yutsuo
/
jupyter-notebooks
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
21 Commits
1 Branch
0 Tags
12 MiB
 
Branch: master
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'master'
${ noResults }
jupyter-notebooks/HDF5.ipynb

2469 lines
408 KiB
Raw Permalink Blame History

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import h5py as h5py\n",
"from mpl_toolkits.basemap import Basemap, cm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"hdf5 = '/notebooks/3B-MO.MS.MRG.3IMERG.20150801-S000000-E235959.08.V06A.HDF5'\n",
"f = h5py.File(hdf5,'r') # Change this to the proper path"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Grid']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(f.keys())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"dataset = f['Grid']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['nv',\n",
" 'lonv',\n",
" 'latv',\n",
" 'time',\n",
" 'lon',\n",
" 'lat',\n",
" 'time_bnds',\n",
" 'lon_bnds',\n",
" 'lat_bnds',\n",
" 'precipitation',\n",
" 'randomError',\n",
" 'gaugeRelativeWeighting',\n",
" 'probabilityLiquidPrecipitation',\n",
" 'precipitationQualityIndex']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" array([-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" dtype=float32),\n",
" ...]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(dataset['precipitation'][0])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3600, 1800)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset['precipitation'][0].shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"precip = np.transpose(dataset['precipitation'][0])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1800, 3600)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"precip.shape"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"theLats= dataset['lat'][:]\n",
"\n",
"theLons = dataset['lon'][:]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" [-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" [-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" ...,\n",
" [-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" [-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9],\n",
" [-9999.9, -9999.9, -9999.9, ..., -9999.9, -9999.9, -9999.9]],\n",
" dtype=float32)]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(dataset['precipitation'])"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['nv',\n",
" 'lonv',\n",
" 'latv',\n",
" 'time',\n",
" 'lon',\n",
" 'lat',\n",
" 'time_bnds',\n",
" 'lon_bnds',\n",
" 'lat_bnds',\n",
" 'precipitation',\n",
" 'randomError',\n",
" 'gaugeRelativeWeighting',\n",
" 'probabilityLiquidPrecipitation',\n",
" 'precipitationQualityIndex']"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Yes, I found in the List\n"
]
}
],
"source": [
"if -17.25 in theLats :\n",
" print(\"Yes, I found in the List\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('<f4')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"theLats.dtype"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"# import sys\n",
"# print(sys.maxsize)\n",
"# np.set_printoptions(threshold=sys.maxsize)\n",
"# np.set_printoptions(threshold=1000)\n",
"# with open('precipitation_short.txt', 'w') as f:\n",
"# for item in dataset['precipitation']:\n",
"# f.write(\"%s\\n\" % item)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3600"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataset['precipitation'][0])"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1800"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(precip)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'GeoAxesSubplot' object has no attribute 'drawstates'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-84-70710365c9ad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mccrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlateCarree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoastlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrawstates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Save the plot by calling plt.savefig() BEFORE plt.show()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'GeoAxesSubplot' object has no attribute 'drawstates'"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"\n",
"ax = plt.axes(projection=ccrs.PlateCarree())\n",
"ax.coastlines()\n",
"ax.drawstates()\n",
"\n",
"# Save the plot by calling plt.savefig() BEFORE plt.show()\n",
"plt.savefig('coastlines.pdf')\n",
"plt.savefig('coastlines.png')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAC1CAYAAAD86CzsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXmwZdlV5vfb0xnu8O6bc6rMysoaM1WlGoVUkkASkhBIIJAQk5iMMeDGOMK47fYf3Y5wOxwOO+zodgeO7rabYGgBDahRSwhNCIGG0lSSSjWp5sysyjnzjXc80x78xz7vZRbRtoHQgEpvZdx3X747nXPuOd9e+1vf+rYIIbAXe7EXe7EX35yQ3+oN2Iu92Iu9+E6KPdDdi73Yi734JsYe6O7FXuzFXnwTYw9092Iv9mIvvomhv9UbsBd78TcNIYQGMiAFEqABqvbWhL2q8F58G4TYO0/34usZQogOsHTNbRFYEkIs3fe2n/6fbFUSXIO1NbZusE2Fa2psU+Oa+PcX/b+psHX8G4BOEpROUNrgvY2P2RrvPVonKGPQJkFqgzYpyhiUic83SYpJM7RJMElKEArV/v7QR//wn3rv14GNa26b7f14D9D34usVe6C7F/+/0WaY+4FDO7ckSY4cf833/zfjzStMR1sUo21m421CCHTm5un058n78b7TH5D1B+T9ASbrYEyKTlOUNqgkxZgEk2YRTE2CUhKkJkkydJKQpAlCaKTW1LXFeQ8E0sRgG4uUguAsTdPQWEtdldimwTcRrJ21OFvjmiqCfF0TvKWpK5q6pqlKysmIYjKkGA8pp0PKyYhyMmQ2HuJsTd4dxP2aW6C3uMypL3/qN4qieB44f83tQgih+lZ9T3vx7RF7oLsXCCEUcBg4Bhx77bt+6d9sXT7L1qVzjNYvMRtt05mbp7+4Sn9plbmlffQWV+kvrtIZLNEbLNIZLJDPDUjSHCEEQiqC9yitEABCkmiJD2B9oHGeRAmEEPgQ8C6AABsEQsByrjHCcXHqybWkLCsEgfi0gBAC6zzBB3wIgIAQCCIgkEgR38e3p7dofyi185hg59QPIRCC332fEAJSgkBgnQNnGW9vMZtsU4y2mWytUU+2GG+usb12kfHmFcYbV5hsr5N1+vQXV5nfd4iF/Yf5wgd+99eAk8Ap4IU9UN6LPdD9DgkhhAD2AbcBx7vd7p37b7nrV7YunWW4fonuYJHB6iHm913H/L5DLO0/zNzKAXqLq3Tnl5A6QQIBUEIglERKCUJgjEZJQd04AJwPnFgyvOZwB60Vw9IivWUyHKJMyteuzLhl34C+9nQ6Gd25AQ+f3ea+G/dTO8fahfPUMqUupix0cz50asbdR5f5q2c3cE2DaxqyPCVIRfABZxuEVC2IAwiklAQhaKwnBAiECLwhkCeKovHMmgjoSIGSgsZ5FIEQQLT7lmlJYx11VeN9BGcENLXFGIPzHoFAaUmWJWxvXGG0foWti2fZunyOrUtn2b50jq3LcQDrzi+xsP8wW2ee/r3xePxQCOFJ4CngTAjBfyvOjb345sYe6L4EQwixCNwphLjrrje945+tnT3F+tmTCCFZPnyMpUM3sHToBhYPXs/8vsMMVg+idQIiZoQRuOK9NgbnXORSpcS6iAtCRjBaTTyvPdJFC8dCpjm/OaGRhuWuYTabcX4KX94y1E3DP3z9UdLE0FhH8IEQAlmW4L2PGawQeA/WBZwPTDbX+Op6w8OXK1ItIuITEMHhPLimQqcZWhvuXDXce+M+Hj+zxptedpAHT23wsWe2ESHg2/cWUuADBOcQMgJqpgWjOmbMCBFvIbSZ8NVr485FwWtu2QdS8X985hyplmglqKsm7gshbp9g914IgTEm0iWAc5bNKxcYXjzHxoXTXHnhJFfOnGTt7EnK6Zil646ycvhGnvjMR/6Jc+5h4BHg/B6f/NKKPdD9No42ez0G3JMkyb3Xv/z+/+7y809TTsfsv+FWDt50gpUjN7F88HoG+69ned9+JtMqYtcOwNLiDAKtJEIqtFEkWmGdw7rAUi4ZVgHnPO++NeUDzzcc6hmWdM1dR5f55BPnWO5l3HZoiWlRcnZY87mLDd99Xc59N+3HS433HtVSCSHE9wohUgPOB7wHHwJNE0AKnPPMxlv8yTNTkBJxzX5779rtlggRM+tDc4ZDScl3Hb+ejz61yROXZ6RKtlRBBNPgPUJKgvdXRxbagxECEK75+7WP7xwjdn8keIRS/NSJHr/58JDEaLQUBMB5j7NxG2PWLMiMQipJ1Xis9+27X/2Mcjpm49xpNs6fZO2FZ7l4+mkunXqK4B37jt7KpWcf/T+Lovgy8BDwRAjBfX3Oor34Zsce6H4bhRBiH/CK1/3Ur37w3NOPcuHZxzBpzoEbT7D/2G0cuOFWVm+4jcXVA1R1jQ+KnbRrBzR2QNZojTKaRCsq65ECfvZmwyMbli9csqQmTt1rD5kWHOxKKut59cGUh6403L0E1y31WdvY5Mj11/PPP30W2k/7oesVh/YtEYSmrCwBgZRxeq+VQmu1m/1ZH5BS7YJuCLCxvs6zV4YEZXh8078oA/W2xjsfXxMCxmiEEDgfIAReczjns+crQiCC7c5GsXOei7+OpwhxFQJrF6it4x13HuDshSs8ObrK/V7zZrv4XHswAnIj+NnjkplN+MCpihsHgi9edKSqfXrY+ZydbyG+3vuAB7SIgJ0rKNzVz5psrXPp1FNcOv0Ul08/xcXnvsZ48wr7j53g0C138Pn/8Ns/AXwJeH4vI/72iD3Q/XsaQggJvEwI8ZrbX/eD/+rskw9RTsccuvkODt1yBwdufBkHbryNudVDEALldIxQSXtNixclckKAFBKpJHmW0LiA956eBgdYFL9we5eTmyUPrTlefSDhw8+XvOGg4XBf8Scnay5MHb9ye86HXmhwAe45ssDDZ7cZFc0umAghcNYitKGxln1dw7vuvo5ubgje4dvsVghB4z0Q6YRIKXga67DWsro8z/j8SX7n2asZaVPOCAiCp+WRFZG7bZNUIVFa7dIEAXaraCEEhLy2eHa18BYPUCyahRCofeDXXn+MpJ7wF0+v8+hGINXXvq59Qft74+HoQPK6wwbvA5mMA0LdCKTwxM0N/N6TzQ6r/B8NKeAnbjU8t90wl8DHXwh4BLJ97NooJyMuPPc45595lAvPPM65Zx7BO8fh43fz7IN/9Y+8958BHgoh1H/7M28vvtGxB7p/T6KVZd2jlHrjsbtf8z+fffJhOnMLHHnZPRw+cQ9Hjt/L8uEb0Fpi65rpZIZSyW7WtsvDwi7ASCFBSoyWuABKSt5xk6KwsF4qPnOu4mDmuDATJEpQ+4CRAhcC3UQyrQMHe4rN0lPUDnaLUR4bJJmRLYjKXTCVsqUqpOTVq3DfiWMEPN56vPdY53E+IJXEuUgNCCGw1jGZzqinI566MiLJMh4famxdIqSClgOOnxP3JcJ2QBuNDwLZKhOsjUoEsZPly0ikBO/jALGLmVezzqvHUfIPvmuFf/HABbJU41pVxa6qoaVIpIC6zUgP9QRvu1FAkFinaZoapQJKtbxvY8mM5D1PBgYJnJ0ElBDonQGD0A4ecSOWc7g8gzuW4YsXA/0kgrsSAin+OvDH1wzXLnD2ia9y5smHOPvEQ2xceIGDN53g0rOP/a9VVX0c+FwIofh6n7d78bePPdD9FkWbyd4upXzjTfe97p+98LUvM1g5wNE7XsnRO17B4eP30FtYbp8dUFIgQqCq6qvFLAFSSgQRcESrKlBSooSgdp5EwrtvEzgHX7zouO9AyqfPezZKxy3zhruWJf/qkRkH5zRvO5pQB0tZBR5ft5yeSn7yRs2fnQ28/YgnWMfnLhbcvNTlLy8JnI162RDC7vR/Z0p/w5zkrXcdJVES5xzOOazz+BCzRtdmpGVV45ynmI5Z39rk+sWU3ztl0BJsXZEkhgh44H0EbkFAKQXBEwCPbIFV7mzG7nFDiJZbDS9OVNuHd7Vk1/C5775V8sTFGY8MU8I1uAzwjpskkybw+QuBYS24ewVed1iwXTiMlnFbr6mo7QCpFAHvHbL9bmwA50HgSLVmu4oDVaYc3sPvPhE55p1N1lJw8zyMajg9jFqMVP3HARgC1WzKuace5oWvfZnnH/0il04/zaGbb+fC0w//07quPw48GEJo/q7n71783WMPdL+JIYRYBt48GAze6aR5l8k63HDnqzh25/0cveO76M4vXn1ue++BREdg9c7F4lZjW3BVKBM7uWXL29Y2pl/XdTxvvSnl9x8refUhxXwmSZTGSCgbT2Yk5yaBvzhryRR890HNg2ueOQPnJp7/5HiK93BxUrOcaxLlmFWOwsLHz3vqoKLMyvsWr3aq/YKfuCnwyPmCG+YTDh069CLe1XqizKsFQSEl25sbrG8P6bgJj1QLXCkFzlm0lLHwFSIdYoymqSqk0lhrQYgIwlLFAUdEnliKAFK1tbGrECgIBHGN3UgI12S4Yvf5qYa3HQlo7fjjZ/Q1X0gEy1++I2a7HzoFz24FfvgmOL4En7uguG+fb986vAgLvQ/44KhcQ246LZ0BPtQoqVvtMQTawRPwXvDBU57zE0hkYLUjqBy886a4T2fHgpsX4EuX4AsXA6kC24rO1M5utp9fTaecefIrnH70i5x+5PNsXTpHNzWf2Nraem8I4aMhhBe+Dqf4XvwNYg90v4HRqgvuTpLkHctHb/snG+dOcfSO7+LGe1/LTfe8loX9h9mZtt61CvcfgNLGqacAxo1g3MAz24JLE892FafVmY4Fn8ZFvm+QwO3L8OAVcA4aH0hE4MhAsb+nKK3nnpWd7FiihefhNcmDFx11gF99eUJV1/zWU6AF3LogWE08+7oaJ+ChK47XHzIsHTjMpbPPM23g+ULx2LqNBaudIp0Aaz33zlcYJfiee04wm1V4wLmADwLrW91sCHjnqcsJZy6tM7d6kOfOX+L0LAFiE0PwEWwRkfOVROWCkrLVzO4c6Jjxt2QCQgry1NA3llEtuW1Q4r3nxKJlVKd89FwGu+QEV1Pj3fcTO5UvaufJFNy8KPjaekRoIwM/dyKgRDzWM6fpG4cUUYlxdmS5fpAS8CRJirOACMyKGYlJ2PGZkjKqNpQUWGdpfIOK/AxFU9FNcxrX4D3kSULAIGkQ0uBdg1QCJTXOtxI84vkRCBgRqJ0nBEHXwG98VdIEUHGkoxqtc+aRz/PElz7Dqa8+QN6fp9y6/C+n0+m/Bx7Yy4K/cbEHul/nEEIkwOt7vd6Pm7z3i+iUW1/5Bm75rtdz+PjdaJNwYknwsmVBR1pCCGi9k6lFvlFJRVk5tra2qBtLnmq01qhOn/Fsm5WOoQkB5yXeOTpphlYJZd3QTSRrleLC2HPTwLNVBp7cFCx3JOcngUwJ1go43Ic7VwQPnA+86XrJ5iyAlPzx05ZfuTPDuZrf+VosBC1mgvsPwEqqsM7z8bOWbavYoTsTLalqRwgeqTXH5uCH7jq6K8MqqjitnlUNTe2YFQXr6xs05Yw0UVyceZ6q+ygR+eFIB0R5l29VCK6uoxSrLQJmWUoIcYAKLUZqJclSg3We/cmYV+3zZKlh5goynbA2GUHbMJFqxYH+gO1G84dPGYwIGAUdI9gu4cRiw+MbhrfeUPHJcwm3LgU2poHXH7YIBEbBHz6T8KM3ViDgD55KefNRz/68xvpAoiLolk2NEQmzqsA2oFQcZLt5hyAarHeUtm61wRKjNFIIRsWUXtYl4BmXM4qm4tD8Cs4G0iRBScHWdIRRmlxnQCwuWmdxLY2xNtmkl3ZRQtJ4S8fk7aDnsc4xl/cwCh6+7GjOP8GnPvVZPv7xT3Du3DmA929vb/874KMhhNG36np6KcYe6H4dQgiRAW95+9t/8P2f+uSnOXj0GPvu+l5O3P+9LF93DCEEtYP/4k5HaT3OebRUzIqKWVkggMY2JJmm9hYhJXmSYoMjNym+doxcQa4TtNKE9p91jqXOPARQWuG850tXDJUNvGJ/4MOnAj91PALSg5cEXSM40g/s60DpJY0TgMIo+OdfrvjP70jxIfBvn2qQBP7hKwxPbTR8bUPy6n0SgkQbzamtis9ejsWkprFIFafyP3HcMJpV3HDd9XSzhGlR4RyUtaUoK5TUjKYzxtubfGIzQQiBFgHf1GR5HjvCZGzv1UruvrcUYK2jqerdzjIhBN1el6bllQGyRFPUFoXnB49O6ZmUJkzZrsY0zmOURgkJSGZVhQoGZQQrvXm6qeHfPGoIAV65f8YXLnV46w0FK5nj/GjIvv4A6zy9NCUQs+odnjpPNI2TXJ4FjsxFKsGGEJtJfFRsbM0mrPQXKIqSEKB2DanRgKCyNaWvGeQ9iroiNymNs6QmQUvFpCponKXxllE5YTHvM5/PURRgtGVcT/EEgvOs9PcRaNBKISTMqpLEpAxnI6SImbH1ll7WQQbJrCnJkpTgPHhBkvTY2FjjIx/5CJ/4xKf4yle+wr333sNnPvPAL4QQPhBC2PoWXWYvmdgD3b9jCCFS4Pvf9P1vff+Dn3uA48dv5bvf9BZ+/G3fS76wyr8/qegawTtucnzolOLufY59WUVRVmw3E1QQLM8t8tS5k8wP5nHBU9sGQuBAd4lRM2M5m2NYTemkWcyGiNIvvyu3ciip8HgW8j4BQaI0Sqmof20LOTs6VClkW+iK2VZZB1Kj+OoVT1cHPnkOEg0gUBIaF/iVOyXWS5SE9zxe8+YDnoVOynuebhA7Ei1gkEresB/2rwyYW1imbix1ExhPS6ZFycVLl1nqGj5wTqJlW2SilUMJwR2L8Oi6Z6Ubi2gH+oKHLjQgYouubSxN3cR91joWpJQEqWIm3JKZUgnuX97ipsUu50cXCMIzqyukcChpEEKhpGJUlHQSyeNbh0llzfW9gqe2F7lndZvHNga8av8GtYNxVVA2DUvdOaxzu4OdlIqDvf1sFOsYZeimc1yceo4NPC5ohJA0rkEJyVYRE8UQoLQ1c0mXTpphlKYoy8j5trI26y2ZTlv1BUilKOqSxll88JG3d5ZEaRpn6ekOicro5Amb021SlTCrSgadRaraIUTF5mybrpxjMN9hUk0IIWC9Y6m3wNp4k/lsjsQYamdJVYJzFu8ESaLY3Nzgc5/7In/6wQ/z2c9+gXvuuYcHHnjgPw0hvC+EMPzmXXEvndgD3b9FtIqD1/7AO971qU/+xcdZvf5mfvFd30//jjeybg6gZeC2geUV+yx1U4OQNCIwq2YkScpkPGHqShJpGNYzVrI5jNKUwZIKQz/LqRuLyQy2dPjg0MZQuJLxdEKWpSTSMKtKhIROktFNc7RUkctExKo+IRbeAHEN6MZKvkSKwJNbko7yrHQUf3rSM22ih4AQcGJZcmIp0DeyrTUZ/uCpkiNpxaVKM3JXFf87TQg/c4ujs3iExGikkownFdujKc9c2OArmyG2wxKwTb0rkU2zDOcDHR24dQFedThlOKpApogw4XeeMuSJoq5qpJTYugIhkUpFmRohFhKvOYWVhB89MkQZuDjdRGCpreVTF08QELzx0GPYoDFSYV1AK8Vfnj/G/s6YC7N5vmff17BekJsEgaDxjgBUtiGRCq1iUTDRKSvJPJeLdTompXINRmm0NCgh8cGjpcS14Fk0FbW3SCFw3pPolL7JIjC3hUHnPUYbrLN004yiqXHeIoXEBb+b9QYCpa0RQtAxGdZ5+mlGohKMSDFGUruaxjV00w6NdVRlYFpMmO/NUTUNWoFVNeOiQEpBnuaoEP00nHUMugMEgdmsQieC4GNW/5d/8Un+7MMf4wuf/yKvfs39fOyjH38H8JE9I5+/eeyB7t8ghBA3ZVn2S2l/4R+lnR4nXv92vu/73sK0f6TlIaP8RwrwQfCDN4xx5YgyWCZNSZ6kSAcyUaQm5fL2GjII5pIuQkqqqqKf9ijrEiklXdOlrmoQAW0UhahJEkPd1CgR+V9PoJdkdJN8VyPrvCfVCQHQUmK9QwmJ84EmSDIVC2nTOvC+U4qfuS1my//hlGJSBw50PJLA2YnkV+4QOJHgGksQikcuznhkqCOYB7+rcw3e08kMbzzkOX7zzVjnqSrLtGh44rkX+NLIxMKXrfFtY8OOUU57cAkB3nGzZCkVCJVycbvk/ac9Ggcyam+9a3DWIbVpJXICoxQersrJArzt2BoLSZdJPWJYTdBKMCpmfGn9DlY6Y24dnCOECIbWeRJtSFXCV9b2c2LhDB2t2CpKaucQQjCrK+aynOB3mjoc/TSncY4kaHQSM2ctFedHGxzsL5KbhNpZjEqwzrbTeklha4qmJlUaJSVNWxwkgAueqmnIkxQlJEGAavl9730sLAJGahpv2SrG7SApMNIwrCYc7C9TNQ0JkrKu8E7QTXso48nTDolJGBdjirqkm+WkKsMYQ2lLJtUMbx29rEPjHYN8jrqpkEJhvaUVJpIkkcYaDSd85CMf488++BEeefJZDP53x+PxvwS+tNcZ9/8de6D7/xJCiK4Q4l1HTtz7O2vnTvHyN7ydu974DpauvxUXaNs7A/esVJxYEmyOtqjxdLKcS1vr6ESzr7tMMaxJuw6BYGM6JNUZQUfBPB6qpiIISSfJkAgMCl87sjRjFCY4AgtZF6kkRVUyrgqSltftmBwhYLW3QONiRtR4h5YxQ1VK4X3U+P7VWcW5ScxQpYAmCH7qNskfPx11r3OpZG3myY2gsY57VzyvWDX8yWkgBDbKsCsJC8HFTNPHabYXkl97zXU0DhrrqWvLY6fO8NUt0CaFEFUHO4OBUCpibgg0TYOUreZWGXA1CIVUCu8csetMIJVCidj4UTc2VvlbTWxjA0fnprxqxZKlAoJiWG9xfrhNZS2PDu8G4C3XP8ukiobnUkpkFGoxqxt8CAyyHICtoqCfJngfuDydoISkn6ZR/ywlW7MpC3mXUVVirQMh6KUZ+/sDzm1v0k8zFvJee6zjdyJabdqsLqNZkHOtFaWA4FFSRTBWOmaxPjBtajKlCQK6aYoQkqppdqVu46povSUkqTak2gBQNDXWO+azPloqTm9ejHy2lOzrziO9Yq6zglIVjbdMxjOC8KQ6ZVJNkUax3F3EeotwKUUzIjcptbcMa8MgrZnLe2ht8N5z8tQpPvxnH+H3/+gDDLoJJ0+98N9aa/9tCOHKN+t6/XaKPdD9ayGEuL3f7/9XTZC/ePj4Xdz95h/llle8DmUSrgre4d23TKlsw6Qq4tTXezyeRTOP9Q4nJ0wnDYv9RbbrEdW0Zi5bwsqSylYorRnVMzKZIkIgzVJkGZjVJVIJ1sptpJYYJJ00Y1TPGKRdrHWkSUJpa3pJh/m8ixIKFzxKysjbtkoI5wPveSonUX/NfgAIAlyIbaa/dLvDKIXD8NiVii9cktQ20NOW6+dTntm00SegtVD015wzQkCD5udv1ejuIhubWxTTGX91hd123B3JqpJyN3OVEuq6AdFmvTvZ8w7/LOMUXbRwNUg925UiBE+eJdSN2/W/fe3+iyybDk1Z0ulJgvZUvuHs5gZr5QIXisMEBK9YeYSqleQZpfDBM6stmTE4H6isRSsVHcSk3rV7TJRkVtU07X4776PmWUk2pjM6icEog5GS0loOzA2YVBXdJCFTCZ7AtK5x3lE7y0p3Dus9RkkujLZx3u8a5YgAvSyPhjlKoEUEeikFzjuMVNTek2qNBKZNzXyWszadAJAqjWkpkNI2bZHWIaTEKBUzbZ2yvzegqWKXoRJd0iTBUzKrCyaTgsW5eUJwKKOR1vGZC5CnHV5zUPCBU4IfvqFEpTlNbdkej9Ai4/mJ4E8feIKvfvxPePrBvyLT8sPD4fB/Bz65l/1ejT3QJcq8hBDvOnz87t/fvHSWe97yY9z7lh/jwP59TG0EgaXMs5g67lka8dhmwj37U4bb2ySdNEqZqoaPXJjn5+5WTMaCj5/zbBWBl81f4VB/mYVEElTGpfWT9NIuF6brBATL+YBTw/NoobHeYW3DfL9PJ0nxBJrG0stSNmcTtFQYpcBDN8mwwdNNchpnyXXKIOsSBBAE732uw64564u6qsQ1PwO/cLunbJoI1kgIjt99OsqxskRSNAEtAO+iMxjRGnHnvBFSYpsak2bcPB+47+CA3390A22SXY2tIGbdSkXAdrZpOdko8I0FoqtgK6REC6itbY3QNcH72O7rYxNAYdu9CJ4jvXVuHyTsW1Q8/MJpgiY2XwBCOE6Ob8VIz9Tm3LH4DGvFEhdni2hRc+vgJLlpP1NKirrBty3T1kdetrSWXGumdbPL0WopWe132JpVZCphVBV0U0NuDL0kwbbFznPDIb0kRQrJpCpBCG5cWmZa14zLEk+gYwzr0ymLnQ7jqsI5Hwfa2YxOmjKtKqSE3ESqxkjFuK7QUiKBorEQoolRxxjKpsFoE2kgoqGQIFA1DZ0kY9ZUpNrQSVKkF4TGkuYp01mJ0ckurdJYiwme+f4+JsWYP79yHUYKQojNMb/wsgbbNHzyrGdaCbatIbVjapkzG494/NMf4at//l4gsHXxzK875357r/j2HQ66QoiVNE1/1XTm/oflw8d4xdveza2vfAOqnaZB7Id3IfJrAO88ug2hYeIaXHAIcv783BILumLos7hmVz3DO4dJMmSSo7Xge1aGDIxgfm6eFy6eRmlFWdRsh4LF7oBL4zWEFG1TgEcjWF1cpiwLJnURMx4hSVXCdjXDSEU3yXZ5vVQZtFLkWvLe0wfQopVSXdvqutslFTuzfv54SePCboYcArzvOc3MQj8N3DFX85kraWx+8B7nLMFd254qkAKUaQ1tEOAjFxoAPCgtYwuzjNSGtVESx06DRJvNBqDfzSiraKBjndulJaQ2CBEbIuYyz6Rq26129gXF66+7DPWE7WpEUAIjA9YLEpOyXs5zergPCGjpefWh03RNYG1SYCQUTYiuawJM22RhffRZmNWWyjpSHTnYVCtSramcZ5Aa1qcFSipsbZEm6oq7SZSmrfa7nNkaMcgjxTKpYvErNwbZ3lvvSLRmYzplPs+Y1g3jqmqlcLH1u7R15HZFiBSUUvTSDOscpY2deyDYN7fAfJKyPRkztjW29UTPTEJRV4QQSI2hcY7caBySqq5JlCaTkl6nQ1VVfHX045OhAAAgAElEQVStx9QmLKWWKmScn+ZoUfOWw5sM7YDPXUyRStLYwLtvHdEUJY/O9vHs5RlV1dDt9fEI6rLCe8+ZJx/ioY/9Mc8/8gW08P/3ZDL530IIz30jr+2/z/EdCbpCiONzc3P/uHLhp2975ffyqrf/LAduPE5AvqjP/tponCAIQSIDbzu6xnQMXx4vMm0igDRVwWg4ptub29WSQsAYxdF+hRAVN3QDMji27YQ5neFs1LrO6oKZsSgVJ9N1U3HD4gqnttboJDm67SJKkFTWYpSOU+sAQsUSh/WOhbyHkZoPnVnZxdhrftnttBICvv/6kkEagUW0vKlzTcziVx1No3huKHnwcuRuY4brYycYrTE4MSMUMpp1l2XRtrBGDwSlZKuYaAt9ziGUAWI32rUJeJqlNI2NA4+U0bTGe7p5QtU45s2Y77lum0+fX2S97LbGL5HuEUKwqDfQYZs1fxNzpuD+Q+cpakvVlHz24ssxKuCC5HDvAj4k3DhYo7SOaW2Zzw1bs5q5zCCEYLWfs936DidasTktCC1/fHCuAyL6J4zLmkQrGhvYKqrorxAg1QrrPau9HB+gbCydxLQZpKVxnnFVkZuExlkWOjmjskJJSdm0VE4IZFozKSsIgk4aKZBJXSGEYKnXY1aWCCGo6gbRzhBW8x46wML8PFvjIQu9PuO6ZFpFAFztDyjqmkznQGCzGCGFpKgrtICDi0s0sxHve+Horo/GzukTbSgFSoTos6FiI8u7btxiMrS87+IKs9E23U5OCB6jFUorqspCCGyvX+KrH3svj3zifSQy/OVwOPwfgU9/p1EP31GgK4R41c2veMPnzz/zCPf9wE9y95t/lIV9hwDQ0tP4axD3msNSe8E7b1zjM2c6vOrQlPVhw5e39lFMR7jGk+UdYslHRIoSUFqjtaKntsjDhFceWqIsJggVp9DOO0pbMWkqilAghWBaV/TyDnVdRc5PCPI0VrN32lLLpiHTCa7lQHc2s5tkLOZ9HtnsMqoNW1UagekaZNvxR2ic4KduGeODJ1EGHxy1d4zKKfN5j37SwXvPHz5jaIKIMifncM6CC1en7ruHKXZT7axLBqB1W7ST0d3LeQ9S453blXvFC1OjjaGum1jcklGH3FQFSimETtAycCAfcvPcGvvnBR949ihTq1/83QIIyUJWsl0Y7lt5mNLGgtWz47tYzYecmy7xytWH+OKVu/juQ0/jvWOll3JmawpCoUWglxoIgYVugnWxCDmrHWuTAiUl1813ODDo8sBzl5jLE1a6KY2Dfm545vKITEuCCKz2O0zKBilgXFkyozBSUjtHUTv6Wcx2a+fZmpWkWrHUzXl+c4RpTde7iaZoLHNpihAwqRoSpRjXTcxufUBrSW0d1jp6nS7LSUbjLUZoktSgvGZ7a8jS8oCt0ZC8m0XlRuPpJRlFcNGOU4J1FhsCnSRja2vIA8M7MMLhfTQy2imk0s584vcraYLk3TetYxFMRlM23RyfOycJIhZ8JYIsTymLAhcETVnwxKc/xIMf+j3y3oALzz72I8AHv1OWK3rJg27rf/DGwWDwv4Skc+8r3/7zvPwNbyfNO7imwqQpAKqtsl/lIOHHbhrzp09rJiHfNURZv3AGpRPybo8gYhf9jtjfIdFKIVvAyWVJpgpunHPcuLTCmbWTUccZ4MJsHaUUg15Oz2gSJXhuaxSnuFqTm5RhMYW21bWylkQbfAjotiKeKUMIIcqeTBIlPUphJHz03FGU8C9KdL9rteSBCx3efsNFbCuJgjj9nNYlUgiWOgOUkGxMR3z03D4E7eKPPsrDAlGetdPYIKXcBfM424+ZtDYm+iGEmPe7VrcshMR5iwiQ5Vns3mqBWCmJD4FqNolLo2tNkhiqumExq7hv5TzPTQ/zzEYas62r3zIQsF7y83e+wKeeX2K7Mtw2eIqLkx5nZ8eQeBCSd9x6Kna4uVgQXZuUVE3M5H3wHJjrYHQsJFrnODTf4fmNKb3UsDEr2T/XYX1ccuNqn41JRT/VbBcNc5nhha0pudFMyobjB+YgwLC09NI4QGgpqGxcQUPKOJiVjWNY1DQ+FvVuWp5jbVqTasnWrGKpm3FhWHLzcp/Hzl9h36BH4wJl08TOPSnxCKZlTS/POdSdYzormVYFS50B47ogyRQyaIJrsIATnsVuDxeio1njA3maYp2NXg5NTYLlj05eR2Ikzl+1oHzxMY8hlaCygp+9bYgUmidfeIbVpRXODHt8+UJAqdYpDsjyhKqMCgznHc996ZN84f2/xXTt3NnJZPKPQwj/LoRgv34I8PcvXrKg24Ltmw7detefF6Mt7n/nf8aJ174FpU0Ug3dy8I7axv1/5cqQx7bnKHxchbaqG6bjEXVZM5iP8pk8TbDWYf3OulvseiaoFnyEACEVSgnSsEktl/i+/etsV2NGdobzDhE8cyZnoymZyxM6iWZaWuY7hkQrLg3HhNiV2XoKaOqm3pUHRT2u2aUYfIDcJEgpMTK2lqZKIAk8Oz7AmUnGifkt9udDrL+6vTLaWiEE5CZlu5jQTXLwULqG+U7Gg5e6nJ+m7ZQ6FvZsqxyIwCFbf9m2EKbkbrYrW27Xh9B62Ua+OjGaylqCi0UykyZxyfS208u1vrcL/Zz1iednTjzNs9urjErL648VBG/57Uev56+fulLAf/mqc/xfXzqI9eB8BIyljuWGhSHPb/e4a/U81y2kjGYNo6ImNZJOoilqRyeRTCrH1qyilxrmOwlSwOa0apcagtW5nPVxRT+JKoK5PMFIgdGSaeU4s1lS1ZaXH+5HBUFl6XdSvItewkrLuPJxe9wDUNWOvGOijtkHxk1czkdKwebYsdKRzKznqctDEqVQxlDVJYudBAhUbau1tY5ebghCMZd2Ga9vcPDAKtuTMaFtC8Y7BJJOJ2c0noBUCBEQeKQUlBU0VRkbMNQyZ2fLTGpFYRO02FnSYodeiJJEset4FFdzfuexNdY21lmeX2I4tXz4dA/hHQuZp8kWqMsqql7sTrETzj7+eT7z3t9kur3B1qWzPx9C+IOXKvi+JEFXCPG6626765Oz0Tav+dFf5rbXfF9bPY8nW9NEU5COkRjh2GoSVKhx1jIZbeOtI0k7qCTBu7h6QZpGsK6r2Joap8jxwhAItJbRNjAEslSBgGW9zu39gnPjTYIS5Klm2jR0nSLJupSioXE1XQ3HDyxxaTSjnxoujws2i4qibtpKeCwqZVrjiOuLaaWZ1BVaKHR78mfaxFULnMX5QKYkX9k6jkDw6tXTUYgvYkYXgK5JKW3NYt5nVM3omAznPWvTEfOdDlemmpPjFQoXp4neNjEztXF7pIpUwM5gI4XcNVA3OprjCNFyga2SwVmHUTJ2YAVBnmdIJbFNg9SGpmnIE02eGiaV4+WrQ1aSy6xXqzy3Pcdbj11Aipr3fO1GjPQvmp1UTnJkUHFkMOGZjTn6ScWVWcabjz4PwZIlOd0ERoWLzQ9Ev4btacliL0UCw8LSeM/hhS5F7VjoGkZFw/asYbGbkhnF2qgkN3Fw0K1xehCC3EieuDilrmruODwglrfYHaCUEhDi+m8A3oGIEuV4zFJFbT3WBaz3XBqW3LTa57kLQ5I0YbVjuDQtmBaOnkmYERhPphxY7NJJJGujilHlSISl25mj9jX7uitU9YRJVZKaBKMVs2KG0JqmsfhWcz0qK1KlObwyIAS4sLaJDYEbDh6hKTf54KmjHF9pePRyh27qaZzYVbOIa2kHIs9/w1zFLekGJjW872SX2XhMohOcUIy21lhcWkQnHZqmISBjsTY4Tj3yIA9+4LcYb66xdensu0MIf/RSox1eUqArhLj75ntf+9CVM6d47U/8A+56/Q/S73cAgZaB0bRkOp1h64pyOiXRhk6/j5N690KQsa0M0U7/lPAIZWI1HYFWAhdaJywRbfmCj6sECClJGWL1Io6EmzqnOZQ2DF0BMhbNkkbQWEu/M8elesQt+/tcN8ho6jjVE0IwKxu01iRGc3lccHZrFDm8lkR1tKvl1nVsNfVXv8NMJ2TasF3BxB3iUrnA9x54Bh8iJbEz/e8lGUpGjti2donOBwrbRI7Xela6c2wWMx4bHkfJ6F1rGxcvlBA1DCKE3YJTINIQUghCsLuqBCEUnig12jUa9w7nA0micTZmzkYbOr0OdR31wI0T/PiJF1juCP71l6/jUL/gB27ZItGCiyNNoj1/8sQ+Cqv49fufZ1wG8lTz/NqYfXM5o6JGK8Wstuyfy5nVlrlMYwM8e3nEUjdh1jgSJSkbRzfV5IlhXNQsdDOSVrIyLupYFNwpKIVo9LPYzYm1wpjBBweNhLPbBTcOMhw+rock2nNEi1hYch5vHUYoRMt5IwTBgRABtKT2jscvDbn70DxPXilo6pquUQwLhzEpCTBsCjJtOLawwrQsuDgacmA5Z1Y3bA1ndHs9qqJGi8DiQg9nLWV9tVhZNIGisfQy0zaMwCA3GCWpGsfGtMR6yZHFRc5evECSdxHBszzo8hcnu7zpxk0++NyN0CpTdjTsO1x/8IH7D5Yc6zt+81GDdBWjjU3yuQFJmrbyQUc1HSMVDBYWSdKMsqw5/egX+dQf/AbeWS6eeuptxFbjlwRYvSRAVwhxZG5u7l94lf7IG3/yF7n5tT8UtaRCAoGqrKhmU3qdLkm3T+NgZ12va5d1CSEWTnZsr0NrkSeVRopoHehCLJaB2K3gSxlbW7VWONugkhQjGt5+g+fy+jl8DtiK7cqRBkVXpsx153HpNgc6BlzbGhsEPjikjpIoT8AJydpowsx6plVD5XzrUhWF9iBo2qxtudvHh0A3idaMn117OfevPEKqYwarhKDxAdP2nm7OCvIkIdcaozVXxmMSHfnH4WzIQud6Htnej9spMIoIEHVVtXIuHwslQhJCpAqAXRpEtFeg0VGf61tbRh/iyg9Ka+q6oSxiFV4bRd7pErxFJwmLScHLV7f46KmDKAX3H7jEQma5flnirOf9T69yYZzys3deJJFV5IuDYlY17JvTbEyjy5lWgjzRKBn9hn3wu6vyhgBXJiWZlgzylMxEmqCXxhWREyXZmFQMcsOstnSzlmKyYZdWCCE6jhFih5syElc1KKnagWaHeokzIL/Dp4uANgZbubj2mwoIL9isKuZzQ6eT4ZBcGRa8sDllJe2hgO2tNbJuTkdLzg2nrCwskUtNcJ610RCfGxpXYEzOnNFIKZi19E03MSgZ0ErjgPGsonCebhKpimkVW9EXOobUaKa1YzSJXZPVbMrq0hyTwtFNAp+/sJ+7D1zkU+dvx0jfDiDsrkm3MwvxPvBzt5f81lclWZJw5ewLiDTWSbQ2LCwMKMqSuqqpypK6mNAbzJN3O5x88BN84g/+NdVw/aHRaPTLIYSvfKPx5Bsd39agK4TodTqd/94J9Y9e/oYf5vY3/AjapJHb1IYkyxFqZ5orYHdlAa5mai0BuePqtCNpFUJGUbkUrb+CABHBQ7Rer6EFYN22tQopsV5woHOFDiNetrjAdDpkrRgy14s84nLeZ9LUSKO4c38XpSW2BqQHKQGPaD1XpVZcGJUY4Rl0Mh6/uIUgAu6sbna3e4cDXez0oh60LpjPM/pZl7XxmNwkZFqzUcxItaZsGipbo6UGX1M1TazaC4UAHhvdy75eyZWxJoi4dLh3fpdKIHiKWdGCMEQBfsxgxdXvpqVf4oKR3rl29YSYHYvWf8E5T1NVgCBJE0yikMqglcCHHeFdtPD55XvO8txml42ZoLKKpzb6/PqrL1J7wawWrI+mrA1nzPdytFIs9Dpc3hxSNZbbrpvn8rAkS6JKoXaBPFGUjWc4q1nsJqyPKw4t5EB0NqudR+8WvDy9VEX9bICqaEgTE0FVBGhnP0JeXZki7C5nBAJ5dTASEk8cMKUS7fGNBUcRPNYGkJBmiicvjZhPu8zKmsIGEqVY6fZY29pEhQqpEmZpl7oqWNEpaLgyHpIlcd24/V1FLQ2T2jIuG7LU4JzlloUuAsG4adAStmvHdtEgiPu8f75P2dQs9lLObRWsbU3pdrv0lOHicMh8L8FIw8WtEc+U9/Gmw8/wifM3RcpEsttdGF3oIqd/sO85MzaUow1s1eClarn+6DExWBgwnUwQxFb0OLsIlNMpj33qT/nyh38fg/vj0Wj0X4cQzn+DYOUbHt+WoCuEEEqpn87nFt5z5MR9fM9P/ir53OJubTUu77JjHRgrxYhWkN/Km0TLbUohWzOT6MwVWs1XdJPacdG6KsYPIVIKkcK66gtwtYLvuWfwCE+PruPW7ibTakxvME9VWWxR0esZeqnm5n3zCCReKHAWRIgnq4hSKqGj3jUIiXOWF7YLJkVN0xbxQgjM6hqtNc45AoLS1gzSHBc8vaTtlGuLXIlWbE9mDDo5mdFcHl7m0fEbkEJyqHOGK+V+rBcY6XjtdRMubo94ZnwdWkYwCCH6tSJomyTaRSatu1pAhN3fd7i9mAmL9njT0jg7TgSRJnHOtWufCZQxLC7OxWy+cbvHXRKovSLTnkHWMMgC91+3SaYsk8oymtVUFla7GiskZRNobENqDHmWMRpPmO93KWuH0VHXnCgwSmNUbMYQwLi0LPUSXEsZFZVlLk/a7QxtESzgrI88fthZwSIWx+TuckDianez/3+oe9MgW7ezvu+3hnfacw+n+8znzlfDlYSEkDVEYjA2pIwthkQ2doEJMR+cimODig/YSRnjomLKQJVjkjIOFJCYwnZwcIVgMI4NsUCOQOO9ku547j33jD137+kd15APa+3dBzsVJ2VN7Kpz7xm6d3e/+93Pep7/8x/iNBBuTpRWCC8xtkMIj4sHNvEw8h2oJEwPIgkc2qrzzM8Ap9jZmrB/+xYizbl2+QJprjk8PaNzgn6msc4wayraziATxe4gpfbw0oMZeZKT0HB5c8BAKZRKouQYSODV4yVd1/HEpRHeC6ZlzUY/46xsuXsWou0fHJ/wrsd22S8tVQuyq/j42dt43+6zfPLoUSo7XPN41wIWD4kKkVOz2ZSjvX3Gm9thyRrv0dU1L4qUqgpWpnmq6WyAxKrFnE/++i/xmX/1K2Dav17X9Y/9YXQ3+0NXdIUQz1x+6m3PdXXJ1/65v8LFx98MQkRYILydbdyUr7brftUriSBz9ZzHca+KAg+9WYCY5nBOkpFi1akElRoEbA0fM7qUAm9JpeFy9iq+eIKt9kWEbRBJwqxdhRJ63vfILp3xpEmCdR2OUPSlkkgdPAcaG7T5SsK8dRzNa+ZNR9WadQdRdi1CKAaR7tNaRz9NGGQpZduhlKJqW4o0Y5Jr9mZLEt9xq/tqnhy+RN3M+MTpu4PUVhiUVoyTivdeOqbsalpj+fTJY5QmWb8xnHP084S6cywXi7BUg1BMYpEVeITU6+hwnWiUknRdCKgMrItVBxgOSOss3gu0khS9HOcsOs3WC0vv4YntlqqFP/b4KcKb4N/QOca9hMNlh5aCo7lBSciUR0m4czhne9Jjd5Tz+uGSsjJYOsb9nIO55V2PTWg6ixKEyPPVQlAIlAjQhHFgumCwjvd0JuDRSggkCumD85qK048jGNyI2MmreLBrlZz/m1Kxww+juNIqqB9XRjgepA4Hf2tbhJAcn1V4IWmrhH5WhObBVOxsDOg6hygyFvUysCQSRZpYqrYjVY48KXh9bnDWIWzF1c0+Cgdd9LlwnjRLEYlnb9myfzbjmStjEq2ZNx3P782RAjKdMFsuuLjRRxEy4w5mHQr4vaM3M+kZTsqUQtV0TkV+L1jT4VeJH0JxeviA6f4+va2LQISqfBDeaKVxzjIe9Tk8PGY4HIEzSJXQmY6zg/t85Jd+itO925zt3/0m7/1vfilqzxfq8Yem6Aohin6//6NOJt//NX/qe3jjB74FHRMLVp3oCl9cdRnrETfitysLxNXP7L0nSXSwBvSe1ZInLMxk2KquvPdikZZiFfXNeYdL8LH1zvG2yefZKwdcG1S8ZXuEk46Fd7yyP+PGpODetOMdF8c4BHXXhE4ri65bEozt0IlmHcaYal7amzOtw9JsWTdxSxy6xarrSJN0vSXf6RfM6lDITSxwzjmGvQH7Jx07446qE0ztLotuwqwrSKSjc5LHR2dspBW1S8lVzWFdcGu2uaaACSEwXRdl0p66LEOaQ1S1iXjIiVUhideHOAEIpdZ0M+8DtrmaPMLvWcMXSid411EUBUKnUW1V866rJ1wZe2aVJdcCLxSNsTSt4e5JhcaxuzXCWMd02aJlwrLtGKSOxy+N2TspKVJJmmhuHy45mje868ltEhFCNgPDgPg9B88IKQXYhs4HlogzrAtqohTCKIwxoShHiXOwXnZBNh3ZLgFaUFgT6H+m7ZBaolMNQtJFO0+BiFQssFj6wwm44N5W9FLqecfZckrtBvjasDlUbI3HnJ4u6aSn9iG2yLkWQYewjl4/w1ofrkWvwLY1wjmSTIdJj3BwSC0wieTuwSnXN/oIFZSFTsDvvXZCohS7A83GIEcA08by2mFJ1zbcOeuzUE+SJIrWhmtmupZVJpxY2XSamv5wxM0XX2Lzwi5dF3LgvI+KRxec23zIeWa8MeH0+IQkzQiW/SFz79ZnPspH/tF/D83iV2ez2fd57/e/+JXoP/zxh6LoCiG+Ybxz+V9euPE0H/jO/4r+eIKxQS+/IuvHj1t3vSsMV3gXx3FY42uCdecm5Kowhw2sWPNtV8/h188t4/MnWiFl2NaH2zVgc89s3WSSazbskvGgH3iWXced44pCSfJEMUw1bdex1c+DUbZUSC3xwuBF7MilCHCH1BwvKu6clgRuv6Q2nkQrZmWJw4cuynt6iaY2JhQw7+lMiHspm5px0WNjfJG9k3vsDHJOlhUO2G+fZtamdC7FOIEWQRU2Smpqm9B0DifOlV/OGFSEM6r5Gch0HYgIqzj4uIGXkkTr9cJSCbARXnAuwBLOObRWcUR3EcaIXytaUoatuqLopeiswDr48PvuMatDjLsQgmXVkSaSk2kNTGjrBmtgUS+YtQvSVPDktU0Ukl4exCMIya3DmhvbOc++fsJTlyeoCCftLwtujJYIodDC8ZG7E1qf8t6rZ6TKYTqH8AJnIhfZhPvp9w/HvF4WdFbw558+w5gOrXLaeolMBInOkEJh43Usy3mMkl9dQ09TNgiVBIWYMTigP8gwsqNcluT9DV793C2uXNtme2Obuq65c1CSZQNSYZHekmQpFknb1vgkRaUwyhIwHUoLXGdJ8jC9mC7ksyVpQtu0pHkKytFKWCyWDHWG1OE1lYlk1lo++/ox73x0B60Frx7X3D+ZolWCMYbnTp8hSRRapzzZf5EXTi/TmdgUxcVqqgVNtcSrlKP7D+hPNoOQJsYfOWfjfRCTlZ2LXXJCZ/xataiUoqlrPv6rv8Dzv/vrdNXiPzfG/NxXOsvhK7roCiGGw+Hwp3xSfPcH/uxf4cZb3x3+PvxjMKFZd7MyJCVE3qzw58Vgvd3xD/3Zc15c5ao7Dh8YniN2vaHOsgpM1DoslqIuIYoM4E2bt0lZ8Oiwx+aox9wpZsdH7LcCZQ1FmrCVBknx7sYQ4T1OSrQO9KoVf1YnCkQc5eMJcfe04u7JErlWpikSFbrKzloSpWmtxXlB3XZcnvQ5W1Y4B+94bIdP3z6lNS1ta0hTzShXdC5jr9zAI1g2jtNuB7G6LmvN/UP3hveYtqZtu7DoeWgJGXDz6LMgRCikROwbgqjCBjK9d47OWHAujNKxw9JKYYyNBjiSLNPUTbt+wZIkIdGCtDdkkrd82xsOWNSWZeMYJB6hNIfHgoO9B1y8UnBwXPL0jW3uVNs0y1vsjDKEEAxzzWY/o7Oem/sVUjgGRcHNe3u8+8lLvHrW41/c2gbv+Y+uz3h0UvJPXthlK5/yLW9YksSllzUEOpgD4y2/8MIVvBe0Ltw0f+ktB0g0OBkYCqYNH6srEpOj04K2XuKERdgAyzjCfdBIS48+tm3wEkRuKUYbLE7nLGrP7N4DnnjDm8EGafLh4RlShcNxaSRZmlKWJVujAq2hax3LpqZIg2WldQ7nWtIkw3uLF6BzBRLaLi5YhUUkks50IVFCSRAeJ4Ih/q3TmksDRZYIhr2Ez9yZMl12zKZLbrmvplcUdMbirIvxP6GAZnlOkQrKxtKUC1757LNcf+rN2DXcBF3XYa1FqTAVCEIDZa1hOCiYLUqyNA/TRWwCju/e5Ld+4W/TnO59dDab/Rnv/Z3/sOrzxXvIf/+HfHkeQogPjLYvza689X3f/af/+s9w463vDm9iZ/DW4J0Jixy7itIOY6G3PshWH+punfVrAjqRyL0y83h49NPyfEQO1q9ivW0WsStW8rwbgyB3feP4eYSreGbnAsbDi3tHHN2/x9RAVlc8vjVC2I5TI1nMltw/LcPCRILwDmvCLxUZAFIEsUWiJW3n2R5kvPvxbXaGKW+5usmVSZ9xr49xnid2Rmz0UsZFyLYqEkmRSkaDMRcnfT7+6hHT+SJYQqoenzl9J3MzYN449hZ97i8vcGZ2opOYpakWmLYO3NFV12kMVVnS2XBdV/+gJSQq+EwEGlikzTkXhRJR2gtre0Hn3HpCUVrjvQ1Ciq7BOUOWapSCqm7WSylBCKb0TiBtQ08t8d4zqzqEtzx764DThaWrGt7/nkdQScHXv/MxDpoxv3dvyN3qSZa1B1JOS8uiCdOPcjWjnqLQLe9+4hKZ1jx/lKGFQ7iG//P1Mb/+bBtGcQsv75UcLgxJkoaForNYHIXWOB+w58fHHX/tHTOkz/FWUBQDtE7JiiFCawa9XZxQeOXxmSHvDZE6ARxKapTUpDZcK+vDgjXrDXjuuWOOziqUmXHpyhZ1fUhl56FTNS3tcs5yPqU8fsDi8C6+bXDOc29vCjon0TmQooWgl6YM+2N6WYGzgl4+RJsUbVIwAq00SivSNKWxFoNZL40Tpci159HNlL15h9Ipn797xlO7o5NIbj0AACAASURBVJB6MiwwnQ1eyZE69u1Pv4bOcpRWDHMBZkHbGaqqQqc5dHUMxgwHscCjlQwwkwwF3+HQSUrZWJRK2N7ZwkbrT2c6tq4+zn/yV/8H3vD1/+l7i+Hkdpqmf16sc6q+sh5fcZ2uECLt9/s/5nX+V77uu36AR9767nUAo5cBLvArOkqECoLhtV9DACGMMXRanbGxiEXJ4kM474q2pLVeiwbWAOaK2h+NXBDneKNUoZPrFxlPDz4FLuHtOxOcANuFEW5/XmOqjsS23HjyGi/dOcEuGx67uMXJ4pTtjREvH53wpssXEInE4/ACEh2pV/HrOQ/705a67dge5RzMKuaNYZJrLm8MQjxO16EEdBZOKsODacl8UTIoCrQyPH/2NEszQEnHleImD8rrOJGACCNd29SBX5xkWGNRSpCmGlQGeOrFDIcEa0I4oxexGxYR9wxvDL2WJq+oUSJkcdl4j3mQWlGXy7AUaTt6RYaxjuV8RpKma2bGsN9jNBlxIT/lpO6DCF3yrFF84NrrvGFXoYXgtLQcnszZmRS8/PoeH7s7It1+hmu9Pbb0HqZ4gk8/KHjrhSk3envszwz9ImXcT2hbyyM7A5y3WK/wxvErL26xP/MopdcH8vlZrbiiPsWlXsYbHr0K9oh+mtK2HfNO8csvXqGznu95xjBRBiEVeZ4giyGmbSjnB+TZBCUF1nRUsynGeLKxZL5cMhlO6GyDWTr0UNC1FqVBqYTlacPGzgaz5SmHpyXTqabfzxgPJPOF4PqNCb6C4+MpaT8jt46uDTaZVbVk5/JV6nJJ03nAokWAb7SSDIcDmuhClqQJTdcEX4YCmnKOVsFTBLV6/wgaL3jlYEYiFbO65sZmwmsnwajoY/ceDUo9KUmSBJ/0+Y4nb3I0X5KlPX773uPUVcnB3dv0s4S0P6ZzHrNKdo588CAjXzVR4R5KtEIpTdd19Hs5i8USnaR4b+naoGo8eP1l/tXP/S262eFvzGazP+e9P/lS1rB/3+MrqugKIR6/cOPpV3qjDf7o93yYrDcKN3+0rbPWhhs2ckPxkCRqje2Fxj0qywjbaLU2XQkPGV34hQg3c/hc+APdbyy2oYiDF+F5QjEJSbleKN4y+iz9PGOrSNhQKWjNaJhTzpYUI43tgsPUg1nJ5Txna7LBfDHDKk+iCAsU50B6VDTS9v58mePxLGpDqoOHgLGWxjiGRcp02TIuEgSeadXhRYrzlkUdCuMre0eMegWfPHkHiXLrDl/I4PKE93RdG69lOMBUlOdqrUizDIGnbSqESrHWYTuLF+78EgkRfGi1ilABUSQSCPlKK2zXhb2UC/SwyJenbWq8swipw7ZdhNidIk/oD0PUTS91vPfSHZrG0NOeNB5Oy1ZwdtZS9Po43/Ev7z+BMw1aKRIteMvFhhdPBvzpp19laVJ+89YVqg6Et7z34j3atmJ7KFh0CaPEMqs6slRza77F84e90H3FH9KjVrux+DN7jIGvHn2c5dzyyKXryFRwcdDx9z/7CKnokFLw2IbizzxlaXURcGkHi2oPbzt66Ta2DUWuaxuatkZnCekoqBGzrMeiCkvEg8MTjvanjDd6ZPmQwTDjbAa27ej1NK/ffJ3y7Ix3vv1NoYjKgrZtUYnl8GDGaBSgrGYVLYSgX2RkvR6z2ZzJKCfLezRti9aSPMuxXYdLHB3BIKctFxS9AoHHuA6dCqwX3Ju13D2a8sjWAO8a/teXn2ZTH3C47CF1OLCd91zZzHnT8GXmXcPTuzn/y2cvsyg7Tg4P2RyPcSqNh7lfJ10HE8nzx6rBWnXhK7YD3iOFC/zpSAtd5en97i//NK99+ndYnBx8rff+X3/hKtV/2OMrpuhqrT+UFIN/9M5v+S6e+fpvDby9SENayUlXPEatgukLMekAIVBKI0XkW0YO7aprlSIk4xLVQVLpFTBMdNFb08bON/XhuZXWcRMdipKNhi1vn3yOSaoDibwy+KYjTRUyTejKjs4bBsOMxsBIFoGRJh1Ih8oU1pmwNZYi2j2CTgLlyDhHlgRZbmvCid+2FisEeaICJOE896cth4uaaVmz2cvItOb+6YzdyYi6qzGMeO74Bp7Q5TflHKmTIEhog8ep9XYNI4gVq907kjRFSkWSKJq2wxqHc2ZNeofgvaCkip/j14q0NEuD81p8PZwLck8fhRwrSlZdLnCRPI9QUQYNW1sTOutQScY3XX6JIoPL4zEOx9msYjLsYekQSvLSvVN+47Ub5HkRFzWCi/5z7PMGGqv4vnc+4NU7x9y4do2f/eQWgwxGacebshe5vjvkzuGM0sIzj2xju5Z/8uIWtRsiBLxhu+FGbx/rPB+5f4VF7dFJ+BmlVLz/wktsjYbU1SH7h5bNQcqvH74xevfCn3jE8PhYkhcZqhjQnZ3QNjNq5gwYg9I4J2ibCic60pGgNpIXXjhEJQaBopf3SXsjvEvZe7CPbeb0c8fVixuYyjLZuYDxLdIWIXFZKRazKWmSopKMqjUILK5rSLQiSUKacrUscd4z6OWk/QnzsyNIBcoLjPe0rkKn4WPPlqd4I9DS0Rv00Am0VnL7eEFnPLWFfp7xa8/vkOgg8EAqVs5zznsG/Zwx9zjsLmNthzMt+/fus7F1IaZsBB53YFKA8+dsljUXOjYGWoUltrV+TQfdmgw5Pp2jVXgOExuz28/9X/yrX/hx6OofKcvyb3wl+Dh82YuuECIZDAZ/R+bDv/jNf/FvsHn1McKSRvH/BMkotZJWrk6/c3cvF7vW9cZcqjUbIfK9WHV14WuzppGptUTTxaSD0HlKHEIG7BEkRZ7x+NaSfPE5dnd2GdYNebGBUC14F4yyH3kXe3d+j6m33N2bcn2Us7Ux4KBsmZ0dcn1nmzRPkBKEjjCC80GdJCVNNJQpUkXZGIpUIwR01sfQSU9tLHtnFdbByaJmd9Jntiwxvsdhs8Hzx1souepKPdViiiMqn6J81688eR+6B1aihaIosPF6g6Bru9CFRKaBkCpCONH0Rgp0EtIr0ixE9XgbOMVCho1913VBKCAlZVmGtAnn0VqHA8AF2h7SMxmPUFmPDz76CqNBxiDrc/P1O/T6AzaLnHk54+dffBwtApsjdEiePMv4M0+/ys984iJpIqjqlndcczw9PuSRCxm/c3fEXtnjXVt7dE3F7mbKb7w8wgnF7sDw2aMN3rF7wqVhxXzZciFTzJclmxtjKu84WKSkdKiqpDfokSrHcWl49GIfaNjbP+Vz1dPcmmW0TvA1lzTfeLWl6yxahk28aWqEDIvS1np0IrEYjKnRA8Vnfv81vuprruN8xv6RJFEZZ9M5s+N9ekryyGMj+v0NQIXE4bZBaol1mvlswWg0YDYr8c5QLpekWULe66OFpK1LxpMR9XJOlveRWnNwcMLWbkLdtZydVXSuIdFFsNU0NbVvkTphc5BRqJZ//MJFPvS2Jbf2T2m6YDR0MquQMuMTh1dJtcZ6SNS5KZRznqLIePvWTX733nW0lty+eZPReANjDA/fgd679fvy36oV679Pk4Smbdfdrsdz5fIu9+/v0+sHtV1ZheDO5ekhv/n3f4Tlwe3fns1m3/Hlhhu+rEVXCLFz+am37idZwR//vv8anfdCNxuXMJFdGyCAyMEV68J5/qIkWp0LIoSg69o1ZmutWzvcq/jiWxuoVR7iSB/e+CJ2zV6EQhPwYhWLuiTNNEKlvHv4SWoheWp7g7Ea0tgOfIvHY4yiVwgWroPWsbGxya2TQ+qyxpolb3nsUYxtwzaYqMuXrNU7xrq1gU2AFTxlaxnlscNSkhfuT2mNJUsKjmczHt8ZUxsJfs6vvfYmcJ62qeNFBmdCces6s54YVl3h+vVfzf2eeNNHVTKQ5T2kFHGZ5fHextcn5G+tYBmdpgz6BXbNwQ3Xr62WWB/USHiPcxbTGZxzZFlK0zSYuEhMU411MB71+Y63nNJPLHmmaFtJ6kN68nTZ8A9ffRyFYTTImc+XdMaSphlKKb7nq0/46Y9NkN7irEErQecTrk0st6dBYvzBx26Ty45Xzgo+f7rLIPO87+JdtkeSptMkCJQHpZNwmCcJXVWzrFqqyjAaD+m6jrwoaMspjc2YVvs8deMie9OU/+3mGKkTrvdavulyQ5pEVk2Yf4MXhhI4mWBFiRfgpce6nM6kHB2eUTWGycaIuzdfJ1Utj17dRWjY3NgN96/ropoS/EqIkuY4J5CE2HpdjAiJxwE3HxYVpgm5aAiB6QzWdPRGI6xradoG7x0yTSnrGWk+4MHJfa5dnDAZpyhv+G8/co2NnuOPP/KAj97b4qsu7LN3MuWTJ28M7xnAOBj2MqbzJWkaDmGlNVpp3jr8NEfLlpvVW7n/6k0GG9vh3jc2CmpEhA+iCX68Z1esGkGAtaRS2K4BqehiSnKiBE0wV6EoeiAEbdNguo5/88t/j9c+87vMDh+8zXv/7Be+ov1/e3zZ2AtCiK8abu3uX3nqbXzr9/8YOgvx11LpYIayvsA+mmWrSFQPna6SYl1Ijemi85XD2y7ACPHFSxIdNPKE3CvrHFKGFyx0a2EBhAcvE4QKvrThe1BrLX3AOXv8x9ef5+1vvEQ/z5ELS9O0YXR2cDqt2JwU1EDawGK+5M7efZwagFW88dpVmrqO5PtwSERiK84HXEpJQZZIqhjrAp7TsuVg3rBsHZ++fULZdHRecjKd8djOmFltuDXr8c9efRPWGNqmQqdpuCmtx9iA365w6YeQl/CI+4v1/yP263345byLNJ7AJRacTwhhBAz+CkqGFAPXlvH1CQVGSBWlwKErblsThROsFzhFkTOeDJFaUeQZIusxTIPvqrGOZV1TtzVFmtDLQ7LF7iRnWRk6K8KiR4SvuSwrCuZkaUKSJDgv6GeSR0dHCNehhOcj9y5wsjDcmHic0BxXmpdnF1hWgmUZDtCiSHGdRUpBJsP3kSUZ3ja0XU0XfQvKRrGVS0wryZIBNy408Z4V3Fqm9HKNSApM5KF2gdsYKqVtEZ1GIYP5T5Nw++4xe8dHJGlGmk/Ihptsbu6gMksxUAjhwHXRJD7cP6EhUYAkyQf4pEdDjwcnJTdfv8frr9/h7t4Jn39tQWUFqjei6A/I85ReL2c0GrNYLpGExqWtFgyHG7RtyTDPyAtN3Xqe3e+z0+9YdBpjE16f9vi1Vx/h3U9u8nVXb9HaENFjTMu8auj1CiQO04X4o0QLXiif4drmgCvJ81x57HGWZ8cBOpBqbQMaBE8BVjg30ffryWxtwi/1espRUtLa8O/WWOpqSVM3KK3Iipz3f+df4t3f9hfIB+PPKKW+9YtY3v5fH1+WTlcp9cG0N/yn3/Dd38+Nt76XXr9P15kwqnoP3gYLujiahu2pxnsXtuxSBqvFyJ213keTaR/hBLnu4GTEa1edsF/1zyu2w1oMIUMIYux8V+GJUqmwHEo0WZ7xvV91QJpYbr5SIuqay9ubLNsGn3pG/SGvP3jAoNPYsWaqJCcHJW+7OsYay2hU4FyHzhTBIjd6I0TGwnFpqFvL/qzk0e0BeRIEFQezhs1BxidvHyOFQiBpqgqpBFujnE8fXue0GWKqGVKH6BvvQw6ZjV68K0zF+2AUrkRIDIgQLi4OeOtaHA+tcCYE7FvrIHdWAqyDJE3WqI1UkiJPqaoKpTRCKpqmRWm1diHrOovt2jW8kGVpuMZSkiSauu2iOslz6eIW77g85ZmtBQfTiq3hACULltMZHYJ/8LkxSdaL32h4TVerl8kg43TpSKSlSCV/7k13mc0X/E/PXiRJM5wLxfnpS56n89v83tEVGp9RdvC+azMuZSVaSKrGopxhe3OD04VhXp2QIOn1FVmeg/PM5h3zBeSJZzLZxpqSl/cP+VT9TEinwPP4BP7UGxNmpyfUdUWvpyhkjsPhVIdvFD5ymY2ZYyUgFHfug9QFeZYhpeDB3dd4zzufIiWwWiQRUtMKIRJ64w0+/uxNNja3wLZkWcZ4mNOUFb1Bj0899xJSaXr9gt4gxTlDUy15cneL5fSYyrZolaFSML5D4Ll5vOCR3THO10xGWSjwpuPvfvwq77+x5HduD9fxTRd7Fe+5esa9kzM+efxk4C23HUWvx+zsjP5giFQa71q+4ZH7HJ2VfOLoKU6PHmCNR+lk7R29ZhMh1rfjSr4fsGmNJ5gkmc7Q1BVJEpayQV3nUcpTVU2g+CEYT0Y0Tcftz3+KX/upv4atF/9NVVU/+qUWU3zJO92iKP5SMdz4px/8/h/jia/5BnqDAayKHUGF03SWRCdonZDp6DRULXHWrrsFF5drJkoYbcAKWFkcyfgLETatPgS2RCwzsBNCLQrXOxRccV5w4aH/h476zRt7zBYLbu/PEF7hrGFeL3HGYFvD/YM9xj5lZ/sC46JgeXLAxZFif2np9/q0bYPUgQpkrcWv/BaiZFYJmFZ1GN+BZWu5d1bhgVcOZuRJiPJW3jDoJySp5qxJ6VxG9+8U3NDVr+Ss50bjATpZ0bhWIo+wfIzbitViTBCupYgjLFFR5oMPbnh4dBImkLqqkFLRdYamaULQphA4Z/C2xftAeBcxC81ag8fTdh1FryBJgqy738sx1lO2gk8/6LE7Lhj1erh2gSmn/PyzE5Ksh/PBMEhqjVQpw1wxKcJo+seemJEpT9k4nj9QXNvpkWZ5NN4O65cXDxK06ri3zKlskOLenvdxWLxXOD+n1x/g8Nx5/fNcv5wxmfTYHI/p2gq8oygcWtU4oEgtpjE8vX2Bb3/zEVYojNd87kRjmwOWpsUbqEtY2g6jGkSnUNqRJQmD/oBebxOsRAjP5kTjTBtVk4K0GHHzzjFG5WEZnIRD1BqDzAYsa5hMxpydTRn0B7Rty70HR0jlOTs54ZEr26Syw7UVTV2ipKFuO17YO+ak9QhdIFRK6zzGSjpjubg5QUjJbNGxsm02UvO9b9/jqEz4L95xm29/Kqhv98qCX/r8VZ7Yyfmq8XM0RqCTFJwlyXLSRIXDwi54dXaRpmt52+Q5hqMxy9kZSsnIVZfAqnGK+5v4X+ccxlqSJCREl1UVlttZCDbVKnyekhLvJGmSrhWr07MZUngeectX82d/5Gfpb1/5m4PB4GeEECtD4C/J40vW6QohxGAw+NvJcPPD3/bhn2Dz4tVYZGPgISuJqImwQRj/VyYYwVZQsfKSXnW4krD1VGtF1MqmUa0XZUFDG3mlQkZrwcABXOncVZC3rZVVqxC+NA0b4URLvvPpW6RpynElkCcl0iuMb7iws4N3jsPDIzZ3J1iRcKEY8uL0AdIqLg4SrDekmcR7icGSakllPakWzFvP2bJFiRCIKIRgf1YxyDQnZcfJvOT61pDWJTTVjCRLqVrDME/4yN0bzJYWjwyYq3dr31rvoqpnZV5OcETDe7wNUejB9lJirCFRKjJDfJweYrEOLyBZlmJd5OXGqSTRKkA13mEJRTwQ3MP1NKYNhjpRcWasDYenCfEwSaIp8hznLWXVUuQZ0+kxw+GEPM/QacJG4dg76fAqw3uHVoIndzQfuHwEviMV0DVRJuodaSIxDv7uJ3bIVDie606AaQMn1zryLMO6EHEz7muqTqBlMJxJu2M2hhmvzzKELvj6rRf5N2ePU9mE7/8jJyjpSESBs2HMVdrhvOLOg0N2t69wenQUnMSEZdopNrcVs9sl2+Mh1lp0nmCcQUvFeKOPJRxIaToKgaVLi2HJvb2OZaOYnh4HsUJecPHiVcqyousaepliY1zQ2IyiCMnDSoUJQ0ZxQZIkzGdTEq3opdH/AU8xGHBwcEhdz/BYEt8xGA4x1iKFgcxhXYe0hPdBAakOGWfWKtrGgHCcVYqff+4KvcQ+BFkJPvjEK8wbx7945RpFvxcCPuuOJM1409aMtH0VgDdd7vGrL+9y68ESU5YYmawNeMI+PHgt6AilSBXzB/Ocot+jLMtg4iOhbdqwN4gc/jU9FEGSKIwJB27YAQWPh1/58Q9zevvF/2M2m/0p7331RS+EfIk6XSGEHAwGPzPYvf7h7/nRn+P6k09TV2VQOXVtNIUOxTZcKIWPcAGsiAcqYjmBKmIccYyAVY+2iqEJ6WAOcJESFkbygCuGAhuzJIHz9IdVt706iFaYpbGWNM94ZbnFZDjCny2YjMfsXN5l59I2KoPXm2Me372Kd9CYJa/du8NESEYyFLUkCfiq8yZISYGyC+qoRdWicFyc5GgluH9WMm8s1ksWi4rWSD772j7e1chE45zn2uaA21PNogp9fcC0g+Wg6Vpc7CLd6vDQScTEo4OWikbpzgWLQQBvsS64jRVpoIIlaYKMAZHOizVlChEKplIS2zUhwy3aNIq4CEmTMKUY66jqhq4LbwrvIU00RZEzGPRJUoVHsL0xCDLl4TgsOKOn8TAXvP2y4T1XDdc2Nd/7TsM3X5/RSzQD26OQEzYGF9jbW+CblHLqSLqcjdTEe0OSakizhEzrSGfzJElKlmZUteXJ4RmPT2qc7aiTbU79Nv1eQb9IkBeeoZ8GnjA4RvkOwvfQWtPLBqS6T6IUFyYjltWUvNej3y+oSsG1YYqYaq5e3GJZd2xfe5y2thgDZdVyvH9CmuZk6Zi9/ZJ7ex0PjhoOjxTHx0ccHd+nNzTkfcWon3FyvEfT1vT6AwbDDRrfozWW6WxOoiRZqtHCrZeIVVVR9PooJfEqpUPSesGdu/voNMNLzcXLV1GDTRociGB6o4EizalMw6CvuX9cMS/ruHS2qDQc7KVVDLKHCy4YJ0i15vRszm7vLJgPVY63XzzgmeHn6PvXGOaaZV3xu587YCNrybKU2fQ0uL1Fq1Xie1oQXOisM9EG1FM3NfPZjK5p4uGdhqUnYVpecX6NsVgXDvnJ9mb09xWouGj70F/9Ka48855vHI1Gvy2EGH4Ryt+/8/iid7pCCD0cDv/h+Mrj3/HtP/gTpHlB03RY61bL8nWRU1KQpQl15JCGy3Mu6X0I4onMhfPI75UrU+jMxJomdp5lxh/4+POfO6JF0QrSR1VbSIUQa2lrlqcUzPngEyc4lzLWY6pyST6W3Nnfp1KeK5sXST20dU2RZbRt4EaqIvwkLvqUtziqznKybCmboLd/6uIQ4zz704baOFIFWkhK60mkp2oN3sNGP2Bx/+y1Z1DCUFdVMEaxodh6LyJHOfxoOnbuEo+Icl3bdQgh6do6Ll/CQ0sQUZuf6IChSyEQSj+EfYduQ+uQLmudX08kznukD/aXlmBGgrN0nYm0IYIjmYBU68guCbJa5yzWi4ijB6x+POxRk/OD77lH6yXzo5Zc5kjrSaUm7/eYzRd0Xctoa5OkGOA1VMuKRHh+5KOKQeLj6yvo2iYazEDdtBRFvpZ9O9tSlhX9XoGXmq5t18yVJvr6FnnCd17bY+fSFfIkwZoGL2pM50EYvIOTaUnTtiA0hUqpS8PVq5dwMkfpJBxQ5SL4ILvgP6DThOmypqot/cGA47MpUkjOTg65dm2LejFjfCnjhdsV2+Occj7g6PCQrd3LDAaDNfVORh+Qzlg2N8Yo4cl7A2azM5w1mK5jY2tM2zRIJcnzgqausLbFY1g2HbaZo0XLKydTnr48Ymejz95ph5bBQtPaiuEwj/7CAa5LteLHf+cSqXLr95T3nkw2VDajn9TMuz5vH3+Sg+YSuyOFkAmH+69w370NhcXLhNs3bzLe2EQKx7LukID1Dh99GFYQg1g7CYbpK89ztNY0bct8Ng/TXvTKRqwMqUKk0qCX0XSeoleE190ZlNL8xv/4o7z00X/+ufl8/n7v/ekXsgb+248vatEVQqjhcPiPJ9ef/vZv+S9/FJmkJEmKNSbY+0HcSEbDY9chZIKS4LxYR7ucc5nCb9ex36wMDs+NpJ0HFbherPTiAtbxISLSxnRMM0CIQHsSK2xTnhdwVp2xJi1yhDMICR+8fpsNNcBbT9JTpIXi4HTK8WyOkIIrgwscUXO116OzLVJILA6dSpzwOBzWwf68xhNiwJ+5NOa144pMC+a1oUhUoIVpSed8jO/uePnsAgf1ZYTvaMpgVG2NiabtKyl08I5YsQq8B2tapNRAGIm11jR1HbPRQiJEGscu4NycHYFOU+A8IUNKYo6ZXJPWI0EavEerAPk0TRey0LqHi3N47n6/T5pq8jzl6GSGs27dObedYXvc45ufWPDkBTg4rKnmjkEvZzMvQMBysaCxgbrl4sKquHg53isSX59BU/Nzz+fsVcEKMJXRMFtJEimpo6ClMSBWNDjvMaZbs2XUygUtXAE+tPsi2+Nd8iLBtR1k4FuP6BXU7Qm9fJubN18h7w0oK0emMpbzBdubE7q6oj8c4j00Vc1oPORsvmR70mc6m3I0rYK3r5I4kfLI9V1efOk5Lmz22b28RWtK9mcV9/YdO5NrYTFpDFubE+bzOd4LLu5sMV0saZqGoijwzlH0epRVzcZkiJSe09NT8ixhWS4pij5FL8d0DUkKKin4nY//PjtbwQ1v0E9R8X3RyyUXN3tr7H31qGvLeCD5m791hVyfaw+284rDqkBJwbXBMVeKAz5z9jR+9gIL/SRttYh2j5L+cERVN7zy3KfYufoIxq64+AEmW1FuVovulW2okoqiyEnznPl8QV2VWBPgynBbxkWcVHFpK+gVCU0bGgqdKLwL0V7/+hf/O1763X/2+fl8/l7v/fQLXA7Xjy9a0RVCyOFw+EuTq09+6E/+5R8jzXOMadE6CZI9OC+68U9aeqTSATs0MQDROSwPsRGkXLuArT5vBZQHk/EVfUYGmamQD6lYAt4bIsND4V6diIF4bZEyWXfTwf7Rg0ziqOz40JsW6KXh8RuXmJ4t8KnhbLkgVZCpnHYeQgzLrqbfKyAJkIeVFhSYGAB4uGiZx47/xvaA2yclnbW0nWN7mFN3FiUkeRI6w9NlyUf33oHwbcCsnKWuW5oquImtdveCoJHXMv681pCmwW/XuNA1rI3EVx2xX//IcWkYfF9VokNH61z0PYOe1gAAIABJREFUEA6QQrM2MwmihJUkU0kJEapJ0pTF7DQwI5wPUIGQKBnSIRCRCmRblM5BCPJUrdkqX/9Iy1OjDuUMSz/i7s2bjIqUy7sXODudcuHCGOssUquQg5YoZC9DJCnehGvkaoNKU7RtaTvD3/pkn0xakJJUSd5/peItGy1Ff8gvfV7x8kEdkpYFJFlKlig6JxAEg5zOSb73qfuoVjDuDcP9qzxCZ8g0pTUzbNuC80gERna89MqMC5MhG+MJ1bLk6GTK9csXmC1LRv0hx9MpCsfGxgivFEeHRyRZQVmWXLxyjVs3XyLPPZdvbNOakjRXWOt45V6JdFc4PTlhe3ODlaClaTsSFQ5KJSWz2Szgn0WOShQnx8cUvQFaSxbLM+q6Y3Nzk0TD3uEJUinu7j9ga6NDqjB1jscbSF8jVcrlDY2xkGlJkoSGprWOVEtqq/jFT29Rdueo5RrFc4bLo4Z0+Sleqv8Irl1St4YsC699lmikVpjOcPulFxhuXwodqwvUMK1kMLghNhMq3Js6SUgSTZJnmLajqiqaugmFdH1YrmtS9OD2aJ0wHOTMFtV6fyOE4F//4t/h5sf++Wfn8/n7vPezL0w1/IOPL0rRjUuznx5dfuz7PvgDPx4J2zZyb8W6m1oT9F1MZIicTi3AIiIe6M+bKKIHQuxgV7SvVc9r2gap1Jo4Hb+X2A+f80pXTlkrGOHcB3b1hYInwMobQCqBlsHJ/088esqwXPKr00f5C09NOe1m0SXMM1A5o40LTE8PkEKRJQleeVpZgRK0pkMryUnZcfdsGReAwahmkCkaEzjJozxhXCRYD50xPJgu+djBO5C+BQSmq3E+/PzlYvkHFGWhO43YuNJ410UKXXDvXxXIFTyzvi2jQETGCux9cFDTOtC/ECCxhET6h7DvuDhbjSFCxMgj2+C8wnRdlGyLdeFeTRcQJo9Ea3pFRmtMTEFW/MA7Gg5nHQ/uHSKsY2uUMYxdYpor1pJtLXDGBwZDkeK6FrHCo9yK0+lQMkhg/8VtzWcOJULCh94ouVxYMC7ASkUGzvLqqedjDyRS1PzRx06Y1opPPNjh3VcMu5nDTpcoFf2CE4mL3HIAJxxdO8d5iRIO5wW37x7Tz8ZMRgX3HxyzszXh+OSUzckEYw3OGi4+cg1b1RjTkUiHS3KOT5acHh8CLTee2Ma6FiE9KvE4B8/fqljMJVd3rnFycsp4c4s81bRNw9lsxmjQRwiCkZCzFEWOd47pvKTpapqqRGcpiVIM+n3mVclsueTV12/xDV+zRVoUIBTL0lCWFXcPZuS9hKeuX2B7oHntoKZsPY/uZKSJoKwd/RR+8t9cI5Hu/CQnvLe1kuSq4WRBhFUC7c7aEETazzXGhaTlV5/9ODuPPB39dQN91Nqwo0nSLPiwiCDvT3RCkmVYEybRrg3Y74p25qMYKHiuBGbEirKuohOeANrOIAT81v/8k7z+id/6vfl8/rXe+/oLXR/1v/9D/v8/er3ej+QbF7/vT/7lv0WSFeG0E5BoTdO5WEB9xFRcpCuJtVjBeRcTDx5aaMWLtvrzSurrXfBglXjyooijR3je1birYsLEmpUQ/9758+cOYoCw9BJSx8IrYzMYDMVbK8jmh/yDozczSi2VbQKf2DvG/SHNtGUxPcG1nqVfUAw3KduKzocobuc9tXHszSs660mU5NK4YG9aYp2lSFIS5VHCsmgCb/j+tOSF6VtQwkRKnMA2MXkhdgHrovuQFWIoiiGayMdC62J68HlUkVv/XsuAeTkpgsWgUg9lnMXr5FibR69GPmPCcxJNyIUQKK2jKiiMh6tvasW7XCWkCSHI0xSpAuUvQCmOtrG8dvsO89KzVfQ5m03p9TZDp12kWGEiC8NFuCDwh4WSiC5MLMI5hMgQkSgopMSg+JbHHF+9Y/l7z2q2RylCGPAGmaa4pkUkijdcGfPETs2rpzBOHZPU8MYLKUr3Odg/pugHg3rbBMGJ9LHIS4XOCpTMaKolWnlS7RgMc6pFw+mZ4/KlXZblkuGoj/eONM3xOKyBrm0xpg0d3/IU5wO8gVW0bYNKfLAbcaCU4PqllOfODnj5VsuTjzzJslzQ1Ipev8/GZBKWnxHvzfMcIRTLqqJX5NR1w3A4IUkUZ6cnOC9o2obZfEGe5vQKHbF7hbPB3W97cwTARl9hnOPKRkrnBKezBWnWozGgteDSsOW41Oddpg/jvXWe0qVo2UAWcWEXHd2cp6wtRS9DJwkXbjyJqUtk1gMUzrr1gW1NUNOF2S749drKkWVZfE8QKYk23mcSwYr/a9fCHynCFOPjMj7RAusEX/dd389vLObv4sWP/4oQ4k96780Xsj6qH/7hH/5CPh9Jkvxn+WjrJ771B3+SfDBhxSwQETdFiLWAIYz1kacqIxMhdrTEzwzCFL+O3BE+FgFvydIkcj5V6OrOmzYQMna7K6PyMApL4VHy4USDUKgeDg4MuHHoXIIhd+h0vZC8f6fls8sR3/30KZ0tqb3htLJIPCrRGGvoTBscn3JJ4zrmdaBNSa1xwLwrOKguMMwV02rOdi/jt+++kVfOLnDzbItZ6/jM4TW28wM2ejnPn15ECrdeEM5PjkAEzHF1oofiqAIcAiGgdlUMXSi4K9vEP3gYuXMMWEqE0sGoPcI4KgoX2q4hZMMFSGbVOdt4aK5EEl4IslTTthWrJIgVW0KyskqUSCUirSoYrdkY3ZNmBVma8PXXC44fHLC5OWE8GpAlMhTzqkFlCpLw2osaRDA1wEfPCmcM6WAT4T12UYbXONGBfuQkxWTENz7ZR2DR6QCR6HD98iAbrcsFiXSMCxA2JxcTXjhxjLOE5ckxGotZzEkHKc5apPBIHZSMtmsROoy/v/YS/JOXUhrZ59HREp06Tk+XjId9js8qnNRkIjQXi+WSwXhCqhXT6Yz+5hbWQleeMpoMmc1K8pFEOo13GucNeSa5tDNg0S2ZLvaZVwu0TullGcYY5suKtjWcnp0xm51RLpeU5RSd5izqJaezGa5zONvR2I5+r0+iNZd3djlcTNmIMJf0ntYmzMuS00WFFJLxMGPReE6XhguTgmdvnyG95c7hkjftdkzSBXfno4dwK3CmixNYQl2VdE0TxA0x3NRbg0fivA0wY2+AaRo8Pnovn8OIYpUOA/R7RWBBdWFXZK17aPpbLx1C7VmbWp3viowxjAY9nBdR6CF47Kvex90XP/2Eq+a7P/RDP/S/fyFr5BcUXhBCfG0xnPz29/7oz9Lfvoqxlq6zeG9IdEqaSDobtdTO4Vc6KBfaf+/MehOOC/6yq61s+AIBWvC4NcywGuvC5658cddt1TlNTKqoUjmXGobx2AUT6Wji8jBksRJH+Pg8xWDIuzeOODhrefO1PjuDigfzBXXdcnHUYzFdsjveZlEvyPt9SjOnyFJQoTNZNJbnDws+fbAdnteDI2DP3tQU/QGdcREK8WzmFe+/dsAvP/8oSobCujg7CYqquEwM9oBtPCh0NCN36xvSrcxtYuQJ66kiVMqVrFoqfW6irpM1xzGQzV0QU8TpwMbk3JX8ctVNy4cnCrvKQvOAJLxMqwNSIOX/zdqbx9q63vV9n2d4h/WuaU9nvOeOHu5gX2PHdoMD1AwuboFCmiAc0lohAiWIqIpUJR1IFFVKlUhNlDZRm6qtWkQHKAqEEhWTMJehAQwYbOxrbO499557xn3O2cPaa73jM/SP3/OuvV1VYIy3dO+595yz117rfZ/39/ye7/Sz7FcNUU85bRXPTBtO7AGPNwN/+W09nNzl0qVLRIRdtllGX69QWYGtSozOCZtWSME8hSDlBmUz1CCB2CGmsfHJeVfXDfP9PZhOUDqHMED06HxKDENaxRGFRduc/uwROpug4sC//NQJH7racnjccHB9n37dSnj5JG3ggwM0qprIZN2gqHLLf/ErDhfgr76rYX16BC7ig6JvBo6OT8jyghff/gLtMDB0Nc4JBr/e1LSD44V3voX65JhApMgyiJF+EJlUzBowgSF4isxw92jD7Xstbijo1o6d5R7TaoKxhrIsIEbyPKOuNzw+OeWVN16ldi0vPvFMcrgFZrMFj9drFlONCyteevaAVavwXUNmDWjF7aMNz15dECLcP2pQoePtT+1z68GGoihwHly34mfvvoOo1Faa6V2/hY82dYvvuiQLVDRtJ9p8pZiUOcpo6rpjUpW89qlPML/8hMCLStahT2sXxtKZTsbxXPo5hmHJaXqEGMSENGauCPQ0hjaJCcBaWaeb1Sk/+vf/GvXR/b/eNM0/+bLVyS9X0VVKPVMt9m5+8/f9Ha68/d0s5nPaeoOP8sGzTIJMxh0qep86j3G+mXRE3g2y46TCMQaPj2oFk2aAmYRdjjDFNgcgETkqqRZG0sykVKeLSUWQ4myTMQJSd506ZKJD0s4EZ3rfUyXf9GzOP/j4gPOB7/tTD/iDhxsyrZkZxW41Z+gleKVxDdOpJSrFgOc37+3w6YdTCdvREsgt5y613XS9dxAgn0xwXvEfvHyLpu/50VeeIc+kqz19/BhtMmxm6PshkQvxHMdNx6rRChmjJOvLteA8NGRUOaTTh9bi7BpHII3hJDG6FC4NKE3fywidmE4NEscnWaYRtlZu6XxVejhGGZv8HGMN1li868BkTNmg5lf47hd6ruzkdJsW1w9gLGpWEvsWkkpDWYudzWDVMAyOkGCg8mAfv6nBBuIwEFYyQ47coE1ONJasLMHD8f1D9t76jGDTfQ8alMkI7QZdVoynyegjOI+ZzHGbU/7n3/HcbzR/5YnbFEVFOZtQHOzizs7wTY+dWHABVVoiHmVKXj81/LPPBKxWvG/vhPdeMTRtz+aspj59xGxnHxVz+qFnvlgwnU/R0WGzgoePHnO2OuHJ5+eYMCM6KSSowBAadJYgIuPH1FK0gjtHNUM3sBkUD+6foP0Sa3KqSc5pUxOJvPHmG+zd2OW9z815sFF88pM3uXTpCvvLA9zQMi0jJgbmy5LFrKQ0A5vO8fC0ZXc5xQ09ZWFxMTUNKA5XA+v1mrIomNqeX3jwFakBkuXj+pYsy8kMbJqe44eHTKqKECJZXtJ3HTbP8UPHYrkkBk/TO2azivtv3CIrSwbnMCbbrm1FJLMyVTtGlcKx2DZdcgoT6FBIdslHds6htTxT8uswFqHtSVhrzenhXX70738f9enRh2KMP/flqJVfFnOEUmpy6em333zvN/1Fnnzp32BaVZytVmR5uS0Eg5MiqoIjMwqUkS4PlR5ccVOp9IGtFVum0kn2lfR2I+seQoomRBG1SvPREiE2dsaoLWzggmCOPsnQttpfxTaycOxuBQMV0kjgHgnm/u1Dw9HpI961v6EoC37l/j6TScngAvlkBgbm1YyzeoMyltrJKBoL3DydbG8wcRy7bfFu2OKqKurtf1udNqQowxsnas2iDInU0pIYlj7TeCsFGRmhGMFTvRcseIyGZuzw0+5/UYKnSFkN8s1EAsPgttczuCFN5ghJzidpYj5IJkbwToTpQSzYIYpjMOE2WwmfTWEmxgrDns0PeG4ZWUxzHj844vRkTd/1KKPhrCbWA/VpiwoWncwZ0Sp0acmKHKMN7cPHnDw6pT0+JdQdOs/JZkvMIDpoW8gMsJgykTcPDtNlS9KNhDdHFCiLGzzKaHSR0fenhM7xkSeOKQz8wP0nyYtMPmfb061ahiEQBk3MNdQdapCH9pmDnO9+r2XwkY8/XvJPfm/B504tn+sW/GT/p2Rta8VsvqCuax4dPgSbcXj4kLrp5G0Fgwpy+nC0DKElKklq84Mn9FrUPFFOhtd2K566tuTlZxZ86ANP8953T7hyteHy1TXVYsWlSx3f8NVP8K5ndyBf8sadx0x3p/Q4jPIQOgKe1jmsrXCDkwkQyjKdlGx6Jzm8BpaV4eGqF5WHgjy3ZNZw3JZJtsj2hB+8xFuasGHoWuazGW6Q5Lph6Mhzw9A2KGM5W9f0g2cxq1DI4IBJNRP7r5FUwegHUEbmuOkLUOIXNFZj0U9Rr1FiBGKC2uzWMDTyN/K9Y01YXLrKN37P36Za7v2sUurJL0e9/LIU3fl8/t8vLz/Bez78EQbv6Lqe2WzGMDTs787ZXU7Z35mxuztHGUvT9sQwoNWFLtfkcsRHjrpaaazWyYk2klypQ0vzy0hRjvIw63RcHwsOadhdTIQLW3JtjIAck4y24d0owpjtmVQNEZk6i4JCOT52b48YB/zQszcZqLuebnCooaVrHHoSuHTlCbKYo5S4r6oc1m1g6Fv6rmfoe4aup287iBo3DAQnhpCylMT9CHzqvuLu5oDMQMOSo+M6DSCM8sD5gExHJOkZJalMgsP9NoUfELKCc8efkISabW6xkuUwbpIAbpAj9yisl3T/sJX5eedxyQl3vnGlo16MaPy5OoKUT5yKjDHCGh/sTLk6VfzZ58XiXRQTZssleVFhFkvIDaaoKIocRQotih6VW1Q/EBKO57DsXN7D6wnHa8e6FQeTnswl46KcCPnSNuzsLaku7Uu3nk3TZq0I6w10GxRgdJCCQIbRFaEwlPMlH7l8B4BffDTlwcNj3rx5l8OHjwlern1mStAGlMEdnaC842A+4cZSU1pFrgO/9njBJ1cLvmZ2m585fRLmu8x35ly+vMcT16+inCOzhv3dBU88+wyxmzLoI7ELIwoVYySfQWnFoNQWFyVK46EVCQqCeZXzlqd2uX6w4H3PP8FktkOnD+h85PbhGZt6w3I5YX+35OjshKgV6IL5YsnZ6ozOWVabXqaIqIDvBz756n02LfS9o2l7Cu2ZlzbZqyO7lSwpFzTBD7i+JS9KbJaxag0nDx8QggyqnC1mzGczgncsFlMIA2GQ58SFiBsGrj7zHIq0pn3HvMrZ2VkkqMCIsSadaMdR71vxxPgsRyDBCijhJpSKENM6jeeQ5Jhk5n3g+gvv4Ss+9O3M5/OPKaWyP2m9/BMXXWPMt6vJ/KPf8F3/6fZ4qbRmcI48Kzg527CuG07Xa4yRUJTRveR8JM8MY0gNupBoRSMdbmDsZpMcKbUkMY472Wj9jdstNZVc+XMSzKC1dJxGbzvaCFt2c4wdRIksbCw62xuFdGxKa77z7Se8eVZgtOL3Hk7pnCcvxE7pgqepDeuzI+rQMC0nuOD44c9cSUdwO3LpjATjuUZQCvwwDOmzwudPr/DKwwm9i7jNI5SSTWMMikGFbRcR02cVe/P5AMlzfDtCMkIQpUB6J3KlUQUBYG2GtTIxYoR3tqqIUSkRPdGLLOdcETKeWhI+H9mOAhq/d+wmVcLUjDXUveely4aobTKraHQ1wUwKYtOCzRmaOilaAhgto2CCyLH84EArhq6h2TQsLi0FF80yvFd4UlfueyDiuh49nab7KxsYyhCHBqLDn9WAk3vuWvrVKUqFlMMc2JsVODSfPNIsphPKSUVRlIQIWWYJXkOWLMyzBbFrCTHyzc8b/uOv32NIrrx6iPgA905a/pfPKv7XVyK/eN9ys5/yq4eWnz6c81N3cv7p7xpMnsl1NEHwcBOxWYGPDqUspdWSEplCjJxX8mgrvS0mMSicl2JcWcfQPObB0TrphafsTCdMcyGDF9OcRZWBimSTEk1gcI4QFb2PnK4bdnfmnDQDzWBYTgsOTzt6L5OexU0pDcdzewLFoVSK2QSUolosyVKuibEZq7MVIQTWm46yKLY8xOZsg7FGIkCJFGWJj4Z1PdB3A0WRE1LnOkJlMiNRn2/3W13/6NpLBTmpJwbnkGGYiR9KkOtYtGOMvPvDf4Hdp19653Q6/XtfUqG88PUnKrpKqRvFdPnPvvGv/B2ycnJeAJI/unOCmY4A99lqxcH+LtWk3HZYLiTMTwujra3dZoPqRJyNRWn0UaT6sAXGCVE6xeATgXN+YhzZ/XPd3tjhabRORNL2RS8cLbbyKylcIf3+xw8Lnt7PqduBzZCzO5PBhGWhmRQZsW8Z+p4qsxxvaj5zVPLg9PxIQ4I1QjoKVmVGUZWMbIM1mpjGmfQ91GHC7sRhygUqn5GpHovbhj2PZEEMEhjkUgrYSGCNXyKPS11sjKnoyfBFYYxH+Z1cC+cFutDWSo5DjITUQQs7HFDp9EEUOCGORFsQijRuH4DxvZJSxiQkZ1IWqBh4/7NzVF8TlSGbT4kESVfxnubkTAweE4spcpQVMinGiJnk2MmEpumYHyxpmgYGxZXrl8gP5rR1TfSebGdXIJbVmnJnga2mgjd3NcPqEK0VQ+dQ1UTYfDfQNx3RaLSVU8R0uY/OFdEUfMv+IYTAD7y5h3MytUErcfB1XYvJl6jgJDrTWJR37ExzDk9rZrnm2b2Mb3hryfvfsssHD04JIXK07vjkvYEf+90jfuN2x+1Tz92NpsoUP/F5hwkTCBXGLAjR0XQ1Sltc6BicQytDALpBJmRvmkBdBwanaZyRousCIcC13QnPP7nL9Us7zBcVi2lO7zweRVXCqu1Y1w23Dg9Z1Q1n9Zp1pzltBvqu5dJuxdMHstFs2haipypzurblxl5JZQZ+8cGLKBV5XGvC8WdQ0dNvjmlqyZRZHx+z3tTS/CQ8ezZfonWkaQfypE4JrqepW9H4VnM2q1OKXGbwNb2nbiU+dD4riYirclYVKCUF1qQ6MptOZM1rcz6CKwWkZ0aUVO7/62RToqMfi+/Xfdd/AtnkbyilPvgnqZtfctFVSqmnXv7KN9/5dd/G5aefl7eaOKGYyC6QAiMhNdC0PXXT0Xc1s8qyvztPiULpaKpk9DRKnT/cyD+QIIUYRG+a2HjnBkLwkPSIUgwiSRm/xXV8JIn0ITdK/hydiiqMuQJbDbDiPIM3SmfmA3zs1ZK3VrfSiBnPSS+DAO89rnEhkk8s63bNG4eHNF5ztBGSZtuwI8V2Wlom0wqnRINojKIsM4LOub5jeetlJeROiJzUsklkaWAkJpfwGiWzosY5UXFbbM8Z45En1Wq7faXjqUVFyTAdSUmxSoZzyU2MBDds4xwhaXXT5jVufiNxMdqCR8hHYca9S64t56NY0rvjb37tnHp1Ak469eA7lA/Q9XRnKyYTQ1YIxKTKHOVkDJDKLKqw6DxjeXkPbQzOw503btLXDa7pWT55jRA9uB616eTzlROia/Cbhjh4+tMN9a1b2Fy6w2x3ij9r0UphshJT5ISmxjUnNGdnlGXB9amiDyLWf9XtEq0iEFidrMjyjL6tqTcDKm2uypaoMDArNX/9a5b82Wdr/vTuGTpqXtjP+ejlm3zLlRPpTKPEISprUcCmD9xa5fyj31pQ2Mes1qeouBALq7ZYnW1PfIOTuFCMYVJYQvSsNg7lBwIBYxXaCOz2qLGAXEuN4aXrc6Z5SVSButsQogTLK8SYoPEo79mZV0wnGd3gubEn8aK9j1zfrbi+N+HkrOYzq2dxUU6wH377CXv2hOOjEx49XnH75qucHh5SLXaJMVJvNqzXa44eH/Po8TEoTVEU4npEnGNDL0M1veu5dO06RVHi+m777FqtOVu3eO/SBJEMazSTokjyRkfb1sQgk7OnkzwFPklBDumENpqkUn1DazFaTSc5WZax3Nvngx/9j1hcuv6LSqnpl1o7v+Sia4z5aH3yiPd907+/PZoS0wO5Lbjya2ZUcqiIQDpEy7p2HJ2s2d9dcjCHqAxD1zAMEpAyvmBMzpNxggRRRPlybDrvaLdYLirl7oYkDRu/L1FJUYY9yl9O4unxSLz9dCoJC84hC6Ulh6DUjh+7+RxKKQYXuFPvE4kUuaIJnqZrCRZOvOfJPcNv3VtusWRjFZPSstyZEqSFwgXp5n2IvHzpHk/NT7h1opmqe3z05df4mifvoZXHe8GRM2uYzwqmVZHiKOUry9IRdPwEiXw0OonAVQogN3KKcN6nTjTNiNNpWkaKfmS8HkpjrMBYI9mZDq1om0suRTkRvM5mWKu3G1fycqSjXVKLRDnaWaN5ZtfQn52Bl2O/62pQnqAHovbYaUYcHGGzIRyf4B+vxOI7JKNIiCgjhJ82hp39OXv7u2TLGcZEmRC8XNIeHtPUNTG3+KZFKU273nD3jTuUe3PMtCS0PbFvadsBb8AUkjchllNFf1ZT2oLBR3YuHfA33hPwGP6fQwv5gsm0Ym9fCkkxm0l+CJLzEPtNer+DSNN6h9/IDLP9y/s88ewLvPXakvfvitPNe0eVG9rBybpVikx5/uvfvE4dI0M45rVHOZEBHyXBTaytBcMgk5bvPT6RQHDl8DHgo6LtPb1XvPFoxTRXfOy1t2KtoXUdbRd46dqU5bTk6v6CIQaMhqO25vWjY+4+PiErLG8+3hCipXMSJ5qpyKyA3nX81Gs3+Knb7+T1s7lI27qOH/6NjN3rz1OUBXmWMV3sYouJEGFRGimJJJVNuW17mrZLGdNW5vRpQ1M39G2PcwM2MxxcvoQ1Gtf3+AiaKGobNEena5puYBh6Mmspiwk+GnaWczJr8T5SFLmE8mvR4SsthJpODZ7RijJNnNnZ2cFmFqMVL3/ga3nibe9kNpv9wy+xdH5pRVcptVvMlj/44e/5z0RapLdIqpAsCnKtKTKFRXSKQzSo4JJU4zw34f7DI4w2XN6bMJtPGfqOGBwhTfW1VkwPwTvpVLUR/CrGFP8oiKtKxMHY96WajEqwgB6rcxzdXJGorIShbw0Sasv8K6VgZPIZ3VwX5E9Ems2aV49nXL+8pPeBg4MJJ3VN0ytmkwn3ThyZEQKvLHJslpEXBf0QeW55yFdeu8lXXXuFvndsVif8yqsLXrnV0pwc8q9fW/A/fvwGv3b3Mn/+xVu8bb/DqYLWad5yACafMClzqrIU4B8wVlQWWZYJoRATTKK0/Owsl+MupL+XY7JMZGRJxxxQGHsxU1jev/BsetvljoqHkU3WRpOn8Tg6WSuNEomO0hptz4Pax6GfH357Idc+M5hJTlCa5tEqabgTC1QWKJuhd+eYnYqoNaqaopKsT0eV4JpAXuVoK4SoWUU9AAAgAElEQVTcmAOsQ8TOF2hticMAEY5uP6apeyaFlo1nGFDBY3eWFETsCDGleFAXQZucYm9B8J7m9JT1ySlPTh1WRU5CgQMeP3xEc7bm5MEjitmMbFzmbkgbfCDGAVXmshFaiy4rjNVUs4pfuINkvrqes8ZtNyuxvMPQd/zkq0tyG9iftWRWTocylimw3tTkuRBt00nJYlIwLSynjafpBoIf+G9//Rr/16sv8oO/fQmrI5NySjGd8fBkjVIlRsnzfNZsUDFQGLmnO8sZ67rn3c/s0PQ9P3fzGj/yylNUkym/eO9FfvBTb2Ez5BRGxgVsSW+bca+7Sp6NI7I0wXtx8mklG7oan1+Br4ahpx88TdulxkROrQFJqhtnqR1cvcru7g4qesqqQmkZ3UWMzKqKuu3ZXYriYbmYASLL7IeeEAKL2YTRhTqrJoSoxOSipD70g6MsCoYkUd3b38UUE775e/8W0eTfq5R6z5dSP7+kojufz//B2973Qa4++4K4omLcgtPL5ZTFrCIrMvquk4fWdYIHRimJzo+jeKRAHG8U9x4c8cTu2F2qbecpRVERkyQkjvF/yUIsX6MuFNTYoaqRR099cfo+IaDi9ggMbN9XCAJkaA0ks4V0qGYLwGslXvHoA2U14+uvvkLTDuztlNRtYOdgwY0rBzy5u0s1sVvRdVEIQdUNHqsDGXe5d/qI37m/z1AfYbJKpHSmZLmzZLmYs5xX9D7j5uoSzy0e8u7lp3CD442TbIuZZ1q6hL5tadYr+ramb2vZaIyRaElj0sgiMTWMmbgheKJzQigpRZZCUkYMHjgfJhmRCQXqvCDLvRRiLiSN5DZicNT9WoO2KadAaSFJlZxG/u7PnFAVgI60ZzXaKqrrlzATCZOhKFCZwT5xDbt3gJ5OUbklrM+ITYfvHH3TE13ADz3ROUwM4GT0jcoKVJGT5Ybi0gE6zzAZGBM5OX5M7AewkhccrcWtVgSriXmOMZmkozUd0UG2mBNRTKcTqt0l8+WMDy3uMzjPT7ymuXncctKuKWdzZvOSEGuBYEKEbCS1EqzV9ejFAl1NwBjy0vBPP2XJTYJfdOqytUo4ekghRQqvCv7hr93g/3jlGr/wxi5DCORZgbGa5byUbGigcZF1j8i9iorMKhbzGfgBfIcfeoIP/NKt6xzHt1O7ANGxnFpyrZlWU5YTg4seFaELirWP/P6dFSZfsPYzQlT8+GcPKGwg1xcblEGkhE7UL03TUZQTipT9EJLsMIYg4TTpZLU1JY2vlFQGJmV/4KVYFqXINDdnZ9iiYDqfEVzHcjHjytXL5EbMFkWR8+DhEU3bpn96IoqdxZKua2nagcV8irWGTSMTmkl8kliFPc71tK1I97reYZTCY/maj3wvi8XiB5RSf+wa+sf+BqXUiw7z3V/17X81DYcDqw15kXH1yr4wlMFzelaLbMwFAiYtnEBIu1QInjGKxocg+Z+nv71lyWKIxDCCxOMwRNLONOrxLnS2jFgjWzx5/PIjmiCfADO63pJ6VZM63IQ7Bmkbk5haJYeXZEJ0bSv3P5FRme7xPvLm4RlHq5aTVU/bNfRD4PQ0Ms8HppMCHyOZFQz5vZc/k/TCntbNiVp2XGsMeWbTEcixXh1Tnzzgp371Fv/itxWfuHuF5XzCB58+5s+9q+H5a5GgCk4e3Gb96C6+qenWK/q2Yeh7sizfdp1d3wnZmG677Esak2XE6LcymXHAZEAldl/uFSlb13kJJRpJPNL1GVnCsYsdR/JoI+SFYMkwZmiGEJgWFu897vExEYdf16BEbqWsQRc5Ki/Sa2uUypNOWYlb0Tm00YQwQLIAK63FWCHvUN5jun9KaQKB+ZU9ljsLGRHeD+iylM45IlBFZlFBCDWIaCvDIKN3ArEMYkoJbsCbAlzLj7+5ZHdqOD46ot60EBTRdRecbkgH7wYwhjgM6LzE2hld0Lyw45KV2CQccisSPwfmEbK0SPGJs0zw9mGQpmYYBoGDlGbwnrNmIMtzjk9XGBy/fHOKwaX7YtLree6cZfT5E9w+PhM9e/QMbkCpjJdu7OPdwKbt0MD94zq5wuQ9vPNyzYeePRy1ONsCOhbVGCJGR4bBU1UVeSFrMkQxTNnUFBhjGUtuSOqaGCNGjRGwsvF4H2jbnsmk2PIcPkTapqWuGx4dPkRbw2JeolBUZcbgvEyXSKT4OIevqgqZspFkZ5nR5JndrmtjLGU52Z54fQrnMRpe/KpvYrJ//SuUUt/xx62hf+zAm+Vy+Y/f961/iWqxI8lBNmOxmLLe1Dw4fExZ5uTWoIH1psWl4qKVTjcGnEOY8xjwTrqv+w+OOC5eRKuQuihx3qQrnrIVxr4VxvEzW8w1FQ0fRteZvN+YwHad7L8jTqtT93cuL0vfk7rgbUKR0mjErNB7jc0K2rYVqcrQ8XNvPsc33Hidy7sLzs7W3Hm85vJuxbX9fV574x7vuDrltx4s0Tiuzk/Y22k5btIUWXWD9WpFUU6FPGhr2rajPlsRgqcop8yqClu+ncU0Y2E3YDx7Rc/p8RFvqzpefvkyHyteZm+WEZWhblrWZzXrszM2ZyvMhSzcrU459f8qZQWYvEgWSMku9UFyHrwfxKyCR0yiAYPogCUqUzashNskVZoI9S9CQ1qBshqdIjrl0CHvQpdLdNkwKafSFa7P6ABjFaZcopTBu7WshSBSNQypM4r44LCTSvKTvSNqhWs61kcd5XwGymJmcyIRXe0SEU5g9+qB7MbdgPcDcfCyBy9KjK0I7QodA8qWEAb8qiHoApVZMmNoNw3lfMlb+4HXzwpyDZ+u97mhT1gsFyL5FAuZrL31Wq56koyhwWYVzrd89qHjd+91aJVE/8no4NNGhxshHkW9acgLS8Dwy7d2eOtuRxY9xmZoK/joeDuUhk3nsXHgB3/nGVa1p5rk567Q0WIf4ZWHe/gAb9tvIC/ZX+Z09Rl3Hg6UkwriwPGqxpYFd9ezpBOG6Fs+e7c/V78ELznNNhflEqIcWk5LJnnE7O/y8PFJMulAnsl4odGWf25qSBxM8IxOsywvCDFuZ6EpPJtNQ5EbistXeXjvDkU5oR/8VlZadykWJ3jqQU7YJ2dr4TX6Ttyb2qBIU0aCYNkhKGbzGdOq5HS1JoRAphTOyfvQ3vPVH/k+/tV/8/3/lVLqn8cY+y+2hv6xbMBKqffP9i7/xl/+L3+Y2WIHa8Tm2Ww2ODcmdCncMGCM3RbHbfmL50y37CRj0Pg4BTiFs2yZ95i6Tym8ga1Sl1FtG7wEkkvnpbZJZPGCPiuz8l5EmmbSeyCxxZID6/04T4xtAHjknNEM3onoPb22STFxbVMzBMXbr3qeym4yKSbszafsLZf81quv8+JT++QmoJTFhcCbxzV3TzdcX2T89GdviMe7brDG0vcdi1lFUU0pixwfIQsr6nZAZXNO1x1D1zEEkXtZetAZZa5Q/QnRTCnKCbYo8X3LarVmva6pqqlkzyrpmgRbEyu2ySSJyyS2HCUyP0nfl88at44zv3UMaj2qF0Y82aQ/N1uDhc0LYpDCY7YaXzk22ywjzzP+wxdWpHwyApo+OCazGaoo8Js1yhrMZEnwHfie2PVEFQmdk4KvtTD9NiM2DcO6xacH6/oLb0GbyXZDjtHjhzVEJXbd3BLrjXyGZJhxJogVddMIPBFk/lasG1xIpKSReVuH9w+Z7l7iU6uMjz+Q7vEvPv2A9UnDJLNcubaPKnM4W6OyEjubC4atBJssy4Lff+MhP/RpRwwi+0KbZMqQ66Q5byhQ54H81srf/dCzj3myasmslKmoZOKIJ3C07jleNQz5Nf7VZyzVdLqVC178GjXtWWb46Es3ee2o53Tdc2le4mPER8vezLAzzag7xydvHXFtZ87P336LRIwqTejWlJMKaxQnpxu2Fn5kQGo5mfBtb7/HzqTnf/r1fbKsYHCOtpHkRO8cUakL0k6FNZaizGXSiZZkO60UQYuWfj4thXRPn6epa3HoAZcu7fLw8CH7u7us1jVd118gy0elEowZu0prurajKLKUYqaxWlFUFYMbyFLTpo1NJiHIDPzQ3/1r3PnsJ75vGIb/7outo38seGFnZ+fvvfff+U6KyRTvZLxJW7eIrNVsF4XNMsY8gC22GuOWzJK5R2wVAqNkTKYfjAuBLRk/2vRG2VFIMANICti2gKtR8O+3N8hamzxbYStl8zGmwnJuHhj1ulk2OlcShBFHHbE9PzL5RAYqsFlOpgJvHE940MzRceD24TH3HxzSnLWcNp6jjeHm/Yb7JwOFNUzzgs+fXMcYQ90OTCYT9nbn3HjyBjv7B5gspw/gfOBxnROzHZwPWA1lkXN5t2Kay3BJXENXNzS+kjAhJZkM1khehKgaErSTXGrj586y7IIxZLRaJwkdSD7G1mkmpEhMZOK4mcZ0L4NP4TfxPNT82lwUK2UmJgQfRjBIflbvYXA9wfd0bYPrewprCE1N2Kyl8217wrAhdi2xE+PICDmNUjmA6Aai8wwJiprNK7zrIcqGB5oYetl0bIEM4/LoSQ5a056cMdQNyhii6wVa0rIO8IFQd1iFzFCrW4yC+XxK3254Yd4SlGDhv/xon3xqQVvBCNseVU5wQwpU8Z4YAlkc+INbr/Mzn7zL0Hf03UDvQrLIjw+BfLoxeD/GkNbueRf4sT+4hIsB5Q1ERWYkHpUYmZY5i8ryxmlJNSm2kMDYUV5suGKCYqpJwVsvT9hd7vFoVaNiJDdS/J2TXOLLy4pHZ+uU3RGxKj1hKVjfaJhO8nRvpOnB90JcR02fQp0mVUVRFtgUIYl3KXQpkdmkDT99fuEb/PZzqFSkvRsoi1wKYlfLe3EDbhhoupTwp9NAggv1KCEhzKqcEBK0pkZuIuACnK3WeBdEl1839H3POMYqRM0H/tz3UBTFf65kkX1RX1900VVKvdRH/aF3fe230qcP4vpOLKzeoaKElGfZeKQc54zJ5RMSKhklUOc21eC3eE0IfnyE0vhu0tEgJImJjLoRnFJMFKNI32jZsUaXy8j4bvOslRGcOALKnOO1cbQHy2L0Y/FIBNKYX6AIWCUqhizL2CaSAXlZYrXi0w8P+D8//yRNeYN7p6fMisCrJwdsNht25hm3Hx1RGE2O4/bpDmU159qVXXZ2d7FFSecCde8Ec3JivS0Ly9A122S02bSk7Tq0sUznC7JyisklgzQbfzWavRmUk4nkOwSxF2dZnvTGAusMPoCKKAJh6IVgSwE/cjIQHHtsCxRQ5kIIxuRhH1PHvHd4n9LMkjrk9UcDrjllUtgkC0olN228y9zhioDLwJSWaB2dqmnY0LoVna/p+5ZhdczQnhGMwzUbfC2/v9mc0bcNfb3GDQ3BRqp5QbGYsrh+WZjy2OF9QwgtSmdk5T7G5NhyhikqQt8T+o5iXhFtRMcAm4HYO8IwoIyWE8Fihht6dFQoHdlsasqFJSs0w+kdei+npltnmt9ZX8YYODvriKbAB0U3dDy+e4fee4bTY77/Yw/431+peJxfJ8sKbJajtWIMp0/VkRHaHTs02dQiwzAkNUikiwoyBV4QDWKkaQLTIvJLt6/z2oMgG14c96sRe+ULfpZSlrsrxeO24udv3eDpq0v6YeC5qwXrTjbfWaG4vjPh2Su7fODK5/kz128RvCcvK7SxKAXVpKQfBlRS/cQYcIPjbrPDmw8b7PCYNgWNb23tPhC1lucrjWIfh1AKv+PlpKuTeShG2t4nUljSx/q2BWOoJgX3HxwR0TR1Te9kMGVZlhCSCknF7bj2o9Oa2STHak03DMmZFs7drlFOelqppJwwSW6mufqWd7Bz422XlVJ/4YutpV90dZ7P53/zxQ9+KybLcW6g7QJEhfOOPAHiZ+sNeZ5Jzq0yuG4clCjYqt+W4NGl5uhDsgAnhpzkUCNIB6wTk6iSDkwh4d/jtNhxAckMLs1IrYmMbMxzvQBrIN2r/H3O4cjUWcc42n/lZhtjBNMbvd1GFuo4pcIYTXJfMqkqFPDbbwx0/VuIMTKbaW7Pn+f51W9xrZrz6Kjm2s6Sf2/5JserFT5ojvqabPIMv/fo8pa8wWSoGOVY5D3BRaaTUsLBtWxsPniC8zLO29iUWRsYouJk47faWeckD0PUC2qL6akYhcwwhsEJFhdSuptKhIYxVkhPrfFuoB9Csk8LvKPV2CWNd1YnXB4ybVjMdxjqY9wgkZExihOxKnO+/pkV2DTfzWh0NDJTToupwvseUxqGADoLxDYyKHEgiUQtJy/k1IWK9MMAJpLhsHUkqAHIUGWJ0hkRn7pf0LbANytCdDgzoLUiwxDXPSFCu6kpywITFa4biF2P1prG1qLYKAMuZJhSsb/zBN+WP+THbx0wNY7SOE7rWtKypiVkJdO5xvUtdz7/OT72+AqL5Q4hyOy7AbMNmN+szzBZntZxejYSETym5ClSUxMiVWH4mZtX+arrt7hSFkStUEGGRf7IJw94uJaGQie4TKXCqxjN8GyfT4PjR1+5nk53kU89usI0Si7y9d0Ck8KBFhNDbgM375/RO8NXXi/5tXsHsul6WUf1Zk1ZzeXZi4G2a/j84wV/6eWKXz96isJa3njzbmqERoBJYL1xsIExMv+srpvUoEqHP2br9n3PbDqh72W0O0TapmE+m5LnOd0gJ51hcJR5QYyBYRhkfJUyGK2YVhWbumY7EzCdyAcncaxZlokMLV2j4B1dG1GTMrlMI+/+xu9gff/m9wP/25et6CqlFkU1+66X/s1vTg+bEow1FUIZdQw7yzlN0wJKjrZWb2MBRUEQR3HC9ggRErspYTdjmjuCMypQeBRiEZYg8jQNLIJS586oGMeJqBeIHTVqHEdiLUEc4xFaiZZ11OnKlY2JcJMFH7xPTrjxssOoKxxxX4KXQp06ZpPllGlw3vHREc71fHr5fv705BPsTiz3Hp5gjaGqcrqgiC5nCHL8s0bj0liSrlkTVIZSFq0ibd+jlEndg2gOJWJRTBAkqCCEHmULiknBZnUiEwDS+xGYJ10jFem6gTb2W5VDnhUonY6SYUw6G6/ZaCE2KO/wCephvCZGsEqj5bH23nN02qBNBiqImy1lITSdw/uaSJBhndGjlSNmJhX5IMSSFxKWqOhDR57n6TpHiIrQ90TlMSZj8BsKU2AyMQ4MbUOkwYYWbUpI1ytkAxrZTFzo0vee4aJFGUPYeLIiQ2WWfqgJOkAR8ToQ8NvuMFhJomuHhuH4Pn/rZcs//uyS3zua8J635Tx6UFO/fhvjGjpd8GP3r6Dyp1H9hmIietw+Des09hwzHKNJx/42XWBpJlKHmmtJ8+oHT91lGFNgSksWFDrP8Mrx0a844h/9yi7jsFVilJHPF15ZRck4MUqS+EYVAmheP6l4334kuMC01Hzy9RN2ZiWPNgMxRObzCfePNrjN6wz+Mjp0GKMpMktnc3m2kDCasprTDZFfvbXLgT1kOctor13l4f37uEHs+FqLy88nHsEoT920hETYBy/8gHIyPVgR0CpgraXvB4y19G2TrpbAXp2TZL/eiVlid2eJCgO9R6zPTYcxluPTNfNZRQwyGNQHxP0ZPUOKM4CYzEZRuAqA4Hnyne8n2PJFpdT7Y4wf/6Pq6RcFLyilPnLjhfewd/k654ntOgmchRUfnGN1tkZrIwES6QKOwRNKOI9tsleqEeQ2BZOrRMhotc3HFbeIIaQuM6QCc9EBNwbi2CS2vhhWoeCi2kZuxUWGNJ4L/7docsKNt/+rJE1rJB/GkeVq1BomzDeOv5c6RIKYOebLXer1hk3r+HT9Evfrgqv7E6pCfPC99yxLw2snM0KaYSa7qRBmo5Y4bgucFGad2vNtFGQqqgEpUEIuBqqqElfUeFESnro9rqaqKZ2EwD4xnIe8yzOe6Mt4HhQ04oznuygJOzvP8R3hl5CkPuNrOudxUXNlPuBixIXIEGQu3jAMeCX/HaPf3psYwWZabN8EQtKFhuhAgw8SfmIyUb2EFIiv05HVhw2OFq+H9PmczBxTMRV5wXB710GQQZyu73A4lEm6cETGqBSYQmOsYt0GojY8+cw1fv/1O/zbe7dZD5r5dMp6c0azPuZBXPAvHl0nKydMMk05nYO2aJMJM+9kgOpIaI34jnSA4QJ2PR7JIl0/SGmMjsLCj3/uSY6bjpDuR2Y0be+pW7flP85fd1xRcm0VIofKM7uF0GIUSOKRu442mrNW8dy1PSa55trOhGmZkxvNlZ0pBzu7vLh7KGsj4fby2YSAHh2Px0cnfPp+zsZXWO14/5V7aRS6jMqRKS0RGRYrskw39EwmE0jrTl/AfIVXEYKt7XrKSYFv1qw3LV3XSc250FAFFKt1zem63qqmRlLMmPQcWU3XyzWfzSZJoytrw6SGaBgG6lqko4OXJuYdX/MtzOfz7/vDK6l8fVFF99rbXv4fnv/AN1IUBSDYKalTHIXwxhjR6EIqxGO4TIpS1Aqrz7HdMVksMIqR0+RZZUiKuguuoLETRgrICEOlTkw6ljA+++cVV6ntgr0oR4nj68cIOkvH4wSDjCTD+HOVwrkU3B1HZ9rFr/GwlkgPogjPL1za2XKfoV7x2hsP+fW7V/jxmy8SJteYVRmXFxMetRlDSGPgI8QUsWjTCJgxwOBgd85kvmS2WFIt5ix2lklFIbu2UorN+hS0pm4GgRKsxfWi18yyQnCs9L59HDcbefDH1LDgh6QsEezLqPPPraIcekOQYO5RhjYW5xA9YziO9x7nHc6LG0tIjxRAFKDtGx6te1at56z1rFpP6xWDj9R9YAiBYDzKejwOrx1YT8SDicRiwGU9Xg14PYD1eDXQuzWDb0AHonUEMxAY8LrDqxYfO3rfSsEGIhLo45SnmFqO+xW9bgiZR+mICz2dkY5aZ6AyyAqFtoZpNeP2G7fQNiOfaWaq5zv2X+NXP+s4nLzAT7Tv4f8+uy4SxXQ6tElutTO1XJsO2zAlNcZmfgHWSrKpj1roxFGk0xDapusLvQv42LPpGw5Xa2ZVWs/b1QnRBwgJKw4j+RmZFJaSYwYnAyTHiSw3T5b86u/f54c+dY3XHhsenQ0sCsNTewWHZw0705L5bIqlRWtFkees1q00AFElKExW2d7+Lnk55b17N/nsw10+8WCfyzuG5c6U3osGWUxI56OzYjr9RkT2SdKWZ5lhVpVCJitNCI7br75KVVVUk0KmXEgHxcVcXaUUu8sdul5S56zWdP2A0ZamqWm6gDGQ5zknx6fkeU5Z5hRZnuJgk1ojQlNv0pXVPPf+r2MI6ruUUvkfVU//yKKrlLr++PZNnvmKD3C2PsMN/YXuKoVbR4kENEbwQSHUzgktCedQ5wRXKnLGSljHdkrsWLxiClhJgvaYVA0qgeuM7PqFuifY0HmXIB1p3HatYxD6yFhuR5CnrF2Vfm9M1h2/J4ZAVoqF0GidgH+2qoeRKtweOQe/VXIw/ggEcpjt7BMCNCcP+IVbVynySJFZhniA7zb0g6NP480HJwElWZ5RTTL2d5dcn3d829P3ebCGdxx0aK2ZzucsdxbMZhPyPGNvb5dpmUnK/hgKolVKNZMRSNuNUMl1k/lp59dO2yJZiJWQI1ryF6y1yWWWkRWS6lTlKV40ZTKQRl+HIJrfGDxqG6JznvozK+BHXnmaO2cFmyHQOGhaT9s5ms6jVZAuOCr6RKD6EMEqVBaJKjDgGVTEa8mECFqhjAIVUCYS6OXUZAJRycRibZDxSRaCUXTRMxBxeLQOtJuaxU4la0V7oo/EHGyl6ZU8qHkhAfuP1wFDy3xxiQcPjtm/PGO6qNCu5rfrfT67Kii1xyqIWYUtp1RlwaWswbmBh6ctrz6Ss6tznq7rIJHDIRVf4heCDDGmFReh7/q0fiNWOX765g1unfTcf1yTWxmVPptkQLxwMkkvkgjP6EdyWTpjq6WYBB/IrJgYducFzaD4pVuX+JdvvMAvf/4hbz5qeN/T+xgFh6uWPe6ggc16jU05INUkJ8sMB3sL8swkmK7n5x68ixAji8rQuJyvfOo+q4cPEiF7rioYlU2rsw1Gxe3/2zQ0tR8G7t65w9H927z+mU+ynFVkO5ep257ewzkUmqzrqRs7Wq2Zz2dpXYoaIs8zljs7GC2wQlWVXLl6ibKaEICizOXnppFUSimMyRlVP/tXnmD32lMA/9YfVVP/SExXKfXvPvXO98tu5SOeQF7kMEA3SHe1szNDKVifrTG2ACUTO61NDCSC9RWZoWnFdqqS00RIq9FlRmr1DcFHtEmK3FFXmFafRnDdUb0w4l1qfM+pCGplEjkW098h5eJeKJap0MurKJSKkhmQ2k6V4rn6rpPCk6Xwl3iePr/9Z4vFpfehLw67FLglL0sZid1t+N1H13hp74idYs2d4oDYdYLn6nPTQZ5Z/vwLd/j05x6ymy+xkyV/+4MPOTqtecd+z8/feY6279h0lp0iQDxj7TNcsJgsE9KHNBhSK7xTSXaTdgQlVxQlqWqh6xiGbivdGbMUYvD4C5CBUkqO9WnzFZY5kOV5Krpxi6VrdU4AjRAPwOA1//rONb7zHa+zaXzaGGMyVSi6ELDRQxjVLIowyOnHRIXOS7qhpY8aozwZOW0/UBiNR2NtkMGKRu60HzxZYelSrizIcXJITKiKEZVrcVwpJ7yN8fgY6eqITWEKnsjpWeB0E+hxzOZ7rNcPOD1ryULFpYNLfOR64Ec+b/nwM44ss9zQhwxdTdSGn314SfTWLmASP+D6lsViIeE16HSCG6Gf8zV1UQEyDA5tCqLvCVHTkVEWlhuX5jjvyeyAUznRt+ccRboHalyX6Umo64bJckGzOiPLJwkmkp/pmMnzGsUk9HrzMk/4V7Ea9qeWw7OMXzn9aozqKcqcTTMwKSx5JuqXTV2LDMtWXJucsukqJrnhID9ltrA8bHYg3paTldbbjndca8YowVcHgal8iJydHDHf2aHKM+xkj8X+VYoyp96IBd5aI1mtVGsAACAASURBVMlkgW1hjEpMW0IcB4KSmYJFXhCVoh/cdlDm4ALBe7oukaxWwaQgNCIOCFEgNnnPiuAGnn33n2H94I3vAH7yD6upf2Snu7u7+51Pv+sDqeMU7Wvb1FJ0gmc5n9J0A2dnoo9zQ4fREgY+Wi/HzNem7dFGnE+pGm7xpgvrSqAJPT6iatsFh0Qi+HHDZiS8OGfcR9fVeV1NPupRNzz+a7QSjz8n4bcJ4xmbAu8DhAFrkpNr1BgzYm7jr+dssLBsmvMfd44REyV5qmlapnbgZDNw2u/QShYL1SQnL0px9mWG3cpT5IpJFXj06IRP3Jty+/Ga1kVOu5z3LP6Al5avszuNvHBwyqVKjkTn8IzoptUXYLmwbcO5EBgakWm+RYnJ5H1oY8nyjKwoKIqcPJfgHpTASCNups04kTmpUMZ6TkwxenHLSivkKBxS1u7nHk3JrKbuexzQDIHWeclPQIp7TBGb0vxpwX19lJCS5KqKPjnpomzILsn/BAE3gtsFkeg6H9Pk1/Pmz6NAGzwSBRo1BEUyc5yvk4j48E9WR3QxcnJyyO7uFOcdWaYpq5KZOuV7X+p4507L8/MNzntmiz2Wu5c4bPU2sGckz4wRdchoRhnXNyPkNH748RmJYjqIQUZc+ZQa988/+zS3jjas254/eFzK6eTCsxUTpBQvvhiyKZ5uHFlRynM5Fn2lyHTEx/OQqt5nfPzhs0QUQwj85uFVIOBdT9e2ZEZTljlomUcmN91gVUSHhuAlJuATd2fEEKjdhBff/W4evXlze1odP3MYH0aUKIY0FIXAZAcH+ywvXyemsCbvxYQhuVgCV47rfowvHevBVg+sUjiTFrPKpCwESkvqCB9EwVNOpsk+7DhX6sgzMPZwz73nq/Hef6u6uEv+/3z9oUVXKWXXTffBJ55/NyBSoxBk9wiJxa6bFu/89uWMzURW5PrU6ckxVuQXlszKgzamVZ0/mmzJNZ2Y29FVdp6lEDn3oo1HfLYF+hy9kquwFfAzFuXzizWiA5JlHBMGxFY3GELEx6QL1BkmK9Ofje4m+byjv3/0dYMUIUm6unAWTJ9rxOMm1Yxfe83yczevs2w/x9XqhEk15am9wPuv3uKpxSE2L/ng00coAi++5QZf+a7L2Myyu7jEpIhMK8XB/pQXbyz50FuOeeFSxzuvwVdef5Uyk8F7IDt7lkmuw+5ymjB2K0XTKBR6GxyPUtvA8xFHGy3RyojVOCqNtqJV1sZCcvkZI98vuL24E60R59RkMmFSTdJxODC0Da5via7ndx8eYLSjLCu8V1iT4by8j3o4J5lclCAbonABvXMp+d8zREXrRK9ddwOrdUOzbmiTSy6ESNAZmyG549DEIPixGws3iqbzeGXxEVwUA3QE2iHFUiasO6BZTmdMigk6n9L2jiuXr+LKkjur+wQ64vqY5mRFWzdUkxzvOrrVimerjm5zJsEwScakTC4PuIa8yFKaXRT8FTlh+C8wNwgk1qUYRKUtYRjIjCYkbPXeWcGH33KXdrhgBoBtUzEW3nFCdl4IYZVlhmFw5Fae3aE74+17m3PoTgXqIcOonh/99BP4oLFJSZSXFZMyF52uNtisTMoEeLp8k9vrA7SCs/UaOX203D2r+Iq9W+hyKhAeYyMj5F6WybTuvMjRSsKjljt7HD54gPOO6bREuBfheJa7O2RZxqQqmc8r8jwXp2dRbLHqwP/L2Zv9WJZdZ36/PZzpTjHkXJU1i6RIUWxSLdGy3OoW4JZlA93otgEbDQP2m4E2/OJnv/of8IMBw/CLH2zAhhswutUeJNiSKLXUoihqoDgUa2JVZeUUmZERcacz7cEPa+9zo9hiya0LVCUyMuLGPefsvfZa3/rW90njTmvN4Dy7fc+L58/Z7Xb4IMaVVV1TlRYUPH32gqOjI6q6maiiMcccpVHGsrhxD10vjoHP/bWDLvDVxckt5sc3sdayWK6mIl6GdZQIJyfKlI8K5yV7rUvpzGY90EUj3DgX1LQx8wLKmSdkf67rp3DmgB7KrJhWTrwWuA/i4zKpk/+ebcSlHFZTQEcdhL1J13Hg9V4T00miHT7hntKBl+adTgMc2XNMsr30UKPkWPmz2RSYro9FN3XBbF7zbv/THDfwD1/7Dsq3/MGjV3nteM+Xbz6lNo4hFqw3W8awYHjwRxR2i8dQpQyyVJGZcTIZqArOt/DG8Qe8eSxi60VVYQpLWZUEbTk6OZJuvUlyi1aLCSQkSEbusxsdfdcx9L24RMSANQJVFNamxgayMcpS+LNKT8RxwVMFKDEpiChtEn4p6k1ulOrpshfQ6Gy756PzdaKKQWU1oxeYYPDyTIag8FFPjTBQlMYwBsWml8NvNitoFgvqsuDpZUtQhs5J1rd3MPjAbgxctpEX+8CjteOTi5YhaK52HbtRyVqFZFB6CPjOQ991UC1Ydz11BS/fu8G6LWDci9oXAReELeFjDxqKuiZqzzcfjozBEJyfpv6c82hTUNWNVHdTNnY9LTmwSshN4TS+LH0PhfcDv/7ua3z/ieFbnzTcXTj+/a+spUJTU7Sd1ujBZUTGaJumJMRIURZ0g6z5dzdv8sXTxwz+GswB/Pd//Aa9PzS2jbVUZZkaTYphGBjHkbZtWS1mvLe5gzGK/b6lKGfcWpb8/OuBv/WWp99f8B/+27fRBKzVlNZQGkNd13gUyhQSIbRiGHps01AvVrI+u47laklZlzS1PONZXbBarVJC5tNnChSFZdZUaY9nT0BFU1doUyTurfDQN5sdfS9WPsYYNusts7oi01sVSlT8NKgYKIuCe5/7CkqpX/msoPpXBd2v33nrZ7DGcHLjlN1eZqqzvF8OQrnpVZbFlMIPDpwbWB0tKUtL61TyasjOAnm8l2kMGOI0lnoQ354QqClAK5hUrA5ZpPzd2DSVlv8tHoQ0MkyRZRoD14N+zt5TlpzmxsW0MBHVY8qiU6Y4pcscylStZNoqf+EgxOPJNbfALVJWBudpQ837z0q+8eEx333gGXZXfPjM88bpyHsPO/7o7Y/Q9Q3+xbe/yy/+/OuixaDAJj7sRRv56Grgaozs25Z7RxVWKebFNo00eow2SSRErjkLlpt0WBhjkgOz8Jp9iEm7WGg5Pk1BRVRSmfLMKlFEy3b3RiuqWsRzCitW63WCJXTStcjPUShk0Pc9m82G//vtW1x2GhflkNwMjqeXe8ZrjR6lFL1LMJA8PExaKy9ax+BGDGI5pJW818cXA0Mo2Ow6uq6jd5Zd72jHSDsGBi96rc4HApptP+KiYtd72iT6nb3ejFa0Y8QasE3N+dMnbC7XDN3IGApUdMTosEHGq/WyJJQaTGQcB7rtJf/jeytUcBIoEwRllCjuOedpu2EaRtHGJNU3lW+c7Ast9zqPpVsr8ERukKro+cb7Ejz/5cOb2BiSGWwOuFLJkUbhjZXDsx9G2nZgWbs0WSow4PNuxm9++Aa/+trHaZgpLW91DaRQ8l7dMAiOb6ThOvQ9t2+esmlH0Jp2v8OlSrm0kf/5m5bQX3Ln5Bg7XHD24EMy9dEFCGiqokAr2K3P2a4vGfadzAPEOGmNtLsd0Y+Mo/j+DePI1dUVs9mMomworKGuZR26oLBWdvXRSoxJBydSqBHRfMgsJ4D9Xkabh6ET54l5nZKPpO/r08CSUrz0Uz/LarX66wfdo6Ojf+vWKz/F8fGK3W6XcE5FHpGRE1fI88LdDAmryhs7NZ3QUwkfAGWyG61KuOA1nqtSU7f9EGgz5poC7XXIJIYJnzE5u0r/6YQfxJhMA0MuCOT/mfM7BUn5B0IaFSRNZ+WTJF77/xRrY/508qfPgObhqEiZQLIESddX2AJti4TLRUxR8cHlSko8ZXg6vE7rAmEcuXe6YHNxyd3TlxiTIEhlNaVW9F5z0Y5sesflvuNZFznbdeK2oRQ//9I5nRPNiXYMU9PSFvYwmTaVEodbezDllGsZfUiiI6KnYIpSNI7zMzRG4Id8ME4cbpH/HAdRZsteahnPjzHiXcT7yG+/f4tVrWkKKe/ns2pqGnVjoB0c+yHQDkJHm1WappTNURiBg3w06ZBVXGw9s7IiDFuiVuzHkRhHnBcuaB4FAJWgLbkXLhwkQl0Qap0E/EBdSDCYF5rVvMITII6cX17xJ29/QEyQikiB+mma0nSOWV3ydCu85esVT9uNE785V2a5EsxppFaHakHunZ9gNKu1aFRz+BFtS4iwHw3tbv0pqC3BpmT+c4zSvARh6PSDn1ZwHr7pnGZVdfzaW2dc233TuiEeYLwIIoblPdoWtIPj8zfWqCj2O1opXr59yhBEBezbj4+5aBXvXN6A6JLBZkxcf4fWirLQ2GLOfHVCtMLK6vueoe9pt1vms4YYYRilCTaMaYAosWi2u3Y6tKRCPjgXB+8xCafVCRI9wJFpQm30GFsyDI79rp2U+Jz3Yg6QWDs3XnkLpdTXf/wWXX99JnuhPn3pP771ylusN9s0c6zxKkCU4BZCcoMlyr85T1mWWaaLnIVO+GwKhEopJqlc4tSp9CFiiNNCg4zFCj6Yz9WJIB1J2ULa6DmzzMIqWououU4z01pNo8IKphsn75MXtwbSxpWdN2XUGROO3hGT/ir8OBxywG4BMjdYKSitaPQaK1zkrt2n7CMQtaIqDQSPtRbnLX2r6NzAveOXKGPDO+//AZ9//Yu4dIuGCO8/26JGx8BAu9cUqmA1r2nDwBCB8IJffXPPRQerMvDOi1Oe7o6oUpc2W6qHxDgQ6EBEc3LDYMLPc1RWB9UrY+Uwy1VJQCWrGghuEJ83AmUlZfMwylQdk2aqSEb6aDDAb773Bj9//wyjtuyHkcrOcM4REeiqHaEuLD96UfON9wyDNzRNhS0qvnr3EV+6FSlswQ+f7QkOjosNvprzeN1yb7Vg04o4TVmINx0x0pSG0Wt65+k91IWhd4HOQRMUVsOskIPchUhdKPwIqzpQmRlRWR4/f8JXf/oupYaWlqNqSdxHlA7EqGHR8McXhuB6RJtSbmVhLV4rnHNYC1EpnCIpwnmsNRONSiXdEeVl8CPH5cGLtGPwPjWqZT/0w8j7jzwfq9vyTGI8PNNrSzaGSNe1aC3TcE436ACnczjvCkJ0FCby7aevcKw/4N99Y80/f/+nMDrkrZeqTcE/rZaqYBhGbhzPWbeOu/WG1b0Lvvf8HlW1pHWOsydnFGXD5brl46LieX9MvbpBXYlRZnBiFlAUhvXVFRhpGOa1KGU/aJNjjuxF78akTQzjMGCsZugHvDOUVUkEykIqgeBHmtkM7wNWe5yHZmbZtwM6BX5JVAJ1Yeic0Ci1ZhLocsEzjA5blNy4/yab3f4tpZSO01TKv0bQvXr2iNXtl+UD5QxWIhTTxFh6wD4kgrkTe/CqqhhHL4pMRkFUSaNARMt1ao4k4dGUyabAqhD6TmoEGZuzZ8jUl6wwdFhAAgEEF9KNSgsKpoxVqeSEcD1ApwZDloQMyb5DcchwRaJOPqVSiMSj0kJaT9mCTsFIX+vy5q8pBD9FIZ5PRLyT8QmPHCJD37FaLti2gywsUzB2HW43UsaC7/zg2/zKv/klYoRKK9a9593na9YXe27euUEday63G6L1LCo40oH3LgdUU7IZOmIIjN5wZ/aQ148e8CdPvig4rzEpK4h0XT/dS2N04trmaiOiUgaW7622BXEcsdYK9JA7NOnemELUm6wRsSGUQBZppvLaS+6hGx3GGr73/D5Lu+ard5/y//xwwZON9AOKwvDy8cjT/RFjewUKCiuNscJE/uLsDnfmP6Iu5rSupts8IixWzIwXgSR8agyKeWmhIrOmlq50hMtuoDKadnTUNjuiQDCGdnTcmBl8NIDFhI6lWbEqC1TlKfVc5P/cQDVqXD9iFhodxCCyLCPff25ZzhT7fcQ7wbKHJF6kjSUi/YjRywbOrIYQfMLLpeQ3pk69hiBBehAxFrRoRA9uRGuD74VT76gpy0ONZ7TB+TBVOMMw0jQzdm3LvvPM5w1+HDmt3uaLNw3fPP88Lmgebmtev2X45LLjK6fv8r3Lt6ZNlhlCAC5IhWCLguB3fP3lCzbtmn0/8srM8N0nK/b7DnQxZf0/fGw4uaF5+bXXePTxx8yPb9DUlZTuEapmTuxE4Sv4gLal9CGUpiqLqfmLijKO7h0ajYsidq+0qA2OTmiNYk0FZVGjgscH8ZjTxtP1Y6JBMkGEUt8fmFdlUbBvk6FBUEnoKVCUlbgWX52/DDz41wq6SqmFLSsWJ7cErzOCbdlkXOgTbhay7kDKfDLlqgudiEV4yWCbWcl+P0yZawbw8whtDAkLjSRstsg1kEAEIZn8hcPoq0mDCjEFP+8PamHTCZyDbyaDR+Fm5iw7z7jnYgKVM4Xr9yKfq9O9mRpw5GsIXjIahA0hHOMkopH1IVKpNvrIOPRobbBGoJmmadh2I8TAzZMF+zFwNG8obl/y3R98xC/9wudxzrPpHO+db/Eh0BSWk9sL+rZj8CNoGXS4ubBYFviy4qJ37AdHoaHzYjfSOcW/8dLb/N6DL0E8lLrpcqYNWdcVw+jS9JOc+D7pWyhlcIOo7hP91CQTuEDEW7KymJIpDJyMwOW6JN3uVMQFGYCIY2C/3bFvKv7pkxtUVeoTRNlE7328IbDDFlVqYIqXlYjzaP7526+j4sjLJ5H7R5qmGWmHlv3oeLFvqYuCECP9INYtP/ey4dI5HlzsWDU1l10n6zpGKmtpk2nivKq42nesZpqnzy+pdEHRaZzfMbs5pyMQx55CGQoMLCKdGymVAmP41lPNx+c92oh2RF6vQrNjUtGLyUEhpIzOK5UKR5VGY21q0oqIO8hwkfdCMxuGISF+kbIsGEaXbrjc9Ty4kjUWYgRjS2xhYHcQ/9ZG8/b6i4zqEXf63+eVN3+WP/j4GEpNg2bv7WGPyTt/qt5TQGU9X739gH0Hl5sNi+M7fOvBXYIXxTfFYU/osib4Hh8Vr3/+82zXG+k5BIMLgXEYxGreWDAiMRqCRtpLmnEccT5Ll8rvH53EJueciAil2BKDTOD5kIBSpVFKnokMeMnkoBYiE9nFY/Syp401ggFrgaKM0YmqFkErVrdeYnd1/sZPCrqfheneblYnCbI8iAtP3DkOGZBJc/8H/EmyutxkkyBlJkpRDn5wwJVUgigKa0XZigNcOk2bxYP+bf7ZBNtOeg3pgx1g1ngdQ4wpsKT3mgADNV3nFKVzOX39ZqXk90AZk+898IqFuC8/Kk2N9B2CcxUFxmjGYUjYt0AYWmsGL0yM4B2DV/zyG+dYY7h1eos2XDCOjkfrjh+crUViLumM7vqB/dBSmmQIaTVDEMz3bCP2LQHhpNpkBUOE7QCVPRDtP30f5Jratp8Csk/CN9J0KwS60WaiY+VJPcjjmjLoID5fYg9+WHRJBF1nbYnUbPR+Ei4ZByez7i7BHz4InSdKE/P6/c+NOZSitHJg7DaXfHCxJEYYE3dbK0U/OlFti4LrPt97doPIkm57aQKN3rPpRzrnuGp7ei9UtEpJxlzOZmgTCAr6fcf6/IqqsFiVBnZmmqChRzRI6iryf/4AjK0STCe6t3GCa2SdKm0IbiRrFB+GTA7NRGOuQXSp8TEOo/Di0zBOiAHnXeKQpsrkx9Zr3oP5Pob84JVOugWKWaV4b/0qVjtuVS+IEZ61NyXhMteqlUiSXTzsFx8NX775eGqUW1vQjwqDTPVdjxfW2lS5RprZnP1+j3OO0QsG65OkgLXSuLWJly3rMk4JXPRZIS9VZmlc1zmHHw/j+8boxIgweeFP07Uu+ORsEpO2jE4ZNFP8gOzEkhPO7MYt/z47OgG4zU94fVbQvdUsj8kDByFIQBI7kVQ2K8E3xjFLn8kGMjokF16Pd+IAvNluKctsuROn8WBjxFteW9nIU3MjlezZjSLjpIfgS/pscRJtOTj1qgQnZ4ggn8LX3gM1/Xl4qR+HZw//rv6yf1ZCpUoy6UZnulSCHzIDIy3+vu/pR3GoyDfeOy+ntncYBbOm4Zdf/YTLx5/w7o+e8qfvnfHqmy/zZ4/XPLxqgciQvOnadN+LumLwA25wROD95zu+e7ZnNS/Ydh0xRk5qybBs5hhGxZdvP5fNfa1i0YdLS4dAmKCfXHk4l9S6Ys6U7GTfQhr1NVpRVPVBoCjf9yBj3M6NxCDGf4oASeTEGCG6u1H+3btRtHqDT4p0ZlpDh2cvgXkcRtzoKauSzt5hUQX2Q4dCYfIej5He+wmnfnC5YdMNKRBK0JRqxPFitydGeLHvuOoDQz+w6washrKucGVAH82ZzVeUxlIEmbQcwohzPbOiYNSG//o3a8rCMo5u0rjI1UTWeJZlGqdDzHkvYiwhEKNKvHG5v85JmZ1LE6U1YZQR4pC1LZxnGEXGUvQwEoyeiEI6YbtlYSnLQu53jCgtTaPzZ8+4uryi1j0vmq+z3nX8yuuf8HxvaeNtTqouqcmlV8yBXDN4y6+9+V2c27HZOba7LUYrFtWOsp7R7feoKILtMQS6vmc+q7FFxTiO3LpxKnBCqo6sET6tLUpSaiuNYCV7Po9Ma5scraOwW3yMDEPPfLGYPqZoQR8aiMJkMhhrD0M2MRfJwpGWhvMBK5epOcdiJpzkDC0oLZOZ9eII4NZfGlX57KC7qOoZ+VSKZOX/lMEG8T7LylJa5SAJPiQRHEWSZ4xC5B4cKnmQiU+9nqaahCamp+BlksBwCm1T8LseIvMsf16w+eSH6WiXn1YStKUs5rDBU0MuT8zlEcs4vQfTg73+mqAI8qiG4D8hJgUtN+ISTpoPCeecBKf8ANMUDZnLC/TDQFFVnK5OePXOfQa15W9++R7rbsB5T20T1S3rRaTPYaKidyO6tOgo63I9jDxb7zGpKThG0TLIz6+yiteP9sJFjYLPmsJ8qlxMCbtUJ5nRoQ4YexYi8d4jdjPJG01LXyO67H0lM/1WBbFEGsXBgRBoqjKR871sfD+iCRRWDvuIfB/TlNahIJEpw0MzJx+i7W6LMZbXVi+m4GX1gdCulHTX8wP2Cd8bvWwylPCqC1vgUmW3CntcfYTVoosxhIpYN5iqQBcO5YQiZQrhPXtr+O13a/7b359TVTXOhXTATit2uh5jTAoY8netk6JeelYxYcsKyVJtWo9ZvMj7SFlVhCAMoqxUNw5OFP9g2kshMUacl/0yJDHx3OuQWK6xRY1zkcEpTuqRP7t4kyp2fOX2U4bg+RcPv8CNpiXGnNgoShP526+8zd959Ye0Y0Xb94BmPmvo+4E7sxm/dP89mlnD6vQmP/NK4M7dW7z80l1+8dUX6KIC4Lb9gLIoWMxnwmBJmbTVJOEluVFGq2QkCUrrFJwPI+8ieWqSD5rscedCmk6TICm48LWYAgLbpANxKny1DMZM6nkimUg3OIpSzC9zMlBWM4BDpP+x12cF3UolARs5FvyUPeZRxZyN5lNOK+lcVjYmzYUk8B2FiD0OQ4IA5NQefXJlUJBYsxTWTFnIND0yNcLUYWg+BVOTV6M6LKz8hRjzIMN1KyCFMlbcZEnUobRY5XUQZZH4fPjZEKf1dfg9KvuF5azcoJSMQOfDyYR2SjEOOg0SRIyRDvZisWAxa/gPvnhOe97y9idv05yu+NNPzhm8sAx2wzhBOyIBKDoRwzhgSsvpvOJkUdE7z+g9nkgf5Ge2g+Bd4zjiQ+ClZcU75ytRHc23UFtxbE1TQNZqrIYYHFmTNQ82jMMo8pPDgBtH2eDjILhuKlNtUSSObpG4sCKuvVotKHVgNqsg84WNwRSFTFaltaUQFahPBarp3gumGWOCmqKUg8fLgno2R2tNO0Kp4KipiCj6YaApMu6upuAlEJcENuliy6Y1aW1bY3lysUWHgX4MjCFSFo6IZr4ouBocmKR8JtGAf/adhncuG1BasvR04Okf2+CZnF9ZlYKj9E5yhSeZrRMecaJPRWTd+yQrGGJguxWRmhwownS/sthR0pKOTGuQIO4IwXv6/T7t02u8dmO4fHHOg/UJF23Duh0ZXeS4OudvvfKAh5vlhCsTI4OX6z+uLVWhWDRz2r4lEvjcKzexheE33n6Zsplzp3jEhxc1uD2xf8xvfq8kuIG6tFz1BahIUZb0XUvbDyjitH6FLx4n1bGYfr/SAkGUyT3Ge09V1/ihF51claEoGUQxSbFQTRxned7iCBQPlcGBv3rAj51YE41D0kuZmFk6idBT/aTA+llBVwlu5BOR2aYbnLIrrbFGrF9AYVNp5r2nGxOVLEnHTfhNBDf6A4xFPslz1ioYDckSPHifwYZrWWMOdnITcsdX0n41BcTrerATPDvVDYcTbAKXSdzHeGgIfKo1kH7mx9GHOL1Fxs6uf175njeOWupCsmyTM24t8nnaGFlc44iyJYV2GFNiynL67HnDTt3nlJnrdLG61JTGcNUNnLcDYwiHjD2qSR93em7a8u0nt3i8tfzaW+eUJjImB48IKJPxd01UBptGfj+lc5HGZmPiJWdMUOb15WfFZyrix566qqhLy77dsb54QVHPiUruQR4EsFoGL2zSe1AqUaumVTctzamxpNOodcYOLzd9Gu1ULKueWd3QjzI1WaWsaHoz9en39UEoWaM7TLpppVk1S165/wrHsxJroLYaN45oY3m+7vjg44+hUEQVcQoc8P65xXs1BfSYJkPy4a64xnhR0PfDtPFzdeETzp/BV4EIhBYmsyvpecQomgHpsBKZyLxsY8ZV/pX1HEGYH9aIiWha4DljI6YkyHUUquej7nU26x21LXDBMy/76dBDgfMWF8Q5elmK6Lg2kiHKVKimqQx13XDWLimbYwa9pFO3WC7mMq3oI0VRolXS0rU9b96dYYuSZjbj9OSIqigPk3nEqbmuiLgx2V15cdx2Q0/XdWit6XuB57RW+OCTNsdhE2f6ar6eHGquV0VyOGsye2o2m0GCFg5vNj3iv/T1WZSxXgSINYqAD56mqmn7QTZkDHgfFN4IawAAIABJREFUMRpctwdvULY6zO3HLEaStV5BJXpYTMLnKmEoQ7JGlgAtSu8hpgmR1N1kwgsPko0xYSgoQ+7KZiFzLcTExL/NIR5JV7OmbF6YSHPncKTkYH19V6rp95JK+ANL4hBg5dnkgwaCj3zvkcfMFLiOOAUvlTqqgX4Y8G7g735uRKnAX/zweyxfPZZubIxTdWG1yNPl7CVrCIMoX2mlWJSW3eipjErlcmIbAEYb8J6qUsyKcyqjmFvF1+4OvH4y8mxn+b2P7xBiQXC98ImdXFhpIuPoiclDTbJ/ORy9d6nU14xJK0EcVRU6Cq2p28ssftUsJYNLjI+x3RGB2XzB6ALokGQH1cFhOJ3ZeY1keCMnIDplubaw0niLEas7fBDjy87JIMHXXz7m4aan6kd2g0/NSOFqZ6qiRuGi9C5smpg0bqTb7qibklUtvGRra2LfoeLAz37hDTrvqaMiKMUPzkrms0rkLEPAu5CW2uF5RdJUmRKBHmUKsoyp0plrLtWgSXBDUUgT00ex05k0MZRAI27skxdaCrAxXrt3OvVjuMYAiqggIux936V2PennYqriYH214ed+KvD28zvcPZXgHGLgp2+c8+dnd6e9YU3g3YtX+SmeMI4eayM3Z3N8UJyvW7519hpaOe4fDZzMP2SsXuPhkwvKZsbT7lQO6aGnbWYTtv2F+x4V3uFUDXyyuY2d3WBWgWPFejuk6wsoZVDBc+NkQT+Ku/XgAio6Tk5XDP3I5YuLSRGvLs3UP8pwgQoBTZgEzfO/+SCUT8GAJRao9EzyxGpTF6w3wm33Yu/T/6TA+lmZ7n7sO3zCUEJUtF1HVtkyKZXXSmGqGkyJ8z6NLMp/KqXz1xJAOUECE1ygzTXWgZz/eB8EA/PivpDVlKTETMLLIRwcaKcsTk0nYIYBDvjrAZ44nP7ydT0da4ew+68eVHEKwjljiClzvh5w87tkU7sYI6Y+JvpAVBZSUyR4L84EQ58wOM9+9Oz9nN6NdEOfMhH9KVw536p8/0pjWFSl3JkY2fQjRilaJ5CJNYrGGu4uaz53a86dk4XAGcWOk7rj0VYzK1pc0BxVnn/w+QeUJnL7yGCKmqqei7V2IdoKKtOXjMIYS0Sy4RCV2Jp4Ep6dtqK2hCiHhHeOfr9haMVQUimDKRuKapaCykGZLubnlf4zqQzPMpMxCkZsjUZHWSP7/R7vR4Z+QOmSF23F6D3z0kB0IlJTV5zUJcvKJOGbnClLEzbkLC/BU33bY4JnNmsgih6DPALPYl7J143oRkSj+NFVze+8K/ShrIoXr71nPilMolmapLGAEtUxbQwqX/u1550pm6MbxR4qNQ8JfqLmjX0PSIU4ZW/psIoxiGRp3hNRoEAfIu12Q93M5LOGA9RCTCYDCr7z8Qyt4Uf95yBCaQz3Vzvuzvd86eazae2f7ZbcXxlO5pYbi5p1N9IUij5WIsdqDDfqDZfrLbX/hLPxBj986NlePIbhgt12w/sPtuw7hwp7vv/sHo/2b7Ixr7I6PuKqn/HS0chXbr3DL732IfdvOGZ1gRsGotbse7FyjzGwNFvGvqMfPScry62XXqIsLN1+J5OdeQ+nKlqBZPzpeeVelTEyZaiVSfrSOQAb+n6gLEtp+KVKyg0dQPvjATW/PivTPW83l9IkIeLciFaapqlFazJGtJLyMYPVpRVcLnjZBDnbUwiG66NwZH2QbNaPQg+K5EmwPKwQJtUnCWJyksUUDCeuocpshCxyQzql1US1uS7dKH8mast1eCFvvqneTBl5TCItU8mXjoUpAOop8IYE+F4vdWKCP4KL6UC49l6pFM/MBjc6fueDE/7Tr7U0q5KisFL+RPAxUKQpvxTuhYgfImPwuDFSWytd+RgptObWrMZHydbOdh2PNh3P96If+dLxnNoaLruB0uxZ95FFadj2IzfnBX//C48wSlEVYr0dsPxPf36bpo5JgUkEWmS+VaNGYW/45BuVGFrobA4apGqRxls6aLVO9itJ5D5NtmWYSWuIUVgWWUKTKFm0TUpwKGHOZKqV0prgI30QZ+Pvnb3ML77ymFU5olE8XLfcXTac7wI35xW3FjXffXKV7qhUCnVRQPSUhWHTeUpbElVB1zlGAqVV2EJ86i43HVEpzp9dcPfGCesY+Y0frjDWpyzXJ3dp+Q1ERe47hRCwRUFTl1hr6AYJngqp0gRaMykzC2m9BxTSyZfgL4ap2khlEVMDTda8jG0HLxQoQqSe16y37cSrdz5NjxUl4+CI0YMSBotQ3iUg2aJkt9twsjJcdQtC37M8XeJD5G/cecz/+6PXydrJRkf+2TuvsSj3gOXVo0sKPfK8P0Yp2O96uPwhvbmFVwrsghC2eF3hqfn3vvKcP350j9v2fV66VfNHj1e8WAd++a1z9ruOG3fuEPbn1PMl49hxu3rA6/ctl13Fo+1NXuwtIsvoCKbBljK2fr6FBY/owjFV00g1pQ9JVra0UlpRlCVWwzA4bGlI8vzopLUgkEqY9vswjMJfV6C0pd1eATz/SYH1szLds3ZzmU4ASaeLwuL9KBNmSFofky0L5EM8UqaFJN1Dua6smJQDoTzgVOJMmWLCLNP3SmBDminpJIo5871+kqfs+sCXzUHxMNAgeO+ndQZ+/Huug7Y5cGY7nINqmWCfwY+JKpJ/JmXPKTmbMNUcfNPzldIs4dMp4+i7nqau+duvD7ixxZYloXM59KMUlBPVRe6DCyHhZ/I1FwKVNcxLsWB/0fUMPvB01xFixCoSbadg2w3sBsdxXXJjVmGUYp/Uviprpqy5d4qu89Ta8/c+/4L9mGyYohOMLjhUODT38kXnACbTQ6JNKnBTpt+QHGfVJDISkYaQznhxGlDQ5tozQ9guPqQqyI2CfTpprGThb6M1Yz+icPzpk9t0buTV0xm1AaKnKYUY31jRedBaczoTK6qjuuDGvKYy8nuOF0vE706CumgpRAoDdbOktJE7d27Se89HLwrq4hoOn6b5Dsd+nJaJTvTCCDR1TdNUE5arUBRVNeGKsuFtOqAPDd0QpaFmjRE3XKWmNULaK9aa6ZmE9HunlnJa1zaNp8e0jkM89GJCauKWRcmu16g4cMV91jufMl7RuwhR9FUABm9Y93Oe72d8+9FLnG+ELRFDoCw0qxs3edS/wtXVGcvKEYPHGst21/H778ik1/fPbvGH71qidwS74tsP7/NsN2MZdyxMjR4sm4sWazTD6JnpS75+9wH3VlsGr7BFKQ1zyQswGvb6Hsva0zQVzrlPKQ5OLyX301pLURg8esJrs06LThs8Q0AH4S0p49v1BcAzfsLrs4LuZYwR37eTGpWUTIrgXJprHmXULze0UJRlRVmWNHWJWNJL1A1RzguTJkJi0jRwzhPcOAXuGKTL7n2Qh5goMFOBpg2FMelUUSnwXjNBvMbZVvoQOPOCv1axkvdCzjwP2K9cjb3W1VTpYeTSVolbIWRjw/xL44F8ToxTmenDNRZFzKwJJy4NCjoHXzt9RLsfaLtR7I60pjCaQhuG5ABx6EYHJsU3wMUgbJAQWfcDow+se8lsTaI/jd5z1fd0zks5PnhKrbizqDluhAPpQqo4SJ12bejGwHHV8o9/4Tm2LCmaJc478bUKuSqQ6/MhWR6l5+GdSzbuh8YHJFHpJEakE1YrjgFmghAE48yNp0gMnqHfQxDmQIzJPUEhEomQiO0peAQIQfPOxX3efbajVNB6xbKQimfwmtdPlpRaRlcrq3m267i3qqms4a2bS5oyUNYaU1jmlbBxPNI02uyvGJM783Zo+cNHNydb7oShfVqg6VqjLCKcUaPhYrMDpWmSaIsLIthSFqXoKaT7o4mHkfQEXxVFwcnxXCqLhAnngOm9Z0xDAUqbJMMapmQheFFX6/uefhgSuCf3Tpqv0qAOIRIVnJ+dQQx872zJd89WaKW5s6xkj6nD8ItUtsLXtybwraev8mS7wruRpqmpyxm9g2L107x+shWBcB84Pl4SzFJcYxTsOitNVzWw3kUeuS/yzbNXuTA1z/onbFzL1cMX4BWlmbPzNa+tzvjlV97FDZ1oudhCHoU2/Nzdh4x9R/BhknuNyftPBzfRygIRjDQX8+CXSdKsNsm4RhJDy42iHaxk3DgEz/r5E4APf1Jg/YlBN8YYj269xOXTB9IUifKgx9HRO/Ey0qYg9TSIKUhJ82Vkt+tQyqATRzMGUXVyTiaN8ulRFgZbFqgYCE74mxoJKM7HFGgOlCxSsFX6Gt0DpixhevCKacEqlZtRIUkKHgYqpv3BIeROGVuKzpngL86sWZxHiPsTnQQm3ynye0XJWlQKSsRI1ucNPo0agnyA4BjCS9w4LnjztMa16fAJkcLoyWUVDoI/OXjL75Nrb51LmzoBJqm8H6bfp1IDTv4sjGZeiTbvzZnQzUCyqcrIpE0IAj8UOvJf/Nwz/vO/+SHdKNfqM7Y+Zbtib+ODTyyAhCcak3z0UsMo3eaJ8x1El1dNE0DipKCVYhx6vBsxxlBWDaaspcGVoAnnkv3QNXA9x3elDR9dNZzOasq6piosL3bScCpN5ObMcHNWE4FXTubcW84IITIrrGikGo32Ldo07HrP6APbEZ5tt8xmDSrAhxct5+4U7wZZG9O6ZVJys0mIpmnqqXJsCs1ms6UpLZv1FcRIXVdklsg4jilQ5xH5VDmkhpwxltmsZtuO9G17aAwhLKCs5BfSuuvaVqC7SUNAgpvNwzp5Ei4k9k8IGGtk/4dIXTecnz0j+oGL4Zg/+KDg/3rnBKUindefSmiuEYmml7Gi0fvNjxpq63n7ac17jzz1rGK1mnN1eYVzju1ujzYFi6NjFvOG2o7YouFO8xS05f0XL9HG+9y8ecLy5ROC9VxuNgybjipoLJav3nxMYx1vrT7h6/cf8ksvf8T//ltPefDgHI/ltLxg7LtD5ZhgoL5t8aNnv2un+yVsHbkoHwJuHBm6jnGQny+tJrhRmC/DwP7qHOCjnxRbP1Pa0W/Pf/3y6QPBmJAT2BjDrKnJDr/aFFLGJInGoe8FAwke7x2mrOkTO0Fce4VcTmowxMjk2pCnSFyIh+WjOGxE2U5pb0mYPFiyq1TdH8qFHNjzPHkunw6jkXHanddLLjiMCydUNwVx+d5pZDItVAl+B0giwx65y5shiKmrnN4/Txm5qBlHx3/3ZwZGx8nJW5ioaYaBMEZcOODIGfOeStYc62LuuMvXjDIpy01YcPBJ9Fx+7pWjmtNZRZkSdmKkLgy1lkXRGHkezkvW7aJKfFPDutW8cmpQJJWylOXqxOiQZozKcPmU+YXgpSpIz0vWkNDmvBvphwE3DoTgUIhkpAsiO1g3jRyCAN7h+pbtZi3YaXKzyL+bdBiM0kXGarGEIUoG+XjnebLuaF1k3TtmZcGd5Qyt4LWTEhc1daEJaEbvCbphN+xZZuaChllVgO94+mLD/ZtHfOG22OZMfPIULHMPwQU4Wc1w3qOtYLW7rqeoF+zagVlTy5QeMnySD55DNSDNRc2hiayUiO8PfSfDK+ownUWMqcoT/qg2BqMNZVEQCQl3VyyWC4ZhxHl3gMSiTIE5LxQs+XmdaIcyCOSd58Ld5Ga95++/8RH/2dce8tqp594y8O+8dc5xPfLmaY9RueMv9DdjDWdbqbxCiFy2inEMtLs9VT1j9IrVaokbWi6enzNfHTH6gqLUPNkeJ10J+MH5XW7PDaWRoaqqqdiGlsdXL1hf7VnWge7iI/7kg5J/8nsj/9vvOu6+9jnmp7f56AffIRZL7iw6rPK4vptogmWCdciQl5cg23cd+90eNw4MnXCatZZhIjGRtfgQuHryCfNZ8yjGOP61gu7V1dXvnX30birLpVutVBp9TSekyMkZjJKMTCeNVVOWmKpBa1JHNk5ZX2GzwpgEp2FwhwGIHDyv1//p9xmjKK3Ix2V3X+DaJJoAAznATZlr+lM2waEJN8kVArnDoVCU9uCeEK8FyAzfSsyVDDJnnNNL5SP+WrYzbcJDkLzu/RRjRFvLotT8/sMTLi7OWNqCZrGk7dpP4XRC4WN678yQyEMEJo1JFnmW3HvK5PBQmMzvhYfrDkPGB4VONIy9ZJNpOCIAlVHJT0xRFA3d0DL6wI0FB2wrMgnaZ3qfmrAc6cznQzOP+U5DKd5N5YZNalukA3qyHdd6Wj0ZXsGUyQSVdOgdMt2Q+OEZzlEKfvB8OU18zUzk/kkj5TaSTDQm0o2Bbe+xRtENo5ATlaWymkIpVMzXYimMoiwXeB94cvaCTScNG4l30yqRa04Q12bfohGep9GJVpegORkPlswy90OmCitGJqH4okRlvFspRif4ep6I+tRaS6wY0ponl8/BC+qHsH26rkNdCwWBPO7PVJWG1EsxxvDi2TO6rsW7wB/8qOLpVvHhowu+futjvnb8Aa+dOP6jn3mOIjAGLQ7iaaH02zXBB7bbtQyFVI1k30YaYEWhqUpL0yy4/+qr1OqSsqpwHkJMcB+wKD1XvVQApdEYC1VlmTcGZRXb3ZateZ227Tg+WnJ08+aUrJzcvsvZpWI3GnQ5wxZiJpDXp6CWYaoogxdh9BiiODCn+6/Imi9QFCWr5ZyLRx9grf32Z8XVz5R2DCF86/H73xdx6pShaC1+QIVhaqDMGxEDHiMY7yitxSiRP/NOUTczNJGrq7UIXyQpOhnLjICmaapJPDljUZIlgbaWrJEboksBUi7WJ/dcuF5dHrJaWUSHV2ZF+OCnibeckUkIDoSoJ1nJXLbnzyLZdOqoh0BI7IMQAkEptApTJjwpL0USNHFo2k3VeNo8wQcefPwQ/8p9vnS35KLzPHn8DFNabsxKnu2GVE5L9jo4EbxRyVgyEqmMiNvcmtecbVuMEjqS0ZIRSNfaoYwleE9p5JBqCs2oLd5HRh9w3jNEWFSWsjAywx8CnRsJGAqreLHL8E86DJCMOaLEcjwdfN7nrnvK2oITVSsjxpHGWgiiAhUT1KDTaKYtijTFqBM9UF6i20DKiJODR9REoyB4ghJ0UmtkiMFaHm4qfnTecTwrqAuLC2BMQTcMKBSdl6DeuUijBE+ubCQMIVUiA01dU9ExjJFFM+eTJ8+5dWtFaUvefY4El5QMSIAOKB0prKXf7rG2SC4UHpRBF1VicMgwidYSHLMAjELR9cPEZ4/I/uv2O7AV83nDbr9PFL20UpVMR43OUZhMvUuZsvdEo4lBOvHEmGh6svh9mmirkjpZTlBCCBNW7UOgrBv22z2X5y+4de8ev/vxHbpuxHxkOFmWvHL+gptLzU8fPaEfbnC2LVhVjqXZUtxcURjNh88LTFHR6B1KGR48a1nNLV0o5KBrwPue1hzhEzuGhG2PQXNvcc7bz+/x1bufsB8iWs95++IO+7HkS7efcmquuH/a0a5eozCGfdex3rQURcG8KRl95HI98vKNC7waGccF68tL5os5RJjPal48f462pUA+PuDDSGGYhHhy4pUhtK7vefje97i8vPzGXzvoAt988fBHMpFSNSgE49NG1KFkeMExjB6lJINVRtR2Yi7PtSL6CEmAWay0JdMdnU8NI8XopCPsg8KWxYEZEEISLgYwKGvB9xTVLClS5eZWTIsrZUNBOuW5vM6TJhlHlPI7sR8AayLZkodrAddYPQm85OxYPpfYwiRuzYQ3i+r8IRClgS0UiV1BzHDaFO19ysyKsqLvOn7r+T3+4etr9h8MMA+0Q8o4IJX4spFKbShKzX4UycbSiMj2xb6n0HrinPrgCGkcdfQC8Syqkk0/ir6vUgw+UhuFMoYxQhx7tLL0fUdpxPvMIa7ILjg+OmvRSrRpjcp8zpiuJVcaMVVBCjVpHwi26RMOrBUoa8mWT5D1PAzeB4qiYEwNCmMMQ9emwGzwUWPSqaaUNM6MyRVUICLND60VjYZFrTmtNLFpCN5z0fXiwGENXbLaHl1k3/XcOb3Ns/MXDOOe2fKYebHEjQFblvjWMeoBXRZcrVtmc7i38Chzik3dKPHZEsJd3w/UdSPVhjU0dcnl1ZroA7aqUnCLIl2oDaoQnWKdhIREaU2YCO3mEl0vUUqxbzuMsYzDIAfWtQRbhiEkUWnKgt45rLG4cUBrMWX0Ifc4gkipIgL6bdcxaxp2ewlSfT9M4lTyM5JMNLM5zx49ZHF0xHJ1lA4LePv5nPgcQlwyDj1GBzYGVHHKFxrL9x8Fxn6XdIYrfuE1xYvzp5xtb3G8sJRVxRjENUSoidcBYoXVgUfbE5SC3/34c9xqtpx3NaMv8cOe7z69xd95dcuq+wuq2c9w2RaMXnO6hN7LAI3WimE0fPjUsZjPuXdqcN3Ao0stzbUYxBMyyZl6N1BVNV0XUd5NiVpu/EpFpXn87ncIIfzOZwXVz4QXYoztct782aN3/4KiKCirCluKBY8LgXEcaPuRYRgS2C8ljRsGjErBrShTeZD0J32cgoHRSrKDhIX6qFFIUyQrmOVSXVyIRWlKmQrNiFYxMSLUJH5hrWxuk7CenFnmUpMQpqxMGy1eR1aM9KTxkm7JNTxSpUUcEtQR03CDSk2KCW+eMNtE4o/5FDxwhrPcnCIdSEl8RyH6p8/PztgPgd/4aM6dW3cIO8NstpAsJV4b70WC7ElTEmNkXsoQwls3ltRWDrLaSla3qEpeP2n48r0lJzMRMSp0ZF5ZCi3X4H0Q7dcoTZjKCvd63UHvI56ItQZrPP/N7ywoi2LaCyGK6Z9kcZmnnLVLQzro8vi3Sl1xJqhgGoRIX4uRJHyTtHmDS7zJPnm2kZxADBO3IR4YFPlPFSWQZd72neWcvYNN59mNgcKYZMfuGWOqSnQBpuThk8ecrJKQOIFxaPFx4Pxyhw+B8/WGWV1xtJrT9z3Ba754uxU4yqjDuLfSGGPxXprEIUTOz5+Lc63SuFGgNWPUQZRlHJPNvZoOW63AECiaBQoxCY1A24pxKFMVpSZtXRJEMTiHRqVBJ4W2UukE79lstrK+Y5CMfBiSZc8g/neRhAVbWdsJw1/O58znM0xR0W53PH/8mI/ee4enj5+wXa8hRuazhuVyhTYlIVrC2PGdj1pOzTNu3Thiuaj48u0Nf/h+iz1+g5unSxyW/eAnoSLgWrP6ACGl7YYPirP9gtHLFOVsNqO0im989Dpn/j60H8nIvdK0rpJDPyVOy6ZgNptxcbnn/Y9fYEpZ16uTE4yVBDG7YRdVw36zprCW+WKeeOe5epRgsbk8Z/PsYQv82V876AKs1+v/9ePv/iFKKcqyQOvDA3PjkMZ0pRE0DD1934l0XZKwU3kjkTBhUqmTmh3ZUdilUdK82OSm5oCW4AQyfhqJiE31NTHIiZYU84bPcEhmPciHmXR902OcGmZ50QNTmZg/RwYaYrbn0WbCladD+NorxIhRhw5yHqJIewGUnjAm5wV60EpRFGKj87AtaMKOn/38F9hvN/Rdj9UWq810ytbW8PBqT6E1tTWMSWz8b9xbiVB6CFgF3TByvhtwAfpxwIfISVOIkI6g4AxesMbeDYQYuOpGCmOwSoRsQvDiUzZG6jIro6W2oMpLKeOV082eJAQlMwXvx/Q81BRsp0MpwUQh8W8zNqmUwUeVfmcefJGnp67d0wzhTHh3FOsWEe0WuxWXxnLLwrLZd1gto7YKqYqcd1itaGYzdoNP6lYygitZTaTtHadHR6KWFhxVYbDK88Wb50k75MAwkSpLYYo69UU0URUCq6QDByJ9L8yHHGhChKHvrzVNQdkCgrggZEw3BgnAcpd/DE/OyUtKCmzqrYj+sVQgOakRxxeS6p+eKkVbyCBKVVcoYNbU5Dud91vTNJR1xXx5hLUlhbHs1ldcnD9nc3WBVkHkAMaRRa255C7tbsfRzPLHD0punJ7QjXHSbAFwQdE7M0kJ5Jf6sc0WgZNmkFRHW7H5CSJov+U+nwxfoHVVcuCQBG05bxiHjt5LU//oeMnoYNxf0PU9Zw8f4J0nuF4qjKJAKcVssZQAXJYURcEssV500pU4++G3aZrmt6KUOT/x9VcGXefcP33nj75BM6sZnFBWpKMs2MvU+IgR5wLDKHYX+e6I6IVmsVxMGY6U3EmMOuMiUbKh0cVpbn9qQpEm1hTT5I33nhCk8x+VQcZRJThnznAO0nkcM63D6XOgxOPLWpuoZ3mR5gZRWszXnnrMZXAKxDI8kQnw8bDQtcZjEiwhwi6EOF1TxrJBiNsKwVOLqub52TNmNvCeu8fZszU37R3UGKirCpdKn9F5zrYyaViaA5Two/M16yFybzXDpU1Rl5rtOHK+69iNMtb9oh2YFVYabkqxqC2b0bGsjPhGlQUvNj1RWcpCyPOBwP/wL1dEJXQqrTXzuhBrJdmGUxNGsPkxNZ7yEIwcVjJYIg0e+d5ISI2kkJxVUyRP90senlGRGH3ivjLhnnnzHYSVcqUkBppGa1y09ONIocTWve8G6UVERWXgjZsFRgVKW2JT9quJVFXJMAYKKx33GyfH1FWBiw5TiEvyvC6IxtKPJUaB946x205Sp9qWLGY1y9UK50assdMEo+LQK4hR1rcbnUwwDi1D10MUnNs5B7bCjSPKWOG4R4i6mCo6OOyZfMrLyKpO8EAkOMcwDJycnuCGXvjnUTSMc0VSVRWr5VyyZq1YrzciqhOlIRvHju12x9FyhougTcHy6Jjl0QpT1RTNkrqZMbrIZr1lt21JqBz9fkvnFJ23rFYL9oObDqt8Ib/8ynNCjPzqW895ednhgpbq+VMpjshJfuH0XCo073BjT9e2DN2esd/R73fsNpes11tUaLm6uqIfHPOjU2whVWJlRhaLJY8uG6xW1MsTXpxfiABV0kdRUYYp6rqUqT88WsNyOWM+n1HPGr7/zd/m8vLyf/mrYupfGXSBt6vZgg+/+8cQhM4zqwq0EipPbg5lPyeloCqMCD5vt4mcrdJJml17SXJ+mX8rWaaZjBulOyyvIKZ6wWNA1KdM6hKnVXsYRmDqoE/jGur6+9kLAAAgAElEQVSweUFNdJzrWWxM5b4tbGquJcGPlDVl1cd4WMdpfPkaFS29P6mUjT5nDodFkjvQmRFhrNwv566JWkdo5guePHrKHz01lP0Zq8WC+/M70EZ2zxwxkcfrwnBvWXNrUXN/VfPK8YwAfHB+xcwqbs5qjpuGbvTMCsNpU2G1TkMXZnJABfFsi0ow4aOmJPqeWSl6AOiCEAN//qgmqjzhJBoZ/TBKc05r8mSC1rJBqqqSYBzkoEg3fIJjVMKhI0o66zFVBtlFVykWi5mU00oaa7lxMT2/w909/GXKkNXU/PlHX+5ou4FhCNTJU0sTGcbAbgis28C2C+zbHdu+JxDY9o7aRLoQacoZftgyuIhRDqstbeepS8vohS1SFy4dfoqoNPOmYBxHhr5liJp/9Pl3xEJeHxIJn77/gMeK6tduswZdigKbVon1AdGNwgaKkWEYJIpNh72ormVhlipN22nNhI1ba6nqOkl3GhlrTWUySKZbVhXGWDbbfcI306CFjxSFoarrZEVvcM5TlQWzWcN+v2e93dG1e/rdmu1mNz0h0TRWFIVhsZwzm89o+zHBfoftgwIXNK+fjPyXv/gJ9+eBv/vaC/7xz3/Cf/KVj9GfeuKBN453vHqiGPsWIlgjMgTOebwL+KAIIcFYuuHo+JSQqsxuv0cbTT9qijIbKRTUpU1YrmLo2zSclY1AIwHJelFSOTgfGLeXfPS9bwP8+mdGU/5/BN0YY7x88vF/9Rff+D9Yro6ZzWZcbbaMTiTU7qwCL90qmc8a5vMZSunkWSU46Ha94fL8nL7rUlaY7mzMCliHUl9FYTzMZw3OZ56hBWWoylK4mCFPmEl5q6cNxtQUC5nkHQ86olKSqyQknQCJKfBKWRqCeJdlvFZpGTGdSlgyyIAQx83h9mWYQT5LmDLaTGWSTEyoWVlVwo8uKWup1FySLEMDs9mM/eVz/smLz3F5+ZzCFJzaJW/dvMXX7n6FRjXgDZvBMS/kELk5K3j5aEbvAo+3A6jIcWN49XiJVZqrfuS1kwVfuLni1aMGkOxRKU3fD7S7FmMqCiKVMUKlGTp2+57vP3X8ycM5MQVWGdGVEd6yatBKFOdKayZ6Ur7XE9803YMQVQomwv/2bjhkhVpjy0rYIzGw2fVoK7xwaYCYaXoxP5O0UpmA+Gs1qNaKW4uC0j/GmpK6jAwp+Dgnh6tVYjteFxL8K5vGnQkMQbEsLY/OnlE1R1gc81nFthsoK8PVrmNwI4SRQqf1CJii5mrbMasbTo6P+YV7T3m2Mf8fb28ea9uWlff9ZrOavfdpbve6eq9aqqBobNMUQaIVRjZSYqWxHCmxRZzYwkGRIzuWowTHli0FmSAIiGBLBkJnDCY2KbqC0FTR1ysXFIFQzYNq3qvXv9ufc3a31ppd/hhjrn0uGFOdWX+8d++5+5yz91pzjjnGN77vGzy8Gi5Vb1WNpw1gPVwWfYNvWnJWi9NiNJmAok5glbIkGOuhL0u1/TQyHryUrIZDafa66NqG1WrB+cUFTdPMlWVSyDDFRJz2LLqOECPTNEhlC+z3ezbbPTlJdjpMUrWsN2vCJKKBvu9puoUICWLF+pFKrcihMk4yykhgJrixDEzJEpLl697yIo0txOTJeSRTuLU2vOOZh+fkygCNLXzBY+dcbCeiXeK8xTdilWoxl5q6olQNIc6j6tfKVFh0LWEKGGPoFz2+EQMv7x1HCw/KvbbaJ8JI4z7GIMWYa+gXHU+9+5dYtP6nSynnf1xM/VgyXUII//wDT/4Cu/UFuRRuPHRDglpOvHQ3cO/eGY0VpcyM4WmTCISwLg0Bdd5Cyh2jd6SWicYawTdzous6VqsVXd/hnHDwGsds7Fy9FED/Vw6zyar94YyYaSYqGYB8/ww35NnqZjZmEfes+no3P+UZR+SS3lovUyJGTV2sWlfqv1z6r+rZqdm5wg2KV9aheVkbet43lBR401VL37c437BaHhHiyBtPX4OdWja7kVYz/zEVNQq3DCHgSqZkw9LCtYWnAVaNZdU6dlOgFOZ5U6tlTwgDIUqzsms93hROjlfcOD3m0aOiAbc8+LkVI0QN3DPM5WrR51uZDPLXPB+QKSVKEt19nexqzOF7yvzzFXZQyKDx1Y3MXI66ivvrH2d4yvLQstB2LYZC0s3ojJizCFwkgpxFIwdD4yxXlp5V51l5yW2Oj09wJXDnfIt1jr4RRZ8YvQsr413PVlNvPZ6tzKwrtuGN1yJPv3Kb1gWl0c2EQvVXNmLzqPcyRfHrlb9qY85Lo9gAqLlQufR5L98LWfOmrmxQzPro+Jjl0VKEJzHO0EMpsFiI6ToUmqZjvd1oZaoYb0lY60lRaHxOq93l0RHjMJJL0Qy6eQCHrVcC2taLUXzjZw71aZv4i2+6QzGGRSPzsl0RmLEgpuLPni9obJqz4QIkhA75zPkVri5E8h5jpF/0LBbtIXtGnnFKCWsKF2f3OD5aSS8pCgNqvx/YbvdsN3vCNPH4q1/Lfred33tOiaC2oU1z8JcWuXXmPT/7rzg7O/unf/hT/+HrYwq6pZQXe29/9nfe8eM435Bz5uj0hKvXrvDYY49wdj6xGWCz2au5si4MY9XYPLLbyWl5ubR01tC0DU3T0HVykuacGCcZUJhTwDhDt1hgnCdo9rFYrVgsF/hGZq5Vs3Mzb2ih2HCpUSD5hx4CdbFWXO3SBij65XqaCnxgZk079Wtcdg2zGKeGJTliSlLqnFxz4FAoQlRTB5kuHJpIVdacU2a1WmBi4Bt/55h2fZcYAuuLPZv1jpgGXnf1IYb9xAdeuuDebmKMmXu7EYPhtBc8ylgIOVKMY9lKIzOkhHedwjzNnAFdOT2FOBBT4mI3ERNijH5xLhS0nB4o3SkIvitGGKD3JMU4N4SYN618vZL+K0yQjQXrZ8y+KERkULcyzV5nmIiD6c/huHxgtepEhEOg+TMnO3bbgDWBzkPr5XtXreW4c7TOsPQih35o1WANTFGrAOsY94FpGrh1tuV4teD52xe4klk2njgVxhDZh8h6Px/ftI1j2Xd0/YKveu0LNN7xyEMnPHFyTmVT5MyB451EPdZ6x35KlwRFAlWlAjkmwjThnGS/JUdd+5o1A3UsFaY2huWexihJwRiicOCtThA2kjxZYwghcLJaStaassAK2hcwRmb87XY7SThKIeik7/t37+Gcl/fftdw/OxcRwbyj4MrxktXREd45Jh0V5Az852++x3/2GTcZdom/9tkv8199zl1MkL2bS6ExHmvhQ/eOefpsNe9LA/zFz7wDKfG5j2553ck51olCNpeCbTquXrvC8dFSmCEaAUoxnJycEnVPp5jZ7oPw1lvPctFhcqaJt7SilmSgzC6IKsa6lEg8/9Rvs7753E3g5z6WePoxBV2As7Ozb3r3236ISRVSKQQKMoJ4dXKVabemVS9Kg3TknQZA5/xs3Xgo6cts3AKFKQS8NZyerAR3dZYpamMhipn36ZUrLBYLht2WOMmEgK6TDDAXM8MP8sDq477ErS3MWbK4WTFn3KUCtprNVrpXXTgPbHAtN2SYpBwEOU6kII5bMclBkEKAFPQzigBizoBVAls0q5273fr7Kt2oaRvybs13P3Oddz69YzHdZb++h3U9x8dLPv30DcRg+NBL95hS5sqi5Uov2G1ImYvNjoghh8AuhEOwixMrnavVeIOMOI9ka8mmsOxaduMAKTFMmb51nJ70hGGjjUJDnT5bZdYzjmpE32QrBlby3LCsR5dMi9B7n6Po4FNWJVrRdaNYZwryPGdvC4URdOFXVR41uzNI0w7hjj++jCz7Dm9h1XqOG8v1hWPZWGwpLLylbazMGrOGxhZuHPccLxfsNhseunrKQ6dLXv3QVRyZq8cLhmnkpfsboinsY2JIC+5OR5QiWfXxquEvfPpN/ovPeJqr7cjFNvLRsyXvfvahGUYqpciUA60Eu7bh+GTFsFuTUzUAsipOytp3aBinih3X6cAcjFmcF7N6I8ZUUVVvOWXCFNherNmutxhrGYeBrPJXp0KZ3TjQtR3jOMjaUAVhClIZWXvA4CX1UB8Q1MQIw5UrJ/R9J/LctsFby7XrVymlMEyRerq+4eqeEx/wqdC2no4JT0HVtXhviRYchq/5Uzd5/emeMVsaV3jNlYkrnQhAWu947+0rCgXIPi7Ko7fe0/c9q1UvvhcKu3hr2e/3TFEw/kXfMY0Tu2FiPww0TcNEr9tdRUZzH0rmAEoTrvC+n/8hpmn6llI773/M9TEHXeBX4vbs/R/8jV+SMs41MjUgBpZHK4aL+5Kq60OareKMVc29dNwNaBdVB+EVlOdrmEJgsxuJcaJrGha9qKSyjlC+d+eOOPrYiqvI6d14aLt29lSYJbuzik23uq2YzEFWO7tf5UQpacbJ5L1dbv7o9tbAnWOUctpKQy1p0E+5NkWkTKx45gPS41KzNBQXNg8EfWuEngeFYT/g2pbWO37v5shPbj+TD4+nXLz8Ue7e2XB6tOAzr7+a17YPc+8s4l3D41c6TvuGIQRe2gZubQaKVUaHdXjrWPS9SEeTZCTWGIwT/XjnW5yxhHGi7VecHnm8iawHx8mVK4IjaqCrksnLDUtjrKgISyWP67QJAdZF5qrqOr0d+hoJ1k5/RmWhFDVosSpntupa5qwEyvp4C2I3WIqMnK8YP8YQp0hjPL7I4EiDzvNrLOpFQ8iZmDN96xnHkWmMnB4fs95vGMbEZj+QbaM1kyPmxJhbfu3ZV/HTT52Kz4EXW9MvefwWpy2s2iPymDnfnvPC9tFLiYY87kas+CSr80aaVwKJ4xQ2MiVBmjCKq85zCfXPAkNI4M4pUqim/6h0X+5x17U0bUu/XKjYosH6hoLg5ctFP98/mbos6kRrnTavtdFJPWTF1D5Txy5J5dotFnSrIx5+7GFOTo954rWPsx6E9TQmw3//+c/xla875ytfc0aJAYfRTLMlROH4D2MkJ3WyNYYpWr76Dff44scvuLd3/Ieffp9hELw4R/iyVz2jPhFaHSHwUgyBpmsPUE+ROX+7/Q6n0uPKfzdA27SklLl54ZTLbw4ub04+ZykF1zRcP274yIc+zHuf/JXNNE3f+bEG0o856JZSyvn5+df/yv/1nRQ1Ss5KAXLeE42dN07bCC5aHbCstfSdTB5g9u3U08zJoikY4b3lLFSyklg0icY7Qsza3YcQMjkGhmGiMZlpGMlFNrJvvMQ5Y8TzV4NwZRhclpLKZ5J3UQCjfqU1GB5wxaI/5/C9VShhKgZLxZo1tzYV+UWT+8ssikOpzKXFcfg3+QGzR0GWcfalwOmNh7j1wrM8Px2zyHvGYaBpe1YLwbvcNvLK3T0pZTpnuLbsyaWwHhLrIdDphOUpjAzTRFJ6kNchfnVE+36cmKZEt1yy3V7QNQLteO+wThqardP3qzxoq9zb+jnqvU7Vb1mxQqNwkDi95fnPOQVZEJqlzXQ9DhVRDGGuWqr4po4iksPsADVYI6X2Llu6vgX0wLcOh52bWMLmkInA1hg6J45gt++e0TQ9y76haQSKsWTO11tSTlyMgdffWPD2jzzC/a1ixqayJQynXcLZwhCziBGKZdhvsa7RAyhrs6vMkMAUETMVFdAUI81i753QwkqZexC+cs3nA0iK7jpEoK696lQnq83MfPi7t2/Rde18rxeLnvOL8wf2hXDdD4ZJ1RdF/lXWaj38uralZJmp5pxjseiFOeFbhikczKewjNHxptMdHostksw0XSMcaSCHwqJp8M6IZNYIxDJNhjdeG/nbX3STcZCJ0a3zrKfA++49gbOGaZJR8ilFxt0Oo8mDtdLEjinReIlVx0cLrPOcnh4rZiECkhRGHrlSGR3y2VFILCUJ7NcXgc88+QDv+cnvB/g/SinrPzJ4/oHr48l0Ad62u/vyU7/3rl9gdjRIicbByZUrmDyRU5pHIVMOXgfjNMnm0wCy7BuMu6TOMob9ftDmSoBSuL/OTNpZlFKrEQ5kEkwsZlG0pTCSwkgpia7vWOj018v2ckb/Us1xjD2cYA+IIDQ7fcBgxgjBvHaaD62SykOoV/2FtXmhTmKVDVEPAZjHzdfvM7aOiXfq8JQY9iMYS8rC5U2pcOXqNV56/gXeNnwWdnnCvTv3MQYefvS1XDm6ynFJ3Ly/Iar++NWnC0Dkv43u06ZpaL1TVZNhmkaBhow01RpvOeoN2/2ezhmmIDaKjctsL+7LAi6S5ViFQKrH6Iy5zTJkBP8LQeHWdODmZjGcwVh81x/oelboUSnG+XkBM0WxetTW5tE8jbkegjnPLJcrC8dPfMjSmA6vWbO1S1FvF4vFChXRG3qvYpqcuXbjBikKzaxoZTOFiHOGzW7gZNnwA7/7OgC6vhePVi9UI2sLlpEhJGIamHLi924fMQVtHpaCUbzU2ULftywXHSUHGt8cRDclCU3OelzTibOYHhK5SBMr5YJROpkwQYI2eZhFESA0zBgix6sVfd/O1UKKkUXfM06B5WIhP89ATFFpVyqBd9q8vHSvQYL/0dGKtvU8+sgNbNPO6tOC0T0ue2QfLf/Lf/AiJhpcVg8IYySpyBlbdAZgMfgMJkLjDJ0ySlpvOW0DjU0KAzm240ApE7f3RxhjabsOY+X/y9VKm16GaRxmOK9tPFeOVwxjwDeOKYqZjdDrHN637KJaEaQw2wKUGoApPH/f8tZ3GX7/yZ/b7Pf7b/5YAyh8nEG3lFIuLi7+5tt/8DuI417LTDkZV8cn3HnlJVJOwoccJ51WKpniLJfD4L1lCHKj67gSCXhaTlnPOEbdPJEcR7yThsOqly60sU6mpVLmmWolG4b9wDhOckqq+7uVOzY7YBV900WzjUpX0s8oG1pNtg8htcIlNXDOaCIHrJfD/zX42CqMqP6l1swAfO1a12+pAcZ7h0UlkEmMR8YgKsCUM1dvPMzd23d5ZuO5enzEZjtiiiOEjEsN20EpayUxxMTpouONN47YhcxeRQghTMr3zHSNpxRpALZtiyGRjMEZR788JmO5tzXzeKX56CmAlrJQ5my3cLi3c1Kf5f0X1fsH9WQWm1jR81cIJoWJeldqo4fqdKU+DJUOxCEmy7goJ/jb8dEC13bsp8R7bhb6yTFOiVwmxjAwjGU2/bbF4IvBIX+PMeON8JZTMTCOUBIL37CPGdNe4Ufe/3rKtMOo/4Qxcng5K9zm+0Nh4Ty5tNw9W/ORu0faZ9D1YiTrLji2uxHnJSPLxZBiJOjY8KTNWnLCe6vNYjsPbW11MkS9EYchrkq/c5bGS5YmpX/HbreTTLlUFoEY8+ciRv25VAHR4eZKEWJFdq/mSdIAb8nA4uiIaMTtzDnLFETANEaDt4VHloG/+6dfZHtRcNEStoG4DaQQscVRQqakiI2G6jdtM5AOc8pizDQWTDI4YzgfRHh1bbHgb33R8zIkV017YkyMk4xrTzHNlLejo5WIvGD2nhijJA5tLz7Ni9WS7W4kpah2nczBFiNBOQ5rfulf/BNyzt9QSjn7mAKoXh9vpksp5Rfz/uIX/81P/fC8IAqWbrHg5MYjkoGmKNlITpQcseS5RKybrhLh90MQg+q57Bba2OUMpuhChMI4ycZ1VsxffNsLnqtKp3oC55yJMaj1pGCCNQOqGWdd//P4H120D9pCHrCQup4P2fHlLFdeZGug1vK2coBlxJFiQ5VupsG//izxi5ADqk4KNn8A2qDIApjCxDP7jr6xgOXhRx4mJ/ksnZrCLxo780ZvbyesEXrTohNJZ0qXJi0rXjeMgX6xlEPFOJzJDFPmqVvq2dC09K2Mmln2rerPzfw+F11zwKfrInMKx2iDU1FaMOrDUA5ClpwLqFx8xoEBLkmWTU5VjF1LDjDQti2+8ZwcL2kXy3kuXe8NH7hoaJuecRSOrq8+x/o+64goh2Hhl2AsS98QppG2bRhDZDJw7WjFh+8d0TmhdJWSZm8AaXpZCp4rCymzd/sNOHEVm6sA5B4452jbjjc/0fP887d0Ccnz902L9S2lIIeQKYJBGjmy6wEVYq2idMwS0oD13s+JhTGCtTqlhlXWjXXNDD9Yvfczt6ccaJ1Vtl0rmHrgWQvTFKT/YAxhEtjRAFOCWAxf/tg5f/VNt/grb7xLzlLJUQo2GwwW79TfN2U8gm9LbNMmbZRYkELdL0YSz1KwJYnXSwaK5fpiO+/YWr1mNdBvGvEhmaaJtu/nCeSlFFZd0ANKsnMJviL2qRL16iNiyWy3G577wO/w4gd+85VxHL/9DwXJP+b6uIMuwPn5+dc++dbvY3f/ljaiMs5oFzQONFUajBhBx6xNLbWf83VyhGKWBbH5yynOJT+gUl8pnyouldSsOepoF+HyOR2HkzlediwXnQYDN+NjCpZq1zjPAaAGvTlYajCsgXcOHkVwLDhwdCVeySKvt/Ky4Y18u/zeNDPYlJ5WimB2psIK8uem8SyXHef3z/ReGN2IZl78ogdvKSnh9vcJY+T5j36U0ytXWLYrLs62bCaZ4NB5S+csnZdhht4kQgxMuQiVxkkpWm0jjYXdMOIsHC8sw37PooncjTfAir/DZjcxTZH1Zss47NULVg7D7X5k9sgwZsa2G+dBWRrOS8mXlahOyTJgEnUdy2m245T+gSoAddp0MepnaoQI5J1jtVxSjNVMJnH3zl12O/UiNpafvXVK2Dn6tmfa71kdHVMNv5umJSczO2kVCjlEtsNWLChLol10OoF34vMfvTUnAsJN9bOU3Rp4y6O36JIljiN3tntGrkKp/sBSaXnvefx0YBgHPvzSnpMj0fGXXPHuQ9PWIFlo1tEy1RQ+paT3UjjwuuCk+tNmZ5XF5yQMoHEYCOMo+2Ve04qTu0NQrjQ/ox3705MTTk5PsBT6tmWx6BVaNJzdO2O7vsA3jojjT5+c83c++yW+7k0v89nHa1zOrNeRsJ0Y9nvCIIwNEyFNkTJmvJFqN/lMFsBeqqVGgrA3DoeRYacqrjn2nsZ5nDWcD4Xry8M9kOVnSCHStEIDHEOcfWMKzK5on3fto8TicQyM+z3SNFMqJLX6bHCI/0hMmZ//3m9iv99/bSll+Hjj5ycUdEspT5c4fsNPf+c/1mwyk2hQZ2QmnXmWkjpMlSxsBe0UxliNlfVBU41QzKy0qVNQBUcR1VTluQIYU2QRGsMwyIh562C92bNZbyQL9rrJle7hG5lSAJfdwS5xcOfAfBnjlSBdStJFPCeo8hpqgL70Bao0uIbxMjecRAos0c3oz7NGwYoss+fu3z8TC8VUub2Gy/Sgpm1Yro54/oMfIE6BHHZcbANnZxtOT69zkSZiSjgjQeCRk5b7u0AukSlLdnay7NSURyacxmRIRUaJhwDn28x2u2UzwtO3DeeD4DLWKF6vEA/K3pizVC5lp5jZKzikOHOVYxC80PvqpCWCh2kcBLqphkgpz43QrNlGzGDMwXrTKg4dYpQZY0Zwfu8a6qh7a6CzhcefeJRpV3B+yW67I0Wh96UoOvp5HZSIRYzhF76ltw0himmLs0ZcsGIELCUVxmGcDWNydnzOwyNtvySbwvnFBb/69GrGn40R2AtjeeGsofHCl62lrjSsikIpUqnFA4mGavxukMNfHkWmbVsKwobJ5dLECOvmde6scG2p1EqtHbLChGDm5mQxKkRxMoomK3ukXy65cvVUuOiNjB63znL99Jg3n4789dc+y1se3bIthdJKiAnTSE1mjI4CIstntf1VSsxkD8nqDEEd12WdpSQDSRKNMhX2G9nrKYrRVuOMDLvsYQwzRqhxJuCsyKWHKWCNjFiv3HrhOBt+9cU38lD3DIEVy6MFZ7dvYxA8OYx74rDj4uyM3SjN2Hf/+PcRN/ffnlJ62ycSPz+hoAuw3+//17ObL/LUO39eGmelsFwuZEHB3Iiq2UTtxFcnpTqNM+rAxabtZsvEUsT8pWai3luWfSN0IueVrynfO01hxkNL8VTrRGct4343B3hjinJHpYyXbFuyyDpvrUbSrJmwNFWSbgTm/5s5y9Xs1Zi5seM0qBtr5jLHmur0JFlwUaiDUh+AZP5N2zJNQTK/GGf+buW7gm405yWoNh2T73nk2gknxyvBtELk/OY9ri5bGmvw1nB7M3G68KpAyozJMIbAopPRNGOSYDaFidNlS0gju92Gn3r6tbz9hdfx7tuvYdic6zjxiknamUITY1D4p3a3y7wGDsHhMGXVefnUqY4LV76v9a3ad0pWWEe1V46uNMrE4rMyZYyV4HC8WmKdJUwDTdMRwjCXhs5aQiqMY8Cbht3FuTjtqiRbgjPUqiaXJD69xhML3FpvsUYmS+QCIXciL3c6Tt7oRIwkI6q2ux2b9Tm5WC7s64lFfA6cAUpkFuLkJJLblLSclXub0sGrYkZXVJ1ltIoqSHOsZvzS5GtkL1XFnnPzJATnBIPNKc5skoqxl2K0WR3nigsMfduKH4ER4cT52QX77Y5xDBydngpOj+Hxxx/lPDq+8vorMml5F9nf3TFtBqzNmFRIY8DERLaQnSpDgby7h1u1kCQQOi+Ck5wTcRvJQyQPhXEzQSqUUIiT8Jd3KTJFYSxsh8zDRzpBW/snq67Bd4tZlBU05iRlgDjvSaWw7C13x9do412EJ+d377DfDYAjFo+xDc45bj/3Yf7fn/9RLi4u/uonGjv/OBPzP/IqpUzGmLf8/Pd9y3te96fewtVHHmexXPL877+f669+owZYMBxK+aylS+OtZhhKPavlvbO0OlJZNkQi4Skpk+JA33ei0288pVhxcvdewHsjwdIrhS3lgvWdLNikFoA69r0qxlyFM10NupLBOZUKG+fVZUns7bxTee/cHapeZvVPzFguILBAfa1ms7lIkLDz94lnQZgmGtfNwxyXq9PZCzbWAO10Lp1CF0nxUdf2pP1ATon3//7v80Vf8Flshz2LtmHQNMkZGa9uDZwPI6ddg3eZMSRSgm0s7IeB33/xNk/de4ytfTjwXsYAACAASURBVDXOhTkzalqhnyVV8dXMpXJOD2wPuQmSgVVvjSr8kOpCNnapJ5Z8n5UgWDe8Meo2hxyUqRQtp73exzJXQalEhnHiqOtwrmG928pGg/nQtQa2u4EOy0hDicLBJKlirh6E1jGmRPaeHGHYbgUj9mLaEnNhjK16Qcf5s8WcsMay34+sFi2bi5EpRH73pRXGFMKkCq2iXNpcm9DCV4/KSS1ZBAdJPUOsYW6e1ca1HDgiJBGqnNznyiM/0BMFh0wFbC40bQNbeS4Yo4f8NBtE6ULFGPBGhAUhRIxvCVPEOUe/6LHesVlvWK5WHB+vWA+Jv/mGjxJyQ5kS48Ue1zYwQozjbNAUcobs8BiSK9jOQYY0BqKDEouY4lhL2SdMBmcbaDNNFi8K1+oeLJZoDJ03GDwnneP1bsevPXuFabcmZcPeSFOsIPex6Re6bpibsgKFGUpJGCPTmZdHR+KmqJhu1RzEceRt/+QfMG0v/lop5aVPNHZ+wpkuQCnlt9Kw/Qc/9q1fT5xkrMijb/gMWmeUNlZfp7/MoLO4ErkINhKj4HQpygk8Tjpgr4B1YrNXs7v9GEg4LSllllXKeaYQlVLNQQ6jz/UdHOhgMOO8uYioIapBT9GgXHHlksWE3SiVqw5RtKZaOqrJtJadAvVKg8Si43DSpIYu8vMap9zkEjBEvBE803oZgY5mfn2r3qtFeJptJ1SipnEMw4D3nusPP8xPnz3KxZ1bnF9c0PULOme4f++MvvUMUcayOwvbKdI46fouGhFevHS2ZYrw4vmO3me6tueF9TGTewxvE2HcY32juKFgblUxVnFv4Wk66rj7S3IGYaVQP1L1ljhwt6VS0MMqy1qocIICN9roU7WTelokfb4lJxVaWFKS9TCNg1Q7WQJuzuLg1nrLL7zYMgwTCcHEbTY0voNs5s03TpEpG0qKND6zOrnCB5+7xW6KfPDekh9+36t5+0euzWq8OvU4Kx+5aSwfvdezLR0/88xVSjaEEC99rgONMCcps7OW2pJxxwPzQ9dg9ehIKnefoZ0iTmnz/SoHuK5odZDUxvTkRKxVxWxIlGMxqVrUWkzdKggHenlyrPCOPAPnPTGO88h2rLiVjTETMjiS2HOWRNM1lGkS5lEuZCvOXKb3mMZQGoNtnOY4Yvhkc8HFQt4n8sUI+yg+thnIgr3a1pKtIevUlyFmxpTYTYH76z0pwyPHk9pXTjPMItCNGufPvQC5Fn1LTtJIi9NISGLiL3EkzfmFc4Zf+1f/lP29V346xvj9n0zc/KSCLsAwDN9477kP/sav/+vvAsT8OWYxpvBq7isNkjJjWkXLf0mKjPIPqzY6zxBDLZ2KKtrI4qGbC1w5PaIoxcW7gx9ALnluEZciQb52fEEoXFlPsJkzqR1VY5w2RA5ltCQFAkUUZVyI7aDyIEuplaD+/IJ3ApckxWStk66sMTJWO0zTDLfEmGThI7+37xosie1mM5+yVYEEqqEvah1Z4ObtM5q4pRjLzVu3ePTx1xFjZj9Kk8Ma8dtNWacsG8l6Q5KGiLVw3LecbQZsGbnLm+Ve5aIZldPGQ5mbjpfZCQdopRrQWIxxokiDAxYvJQBZG6FFnz36zOumqMMoBUrSLFkPVfGEFeZEbf6kLGtnueg10ArLpUILIGKHUgpjgmwMKSRMsdIZr6W9PuvOe0zxdO0Cg8c7OF0uWHj4xadvzI1P5/0Ml4nRuMBGnTf87DPX+ZH3PcTdTcVLDwY+KecaEWWtz3lBnisY+VwShAUa8KQYEJ9hZoaOuLQdZO6XXe9qtKjwlvOOrFNaZKKL0/5ENV4yM0b++OOPMY5BYQg9IK3AFlMIqpiz4iSXC9e7QC6WHHRu4JS0KrMYLzWd8QqfFPlzsWWW7FcoD+XtEhK+73DGk30mbyO5KTCBTcL5DqlQUiJMgeOuo+s6NiHx8pmRitU2WOsYQ4Cc8W0va8uIijYp66Ma76QYCTFScsLp2CkjPgEYC8/97pO89x0/dufi4uJrSj09P8Hrkw66pZR0cXHxn7zn5/5v3v/k2+n7juXqiLjfEmPSZouM+zBI2Y7hoJJRqaGobfJ8ylPEVDnEqPO0ZI6TQSzaztZ72sbgnUiAu7aRxplmLMyOWNqI43JDxsxOXpKtFcXq1OJR2IjEKFLEotNAcwozRimwiUoDTcaZNJ/sIURCEO23b8W5KSWFO6x4EGQsxnci7TRiZzjFwhQLxvW4boV0pM18+Fgnc8Nc08xKoTsvPUe3XLBcdEypcPfWK0xruLoQrHHZCjvhqPNYI6R3a6S0ev7+lhgjQwiM2fHi2Z7PufYCgisGHQiasd4p5qfNOTIpRFIUfwTvKruiwkl57rSj/IWcAkUnRxedACxxQcaBW6dTcOtEYOsOfgLW4qsFqLHzMFLfdnjvOD09IeXIrVdeIoYaCPXgTplpkibXn7meObl2DRccm82eVJi9CYw1WDw5iZ5+GnfkElnvN/St5Wwf6JxmoRoEYwjEkChZm4cFdsPEUWc5OWrpV8cCs6Qsr0n1jkA1g6pMhEp5bJuGVmXfXdsqfJAl4GoCMX+tHsbaZExV/j4jQAXvPMvlgikEqRTzwVXMzWwSqVQWfcNi2fPyzbukKHCCU+w8hkBMgnkO+x3zXELgr3/6y0zGSiBvLHbpobMUA661WA3wKRfKmAj7SB4TcTsy7EbCNJHGQFbIavnIQ7jrS3Iq5DFL4N1EksmqcjXspsC1rsEjh/p6mnj3Cw8x7Pa6XwROaS20/YJCIaVCDIkUBB9fLTs26zXTOIpxvHMPHGRWh86evfwcP/Ed/5DdbvcflVLuf7Ix85MOugCllFcu7t78kp/+p/+IOy9+VHiZVqYLtK2A+imLrRsPKLu0fCzSOa5zh2rZWuW7Sd2MSgqA3PgUEyEZQszstxu6vpEsqLFqRlFHusslY781DhcpM5gztiLkNs3a5skS+h6LMUizywtNKCWK8iLnBo/xCgV4quRVlG06A82amVuZCwruC7/SOatNKvV8oKrYHJWja63QfkylyWU56W88+iqC72hMpoTAYjHw+I0Vruk47jwhFVatY9EIju6sYdFasnV83hPXWHSeh447rh+1PHL1GuuLu/O9kykGkyzSXFiulkT1OigIxiyQjkyQrRzjBzI2PVAPh4cao9dLGz5CXjfzCBljlItqBaxI5fB665yOULG0bUvKmd1uwrh2bnDW7rzR+waGH/8INLs7vHzvPmHYYTEymNcYcnHc31yQioz16doeZz02C9nfGVHqgazPzcWFgijMwT3nwtGiY5wmSpyYhoHFajlXXnXCSF2XpWQaZ8UNrmQWfadjfCrLY5TsuHCQoqICiZm2KKovhWkfWGNW6YA5BcIgzBDvnIx514Da+Eb+37Rcv35VvAyQeX2zQVGRjoXeUUqG3VamEI/JMBmYtoFIkuqhkczaOiuih1hIU8QEwaHJmTQE0pjwQFyPIoLIlq4/plktCWcj5lghCGMwzqgyTg73zhs2KbFNYti+jUtePkeSn1I4PT0RvJ/DxG+Q3glGqp/tZo8xIlkuxtB2nY6CL3j1z03jjh/7tv+JsN/8d6WU3/hk4mS9PiVBF6CU8mTYb/7bH/yHX0cc95xcOeXi/h3GYcA7bUIkKb0xYjZSV6Op5eYcbEXP3epJXzuOMUtTQVQzWTLRXMA4Nusd0xQEP+NgxKLvTQPswQCnNnYqNpAUcphpaqjhiLIv5uaErU2hAzMShSWs7xRvTCrFPBwuNejoD1GZtCwYqlR4nkJ8wP+sqfaYNbPR3FFSbtYXF7yyTbxyd02JO6xZ0eK5efs+npZlK7hq1wj52xrDGDIPHXdgCn0jVoIyFTby6LWrsulTIieBJMZxZBxH9rv9zE5ZdF519pZFL5LL6w8/xNHJMcvVSpjzc6dd75uV54bKMSvpnFJEM6+shfr5q7lKxYSrQlAmPFu61ouV4N27Sg2rpvHVXEjEINWPllL45t+7zo2rVzk6uYpfHlGc03UzcLxYYgtE5cnevn8GtmGc9vzwe1+DSJD1iWrTtVZPhULfiX9HTlmyQucZh2HG/iXgeWGlGE04JBWWqdNJaJVRFZapKEKuApIUEznHGZKQMVBW35dUeI3anTpXXdxESozz+EbGiTvnWa0WLFYLnNIoVydHnG+2Qq20TjL5OGFLIscwr/uclUVhjDYEC03vcL2sMzrJbI0z2IX2X3w5NMBA3ORCwjWG6XxHmhJNI83J7upDjOdbutWCcLbDtAJ5lM5g9Xe0raVpLddPOlZtw4vnW/71e0TRFxX6SCnStqKYq/t83O9p24am8XRdS9d3IuywjqNlzzCMOuSzcvEzP/Htf4/h/s3vCyH8s08qQF66PmVBF2Acx++a1ve+/Uf+8d+i8Ybrr3o1jbNMk1iwSbOszFmn15E8tTSN8bBZctGmgpYxXkuiKWTp9HunmalYBwZtxCXNOEoNUNqEcLY2S9AufJ69QutprrClvCYfTHYENkgPvKaWdNWnoVQQmcP7L6hlb81YS8Ua5bOCNv1qEEUy4pLjfBDNDsCagVd4BKQhEwv83L3HiHEihMgTV49wx4VHTk9IpYEiY1KmUGid1eArjaYQAuvdQE6BxsP5xZYnX34CjMwWg6INQzGLFoVgwTiPbxoWi44rJytygc1my8X5hWa+4HyrnGguTQxR5dMlr92KiXaNwzpxvKpBesY+szY1zeGAXnQytimlOM/pk3Hdds5qvNcRN3OJI4qt46stLgXWZ/cF3zXgioNsKNYyjolnXzjn4jzy8v0zfvzpz6TrmplNQakudlkPiULfSSN0GEe6vsM6x7jfKwQjnhLOVWxUIDdjoHVOsXWpGsZpkqBahT3a1IFDxlkNboQy51DXC6FgKlYpzbEiUz2c06EAC4x3HB8vFd8XuqR1RvoMlUOLHOwG9JBwusPl9c7Vvxeu9JGQDaVvKZ2FxlIsuL4RSMEWXO8p3mMXDc1pi20dfiFZZdd29N5BEIP0/e0XCeNI3O1Ak55IIAHGCa3CtIDvGEvLe1+6yzMXp3jnCDFxvFywOlqKjNjrTLgiJuRd1xFTYtG17LZbpmmc5dRRm5rGOm00Zn7xB76VWx/+3V9fr9d/45PFcS9fn9KgC7DZbP7OvWd//21v/d+/Hmvh5rMfEvefKOWzlRpIAp423GonukplY4pitwcKOfBAgATDNMrYd/LBsUqCoWbAOmmgqU0X5MaLRk4WqNNSteJWFXqoeKMkqkb115VHqRxdW7U8HHA2vUrFrhXPjDlTF+z8/bUtitKCLvk8SGC59PNnToA6OyXJxLx3rI5PmTYbXvfqY4zz9N7iSISSafuG04Vj2S3pGnnUIRYa1eTnUlgtVxwtllDgZnoDGMM0bAnTMDfH/CwoEcGCd1ZwXmsZQqTpWparFcZYYqhDSfUAcl4pd3pQFPV+tfKZC6IMSkYDutJzxE3L6P2S40eaG6gHRWK/3UiVEMMsGZ8XtDE6n08y5imm+dDbFcvFsJFAZ2EaomCuOZPGLJM8TOT4xPMzH3qMxhVimPQZaZk7Pxl05pzcE9+2hJgV843aq5Bhmo0yLUoRc5zVakG36IiFgwTXygEibJeKfVeGbpkluLlc8gsxB+l7tc1MOlG57yW4pTCy2axxCs+1XTtP414tF3MglQNR1rmFWWZc6zWrg0Yr9/oLX3UBBnoPIWZCEtbIJkT2MWG10jK+4BdeqtzeiYMTQOOwyxbbGvzxFe7cv0fTt6RUwGSKE4rceYiSPTee80ESlfd88Hned+sJfvPpRuEfx36axMKy74WBoPc/RWnudX3H+flabWalMogxMOx36o0hWe5vvu1f8NSv/tSHLy4u/kIppTYoPiXXpzzollLyer3+Sy+8793v+qUf+DZOX/U6pvV9IYYbK2q1LPlMUnpXnTxwOajWKQMyZwtERlmXg7wuhCAmHcosqMbFZJ3UUI1RisqJrcp1TS3tjfroCq2qMrbgEpxgNAheIquX8iA0op97hgSMQaeWSnSd8bAHuv815JaZf1t/b0GmBoiEsx6wwnEtRfilwuqQIGSd4XiVeONnfDbv/cjTrO+ID2+KUTxK0yAeBI0E2kk31hAyJYsZ/aIxjNFAmpRG5OcDsZbPNbuXBlKUg89apnFQeMToxjxIluuHla+rF4Q5jAUvGnCqIU79uDNH1ToNvk56AiXTdR3TFHG+lUGJOo+rro+u6+afA2hZnmT4oDX8+isn9H1PSon9Zs80SnAcpkBxnn2IGJ9ZTwnjpQlTlPmRc2SzXjN7FOhz7dTPGYUDplEUlrX5VyW1aMA2WZqzMRV2F2dSBapcXjoIEmz0yc+TrkXc4+eqCvSAKkW9XkW11rUyKHZKme1mzX4/kUJis96yuVgTpnH26510RFOjU59jCKSYGYPEmlyqxNnIHkGSodOV480PT/JerCFbQyKTDUw5s2j9DKk5U/AmgMl4W3CthHW/PMK2Peb4Oufrm9y4eo0wjGSbSRSMd2xIXD9qMc6wDYXTZc9zt8/oV0esB60Ac6ZpGq5duyIjp4qkKilMVO9gjGHc76ljhYw1bNbrAzXOStP2/b/y47zrrd/z8nq9/rLyMcw8+3ivT1gc8e+6SimjMearP/jkzz6ZS/mcz/qK/5TT5QneVZpJout6BvW5DCHS6KTXlIJgtlgxLDeGEqPaq2UMagdZ6ibIFINOJpU5baUUiJliLVMto4z6s2o5AVDCKN1yY5RwLRmV881M1BeenwSAqguQgz8rhFGI0wQU4fQWmQRnOXgsVHP1+VAxaLYkxPUH8c3Ky7QKJagDW5HMI/+BQNY0DWd3b/EvP/IEn29fxC1u8OpXnTC4KLLfKeK8o+8c9zeBVecIqTCExOmyIWbofaFx8Mq2J+dRqF0xC/Xv0uamiKObwTAOI03XgUJGMYjPwTxoURsROR0Mzu3lQZ5W5SGKrES1ELSuZsSXTYhQypHIXYdpwptCxnJ2doZx7Uzwt8YwTBIITFF7SVPIMROCPPen7vZ80XHCtZawCRQSTDDmRBpHIonb05InX3xk/gwFaBuZfivjjSR7t07MZYz3OF1jKWd828wQRIwBR5RROxZ8I+rA/TDRtgbfdFDN3kuGonP0dI2bIpM3chJ6oXh2SKUgs9SkCvPeafNIAtFiuWK735OUm1swmCI+J9N+L14YRoJubUxOo/BcW+9EwJQUYzeHisw1jhtXT3jzQ1tCypgkI6FiSSQsEThuLIbIb9455dau49ntEc5Ir8WbxFseveBzr28lmRkzdFtaHNM0kuJEe7zEtLBPSWAlb5gKhDhx82zkHc8+wd27FxibaL3Desc4BfwU5wqt6F7yzs/JQQxixtV24v871dcrDfCpX/sp3v6933x3t9t9WSnllU9dVDxc/16CLkApZW2M+dIPv+vnnrTWfdbnfMV/zNGNR9XOzjCMgeWiYzdMMklC9eTO6lwmbcIY7VyiC0nwPqjRa9b7p2nuPBo165BFJiGwdndnbq4xOOslay1JVUmQVeftLeIBnBuqA1bSMrGWziIDdfi2ET6QZoTOOvVKFc/QmeNqBWc2FLJBMlrN0qFm06Ym0RgOGFu1oPQWzeKzZkYJrGcMhle/MfPU7zXsy4DFM+x3mCbPwwwfOm15/4trHj1Z0Tg5RBpnGVLg37xwXXo63hPGEWOqZl/euxD3szZRhMM47Ac90A5NndWiI2aDCTK+yGpjSbBqqHzqmlkcUBkJDHoH5oBb/1mEK+IN0fcL9rsd1iT6tmU3xrncTznP76mURAxQ2RgF6Jc9f/kzbpFG2N9Z4/sWM0U4auk6j7Mjd7Yt73zuCtYbZWkVFgvxqtjvtlpx1HWXyPYwkaDerxjlUK0Hah1d45zHNY60Fe5ozpGEqLDQ3oC1On1F37O18vyMNnf0blHiiHOtrAeKVoVi8OO7jt0w1tKQ6g8io5RUWh0iU5E9EsN+DlTWwBSijMuJiZOTYxZHR4z7gZQLRycrzveFz3/VhjIJF3kzBW5c8QwBzneZ7/nA6+msJE0xJVobgNqEtqzaBI2lzRPmdMX5y2vazuEWDZbqVFc4WTW4rrAZLM/c2RJy5uc++Cpy2GOdTDTBiMDj6tUjpih85ypWkakXEg/arqdtBA8PGWIKNI062lnH77zjx3j79/xv93a73ReXUj7yqYqFf/D69xZ0AUop58aYL/3gO3/m13eb9Wf9ua/52xQVReRS2O1HaQo4o1iRZgmOeWzzoSQvqkiiMqvmLEo2rjTqxOBZy7wimTHk2V+hqKTSUEnhBW+9PCCJiGo8IjS1xqFsBOEZm0tMgqy8U+dEbNC0VtkLkh3UrDArGyNr4C3qHWD1a0nds2rnHexhoxjFdq1RI3Ap9733OkNMXtP6QtdlXvPqh4nhLs4kFm2PsQEQjmTKhTc/doQBnrnX8vjRjmJg1cJvv3JCU/Z6llmmadLgXiETcykDz5KRofxcgBRIxTGGJE5WIdA2Lb5pqGPo5flJh95QZa0GZ2RSLLPYpCqhlJpqBVdfdI7dPhCHNd534nCWxW8gU2bqag3aFTdPqejMtIDZZv6fD1/lyx65wJ6ecDs43n3vmOXacD72bDcboYbZWv3A0ckRIQRKNgzDIJloYe5BlCI+rhUzlrExFcKypCgULGMNIQb2w6BqMbm/BxGIEW7qperCXoIwTClYLxxh13bgOx0aIEE/TJGMsBaG/V73Qe0OVBMmwKj/QNEhocaxX2/o+1azw+q2JnBC2/fEUjBNo5aYiT/3ac/iXU80iR966gYXQ4P3svf26/ukdJtgrDRdnWOxWjIPdLTw0u6Y83HP5z7Rca2JDEcdFsN6mFg2MuSyNIIFp2y5vd7jneet73HYdo8pqGVoCymzWDZi1+jErL1WVcKyyDM3uSCj2xvrxR42Rtqm4Z0/+UP88j//1nu73e5LSikf/JQHw0uX+RQ25f7oX2LMyfHx8S8++plf+AVf9V//z7i2vfRv0DUe45xmCorzJZF4higiBEPdtLJyclFZ3/xzLjV9Lit9jGCJc9Km31N9QRsvktiKv+WcZ/w5hhHn2znAeiucSWsKBYv1jW6urJQoS4pSToqSyM3AfM2OJGDPTg3aNKnMBPTz1xYSBwK9vocHG3ZAyex3e173msf4S296mvubGyzjbZIt9MuGbApdZ2kaoTtViKRvCj/7wSv89ktHWAveZqb9Fus84zAJ7/Sy1I5LN3RucB3GoDvNkmrVcvmZVMls5UHXUniecxamOdhrq1IDpzSTfNNScpodwTBycJWa3cY4g/H1EFCxE9YI+T8lCYBdLzhwMX526ArjgLVWp0SYOWKXUjQTKqJMAjYXF6A2hFIYWLq+l8MlSyWQc1G/BJ2OPO7wbYNrOuU+R7KxlCQQlWCJl6Zm5HxICqzT9SZQV0F8dQVukCkRYRzE7S0DplzCsw+HdhUjRcXNK63Smjq/zzLsNweBQFZVFmYeWplTomlbfNOy6C1jNAybC7CSwYvrn8wvTLOPtZjtHJ0cy4GdE9gGYwqdL/zlz7nFSW/Y7IVlYw2UZHAeuqXnAy9PnLTw3b/qsU0nLBZb15GYJi0WC+EFez/j4Bij3gp6H3QPhmx5/ZULXrhYYQxM+4F3/cT38+63fs/N7Xb7xaWUp/8doexTcn3KG2n/tquUcrFer7/85u+955d//rv+EWHY1a9TimBKYRypkyKsczIvrQiOJgV55X3WEkhdplTCmVVKGmJkitIcSzqeutpJztaQ6r1gjBFryizOZ1mtBJM6mHnfqJZfsulUJJAUcxB5eCtlZA0mzlsZ+OcaZOsYJbpPmuHlufFhDTrbSt5TyfJvVXefwijqLTTLsZWKdmhsVVzRmELjCtkGXnplx+58pFDwxrKb8tzkqZ3nMVq+6tPOaVzBm0NwjeEgwZ7lqxVfLfV36jC/kmchSdYAIRxiaVjOGwAkZQVm+Wsuan4NtmkxrlFqUvXHUHqSEUK7VD5yOMUw6L1KehBJcHfG4J0MxNQkd2Zc1EZoCIH9blShjWEcBgqG7W7AAn3nD2tH30pGDtDddjNn/t5ZmlapakkhHCvTNchZ1VyW1WrB6viYXAzTNBGDjFMvSYZgavuUfOn5ylsXXrN4M/DAsxPVHzqd1qkwB+kV1OrIKEyhMFt9ltW1zlk7w2Agwcs3rVRSCsG1TUPXNtKYignnGlLMTCGy3gSG3UDBkWNiGgZiTIRpZApSJYUpEGNkHEfu3r7D2b17rM/PoUSudIF9sLy06cnA0dLRtQ7XWvpjx8t7wzs/eMHzN2/xz37ZYX0r8KMeCppxaK8lK/WrzAcdpeAbL01qJ/u1lMKXvuYuz52vCCGSpol3/OC38xs/9r3PbrfbL/iTCLjwJxR0AUopu4uLiz9/+yPv/aGf+ra/i0mDZBHG0HqvFBgJoH3rCTGzWjYyjbR2hZFSzKvSpp5ign/KhqtKtqzYbghRBjBSMyfZUFmFFbIp06WTHeHJgsiXeymLSgGTZMpFbRjFmJgiFCPS1qwTDS43jHIWfC8XgUxiPhiUFNSyL01yyltzMCzB4HxLMQePgUpKpwZA/Sld17EfEr/5yoLSTLjGYkMmXIyQwSM8XTC0vo7MllE0V/oA6Mj4mPTwylJJZIU79PfNzcBaHWkQzirVrjPNeh14CFAjV830hZ8trm0xJIraC1b4oqRhnjxnDTIEs1/I/UEMzaU6MZeCi3b7L2XZmIql5wdl4DqqyBnYnt8nTgO77Z7loqddLKT8bBsNTgKlWGPZ73akKFQvoWfLfey6bibWkwX26XUElW88w3YtGbSgsESFn9KcCed5zVIqa8FSna2ME+erCpU45+j7HudkhPgwjHPz0mAO9Ecqo8HqehX4QuAEqzh9nl9bGUSu7aVPUScLl0zXiDot6d9NjpCiMDDihMTAMotb5maqrfvAzNhuSpnN+Rm3zwOf8dDIG2+M5ebfOwAAIABJREFU3FwXNlPhYgw8ezfytt96iQ+98BK3tgNPfvQJmlZ4z9bK6K0YkwzYRA8ntaZs2kZMh/QArF4gtZqMxfGa04mC+AS/9du+ng/80o/91maz+bxSyoufdJD7GK8/saALUEoJ6/X6a85f/ug3/ODf/29Y33mRpvEiWdRmhHNeWA2miKl2NQxRbCCEhLFWM2CQtsBBjlkDsgRKKfvrbLGaYZZLWVddruVS+VWom+GgXDPGQNNhnJDRU+VVcsA6vY6Wr7PPjGbmaPZXpwsUmAnZQoTXe2AODbPqZiZ77VB0Gy2VlEIh795Y7p+d8/zmOicLQzk+JpOZ9uJlagvsBmn0hSgZuWyAwlEr4oBKP6tiEnIlRR3Mgg4PsgIMemlA0Luhc7KcHqraPNOyX7xtD/Qu62Rcu0GaTVjBPqtSrWk7SgqEmIipcnQPWfiltzQLaeoX5owXkXUCMzx1dr4mhMA4Ck5eBzKK/DkpfapCVkKTszMlzuoBKa9hLskP+n1rNSvP9aDK8/ufiTJ1XYOq6fRz6L8YhT9KzXK9ZJ4xF7brDda1escl4BpNAI3R+W9UpERJi+XwuATyqc9WAry1RpkSDb5p6RZL6Vn4BuO9eAKruq+6vfmmUwm7GPOIl6/c9Xz5+dQ1U+S9Nk3Dl7zmLo3NPHwM47jjfS/teOH2HaK/xivjDf6/Zx8SdpGuhTq1uaD+xcq9NcqKijFptZAv7RvZKhnL33jLy/zac9d5nX0v3/0//hWe+513/tTFxcWXfir8FD6e608E0/23Xd77/7JdnfzwV3/t3+NNb/lyQqizz6yodepyLDJqeQp5XoD1AXpnBRubG1CHxZySLGjvBGMCOWkN6vqlJiNogBZpaU3edD5UlfwqhtjomJlSG0w1SKoAwyuG6301yykHjE/enbz/SlnTYOq81xldRrP2iqFVhyqI04jV11UrwIoVZ8WKH75xyle85sM8e2viDSazbBr60x7rDMlKQ+r0yPAdv/EYIcn4nkWj0z1iYL9ZEzIUDQxQAyZwQKHnq1Ka6tyrmlmJZ+6B9FwbgZWjXL/HWYPxDnSUjDBEsma5h4DgvWO725Nn4YVkO0Z/eF3CSRWEIrsVbNQ7hyESi2CmdbBN0QzTWsvJyRHON6QiJkslZ0JINI3OHcuJ/XYn+KoTbLfTbLPre/a7rZrrx3mici4wDgNdK8bspaAWjwIX1Azae+UuW6Pc6MsrpXonHzik6H0puR5xsuhnhrtWDLZyvCtUVOrarr2Qg+z9sBYFfy358PPqfpBSXtZuqb8nV8GKnXFjOSiSeGVowKceC1qZ1BHt3luC6Wk9jBGxQgXCbs04yftsbFE59KFSNbOFQOHo+Ii2bRnHkb7vCTHPCVrbOHZT4c++/sPY6Hnq7hV+63ee4Ue/6X8g7dffuN1u/355YADhn8z1J5rpXr5ijP9yf3H/C9/x/d/CO3/0/xSDjaYRkw6UA6tY7hjEtUqED5KZYZDpEop5SrTSTW6sTIV1VrFcwXVt1f/rmJ9c7fOsBOSa7c4cv5qRZem2ixsa1AU0N1yUy1i5viklKUNdnbt1SXUhv4CaPUvpexk3hVLMnLVVjm4dNHjZjlIgi0wlxoeYefas42jZsDpZ0B53snGLpbFCf7rYw5//tDOcLXPABXQDyWbOueZgNTt6MNzWbMzMmz7PB1QpKl3W9z37asAsja5QkTzOdKDNlZrNV8N7aZrstmtM0eymFJ2PZ1WuabWnUuafY+YgpkHKNCoD19eV+u7l01RIp1YWScUjlbo1jqMM6XQCfDgvjV/nPcN+T9t1oqA0bmapHEp3MNapKvFQHTRO1mjN+Ov6nb9HP0M1Ki9I9UNdM6ZS9erjUQ9rrbgq/Db/bE1UauAzHEYgoZ9dKI7KnpjXYl2yB8MbkP2UqtKSWucUPXyVcTO/PzNj3FBw/397bx502XXVh/72dM65937f/b6vB/WgVnerJcuaLFm2ZSzZmBmXg02cvATHZYoQkgohFO8BVUllqMqrQIEfLxAX7/HCUDgvJDhJUdjEENsYP+NJFmBbsubWPPakVvc33uFMe+/3x1prn9PCBgxGakl3V3VJ33TvuWdYe63f+v1+yxFpyjiHfUsVtIoobMRK0WC+s80BN8JqoBFRU7o/uis3ZCXdzvYO8pyUgNTMZSZICLhx70kUbYvPnTyEj3/kD/Dff/qfYrrx3Lsnk8m/ejECLvAiZrrpAJTaNx6Pf3fP5de98Z3/28+gGI6RZTSxofHkxq8ANE1NgRMKLneYTecg9zKe9KBwQeeXmhMh4XLBi4yYfBygRNGl6CFkLTxUDwpAh91Rswagh47NbCS7lokGAglouTUijXpuRFUXU2ZNNxFtCJlzxIITLAwUVCWYeR/YfIdnc3GDBeCBjYL5RmB1dQXfdfg4njobcdOeZUyeW8d4zzJslmGjUdi9XMPkBr/ypQPpgYKiTHe2s42mDTw+phO6amuSyTUXEikTkm8aa1LQhBEowVyw2fRVd4obOtaYND6m/4L0mXizjB51G1KHH5H8H8jwiK8BZ5NaKVJ+8XUNMWJ1ZYTz59ehNTNV+A4JIaAoCpqowAyCuqrIfId3wLqcw7gMzjmSP2sDxdAJwJN6YyDXOaVSVpmm9dYNS1FBOHlMYVBSBRImsKw2IEL40ZobZ84azhzJGyCKFSRv8jEEaGfJZ1muD2/YQTJVvmaRMXtiJegEfdC0ih47hl8r8YWDsB6IveMT55qom0BM3HSh+UV5nDhJkQ3E5QWWloZoosHQUgI0sC1On6tTNaAiTW4B6JqK37LQkKwmbHpQZNAu5z2LhhJcvXoWz0z34E2HtrAzq3D7kyN88j/+Ozz4+Y+enEwmb48x3vtnR6W/3vWiB10AUErZ0Wj0c2aw/JPv+on34cj1b6CHXAEyW2w6nXMgjGxqE5Bn1J1teOKAApKrVZel0U0jnEgC/AkCQJTg7HkXpiw5lU+8s0pAj6AMJUiQQe/h4UzhAvzSmNSo6weqtvVMQAVlr21LmCabzACAZgUSdatJhqoTVSYCyqQmmzy8iGTufsnwOew2G9g/WsK4mmG4MsLmufNYWxnhPzx+FAgRmRb8gP7jmxqzaYm6KlMgo+CnuHkkGQ4gFyedHz7qRHviBw297FdpOT9IDQ7N+KC4yAEqlf3yMSV4SN0ToOGcRZY5aBWxtbnNI7iRzjtAzbBMBZTVnKEFskT0jNkT9GSRFzmss6zFJ8lwVVUQgyJxBbOGvH6dY49f59DOp4BxvCkK60JTQ5KN34UmKD0GGitF3zMMYWmGZCTT1ao7987ZlIVDKcRInPFIkZ1+VzOzh19bpgkDgqt2EJdsJs+XpgseKhtelJuKz33g4AwwBZAn6tLFDynQa63ThiN+1ULx8jwI0rJXx+v2PYJr9i/j0w82eOTcWoLNlDbJNU2q0sjPi9GU9RtNFpVZkVNlaw2uXDmPywbbeGB9F06stwhmiK1nT+JDv/DPMT37zCe2t7ff/dch6/1610URdGUZY/7GaGX3R29++/fh27//R5lyRJQc2YV961FXJeZllbCRGMjUmmBWldgDcqOnEgni2MS7uxiYoLuhpGts2TIP8n0ubZPuXbFxiwJnJt0NnAIMO3QFzsIg2BooExJ+rihnyF9Cuq5KDjo9IpazWml4CMQgJSEL3nDskgoWDfboDVwxyGEQMW9LjAcF8sLgvu1l3H56JQXcppzDWIud7R20jUy6RQokMjlA/uBCwYqUu3Kw1LRShhsqKcPiRqFg5Zrn4qmYsvgI4oe2gaSfnvmukhuG4GFCg1lVI89z5MUAQMRsXtMGDQromdVAaDBv6DyGpoRxOWe+sq1FgrO0pvEzQPL6kBFQzlB/wTkyyFeahDDaWJTTbcQYkRWjxAkm2ImkuUqRWUwIgQQPnDBIf0EaluS/bBK/OIJezGrNMt0Ot5WgrBQNkUwCBt520+bL10juC4FuJJAq1d2Lafqv/FyOQq4bH69QscjsnX4mRlUJuWdYDoomcAfBJ2JM4iSlNMarK3jt3oewf5Thw/ftgjGOJLlNw+8lBUFMkBpBhsBgOEJd11hZWU60z+WBx1H9ILJ8hHt3rqANPAJ3/n+/g4/+2vvgy9lPVFX1i/EiCXYXVdAFAKXUgfF4/Nvjg8dufddPvA8HLzuAylsuYYC6KlFzx7lhLJWyAwVrNbLMYT6vuhsMPR9bgJsHgjX2fRE6vieVdarbYRP6R2UtYoBSho1ndNKk96p18h7oNZM6wrsYqkco/n9KbELC8DTDJjEIqVulIC+fQbIksP+qNTzDLEasLA8wrRVuGt+H1+xeJrPooOFyA+sCfuH4USw732XaDXlgbG1ukWG8ZxMgpun0G3xyHlKOrxSij+mHkk3JRIIYhYusWG3I2R1/L0m904PGvFpETqgVn29pEAFtPQds0euaE06fGpiRt+PYQkGjaRsaUc5BV/DOkAITTXbwfI20JhMlm+XJ9Ns4x4IHagLWVYWoLVzmIA2pGJEy9RgDYlDwgSZsKNWN6okhdJsPy6f7ikUJPIZFCrx3pfOvRS2XTrqoMjn8aZqaHEMveMpGGWTLAR+jTJKIEte75IFFRhL0BT6RpMN7Tx4o5nnG9CkMy7mNaQNZWhrBmIgAi7qsUAxyTKdTaqRzYmI1NTuF/mcY+guRuMPFoMB4Kcdrdz2LUXMSWD2Kjz04Bhhxnu1s4uO/+jN48s7PPT2ZTN4ZY7znzww6L/C66IIuACildJ7nP66zwS98y3t+BK/77u8j7qzSKMsamiWxdKOoXilIQUkbCwWhZykUmcaM1UaaH16kB51vZ7njOHcIiNCRsjAfQjKLDoHGtUjJJ+V/ykKM6W0EHFy0jLtm4INPecpCwDd67Ka9CsYvFKeuLGfcV6cCkZqBSsE6Bx8CRkWOo8sPY6tew5GlTbzKWYRgkFmDxrX44OOH0+soAG1Tw1iLydY2yqqCl3qdP4fhrFWzHWOX1NLvkEdySKWr1obMw6VsRuexIBCQsBlkCKi+YIMS3DB2pTUiMkOj40M0aJqa1Xoe4/ESprMqqdSkESiUQAXCydtIeLwxBhVbgwrWL3xZwxuCyxyib7E8HiPLHBr2IqiDSu5nmTWY1+R97BtyZ2u95wYsXdtOdBPT+ZEQarTmjU2BvccYsoysbvTUP+htd5JAiGG7uLVJtpoCcOyarHwhL8h2EWK6xxCB2NuIujuDfywzCtGNmheOMcSQif9GrrPRnPFrzUrLiPHSCG3wqOsGimX/bdOg5b6M9C7atqUqVmSk/GwVucOhS5ZxbXY/losMd21fgifWHfHZ+fge/tJn8dH/8G+BZv6rOzs7PxljnH3VIPMirosy6MpSSr16eXn5t1cvu+r67/6H/wK7DlCw8BxwpXtK9C0OTujwVbkDYgzJxaxp26Roib1AC3S3mTQfKMmKJF4ohsmNLKLrjItkWDNeCW64WQ2E2GG8mm8qKftk5+7GwwNAhIyXETxXspoYQe5nkTCv0LbET+QMm4ASD23JfMcNlnEwfxKHlzyOLmmYKiIfDfHhJ3Kcb5bTwyEreo+6qjCbzdPgTDIzIA9VKJmITEFHKHkqoa0RgEfw5HwGsAG6NohcDRD8oTl4iximm/grHgJSNYhQgy5KYFYEB+FAtp4tz/IKMSBzGYtRPCKbzRhN5wbMRIiRqoKavW5FKJE+idYwSmE4GkIhom5qDJbGjLPy1LoYSMzDUBTRCS2ss5jP5xT4uSEZ+vzwSHHEsgHLaFBgOBpgc3uSNiDJhoUtIEmCEriFqxOobpqyBPIYVQq+ncGQ3NnSWIsQSlnoBWXKgj36N0YfthKpcF/Yc+ExIF1PhZgER2CMejAagpy+GoSmQsMQlsuKBGcRfqsJq7Y2wSzOarzmsMc142ehtMVtT49xttrVbWSR7DE/+Z9+Ho/+8Sefm06n744xfvrrjzgvzLqogy4AKKVsURQ/qVzxc2/4nvfitW97N4sQhGpFvxe55Af6MkjNWWpXlst03dZLNqfoIuse9irZcMIiI4x1CdMSDBdg9ZA0OeTRTdko+59a4W6mg00PQAhdo0jcsKTslSZU2hS44x858Ctwo4dL8LomQ3EFkiwfXD6LbztyAG3YxLAqkWUW1kZ88MmDKLu5kZS1hpCk2JOdCVrfyNtyedt54BpjEcENQCV4X0if2XC5aRh+Ed8Cy1OJyeCFYgeR6eW0xDS2HeDy2reoWzYNl5JWdblfWzd0TbTBcJChbhpUZQMVPZRxiKHlQYMadTlD23oMhkP6XgiwRqGsG8C3sFkBhYCsKDBeGmGyswWvONOMHsYVLLIJjMWK+IWkyuLloLRG45l2GDroSk63sw6j0QDakLm6M4oDVUBVs29FpLtJICDqA8QLgihfnXT/Ez3RpM1AoJ4IuWe682y0QdPyNRZsh+G4yI0xCrSRslIw1BBCkntT5dUBHwKzBd8m+KP1HuPVMQtJFJpyjq3taYI0JDum5ILuqUGRsagkYG1tBaXPsGcwxeY0IOosKesAUnTef9sn8Ilf/z+ApvwAZ7fbXyueXAzrog+6spRSx8bj8X8Z7jl461vf+xM4cMV1aaejHR6cPdJ/RRJMGJlO02mVUjxQkQKEb1tWuFlyKQK5LEkCwb1bahgonfArCTbCkZVMiCarSJdeDL275hhlvDJBtsMBO9ih/1/u+CoK/JQ1Gy4BJfh3gZpKd+I2hxiwNl7GVasnsapnuEQrDN0QHjUcHD55fhdOz/PuQeSgGdoW08kEdd3As7SZzltvo6GnhCo/8LghxnFlo4lgbjFUGtvDVxJATGW1Upr9h+m1lNIkw7UGbd0kbJumGMv7KLQMqbQ+dL4UIWA0KlCVc1J/ceAhQYPiyb+hw0eVSI+peVYUBZX11QyD0RICq8iSAIDSuhSYRDUofYKYNgPGeANShWMMqyiVRj4oQJJgAIwzI1DwLauKPA6YEy4bkk/ycboviZLVfS1DQZOYh3npgZoIkN01wVlA2vAFvrlA4Rc8QlQwWiiOAdq6Do5Ddy46HBipmgMinDWwLmMmTovQtiirOrEruuMnY6EYPJtIaQwGpIqrWkBFzoDlOeLjXD/9ND7+az+L08fveHoymbwnxnj7nxtILoL1kgm6AKBovWdpbe8HD1/3Brz5+34Eg+VVsrJVItmVUiemHZ2aQsTrJD/fDieSjnoieiuFGFoubSwkP4j84IXQ0nvwe0nJLc2gfsmlomfzdXCZzlkhGLaQ7jI3cNLE2152qxV5m8rqgnhMfhVyH2pDDwFRsALGowKHV87jf3n9jXj8ibugqgq50zAo8JtP7oLvc2P5vTXV2tBaoS4rbG1soK7rFHC6cxQ5k2V6D6j51IaIPLOILDComwZ5MaCx6cZRNaA69V6inSUZODczGZIJ7DznexWBbHY+dudDq4CmoenFeUGk+Yr5toazKLHiFDjE+wCNiKZt4axF5sjsZjAYYHtnQtg9Bziup5MAQ/MGH1UHsQhI0dEVhR1AUy/y3CGqzmLUs/+Fiw2xY6QJlzBgetku4AaGODsqnlYA7WlMedQsnY8dNCFNy4gI3zRUtQnMwDxwrWJSIQbfdgo7yDBOBZl+LI1VWYYHAwR20JOmt7EGdVVjtDSCcQ47WzuE47Y+TTSOMaKTvAfoGJCNlmmzECMjNmeHIkqfb2rc9qFfxxd/7zcRm+rflGX5f8YYq79QELkI1ksq6MpSSo2XlpZ+1iv7o7e86x/gW9/9j3D22ecA9hIFkMqlfumTPDUlQ+afkleCyDEJoojsU9pG4aPKOJQLsTEwvkv0KpJeUqZHgdGyxt3I7CovHE7CGkMEvJjKAKm0i/IZqPa/4METYnvgTJMwU3kgwHsC4YYrSwWuGK/jyqUCBbaxOl5GOZ3hy9tLeHBz1D08Uf6upw7rP7QxQubRhUAiFW1pkmwEMNmZoCxLYlVAGoC8+WgNpQK07rx1NVOi5GGSpqTAQ1qRaEDGAKkoUCZltC1zPgVjrpsG0dcIIH+CCOFHM2NAGfLt5ayUJpbUiCEicxaIREUcjJbhQ0RdlSxHB7vG6dRwSk2o3iYPdOW/3GcySHSQUYNtvDKmgZVVDQDYnXucnevEg66rGhE05SLw9e88lpECrUAMsjnJHi0lN30Z04bkPfcxoFKgjQBUaDgzBYzL4NuGaImB/C2IEUKThOVVidpIXwnDp6qqdK+oGJNFq5iuCyQh+LtsOjZ5VgTsXl3CtPIwzl3wbMr9QVNFNO759O/hU//lF4Fq+vGtra1/EmN8+usIHRfFekkGXVlKqWtXVlZ+WRXLb33re34UR264lZsn4qTVSxqV3IQ8RiZGtHUDl2XpJhY+qWFIQmkZ8Edgft20pA4SvDV0fN/UfVddMw+c/aYOs/CHpWEQRTEUEuYrWUai2nD5JvJf4VUy+tbjPzJ8oTSgInuv0jEuLQ9w896nsTfX2D2kEvqX792DwsR+zKAHNMhECVCWyYGYONJt+nzEi6bjC75FyZNv6WfUTJNRRMRVbTEaDqGMwXw+J20In3fZ9CSBktcHB2JjLcE8UZp34MBOsE9VN9AAbJazlzH5aORZhnlZsiNcC+cyNK1P2GPTNGhbclnLDGH6dctkfm2YEUI8WssKtMD+Di1DRHROkOTkWeagADRti7YNMMpD2zxBLi5zcFkO3zZwzlHlz6VK21TwLWOwvXuin0GL4x4gMb93AdFBDrZXYUlmHiOYEy73oWe4jP5SoBoFNvDPXLpP6f7lhCS9nkLTtskPRGsyT3eO/bJVQAhSHXXNOIHKtCbGTfAt9qyNsD4hVkP/nkSMCG2DR+76E3zuv/0Sdk4/8djOzs4Pxxg/9XWEiotqvaSDriyl1NuWl5d/ZfnAsaO3/p1/jH1XXE/ZgZJBft2YdMOZFylfdGIIVFVDKiemd4WI1PCRrEL4okprmukVSUAQ2gbKWOI89ueASTc3vQ/9Ey5nmgALJFxQcYbcYV6s0oriq9rhcIJZUslOJkHa9HyHgSRA2LeWYdIYHHDP4DuuHKKwDr/0lVVY3eGCUMS6uPXABiY1cMeZcXqoU0nJm4QE+hDInrH1sqkhTeEdLS+jLstUIZTzOYajISaTCZ9TnbLC1ByJIcl4nSFjapLZaoZ9CD9v6goIHsblNELHZJAYpLRGpjybXnue/wxWXbEIIQJVVUKFFjbLUVUVzwTzlN2BMkJrMxhnkWf0vWQGzmbknsfCOMuuZAhoA9D6lhy7uAoKIIqUcxYhRFirUeQ52qjSOW7rikv0nuVj6AQodCEYSKAPKndaD0bgRmUMzAfvMmC5V8CfP0ZARXKbk5uAmpQRMN056OO2Eihp9lubBAqEmTcdTtzL9jXzbFUM5HEbalSeqsHR0oigEEt2n0k5Fzy2NjaweeYkbv/wr+PEvbdv1nX9z9q2/X+juPa8RNfLIugCgFLKGmN+YLCy+wN7D1+Fb/pbP4Q9h45xeUW7sO41vBRncNJpp5SOJZFNgyzP4VsxGwFjjL0SHoRBEaVJoWk9l0sqNaf6EtgkAuCOvNaMg4X0GHQ4HiIkFewHY5nRJlibYGd07Px3DD0ATGPjRCV3Bso6HF7extDV2DXIsDLK8PuPjnqJBf19y9muiTR3zrcNQggpsLStR5Y5hhCoYbYzmSFGmqRgDHFMrbWo6woyprPhAZ4qciCBeL8qZoXIJkENSfHIUKAqwWUZ+Q+zSjCCM0+GEMD4el3OQDOxMtoAes0XyQaNsWjriji4dYtZWfF9QhtsluXwgQZhFkWO1tNkZWcNyqom5V5TwaiIeVlhaTRCVddoA2PRPkAjILK4pMgzVFWTNmCXOWRssl3XVPloUDVTlRVPeBDtV9fhl8+QGpuC53PAFeGMTH2A6uiKdIljarqKX0USTvB56fPBpX8AdE1Ca23yk5BT27YNIsAQDhh7VonfjAg09Rz5YIQYIoaDDNHQhF4Ej8i9idC22NmeYLp5Dn/04Q/gkS99GsrX/3Y+n/98jHHylw4QF9F62QRdWUqpIsuyHzXF6OcPvfom3Py9P4jdl17OzU9Fj3+6yWgnNtax32qE0jaV00Z3+nSRqQo0IR6fSmlYR9hU21BDpJzNiHPKuCrJhXsCgYT5MgeXebh9sQT5LVC2LpMW5AGRvk4oJ4Ab0NQCRaVeDIzH8ntKJi/ZiMsslM1w/a4deJVj3/A0dlqLu88ehI+m1+BAKvc1IqaTHTRVSZmTseSj2rYwMthP8dw2a3FsrcBjmw2q2RTTyQ4AA2iCJ6yx5FWrqGjWikpxbbIkvqDNhjZKUdppTbi4NMO0Nqgnm1haWcP29jYQyY6xGC2hLCuCH/h8Eoui8+VAjDxZ16Au52gCzTUTnFwrDetotlcxJKlx23j2aWU4pZxjUAywsmsVmxsbadKwbxpYZxCjTifQ8L0mzBlrDDJL457YNh8yaQGgceVl1fTaEsKS0Qnm15rOIU2U7mWyjIcapkDK/YNe40zwdhFYkCVmTA06wdg7DjAnCyESXq4oBCtENqhh2IttRruYErFnbQnPPreZ3m911ypt6qLg8w22NjdADCSD+c4mvvh7/xnHv/BxWIT/azqd/lSM8fw3Ok68mOtlF3RlKaWW8jz/X3U+/JlLX30T3viOv489h48hqaCkW85ZiTjnC41J6U555Sx1sUVOmbJQDhpQPJtNEc1L8DGEQKoco5HnBepyBpfTdAJKEUBl4AVyTfSkmsJFjl3Gy5tBy5nLfDqBtRZZXnBwDly+mvQ7NMSyw8piCMgHQ4wLjTcdsDi18QxGRY0HNi5BjAob2zWUNiQUsA6+adBUZSKyi+dE01AJDa3gspxeO9LwxMwoGJfhzKkzLLwoUVUltDZY27sPeU5j0+u6hvceWZZRQADYQJ2CjLUObeu5Q08PpnPUlAEHlbquMShyABpzHhuD4NEGyfwJk5UAprVCqCtMq4aphMyiPJ/mAAAep0lEQVSHZihGGjxFUfBm4RN3mm8J1HUNZykjDJGqF5lhJjPUAIXhgAQbje/xaBWQ5TlxWZXMyAuYz2ZJvSYiG/HFFX9gAHxtQNMbjE28bYB9dEU2DaSRVQki43uMLDVpUGiiNKLjHXdNVJ2CsGZmiRxLVdbp3q+b5gK2ROYsqrIiOEYB4+URBJ0OIWA+3UFVtampOds6jy9/7IM4/oXfh1PxVyaTyU/HGE99Y6PCxbFetkFXllJqKcuyHzPG/IsDV79+/IZ3fD/2H7s2lbHSSNAKqdmipBskYgiGBmhiL2d/xkB8Ptumgm8aFMMR43EUNELDQxQlWEtgZe5tVVawziLLckRFiiNWgTL21iluwHJnodkk7X30VLarjh2hGLSkzMo8L4PRPE/KEyUqeORFDp0VqGc7ZAgUAsjsLzJ/Ncd0OkNmydks+ACXUdBs2gbO5RCbSseNpKppgUjMALGyjDGiKByqxuPpRx/BvkOHQeOTKJg4a5OcmLwlDJQSbNUk7ijAqrMYobRBXc2RZQXATbU0fZgbakopFEVOzBF27iqnUwQwy4LPV18AohW9NvFxbcreZFKxeM5K2a+0Rp5lqJqGcsBIYpC8yDEY0IZYlhUGeQYP6gmQVwRl3JOdSXLYEhxUegDyeaRCS7g+VyS05P5CD6ai7wsUITgxPxdIdD3VMSE0Y+JS6Ugiktgxhu6PtqW5cKQM7VgbmY4oG3qfuqrpfMkx98zqpR+yffYUvvSx/4rH/uQP5lrr3+DM9vQ3PBBcROtlH3RlKaUG1tofGq7u/aXl3fvwhu95Ly6/4RYo083CotlbMhWCMl7ikwqLQAGaIIrUXefGmW9Jc+9jQGhrGvIHnZpPAAfSlLGKWbbigO8R2oaI5HxNLA/U6+a+UQlnjO5hdKwEU6ye4zKwZVqaihE2cymDMjzlQlRV0ngDgLYpoTQJGpzrAk3wbVK+lWUFx+5ciMzIiOTY1bYtpBmPKKVu7EyDYgCUQeYUytpjvrMDl+dJnRSB1ASiyRL8wBs29gmgaRxRzgc46/c0nj6Q+Y/4bSlEQGsYrlyiUsxrNZjPS0FL5cpA+La0wfIg0gvvoWTGo5lvS2PY6X36RuvWaOaGN/C+wfLqGh9fC4AFIOJboIDYNNiZTCEmCARLsdScxR20AfPYeq0SjTDxcOV6hd4gUdV9n4Jy4AAqCK3AEVwB8mUylhtonN0C6CZX8PX0PFjAOQuDgFnVwFmLuiLsvq4qopiBM32ABSQKzz31EO74+H/FieN3Am31vvl8/v4Y43N/+Sf8pbNeMUFXllLKKqXePR6P/3c9WHnV6972fbjmLX+Dh98xTqs7W0etqasqY1oU8x37LmKEErC/aBDKj0wP0Mn+LoJMx5Men1OTbjBhTDe10iphxEJ8j5EtIHlRRkPvYbTGcJBjZzJN+LGMC5IHULL2fijpxAj8+diwhzBPCnY+RPgY6Vywosvy6JoIqhIgND32jgUihsMByqqENfIgAkJ419bg2ZMnMF5ZpeAom0nbEEsjNISvasXyW3Zfa+pkG+nbOjVJQwhwWY6mbngQqYKzlmwiNQVdmQocU/OyF1IF7pFArJH4yaQYS3tnwua5TwXp7ss3SCEYYDUFXskws8xhNCxgsoyybnSTa+uyQtM0FzAWpOynvlbHiolAavKmjJUDdQTxv2XwpRKJpkBLkrHH7nPQx5Rms00fUoJw39FONiUfCC6Zbm/D5hkyl6Es59DaoG0643Zp9BlN3g6P3fl53PmJ38LWqcfONU3zc3Vd/2qMcecv9vS+PNYrLujKUrR9f/Pq6uq/Ltv43de/9R14zbf9Tew5eIQCnvdpsioUkrOSEm8BabYZ6cADQtNBcrnqpL4KnNkqeihiaDuOJsDlLXfj+QYPHCwF01WahyEqxd3iC6XARuuU6ZFVYBfQk1IKEc7SZA7eRdhkmmAHmRWH0CKAZ58FmowhWbTiEjRwcAvS5AkeirM4JcfCaa80eQIzAuazGeq6SYwPep+WVXYsz1WGxSQ0nZkc3riZxhlUKld7lKrUgIJk9y6d40aGF0o1I39Ev0CZJrrXSqtLFvnzd39Jl5C77yCj+7b1ie5GOLLB8sq4t8Gyaow3vZ2tHcZ4pUGrLjysVKXQvShcZKkQ0j2XDpIyc4FNojjY9TJc+vzCmdWQ+X+isuSXSVxp4YhLFj6fTmB4NHpZlrDWEuwj9xxvGJOtdRy/7eO4+1Mfhm7LezY3N38awP+IMXZSy1fQesUG3f5SSh0bjUY/HrT7sf3HrsZN3/V3cMVNb4Y2DsYaHi1OGKiRkjAxAsAloEmQgbhKSckXJNsMPBEhdAM2hVnQfw8ZTZLUaT2KlEQUmagBfqCoCcIUI/EBUKqXsUqpecEnT+wGceoS854sI4VSADd6eBICkeYz5M6hqWZMoWpQDAfwVYXJvMbarlVy8YLCIHeoq5KwP+9RDEbwwaMuS0y2NrGyay9a72ENK8tayvRITRZT1tqy3WTHOulYHPJZUuamOwN0JSPJuWEpstxUeii5Bxj96JfjvdfuB2KiYknAlQYUZ/0aCOz2RVJwOu/GcjO1KmGsg8uzBPnUVUlTrnubQBCVIh9fENqjVokSJp85RvRwbPRScRmlQ6/b2cGn+57+q9XzvhdhneUMVacNRjB0BZITx/T65LnQWUkS/HHiobtw96d+B49/5TbkVv/29vb2v4sxfvGrP4WvnLUIur2llBoopf7uysrKP/cmv+6Gb/1eXP8t34PdBw4nNoPqPVTyUMQQyWYRRA9quWRs2UYyMCXHM9+WDM1J1UT4qk8PK4SWxLil5saKYL/SDY4AdC9T7o+elt6QBAR5aLqaUg6cX4ezGisZPLrNIEY2PkHE2toYq8Mcz5zfQWRBSF2VMIaajzZzqQye7WxhdXUV6+eJ7WNdhqZpYF2G4Bu2MUSCFjrPVmr+NU2LCBqpI2q8/q0aGQqQ6Ke1YmpW7ChRfI0EI08ig/45kGuPXgDv/ThNJ8GFf6K4zIYEXUXXSsIaVUWMF/MfW0Om5zZziecrPG8ATBEkzDMgAAFdY5QDu+DFVAQFIGoo/bxnOEqQlOw3XvDaya2uO5Hpc0tFopnaFyNP1GaIw7dNwrJFyFCVFWIk05qt8+dw/PY/wD2f/gjm66dPl2X5/rZt/+PLjfb1V1mLoPs1llLqxqWlpR/xyv7wJUdehRu//W/imjd9B2w+oFLMGGi2u1PcVBKJqjS5YpCMlV5TOscSbGWQYGhrhFT4yYpJd97PZi/8Df5vFEvJDmNMfF9uCD7vT9NrSQlotGIPB86euE4X+GTP2hK2pyX5L7gcQ6tR1g2cs5jPSgAi4giom5a4tIhoGn9BwxBA8rloGsFthczfNRn7IZGJAikL7N+zCh04qfi166aBYyvB0Gfw905CwmfT9f5q94ChoZwgs+1+aKZNWCcDHGttKu9lxI6IExJOjAhtFKzLOMOlCSd9P+EQiQvbNA0yRzCQ9wFN6xP+3jL9TKUDFw43wVndphqT3DqEAGM1jDKEVwNomarYP4cdpzsSrS0KjbEzLhc4SoFhtxhx/xc/i/s/+z/x+N1/hMKZ39na2vp/AHw6vkgTdy/mtQi6f85SSuUAvndtbe3H53V761Vv/Da85lvfgctf803cTDPwbGwixPnU0GDJb8o4orhk6eQuBiA14oS+lDrPAP709bkgHH2NfK0fQeiBJoaGT5Jc6ZILLi1d7QhujIFEH0opjAYZZrXHoXGOZzZneNshi3awhj98fAOhqenhBvN2g4ePiifOdseuBX/sVQdSqgvLIYW0eOHH6V5FghcwyDMsryzDQ2E+mxMnlLMwa/rNxtB/5T91BjXzXMGnXGkaxKkUoLVJxuoyQSQyRAHVjR8CVMr+ZIMTybFcQ9k4OPlNwVopBeccjNEY5GTUUzWeDV4MPE9WiNy4UoamA7c+EGMmiDSdlZdKwVqTBDM+kEctGbvLsTBmLImCki2hvzEqdBscB2UOvobFJicfvg/3fvZ/4oHbPgGL9qHt7e1fDCH8txjjJhbra65F0P06llLqgDHm7+09ctW/n2ycw3Xf/Ha85q1vx3729gUYT2xDojUBCsmTlvEwIqYzi4AxWuF9AhQoPGspJYNNWW/sl9Z/6vj4dwXvExlpTAFVRY+11TE2dqb02lFxp14MZ3qeDYac+03mgEi0n3ffuB8fuusUgmYTb36/EMhaMbClI1G7eoFOHl487/j5+5IZPv9+fH4GKo27ldUxIhSapkZTNx22qsBBzGA6K1kcQFS1KJh6L7jmeY66mqNpmWkCwFkDwx4DWUH+DUYp1AG0qYRII2egoA3R63wbCGLqNS3FIAgAKbkCbU7WGlbb0TGXdYsf+KYj+NXbHqes33vsXxlgWrVpuzBGo2w8hplB07LCDJ1yzGjKXNu2TefIGMPZM53npvU9bD+mc0H/QsqcNQ1voKuS4Bgy9zl/+ikc/8If4IHbPg7ftii3zr2vLMvfiDE+9Bd5hhZrEXT/0kspdXVRFD8w3LXvX/qmxjW3fBeuvuU7celVN6Tg2I0y7zIMuelThz6mQjSFUcF8YySqlmQlAOOvsZ8Bx68SqDjYMubHxwthVEiaR8o8zmhC6GbMKXC2R5nS8tIAVeOxOjD4uzddhvlkC7t3reEXP/M4f04etxIDfBtTh14ydhGL0GcCf9LeJpP+vwvM/c8S5ZyBDehDRFZk1CTkUfWiwhOZdlVVTGHuvAdkYxFHuKWlIcr5DFXV8rnVyDMHbQyy3MGyQu6H3nQY+8Zk+P5TH3uQTJMUc7N5ozAMQ2itUdUNFIBB4RAjnfeq9bjx4CqOn9nGTt1iObeo2oAfetNRDDOSBdsY4CPwh4+cw2PnZ9ie1/jhW47i4O4B3v/px7E5rXmfjmhDRGY1br5sFV96agPffNVe7Bo61E3AMDP43XtOY9a08J5MjKJSyIxO0na5LiF2jbbcGdQ8DRkRyeD//Mkncf8XPoGH/uiTmG6tQ/v6Azs7Ox8A8MdxEUC+7rUIun/FxdSz64uieM/S7v3/sirnuPL1b8Wr3vAtOHz9zcQCiFFMz5gJwON22E1MeK3Jck8CLZDoNxKoJbORB8dZjdb3ZcTiDMUKpEjvkcx3QL8n0Id1Mi6n50nBm4HLHGVlrMi6+dIlnNku8c7XHaEgpxX+7888CpnAEWgcRuqkkxKaTd9jf+OR8JrSrISH9s4sGFnm7IvUgM5axn9pA2qblrwcjCGXOOtQzks+rx0OaS9wjAN2ry1jXrVomhZ106DIiFEAADdftoq3HNuFnWmFlXGB1kfkjihwPkTslB6tDzg3qfDE+RnW5w1ObM7TZhgB+AAc3jXAP3jT4cRzlnOs+ToaDUymcxjngAgMcouy9jSeSCnkTqfzkFkadVQ1Ac5qMqPnAFrWnjJ1sKevBsqyxrnzW9jUBX7/+HOIAKzQwNL9Fbsmb9tCs9G/jgHPPHI/Hv7SZ/HIlz5D4+ar2a9NJpPfBHB7fIm7fL3YaxF0v8FLKXWtc+5dS0tL753X7bVHb7gFV7z+m3HstbdiuLwKdlNIJagCZZYp64jq+a+XMkGBCaCIyiNBkjDGbmQ30DWniF7kuQFCmaxnl3+leYJDBJaGGao2dGwLTdNXiSZEgf3QriEePbsDQOEn33IpWpNDaY1f+fzjAEDCCOq6IIaAhsUIqQF2Ab7dZesiR5WNpF/+KhDdyjmHosgxm83ZaFumE9gkp7XO0gw2ZjLULEX20lQEkOcORe6wf5ThkecmSc31j998FPvHORovzU7yzxjkJrFB2kCDRAe5wXTeIMSAedngbBnxobtOQkXge67fj5uPrhEuG2XUT69pCfZrVvQeAJipAbQtkboyNrx3Vif6mxYoB0hfT2YNiKMNDHIybaKqyJNbGCIeOzfDSmbxG18+gfVphaXCoaoJtoCmyRwhBLR1iUe/cjse/fLn8egdn4P21YmmaX5rPp9/CJTRLhpi36C1CLp/jUsptV8p9Y61tbX3TufVt+4+dAyX33grLn/tLdh/xTUQD9znN4+oLBclUuqpJcZXHxM1hqbris8sfW1oQgJTrVLXW4QBbMQdOEAtDQtErdE2LbKMRupUTUgBI83QgsKPfcsVmO5M4G2G1VxhurONfHkXfulzj1HGmkBCpGAPkDcNIZrSMafMm4aBdg5acj/6EOCMxvLyCFEb5FZjbZTBALj24Aqu2D3ExrzByc0Zrty7jGFmsF2SCc54kEEDmNYtitzh9FaJPSPKYq1WyI3CsKCBk/OyRZZ17200uGnWiVIAcuKKIWJnViJGDbgcxlDmuHfk0IaIqvbcTFMJc5VrF0KADx6Zc3xvdD+czps04wygDN45A6s7OAmIqJqIum55vJChGWQGKdjWLQVjazQaT30B5zTapsZnHt/GHz12Lm20bV1j/dQTeOSO2/DoHZ/HiQfvxtKwuGtra+uD3vuPxBgf+UY/D4tFaxF0X6DFLIi3DIfDdzrn3lUHdeSKm27FZde+Hpdd83qs7DuEDnCVzKwTQFBq0pXmqnthAJTlakPNIfoF+r7jiQfiGSBDARV7HBSZw6HVHE4rPLlRYl57KBVhuKnEuSgAoI1A3QT86++4HNuzEqGtoUyG3FlsbG7h1+/exCizpJzTKmWqZB/o4RtPgdh3r6ognhIGMcowRoXRqEBQBrlR+MGbD8JZi/HQYV61zChQHVwTIjWXgGQP2d+stqc1rNEY5IYrg4hn1+e4bN+oy6rBDcG0sQU0LWfsbWciP6s8xqvLmM9q7B5n7KzF78ufSiZmeMnse01WoxSgKGP2jENL9msMuz/wsXvPfhMhJupYnhvEqGANey8g9WKhjU4b5GRnApgcmTOYTCZYGg7wUx/5Mk7e/yU8ce8X8egdt6GZbp03xnxse3v7dwD8YYxx6xt0uy/Wn7EWQfdFWkqpQwC+c3V19Z3eZH9bKY2jr3kjDl/3Blz66tdhvHcf+np7IHJTiUv23tQAgIKcc8QoICtH9mtAF6DTkM1ephwiMHQGP3zLYczaiLZtsDQocG5S4+z2DCe35rj71BSZVWh8xN6lHG+75hL81p0n8E/ffBjTymPf6hAKEafPrmNtbQ3//tOPotso6A2F0B9iRGjaC/wmvO8mGmfWwDmLv/W6w7hkycFphdwZWNMp7LQxybIwRKCqW4IcOCuVkUp1E1i5RlCF/E7VBAxynehnIp6IIDGCb0Mak1NVHq33mM5brO0ZwwSCF7RWyKxhSIgy4hBV2qhIDhtTpu+cSRuKXDanFRl8p8YrB9cINjzvJLcxAs7qtCkopdA0npqHrCjzvoW1GaxRePLpE9jY2ML7/8dn8cQ9f4In7/5jTDbOYZCZT21sbHwk0rib44tG2Au/FkH3IljcjHuVUurbV1dXv7eO5u3aGBy+5iZcds1NOHTNTdh3+EpAa3jPk2hZJUrOC/TcWquxsjxEVERDagJw6UoBZxRWBw5vOrJGXNC2RhU0po3H2Z0KG5MSd57cwaxukVuNH7nlEIrMIXfA9k6JqAxWlwfJxKRuI6qmgTWK6FVGwwegrUts70wQswH+8x1nuuCegrzihiD7s7J3rRDwvff4h2++HA+e2cF1B8bIeDJxRkI5OGswrxo0jefRSuSRIfVA03hkzqRJBVA6SVPJIUwcuSgzBZghwlCCQAgxRrQ+omkphbTWYphb7vTTppU7gnUoo2WRC5uhR/nI/LvWapqooRQyd2EW3rSB34vG5hjDVYvWMMw6uLDHGJNnMmLAF++8H0/p3Xh6s8Lms8/gxPGv4OkH7sAzD9yJyeZ5jHJ3+9bW1ke8958CcNeiCfbir0XQvQgXB+FjAN4yHo+/M1vZ+/0755/FwSuvw2VX34BDr74Bl111A1b27kdVt4mnai05jV22a4TvumoPdi9lSVp76vwMw0wjzx0P2WRurQ8YZIZmnBmTLCJzqzHnZlDdtHBGI3M8Nj0EDnLkFxEVGZ04qzGbTmDyIWyWobAan3/sPG57/DwG1nReMnLPJU4xYJ1BWQf8s+94FerWY+Bkvh136KuG5pdpzUGOS3bmJpN/RYBzQt3qlFsxMTIoMPcbVJJ4VpVHWRPmnTvy0UjOctzEos2BmBCkOowpSw4+JoUXQEHZWMrQjZa/l4ZpTMFaNiXBbuV40lMZiQkhm4OxBr/8+ccx2dnCyYfuwalHH8Cph+/BiYfvgc1ymLb83c3NzU/EGL8A4L5FkL341iLovkSWUmoPgJuttW+6/KY3/5uTD98LrTUuveoGHLjyeuw/djUuOfwqrO6/NJmvzMoa/+ptV2NaVlgdOgCKnc5UwkKFNkUNNAVnO3mpDL8MIaLIafJC3QZWLQWUVQMoIuBXgnu2HvlolIIpQAEkNxofvf8MHjk7YTP42DXeAASlcc0lI7zz2v0o2wBECqCZM6iqBmJjKV6yWiExDYzpJNRiNB9jN2pdJhfL0MTgIwwHXnAAJHc2JMvOEIGmEYYHB0sOksIYEaoeYbLo2TEiUfSsJUtJ31LzEqrjZPN+wTUAMxxEXAOFPKNx7T/70buw/vTDOPXYgzj92AM4/eh9mG2ex4Err8Wzj973/vl8fjuAL8aX4DjyV+JaBN2X6OJs+DCANxZF8U2j0ehW67JbynKO666/AVdfez2OXXM9rrjyatx682sxGC1D+xplWQEAjHFUHnMgieioScR2QCL7E7YodpfUWReamw/AbDbD+sYm1vbuRQwRxaBAjBF1WSMrsq96/BvrEwxyheXxMjEpfMD6uXUURQ5tHPJMo6rbFNiMoU1AmmF8DmhenSK6lVJIU4WTiATMHOBufgidC1nmSFIrHOCWg2ySyXJIDJEmVQjNjQJrTFkwmDUivFnBZp0zZGzDjcO+uk6BjMIpkJN4Yzbdwb3334czZ87ggfvvw/333YP7770H89kMztm7qqr6k8lk8scAvgjgoUUW+9Jci6D7MltKqUsA3KiUunF1dfXNlx46/K7HHnsEa2u7cOVVr8arrroaRy8/hmuvfw2uevWrsWfvPoSmhm+IHC9THAyXxdYo6t4zvYxsAEWuq1Kga1n67Fn4MStbLK+MABBWa0gKdsGx7mxXMKrF0tAhmEyOn2ectYjc4ddaw1kKwkXWNdW0Ujx7DKgbHq/OjAlJfWPoSvmufJeg3TW2er3F9DOZmsuvlH4/MQW40dVfZJTeWU9KQ5E2AtoAnju/jnvvuQfPPncWDz94HI88/CAeeehBnDp5AoePHMX6+XOf3Nraur1t2zsB3A3g6UXD6+WzFkH3FbAUUR2OALhGKXX1eDx+vTHmBkBdX1Yljhy9HEeOXo7DRy7Hkcsvx+HDR3Ho4CEcOHgZisEAgEwRoHvFGZWwYsP+AclnIpL8Q3G22fquidX6iCyLsFmOC8IZZ4912cAo8jMAB0jDHX7vA4qMRidZTeN+6sansl+m0PYZAroXVPtLvGhFbCDPgNDctOqkwhI3xYKxYXhFPh8im4szL1c2jaefOYHTJ5/CAw8cx8kTT+OZEyfw1JNP4KknHkfTNsicexTAfdvb23d47x8AcBzAozHG5q/jHlisi2ctgu4rfCmlVgBcDuCYUurY8vLyda97wxt/8Kknn8DJE89gebyC/fsPYN/+Azhw8CD279uPS/YdwMFLD2H//oM4eOlB7N2zmwdH8rQLsH+E6jDOwBLWtu1mclkTEUGNsYEDsixD4wPmJQ25zJ1G07Y8b0wnPmxZtQmThopIjmn8un0zl/4SKEJ+IFm7LOnxiZSX/ByIMeKshjHAZDLByRMncfbsGZw+fRpnTp/C6dOncOrUKZx85hmcefYMNtbP45J9+3H46OU4ft89/317e/v+EMJjAB4H8BiA84vM9ZW7FkF3sb7m4gz5EgCXyj9r7WXLy8tXaq2P7N17yRtPnz6N6XSClZVVrK6tYW1tF/bs2YPV1TXs2r0bWhtcceUxLC2tYm1tDcVgiKVhgWKQAyrD6ngIZRyszVEMBjTkUHcqqxhJhSUwRgyUMaeR6KoTFHj2gxATeaVJXuK9R1VVqOsKTV2jrErUdU3fqyrMyxIbGxvY3FjH5uY6NjY2sLGxjs2Ndayvr2Njnb63vn4ezmXYv/8AtrY2vhJjfHo2mz1WluVTAE72/p1ZZKyL9bXWIugu1l95KaUcgDUAu5//L8uyvcPh8KAxZt811173nWU5R1mWqKoKVVWjKkvUdYWqqlCWJQAgz3PkRYE8y5HlGX2d5SiKgr7Ocjjn0HqPeVnya9QUSMsSVV2hrur0ut57eo28+3t6rRx5nqHICzz88EOfCSGcnc/np6uqOsOTDtYBnO//izFWL8Y5XqyXz1oE3cW6qJZSygLIe/+Kr/G1A9ACqHr/yq/xdbso5xfrYlmLoLtYi7VYi/UCLv3n/8piLdZiLdZifaPWIugu1mIt1mK9gGsRdBdrsRZrsV7AtQi6i7VYi7VYL+BaBN3FWqzFWqwXcP3/m2DzK4NhiJQAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"\n",
"ax = plt.axes(projection=ccrs.Mollweide())\n",
"ax.stock_img()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"\n",
"ax = plt.axes(projection=ccrs.PlateCarree())\n",
"ax.stock_img()\n",
"\n",
"ny_lon, ny_lat = -75, 43\n",
"delhi_lon, delhi_lat = 77.23, 28.61\n",
"\n",
"plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],\n",
" color='blue', linewidth=2, marker='o',\n",
" transform=ccrs.Geodetic(),\n",
" )\n",
"\n",
"plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],\n",
" color='gray', linestyle='--',\n",
" transform=ccrs.PlateCarree(),\n",
" )\n",
"\n",
"plt.text(ny_lon - 3, ny_lat - 12, 'New York',\n",
" horizontalalignment='right',\n",
" transform=ccrs.Geodetic())\n",
"\n",
"plt.text(delhi_lon + 3, delhi_lat - 12, 'Delhi',\n",
" horizontalalignment='left',\n",
" transform=ccrs.Geodetic())\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}