDR0dFs5MiRNyw/ZcoUtmTJEpPHw8PD2YYNGxhjjGm1WtavXz/29ddfm9S9/fbbRY9NmDCBLV++nFVVVTG5XM52794tHKuoqGCurq7C9fLy8hgAdv78eYtimzJlCrvttttEj1VXVzO5XM62b98uPKbRaNjAgQPZq6++2mq8Op2O9erViz3++OPCY0VFRQwAy8nJMbkuAPbpp5+2GishxL5QCxAhToTneTz11FNYtGgR/vKXv+C3336DVqu1+DxnzpxBbm4uZs+eDQCQyWSYNWsW3nnnHZOyt956q+i+r68vSktLce7cOWi1WkycOFE45unpieHDhwv3Q0JCMG3aNAQHB+Phhx/G22+/jfLycrNiHDdunOj+2bNnodVqMXnyZOExuVyOiRMn4tSpU63GK5VK0bdvXwQHBwuPeXt7AwBKS0vNioMQYp8oASLEibzxxhsoKyvDyy+/jODgYGi1Wpw+fdri87zzzjvQ6XQYOHAgZDIZZDIZ3nrrLXz88ceorKwUlZXL5aL7HMeB53mzriOVSpGVlYWvvvoKI0eOxBtvvIHhw4ejoKDghnV79epl/hO6QbzGj3EcBwBmPwdCiH2iBIgQJ3Hp0iW8+OKL2LRpE3r16oXA/2/njkEaB8Mwjv9baxSMigU7KBRRFFIQRCfp4lCEiqDQUcShDnVx0k0QNxFSdHHr4tKxSxcHHdyCYtRNURDBRQdRUAcVb/CuUE5se+hJzfODLkl435Dp6ZvvS3c3dXV1Fa8Den5+ZmNjA9u2OTg4KPwODw9pa2sjm82WVaezs5Pa2tqiRci3t7ecnJwUXefz+YhGoywtLeG6LoZhkMvlADAMg5eXl7L6dXV1FdYE/fH09MTu7i6RSKSsGiLycwRKXyIiP8Hs7CzxeLzwzZ5AIIBlWRUHoHw+z83NDclkkubm5qJziUSCTCZDKpUqWaexsZGpqSnm5+cJBoOEQiEWFxfx+/2FKYvjOGxtbTE8PEwoFMJxHK6vr7EsC4COjg4cx+H8/BzTNAkGg/j97/+va2hoYGZmptAvHA6zsrLCw8MDyWSyomcgItVPEyARD8jn82xvb7O2tlZ0vLe3t+IAlMlkiMVif4UfeAtAe3t7HB0dlVUrnU4zODjI6OgosViMaDSKZVnU19cD0NTUxM7ODiMjI/T09LCwsIBt28TjcQDm5uaoqakhEonQ2trKxcXFh/2Wl5dJJBJMTk7S39/P6ekpm5ubtLS0VPQMRKT6+V5f39m3KiLy29DQEH19fayurn55r/v7e9rb27FtuyqnMj6fj1wux/j4+HffioiUoAmQiJS0vr6OaZplfzm6XK7rks1mOTs7Y39/n4mJCQDGxsY+tc9XS6VSmKb53bchIhXQBEhEPnR5ecnj4yMA4XAYwzA+rbbrukxPT3N8fIxhGAwMDJBOp4u2nVeDq6sr7u7ugLdt/v+6A01E/h8FIBEREfEcvQITERERz1EAEhEREc9RABIRERHPUQASERERz1EAEhEREc9RABIRERHPUQASERERz1EAEhEREc9RABIRERHPUQASERERz/kF2RAiLl7SHzMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "for i in range(len(ssp.metallicity)):\n",
    "    plt.plot(ssp.wavelength,ssp.flux[i][0], label=r'Z=%0.3f'%ssp.metallicity[i])\n",
    "plt.xlabel(r'$\\lambda$ [%s]'%config[\"fields\"][\"wavelength\"][\"units\"])\n",
    "plt.ylabel(r'Flux [%s]'%config[\"fields\"][\"flux\"][\"units\"])\n",
    "#plt.yscale(\"log\")\n",
    "plt.xlim(0,10000)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7197ec34f290>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "ages = np.linspace(0,len(ssp.age),10)\n",
    "for age in ages:\n",
    "    plt.plot(ssp.wavelength,ssp.flux[0][int(age)], label='%.2f %s'%(ssp.age[int(age)], config[\"fields\"][\"age\"][\"units\"]))\n",
    "plt.xlabel(r'$\\lambda$ [%s]'%config[\"fields\"][\"wavelength\"][\"units\"])\n",
    "plt.ylabel(r'Flux [%s]'%config[\"fields\"][\"flux\"][\"units\"])\n",
    "#plt.yscale(\"log\")\n",
    "plt.xlim(0,5000)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reload the created FSPS template"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is only for tutorial purposes as you would need to run fsps like above first to have a pre-existing template..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NBVAL_SKIP\n",
    "from rubix import config as rubix_config\n",
    "rubix_config[\"ssp\"][\"templates\"][\"FSPS\"][\"source\"] = \"load_from_file\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HDF5SSPGrid(age=Array([9.9999997e-05, 1.1220184e-04, 1.2589252e-04, 1.4125378e-04,\n",
       "       1.5848933e-04, 1.7782794e-04, 1.9952621e-04, 2.2387206e-04,\n",
       "       2.5118870e-04, 2.8183832e-04, 3.1622776e-04, 3.5481335e-04,\n",
       "       3.9810708e-04, 4.4668370e-04, 5.0118729e-04, 5.6234130e-04,\n",
       "       6.3095725e-04, 7.0794561e-04, 7.9432840e-04, 8.9125102e-04,\n",
       "       1.0000000e-03, 1.1220183e-03, 1.2589252e-03, 1.4125379e-03,\n",
       "       1.5848933e-03, 1.7782794e-03, 1.9952620e-03, 2.2387207e-03,\n",
       "       2.5118869e-03, 2.8183833e-03, 3.1622776e-03, 3.5481334e-03,\n",
       "       3.9810711e-03, 4.4668368e-03, 5.0118729e-03, 5.6234132e-03,\n",
       "       6.3095726e-03, 7.0794565e-03, 7.9432838e-03, 8.9125102e-03,\n",
       "       9.9999998e-03, 1.1220183e-02, 1.2589254e-02, 1.4125375e-02,\n",
       "       1.5848933e-02, 1.7782794e-02, 1.9952621e-02, 2.2387212e-02,\n",
       "       2.5118863e-02, 2.8183833e-02, 3.1622775e-02, 3.5481334e-02,\n",
       "       3.9810721e-02, 4.4668358e-02, 5.0118729e-02, 5.6234132e-02,\n",
       "       6.3095726e-02, 7.0794582e-02, 7.9432823e-02, 8.9125104e-02,\n",
       "       1.0000000e-01, 1.1220185e-01, 1.2589255e-01, 1.4125374e-01,\n",
       "       1.5848932e-01, 1.7782794e-01, 1.9952624e-01, 2.2387213e-01,\n",
       "       2.5118864e-01, 2.8183830e-01, 3.1622776e-01, 3.5481340e-01,\n",
       "       3.9810717e-01, 4.4668359e-01, 5.0118721e-01, 5.6234133e-01,\n",
       "       6.3095737e-01, 7.0794576e-01, 7.9432821e-01, 8.9125091e-01,\n",
       "       1.0000000e+00, 1.1220185e+00, 1.2589254e+00, 1.4125376e+00,\n",
       "       1.5848932e+00, 1.7782794e+00, 1.9952624e+00, 2.2387211e+00,\n",
       "       2.5118864e+00, 2.8183827e+00, 3.1622777e+00, 3.5481341e+00,\n",
       "       3.9810719e+00, 4.4668355e+00, 5.0118723e+00, 5.6234131e+00,\n",
       "       6.3095737e+00, 7.0794582e+00, 7.9432821e+00, 8.9125090e+00,\n",
       "       1.0000000e+01, 1.1220183e+01, 1.2589254e+01, 1.4125375e+01,\n",
       "       1.5848933e+01, 1.7782795e+01, 1.9952621e+01], dtype=float32), metallicity=Array([4.4904351e-05, 1.4200003e-04, 2.5251572e-04, 4.4904352e-04,\n",
       "       7.9852482e-04, 1.4200003e-03, 2.5251573e-03, 4.4904351e-03,\n",
       "       7.9852482e-03, 1.4199999e-02, 2.5251566e-02, 4.4904340e-02],      dtype=float32), wavelength=Array([8.950e+01, 9.250e+01, 9.450e+01, ..., 9.817e+07, 9.908e+07,\n",
       "       1.000e+08], dtype=float32), flux=Array([[[3.69801944e-25, 1.71711785e-25, 1.01008924e-25, ...,\n",
       "         4.20808249e-11, 4.13591869e-11, 4.06485991e-11],\n",
       "        [2.95627621e-25, 1.37270093e-25, 8.07487082e-26, ...,\n",
       "         3.36403162e-11, 3.30634235e-11, 3.24953640e-11],\n",
       "        [3.62052076e-25, 1.68113235e-25, 9.88920961e-26, ...,\n",
       "         4.11989401e-11, 4.04924289e-11, 3.97967319e-11],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         6.87085782e-21, 6.62186151e-21, 6.38153848e-21],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         6.64786358e-21, 6.40694763e-21, 6.17442546e-21],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         6.16116230e-21, 5.93788412e-21, 5.72238539e-21]],\n",
       "\n",
       "       [[2.47674418e-25, 1.15003767e-25, 6.76506097e-26, ...,\n",
       "         2.81835822e-11, 2.77002657e-11, 2.72243512e-11],\n",
       "        [2.70331983e-25, 1.25524459e-25, 7.38393714e-26, ...,\n",
       "         3.07618514e-11, 3.02343220e-11, 2.97148695e-11],\n",
       "        [3.59155428e-25, 1.66768228e-25, 9.81008994e-26, ...,\n",
       "         4.08693253e-11, 4.01684623e-11, 3.94783338e-11],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         8.89014787e-21, 8.56769041e-21, 8.25660749e-21],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         8.48373481e-21, 8.17602604e-21, 7.87917160e-21],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         6.92724666e-21, 6.67610364e-21, 6.43376153e-21]],\n",
       "\n",
       "       [[2.81423712e-25, 1.30674723e-25, 7.68690040e-26, ...,\n",
       "         3.20240119e-11, 3.14748366e-11, 3.09340713e-11],\n",
       "        [2.62237481e-25, 1.21765895e-25, 7.16284100e-26, ...,\n",
       "         2.98407549e-11, 2.93290184e-11, 2.88251211e-11],\n",
       "        [2.73612140e-25, 1.27047540e-25, 7.47353201e-26, ...,\n",
       "         3.11351084e-11, 3.06011778e-11, 3.00754213e-11],\n",
       "        ...,\n",
       "        [1.32808084e-10, 1.46964954e-10, 1.78483950e-10, ...,\n",
       "         8.73207478e-21, 8.41537447e-21, 8.10981686e-21],\n",
       "        [5.43437206e-09, 5.32875122e-09, 5.96376948e-09, ...,\n",
       "         8.36363771e-21, 8.06031204e-21, 7.76765247e-21],\n",
       "        [9.67010383e-09, 9.52066159e-09, 1.06839435e-08, ...,\n",
       "         7.63764698e-21, 7.36066264e-21, 7.09341060e-21]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[3.35264627e-25, 1.55674908e-25, 9.15752880e-26, ...,\n",
       "         3.81507222e-11, 3.74964816e-11, 3.68522574e-11],\n",
       "        [3.28182234e-25, 1.52386307e-25, 8.96407792e-26, ...,\n",
       "         3.73447974e-11, 3.67043756e-11, 3.60737620e-11],\n",
       "        [3.33899035e-25, 1.55040824e-25, 9.12022862e-26, ...,\n",
       "         3.79953291e-11, 3.73437531e-11, 3.67021517e-11],\n",
       "        ...,\n",
       "        [8.59993882e-11, 1.15849191e-10, 1.53091637e-10, ...,\n",
       "         1.75658879e-20, 1.69284419e-20, 1.63133959e-20],\n",
       "        [4.22778722e-11, 5.95679478e-11, 7.97761926e-11, ...,\n",
       "         1.53844511e-20, 1.48262221e-20, 1.42875434e-20],\n",
       "        [2.23467148e-11, 3.41603967e-11, 4.68701189e-11, ...,\n",
       "         1.51603821e-20, 1.46103038e-20, 1.40795349e-20]],\n",
       "\n",
       "       [[3.28365965e-25, 1.52471627e-25, 8.96909643e-26, ...,\n",
       "         3.73657043e-11, 3.67249252e-11, 3.60939577e-11],\n",
       "        [3.06845691e-25, 1.42479015e-25, 8.38128412e-26, ...,\n",
       "         3.49168507e-11, 3.43180658e-11, 3.37284506e-11],\n",
       "        [3.15609887e-25, 1.46548539e-25, 8.62067320e-26, ...,\n",
       "         3.59141536e-11, 3.52982678e-11, 3.46918119e-11],\n",
       "        ...,\n",
       "        [4.62700885e-11, 7.34858702e-11, 1.02663184e-10, ...,\n",
       "         1.95786429e-20, 1.88677474e-20, 1.81815365e-20],\n",
       "        [5.22229378e-11, 7.36018815e-11, 9.85654544e-11, ...,\n",
       "         1.83188566e-20, 1.76538179e-20, 1.70122553e-20],\n",
       "        [3.85684748e-11, 5.60884464e-11, 7.57659144e-11, ...,\n",
       "         1.75572090e-20, 1.69198550e-20, 1.63050046e-20]],\n",
       "\n",
       "       [[3.02441580e-25, 1.40434041e-25, 8.26098899e-26, ...,\n",
       "         3.44156960e-11, 3.38255049e-11, 3.32443517e-11],\n",
       "        [3.15393394e-25, 1.46448021e-25, 8.61475921e-26, ...,\n",
       "         3.58895171e-11, 3.52740545e-11, 3.46680150e-11],\n",
       "        [3.26780774e-25, 1.51735571e-25, 8.92579844e-26, ...,\n",
       "         3.71853208e-11, 3.65476364e-11, 3.59197151e-11],\n",
       "        ...,\n",
       "        [3.19179711e-22, 1.78760657e-21, 5.73130869e-21, ...,\n",
       "         1.93975841e-20, 1.86948069e-20, 1.80162264e-20],\n",
       "        [6.14501020e-11, 8.57527313e-11, 1.14455764e-10, ...,\n",
       "         2.07055004e-20, 1.99535222e-20, 1.92279652e-20],\n",
       "        [1.24949378e-12, 2.30248633e-12, 3.44144765e-12, ...,\n",
       "         2.00280460e-20, 1.93006602e-20, 1.85987619e-20]]], dtype=float32))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "ssp2 = get_ssp_template(\"FSPS\")\n",
    "ssp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "ssp.wavelength.shape == ssp2.wavelength.shape"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "rubix",
   "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}