notebooks/01_calculate_ECS_Gregory_method.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimating Equilibrium Climate Sensitivity (ECS) in CMIP6 models\n",
    "\n",
    "*Definition:* Equilibrium Climate Sensitivity is defined as change in global-mean near-surface air temperature (GMST) change due to an instantaneous doubling of CO$_{2}$ concentrations and once the coupled ocean-atmosphere-sea ice system has acheived a statistical equilibrium (i.e. at the top-of-atmosphere, incoming solar shortwave radiation is balanced by reflected solar shortwave and outgoing thermal longwave radiation).\n",
    "\n",
    "This notebook uses the [\"Gregory method\"](https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2003GL018747) to approximate the ECS of CMIP6 models based on the first 150 years after an abrupt quadrupling of CO$_{2}$ concentrations. The \"Gregory Method\" extrapolates the quasi-linear relationship between GMST and radiative imbalance at the top-of-atmosphere to estimate how much warming would occur if the system were in radiative balance at the top-of-atmosphere, which is by definition the equilibrium response. In particular, we extrapolate the linear relationship that occurs between 100 and 150 years after the abrupt quadrupling. Since the radiative forcing due to CO$_{2}$ is a logarithmic function of the CO$_{2}$ concentration, the GMST change from a first doubling is roughly the same as for a second doubling (to first order, we can assume feedbacks as constant), which means that the GMST change due to a quadrupling of CO$_{2}$ is roughly $\\Delta T_{4 \\times \\text{CO}_{2}} = 2 \\times \\text{ECS}$. See also [Mauritsen et al. 2019](https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2018MS001400) for a detailed application of the Gregory Method (with modifications) for the case of one specific CMIP6 model, the MPI-M Earth System Model.\n",
    "\n",
    "For another take on applying the Gregory method to estimate ECS, see [Angeline Pendergrass' code](https://github.com/apendergrass/cmip6-ecs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extrapolate linear relationship between GMST and radiative imbalance\n",
    "# between gregory_limits[0] and gregory_limits[1]\n",
    "gregory_limits = [0,150] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/ipykernel_launcher.py:7: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import xesmf as xe\n",
    "import cartopy\n",
    "from tqdm.autonotebook import tqdm  # Fancy progress bars for our loops!\n",
    "import intake\n",
    "# util.py is in the local directory\n",
    "# it contains code that is common across project notebooks\n",
    "# or routines that are too extensive and might otherwise clutter\n",
    "# the notebook design\n",
    "import util\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = 12, 6\n",
    "%config InlineBackend.figure_format = 'retina' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data catalogs\n",
    "\n",
    "This notebook uses [`intake-esm`](https://intake-esm.readthedocs.io/en/latest/) to ingest and organize climate model output from the fresh-off-the-supercomputers Phase 6 of the Coupled Model Intercomparison Project (CMIP6). \n",
    "\n",
    "The file `https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv` in google cloud storage contains thousands of lines of metadata, each describing an individual climate model experiment's simulated data.\n",
    "\n",
    "For example, the first line in the csv file contains the precipitation rate (`variable_id = 'pr'`), as a function of latitude, longitude, and time, in an individual climate model experiment with the BCC-ESM1 model (`source_id = 'BCC-ESM1'`) developed by the Beijing Climate Center (`institution_id = 'BCC'`). The model is *forced* by the forcing experiment SSP370 (`experiment_id = 'ssp370'`), which stands for the Shared Socio-Economic Pathway 3 that results in a change in radiative forcing of $\\Delta F = 7.0$ W/m$^{2}$ from pre-industrial to 2100. This simulation was run as part of the `AerChemMIP` activity, which is a spin-off of the CMIP activity that focuses specifically on how aerosol chemistry affects climate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>piClim-CH4</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>ch4</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>piClim-CH4</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>clt</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>piClim-CH4</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>co2</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>piClim-CH4</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>evspsbl</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>piClim-CH4</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>hfls</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  activity_id institution_id source_id experiment_id member_id table_id  \\\n",
       "0  AerChemMIP            BCC  BCC-ESM1    piClim-CH4  r1i1p1f1     Amon   \n",
       "1  AerChemMIP            BCC  BCC-ESM1    piClim-CH4  r1i1p1f1     Amon   \n",
       "2  AerChemMIP            BCC  BCC-ESM1    piClim-CH4  r1i1p1f1     Amon   \n",
       "3  AerChemMIP            BCC  BCC-ESM1    piClim-CH4  r1i1p1f1     Amon   \n",
       "4  AerChemMIP            BCC  BCC-ESM1    piClim-CH4  r1i1p1f1     Amon   \n",
       "\n",
       "  variable_id grid_label                                             zstore  \\\n",
       "0         ch4         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...   \n",
       "1         clt         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...   \n",
       "2         co2         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...   \n",
       "3     evspsbl         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...   \n",
       "4        hfls         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/piClim-CH4/...   \n",
       "\n",
       "   dcpp_init_year  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file `pangeo-cmip6.json` describes the structure of the CMIP6 metadata and is formatted so as to be read in by the `intake.open_esm_datastore` method, which categorizes all of the data pointers into a tiered collection. For example, this collection contains the simulated data from 28691 individual experiments, representing 48 different models from 23 different scientific institutions. There are 190 different climate variables (e.g. sea surface temperature, sea ice concentration, atmospheric winds, dissolved organic carbon in the ocean, etc.) available for 29 different forcing experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pangeo-cmip6-ESM Collection with 204562 entries:\n",
       "\t> 15 activity_id(s)\n",
       "\n",
       "\t> 26 institution_id(s)\n",
       "\n",
       "\t> 58 source_id(s)\n",
       "\n",
       "\t> 100 experiment_id(s)\n",
       "\n",
       "\t> 128 member_id(s)\n",
       "\n",
       "\t> 29 table_id(s)\n",
       "\n",
       "\t> 297 variable_id(s)\n",
       "\n",
       "\t> 9 grid_label(s)\n",
       "\n",
       "\t> 204562 zstore(s)\n",
       "\n",
       "\t> 60 dcpp_init_year(s)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = intake.open_esm_datastore(\"../catalogs/pangeo-cmip6.json\")\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we show the various forcing experiments that climate modellers ran in these simulations. A few examples are:\n",
    " - `piControl` which fixes CO2 levels at pre-industrial concentrations of 300 ppm\n",
    " - `historical` which includes the historical evolution of greenhouse concentrations as well as historical volcanic eruptions, changes in solar luminosity, and changes in atmospheric aerosol concentrations (and some other, less impactful forcings).\n",
    " - `abrupt-4xCO2` in which the CO2 concentrations in an pre-industrial control simulation are abrupted quadrupled from 300 ppm to 1200 ppm.\n",
    " - `ssp585`, a `worst-case scenario` in which fossil-fueled development leads to a disastrous increase of $\\Delta F = 8.5$ W/m$^{2}$ in radiative forcing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['piClim-CH4', 'piClim-NTCF', 'piClim-control', 'ssp370',\n",
       "       'piClim-2xDMS', 'piClim-2xdust', 'piClim-2xfire', 'piClim-2xss',\n",
       "       'piClim-BC', 'piClim-HC', 'piClim-N2O', 'piClim-OC', 'piClim-SO2',\n",
       "       '1pctCO2-bgc', 'esm-ssp585', 'hist-bgc', 'amip-4xCO2',\n",
       "       'amip-future4K', 'amip-m4K', 'amip-p4K', 'amip', 'abrupt-2xCO2',\n",
       "       'abrupt-solp4p', 'abrupt-0p5xCO2', 'amip-lwoff', 'amip-p4K-lwoff',\n",
       "       'abrupt-solm4p', 'aqua-4xCO2', 'aqua-control-lwoff',\n",
       "       'aqua-control', 'aqua-p4K-lwoff', 'aqua-p4K', '1pctCO2',\n",
       "       'abrupt-4xCO2', 'historical', 'piControl', 'esm-hist',\n",
       "       'esm-piControl', 'ssp126', 'ssp245', 'ssp585',\n",
       "       'esm-piControl-spinup', 'piControl-spinup', 'hist-GHG', 'hist-aer',\n",
       "       'hist-nat', 'hist-CO2', 'hist-sol', 'hist-stratO3', 'hist-volc',\n",
       "       'ssp245-GHG', 'ssp245-aer', 'ssp245-nat', 'ssp245-stratO3',\n",
       "       'dcppA-assim', 'dcppA-hindcast', 'dcppC-hindcast-noAgung',\n",
       "       'dcppC-hindcast-noElChichon', 'dcppC-hindcast-noPinatubo',\n",
       "       'dcppC-amv-neg', 'dcppC-amv-pos', 'dcppC-amv-ExTrop-neg',\n",
       "       'dcppC-amv-ExTrop-pos', 'dcppC-amv-Trop-neg', 'dcppC-amv-Trop-pos',\n",
       "       'dcppC-atl-control', 'dcppC-atl-pacemaker',\n",
       "       'dcppC-ipv-NexTrop-neg', 'dcppC-ipv-NexTrop-pos', 'dcppC-ipv-neg',\n",
       "       'dcppC-ipv-pos', 'dcppC-pac-control', 'dcppC-pac-pacemaker',\n",
       "       'faf-heat', 'faf-passiveheat', 'faf-stress', 'faf-water',\n",
       "       'faf-all', 'amip-hist', 'highresSST-present', 'hist-1950',\n",
       "       'control-1950', 'land-hist', 'deforest-globe',\n",
       "       'esm-ssp585-ssp126Lu', 'hist-noLu', 'land-noLu', 'omip1',\n",
       "       'lig127k', 'midHolocene', 'lgm', 'piClim-4xCO2', 'piClim-aer',\n",
       "       'piClim-anthro', 'piClim-ghg', 'piClim-lu', 'ssp119', 'ssp434',\n",
       "       'ssp460', 'ssp534-over'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['experiment_id'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Climate Model Output Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading data\n",
    "\n",
    "`intake-esm` enables loading data directly into an [xarray.DataArray](http://xarray.pydata.org/en/stable/api.html#dataset), a metadata-aware extension of numpy arrays. `xarray` objects leverage [dask](https://dask.org/) to only read data into memory as needed for any specific operation (i.e. lazy evaluation). Think of `xarray` Datasets as ways of conveniently organizing large arrays of floating point numbers (e.g. climate model data) on an n-dimensional discrete grid, with important metadata such as units, variable, names, etc.\n",
    "\n",
    "Note that data on the cloud are in [zarr](https://zarr.readthedocs.io/en/stable/) format, an extension of the metadata-aware format [netcdf](https://www.unidata.ucar.edu/software/netcdf/) commonly used in geosciences.\n",
    "\n",
    "`intake-esm` has rules for aggegating datasets; these rules are defined in the collection-specification file.\n",
    "\n",
    "#### Choice of simulated forcing experiments\n",
    "\n",
    "Here, we choose the `piControl` experiment (in which CO2 concentrations are held fixed at a pre-industrial level of ~300 ppm) and `abrupt-4xCO2` experiment (in which CO2 concentrations are instantaneously quadrupled - or doubled twice - from a pre-industrial controrl state). Since the radiative forcing of CO2 is roughly a logarithmic function of CO2 concentrations, the ECS is roughly independent of the initial CO2 concentration. Thus, if one doubling of CO2 results in $ECS$ of warming, then two doublings (or, a quadrupling) results in $2 \\times ECS$ of warming.\n",
    "\n",
    "Ideally, we would choose the `abrupt-2xCO2` forcing experiment, but this seems to be currently unavaiable in Google Cloud Storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_tas = col.search(experiment_id=['abrupt-4xCO2','piControl'],  # pick the `abrupt-4xCO2` and `piControl` forcing experiments\n",
    "                 table_id='Amon',             # choose to look at atmospheric variables (A) saved at monthly resolution (mon)\n",
    "                 variable_id='tas',           # choose to look at near-surface air temperature (tas) as our variable\n",
    "                 member_id = 'r1i1p1f1')      # arbitrarily pick one realization for each model (i.e. just one set of initial conditions)\n",
    "\n",
    "cat_rad = col.search(experiment_id=['abrupt-4xCO2','piControl'],  # pick the `abrupt-4xCO2` and `piControl` forcing experiments\n",
    "                 table_id='Amon',             # choose to look at atmospheric variables (A) saved at monthly resolution (mon)\n",
    "                 variable_id=['rsut','rsdt','rlut'],           # choose to look at near-surface air temperature (tas) as our variable\n",
    "                 member_id = 'r1i1p1f1')      # arbitrarily pick one realization for each model (i.e. just one set of initial conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |█████████████████████████------------------------------------------------------| 31.2% Dropped\n",
      "Progress: |███████████████████████████████████████████████████████████████████████████████| 100.0% \n",
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n",
      "             \n",
      "--> There are 48 group(s)\n",
      "Dropped\n",
      "Progress: |-------------------------------------------------------------------------------| 0.0% Dropped\n",
      "Progress: |███████████████████████████████████████████████████████████████████████████████| 100.0% \n",
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n",
      "             \n",
      "--> There are 45 group(s)\n"
     ]
    }
   ],
   "source": [
    "# convert data catalog into a dictionary of xarray datasets\n",
    "def drop_time_bounds(ds):\n",
    "    if 'time_bounds' in ds.coords:\n",
    "        ds = ds.drop('time_bounds')\n",
    "    elif 'time_bnds' in ds.coords:\n",
    "        ds = ds.drop('time_bnds')\n",
    "    return ds\n",
    "\n",
    "dset_dict_tas = cat_tas.to_dataset_dict(preprocess = drop_time_bounds, zarr_kwargs={'consolidated': True, 'decode_times': False})\n",
    "dset_dict_rad = cat_rad.to_dataset_dict(preprocess = drop_time_bounds, zarr_kwargs={'consolidated': True, 'decode_times': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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    {
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     "output_type": "stream",
     "text": [
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)\n",
      "/srv/conda/envs/cmip6-temperature-demo/lib/python3.7/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ds_dict = {}\n",
    "gmst_dict = {}\n",
    "imbalance_dict = {}\n",
    "for name, ds_rad in tqdm(dset_dict_rad.items()):\n",
    "    model_name = name.split(\".\")[2]\n",
    "    \n",
    "    if not (('rsdt' in dset_dict_rad[name].keys()) & ('rsut' in dset_dict_rad[name].keys()) & ('rlut' in dset_dict_rad[name].keys())):\n",
    "        continue\n",
    "        \n",
    "    ds_tas = dset_dict_tas[name]\n",
    "    \n",
    "    # rename spatial dimensions if necessary\n",
    "    if ('longitude' in ds_rad.dims) and ('latitude' in ds_rad.dims):\n",
    "        ds_rad = ds_rad.rename({'longitude':'lon', 'latitude': 'lat'}) # some models labelled dimensions differently...\n",
    "        ds_tas = ds_tas.rename({'longitude':'lon', 'latitude': 'lat'}) # some models labelled dimensions differently...\n",
    "        \n",
    "    ds_rad = xr.decode_cf(ds_rad) # temporary hack, not sure why I need this but has to do with calendar-aware metadata on the time variable\n",
    "    ds_tas = xr.decode_cf(ds_tas)\n",
    "    \n",
    "    # drop redundant variables (like \"height: 2m\")\n",
    "    for coord in ds_tas.coords:\n",
    "        if coord not in ['lat','lon','time']:\n",
    "            ds_tas = ds_tas.drop(coord)\n",
    "            \n",
    "    # drop redundant variables (like \"height: 2m\")\n",
    "    for coord in ds_rad.coords:\n",
    "        if coord not in ['lat','lon','time']:\n",
    "            ds_rad = ds_rad.drop(coord)\n",
    "\n",
    "    ## Calculate global-mean surface temperature (GMST)\n",
    "    cos_lat_2d = np.cos(np.deg2rad(ds_tas['lat'])) * xr.ones_like(ds_tas['lon']) # effective area weights\n",
    "    gmst = (\n",
    "        (ds_tas['tas'] * cos_lat_2d).sum(dim=['lat','lon']) /\n",
    "        cos_lat_2d.sum(dim=['lat','lon'])\n",
    "    )\n",
    "    \n",
    "    ## Calculate global-mean top of atmosphere radiative imbalance (GMST)\n",
    "    net_toa = ds_rad['rsdt'] - ds_rad['rsut'] - ds_rad['rlut']\n",
    "    \n",
    "    cos_lat_2d = np.cos(np.deg2rad(ds_rad['lat'])) * xr.ones_like(ds_rad['lon']) # effective area weights\n",
    "    imbalance = (\n",
    "        (net_toa * cos_lat_2d).sum(dim=['lat','lon']) /\n",
    "        cos_lat_2d.sum(dim=['lat','lon'])\n",
    "    )\n",
    "    \n",
    "    imbalance_dict[name] = imbalance.squeeze()\n",
    "\n",
    "    # Add GMST to dictionary\n",
    "    gmst_dict[name] = gmst.squeeze()\n",
    "    \n",
    "    # Add near-surface air temperature to dictionary\n",
    "    ds_dict[name] = ds_tas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split dictionary of all simulations into `piControl` and `abrupt-4xCO2` dictionaries\n",
    "\n",
    "And get rid of any models that don't have both experiments available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctrl_dict = {} # dictionary that will hold spliced DataArrays\n",
    "for name, ds in ds_dict.items(): # Loop through dictionary\n",
    "    model_name = name.split(\".\")[2]\n",
    "    if 'piControl' not in name: continue\n",
    "    ctrl_dict[model_name] = ds\n",
    "    \n",
    "abrupt_dict = {} # dictionary that will hold spliced DataArrays\n",
    "for name, ds in ds_dict.items(): # Loop through dictionary\n",
    "    model_name = name.split(\".\")[2]\n",
    "    if ('abrupt-4xCO2' not in name) or (model_name not in ctrl_dict.keys()): continue\n",
    "    abrupt_dict[model_name] = ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctrl_gmst_dict = {} # dictionary that will hold spliced DataArrays\n",
    "ctrl_imbalance_dict = {} # dictionary that will hold spliced DataArrays\n",
    "for name, gmst in gmst_dict.items(): # Loop through dictionary\n",
    "    model_name = name.split(\".\")[2]\n",
    "    if 'piControl' not in name: continue # keep only SSP simulations\n",
    "    ctrl_gmst_dict[model_name] = gmst\n",
    "    ctrl_imbalance_dict[model_name] = imbalance_dict[name]\n",
    "    \n",
    "abrupt_gmst_dict = {} # dictionary that will hold spliced DataArrays\n",
    "abrupt_imbalance_dict = {} # dictionary that will hold spliced DataArrays\n",
    "for name, gmst in gmst_dict.items(): # Loop through dictionary\n",
    "    model_name = name.split(\".\")[2]\n",
    "    if ('abrupt-4xCO2' not in name) or (model_name not in ctrl_gmst_dict.keys()): continue # keep only SSP simulations\n",
    "    abrupt_gmst_dict[model_name] = gmst\n",
    "    abrupt_imbalance_dict[model_name] = imbalance_dict[name]\n",
    "    \n",
    "ctrl_gmst_dict = {} # dictionary that will hold spliced DataArrays\n",
    "ctrl_imbalance_dict = {} # dictionary that will hold spliced DataArrays\n",
    "for name, gmst in gmst_dict.items(): # Loop through dictionary\n",
    "    model_name = name.split(\".\")[2]\n",
    "    if ('piControl' not in name) or (model_name not in abrupt_gmst_dict.keys()): continue # keep only SSP simulations\n",
    "    ctrl_gmst_dict[model_name] = gmst\n",
    "    ctrl_imbalance_dict[model_name] = imbalance_dict[name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pick first model arbitrarily for example application of Gregory method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = 'CESM2'\n",
    "for name in ctrl_gmst_dict.keys():\n",
    "    if model == name:\n",
    "        ctrl_gmst = ctrl_gmst_dict[name].groupby('time.year').mean('time').compute()\n",
    "        ctrl_imbalance = ctrl_imbalance_dict[name].groupby('time.year').mean('time').compute()\n",
    "        abrupt_gmst = abrupt_gmst_dict[name].groupby('time.year').mean('time').compute() - ctrl_gmst.mean(dim='year')\n",
    "        abrupt_imbalance = abrupt_imbalance_dict[name].groupby('time.year').mean('time').compute() - ctrl_imbalance.mean(dim='year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 236,
       "width": 718
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DBcxtJiLOAj4BnA70AzYDzwI/Sind1pZrlxXnNz2urDFwliRJyhUDZ0mSJKkLSikta8/1IuKLwNeBjcCtwBpgADANOBdo08C5V4kjNSRJkjoCA2dJkiSpi4iIHwF/Ax5IKaV2XPctZMLmu4A3ppS273W8sK1rcKSGJElSx5CX6wIkSZIkZc1HgHuBdRHxm4h4dUQUteWCEZEHfAfYCbx977AZIKVU25Y1AJQV7QmcK+1wliRJyhk7nCVJkqSuYxjwBuBi4J3Ae4DKiLgNuBG4bX+BcCudTmZe9PXAloi4CDgBqAYeTyk9kuX19qtnsSM1JEmSOgIDZ0mSJKmLSCmtB34F/CoiegOvIRM+vxq4BNgVEXeTCZ9vTiltyMKyJzferwPmAFOaH4yI+4E3H2qtiHjyAIcmHU4RZXsFziklIuJw3ipJkqQscqSGJEmS1AWllCpSSn9OKb0FGEim8/la4FTg18DqiLgvIj4eEaNbsdSgxvsPAj2AC4BeZLqc/w84G7iuFdc/LEUFeRQVZP5605CgurahrZeUJEnSftjhLEmSJHVxKaVdwM3AzY0zl88B3gi8Hvgh8IOImJtSmnEUl89vvA8yncxPNz5/PiIuBl4AzomIWQcbr3GgtRs7n6cfTiE9iwvYXFcDZLqcexTlH+IdkiRJyjY7nCVJkqRuJKXUkFK6N6X00ZTSKOAUMpv+9TjKS25pvF/cLGzevVYVmS5nGtdpU83nOLtxoCRJUm4YOEuSJEldREQUHul7UkqzU0pfSCkdd5TLLmy833qA47sD6aMNtA/b3nOcJUmS1P4MnCVJkqSuY0tE3B4Rn4mIwxpDkQX3A3XAsRFRtJ/jJzTeL23rQnoW7xmhYeAsSZKUGwbOkiRJUtdRALwC+DbwRERsiojrI+JDEXFsWyyYUtoI/AUoB77U/FhEvLyxnm3AHW2xfnOO1JAkSco9Nw2UJEmSuo4+wBnAyxpvM8lsDngxQESsAu7efUsprcnSulcCpwL/HhFnA48DoxvXrQeuSCkdaORG1jhSQ5IkKfcMnCVJkqQuIqVUzZ5AmYjoBZzDngB6CvBu4NLG4wuBu1JKH2vluusj4lTgi2RC5tOA7cA/gG+llB5tzfUPV08DZ0mSpJwzcJYkSZK6qJTSduDWxhsR0Z894fOFwCRgItCqwLlxrc1kOp2vbO21jpYjNSRJknLPGc6SJElS9zGq2W1QjmvJuhYjNaoNnCVJknLBDmdJkiSpi4qISezpaD4X6AsEsAa4Gbi38dYltBypUZ/DSiRJkrovA2dJkiSpi4iI0ewJmM8DhpIJmNcDdwH/Au5NKS3MVY1tqWeJIzUkSZJyzcBZkiRJ6joWN95vAu6nsYM5pTQvdyW1nxYjNWoMnCVJknLBGc6SJElS1xGN988ATwKzgQW5K6d99SzOb3rsDGdJkqTcsMNZkiRJ6jrewZ6RGv8JJGBHRDxAptv5npTSUzmsr02VFTlSQ5IkKdcMnCVJkqQuIqV0DXANNM1zvoA9Gwa+GkgRsRW4jz0B9PO5qTb7ms9w3mHgLEmSlBMGzpIkSVIXlFJaBvy28UZETAbOJxNAnw28vvH1DSmlIbmqM5t6Fhs4S5Ik5ZqBsyRJktQNpJTmA/Mj4kbg5cAngSnAwJwWlkXNNw10pIYkSVJuGDhLkiRJXVhE9AfOI9PZfD5wzO5DZGY8P5Oj0rKuZ4vAuT6HlUiSJHVfBs6SJElSFxIRPYFz2BMwn0AmXI7GU14C7gbuITPDeWMu6mwLxQV5FOQFdQ2JmvoGqmvrKSnMz3VZkiRJ3YqBsyRJktRFRMTDwEwgnz0B82oyGwTeDdydUlqRo/LaXEQwrE8Plm/eCcD8NRVMG9U3x1VJkiR1LwbOkiRJUtdxGrAF+BeNXcwppQU5raidzRzdtylwnr10i4GzJElSO8vLdQGSJEmSsmYmMCCl9KaU0s+7W9gMMHNMv6bHTyzdnMNKJEmSuicDZ0mSJKkLiIgeKaU5KaXUmmtks6ZcOHnMno7m2cu20Io/DkmSJB0FA2dJkiSpa1gSER+PiOIjfWNEnBgRfwc+3QZ1tavxA3vSp7QQgM2VNSzeWJnjiiRJkroXA2dJkiSpa7gT+AGwJiL+JyLOO1jHckSMi4gPRcQjwBzgRDKbC3ZqeXnBzNHNupwdqyFJktSuDJwlSZKkLiCldClwKjAbeD9wF7AtIp6OiDsi4pqIuDEi7o+IdcAi4GfAGODfgYkppQdzVH5WtZzjvCWHlUiSJHU/BbkuQJIkSVJ2pJRmAxdGxLHA5cD5wEnAlL1O3QD8DbgBuCGlVNuuhbaxFnOc7XCWJElqVwbOkiRJUheTUloEfB4gIkqB4UB/oApYn1Jak8Py2twJw8spKsijpq6BpZt2sn57NYN6leS6LEmSpG7BkRqSJElSF5ZS2plSWpRSejSl9HRXD5sBigvyOWlEn6bnTzpWQ5Ikqd10u8A5Ii6KiDsjYmVEVEXE4oi4LiJm5bo2SZIkSdkxs9lYDec4S5IktZ9uFThHxHeAW4HpwB3Aj8nsyP164KGIeGcOy5MkSZKUJSeP3bNx4OxlznGWJElqL91mhnNEDAE+DawDpqaU1jc7dh5wD/A14I+5qVCSJElStkwf1ZcISAmeX11B5a46yoq7zV9/JEmScqY7dTiPJvN5H2seNgOklO4FtgMDc1GYJEmSpOwq71HIxMG9AKhvSMxdsTXHFUmSJHUP3SlwXgTUAKdExIDmByLibKAXcFcuCpMkSZKUfSeP2TNW44mljtWQJElqD93mN2Uppc0R8TngB8C8iLgJ2ASMB14H/BP4wKGuExFPHuDQpGzVKkmSJKn1Zo7pyx8eXQYYOEuSJLWXbhM4A6SUfhQRS4HfAVc0O/QicNXeozYkSZIkdV7NO5yfWr6V2voGCvO70488JUmS2l+3+rYVEZ8FrgeuItPZXAbMABYDf4qI7x7qGimlGfu7AQvasHRJkiTpqEVEWURMi4izcl1LexrWpwfDyksA2FlTz+INlTmuSJIkqevrNoFzRJwLfAe4OaV0ZUppcUppZ0ppDnAxsAr4VESMy2WdkiRJUrZExIiIuAHYAswG7m127MyImNf4PbnLmjy0d9PjBWsrcliJJElS99BtAmfgNY339+59IKW0E3iczJ/HtPYsSpIkSWoLETEUeAx4PXAr8AgQzU55DBgE/L/2r679TBraq+nxgrXbc1iJJElS99CdAufixvuBBzi++/WadqhFkiRJamtfJhMoX5BSeiOZTbKbpJRqgQeAM1q7UEQsjYh0gNva1l6/NSYNadbhvMYOZ0mSpLbWnTYNfAD4CPD+iPhlSmnV7gMR8SoyX7SrgYdzVJ8kSZKUTa8mM07uXwc5ZzmQrbnO24Af7ef1HVm6/lGZNGRPh/NCO5wlSZLaXHcKnK8H7gIuAOZHxI3AWmAymXEbAXw+pbQpdyVKkiRJWTMYWHSIc2rJbKSdDVtTSl/J0rWyZuyAMory86ipb2D1tmq27aylvLQw12VJkiR1Wd1mpEZKqYFMl8cngXlkNgr8FHAacBvwipTSj3NXoSRJkpRVm4GRhzhnApkmjC6rID+PYwb1bHq+cJ1dzpIkSW2pO3U4755T9yP2/1M/SZIkqSt5CHhdRAxJKe0TKkfEscArgT9mab3iiHgnMAqoBJ4B7k8p1Wfp+kdt0tBezGuc37xgbQWnjO2X44okSZK6rm4VOEuSJEndyPeA1wP3RcQngFKAiCgDzgZ+CDQA38/SekOAP+z12pKIeE9K6b5DvTkinjzAoUmtLaz5HOf5a+xwliRJaksGzpIkSVIXlFJ6LCLeD/wCuLXZoYrG+zrgvSml57Ow3O/JbNL9PLAdGEfjht3A7RExK6X0dBbWOSqThvRuerxwbcVBzpQkSVJrGThLkiRJXVRK6fcR8SDwb2T2LukPbAMeBX6aUlqYpXW+utdLzwEfjIgdZPZN+QqZPVQOdo0Z+3u9sfN5emvqmzR0T4fzwrXbaWhI5OVFay4pSZKkA8ha4BwRAVwAvJzMT/RGAQOAKmA9MBe4B7g5pbQqW+tKkiSp+9iwfReLN+xg6aZKlm7ayfLNO3NdUoeXUlpEZuPsXPgFmcD57BytD8DAnsX0Kytic2UNlTX1rNpaxch+pbksSZIkqctqdeAcEaXAx4APkAmZd7cKVJMJmnuQ+UndeOBNwI8j4hbg+ymlh1u7viRJkrq+ZZsq+dot87h7wfoWr1dU1eaoIh2m3f+BleWyiIhg0pBePPzSJgDmr6kwcJYkSWojea15c0S8B1gEfJNMJ/NXyXQ490kplaaURqSU+pMJto8D3gvcALwKeCAi/hIRo1pTgyRJkjqe+obEvQvXc/UjS5m7YisNDemorlNdW88P/vkCL//h/fuEzTq4iHhLRNwTEcMOcHx4RNwdEW9swzJmNd4vbsM1DsvEZhsHLljrxoGSJEltpbUdzr8FbgK+lVJ64kAnpZQSsKDxdlVE9AbeDXweuAz4WivrkCRJ6tYaGhKPLt7E2IFlDC3v0WbrpJTITFLbv+3Vtfx19kr+9+GlLcZd9C8r4pyJAzl/0mAuOG4QxQX5B12nurae255dww/veoEVm6uaXo+AqcPLGTOgjNH9y/jZ30tZvK71n6uLeh+ZRpDV+zuYUlrV+L38fcDfjnaRiDgeWJNS2rzX66OBnzY+/ePRXj9bJrfYONDAWZIkqa20NnCemVKac6RvSilVAD+JiF8DY1pZgyRJUrf3zdvm85sHl1BUkMd/veVEXnfifptaj9r9L2zgG/+Yx9adtXziggm87ZSRLYLnbVW1/OzeF/nTo8uorKnf5/2bKmv425xV/G3OKoaWl/Dh847hkpkjKSpo+YO7lVt28qfHlvOXJ1awubKmxbGpI8r5+utP4MSRfZpe+1NpYVY/ZxczBbj1EOfMBl7bynXeAnw+Iu4FlgDbyYzTuwgoAW4D/quVa7Ra8w7n+WsrcliJJElS19aqwPlowua93l9NputZkiRJR2nZpkp+//BSAGrqGvjYNU+xdGMlH33ZMQftRm5u285avnX7fHbW1HPBcYM5f9IgyooL2Lqzhq/fOp8b5qxsOvcLNz7LLU+v5ttvmsKIvqVcN3sF3/u/hWzaKyDuXVLArPH9eXLZFjbu2HNszbZqvnjTc/zPv17iPWeMobq2noXrdrBo3XZeWLedvadv9Ckt5LOvmMT/O3kk+XmH93kEQD/2zFA+kE1kNvpujXuBicA0MiM0yoCtwIPAH4A/NP7iMacmDO5FBKQESzdWUl1bT0nhwTvtJUmSdOSysWlgD+AO4EXg31JKu1pdlSRJkg7bj+9eRP1eKe0P/vkCSzZW8u03TTnk+Ir6hsRHrpnDA4s2AnDz06spKczjnAkD9wmLd3tk8SZe8aP7GdO/bJ95uMcM6sllp4/hjdOHU1pUQEND4tlV27h7/jr+/Pjypuut2lrFN/4x/4B1DSsv4R2njebtp4yib1nRYf1ZqIWNwLGHOOdYMuHwUUsp3Qfc15prtIceRfmM7V/G4o2VNCRYtG4HU0aU57osSZKkLqfVgTOZGcxnAXcYNkuSJLWvF9dv56anVjU9P35Yb55fnRkXcONTq1i5ZSe/fNdM+h0ksP3JPYuawubdqmsb+L/nWw5Hfs3UoQzv04NfP7CYhpQ5p3nYPKy8hC9cNJmLpgxt0VmdlxecOLIPJ47swwfPHc8fH13GL+9bvE9H9G5nHjOAd80azfmTBlGQ36o9rru7h4DXRcSklNI+vyqMiMnA64Fb2r2yHJk4pBeLN1YCmbEaU0aU8/iSzfz6gcWcOrYf7ztrXI4rlCRJ6vyyETi/kcxP8X5wsJMi87eOa4FdwMdTSluysLYkSVK39sO7FjWNoDhnwkB+8+6ZfOnvz3HN4ysAeGLpFt7ws4f43WUnc8ygnvu8//4XNvDjuxc1PX/ZpEGs2lLFwnV7guQhvUv4xhtO4ILjBgNw0dShfPb6Z5rC5uKCPD54zng+eM54ehQdvJu6tKiA9589nnecOpo/P7acOcu3MLh3CROH9GLC4J4cO7gXvUucy5wl/0Xmu/qDEfE1Mr9KXAUMB14F/AeQTweYr9xeJg3pze3PrQUyG3vooDMAACAASURBVAf+4dFlfPXm56lrSPxz3jrOOGYAk4f2PsRVJEmSdDDZCJxPBO48VHdzSilFxFVkNi65kw6wU7UkSVJHlVJiycZKhpb3OGCIO291Bf94Zk3T809dOIHC/Dy+efEUxg3oyTdvn09KsHzzTi7++UP84p0zOOOYPeN6V2+t4uPXPsXu6bqzxvXnV++aQUF+Hi+s286dz2eCuUtPH9MiBJ46og83f+RMrnl8Oau3VfHOU0czsl/pEX2+suICrjjbbtK2lFJ6IiL+DfgZ8MPGW3P1wIdSSo+1e3E50nzjwD8/tpyq2pYbXD65bIuBsyRJUitlI3DuAyw7nBNTSrdHxCrgNRg4S5Ik7Vd9Q+LDf5rDHc+vpUdhPudPHsRrpg7l3ImDWmxy9oN/vtD0+MLjBjN1RB8AIoIrzh7H6P6lfPzauVTV1rO9uo53/+5xLj9zLOWlhaQEdzy3li07awEY1KuY/37btKYRFhMG92LC4F4cSFFBHu8+fUwbfHplU0rp1xHxIPBvwKlkvrtvBR4F/ieldOAh2l3Q5KF7/ju9d9gM8Pzqbe1ZjiRJUpeUjcB5K3Aku208CEzJwrqSJEld0nfuWMAdjd3FVbX13PrMGm59Zg1lRfkcO7gXA3sV07ukkLvmZ2YsR8CVF07Y5zoXHj+E6z44i8v/9wnWVeyiriHxy/sX73Nefl7w07dPZ2Cv4rb9YMqJxlD5o7muoyMY2beU0qJ8dtbsCZtPGN6b51Zl5p7vvpckSdLRy0bgvByYegTnrwAuzMK6kiRJnca9C9fz96dWUVufSCQaGqBHUT4XTRnK+ZMHNW2yd/2TK/nVfkJhgMqaeuau2LrP66+ZOoxJQ/Y/BuCE4eX8/cNncvn/PtG0meDePvfKiZwytt9RfjKp88jLC844ZgD/nJf5x5rPvGIibz9lFNO+/k8gM9e5pq6BogI3q5QkSTpa2Qic/wl8NiJOSCk9dxjnFwL77lgjSZJ0mFJKLFi7ncG9S+hXVpTrcg7p2ZXbuOJ/Z1O3e3e/Zm58ahVnTxjIl197HNuqavnC355tOnbB5MF86sIJ3P5spsN58cbKfd5fVJDHJy449qDrDykv4boPzuLGp1axfNNOCAiCCJg8tDevnTq09R9S6iS+9cYpnDq2H9NH92X6qL4AjOjbg5Vbqqipb2DR+u0cP+xIfsApSZKk5rIROP8W+CTw54iYlVLa929CLU0ANmRhXUmS1E39+O5F/OiuRfQtLeTa989qsRFYR1NdW8+Vf52737B5t/tf2MArfng/pUX51NQ3ADBxcC9+9NaT6FlcwOShvfnkyyewcksVayuq2bB9Fxt37KKiqpYzjhnA+IGH/rf80qIC3nHq6Kx9LnUOEXEO8BngFKAvsL/W3ZRSysbfCzqFAT2Led9ZLTesnDK8nJVbqgB4flWFgbMkSVIrtPqLZUrpxYj4LvBF4NGIuORAm49ExEQy4zRuae26kiSpe1q0bjs/vedFALbsrOV9Vz/B3z985kE7nWvrG1i9tYp+ZUX0Kilsr1IB+P6dC1m0fgcApUX5fP31J1BSmE8EPPzSRv782HIaEtQ1JCqq6wDoW1rIb949k57Fe76qRQQj+5Uysl9pu9avzisiLgJuAvLJjMFbCNTltKgO6oTh5dz+XGZu+rOrtnHJySNzXJEkSVLnlZVOhpTSlyJiJPBuYG5EXA38EXgspVQdmaGE5wI/J/OF99fZWFeSJHUvKSW+csvzLbqFV2yu4kN/fJI/XH5q09zVtduqufqRpTy/uoKlmypZuaWK+oZEYX7w5hkj+NA5xzCq/5EFtxXVtSxYs526hgbqGxL1DYkBPYs5fljvpvnLe3ts8SZ+8+CSpudfePVk3jRjRNPzV08ZyttOGcWX//48s5dtAaAgL/j5O2YYLCsbvgLUAhellO7McS0d2vHD9sxAf271thxWIkmS1Pll7adzKaX3RMSzwDeAy4H3AikiKoAeQBEQwG9TSndka11JktR93PHcWh56cRMAeQEJSAkeW7KZr9zyPF949WR+ed9L/PqBxVTXNuzz/tr6xDWPr+Cvs1fyhpOG8+HzxjPuEOMoUkr88bHlfPMf86mqrd/n+DkTBvJfbzmRgb2KW7y+Y1cdn77+aVJjNn72hIG849RR+7z/+GHlXPfBWdzyzBruW7iBi6cNZ9b4/of5JyId1AnAtYbNh9Z8hMb8NRXU1TdQkO/GgZIkSUcjq9+iUko/AMYB3wMWNV6/D1AMLAM+kVK6IptrSpKk7qGqpp5v/GPP1K53nTaaT184sen5nx9bzqxv3c1P7nlxv2Fz85Eb9Q2JG+as5MIf3s+v7n+JlPY/X3nNtiou/d3j/MdNz+03bAa474UNvOrHD3D/C5ktKlJKzFtdwZV/mcuKzZmZsL1LCvjum6YesBM6InjdicP4/iUncuaxAw7xJyEdth3A5lwX0RkM7FXMkN4lAFTXNux3g05JkiQdnqxvDpJSWgt8DvhcRJQBA4HKlJIbBUqSpENKKfGbB5bwP/e9xLgBZVxy8kheM3Uov7xvMau2ZgLcfmVFXPnyifTuUcAL67bz97mrAdhevWc87XFDe/OBc8YxcUgvRvcro6Qwj0cXb+Yn9yzi4ZcyXdJ1DYlv3raA2Uu38L23nEh5j8x858pdddz89Gq+edv8Ftcc0bcHI/r2ID8vqG9IPLo4k+Vt3LGLS3/3OK+ZOpT5ayp4aUPLsOprrz+BIeUlbfeHJu3f3cCsXBfRWZwwvDdrK6oBeG7VNiYM7ribkUqSJHVkbbobdUqpErA9QJKkbq6mroHnV29j8tDelBTmH/C8hobE126dx1UPLwVgc2UNs5dt4eu3zGNX/Z6u5c+8YiLlpZlw+DtvmsrSjZU8vTIzd3VoeQmfvnAiF08bTl5ey47iWeP7M2t8f55ctpmv3zqfuSu2AnDnvHUs/OmDfODs8Tz44gbuWbC+RZd0BLz/rHF88uUTWtT/wKINXPnXp9mwfRcAtz6zZp/P9Mbpw3n9ScOO5I9LypbPAY9HxBeB/0wHauUXkBmrcdf89QA8t6qCN07PcUGSJEmdVJsGzpIkSdura7nkl48yf00Fw/v04Htvmcrp4/cdG1FT18CnrnuaW55eve81du3pMp4yvJxLZo5sel5SmM/V7z2V3z64mAG9irlk5siDhtoAM0b3468fmMW3bp/P7x9aCsCyTTv5wo3P7nPuqH6lfP+SEzl5TL99jp117EBu//hZfOa6p7l34Z4fc5UW5XPB5MG89sRhXDB50AFHaUht7MvA88BXgfdGxFxg637OSymly9u1sg7ohOF75ji7caAkSdLRa3XgHBFvI/NTvdkppatbX5IkSeoqUkp89vpnmL+mAoBVW6t4+68f4z1njOFzr5xESWE+KSVWbqniCzc+ywOLNja996IpQzlxZDnXPr6iaZ5qBHzldceTv1fncnlpIVc2m+d8OIoK8vjya49n5uh+fPb6p6msaTmjecLgnrzuxGG854yxlBUf+CvTgJ7F/O6yk7lu9kqeWrGVs44dwHkTB9Gj6OCht9QOLmv2eEzjbX8SmU2/u7UpzQLneasraGhI+/xKQpIkSYfWqsA5Ir4GTACuBt4WEcemlP4jK5VJkqRO7zcPLOH259bu8/rvH1rKvxZuYHDvYuatrqCi2ZxkgEtnjebLr80Ey1ecNY7Hl2zmoZc2cfKYvswY3TerNV40dSiThvbiS39/jm1VtZw/aTAXTR16RPNbI4JLTh7JJSePPPTJUvsZm+sCOpPBvYsZ0LOIjTtq2LGrjqWbKhk3sGeuy5IkSep0WtvhfDEwK6W0IyLuBx4CDJwlSepgFq7dzo5dtUwd0YfC/Lx2WfOxxZv49h0Lmp6/ecYINu3Y1TR6YsnGSpZs3HerhytfPoGPvuyYpjEUEcGp4/pz6rj+bVbr+IE9+dP7Tmuz60u5kFJalusaOpOI4Phh5dz3QuZ/o55bXWHgLEmSdBRaGzg/DHwhIq4G3gk83vqSJElSNv3psWV88abnSAl6lRRw7sRBXDB5ENNH9aUgPwiCiMzc4V4lhVlZc31FNR/+81PUN2T2KJs2qg/fvHgKhfnBX55YwddvnddihEV5j0KOG9qbd80azaunDM1KDZJ0pE4Y3rspcH5+1TZed6IbfkqSJB2p1gbOnyCz+/V/AU81PpckSR3EXfPW8R+NYTPA9uo6bnl69X435gMY0LOIMf3LGDOgjElDevHG6SPoV1Z00DXq6hu45ZnV3LtgA6u3VrFmWzVrK6qbwuZ+ZUX8/B3TKSrIdFa/9ZRRnDtxEA+9uJHePQo5blhvhpWXuLGe1EYiYihwPjAcKN7PKSml9PX2rapjOmGYGwdKkiS1VqsC55RSFfCV7JQiSZKyae6KrXzkmjk05r4U5edRU99w0Pds3FHDxh01zF62BYDv3/kCbztlFFecPZah5T1anFvfkLj1mdX8+K5FTZv67S0v4Cdvm7bPe4eUl/CmGSOO8pNJOlwR8VXg87T83h9kNgps/tjAGTih2caBz62qIKXkP4ZJkiQdodZ2OEuSpA5o2aZKLr/qCaprMwHzyH49+NuHzmDD9l3cPX8d9yxcz5qt1SQSKWXSpm07a/cJpKtq6/ndQ0v4w6NLedUJQ+nfs6hpBMcDizbwwrodB6xhUK9iPnHBBM44ZkBbflRJBxAR7yCzv8o9wM+AG4CrgDuBc4HLgeuAX+amwo5nRN8elPcoZFtVLduqalm5pYqR/UpzXZYkSVKnYuAsSVInU1Fdy0/uXkR+Xh7nThzIjNF9mzYC3FZVy78WrueH/3yBTZU1APQpLeSq95zCwF7FDOxVzHHDevPR84/d57r1DYnVW6tYuqmSF9fv4K+zVzJ/TQUAtfWJmw8whgMys6Hfe8ZYTh3bj6F9ejC0vISSwvw2+PSSjsCHgJXAK1NKdY2duktTStcC10bEjcA/gGtyWGOHEhGcMLw3D724CYA5y7cYOEuSJB2hVgXOEdGjcaxGTq8hSVJnsmNXHfcsWM/AnsWcMLx3i436Vm7ZyUMvbuT51RWcOrY/F03ddwO9z173DHc8vxaAX9z3Er1LCjh7wkC27qzl0cWbqNs9QwMoLsjjt++eyfiBPQ9ZV35eMLJfKSP7lXLWsQO57PQx/GvhBn5674s82ThiY29lRfm898yxvO/McZSXZmfDQUlZMwW4JqVU1+y1pn8JSin9X0T8H/AZ4Jb2Lq6jmjm6X1Pg/Ojizbz+pOE5rkiSJKlzaW2H85KI+Bbwi5TSriN5Y0ScCHwNmI0z4yRJ3cSG7bt4268f5cX1e0ZRjBtYxrGDerJw7XaWbtrZ9PrVjyyjX9lpzBrfv+m1easrmsLm3Sqq67j1mTX7rFWYH/z4rScxY3S/o6o1Ijhv0iDOnTiQOcu38PSKbTQ07j6YEvQsKeAVxw855KaCknKmENjU7HkVUL7XOc8BH2y3ijqBWeP78+O7FwHw6OJNhzhbkiRJe2tt4Hwn8APgyxHxF+CvwKMH6liOiHHAK4BLgVOAFcD3WlmDJEmdwv7CZoDFGypZvGH/m+59/dZ53PLRM8nPy2xa9d+NIQhkgurqmnpWb6tu8Z6pI8p5+eTBvPbEYYwZUNbquiOCGaP7HXVwLSln1gDNfyaxHJi61znDgTrU5KSRfSgqyKOmroElGytZu62aIeUluS5LkiSp02hV4JxSujQi/hv4JvD+xlt9RMwn8wV3C1AC9AcmAgPI7IS9Dvh34IdH2hktSVJHVrmrjhfX72D99l1MGNyTUf1KiQg2bN/F25uFzXkBEwb3YtH6HdQ3G4FRUpjHyWP68cTSzVTXNjBvTQU3PLmSS04eyfw1Lbubf/q26Uwe2ov5a7bz8EsbKS0q4LxJAxla3qPdP7ekDukpMmM1drsHeH9EvAv4G5mNA98EPNT+pXVcJYX5zBjVl0cW7x6rsYk3THOshiRJ0uFq9aaBKaXZwIURcSyZna7PB06i5ZdbgA1kvtjeANyQUqpt7dqSJOVaSomb5q7i73NXs2jdDlZtbfkjnyG9Szh1XD+eX13RImz+8Vun8doTh1FdW8+8NRW8tH4Hw/v2YMbovhQX5PPDf77Q9JPu7/7fQl49dWiL7uYLjxvMccN6A3DcsN5NjyWpmVuBn0fE2JTSEuDbwP8Drmq8AdQCX8xJdR3YrPH9mwLnR14ycJYkSToSrQ6cd0spLQI+DxARpWR+ntefzKy49SmlfYdLSpLUib2wbjtfvPE5Hl+6+YDnrK2o5u9zVzc9bx42Q6aTbvqovkwf1bfF+z5wzjiufWI56yp2sXHHLj5z3dPc/tye7uaPnX9slj+NpK4mpXQVe4JlUkorIuJk4FPAeGAp8POU0rO5qK8jO23cntn5jzjHWZIk6YhkLXBuLqW0E1jUeJMkqVO75vHlPPjiRgb1KmZE31JG9u3BUyu28uv7F1PXbBwGQEFeMGZAGQN7FvPsqm3s2LVnNGpewI+ahc0HU1pUwOdeOYkr//o0QIuw+eXHDeaE4Xvv+yVJh9bY6fyRXNfR0Z04spySwjyqaxtYvnknq7ZWMbyP44okSZIOR5sEzpIkZdPyTTvp3aOAPqVFWb92Q0PisSWb2bKzhgsmD6aoIK/F8f/510t8544FB71GQV5w+ZljefOMEYzuX9Z0jbr6zAzmxxZvZtH67bz2xGGcdezAw67tDScN538fXsrTK7e1eP3jdjdL6gQaZ0Vf3fj0ipTSb3JZz5EoLshn5uh+PPjiRgAefWkTb5oxIsdVSZIkdQ4GzpKkDu23Dy7hG/+YR0Fe8M7TRvOxlx1L37L9B8/rKqr5xzNruHvBOgBeecJQXjd1GOWlhfucu3RjJTfMWckNT65k9bZqAKaOKOdnb5/OyH6lAPz5seWHDJtPHtOXb7xhChOH9NrnWEF+HlNH9GHqiD5H9Jl3y8sL/uM1x/HmXzzS9NoFkwfZ3Sypw4uIkcBPgB1AzxyXc1ROG9cscF5s4CxJknS4DJwlSR3W7KWb+eZt80kJausTv39oKdc/uZIPn3cMr5k6lPXbd7FmazUrtuzkngXreWLpZlKzCRcPvbiJr986jwuPG8yM0X1ZtaWKZZt3smxTJS+s27HPes+s3MZF//0A37/kJKpr6/n3m/aMNT1lbD/OmziIlVt2smJLFQ0NidedNIw3Tx9BXl602Z/BzDH9eOO04fztqVUU5efxiQsmtNlakjq3iPjdUb41pZQuz2IdAfwe2ERm0/BPZ+va7WnWeOc4S5IkHQ0DZ0lSh7R1Zw0fu+Yp6veakby9uo5v376Ab99+8M7j3WrqGrj1mTXc+syB967tW1rIjl111NYnKqrruOLq2eTnRVN4PXVEOb+77GR6Fufm/za/8+apnDVhAOMH9rS7WdLBXHaU70tA1gJn4GPAy4BzG+87pakj+tCjMJ+q2npWbqlixeadTb+AkSRJ0oEZOEuS2lRDQ+K51dvYuGMXVTUN7KzJBLuzxvdn7ICy/b4npcRnr3+madRFeY9C/v3Vk/nFfS+xeGPlAdfKi0xH2kVThlHX0MD1T67kmb3mH++WnxecN3Egb54xkpdNGsRzq7fx0T8/xaqtVQBNQfexg3py1XtOyVnYDFCYn8fF0/wpt6RDGpvrAiJiMvBt4McppfsjotMGzoX5ecwc05cHFu0Zq2HgLEmSdGgGzpKkNrNyy04+es1TPLV86z7Hyory+du/nbHf2cdXP7KMO+eta3r+vTdP5cLjh3Dx9OFc+8QKfv/QErbtrGVonxKGlvdgWHkJE4b04sLjhjCwV3HT+y6dNYYFayu4ee5qNu2oYWS/HozqX8bofqWMHVhG75I9s52nj+rLrR89k09d9zT3LFgPwIi+PfjD5afS7wAzoyWpI0kpLcvl+hFRAPwBWA584Sje/+QBDk1qTV2tMWt8/6bA+ZHFm3jLzJGklFizrZqyooL97hEgSZLU3bU6cI6IS4G5KaVnslCPJKkTSSkxZ/lWnl+9jfEDe3LK2H4U5ucBcNe8dXzquqfZVlW73/dW1tRzxdWzufkjZ9CndE+gO3fFVv7zH/Obnl92+hguPH4IkOk2e9dpo3nXaaMPu8ZJQ3oz6ZW9D+vcvmVF/ObSmdw0dxUvrNvBZaePYUh5yWGvJUnd3JeAacCZKaWqXBeTDbPG7Znj/MCijXzl5ue5Z8F6lv9/9u47vK3y7OP495G8dxyP2Nl7bxKySVhlbyiUUfZoC5S29IWWtpTSRemgpZRRKJS9NyWsLMheTuIsx4kd772XLOl5/5AsnSPJW3bW/bkuX9Y4S3bG0a37/O7KRqwWxakTUrhq7lBOGZeCtQ/z/IUQQgghjiXB6HB+HngQkIKzEEIcB/aX1JFdWs/S8SlEhlkDLlPX3Mp72wt5eX0ue4vrPI8nRIVy2oRUIkItvLzhsOdxq0WxcEwS0WFWIkOtLM8spsHm4HClqwP6+RvmYrUoPsss5oevb8fmcAIwOT2O+8/p38Y2i0VxySyJrxBCHD+UUmnAacBgIDzAIlpr/Zte7mMurq7mP2ut1/VkG1rr2e1sewswqxeH12NTBscTHWalweagrK6F59fmeJ5zODWf7y7h890lpMVHcO384dy2ZLQUnoUQQghxwpNIDSGEEDidmlVZZTy75hBfH3BdOjwmJYYnr5nNmJQYz3JNNgd//yqL/67NocHm8NtOdWMrb2/NNz2WHh/BP74zk9nDEz2PfbqriNtf2gq4OsYeWb6XhMgwHlm+1zOoLzYihH9cNZPwkMBFbyGEEJ1TSv0auA/zeb/CNSjQeLvHBWdDlMZ+4Bc93c7RKNRqYe7IRFbsKzM9HhFqobnV6blfVNPMI5/uIyk6nCvmDO3vwxRCCCGEOKpIwVkIIU4whdVNbM6toqapldqmVmqaWvlqbykHSutNyx0orefCx7/m0cunc/bUNNZlV3DfOzvIrWg0LRcRamHJ2GR2FtRQ5B7y1+a0CSk8evl0BvhkIJ81JY07Tx3DP746AMBTqw6anh+WGMWz3z2JUckxCCGE6Bml1NW4CsBfAf8E3sZ1deJnwFLgJuBN4Kle7ioGGOe+3axUwA7fZ5RSz+AaJvjDXu6vX/3g1DHsK64jPNTK0vHJnDYhlbkjE8mvauT1TXm8uSWfygYbAG9tzZeCsxBCCCFOeFJwFkKIfnS4opF/f32Q9IRIrjhpaL8Oo7PZnTyx8gD/XHGAVodudzmLghCrBZvdSYPNwR0vb+XkkYlsOFRpWm5sSgzXzBvORTMHEx8ZitaaHfk1LM8sJiO/mjMnDeK6+cNpp/DAPaePI7Ow1jOgr83cEYk8ee1sGdQnhBC9dweQD5yltba7/z3O0Vq/BrymlHoX+Bh4tZf7aQGebee5Wbhynb8G9gE9its4kmYPT2Tt/af5PT4qOYb7z5nITYtHMu93X+LUsPFQJYXVTaQnRB6BI+0drXW7/2cLIYQQQnRHsArOCUqpYd1ZQWt9uPOl+oZSajHwQ2ABkAhUAjuBv2mtPzlSxyWEOL4dKK3jyqc3UF7fAsBfP9/PJbMGc8PCkYxLje3Tfe8qqOEnb2aY8pZ9xYSH8O05Q7l+wQjqmu3c/tIWDle6upmNxebYiBAeOHciV5w01PTGVCnF9KEJTB+a0KVjslgUf/32DC765zccKm8A4PLZQ/jtxVMJC7H05GUKIYQwmwq8qrW2Gx7z5BRprZcrpZYD9wIf9nQn7gGBNwd6Tin1IK6C8wta63/3dB9Hs5TYCBaOSWJNliuS6qMdhdy6ZHRQ97Elt5KtudVcMWco8ZGhQd02wPvbC/jNR7s5Y1Iqv7t4qhSehRBCCNErwSo43+3+6iodxH13i1LqAVwZdeXAR0ARkITrRHgpIAVnIUTQZZXUcdUz3mIzQIvdyasb83h1Yx4XzkjnkcumdTuvOL+qkfe3F6K15oqThpISF2F6vqrBxpOrs/n3mkM4nN6u5imD45g6OIH4yFDiIkMYFBfB6ZNSiYvwvon98AeL+NEb2/nS0IF85qRUfnPRFFJ99tNT8ZGhvHLLyTy9+iBTB8dz8czB8iZXCCGCJxSoMNxvAuJ9ltkF3N5vR3ScumB6uqfg/P724Bacy+pauOqZDdjsTj7eWcRbt88nxBrcD2b//mUW5fU2Xt2Yx3XzRzAxLS6o2xdCCCHEiSVYRd9aoDpI2+ozSqnLcRWbvwAu0VrX+Twf/HYBIcQJz1VsXk95vSvfMTrMysjkaHYV1HqWeX97IWNTYvjBqWM73V5zq4PlmcW8uTmfb7LLPUP2/v7lAS6dPZhbl4wmOszKM2sO8vKGwzQahvtFhFq491sTuH7BCKyWjgu78VGhPHPdSfx3XQ5rsyu4eOZgzpoyKOgF4bT4SH51/uSgblMIIQTgaqxIM9w/DEzzWWYwYEf0yremDOLn7+3CZneSWVjLgdJ609Dd3tieV43N7vTcfmJlNned1vn5Qlc5nZq8yibT/qTgLIQQQojeCFbB+a9a64eCtK0+oZSyAH8EGoHv+BabAbTWrf1+YEKI447WmrL6FvYW1bGvuI6nVmebis3P3ziXk4YPYHNuFf9ame3JMH58xQEumjmYIQOi2t32in2l/OSNDCrcw4mMbA5Xx/Rrm/IItViwOZym5+eOTOSRS6cxIim6y6/FYlFcv3Ak1y8c2eV1hBBCHDW24YrVaPMVcKtS6lrgHVxX910KfNNXB6C1fhB4sK+2f7SIiwjl1PEpfJpZDMAHGYX86IxxnazVNVml5rctf/8yi2XjU5g6xLdZvWfKG1pM5wwZedVcNbdbaYlCCCGEECYnUkjmAmAkrsiMKqXUuUqp/1NK3a2Umn+Ej00IcRzQWvPiuhxO/t2XzP3tl1z33EZ++8keU7H5hRvnMmdEIkop5oxI5JnrTmKSu4uoudXJbz7a3e62/73mIDc9v8lUbFYKloxLZoYhN1lrglDkIAAAIABJREFUTG8cx6fG8tiVM3jtlnndKjYLIYQ45n0ETFZKtX1q+AegBnge1xWKHwAKeOCIHN1x5sIZ6Z7bH2a44q6C4UBJvem+3am5543tNLc62lmjewqrm033t+cd9ReuCiGEEOIod0RylI+QOe7vJcBWzN0eKKVWA5dprcs62ohSaks7T03o9REKIY4Jgaa42+xOfvXBLl7dmBdwnbZi80kjEk2PWy2K31w0mUv/tQ6A5ZklrNxXytLxKZ5lWuwOHnh3F29uyfc8lhIbznXzh3PJrCGkJ0SitWbDoUqeXJXNyn2uf8amD03gB8vGcNqEFCydxGcIIYQ4/mitn8dVXG67n6eUmgP8GBgN5ABPaK13HonjO94sm5BCTHgI9S12DpU3sLOghmlDujZItyNZpfV+jx0orefR5ft44LxJvd5+UXWT6X5WaT2NNjtRYSfSW0UhhBBCBNOJdBbRVr25HTgEnA5sAIYDfwa+BbyJ69JCIYTw43RqfvVBJq9tOsyUwfFcOmsI509Lx6k1t7+0hQ2HKj3LRoVZGZcay8S0WManxnLO1DS/gX5tZg9P5LLZQ3jLXVB+8INMlt8zkDCrhQ2HKvnT8n1sya0yLD+AJ6+ZTXJsuOcxpRTzRg1k3qiBHCpvoKHFzuT0OBnAJ4QQwkRrfQj4wZE+juNRRKiVb00exNtbXf+fv7+9kKmD41l/sJLXNx3GqeHn507s1uBdp1NzwFBw/smZ43j0s/0APPvNIU6bmMr80QN7ddwFPgVnh1OTWVjLHJ8PyYUQQgghuupEKjhb3d8Vrk7mDPf9TKXUxcB+4BSl1Hyt9br2NqK1nh3ocXfn86xgHrAQou9ordldVMvKfWWU1jZz46KRDB/YftyE1poHP8zkxfW5AGw7XM22w9U89NFu4iJCKa9v8Sx70Yx0/nDpNCJCre1tzs99Z09geWYxdc12cioa+d5LW8kuqyenotG03KWzhvC7S6YQHtL+tkdKbIYQQghxRFw4I91TcH53WwHrsivYXeQdEpxdVs9bty8gMqxr5wgF1U00uaMzBkaH8f1lY9iUU8Wq/WVoDb/6YBfLf7ikVx8w+0ZqgCvHub8LzlprtEauyhJCCCGOA70uOGutj5Uc6Lb2wIOGYjMAWusmpdRy4CZgLtBuwVkIcexqdThZk1XGZ5klrNhXSkmtt0i8PLOED+9cZOoaNvrbF1n8d12u3+M2u9NTbFYK7v3WeO44ZXS33/glxYTz4zPG8eCHrgznL92DBNsoBfefPYFbFo+SrmUhhBDdopSKAS4GZgLxuHKctwHvaq398xpEjy0YPZCkmDDK621UNri+jDILa/m/t3fw2JUzuvT/ubG7eUxKDEopHrlsGsseXUmjzcH+kno2HKpk3qiedzkX+nQ4A2Tk1/R4ez1R19zKlU+vp7y+hWe/O4cpg4MzEFEIIYQQR0afF4uVUhcopf6mlHpMKXVpX++vA/vc39ubgtFWkI7sh2MRQvQTrTW7Cmp46MPdzP/9l9z4/GZe25RnKjYDFNc2c8dLW7DZnX7beGFtDo99meW5f+60NH59wWSmGt4MRYVZefKa2Xxv6ZgeF4SvmTecie4Bgm1iI0K4Zt4wPrlrMbcu6X4hWwghxIlNKXU5kIsry/mHwA3u788DuUqpy47YwR2HQqwWzp2aZnosItTC0vHJnvsfZBTy9OqDXdpeVmmd5/bY1BgAUuMiuGjmYM/jL633/0C8OwprAhSc+3lw4GeZJWQW1lJS29Lln40QQgghjl697nBWSp0P3Av8Qmu9yue5/wDX4YqxAPiBUuo9rfWRKDyvBuzAWKVUmNba5vP8FPf3nH49KiFEn9ldWMvP3t3Z7rT1+MhQTh6ZyBd7SnBq2JxbxYMfZvK7i10zRR1OzSsbD/OrDzI96ywZl8xfr5hBWIiF7y4Ywf6SOjYeqmTx2KQOIzm6IsRq4fHvzOTn7+4kLMTKxTPTOXtKWreiOYQQQog2SqkzgFcBJ/BfYCVQDAwClgHfAV5VSlVrrb84Usd5vLntlNGsO1iBze7kijlD+c7cYSREhXH/Ozt5deNhAP746V4mpMVxyrjkDreVVeLtcB6bEuu5fc3Jw3llg2tbyzOLKatrafcqrc4EitQ4XNlIZYONxOiwHm2zu8oM0WSZhX3TXZ2RV80nO4u4eNZgJgyK63wFIYQQQvRYMDKcL8CVXbzB+KBS6jzgu0AD8FegDrgVuEgpdZXW+tUg7LvLtNblSqnXgauBXwIPGI71DFxDA2uAT/vzuIQ4nny6q5iHPsxkzshEfn/J1CM23by51cE/vsriqVUHsTu16bnUuHAunDGYb01OZfqQBEKsFp5alc3v/7cXgFc2HGbioFhiI0L5x1dZZJc1eNadOSyBJ6+ZRViI9+KQcamxjEuNJVhGJ8fw2q3zg7Y9IYQQJ7RfAi3AYq31Vp/nXlBKPY6rKeOXgBScgyQ9IZLP7jnF7/FfXzCZrJI6NudW4dRw5ytb+fDORR1+YJ3lE6nRZlJ6HLOHD2BLbhWtDs0bm/P4/rIx3T7W5laHJxrMalFMSotjZ4Gr4JuRX82y8SkdrR401Y2tntsHyxtotNmDeh5pdzi57cUtFNc2s2JfacDfjxBCCCGCJxiRGnOBdVpr34/GbwQ0cIPW+pda6z8Bi4FmXEXfI+FHwAHg50qp1UqpR5VSbwL/AxzALVrr/r1+TIjjRHl9C/e+mUFhTTPvby/k+v9sor7F3u/HsTmnknP+voZ/rsj2FJvDQixcPHMwL940l7X3ncbPzpnI7OGJhFhd/wTeumQUF0xP92zjF+9n8sPXt5uKzeNTY/nP9XOOWBFdCCGE6IGZwOsBis0AaK03A28gg6/7RViIhSeumcWguAgAapvtPP7VgXaX11qbMpzHGgrOANfMG+a5/cqGwzh8PmTviuIa71u4QXERzB4+wHN/R17/5TjXNHkvPtUa9hbXdbB095XWtVBc63qt+0vqA0aoCSGEECJ4glFwHgRkB3h8Ca685LfbHtBaFwMf4zr57Xda61LgZFwd10OBu4BT3ce0WGv95pE4LiGOdlprMvKquffNDJb+aQWPLt+H1uY3NX/+bB91hgLzxkOVfPe5jdQ1t/puzrTdzMIaigJkBwLsKarl3L+v4bx/rGFNVlmnx/niuhyueGodBw2F4rkjEvn07sX89dszWDw2GWuAyedKKf546TQmpflfXhkbHsJdp47hrTvmkxDVP5eVCiGEEEHSAhR1skyheznRD1JiI/jLt6d77q/YV4qznUJxcW2z58P7uIgQv8iMs6ekeSIvCqqbWOEzcLgrjAMD0+IjmDbEO58iI7//+nCMHc7gikULpqIac29UVaNvuqIQQgghgikYrXoDgErjA0qpYUAi8KH2rUrBIVwxHEeE1roSV6fzj47UMQhxrGhudfDetgJe2pDLrgLvif/jKw4QExHC7aeMBmBXQQ2vbcrzW39LbhXXPLuR/944l/jIUNNz6w9W8MdP97LtcDWhVsVfrpjB+YYu47zKRq57biNlda73wNc+u5Hr5g/nvrMn+HUZO5ya332yh2e/PuR5LCY8hPvOnsB35g7DEqDI7CsyzMrT183mon+upby+hdiIEG5cOJIbF44kPiq00/WFEEKIo9AaYFEnyyzEFash+sm8kQMZGB1GRYON8nobuwprmDYkwW85U35zaqzf4OCIUCuXnzSEp1a5huy9tCGX0yeldutYCgwF5/SESKYP9R5HRl41Wut+GVhc0+RTcC4KbsG52KfgXNlgI9XdaS6EEEKI4AtGwbkOGOLz2Gz3923trOM/mUIIcVQprG7ixuc3tXtJ4x8/3cv41FiWjk/m1x9m0vbR0rLxySwZl8yvP9wNuN6snP6XVcwdkci0IfGMSIrmlQ2HWbXf27Hc6tDc9do2GlrsXDl3GBX1LaZic5v/rstl9f4yHr5oKuMHxTIgKhSbw8ndr23n890lnuWmD03gX1fPIj0hsluveciAKD6/Zwnb86qZNXyAX5FcCCGEOMb8H7BOKfUH4Ddaa88lQEqpaOBXuAZnLzhCx3dCslgUp4xP5p2tBQCs2FsWuODcQZxGm6vnDufp1QfRGlbtL+NwRSPDBkZ1+ViMAwPTEyIZOTCa2IgQ6prtVDTYKKhuYsiArm+vp/q+w9l8NV1Vg3Q4CyGEEH0pGAXnncC5SqkYrXXbWdHFuPKbvw6w/Eg6v7RPCHEE7S6s5YbnN1JS6y34hodYOG9aOofK69l6uBqt4a5Xt3Hz4lFsyqkCIMSieOC8SYxOjiHEauEX7+0CoKyuhY93FvHxzvb/6msN972zk4oGG59lFnOo3PWeOCzEwtwRiXx9oByAnIpGrnnWO6M0PMRCiyGH76zJg/jrt2cQGWbt0WsfEB3Gsgn9MyBHCCGECCal1HMBHt4B3AvcqpTaCpQAqbhym+NxdTf/FLipv45TwLLxKd6C875S7j59rN8yB9oZGGg0bGAUp4xLZuW+MrSGlzfmcv/ZE7t8HMZC7OCECCwWxfQhCZ7zroy8mn4pOPt2OO8trsXh1AGj0HrCt8O5QgrOQgghRJ8KRobzy7hiNVYppe5yT7u+GigGVhgXVK7rsRYBu4OwXyFEH1i9v4wrnlrnKTaHWhU/PWs8G352Gn++YjpPX3cS6fGuSxDrWuz89Yv9nnVvWDiC0cmuN0TXzhvOny+fTlxE4M+1LAounz2Ej+5cxNTB3rzAPy3fR0a+a0iNUvD3K2fw4k1zefTy6cSE+2/LWGy+bckonrh6Vo+LzUIIIcQx7voAX0sABSTgml1ylft7gvvxU9zLiX60ZGwybbXUjPxqKur9Y7QPlHqvMhubGtvutq45ebjn9ttb8rE7uj4QzzdSAzgiOc7VPpnKza1OT/NBMEiGsxBCCNG/gtHh/CxwCfAtYAauE9dW4G6ttcNn2dNwDRn8Igj7FUIE2Rub8vjZuzuxu4fXxIaH8NS1s1kwJsmzTFJMOE9fdxKXPbmW5lbvG5qB0WHceZq5O+fS2UO4YEY6+4rryMivZkdeDftL6xg5MJo7lo72vHl6+ZaTufn5zWzMMcXB89CFUzhrShoAl80ewrxRiTy6fB87C2qobLBR3dSK1q4u6F+dP4mrDW+4hBBCiBPQyCN9AKJr4qNCmT18AJtyqtAaVmeVcfFMb0qh1pr9JZ1HagAsm5BCSmw4pXUtlNfb2Hio0nTu1pHCAAVn3xznvmazO2mw+b5thMzCmnY7u7vLN1Kjol4KzkIIIURf6nXBWWvtVEqdi6tbYgFQAbyjtd4eYPEk4DHgg97uVwgRPHaHk9//b69p6F5afAT/uWEOEwbF+S0/ZXA8f7psOne+6o1pv/db44mL8M88DrVamDI4nimD47n65MD7j4sI5YUb53L7S1s82c53nTqGa+eZC8hDBkTxtytneu47nJrqRhthIRZiA+xbCCGEOJForXOP9DGIrls6PsUTS7Zir7ngXF5v88RMRIdZSYtvf8Cd1aI4Z2oaz6/NAeCjnUVdKjhrrc0ZzvGugvMMQ8F5Z0FNUKMtAvGN02izu6iWC2cMDso+fCM1pMNZCCGE6FvBiNRAa+3UWr+stf6+1vqX7RSb0Vq/prW+R2tdEIz9CiHap7XmhbU53PXqNta6c/gCqWlq5cYXNpuKzRMGxfLu9xYGLDa3OX96Og+cO5HoMCsXTE/n8pOG9up4I8OsPHPdSTxy6TSeuHoW95wxrtN1rBbFwJhwKTYLIYQQ4pizbLx3ZsSq/WU43FeYAWQZ4jTGpMbiSiZs37nT0jy3P91V3KVYjerGVppaXZ3F0WFW4iJdvUipcRGkxoUD0GhzkF1W3+422mitWXugnD1F3R/2127BOUiDAx1OTYnPIOpKyXAWQggh+lQwIjWEEEeh/3yTw0MfueLSP8go5Lr5w7nv7AlEhXn/2u8qqOGuV7dx0JCRd+akVP7y7RkB85J93bx4FDcvHhW0Yw4LsXDFnN4VroUQQgjhpZQaAtyDK/puCBDoU1qttR7drwcmmJgWS2pcOCW1LdQ0tbI9r4rZwxMBn4GByZ3HSsweNsCzrcoGG+sPVrJobMddzr75zcai9pT0eEpqSwHYW1zHuA4ypAFe25TH/e/sJMSieO/7C5limM/RmZomb/E3MTrMUwzeXViL1rrTYntnyutbTMV8kIKzEEII0dd6XXBWSl3Xk/W01v/t7b6FEIFtya3kd5/sMT3233W5rNpfxv1nT2BfcT0f7yw0ZQOCK8bih6ePw9KHl00KIYQQon8opZYCnwARgB0ocX/3W7QfD0u4KaVYNj6F1zblAa5YjbaCc5Yxvzm184KzxR2r8Z9vcgD4eGdhpwVn4yC9tvxm7z5j+XJvqftY6ujM1+6r6exOzVtb8gMWnJ1OTavTSXiIebhzdaO3w3nK4Hi25FTSYHNQ0WCjrK6FlLj240S6wndgIEjBWQghhOhrwehwfh7QnS1koNzLS8FZiE4UVDdR32xnbEpMl4vA5fUtfO/lrabBf3UtrveWuRWN3P7SVr91IkItPHr5dM6blh68gxdCCCHEkfYIYAWuA17RWneesyD61VJjwXlfKT/51njAHKnR0cBAo/OmeQvOn+4q5qELpxBqbT9BMdDAwDbjB3n3ua+484JzuSGy4su9Jfzq/EmmzuQDpfVc8dQ6LErx+m3zGG3o2jYWnAdEhTIxLY7Nua5s68yi2l4XnIt9BgaCFJyFEEKIvhasSA078BGwO0jbE+KE9/72An7yZgatDk1ybDinjk/h1IkpLBqTRHQ7cRcOp+auV7dRUus66U+ICuWjOxex4WAlD36YSV2zuakpMtTKqRNTuPPUMR3mNQshhBDimDQVeFVr/dKRPhAR2MIxAwm1KlodmszCWkpqm0mNizBFaoxN6TjOos3MoQMYFBdBcW0zVY2trMuuYMm45HaXNxacByeYi7rGfWaVdp7hXFbvLTjnVTZxoLSesYYYjidWHPAUed/fVsCPzhzvea7akOGcEBlKfGSop+C8u7DWlHXdE4E6nKsabUGJ6xBCCCFEYMEoOK8ClgAXASnAM8AbWmv//9mFEF3yYUYh97y+nba4ubK6Fl7fnMfrm/MIC7Ewf9RATpuYwqkTUhicEElZfQsHSuv5YHsha7MrAFAK/vbtGQwZEMWQ2VEsGDOQX72fycacSuaPGsi509I4dUKKKdNZCCGEEMeVKqDySB+EaF9sRChzRiR6zt+++9xG4iJCKa93FWcjQi0MHhDZ0SY82mI1nvvGNQj64x1FHRacjRnOafHmfYxJicGiwKkht6KB5lYHEaFW3014lPsM5ftyb6mn4Nxos7M8s9jzXFtjRJuaRm+3cXxUGOnx3uJ3MAYHFgcoOLc6NPUtdhk8LYQQQvSRXleatNbLlFJjgFtwXa73H+AxpdRLwDNa6x293YcQJ5JPdhbxQ0Oxue1kv43N7mTV/jJW7S/jl+9nEhMeQn2LfxzjXaeOZamhIyQtPpKnrzuprw9fCCGEEEePj4BTjvRBiI4tG5/iKTjv9YmvGJUUg7UbszXOneYtOH+aWczDF7cfq9FRpEZEqJXhA6M5VN6AU7siMdobBNhid1DrcxXdV3tKuf0U1xzKz3eX0GBzeJ4zdkMD1Ph0OE9K9151t7uo9wXnwgAFZ3DFakjBWQghhOgb7Yd6dYPW+oDW+v+AocAVwAbgDmCbUmqjUuompVR0MPYlxPHs013F3PXqNs8k7TEpMay//zTevmM+31s6mgmD/C+pDFRsPmVcMnedNrbPj1cIIYQQR7WfAfFKqX/KufjR68IZ6SREBS58XnHSkG5ta+bQBE+HcE1TK9+4h/kFYoyaGJzg30VtzI42Zkr7qqj3z0PenFtJtbtz+f3thabnyny6oU2RGlGhjEuN9RTZcyoaAp7rdkegDGfo+xznguombntxM794bxfNrY7OVxBCCCGOI0G9ll5rbQfeBt5WSg0HbgauB54G/qKUOktrvS6Y+xTieLC3uJZnVh/ive0FnmLzqORoXrnlZFJiI0iJi2D28ER+etYE8qsaWbG3lC/3lrI2uwKb3Ul0mJUxKTGMTolh1rABfHvO0G51wwghhBDi+KO1LldKnYWrGeQ6pdR+oCbwovq0/j060SYlLoI1P11GZmEtWrti0RSQGhfBiKTufU7QFqvx76+9sRpLA2QgtzqclNS6Cs5KQWp8uN8y4wfF8tnuEgD2Fbef41zu07EMrqvzVu0vY9GYJFbtLzM951dwbjQXnCNCrYxJjmFfSR1aw77iWmYPT2x3/50xFtaHJUZxuLIR6PuC85Mrs1me6fr5WRT8+sIpfbo/IYQQ4mjSZ+GtWutc4BdKqbXAU8BgoP0QMSGOMZUNNl7deJhJaXEsHZ/sN3Tk66xy/vrFfgYnRPLAuRMDTthem13OU6sO+p2Ij0yK5tVb5pES67/OkAFRXDt/BNfOH0Fzq4Pa5laSY8Jl6IkQQgghTJRSk4EVwAD3QzPbWVS387joJ7ERocwbNTAo2zp3mrfg/NnuEv7g1H6NCCW1zZ7ItuSYcMJD/POZjUP/skra73AOVHAG+HJPKdWNrZ5mCuPyTqfG4j4mY4dzfGQYAJPS49jn3ufuwp4XnJ1O7SmsA0xKi+u3gvMeQxzIC+tyWTo+hWUTejcAUQghhDhWBCVSw5dSKl0p9YBS6iCu7LiBwEvA1r7YnxDBYLM72ZRTSVUXTj7rmlu5/Mm1/Gn5Pm54fhP3vL6dumbXybLWmqdWZXPdcxvYklvFBxmFnP3YGlYbisp5lY3c/MImvvPMBr9i87xRibx6yzxSAxSofUWEWkmJjZBisxBCCCEC+Quu8/BfAsOBUK21JcBX+9PgxDFnxtAEUmJdHcs1Ta3sKvBvai+s9hZh0wLEaQCMNxSc93VUcK7znjsb499W7ivl7a35fsvbndpUZDYNDYx0RYtMSgtOjnNFg41Wh/Zs2ziAsa8LzgfLG0z3730rw6+7W/ReflUjWw9XobV8biaEEEeToHU4K6UswHm4YjTOcm97J3A38KLWOtDle0IcFVrsDq56ej1bD1cTalWcOiGFS2YNYdn4FMJCzJ/LOJ2aH7+RQXaZ9yTyve2FbD1czR8vncYrGw/zYYY5q66iwcZ3/7OR7y0dTZjVyhMrD9Bid3qeVwrOmjyIW5aMYtawAQghhBBCBMF84B2t9cNH+kBE/1FKsXBMEu9uKwDgm+xypg9NMC1jHBg4OCFwk8PIpGhCLAq7U5Nf1URDi53ocP+3j8YhgIvHJlHT1EpRTTO1zXZ25LveAoZYFAOiwzwF17K6FhKjXd3MvhnOAGNSvfnRuRWNXX/xPooNcRpp8RGefQJUNvZdwbm60eZX0C6vt/HTtzJ47vo50iwSJIcrGjn9r6uw2Z08fNEUrpk3/EgfkhBCCLdedzgrpUYqpR4G8oD3gGXAC8A8rfV0rfXjUmwWR7vff7KXrYerAWh1aJZnlnDbi1s4+Xdf8OfP9tFgGFbyxMoDnjw7o8OVjVz1zHpTsXn60ASS3R0mWsM/V2Tz1y/2m4rNl88ewoofL+Vf18yWYrMQQgghgskG5BzpgxD9b8FobzzH2gMVfs8XGArO6fGBO5zDQiymDOkDpYFznI2RGsmx4ZwaIDZi6fhkRid7t9VWeHY6NbWmSI1Qv2MyFo27q8gwMHCQT8G5K1c19tQhQ3dzbIS3SL9iXxkvrs/ts/0eb7TWrD9Ywar9ZQE7mD/bXYzN/b4q0PszIYQQR04wIjUOAPcD+cCtwCCt9S1a641B2LYQfe7z3SU8vzYn4HNVja3846sDLHt0JW9tyeervSX8+fP9nudvXDiSv181k9gA3R7XzBvGm7fN55O7FrNoTJLf81MGx/Hu9xbwp8und3sgjBBCCCFEF6wE5h7pgxD9b6Hh3HNTTiXNrQ7T88ZCbHo7kRrQtVgNY0xEUkw4p09M9VvmwhmDSTbMJimrdxWR61rsnizpmPAQQq2ut6eD4r3LFtU09zguobjW2OEcyYAoQ4dzHxacDxquhFw8NombF4303P/tx3vI8YnbEIGty67gyqfX893nNvLxziK/541xK7kV8jMVQoijSTAKzgqwA2m48uH2KKUOd/IlH+uKo0JhdRP3vpXhuX/GpFS+/PEpfH/ZaNIMJ7qldS385M0Mbnx+M23nu/NGJXL/ORO4YHo6H9+1mBnuSxXDrBb+eOlUHr5oKmEhFpJjw3nhxrn8+IxxhIVYSIgK5TcXTeH97y9ipnQ0CyGEEKLv/BSYpJS6T8k1/CeU9IRIRrobGlrsTrYerjI9b8xw7qjgPNYQbdHe4EDfDuf5owcSEep9mxkTHsLpE1NJjgn3PNZWpK5p9O9uBoiLCCE6zBUt3tTqoMbQBd0dRT6RGgNj+qfgbOxwHpkUzb1njffkW7fYnby1xT/b+mjhdB49WchrDpR7bv9vV7Hf83uKvH8m86uaaHU4/ZYRQghxZAQrwzkUGBKkbQnRL+wOJ3e/to1q94luenwEf7psGglRYdz7rQn86IzxvLutgEc+3Uupz4CPtPgIHv/OLE8XxrCBUbx1+3w25lQyYmC034m71aK487Sx3LBoJBEhFkKsfTKvUwghhBDC6AFgF/Bb4Bal1HYgUNSd1lrf1JsdKaX+CJwEjAOSgCYgF1fk3uNaa/9cB9GnFowe6Cl8rj1QwYLR3q5nc4ZzVzuc24vU8BZuk2LCiQi1smhMEl/sKQXgrCmDiAyzemLmwFtwrm7yrtuW3wyuHOpB8RGemSlFNc0kGLqTu6qo2hyp0W8dzuXen9WopBjCQ6zcfdpY7nh5KwCrs8r4ybfG99n+e+qxL7J4clU2NywcwU/PmtDt9ddlV/Dzd3cyfWgCf7lieq+zqguqvL+/nfnmf7psdicHSr0FZ4c7a3ykXDkqhBBHhV5XvdqZdN3pVzAOXojeeOzLLDa7NpMXAAAgAElEQVTluLo9rBbFY1fNNJ3IWi2Ky2YPYcVPlnLnqWMIdw8PDAux8OQ1s0kydGkAhFgtLBid1GGXSEx4iBSbhRBCCNFfrgdOwXVF4kjgYvdjgb566x4gGvgceAx4GddVkA8CO5RSQ4OwD9ENxliNb7K9naKHyhtM8RhDEzvqcPYWnLvS4dx2fnz9gpEo5TpvvnGhK04iYMG5nQ5nMHdeGyNAusOvwzm6/yM1RrqzqxeMScJqcRVgdxbU9On+e8Lh1PxzxQGaWh08sTKb0rruZWfb7E5+8mYGB8sbeHdbgWc+Tm8Ys8YPVzaacrezSutodZi7sXMkVkMIIY4awepwFuKY8tXeEh5fccBz/57TxzJnRGLAZaPDQ/jxmeP59pyhrNhbyrxRA00n30IIIYQQR6mRnS8SNHFaa78KlVLqt8DPcM18+V4/Hs8Jb/6ogSjlGly9I7+GuuZWYiNCee7rQ56IuKXjkzvsHB4xMIowqwWbw0lRTTO1za3ERXgLw60Op6dobFF4hvItGpvE2vtOJcxqYaC7CG0qONe3dTh7C87GDmeAQXHmHOeeMGc4RxAXGYpFgVNDbbOdVofTc8VisDid2lT4HOXuuI2PDGXG0AS25FahNXx9oJwLpqcHdd+9UdHQgs0QSfF1VjmXzOr6Rczvbss3FYgLqpuYPbx38YH5VY2m+zsLalgyLhmA3YW1fsvnljfA0dc4LoQQJyRptRTHBbvDycsbcvnFe7vYVxy4+6LNofIG7n5tu+dEe+GYgdyxdEyn+xgyIIpr54+QYrMQQgghjgla69yufgVhX+1VBN9wfx/b232I7hkQHcaktDjA1b264WAlVQ023tyS51nmlsWjOtxGiNXCqGRvRIFvl3OFIU4jMTrM08ELriF9Aw1XBAbMcG4ydjibC9/GeSrFPSg4a61NhepB8ZFYLcpUYK9qDH6XcVFtM82trsJtYnSYaX9LxiZ7bq/eXxb0ffdGmU+E4Jqs8naW9Gd3OHliZXaH2+sum93pF2u4I9/bNW0cGNgmp6LR7zEhhBBHRlAKzkqpO9zDSCyGx+5WSh0M8PWfYOxTiDY782u48J/f8PN3d/Hi+lwu/OfXvLM18CCOhhY7t724mbpmO+DKrPv7lTNNJ8dCCCGEECJoznd/33FEj+IEZYzVWJtdwcsbcj3F0ElpcSwYPbDTbYwzNFvs98lxDhSn0Z5AkRo1jYEznAHSDJEaxiGHXVXV2IrN7nqtseEhxIS7Lu5NNMRqVDX0bBhhRw6VmQcGGi0Z5/19rMkqQ+uOB/RprTtdJlj8C85lXR4g+OGOQnJ9ir29LTgX1TTh+9J3GHKcA3Y4S6SGEEIcNXodqaGUmgU8Dvxea20cC5sAjAiwynCl1GNa6+293bc4fmSX1fPy+sNkl9XTYnfQYnfS0uokOTachy+awtDEKL91Gm12/vLZfp775hDGc6HmVic/eiODLblV/PL8SYSHuCZca6356ds7PCfKYSEW/nXNLFPnhRBCCCGE6Dml1E+AGCAe1xDBRbiKzX/owrpb2nmq+9PLBADzRw/k6dUHAVi1v5Rad9MFwC1LRnZpqNv4QbGQ4brteyVhWTcKzonRYZ44i7ZisDHDOcEnw3mQscO5tvsZzsbcZ+O2Eg0dxxUNLYCroL4mq4ynVx/kgunpXH6Sf+T4nqJa/vL5fuaOSOTmxe3/7MwDA80F52lDEoiPDKWmqZWS2hb2ldQxYVBcwO3UNLZy5TPrKa9v4aZFI7lh4QjP+5q+4FsgLq+3sbuolimD4ztcz+nUPP7VAb/He1twNg4MbNNWcNZaH7cdzsU1zdz7VgYJUWH86bJpRIT23e9cCCH6UjAynK8CbMDfAjyngVBcg0oABgB5wDWAFJxPcFpr1h2s4Nk1h/hyb2nghYrgrte28fbtC7AYupCrGmxc+uRa00CO8BALKXHh5FW6Tk5e3nCYHfk1zB4+gCabg5K6Zlbu81669vBFU5g2JKFvXpwQQgghRD9TSj2H6/z7Z1rrEvf9rtBa65uCdBg/AVIN9z8FrtdaH135ASeIuSMSCbEo7E5NtuG8eVBcBOdN61p+8NiUGM/trFJzwbm8zlhwbj8LGlwDuQfGhHsKkRUNLR1mOBsjNXqS4WyM4TB2SwfqcNZac9/bOymobmJddgWnjE8mJda7f4D739nJ9rxqPt9dglNrbjtldMD9BhoY2MZqUSwak8THO4sAV6xGewXnd7bls8ddVP3D//byyobD3H/2BM6aMqhLHxR0V3m9f7zI6qyyTgvO/9tVbPqz1cb4YURP5Ff7F5yLa5sprW2mxe70XLEaHWalweYAIK+yEbvDeUwPaf/nigOeOJMFowdy1dxhR/iIhBCiZ4LxL/FiYJ3WOmDIk9baqbV2uL/KgS/c64gTWGF1Exc/sZbvPLOh/WKz27bD1byy8bDnvtaa+9/ZaTqZWzhmIMt/uIT/3b2Ec6eleR7fWVDD82tzeH1znqnYfM28YVwRoHNBCCGEEOIYdr37K97nfle+gkJrPUhrrYBBwCXAKGCb+6rIztadHegL2Bus4zvRRIeHMHOYf4PFDQtHdHlYXkeRGt3pcAb/HGdjh7N/hrO3SFxU3dztaAljkTrNMIBwgKHgXNngOv6S2hbPwDu7U/O1T35xeX0L2/O8+cF//HQva7ICf4ZyqNx/YKCRMVZj9f72c5KN+wM4XNnIHS9v5cqn15u6t4MlUEdyZznTWmv+8VWW5/7isd7X1hcdzuDqcjZ2N08bkkCKO67F7tQ9il85mmw8VOm5vTW36ggeSd/Jq2zkgfd28u62wDGY3dXc6uDBDzL5xXu7aLTZO19BCNEvglFwHkvgTDaFt7PZKAcI/HGwOCHUt9i58flNfidRp09M4V9Xz+KVm0/m7Tvmc+NC72D1P366l1L3lOm3tuTzaWax57mHL5rCSzedzIikaGLCQ3j8qpn84rxJhLSTyzx3RCK/PG9yH7wyIYQQQogjaiSuAu9Bw/2ufHU8Oa4HtNYlWut3gTOBgcB/g70P0TULRieZ7keHWbmyG12TQxOjiAh1vW0sq2uhqsHbCVte571tzGhuj2+Oc02Td/14n0iNuIgQosJccQJNrQ5qm7pXSCo2DQz0FpwHmgrOroJ3ZqE3Gxj8B+b5FqCdGu58dRt5lf4RDqZIjeQYv+eXjPMODtyYU0mTuzvXl/G9UtvPH2DDoUoeXb4/4Dq9EagjeUtuFQ0t7f/cv9hTyl53zEpkqJX/O8ubftPrgrOhwzk2wnth9o6CGlN+86T0OEYM9Bb2c47hHOfa5lb2G64i2FlQ08HSx67ffryHl9Yf5sdvZJBT3vvf15tb8nl+bQ4vrs/llQ2HO19BCNEvghGpEQvUBXj8P8CKAI9X0xaUJU44Dqfmrle3eU5MQiyKK+cO5YaFIxntc0I2OT2eFftKOVTeQF2znV9/tJuffms8D36Q6Vnm6pOHcc284ab1lFLctGgkp4xLYuW+MixKERFqJSLUQnxkKAvHJBEWcuxeZiWEEEIIEYjWOrej+0eC1jpXKbUbmKGUSmrvqkjRdxaOSeKxL71dqN+eM8yvuNsRq0UxJiWGXQWuIt++kjrmjXING+zO0EAIVHBuP1JDKcWg+AjPVY2FNU3ER7V/3Cv3lbI2u4LLZw9hbGoshYYuYGM8h7HDuco9tDDTZwBd28C8tki/QJ2+1Y2t3PriFt65YwGR7sJ4i91BvrszVykYFmAOTVp8JGNTYsgqrcdmd7LhUAVLx6eYlqlqsHmG8IVZLaz+6TIe/+oA/13n+iu9ar9r4GAwozXK6rwF+ras7VaHZv3BCk6bmBpwnadXZ3tuXzNvmKkbvrKhBYdT93g4u7HD+YyJqbyzrQCAHfnVhBm68yelxVHb1MrGHFdnsGtwYDLHou2Hq02DErNK62ludQQ9x/nDjEK251Vzy+JRpg9j+ssu9wc8Tg0Z+dWMCHAlQHfsNnxg5Pt3WQhx5ASj6lYHJPo+qLXO1VqvCrB8InDsfuwoeuXhj3fzlSFC4/eXTOXhi6b6FZsBIkKt/PaiKZ77H+8o4up/b/BkdI1Kiubn505sd19jUmK5efEoblw0ku+cPIxLZg3htImpMnhBCCGEEKJ/tYUFB27lFH1qxtAET4eo1aK4YeGIbm9jfKo3Z3ivIc7AVHDuQYezaWhggGJyuiFWo7iDHOctuZXc+Pwmnl59kPP+8TUvrs+lqDpwh3NitHc/Fe5u7V0+naTl9Tb2FLtep9aa1YYO519fMNlT8NxTVMv/vb3DE/eRW9HoKRgOGRDZ7vsOY5dzoFiNjHxvd/PE9DhSYiN48PzJnp9ReX2LX7xJV3y1t4T/e2uHX0c3mDuSF481Hl/gWI28ykY25bgiH6wWxc2LRxEWYmGA+xidum0oY88YO5zPmjLIc3tnfo2pqDgpPc5UsDxUfuwODtziE6HhcAYejtgbOeUN3PXaNp79+hAPfZTZ+QpB5nBqSmq9fzcPBaHD+bDhSoNjucNdiONNMArOOcDcbiw/172OOMG8uC6H/3yT47n/vaWjA06ANlowJolLZg323G/rGAixKP525QyiwoLRpC+EEEIIIXpKKTVBKTUowOMWpdRvgRRgrdb6+AwkPcqFhVh45NJpzBkxgN9fMpWhAbpuOzMxzdu52nalIvh2OHc8NBDMGc6ldT5DAyP91x/UhcGB9S127nk9A6e70Ntid/KL93ax7mCFZxljHnRitPcY2uJBAnVFthWC9xTVeV7ngKhQrpk3nIcu9MbzfZBR6IngMA0MTPJvqGljKjgHyILOyPMWhGcMcUWyWyyKhYZ4lK8PdO9igdrmVn7wyjZe35zHfW/v9HveWHC+dPYQw/EF3s/72ws8t5eMTSLVnZPt+6FCZ8f0yKd7eXJVtimj2+nUppzqhWOSiA13ve+raLB5itFhVgujk2NMkRq5x3DBceth/38ifT8M6a0dBTWeD0XW7C/H4exeNnpvlde30Orw7vNggIGT3WUsOLddGSCEOPKCUXBeBcxSSs3rbEGl1HxgNoGjNsRxyu5w8vTqbB78cLfnsXOmDuInZ47v0vo/P2ei55PyNvecMY5pQ/wHoAghhBBCiH53FpCnlPpSKfW0Uur3SqnngCzgZ0AxcMsRPcIT3NlT03jz9gU9Hpo9Mc3b4bzH1OFsyHDuZqRGXlUjNrsTcBXFjTnFbdJMBefAQ+Qe+jDTVHAKxNThHOUtbFc02KhutJm6adu0DQU0FoQXjknCalFcOXcYl87yFmWfWeOKTe9sYGCbk0cmeiL+DpTWU+iz/+153sLj9KHe9zwLx3gLzt90s+CcVVJHo/tK0T1FtdgdTs9zza0OaptdWc1Wi+LMSame38eh8ga/rGqtNe9u8xacL5rpbRDqasG51eHk1v9u5omV2fzhf3v5ZKd3Rk9pnbcomRgdRnR4CFOHxPttY0xKDGEhFoYP9H6I0tsO1w0HK/jV+7sCdoH3JYdTs/1wtd/jO/L9j6Oktpn8qp4VVo2/y7oWO1mlgdJR+47v3zVj5nlPtDqcpkGRlQ02aptbO1hDCNFfglFw/heggVeVUhPaW0gpNR54BdeldE8GYb/iGLCnqJZL/rWW332y1/Pp6fQh8fz58hmeTLTODIwJ52fneKMz5owYwO2nyNxJIYQQQoijxBfA07iGA14C3AtcClQCvwYma613t7+6ONoZC877SupwODV2h9OTgayUqzDYGWMxMssQCZEQGRowj9jYmRyow3l5ZjFvbM733H/k0ml8d755vktUmJU4w9C5REMndlWDzdTdnG4oTG/OqaLRZvcUnsHcmfzD08fS9nZmTVY5uwtrOVhmHBjYfsE5ItTKySO9qZQr93n3obUmw1BknGEoOC8yFJzXH6yg1VA07owxasLu1JQYisG+neoRoVZPTje4MqONdhXUku3uTI0Ks3LGJG/Gs/GDh44Kzg99uJv1Bys997/J9hbQC6q9xzo4wfVnIFCz0aR0159LY8E5r7Kpx127dc2t3PzCZl5Yl8v3Xt5q6rrua1mlddQFGNDo2+G8Pa+ahX/4iiWPrGC9oYu/qw77dAD7xnj0NWPUDcChsoZe/ZwLq/1/376vUQhxZPS64Ky1zgJ+AwwHtiml/quUukEpdaZS6gyl1PVKqReB7e5lfuNeRxzHWh1O/vL5fs7/x9emT2Unp8fxzHdP8gzW6KrLZg/hD5dM5bZTRvHMdSf1ePiEEEIIIYQILq31Lq3197XWM7TWSVrrEK11vNZ6jtb6Qa11ZedbEUezxOgwUuNchcTmVic5FQ1UNtg8l+YPiAojxNr5W0tjwdnY6RgovxnMHc6+Gc6ldc3c/443GuL86elcMWcov75wCk9fO9uzzdMmppqK2cYO58pGm6mgt2RcMhMGueJDbA4nK/aWsemQtyC3eKy34Ds0MYqzp6Z57v97zUFTh/PITgahGQcFfrKzyHM7v6qJSnfUR1xEiCkuYtjAKIYmugqwjTYH2/P8O2Lbc8ink9Q4lM9YGG77HS3pIMf5PUOcxlmTB5liDk0dzvWBC84vb8jlxfXmmaY7DLnV+YZj8xac/TucJ7k/CImNCPVEutgczna74TvzxZ4ST9E3t6IxKPnCXbU11/v6F49Nou2PbFZpPU02b/z9898cwu7UODV8tKOw2/vJrTS/pi05/Vtw9u3mb7A5KKntedZ3oAgNyXEW4ugQlABcrfWv3f+J/xy4BrjaZxEF2IEHtdYPBWOf4uhV32Lnjpe2eLLMwJWvdffpY7l1yShCu3Ay6ksp16VrQgghhBBCiP43YVAcJbWuwuOeolpTIbQr+c1gLkYaxUe2U3BO8BacCw1FRK01//fWDk9hNi0+gocv9A4bP3PyIOaNHsjuwlpmDx9g2mZkmJXIUCtNrQ5sdiebcryfh0xOjyM2IsSTU/3XL/Zjc3cRj0uNMXVcA9y6eBQf73AViz/IKCQ8xPs+p7OC8zlTB/Hwx7vRGtZml1Na20xKXISpiDx9aILfVaGLxiTx6sY8wNVZPWdEIl3hWzzNr2pkrrvL2hiNkuTuUDZ2c39zoJyS2mZS4yJwODUfZHgLncY4Deg8UsMVWeE/rG5vUR3NrQ4iQq2mDyMGD+ig4Jzu7bwfPjDa8zpyyhsZMqD7WeVtv0vPsR6qZFSA4fZ9wdhpvHhsEoXVTWSXNXgGB84ePgCb3cmXe0o9y+X0YEBiXqW54LslQG50XwoUX3OwvN4Ue9MdgeJ0JMdZiKNDMCI1AFfRGRgP/BZYCewF9rlvPwxMkGLz8a+0tpkrnlxnKjbPHj6AT+5ezPeXjelRsVkIIYQQQvSOUipaKTVTKbX4SB+LODb55jibYxg6z28GiA0PMRVl28QHGBgIkBbnLfAW1zR7Lr3fVVDLCkMMxaOXTyfep0s6LiKUeaMGBnz/YYz/WJftjSWYlB5vKrQeKPV2BRs7fttMH5rAXHfB1+7UNLg7UcNDLKT7FKf9Xlt8pCdWw9Wt6ip2mgrOAWIkeprj7Ducrd0OZ/fvcnRytKdo3mBz8L2Xt2KzO1mbXe5ZPjk2nAWjB2LUUcG5oLqJO17eit0dgTBlcBxD3AVlu1N7Cv0FATqcBydE+sW2GP9MGj8A6UmHa01Tq2dQZJuNh/rv4gzjwMDZwwcwdbC3wN7Whf9NdrkpdqO7Hdg2u9P0wQ24irOdDXcMpkDd570ZHOibLw7H9uBIIY4nQa3+aa0Paa1/obU+TWs9WWs9yX37l1rrg8Hclzj6HCit4+In1rLbMEjkrtPG8uZt8xmT0j+fDAshhBBCCC+l1BCl1NtAFbAZw/BupdQipdRupdTSI3V84tgxMS3Wc3tvUZ15YGA7ncu+lFIBl20vUiMuMoTIUFcUX6PNQW2Tq9j2taHQeu60NFMRtisGRHv311YktijXa5wzIjFgUXzxOP+CM8AtS0b5PTYyKbpL82ounOHtDn7f3TWcYSg4G/Ob2ywY7X2t2/OqqevCgDSnU/sVYY2dpoEiNZRS/PaiKZ6c6i25VTz0UaZpWOD509L9olSSY7ydqr6FzKdXZXu60pNiwnj62pOYNczbgd4WqxGow1kpZepyHjIg0tQZP8KQ49yTguMXu0s83extNhys6Fa+cE1TK5/uKuIvn+9nQzfylSsbbJ7icZjVwuT0eKYaPmzY6S44L99VbFqvoLqJ5lYHXVVQ3USgl9OfOc6F1f5Z7L0pOAeO1JAOZyGOBtJuKnrN4dS8vSWfS/+1znNyYLUo/njpVH50xrguDwcUQgghhBDBo5RKAzYAFwIfAetwRd212QCkAN/u/6MTx5pgdDhD4OJ0QjuRGkopU6xGUa3rvcY6QzHvlHYKwR1JjPY/hlHJMUSFhbgG+o0yd+2Gh1hMQ/6MTpuQwiif+IyOBgYanT1lEKFW11/JjLxqDpTWs6vQmyk9bah/jERidBiT3VESDqdmw8HOu3CLa5tpbjUXU405yWX13iKg8fezYEwS9509wXP/pfWHeX+7N07jYp84Dd/1fTOcjQMaH7pwCukJkaYickae67UH6nAG8+DASYY/jwDDk4wdzt0vOAbKQy6saTb9nAKpbLDxty/2c8kT3zDzoc+4/aWt/P3LLK59dqOpQ74jWw0F38mD44gItZo6nHfm1+Bwaj7bXeK3bne6uQPFT4C5u7qv+WY4gytSo6cCR2r4/0yqG23H3DDBhhZ7nwyu1Fqzen8ZN7+wmaueXu83mFKIYJGCs+gxrTXLM4s5+7HV/PjNDGqaXJ+uR4Za+fd1J/HtOZK5LIQQQghxBP0KV0H5dK31JcDnxie11q3AGmDhETg2cYwZlRRNmLubtbCmmWxDMa1bBecAy7bX4QzmwYFFNc20OpxsNuQuz/cpDndFYoD9TTbkAS8Za+6YnjsykYjQwEPPLRbFzYvNXc6d5Te3SYgKMxXMH12+z1MYHpwQSUps4FzbRYaO7q+7EKsRKHqhsw7nNrcsHsV507zDER3uOIzRydFMGWwu+vqu79vhfNBwHG2F5mmmTt5qtNamY2uL3AA4d2qaZ3j8edPTTdvuTYdzTWOrKRJyrOHq3A2dxGp8/+Wt/O2LLLYersZpqA3aHE6eWHGgS/s35ijPdnd8T06PMwwOrGP1/jJPd7hRTjdiNYzFWWPuen91ODe3OqgI8Bo663A+VN7A+f/4mu8+t9E0QFFrHTBSo6S2xbRcflUjC//wFUv+tMI0oPNo9vGOImb+5nPOfmwNLfaud7F3xO5w8v72As75+9dc99xGvthTwrqDFVz59HpTtJAQwSIFZ9EjB8vqueRfa7ntxS3sL/GebCbHhvP6bfNYNiGlg7WFEEIIIUQ/OAf4QGu9soNlDgPpHTwvBAAhVgtjU72FOGN+cFeHBkLgDuf4qPbXH2TIcS6qbmZHfjWN7mLSkAGRDE3s/nC4QB3OU9K9HaVLfLqmA+U3G10yazADDfnCI5O6Hid4gSFW49NMb2TC9ADdzW0C5TjvLa7l2mc3cPLvvuDLPeZO2IPtFJyd7gppoAznNkopHrlsGhMGxZoev2jGYJTyv5I1ITLU07Vd12z3RD5UN9o8BVNjxvWUwXGe2I4DpfUUVDd5fr/RYVZTbMb4QbGs+PFSPrpzEecbiuDgGhrYJrei0fPaumL57mJPrvT0IfGmQYgdRWMUVjeZuu2VMl8J8H5GYZcKwsYO51nuIZfR4SGMdg8sdGrXAEvjftoE+t3WNreSX+VfiD1sKMSfN837z/7O/JpuRXP0VFGNt5M+KSbc8zryqxo7LKr+e81BdhbUsGp/GW9tyfM8XtXY6sm0jgqzMtzwoYOxuP7+9kJPdE6gTvaj0Qtrc7DZnewtrvPLFu+J0tpmzvzbau5+bTt7DBGoAPUtdr77n418llncztpC9IwUnEW3FVY38Z1nNrDtsDdfLDrMyt2njeWrH59i+pRaCCGEEEIcMalAVifLtAJda8cUJzxjMa3QWDzqYoYzBO6Gjm8nUgMg3RCpUVzTZOrE60l3M0BidMcdzmNTYkyd1aeM77jgHBFq5cdnjgdc3dqndqP55vSJKUSF+XdPB8pvbjNnRKKn2zyrtJ5fvr+L8/7+NWuyyimpbeHRz/ablj8UoIPUZnd6YlGM0ReBPhCICgvhqWtnExcRArjiEy8KEKcBro5v4++4rZhtLIwaM66jwkIYm+IqZjs1fJbpLZYPHhDpV9QeNjCKKYPj/R6Pjwz1DBVssTsprvXPCm7Pxzu8Xa/nTkszxadszGm/w/lzQ8TFnBED2PrAGfzv7sWeDnSHU/PEyo67nFsdTjLyve+rZw/3ZlpPM8Rq7Mj3xh6cOSnVc9v3d1tc08yC33/Foj+u4MMMc3HVWISdOSzB04lvczjJLGw/VmFLbhW//2QPWSV1Hb6WzhjjNEYmRXniUpyaDuMu9hv2a+zGNr6eYYlRpg8djFEjxnVKa/tvQKLRroIa7n0zw+/DoPYY/74cLOt55EibNzbnmTrJI0ItXH3yMFLcf99tdie3v7SFNzbntbcJIbpNCs6iW6obbVz33EbPf+BhVgs3LhzJ6p8u454zxhEb0f7JohBCCCGE6FeVwNBOlhkHSFuT6BLfLtc2gWIy2tOdDGeAQT6RGmuNBefRPS04+x/DJEPBWSnFQxdOYVxqDD9YNoZxqYFft9F3Th7G6v9n777D26zu9oHfR8OW9952vLL3DgkECCOslE2hgy5o+xYobWnppi0db/u+bX9ddEAHdEILZbSBt+wwE8jeO/G243jvIen8/pD06DyPHtmyLVke9+e6ckWyZOlYkhP7fr66z70b8Nq9G7TgMxTxMTZdgOizZIghnrgYqy6Y/NPWSm1CF/BMO3comwmeDtKRW9PWCynlkJUaPsUZCXj0E+fg+mUF+Pkty4acLDfrcVaDUWPH9SKlx/n/DvjDX7W/ORTqhGuo3cat3QO6af0rF+VhcbYNh3cAACAASURBVGGqtnFkZXMPGtrNw+vnlYnQq5cWIM37vN998Szt40/uqjWtffA5XN+hq1HJSfa/3hcWBE65p8bbcYtSXWmsS9m8rw5d3qnfv2/Xh4dVLf7Ad0Z6vG7Dxh0V5rUafYMufPxPO/Dg66dw1992B/06QqEGzvmpcbrqmZND1GqoX+NuZVNNY+BsVqvidkt94NwZncD5i0/sw+M7a3D3o7u15yeYrn6nriPfrBJnpLYpXe/vWz0Db3/5YnzvukX456fWaY+bW3rW+cqR0EJxouEwcKaQ9Q26cPsfd2ibH9itAn/4yCp84z3zkTGCHzKJiIiIaFy8BeBqIUSu2YVCiFkALgfw6riuiiYt40ZtPmPeNDDEDufK5h5deDT6wFl/f4VpcUg11HpcOj8HL3zuAnzhsjkh3+6MjPghp7WDuWapflrYIszDRtV5hp5pwF+1ICV070ZVAys1lK1t7UVXv1MLPB12CxJjbUHvc0F+Cv7fzUtxlaHOwijLdMLZH3obO66XKIHzDuX5LUgbWeBcYqjVCMULSp3G0qJUFKbFI8Zm0YWx75wOrNVo6xnQ9TtfOs9/0GB1abo2Je10S/z6tZNB79+sTsNHDeLV+1GrbYxh5F5lEnpvTZtWLWLsOy7OSMDKEv/9BetxPnW2W6tCOXqmM2j4Hoq6Nv/n5qfGaZUhQPCNA9t7B9HU5e99rmzu0cJYtSLEOOHse/5Pnu3S9poCgDMdfRHZiG8ogy43jjR4aiy6B1zD1qwYO8jNalOM+gZd+H8vHMVvXjsZ8PUNuty65/eui2ZqB8WK0uPx+H+t0/3b/ti7nHKm8GDgTCFxutz49KO7dT8A/OimJaY/6BARERHRhPBDAA4ArwkhrgAQDwBCiATv+X8DcAP4cfSWSJPJ3CCBc8YYO5xT44J/fl6KP3TcUdmCfqcnHC3NTNBdNhJphnBZrdOIhvNmZSJNCd1n5yQhYYjgF/DUKvg20EuJs+N/bliED51TrF2+01sFMeB0o7rVP1m6rtz/+1tNa68uzMtKijXtZR4ps40D1WC0zNBxrVYyqllZQerI+rlHM+G8WanTUDdHXFPmr9Uw2zjw5cON2gaKS4tSdZP4APAZZcr58R3Vuule1fNKhcjyGfqp9vl5/n5rn8sX5iI/JQ4x3gns5u4BXaC6T6nn6OxzamFla8+gNlmbGGtDWrxdNyW/q6rVNIg1BsF7qke/waBuwjnFoZt0D7ZxoNl07x7vwZQqXYAej+J0dcLZc9kOQ5De73Sjo2/oCWOny43tFS14+fAZDHj/vRmLhvY+3YaSwV4LPhVN+oMloUw4f+XJ/fj5Kyfwg/87gsd31Ogu21fTjt5Bf++98Z0DWUmx+OZ75mvn1elqorFg4EzDklLi608f0HVUff2qeQFH4omIiIho4pBSvgPgEwBKAGwG8AXvRR3e86UAbpNSHozKAmnSSU+IQU6yPjBOjbfDbg3910qz+o2UECec1dDmnFH2NwOBAfmC/KGniSPNbrXgykX+sHOoOg2fWTlJ+ONHV+O+TfPx8ucvwM2rZmBliT8k9QVt1a09WjBakBqHciXkq23r0dVpjGRSfShmgbMaKJYaKjXm5iVpGw2qxjLhfLRh+L7hP2+rxJtKncYVynOwWu1xNgmcXzjkr9PYuCCwEmVteYYW6A66JB40mXLeWdmibTpotQhcMk9/O+rGgYBn36RzZ2bCYhEoVfuKfaFy90DAZPcebwWFOjVblB4PIQRmZiVqvdxNXYGfCwAnG/Vhpzo5P1J17fpKDfXAQ7BQ1ay/eLc39FbXW5Qej5LMwAMO2006uBtN+r27+p14enct7n50N1Z89yXc9JutuO2PO/CNZw4M92UNq9qwgWPtcIGz4WDJ2c7+IWs4thxtxFO7a7Xz/zZsjKhO6K8pNf93U/030TfRPhGM9zQ6hRcDZxqSlBL3//sQHlP6nz5xfhluX18WxVURERERUSiklA8DWAjg5wDeBXASwC4AvwKwWEr51ygujyaheYYp55H0NwOBE84WASQNMc2bEmdHnD1wU73R1mkAgRPOCwuiO+EMeH7HykiIQVKsDbeuLR7+E+CZjL7tvFItKFYnVvdUt8Hpcuu6k0szE1CYpq/U0PU3RyJw7uqH2y0NE876wDnWZsXc3MDnYKQdzupGi28eb0JzkElNKSV+9PxR3Pf0AW2i+vzZWbr7Wz4jTduY8URjl27qs3fAhdeOndXOX7YgsLVICKHrcn50e3XAxngPvOLfUPCapfmmvdhqrcZF83Lg8H4vqOGq77HdVxu48Z9vIlnfd+z5Oi0WoavxMKvVOGkIfMcUOBs6nPUTzuaVGmaTz7sqPWuoNnQ4F6bFa7UydW29GHC6Tb8mY49zd78Tl/3kdXz273vwr711uonxJ3bWBH0dhaqmVR8wDzfhbKzUABC0hqOr34mvPaUPxbedatZ1uKv9zerkvkrttZ8IgbPLLfGLl49j5Xdfwp1/26VVw9DkwsCZgpJS4n+fP4pH3q7QPnbD8kJ8+fK50VsUEREREY2IlPK4lPJzUsq1UsrZUspVUspPSymPRnttNPkYg8GRTsU67FYkOfwBc0qcHRZjb4BCCKGbcvY5J0hwEorU+BhdVUG0J5wBT6fuO1+9GNu/fsmw/c3B5KfGId/7WPUMuHC4vlMX9HoCZ3+oWtPai7Od/mnPYBsGjpSxw7muvVerQklPiAnoywaAxSZ9xYUjnXDOTNBCd6db4pk9dQHXGXS58aV/7sMDr/rD3iWFKfjJe5foruewW7GkyL+m7cqU8xvHz2q91zOzE3VTyKrzZ2ViiTcEH3C6cc8/9mjT5vtr2vHqUU9oLQRw54aZprfxniX52nVuVSpTSjPV/mNv4FwdGAbvrfaE0MZw1meF0lW9syownDVWauyrbcOga+Q1E1JKfYdzShxykx1w2D2RVGvPIFpNgk6zyee9NW3oHXCh3jupLARQmBYPh92KfG/Njlt6DrqYTW2fMUw4v1vREjB17Auug72ORsIYOA874dwUuOZgPc4/ev5owO0NuiS2eF9bTpdbq9cBgLVB3hmSEmfXvuaOPueonuNwqW3rxfse2oYfv3gMzd0DeHZfvW6zWJo8GDhTUA+8cgK/3uJ/689Vi/PwPzcsGvIHQiIiIiKaGIQQw78vn2iE5uUl6c5njiKkVIPNUDbZM/bjzsxORHZSYAgdKqtFYNNiT5B34ZwsZIcpaB0rm9WiTbCO1gpdrUaLLqgyBs61bb26ac+wBc6GSo2hppt9jIFzjNUyqonrG1cUaqef2KnvsnW7Je786y78Q+m4vXBOFv728XOQYXJfaq2G2uOs9i5vnB9Yp+EjhMC3r16gdW3vqGzFg697fr9+4NXj2vWuWpQXNLTeMCcb//nserz4uQt061EfR9/ju7cmMHA+XN+BvkGXYcJZCZyVjQN3GaaB3W4ZUKnRN+gOqa7EqL13UOsRToixIjnO5qkGyRx640DjhDXgOZjyypFGbTpd7bRWv7Ynd9UEfC4QOOGsBsJrStPx3N3r8d1rF2of+2eQ2wlVTUClxtAbL5r1j582mfTeWdmCP26t0M6rm2/66lAP1HWge8DzuOenOIIexLFahO6dH6090Zlyfm5/Pa746et411CF8tqxxqisp7GzT9vwcbKKZi0JA2cCALT3DOIPb57G157ajw/94V1c9KMt+PGLx7TLL5mXg5/evBS2EfSzEREREVFU1Qsh/i6EuFIIwR/iKCzm5xknnEPfMNBHDRJTTKZdjYybAwab0huJn92yFC/dcwF+/+FVYdkob6JYqVQk7KhsxWklxCvNSkBKnB0JMZ5Qu2fAheON/ssjFTifMtR6mFls6K3OS3WMatDpqsV5iPWGj4fqO3Cwzl8z8dTuWryg7Et044pC/PZDK4Nu0Kj23b50+AyqmnvgdLnx8hH/bZjVaaiWFKXi7ov81Ro/efEYntxVowutg003+8zNTcbMbH0grfZgn27qgpQSe2v8X6vvOXa6JQ7WtesDZ6X/eXFhqjbZeryxC33eUBgAGjr6tJBYtdtkkno4tYY6Dd/3nFqrcdIQqrrdUhe+qt/3T+/xdxYXpfv/fVCrRp5VNoSMj/EfyDFOONcaNtWcn5+MTYvztdfRwboOHK4ffegYMOHcGnzCuWfAGRCIA9B9HwNAv9OFL/1zvxa6XzgnCz+4YbF2+ZYjjRhwuvHOKaW/uSxjyH/r1I1Lo1Gr8eBrJ3HHX3eZbuqoVtiMl+qWHlz8o9dw+U/fwKPvVo3qNlxuiarmnqiFvnur27D2+6/gul+9NWQPeKTwB09CdUsPrvz5G/j25kP46ztVeP3YWd2R8PWzMvHA+5eNaDMQIiIiIoq6CgA3Afg3gFohxA+FEIuiuySa7EozE7RpQmB0G82pgWRqCBPOxkqNdWPob/YRQmBmdqI2fTpVqD3OOytadWFvWWYChBC6zfjUTt5wdTirr4mzXf26ft6yIJO8s7ITtXoFYOT9zT7JDjsuX+gPgf+50xNMdvc78b/PH9E+/sFzZuCHNy4e8nfcFcX+Huea1l5c+pPX8IXH96Ktx9OPm5vswKIQ6k/u3FCu9UsPuiTu+cde7bJL5+cE9KKHQg3uK5p6UN/ep/VxJ8bacKkyeb27qk3XH61OASfG2rTbcrklDinBqll/suf2Aqs3hqPWaeQpz225yaS2T31Hn1Zdkp4Qgw1zs7TLthz1T7wWp/tvo1gJ0zuVgO2iudnaaWOgqw/DPf/WpMTZsXGB+joa/ZSzMWBu6urXBfsqszoNIPCx+fPWSpzwHixKiLHie9ctwtzcJG2CubPfiXdON+sm89eUDl1DlBHlHuc/KlWuBalx+Mtta7T/a46d6Rq2+zrcHttepb2GnttfP8y1A7ndEu97aBvO/+Gr+PbmQ2FZ0zunmnHtL9/Cj54PrRHtT1sr0dDRh91VbfiHsi/beJnWCaIQ4lYhhPT+uT3a64mGiqZuvPfBrUF7hK5anIeHbl055rd2EREREdH4klLOA7AGwG8A2AF8HsAeIcROIcTdQojMqC6QJiWb1YLZOf7QcDQhpS5wjh95pcaaMEw4T1Vzc5O06daGjj4tXLNbhRbiqhsHqpvhhWvCOSHWpq1hwOnWTd4Gm3C2WS1YqHRpjzZwBvS1Gk/vqcWA040HXzuJMx2erzUrKRZfuWLesJPtCbE2fO2qedoEcL/TjaeVPt+NC3JCmsK2WS34yc1LTTe/vGuY6eZgfBtMAp6N414+7J+YXliQrNsMcHtFi67v2PjYqo/7QWXjQbXOQp2w3hPCxoHGDuD6dn/eUJDq/35WD0AYNw48bThYslzpmx50+SdGZ2T4X88lGYEbLwLAFQvztNONhglnNchUD8bcsLxAO/30nrpR9RoPuty6r92nvt28VkPdMHBBvv9AxKmmbt2U7IvKpP49G+egwDs1rh5o+M+BBl33+HD/bqYlRG/CuWfAiTrvY2KzCDx393qcNytTF5K/Po5TzlJK/HuvP2Q26wIfzrHGTq0a5LF3q8Oy8eE3njmIPdVteODVE9hfE7hJqJH62t4ShSnxaRs4CyGKAPwCgPl2qNPAybNduPmhrdo/djE2C+69bA4eunUF/u8z67H/Wxvxy/cvR1wMw2YiIiKiyUhKuV1KeSeAPHimnZ8FsAjAT+GZen5aCHFtNNdIk8/5s/yThktnjLwqXO1fTguhUkMNgebmJiE9YeQ1HtOFzWrBMiWY85mRHq/VIwYLc8MVOBtva78SYpZnmQfOALQN9gCgOEhwGIp15ZnaVHxL9wD++k4lHnz9lHb5Fy+bE7RGw+jD60rw1B3nYmFB4BTycHUaqtLMBHx90zzdx86fnaX7mkdCCKGr1VCD8CVFqdpENQBsOXrWtO/YR53S3h8kcL56ST7sVk+4fqqp23SDP5/fvHYS8+77D+786y4tZNNNESsVOWqlhnGiWu10Ls1MwMKCFNhMAv4iZWJ7Rnrg66s8KwFzcv3d8wETzsoEcmGq/7bWz8pCTrLnddzU1T+qwLOhvQ9mOWOwad0KJdhcVZKuHbjp7HOi2fuY9w26dLUm71niD9PVwPmJnTXahG52UmzQMN4nXZlwHur5jQT1uZ+REY8U74HIC2b7/68Zz1qN/bX6Gpratt4RH3DYpwTCvYOuYTeLHE51Sw+OnvH3p791smnYzzmrHFDcdqoZvQPmk/WRMi0DZ+E5lPkwgGZ4Jj6mnd1Vrbj5wW3aUd5YmwW///BK3LlhJjYuyMW8vGQkOYafNiAiIiKiiU9KOSil/KeU8moA+QDuAXAAwNUAnojq4mjSueuimbj/6gV45KOrMDsnafhPMNi0OA8JMVbE2Cy6sCSYdeUZmJubBJtF4FMXlo9mydOKWqvho27OVhBk47DR1KMEowbOLm/iZhH6aVSjj6wrwYz0eJRlJeDGFUWjvm+rReB6ZTr125sPod/pCYsWFiTjhuWFwT7V1NKiVDxz53n4zjULkOTwBNUlGfG6TfxC8f7VM7RqB6tF4DMXzxrmM4amTovvVDb8W1KYirm5yVqw7PvaAX3fsc9CXeBsXqkxLy9Z19++x2SDQsAThv7speNwuiWe3V+vVToEq9RQv4bK5h7ttWK8/7KsRDjsVszPDwz+i5XA2exAxcridGQn+1+PZzr6tGnhAacbZzr909/quymsFoFrl/lfR6PZPLC61XwyNliPc4Vhk099V7fnsn017RjwPqdlmQm6A3irS9K1jVjV5324/mYASFcmnJvHOXBWD26UKf9WqYHzm8ebRjVlPhqb9+krNFxuOWT3tpkDtfoJ5GNnRr7ZpkqtkQGArSebg1zTT53mH3C6se3U8J9j1D2G7udpGTgDuBvARQA+CsC8mGgK6ht04fEd1bj2l2/hul+9rb19Ks5uxcMfXYX1yqQCEREREU1ZzQAOAjgMYBDA1CqxpYiLj7Hhw+tKcOGc7OGvbKIoPR7bv34J3v3qxVhRPHxoF2uz4rm712PvNzfimqUFw15/ultZEhg4q5OkhSaBc7LDFtYaRbNp6cK0eMTagt9HUXo8Xrv3Qrx8zwUBNSojpYbK6n5d39i0YFSbEVotAreuLcGWL1yIB96/DP/45NoR73EkhMCvPrAc3712If5y2xrTAwMjEXwDxhTE2Cy6SgYftb/ZZ4EyvX38TKfWL6yGgOVZCbrJ+WC1Gm8eb9JtNPj37Z7N1upNepIBIMlh114rAy43apSA9lRT4GaTy4NM7/skxNoCXnsrS9KQFGvTKk36Bt3a5G9De5/2+shJcgRMf9+ovI5eOtSItp6RBbHGDQN9gk27qpskFmfE6w4U+SpG3j3tDw2NBz1sVouur9rnnLLh/52dKBPO5dn+1/XM7ETke/8t6Ox3Ys8oNqwcKSmlbsNJH/W5CcV+Q+CsbtA6Gq8e1U9476hoGTKA7xt0BWzAaAyth7OjoiWgP3wkpl3gLISYB+AHAH4mpXw92usZL3/fXoU1//0y7n1in+6bNCHGij9+bDXWlbPCj4iIiGgqE0LMFUJ8H0AVgP8AeD+ASgDfiOrCaFqKj7EhNYQ6DR+LRYRcgzDdLS1KhTFTVcNJs0qNcNZpAObd3sECUpUQYthJzFCUZSUGBLpXLcob8VSyUUZiLDYtzkd28ugCcYfdig+eU4y1Ydj40uzxzEyM0Z7fpSZ1Heqmej7JDrt2W063xJGGTnT3O7XqTZtFoCg9HsuU+pzdQYK/Fw416M4/d6AB7T2D+p5kw+tPrVlR+75PN3UFXGeZocInKdYW0ANfbAjVV5akQwihm3L2TX7WtPkDbrPJ/1k5SVhS6JkAH3C58e+9dQHXGYoaOKtVQMErNQwTzspz7AvgdRsBmgTJaq2Gdr3S4V9v0ZxwVg8ulCshuxACF8xRajWOhq9Wo3fAhY//aQc++Lt3dJtq7qpqMz0goFZsDMfpcuOwsgEnMLYJ575BF942VGh0D7gCQm3VWUN1DDDyWhLjpPdITavAWQhhA/BneH7I/uoob2On2R8Ac8O51nB69WgjvvTP/WjvHdQ+ZrcKXL0kH0/dee6Y/9MlIiIioolJCJEmhLhDCPEOPFPNXwKQBOD3AM6TUs6WUn4vqoskorBKctgxJ1c/3aoGV+qmgT7hrNMAzAPssiH6myNB3TwwxmbBl6+YsL+yj4pZ4Ly4MFUL7M0C5yKTCWfAWKvRrptqLM6Ih91qwbIidcK5NWATNJdb4qXD+gnKAacbT+yqQYPy1n7j9PraMv/w27P7PIFu36BLC2vVKhZ1DYDn48YDFGqonpEQo3UX5yjVE43ealG1JiE/SLf5Dcrr6LHt1brN+3xauwfwwsEGdPYN6j6uTmyvLvHnLmaBZs+AU6s8tVk8m3yWKc9xRVM3Bl1uXX3KapMg+fzZWYhRpu8zE2OH7E730U04j3CSe6zUDSON/06E2uMspUTPgBONHX04dbYL+2rasPVkM5q7AoNXAPjbu1V48dAZvHmiCZ/48w5tsn/zPv9BBV9vOQBUNIUeOJ8424W+Qf308fEzo59w3naqOeD2fB8PxthVDng6witGMLH81onhe6KHMt0OEX8DwDJ4frgeW2P3JFHX1ot7/r5HO1+QGocPnlOMm1YWhv2HCiIiIiKaOIQQTwDYBMAOQAJ4CcAjAJ6SUvYN8alENMmtLE7TTdipwVVmYgxibRZdx2vYJ5zNAucQJpzD6Zql+fjLtkocqu/AfVfNCxq2TlYlJo/nkkJ/yGwMZwHzSg0AWFSQrE3vHqhpR7LDHxWVZ3kmTovS45CREIPm7gF09DlxqqkbM7P906g7K1vRYjIZ+9DrJ7WN8zITYwNqVTYtycNPXjoGwFMb0Nk3iHql6kKtYilKj0NmYgyaugaCfj3q5ngritO0QDpL7XH29jar3dLBNtO8ekk+vvfsYfQ73ThY14HXjp3V1Qn1O11474NbcbyxC8tnpOLJO87VLlMnnFeXpuM/Bxu89zv0BG1hWhxsVovuoMLppm4crOtAj3fjt4LUONM1J8basG5mBrZ4p4HXlKaH9K6BdOUdJ81dQwfOnX2DeON4E/ZUt6G+vQ8N7b3ac3bvZXN03dfDcbtlQF+3at3MTFgtAi63xP7adpzt7EdWUix2VLTgxy8cQ0VzN7r6nejud5pu0Gi3Cjx393rMMuw3sE/pIT/S0In/fu4wvvmeBbo6jRuWF+Kx7dUAgKqW0INadcNAnxONXXC75agqfbYok925yQ7tAM7Wk82448KZpp9jNuHsua1GfCSzdNj7PNPRN+YakGkz4SyEWA3PVPOPpZRbR3s7UsoVZn8AHAnbYsNk0OXGpx/djdYez1G2nORYPHPXufjUheUMm4mIiIimvusBnAbwNQDFUsrLpJSPMmwmmvrUHueEGKsuABZCBARV4xE4q3204yE+xoZ/3XUe9n1zI25dWzKu9z0ekh32gN/rFxf5J5WL0uN0NQ5A8MDZOOF8slGdOPU8b0II3dT07ir/pC0AvKjUaVy1OA8Ouydu8k3tAkBBamAVSXlWIuZ5NyQccLrx0uEzuolXNXT1rMH/2jb7ei5bmAurN9R770r/5pOmE87DVGoAQGp8DN63eoZ2/hevnNBNOf/x7QotmNtV1aYLk2sNgbNPXVtfwIS4OnnqO5igHlQ43dyt2yjOrE7D5yZl080rFw2/MSsApCf6XytmE87NXf34/Zun8YHfbcOyb7+IO/66Cw+9fgr/3luH7RWtqGntRW1bL769+ZDpFHgwDR19Wu93Wrw94DWb7LBjhdLd/fqxs/jz1grc8tA2bD3VjPr2PnT2mYfNADDoknh6T23Axw/V6Ssv/rS1Ev/93GFtMjgjIQY3r/I/jhXNoU84GzcMBIBeZWp/JKSUeOWI/50Dn7vUv9nojorWoD3OZzv9P+rFKf38W0Ks1TBWeIzGtAiclSqNYwDui/Jyxs0Pnz+qvd3CahH4xfuWM2gmIiIimj7WSSnnSSl/IKUM/G2LiKastWUZ2gZoy5UpTx9juBb+DufAYHG8KzUAz+/BSQ778FecpIxT4+qEsxBC6x8GPJOvafHmj4UaOB8704lD9f6+WbWOIViPs5QSLxw6o52/cUWhadCZl2Ie6m5a7L/u5r31OKmbeNV/jRsX+DuKzbqwZ+ck4Y0vbsDLn78Alyh9xmqH8xktcPYHgIVBJpwB4JMXlGn1CjsrW7HVW2XQ3NWPX7x8Qnfd7RWejuVBlxv17f7bn5WTqPVND7jcaOrWT6CqgWaJtxYkJc6ODG8AO+B04+nd/v/K1wxRjXrlolw8/JFV+P2HV+LKRblBr6dSJ5xbugd0oXHPgBOX/+wNfGfzIbx1ohnOYOmu93ODbYpoZqjpZh+1x/k7zx7Cfc8cNF1DrM2CjIQYFGfE6w5GGCeO+wZduk0xfX7/5mnt9OULc3XrqWrpCThIEIzaraxuRHm8Ud/jXNHUjV9vOWm6Fp9TTd3a9HtCjBXXLSvUNn7tHXTpJrVVaqXGFcprYOvJZq0+ZChvnQhe1xGq6VKpkQhgtvd0X5C3E/xWCPFbeDYT/Oy4rSxMDta148VDZ7QR/a4+J36nfLN8YeMcdjUTERERTSNSym3RXgMRRUd2sgMP3roCbx1vwq1riwMuLzQGzhHucHbYLcgd5UZ7FFxpZgLe9QacZhPNS4vS8Kr37fgz0gP7jn2SHXaUZMSjorkHTrfE68f9U5DlSm3GMmXSdFdlK6SUEELg2JkuVDb7Q7F15RlIiLHhyV36Y53BepLfszgfP3z+KADg9eNndbUDxhDyxuWFiI+xIsZq0fX7Dnc/Oeqmgd7pT3UCOdiEM+AJym9cUYRH360CADzwygmsK8/Ez14+js5+p+662ytacM3SAjS092lTtznJniqR/JQ4tHnfgV7b2otsZeq6UtkwUK0FKc1M0DbxO6psPGfW3+wjhMCGudlBLzcTF2NFnN2KelzysgAAIABJREFU3kEXBl0SXf1O7WDNnuq2gIqGhQXJuGB2FsoyE5GX4sAPXziK3VWe8PNgXYdpV7wZNWwNVrtzwews7fXhe/x8a/jRTUuQm+xAQqwNdqW7urK5Gxf8cAsATwDse60CwNGGTu258b3bwxiSb1qcj5Q4O9Li7WjtGcSA042Gjr6gr2Efp8utm56+dF4Ont3vqek4dqYLF8/zHARxuyU+9sh2nGrqxmPbq/Dq5y80rdt4VZluPm9WJmJsFqwty8DjO2sAANtOtWBFcWDWpz5fy2akYW91G06e7Ua/041tp5p1tTBGUkq8Pcb+ZmCaTDgD6IdnYxSzP7u913nTe37UdRvR0tzVj1se3IafvnQcP3/lBH760nFd2HzR3Gx88vyyKK6QiIiIiIiIxtOGOdn4+qb5uk3UfIxhULgnnDMSY6Bmm6WZiaPqLqWhlSrTv4sLAzcJXDfTH0ouyE8OuFylTjkPKP3e5UoVyuLCFPiexiMNnXj4rQoA+jqNC+dkI9ZmxaqStIAAMd+kUgPwbP632DuNPeiSeOmwf1raeBsWi8CmxfnYuCA3pG5in2xDpYbbLXUdzsMFiZ+6oFyr6nj7ZDP+sb0af32nKuB620973mVe3ap2Mnu+39RQW71vAPqNGpWv2WxzyKykWF0oHS7qAQu1j1td69qyDGz7ysXY/On1uPeyubhhRSHWzczEcuVgxEFDXcVQ9BsGmk84z89LDni3/vXLC/DEf63D3NxkpMbH6MJmwHOAxddF3tYzqKuzOKT02y+dkYqfv2+p9twCnsfXN7Cp/vtZGUKtxvHGLq0fPz/FgXOU6pPjygGDQ/UdOOV9ziube3A2yOaGan/zBm9IfE6Z//tarVlRqRPO2UmxuoBZvU0zFc09qGv3POeWEXyPGU2LwFlK2SulvN3sD4B/ea/2R+/H/h7NtY7GU7trA46q+RSkxuHHNy3hf+5EREREU5wQ4g9CiN8LIXKU86H8+X20105E4yvSHc52q0X3Fv3x3jBwuti0OA9JsTZYLQIfUHqGfVaVpOO+TfNx88oifO7S2Sa34KcGzj6ZiTFIUWo4khx23KLcz3efPYQ3jp/V1Wn4Ki+EELoOXGDoUPc9i/O102oFcLiqWIwTzk1d/Rjw9t+mxNmRGDt0AcCMjHhcu9S/Gd4X/7kPLu+Y7MriNC2wPHqmE+2GgNP3jgL1+07tjwb0YWaJEnKWmnz9oW4EOFLBAmd1EnzpjFTkpgQeOFAPaByqC+wwDuaUErSXB3muLRaBG1cUAvDU5HzzPfPx45uWwGG3ml4f8Lz+FhXqu8n96/MHzvPzkrGiOB33KN8fNywv1J7PYiXYV6fQg1HvZ2FBim6zwmNKpcZrhi5lddNIn+5+J9457Q+UfaGxWiWzo7IF/c7AioxGpcM5KykWFyq1JFuONmJfTRt+98Yp3PW3XfjWvw6id8B/G28p080Jw3xfDGW6VGpMWVJKbddMwLMTb3F6PFxSIsZqxQ0rCpBmeFsNEREREU1JHwEgAfwPgDPe86GQAG6LzJKIaCKKdIez7zZ9VQDR6G+eDgrT4rHtqxdjwOkO+nv/beeVhnRbi0wCZ7OJ029smo9DdR3YU90GtwTu+OsudPZ5BuBsFqGbpLx+eSF++PxRrW83zySo9LlqcR6+99xh3cfi7FbdZn9jkaXczpmOftQoFQrGAzDB3LGhHE/urtEF4kIA91+zAF99cj/2eruCd1S2DBs4q1PDfYMu1HsnSq0Woau8MTtYM1R/81gEDZyVcDzYQYMF+f7Xz8gmnIfvcAaAey6djTWl6SjNTNBtpjiURQWpWhfxvpp2rVdcnXCe7w3KP3VBObISY3G2qx+3r/d/z+gmnE1CYSN1w8BFBSmYrQTOJxq7tBrc1w2Bc3VLD1aV6J/XN080YdDlebHNy0vWgv781DgUZ8SjsrkHfYNu7KtpD/jcs4YJ56z8ZK0ypaK5B1c/8Jbu+m4p8e1rFgLQbxiYEGvFaNucp8WE81S2q6oVJ7w7oibEWPHf1y3CPRvn4N7L5uIzl8wKuTeHiIiIiCa9UgBlAE4p50P5w+41omlGDbQsAshIiEzg7GNWC0DhkRBrC8uQ2cL8wMDZbOLUYbfioVtXaBPDvrAZ8LzVPyXOPxGdlRSL93knogtS43ShpFF+ahxWFKfpPlaamRC2d2snO2xw2D0RWO+gS1dvMFR/s6o8KxFXGTZDvGlFIRbkp+gCv3crWlBjUqmhhrVqIK1ONxemxenqIUozA0PYofqbxyJ44Dz85orlWQmI9W6QV9/ep/t8wNNtfLCuXTeN2zPg1G7bahG6jf6MYmwWbJibHXLYDECraQGA/bWefmm3W+KwEjgvyPMEzhaLwHtXFeHODTMRa/NPTpeMYcJ5UWEK0hNikJnoeVz7Bt2oae1FZ98gdla26j7PbMJ5y1F/f/OGOfq+8nNKg9dquNwSTV3+xz8rydMhvs5kk02fv2yrxMG6drjdUnd7w03+D2XaB85Sym9JKYWU8nfRXsto/F2Zbt60OH9M4+5ERERENHlJKSu9f5yG88P+Gcv9CiEyhBC3CyGeEkKcEEL0CiHahRBvCiFuE0JM+985iCaanCQH5ngn79aUZuj6S8PFV5GQGm/Xukdp4kqJtwcEfuVBJk6zkx146NaVWsDo46vTUH3zPfPx5B3r8J/PrkeMbej/DjYt1oe5ZnUSoyWE0PU476ps006HOuEMAHddNFM7HR9jxec3zgEArFQC5x0VreYTzroOZ//lFUqQaexcL86I1/Whp8XbMSs7+CTwWITS4RwsnLdZLZib65/mPWio1fivv+zCVT9/Ex/6/buQ3hFxtbd6Rnr8sK+PkVKn9vfVeDYOrGrpQY+3PiIzMWbYd3foKzWGnnA2bhjou/9Z2UqtxplObD3ZrE39+5gFzm8rwa9xE0i1VmPbKX3g3NI9oNW9pMTZtQD94+eXaR3seSkOXL0kH0uKPN3vbgl845mDOFTfgVbvxoyZiTFD1pYMh+nkJNbV78TmffXa+ZtXFw1xbSIiIiKaToQQHwKwR0q5b4jrLAKwTEr5pzHc1U0Afg2gHsCrAKoA5AC4HsDvAFwhhLhJSimD3wQRjSeLReDPt6/G1pPNuHB2ZMLg964qwqrSdGQlxY5pSo7Gz6KCFF3wFSxwBoAlRan43xsX4zOP7dE+dsm8wMDZZrXoNpQbypWL8vDtzYe0yoryME/G5yTHal/f7mr/hGlhiBPOADA3Nxk/uH4RntlTh09eUIacZE+IvarE/zXuq2lDSpw/vPVPOPsDb3VquEIJXksNmwE67Fbkp8Rp119Vkh6xPbp0gXOPJ3B2u6VurUP1cM/PT9FqRQ7VdWD9LM9Ubk1rj7YR5DunW7CjshWrStJ1dRrB+pvHojAtDmnxdrT2DKKzz4nK5h5dnca8vORhu7DVAwBVzT2QUgb9HOOGgRnejQ5n5yRiqzcUPtbYqTvY4FNtCJz7Bl3aa9UiAitv1I0Dd1a2ot/p0oJlY52G+jm779uI7gEn8lIcEELg1NkuXPbT1zHokthZ2YqvP31Au/7a8ky8HeyBCQGnDSaxzXvrtCMzs7ITsawocFdaIiIiIpq2HgFw7TDXuRrAw2O8n2Pe2ymUUn5ASvkVKeXHAMwFUA3gBnjCZyKaQLKTHLhmaYFuU7hwK81MYNg8iRg3DhwqcAaAa5YW4P6rF6AkIx5funzukGFkKHKSHbp+4vIwT/KqE87HvdWkwNAhqplbVs/Ao584R9dXnZEYq4Wmgy6Jpi5/6OcLmjMTYrUp3vbeQXT1e+pIKpTJWeOEM6DvQF8dof5mwBA4eysZmrr7MeAMbXNFdeNAtcf5lSONuus9vsPzTv2TZ/3PwVD9zaMlhNC9pvfVtus3DFTWG0xGQgwSYjxBbme/M6AqRGWs0/CZqfQ4Hz/TFbBhIBA44VzR3K0deClKjw+YNM5NcWhVRf1ON/bX+O/buGGgKiXejvzUOC00L8tKxO3r/c1qe6r9k//nDlHBEQoGzpOYulngzauKIrJLKRERERFNaVZ4Ng0cNSnlK1LKf0sp3YaPNwD4jffshWO5DyIiijx1ijLGZgmp2/jD60qw5d4N+NSF5WFZw5evmIf8FAdWl6bjsgW5YblNn+xkf/imvudmJJUaQzFu3AZ4pqp9k6cWi0C+snGib9L1kFI/YdZ3fvUSTz1NksOGTd6qmkhIi/cHzq3eCWddncYwj9N8XeDs/5pePqwPnDfvq0d3v1O/YWCEet7VHucDte36DQPzhg+chRAhbxxo3DDQZ7Zy4OT1Y2dR3eJ53uNjrFqd0ZmOfvQN+vutTzaq09/mYbz6tR074w/vg004B3PXhpmmG3qeOzNz2M8dCgPnSepoQ6d25MFuFbh+eWGUV0REREREk9BsAK3DXmv0Br1/O4e8FhERRd2SohRt0781pekR6fYeztKiVLz9lYvxj0+uHVN/rBl1wlkV6qaBwzELnH11Gj7qNHVtWy/ePtGk1VDYLAILCgJD0JtWFuGVz1+AN794EXJNgsFwyUj0B87N3kne2tbQ6jQAYF5ustYRfKqpGz0DTnT3OwM2tesZcOG5/fU41RTZCWcAWFTgbwLYV9Omm3BeEMKEM2DscQ6+caA64axOVs9WJpyblQnpdeUZupoVtfdbnf4OVjeidnmfUCb2G9XAOXn410tCrA1fu2qe7mNF6XEoGmITx1DwvS2TlLpZ4Mb5ubq3PhARERHR9CSE+IPhQ9cKIUpMrmoFMAPAegDPRmgtNgAf8p79TwjX3xnkorlhWxQREQWV5LDjL7etwZsnmnDdsoJoLyfscpIDpz0ddgsywpSnmNVdGPuh1Snh2tZePL27Vjt/08rCoKF4pAJZlW7C2Rc4t/kneofruo6LsaIsKxEnGrsgJXC4vhNnO/sx4HIHXPcfO6oj3uEM6KeAd1e1aR3LsTYLSkzqS8zoJpyDbBzodLlxuD5ww0AASEuIQWZirK5mBQDOn52FngGXNvFc3dKDmd4QWR84mz/3M5XNCI83dmqn1QnnrMThJ5wB4KpFefhbeZW2UeG55WObbgYYOE9KfYMuPLW7Rjv/3lXcLJCIiIiIAAAfUU5LAEu9f8xIAO8A+FyE1vIDAAsBPCelfD5C90FERGG0qDBF1z87lZiFuWqf7VgVpsUhN9mBho4+3ceM9+fz+I5qbbrZbhW4c8PMsKxjtNTg3WzCOZTqkQX5ydq07aG6dt3U780ri/DErhq43BLbK/xvrkqJs0dsiDIvxYHMxBg0dQ1oYTMAzM1Ngs0aWumDfsLZPHD+7Run0Tfouf2C1Dhtw0Cf2TmJgYHzrCwcquvQQl61x1kXOAfpMp8ZZMJZV6lhcpDFjBAC379+EW774w70Drhwx4Vjfy0ycJ6EfrXlJFp7PO9OzE9x4Lwx9qoQERER0ZRR6v1bADgF4KcAfmZyPReAVill8PeGjoEQ4m4AnwdwBMCtoXyOlHJFkNvaCWB5+FZHRETTkdmEc7j6mwFPaLeyJA2b99VrHzNWaqj1HXuVjd5uWTUj4LrjLSXODosA3BLo7HNi0OVGrdLhHMrmigvyk/HMnjoAnoqJV474N8i7eXURmrsH8NLhM7rPKctKiNieZEIILCpIwatH9Rv1hbJhoM9wlRr7a9rx4xeOaudvMRkKnZ2TpAXLvtssyUzQ1Vb4Ame3W+o6nIP1WxdnxMNuFRh0SdS396GzbxBJDvuQmwYOpTgjAS9+7vywPRfscJ5kTp3twm+2nNTO37FhZlR6lYiIiIho4pFSVnr/VAC4H8DTysfUPzURDJvvhCfkPgRgg5SyJRL3Q0RENBJmE87hDJyBwFqNImPgbHJ/MTZL1KebAc+mhsZajdo2ZcI5hK7rBfn+6fjn9jdoU70ZCTFYWpiK964M3H+sLDOydSFqvYVPKBsG+gxVqdEz4MRnHtsNp9uzC+WyGammG2jONEwpnz8rCwAwwyRwbujoQ693A8HU+ODT33arvhbkpLeipHGEmwaqwhn8M3CeRKSUuO+ZA1r/zdKiVLx/9Ywor4qIiIiIJiIp5f1SytfH8z6FEJ8F8ACAA/CEzQ3jef9ERETBJMfZEGvTx2DhDpyNGwcO1eHs84E1MyK6GeBIpCnhZkvPAGpb/QFrKI+VGuR29fv3C94wNxsWi8CGudnITNQHqOXZkelv9llUmBrwsZFMOOclOxDjfd00dw+gs29Qu+w7mw/hVJMn6E2IseKnNy81repQNw4EPP3NgD5wrvYGzsb+5qFC4Fk5gbUaug7nIJ3g44GVGpPIv/bW4a0TnhF8iwC+e+1CWDjdTERERERDEELkAbgYQAEAs1EXKaX8Thju50vw9DbvAXCplLJprLdJREQULkIIZCfHapu0AaFN7Y7E7JwkZCXF4mxnP5IdNuSl6gM/Y7DssFtMJ2KjRZ2mrWruQUefJzSOsYW2uWJaQgzyUxyoa+/TffySedkAPFO51y0rwG/fOK1dFukJ58WGTnIhgDm5oQfOFotAUVqcNkFc2dyDhQUp+M+BBjz6brV2vfuvWaibhlbNzkmEEICUnr7uteUZAAIDZynliDZT9Gwc6Dm2f7yxE139TvQMeKajY2wWJDuiF/sycJ4k2nsH8Z3Nh7XzH1lXioUmbwsgIiIiIvIRQtwP4MvQ/9wv4NkwUD09psBZCHEfgG8D2AlgI2s0iIhoIspJcugD5zBPOFstAj+/ZRn++k4lrl9egFibVXe5w27VAmkA+NDaEtOqj2hJVyo1Digb/hWkxoU88Dg/P0UXOMdYLTjPWyEBADetLNIFzsOFqmOVk+xAdlKsVjVRkpGAxNiRxaElGQla4HykoROvHGnEb17z191uWpyHG5YXBP381PgYfPL8cvxlWyXu2FCu3X9qvB2JsTZ09TvRPeBCS/dAwITzUNSqjpONXfoNA5NiI9aNHQoGzpPEj54/qnXf5CY7cM/G2VFeERERERFNZEKIDwC4D8ArAH4J4J8AHgHwAoALAdwG4HEAD47xfj4MT9jsAvAGgLtNfsGpkFI+Mpb7ISIiGqtsw8aB4Z5wBoC15RnaBKuZ9TMz8eTuWmQmxuAT55eF/f7HIl2pu9hvCJxDtSA/Wbcx4JqydF3AOzsnCRfOycKWo2dRkhGP0iCb4oXT4sIUvHS4EcDI+pt9ZigbB977xF5I6b8sP8WB7127aNhw98tXzMWXLp+ju54QAkXp8Thc3wHA0+M8ksB5lhI4H2/sQmOHP+gfaX9zuDFwngRONHbhL+9Uaue/8Z75Iz4aQ0RERETTzqcA1AC4XErp9P6CUyGlfAzAY0KIpwA8C+DRMd5PqfdvK4DPBrnOa/CE3URERFGjThNbhGf6dbx9/4ZFuGxhLhYWpCAzMbqhoJE64TyWwFl1ybycgOs88P7l2HayGSuK00w7j8NtVUm6FjgvmxHY6TwcdXM+NWyenZOIn92yDCnx9pBuxyyUnpEepw+cG5VKjeyhA+fSzARYBOCWns+tbvVP72cxcKbhPL6jWntBr5+ViSsW5kZ3QUREREQ0GSwC8KiU0ql8THtvr5TyeSHE8wDuBfDv0d6JlPJbAL412s8nIiIaL+qEc26yA/ZxCDuNYm1WXLZgYuY6aodzU9eAdjp/JIGzof71ornZAddJjLXhkvmBQXSk3Lq2GBXNniD3A2uKR/z5Mw3Br6954IblhbCOcW81tcf5cH0nGrxTynarpzt6KA67FTPS41HR3AMpgXdONWuXRbuqhYHzBOd0ufHk7lrt/EfPLYlqBwsRERERTRp2AM3K+V4Axk1ADgD4r3FbERERURTlKCFcJOo0Jrv0IBsDjuSxyk9xYHVJOt6taMFFc7NRpASq0RIfY8P3r1886s8/pywDVy7KxcG6Drx3ZRE+dm4p4mKsw39iCNTAecvRRu10cUZCSNPfM7OTUNHcAwB4+6QaOHPCmYbw+vGzWul3dlIszleK1omIiIiIhlAPIE85XwXA+NtWAQAniIiIpoGVJWmwWgRcbom15ZnRXs6EEzRwHsGEsxACf759NQ7Xd2JeXlK4lhZVVovArz6wIiK3XagEzkcaOrXToW6mODM7UevMrm1jpQaF6PEdNdrp65YXjEu3DRERERFNCbvhqdXweQXAJ4QQtwJ4Ep6NA28A8Nb4L42IiGj8FWck4B+fPAenm3qwaXHe8J8wzYQjcAY8tSFLi0belTwdzQgyAT7choE+s4L0PBs3yBxvTC8nsJbuAd3OnjetKIziaoiIiIhoktkMYIEQwrep3w8AtMOzeV8HgH8BEAC+HpXVERERRcGK4nTcuKIQDnt4KhGmErPAWQggNyW6fcBTWUFqHMyac0MNnI390j7R7nBm4DyB/WtPLQZdnt0ClxalYmb21HgrAhERERFFnpTyESllvJTytPd8NYBVAH4N4AUADwFYJaXcFsVlEhER0QRhFjjnJDkQY2N8GCkOuxW5yYHhcHmQIDnU67FSg4J6fKe/TuOmlZxuJiIiIqKx8YbPd0V7HURERDTxOOxWxMdY0TPg0j6Wn8rp5kgrSo9HfXuf7mNlIXY4J8baUJAap+tvFgLICFKPMl54iGKCOlTXgYN1HQCAWJsFmxbnR3lFREREREREREQ0laXF64PKgjTzjmEKH2OPc3ZSLJId9pA/3zjlnJEQE/U94DjhPEE9oUw3X7YgFylxob/QiIiIiGj6EUL8AYAE8FUp5Rnv+VBIKeVtEVwaERERTRIZiTG6admRbhhII1dkCPVD7W/2mZWdiNePndXOZ0W5vxlg4DwhDTjdeHpPrXaedRpEREREFIKPwBM4/w+AM97zoZAAGDgTERFR4IQzKzUibkaGPtQPtU7Dx7hxYHaU+5sBBs4T0guHGtDSPQAAyE9xYF15ZpRXRERERESTQKn371rDeSIiIqKQGLt/C9I44RxpxkqN0Uw4q6K9YSDAwHnCkVLit2+c1s7ftLIIVouI4oqIiIiIaDKQUlYOdZ6IiIhoOGnGwDmVHc6RVmQMnLNHFjhPxAlnbho4weyqasXe6jYAQIzVgg+eUxzlFRERERERERER0XSQbgic81mpEXFZibGIs1u18+UjrNRIjY9BZqI/ZGbgTAF+/6Z/uvnaZfkTYgyeiIiIiIiIiIimPjVwTnbYkOSwR3E104MQAh9a6xk4vWRe9qg2alRrNbKTo3+QgJUaE0h1Sw/+c6BBO3/beWVRXA0RERERTSZCiFOj/FQppSwP62KIiIhoUlID54I01mmMl69cOQ8fP78MGQkxEGLk1bq3ri3G9ooW5KY4cMHsrAiscGQYOE8gD79VAbf0nF4/KxNzcpOiuyAiIiIimkwsAKThYzEA8rynnQCaAWTA/3tAPYCBcVkdERERTXgLC1Jgswg43RKrS9KivZxpRa3FGKkrF+Xh3PJMxMdaYbdGv9CCgfME0dE3iL9vr9LO376e081EREREFDopZYl6XgiRDOAlAJUAvgLgDSmlWwhhAXA+gO/DE1JfMs5LJSIiogmqIDUOf7l9DY7Ud+D6FYXRXg6NQEr8xKk/YeA8Qfz93Wp0D7gAeHpXzp+VGeUVEREREdEk9z0AqQAWSim1KWYppRvAFiHEBgD7vde7OzpLJCIioonmnLIMnFOWEe1l0CQW/RlrgtPlxsNv+TcLvO280lH1tRARERERKa4D8IwaNquklH0AngFw/biuioiIiIimNAbOE8DLRxpR194HAMhIiMG1ywqivCIiIiIimgIyAAz33kq793pERERERGHBwHkCeP5Ag3b6vauK4LBbo7gaIiIiIpoiTgK4UQiRYnahECINwI0ATo3rqoiIiIhoSmPgHGWDLjdePtKonb9yYd4Q1yYiIiIiCtlvAOQDeFcI8SEhRIkQIs7794cBvAMgF8Avo7pKIiIiIppSuGlglG0/3YL23kEAQF6KAwsLkqO8IiIiIiKaCqSUDwghZgH4NICHTa4iAPxCSvmr8V0ZEREREU1lDJyj7IVDZ7TTG+fncLNAIiIiIgobKeVnhBCPAfgYgGUAUgC0A9gF4BEp5dvRXB8RERERTT0MnKNISokXlcD50vm5UVwNEREREU1FUsqtALZGex1EREREND2wwzmKDtZ1oLatFwCQ5LBhTVl6lFdERERERERERERENHqccI4itU7j4rnZsFuZ/xMRERFReAkh8gBcDKAAQKzJVaSU8jvjuyoiIiIimqoYOEeRWqexcQHrNIiIiIgovIQQ9wP4MvQ/9wsA0nCagTMRERERhQVHaqOkuqUHh+s7AAAxNgvOn50V5RURERER0VQihPgAgPsAvAHgRnjC5T8CeD+A3wJwA3gMwEXRWiMRERERTT2ccI4StU7jvJmZSIzlU0FEREREYfUpADUALpdSOoUQAFAhpXwMwGNCiKcAPAvg0SiukYiIiIimGE44R8kLBxu00xvn50RxJUREREQ0RS0C8JyU0ql8zOo7IaV8HsDzAO4d74URERER0dTFwDkKWroHsL2iBQAgBHDxPAbORERERBR2dgDNyvleACmG6xwAsGTcVkREREREUx4D5yjYcrQRbu82LctnpCEryWyzcCIiIiKiMakHkKecrwKw2HCdAgBOjJEQ4kYhxC+EEG8IITqEEFII8Zex3i4RERERTT4MnKNge0WrdnrDHG4WSEREREQRsRueWg2fVwCsF0LcKoRIEEJcBeAG7/XG6usA7gKwFEBtGG6PiIiIiCYpBs5RsLvKHzgvL06L4kqIiIiIaArbDGCBEKLUe/4HANoBPAKgA8C/AAh4wuKx+hyA2QCS4dmskIiIiIimKVu0FzDddPU7cfRMJwDAIoAlhalRXhERERERTUVSykfgCZd956uFEKsAfB5AOYAKAL+SUu4Pw3296jsthBjrzRERERHRJMbAeZztrW6D9PY3z8lNRkIsnwIiIiIiCj8hxPkAOqSUe3wfk1Kehqf73YY8AAAZ1UlEQVT6goiIiIgoIqZN2imEyABwHYCr4OmyKwAwAGA/gIcBPCyldEd6HbsqlTqNGZxuJiIiIqKIeRXAgwDuiPZCQiGE2BnkornjuhAiIiIiGpNpEzgDuAnAr+HZrftVeHbpzgFwPYDfAbhCCHGTlL7548jYpfY3z2B/MxERERFFTBOA3mgvgoiIiIiml+kUOB8DcDWAZ9VJZiHEVwG8C88O3dcD+GekFiClxO7qNu38Mk44ExEREVHkbAGwLtqLCJWUcoXZx72Tz8vHeTlERERENEqWaC9gvEgpX5FS/ttYmyGlbADwG+/ZCyO5htNN3WjrGQQApMXbUZqZEMm7IyIiIqLp7esA5gghviOEsEd7MUREREQ0PUynCeehDHr/dkbyTnZVqdPNadzBm4iIiIgi6SsADgD4KoDbhBB7ATQAMFbISSnlbeO9OCIiIiKamqZ94CyEsAH4kPfsf0K4/qg3M9H3N7NOg4iIiIgi6iPK6VzvHzMSAANnIiIiIgqLaR84A/gBgIUAnpNSPh/JO9pV6Q+cl3HDQCIiIiKKrNJoL4CIiIiIpp9pHTgLIe4G8HkARwDcGsrnjHYzk65+J46d6QQAWASwpIgTzkREREQUOVLKyvG6LyHEtQCu9Z71TVKvFUI84j3dJKX8wnith4iIiIiiZ9oGzkKIOwH8DMAhABdLKVsieX/7qtvg9rblzc5JQmLstH3oiYiIiGjqWQrgw4aPlXn/AEAlAAbORERERNOAJdoLiAYhxGcBPADPJiobpJQNkb5PXX9zMes0iIiIiGjqkFJ+S0ophvhTEu01EhEREdH4mHaBsxDiSwB+AmAPPGFz43jc766qNu30MtZpEBERERERERER0RQ0rQJnIcR98GwSuBOeGo2m8bhfKSV2c8KZiIiIiIiIiIiIprhpUyQshPgwgG8DcAF4A8DdQgjj1SqklI+E+74rmnvQ2jMIAEiNt6MsMyHcd0FEREREREREREQUddMmcAZQ6v3bCuCzQa7zGoBHwn3Huyr9083LilJhEnQTERERERERERERTXrTplIjhI1MhJTywkjc95GGDu304kL2NxMREREREREREdHUNG0C52g63dSjnS7PToziSoiIiIiIiIiIiIgih4HzOKho7tZOl2awv5mIiIiIiIiIiIimJgbOEeZyS1Q1+yecSzLjo7gaIiIiIiIiIiIioshh4BxhdW29GHC5AQCZiTFIctijvCIiIiIiIiIiIiKiyGDgHGFqnUYJ6zSIiIiIiIiIiIhoCmPgHGEVTUrgnMnAmYiIiIiIiIiIiKYuBs4RdrrJ399cysCZiIiIiIiIiIiIpjAGzhHGSg0iIiIiIiIiIiKaLhg4R5i+UiM+iishIiIiIiIiIiIiiiwGzhHkdLlR1eKv1OCEMxEREREREREREU1lDJwjqLatF063BABkJ8UiIdYW5RURERERERERERERRQ4D5wg6ravT4HQzERERERERERERTW0MnCNI7W8uZZ0GERERERERERERTXEMnCOoolnpb+aEMxEREREREREREU1xDJwjSK3UKM2Mj+JKiIiIiIiIiIiIiCKPgXMEVTazw5mIiIiIiIiIiIimDwbOETLocqO6tVc7X5zOwJmIiIiIiIiIiIimNgbOEVLT2guXWwIA8lIciIuxRnlFRERERERERERERJHFwDlCKpT+5uIM9jcTERERERERERHR1MfAOUL0GwayToOIiIiIiIiIiIimPgbOEVKhbhiYwcCZiIiIiIiIiIiIpj4GzhGiTjiXcMKZiIiIiIiIiIiIpgEGzhGiTjizUoOIiIiIiIiIiIimAwbOETDgdKO2tRcAIAQwI52bBhIREREREREREdHUx8A5AqpaeuCWntP5KXFw2K3RXRARERERERERERHROGDgHAEVuv5mTjcTERER0dQnhCgUQvxBCFEnhOgXQlQIIX4qhEiL9tqIiIiIaPzYor2AqUjtby7JYH8zEREREU1tQohyAG8DyAbwDIAjAFYD+AyAy4UQ50opm6O4RCIiIiIaJ5xwjoDTTdwwkIiIiIimlV/BEzbfLaW8Vkr5ZSnlRQB+AmAOgO9FdXVERERENG4YOEdAaWYCVpemIzsploEzEREREU1pQogyABsBVAD4peHibwLoBnCrEII/GBMRERFNA6zUiIDb15fh9vVlAAApZZRXQ0REREQUURd5/35BSulWL5BSdgoh3oInkD4HwMvjvTgiIiIiGl8MnCNMCBHtJRARERERRdIc79/Hglx+HJ7AeTaGCJyFEDuDXDR39EsjIiIiovHGSg0iIiIiIhqLFO/f7UEu9308dRzWQkRERERRxglnIiIiIiKKJN9b/obsmpNSrjD9ZM/k8/JwL4qIiIiIIoMTzkRERERENBa+CeaUIJcnG65HRERERFMYA2ciIiIiIhqLo96/Zwe5fJb372Adz0REREQ0hTBwJiIiIiKisXjV+/dGIYTu9wshRBKAcwH0Atg23gsjIiIiovHHwJmIiIiIiEZNSnkSwAsASgDcabj4fgAJAP4kpewe56URERERURRw00AiIiIiIhqrOwC8DeDnQoiLARwGsAbABniqNL4WxbURERER0TjihDMREREREY2Jd8p5JYBH4AmaPw+gHMDPAayVUjZHb3VERERENJ444UxERERERGMmpawG8NFor4OIiIiIoosTzkREREREREREREQUFgyciYiIiIiIiIiIiCgsGDgTERERERERERERUVgwcCYiIiIiIiIiIiKisBBSymivYUoQQjTHxcWlz5s3L9pLISIiIpo2Dh8+jN7e3hYpZUa010KRwZ+ziYiIiMbfWH7OZuAcJkKIfgBWAHujvZZJbK737yNRXcXkxsdw7PgYhgcfx7HjYzh2fAzHbjI8hiUAOqSUpdFeCEUGf84Om8nw/TzR8TEcOz6GY8fHcOz4GI4dH8PwmOiPYwlG+XO2LfxrmbYOAICUckW0FzJZCSF2AnwMx4KP4djxMQwPPo5jx8dw7PgYjh0fQ5og+HN2GPD7eez4GI4dH8Ox42M4dnwMx46PYXhM5ceRHc5EREREREREREREFBYMnImIiIiIiIiIiIgoLBg4ExEREREREREREVFYMHAmIiIiIiIiIiIiorBg4ExEREREREREREREYSGklNFeAxERERERERERERFNAZxwJiIiIiIiIiIiIqKwYOBMRERERERERERERGHBwJmI/n97dx8kSV3fcfz9lUcDCEggEGJABZTCWEVhiBwJ3BEFNSRCCYQiEC4VQKIRESxIWQRIGR/QqFgSCk+UM0EDAhEDXAIR7kRFUUtIRDSAxyUqz4c8w/H0zR/9W2lnZ3bnoWdnZ+f9qurq234Yuj/s7Hy2tx8kSZIkSZKkRnjAWZIkSZIkSZLUCA84S5IkSZIkSZIa4QFnSZIkSZIkSVIjPOAsSZIkSZIkSWqEB5wHFBG/FRGfi4i7ImJdRKyJiLMjYstRb9t8ERFbRcQxEfHliLgjIp6MiIcj4hsR8ZcR0fb7MCIWRcSKiHgwIp6IiP+OiBMjYr253of5KCKOiogswzEdljHDNiLiDyLisoi4u7xv746IayLiLW2WNcMWEfFHJa+flffz6oi4JCL26rD8RGYYEYdExKci4usR8Uh5r144yzo9ZxURR0fEdyLisfKzdVVEHNj8Hs29XjKMiJ0j4tSIuC4ifhoRT0fEvRHxlYhYMst/xww7r//Z2mfNTjMst2Az1OjYs2dnzx4eu3b/7NqDsWvPzp49OHt2M+zaM8hMhz4H4JXAvUAClwMfBq4rX/8Y2GrU2zgfBuD4ksldwBeADwGfAx4q0y8FomWdtwLPAo8BnwU+WjJN4JJR79OoB+BlJb9HSybHtFnGDNtnd1rJ4H7gAuCDwDLgu8BHzHDW/M4q+/8AcH75uXcp8DTwPHCkGf5y328u+/ko8KPy7wtnWL7nrIB/KPN/CnwC+EdgbZn216POYC4zBC4q838IfLp81vxryTSBE8xw9u/DlnX/uLZuAjtNYoYOoxmwZ3ebkz17OLnatfvPzq49WH527e5ysmfPYYbYsxv7XmxZd0F37ZFvwDgPwNXlf/C7WqZ/vEw/b9TbOB8GYL/yRnpRy/Rtgf8rWb2tNv0lwH3AOuB1tekbAzeU5Q8f9X6NMM8Avgr8pHxQTivBZtgxu0PLvv8nsFmb+RuY4Yz5bQs8B9wDbNMyb0nJZLUZ/komO5f37OKZykc/WQGLyvQ7gC1r03csBeQpYMdR5zCHGS4Fdm8zfV+qX9LWAduZYXclGNi6vNcvAlbRoQRPQoYOoxmwZ3ebkz27+Uzt2v1nZ9ceLD+7dvdZ2bPnNsOl2LMHzrFlvQXftb2lRp8i4hXA/sAaqr8u1J0BPA4cFRGbzPGmzTuZeV1mXpGZz7dMvwc4r3y5uDbrEKo330WZ+b3a8k9R/cUc4K+Gt8Xz3glUv1z8BdX3WTtm2KJcUnoW8ARwRGY+2rpMZj5T+9IMp9uB6lZMN2bmffUZmbmS6i+zW9cmT3SGmbkyM2/P0ghm0U9Wx5fxBzLzF7V11lB9Lm1E9XNibPWSYWYuz8yb2kz/GlWJ25CqsNWZYWfLyvidsyy34DPU3LNnd8+ePRR27T7YtRth1+6SPXtw9uxm2LU784Bz//Yr42vaFLxHgW8Cvwa8fq43bMxMlY5na9Omsv2PNstfT1ViFkXERsPcsPkoInaluqzqk5l5/QyLmuF0i4CXAyuAX5R7o50aEe/ucD80M5zudqq/YO8ZEb9enxER+wCbUZ0RNMUMu9dPVjOt8+8ty0y6dp81YIZtRcRS4CDg+MxcO8viZqhhsGc3w57dI7v2QOzag7NrD4c9e7js2T2alK7tAef+vaqMb+sw//Yy3mUOtmUsRcT6wJ+XL+tvno7ZZuazwJ3A+sArhrqB80zJ65+pLo983yyLm+F0v1vG9wLfB66k+oXibOCGiPhaRNTPGDDDFpn5IHAq8BvArRGxLCI+FBFfAq6hunzy7bVVzLB7PWVVzurbHngsM+9u83p+BhURsQPwh1S/TFxfm26GbZS8Pkl1KeDlsyxrhhoWe/aA7Nm9s2sPzK49ILv20Nizh8Se3btJ6trrj3oDxtjmZfxwh/lT07eYg20ZVx8GXgOsyMyra9PNtr3Tgd2B38/MJ2dZ1gyn26aMj6cqFW8AbqS6dO1jwAHAJbxw2akZtpGZZ0fEGqoHEh1bm3UHsLzl8j8z7F6vWZltF8qZKl+gutTslPplaJjhNOVy6M9TPVDnhC5WMUMNi99bg7Nn986uPRi7dgPs2kNhzx4Ce3bvJq1re4bz8EQZ93ofl4kQEScAJ1M9GfaoXlcv44nJNiL2pDrT4mOZ+a0mXrKMJyZDYL0yDuCQzLw2Mx/LzB8CBwM/A/btcMlfO5OYIRFxCtWTspcDrwQ2AfYAVgNfiIiP9PJyZTxRGfap36wmNtuIWI/qTLW9gYupnu7cj0nK8D1UD385tuWXhkFNUoaaG35+zMCe3Tu7diPs2g2wa4+EPbtH9uy+TVTX9oBz/6b+krB5h/kvaVlORUS8k+oSgluBJeXSoTqzrald3ncb8LddrmaG0039QF+dmf9Vn1HOYpk6+2fPMjbDFhGxmOphMP+WmSdl5urMfCIzv0/1i8TPgZPLw57ADHvRa1azLT/bX8MXtFKCLwQOBb4EHNnmQR5mWBMROwMfAC7IzBVdrmaGGhY/P/pkz+6dXbsxdu0B2bWHxp7dIHt2fyaxa3vAuX//U8ad7pWycxl3uvfcRIqIE4FzgFuoSvA9bRbrmG0phC+nuiH96mFt5zyzKVUWuwJPRURODVRPagf4TJl2dvnaDKebyuShDvOnSvKLW5Y3wxccWMYrW2dk5hPAd6g+V3Yvk82wez1llZmPU/3SsWlEbNfm9Sb2M6jk9S/A4cAXgSPK/fl+hRlOsxvlKdf1z5nyWbNvWeb2Mu0gMEMNlT27D/bsvtm1m2HXHpxdezjs2Q2xZw9k4rq2B5z7N/UhsH+5D8svRcRmVJcWPAl8e643bL6KiFOBTwA3U5Xg+zosel0Zv6nNvH2onkp+Q2aua34r56V1wGc7DDeVZb5Rvp66BNAMp7ueqkjsHBEbtpn/mjJeU8ZmON3Uk5u37jB/avrTZWyG3esnq5nWeXPLMhOhvLcvpTrj4p+AozLzuRlWMcMXrKHzZ83UQatLytdrauuZoYbBnt0je/ZA7NrNsGsPzq49HPbsBtizB7aGSevamenQ50B1WVAC72qZ/vEy/bxRb+N8GaguT0vge8BLZ1n2JcD9VOXvdbXpGwM3lNc5fNT7NB8G4MySxzFm2FVeF5Z9//uW6W8Enqc6I2MLM+yY32Flv+8Btm+Z9+aS4ZPAVmY4LbvFZX8v7DC/56yARWX6HcCWtek7AmuBp4AdR73vc5jhRsBVZZnzgRd18Zpm2N16q8p6O016hg5zN2DP7iUre/bwsj0Tu3Yvedm1B8vPrt1fbrN1RHv24BnasxvIcYb1VrEAu3aUDVUfIuKVVD+gtgG+AvwI+D1gCdUp7Ysyc+3otnB+iIijqR568BzwKdrfX2ZNZi6vrXMQ1V/PngIuAh4E/gR4VZl+WPrNS0ScSXWp37GZeX7LPDNsERHbAN8EdgK+TnVZ2g5U90RLqkuCLqktb4Y15Syzq6meOv4o8GWqQrwr1SWAAZyYmZ+srTOxGZZ9P6h8uS3V09lXU33vATyQme9tWb6nrCLiY8BJVA/iuRTYEPhTYCuqgzTnDGXn5kgvGUbEBcBS4AHgXNo/PGNVZq5q+W+YYe37sMNrrKK61G/nzLyjzfwFnaFGw57dHXv2cNm1e2PXHoxdu3v27MHZs5th157BqI94j/sAvAy4ALib6tKW/6V6UMeMZxdM0sALZwbMNKxqs97ewAqq+309CfyA6qme6416n+bLQIezLsxwxsxeSnV21J3lPbuW6hfZ15thV/ltAJxIdRnzI1SXTt4HXAnsb4a/st+z/exb00RWwNHAd4HHqX45+Rpw4Kj3f64z5IUzA2YazjTD2b8P27zGVLbTzrqYhAwdRjdgz+4mo9ne4/bsZvK1a3efmV17sPzs2t3l1HO/6SenhdxveskQe3aj34ttXmMq3wXVtT3DWZIkSZIkSZLUCB8aKEmSJEmSJElqhAecJUmSJEmSJEmN8ICzJEmSJEmSJKkRHnCWJEmSJEmSJDXCA86SJEmSJEmSpEZ4wFmSJEmSJEmS1AgPOEuSJEmSJEmSGuEBZ0mSJEmSJElSIzzgLEmSJEmSJElqhAecJUmSJEmSJEmN8ICzJEmSJEmSJKkRHnCWJEmSJEmSJDXCA86SJEmSJEmSpEZ4wFmSJEmSJEmS1AgPOEuSJEmSJEmSGuEBZ0kaIxHx6ojIiLhuhmV+EBHPRMS2tWkHRMSKiHggItZFxE8i4qMRsUWb9ZdExLKIuDUiHomIJyPilog4IyI2brP8mWWbFkfEERFxY0Q8FhFrGttxSZIkacjs2pLUjPVHvQGSpO5l5o8jYiWwJCJ2yczb6vMjYhHwGuCyzLynTDsd+DvgQeBK4D7gtcB7gbdExF6Z+UjtZU4FXg3cAFwFbAzsDZwJLI6IN2Tmc20272TgjcAVwEpg82b2WpIkSRo+u7YkNcMDzpI0fs4FlgDHURXZuuPK+NNQnUFBVYC/BbwlMx+aWjAilgIXlPnvqb3GO4A7MzPrLxwR7wdOAw4BLm6zXfsBe2XmTX3tlSRJkjR6dm1JGpC31JCk8XM5cBewNCI2mppYLtk7DPgJ8NUy+YQyPrZegAEyczlwM/BnLdNXtxbg4uwyPqDDdi2zAEuSJGnM2bUlaUCe4SxJYyYzn42I84HTgbcBXyyzjgJeTFVGp0rsXsAzwKERcWibl9sQ2DoitsrMtQARsQnwbuBgYBdgMyBq62zfYdO+0/9eSZIkSaNn15akwXnAWZLG0zLgfcDbeaEEHwc8TXXp3pStqH7WnzHL620KrI2IDYDrgD2BW6gu57ufqkhTXmejtq8A9/S2C5IkSdK8ZNeWpAF4wFmSxlBm/jwirgAOjohdgS2pHmBycWbeX1v0YeBFmfnSLl/6rVQF+POZubQ+IyK2Y+Yy3e7SQEmSJGms2LUlaTDew1mSxte5ZXwcLQ8wqfk2sGVE7Nbla+5Uxpe1mbdvb5snSZIkjS27tiT1yQPOkjS+rgVuA46meoDJbZm5smWZT5TxZyLiN1tfICI2iYjX1yatKePFLcu9AjirgW2WJEmSxoFdW5L65C01JGlMZWZGxHnAx8uk1jMuyMxrI+JvgA8Bt0fECuBOqvvI7UB1JsU3gDeVVa4A7gBOiojfAW4Cfhs4ELiq/FuSJEla0OzaktQ/z3CWpPG2HHgeWAd8vt0CmXkWsA9Vid0bOBE4lOoJ2MuA02rLPg7sR/VwlN2AE4DXAu8HjhzSPkiSJEnz0XLs2pLUs8j0vvOSNK4iYjGwErgwM48a8eZIkiRJC4ZdW5L64xnOkjTeTinjc0a6FZIkSdLCY9eWpD54D2dJGjPlfm8HAnsAbwauzMwbR7tVkiRJ0viza0vS4DzgLEnjZw/gg8AjwCXAO0a7OZIkSdKCYdeWpAF5D2dJkiRJkiRJUiO8h7MkSZIkSZIkqREecJYkSZIkSZIkNcIDzpIkSZIkSZKkRnjAWZIkSZIkSZLUCA84S5IkSZIkSZIa4QFnSZIkSZIkSVIjPOAsSZIkSZIkSWqEB5wlSZIkSZIkSY3wgLMkSZIkSZIkqREecJYkSZIkSZIkNcIDzpIkSZIkSZKkRnjAWZIkSZIkSZLUCA84S5IkSZIkSZIa8f//JU8JHpTeqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 236,
       "width": 718
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3.5))\n",
    "plt.subplot(1,2,1)\n",
    "abrupt_gmst.plot()\n",
    "plt.ylabel('GMST ($^{\\circ}C$)')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "abrupt_imbalance.plot()\n",
    "plt.ylabel('radiative imbalance (W/m$^{2}$)');\n",
    "plt.savefig('../figures/4xCO2_temperature_evolution.png', dpi=100, bbox_inches='tight')\n",
    "\n",
    "plt.figure(figsize=(12,3.5))\n",
    "plt.subplot(1,2,1)\n",
    "abrupt_gmst.plot()\n",
    "plt.xlim(gregory_limits)\n",
    "plt.ylabel('GMST ($^{\\circ}C$)')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "abrupt_imbalance.plot()\n",
    "plt.xlim(gregory_limits)\n",
    "plt.ylabel('radiative imbalance (W/m$^{2}$)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = abrupt_imbalance.isel(year=slice(gregory_limits[0],gregory_limits[1]))\n",
    "x_data = abrupt_gmst.isel(year=slice(gregory_limits[0],gregory_limits[1]))\n",
    "a, b = np.polyfit(x_data,y_data,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECS =  5.161240071872432\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 375,
       "width": 721
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(abrupt_gmst, abrupt_imbalance, c = abrupt_gmst.values, cmap='viridis')\n",
    "plt.scatter(x_data, y_data, marker='.', c = 'k', cmap='viridis')\n",
    "x_extrapolate = np.arange(abrupt_gmst.min()-20,abrupt_gmst.min()+20., 0.1)\n",
    "plt.plot(x_extrapolate, a*x_extrapolate + b, color='grey')\n",
    "\n",
    "twoECS = b/(-a)\n",
    "print(\"ECS = \", twoECS/2.)\n",
    "\n",
    "plt.plot([0,twoECS + 1.], [0,0], \"k--\")\n",
    "plt.xlim([0,twoECS + 1.])\n",
    "plt.ylim([-2,8])\n",
    "plt.xlabel('GMST ($^{\\circ}C$)')\n",
    "plt.ylabel('radiative imbalance (W/m$^{2}$)');\n",
    "plt.savefig('../figures/Gregory_method_CESM2_example.png', dpi=100, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eagerly compute ECS for each model, in preparation for plotting\n",
    "\n",
    "The operations we have done up to this point to calculate the global-mean surface temperature were evaluated lazily. In other worse, we have created a blueprint for how we want to evaluate the calculations, but have not yet computing them. This lets us do things like multiply two 1 Tb arrays together even though they are each individually larger-than-memory.\n",
    "\n",
    "Now we call xarray's `compute()` method to carry out the computations we defined in the for loop above for calculation the global-mean surface temperature anomaly roughly 100-150 years (1200-1800 months) after instantaneous quadrupling of CO2 relative to the last 50 years (600 months) of the control simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d61a34f8e806473283d6243e05f50da7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ECS_dict = {}\n",
    "results = {'ECS': {}, 'ctrl_gmst': {}, 'ctrl_imbalance': {}, 'abrupt_gmst': {}, 'abrupt_imbalance': {}}\n",
    "\n",
    "for name in tqdm(ctrl_gmst_dict.keys()):\n",
    "    results['ctrl_gmst'][name] = ctrl_gmst_dict[name].groupby('time.year').mean(dim='time').compute()\n",
    "    results['ctrl_imbalance'][name] = ctrl_imbalance_dict[name].groupby('time.year').mean(dim='time').compute()\n",
    "    \n",
    "    results['abrupt_gmst'][name] = (\n",
    "        abrupt_gmst_dict[name].groupby('time.year').mean(dim='time') -\n",
    "        results['ctrl_gmst'][name].isel(year=slice(gregory_limits[0],gregory_limits[1])).mean(dim='year')\n",
    "    ).compute()\n",
    "    results['abrupt_imbalance'][name] = (\n",
    "        abrupt_imbalance_dict[name].groupby('time.year').mean(dim='time') -\n",
    "        results['ctrl_imbalance'][name].isel(year=slice(gregory_limits[0],gregory_limits[1])).mean(dim='year')\n",
    "    ).compute()\n",
    "    \n",
    "    # Apply Gregory method to estimate ECS\n",
    "    if results['abrupt_imbalance'][name].size >= gregory_limits[1]:\n",
    "        y_data = results['abrupt_imbalance'][name].isel(year=slice(gregory_limits[0],gregory_limits[1])).compute()\n",
    "        x_data = results['abrupt_gmst'][name].isel(year=slice(gregory_limits[0],gregory_limits[1])).compute()\n",
    "        a, b = np.polyfit(x_data,y_data,1)\n",
    "\n",
    "        results['ECS'][name] = b/(-a) / 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MIROC6': 2.571679992793863,\n",
       " 'E3SM-1-0': 5.327854275356072,\n",
       " 'CanESM5': 5.614675078786822,\n",
       " 'BCC-CSM2-MR': 3.0219041781154377,\n",
       " 'GFDL-ESM4': 2.679879548908625,\n",
       " 'CESM2-WACCM': 4.746858814991468,\n",
       " 'BCC-ESM1': 3.243438196242251,\n",
       " 'GFDL-CM4': 3.934080254287022,\n",
       " 'CESM2': 5.225059336140391,\n",
       " 'GISS-E2-1-H': 3.0841129356317167,\n",
       " 'CAMS-CSM1-0': 2.288186201608668,\n",
       " 'GISS-E2-1-G': 2.7174072964820772,\n",
       " 'NESM3': 4.612541962433655,\n",
       " 'MRI-ESM2-0': 3.1627661850484663,\n",
       " 'EC-Earth3-Veg': 4.300457395775987,\n",
       " 'SAM0-UNICON': 3.6779398988613416}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['ECS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 387,
       "width": 720
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.array(list(results['ECS'].values())), bins=np.arange(0,max(list(results['ECS'].values()))+0.5,0.5))\n",
    "plt.xlabel(r\"ECS ($^{\\circ}$C)\")\n",
    "plt.ylabel(\"number of models\")\n",
    "plt.title('Equilibrium Climate Sensitivity (ECS) in CMIP6 models')\n",
    "plt.annotate(s=fr\"$N = {len(results['ECS'])}$\",xy=(0.025,0.90), xycoords=\"axes fraction\", fontsize=14)\n",
    "plt.savefig(f\"../figures/ECS_Gregory_{gregory_limits[0]}-{gregory_limits[1]}_hist.png\",dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = sorted(results['ECS'].items(), key=lambda x: x[1])\n",
    "ordered_ECS = { pair[0]:pair[1] for pair in tmp }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 352,
       "width": 710
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(np.arange(len(ordered_ECS)), np.array(list(ordered_ECS.values())), align='center', alpha=0.5)\n",
    "plt.xticks(np.arange(len(ordered_ECS)),ordered_ECS.keys(), rotation=90)\n",
    "plt.ylabel(r\"ECS ($^{\\circ}$C)\")\n",
    "plt.title('Equilibrium Climate Sensitivity (ECS) in CMIP6 models')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"../figures/ECS_Gregory_{gregory_limits[0]}-{gregory_limits[1]}_bar.png\",dpi=100,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Speed up your code with dask-kubernetes (if available)\n",
    "```python\n",
    "# Cluster was created via the dask labextension\n",
    "# Delete this cell and replace with a new one\n",
    "\n",
    "from dask.distributed import Client\n",
    "from dask_kubernetes import KubeCluster\n",
    "\n",
    "cluster = KubeCluster()\n",
    "cluster.adapt(minimum=1, maximum=10, interval='2s')\n",
    "client = Client(cluster)\n",
    "client\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cmip6-temperature-demo",
   "language": "python",
   "name": "cmip6-temperature-demo"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}