{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from salishsea_tools import viz_tools, places, visualisations\n",
    "from matplotlib import pyplot as plt, dates\n",
    "from datetime import datetime, timedelta\n",
    "from calendar import month_name\n",
    "from scipy.io import loadmat\n",
    "from tqdm.notebook import tqdm\n",
    "from salishsea_tools import nc_tools\n",
    "from dask.diagnostics import ProgressBar\n",
    "import cmocean\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 12, 'axes.titlesize': 'medium'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data for original cold and warm years\n",
    "\n",
    "monthly_array_temp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['votemper']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 original temp      \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)\n",
    "\n",
    "\n",
    "### 2019 original               \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/391916811.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_temp_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_temp_slice[monthly_array_temp_slice == 0 ] = np.nan\n",
    "monthly_array_temp_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_temp_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_temp_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data for Experiments 1 and 2\n",
    "\n",
    "\n",
    "monthly_array_temp_exp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['votemper']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "          \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "                \n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "                \n",
    "\n",
    "###             \n",
    "## Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/2338418733.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_temp_exp_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_temp_exp_slice[monthly_array_temp_exp_slice == 0 ] = np.nan\n",
    "monthly_array_temp_exp_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_temp_exp_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_temp_exp_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Degrees C')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_temp_slicemean[12,:],color='r',linestyle='-',label='Original WY')\n",
    "ax.plot(xticks, monthly_array_temp_exp_slicemean[12,:],color='k',linestyle='-.',label='WY with CY winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY SST with CY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,25)\n",
    "ax.set_ylabel('Degrees C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.94768824,  5.03149186,  6.92260036, 10.4741395 , 13.69086769,\n",
       "       17.51762955, 18.32682018, 18.80116005, 16.19822578, 10.88716039,\n",
       "        8.96325978,  6.76106302])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_temp_exp_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Degrees C')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_temp_slicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_temp_exp_slicemean[1,:],color='k',linestyle='-.',label='CY with WY winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY SST with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,25)\n",
    "ax.set_ylabel('Degrees C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.59662968,  5.90259706,  7.60633249,  9.04497093, 13.09915475,\n",
       "       16.51518926, 20.73906041, 19.60519318, 16.00876067, 11.95839157,\n",
       "        9.37915939,  6.13304258])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_temp_exp_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Surface PAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PAR data for original years\n",
    "\n",
    "monthly_array_PAR_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['PAR']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 original temp      \n",
    "\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)\n",
    "\n",
    "\n",
    "### 2019 original               \n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/2771304440.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_PAR_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_PAR_slice[monthly_array_PAR_slice == 0 ] = np.nan\n",
    "monthly_array_PAR_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_PAR_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_PAR_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PAR data for experiments 1 and 2\n",
    "\n",
    "monthly_array_PAR_exp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['PAR']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "            \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "                \n",
    "###               \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/178454329.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_PAR_exp_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slice[monthly_array_PAR_exp_slice == 0 ] = np.nan\n",
    "monthly_array_PAR_exp_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_PAR_exp_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_PAR_exp_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'uE/m$^{2}$/s')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_PAR_slicemean[12,:],color='r',linestyle='-',label='Original WY')\n",
    "ax.plot(xticks, monthly_array_PAR_exp_slicemean[12,:],color='k',linestyle='-.',label='WY with CY winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY PAR with CY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('uE/m$^{2}$/s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.33108848,  25.43487056,  53.40414242,  62.65396427,\n",
       "        90.50300235, 103.02960905,  93.25852068,  79.68039158,\n",
       "        51.27869687,  27.85621855,  20.32929689,   8.03467567])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'uE/m$^{2}$/s')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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edfdIsJSA8d711VLcu+HBrl27Hnt9tceV1+t/1DUbq1evzvHjx1m4cCEvvvgiVatWJTo6mnXr1vHqq69SsWJFDh06lMXVioiI5D0K4kRERCSN5s2bm9aYWrx4sZmryTqhoaH8+eefme6fstvkwxo+fDg+Pj5cvXr1saY7poyg2rBhA0FBQVy4cIGqVavi7u4O/BfW3B3U3d2eF9nb25umVqYEWPeur5bC29ub0qVLc/HiRU6ePGnqf/dabTktL9Z/95pwKevlpefixYv3vY6NjQ3du3fnl19+4dChQ1y7do2ff/4ZV1dXzp8/b1p/UUREpCBTECciIiJpFC9enB49egAwe/ZsgoKCMn1udqwvl1VmzZpFbGwsxYoVIzw8nMjIyAxfb775JvDg6anpsba2Nm1mMGXKlId6fne7O2i7d1oqJC/47+vry6lTp1i7di2XLl0C0gY+D3L3wv654ft39zprgYGBXL16lXLlyqU7NfLuUWUpz6hx48aPvDZfVshr9Xt7e5s2Zbl3k4m73bsxyIO4ubnx0ksv8eWXXwLJo2u1mYOIiBR0CuJEREQkXZ999hmOjo5ER0fTvXv3B46GuXHjBj169CA8PDyHKnx4KTuhdu/eHWdnZxwdHTN89enTB4CdO3dy9OjRh75Xv379KFOmDImJiYwaNeqR6k0J3c6cOcO0adNStaVICX1SRt5VrFgRT0/Ph7qPs7Oz6eubN28+Uq1ZKeUz3bp1i6+++grIOFxMCbLWrFnDzp07U51vLnmtfoPBQO/evQH4+eefuX79epo+R48eZcGCBemeHxsbe9/r29vbm762tLR8jEpFRETyPgVxIiIikq4KFSowc+ZMbGxsOHLkCDVq1ODLL7/k5MmTpj6JiYns37+fESNGUK5cORYtWmTGiu9v9+7dBAYGAphCh/upX78+Xl5ewH8B3sOwsrIybdzwxx9/PFKYV758edMoql27dmFpaZlmnbGUYG7Xrl3Ao4U4hQsXNi3SP23aNNNun+ZSv3597OzsAJgzZw6Qdn21FCntixcvJjo6GjB/EJcX6x82bBhOTk5cv36d1q1bs2fPHiB5hOSaNWto164dDg4O6Z77xx9/0KhRI3755RdOnz5tak9MTGT16tUMHToUgAYNGlC4cOFs/ywiIiK5mYI4ERERyVDXrl3ZsGEDvr6+XL9+naFDh1K+fHlsbW1xc3PDxsaGWrVq8emnnxIeHs4zzzyTaofS3CRlimmxYsVo2rRpps5J2VX1t99+e6QdQF944QU8PDxISkpi5MiRD30+pA5latWqlWbH2XtDm0cNcV5++WUAJkyYgKOjI15eXpQtW9Y0MjAn2dra0rBhQyA5zIGMR5SVL18eT09PUz9nZ2dq166dI3VmJC/W7+XlxZw5c7C1teXAgQPUrVsXZ2dnChUqxFNPPUV8fDzffPNNuucajUa2b9/Oyy+/jI+PD3Z2dhQtWhQbGxvatm3LhQsXKFGixCMF2iIiIvmNgjgRERG5r0aNGnH8+HHmzJnDs88+i6+vL3Z2dkRGRuLq6krjxo358MMPOXbsGLNnzzbr2lwZiY6ONu2E2qNHj0xPj0sZOXft2jWWLVv20Pe1s7PjnXfeAWDhwoUcPHjwoa9xd7CW3m6oxYsXp3LlykDyFMOHXR8uxfDhw/nuu++oU6cO1tbWXLhwgXPnzhESEvJI13tcd3/usmXLmkYnpufu0WZNmjTJFdMf82L9HTp0YN++ffTp04dixYoRFxdH8eLFef3119m/fz/e3t7pnte5c2d+++03BgwYQPXq1XFxcSE8PBwnJyfq1avHp59+ypEjR6hYsWIOfyIREZHcx2DMDSvyioiIiIiIiIiI5HMaESciIiIiIiIiIpIDcm0QFxkZyZAhQ2jTpg3u7u4YDAYCAgLS9Hv++ecxGAxpXhkNfZ8wYQIVK1bE1tYWb29vRo0a9UhrvoiIiIiIiIiIiDwMK3MXkJHQ0FAmTZpE9erV6dq1K5MnT86wr729PRs2bEjTdq/PP/+cjz/+mKFDh9KmTRt2797NRx99xMWLF5k0aVKWfwYREREREREREZEUuTaIK1OmDDdu3MBgMHD9+vX7BnEWFhbUr1//vtcLDQ3ls88+Y/DgwXzxxRdA8u5V8fHxfPTRR7z11lumhY5FRERERERERESyWq6dmpoyxTSrrFq1ipiYGAYMGJCqfcCAARiNRpYsWZJl9xIREREREREREblXrg3iHkZ0dDQeHh5YWlpSqlQpXn/9dcLCwlL1OXz4MADVqlVL1e7p6UnRokVNx0VERERERERERLJDrp2amlnVq1enevXqVK1aFYDNmzfz7bffsn79enbv3o2joyOQPDXV1taWQoUKpbmGq6sroaGhGd4jNjaW2NhY0/ukpCTCwsJwc3PL0lF7IiIiIiIiIiKS9xiNRiIjIylRogQWFhmPe8vzQdzbb7+d6n3r1q2pWbMmPXv25Ndff011/H6h2f2OjR49mlGjRj1+sSIiIiIiIiIikm+dP3+eUqVKZXg8zwdx6enWrRuFChVi586dpjY3NzdiYmK4ffs2Dg4OqfqHhYVRu3btDK83bNgw3nnnHdP78PBwvLy8OH/+PM7Ozln/AUREREREREREJM+IiIigdOnSODk53bdfvgziIHlI4N1DAVPWhjt06BBPPvmkqT0kJITr16+bpramx9bWFltb2zTtzs7OCuJERERERERERAS4/4xLyCebNdxrwYIF3L59m/r165va2rZti52dHdOnT0/Vd/r06RgMBrp27ZqzRYqIiIiIiIiISIGSq0fErVy5klu3bhEZGQnA0aNHWbBgAQDt27fn2rVr9O3blz59+uDr64vBYGDz5s2MHz+eKlWqMGjQINO1XF1d+eijj/j4449xdXWlTZs27N69m4CAAAYNGkTlypXN8hlFRERERERERKRgMBiNRqO5i8hI2bJlOXfuXLrHzpw5g4uLCy+88AL79+/nypUrJCYmUqZMGbp168bw4cNxcXFJc97333/PDz/8wNmzZ/Hw8GDAgAF8+OGHWFtbZ7quiIgIXFxcCA8P19RUEREREREREZECLrNZUa4O4nIrBXEiIiIiIiIiIpIis1lRvlwjTkREREREREREJLdRECciIiIiIiIiIpIDFMSJiIiIiIiIiIjkAAVxIiIiIiIiIiIiOUBBnIiIiIiIiIiISA5QECciIiIiIiIiIpIDFMSJiIiIiIiIiIjkAAVxIiIiIiIiIiIiOUBBnIiIiIiIiIjkCzt37qRXr154enpiY2ODh4cHPXv2ZMeOHQ91nYCAAAwGwyPVsGnTJgwGA5s2bXqk8zPL398ff3//TPVNSkpi5syZtGrViqJFi2JtbU2xYsXo2LEjy5YtIykpiY4dO1K4cGHOnz+f5vywsDA8PT1p1KgRSUlJWfxJChYFcSIiIiIiIiKS502YMIFGjRpx4cIFxo4dy7p16xg3bhwXL16kcePGTJw4MdPXGjRo0EOHdylq1arFjh07qFWr1iOdn9ViYmJo3749/fv3p1ixYvz0009s2LCBn3/+mRIlStCrVy+WLVvG5MmTsbKyYtCgQWmu8frrrxMZGcmMGTOwsFCU9DgMRqPRaO4i8pqIiAhcXFwIDw/H2dnZ3OWIiIiIiIiIFGjbtm2jadOmtG/fnsWLF2NlZWU6lpCQQLdu3fjrr7/YsmULjRo1yvA6t2/fxsHBISdKfmwpo+EeNPLu1Vdf5aeffmLGjBk899xzaY6fOHGC6Oho/Pz8mDdvHk8//TQ///wzL730EgCLFy+me/fu/Pjjj7zyyitZ/THyjcxmRYoxRURERERERCRPGz16NAaDgZ9++ilVCAdgZWXFjz/+iMFgYMyYMab2lOmn+/bto2fPnhQpUgQfH59Ux+4WGxvLu+++i4eHBw4ODjRt2pS9e/dStmxZnn/+eVO/9KamPv/88zg6OnLy5Enat2+Po6MjpUuX5t133yU2NjbVfUaNGsWTTz6Jq6srzs7O1KpViylTpvAo46hCQkKYPHkyTz31VLohHED58uXx8/MDoHfv3vTp04f33nuPs2fPEhoayssvv0zr1q0VwmURqwd3EREREREREZH8yGiE27fNXUVqDg7wMMuzJSYmsnHjRurUqUOpUqXS7VO6dGlq167Nhg0bSExMxNLS0nSse/fu9OnTh5dffplbt25leJ8BAwYwd+5chgwZQosWLTh69CjdunUjIiIiU3XGx8fTuXNnXnjhBd599122bNnCp59+iouLCyNGjDD1O3v2LC+99BJeXl5A8rp3b7zxBhcvXkzVLzM2btxIfHw8Xbt2zfQ5P/zwA5s3b2bgwIG4u7sTFxfH1KlTH+q+kjEFcSIiIiIiIiIF1O3b4Oho7ipSi4qCQoUy3//69evcvn0bb2/v+/bz9vbmn3/+ITQ0lGLFipna+/fvz6hRo+577tGjR5kzZw4ffPABo0ePBqB169YUL16cZ555JlN1xsXFMWrUKHr16gVAy5Yt2bNnD7Nnz04VsE2bNs30dVJSEv7+/hiNRr777js+/vjjh9pEIjg4GOCBz+Zurq6uTJkyhfbt2wMwc+bMDANOeXiamioiIiIiIiIi+V7K1M57g6wePXo88NzNmzcDyVM379azZ880U2EzYjAY6NSpU6o2Pz8/zp07l6ptw4YNtGrVChcXFywtLbG2tmbEiBGEhoZy9erVTN3rcbVr14769etTvnx5+vXrlyP3LCg0Ik5ERERERESkgHJwSB6Blps87F4JRYsWxcHBgTNnzty339mzZ3FwcMDV1TVVu6en5wPvERoaCkDx4sVTtVtZWeHm5papOh0cHLCzs0vVZmtrS0xMjOn9P//8Q5s2bfD39+fXX3+lVKlS2NjYsGTJEj7//HOio6Mzda8UKdNbH/Rs0mNra4uNjc1Dnyf3pyBOREREREREpIAyGB5uGmhuZGlpSfPmzVm1ahUXLlxIdxrlhQsX2Lt3L+3atUu1PhykHSGXnpSw7cqVK5QsWdLUnpCQYArpssIff/yBtbU1y5cvTxXaLVmy5JGu17x5c6ytrVmyZAkvv/xyFlUpj0NTU0VEREREREQkTxs2bBhGo5FXX32VxMTEVMcSExN55ZVXMBqNDBs27JGu37RpUwDmzp2bqn3BggUkJCQ8WtHpMBgMWFlZpQoLo6OjmTlz5iNdz8PDg0GDBrF69Wp+++23dPucOnWKwMDAR7q+PDyNiBMRERERERGRPK1Ro0aMHz+et956i8aNG/P666/j5eVFcHAwP/zwA7t27WL8+PE0bNjwka5fpUoVnnnmGb7++mssLS1p0aIFR44c4euvv8bFxQULi6wZ59ShQwe++eYb+vbty4svvkhoaCjjxo3D1tb2ka/5zTffcPr0aZ5//nlWr15Nt27dKF68ONevX2ft2rVMmzaNP/74Az8/vyz5DHJ/CuJEREREREREJM974403qFu3Ll9//TXvvvsuoaGhuLq60rhxY7Zu3UqDBg0e6/rTpk3D09OTKVOm8O2331KjRg3mzZtH27ZtKVy4cJZ8hhYtWjB16lS+/PJLOnXqRMmSJRk8eDDFihXjhRdeeKRr2tnZsWLFCn7//XdmzJjBSy+9REREBEWKFKFOnTpMnTo1zSYSkn0MxpRtQyTTIiIicHFxITw8HGdnZ3OXIyIiIiIiIiJmsH37dho1asTvv/9O3759zV2OmFFmsyKNiBMREREREREReYC1a9eyY8cOateujb29PQcPHmTMmDGUL1+e7t27m7s8ySMUxImIiIiIiIiIPICzszNr1qxh/PjxREZGUrRoUdq1a8fo0aNT7XAqcj8K4kREREREREREHuDJJ59k69at5i5D8ris2dZDRERERERERERE7ktBnIiIiIiIiIiISA7ItUFcZGQkQ4YMoU2bNri7u2MwGAgICEjVJzExkW+++Ya2bdtSqlQpHBwcqFSpEkOHDuXmzZtprmkwGNJ9jRkzJmc+lIiIiIiIiIiIFFi5do240NBQJk2aRPXq1enatSuTJ09O0yc6OpqAgACeeeYZBg0aRNGiRdm3bx+fffYZy5YtY8+ePdjb26c6p2fPnrz77rup2ry8vLL1s4iIiIiIiIiIiOTaIK5MmTLcuHEDg8HA9evX0w3i7O3tOXPmDG5ubqY2f39/vLy86NWrFwsXLqRfv36pzilevDj169fP9vpFRERERERERETulmuDOIPB8MA+lpaWqUK4FPXq1QPg/PnzWV6XiIiIiIiIiIjIo8i1a8Q9jg0bNgBQpUqVNMdmz56Nvb09tra21K5dm2nTpuV0eSIiIiIiIiIiUgDl2hFxj+rixYsMHTqUOnXq0LFjx1TH+vbtS4cOHShdujRXr15lypQpDBw4kNOnT/Ppp59meM3Y2FhiY2NN7yMiIrKtfhERERERERERyZ/y1Yi4sLAw2rdvj9FoZO7cuVhYpP54v//+O3379qVJkyb06NGDv/76i44dOzJmzBiuXbuW4XVHjx6Ni4uL6VW6dOns/igiIiIiIiIi8pACAwMZMGAA3t7e2NnZ4ejoSK1atRg7dixhYWHMnz8fg8HAhAkT0j3/xRdfxNbWlsDAwCyvzWAwEBAQYHp/9OhRAgICOHv2bJq+/v7+VK1a9ZHuU7VqVSpVqpSmffHixRgMBho0aJDm2MyZMzEYDCxdupSOHTtSuHDhdJf7CgsLw9PTk0aNGpGUlPRI9WVk+vTpGAyGdJ/Ho9q0aRMGg4FNmzZl2TUfV74J4m7cuEHr1q25ePEia9eupVy5cpk6r1+/fiQkJLBnz54M+wwbNozw8HDTS2vPiYiIiIiIiOQuv/76K7Vr12b37t28//77rFq1isWLF9OrVy9+/vlnXnjhBXr16kXfvn0ZOnQoJ0+eTHX+mjVr+PXXXxk1ahR+fn5ZXt+OHTsYNGiQ6f3Ro0cZNWpUlgZPAM2bN+f48eOEhISkat+0aROFChViz549REZGpjlmYWFB06ZNmTx5MlZWVqlqTfH6668TGRnJjBkz0gx+elwdOnRgx44deHp6Zul1c5t8EcTduHGDVq1acebMGdauXftQ/8IYjUaA+/4A2dra4uzsnOolIiIiIiIiIrnDjh07eOWVV2jVqhV79+7l1Vdfxd/fn9atWzNs2DCOHz/OgAEDAJg4cSKFCxfm+eefN43qioiIYNCgQTRo0ID3338/W2qsX78+pUqVypZr36158+YAaUaBbdq0iUGDBmEwGNi6dWuaYzVr1qRw4cJ4eHjw448/smbNGn755RdTn8WLFzNnzhy++uorfH19s7xud3d36tevj62tbZZfOzfJ80FcSgh3+vRp1qxZQ82aNR/q/JkzZ2JtbU3t2rWzqUIRERERERERyU5ffPEFBoOBSZMmpRvk2NjY0LlzZwCKFCnClClT2LZtG99++y0Ab7/9NqGhocyYMQNLS8sM7/PDDz9gYWHB1atXTW1ff/01BoOB1157zdSWlJREkSJFePfdd01td09NnT59Or169QKSgzODwYDBYGD69Omp7rd7926aNGmCg4MD5cqVY8yYMQ+cEurv759mOmZoaCiHDh2iQ4cO1K5dm40bN5qOnT9/ntOnT5sCPIDevXvTp08f3nvvPc6ePUtoaCgvv/wyrVu35pVXXrnv/evWrUuHDh1StVWrVg2DwcDu3btNbYsWLcJgMHDo0CHTM7l3amrKFN3MPIfjx4/Ttm1bHBwcKFq0KC+//HKakX8A+/fvp2PHjhQrVgxbW1tKlChBhw4duHDhwn0/V1bJ1Zs1rFy5klu3bpke3NGjR1mwYAEA7du3x2Aw8NRTT7F//37Gjx9PQkICO3fuNJ3v7u6Oj48PAF999RVHjx6lZcuWlCpVyrRZw5o1awgICKBo0aI5/wFFREREREREcoFbt2499Dm2trZYWSXHCgkJCcTGxmJhYYG9vf0jX7dQoUIPXUdiYiIbNmygdu3amV7TvW3btrz00kt89NFHWFhYMHXqVCZOnEj58uXve16rVq0wGo2sX7+eZ555BoB169Zhb2/P2rVrTf327NnDzZs3adWqVbrX6dChA1988QXDhw/nhx9+oFatWgCmDAMgJCSEZ599lnfffZeRI0eyePFihg0bRokSJXjuuecyrNHV1RU/P79UYdvmzZuxtLSkYcOGNGvWjA0bNpiOpfS7O4iD5NBx8+bNDBw4EHd3d+Li4pg6dep9n0/KM5o4cSLx8fFYW1tz5coVDh8+bHpGdevWNT234sWLU61atfteLzPP4cqVKzRr1gxra2t+/PFHihcvzu+//87rr7+e6lq3bt2idevWeHt788MPP1C8eHFCQkLYuHFjuqFdtjDmYmXKlDEC6b7OnDljPHPmTIbHAWP//v1N11q6dKmxcePGRnd3d6OVlZXRycnJ2KRJE+OcOXMeuq7w8HAjYAwPD8/CTysiIiIiIiJiHvf73Tqj17x580znz5s3zwgYmzVrluq6RYsWfahrPoqQkBAjYOzTp89DnRcZGWksV66cETC2atXKmJSUlKnzSpUqZRw4cKDRaDQaY2NjjYUKFTJ+8MEHRsB47tw5o9FoNH7++edGa2trY1RUlOk8wDhy5EjT+/nz5xsB48aNG9Pco1mzZkbAuGvXrlTtlStXNj711FMPrPGtt94yAsZLly4ZjUaj8Y033jDWr1/faDQajX/99ZfR0tLSlGkMGDDAaGlpaYyIiEhznb/++sv0vZk5c+YD72s0Go3r1q0zAsYtW7YYjUajcdasWUYnJyfjq6++amzevLmpX/ny5Y19+/Y1vZ82bZop73nY5/DBBx8YDQaD8cCBA6n6tW7dOtUz3rNnjxEwLlmyJFOf5WFkNivK1VNTz549i9FoTPdVtmxZypYtm+Fxo9GYakhnp06d+Pvvv7l69Srx8fFERESwZcsW+vTpY74PKCIiIiIiIiJm4ejoyJAhQwAYNWoUBoMhU+e1bNmSdevWAbB9+3Zu377NO++8Q9GiRU2j4tatW0eDBg0eaYRfCg8PD+rVq5eqzc/Pj3Pnzj3w3HvXidu0aRP+/v4ANG7cGIAtW7aYjtWpUwcnJ6c012nXrh3169enfPny9OvXL1N1N2rUCDs7O9MzWrt2Lf7+/rRt29b0vM6fP8+JEycyHDF4t8w8h40bN1KlShWqV6+eql/fvn1Tvff19aVIkSJ88MEH/Pzzzxw9ejRTnykr5eogTkRERERERESyX1RU1EO/unXrZjq/W7duREVFsXLlylTXPXv27ENd81EULVoUBwcHzpw589DnpqwnZ2Njk+lzWrVqRXBwMCdOnGDdunXUrFmTYsWK0aJFC9atW0d0dDTbt2/PVMh0P25ubunWGx0d/cBzmzVrhoWFBRs3biQ0NJTDhw/TrFkzAJycnKhZsyabNm0iODiYM2fOpJmWeu89H+b52NnZ0ahRI1MQt379elq3bo2/vz+JiYn8/fffpsAyM88oM88hNDQUDw+PNP3ubXNxcWHz5s3UqFGD4cOHU6VKFUqUKMHIkSOJj4/P9Gd8HLl6jTgRERERERERyX6PM3ILwMrKyrReXFZeNzMsLS1p2bIlK1eu5MKFC9m+M2nLli2B5FFva9eupXXr1qb2jz76iC1bthAbG/vYQdzjcHFxMYVtmzZtwsLCgkaNGpmON2vWjI0bN5rWZ7tfEPcoWrZsyYgRI/jnn3+4cOECrVu3xsnJibp167J27VouXbpEhQoVMr2m34O4ubkREhKSpj29tmrVqvHHH39gNBoJDAxk+vTpfPLJJ9jb2zN06NAsqed+NCJORERERERERPK0YcOGYTQaGTx4MHFxcWmOx8fHs2zZsiy5l6enJ5UrV2bhwoXs3bvXFMS1bt2aa9eu8c033+Ds7GzalCAjKaPxMjPC7VE0b96cEydOMHv2bGrXrp1q6mmzZs04cOAAS5YswdraOlVIlxVatWpFQkICH3/8MaVKlaJixYqm9nXr1rFhw4YsDSqbN2/OkSNHOHjwYKr22bNnZ3iOwWCgevXqfPvttxQuXJh9+/ZlWT33oyBORERERERERPK0Bg0a8NNPP7Fu3Tpq167Njz/+yObNm1m3bh1fffUVlStXztSOn5nVsmVL1q9fj42NjSnE8vb2xtvbmzVr1uDv75/uCMG7Va1aFYBJkyaxdetW9uzZQ2hoaJbVmDLKbfHixaZpqSmaNGkCwJ9//km9evWyfORi7dq1KVKkCGvWrDEFlZAcxB08eJArV65kaRD31ltvUbRoUTp06MD06dNZuXIl/fr14/jx46n6LV++nPbt2zNp0iTTiMZXXnmFmzdvpqozOymIExEREREREZE8b/DgwezZs4fatWvz5Zdf0qZNG7p27cqcOXPo27cvkyZNyrJ7pYRIjRs3xs7OLk17ZkImb29vxo8fz8GDB/H396du3bpZNmoPksM2KysrjEZjmiCucOHC+Pn5YTQaTZs4ZCULCwvTde9+FikbWFhYWGTpdFgPDw82b95M5cqVeeWVV+jXrx92dnZMnDgxVb/y5ctTuHBhxo4dS+fOnenVqxf79u1j+vTpDB48OMvquR+D0Wg05sid8pGIiAhcXFwIDw/H2dnZ3OWIiIiIiIiIiIgZZTYr0og4ERERERERERGRHKAgTkREREREREREJAcoiBMREREREREREckBCuJERERERERERERygII4ERERERERERGRHKAgTkREREREREREJAcoiBMREREREREREckBCuJERERERERERERygII4ERERERERERGRHKAgTkREREREREREJAcoiBMREREREREREckBCuJERERERERERERygII4ERERERERERGRHKAgTkREREREREREJAcoiBMREREREREREckBCuJERERERERERERygII4ERERERERERGRHKAgTkREREREREREJAcoiBMREREREREREckBuTaIi4yMZMiQIbRp0wZ3d3cMBgMBAQHp9t23bx+tWrXC0dGRwoUL0717d06fPp1u3wkTJlCxYkVsbW3x9vZm1KhRxMfHZ+MnERERERERERERycVBXGhoKJMmTSI2NpauXbtm2O/48eP4+/sTFxfHvHnzmDp1KkFBQTRp0oRr166l6vv555/z5ptv0r17d1avXs2rr77KF198wWuvvZbNn0ZERERERERERAo6K3MXkJEyZcpw48YNDAYD169fZ/Lkyen2GzFiBLa2tixfvhxnZ2cAateuTfny5Rk3bhxffvklkBzsffbZZwwePJgvvvgCAH9/f+Lj4/noo4946623qFy5cs58OBERERERERERKXBy7Yg4g8GAwWC4b5+EhASWL19Ojx49TCEcJId4zZs3Z/Hixaa2VatWERMTw4ABA1JdY8CAARiNRpYsWZKl9YuIiIiI+UVFRXHz5k3T+7Nnz/J///d/PPvss6n6nT17ltjY2ByuTkRERAqaXDsiLjNOnTpFdHQ0fn5+aY75+fmxdu1aYmJisLOz4/DhwwBUq1YtVT9PT0+KFi1qOi4iIiIieUNiYiIhISEEBwcTHBzMuXPnTF+nvG7cuMGHH37IkCGfcekS7NljZNasWVhZ2eLqOou4OANt2sBXX/Xl2LEjdO7cmV69etGmTRvs7OzM/RFFREQkn8nTQVxoaCgArq6uaY65urpiNBq5ceMGnp6ehIaGYmtrS6FChdLtm3Kt9MTGxqb6P6QRERFZUL2IiIiI3E9UVBSXL1+mfPnypraAgAA2btxIcHAwFy5cICEh4YHX+fLLq3z+ecq7UsCXJCR4MXFiImDFpElRGAznMRojmDVrFrNmzcLJyYkuXboolBMREZEslaeDuBT3m8J697HM9rvX6NGjGTVq1KMVJyIiIiJpJCUlcfny5VSj1ypVqkTHjh0BOH36LD4+3tjY2LJwYTSXLhm4dAlmzDjE2bNb7rqSJcnhmtc9rzJ3/lmahITkJUycnaFkSWtKlBhCyZJQogTExsLixY6cPXsO2AHMBxYQGXkxVSjXuXNnevfurVBOREREHkueDuLc3NwA0h3NFhYWhsFgoHDhwqa+MTEx3L59GwcHhzR9a9euneF9hg0bxjvvvGN6HxERQenSpbPgE4iIiIjkT7du3UoVst09bfTcuWAuXrxAfHx8qnO8vP6Pzz/vyKVLcOlSCcBAXJw9nTrdAFJmQLwK9OS/sM0TGxtLSpRIDtZSArZ7/1miBDg6pl/r11/Dvn0WzJ/fiAULGnHq1DfATmAeKaHc77//zu+//24K5Xr16kX79u2xtrbOlucnIiIi+VOeDuJ8fHywt7fn0KFDaY4dOnQIX19f0/+xTFkb7tChQzz55JOmfiEhIVy/fp2qVatmeB9bW1tsbW2zuHoRERGRvO/27dv8+OOPnD9/nrFjxxMSkjxy7e23n2PXrkUPONsSKEnK6LXg4CYEB6ccswHCKV7c6Z5grWWaoM3NDR6wx9d9GQxQu3bya/RoOHjQggULGjJ/fkOCglJCufnAfFMot2LFX1y5EmK6htFofOBGYyIiIiJ5OoizsrKiU6dOLFq0iLFjx+Lk5ARAcHAwGzdu5O233zb1bdu2LXZ2dkyfPj1VEDd9+nQMBgNdu3bN6fJFREREcqVbt25x/vx500i2c+eCOXEimNOng7lwIRhf3+a0bDmZS5fg/HkrVq0aAhj5/vsPgWJ3ruIFuJB6muh/L0dHL0qW9KRUKasMR7J5eDiR0wPODAaoUSP59emncPjwf6HcsWNfkxLKhYfb06WLDT17QufORlq3rkmVKlUYN24cnp6eOVu0iIiI5BkGo9FoNHcRGVm5ciW3bt0iMjKSgQMH0qtXL3r37g1A+/btcXBw4Pjx49StW5datWoxdOhQYmJiGDFiBGFhYRw4cAB3d3fT9T7//HM+/vhjhg0bRps2bdi9ezcfffQRzz33HJMmTcp0XREREbi4uBAeHo6zs3OWf24RERGR7JKUlITBYDCN3po7dynLl6/j9OlgLl4M5tq1YG7fzngTq2TNgE13vX+V5NDtHayt3e9MBU2gZEmrDKeKZjRNNDc7ehQWLEh+3T0hw8JiL0lJdbCxceDff69RtmzyMihBQUGULl0ae3t7M1UsIiIiOSWzWVGuDuLKli3LuXPn0j125swZypYtC8DevXv54IMP2LFjB1ZWVrRo0YJx48bh4+OT5rzvv/+eH374gbNnz+Lh4cGAAQP48MMPH2p9DwVxIiIikluljGY7dSqYqCgbvLz874xcS+STTyoSHn6Ohg0vcO1aMS5dgsjIt4Hx6VzJmXtHsjk7e1G8eBm8vMri41My3YDNzQ0sLHLwA5vJv//+F8odOJAE7AKCsLDoT7Nm0LMnfP99VS5ePEenTp3o1asXbdu2VSgnIiKST+WLIC63UhAnIiIi5pCUlERIyBUCA4M5dCiYf/8N5uzZlJFs54iICCY+/u7RbPeOXCsJXAL+AereaVuJjc1mihRJCdm88PX1olw5l1QBm4cH2NjkzOfMa06e/C+U27s3pTUUqAmcN/VzdHRUKCciIpJPKYjLRgriREREJDtERcGlS7B06TqOHz+Ll1dvwsKcuXgRdu36lPPnPwXiH3gdcALKYDA8iZfXZFOgZm19gDJlXKlUqSSlS/+30+idZXYlC5w5AwsXwvz58M8/SSSHnsm7r94bynXs2JHevXsrlBMREckHcjSI27BhA6GhofTq1QuAK1euMGDAAPbt20ebNm2YNGmSaffS/EBBnIiIiDyKmBgj//wTwpYt/7J/fxAnTgQREnKKuDh3jMZJRESk9EwZubYbqHOn7VvgHcACKIG1dRkcHb1wdfXCw8MLLy8vypf3okoVLypUKGzaTbQgTBPNrc6dg0WLkkfKbd+eEsrNv/NKG8r16tWLLl26YGlpaaaKRURE5FHlaBDXuHFjWrduzciRIwHo378/ixYtonXr1qxatYphw4bx8ccfP+5tcg0FcSIiIpKRxEQ4fPgmmzefYO/eII4dC+L8+SDCwoKIiwsCotI5yxs4DSSPTjMY+mFtHUb9+p9TtWpNSpYEJ6dQXFyi8PMrQenS1pommsdcuPBfKPf330ZSj5QLBqB48ZJcuhSMxZ30NCkpyfS1iIiI5G45GsQVK1aMKVOm0KlTJxISEnB1dWXMmDG8+uqrjBs3jqlTp3L06NHHvU2uoSBORESkYDMaISQE1q//l+3bj2IwNOTixeKcOAH//juBxMT/3edsC2xtvXF1rYCXVwUqVPClcmUfunVrp2miBcTly/+Fcps3GzEaU0bKuVGz5jB69oSuXRPo2LEC9erVY8KECbi7u5u7bBEREbmPHA3i7OzsWLt2LU2aNGHXrl00bNiQc+fOUapUKbZs2UL79u2Jikrv//7mTQriRERECobQ0ET+/juY7duDOHQoiAsXwrCxGUlQUPJ6blCf5N0yFwA97py1FOiClZUnLi4VKFmyAk88UYGaNSvQpEkF6tYth62thrNJsitXYPHi5FBu40ZISko5shnwx8HBjZ07Q6hWzQqAw4cP4+PjozXlREREcpnMZkVWWXGzYsWKceLECZo0acK6desoU6YMpUqVAiAyMhJra+usuI2IiIhIlrt928g//1xj69Yg9u8PIigoiEuX/iU8PIjExJNA3F29rYDhgDUWFuDoWBuDIYnGja1o1w7Kl4cyZdpQvHgEhQtraJs8WPHi8PLLya9r1+DPP5M3eli3rilJSbu4ffscfn5WVKkCPXoY+fXXDkREhJrWlGvXrh0ODg7m/hgiIiKSSVkSxLVt25bhw4dz5MgRpk+fTv/+/U3Hjh8/TtmyZbPiNiIiIiKPJD4ejhyJ4sSJJC5ccObECdi9exeBgW/cWbct/D5n2+Dg4Iu7ewW8vSvw0ksx1Khhjbc32Nr+kE5/uzsvkYfj7g6DBiW/QkMNLF1ajwUL6rF2LRw5AkeOXAIMwC3mzp3L3LlzKVSokEI5ERGRPCRLpqZev36dfv36sX37durVq8e8efNwdXUFoHbt2tSvX58ffkjvP1TzJk1NFRERyX2SkuDMmTi2bj3Drl1B2Np24MQJC4KC4OTJFzEafwW+JnnnUYADQM07XxuwtS1jWretUqUK1KlTgaZNK1C5spd2sRSzunEDli1Lnr66apWR+Pg9JG/0MB84Z+pXqFAhOnToQO/evRXKiYiI5LAcXSPuQYXY2dlhk4+29lIQJyIiYh5GI1y7lsT27ZfYvv1fAgODOHUqiCtXgoiKCsJoPAMk3ul9DvC68/XHwGcULfo2/v7fUKEClC0bzfXrK2na9Alq1/bBzk6j2CT3Cw+H5cuTQ7m//jISF7eH5EBuHneHcg4ODqaRct27d9fuqyIiItks24O47777jh49epjWgitIFMSJiIhkr6goOHECgoLgwIFQli//lkuXgu6s23YCuJ3huQZDIZycKtClywwaNapG+fLg7h5KiRIWuLkVybkPIZLNIiPhr7+S15RbscJITExKKDcfOAtAmTIVOXXqKJaWBgASEhKwssqS1WlERETkLtkexBUvXpzr169Tp04devbsSffu3fHx8XnkgvMSBXEiIiKPLzYWjhy5zZkz1pw+bc2JE7B161xOnvyO+PhWwCd3eoYBbvecbYWDg49p3baqVStQv34FGjeugJeXJwaDIWc/jIiZ3boFK1cmj5RbtszI7dt7SR4l50WpUq/Towd06hTNs89606RJEyZPnoyLi4u5yxYREck3sj2IS0pKYvPmzSxcuJDFixcTEhJCtWrVTKFc5cqVH7n43E5BnIiISOYkJsLp0wls3XqWf/4J4siRIM6eDeL69SCio4OA88AWoMmdM34FXgTa4e7+FxUqQIUKcOLEu/j6lqZu3Qo0aVKBSpXKalSPSAZu34bVq1NCueSRc8lWAB1xcvJi6dKzNGliwNIS9uzZQ6VKlShUqJAZqxYREcnbcnyNuG3btrFgwQIWL17M+fPnqVChAj169KBHjx7UrFnzwRfIQxTEiYiI/MdohCtX4PjxJFas+JvAwCBOnw4iJCR53TY4BcRneH6ZMtNo2PB5ypeHIkXOEh29mxYtqvLkk5Vy7DOI5FcxMbBmTXIot2SJkcjIfUAI0IFixaBLl3j++MODhIRoOnToQK9evejQoYNCORERkYdk1s0a/vnnHxYuXMiiRYs4ffo0ZcqUoWfPnowdOzarb2UWCuJERKQgunkzec22Eydg+/Z/2bx5NjdvOhMR8e6dETdGwAWITHOuwWCHk1N5PDwq4OtbAT+/CjRsmDydtGhRN00lFckBsbGwbl1KKJf873RyUN4GOG3qZ29vr1BORETkIeWaXVMPHDhgCuWOHDmSnbfKMQriREQkv4qOhiNHYti69RR79gRx/HgQ588HERYWRELCO0C3Oz3XAE8BlYEjWFhA2bJw61Z37OxiTOu2PflkBRo1qkCZMqW0a6NILhIXBxs3Jm/0sGiRkRs39pO8ptx87g3l2rdvbwrlHB0dzVWyiIhIrpZtQVxMTAwnTpzAx8cHBweHVMe2bdtGo0aNHq3iPERBnIiI5Afx8TBx4hEWLdpoWrctJiaI5N0W0/vPgxF4eo6iQgUoWfISp04FUK1aVd5553+UKwe2tjlbv4hkjfh42Lw5eaTcwoVGrl/fT3IgN4/0QrnevXvTq1cvjWQVERG5S7YEcTt27KBz584kJSURExPDxx9/zNChQ03HnZ2diYiIeLzK8wAFcSIikpedPQtvvvk7K1f+QHz8jnT7WFo64+JSgVKlKlChQgVq1apA+/b1qF69YOyQLlJQJSTA338nh3ILFhi5ejUllJtP8jRWKF++HgcP7sLePvmcuLg4bGxszFWyiIhIrpAtQVzDhg15+eWXee655zh+/DjPPfccVatWZfLkyVhYWODk5ERkZNp1YfIbBXEiIpLXJCTAX3/Bzz/DqlVgNPYm+RdrK8qWbUXlyn6mddvq1q1A8eLFNNpFpIBLTIRt2/4L5S5fPkDyKLmKODr2p2NHaN/+Jm+8UZaWLVsyc+bMNDNmRERECopsCeIKFy7MzeRVXQGIjo6mV69e2NjY8Mcff+Dm5qYgTkREJBe5eBHeeGMGK1b8TFzcDKACAHXrbqNChS188cXzeHl5mrdIEcn1kpJgx46UUA4uXEg5Mhfog7NzFX799TAdOkChQslL1lSvXl1ryomISIGRLUGcl5cXO3bsoGTJkqa2hIQEnnvuOUJCQti1axe3bt16vMrzAAVxIiKSmyUlwdq1yaPfli2DxMROwHLs7d/l9dfH8eKL4Otr7ipFJK9KSoLdu5MDuXnzjAQHHwDCgJbY20Pr1rdZvdodgyGJdu3a0atXLzp27IiTk5OZKxcREck+2RLEDRw4kHLlyvHRRx+lajcajbz44otMmTKFpKSkR686j1AQJyIiudGZM1G8994frFgxmdjYuUAZAPz8NlKlym7GjOmPl1dx8xYpIvmK0Qh79yaHcvPnw+nTAEdI3mH5hKmfnZ2dQjkREcnXsiWIi4uLIyEhIcO1H4KDg/Hy8nr4avMYBXEiIpJbGI3w66/7GDt2EqdO/Q5EAWBr+zEvv/wJL74IlSubt0YRKRiMRjhw4L+RcidPHuS/jR4UyomISP6WLUGcJFMQJyIi5nb2bATvvz+HZcsmERu7z9RuZ1eezp1fZOzY/pQp427GCkWkIDMa4dCh/0K5f/8NJHmjh9ShnK2tLW3atKF3797069fPXOWKiIg8NgVx2UhBnIiImENSkpFp0/YwZswkTp6cA6Ssy2qDr28PPvjgRV54oZl2OxWRXOfo0eSpq/PnGzlyJG0oV7FiG1avXk3K5Jrr169TtGhRc5UrIiLy0HI8iFuyZAm///47586dIyYmJvVNDAYOHjyYFbfJFRTEiYhITrpwIYL33vudP/+cREzMAVO7rW1FOnV6ka+++j/KltUvrCKSNxw/nrKmnJHAwEPAYqAa0J3ataFFi4t8/bUX9evXZ+PGjdjY2Ji5YhERkQfL0SDuq6++4oMPPsDd3R1fX990/7LcuHHj494m11AQJyIiOWHvXvjlF5g5cxMxMc3vtNri49OL999/kcGDG2NhodFvIpJ3nToFS5Ykv7ZtS57Smjxa7mns7Bry+uvb6NoV6teH33//jVq1alGlShWN/BURkVwnR4M4b29vWrZsyS+//IKlpeXjXu6hPP/888yYMSPD4zt27KB+/foZ9nviiSc4fvz4Q91TQZyIiGSXixdvMmTI72zZEseFC2/faTXi6NiF1q1bMm7c/1GunKtZaxQRyQ5XrsCyZcmh3Nq1F4mLuwbUAMDdPYzr14tjNCbg61ue7t270b17d+rWrYuFhYU5yxYREQFyOIhzdnZmyZIltGjR4nEv9dBOnTrFtWvX0rR36tQJW1tbzp07h6WlJc8//zzz5s1jw4YNqfrZ29tTvXr1h7qngjgREclqhw8nj36bOnUVt2+3A4pgbX2JXr3sePllaNwYNABERAqKyEhYvRoWL4YVKyA8/CTwDrAGiDX1K1GipCmUa9KkCVZWVuYqWURECrjMZkVZ8jdVo0aNOHbsmFmCOB8fH3x8fFK1bd68mevXr/PRRx+lGqFnYWFB/fr1c7pEERGRdF2+fIMhQ2ayfbslp0+/dqe1NQ4O7Wjduh3ff59kWrhcRKQgcXKCnj2TX3FxsHmzL4sXL2Xx4khCQlYCi4AVXLp0kYkTJzJx4kSKFHGjW7cudO/enVatWmFra2vujyEiIpJGloyI+/fff+nWrRtjxoyhbdu2Zl9Q9bnnnmPWrFmcOnUKb29vIHkK64IFC4iKinrs62tEnIiIPCqj0cicOdv47LNJHDs2H4gBimFhcZ5u3Wx4+WVo0QI000pEJK2kJNizJ3mk3KJFMQQFrSc5lPsTCDX1K1TIiT59ejN58mRzlSoiIgVMZrOiLPnPfF9fX1q1akW3bt1wcHDA2dk51cvFxSUrbpMp4eHhLFiwgJYtW5pCuBTR0dF4eHhgaWlJqVKleP311wkLC8ux2kREpOC6fDmU558fj6NjFZ59tgnHjs0EYrC29qNTp5GcPJnEggXQqpVCOBGRjFhYQL16MHo0/PuvHceOdWDMmCnUqxcCbABeB0py61Yk8+aFM3w4/PNPcoA3f/58QkNDH3AHERGR7JUlU1OHDBnCxIkTqVGjBpUqVTLriLg5c+YQHR3NCy+8kKq9evXqVK9enapVqwLJ01e//fZb1q9fz+7du3F0dMzwmrGxscTG/rcWRURERPYULyIi+YrRaGTu3C18+ukkjh5dyH/rGjng5fUMb775Iv/7X12srLT4m4jIo6hYMfn1wQdWXLrUnKVLm7N48XesX7+byEgbRo9ODu2KFfuXq1d7Y2trT0jIdQoXdjB36SIiUkBlydRUNzc3XnzxRUaPHp0VNT2WunXrcubMGS5evPjAdSEWLlxIz549+eabb3j77bcz7BcQEMCoUaPStGtqqoiIpCck5DrDh89g3rxfuXXrX1O7lVUN2rR5ibFj+1Kliv7+EBHJLjdvwsqVyTuw/vUXREVtJXm0nAcuLqvo0AG6dYMVK16kSpUKdOvWLc260yIiIg8jR3dNLVy4MIsWLTLLZg13CwwMpHr16rz55puMHz/+gf2TkpJwdnamQ4cOzJ07N8N+6Y2IK126tII4ERFJ5eJFmDwZPv+8FvHx+++0FqJkyb78738v8tZbtbGx0eg3EZGcFBMDGzYkh3JLlkRz7Zr9nSPngf92xKlSpTq9e3ene/fuVKlSBYO2qhYRkYeQo2vEtWnThp07d2bFpR7LlClTABg0aFCmzzEajVg8YDEeW1vbNOveiYiIAISEXGXw4K/p2PEWZcpAQADEx/fHyqo2bdr8wv79l7lwYRJDhtRRCCciYgZ2dtC+PUyaBJcv27N1K7z3HpQt6wT8ALQELDly5CAjR46kWrVqeHtX4IMPPmDXrl0kJSWZ+ROIiEh+kiUj4g4dOsTTTz/NSy+9RIcOHXB1dU3TJ722rBQbG0uJEiXw9fVl165dmTpn3rx5PP3004wfP54333wz0/fSrqkiInLlCkydamTkyMrExx8HpgHP06wZvPhiIj16WPKAFRJERMSMjEY4ciR5pNyCBaEcPLiM5B1Y1/Dfmp5QrFhJevXqRo8e3WnSpAlWVlmyzLaIiOQzOTo1NWVE2f2GbycmJj7ube5r7ty59OnTh0mTJjF48OBUx86dO0ffvn3p06cPvr6+GAwGNm/ezPjx4/Hx8WHXrl0UKlQo0/dSECciUjCFhFxh1KhZXLv2OkuX2hIfDzAGS8vFtG//MV9+2ZFKlcxdpYiIPIrgYPjzT1iwIJK//16J0bgIWAFEmfqULv0Ep04dw9paI5xFRCS1HA3iAgICHriGwsiRIx/3NvfVpk0btm/fzuXLl3Fyckp17MaNG7zwwgvs37+fK1eukJiYSJkyZejWrRvDhw/HxcXloe6lIE5EpOBISkpi8eJ1fPLJJAID/wQSgDlAH+rXTx799vTTljhoAz4RkXwjLAyWL4eFC2NYtWo9cXGLgD+Bzri6TqVTJ+jSxchff71K69bN6dKlywM3ihMRkfwt24O47777jh49elCqVKlHLjKvUhAnIpL/Xbp0mVGjpjF79mSios6Y2i0sGtCu3Ug+//wpqlc3Y4EiIpIjbt+GtWth0aIEli2L4MaNlCV39gB1sbQsxE8/XaN7d3vc3CA+Ph5ra2tzliwiImaQ7UFc8eLFuX79OnXq1KFnz5706NGDcuXKPXLBeYmCOBGR/CkxMZHFi9fw6aeTCAxcBqQsq+CCu/tzvPLKYN57rxr3DLwWEZECIiEBtm2DxYthwYKzXLz4E8kjpb/G0hKaNoXjx/0oV64Yfft2p2vXrpQoUcLcZYuISA7I9iAuKSmJzZs3s3DhQhYvXkxISAjVqlWjR48e9OjRg8qVKz9y8bmdgjgRkfzl4sWLfPbZVGbNmkxUVLCp3cKiEY0avchnn/WkSRMHHrAKg4iIFCBGIxw8mBzKLVkCgYEApwDfVP2qV2/As892p3v3bvj4+JihUhERyQk5ukYcwLZt21iwYAGLFy/m/PnzVKhQwRTK1axZMytukWsoiBMRyR9u3YKhQ5czcWIXIOlOaxFcXZ/jpZcGM2RIFQoXNmOBIiKSZ5w+nRzIzZlzij17FpO8A+uOVH18favz7LPd6dmzO1WqVHngOtsiIpJ35HgQd7d//vmHhQsXsmjRIk6fPk2ZMmXo2bMnY8eOzepbmYWCOBGRvOv8+fNs336Vv/+uzcyZEBFxEyiBwVCHBg1e5JNPetCihb1Gv4mIyCO7ejV5s4fZsy+yefOfJCQsAjbx35IH4OnpyzPPdKd37+7Uq1dPoZyISB5n1iDubgcOHDCFckeOHMnOW+UYBXEiInlPdDR88MFCJkzoDdQG/gHA1xf69r3CG28Up2hRs5YoIiL5UFQUrF4Nc+eGsnz5MqKjFwOrgVgA7O1L8+uv5+jQwUDhwmA0GhXKiYjkQbkmiMuPFMSJiOQNwcHB7NsXxpYtNZgxA8LCrgGlgAZ06bKMN95wonlzsLAwd6UiIlIQxMfD5s0wb14kCxeuIixsEeADfIaVFTRrlsDhw1Xx92/ETz+No0iRIuYuWUREMinbg7gtW7ZQq1YtHB0d79vv+vXrLF26lIEDBz7KbXIlBXEiIrlXQkICS5as4IsvJrF//0qgMbAFgDJl4JlnQnjzTQ88PMxapoiIFHBJSbB3b/K6ckuWwNGjABuAlkBR6ta9TPfuVnTtCjExB/D19X3g714iImI+2R7EWVpasmPHDurVqwck76JqZ2fHrl27Um3OsGvXLho2bEhiYmJGl8pzFMSJiOQ+Z8+e5euvpzB9+lSioi7ddaQF7dsv57XX7HnqKbC0NFuJIiIiGQoKgoULE5g162+OHr0M9L1zJAkrKy+Mxus0aNCGAQO606VLJ9zc3MxZroiI3COzWdEjT8a5N78zGo0kJCSkaRcREcku8fHxLFiwmNq12+HtXY6JEz+7E8K5U6jQEF57LYjg4PWsWGFP+/YK4UREJPeqUAGGDbPiyJHmXLrUl59/hrZtwcoqhIQEexITY9m6dRkvvDAAd/fi1KzZiu+//5FLly49+OIiIpJraFUcERHJc06fPs3rrw/H1dWLXr26s2/fKsAItMLPbx5z517gxo0vmTixPKVLm7taERGRh+PpCS+9BCtXwvXrJZg9O4inngrExiYA8MNoTOTAgfW8+eZrlCxZkieeaMjnn4/j1KlT5i5dREQeQEGciIjkGQkJRho16oGPjw8//DCaqKgQoDj29kN58cWTnDy5loMHe9G7tw3W1uauVkRE5PG5uMAzzxhYtaoaEREj+euvg/TpcxJHx6+ABgAEBe3go4/ex9fXl9Klq/P++6NISEgwb+EiIpIuBXEiIpKrnTt3jpAQI6NHQ/nyBrZvdwUMwFNUqbKQmTPPc+PGaH75xQcfH3NXKyIikn1sbaFdO5gzx4fw8PfYvn07L798AXf3H0je5MGSCxcCGTduPv7+VowbBydPwsmTJ0lKSjJ3+SIiAlg9zsn//vsvVlbJl0jZjOH48eOp+tz7XkREJDOSkow0atSZnTuXY2n5D4mJdQFwcvqQnj2H8/773lSqZOYiRUREzMTCAho0gAYNSvLjj69y7Nir/P57KLNnL+PsWVu2bYNt2+D9929jMFTH0bEIc+fuom3bkhgM5q5eRKTgeuRdUy0sLDDc8ye40WjMsE27poqIyIOcO3cOR8cyzJgBv/wCQUH/B/wOfEODBm/x0kvQuzfY25u7UhERkdzr/Hn4809YsgQ2btxNUlILwA04Q+nSBrp2BVvbqTRuXJwOHZ4yDa4QEZFHl9ms6JGDuBkzZjxU//79+z/KbXIlBXEiIlknJiaGRYsWM27cJPbv34S1dSDx8dUAKFToFN26WfHee2WoXt3MhYqIiORBYWGwZEkMc+acYfv2Sty+DRAPFAdu4Opagc8//5jBg5/BUtuLi4g8smwP4goyBXEiIo8nNDSUFSv+YvbspWzatIrY2Kg7RyyAidSs+QqvvALPPAOOjuasVEREJP+IjoZ162Du3BssXDiCmJjZQBgAbm5P8MUXI3jhhacVyImIPAIFcdlIQZyIyMMLCgpi0aKlzJ69lMOHt2E03r1odCmsrF6ge/eBvPeeF3XqoPVrREREslFCAsydG8l7700kJGQc/wVyFRk9egQDB/ZWICci8hByNIhLb724e2mNOBGRgmffvn1MmfIHixYtJSTk33uOVsfCojN163amX79a9OtnQeHC5qhSRESk4EpKgtmzI3jvvQlcufI1cAOAokUrMWbMSAYM6IWFhYV5ixQRyQNyNIgLCAhIE8Rdu3aNNWvWkJiYyHPPPcfIkSMf9za5hoI4EZH0RUVFYWVlw5EjNixfDr/88iGXL39x56g14I+TU2fatetEnz5laNUKnJzMWbGIiIhAciD3++/hvP/+91y58g1wEwB39yp8+eVI+vfvoUBOROQ+csXU1Li4OJ566il69+7NK6+8kl23yXEK4kREUouKgu7dX2T9+hm4uMznxo3Od47sBb6lXLnO9O79FD16uFCrFui/40VERHKnlEDuvfe+4+rVb4BwADp3ns706f0pUsS89YmI5Fa5IogDWLRoER988AEnTpzIztvkKAVxIlKQGY1GDhw4wKxZf1G69BBWrrRm0yaIi/sfMAF4n0KFxtKmDXTsCO3agaenmYsWERGRh5KUBLNm3eT998dz9ep8YC/Ozna89Rb07n2RSpU8NUJOROQuuSaIW7lyJb169SIqKurBnfMIBXEiUtDExsayfv0mfv11KevXLyMy8vydIxsBfwBKlz5N06YxPPdcJZo1M2Bra65qRUREJKskJcHChUl8+qkFhw4BJGJhUYVixexZsmQOTz5Z0dwliojkCpnNiqyys4hr167x1Vdf8cQTT2TnbUREJBuEhoYyb95fTJ++lH37VpGQcPf/UHEA2lCzpj19+yaPfHviiXLa6VRERCSfsbCAXr0s6NEDFi+GoUOPcPLkJUJCrGndugRvvw1vvYWmrIqIZFKWjIjz9vZOs1lDbGwsV69excLCgqVLl/LUU0897m1yDY2IE5H8KijoBJMmLWXRoqWcObMVSLrrqCe2tp1o0KAzAwe2oFMne+1yKiIiUsAkJcGMGaF8+ulhzpxpBoCzs5Fy5V5gyJBu9OnTMc3vhiIiBUGOTk19/vnn0/xha2dnR9myZXn66acpW7bs494iV1EQJyL5SXQ0bNwIb73VlxMn5txz1I9ixTrTvn1nXnihNg0aWGBpaZYyRUREJBdJSoJFi2DUKDh8eBmQvFGTp2dtvvkmgKef7qBATkQKlFyzRlx227RpE82bN0/32I4dO6hfv77p/b59+xgyZAg7d+7EysqKFi1aMG7cOMqVK/dQ91QQJyJ5mdFoZPLkxUybtgxn52/YsqUI0dEAo4ERWFj4U7FiZ/r27cT//V9ZvLzMXLCIiIjkWklJMH36dT74YBzXr08EbgFQokRdvv02gF692imQE5ECocAFcV988UWaQK5q1ao4OjoCcPz4cerVq0eNGjUYOnQoMTExjBgxghs3bnDgwAHc3d0zfU8FcSKS14SG3uTEicIsXw4rVsCBA1WBI8DvQF9KlYLWrW/Qtq0FHTu64OBg5oJFREQkT0lKgmnTrvHBB18RGvoDcBuAkiWfZPz4AHr0eEqBnIjkawUuiJs/fz49e/bMsF/v3r3ZuHEjp06dMj2Qc+fOUb58ed5++22+/PLLTN9TQZyI5HZGo5GtWw8yceJS1q9fSmjoMeA6YH+nx3d4ep6lZ8+BDBpUjWrV0EYLIiIi8tiSkmDq1KsMHZoSyEUDUKpUfcaPD6B79zYK5EQkX8psVmSRgzWZTUJCAsuXL6dHjx6pHkaZMmVo3rw5ixcvNmN1IiJZIzY2lqlT19C48evY25ehadOazJs3ktDQvUA0hQrtpndv+O03uHr1TS5d+pbvv6+Gn59COBEREckaFhYwaFAxrl79ikmTzuDm9g5gx4ULO+nZsy1eXo1YvHgteXw8iIjII8s3Qdxrr72GlZUVzs7OPPXUU2zdutV07NSpU0RHR+Pn55fmPD8/P06ePElMTExOlisikiVCQsIYNmwWFSr0xt7enRdeeIpt234gNvY8YI+jYxeeemoKCxde5saNpsydC//3f/AQs/FFREREHpqFBQweXJyrV7/ml1/O4Or6FsmB3A66d2/Pe+9d5OZNMxcpImIGVuYu4HG5uLjw5ptv4u/vj5ubGydPnuSrr77C39+fFStW8NRTTxEaGgqAq6trmvNdXV0xGo3cuHEDT0/PdO8RGxtLbGys6X1ERET2fBgRkUy4cgXefXciq1cv4Pr1rUDiXUc9KFmyE+3bd+Z//2tJ1ar2GV1GREREJNtZWMCLL3owaNC3/PrrEIYP/5KwsES++aYUU6fC229DmzZHqF+/irlLFRHJEXl+jbj03Lx5k2rVquHq6srBgwfZvn07jRo14o8//uDpp59O1Xf06NEMHz6cy5cv4+Hhke71AgICGDVqVJp2rREnIjkhISGRefMCOXWqJsuXwz//ALQB1gJgZeVHlSqd6du3My+9VBsXl3wz2FlERETymaQkWLAARo2Co0cBDgF+lC3rzz//rMbd3cbMFYqIPJoCvUZc4cKF6dixI4GBgURHR+Pm5gZgGhl3t7CwMAwGA4ULF87wesOGDSM8PNz0On/+fHaVLiICwK1b8OefMGDALWxtS/Lss7UYMeLSnRAOfHxep23b71my5AyxsQc5cOBThgypqxBOREREcjULC+jdGw4dgrlzoUSJPYANZ8+6U6GCDZ9+CuHh5q5SRCT75PmpqRlJGehnMBjw8fHB3t6eQ4cOpel36NAhfH19sbOzy/Batra22NraZlutIiIAO3Zc5LvvlrN7dzAXL35O8oz4QkBZIJZGjY4xYEAJ2rcHT8/OZq1VRERE5HGkBHI9egzgl19a8803CZw6BSNGwFdfnaFYsZeYMOFj2rVrYu5SRUSyVL6cmnrjxg2qVauGu7s7+/fvB+Dpp59m06ZNnDx5EicnJwCCg4MpX748b7/9NmPGjMn09TM73FBE5H7i443MnHmQqVOXsnfvUmJi9t45YgVcw9u7MB07Qr165+na1QNHR2tzlisiIiKSbRIT/5uyeuzYYGAyAOXKteTHH0fx1FONzFugiMgDZDYryvNBXN++ffHy8qJOnToULVqUEydO8PXXX3Pq1ClWrlxJq1atADh+/Dh169alVq1aDB06lJiYGEaMGEFYWBgHDhzA/SG2EFQQJyKP6vLlWL77bjOLFi3l5MmlGI13T3U34OT0JI0adWbkyJd58skiGAxmK1VEREQkxyUmwk8/nePjj7/g5s2pQAIAPj6t+fHHANq0aWjeAkVEMlBggrgxY8Ywd+5czpw5Q1RUFK6urjRu3Jhhw4ZRt27dVH337t3LBx98wI4dO7CysqJFixaMGzcOHx+fh7qngjgRySyjEbZvD2PChL9Yv34p16+vAiLv6mFPqVKtad++M2+/3YGKFdPfNEZERESkIElMhB9/PMuIEV9w8+Y0UgI5X982/PzzKFq2rG/eAkVE7lFggjhzUBAnIvcTEwObNsHy5bBsWTzBwUWBCNNxKysPqlbtRJ8+nXj11ZY4OTmYrVYRERGR3CwxEX744QwjRnxOePh0IBGA8uXb8vPPAbRo8aRZ6xMRSaEgLhspiBORe128CH/8cY2ff/6aM2eOk5i4xHTMwqI7hQqdpFGjzrzySmc6dqyDhYV2NxURERHJrMREmDjxNCNHfk54+Az+C+Ta8csvATRvXs+8BYpIgacgLhspiBORpCTYsuUW8+dfYseO8iTvC3MTcAcSKF78BF27+tKxIzRsGIOra8Y7M4uIiIhI5iQmwoQJpwgI+Izw8JlAIpaWtQkI2M3//mdAv56JiLkoiMtGCuJECqaICPjjj0tMn76cvXuXEhe3DqgB7MRggCefBEfHz2nVyodXX+2Ik5OjmSsWERERyZ8SE+H7708SEPAZERG9gA4UKQKvvx5Jq1YnaNq0lrlLFJECRkFcNlIQJ1JwBAUZmTQpkEWLlnHmzFJgd6rjjo4+fPvtIbp0sechNl8WERERkSyQmAjz5sEnn8Dx4wBfAB/SsOFbrFz5rUbIiUiOyWxWZJWDNYmI5HpxcbBxYxyTJm1m/fqlhIcvBYLv6mHA0/NJOnTozGuvdaZ69coYDAZzlSsiIiJSoFlawjPPQO/eMHcuvPZaCDdvWrB9ex28veHdd+G114y4uOi/10Qkd9CIuEegEXEi+UtICKxeDQsXXmflytdISFjF3bucWljYU7lya/r27cyAAR3w8PAwX7EiIiIikqHERBg/PohJk3wICrIEwMHhR7y91zNp0kgaNvQzc4Uikl9pamo2UhAnkrdduwarV99m3rzt7Nt3k4sXe945kgAUB8Kwty9Oo0adGDSoM506tcTBwcGMFYuIiIjIw0hMTB4hN2pUAkFB5YDzAFSp0oPJk0dSv3418xYoIvmOgrhspCBOJG8JDYXVq2+xYUM0O3cW5cgRgHVAa6A0cI5atQx07Ah2dvPx9/fiySfrYmFhYda6RUREROTxJCbCuHFH+OyzT4iKmg8k//pbtWovpkwZSb16VcxboIjkGwrispGCOJHc7cYNWLUqinnztrFt22auXdtE8iYLbwLjAKha9RbBwX7UrNmQ33+fRMmS9masWERERESyU2IifPXVYT7/PCWQAzBQrVpvpkwZQd26lc1an4jkfQrispGCOJHcJTwcVq2KZO7cbWzfvpkrVzYBe0ieavqfMmU68PXXy2nWDIoWNUelIiIiImJOyYHcIT7/fBRRUQvvtBrw8+vDlCkfU6dOJbPWJyJ5l4K4bKQgTsS8IiJg9epI/vhjK9u2beLKlc0kB2+Jqfo5OZWhVi1/unf3p1OnZpQtW1Y7nIqIiIgIiYnw5ZcHGT16FFFRi++0Gqhe/RmmTh1BrVpPmLU+Ecl7FMRlIwVxIjkrKgpWr45g61ZLtm8vxN69kJj4DfBuqn5OTt53grdmdO6cHLyJiIiIiGQkIQG+/PIAo0eP4tatJQDY249ixIgRvPYaODmZtz4RyTsUxGUjBXEi2evWLdi+HTZuTH7t2vUCRuN0YCrQH4BSpfYSEfE0NWs2o1s3f7p1a4aXl5cZqxYRERGRvCohAcaM2c+XX35FVNRPgAtubtC3734GDHCkZs3y5i5RRHI5BXHZSEGcSNaKjobVq28wZ87fbN26mcuXt2A0rgdS/v0aBoyhatX3GDLkK/z9oVQpo6aZioiIiEiWSkiAP/6ATz6BEyeMwJPAPvr0mcWkSX00Qk5EMqQgLhspiBN5PDExsGZNGLNn/83WrZu4dGkzRuMBUraTB3B3/4v27dvh7w+VKl2iZMkkSpUqZa6SRURERKQASUiAqVMjePvtvty+vRE4g5tbMd5/H15+OREXF0tzlygiuYyCuGykIE7k4cTGwtq1Yfz++xb+/nsTly5twmgM5O7gDcDJqQK1avnTtWsz+vV7iqJF3cxRroiIiIgIkBzI/fjjJSZOLMGJE8lt1tbtqV7dk6lTP6RatXLmLVBEcg0FcdlIQZzI/cXFwe7dKWu8Gdm0qSlJSVvT9HNyeoKaNf3p1s2f3r2bUaKEZ47XKiIiIiLyIAkJMGcOfPjhYc6fr3an1Yo6dfozffpHVKlS1pzliUguoCAuGymIE0ktPh727oWlS68wa9bnXLx4hqSkZXf1aA+sxNm5EtWrJ2+u0KdPMzw9PcxUsYiIiIjIw0tIgE8+2cG4cQFER6+502pFvXoDmDbtQypXLmPW+kTEfBTEZSMFcVLQJSTAunVXmTVrM8eO2RIU1JmoKIAIoAiQRJEi52nZshTNm0OZMv9Su3ZhPDyKm7dwEREREZEskJAAo0Zt5+uvRxIdve5OqzVPPjmQadOGU6mSl1nrE5GcpyAuGymIk4ImMRHWr7/CzJmb2bJlExcubCYp6eidow2A7bi6QrNmkJDwJc2b+zJoUFucnAqZs2wRERERkWyVkAABAVv55psAoqPX32m1pn79QUybNoyKFUubtT4RyTkK4rKRgjjJ75KSYP36EFPwdv78JpKSjqfp5+xcjVq1WvPtt+Pw8zNgYZHztYqIiIiImFtCAowYsYVvvw0gJmbjnVYbGjQYxMyZH+Hjo7WQRfI7BXHZSEGc5DdJSXD4MMyefYS5cycQHLyJpKR/0/Rzdq5O9erN6NLFn379mlC8eFEzVCsiIiIikjslJMDHH29m/PiRxMRsBqBw4QMMH16dV1+FQpowIpJvKYjLRgriJK8zGmHz5stMn76Rc+d8OXSoHqGhADuAhnd6GXB2ro6fnz9dujTjueeaUKyYm/mKFhERERHJIxIS4KOPNjFp0mZu3BgJgLs7tGkzj4CAxvj6ljBzhSKS1RTEZSMFcZLXGI2wadMFAgPd2bbNlk2b4Nq1d4BvgZeAnylUCBo2jCcmZhidOjWhf/8mFCvmat7CRURERETysIQEmDULPvsMTp06B5QHLBk27BgfflhWI+RE8hEFcdlIQZzkdkYjbNlynhkzNrNp0yaCgzeRmHgKWA+0AMDGZjm2tgE0atSHESPeo04dsLY2a9kiIiIiIvlSQgKMHn2Izz9/mdhYe2Ad7u7QvTtYWv5G7941adq0KgaDwdylisgjUhCXjRTESW5jNMLffwczffomNm/exLlzm0lMPH1PLwsqVJhAv36v0rw51KsHNjZmKVdEREREpECKizMyZUok48Y5c/o0wDWgGACWlu74+LSgVasWDB7ckurVyymYE8lDCkwQt2HDBmbNmsX27ds5f/48hQsXpk6dOowYMYLatWub+j3//PPMmDEjzflPPPEEx4+n3Q3yfhTESW6wdes5pk1LDt7Ont1EYuLZe3pY4uRUGz8/fzp3bsbzzzemWDH9vIqIiIiImFt8PKxeDfPn/8uiRW8SFfU3cDtVHxsbL554ogXt2rVk8ODm+PqWNE+xIpIpBSaI69WrF6GhofTq1YvKlStz7do1vv76a/bs2cPq1atp0SJ5Gt7zzz/PvHnz2LBhQ6rz7e3tqV69+kPdU0Gc5DSj0cjOnRcICirNxo2waROcO9eK5KmmKSxxcqpDtWr+dOrUjAEDGlG8uH4+RURERERyu4sX45gyZRfLlm3gyJH1REfvBOJT9bG3f4KqVVvSqVML3n23Cw4OVuYpVkTSVWCCuKtXr1KsWLFUbVFRUfj6+lK1alXWrVsHJAdxCxYsICoq6rHvqSBOcsKFC7BxI6xbF8Ps2ZVISDgLXCFl6LqFxZc4OCzBz8+fjh39GTiwIcWLO5mzZBERERERyQJBQbf49detrFq1gX//3UB8/F4g5Vf3otjbX6FJEwtatoTSpQNp374cLi6O5ixZpMArMEFcRlq0aMHFixf5999/AQVxkrsZjUZ27DjN9Omb2LhxEyEhSURF/X5Xj+rAUSpWXEXXri1p3hwaNgRH/V0rIiIiIpKvGY2wf/8NJk/ezLp1G7hwwY7o6LEpR4FSwFWaNt1Ojx51adECqlQBLS8nkrMymxXly7Gs4eHh7Nu3zzQtNUV0dDQeHh5cu3YNT09PunbtyieffIKrq6uZKpWC6PbtWNauPca6dYHs2RPIqVOBhIYeJCnp6l29bDEYplCnjh3+/lC+/Hw6dSqJh4f2NxcRERERKUgMBqhVqwg//tgV6IrRCEeOwIYNsHLldVavtsVotGTLlqps2ZJ8joPDhzg5/UPDhi159tkWdO5cC2vrfPnrv0ieky9HxPXr14+5c+eyc+dO04YN3377LQBVq1YFYPPmzXz77bd4eXmxe/duHO8ztCg2NpbY2FjT+4iICEqXLq0RcfJAYWGwdm0ws2bN5ujRQC5fDiQ6+jiQmE5vGxwdn6Rq1WZ07OjPSy81pWhR65wuWURERERE8pDERFi7NoTAQA82bIC//4bbt6sDgaY+BoMzpUr507RpC/r3b0GrVlW1I6tIFiuwU1M//vhjPvvsMyZMmMDrr79+374LFy6kZ8+efPPNN7z99tsZ9gsICGDUqFFp2hXESYr4ePj3X5g1azXr1v0FtCYkpCMXLwL8Azx5zxlFcHT0w8urOn5+fjRt6keXLlUpUcI+x2sXEREREZH8Iy4O5s49yh9/bGDXrvWEhm4CbqbqY2npTtmyLWjZsgUvvNCSunXLKZgTeUwFMogbNWoUAQEBfP755wwfPvyB/ZOSknB2dqZDhw7MnTs3w34aEScpkpKM7Nlzlr/+CmTHjkCOHw/EyekngoKKEh8P8AEwFngV+AGAMmVukZAwiAoVqvPkk360betHo0YlsbLSX3QiIiIiIpK9IiMT+e23AyxYsJ79+zcQHv43cDtVH2trLzp0mMTAgU/RtCm4uJinVpG8rMAFcSkhXEBAACNHjszUOUlJSTg5OdG5c2fmzJmT6Xtps4aC4cqVCFasOMymTYEcPBjIuXOBREQEYjRG3tNzPdACJyfw8tqAldUKmjRpzTPPtKVqVdCPiIiIiIiI5BbXrsUxZcou/vxzA4cObeDWrR1APHAQ8MPCAnx8/sTRcTV9+/bm1Vf9cXAwc9EieUCB2qzh008/JSAggI8++ijTIRzAggULuH37NvXr18/G6iS3MxohOBj27o1jwoTPCAoK5Nq1QOLjz2Rwhg22tpXx8PCjUiU/uncvR+vWUKYMGAwtgBYZnCciIiIiImJe7u42DB3ahKFDmwAjOXv2FlOmbOfataps3AhBQXDixAJgFvv3F+HDD/2pXx8aN46kcOG/GTiwCW5uTub+GCJ5Vp4fEff111/z3nvv0bZt23RDuPr163Pu3Dn69u1Lnz598PX1xWAwsHnzZsaPH4+Pjw+7du2iUKHM70apEXF5V2QkHD4MK1YcYNGiH4iIcCUy8ksiIiB562834Iapv4VFSYoU8cPb249atfxo3tyPdu2ewMVFmyiIiIiIiEj+c+ECTJiwlqVLlxEW9ixXr6asd70M6AxYUbhwPWrVakHv3i3p168+hQrZmbFikdyhwExN9ff3Z/PmzRkeNxqN3LhxgxdeeIH9+/dz5coVEhMTKVOmDN26dWP48OG4POQEeAVxuV9sbALr1wexZs1Bdu8O5NSpQIzGwVy92vVOj40kj1wrB5zC2hoqVQJLy7GULm1Ho0Z+dOhQjcqV3dCapSIiIiIiUhAZjXDqFGzYANOnz2XXruEkJZ2+p5cd7u6NefLJFjzzTAt69aqNtXW+mHwn8lAKTBBnDgricpfjx6+yYkUgW7cGcuRIIBcvBnL79hEg7p6e7wNjKVECKlW6we3b31C3bnUGDerJE0+AjY0ZihcREREREckjjEZYs+YsM2Zs4O+/13Px4gaMxpBUfQwGZzw9m9G4cUuef741bdtW1uAGKRAUxGUjBXHmER8P//4LgYEwZcq3HDy4khs3AklKupLBGY4UKlSN0qWrU62aH+3aNaFz56q4ueVo2SIiIiIiIvlSQoKRxYuP8/vv69mxYwNXr24Ebt7VoyNeXsto0QJatAAfnzM0aFAWg5I5yYcUxGUjBXHZy2iEkJDkwG3XrnCmTXuVK1eCiI/fSUKC5Z1efYGUnW4NWFn54u7uR4UKftSr58dTT/nRtGlZrK0tzPQpRERERERECpbo6ER+//0A8+ZtYM+e9YSHdyUp6eU7R88BZbG2Lsfzzx+ndWtr/P3B3d2MBYtkIQVx2UhBXNYJDb3NihVH2LgxkAMHAjl7NpCYmMrExPxwp0ci4AREA0E4OZXHzw+KFFmLi8tZ/P2r06FDFTw9M7/ZhoiIiIiIiGS/27dh27bkNeYWLVpBUFA3oA6w3dSncOG+eHkVoX37Frz4oj/e3prCJHmTgrhspCDu4SUlGdm27SyrVgWya1cg//4byNWrgcTFnSB5t9K7VcPCIpAKFcDPD+Ljp1K1anH69GlGpUqOWl9AREREREQkD7p06RbLl4dw7JgPGzZAYGA44Aok3elhwMGhBlWrtqBz5xYMGtSE4sWdzFixSOYpiMtGCuLuLyICDh9Onlq6ZMkitm//hsjIQCAy3f4GgzuFC1fH29uPmjX9aNmyBl27VsfePmfrFhERERERkZxz/nwMEyasZuXK9QQFbSAu7sg9Paxwdq5HjRot6NmzJf3718fZ2c4stYo8iIK4bKQgLllCgpHTpw0EBiaHbjNnvsqFC6tISJgGNLvT6zeg/52vbbCzq0yJEn5UquRHo0Z+dOjgR7VqxTXKTUREREREpIDbuzeEyZM3sn79ek6f3kBi4pl7etjh6tqIV1+dRZcuHtSsCZaW6V5KJMcpiMtGBTGIO3kyjOXLD/H334EcPhzIxYuB3L59GaPxHJCSonUBlgLfUbLk//Dzg7JlL2AwbKFVKz/atHmCQoWszfchREREREREJE8wGmHTpjNMm7aBzZs3cOHCBpKSQgBnIBSwwsUFypT5ER+fOF59tQctW5bWIA8xGwVx2Sg/B3G3byewZk0Q69YFsndvIKdOHSQ0NJCkpAvp9rezu0i1aiXw8wMnpx2UKRNL5841KFeucM4WLiIiIiIiIvlWUpKRZcuOsWLFKa5e7cSmTRAeDuADnAaWU6xYB1q0gBo1zlOnTgzNm/tiYaFkTnKGgrhslN+CuBMn4N13j7F27bPExBwFYtPtZ2VVlqJF/Shf3o+6df1o3dqPFi18sbHRWGARERERERHJOYmJsHt3Ip988jX//LORW7fmEhOT8vv5cGA0lpalKVu2Ba1ateSFF1pQt25Jc5Ys+ZyCuGyU34K4kyehfPlrQLE7LY44OVWjdGk/qlevTrNmfnToUJVSpVzMWaaIiIiIiIhIumJj4Z9/YMMG+OWXV7l8eTIQn6qPtfUTlC/fgrZtW/Lii/488YSbeYqVfElBXDbKb0FcUhKMGQMxMctp3boyDRuWxdLSwtxliYiIiIiIiDySa9duMXnyNpYu3cChQ+u5dWsfkHRPLwcMBkcsLZ0oVuw9ypR5GUdHsLQM5sSJkbi4eNK69Rc4OoKjI5w9uxKDIYrChR1xdXXEzc0Rd3cn3N0dKV7cEXd3B6ys9Lt0QaUgLhvltyBOREREREREJD87d+4GkyZtYcWK9Rw/voHY2CP39PgaeOfO17uA+kBZ4O6dW+sCe+5zFwNQCAsLRywtk1+engN54ok3cHQEa+swjhwZhZNTYdq3H4WjIzg5waVLOzAaI1KFe8WKJYd7bm72Wucuj1AQl40UxImIiIiIiIjkXcHBNzl3Loxr16IIDY3C0dELe/tSREVBcPAFtm79HaPRgcqV3yAyEqKiYPv2l7l58xjx8VHEx0eSmBhFUlIUEAVkFK18DHxy5+vjQCWgCBB2V59WwPoMzrcAHE3hnpWVIzY2TpQo0ZVq1d7C0RHs7GI4ePBznJwcadv2XQoXtsLREa5fP4zRGIG7uxNFizri7u6Ip6cTLi62CveygYK4bKQgTkREREREREQgeUfXGzduExISxbVrUVy/HsX165GEhkbh7OyNi8sTREbCpUuX2bx5IgkJllSt+glRUckB3759L3Pz5i4SEqLuCffu5w3g+ztfXwZKkDwiL/HOPwF6AgvTOdeS5HDPCSur5HDP2toROzsnSpRoQe3ab92Zjmtk376vcXIqROvWz+Pqao+jI0RGnsPCIoqiRR0pVswRDw9HnJxsMRTwbE9BXDZSECciIiIiIiIi2SUxMYnQ0NtcuRLF1auRXL+ePHLv+vVIbt6MwsmpHEWL1iEqCq5eDWXDhgBiY+Px8/uZqCiIjIRjx14hPHwNSUkp4d7tTNz5OWDGna+jAYc7X0cATne+fgGYes951hgM/43cs7Z2xNraCTs7R2xtHfH0rEOjRm+b1ts7d24V7du3pV27x31SuUdmsyKrHKxJREREREREREQewNLSgmLFkkecgccDersBE9Jp/ynVu/j4RK5du8XVq1FcvRp1J9xLHrl382YU4eFRODqWw8MjeaReWFgi69c/R3R0JFWrOnDrVnLAd/asPZGRRTEao4CYlKtjNN4gMfEGiYkQF5e6knPnoti58+1UtR08mL+CuMxSECciIiIiIiIiks9ZW1tSooQzJUpkdmafI/+NjrvbxDsviI1NuDNq779puaGhkYSFRXHjRnK4FxERib19Wby8kgO+8PBEDhxoTJMmWfTB8hgFcSIiIiIiIiIi8tBsba3w8iqMl1fhhzjLEng/myrK/SzMXYCIiIiIiIiIiEhBoCBOREREREREREQkByiIExERERERERERyQEK4kRERERERERERHKAgjgREREREREREZEcoCBOREREREREREQkByiIExERERERERERyQEFLoiLiorirbfeokSJEtjZ2VGjRg3++OMPc5clIiIiIiIiIiL5nJW5C8hp3bt3Z/fu3YwZM4YKFSowe/ZsnnnmGZKSkujbt6+5yxMRERERERERkXzKYDQajeYuIqf89ddfdOjQwRS+pWjTpg1HjhwhODgYS0vLB14nIiICFxcXwsPDcXZ2zs6SRUREREREREQkl8tsVlSgpqYuXrwYR0dHevXqlap9wIABXLp0iV27dpmpMhERERERERERye8KVBB3+PBhKlWqhJVV6hm5fn5+puMiIiIiIiIiIiLZoUCtERcaGkq5cuXStLu6upqOpyc2NpbY2FjT+/DwcCB52KGIiIiIiIiIiBRsKRnRg1aAK1BBHIDBYHjoY6NHj2bUqFFp2kuXLp1ldYmIiIiIiIiISN4WGRmJi4tLhscLVBDn5uaW7qi3sLAw4L+RcfcaNmwY77zzjul9UlISYWFhuLm53TfYy0siIiIoXbo058+f1wYUZqDnb156/ual529eev7mpedvXnr+5qXnb156/uan74F56fmbV358/kajkcjISEqUKHHffgUqiKtWrRpz5swhISEh1Tpxhw4dAqBq1arpnmdra4utrW2qtsKFC2dbnebk7Oycb/4lyIv0/M1Lz9+89PzNS8/fvPT8zUvP37z0/M1Lz9/89D0wLz1/88pvz/9+I+FSFKjNGrp160ZUVBQLFy5M1T5jxgxKlCjBk08+aabKREREREREREQkvytQI+LatWtH69ateeWVV4iIiMDX15c5c+awatUqZs2ahaWlpblLFBERERERERGRfKpABXEAixYt4sMPP2TEiBGEhYVRsWJF5syZQ58+fcxdmlnZ2toycuTINFNwJWfo+ZuXnr956fmbl56/een5m5eev3np+ZuXnr/56XtgXnr+5lWQn7/B+KB9VUVEREREREREROSxFag14kRERERERERERMxFQZyIiIiIiIiIiEgOUBAnIiIiIiIiIiKSAxTE5WPTp0/HYDCwZ88ec5dS4KQ8+/Re7733Xqav8/zzz+Po6JiNleY/dz/7TZs2pTluNBrx9fXFYDDg7++f4/UVNN9//z0Gg4GqVauau5R8Tz/7uYf+/s09Hud7YTAYCAgIyPqi8jn9uW8+u3btolu3bnh5eWFra0vx4sVp0KAB7777rrlLK3B27txJr1698PT0xMbGBg8PD3r27MmOHTse+lpHjx4lICCAs2fPZn2h+UTKn/V2dnacO3cuzXF/f3/9mZSN7v3d187ODg8PD5o3b87o0aO5evWquUvMdRTEiWSjadOmsWPHjlSv//3vf+Yuq0BwcnJiypQpado3b97MqVOncHJyMkNVBc/UqVMBOHLkCLt27TJzNQWDfvZFxJz05755rFixgoYNGxIREcHYsWNZs2YN3333HY0aNWLu3LnmLq9AmTBhAo0aNeLChQuMHTuWdevWMW7cOC5evEjjxo2ZOHHiQ13v6NGjjBo1SkFcJsTGxvLRRx+Zu4wCK+V337Vr1/LDDz9Qo0YNvvzySypVqsS6devMXV6uoiBOJBtVrVqV+vXrp3p5eXmZu6wC4emnn2bhwoVERESkap8yZQoNGjTI0u9DdHR0ll0rP9mzZw8HDx6kQ4cOAOmGQ4/j9u3bWXq9/CInf/ZFRO6W3X/uS8bGjh2Lt7c3q1evpk+fPjRr1ow+ffowbtw4goODzV1egbFt2zbeeust2rdvz99//83//d//0bRpU/r168fff/9N+/btefPNN9m2bZu5S82X2rZty+zZszl48KC5SymQUn73bdKkCT169ODbb78lMDCQQoUK0b17d65cuWLuEnMNBXEFyJ49e+jTpw9ly5bF3t6esmXL8swzz6QZvpsytHTjxo288sorFC1aFDc3N7p3786lS5fMVH3+M3fuXBo0aEChQoVwdHTkqaeeYv/+/en2PXLkCC1btqRQof9v786DmjjfOIB/A0lAOVRQQVQCHuA9olCroIhHFVFxMKIgCFIr40mLB6KOFKsieNEOKtgC3heK1mOAaRWvWgV1vMartiIe4IGiwCBCfH5/+EvaNUHRGmji85nZP3bz7s777r55982b9zBBkyZNMHXqVG6EeAc/Pz8AwLZt21THnj17ht27dyMkJEQtfHR0NHr06AELCwuYm5ujW7duSE5OBhEJwtnZ2WHo0KFIT0+Hk5MTjI2NER0drd3E6CjlD7ClS5eiV69e2L59uyDf5uXlQSQSIS4uDosXL4atrS2MjY3h7OyMQ4cOCa717bffQiQS4dy5c5DL5WjUqBFat25dq+nRFdrI+19++SUsLCw0ljv9+vVDx44dtZAS/dG3b1+Nw4GDg4NhZ2en2ld+J5YvX46VK1fC3t4epqam6NmzJ06dOlV7EdZjNX0W7MO8q9w/cuSIxuHzyry/fv16wfEff/wRDg4OMDIyQocOHbB161Z+VtUoKipC48aNIRaL1T4zMBD+5KtJHVQ5PQrXQd9PTEwMRCIR1q5dq/YsxGIx1qxZA5FIhKVLl6qOX7t2DX5+frCysoKRkRFsbW0xbtw4VFRUYP369Rg1ahQAwMPDQzX0783vCntt9uzZsLS0RERExFvDvXjxApGRkbC3t4dUKkXz5s0xZcoUFBcXq8KMGDECMpkMr169Uju/R48e6Nat28eOvl6ytbXFihUrUFJSgqSkJNXxM2fOYPjw4bCwsICxsTGcnJywc+dOtfPv3buHiRMnomXLlpBKpbCxsYFcLtf5Rj1uiPuE5OXlwdHREfHx8cjKykJsbCwKCgrg4uKCx48fq4WfMGECJBIJtm7diri4OBw5cgQBAQF1EHPdpVAoUFVVJdgAYMmSJfDz80OHDh2wc+dObNq0CSUlJejduzeuXLkiuEZlZSWGDBmC/v37Y+/evZg6dSqSkpIwevToukiSzjA3N4dcLlcNkQFeN0wYGBhovHd5eXkIDQ3Fzp07kZ6eDh8fH0ybNg3fffedWthz585h1qxZmD59OjIzMzFy5EitpkUXlZeXY9u2bXBxcUGnTp0QEhKCkpISpKWlqYVNSEhAZmYm4uPjsXnzZhgYGMDT01PjPCo+Pj5o06YN0tLSkJiYWBtJ0TnayPthYWF4+vQptm7dKjj3ypUryM7OxpQpU7SXoE/Q6tWr8csvvyA+Ph5btmxBWVkZhgwZgmfPntV11Bir1vuU+zWxbt06TJw4EV26dEF6ejrmz5+P6OhojXNgMqBnz544ffo0pk+fjtOnT6OyslJjOK6Dao9CoUB2djacnZ3RokULjWFatmyJ7t274/Dhw1AoFLhw4QJcXFxw6tQpLFy4EBkZGYiJiUFFRQVevnwJLy8vLFmyBMDrd4Nyqhtlr1MmZGZmhvnz5yMrKwuHDx/WGIaIMGLECCxfvhyBgYE4ePAgwsPDsWHDBvTr1w8VFRUAgJCQEOTn56td59q1a8jJycH48eO1nh59MWTIEBgaGuLYsWMAgOzsbLi6uqK4uBiJiYn4+eef0bVrV4wePVrQyHzv3j24uLhgz549CA8PR0ZGBuLj49GgQQM8ffq0jlLzkRDTW6mpqQSAcnNzNX5eVVVFpaWlZGJiQt9//73aeZMnTxaEj4uLIwBUUFCg1XjrA+U91LTl5+eTWCymadOmCc4pKSkha2tr8vX1VR0LCgoiAILnQ0S0ePFiAkAnTpyolfTokn/m++zsbAJAly9fJiIiFxcXCg4OJiKijh07kru7u8ZrKBQKqqyspIULF5KlpSW9evVK9ZlMJiNDQ0O6fv261tOiyzZu3EgAKDExkYhe529TU1Pq3bu3KsytW7cIANnY2FB5ebnq+PPnz8nCwoIGDBigOhYVFUUAaMGCBbWXCB2j7bzv7u5OXbt2FYSfNGkSmZubU0lJiXYSpaPefP+6u7trvOdBQUEkk8lU+8rvROfOnamqqkp1PCcnhwDQtm3btB11vfOhz4KICABFRUVpP5J6oiblvrJsys7OFpyrzPupqalE9Lossra2ph49egjC3b59myQSidqzYkSPHz8mNzc3VX1TIpFQr169KCYmRlVGcx1UuwoLCwkAjRkz5q3hRo8eTQDowYMH1K9fP2rYsCE9fPiw2vBpaWkavzfsb/8s6ysqKqhVq1bk7Oysqse4u7tTx44diYgoMzOTAFBcXJzgGjt27CAAtG7dOiIiqqysJCsrK/L39xeEmz17NkmlUnr8+HEtpEw3vKvdgYjIysqK2rdvT0RE7dq1IycnJ6qsrBSEGTp0KDVr1owUCgUREYWEhJBEIqErV65oL/J1hHvEfUJKS0sRERGBNm3aQCwWQywWw9TUFGVlZbh69apa+OHDhwv2u3TpAgAaV6Jhmm3cuBG5ubmCLSsrC1VVVRg3bpygp5yxsTHc3d01/tM7duxYwb6/vz+A1/8msOq5u7ujdevWSElJwaVLl5Cbm6txaB4AHD58GAMGDECDBg1gaGgIiUSCBQsWoKioSG2lny5dusDBwaE2kqCzkpOTUa9ePYwZMwYAYGpqilGjRuH48eP4448/BGF9fHxgbGys2jczM8OwYcNw7NgxKBQKQVjufVgz2sj7YWFhOH/+vGpem+fPn2PTpk0ICgri1Z0/Mi8vLxgaGqr2+f3LdMH7lPvvcv36dRQWFsLX11dw3NbWFq6urh8tzvrE0tISx48fR25uLpYuXQpvb2/cuHEDkZGR6Ny5Mx4/fsx10P8I+v/UD+Xl5Th69Ch8fX3RpEmTOo6V/pBKpVi0aBHOnDmjcaijsodbcHCw4PioUaNgYmKimh5FLBYjICAA6enpqh7pCoUCmzZtgre3NywtLbWbED2jzPc3b97EtWvXVGXLP8uiIUOGoKCgANevXwcAZGRkwMPDA+3bt6+zeGsLN8R9Qvz9/ZGQkIAJEyYgKysLOTk5yM3NRZMmTTRONv9m4WJkZASAJ6Z/H+3bt4ezs7NgU45nd3FxgUQiEWw7duxQGyYsFovVnoW1tTWA1/OBsOqJRCKMHz8emzdvRmJiIhwcHNC7d2+1cDk5Ofjiiy8AvJ6P5rfffkNubi7mzZsHQD3PN2vWTPuR12E3b97EsWPH4OXlBSJCcXExiouLIZfLAUAwZBL4Oz+/eezly5coLS0VHOd7XzPayPve3t6ws7PD6tWrAbyeT7SsrIyHpWoBv3+Zrnnfcv9dlPUbKysrtc80HWN/c3Z2RkREBNLS0nD//n188803yMvLQ1xcHNdBtaxx48aoX78+bt269dZweXl5qF+/PsRiMRQKRbXDWNmHGzNmDLp164Z58+apDdMuKiqCWCxWa/wUiUSwtrYW5O2QkBC8ePEC27dvBwBkZWWhoKCAh6W+p7KyMhQVFcHGxkZVDs2cOVOtHJo8eTIAqMqiR48e6e33Q302T6aXnj17hgMHDiAqKgpz5sxRHa+oqMCTJ0/qMGafnsaNGwMAdu3aBZlM9s7wVVVVKCoqElSECgsLAaj/WGPqgoODsWDBAiQmJmLx4sUaw2zfvh0SiQQHDhwQ9Mzau3evxvAikUgbUdUbKSkpICLs2rULu3btUvt8w4YNWLRokWpfmZ//qbCwEFKpVK2nFd/7mvvYed/AwABTpkzB3LlzsWLFCqxZswb9+/eHo6OjtpKgN4yNjTXO76ZpflamXfwstKOm5b6ynFHOwaT05v1X1m80Tcat6Z3BNJNIJIiKisKqVatw+fJleHt7A+A6qLYYGhrCw8MDmZmZuHv3rsYGhLt37+Ls2bPw9PSEhYUFDA0Ncffu3TqIrX4TiUSIjY3FwIEDsW7dOsFnlpaWqKqqwqNHjwSNcUSEwsJCuLi4qI516NABn332GVJTUxEaGorU1FTY2Nio/sRkNXPw4EEoFAr07dtX9Vs4MjISPj4+GsMr65ZNmjTR2+8H94j7RIhEIhCR6l91pZ9++klt6BfTrkGDBkEsFuPPP/9U6y2n3N60ZcsWwb5ywnRNK78xoebNm2PWrFkYNmwYgoKCNIYRiUQQi8WCoWDl5eXYtGlTbUVTbygUCmzYsAGtW7dGdna22jZjxgwUFBQgIyNDdU56ejpevHih2i8pKcH+/fvRu3dvwTNh70cbeX/ChAmQSqUYO3Ysrl+/jqlTp2ol7vrGzs4ON27cEDQ+FBUV4eTJk3UYq08TP4uP733KfeVqpxcvXhRcY9++fYJ9R0dHWFtbqw0ry8/P52dVjYKCAo3HldPP2NjYcB20FkRGRoKIMHnyZLXfWAqFApMmTQIRITIyEvXq1YO7uzvS0tLe+mcA94r+MAMGDMDAgQOxcOFCwQiL/v37AwA2b94sCL97926UlZWpPlcaP348Tp8+jRMnTmD//v0ICgri+ul7yM/Px8yZM9GgQQOEhobC0dERbdu2xYULF6oth8zMzAAAnp6eyM7OVg1V1SfcI+4TIBKJYG5ujj59+mDZsmVo3Lgx7OzscPToUSQnJ6Nhw4Z1HcVPip2dHRYuXIh58+bhr7/+wuDBg9GoUSM8ePAAOTk5MDExQXR0tCq8VCrFihUrUFpaChcXF5w8eRKLFi2Cp6cn3Nzc6jAluuOfS8Rr4uXlhZUrV8Lf3x8TJ05EUVERli9frtZwzd4tIyMD9+/fR2xsrMZKeqdOnZCQkIDk5GSsWrUKwOt/kAcOHIjw8HC8evUKsbGxeP78ueB7wD7Mx877DRs2xLhx47B27VrIZDIMGzZMG9HWG8oenIGBgUhKSkJAQAC++uorFBUVIS4uDubm5nUcw08HPwvteZ9yf+jQoRgwYABiYmLQqFEjyGQyHDp0COnp6YJzDAwMEB0djdDQUMjlcoSEhKC4uBjR0dFo1qwZDAy4L8GbBg0ahBYtWmDYsGFo164dXr16hfPnz2PFihUwNTVFWFgY10FrgaurK+Lj4/H111/Dzc0NU6dOha2tLfLz87F69WqcPn0a8fHx6NWrFwBg5cqVcHNzQ48ePTBnzhy0adMGDx48wL59+5CUlAQzMzN06tQJwOuVhM3MzGBsbAx7e3vulVgDsbGx6N69Ox4+fIiOHTsCAAYOHIhBgwYhIiICz58/h6urKy5evIioqCg4OTkhMDBQcA0/Pz+Eh4fDz88PFRUVanPLsb9dvnxZNd/bw4cPcfz4caSmpsLQ0BB79uxR9UBMSkqCp6cnBg0ahODgYDRv3hxPnjzB1atXce7cOdVq28qVhPv06YO5c+eic+fOKC4uRmZmJsLDw9GuXbu6TO6/U1erRDDtW716NQGgS5cuERHR3bt3aeTIkdSoUSMyMzOjwYMH0+XLl0kmk1FQUJDqvOpWPalupSumriYrx+zdu5c8PDzI3NycjIyMSCaTkVwup19//VUVJigoiExMTOjixYvUt29fqlevHllYWNCkSZOotLS0NpKic2py74nUV45MSUkhR0dHMjIyolatWlFMTAwlJycTALp165YqnEwmIy8vLy3FXveNGDGCpFLpW1f/GjNmDInFYjp16hQBoNjYWIqOjqYWLVqQVColJycnysrKEpyjXDX10aNH2k6CztJ23lc6cuQIAaClS5d+5BTojzffv0REGzZsoPbt25OxsTF16NCBduzYUe2qqcuWLVO7JngFzw/yoc+CiO95Tb1PuV9YWEgFBQUkl8vJwsKCGjRoQAEBAXTmzBnBqqlK69atozZt2pBUKiUHBwdKSUkhb29vcnJy0nKqdM+OHTvI39+f2rZtS6ampiSRSMjW1pYCAwPVVhzkOqj2/f777ySXy8nKyorEYjE1bdqUfHx86OTJk2phr1y5QqNGjSJLS0uSSqVka2tLwcHB9OLFC1WY+Ph4sre3J0NDQ43flU/d2+pA/v7+BEC1aioRUXl5OUVERJBMJiOJRELNmjWjSZMm0dOnTzVeX3kNV1dXbSVBpynvv3KTSqXUtGlTcnd3pyVLlmh8P1y4cIF8fX2padOmJJFIyNramvr166daeVvpzp07FBISQtbW1iSRSMjGxoZ8fX3pwYMHtZU8rRAR/X/5CqZ3wsLCkJCQgOLiYlX3TsYY+6/Iy8uDvb09li1bhpkzZ9Z1dFgNzZgxA2vXrsWdO3f43/hq8Pv3v4OfhX4pLi6Gg4MDRowYoTbvE/u4goODsWvXLrVFkxhjjP17PDRVD509exa5ublISUnB8OHDueLJGGPsXzt16hRu3LiBNWvWIDQ0lBvhNOD3738HPwvdV1hYiMWLF8PDwwOWlpa4ffs2Vq1ahZKSEoSFhdV19BhjjLEPxg1xekgul+PZs2cYPnw4fvjhh7qODmOMMT3Qs2dP1K9fH0OHDhWsesv+xu/f/w5+FrrPyMgIeXl5mDx5Mp48eYL69evj888/R2JiomquJ8YYY0wX8dBUxhhjjDHGGGOMMcZqAS85xBhjjDHGGGOMMcZYLeCGOMYYY4wxxhhjjDHGagE3xDHGGGOMMcYYY4wxVgu4IY4xxhhjjDHGGGOMsVrADXGMMcYYY4wxxhhjjNUCbohjjDHGGGOMMcYYY6wWcEMcY4wxxhhjjDHGGGO1gBviGGOMMcYYY4wxxhirBdwQxxhjjDHGGGOMMcZYLfgfWb5GVoLwxnMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_PAR_slicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_PAR_exp_slicemean[1,:],color='k',linestyle='-.',label='CY with WY winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY PAR with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('uE/m$^{2}$/s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.22929443,  25.46042814,  40.97263571,  59.96603881,\n",
       "        84.80457427,  94.07934195, 101.42659579,  80.66420632,\n",
       "        60.70089652,  29.308822  ,  13.13657237,  10.14639563])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Halocline Strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Halocline Strength data for original years\n",
    "\n",
    "\n",
    "monthly_array_halocline_depth_orig_SSslice = np.zeros([14,12,50,50])\n",
    "monthly_array_halocline_strength_orig_SSslice = np.zeros([14,12,50,50])\n",
    "\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask,depth = [mask[var].isel(**slc).values for var in ('e3t_0', 'tmask','gdept_0')]\n",
    "years, variables = range(2007, 2021), ['halocline','strength']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "         # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_orig_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "          \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "         # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_orig_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/1288103633.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_halocline_strength_orig_SSslice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_halocline_strength_orig_SSslice[monthly_array_halocline_strength_orig_SSslice == 0 ] = np.nan\n",
    "monthly_array_halocline_strength_orig_SSslicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_halocline_strength_orig_SSslice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_halocline_strength_orig_SSslicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data for Experiments 1 and 2\n",
    "\n",
    "monthly_array_halocline_depth_SSslice = np.zeros([14,12,50,50])\n",
    "monthly_array_halocline_strength_SSslice = np.zeros([14,12,50,50])\n",
    "\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask,depth = [mask[var].isel(**slc).values for var in ('e3t_0', 'tmask','gdept_0')]\n",
    "years, variables = range(2007, 2021), ['halocline','strength']\n",
    "\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "            \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "       # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    " \n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "            \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "       # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "        \n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/3661973807.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_halocline_strength_SSslice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_halocline_strength_SSslice[monthly_array_halocline_strength_SSslice == 0 ] = np.nan\n",
    "monthly_array_halocline_strength_SSslicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_halocline_strength_SSslice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_halocline_strength_SSslicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'g/kg m$^{-1}$')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_halocline_strength_orig_SSslicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_halocline_strength_SSslicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY Halocline with CY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,5)\n",
    "ax.set_ylabel('g/kg m$^{-1}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.27275877, 0.43238446, 0.42162363, 1.29093306, 1.82034273,\n",
       "       2.08958861, 1.36382405, 1.45949038, 1.19413448, 1.26065329,\n",
       "       0.80995226, 0.6541769 ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_halocline_strength_SSslicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'g/kg m$^{-1}$')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SUrBgwXSPpayImpiYaLX/3Llzlo8z2gMwJRTJiFtXxUwJxI4dO8bJkyepUqUKRYsWtYQrt4ZnmZnvLCsy8sxS/j4zo0CBAjRo0ACwDpXWrVsHWPfcKlOmDGXKlOHMmTMcPXrU6pxbr3Mnt35dZsfXaE7Xn5Z7+btJ+dq909ctkO4KxN26deOll17CZDKxePFinnzySfz8/KhQoQIDBw606oUnIiJiKwrPREREsujWHkKZnSQ/rwkPD6dXr17ExcXRokULQkNDuXHjBhEREVy4cIHz58/z3Xff5UgtJpMpQ/tym6SkJABcXV3v2gMwZbs1OMmI23uWpfyZEpqVLVsWf39//v77b86dO8eRI0c4ffq01bl5UUrtGzZsIDExkQMHDnDhwgUeeuihVOHO7UMfU/68tQdYTsvL9Wfla2/69On8/fffvPfeezzyyCN4eXlx5MgRZs+eTb169Xj55Zezr1AREZF7oPBMREQki5o3b46dnfm/1KVLl9q4mvvr119/JTIyEm9vb5YvX06zZs1wdXW1anP+/PlMX/fWIW93Gu6ZcszBwcHSw61QoUI4OjoC5lUsc7uiRYsCEBMTw5EjR+7LPVJCssOHD3PmzJlU4RlYB2wpx+80vC4vSHlP0dHRbNu2zdIbK63w8dahj/Hx8WzatMnqGraQF+tP+dq92zDts2fP3vF4+fLlGTlyJL/++ivh4eFs2rSJJ554AjCvyPnTTz9lS70iIiL3QuGZiIhIFhUpUoROnToB8PXXX/PPP/9k+Nz7MV/a/ZTSO6lSpUppTuoPsGrVqkxft06dOpYA8s8//0y3Xcq1a9asaQnMHBwcLMPUli9fnul757TGjRtbeuksXrz4vtyjcuXKFCtWDDCHY6GhodjZ2VmFMLcO3czKkM2Uv7fc8LkcEBCAs7MzYO6JldZ8YSluDZ82b95MTEwMYNvwLC/WX69ePcA891lUVFSabVJC3Iyys7OjUaNGfP/995QuXRqAP/74I+vFioiI3COFZyIiItlg3LhxuLu7ExMTQ8eOHe/ay+Lq1at06tSJiIiIHKowe6TM7fTPP/8QGxub6viuXbv4+uuvM31dLy8vy2p+kydPTnOOr927d7NkyRIAunbtanWsb9++gLln3K+//prp++ekwoULW1binDx58l3D1nudRy8lRJk9ezb//vsvNWvWtFoN9taeZykhzb0ELymTxF+7du2e6sxOLi4ulhU7Q0NDWbt2LZB2z62UoZBnz57l008/BcDd3d0SBtlCXqy/U6dOODg4EBMTw9SpU9Ns8/bbb6d7flxcXLrH7O3tLUNQ7e3ts1aoiIhIFig8ExERyQYVK1ZkwYIFODk5sX//fmrVqsXEiROthuUlJSWxc+dORo0aRbly5fjhhx9sWPG9adOmDXZ2dly5coVnn33WEhLGx8fz7bff0qZNmztOOn4n7777Lo6Ojhw5coS2bduyd+9eAJKTk/n1119p3749iYmJPPTQQ7zwwgtW5/7vf/8jKCgIwzDo1KkTkydPtqzYl5SUxIkTJ3j//fcZPnx4Ft599pk6dSqFChUiMjKSoKAgvvjiC6sg9fLly/zwww907NgxVVCYUSlB2JYtW4DUKzAWL16cSpUqcfLkSctQ28zOrQZQrVo1ANavX8+hQ4fuqdbslPK+//zzTy5evIi/vz+lSpVKs21K761FixYB0KRJE8vE+LaS1+ovUaIEAwYMAOCdd95h/Pjxlh5oly5dYtCgQSxcuDDdRRUaNmzIiy++SGhoqNUiI+fOnWPw4MGWf0NTVvEVERGxBYVnIiIi2eSJJ55g9erVlC9fnsuXLzNixAgqVKiAs7MzhQoVwsnJiTp16vDOO+8QERFB165dcXNzs3XZmVKhQgWGDRsGwA8//EDJkiXx8vLC3d2dZ555Bnd3dz788MN7unbt2rUtAeSGDRuoUaMGnp6euLm50aFDB86dO0epUqVYvnw57u7uVuc6ODiwdOlSmjRpQmxsLK+99hqFCxfG29sbV1dX/P39efXVV/n777+z/AyyQ7ly5fjjjz8oW7Ysly5dom/fvnh7e+Pj40PBggXx8/OjU6dOLF26lOTk5Hu6x+1h2e2vwbqnWdWqVSlSpEim79OpUyf8/Py4evUqVapUwc/Pj7Jly1K2bFk2b96c+cKzKOU9pSzMcKdAMCV8SmmbGxZLyIv1T5w4kVatWpGcnMzrr79u+VwuUqQIs2bNYvjw4dSqVSvNc69du8aMGTNo3rw5BQsWxNvbG3d3d0qUKMHMmTMBeOWVV2jTpk0OviMRERFrCs9ERESyUWBgIIcOHWLRokU8++yzlC9fHhcXF6KiovDx8SEoKIg33niDgwcP8vXXX1vm7cpLJkyYwPz582nQoAGurq4kJCRQvnx5Xn/9dXbu3Enx4sXv+drPPPMM+/fv54UXXuChhx4iLi4OBwcHatWqxdixY9m3bx9VqlRJ81xfX19CQ0NZuHAhjzzyCH5+fly/fh1vb2/q1q3LiBEjeO+99+65tuxWu3ZtDhw4wMyZM2nVqhW+vr5ERUWRnJxMhQoV6NatG4sXL77nHorlypWjTJkygDlcbNKkSao2aS0gkFne3t6sW7eOLl26UKJECSIiIjh58iQnT55Mc2jv/dawYUOrRSzSmi8svWO5ITzLi/W7uLjw22+/8cEHH1CrVi2cnJwwDIMmTZrw7bffMmHChHTPXbx4MWPHjqVly5b4+/sTHx9PQkICZcqU4ZlnnuHPP/9k2rRpOfhuREREUjMZuWF2VxERERERERERkVxIPc9ERERERERERETSkevDs9DQUEwmU5qbLebREBERERERERGRB4dtlxPKhPfeey/VPA4pqzuJiIiIiIiIiIjcD3kmPKtQoQKNGjWydRkiIiIiIiIiIvIAyfXDNkVERERERERERGwlz4RnAwcOxMHBAQ8PD9q2bcuGDRtsXZKIiIiIiIiIiORzJsMwDFsXcSc7d+5k3rx5BAcHU6hQIY4cOcLkyZP5559/+OWXX2jbtm2a58XFxREXF2d5nZyczJUrVyhUqBAmkymnyhcRERERERERkVzGMAyioqIoXrw4dnZ37luW68OztFy7do3q1avj4+PD7t2702wzZswYxo4dm8OViYiIiIiIiIhIXnH69GlKlix5xzZ5MjwDCAkJ4aOPPuLGjRu4urqmOn57z7OIiAhKly7N6dOn8fDwyMlSRUREREREREQkF4mMjKRUqVJcu3YNT0/PO7bNM6tt3i4l80tvCKazszPOzs6p9nt4eCg8ExERERERERGRDE3tlWcWDLjV1atX+fnnn6lVqxYuLi62LkdERERERERERPKpXN/zrFu3bpQuXZp69erh6+vL4cOHmTp1KhcuXGDu3Lm2Lk9ERERERERERPKxXB+e1ahRg2+++YaPPvqI6OhofHx8CAoKYsGCBdSvX9/W5YmIiIiIiIiISD6WZxcMyKzIyEg8PT2JiIjQnGciIiIiIiIiIg+wzOREeXLOMxERERERERERkZyg8ExERERERERERCQdCs9ERERERERERETSofBMREREREREREQkHQrPRERERERERERE0qHwTEREREREREREJB0Kz0RERERERERERNKh8ExERERERERERCQdCs9ERERERERExGY2b97MU089RbFixXBycqJo0aJ07tyZTZs2Zeo6Y8aMwWQy3VMNoaGhmEwmQkND7+n8jAoODiY4ODhDbZOTk1mwYAGtWrXC19cXR0dHChcuzKOPPsry5ctJTk7m0UcfxcvLi9OnT6c6/8qVKxQrVozAwECSk5Oz+Z08WBSeiYiIiIiIiIhNzJgxg8DAQM6cOcOkSZNYtWoVU6ZM4ezZswQFBTFz5swMX6tfv36ZDtxS1KlTh02bNlGnTp17Oj+7xcbG0r59e3r27EnhwoWZM2cOq1ev5qOPPqJ48eI89dRTLF++nM8++wwHBwf69euX6hqDBg0iKiqKefPmYWen+CcrTIZhGLYuIidERkbi6elJREQEHh4eti5HRERERERE5IEWFhZG06ZNad++PUuXLsXBwcFyLDExkSeffJJff/2VdevWERgYmO51bty4QYECBXKi5CxL6XV2tx5uAwYMYM6cOcybN48ePXqkOn748GFiYmKoUaMG3377Lc888wwfffQRL7zwAgBLly6lY8eOzJ49m5CQkOx+G/lCZnIiRY8iIiIiIiIikuPGjx+PyWRizpw5VsEZgIODA7Nnz8ZkMjFhwgTL/pShmTt27KBz5854e3vz0EMPWR27VVxcHEOGDKFo0aIUKFCApk2bsn37dsqWLUuvXr0s7dIattmrVy/c3d05cuQI7du3x93dnVKlSjFkyBDi4uKs7jN27FgaNmyIj48PHh4e1KlTh88//5x76a90/vx5PvvsM9q2bZtmcAZQoUIFatSoAcDTTz9Nly5dGDp0KCdOnCA8PJz+/fvTunVrBWfZxOHuTUREREREREQktzAMuHHD1lXcVKAAZHaqsaSkJNasWUO9evUoWbJkmm1KlSpF3bp1Wb16NUlJSdjb21uOdezYkS5dutC/f3+uX7+e7n169+7NN998w2uvvUaLFi04cOAATz75JJGRkRmqMyEhgccee4y+ffsyZMgQ1q1bxzvvvIOnpyejRo2ytDtx4gQvvPACpUuXBszzuA0ePJizZ89atcuINWvWkJCQwBNPPJHhc2bNmsXatWvp06cPfn5+xMfH88UXX2TqvpI+hWciIiIiIiIieciNG+DubusqboqOBje3zJ1z+fJlbty4gb+//x3b+fv789dffxEeHk7hwoUt+3v27MnYsWPveO6BAwdYtGgRw4cPZ/z48QC0bt2aIkWK0LVr1wzVGR8fz9ixY3nqqacAaNmyJdu2bePrr7+2CsW+/PJLy8fJyckEBwdjGAYffPABb731VqYWMjh16hTAXZ/NrXx8fPj8889p3749AAsWLEg3lJTM07BNEREREREREcmVUoY93h4+derU6a7nrl27FjAPa7xV586dUw0TTY/JZOL//u//rPbVqFGDkydPWu1bvXo1rVq1wtPTE3t7exwdHRk1ahTh4eFcvHgxQ/fKqkceeYRGjRpRoUIFunfvniP3fFCo55mIiIiIiIhIHlKggLm3V25xL3P1+/r6UqBAAY4fP37HdidOnKBAgQL4+PhY7S9WrNhd7xEeHg5AkSJFrPY7ODhQqFChDNVZoEABXFxcrPY5OzsTGxtref3XX3/Rpk0bgoOD+fTTTylZsiROTk4sW7aMd999l5iYmAzdK0XK0M+7PZu0ODs74+TklOnz5M4UnomIiIiIiIjkISZT5odJ5jb29vY0b96cFStWcObMmTSHGJ45c4bt27fzyCOPWM13Bql7oqUlJSC7cOECJUqUsOxPTEy0BGvZYfHixTg6OvLzzz9bBW3Lli27p+s1b94cR0dHli1bRv/+/bOpSskKDdsUERERERERkRw3cuRIDMNgwIABJCUlWR1LSkoiJCQEwzAYOXLkPV2/adOmAHzzzTdW+7///nsSExPvreg0mEwmHBwcrAK+mJgYFixYcE/XK1q0KP369eP3339n/vz5abY5evQoe/bsuafrS+ap55mIiIiIiIiI5LjAwECmT5/Oyy+/TFBQEIMGDaJ06dKcOnWKWbNmsWXLFqZPn07jxo3v6foPP/wwXbt2ZerUqdjb29OiRQv279/P1KlT8fT0xM4ue/oTdejQgWnTptGtWzeef/55wsPDmTJlCs7Ozvd8zWnTpnHs2DF69erF77//zpNPPkmRIkW4fPkyf/zxB19++SWLFy+mRo0a2fIe5M4UnomIiIiIiIiITQwePJj69eszdepUhgwZQnh4OD4+PgQFBbFhwwYCAgKydP0vv/ySYsWK8fnnn/P+++9Tq1Ytvv32W9q1a4eXl1e2vIcWLVrwxRdfMHHiRP7v//6PEiVK8Nxzz1G4cGH69u17T9d0cXHhl19+4auvvmLevHm88MILREZG4u3tTb169fjiiy9SLWQg94/JSFm6Ip+LjIzE09OTiIgIPDw8bF2OiIiIiIiIiNjAxo0bCQwM5KuvvqJbt262LkdsJDM5kXqeiYiIiIiIiEi+9Mcff7Bp0ybq1q2Lq6sru3fvZsKECVSoUIGOHTvaujzJIxSeiYiIiIiIiEi+5OHhwcqVK5k+fTpRUVH4+vryyCOPMH78eKuVMUXuROGZiIiIiIiIiORLDRs2ZMOGDbYuQ/K47FlaQkREREREREREJB9SeCYiIiIiIiIiIpIOhWciIiIiIiIiIiLpUHgmIiIiIiIiIiKSDoVnIiIiIiIiIiIi6VB4JiIiIiIiIiIikg6FZyIiIiIiIiIiIulQeCYiIiIiIiIiIpIOhWciIiIiIiIiYjN79uyhd+/e+Pv74+Ligru7O3Xq1GHSpElcuXKF7777DpPJxIwZM9I8//nnn8fZ2Zk9e/Zke20mk4kxY8ZYXh84cIAxY8Zw4sSJVG2Dg4OpVq3aPd2nWrVqVKlSJdX+pUuXYjKZCAgISHVswYIFmEwmfvrpJx599FG8vLw4ffp0qnZXrlyhWLFiBAYGkpycfE/1pWfu3LmYTKY0n8e9Cg0NxWQyERoamm3XzCqFZyIiIiIiIiJiE59++il169Zl69atDBs2jBUrVrB06VKeeuopPvroI/r27ctTTz1Ft27dGDFiBEeOHLE6f+XKlXz66aeMHTuWGjVqZHt9mzZtol+/fpbXBw4cYOzYsdkaFgE0b96cQ4cOcf78eav9oaGhuLm5sW3bNqKiolIds7Ozo2nTpnz22Wc4ODhY1Zpi0KBBREVFMW/ePOzssjcG6tChA5s2baJYsWLZet3cJs+FZ5999hkmkwl3d3dblyIiIiIiIiIi92jTpk2EhITQqlUrtm/fzoABAwgODqZ169aMHDmSQ4cO0bt3bwBmzpyJl5cXvXr1svSeioyMpF+/fgQEBDBs2LD7UmOjRo0oWbLkfbn2rZo3bw6QqrdVaGgo/fr1w2QysWHDhlTHateujZeXF0WLFmX27NmsXLmSjz/+2NJm6dKlLFq0iMmTJ1O+fPlsr9vPz49GjRrh7Oyc7dfOTfJUeHb27FmGDh1K8eLFbV2KiIiIiIiIiGTBe++9h8lk4pNPPkkzfHFycuKxxx4DwNvbm88//5ywsDDef/99AF555RXCw8OZN28e9vb26d5n1qxZ2NnZcfHiRcu+qVOnYjKZGDhwoGVfcnIy3t7eDBkyxLLv1mGbc+fO5amnngLMYZfJZMJkMjF37lyr+23dupUmTZpQoEABypUrx4QJE+46XDI4ODjVUMXw8HD27t1Lhw4dqFu3LmvWrLEcO336NMeOHbOEbgBPP/00Xbp0YejQoZw4cYLw8HD69+9P69atCQkJueP969evT4cOHaz2Va9eHZPJxNatWy37fvjhB0wmE3v37rU8k9uHbaYMX83Iczh06BDt2rWjQIEC+Pr60r9//1Q97AB27tzJo48+SuHChXF2dqZ48eJ06NCBM2fO3PF9ZReHHLlLNunfvz9NmzbFx8eH77//3tbliIiIiIiIiNjM9evXM32Os7MzDg7mKCAxMZG4uDjs7OxwdXW9p+u6ubllugaApKQkVq9eTd26dSlVqlSGzmnXrh0vvPACb775JnZ2dnzxxRfMnDmTChUq3PG8Vq1aYRgGf/75J127dgVg1apVuLq68scff1jabdu2jWvXrtGqVas0r9OhQwfee+89Xn/9dWbNmkWdOnUAeOihhyxtzp8/z7PPPsuQIUMYPXo0S5cuZeTIkRQvXpwePXqkW6OPjw81atSwCsjWrl2Lvb09jRs3plmzZqxevdpyLKXdreEZmIPCtWvX0qdPH/z8/IiPj+eLL7644/NJeUYzZ84kISEBR0dHLly4wL59+yzPqH79+pbnVqRIEapXr37H62XkOVy4cIFmzZrh6OjI7NmzKVKkCF999RWDBg2yutb169dp3bo1/v7+zJo1iyJFinD+/HnWrFmTZtB2Xxh5xIIFC4yCBQsap0+fNnr27Gm4ubll6vyIiAgDMCIiIu5ThSIiIiIiIiI5B8j09u2331rO//bbbw3AaNasmdV1fX19M3y9e3X+/HkDMLp06ZKp86Kiooxy5coZgNGqVSsjOTk5Q+eVLFnS6NOnj2EYhhEXF2e4ubkZw4cPNwDj5MmThmEYxrvvvms4Ojoa0dHRlvMAY/To0ZbX3333nQEYa9asSXWPZs2aGYCxZcsWq/1Vq1Y12rZte9caX375ZQMwzp07ZxiGYQwePNho1KiRYRiG8euvvxr29vaWTKN3796Gvb29ERkZmeo6v/76q+XvZ8GCBXe9r2EYxqpVqwzAWLdunWEYhrFw4UKjYMGCxoABA4zmzZtb2lWoUMHo1q2b5fWXX35pAMbx48cz/RyGDx9umEwmY9euXVbtWrdubfWMt23bZgDGsmXLMvReMiozOVGeGLZ58eJFXn75ZSZMmJDhscZxcXFERkZabSIiIiIiIiKSd7m7u/Paa68BMHbsWEwmU4bOa9myJatWrQJg48aN3Lhxg1dffRVfX19L77NVq1YREBBwz73pAIoWLUqDBg2s9tWoUYOTJ0/e9dzb5z0LDQ0lODgYgKCgIADWrVtnOVavXj0KFiyY6jqPPPIIjRo1okKFCnTv3j1DdQcGBuLi4mJ5Rn/88QfBwcG0a9fO8rxOnz7N4cOH0+2Zd6uMPIc1a9bw8MMPU7NmTat23bp1s3pdvnx5vL29GT58OB999BEHDhzI0HvKTnkiPBswYACVKlW66xjdW40fPx5PT0/LltFuoCIiIiIiIiJ5QXR0dKa3J5980nL+k08+SXR0NL/99pvVdU+cOJHh690rX19fChQowPHjxzN9bsr8aE5OThk+p1WrVpw6dYrDhw+zatUqateuTeHChWnRogWrVq0iJiaGjRs3ZigYupNChQqlWW9MTMxdz23WrBl2dnasWbOG8PBw9u3bR7NmzQAoWLAgtWvXJjQ0lFOnTnH8+PFUQzZvv2dmno+LiwuBgYGW8OzPP/+kdevWBAcHk5SUxPr16y0hY0aeUUaeQ3h4OEWLFk3V7vZ9np6erF27llq1avH666/z8MMPU7x4cUaPHk1CQkKG32NW5Po5z5YsWcLy5cvZuXNnhhNlgJEjR/Lqq69aXkdGRipAExERERERkXwjKz2kABwcHCzzn2XndTPC3t6eli1b8ttvv3HmzJn7vqJly5YtAXPvsj/++IPWrVtb9r/55pusW7eOuLi4LIdnWeHp6WkJyEJDQ7GzsyMwMNByvFmzZqxZs8Yy39idwrN70bJlS0aNGsVff/3FmTNnaN26NQULFqR+/fr88ccfnDt3jooVK2ZbtlKoUCHOnz+fan9a+6pXr87ixYsxDIM9e/Ywd+5c3n77bVxdXRkxYkS21HMnubrnWXR0NAMHDmTw4MEUL16ca9euce3aNeLj4wG4du1auhMZOjs74+HhYbWJiIiIiIiISO4wcuRIDMPgueees/ycf6uEhASWL1+eLfcqVqwYVatWZcmSJWzfvt0SnrVu3ZpLly4xbdo0PDw8LBPjpyel11tGepLdi+bNm3P48GG+/vpr6tatazUss1mzZuzatYtly5bh6OhoFaxlh1atWpGYmMhbb71FyZIlqVy5smX/qlWrWL16dbaGi82bN2f//v3s3r3bav/XX3+d7jkmk4maNWvy/vvv4+XlxY4dO7KtnjvJ1eHZ5cuXuXDhAlOnTsXb29uyLVq0iOvXr+Pt7c2zzz5r6zJFREREREREJJMCAgKYM2cOq1atom7dusyePZu1a9eyatUqJk+eTNWqVTO0UmRGtWzZkj///BMnJydL8OTv74+/vz8rV64kODg4zZ54t6pWrRoAn3zyCRs2bGDbtm2Eh4dnW40pvcmWLl1qGbKZokmTJgD8+OOPNGjQINt7CNatWxdvb29WrlxpCRfBHJ7t3r2bCxcuZGt49vLLL+Pr60uHDh2YO3cuv/32G927d+fQoUNW7X7++Wfat2/PJ598Yuk5GBISwrVr16zqvJ9ydXhWtGhR1qxZk2pr27YtLi4urFmzhnHjxtm6TBERERERERG5B8899xzbtm2jbt26TJw4kTZt2vDEE0+waNEiunXrxieffJJt90oJfoKCgnBxcUm1PyPBkL+/P9OnT2f37t0EBwdTv379bOsdB+aAzMHBAcMwUoVnXl5e1KhRA8MwLAsJZCc7OzvLdW99FimLKNjZ2WXrUNGiRYuydu1aqlatSkhICN27d8fFxYWZM2datatQoQJeXl5MmjSJxx57jKeeeoodO3Ywd+5cnnvuuWyr505MhmEYOXKnbNSrVy++//77TE1OGBkZiaenJxERERrCKSIiIiIiIiLyAMtMTpSre56JiIiIiIiIiIjYUp4Mz+bOnZulJXFFREREREREREQyIk+GZyIiIiIiIiIiIjlB4ZmIiIiIiIiIiEg6FJ6JiIiIiIiIiIikQ+GZiIiIiIiIiIhIOhSeiYiIiIiIiIiIpEPhmYiIiIiIiIiISDoUnomIiIiIiIiIiKRD4ZmIiIiIiIiIiEg6FJ6JiIiIiIiIiIikQ+GZiIiIiIiIiIhIOhSeiYiIiIiIiIiIpEPhmYiIiIiIiIiISDoUnomIiIiIiIiIiKRD4ZmIiIiIiIiIiEg6FJ6JiIiIiIiIiIikQ+GZiIiIiIiIiIhIOhSeiYiIiIiIiIiIpEPhmYiIiIiIiIiISDoUnomIiIiIiIiIiKRD4ZmIiIiIiIiIiEg6FJ6JiIiIiIiIiIikw8HWBYiIiIiI3E1SUhK7d+8mLCwMT09PunXrhoODvpUVERGR+0/fcYiIiIhIrhMTE8OWLVvYsGED69evZ9OmTURFRVmOz549m3nz5lGpUiUbVikiIiIPAoVnIiIiIpIr7Nmzh4ULF7Jhwwa2bdtGQkKC1XEPDw8aNWrEli1b2LJlC7Vr1+azzz6jW7duNqpYREREHgQKz0REREQkx505c4Z169ZRv359KlSoAMC+ffuYPHmypU2xYsVo0qQJTZo0ISgoiOrVq2Nvb8/p06fp27cvq1evpnz58rZ6CyIiIvKAMBmGYdi6iJwQGRmJp6cnEREReHh42LocERERkQdGcnIyf//9N5UrV8ZkMgHQsWNHli5dyoQJExg+fDgAZ8+eZcyYMQQFBdGkSRP8/f0t7W9nGAZbt26lQYMGln0HDhygSpUq6Z4jIiIikiIzOVG29TxLTk5m4cKF9OjRI7suKSIiIiJ5UHx8PDt27GD9+vWsX7+esLAwrly5wtGjRylXrhwAzZs35/Tp0/j6+lrOK1GiBJ9++mmG7mEymayCs3379lG3bl3atWvHokWLKFCgQPa+KREREXlgZVt4lpCQQO/evRWeiYiIiDxgoqKi2LRpE+vXr2fDhg1s2bKFmJgYqzaurq78888/lvBs8ODBDB48ONtq2LlzJ4ZhkJSUhKura7ZdV0RERCRT4dnbb7+d7rHbJ3QVERERkfzJMAyWLFliWQlz165dJCcnW7UpVKiQZfhlUFAQderUwdHR8b7V9L///Y+aNWvi5+dnGbYZERGBYRh4eXndt/uKiIhI/pepOc+cnJx48skn0xwLmpSUxLx580hKSsrWArOL5jwTERERyTzDMDh69CjHjh2jTZs2lv3ly5fn6NGjltdly5a1BGVNmjSxmt/MVnr06MHq1av54osvrGoXERERuW9znlWvXp0ePXrQoUOHVMdiY2OZO3dupgoVERERkdwlKSmJqKgoS2+tPXv2UKtWLTw8PLhy5Qr29vYAdO/enUuXLlkCs5IlS9qw6tSuXbvG5s2bOXv2LG3btmXAgAFMmjQJNzc3W5cmIiIieYxdZho/99xz6fYsc3R0ZPTo0dlSlIiIiIjkjJiYGNauXcu4ceNo164d3t7evPLKK5bj1apVw8/Pj+rVq3P58mXL/jFjxjBr1iy6dOmS64IzAC8vL3bu3MmgQYMAmD17NrVq1WLjxo02rkxERETymkwN28zLNGxTREREBK5cuUJYWJhlvrJt27almru2Vq1a7Ny50/I6MTERB4dsW2cqx61atYrevXtz5swZ7OzsGDZsGGPHjsXZ2dnWpYmIiIiNZCYnylTPs7Tc795mu3btokOHDpQuXRpXV1d8fHwICAhg4cKF9/W+IiIiIvnBhQsX+PrrrwkJCaF69eoUKlSIxx57jEmTJrFp0yYSEhIoVqwYTz/9NB9++CE7d+5k27ZtVtfIy8EZQKtWrdi7dy89evQgOTmZiRMnUr9+fXbv3m3r0kRERCQPyHLPswIFCnDjxo3sqieV0NBQFi9eTFBQECVKlOD69et89dVXLF68mHfeeYc333wzQ9dRzzMRERHJ75KTkzl48CC+vr4UKVIEgHnz5tGrVy+rdpUqVbKa3N/f39/mk/vnlGXLlvH8889z6dIlHB0dGTNmDK+99lqeDwhFREQkczKTE2U5PHN1dSUmJiYrl7gnjRo14ty5c5w6dSpD7RWeiYiISH4THx+Pk5OT5fUzzzzDt99+y7Rp0yzzlh07doxnnnmGJk2a0KRJEwIDAylcuLCtSs4VLl68SP/+/Vm6dClg/r5y3rx5VKxY0caViYiISE7J0WGbtvotpa+vr35DKCIiIg+UqKgoVq5cyVtvvUVwcDCenp6cPn3acrxOnToUKFCAa9euWfaVK1eOrVu3Mm3aNJ588skHPjgDKFy4MEuWLGHevHl4eHiwefNmhgwZYuuyREREJJfKM+lTcnIyycnJXL16le+++47ff/+dmTNn2rosERERkfvmwoULrF+/3jK5/65du0hOTrZqExYWRpcuXQAYOHAgr776Ko6OjrYoN08xmUz06NGD5s2b89JLLzF9+nRblyQiIiK5VJ4JzwYMGMDHH38MgJOTEx9++CEvvPBCuu3j4uKIi4uzvI6MjLzvNYqIiIhkxZEjR1i/fr0lMDt8+HCqNv7+/pa5yoKCgqhcubLlmLu7e06Wmy+UKlWKH374wWrfa6+9xsMPP0yPHj0emLngREREJH15Zs6zU6dOcfHiRS5evMjy5cv55JNPmDhxIkOHDk2z/ZgxYxg7dmyq/ZrzTERERHKDpKQkTp48Sbly5Sz7ypQpYzWfq8lkonr16pagLCgoiJIlS9qi3AfG+vXradq0KQA7d+6kVq1ati1IRERE7oscXTCgVatWrFq1KiuXuCchISF89tlnnDt3Dj8/v1TH0+p5VqpUKYVnIiIiYhOGYVh6Mf3999/Ur18fR0dHLl26hJ2deRraPn368M8//1jCssaNG+Pt7W3Lsh84iYmJTJ48mStXrjB58mRblyMiIiL3SY6GZ7by5Zdf0qdPHzZv3kzDhg3v2l6rbYqIiEhOunLlCmFhYZYhmHXq1LHM15qQkICXlxcODg7s3buX0qVLA9YBm+Qex44dY/z48UyePBkvLy9blyMiIiLZIDM5UZ6Z8+x2a9aswc7Ozmqog4iIiIitnDp1yjKx//r169m/f7/V8atXr1o+dnR0ZM+ePZQtWxZ7e3vLfgVnuY9hGPTt25fQ0FBWrFjBF198QevWrW1dloiIiOSgXB+ePf/883h4eNCgQQOKFCnC5cuX+e677/jmm28YNmxYmkM2RURERO6n5ORkDh48aLUS5q1zlaWoVKmSZQhmkyZNrI499NBDOVWuZIHJZOLdd9+lR48eHD16lDZt2jBgwAAmTZqEm5ubrcsTERGRHJBtwzaXLVvGV199xcmTJ4mNjbW+icnE7t277+m6X375JV9++SUHDx7k2rVruLu7U7NmTfr160f37t0zfB0N2xQREZHskjJ9xK3s7e2pXbs2TZo0sQRm+iVf/nH9+nVee+01Zs+eDUD58uWZN28ejRs3tnFlIiIici9yfM6zyZMnM3z4cPz8/ChfvjxOTk6p2qxZsyart8kShWciIiJyLyIiIpg/fz6VK1e2DNc7dOgQdevWpVGjRpagrFGjRri7u9u4WrnfVq5cSZ8+fTh79ix2dnYMHz6c0aNH4+zsbOvSREREJBNyPDzz9/enZcuWfPzxx1bzduQmCs9ERETkXowaNYp33nmHFi1a8OeffwLmebASExNxdHS0cXViC1evXuWll15iwYIFANSoUYMFCxZQo0YNG1cmIiIiGZWZnMguO24YHh5Ot27dcm1wJiIiIpIRMTExzJs3j7CwMMu+fv36UaNGDTp16kTK7xxNJpOCsweYt7c38+fPZ8mSJfj6+rJnzx7q1avH+PHjSUxMtHV5IiIiks2yJTwLDAzk4MGD2XEpERERkRx3+PBhhgwZQsmSJenVqxfjx4+3HCtdujS7d+9mwIABWg1TrHTs2JF9+/bx+OOPk5CQwOuvv07Tpk1Tzf8rIiIieVu2hGfTp09n1qxZ/PTTT8THx2fHJUVERETuq8TERJYuXUqbNm2oWLEi06ZN48qVK5QpU4amTZuSTWsqST5XpEgRli5dyty5c/Hw8KBatWq4uLjYuiwRERHJRg7ZcZHy5cvTqlUrnnzySUwmEwUKFLA6bjKZiIiIyI5biYiIiGTJuXPn+PTTT/n00085e/YsYP5e5ZFHHiEkJIRHHnlEU1FIpphMJnr27Enz5s3x9va27D937hxJSUmUKlXKhtWJiIhIVmVLePbaa68xc+ZMatWqRZUqVdJcbVNERETEVgzDYPXq1cyZM4dly5aRlJQEgK+vL/369eP555/H39/fxlVKXle6dGnLx4Zh0KtXL/766y++/vpr2rdvb8PKREREJCuyJTybO3cuw4cPt5ofRERERCQ3SEhIoE6dOuzbt8+yLygoiJCQEDp16oSzs7MNq5P86urVq0RGRhIXF0e5cuVsXY6IiIhkQbbMeZaUlETr1q2z41IiIiIiWXbo0CHLx46OjlStWhV3d3cGDBjAnj17WL9+Pd26dVNwJveNj48PGzZsYN26dVSuXNmyX4tsiYiI5D3ZEp61adOGzZs3Z8elRERERO5ZfHw8jRo1okqVKlYB2tSpUzl37hyzZs2ievXqNqxQHiQODg7Ur1/f8josLIxq1arRs2dPrl27ZrvCREREJFOyJTx76623WLhwIR988AFHjhzhypUrqTYRERGR+yFl0n8AJycnChcujJOTE1u3brXsL1myJAULFrRFeSIWKZ+T8+fPp3r16qxatcrGFYmIiEhGmIxsWIfdzs6cwZlMpnTbpEzMayuRkZF4enoSERGBh4eHTWsRERGRrElISODHH39kzpw5hIaGcuTIEcuE/0ePHsXDwwM/Pz8bVymSWlhYGD179uTo0aMADBo0iIkTJ6ZarV5ERETur8zkRNmyYMCoUaPuGJyJiIiIZIczZ87wySef8Nlnn/Hvv/8C5l/irV271hKePfTQQ7YsUeSOAgMD2bVrF6+99hpz5sxh5syZ/P7778yfP59GjRrZujwRERFJQ7b0PMsL1PNMREQkb0pOTmbVqlXMmTOH5cuXW3qzFylShH79+vH8889TunRpG1cpknkrV66kT58+nD17Fjs7O4YPH86YMWNwcnKydWkiIiL5XmZyIoVnIiIikiuFh4czd+5cPvroI44cOWLZ36xZM0JCQnjyyScVMkied/XqVV588UUWLlwIQM2aNZk/fz41atSwcWUiIiL5W2ZyomxZMEBEREQkuxw6dIhevXpRokQJhg4dypEjR/Dw8GDw4MHs37+f0NBQnnnmGQVnki94e3uzYMECvv/+ewoVKsTu3bupV68eEyZMsPmcwSIiImKmnmciIiKSq6xdu5bg4GAAateuTUhICF27dsXd3d22hYncZxcuXOD555/np59+wsnJid27d1O5cmVblyUiIpIv5fiCASIiIiL34ujRo0yfPh1vb2/efvttAJo2bcprr71Gx44dadCggRYlkgdGkSJFWLZsGfPmzSMqKkrBmYiISC6hnmciIiJiMytWrOCRRx7B09OTc+fOUaBAAVuXJJLrbNu2jVGjRvHJJ59QsmRJW5cjIiKSL2jOMxEREcl1Tp06xZtvvsmUKVMs+9q0acMLL7zAt99+i4uLiw2rE8mdDMOgf//+/Pbbb4waNcrW5YiIiDyQ1PNMRERE7pvk5GRWrlzJ7Nmz+eWXX0hOTsbPz4/Tp0/j7Oxs6/IyxTDg5EnYuBEKFoRHHgEHTYAhOeDvv//mtdde4/PPP8fX19fW5YiIiOQLOT7nmZ2dXbrzkZhMJry8vKhXrx7Dhw+nefPm2XFLERERycUuXbrEl19+yccff8yxY8cs+1u0aEFISAj29vY2rC5jkpJg/35Yvx42bDBvZ87cPF6mDAweDP36gaen7eqU/K9SpUr8+OOPlteGYTBgwADatWvH448/bsPKREREHgzZ0vNszJgxzJ8/n6ioKP7v//6PIkWK8O+///LLL79QsGBBHnvsMVatWsWhQ4f47bffaN26dXbUninqeSYiInJ/GYbBxo0bmTNnDt999x3x8fEAeHp60qtXL/r375+rJ0CPjYWtW80h2fr15h5mERHWbRwcoHZtOH4cLl8273N3h7594cUXoVy5nK9bHjxLly6lY8eOAPTq1Yvp06fjqQRXREQkUzKTE2VLePbhhx+yePFi/vjjD9zc3Cz7o6Ojad26NR07duSVV16hdevWGIZBaGhoVm+ZaQrPRERE7o+oqCi++uorZs+ezd69ey3769aty4ABA+jSpUuuXAjgyhVzQJYSlm3bBv/lfRbu7hAQAE2aQFAQNGgAbm4QEwNffQXvvw8HDpjb2tnBE0/Aq69C48agRULlfomLi2PUqFFMnjwZwzAoXbo0X375JS1atLB1aSIiInlGjodn5cuXZ9KkSZbfgN1qyZIlDB06lOPHj/PNN9/w3HPPERkZmdVbZprCMxERkfujUaNGbNmyBQAXFxe6du1KSEgI9evXt3Fl1k6dsh6CuW9f6jZFitwMypo0gRo17jyvmWHAypXmEO3332/ur18fXnkFOncGR8fsfy8iABs2bKBnz56WodGDBw9mwoQJuTKsFhERyW1yfLXNM2fO4JjOd4YODg6cP38egGLFipGQkJAdtxQREREbiIuLY9GiRVy/ft2y79lnn6VixYq8//77nDt3ji+++MLmwVlyMuzdC3PmQLduULq0eY6y7t3ho49uBmcVK5qHXH75JRw+DP/+C999By+9BHXq3H1BAJMJ2raFFSvM1+zXD5ydzcM/u3UzD+OcNAmuXr3/71kePEFBQezevZv+/fsDMGPGDGrXrm0Js0VERCR7ZEvPs5o1a+Ln58eKFStwuOW7zMTERNq0acOVK1fYtWsX33zzDcOGDePUqVNZvWWmqeeZiIhI1gUEBLB582Y+++wz+vbtC0BCQgIODg7pLh6UE+Libs5XtmEDhIXBtWvWbeztzYFYSq+ywEAoXDj7a7l40RzQzZpl/hjMQz179zaHcuXLZ/89RVasWEHfvn05d+4cdnZ2jBw5klGjRuHk5GTr0kRERHKlHB+2+eOPP9KpUydKlizJE088QZEiRbhw4QLLli3j7NmzLFmyhMcee4x+/foRFRXFN998k9VbZprCMxERkcxJSkpixYoVtGrVCmdnZwAmTZrEhx9+yDvvvEPv3r1tVtu1a+b5ylKGYW7dag7QbuXmZp6vLCjIvDVqZN6XU2JjYdEi85DOlKngTCZ47DHzvGhNmmheNMleV69eZdCgQXz99dcA1KpVi/nz51O9enUbVyYiIpL75Hh4Bubfdo0aNYrt27djGAYmk4l69erx9ttv07Zt2+y4RZYoPBMREcmYixcv8vnnn/Pxxx9z8uRJvvrqK7p16wZATEwMjo6OVj3Nc8Lp0zd7la1fbx4ieft3MIUL35yvLCgIatW6+7DLnGAY8Oef5hDt119v7q9Txzwv2tNPgzoHSXb6/vvv6d+/P+Hh4Tg6OrJ48eI05yYWERF5kOV4eHb58mV8fX0BuHHjBlevXsXb29syWemOHTuoU6dOVm+TJQrPRERE0mcYBuvXr2fOnDksWbLEMkept7c37733nmVOpZyQnGxewfLWsCytGR8qVLg5BDMoyDwcMrf35Dp4ED74AObNM/dMAyheHAYNghdeAB8f29Yn+cf58+d57rnn2LRpE/v27aNo0aK2LklERCRXyfHwLCAggDVr1uDi4pLq2P79+wkODubSpUtZvU2WKDwTERFJLTIykgULFjBnzhz2799v2d+wYUNCQkJ4+umncXV1va81xMXB9u03h2CGhaWeYN/eHmrXvtmrLCjIvDJmXnX5Mnz8McycCf+tq0SBAtCzJ7z8snkhA5GsMgyDkydPUrZsWcu+VatW0bJlS5vOUSgiIpIb5Hh4Vq5cOWrXrs2SJUus9h85coSmTZtSvnx51q1bl9XbZInCMxERkZt27drFnDlz+OqrrywrZxYoUIBnn32WkJAQateufd/ufe0abNp0s1fZX3+lnq+sQAHzHGUpvcoaNoSCBe9bSTYTFwfffGMe0rlr1839jz5qnhctODj396aTvOOHH36gU6dOtG/fnp9++gl7e3tblyQiImIzmcmJsmUmkF9//ZXAwEBeffVVpk2bBsCpU6do2bIlxYsX55dffsmO24iIiEgWHTp0iD59+rBp0ybLvipVqhASEkKPHj3w9PTM9nuePXuzV9mGDbBnT+r5yvz8bvYoa9LEPF+Zo2O2l5LrODtDjx7wv//B2rUwbRosXw4//2zeatY0z4vWpYu5rUhWXLlyBRcXF2rWrKngTEREJBOybcGAtWvX0rZtWyZNmsQzzzxDUFAQzs7OrFu3Dp9cMIGHep6JiMiDKjY21jK1QkREBMWLFyc+Pp6OHTsSEhJCs2bNsm0IV3IyHDpkHZadOJG6Xfny1kMwK1ZUD6sU//xjnhdt7ly4ccO8r2hRGDgQ+veH/6aZFbkn//zzD2XKlLGsoHv8+HEKFixomb9YRETkQWGT1TYBFi5cSO/evSlZsiQODg6sX78+y5OTrl69moULF7Jx40ZOnz6Nl5cX9erVY9SoUdStWzfD11F4JiIiD5rDhw8zePBgwsPD+euvvywB2S+//ELdunWzZQLx+HjzfGUpQzDDwuDKFes2dnbmnmQpQzADA6FYsSzfOt+7cgU++QRmzIBz58z7XFzMPdVefhmqVLFpeZIPJCQk0LhxY06fPs2nn37K//3f/9m6JBERkRyTI+HZldu/M/7PuHHj+Oqrr1ixYgVlypSx7L/X3mdPPfUU4eHhPPXUU1StWpVLly4xdepUtm3bxu+//06LFi0ydB2FZyIi8iBISkqyDMe6fPkyJUuWJD4+noMHD1KpUqUsXz8yEjZuvNmrbMuWm6tGpnB1Nc9XljIEs1Gj/DlfWU6Jj4fvvjPPi7Z9+839jzxiHtLZqpV67cm9OXPmDO3atbMsFtK7d2+mT5+u75VFROSBkCPhmZ2dXbpDPAzDSHUsKSnpXm7DxYsXKVy4sNW+6OhoypcvT7Vq1Vi1alWGrqPwTERE8ivDMAgNDWXOnDlcvnyZ1atXW44tXryYBg0aUK5cuXu69rlzN3uVpcxXlpxs3cbX13oIZp06D8Z8ZTnNMMx/B9OmwY8/3pw3rnp1c0+0bt3MPdNEMiM2Npa33nqLqVOnYhgGpUuXZu7cuTRv3tzWpYmIiNxXORKejRkzJlPzo4wePfpebpOuFi1acPbsWf7+++8MtVd4JiIi+c21a9eYP38+c+bM4dChQ5b9//zzDxUqVMj09QzDPF9ZSq+y9evh+PHU7cqVuzkEMygIKlVSz6ecduQIfPghfPEF/LdYKoULw4ABEBJi/lgkM9avX0/Pnj05/t8X/Ysvvsj48eMpUKCAjSsTERG5P2w251lOiYiIoEyZMrRo0YIffvghQ+coPBMRkfxiz549zJgxg6+//pob/80o7+bmRvfu3QkJCaFmzZoZuk58POzYcTMs27ABwsOt29jZmVd8TBmCGRgIxYtn9zuSe3XtGnz6qXletNOnzfucnaF7d/OQzocftml5ksdERUUxdOhQPvnkEwAqVarE/PnzadCggY0rExERyX45Ep598MEHdOrUiZIlS95TkVnRvXt3vvnmGzZv3pzuogFxcXHExcVZXkdGRlKqVCmFZyIikmdduXKF119/nU8++YSU/76rVatGSEgI3bt3v+v/b5GRsHnzzSGYW7ZATIx1GxeXm/OVBQVBQADov83cLyEBliwxD+ncuvXm/jZtzCFa27bqHSgZ99tvv9G3b1/+/fdf7O3tGTlyJG+99RZOTk62Lk1ERCTb5Eh4VqRIES5fvky9evXo3LkzHTt25KGHHrqngjPjrbfeYty4ccyYMYNBgwal227MmDGMHTs21X6FZyIiktcYhsG8efMYNmwYly9fBqBz58689NJLBAYGpjuNwr//Wg/B3L079XxlPj43e5WlzFemn4/zLsMwL+jw/vuwdOnNv++qVc3zonXvbl7QQeRurly5wqBBg1i0aBEAtWrVYsGCBVSrVs3GlYmIiGSPHAnPkpOTWbt2LUuWLGHp0qWcP3+e6tWrW4K0qlWr3lPxdzJ27FjGjBnDu+++y+uvv37Htup5JiIi+cGhQ4d47rnn2LBhAwBVq1Zl9uzZNGvWzKqdYcDff1sPwTx6NPX1/P1v9ipr0sQ8X5mdXU68E8lpx4+b50X7/HOIijLv8/U1z4k2YAAULWrb+iRv+O677wgJCSE8PJxXX32VqVOn2rokERGRbGGTOc/CwsL4/vvvWbp0KadPn6ZixYp06tSJTp06Ubt27SxfPyU4GzNmzD0tPqA5z0REJC/as2cPderUwdnZmdGjR/PKK6/g6OhIQgLs3HlzCOaGDfBfpzQLk+nmfGUpW4kStnkfYjsREeYA7cMP4eRJ8z4nJ/PqnK+8AjVq2LY+yf3Onz/PuHHjmDx5Mq7/dV1ctmwZbm5uNG7cGDc3NxtXKCIiknk2XzDgr7/+YsmSJfzwww8cO3aMMmXK0LlzZyZNmnRP13vnnXcYNWoUb775Ju+88849XUPhmYiI5AWGYbB//36roVHz5s2jefPmlC5dmr//hhEjYOVK+G+tAAtnZ2jY8OYQzIAA8PTM4TcguVZiIixbZp4XbdOmm/tbtjSHaI88ol6IknEVK1bk8OHD/PLLL7Rv3x6Ac+fOkZycbJM5kUVERDLL5uHZrXbt2mUJ0vbv35/p86dOncrQoUNp165dmj3OGjVqlKHrKDwTEZHcLiYmho4dO/LHH3+wa9cuqwAtKgrGjTPPZZWQYN7n7W09BLNOHXOAJnI3mzebP5eWLIGkJPO+SpXM86L16AEFCti0PMnlEhMT6d27N2FhYWzfvh1vb28A3njjDd577z1Kly5NUFAQgYGBBAUF8fDDD2Nvb2/jqkVERKzlqvAsq4KDg1m7dm26xzNavsIzERHJCzp27Mgvv/zCxx9/TK9evTAMWLwYhg6Fc+fMbTp0MAdpNWqop5BkzcmTMGMGfPqpeTVWMC8i0b8/DBwIxYvbtj7JW55//nk+//xzkm9bmcTDw4PGjRsTGBhIYGAgDRo00FBPERGxuRwPz+zs7NJd6ctkMuHl5UX9+vV57bXXaN68eVZvd08UnomISG70+++/U7NmTYr+N3v72bNnuX79OhUrVmTvXhg0CNatM7ctVw4++AAefdSGBUu+FBUFX3xh/vw6fty8z9ERunQxD+nMhulr5QERFRXFli1bCAsLY8OGDWzevJno6GirNg4ODtSuXZugoCA6d+5M48aNbVStiIg8yHI8PBszZgzz588nKiqK//u//6NIkSL8+++//PLLLxQsWJDHHnuMVatWcejQIX777Tdat26d1VtmmsIzERHJTc6cOcMrr7zC999/T/fu3VmwYIHl2LVrMGYMzJxpHlLn6gqvv27ufebiYrOS5QGQlAQ//WSeF+2/BV4BCA42h2iPPqrejpI5iYmJ7NmzxxKmhYWFcfbsWcvx9957j5EjRwJw8eJFfvzxR5o0aULlypVtVbKIiDwgMpMTOWTHDX18fChatCh79+616oIdHR1N69atKVGiBLt27aJ169a8++67NgnPREREcoOEhARmzJjB6NGjiY6Oxt7eHj8/v/+GOdkxfz4MHw4XL5rbd+oEU6dCmTI2LVseEPb28OST5m3rVvO8aN9+C6Gh5q1CBXjpJejVCzTqTjLCwcGBOnXqUKdOHQYPHoxhGJw6dcoSpLVp08bSds2aNTz//PPUqVOH7du3W/bv3r2bSpUq4aLfHoiIiI1kS8+z8uXLM2nSJDp27Jjq2JIlSxg6dCjHjx/nm2++4bnnniMyZVKNHKSeZyIiYmthYWGEhISwd+9eAAICApgzZw41a9Zk+3bzEM3Nm81tK1Uyz0Wl3zeJrZ0+be4F+ckn5l6RAF5e8MIL5s9ZLawo2eXnn39m6tSpNGzYkAkTJgBw48YNPD09sbOzo379+pZ50xo3boyvr6+NKxYRkbwsMzlRtnS8P3PmDI6Ojmkec3Bw4Pz58wAUK1aMhJQlwkRERB4Qly5dok+fPgQFBbF37158fHz47LPP2LBhAyVL1qR/f6hf3xycubvDpEmwZ4+CM8kdSpWCiRNvhmjly5tDtIkTwd8fnn0Wtm2zdZWSHzz66KOsWbPGEpwBnDhxgkKFChEfH09YWBiTJk3i8ccfx8/PjypVqvDcc88xd+5cDh8+nOGFxERERDIrW3qe1axZEz8/P1asWIGDw82RoImJibRp04YrV66wa9cuvvnmG4YNG8apU6eyestMU88zERHJacnJyXz22WeMGDGCq1evAtCvXz/Gjx+Pt7cvn34Kb7wBV66Y23frZg7OSpSwYdEid5GUBL/8Yp4X7dYF0Zs0Mc+L9thj5uGfItnFMAyOHj1KWFiYZe60gwcPpmpXuHBhAgMDCQoK4oUXXtCKniIickc5vmDAjz/+SKdOnShZsiRPPPEERYoU4cKFCyxbtoyzZ8+yZMkSHnvsMfr160dUVBTffPNNVm+ZaQrPREQkJ+3cuZOQkBC2bNkCmH/RNGfOHAICAti0yTzcbccOc9vq1c09epo2tWHBIvdgxw7zvGiLF0NionlfuXLmedF694aCBW1bn+Rf4eHhbNy40RKmbd26lfj4eABcXV2JiIiwjIz58ccfcXR0JCgoSD8HiIiIRY6HZwArVqxg1KhRbN++HcMwMJlM1KtXj7fffpu2bdtmxy2yROGZiIjklD179lC7dm2Sk5MpWLAg77zzDgMHDiQ83IERI2DuXHM7T0945x0ICQGHbFnCR8Q2zp6FWbPg449v9qT09ITnnoPBg6F0advWJ/lfXFwc27dvZ8OGDURERPDuu+9ajlWrVo39+/ezbNkyHn/8cQDOnTtHfHw8ZcqUwWQy2apsERGxIZuEZylu3LjB1atX8fb2pkCBAtl56SxReCYiIjnFMAw6dOiAp6cnU6dOpXDh4syaBaNGQcqaOb17w4QJULiwbWsVyU43bsD8+ebeaP/8Y95nbw+dO5uHdDZsaNv65MGTlJTEc889x/r169m4cSN+fn4AjB49mrfffpvixYtbhnoGBgZSs2ZNq2loREQk/7JpeJZbKTwTEZH75dChQ7z55pt89NFHltXf4uLicHZ2Zu1a8xDNffvMbevWNQ/RbNTIhgWL3GfJyfDrr+YQbfXqm/sbNzaHaE88od6WYluDBw/mo48+IjFlvPF/3NzcaNSokSVMa9SoEQU1/lhEJF9SeJYGhWciInI/GIZBgwYN2LZtGy+88AIfffQRYB7GNmwYLFpkbufjA+PHQ9++mkxdHiy7d5tDtK+/hpRF18uUMc+L1rcv6NsysZUbN26wdetWNmzYQFhYGBs3biQiIsKqjZ2dHTVr1iQwMJDOnTvTrFkzG1UrIiLZTeFZGhSeiYhIdkpOTsbOzg6AsLAwxo8fz4cffkjJkuWYPh3efhuuXweTCfr3N89tVqiQbWsWsaXz52H2bPMWHm7eV7Ag9OsHL74IZcvatDwRkpOT2b9/v2URgrCwME6cOGE5PnbsWEaNGgXA5cuX+e677wgKCqJ69eo2qlhERLJC4VkaFJ6JiEh2OHnyJC+99BL169fnjTfesDq2cqV5cvSUuZ4CAsxDNOvUsUGhIrlUTAwsXGjujXbwoHmfnR107Gge0hkQYA6dRXKDs2fPEhYWRlhYGM8++ywNGjQAYMmSJXTu3Jnq1auzZ88eS/sdO3ZQuXLlXDX3s4iIpE3hWRoUnomISFbEx8czbdo03n77bWJiYvDw8OD06dN4eHhw4gS8+iosXWpuW7gwTJoE//ufORQQkdSSk82B87Rp8McfN/c3aGD+eurUSfOiSe61cuVKpkyZQs2aNZk8eTJgnuvS09OTpKQk6tSpY7UQQZEiRWxcsYiI3E7hWRoUnomIyL1as2YNAwYM4NChQwA0a9aM2bNnU65cVSZPhvfeg9hY81xmgwfDmDHg6WnbmkXykr17Yfp0+OoriIsz7ytVyjycs18/8PKyZXUiGXP48GGaN2/O2bNnUx176KGHLEFaYGAglStXtgz9FxER21B4lgaFZyIiklnnz59n2LBhLFy4EIDChQszZcoUnn22O7/8YuLll+HYMXPb4GCYMQOqVbNZuSJ53sWLMGcOzJoFly6Z97m5mRcWePFFeOgh29YncjeGYXDq1CnLnGlhYWHs3buX23/k8vHxoXHjxgQGBjJgwAD9fCIiYgMKz9Kg8ExERDIqKSmJjz76iDfeeIOIiAhMJhMhISGMGzeOy5e9efll+PVXc9sSJWDqVHj6ac3TJJJdYmPNq3NOmwb795v3mUzwxBPmedGCgvT1JnnHtWvX2Lx5syVQ27JlCzExMQA4OTkRERGBi4sLAMuWLcPOzo6mTZvipS6XIiL3lcKzNCg8ExGRjPjrr78ICQlhx44dANSrV485c+ZQpUo93nsPpkyB+HhwdIQhQ+CNN8Dd3cZFi+RThgGrVplDtBUrbu4vUQI8PMDFBZydzX/evqW3/16OOTubh2WLZIeEhAR27txJWFgYly5d4r333rMcq1WrFrt37+a7776jc+fOAPz7779ER0dTvnx5TEqNRUSyTWZyIk3DKiIiAkRHRzNs2DA+/vhjDMPA09OT8ePH89xzz7NsmT0dO8Lp0+a2bdrAhx9CpUq2rVkkvzOZoHVr83bgAHzwAcyfD2fPmrec5OiYM0FdesecnNTbLr9wdHSkQYMGlpU7UxiGQaNGjYiPjycwMNCy//PPP+ett96icOHCBAYG0rx5c/73v/+pZ5qISA5SzzMRERHMq6TVqFGDf/75hx49ejBp0iTCw4vw4ovw55/mNmXKmCc1f/xx/RArYitXrsDff5sXFoiNNW+3fnzrltn9tx6LiTGvCJqb3C10y+4Q7/b9Wv3UNoYOHcqMGTOIj4+37HN3d6dfv3689NJLlC1b1nbFiYjkYRq2mQaFZyIicruDBw9Svnx5HB0dAQgLCyMxMZHatZvx9tvmXi6JieYfIEeMgOHDwdXVxkWLSI5JTMyeIC4r+3MTe3tziObmBoULmzc/v5sf3/7az8+88rB+2ZB1cXFxbN++nfXr17Nw4UL27dsHgL29PU899RRDhgyhXr16Nq5SRCRvUXiWBoVnIiJyq3fffZfRo0czceJEhgwZApjnV/rqKxg2DM6fN7d77DF4/30oV86GxYrIA8kwzHMs2iq4i42FhISsvQdHx4yFbCkfu7llz7PLzwzDYOXKlUyZMoVVq1ZZ9gcHBzNkyBDat2+PnZ2dDSsUEckbNOeZiIjIXRQrVoykpCR2794NwO7dMGgQbNhgPl6+vLnnWfv2NixSRB5oJpO556uzs3mBBFtITk4drEVGwqVLcPHizT9v//jiRYiKModvmZmjztU1473a/PzMPeEeNCaTibZt29K2bVt27drFtGnTWLRoEaGhoYSGhlK5cmVWrlxJqVKlbF2qiEi+oZ5nIiLyQDh69Cjnzp2jSZMmACQnJ7NhwwaqV2/KqFEwe7b5h8QCBeDNN+HVV80/sIqIyL2Jjc1YyHbpEly4cG/DVD080u/FdvtrX9/8O2/bmTNn+PDDD/n4448pWrQoBw8etPQ+S0xMxCG/vnERkSzQsM00KDwTEXkwxcbGMnHiRMaPH4+fnx8HDx7E3d2d5GT48kvzXGaXL5vbPv00TJkC+mW9iEjOMgy4fv3uIdutH9/LkFIfn4wPI/Xxgbw2+jEyMpITJ05Qo0YNAGJiYqhatSodOnTg3XffxdPT08YViojkHhq2KSIiAqxcuZKBAwdy5MgRACpXrkxkZCQHD7ozcCBs3WpuV7UqzJgBLVrYsFgRkQeYyQTu7ubN3//u7Q0DIiIyFrJdvGj+JUlysnm11itX4NChu9/Dzs7cWy0vLY7g4eFhCc4AfvrpJ06cOMHy5cuZPn267QoTEcnjFJ6JiEi+c/bsWV555RW+++47wDy/2fTp02ne/CneeMPEZ5+Zf/AqWBDGjjXPdfbfgpsiIpIHmEzg5WXeKla8e/ukJHNoll7PttsDuKtXzWFbyuuMyI2LIzz99NP4+fkRFRVlGbqZkJBA165d6d69O//3f/+Hvb39/S9ERCSP07BNERHJNxISEpgxYwajR48mOjoae3t7XnzxRd56awyLFnnw5pvmH4gA/vc/mDgRihWzbc0iIpL7JCSYe6tlZL62lMURMstWiyN89dVXdO/eHYAKFSrwyiuv0LNnTwoUKJA9NxARySM051kaFJ6JiORvYWFhhISEsHfvXgACAgKYM2cO0dE1GTQIdu0yt6tZE2bOhKAg29UqIiL5y62LI9xtKOnFi1lbHKFkSahTB+rVg/r1zatDZ2a46L///suMGTOYM2cO165dA6BQoUIMHDiQgQMHUrhw4cwXJyKSByk8S4PCMxGR/Ony5cu89tprfPnllwD4+PgwadIk2rXrzciRdixYYG7n5QXvvgsvvAAaoSIiIraSsjjC3YaP3vpxYmL61/PyMgdpKWFa/frmgO1ugVp0dDRffPEF77//PidOnADA2dmZnj178uqrr1KpUqVse88iIrmRwrM0KDwTEcl/EhMTqVSpEseOHQOgX79+vP32eBYv9mX0aPMwGpMJ+vUzB2d+fjYuWEREJJMMA65duxmmHT0K27aZF73ZtQvi4lKfU6SIdZhWv376/wcmJiaydOlSJk+ezNaUlXSAxx57jKFDhxIUFITJ1ishiIjcBwrP0qDwTEQkf5o1axaffvopc+bMISYmgMGD4cAB87EGDcxDNOvXt22NIiIi90N8POzbdzNM27rV/DopKXXb0qWtw7S6dc0rhKYwDIMNGzYwZcoUfvrpJ8v+Bg0a8OWXX1K1atUceEciIjlH4VkaFJ6JiOR9ERERjBo1irZt29K+fXsAkpKSOHXKYMQIB7791tzO1xcmTIDevcHOzoYFi4iI5LAbN2D37pth2rZtcOhQ2m0rVrwZptWrB7VrQ4EC8PfffzNt2jTmzZuHg4MDp0+fxtvbO2ffiIjIfabwLA0Kz0RE8r7Ro0fz9ttv4+/vz6FDhzAMJ6ZNg3HjzD8s2NnBgAHw9tug7/FFRETMIiNh+3brQO2/ac6s2NvDww/fDNMeeugiMTHbeeyxRwBz77RHH32UWrVqMWTIEHx8fHL2jYiIZKN8FZ5FRUXxzjvvsGvXLnbu3Mnly5cZPXo0Y8aMydR1FJ6JiORNycnJ2P3XfSw6OponnniC4cOHk5TUmhdfhMOHze2CgsxDNGvWtGGxIiIiecSlS+YQ7dYhn+fPp27n5AS1apnDNB+fzYwbF4CTkxOnTp2iSJEiOV63iEh2yUxO5JBDNd2z8PBwPvnkE2rWrMkTTzzBZ599ZuuSREQkB9y4cYN3332XjRs38ueff2JnZ4e7uzuffrqKV16BH380tytaFKZMgW7d7r6ymIiIiJj5+cEjj5g3MC9McPasdZi2bRtcvQp//WXeoD6wFDjKU08VsQz5XL/+TTp1aknz5sFaXEBE8qVc3/MspTyTycTly5fx8/NTzzMRkXxu+fLlDB48mJMnTwLw66+/Ehz8CBMnmucyi4sDBwd46SUYNQr0z7qIiEj2Mww4duxmmLZ1K+zYAdev39pqB1AXgIIF69CixVC6d+9Mo0aOlCihX2yJSO6Vr3qe6TcXIiIPjhMnTvDSSy9ZVvkqXbo0H3zwIbGx7aha9eb8LC1bwocfghb+EhERuX9MJnjoIfPWpYt5X1KSeQGClDAtLMyPvXsHkpz8BVFRO/jxx278+OMI4GUKF+5Hw4YFLXOo1a9vXtRHJDMSExPZsWMHa9as4cCBAxQvXhx/f3/8/f1p2bKlZXoPkfsp14dnIiKS/8XHxzNt2jTefvttYmJicHBwYMiQIXTp8hYjR7qxYoW5XalSMG0adOqk32SLiIjYQsqiAg8/DL16AZQiPn4m69eP5YMP5vDHHzOIjT0FvMrFi2NZvvwFli9/ESgBQNmyWIVpdeuqB7lYS05OZu/evaxevZo1a9awdu1aIiMjU7UrUKAA0dHRltdvvfUWp06dYuDAgTRo0ACAhIQE7OzssLe3z7H6JX/Kt+FZXFwccXFxltdpfbGJiIjtrVmzhgEDBnDo0CEAmjVrxpQps1mypCoNGkBCgnmy4mHDYORIcHOzccEiIiJixckJWrYsRMuWbxIbO5SFCxcyZcpU/v77EDAJk2kaBQt2IzJyCCdO1ODECfjuO/O5JhNUqnQzTKtf37xAgaurDd+Q5LjLly/z7bffsnr1akJDQwkPD7c67uXlRXBwMHXq1OHChQscP34ce3t7q5Fqy5cvZ/fu3Tz99NOWfd9//z09e/akdOnSlt5qt29+fn4a8SZ3lW/Ds/HjxzN27FhblyEiIuk4f/48Q4cO5auvvgKgcOHCTJkyFUfHZ3niCRNnz5rbtW8PH3wA5cvbsFgRERHJEBcXF/r160efPn349ddfmTJlyn89h+YD86lbtzWBgZM4e7YWW7fCqVPmYaCHDsHCheZr2NtDtWo3w7T69c2vHR1t+tYkGx0/fpz4+HgqVaoEwMWLFxk4cKDluJubG02bNqV58+a0aNGCWrVq3bX32OjRozl06BC1atWyuk9CQgJHjx7l6NGjaZ5XoEAB/P39KVu2LP7+/lSuXNmqFhHIAwsG3CozCwak1fOsVKlSWjBARCQXmDNnDiNGjCAyMhKTycSAAQPo1m0cb7zhRWiouY2/vzk0e/RRDdEUERHJy7Zt28bUqVP57rvvSEpKYteuXdSsWROAixdvrvCZ8ueFC6mv4exs7pGWEqbVq2fusabReHmDYRiW3l1Tpkxh2LBhdOnShUWLFlmOd+7cmdq1a9OiRQvq16+PYzakpUlJSZw9e5bjx4+n2k6cOMHZs2e5PRKpXLkyBw8etLxu1aoVV69e5eOPP6ZevXoA/Pvvv1y7do2yZcviqm6SeVa+WjDgXjk7O+Ps7GzrMkREJA0HDhwgMjKSevXqMXnyHH76qR5Nm5onIXZxgddfNw/TdHGxdaUiIiKSVfXq1WPRokWMHz+eX375xRKcAXzwwRt4enry0kvP4+XlhWHAmTPWYdq2bXDtGmzZYt5SuLub50y7dQ41f3/90i03uHz5MqGhoaxevZrVq1fz3nvv0bFjRwAaNGiAg4MDsbGxlvYmk4klS5Zkex329vaULl2a0qVL06xZs1TH4+LiOHXqlFWo5uXlZdVmx44dXL161SpfmDdvHiNHjgSgSJEi6Q4JLVWqVLaEgGJ7+TY8ExGR3OPq1atERUVRunRpAN555x2qVauBs3MfunSxt/yGuWNHmDrVPJmwiIiI5C9ly5a1Gg53/vx5pkyZQnx8PEFBQTRu3BiTybxAUKlS5u8LAAwDjhy5GaZt3Qo7dkB0NKxda95SFCpkDtJunUOtePEcfqMPoIiICNatW2cJy/bs2WN1fPXq1ZbwrHHjxly9ehV3d3dblGrF2dmZChUqUKFChTSPG4bB2rVrOXHiBOVvmUMkPj4eDw8PIiMjuXDhAhcuXGDz5s2pzrezs6NUqVKULVuWoKAgxo0bZzl26dIlChUqpNVC84g8MWzzt99+4/r160RFRdGnTx+eeuopyySA7du3p0CBAne9Rma644mISPb5888/6dq1K9WqVePPP//EZDKxcycMGgQbN5rbVKoEH34IbdrYtlYRERHJOfHx8SxevJjQ0FC++OILy/758+fz8MMPU7du3XTPTUw0z5OWEqZt3Qq7d5sXGrpd8eLWYVq9euaQTe7d9evXCQsLs4Rl27dvJzk52apNtWrVaNGiBS1atKBp06Z4e3vbqNr7wzAMrl69mu6Q0BMnTlj1rmvbti0rUpaQB3x9fYmOjmbnzp1UqVIFgL/++ouTJ09aeq75+PhoMYP7KDM5UZ4Iz8qWLcvJkyfTPHb8+HHKZqCLgsIzERHbOH78OFWrVqVcuXL88MMaPvigMB99ZP4tspsbjBoFL79sXqlLREREHmxXrlyhVKlS3Lhxg+bNmzN06FDatWuXod45cXGwd+/NMG3bNti/H27LdADz8M5bFySoUwcKFrwPbygfevPNN5k0aRIJtyWVFSpUsIRlwcHBFC5c2EYV5g7JycmWlUGPHz+Ot7c37du3B8zho6enJ0lJSVYZRUhICB999JHlGgULFrQsZJDWlht67+Vl+S48yw4Kz0Tyj3///ZdNmzbh5+dHkyZNLPsPHz6Mu7s77u7uuLm5qQu0jURHR/PLL7/wzDPPWPZt2LCJvXvr8dZbjqSsPN61K0yeDCVK2KhQERERyXXOnj3LyJEjWbRoEYmJiQBUqVKFIUOG8Oyzz+KSyQlRr1+HnTut51A7fDh1O5MJKle27p1Wq5bmX506dSorVqzgo48+4qGHHgJg5syZDB48mFKlStGyZUtatGhB8+bNKVmypI2rzVsSExM5c+aMVWegSZMm8eOPP3L8+HH+/fffu17D19eXZ555hpkzZ1r2rVq1ilKlSlG+fPm7rlD6oFN4lgaFZyJ5U3x8PDt37qRq1aoU/O/XgW+88Qbvvfce3bp146uvvrK0u32REDc3N9zd3SlYsKAlVEv5OOXP5557jmrVqgFw4sQJduzYQalSpahfv77lOuHh4bi5ueHs7Kxu03dgGAY//PADL7/8MmfOnGH9+vUEBQWxZYt5iOa2beZ21arBzJmQxpytIiIiIgCcOXOGDz/8kI8//pjIyEjAPDH74MGD6d+/P4WyMO7y6lXYvt16DrXTp1O3c3CA6tWtFyR4+GHIj/O/Jycns2fPHnbv3k3Pnj0t+5s1a8a6dev45JNPeO655wDzYgARERGUK1dO3xvfRzExMZw8eTLdYaFXrlwB4LnnnuOTTz4BzL/ETvmZ6dq1a3h6egLmodBHjhyx6rVWsmTJBz5cU3iWBoVnInlDSq+yjRs3smnTJrZv305cXBw///wzHTp0AGDFihW8/vrrdO/enVdffRXAslR0VFRUqvkW7uS3336jXbt2AHzxxRf07duXDh068PPPP1vauLm5cePGDRwcHNIN4W7/s2PHjlSvXh0wTwa6b98+ChcuzMMPP2y5bnJycr7pHXf06FEGDx7Mb7/9BoC/vz9Tp37Bzz8HkzKFiYcHvPMODBhg/mZURERE5G4iIyP57LPPmD59Oqf/S7gKFChAnz59eOWVVyhXrly23OfCBeswbetWuHQpdTs7O/D0NG9eXjc/zsw+V1fbrwhqGAaHDh2yzFkWGhrKlStXMJlMXL58GR8fHwC+/fZbLl68yKOPPpqh6ZIk50RERHDixAlcXV2pWLEiAKdOnaJDhw5ERERw6tQpS9v27dtbvk9P4eDgQOnSpdMdFlqkSJF8H44qPEuDwjOR3CchIYHdu3dbgrJNmzalOb+hr68v06ZN43//+99dr2kYBrGxsURHRxMVFXXXP/v27Wvpgv7jjz8yZcoUAgMDmTBhAgBJSUk43EPSs2jRIrp06QLAkiVL6Ny5M4GBgWzYsMHSpnjx4ly9evWuQdytPedatGhh6SkXFRXF0aNH8fb2pkyZMpmuMTvExsYyadIk3nvvPeLi4nBycmLYsOF4e4/knXdciYgwt+vdG8aPhyJFbFKmiIiI5HEJCQl89913TJ48mV27dgHmlQw7duzIG2+8Qa1atbL1foZh7o12a5i2bRv81wkuSxwcMhay3anNbQMuMvB+DI4fP24Jy9asWcP58+et2ri7u9OsWTOmT59utbKk5H2ff/45f/31FydOnOD48eOcPHmS+Pj4O57zwgsvWOZfi4mJYfbs2TRr1ox69erlRMk5IjM5kX73LyI5bvz48fz2229s27aNmJgYq2N2dnZUr16dgIAAy1a+fPkM/9bDZDLh6uqKq6srfn5+marr8ccf5/HHH7faZ29vT2JiItHR0Zbt9hAurX2VKlWyXMPFxYUqVaqk+s1oVFQUsbGxxMbGcimtX22mYc6cOZbw7K+//qJVq1Y8/PDD7Nu3z9KmUaNGnDlzJt1ecml9XLduXcsqP3FxcVy4cAEPDw+8vLzSrWXlypUMHDiQI0eOANCqVSt69pzJpEmV2LvX3KZOHZg1Cxo1ytDbExEREUmTo6Mj3bp1o2vXrqxZs4YpU6bw22+/8f3339OxY8dsD89MJihd2rx16mTel5xs7qF27RpERJi3Wz++277ISPM1EhPh8mXzdq+cne8esrm5xfP3399w5MhqDhxYw4UL1r+kdnFxISgoiObNm9OiRQvq1q2LY34ckyr07duXvn37Wl4nJydz7ty5NIeEHj9+nDNnzlCqVClL++PHjzN06FDefvvtfBWeZYbCMxG5b6KioliwYAEHDx5kxowZlv1hYWGsX78eAG9vb6ugrEGDBpZx+rmFvb09np6eljkDMqtDhw6WIae3On78+F1DuDuFcoZhULRo0VQrGZ09e5azZ89mqsYJEyZYwrN9+/ZRr149SpYsaRkeAdCpUyf++ecf3N3dMQyDLVu2AFCsWDHeeut9Nmx4mv/9zxxy+vjAe+9Bv37wgE+lICIiItnIZDJZVnTct28fn3/+OZ07d7YcX7hwIdHR0fTs2RNXV9dsvbedHRQrZt7uhWFAdHTmgrfbX6f0fIuLg4sXzdtNF4ETQIP/XjsALwFX/3vtCDQEWgAtMJkasX+/M2fOwE8/ZX4oqoeHvs/Lq+zs7ChZsiQlS5a0WoAtRXx8vNVqqvb29nTp0oUGDRqkavug0LBNEckWly9fZvPmzdjZ2VmWYI6OjsbLy4ukpCROnz5tWYHn119/5cKFCwQEBFCxYsV8M+9XbnH06FEiIyMzNGw1JbTr378/HTt2BMzhZsuWLfH39+fgwYOW61avXt2qh5udnR0DB75I4cJjmTjRg+ho829pX3gBxo2DLMzjKyIiIpJpCQkJlC9fnlOnTvH555/Tp08fW5eU7ZKSICoqJVgziIw0EREBW7ZsYNy4Jnh5lea5504QGWni2jX466+RxMUZODi0ID4+kKgoN65fz7563N0zN9/b7a8LFjSHkiK2oDnP0qDwTCT7JCUlsX//fss8ZZs2beKff/4BoGHDhmzevNnSduDAgfj5+dG/f3+KFi1qq5LlHiQmJlrN97Zr1y7Cw8MtgVtsbF2mTKnMoUPm440amYdo1qljo4JFRETkgRYXF8cnn3zC119/zZo1a3BxcQFg3bp1FC1a1DKpel4VHR3Nhg0bWLNmDatXr6Zly5aWeXqvX7+Oj48PlStXJjQ0FG9v73Svk5ho7sGW2WGnt76+beaVe2YymXuwZWbhBR+fm5uXl3q/yb1TeJYGhWeSVYYBx4/fnDB0927zMtWFC6e/+fllfjLP3Ojq1ats2bLFMrH/li1biIqKStWucuXKBAcHM3v27Hy/Mkt+lzIXx8WL5pWmUoYFpGxHjkBoqLlt4cIwcSL06KHfHIqIiEjukpycTOXKlTly5AiPPfYYQ4cOJTAwME98rxobG8umTZssYdmWLVtITEy0HK9duzY7duywvI6IiLjnaUYyKz4+dcCWkWGnt+67y3z1GeblZR2opbV5e6fe5+SUPfeXvEvhWRoUnklm/ftv6tV1wsMzfx1PzzsHbLduPj65K3z4448/eOmll6yG7qVwd3enYcOGlrnKGjVqZFnSWnIfw4CrV9MOwlK2W49l5HPd3h4GDYIxY8zftIiIiIjkNuHh4fTp04effvrJsq9hw4YMHTqUJ598Evtc1G0pISGBbdu2WVbEDAsLIy4uzqpNmTJlaNmyJc2bN6d58+aUKFHCRtVmXWxs5hdeuHrVvF25Yh6+mhVubncP3dIK3woUMPeYk7xP4VkaFJ7JnVy9ag7Hbg3L0ppv3dERataE+vWhbl1z0JVeEHHxorn3TmbY2Zl7q2U0bHNzy75/uOfPn8+iRYvo3bs3Tz/9NABbtmyh0X/LJJYvX56AgAAaN25MQEAA1apVy1XfbDyIrl/PWBCW8vpePh8LFUq/V2WzZlC58v15byIiIiLZ6e+//2batGnMmzfPEkj5+/vz6quv0rt3b9zc3Gxa3/fff0/v3r2Jjo622l+sWDHLapgtWrTA39/fRhXmPgkJ1mFaRrerV82/WL5XTk4ZD91uDd48PHJXRwlReJYmhWeS4sYN2LHDukfZ4cOp25lMULWqOShL2WrUyPgwTMMw/7bkTuHahQs3P7569a6XTMXVNeNBm58fODgY/PPPP5Z5yiZNmmTp2v3aa68xefJknn/+eT7++GPAvMrKihUraNSoUaoVHSX7xcenHXql9/lzL3NN3N4T8k5hrY+P5pAQERGR/OXixYvMmjWLWbNmEf5fV3tvb28GDBjAoEGDcmSO3u+//56vvvqKZ5991rJS6I4dO6hbty4+Pj5WYVmlSpXyxBDTvCQ52dyL7fZALSPB2y0LUGaanV3aw0fvtnl5wS3TEEs2UniWBoVnD6aEBNi7F/7662ZYtn+/+R/M25UrZx2U1aljXj0mp8TH35xj6m7bhQvmbs53Fw38BWwCNmEybcIwrliOPvroSurWbU3hwhAZuZXz57fQsmVzAgMfxstLvxnJqqQk83+yGQnCLl0yh62Z5eICRYrcOQRLOZZf5uATERERyaobN24wb948pk2bxpEjRwBwcnKie/fuDBkyhKpVq2b5HoZhcPToUdasWUOXLl0oWLAgACNGjGDixIn06tWLL7/8EjAvyLV3715q1KihlehzKcMwj/xIqyfb3UK3Gzeydm8Pj8yHbt7e5p8VJH0Kz9Kg8Cz/S06Gv/+2Hnq5axfcNk0AAMWKWQdl9eqZh6flFSn/cFsHagaHDh1l375NHDu2ifPnNxEdvQe4PSl0BuoBAUBvIO1vDBwcMjeEtECB+/qWcwXDMK9MdKcg7Nb9ly+nHdTeib19+s89rf3ZOXRXRERE5EGTlJTE8uXLmTJlCmFhYQC88cYbjBs37p6ud/r0acsE/6tXr+b06dMA/PrrrzzyyCMAbNu2jVWrVtGuXTtq1aqVLe9DcrfY2DuHbOkdi4jI2n0LFLi33m4Pys8YCs/SoPAsfzEMOHnSOijbvj3tSSO9vKyDsvr1IQ/Pq2mRlJRkmXPMMAwqVarE4TTGn5YqVYpGjQKoUSOA8uUbU6hQLa5edbpjz7Z7+UfazS3jQZuvb+7pehwTk7EgLGW7l1WBfHwyPlRSPf5EREREbGPTpk1Mnz6dDz74wDJ8c+PGjZw8eZLOnTvj6OiY6pwLFy4QGhpqCctSerGlcHR0JCAggDfeeIM2bdrkyPuQ/CMx0Tw6JSPzuN2+L7O/xL+Vo2P6q5S2bQvt2mXbW7SpzOREueTHV5E7u3DBOijbutXcq+d2BQqYh1veGpQ99FD+Ss3//vtvunfvTlRUFIcOHQLAZDLh7+/PyZMnqVOnjmUFzICAAEqWLJnpe8TF3XmI4e1bXJy5J9zx4+YtI9KbiD6tzdMz43+HiYlpD39N7/3cNidrhri73z0ESznm62v+z0dEREREcreU759vNXr0aFatWsWBAwd45513APjtt9/47bffWL16Nfv377dqb2dnR/369S1zljVu3JgCD8IQDbkvHBzMP0/4+mbuvORkc8eSzC6kEB5u7iyQkGD+GfzChdTX9vDIP+FZZqjnmeQ6ERGpV778r7ezFUdH8wT+twZlVarknh5NWREbG8v27dstE/s3aNCA4cOHAxAREYG3tzeGYXDhwgXLRP5nz56lUKFCuOTwwHbDMP/DnNGg7fLlzK9u4+iYdjgVG5s6IPtv3tcsXz+9XmJ+fg/GEFURERGRB11ycjLjx49nzpw5bNiwgbJlywLQvn17fvvtN0u7WrVqWSb5b9KkiWVBLpG8xjDMI3PuFLK1amXe8gMN20yDwrPcKSYGdu60Dsr++Sd1O5MJKlc2B2QNGtxc+TK/TIB45swZNm3axMaNG9m0aRM7duwg4ZalXJo0acK6dessr3/++WeqV69O6dKl89zqO0lJ5oAro2FbWkNx78ZkMv92JqNDJT088lfvRBERERHJPrdOlwIwb948/vrrL1q0aEGzZs3wzWy3IBHJFRSepUHhme0lJMC+fdZB2b595jDldmXLWvcoq1sX/lucJs+Lj49n586dll5lGzdu5MyZM6naFS5cmICAABo3bkyTJk1SdSF/UNxpTjJX17QDskKFzBPvi4iIiIiIiKRFc549IJYuXUr//v1xcXGxbK6urlav09rGjx9v6a30xx9/cO7cORo3bkyFChUA87DAf/75J93zHRwc7trbKTnZ3IPs9pUvY2NTty1SJPXKl35+2f20bOfff//F3d3dsjT1hx9+yLBhw6za2NvbU6NGDUtYFhAQgL+/f57rVXY/uLpC6dLmTURERERERCSnKTzLwyIjI7l48WKmznF2dmbChAmW1zNmzGD58uV8+umnlvBs8+bNtLvDDIB2dnapwjp7exfefHMT+/YVZOtW2LRpJrGxocD/gMf/O/Mczs5zKFHChTJlXChf3pWKFV0oVswFV1fz9ZydXThyxJUzZ26GdSVLlkxzZZvc6PYu3U8++STLli1j4cKFPPvsswA0atSIQoUKWSYkbdy4MfXr18fNzc1WZYuIiIiIiIhIOhSe5WGPP/44e/bsITY2Nt0tJibG6vXto3Tr1q1LQkICZcqUsexzcHCgTJkyVufFxcVZjicnJ3Pjxg1u3Lhhda0ePW4NuP4CllCmTABPPGHuUebkdJqnnx7HsWNw7BisWZOx93ns2DH8/f0BGD58OB988AFDhgzh3XffBeDcuXO0b98+3V53ae13dXXlmWeesUy2f+zYMY4ePUqpUqWoXLmy5X2eO3cuVa+7W126dMlqrrJdu3Zx7tw5SxBWtmxZ7OzsOHHihOWcxo0bc+nSJfUqExEREREREckDFJ7lYV5eXnh5eWXpGqNHj061r2XLlpawJyICtm+HLVuS+euvOLZti+XMmVjAerOzi6VGDWfLZP6G0YvY2EY0adKYWrXM1z12zI9BgwbdNeC7fb+rq6ultpiYGOLi4qyCp+joaHbv3p3p996kSRNLeLZ48WLeeOMN+vbty2effQZAVFQUpUqVsjrH3t7eEqTZ29un2fNv69atBAcHAzBy5EjGjh1rNX7azs4u07WKiIiIiIiIiG0oPBOLmBjzvGS3zlP2998pR+0AV8AVkwkqVbKep6xmTfPcVDe1+G+7qVy5csyYMSNLNY4bN45hw4ZZDXEsXrw4v//++x3DuLT2FSpUyHKNQoUKUaNGDaseeHFxcTg5OREfH2/Zl5SUxPXr17l+/bplX9WqVS3zlAUEBFCpUiXLsZRwTkRERERERETyJq22+YBKSID9+1OvfJmYmLptmTKpV758kB5hUlIScXFxqcK3uLg4/P39s9z7T0RERERERERyllbbFCvJyXD4sHVQtnNn2itfFi6ceuXLB73zlL29PQUKFKBAgQK2LkVEREREREREcpjCs3zGMOD0aeugbPt289xlt/PwMIdjt4ZlpUqB5rEXERERERERETFTeJbHXbpkHZRt3QppzGGPiwvUrm0dlFWoAJq7XkREREREREQkfQrP8qi5c2HMGDh5MvUxe3uoVs0ckKWsfvnww+DomNNVioiIiIiIiIjkbQrP8ignp5vBWcWK1j3KatUCTc8lIiIiIiIiIpJ1Cs/yqNatYdUq88qXWuxRREREREREROT+UHiWR/n5QcuWtq5CRERERERERCR/03TxIiIiIiIiIiIi6VB4JiIiIiIiIiIiko48EZ5FR0fz8ssvU7x4cVxcXKhVqxaLFy+2dVkiIiIiIiIiIpLP5Yk5zzp27MjWrVuZMGECFStW5Ouvv6Zr164kJyfTrVs3W5cnIiIiIiIiIiL5lMkwDMPWRdzJr7/+SocOHSyBWYo2bdqwf/9+Tp06hb29/V2vExkZiaenJxEREXh4eNzPkkVEREREREREJBfLTE6U64dtLl26FHd3d5566imr/b179+bcuXNs2bLFRpWJiIiIiIiIiEh+l+vDs3379lGlShUcHKxHmNaoUcNyXERERERERERE5H7I9XOehYeHU65cuVT7fXx8LMfTEhcXR1xcnOV1REQEYO6WJyIiIiIiIiIiD66UfCgjs5nl+vAMwGQyZfrY+PHjGTt2bKr9pUqVyra6REREREREREQk74qKisLT0/OObXJ9eFaoUKE0e5dduXIFuNkD7XYjR47k1VdftbxOTk7mypUrFCpU6I5hXF4SGRlJqVKlOH36tBZBsAE9f9vS87ctPX/b0vO3LT1/29Lzty09f9vS87ctPX/b0vO3rfz4/A3DICoqiuLFi9+1ba4Pz6pXr86iRYtITEy0mvds7969AFSrVi3N85ydnXF2drba5+Xldd/qtCUPD49888mbF+n525aev23p+duWnr9t6fnblp6/ben525aev23p+duWnr9t5bfnf7ceZyly/YIBTz75JNHR0SxZssRq/7x58yhevDgNGza0UWUiIiIiIiIiIpLf5fqeZ4888gitW7cmJCSEyMhIypcvz6JFi1ixYgULFy7E3t7e1iWKiIiIiIiIiEg+levDM4AffviBN954g1GjRnHlyhUqV67MokWL6NKli61LsylnZ2dGjx6daniq5Aw9f9vS87ctPX/b0vO3LT1/29Lzty09f9vS87ctPX/b0vO3rQf9+ZuMjKzJKSIiIiIiIiIi8gDK9XOeiYiIiIiIiIiI2IrCMxERERERERERkXQoPBMREREREREREUmHwrNcZu7cuZhMJrZt22brUh44Kc8+rW3o0KEZvk6vXr1wd3e/j5XmP7c++9DQ0FTHDcOgfPnymEwmgoODc7y+B82HH36IyWSiWrVqti4lX9Pnfe6i/39zj6z8XZhMJsaMGZP9ReVz+nffdrZs2cKTTz5J6dKlcXZ2pkiRIgQEBDBkyBBbl/bA2bx5M0899RTFihXDycmJokWL0rlzZzZt2pTpax04cIAxY8Zw4sSJ7C80n0j5t97FxYWTJ0+mOh4cHKx/k+6j23/2dXFxoWjRojRv3pzx48dz8eJFW5eY6yg8E7nNl19+yaZNm6y2F1980dZlPRAKFizI559/nmr/2rVrOXr0KAULFrRBVQ+eL774AoD9+/ezZcsWG1eT/+nzXkRsTf/u28Yvv/xC48aNiYyMZNKkSaxcuZIPPviAwMBAvvnmG1uX90CZMWMGgYGBnDlzhkmTJrFq1SqmTJnC2bNnCQoKYubMmZm63oEDBxg7dqzCswyIi4vjzTfftHUZD6yUn33/+OMPZs2aRa1atZg4cSJVqlRh1apVti4vV1F4JnKbatWq0ahRI6utdOnSti7rgfDMM8+wZMkSIiMjrfZ//vnnBAQEZOvfQ0xMTLZdKz/Ztm0bu3fvpkOHDgBphjpZcePGjWy9Xn6Qk5/3IiK3u9//7kv6Jk2ahL+/P7///jtdunShWbNmdOnShSlTpnDq1Clbl/fACAsL4+WXX6Z9+/asX7+e//3vfzRt2pTu3buzfv162rdvz0svvURYWJitS82X2rVrx9dff83u3bttXcoDKeVn3yZNmtCpUyfef/999uzZg5ubGx07duTChQu2LjHXUHiWy23bto0uXbpQtmxZXF1dKVu2LF27dk3VtTWl2+WaNWsICQnB19eXQoUK0bFjR86dO2ej6vOfb775hoCAANzc3HB3d6dt27bs3Lkzzbb79++nZcuWuLm54efnx6BBgxQc3EXXrl0BWLRokWVfREQES5YsoU+fPqnajx07loYNG+Lj44OHhwd16tTh888/xzAMq3Zly5bl0Ucf5YcffqB27dq4uLgwduzY+/tm8qiUH5omTJhA48aNWbx4sdXn7YkTJzCZTEyaNIl3332X0qVL4+LiQr169fjzzz+trjVmzBhMJhM7duygc+fOeHt789BDD+Xo+8kL7sfnfd//b+9Og6I42jiA/xd2F5RDBZRDZcEDVNQShRgFg3hEERELEQEPkNdIiQcJHohaUviqCF4k5QEmgveFQeNRQCURrxgEtbxKxZiIiAIKihyFCOvzfvDdTcZdFI0rh8+vaj7MbM9U90xvT29vH//5D4yMjNSWOUOGDIGdnZ0GUtK8DB48WO1w2cDAQFhZWSn3Fd+JNWvWYN26dbC2toa+vj4GDBiAzMzMjxfhZqy+z4K9n7eV+ydPnlQ7vFyR97dt2yY4/v3338PGxgY6Ojro0aMH9uzZw8+qDiUlJTAxMYFYLFb5TEtL+DOtPnVQxdQhXAd9N9HR0RCJRNi8ebPKsxCLxdi0aRNEIhFWrVqlPH7r1i34+fnB1NQUOjo6sLS0xJQpU1BdXY1t27Zh/PjxAABXV1flsLjXvyvslQULFsDY2Bjh4eFvDPf8+XNERETA2toaUqkU7du3x8yZM1FaWqoMM3bsWMhkMrx8+VLl/P79+6Nv374fOvrNkqWlJdauXYvy8nIkJCQoj1+4cAFjxoyBkZERdHV1YW9vjwMHDqic/+DBA0yfPh0dO3aEVCqFhYUFvL29m3xDHDeeNXK5ubmwtbVFXFwc0tPTERMTg4KCAjg6OqK4uFgl/LRp0yCRSLBnzx7Exsbi5MmTmDRpUgPEvOmSy+Wora0VbACwcuVK+Pn5oUePHjhw4AB27tyJ8vJyDBo0CDdu3BBco6amBqNGjcLQoUNx+PBhzJo1CwkJCZgwYUJDJKnJMDQ0hLe3t3L4CPCqQUFLS0vtvcvNzUVwcDAOHDiAlJQUeHl5Yfbs2fjvf/+rEvbSpUuYP38+5syZg7S0NIwbN06jaWmKqqqqsHfvXjg6OqJnz54ICgpCeXk5kpOTVcJu2LABaWlpiIuLw65du6ClpQU3Nze184J4eXmhS5cuSE5ORnx8/MdISpOiiXwfGhqKp0+fYs+ePYJzb9y4gYyMDMycOVNzCfpEbdy4ET///DPi4uKwe/duVFZWYtSoUXj27FlDR42xOr1LuV8fW7ZswfTp09G7d2+kpKRgyZIliIqKUjuvIwMGDBiA8+fPY86cOTh//jxqamrUhuM6qObI5XJkZGTAwcEBHTp0UBumY8eO6NevH06cOAG5XI4rV67A0dERmZmZWLZsGVJTUxEdHY3q6mq8ePEC7u7uWLlyJYBX7wbFNDCK3p1MyMDAAEuWLEF6ejpOnDihNgwRYezYsVizZg0mT56M48ePIywsDNu3b8eQIUNQXV0NAAgKCkJeXp7KdW7duoWsrCxMnTpV4+lpLkaNGgVtbW2cPn0aAJCRkQEnJyeUlpYiPj4eP/30E/r06YMJEyYIGoYfPHgAR0dHHDp0CGFhYUhNTUVcXBxatWqFp0+fNlBqPhBijUpSUhIBoOzsbLWf19bWUkVFBenp6dG3336rcl5ISIggfGxsLAGggoICjca7OVDcQ3VbXl4eicVimj17tuCc8vJyMjMzIx8fH+WxgIAAAiB4PkREK1asIAB09uzZj5KepuSf+T4jI4MA0PXr14mIyNHRkQIDA4mIyM7OjlxcXNReQy6XU01NDS1btoyMjY3p5cuXys9kMhlpa2tTTk6OxtPSlO3YsYMAUHx8PBG9yt/6+vo0aNAgZZi7d+8SALKwsKCqqirl8bKyMjIyMqJhw4Ypj0VGRhIAWrp06cdLRBOi6Xzv4uJCffr0EYSfMWMGGRoaUnl5uWYS1YS9/v51cXFRe98DAgJIJpMp9xXfiV69elFtba3yeFZWFgGgvXv3ajrqzc77PgsiIgAUGRmp+Ug2E/Up9xXlU0ZGhuBcRd5PSkoiolflkZmZGfXv318Q7t69eySRSFSeFSMqLi4mZ2dnZX1TIpHQwIEDKTo6WllOcx1UswoLCwkA+fr6vjHchAkTCAAVFRXRkCFDqHXr1vTo0aM6wycnJ6v93rC//bOsr66upk6dOpGDg4OyLuPi4kJ2dnZERJSWlkYAKDY2VnCN/fv3EwDasmULERHV1NSQqakp+fv7C8ItWLCApFIpFRcXf4SUNQ1va3cgIjI1NaXu3bsTEVG3bt3I3t6eampqBGFGjx5N5ubmJJfLiYgoKCiIJBIJ3bhxQ3ORbyDc86yRq6ioQHh4OLp06QKxWAyxWAx9fX1UVlbi5s2bKuHHjBkj2O/duzcAqF3BhKm3Y8cOZGdnC7b09HTU1tZiypQpgh5purq6cHFxUfuP6sSJEwX7/v7+AF612rO6ubi4oHPnzkhMTMS1a9eQnZ2tdugaAJw4cQLDhg1Dq1atoK2tDYlEgqVLl6KkpERlhZjevXvDxsbmYyShydq6dStatGgBX19fAIC+vj7Gjx+PM2fO4I8//hCE9fLygq6urnLfwMAAHh4eOH36NORyuSAs9/J7O03k+9DQUFy+fFk5R0tZWRl27tyJgIAAXhFYA9zd3aGtra3c5/cvawrepdx/m5ycHBQWFsLHx0dw3NLSEk5OTh8szs2JsbExzpw5g+zsbKxatQqenp64ffs2IiIi0KtXLxQXF3MdtJGg/0+NUFVVhVOnTsHHxwdt27Zt4Fg1H1KpFMuXL8eFCxfUDgNU9CQLDAwUHB8/fjz09PSUU4eIxWJMmjQJKSkpyp7fcrkcO3fuhKenJ4yNjTWbkGZGke/v3LmDW7duKcuWf5ZFo0aNQkFBAXJycgAAqampcHV1Rffu3Rss3prCjWeNnL+/PzZs2IBp06YhPT0dWVlZyM7ORtu2bdVOeP56gaCjowOAJ0d/F927d4eDg4NgU4zPdnR0hEQiEWz79+9XGUIrFotVnoWZmRmAV/NbsLqJRCJMnToVu3btQnx8PGxsbDBo0CCVcFlZWfjyyy8BvJpf5bfffkN2djYWL14MQDXPm5ubaz7yTdidO3dw+vRpuLu7g4hQWlqK0tJSeHt7A4BgSCHwd35+/diLFy9QUVEhOM73/u00ke89PT1hZWWFjRs3Ang1N2ZlZSUP2dQQfv+ypuZdy/23UdRvTE1NVT5Td4z9zcHBAeHh4UhOTsbDhw/xzTffIDc3F7GxsVwH1TATExO0bNkSd+/efWO43NxctGzZEmKxGHK5vM4hnuz9+fr6om/fvli8eLHKEOaSkhKIxWKVBkuRSAQzMzNB3g4KCsLz58+xb98+AEB6ejoKCgp4yOY7qqysRElJCSwsLJTl0Lx581TKoZCQEABQlkWPHz9utt8P1dkpWaPx7NkzHDt2DJGRkVi4cKHyeHV1NZ48edKAMfv0mJiYAAAOHjwImUz21vC1tbUoKSkRVF4KCwsBqP7AYqoCAwOxdOlSxMfHY8WKFWrD7Nu3DxKJBMeOHRP0gDp8+LDa8CKRSBNRbTYSExNBRDh48CAOHjyo8vn27duxfPly5b4iP/9TYWEhpFKpSq8mvvf186HzvZaWFmbOnIlFixZh7dq12LRpE4YOHQpbW1tNJaFZ0dXVVTtfmbr5Rplm8bPQjPqW+4qyRjGnkMLr919Rv1E3IbS6dwZTTyKRIDIyEuvXr8f169fh6ekJgOugmqKtrQ1XV1ekpaUhPz9f7Y/+/Px8XLx4EW5ubjAyMoK2tjby8/MbILbNm0gkQkxMDIYPH44tW7YIPjM2NkZtbS0eP34saEAjIhQWFsLR0VF5rEePHvjss8+QlJSE4OBgJCUlwcLCQvnnI6uf48ePQy6XY/DgwcrfwhEREfDy8lIbXlG/bNu2bbP9fnDPs0ZMJBKBiJT/Xiv88MMPKsOimGaNGDECYrEYf/75p0qvNMX2ut27dwv2FRN3q1sxjAm1b98e8+fPh4eHBwICAtSGEYlEEIvFgmFSVVVV2Llz58eKZrMhl8uxfft2dO7cGRkZGSrb3LlzUVBQgNTUVOU5KSkpeP78uXK/vLwcR48exaBBgwTPhNWfJvL9tGnTIJVKMXHiROTk5GDWrFkaiXtzZGVlhdu3bwsaDEpKSnDu3LkGjNWniZ/Fh/cu5b5ilcyrV68KrnHkyBHBvq2tLczMzFSGXOXl5fGzqkNBQYHa44qpWSwsLLgO+hFERESAiBASEqLyG0sul2PGjBkgIkRERKBFixZwcXFBcnLyGxvwuffx+xk2bBiGDx+OZcuWCUYyDB06FACwa9cuQfgff/wRlZWVys8Vpk6divPnz+Ps2bM4evQoAgICuH76DvLy8jBv3jy0atUKwcHBsLW1RdeuXXHlypU6yyEDAwMAgJubGzIyMpTDOJsT7nnWSIlEIhgaGuKLL77A6tWrYWJiAisrK5w6dQpbt25F69atGzqKnxQrKyssW7YMixcvxl9//YWRI0eiTZs2KCoqQlZWFvT09BAVFaUML5VKsXbtWlRUVMDR0RHnzp3D8uXL4ebmBmdn5wZMSdPxz+XA1XF3d8e6devg7++P6dOno6SkBGvWrFFpbGZvl5qaiocPHyImJkZtxbpnz57YsGEDtm7divXr1wN49U/t8OHDERYWhpcvXyImJgZlZWWC7wF7dx8637du3RpTpkzB5s2bIZPJ4OHhoYloNyuKnpKTJ09GQkICJk2ahK+++golJSWIjY2FoaFhA8fw08HPQnPepdwfPXo0hg0bhujoaLRp0wYymQy//vorUlJSBOdoaWkhKioKwcHB8Pb2RlBQEEpLSxEVFQVzc3NoafF/9q8bMWIEOnToAA8PD3Tr1g0vX77E5cuXsXbtWujr6yM0NJTroB+Bk5MT4uLi8PXXX8PZ2RmzZs2CpaUl8vLysHHjRpw/fx5xcXEYOHAgAGDdunVwdnZG//79sXDhQnTp0gVFRUU4cuQIEhISYGBggJ49ewJ4tQKtgYEBdHV1YW1tzb3/6iEmJgb9+vXDo0ePYGdnBwAYPnw4RowYgfDwcJSVlcHJyQlXr15FZGQk7O3tMXnyZME1/Pz8EBYWBj8/P1RXV6vMlcb+dv36deX8ZY8ePcKZM2eQlJQEbW1tHDp0SNnTLyEhAW5ubhgxYgQCAwPRvn17PHnyBDdv3sSlS5eUqzQrVqD94osvsGjRIvTq1QulpaVIS0tDWFgYunXr1pDJ/XcaaqUCpt7GjRsJAF27do2IiPLz82ncuHHUpk0bMjAwoJEjR9L169dJJpNRQECA8ry6Vsuoa4Ukpqo+K44cPnyYXF1dydDQkHR0dEgmk5G3tzf98ssvyjABAQGkp6dHV69epcGDB1OLFi3IyMiIZsyYQRUVFR8jKU1Ofe49keqqg4mJiWRra0s6OjrUqVMnio6Opq1btxIAunv3rjKcTCYjd3d3DcW+6Rs7dixJpdI3rhrl6+tLYrGYMjMzCQDFxMRQVFQUdejQgaRSKdnb21N6errgHMVqm48fP9Z0EpokTed7hZMnTxIAWrVq1QdOQfPy+vuXiGj79u3UvXt30tXVpR49etD+/fvrXG1z9erVKtcEr/z4Xt73WRDxPa+vdyn3CwsLqaCggLy9vcnIyIhatWpFkyZNogsXLghW21TYsmULdenShaRSKdnY2FBiYiJ5enqSvb29hlPV9Ozfv5/8/f2pa9eupK+vTxKJhCwtLWny5MkqK9VxHVTzfv/9d/L29iZTU1MSi8XUrl078vLyonPnzqmEvXHjBo0fP56MjY1JKpWSpaUlBQYG0vPnz5Vh4uLiyNramrS1tdV+Vz51b6oH+fv7EwDlaptERFVVVRQeHk4ymYwkEgmZm5vTjBkz6OnTp2qvr7iGk5OTppLQpCnuv2KTSqXUrl07cnFxoZUrV6p9P1y5coV8fHyoXbt2JJFIyMzMjIYMGaJcsVnh/v37FBQURGZmZiSRSMjCwoJ8fHyoqKjoYyVPI0RE/19CgTUKoaGh2LBhA0pLS5VdHxljrLHIzc2FtbU1Vq9ejXnz5jV0dFg9zJ07F5s3b8b9+/f5H+834Pdv48HPonkpLS2FjY0Nxo4dqzKPEfuwAgMDcfDgQZWFexhjjP17PGyzkbh48SKys7ORmJiIMWPGcGWRMcbYv5KZmYnbt29j06ZNCA4O5oazOvD7t/HgZ9H0FRYWYsWKFXB1dYWxsTHu3buH9evXo7y8HKGhoQ0dPcYYY+y9ceNZI+Ht7Y1nz55hzJgx+O677xo6Oowxxpq4AQMGoGXLlhg9erRgpVQmxO/fxoOfRdOno6OD3NxchISE4MmTJ2jZsiU+//xzxMfHK+cuYowxxpoiHrbJGGOMMcYYY4wxxlgdeNkbxhhjjDHGGGOMMcbqwI1njDHGGGOMMcYYY4zVgRvPGGOMMcYYY4wxxhirAzeeMcYYY4wxxhhjjDFWB248Y4wxxhhjjDHGGGOsDtx4xhhjjDHGGGOMMcZYHbjxjDHGGGOMMcYYY4yxOnDjGWOMMcYYY4wxxhhjdeDGM8YYY4wxxhhjjDHG6vA/K6Yv5QGtUQoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_halocline_strength_orig_SSslicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_halocline_strength_SSslicemean[1,:],color='k',linestyle='-.',label='CY with WY winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY Halocline with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,5)\n",
    "ax.set_ylabel('g/kg m$^{-1}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Depth-averaged Nutrients (0-10m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for original cold and warm years\n",
    "\n",
    "\n",
    "monthly_array_nitrate_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "          \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/3312634990.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_nitrate_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slice[monthly_array_nitrate_orig_slice == 0 ] = np.nan\n",
    "monthly_array_nitrate_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_nitrate_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_nitrate_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "### Silicon data for original cold and warm years\n",
    "\n",
    "monthly_array_silicon_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['silicon']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "   \n",
    " ### \n",
    "## Experimental Year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "       \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/241793216.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slice[monthly_array_silicon_orig_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for Experiments 1 and 2\n",
    "\n",
    "monthly_array_nitrate_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)                \n",
    "\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "     \n",
    "        # # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/231329215.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slice[monthly_array_nitrate_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_nitrate_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_nitrate_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "### Silicon data for Experiments 1 and 2\n",
    "\n",
    "\n",
    "monthly_array_silicon_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['silicon']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    " # Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "         \n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/3737416097.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slice[monthly_array_silicon_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-3}$')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_nitrate_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY Nitrate with CY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-3}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.64834195, 22.38691698, 16.99383033,  7.63730902,  4.96273598,\n",
       "        1.56547187,  1.23107567,  1.5230891 ,  7.95614177, 16.79074658,\n",
       "       19.52305174, 21.62759183])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.80685063, 22.63941779, 18.68073904,  6.54336744,  5.49402545,\n",
       "        1.67369885,  2.81440205,  3.81694601, 11.47654414, 19.18559774,\n",
       "       21.74671523, 22.24619177])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol Si m$^{-3}$')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_silicon_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY Silicon with CY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol Si m$^{-3}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.06238141, 49.17238747, 41.59249494, 18.0018168 ,  5.46358173,\n",
       "       12.1107789 , 19.87997362, 28.03714477, 36.00806573, 41.96864256,\n",
       "       44.54149611, 47.0336623 ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([47.79922893, 49.19336588, 44.03685091, 16.95528149,  8.04410933,\n",
       "       11.85676838, 23.22130531, 29.99828923, 38.12645109, 42.5020271 ,\n",
       "       45.51880128, 47.52354357])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-3}$')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_nitrate_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY Nitrate with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-3}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.13891096, 23.43445999, 20.16081944,  9.53873272,  5.7397893 ,\n",
       "        2.06754755,  0.95348431,  2.25127683,  8.106326  , 16.05059715,\n",
       "       20.61628598, 23.32419151])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol Si m$^{-3}$')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_silicon_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY Silicon with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol Si m$^{-3}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.06105751, 51.39125156, 49.65665279, 23.79865366,  8.11240424,\n",
       "       10.9729251 , 13.07850414, 11.59949357, 21.74708727, 35.04296807,\n",
       "       42.83359454, 47.61766334])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Depth-integrated 0-100 m Diatoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Diatom data for original years\n",
    "\n",
    "monthly_array_diatoms_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 using higher temperature threshold       \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "                \n",
    "### 2019 using higher temperature threshold                \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/3403781678.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_orig_slice[monthly_array_diatoms_orig_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#years, months, data\n",
    "monthly_array_diatoms_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_W19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n",
    "        \n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3222745/2320522072.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_depthint_slice[monthly_array_diatoms_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mrFixwjpc9/jx49Y/UKRJk8a4d+9eHO7YMHr37m19Xv39/eN07tOuX79uXWzg9ddfN44fP264uroagDF8+PB499uhQwcDMAoVKhTpWLly5QzA2Lt3r2EYhlGrVi0DMLZu3WrT7qeffor2fTA2r1G6dOkMJycn48svv7Q+T7du3bImVKO6pmEYxuTJk63H33nnHePmzZuGYZhJ/eHDhxsWi8X6vvv098GPP/5oTbx+//331u/n8PBw4/r168ayZcuMVq1axeo5FBGR5KHEmoiIJLuID8GA8euvv9oci5hfLSJZMm/ePAOItHLb559/bv3wEdPcQL/99pt1xcPs2bMbgJEjR444fxB9UlwTa4ZhGPnz5zcAo1KlSjb7Y0qsPcuBAwcMwHB3d4+UIIptYq1Pnz7WD5BXr16NdPzSpUvWxMLTK/Q9eY0iRYpE+Tp07tzZ2qZOnTpRrk5ZpUoVAzDeeOMNm/1jx441wFx5MzENGTLEAIyiRYtG2yZi1UMgxsTDpEmTrO3imtCJTmwTa++//771tYvJe++9Z4C5Qu2TWrZsaQDGq6++GuP5ERVJpUuXjlX8EZ783n7Waw8YPXv2jHQ8NDTUyJUrlwEYI0eOjPZaN2/etPazbdu2OMUZ8Ro+nXj84YcfrN+X2bJlMywWi3Hr1i3r8YsXL1qvGZEsjpCYiTVnZ2fj2LFjkY7fuHHDWv32008/xf6Gjf/m1sufP3+czovOsmXLrM9FxPts6dKlo5xDMLZmzJhh7fPatWvW/QEBAYaDg4Ph7u5uTTR+9tlnBmAMGTLEpo+IBFiuXLki9R+b1yimn8PSpUtH+X0bFBRkeHt7R/l7K8KgQYOs/T/9ffDWW28Z8F/VtoiIpH6aY01ERJJdpUqVrPM5+fn5Wff/888/XL9+nfz585M1a1YAqlWrFqndk9sVK1aMcW6gsmXLWueJunz5snV1Ok9Pz8S7oVjw9vYGiDTHWkKUKVOGjBkz8uDBA/744484n28YhnW1xz59+lhXYX1S9uzZratmLliwINq++vfvH+XrUK9ePevXgwYNinIuuYg2f/75p83+iDnDbt68Ge0KsvERsVJshgwZom1z//5969dPzt/3tCePPXlOcoi4XkzxPXn86fgSen5cDBw4MMbXHv6bz+1J9vb21jm0nv7+eJK3tzd2duZ/a6NbCTg6EXNv/fXXX9y8edO6P+I9pmbNmtSoUQPDMGzeh7Zt2waYcxj6+vrG6Zpx0bp1awoWLBhpf4YMGahYsSIQ83MTldu3bwP/vS8lVIsWLejcuTNgvs+6uLgwb968BM0h+PT8dBEi5ld77bXXrL9HnvV74un51WIrR44cdOnSJcpjTZs2BSI/95s2bbK+z0eszPu0QYMG4eLiEuWxiPe9a9euxSdkERFJAUqsiYhIsnNzc6NcuXKA7QemiK8jPiQB5MyZk1y5cnH58mXOnDkDmBNv79u3DzA/9D7LkCFDrEmUZs2axeqcxGY8Y7Lq6ISEhDBt2jTq1q1L1qxZcXFxsZns/MaNG4D5YTauzp07Z/0AGJG8iEqdOnUA88P4uXPnomwT8Xo+LVOmTNavy5YtG2Obu3fv2uyvXbs2Li4u/P7771SpUoVZs2ZFe/24iEieJFZSQZ7tWd8f3t7e5MmTJ8Y2T39/PMnOzs6aLH8yORYbxYoVI3369JESZ08n1uC/ZNqTX8c3aRNb5cuXj/ZYxB8g4pqwj3g/iirZGV9Tpkyxft2nTx8KFy6coP7y589P9uzZgWf/nihfvjwuLi789ttvBAUFAXDlyhXr74z4vueXLVvWmrB9WnTP/cGDBwEzKZcvX74oz/X09KR06dJRHmvYsCEWi4VVq1bRoEEDfvnllzgni0VEJHkpsSYiIiki4sPo7t27rasCRnxgenqVtogPUBHHf/vtNx4+fGjTT0wcHR2tVTfJXakWISIp4OPjE+tzbty4QZkyZXjrrbfYvHkzV69exWKxkD59ejJlykSmTJmsH/qeXHkzLv1HyJYtW7TtIj7cPn3Ok9KmTRvl/icrVp7V5umVQfPkycP3339PmjRp2LdvHz179iRPnjxkzJiRdu3asXLlynglLCNWEY2p0vHJWCO+16Ly5LEnz8mcOXOUj379+sU53mfFGFN8Tx5/+vlP6Plx8azXPqa+o/v+eJqrqytAnFeJfXJlyIhk2dmzZ7lw4QKFChUic+bM1sTMk4m1hFZDxVZiPDdPS58+PfBf5VpiePK9NbHeZyNel6gSa0/+nnB2dqZ8+fKEhISwd+9ewLZ6LaqVP2MjPs99xHtkTO+pYPu++qTKlSszduxYnJyc2LBhAx07diRbtmzkyJGD7t27R6rKExGRlKfEmoiIpIiID6OBgYHWv/Dv2LEDsK1EeHI74gNFxL9PVr6lZoGBgZw9exaAvHnzxvq8/v37c/ToUXx8fJg9ezZXr14lKCiImzdvcu3aNa5du2atmohvRVyE2FauJGaFS2y8/vrrXLhwgWnTptGuXTty5MjBzZs3WbRoEc2bN6datWoEBATEqc+I5GZMFVARzyuYlS/RiTjm4eFBmjRprPuvX78e5cPf3z9OscYkIsa7d+/GmByLiPHJe3pyO6b7i+n81CaiciguyesIT1ekRfwbkVDLlSsXuXPn5sSJE/z777+cPn2aS5cu2Zz7PClSpAgAZ86cifPPT3KKeG6PHz/OtWvXuH//PocPH8bV1TVSBWx0vyeerHxLTgl5r/zwww85d+4cX331Fc2bNydjxoxcvnyZuXPnUrNmTdq0aRPnZKqIiCQdJdZERCRFvPbaa9aKoe3bt/PPP/9w48YN8ubNG+lD0NMVaxH/Vq5c2TrHTmq2YcMG6xxhsa2cePz4McuWLQPgm2++oXv37pHmQAsLC+PWrVvxjitjxozWryOSBFF5cphpTPOSJRVvb2969+7NggULuHjxIqdPn7bO17Zr1y6GDx8ep/4i7iGm4XNFixa1fv3XX39F2y7i2NPD3gxzgahIj7lz58Yp1pg8GePff//9zBgjkilPn3/jxo1oh0+GhYVx/PjxKM9PTYKDg62VavH5Ho1IoJ06dYrLly9HSqyBbfIt4vgrr7zyzMqk1KhWrVoAhIeHs2rVqhSOJnpPJi23b9/Orl27CAsL47XXXsPJycmmbXS/J5I78Rnxvvqs4fnPSmhnzZqV9957j+XLl3P9+nX+/PNPevbsCcCSJUv47rvvEidgERFJMCXWREQkRbi4uFChQgXArCyIat6cCHny5CF79uxcuXKFv//+2zq/2vNQKRISEsKoUaMAc3hU8+bNY3XezZs3rYmCV199Nco2u3fvjnbY25PzAkVXzZY7d27rPGNbt26NNpYtW7YAZiVQ7ty5nx18EsubNy+jR4+mY8eOAGzevDlO50ckwSKqCKNSoEABcubMCZiJ0ag8ePCAXbt2AVC3bt04xZAYKleubB3+GF2MFy5c4NixY0DkGCPmzovp/D179lgXLUiJe4ytJ1/LQoUKxfn8ggULkiVLFsBMnG3fvh07OzubRPiTw0ETMgw04mczoVWmCdGsWTPrHzBGjx4d6+Gz4eHhSRlWJE8uDPGs3xMVK1bEycmJ3377jePHj1u/J5L790SZMmUA848VEXO8PS0gIIBDhw7Fqd9ixYoxc+ZMKlWqBMT9fU9ERJKOEmsiIpJiIj7w7Nmzx/ohIbqKrogPUmPGjLFOTp3aE2tBQUF069aN33//HTBXPYxY8e1ZPDw8rEOJjhw5Eul4aGgon376aYznR7h3716UbSwWC+3atQNg+vTpUa5C9++//zJ9+nQAOnToEKvYE8ujR49iPB6RVLK3t49Tv1WrVgXM5OX58+ejbRexGuCCBQuibDd16lQCAwOxt7fn9ddfj1MMicHd3Z1WrVoB8N1330U5zHTs2LGAOVfU00ndPHnyULlyZQAmTpwY5dCyMWPGAODr62t93lKjX3/9FTAXOihQoEC8+oh4P/n222+5evUqJUqUsFng4smKtYRUQ0X8bEb3c5kcnJycGDduHGCuxty1a1dCQkJiPOeff/6JdoXMpBTxHG/fvj3aeTgB6/DQx48f88UXX1j3x3d+tfiqU6cO6dKlA2DkyJFRthk3bpz199jTkup9T0REko4SayIikmIiPjA9ePCA1atXA1FXIjy5/5dffgHMREF0q6qlpPDwcP766y++/PJLihQpYo23c+fOfPTRR7HuJ02aNNbKhPfff59t27ZZq0X++usvGjZsyMGDB3F3d4/yfC8vL+sQtTlz5lgXiHjaJ598gpeXF3fu3KF27drWib/BTHjWrl2be/fu4e3tzaBBg2Idf2J45513aNu2LUuXLrVZNCEwMJBp06Yxb948wFxFLy4KFSpkHa4VkZCJygcffEDmzJl5+PAhjRo1slaYhISE8N133zFkyBAAevXqxSuvvBKnGJ5069Ytm0eEwMBAm/1RzaP22Wef4e7uztWrV2nSpAmnTp0CzJ+pzz77jGnTpgEwePBg64f9J40bNw57e3uOHDlC+/btrcPT7ty5Q9++fVm/fr1Nu9Qq4nWM7v0jNiLejyL6enolyaxZs1KgQAEuXLhgTULHJ2kTMQR3165d1mG2KaFDhw7W96RFixbx6quv8uOPP9oMCw4ODmbr1q306NGDEiVKxDgsOqlEvC4nT57k0KFDuLi4RDu35tO/JyIWn0hOrq6u1veGH374gffee8+6SERAQAAjR45k1KhR0f6RpXnz5vTo0YP169fbJF/v3LnD559/bq0ujuv7noiIJCFDREQkhTx69MhwdXU1AAMwcufOHW3bEydOWNsBRsOGDeN0LV9fXwMwunbtmsCoDWPYsGHWODJlymR9eHl5GXZ2djZxpk+f3pg2bVq0ffn5+VnbPu3gwYOGu7u79bizs7ORNm1aAzAcHByMefPmWe9rzpw5kc4fOXKkzbk5cuQwfH19jXbt2tm02759u+Hp6Wlt6+7ubnNdLy8vY+fOnZH6P3funLXNuXPn4nx/EebMmWMAhq+vr83+rl272jyXadKkMby8vGz2Va5c2QgMDIy27+j069fPAIyOHTvG2O7gwYOGj4+P9Xpp06Y1HB0drdt169Y1goOD43z9Jz15PzE9hg0bFuX5a9euNdzc3KztPD09DXt7e+t2t27djPDw8GivP3PmTMPBwcHm9bZYLM+87rMk5LV/UsTPW7Vq1aI8HhYWZmTPnt0AjBUrVsQrVsMwjDNnztg832vXro3Upk+fPtbjhQsXjlfMd+7cMTJkyGDzHuHr62v4+voa+/bts7aL6Wc7QsTPSELe16ZPnx7p58rNzS3KfePGjYuxr4R+z0Tl4sWLNnHUqFEj2rYbN260adu3b99o28b0GsXmeY3pezcsLMzo3LmzNQ47OzsjXbp01p/L9u3bR3uNatWq2dyDh4eH4eHhYbOvdevWRlhYWLSxiYhI8lLFmoiIpBgnJydee+0163ZM1SavvPKKTeVBahkGGrHa440bNwgNDSVz5sxUqFCBt956iyVLlnDlyhV69+4dr75Lly7Nb7/9Rtu2bUmfPj3h4eGkTZuWtm3bsnfvXjp37hzj+Z988gmTJ0+mTJkyODo6cvnyZZtqmwjVqlXj+PHjDBgwgEKFChEeHo5hGBQqVIgPPviAY8eOUaVKlXjdQ0IMGTKEr7/+mhYtWlCwYEEcHBwIDAwkY8aM1KlTh9mzZ7N9+/Zoq/ZiEvGarFy5kgcPHkTbrnTp0vz999/079+f/Pnz8/jxY9zd3alcuTIzZ85k/fr11kU4UkrDhg35888/efPNN8mVKxdBQUF4eXlRp04dlixZwpw5c2JcobBnz578+uuvdOzYkWzZsvHw4UMyZsxI8+bN2bp1a5wXh0huO3bs4PLly2TLlo3GjRvHu588efJY5/NycHCI8ns+qsUM4ipdunTs3LmT9u3bky1bNvz9/blw4QIXLlyI9VxnialXr16cP3+eSZMm0bBhQ3LkyIFhGAQFBZEtWzYaNGjA5MmTuXDhAh9++GGyx5cjRw6b1ZRj+j3x2muv4eDgYN1Oqd8TdnZ2zJs3j3nz5lGhQgVcXV0JDQ2lVKlSTJs2jZ9//jnac6dMmcLYsWNp2LAh+fPnt74WWbNmpWnTpixdupTFixfbzKMpIiIpy2IYKThzqoiIiEgKqVmzJn5+fvzwww8pMneUJI4ePXowZ84cRowYwdChQ1M6HBEREXnJKLEmIiIiL6V9+/bx2muvUaRIEY4ePRpjVZekTpcuXSJfvnx4enpy+vRpm0U7RERERJJDqqwh3r59OxaLJcrH/v37bdoePnyY2rVrkyZNGry8vGjZsqXNkusiIiIiUalYsSKtW7fm77//ZvHixSkdjsTDqFGjCAkJYfjw4UqqiYiISIpweHaTlDNq1KhIcyNErKQEcPz4capXr07JkiVZtGgRwcHBDB06lCpVqvDHH3+QIUOG5A5ZREREniPjx4+nSJEiPH78OKVDkTgKDw8nZ86cfP755/Tq1SulwxEREZGXVKocCrp9+3Zq1KjB4sWLad26dbTt2rZti5+fH2fOnLH+lfLChQvkz5+f/v37M3bs2OQKWUREREREREREXjKpcihobISGhrJmzRpatWplU/rv6+tLjRo1WL58eQpGJyIiIiIiIiIiL7pUnVh7++23cXBwwMPDg3r16rF7927rsTNnzhAUFETx4sUjnVe8eHFOnz6dIkuWi4iIiIiIiIjIyyFVzrHm6elJv379qF69Oj4+Ppw+fZrx48dTvXp11q5dS7169bh9+zYA3t7ekc739vbGMAzu3r1LlixZIh1/9OgRjx49sm6Hh4dz584dfHx8tCKYiIiIiIiIiMhLzjAM7t+/T9asWbGzi74uLVUm1l599VVeffVV63aVKlVo0aIFxYoV46OPPqJevXrWYzElwqI7Nnr0aEaMGJF4AYuIiIiIiIiIyAvn0qVLZM+ePdrjqTKxFhUvLy8aN27MtGnTCAoKwsfHB8BaufakO3fuYLFY8PLyirKvjz/+mPfff9+67e/vT86cObl06ZKWahcREREREREReckFBASQI0cO0qZNG2O75yaxBmYZHpiVaHnz5sXV1ZWjR49Ganf06FHy5cuHi4tLlP04Ozvj7Owcab+Hh4cSayIiIiIiIiIiAsQ8UhJS+eIFT7p79y5r1qyhZMmSuLi44ODgQJMmTVi2bBn379+3trt48SJ+fn60bNkyBaMVEREREREREZEXXaqsWOvYsSM5c+akTJkypE+fnlOnTjFx4kSuX7/O3Llzre1GjBhB2bJlady4MYMGDSI4OJihQ4eSPn16BgwYkHI3ICIiIiIiIiIiL7xUWbFWvHhxNm7cSM+ePalduzaffvophQsXZu/evdSuXdvarmDBgmzfvh1HR0dat25Nt27dyJcvHzt37iRDhgwpeAciIiIiIiIiIvKisxgRE5e9xAICAvD09MTf319zrImIiIiIiIiIvORimytKlRVrIiIiIiIiIiIiqZ0SayIiIiIiIiIiIvGgxJqIiIiIiIiIiEg8KLEmIiIiIiIiIiISD0qsiYiIiIiIiIiIxIMSayIiIiIiIiIiIvGgxJqIiIiIiIiIiEg8KLEmIiIiIiIiIiISD0qsiYiIiIiIiAgA+/fvp02bNmTJkgUnJycyZ85M69at2bdvX5z6GT58OBaLJV4xbN++HYvFwvbt2+N1fmxVr16d6tWrx9gmLCyML7/8kvr165M9e3bc3NwoVKgQgwYN4t69e1GeM2XKFAoWLIizszO5c+dmxIgRPH782KbN5cuXee+996hWrRpeXl5YLBbmzp0bZX8hISEMHTqU3Llz4+TkhK+vLx9//DFBQUHxuGtJbEqsiYiIiIiIiAhTpkyhUqVKXL58mXHjxrFlyxYmTJjAlStXqFy5Mt98802s++rZs2eck3ERSpUqxb59+yhVqlS8zk9MQUFBDB8+HF9fXyZNmsS6det48803mTFjBpUqVYqU3Priiy/o168fLVu2ZOPGjfTt25dRo0bx9ttv27Q7ffo08+fPx8nJiYYNG8YYQ4cOHRg/fjy9evVi3bp19OzZky+//JJ27dol+v1K3FkMwzBSOoiUFhAQgKenJ/7+/nh4eKR0OCIiIiIiIiLJas+ePVStWpWGDRuyfPlyHBwcrMdCQ0Np0aIF69atY+fOnVSqVCnafh4+fIibm1tyhJxgEdVqMVXGhYWFce/ePXx8fGz2L1myhDZt2vDjjz/SqVMnAG7fvk327Nnp0qUL06dPt7YdNWoUgwcP5q+//qJw4cIAhIeHY2dn1jodPHiQsmXLMmfOHLp162Zznf3791OxYkUmTpzI+++/b90/evRoPvnkEzZt2kSdOnXi+xRIDGKbK1LFmoiIiIiIiMhLbvTo0VgsFr777jubpBqAg4MD3377LRaLhTFjxlj3Rwz3PHz4MK1btyZdunTkzZvX5tiTHj16xIABA8icOTNubm5UrVqVQ4cOkStXLpuEUlRDQbt160aaNGk4ffo0DRs2JE2aNOTIkYMBAwbw6NEjm+uMGDGC8uXL4+3tjYeHB6VKlWLWrFnEp67I3t4+UlINoFy5cgBcunTJum/Dhg0EBwfTvXt3m7bdu3fHMAxWrFhh3ReRVHuWPXv2AESqamvcuDEAS5cujVU/knQcnt1ERERERERERKJkGPDwYUpH8R83N4jj3GZhYWH4+flRpkwZsmfPHmWbHDlyULp0abZt20ZYWBj29vbWYy1btqR9+/b06dOHBw8eRHud7t27s3DhQj766CNq1qzJP//8Q4sWLQgICIhVnI8fP6Zp06a88cYbDBgwgJ07dzJy5Eg8PT0ZOnSotd358+fp3bs3OXPmBMyqr3fffZcrV67YtEuIbdu2AVCkSBHrvr/++guAYsWK2bTNkiUL6dOntx6Pi5CQEACcnZ1t9kds//nnn3HuUxKXEmsiIiIiIiIi8fXwIaRJk9JR/CcwENzd43TKrVu3ePjwIblz546xXe7cufntt9+4ffs2GTNmtO7v2rUrI0aMiPHcf/75h19++YWBAwcyevRoAOrUqUOmTJno0KFDrOIMCQlhxIgRtGnTBoBatWpx8OBBfv75Z5uE2Zw5c6xfh4eHU716dQzDYPLkyQwZMiTeiypEuHLlCoMGDaJMmTLWyjEwh4I6OzvjHsXz7+3tze3bt+N8rYiho3v27LF5fXbv3m29pqQsDQUVERERERERkWeKGEr5dGKqVatWzzx3x44dALRt29Zmf+vWrSMNPY2OxWKhSZMmNvuKFy/OhQsXbPZt27aN2rVr4+npib29PY6OjgwdOpTbt29z48aNWF0rOnfu3KFhw4YYhsHChQsjDemMKWkXn4RegwYNyJcvHwMHDmTz5s3cu3ePDRs28Mknn2Bvbx/rIaWSdFSxJiIiIiIiIhJfbm5mlVhqEY+FA9KnT4+bmxvnzp2Lsd358+dxc3PD29vbZn+WLFmeeY2IyqpMmTLZ7HdwcIhyDrOouLm54eLiYrPP2dmZ4OBg6/Zvv/1G3bp1qV69OjNnziR79uw4OTmxYsUKvvjii0ireMbF3bt3qVOnDleuXGHbtm3kyZPH5riPjw/BwcFRLuBw584dSpcuHedrOjk5sX79ejp37kzdunUBcHd3Z9SoUYwcOZJs2bLF+34kcSixJiIiIiIiIhJfFkuch16mNvb29tSoUYMNGzZw+fLlKOdZu3z5MocOHaJBgwY286tB7CqxIpJn169ft0kGhYaGJupwxgULFuDo6MiaNWtsknBPLhwQH3fv3qV27dqcO3eOrVu3Urx48UhtIuZWO3r0KOXLl7fuv3btGrdu3aJo0aLxuna+fPnYt28fV65c4c6dO+TNmxd/f3/69etH1apV43dDkmhUMygiIiIiIiLykvv4448xDIO+ffsSFhZmcywsLIy33noLwzD4+OOP49V/RAJo4cKFNvuXLFlCaGho/IKOgsViwcHBwSb5FxQUxI8//hjvPiOSamfPnmXTpk28+uqrUbarX78+Li4uzJ0712b/3LlzsVgsNG/ePN4xAGTLlo1ixYrh5ubG+PHjcXd354033khQn5JwqlgTEREREREReclVqlSJSZMm8d5771G5cmXeeecdcubMycWLF5k6dSq//vorkyZN4rXXXotX/0WKFKFDhw5MnDgRe3t7atasyd9//83EiRPx9PRMtLnCGjVqxJdffknHjh3p1asXt2/fZsKECZFW1YytoKAg6tWrx++//86kSZMIDQ1l//791uMZMmQgb968gLlAweDBgxkyZAje3t7UrVuXAwcOMHz4cHr27GldiCDCkiVLADh79iwABw8eJM3/L4TRunVra7tx48aROXNmcubMyfXr11m0aBErVqzgxx9/1FDQVECJNRERERERERHh3XffpWzZskycOJEBAwZw+/ZtvL29qVy5Mrt376ZixYoJ6n/OnDlkyZKFWbNm8dVXX1GyZEkWLVpE/fr18fLySpR7qFmzJrNnz2bs2LE0adKEbNmy8eabb5IxY8Z4VXddv36dAwcOANCvX79Ix7t27WpTofbpp5+SNm1apk6dyoQJE8icOTODBg3i008/jXRuxOqmEaZOncrUqVOB/xaKAAgODuazzz7j8uXLuLq6UqFCBbZv306VKlXifD+S+CzGk6/WSyogIABPT0/8/f3x8PBI6XBEREREREREXgp79+6lUqVKzJ8/n44dO6Z0OCJWsc0VqWJNRERERERERJLc5s2b2bdvH6VLl8bV1ZUjR44wZswY8ufPT8uWLVM6PJF4UWJNRERERERERJKch4cHmzZtYtKkSdy/f5/06dPToEEDRo8ebbOCp8jzRIk1EREREREREUly5cuXZ/fu3SkdhkiiSpxlN0RERERERERERF4ySqyJiIiIiIiIiIjEgxJrIiIiIiIiIiIi8aDEmoiIiIiIiIiISDwosSYiIiIiIiIiIhIPSqyJiIiIiIiIiIjEgxJrIiIiIiIiIiIi8aDEmoiIiIiIiIiISDwosSYiIiIiIiLyktu2bRs9evSgYMGCuLu7ky1bNpo1a8ahQ4eibH/48GFq165NmjRp8PLyomXLlpw9ezZSu0mTJtGyZUty586NxWKhevXq0cawceNGKlWqhKurK56enjRp0oS///47sW4xShaLheHDh1u3//nnH4YPH8758+cjta1evTpFixaN13UCAgL44osvqF69OpkzZyZNmjQUK1aMsWPHEhwcHKn948ePGTFiBLly5cLZ2ZmCBQsyZcqUKPs+e/YsLVu2xMvLizRp0lCnTh0OHz4cZQyffvopr7zyCm5ubmTLlo02bdok6XM8fPhwLBZLovY5d+5cLBZLlK9RSlBiTUREREREROQl991333H+/Hn69evHunXrmDx5Mjdu3KBChQps27bNpu3x48epXr06ISEhLFq0iNmzZ3Py5EmqVKnCzZs3bdpOmzaNCxcuULNmTTJkyBDt9VeuXEmDBg3ImDEjS5cuZdq0aZw6dYoqVapw5syZJLlngH379tGzZ0/r9j///MOIESMSPWlz8eJFJk2aRKlSpZgxYwarVq2idevWDB8+nMaNG2MYhk37vn37Mnr0aN5++202btxIixYt6NevH6NGjbJpd/PmTapUqcLJkyeZPXs2ixYtIjg4mOrVq3PixAmbtk2aNGHSpEm8+eabrF27ljFjxvDHH39QsWJFLly4kKj3G6Fnz57s27cvSfpONYznwMyZMw3AcHd3j3Ts0KFDRq1atQx3d3fD09PTaNGihXHmzJk49e/v728Ahr+/f2KFLCIiIiIiIvLcuH79eqR99+/fNzJlymTUqlXLZn+bNm2M9OnT23yGPn/+vOHo6Gh89NFHNm3DwsKsXxcpUsSoVq1alNcvUKCAUbx4cSM8PNymTycnJ6Njx47xuaV4Wbx4sQEYfn5+kY5Vq1bNKFKkSLz6DQwMNAIDAyPtHz9+vAEYu3btsu7766+/DIvFYowaNcqm7Ztvvmm4uroat2/ftu778MMPDUdHR+P8+fPWff7+/kb69OmNtm3bWvedOnXKAIzBgwfb9Ll3714DML788st43VdKmDNnjgEY586dS9LrxDZXlOor1q5cucIHH3xA1qxZIx2LS5ZcRERERERERKKWMWPGSPvSpElD4cKFuXTpknVfaGgoa9asoVWrVnh4eFj3+/r6UqNGDZYvX27Th53ds9MOt2/f5sSJEzRo0MBm2KCvry9FixZlxYoVhIWFRXv+1KlTsbOz48aNG9Z9EydOxGKx8Pbbb1v3hYeHky5dOgYMGGDd9+RQ0Llz59KmTRsAatSogcViwWKxMHfuXJvrHThwgCpVquDm5kaePHkYM2YM4eHhMd6ju7s77u7ukfaXK1cOwOY5XrFiBYZh0L17d5u23bt3JygoiA0bNlj3LV++nJo1a+Lr62vd5+HhQcuWLVm9ejWhoaEAODo6AuDp6WnTp5eXFwAuLi7Rxm4YBpkyZbJ5LsPCwkiXLh12dnZcv37duv/LL7/EwcGBe/fuAVEPBc2VKxeNGzdmw4YNlCpVCldXVwoWLMjs2bMjXXv//v1UqlQJFxcXsmbNyscff8zjx48jtdu2bRvVq1fHx8cHV1dXcubMSatWrXj48GG095VYUn1irU+fPlStWpU6depEOjZ06FCcnZ1Zs2YNDRs2pGXLlqxdu5abN28yYcKEFIhWREREREREXkYPHjyI8yMi6QFmwurBgwcEBQXFu9/E5u/vz+HDhylSpIh135kzZwgKCqJ48eKR2hcvXpzTp09HOWdYTEJCQgBwdnaOdMzZ2ZmHDx/GOBy0du3aGIbB1q1brfu2bNmCq6srmzdvtu47ePAg9+7do3bt2lH206hRI+tQy6lTp7Jv3z727dtHo0aNrG2uXbvG66+/TqdOnVi1ahUNGjTg448/5qefforTPUeIGGb75HP8119/kSFDBjJnzmzTNuI5/+uvvwAICgrizJkz0b4WQUFB1nnvfH19adasGV999RV+fn4EBgZy/Phx/ve//5EzZ07at28fbYwWi4WaNWuyZcsW676I59LFxSXS8166dGlrwi46R44cYcCAAfTv35+VK1dSvHhx3njjDXbu3Glt888//1CrVi3u3bvH3LlzmTZtGr///juff/65TV/nz5+nUaNGODk5MXv2bDZs2MCYMWNwd3e3fm8lpVSdWPvpp5/YsWMH3377baRjcc2Si4iIiIiIiCSVNGnSxPnx5OfW5cuXkyZNGho0aGDTb65cuWLdX2J7++23efDgAZ9++ql13+3btwHw9vaO1N7b2xvDMLh7926crpMpUya8vb3Zs2ePzf579+5Zk0gR141KgQIFyJ49uzXxExISwq5du/jf//7HqVOnuHjxImAmfRwdHalatWqU/WTIkIH8+fMDULhwYSpUqECFChVs5oa7ffs2P/30E71796Z27dpMnTqVwoUL8/PPP8fpngH+/PNPxo0bR4sWLWySY7dv347y+XV3d8fJycn6XNy9exfDMKJ9LSL6irB48WIaNWpEzZo1SZs2LYUKFeLGjRvs2LGDdOnSxRhr7dq1OXnypLWybsuWLRQsWJA6depYn/fHjx+zc+fOaBOXT7p16xbr16+nc+fO1K1bl3nz5uHt7W3zPH722WcYhsG2bdto3749TZs2Ze3atbi5udn0dejQIYKDgxk/fjzNmjWjWrVqdOzYkXnz5j0zwZcYUm1i7caNG7z33nuMGTOG7NmzRzqekCz5o0ePCAgIsHmIiIiIiIiIiGnIkCHMnz+fr776itKlS0c6HtNKj3FdBdLOzo63336brVu3MnLkSG7cuMHp06fp1KmTdSjfs4aU1qpVy5rg2bt3Lw8fPuT9998nffr01qq1LVu2ULFixSiHZMZW5syZrcM3IxQvXjzOk/+fP3+exo0bkyNHDr7//vtIx+Py/Ma27VtvvcXSpUv56quv2LFjBwsXLsTJyYmaNWs+M/6IZFnEc7x582bq1KlD7dq1rc/vvn37ePDgQawSayVLliRnzpzWbRcXF1555RWbOPz8/KhVqxaZMmWy7rO3t6ddu3aR+nJycqJXr1788MMPUa5Om5RSbWKtb9++FChQgLfeeivK4wnJko8ePRpPT0/rI0eOHIkXuIiIiIiIiLx0AgMD4/xo0aKF9fwWLVoQGBjI+vXrbfo9f/58rPtLLCNGjODzzz/niy++4J133rE55uPjA0RdQXbnzh0sFku8qoSGDh1K//79+fzzz8mUKZO1cixinrFs2bLFeH7t2rW5ePEip06dYsuWLbz66qtkzJjROoQxKCiIvXv3xirpE5OI+3+Ss7NzpCG8Mblw4QI1atTAwcGBrVu3Rspr+Pj4RPn8PnjwgJCQEGv7dOnSYbFYon0t4L+cyYYNG5g1axbTp0/nvffeo2rVqrRt25bNmzdz584d6zxz0fH19SVv3rxs2bKFhw8fsm/fPmti7fLly5w4ccI6/Pa111575nMQm+fx9u3bkYbDApH2RcSVMWNG3n77bfLmzUvevHmZPHnyM+NIDKkysbZ06VJWr17NzJkzn5npjk+W/OOPP8bf39/6eHKSQBEREREREZG4ipicPi4PBwcH6/kODg64u7vj6uoa734Tw4gRIxg+fDjDhw/nk08+iXQ8b968uLq6cvTo0UjHjh49Sr58+WKcCD86Dg4OfPnll9y+fZs///yTf//9lzVr1nDx4kVy584d5Ui2J9WqVQswK6oiqqki9m/dupWdO3fy6NGjBCfWEurChQtUr14dwzDw8/OL8r6KFSvGzZs3uXbtms3+iOe8aNGiALi6upIvX75oXwtXV1fy5MkDwB9//AFA2bJlbdp5eXmRL18+65DbmEQ8lzt27CA8PJzq1atTqFAhsmbNyubNm9myZQtVqlSJcq68+PDx8Yn0HABR7qtSpQqrV6/G39+f/fv3U7FiRd577z0WLFiQKLHEJNUl1gIDA3n77bd59913yZo1K/fu3ePevXvWCefu3bvHgwcPEpQld3Z2xsPDw+YhIiIiIiIi8jIbOXIkw4cPZ/DgwQwbNizKNg4ODjRp0oRly5Zx//596/6LFy/i5+dHy5YtExRDmjRpKFasGFmyZOHw4cNs3bqVfv36PfO8LFmyULhwYZYuXcqhQ4esibU6depw8+ZNvvzySzw8PCIllp4WkRSKSwVabF28eJHq1asTFhbGtm3bbFbyfFKzZs2wWCz88MMPNvvnzp2Lq6sr9evXt+5r0aIF27ZtsykYun//PsuWLaNp06bW5G3WrFkBc5XNJ92+fZuTJ08+M3EJZlXg9evXmTRpEhUqVCBt2rSAmXBbvnw5Bw4cSNTEZY0aNdi6davNqqNhYWEsXLgw2nPs7e0pX748U6dOBeDw4cOJFk90HJ7dJHndunWL69evM3HiRCZOnBjpeLp06WjWrBlLlixJkiy5iIiIiIiIyMtm4sSJDB06lPr169OoUaNICZgKFSpYvx4xYgRly5alcePGDBo0iODgYIYOHUr69OkZMGCAzXkHDx7k/PnzAAQEBGAYBkuWLAHM6qmI5NL27ds5cOAAxYsXxzAMfvvtN8aOHUv9+vUjDUeNTq1atZgyZQqurq5UqlQJgNy5c5M7d242bdpkk2iKTkQ12IwZM0ibNi0uLi7kzp07yqGLcXHjxg1q1KjB1atXmTVrFjdu3ODGjRvW49mzZ7cmt4oUKcIbb7zBsGHDsLe3p2zZsmzatIkZM2bw+eef2wwd/eCDD/jxxx9p1KgRn332Gc7OzowZM4bg4GCb4Z0tW7Zk6NChvPXWW1y+fJlSpUpx9epVxo8fz8OHD2OVvKxZsyYWi4VNmzYxYsQI6/7atWvTtWtX69eJZfDgwaxatYqaNWsydOhQ3NzcmDp1aqQVcKdNm8a2bdto1KgROXPmJDg4mNmzZyd6PNEyUpmgoCDDz88v0qNevXqGi4uL4efnZxw9etQwDMNo27atkTFjRiMgIMB6/oULFwwnJydj4MCBsb6mv7+/ARj+/v6Jfj8iIiIiIiIiqV21atUMINrH0w4ePGjUqlXLcHNzMzw8PIzmzZsbp0+fjtSua9eu0fY5Z84ca7s9e/YY5cuXNzw8PAxnZ2ejaNGixoQJE4yQkJBY38PKlSsNwKhTp47N/jfffNMAjK+//jrSOYAxbNgwm32TJk0ycufObdjb29vEWa1aNaNIkSJR3qOvr2+Msfn5+cX4/D4dQ0hIiDFs2DAjZ86chpOTk/HKK69EGb9hGMbp06eN5s2bGx4eHoabm5tRq1Yt49ChQ5HaXb161XjnnXeMfPnyGS4uLkbWrFmNRo0aGfv27Ysx9ie9+uqrBmDs2bPHuu/KlSsGYPj4+Bjh4eE27YcNGxbp+8fX19do1KhRpL6rVatmVKtWzWbfnj17jAoVKhjOzs5G5syZjQ8//NCYMWOGARjnzp0zDMMw9u3bZ7Ro0cLw9fU1nJ2dDR8fH6NatWrGqlWrYn1fUYltrshiGIaR9Om7hOvWrRtLliyxmZDx+PHjlC1bllKlStlkye/cucMff/xhsyRuTAICAvD09MTf31/DQkVEREREREREXnKxzRWlujnW4qJgwYJs374dR0dHWrduTbdu3ciXLx87d+6MdVJNREREREREREQkPp6birWkpIo1ERERERERERGJ8FJUrImIiIiIiIiIiKQUJdZERERERERERETiQYk1ERERERERERGReFBiTUREREREREREJB6UWBMREREREREREYkHJdZERERERERERETiQYk1ERERERERERGReFBiTUREREREREREJB6UWBMREREREREREYkHJdZERERERERERETiQYk1ERERERERERGReIhTYi04OJijR4/y8OHDSMf27NmTaEGJiIiIiIiIiIikdrFOrO3bt48cOXJQvXp1MmTIwJgxY2yON2jQINGDExGR59Dhw9CnD/z4I9y/n9LRiIiIiIiIJJlYJ9YGDBjAxIkTuX37NocOHWLZsmX06NGD8PBwAAzDSLIgRUTkOWAY8P338NprMH06dOkCmTJB+/awejWEhKR0hCIiIiIiIonKYsQyI+bl5cW9e/es20FBQbRp0wYnJycWLFiAj48P95/TyoSAgAA8PT3x9/fHw8MjpcMREXn+BAXB22/DnDnmdtWqcPUqnDr1Xxtvb2jTBl5/HSpVAjtN8ykiIiIiIqlTbHNFsf5U4+HhwZUrV6zbrq6urFixAhcXF+rXr2+tXBMRkZfMmTNmldqcOWaybPRo8PODEyfgwAF47z3InBnu3DEr2apWhdy5YdAgOHo0paMXERERERGJt1hXrPXo0YM8efIwePBgm/2GYdCrVy9mzZr13CbXVLEmIhJPq1dD587g7w8ZMsAvv0CtWpHbhYWZybaff4alSyEg4L9jRYuaVWwdOoCvb/LFnkLCwsIICQnB1dXVuu/KlSvcvXuX4OBgHj16FO3jyeOFChWiZcuWADx+/JjevXvz6NEjZs6ciZubW0rdnoiIiIjICyG2uaJYJ9ZCQkIIDQ2N9j/rFy9eJGfOnPGLNoUpsSYiEkdhYTB0KIwaZW5XrAiLFkH27M8+NygI1q2D+fNh7VrbudcqV4aOHc0ho+nTJ1KoYTEmqyIevr6+5M2bF4A7d+6wZMkSHBwc6NGjh7Wv7777jhMnTsSY8Hr60alTJz777DMArl69StasWbGzsyMsLMzab4sWLVixYkWc7qtt27YsXLgQgPDwcOzt7QG4efMm6f//uQsPD8dOQ25FREREROIstrkih9h26OTkhJOTU7THn9ekmoiIxNHNm2Z12dat5va778KECfDE74iQkBAsFguOjo6A+UvpwoULODs788orr0CrVtCqFesWLSJg61Ye7dzJo+PHebR7t/l45x0e5c3Lo4IFeZQjB4/Cw63Jq65du1KvXj0ADh8+zFtvvUX27NlZunSp9foVK1bk6NGjBAcH2ySwYjJ06FBGjBgBmAmw3r174+PjY5NYW7RoEdu3b4/T03Xjxg3r1xG/R8PDwwkLC7Mmw7y9vcmQIQPOzs44Ozvj4uJi/Tqqh4uLC2XLlrX2a2dnx5gxY3BycrJWwp0/f57mzZvz3XffUbFixTjFLCIiIiIisRPrxFpUhg0bZv0QIiIiL4H9+81qssuXwc3NXAW0QwfrYcMwGDFiBKNHj2bu3Ll0+P9jW7ZsoVWrVlSqVIndu3db27/Rrx/Xrl2LfJ3wcHPhgycXP/h/5cqVsybWgoKC+O2337hz545Nm6CgIB48eBDlLUSVpHJ2drZWeQGkS5eOZs2a4eXlZXNuhw4dqFixYowJr6f3ZcmSxabfmzdv4uzsbFNJNmvWrChjjYuBAwfabA8bNowjR45Qv359Nm/eTLly5RJ8DRERERERsZWgxNr48eOVWBMReRkYBkydCu+/D48fQ4EC5lxpRYpYm4SGhtK7d29mz54NmMmtCGnSpCFjxoyRElWVKlXizp07kZNUQUE4nz+P86lTOPv74wzmw92dqvv2QfnyUKEChQsXZtWqVXh6etr0u3z5csLDwyMluxwdHbFYLM+83axZs0Y5NLNXr16xfsqiYmdnZ5PAS0rffvst58+fZ+fOndSrV48tW7ZQunTpZLm2iIiIiMjLItZzrEXF1dXV5oPT80pzrImIxODBA+jVy1x4AKB1a5g9G9KmfaLJA9q1a8fatWuxs7Pj66+/pnfv3jg4JOjvN2ZC79dfzfnYFi40h6FGyJ3bnI/t9dehUKGEXecFdf/+fRo0aMCePXtIly4d27Zto2TJkikdloiIiIhIqpfoixdExc3NjYcPH8b39FRDiTURkWicOGHOh/b332BvD+PHw3vvwRNVXzdv3qRx48b89ttvuLi4sGDBApo1a5b4sYSGwpYtZoJv+XIIDPzvWMmSZoKtffvYLaDwEgkICKBevXrs378fHx8f/Pz8KFasWEqHJSIiIiKSqimxFgdKrImIRGHpUujeHe7fhyxZzIqxKlVsmpw9e5b69etz6tQpvL29Wb16Na+99lrSx/bwIaxaZSbZ1q83k25gJvyqVTOTbK1aQbp0SR/Lc8Df3586depw4MABMmTIwPbt2ylcuHBKhyUiIiIikmrFNldkF+0RERF5OT1+DB98YA75vH/fTFQdPhwpqXb48GFee+01Tp06Rc6cOdmzZ0/yJNXAXDihfXszuXbtGkybZsZnGLB9O7z5JmTODC1awOLF8AJMW5AQnp6ebNy4kVKlSnHz5k1q1qzJ8ePHUzosEREREZHnnuZYQxVrIiJWV69Cu3awa5e5/eGHMGoUPDVX2ubNm2nZsiWBgYGUKFGCdevWkTVr1hQI+CkXLsCCBeacbEeP/rc/bVqzgq1jR6hZ0xzW+hK6ffs2tWrV4siRI2TJkoUdO3aQP3/+lA5LRERERCTVSZaKtUqVKiXkdBERSU127oRSpcykWtq05lDQceMiJdV++uknGjZsSGBgIDVr1mTHjh2pI6kG4OsLAwfCn3+aj0GDIGdOs/Ju7lyoW9ecg+299+DAAbPC7SXi4+PDli1bKFq0KFevXqVGjRqcOXMmpcMSEREREXluJahi7UWhijUReakZBnz5pZmQCguDokXNpNorr0RqunPnTqpVqwZA+/btmTt3Ls7OzskdcdyEh8PevWYV26JFcOfOf8fy5/9vZdGXqHLrxo0b1KhRg3/++YemTZuycuXKlA5JRERERCRVSZbFC14USqyJyEsrIAB69DATaWAmmKZPB3f3KJsbhkHXrl3JkCED48ePx87uOZuqMyQENm0yFz1YscJ27rUyZf5bWTRz5hQLMblcu3aN999/n2+++QZvb++UDkdEREREJFVJ1sTaihUrmD9/PhcuXCA4ONj2AhYLR44cSeglkpQSayLyUvrrL3PesZMnwdERJk+GPn3MlTWf8OjRIwzDwMXFBYCwsDDsX4Q5ygIDzeTazz+bybawMHO/nZ05D9vrr5uLH3h6pmiYySkoKAhXV9eUDkNEREREJMUl26qg48ePp2XLluzcuRNHR0d8fHxsHvoruIhIKvTzz1C+vJlUy57dnFftrbciJdXu3btHvXr16NSpE2H/n3h6IZJqAGnSQKdOsG4d/PsvfPMNVKxoDh3dsgW6d4dMmaBNGzMB9+hRSkecpKZMmUKJEiX4999/UzoUEREREZHnRoIr1nLnzk2tWrWYPn36c/thSxVrIvLSCAmBAQPMJBJAnTrm3GMZMkTZfO/evdSoUQNnZ2d+/fVXChUqlIzBppCzZ+GXX8zn5dix//Z7eUHr1uacbNWqmZVtL4jAwECKFSvG+fPnmTBhAgMGDEjpkEREREREUlSyDQX18PBgxYoV1KxZMyHdpCgl1kTkpXDpkll99euv5vbgwTB8ODzjjyIrVqwgV65clCxZMslDTFUMA44cMRNsv/wCV678dyxbNujQwUyylSwZqdLveXT+/HmWLFnCgAEDsLwA9yMiIiIikhDJNhS0UqVKHHvyL/qJ4I8//qBRo0bkzJkTV1dXvL29qVixIj/99FOktocPH6Z27dqkSZMGLy8vWrZsydmzZxM1HhGR596WLVCqlJlU8/KCNWtg5Mgok2q7d+/m77//tm43b9785UuqgZksK1kSxo+HCxfAzw969jSfvytXYMIE8zktUgQ+/9ysdHuO5cqViw8++MCaVAsODub27dspHJWIiIiISOqW4MTapEmTmDp1KqtWrSIkJCQxYuLevXvkyJGDUaNGsW7dOubNm0euXLno3Lkzn3/+ubXd8ePHqV69OiEhISxatIjZs2dz8uRJqlSpws2bNxMlFhGR51p4OHzxBdStC7duwauvwuHD0KhRlM2XL19OnTp1qF+/PleerNB62dnbQ/XqMHMmXLsGy5eb1X8uLuZw0SFDIG9eeO01c5jtjRspHXGCPHz4kKZNm1KrVi0l10REREREYpDgoaBhYWH079+fqVOnYrFYcHNzs72AxYK/v3+CgoxQoUIF/v33Xy5evAhA27Zt8fPz48yZM9ayvAsXLpA/f3769+/P2LFjY9WvhoKKyAvp7l3o0sWsTgN44w2YMgWiWfXx22+/5Z133sEwDJo0acKCBQsivafLUwICYNkyczGIrVvNRCaYibg6dcyVRZs3NxdKeI6cPXuWSpUqce3aNV599VW2bt1KunTpUjosEREREZFkk2xzrA0YMICvvvqKkiVLUqhQIZycnCK1mTNnTkIuYdW4cWP++ecfzp49S2hoKB4eHnTp0oVp06bZtKtXrx7nzp3j5MmTsepXiTUReeH8/ju0agXnzoGzM0ydaibWomAYBoMHD2bUqFEAvPnmm3z77bc4ODgkZ8TPv6tXYdEic062Awf+2+/qCs2amUm2unUhit+TqdE///xD9erVuXnzJmXLlmXz5s14enqmdFgiIiIiIskitrmiBH9qmjt3LgMHDmT06NEJ7SqS8PBwwsPDuXv3LosXL2bjxo188/8r2Z05c4agoCCKFy8e6bzixYuzefNmgoODcXFxSfS4RERStTlzoG9fCA6G3LlhyRJzLrAoPH78mN69e1v/ADJixAiGDBmiyevjI0sW6NfPfJw8+d/KoqdOwYIF5sPHxxxC2rEjVKqUqlcWLVy4MNu2baNGjRocOHCA+vXrs3HjRv0BSkRERETkCQn+H31YWBh16tRJjFgi6du3L46OjmTMmJH+/fvz9ddf07t3bwDrnC/e3t6RzvP29sYwDO7evRtlv48ePSIgIMDmISLy3AsOhjffhB49zK8bNYJDh6JNqj148IBmzZoxZ84c7OzsmDlzJkOHDlVSLTG88goMGwYnTpjVa++9B5kzw+3bMG0aVK1qJj0//hiOHk3paKNVtGhRtmzZgre3N/v376dhw4YEBgamdFgiIiIiIqlGghNrdevWZf/+/YkRSySffPIJBw4cYO3atfTo0YN33nmHCRMm2LSJ6QNgdMdGjx6Np6en9ZEjR45EjVtEJNmdO2dWQH3/vbma5eefw6pVEM28WDdv3qRGjRqsX78eV1dXVq5cSc+ePZM56JeAxQJlysBXX8Hly7B5M3TrBmnTwsWLMGYMFC9uPsaONfelMiVKlGDz5s14eXmxZ88eGjVqxIMHD1I6LBERERGRVCHBc6wdPXqUdu3a0bt3bxo1ahRtBVlieOutt/j+++/5999/uXPnDgULFmTq1Kn07dvXpt2HH37IxIkTefjwYZRDQR89esSjR4+s2wEBAeTIkUNzrInI82ndOujUyVyswMfHHIIYQyXx2bNnqVevHqdPn8bHx4c1a9ZQoUKFZAxYCAqCtWvNoaLr1sGTq2pXqWIOFW3Txnw9U4kDBw5Qu3ZtAgICqFGjBmvWrNHiFiIiIiLywkq2xQvs/n9+mJgqx8LCwhJyCas5c+bQo0cP9u/fT+nSpfHw8KBr16589913Nu3q16/P2bNntXiBiLzYwsJgxAgYOdLcLlcOFi+GnDmjPeXQoUM0bNiQGzdukCtXLjZs2ECBAgWSKWCJ0t27sHSpubLo9u0Q8WvZwQHq1zcXPWjaFFJBEmv//v3UrVuX+/fvU7t2bVatWoVrNKvMioiIiIg8z5Jt8YLknI/Hz88POzs78uTJg4ODA02aNGHZsmWMGzeOtGnTAnDx4kX8/Pzo379/ssQkIpIibt0yEy6bNpnbffvCl1+aK4DGYPTo0dy4cYOSJUuybt06smTJkgzBSozSpYOePc3H5cvmIgc//2yu7Lpmjflwd4cWLczXvHZtM+mWAipUqMD69eupV68eW7ZsoWXLlixfvlwLBYmIiIjISyvBFWtJoVevXnh4eFCuXDkyZcrErVu3WLx4MQsXLuTDDz9k3LhxABw/fpyyZctSqlQpBg0aRHBwMEOHDuXOnTv88ccfZMiQIVbXU8WaiDxXfvsNWreGS5fA1RVmzDCHgsbC/fv3GTx4MCNHjtT7XWp37JiZYPv5Zzh79r/9GTJAu3Zmkq18eXMet2S2c+dOGjRogKurK3v27FHVo4iIiIi8cJJtKGhSmDNnDnPmzOHYsWPcu3ePNGnSUKJECXr27Emnpz48Hjp0iIEDB7Jv3z4cHByoWbMmEyZMIG/evLG+nhJrIvJcMAxzRcl+/eDxY8if3xxCWKxYDKcYbNmyhdq1a2u1z+eVYcCvv5rzsS1cCDdv/ncsTx5zPrbXX4eCBZM1rO3bt+Pj40OxGL7/RERERESeV891Yi25KbEmIqnew4fQpw/8+KO53aIFzJkDnp4xnvbee+8xefJkRo8ezaBBg5IhUElSjx/D1q1mkm35cnhydc5XXzUTbO3bQ7ZsyR7aX3/9RYECBXB0dEz2a4uIiIiIJLbY5orskjEmERGJj1OnoEIFM6lmbw/jx5uVas9IqgHkzp0bQHNgvSgcHc0FDX78Ea5fN1eAbdzYnHPt99/hgw8gRw6oWRP270+2sHbs2EGFChXo1KkToaGhyXZdEREREZGUpsSaiEhqtmIFlCkDR49CpkxmtdIHH8R6Xq1+/frxxx9/8N577yVpmJIC3N3N6rTVq+HaNfjuO6hc2Rw66ucHVavCzJnJEkpgYCAhISHcvXuXx48fJ8s1RURERERSAyXWRERSo9BQGDjQHPIZEGAmTA4fhmrVYjzt8uXLtGvXjnv37ln3lShRIomDlRTn42MOFd61C86fh1atzGGjvXrBW29BSEiSXr5Ro0Zs27aNVatW4erqmqTXEhERERFJTZRYExFJba5dgzp14P9XQOb992HbNsiaNcbT/v77bypWrMiiRYt46623kiFQSZV8fWHxYvjiC7Oycdo0qFXLHDqahCpXrmwdcmwYBsuWLSM8PDxJrykiIiIiktKUWBMRSU327IFSpWD7dkiTBhYtgokTzbm1YrBr1y4qV67M5cuXKViwIGPGjEmeeCV1sljgk0/MYaIeHrB7N5QuDQcOJMvlP/nkE1q1akWfPn2UXBMRERGRF5pDfE4qXrx4rNtaLBaOHDkSn8uIiLw8DAMmT4YPPzSHgRYubC5QULDgM09dunQpr7/+Oo8ePeK1115j9erVeHt7J0PQkuo1agS//QbNm8Px41ClCsyYAV26JOllixUrhp2dHTNnzsTBwYGpU6diieW8gCIiIiIiz5N4Jda8vb2f+R/kwMBADh06pP9Ii4g8y/370LOnWZ0G5oT0M2eaFWvP8M033/C///0PwzBo3rw5P//8s+a4ElsFCsCvv0LnzrBqFXTtas7XN378Mysh46tjx46EhobSrVs3vvvuOxwcHJg8ebL+TyAiIiIiL5x4Jda2b98e7bHQ0FBmzJjBZ599hsVioWPHjvGNTUTkxXfsGLRsaVYTOTjAl1/CO+88c9VPwzD49NNPGT16NAB9+vThm2++wd7ePjmilueNhwcsXw6ffQYjRpjVkX/+aSZz06dPkkt26dKF0NBQ3njjDaZMmYKDgwMTJ05Uck1EREREXiiJOsfa4sWLKVy4MO+++y4lSpTg0KFD/Pjjj4l5CRGRF8fChVC2rJlUy5YNdu6Ed999ZlLt8ePHdOvWzZpUGzlyJN9++62SahIzOzsYPtxMsKVJA35+UKYM/PFHkl2yR48eTJ8+HYCvvvqKQYMGYRhGkl1PRERERCS5JUpibfv27ZQvX5527drh4eHBpk2b2LhxIyVLlkyM7kVEXiwhIfDee+aQzwcPoGZNc2hexYrPPDUwMJAmTZowb9487O3tmTVrFoMHD1YVkMRe8+awfz/kywcXLsBrr8GCBUl2uV69ejF16lQAxo0bx+DBg5VcExEREZEXRoISa0ePHqVhw4bUqlWL27dv8/PPP3Pw4EFq1aqVWPGJiLxYrlyBGjXMoXgAH38MmzZBxozPPPXGjRtUr16djRs34ubmxsqVK+nRo0cSBywvpCJFzEUN6teHoCDo0AEGDoSwsCS5XN++fZn8/9/zo0aNYsSIEUlyHRERERGR5BavxNqlS5fo2rUrpUqV4tChQ0yaNIljx47Rvn37xI5PROTF4ecHpUrB3r3g6QkrV8KoURDLIZyGYXDnzh3Sp0+Pn58fjRo1SuKA5YWWLh2sWWMm1ADGjYOGDeHu3SS53P/+9z8mTpwIwIgRI/j888+T5DoiIiIiIsnJYsRjPIarqyshISHUr1+fjz76iLRp08bYvlSpUvEOMDkEBATg6emJv78/Hh4eKR2OiLxoDMNMWnzyCYSHQ4kSsHQp5M0b565OnTqFYRi88sorSRCovLQWLoTu3c3qtbx5zaRvkSJJcqlx48Yx8P+TeWPGjLF+LSIiIiKSmsQ2VxSvxJqd3X+FbjHN62MYBhaLhbAkGlqSWJRYE5Ek4+8PXbuaiQowv/72W3Bzi9XpGzZs4NatW3Tq1CkJgxQBjhwx5187fx7c3WHePHPF2iTwxRdfMGzYMH766SdVu4uIiIhIqpSkibUffvghTu27du0a10skKyXWRCRJ/PkntGoFp0+DkxN88w307PnMVT8jHDhwgIoVK2KxWNi1axcVKlRI4oDlpXfrFrRrB9u2mdtDhpgridol6iLiAPzzzz8ULlw40fsVEREREUkMSZpYe9EosSYiiW7ePOjTxxxa5+sLS5ZAmTJx6iI8PJwuXbpgsViYNWsWTk5OSRSsyBNCQ+Gjj+Crr8ztxo3hp5/MeQGTyJUrV9i2bRudO3dOsmuIiIiIiMRFbHNFDskYk4jIi+/RI3jvPZg2zdyuX99MSvj4xOr0sLAwQkNDcXZ2xs7Ojjlz5mBvb28zBF8kSTk4wJdfwquvwptvmgsclC9vDmcuUCDRL3f37l2qVavGmTNnAJRcExEREZHnij6piYgklgsXoHJlM6lmscCIEbB2bayTakFBQbRt25ZOnTpZ56Z0dHRUUk1SRufOsHs3ZM8OJ05AuXJmki2ReXl50bx5c3Lnzk3VqlUTvX8RERERkaSkoaBoKKiIJIKNG6FjR7hzB7y9Yf58s1otlu7evUvTpk3ZvXs3Tk5O7Nu3L9WvqCwvievXoXVrM8lmscBnn5kr3CZiwtcwDO7evYu3t3ei9SkiIiIikhCxzRWpDEJEJCHCw81EQ4MGZlKtTBk4fDhOSbWLFy9SuXJldu/ejaenJ5s2bVJSTVKPTJlg61bo2xcMw1zQoE0buH8/0S5hsVhskmrLly9n/vz5ida/iIiIiEhS0RxrIiLxdfu2OVxu/Xpzu3dvmDwZnJ1j3cXRo0dp0KABV65cIVu2bKxfv55ixYolUcAi8eTkBFOnmvOu9e0Ly5aZw0NXroS8eRP1UgcOHKBNmzYYhoGDgwPt2rVL1P5FRERERBKTKtZEROLj0CEoXdpMqrm4wNy55txqcUiq7dixgypVqnDlyhUKFy7Mvn37lFST1K1nT9ixA7Jkgb//hrJlYdOmRL1E6dKl6dq1K+Hh4bz++ussXbo0UfsXEREREUlM8Zpj7eLFi3FqnzNnzrheIllpjjURiTXDgO+/h3fegZAQs1pn6VIoUSJO3SxevJhOnToREhJC5cqVWblypeaXkufHv/9Cy5bw66/mXGtjx8KAAeYcbIkgPDyc7t27M2/ePBwcHFiyZAnNmjVLlL5FRERERGIjtrmieCXW7OzssMThP88Rq9ulVkqsiUisBAWZw+DmzjW3mzaFH34AL684dTNlyhT69euHYRi0bNmS+fPn4+LikujhiiSpR4/Mn4fZs83tDh3MpLObW6J0HxYWRpcuXfj5559xdHRk2bJlNG7cOFH6FhERERF5ltjmiuI1x9rs2bPjlFgTEXnunTkDrVrBkSNmhc6oUfDhh3FaGdEwDD7++GPGjh0LQN++ffn666+xt7dPqqhFko6zs5lIK10a+vWDX36BY8dgxQrw9U1w9/b29vzwww+EhYWxcOFCWrVqxcqVK6kfh4VBRERERESSWrwq1l40qlgTkRitXm0uUuDvDxkzwoIFUKNGnLt57733mDx5MgCjRo1i0KBB+iOFvBh27oTWreHmTUifHhYtitfPSFQeP35Mhw4dWLp0Kc7OzqxevZo6deokSt8iIiIiItGJba4oURcvOHnyJPv27ePUqVOJ2a2ISMoIDYVPPjGHfPr7Q8WKcPhwvBMGHTt2xNPTk7lz5/Lxxx8rqSYvjqpVzQU9SpWCW7egTh34+mtzTsIEcnR05JdffqFZs2Y8evSIpk2bsm3btkQIWkREREQk4RIlsbZ48WJ8fX0pVKgQlStXpmDBgvj6+rJkyZLE6F5EJPnduAH16sHo0eZ2v36wfTtkyxanbsLDw61flytXjnPnztG1a9dEDFQklciRA3bvhk6dICzM/Jnp3h2CgxPctaOjI4sWLaJx48YEBwfTpEkTduzYkQhBi4iIiIgkTIITa+vWraN9+/Z4enoyZswY5s2bx+jRo/H09KR9+/asX78+MeIUEUk++/aZlTfbtoG7uzl31KRJ4OQUp25OnTrFq6++ysGDB6370qVLl8jBiqQirq4wbx58+aU5/+APP5jVbJcvJ7hrJycnlixZQoMGDXj48CGNGjXi9OnTiRC0iIiIiEj8JXiOtUqVKuHh4cHatWuxe2ISb8MwaNCgAffv32fPnj0JDjQpaY41EQHMYWvffAPvv28OAy1YEJYuhcKF49Vdhw4dWLBgARUrVmTPnj0a+ikvly1boF07uHMHMmUyf5YqVUpwt8HBwTRt2pSiRYsyceJE/VyJiIiISJKIba4owYk1d3d3FixYQJMmTSIdW7VqFR07diQwMDAhl0hySqyJCIGB0KuXWZ0G0KYNzJoFadPGu8uAgAD69evHmDFjyJQpUyIFKvIcOXcOmjeHP/8ER0eYMgV6905wtyEhITg6OiqpJiIiIiJJJtkWL7C3tyckJCTKY48fP7apYhMRSZWOH4fy5c2kmoMDfPUVLFwYr6Ta/v37ifh7hYeHB3PmzFFSTV5euXPD3r3Qti08fgx9+piJtWj+3xBbTk5O1qRacHAwnTp14tChQ4kRsYiIiIhInCQ461W2bFnGjRtHUFCQzf5Hjx4xYcIEypcvn9BLiIgknSVLoGxZ+OcfyJIF/PzgvfcgjpUwhmHw+eefU7FiRcaPH580sYo8j9zdYcECGDPG/LmaMcNcWffq1UTp/rPPPmP+/Pk0a9aM4ERYKEFEREREJC4SnFgbMWIEf/zxB3ny5OF///sfo0aN4t133yVPnjz8/vvvjBgxIs59btu2jR49elCwYEHc3d3Jli0bzZo1i/Kv0YcPH6Z27dqkSZMGLy8vWrZsydmzZxN6WyLyonv8GAYMMId8BgZC9epw+DBUrhznrsLCwujbty9DhgwBwN/fP5GDFXnOWSwwcCCsXQuenmYVW5ky8NtvCe76448/platWsybNw8XF5dECFZEREREJPYSPMcawI4dOxg0aBC//fYbhmFgZ2dH+fLlGT16NFWrVo1zf23atOH27du0adOGwoULc/PmTSZOnMjBgwfZuHEjNWvWBOD48eOUK1eOkiVLMmjQIIKDgxk6dCh3797ljz/+IEOGDLG6nuZYE3nJXL1qTqq+a5e5/dFH8MUX5jDQOAoKCqJjx46sWLECi8XC119/zTvvvJPIAYu8QE6dgmbN4Ngxc6XdadOge/cEdWkYhs18a09vi4iIiIjEVbItXvCkhw8fcvfuXdKlS4ebm1u8+7lx4wYZM2a02RcYGEi+fPkoWrQoW7ZsAaBt27b4+flx5swZ601euHCB/Pnz079/f8aOHRur6ymxJvIS2bnTnO/p+nXw8IC5c6FFi3h1defOHZo0acLevXtxdnZm/vz5tGrVKnHjFXkRBQRAly6wcqW5/e67MHGiucBBAv3999+8/vrrLFiwgIIFCya4PxERERF5OSXb4gVPcnNzI1u2bAlKqgGRkmoAadKkoXDhwly6dAmA0NBQ1qxZQ6tWrWxu0NfXlxo1arB8+fIExSAiLxjDgAkToGZNM6lWtCgcPBjvpNqFCxeoVKkSe/fuxcvLi82bNyupJhJbHh6wbBkMH25uT5kCderAzZsJ7vr999/nyJEj1KxZk1OnTiW4PxERERGRmCRKYm3FihW0adOGcuXKUbx4cZtHiRIlEuMS+Pv7c/jwYYoUKQLAmTNnCAoKonjx4pHaFi9enNOnT2sSYxExBQRA69bw4YcQFgadOsH+/ZA/f7y6O3LkCBUrVuT48eNkz56d3bt3U6VKlUQOWuQFZ2cHw4bBihWQJg3s2GHOu/b77wnqdv78+RQrVoyrV69So0YNzpw5kzjxioiIiIhEIcGJtfHjx9OyZUt27tyJo6MjPj4+Ng9vb+/EiJO3336bBw8e8OmnnwJw+/ZtgCj79/b2xjAM7t69G2Vfjx49IiAgwOYhIi+ov/4yP6wvW2YOM/v2W5g3z1ypMB78/PyoWrUqV69epUiRIuzdu9ea8BeReGjWDH791Ux0X7wIlSrBzz/Hu7v06dOzZcsWChcuzJUrV6hRowbnzp1LxIBFRERERP4T95m6n/Ltt9/So0cPpk+fjr29fWLEFMmQIUOYP38+U6ZMoXTp0jbHYpqcOLpjo0ePjtdqpSLynJk/H3r1gocPIUcOWLIEypWLd3cLFy6kS5cuhISEULVqVVasWEG6dOkSMWCRl1ThwuYKoR07wvr18PrrZuXa6NHxWlQkY8aMbNu2jerVq3P8+HFq1KjBjh078PX1TYLgRURERORlluCKtdu3b9OxY8ckS6qNGDGCzz//nC+++MJmpT0fHx/r9Z92584dLBYLXl5eUfb58ccf4+/vb31EzNsmIi+IR4/gnXfMIZ8PH5pzNx0+nKCk2sWLF+ncuTMhISG0atWKjRs3Kqkmkpi8vGD1avjkE3N7wgRo2BDu3IlXd5kyZWLbtm3kz5+fCxcuULNmTf2+FxEREZFEl+DEWqVKlTh27FhixBLJiBEjGD58OMOHD+eTiP9o/7+8efPi6urK0aNHI5139OhR8uXLh4uLS5T9Ojs74+HhYfMQkRdEQAA0aABTp5rbQ4eaFTDp0yeo25w5czJ9+nTeeecdFi5cGO37i4gkgL09fPEFLFoEbm6weTOULQtR/K6PjSxZsuDn50fevHk5e/YsNWvW5MqVK4kctIiIiIi8zBKcWJs0aRJTp05l1apVhISEJEZMAIwcOZLhw4czePBghg0bFum4g4MDTZo0YdmyZdy/f9+6/+LFi/j5+dGyZctEi0VEnhPXr0P16uDnB2nTwpo1MGKE+WE9HkJCQmw+hHfv3p0pU6YkWYWuiPy/Nm1g3z7InRvOnoWKFc2h3PGQLVs2tm3bRq5cuTh9+jQ1a9bk6tWriRywiIiIiLysLIZhGAnpICwsjP79+zN16lQsFgtubm62F7BY8Pf3j1OfEydO5IMPPqB+/fpRJtUqVKgAwPHjxylbtiylSpVi0KBBBAcHM3ToUO7cucMff/xBhgwZYnW9gIAAPD098ff3V/WayPPq3DmoWxdOn4YMGWDDBihVKt7dBQQE0KpVK86dO8fevXvJmDFjIgYrIrFy+za0awdbt5rbn3wCn30Wr2T5+fPnqVatGhcvXqRQoUL4+fmRKVOmRA5YRERERF4Usc0VJTixNmDAAL766itKlixJoUKFcHJyitRmzpw5ceqzevXq7NixI9rjT4Z86NAhBg4cyL59+3BwcKBmzZpMmDCBvHnzxvp6SqyJPOeOHoV69eDqVciVCzZtMlcYTIBr165RsWJFbt68yfr166lSpUrixCoicRMaCoMGwcSJ5najRubCJJ6ece7q7NmzVKtWjcuXL9OrVy+mT5+eyMGKiIiIyIsi2RJrPj4+9OrVi9GjRyekmxSlxJrIc2z3bmjSBO7dg6JFYeNGyJo1Ubo+efIk9+/fj7QasYikgJ9+gjffhOBgeOUVWLkSChaMczenT59m+PDhTJ8+HXd39yQIVEREREReBLHNFSV4jrWwsDDq1KmT0G5EROJuzRpzxc9796BSJdi5M0FJtV9//ZUFCxZYt1955RUl1URSi06dzER6jhxw8qS5yu/q1XHuJl++fPz00082SbXg4ODEjFREREREXiIJTqzVrVuX/fv3J0YsIiKxN28eNG9uVq80amQO/0yXLt7drVmzhho1atC5c2d27dqVeHGKSOIpXRoOHoSqVeH+fWjaFEaOhPDweHVnGAbDhg2jSpUq3Lt3L3FjFREREZGXQoITa0OGDOGnn35i8uTJnD59mjt37kR6iIgkqokToWtXCAuDLl1g+XJ4auGUuPj+++9p3rw5QUFB1KlTh1dffTURgxWRRJUxI2zZAu+8Y24PHQqtW5uJtji6ceMG3377LQcPHmR1PKrfREREREQSPMeanZ2Zm7NYLNG2CQsLS8glkpzmWBN5ThiGOYn5uHHm9oAB5td28fsbgWEYjBw50rr6cPfu3Zk+fTqOjo6JFbGIJKXZs+GttyAkBAoXNuddy5cvTl0cOXKEPXv20Ldv3yQKUkRERESeR7HNFTkk9EJDhw6NMakmIpIoQkOhd2/zgzTA2LHw0UcJ6C6Ut99+mxkzZgDw6aefMnLkSL2fiTxPevSAIkWgZUv45x8oWxZ++QXq1491FyVKlKBEiRLWbX9/fxwcHLSwgYiIiIjESoIr1l4EqlgTSeWCgqB9e1i1yqxOmznT/EAdTw8fPqRDhw6sWrUKi8XC1KlTeeuttxIxYBFJVlevQqtWsG8fWCwwerSZeI9jovzevXvUq1cPNzc31q5di1sChpiLiIiIyPMt2VYFFRFJUv7+ZvXJqlXg7AzLliUoqXb79m1q167NqlWrcHFxYenSpUqqiTzvsmQBPz/o2fO/IeMdOsCDB3Hq5ty5cxw7dozt27fTtGlTgoKCkihgEREREXlRJErF2ooVK5g/fz4XLlyItGS9xWLhyJEjCb1EklLFmkgqde2amVQ7cgQ8PGD1anM1wHg6f/489evX58SJE6RLl47Vq1dTqVKlRAxYRFKUYcD06fDuu+bw8RIlYMUKyJUr1l3s3buXevXqERgYSL169VixYgUuLi5JFrKIiIiIpE7JVrE2fvx4WrZsyc6dO3F0dMTHx8fm4e3tndBLiMjL6MwZqFTJTKplygQ7diQoqfbHH39QsWJFTpw4QY4cOdi9e7eSaiIvGosF+vSBbdvM1UOPHIEyZcztWHrttddYt24dbm5ubNy4kVatWvHo0aMkDFpEREREnmcJrljLnTs3tWrVYvr06djb2ydWXMlKFWsiqcwff5iVatevQ548sGkT5M2boC7Hjh3LoEGDKFasGOvXrydbtmyJE6uIpE6XLpmLGhw8CPb2MGEC9OsX63nXtm/fTsOGDQkKCqJp06YsXrwYJyenJA5aRERERFKLZKtYu337Nh07dnxuk2oiksrs2AHVqplJtRIlYM+eBCfVAD766CMmT57Mzp07lVQTeRnkyAE7d0KXLhAWBv37Q7du5mIosVC9enVWr16Ni4sLq1atokOHDjx+/DhpYxYRERGR506CE2uVKlXi2LFjiRGLiLzsVq6EevUgIMAc9rl9O2TOHO/ufvzxRwIDAwFzvsf//e9/eHl5JU6sIpL6ubrC3LkwaZJZtTZvnvneculSrE6vVasWK1aswMnJiWXLlvH6668TGhqapCGLiIiIyPMlwYm1SZMmMXXqVFatWkVISEhixCQiL6PZs81hW48eQbNmsGEDJCAJNmLECLp06UKbNm0ICwtLvDhF5PlisZhDQDdtAh8fc2homTKwa1esTq9Xrx7Lly/H0dGRxYsX06VLF72niIiIiIhVghNr+fLlo3bt2rRo0QI3Nzc8PDxsHp6enokRp4i8qAwDxo2DN96A8HDo0QOWLDErTRKgfv36uLu7U7NmTezsEvxWJyLPu5o1zaRaiRJw44a5/d135nvQMzRs2JClS5fi4ODAL7/8Qvfu3ZVcExEREREgERYvGDBgAF999RUlS5akUKFCUU7sO2fOnIRcIslp8QKRFBIeDh99BBMnmtsDB8Lo0bGeXPxpV69eJUuWLNFui4jw4IGZyF+40Nzu2RO++QacnZ956rJly2jbti1Zs2Zl//79ZM2aNYmDFREREZGUEttcUYITaz4+PvTq1YvRo0cnpJsUpcSaSAp4/Nj8QDtvnrk9YQIMGBDPrh4zfPhwvvzyS/bs2UOpUqUSMVAReeEYBowfD4MGmV9XrAhLl0IsEvErV66kWLFi5MmTJxkCFREREZGUkmyrgoaFhVGnTp2EdiMiL5OHD8351ObNMycU/+GHeCfVzp07R9WqVRk1ahTBwcGsWbMmkYMVkReOxWJWy65bZ87luG8flC4N+/c/89RmzZrZJNWOHj1KAv9GKSIiIiLPsQQn1urWrcv+WPxHVEQEgLt3oW5dWLMGXFxgxQro0iVeXS1cuJCSJUuyf/9+vLy8WLx4MUOHDk3ceEXkxVW/Phw4AEWKwNWrUK2auZBKLK1evZrSpUvTr18/JddEREREXlIOCe1gyJAhtGvXDnd3dxo1aoS3t3ekNlHtE5GX0L//Qr168NdfZpXI6tVQuXKcu3nw4AH/+9//mP3/H4ArVarE/Pnz8fX1TeSAReSFly+fWbHWtSssX27Ov3b4MHz1FTg6xnjq7du3CQ0N5dq1a4SFheHgkOD/VomIiIjIcybBc6xFrLZniWGy8dS+cpbmWBNJBqdOmZVq58+b8xht3AjFisW5m99//50OHTpw4sQJLBYLgwcPZujQofpAKyIJEx4Oo0bBkCHmdtWqsHgxZMwY42lbt26lWrVqeg8SERERecHENleU4P8FDh06NMakmogIhw+bQ65u3jSrQzZtgty549SFYRhMnjyZgQMHEhISQrZs2fjpp5+oXr160sQsIi8XOzsYPBhKlIDXX4edO6FMGbOKrXTpaE+rVauW9evw8HBWr15N06ZN9X8jERERkZdEgivWXgSqWBNJQn5+0KwZ3L8PpUrB+vXPrAB52s2bN+nWrRvr1q0DzMnDZ82ahY+PT1JELCIvu+PHzfetkyfNuSBnzoROnWI8xTAM3nzzTWbNmsWwYcMYPnx48sQqIiIiIkki2VYFFRGJ1rJlZqXa/ftQo4aZZItjUi0wMJBSpUqxbt06nJ2dmTp1KsuXL1dSTUSSTsGC8Ntv0KgRBAdD587mysWhodGeYrFYKFKkCAAjRoygTZs27NixQ4saiIiIiLzglFgTkaQxcya0aQMhIdCyJaxbB/GoCE2TJg1vvPEGhQsX5sCBA/Tt21dDrEQk6Xl6wqpV8Omn5vaXX0KDBnD7drSn9O/fn3HjxgGwZMkSqlevTuHChZk0aRJ37txJjqhFREREJJlpKCgaCiqSqAwDRo/+78Nor17w7bdgbx/rLs6cOUN4eDj58+cHIDQ0lJCQENzc3JIiYhGRmC1ZAt26wYMH5vyQK1ZA8eLRNv/999+ZNm0a8+fP58GDBwC4uLjQrl07evfuTYUKFfQHAhEREZFULra5IiXWUGJNJNGEh8P778Pkyeb2p5/CyJEQhw+QW7ZsoWXLluTNm5f9+/fj7OycRMGKiMTB0aPQvDmcPQtubjB3rlmVG4OAgADmz5/PtGnT+PPPP637ixcvTp8+fXj99df1/w4RERGRVEpzrIlI8nr8GLp0+S+pNmkSfP55nJJqAIULF8bZ2Zk0adLg7++f+HGKiMRHsWJw4ADUqQMPH0LbtvDJJxAWFu0pHh4evPXWW/zxxx/s27ePbt264eLiwp9//knfvn3p3r17Mt6AiIiIiCQFJdZEJOEePDBX0Js/Hxwc4KefoF+/WJ9+6dIl69dZs2Zl165d+Pn5kTGOCx2IiCQpb29zvsgPPzS3R4+GJk3g3r0YT7NYLFSoUIE5c+bw77//MmnSJAoVKkSPHj2sbS5evMisWbOsQ0dFRERE5PmgxJqIJMydO1C7NqxfD66u5mTfr78eq1PDw8OZOHEiefPmZdmyZdb9BQsWxMHBIakiFhGJPwcHGDcOfv7ZfM9bvx7KlYN//onV6enSpaNfv378/fffNGjQwLp/xowZ9OzZk44dOyZV5CIiIiKSBJRYE5H4u3wZqlSB/fshXTrYutVcNS8Wrl+/TqNGjfjggw94/Pgxa9euTeJgRUQSUYcOsGcP5MwJp05B+fLmogaxZLFYsLP7779huXLlIm/evHTu3Nm67+rVq/z4448EBQUlZuQiIiIikoiUWBOR+DlxAipVMqs0smWDXbugYsVYnbpp0yZKlCjBhg0bcHFxYdq0aXz//fdJHLCISCJ79VU4eBCqV4fAQGjRAoYPNxdyiaOePXty8uRJWrRoYd03a9YsunTpQrZs2Xj//fc5ceJE4sUuIiIiIolCiTURibsDB6ByZbh4EV55xazaKFLkmaeFhITw4YcfUq9ePa5fv07RokU5ePAgvXv3xhLHRQ5ERFKFDBlg0yb43//M7REjoGVLuHEjzl3Z2dlhb2//RNcZyJkzJ3fv3uWrr76iYMGC1KhRg4ULFxISEpJYdyAiIiIiCWAxDMNI6SBSWmyXUBURYMsWaN7cXLCgTBlzIu8MGZ552qlTp+jYsSMHDx4EoG/fvkyYMAFXV9ckDlhEJJnMnQt9+sCjR2BnZ1b1NmtmPvLli1eXYWFhbNiwgenTp7N27VrC/78aLmPGjHTv3p1evXqRJ0+eRLwJEREREYHY54pSZcXa/fv3+eijj6hbty4ZMmTAYrEwfPjwKNsePnyY2rVrkyZNGry8vGjZsiVnz55N3oBFXhaLFkHDhmZSrXZt2LYtVkm1H3/8kVKlSnHw4EHSpUvH8uXLmTp1qpJqIvJi6dYNdu6E0qXN4aC7dsEHH0D+/FC0KHz6qVnxG4ehovb29jRq1IhVq1Zx7tw5hgwZQpYsWbhx4wZjx44lb9681K9fn+XLlxMaGpp09yYiIiIiUUqVibXbt28zY8YMHj16RPPmzaNtd/z4capXr05ISAiLFi1i9uzZnDx5kipVqnDz5s3kC1jkZfDdd9C+PTx+DG3awJo1kDZtjKcEBATQuXNnunTpQmBgIFWrVuXIkSMx/lyLiDzXypUz5107fx6+/hpq1QJ7e/j7bxg1yjyeIwe89RZs3AhxGNKZM2dOPvvsMy5cuMCyZcuoW7cuABs3bqRly5b4+vpy5syZJLoxEREREYlKqhwKGhGSxWLh1q1bZMiQgWHDhkWqWmvbti1+fn6cOXPGWpZ34cIF8ufPT//+/Rk7dmysrqehoCIxMAz47DNzQm4wPwxOmWJ+UIxBeHg4ZcqU4ffff8fOzo7hw4fzySef2MwfJCLyUrh71xw2v3IlrF9vLnQQIW1asxK4WTNzVWUvrzh1febMGWbOnMns2bNxd3fn9OnT1vfZU6dOkSdPHr3vioiIiMTDcz0U1GKxPHMi89DQUNasWUOrVq1sbtDX15caNWqwfPnypA5T5MUXHg7vvvtfUm3YMJg69ZlJNTAn4f7ggw/ImTMnO3bsYMiQIfpwJyIvp3Tp4PXXzeH0t26ZSbbevSFzZrh/HxYuhI4dzaH1deqY77OXLsWq67x58zJmzBguXbrEunXrrO+zISEhVK5cmbx583Ly5MmkvDsRERGRl1qqTKzFxpkzZwgKCqJ48eKRjhUvXpzTp08THBycApGJvCBCQswPelOngsUC33xjJthiSHpfu3aNAwcOWLc7duzIP//8Q+XKlZMhYBGR54Czs1mZNm0aXLkC+/fDxx9DoUIQGmouEPPOO5AzpzlX28iR8OefZvVwjN06U6hQIev2sWPHePz4MSEhIeTOndu6/8KFC9YFEEREREQk4Z7bxNrt27cB8Pb2jnTM29sbwzC4e/dulOc+evSIgIAAm4eIPCEwEBo3NqsoHB3h55/h7bdjPOXIkSOUKFGCpk2b2sxx6O7untTRiog8n+zsoHx5c+61f/6Bkydh/HhzNVGLBQ4fhqFDoUQJyJMH+veH7dvNBNwzlChRgitXrrB+/XocHR0Bs9q/cuXKFChQgAkTJnDr1q0kvkERERGRF99zm1iLENOQ0eiOjR49Gk9PT+sjR44cSRWeyPPn1i1zsu3Nm8Hd3VykoH37Z56WP39+MmTIQMaMGZWsFhGJj/z5zVVEd++Ga9dg1ixo0gRcXMzFECZNgho1IFMm6NoVli0zV2mOhqurKyVKlLBuHz9+nICAAE6fPs2HH35ItmzZeP3119m1axepcMpdERERkefCc5tY8/HxAf6rXHvSnTt3sFgseEUzAfDHH3+Mv7+/9XEplvOYiLzwLl6EypXht9/Axwe2bYP/X3UuKufPnycsLAwANzc31q5dy6+//krevHmTK2IRkRdTxozQowesWmX+wWP5cjOZ5uMDd+7AvHnQqpW53aQJfP89XL8eY5dFixbl33//5fvvv6dMmTKEhITw888/U7VqVYoUKcLXX38dbbW/iIiIiETtuU2s5c2bF1dXV44ePRrp2NGjR8mXLx8uLi5Rnuvs7IyHh4fNQ+Sld+yYOfzoxAnIkQN27YJy5aJsahgGc+fOpWjRoowfP96639fXN9qfOxERiSd3d2jeHObONSvZduwwh4XmyQOPHpmVxW++CVmymO/j48aZ7+VRduXOG2+8wYEDBzh48CBvvvkm7u7uHDt2jH79+pE1a1a6d+/Or7/+qio2ERERkVh4bhNrDg4ONGnShGXLlnH//n3r/osXL+Ln50fLli1TMDqR58yvv5qVapcvmxNo79lj/huFgIAAXn/9dbp3786DBw/w8/PTRNgiIsnFwQGqVoUvv4TTp+HoUXOBgzJlzAUO9u6FgQOhYEHzfXzQINi3z1zl+SmlS5dmxowZ/Pvvv3z77bcUK1aM4OBg5s6dS4UKFXj11VdZtGhRCtykiIiIyPPDYqTSP0euX7+eBw8ecP/+fXr06EGbNm1o27YtAA0bNsTNzY3jx49TtmxZSpUqxaBBgwgODmbo0KHcuXOHP/74gwwZMsTqWgEBAXh6euLv76/qNXn5bNwILVvCw4fmJNpr15pDi6Lw66+/0qFDB86dO4e9vT2fffYZAwcOxN7ePpmDFhGRSC5fNoeOrlwJfn7w+PF/xzJlgqZNoVkzcx7NKKqLDcNg//79TJ8+nYULFxIcHMyYMWMYOHCg9XhMc9uKiIiIvEhimytKtYm1XLlyceHChSiPnTt3jly5cgFw6NAhBg4cyL59+3BwcKBmzZpMmDAhTnM8KbEmL61ffoEuXcwV5urVgyVLIE2aSM3Cw8MZN24cQ4YMITQ0FF9fX3755RcqVqyYAkGLiMgz+fvD+vVmkm3dOnhyURl3d6hf30yyNWoEUaywfufOHebNm0fHjh3JmDEjAL/88gtfffUVH330Ea1bt06uOxERERFJEc99Yi05KbEmL6UpU6BfP3PoUIcO5tw9Tk6Rmv3777906dKFrVu3AtCuXTumTZsW7eIgIiKSyoSEwPbtZpJt5Uq4cuW/Y/b25tDSZs3Mx///4TIqtWvXZuvWrQwbNozhw4cDqmITERGRF5cSa3GgxJq8VAwDhg0z5+QBePddmDQJ7CJPubh27Vq6devGrVu3cHNzY8qUKXTv3l0fokREnleGAYcO/Zdke3oRqBIlzARb8+ZQsiQ88X5/48YN5s6dS4cOHciRIwcAK1euZPz48fTu3Zs2bdpoARsRERF5YSixFgdKrMlLIywM3n4bpk83t0eOhE8/tfngBPDo0SMGDhzI5MmTAShRogQLFiygYMGCyR2xiIgkpbNn/0uy7dplu8hBzpzmvGzNm5tVbY6OkU5v1KgR69atA8Db25tu3brRq1cvChQokEw3ICIiIpI0lFiLAyXW5KXw6BF06mTOo2axwHffQe/eUTZt0aIFK1asAKBfv36MGTNGVQgiIi+6W7fMBWxWrjQXtnn48L9jXl7QsKFZzVa/Pvz//5f+/fdfZs+ezYwZM7h06ZK1eY0aNejTpw/NmzfHKYppBkRERERSOyXW4kCJNXnh3b9vVhxs22bOozZ/PsQw8fS+ffto2bIlM2fOpHHjxskXp4iIpA5BQbBli5lkW7UKbt7875iTE9SsaSbZmjaFrFkJCwtjw4YNTJs2jbVr1xLx38uMGTPSo0cP3nzzTfLkyZNCNyMiIiISd0qsxYESa/JCu3HDrDI4dMhc8XPFCqhVy6aJv78/v/32G3Xq1LHuCw4OVpWaiIiY0wjs328m2VasgFOnbI+XK/ffvGyFCnHx0iW+//57vv/+e65evQqAxWKhbt269OnTh8aNG+Pg4JDstyEiIiISF0qsxYESa/LCOn8e6tWDkychQwZYvx5Kl7ZpcuXKFSpXrsy1a9c4cOAARYsWTZlYRUQk9TMMOH78vyTbr7/aHs+Xz5pke1ymDKvXrWPatGls3rwZACcnJy5fvkyGDBmSP3YRERGROFBiLQ6UWJMX0l9/mUm1f/8FX1/YtAleeSVSs/DwcBo1asSJEydYvHgxpZ9KvImIiETr6lVYvdpMsm3dCiEh/x3LkAEaN4bmzTmdOzczf/qJoKAgvv76a2uTTz75hCpVqlC3bl3s7e2TP34RERGRaCixFgdKrMkLZ+9eaNQI7t2DIkXMSaizZbMe/vfff/Hw8CBNmjQA3Lp1C0dHRzw9PVMoYBERee7dv2/+vlmxwlwE4d69/465ukLduuZw0caNIX16/v77b4oWLYq9vT0XLlwg2xO/p0RERERSmhJrcaDEmrxQ1q0zFyYICoLXXjMrCby9rYdXr15N9+7dadasGbNmzUrBQEVE5IX1+DHs2mUm2VauhIsX/ztmZweVKnGpWjW+vHyZADs7m99HX3zxBRUqVKBGjRrY2dklf+wiIiIiKLEWJ0qsyQvjxx+he3dzoulGjWDRInBzA8zFCD766COmTJkCQKlSpdixY4e1ak1ERCRJGAYcOfJfku2PP2yPFylinZftjJcX+f5/2oL8+fPTq1cvunXrRvr06ZM9bBEREXm5KbEWB0qsyQvhq6/g/ffNrzt3hlmzwNERgGPHjtG+fXv+/PNPAN5//31GjRqFs7NzSkUrIiIvq/PnYdUqM8m2Y4f5x6D/dylTJsamT8+8c+e4//AhYC540Lp1a/r06UPlypWxWCwpFLiIiIi8TJRYiwMl1uS5Zhjw6acwerS53b8/TJgAdnYYhsGsWbP43//+R1BQEBkyZOCHH36gQYMGKRuziIgIwJ075hQGK1fChg0QGAhAILDA2Zlpbm4cunvX2rxw4cL07t2bLl264OXllTIxi4iIyEtBibU4UGJNnluhodCnj1mdBjBmDHz0EVgs3Lt3j169erF48WIA6tSpw7x588icOXMKBiwiIhKN4GDYts1Msq1aBdeuAXAQmG6x8LPFwsPwcABcXV1p3749vXv3ply5cqpiExERkUSnxFocKLEmz6XgYOjQwZyzxs4OZsyAN94AYO/evXTo0IGLFy/i4ODAqFGjGDBggCaBFhGR50N4OPz2m5lkW7kSjh3DH5gPTAOOPtH040GDGBVRtS0iIiKSSJRYiwMl1uS54+9vTvS8Ywc4O8OCBdC8OWFhYYwaNYoRI0YQFhZG3rx5+eWXXyhbtmxKRywiIhJ/J09ak2zGnj3sA6YDC4EtWbJQuV07aNaM89mzc/f+fV599dX/a+/O42O6+geOf2bJzJBJZEEiNEtp7B7UUkVDaymqWo21lKq2P63SRxd0oZRaqqV9aOnz2B5KlapqFd1oadXSRZBSWrFUYkmM7JPMzPn9MYuMhIpHjInv+/W6r7n35tyTc8+9c+bOd86518cFFkIIIYS/k8BaKUhgTfiVkyfh7rudT1ULDnYOl0lIAJwPJZg5cyYAAwYMYM6cOXJOCyGEKF9OnoTPPoNPPuHsF18QYrXiHgg63GhkjtXKlJ49GTN6NNSvj6WwkN27d1OvXj2qVKni06ILIYQQwn9IYK0UJLAm/Maff0KnTvDHH1C1qvNGz0V+lT9y5Aht27Zl8uTJDBw40IcFFUIIIa6BnBz44gtnb7ZPP+X/MjJYACwFegNoNHxerRrdTpwAoHJwMPVq16Ze06bUq1+fevXqUa9ePSIjI+U+bUIIIYTwIoG1UpDAmvALSUnQubPzZs5xcfDFF+RVr8769evp2bOnJ1lBQQEGg8GHBRVCCCF8wGaD778n++OP0SUlUWHvXjh9mtXAc8Bh4GIXvSFmszPI1qiRJ9jWqVMnCbYJIYQQNzAJrJWCBNbEdW/LFuje3XlvtUaNYMMGcitV4rbbbmPPnj18/vnndOnSxdelFEIIIa4vJ0/Cnj2QlETuzz9zYNcukg8dItluZx+QDPwBOC7YrJrZzIn33oOGDaF2bWa89RYAffr04aabbrrGOyGEEEIIX7jcWJH+GpZJCHElPv0Uevd2PgW0bVvnPdVCQqgItGvXjpMnTxIQEODrUgohhBDXn4gI59ShAxWBJkATmw0OHnT2BE9KIv+XX/j9119JTk0lGWewLSQ7G/r3d+YREMAs4K/CQlqnpHBT9+7QqBEfb9vGx2vWeHq41a9fn9jYWHQ6na/2VgghhBA+ID3WkB5r4jq2aBEMHQp2O3TvTsa775Kv0RAVFQVAfn4+586dIyIiwrflFEIIIfydxQJ79zoDbq5ebiQl4cjOZhLOgNtcIMSVfKTJxNv5+V5ZmEwm6tSp4wm2uaeaNWui18vv2UIIIYQ/kaGgpSCBNXFdev11eP555/zgwWwZNIgHH3qIuLg4vvnmG/lFXAghhChrDgccOVIs2MbBg3zvcLAZPL3c9gP5F8kmICCA2rVr06NHDyZNmuRZb7PZJOAmhBBCXKdkKKgQ/kopZ0BtxgwAbM88w6TAQF696y4cDgdGo5HU1FRq1Kjh44IKIYQQ5ZxW63xgUFwc9Ohxfn1eHq2Tk2ntDrQlJWHfvZuU9HRPoM09/QbkFBayd+9emhUUQFQUNGpEQe3aVIqOJjY2lh9++IHQ0FAATp06RXBwMCaT6drvrxBCCCFKTXqsIT3WxHXEZoNHH3UOAQWOjh3LgK1b2bJlCwCDBg3iX//6F0FBQT4spBBCCCGKUcr5sIQiwTb27MGxbx/HCgtJBioDzV3J9wENgGC9Hstzz6H5xz+gYUN6vvgin6xdy80330z9+vW9hpTWqVOHihUr+mwXhRBCiBuJDAUtBQmsietCXh706eN8WIFOx+r/+z+GLlvG2bNnMZvNzJ07lwcffNDXpRRCCCFEaRQWwu+/ewXbSEpCHTvGX8Bx4LYiyVtqNOy4yOW5RqMhNja22D3c6tWrh9lsvhZ7I4QQQtwwJLBWChJYEz5nsUD37rB1K7lGI6Pat2fehg0ANG/enOXLl1OzZk3fllEIIYQQV8/Zs84gW9F7t+3Zg8rJ4SQUG1KarNFw+iKX7S+//DITJ04E4MyZM6xZs4aGDRvSsmXLa7QzQgghRPkj91gTwl+kpsLdd0NSEnvNZvpWqcI+V1Bt9OjRTJw4EYPB4ONCCiGEEOKqCg2FO+5wTm4OB5rDh4ncs4fIpCTudAfcDh0CpTiN855tyTiHkiYDyTod9b78EoxGaNSIXRkZPProo9SvX5+9e/d6sn799dcJDg729HALDw+/tvsrhBBClFPSYw3psSZ86NAh6NQJdfgw7wYH84zVSr7VSkREBEuWLKFjx46+LqEQQgghfC0nB5KTve/flpQEGRkAKEDjSroZmKLTUatKFebcfz80aoSjQQOC776bnJwcT5ZVq1YtcUhp1apV0Wg0F5ZACCGEuOHIUNBSkMCa8IlffnH2VDt1irfDwxmZng5Aly5dWLRoEVWrVvVxAYUQQghx3VLK2ev9gnu38dtvzvu6FZEHjAOSK1QgWaMhJTf3otmGhYV5gmxPPfUUDRo0KNv9EEIIIa5TElgrBQmsiWtu82bo0QMyM6FxY7JWraJVjx4MHTqUESNGoNVqfV1CIYQQQvijggI4cMA72JaUBH/95UmSDezn/FDSZLOZZIeDP7OzKfrVYOvWrbRu3RqA999/nzlz5tCnTx9GjhzpSaOUkh5uQgghyiW5x5oQ16uPP8bWty/LCwoYkJCA5pNPCKpUiV9++YWAgABfl04IIYQQ/sxggIYNnVNRGRmeQJs5KYlmSUk027sXcnPh3DnA2bPtAK57uAUG0uDll6FpU+e92774gm3bttGqVStPlunp6cTGxlK3bl2v4aTVq1cnNDSUkJAQgoOD5QdDIYQQ5Zr0WEN6rIlr6D//QT32GB2V4mvgrRkzGPHMM74ulRBCCCFuRA4H/Pmn933b9uyBP/5wDjUt4k9gl0ZDrbg4mrZoAY0asVWno+3o0Zf8FxqNhkqVKhESEuIJti1dupSoqCgAvv32W/bs2UOLFi1o0aIFADabjVOnThEaGorJZJIecUIIIXxCeqwJcT1RCqZNg7Fj0QD3tWrFzn37iKhRw9clE0IIIcSNSquFWrWcU8+e59dnZ8O+fV7BtpuTkrj57FlnIO7PP+GDD2iJazhpxYokV6lCssHAb3l5nMrNxZKTQ57VilIKi8WCxWIhJSUFAJ1O5/lXq1atYvbs2bzwwguewNrhw4eJj48HwGAweAXlis5fuK5Tp05UqlQJAKvVik6nQ6+XrztCCCHKlt/3WMvOzuall17iww8/JCMjgzp16jBmzBj69u172XlIjzVRphwOckeO5K/Zs7kF4IUXUK++SmpamufXWiGEEEKI65pSzvu0XXjvtv37wWYrcRMrYAHOul4twFmdjsTgYAKCgsBsZkFeHuuzs+kdE0OvW24Bs5mfc3JosXw59lJ+Tfntt9+oU6cOAOPGjePVV1/ln//8J2+++SYAFouFQYMGXTRAd+Gr2WyW3nJCCHEDu2F6rPXs2ZOdO3cydepU4uPjWbZsGf369cPhcNC/f39fF0/c6AoLSbr/fvquW0cB8POUKQSPGYMGJKgmhBBCCP+h0UCNGs6pa9fz663W8w9LcAfd0tMhJwdjdjYRromCAmd6ux3OnnVOwBDXxOnTsGsXAE2BQpwPWfAKypUw71mn1VI5IQGCg8FsxnLyJAAV1693/i+zmdNWK2vXrr3sXdbpdISEhPDVV1/RuHFjANatW8cnn3xC+/bt6devn2uX7Hz55ZfFetEZjcbL/l9C+C2lnMH1ggJne2C1es+XdvmCvymrFUd+PpqCArQFBVBQQH5eHudyc7Hl52MrKHBOViu2wkJwONDp9ej1ek+vVZ1rqmo0otHrQa+nQKsFvR59QABa1zrPpNN5L5e07lqmuZztdDpnOy18wq97rH3++ed069bNE0xz69SpE/v27ePo0aNeXc0vplz2WPv3v5m/di1/WCzOhkSn825YXMvuRsbd+NSpUYPW9euDTkehUqzdtQudXk/31q3RGQyg07HvyBFOZ2WhCwhwTno9eqPx/HJAgPeywUBgUBBhVao43/A6Hdl5eegMBkwVKpTbXwJVTg7vtGjBM8nJWIFqISGs37yZf/zjH74umhBCCCHEtVVQADk5zmGmJU2X+tul0l+EDWfATQ+EuNZlAKu4SGDugnWFRfLaX60atUNDwWxm3KlTvJqSwhO33MKc9u3BbOaMVkuVGTOKlaGC0UhIcDChrkBbSFgYoZUrewJwQ4YMIS4uDoDU1FTS0tKoVq0akZGRV1bHotxxOBxoNBrn9yWlyLZYyLVYsOXmOqe8POz5+djy8s5P+fnOKS8Pm9VKpYAAmlar5glaffLzz+Tl5dE1JoZgjQasVrYfO8bOEyew22zYCgvPT+5lmw2b3e58tdmw2+1EKsU41/YoxUjgGDAZqOsq/0fA2zjfjxeb7BcsV8b51GK3NsD3wGrgfte6ZcCDV1CfRQMfD7jyfAcY5lr3LdAV0LkmfZH5C5eLzn8NhLrymAl8BjwMDHCtOwqMusT2F8t7BFDVlcdWYDvOHx7au9bluerCs61Gg16nQ6fVOr/7u2MA7u/+rvkGFSsSbDSCXk+6Upx0OAgxGokKDAS9HqXVkma3O7/Xu77v6/R69AaD5zv+JYORI0ZAWNgVHKHrzw3RY+3jjz/GbDbTq1cvr/UPP/ww/fv3Z/v27dx+++0+Kp2PLVzI8m3b+LqUmz0KtHbNZwOJrvkCnG9WcDaWy0uZbw9gTZHlEJyN6HGNhuquN+Iou505hYWeRsHTOLiXXZO+yPytgYEsiY/3BOx67NvHWZuN+Y0acUtwMOj1rDhxgqXHjqHTas83NK7J08hc0PhEBAUx5o47PA3EvJ07OZ2by4PNmxMXEQE6HXvT0th88KBXYLJooPKDhQtZ6/o19p4WLVi4bh2VK1cuZc0JIYQQQpQDBoNzCg39+7SXy+GAvLwSA2/67GwqXxCIC8vO5rG/C9hlZaFsNvI4H2SLS02F1FQAOgABwK0HD8LBg4BzyGsTzgfmzuH8Ap9ntZJ3+jSpp0+XWPxuixYRV6UKBAbywZkzjNq/n35xcSzr1g3MZgorVCD+rbcICQwkJCiI0KIPgQgPJ6RKFUKrViUkMtIrYFelShXPk+azs7OxWCyYTCbPdahSikOHDqGUwuFweF7/bv6WW24hPDwcgFMnT7Jv714qBQfTtHFjZ68lh4Ovvv4aa34+Drsdh92Ocm3vmXetd9jtzvztdlr84x/UqVkTHA5OpKXx2VdfERIURO8uXZz5KsWSNWs4k5HhLIs7P3f5iuTlKbfdTvsmTbizaVNQirQzZ5jxwQcEmkxMeOgh57mjFLNWr+b348cvue8XLndq2JBH7rgDlMKSnc2gf/8bh1J8Ony4s7yFhYz/5BM2Hzx4PiBlt2N3vdrsdmwOh2eyOxzYlOLekBDmV68OVit2qxX9n38CcCYoiPDCQrBaeVYp5pXybdIe+KbI8iNAOs77I7rDBOuAV0uZb31gXJHlL3AGxEYCdU0mMBpJtdv57hIB8JJoK1SAJ590thdGI7pFi+DwYeyPPAKtWoHRiP6nn2DWLPSu7296dwcSV6cau92O3eHw1Lnd4UAD8Pnnzt51djv2SZNgxw60jzwC7dqBzUZBUhK5M2eWsiaAQYOc3xvtdn77/nu+OXSI9vXqQXw82Gyczcjgox9+KHW2A0NCnIE1m40NeXlMttt5ivOBtbPA0KIbuHsP/o3vgLau+fdxHrM+wAeudXbg78ZWaSgeEFwKdAdnfZSTwNrl8uvA2t69e6lbt26xm5I2atTI8/cbNrCWmMgDBgMNMjI8DYutSANjdzfirlf31DA0FKpVA5sNrdVKmwMHsCuF7uabnV337XaiTp6kTk4OdqU8kw2c80Ve3b9A2HFegBTlcL3qXB8+FBZSgDOAB5x/EtXfdKisnJ8P27Z5ln8ETgF5333nWXcA568GpVEbGPPN+Y+g2cBeoNWGDcS51m0FnvqbfAzAjKefZvibb5bbnnlCCCGEED6h1UJgoHOKiLhq2WoKCqiYk0PF7GyiLgi83eGaiq6rnp3Nz0V63DmyssjMzMSSmcnZ7Gwsubmczc8v1jPupuPH4fhxwPmlLAqIPHwYZs8GV5oUgIyMUpV/U7VqtDOZwOFgcWYmw8+eJdFkYmVYmDOg5HAQf+pUqevlQ62WXq5A12acX8QTgM1F0vQDzpQy39lAHdf878DjQD2g98iRnjRTcQaCSkO3eDF3uubTgTdw9oaasGSJJ80anL2USiN8+3Ye+c9/AGfPRvfgYvXoo7iv9n/DGbwojXOnTzuHRAPaIuvtWVmeeXdHB3cwQ++eXJ0P9BoNeq32/KtWS2xICDRt6glUtdm6lSy7HVOHDlC1KhiNNPj9d3rt348+IAC9a3ikPiAAvcFw/tXVW0lvMKA3GomIiIDBgz35jvvkE87l5lKrRw/nkHHg7kOHWPnrr848i0zukVQlTQaDAWrW9OzzmqeeQimF2Wx2/i+g14MP0ruUATCllNdQyeUdOmC1WjGZTGAyAdAmL4/DI0Z4euW5Xy827341d+gArmD2ozt30u7gQWdMokEDAKqfOcOcDz+8rDyLLoe+9JLzGAGNV61i4Nq1NO/YER58EGw2DGlpdHvsMew2m7O3oevVbrdjKyy8aN4VX37Z+dAamw3jp58SvngxwbfdBo8+CjYb9rw8tEOH4rjEd3HF+V6Gbo5u3SAmBlwPkbmR+PVQ0Pj4eG6++WY2bNjgtT41NZWoqChee+01xo4dW2w7q9WK1Wr1LJ87d47o6GiOHTtWfoaCXk8cDk9Qzv0LQW52NvbCQgINBrRKgd2OxWIhOzMTu6vrsaOwEHthIfaCAk9j4ZkvLMRusxFkNNI4OtqT/xe//EJufj7t4+OpZDSC3c7uo0f59cgRZ0PiblQuaFzsrl/O3MHHcIOBp+rX9+Q7dfduTuTkMLxmTeIDA8Fu5+vUVP577JhXkNLhDjY6HASbTIyeMYNG993n6yMghBBCCCF8yeGA3NzzQ1iLzufklDhfkJnJ7r/+wpKVhSU7m3O5uZzLzcVitXLOauVcYaGnh5yF873l1uAMeAHMB57HOXpkQZHi1MDZ40TrenXPay8xPx3o7Nr+K+AFnD31ivagus9VhqJ5lpRf0XVDtVq66vWg0ZCsFJNsNmpotUyvUMEZCNFqeTkvj1SlnPlqNM5tXUMkta5J41rnnjpUrEinwEDQajllt/Ovs2cJ1OkYU7WqMyir0fC+xcLRggJPPhqt1rk9nJ8v8n80Gg31zWbahoeDRkO+UqxIS0Oj0TCwRg00Wi0EBLA9K4sThYVegSmd0YjOFZTSGwzoTCb0JpNz2WQiuFIloiIjPYGqU9nZ6CtWJCQ8HK2rB5hDr0djNKIxmZxD7oQoQ+6emhcGAC+2LiIiwhkALUcyMzO56aabsFgsnqdOl8TvA2s1a9Zk/fr1XuvdgbUpU6YwZsyYYtu98sorTJgw4VoVUwghhBBCCCGEEEL4oWPHjlHD1ROzJH4d5g4PDyc9Pb3Y+gxXd+mwi4zrHTt2LKNGjfIsOxwOMjIyCA8PLzfD9dyRVemF5xtS/74l9e9bUv++J8fAt6T+fUvq37ek/n1L6t+3pP59S+rft8pj/SulyMrKIirq0ned8+vAWsOGDVm+fDk2m83rPmt79uwBoIFrTPOFjEZjscdfh4SElFk5fSk4OLjcnNT+SOrft6T+fUvq3/fkGPiW1L9vSf37ltS/b0n9+5bUv29J/ftWeav/Sw0BddP+bYrr2P333092djYfffSR1/rFixcTFRVFy5YtfVQyIYQQQgghhBBCCFHe+XWPtS5dutCxY0eGDRtGZmYmtWrVYvny5WzYsIGlS5d6HrcrhBBCCCGEEEIIIcTV5teBNYDVq1fz4osvMm7cODIyMqhTpw7Lly+nb9++vi6aTxmNRsaPH19syKu4NqT+fUvq37ek/n1PjoFvSf37ltS/b0n9+5bUv29J/fuW1L9v3cj179dPBRVCCCGEEEIIIYQQwlf8+h5rQgghhBBCCCGEEEL4igTWhBBCCCGEEEIIIYS4AhJYE0IIIYQQQgghhBDiCkhgzc8sWrQIjUbDrl27fF2UG4q73kuann322cvOZ/DgwZjN5jIsaflTtO43b95c7O9KKWrVqoVGo6Fdu3bXvHw3mrfffhuNRkODBg18XZRyT87964t8/l4//pdjodFoeOWVV65+oco5aft9Y/v27dx///1ER0djNBqJiIigVatWPPPMM74u2g3nxx9/pFevXlSrVg2DwUBkZCSJiYls27at1HklJyfzyiuvkJKScvULWk6423mTycSRI0eK/b1du3bSHpWxC7//mkwmIiMjad++PVOmTOHUqVO+LuJ1RQJrQpTCwoUL2bZtm9c0YsQIXxfrhhAUFMT8+fOLrf/222/5448/CAoK8kGpbjwLFiwAYN++fWzfvt3HpbkxyLkvhPA1afuvvXXr1nH77beTmZnJ9OnT+eKLL3jrrbdo3bo1K1as8HXxbij/+te/aN26NcePH2f69Ol89dVXzJgxg7/++os2bdowe/bsUuWXnJzMhAkTJLB2GaxWKy+99JKvi3FDc3///fLLL5kzZw6NGzdm2rRp1K1bl6+++srXxbtuSGBNiFJo0KABt912m9cUHR3t62LdEPr06cNHH31EZmam1/r58+fTqlWrq3oc8vLyrlpe5cmuXbvYvXs33bp1Aygx2PO/yM3Nvar5lRfX8twXQogLlXXbL0o2ffp04uLi2LhxI3379iUhIYG+ffsyY8YMjh496uvi3TC+//57nn76abp27cqWLVsYOHAgd9xxBwMGDGDLli107dqVkSNH8v333/u6qOXS3XffzbJly9i9e7evi3LDcn//bdu2LQ888AAzZ84kKSmJwMBAevbsycmTJ31dxOuCBNb83K5du+jbty+xsbFUqFCB2NhY+vXrV6zLrLsr56ZNmxg2bBiVK1cmPDycnj17cuLECR+VvnxZsWIFrVq1IjAwELPZTOfOnfnll19KTLtv3z7uuusuAgMDqVKlCsOHD5egwt/o168fAMuXL/esO3fuHB999BFDhgwpln7ChAm0bNmSsLAwgoODadq0KfPnz0cp5ZUuNjaWe+65h9WrV9OkSRNMJhMTJkwo253xU+4vU1OnTuX222/ngw8+8DpvU1JS0Gg0TJ8+ncmTJxMdHY3JZKJZs2Z8/fXXXnm98soraDQafv75ZxITEwkNDaVmzZrXdH/8RVmc+4888ghhYWEltjt33nkn9evXL4M9KV/atWtX4hDcwYMHExsb61l2vy9mzJjBm2++SVxcHGazmVatWvHjjz9euwKXY5d7LMSV+bu2f/PmzSUOWXef+4sWLfJa/+9//5v4+HiMRiP16tVj2bJlcqxKkJ6eTuXKldHr9cX+ptV6f4W7nGtQ9+1I5Bq0dKZMmYJGo+Hdd98tdiz0ej3vvPMOGo2GqVOnetbv37+ffv36ERERgdFoJDo6moceegir1cqiRYvo1asXAO3bt/cMs7vwfSKcnn/+ecLDwxk9evQl0+Xn5zN27Fji4uIwGAxUr16dJ598EovF4klz3333ERMTg8PhKLZ9y5Ytadq06dUufrkVHR3NG2+8QVZWFvPmzfOs37VrF/feey9hYWGYTCaaNGnChx9+WGz7v/76i8cee4ybbroJg8FAVFQUiYmJfh2kk8Can0tJSaF27drMmjWLjRs3Mm3aNFJTU2nevDlnzpwpln7o0KEEBASwbNkypk+fzubNmxkwYIAPSu6f7HY7NpvNawJ47bXX6NevH/Xq1ePDDz9kyZIlZGVl0bZtW5KTk73yKCwspGvXrtx1112sWbOG4cOHM2/ePPr06eOLXfIbwcHBJCYmeoajgDPQoNVqS6y7lJQUHn/8cT788ENWr15Nz549eeqpp3j11VeLpf3555957rnnGDFiBBs2bOCBBx4o033xR3l5eSxfvpzmzZvToEEDhgwZQlZWFitXriyWdvbs2WzYsIFZs2axdOlStFotXbp0KfE+JD179qRWrVqsXLmSuXPnXotd8Ttlce6PHDmSs2fPsmzZMq9tk5OT2bRpE08++WTZ7dANas6cOXz55ZfMmjWL999/n5ycHLp27cq5c+d8XTQhLqo0bf/leO+993jsscdo1KgRq1ev5qWXXmLChAkl3kfyRteqVSu2b9/OiBEj2L59O4WFhSWmk2vQsmO329m0aRPNmjWjRo0aJaa56aabuPXWW/nmm2+w2+3s3r2b5s2b8+OPPzJx4kTWr1/PlClTsFqtFBQU0K1bN1577TXA+bngvrWMu0eo8BYUFMRLL73Exo0b+eabb0pMo5TivvvuY8aMGQwcOJB169YxatQoFi9ezJ133onVagVgyJAhHD16tFg++/fvZ8eOHTz88MNlvj/lSdeuXdHpdHz33XcAbNq0idatW2OxWJg7dy6ffPIJjRs3pk+fPl6B47/++ovmzZvz8ccfM2rUKNavX8+sWbOoVKkSZ8+e9dHeXAVK+JWFCxcqQO3cubPEv9tsNpWdna0CAwPVW2+9VWy7J554wiv99OnTFaBSU1PLtNz+zl1/JU1Hjx5Ver1ePfXUU17bZGVlqcjISNW7d2/PukGDBinA69gopdTkyZMVoLZu3XpN9sefFD3nN23apAC1d+9epZRSzZs3V4MHD1ZKKVW/fn2VkJBQYh52u10VFhaqiRMnqvDwcOVwODx/i4mJUTqdTh04cKDM98Wf/fe//1WAmjt3rlLKeX6bzWbVtm1bT5rDhw8rQEVFRam8vDzP+szMTBUWFqY6dOjgWTd+/HgFqHHjxl27nfAzZX3uJyQkqMaNG3ulHzZsmAoODlZZWVlls1N+7MLP34SEhBLrfdCgQSomJsaz7H5fNGzYUNlsNs/6HTt2KEAtX768rIte7lzpsVBKKUCNHz++7AtZTlxO2+9unzZt2uS1rfvcX7hwoVLK2R5FRkaqli1beqU7cuSICggIKHasbnRnzpxRbdq08VxvBgQEqNtvv11NmTLF00bLNWjZSktLU4Dq27fvJdP16dNHAerkyZPqzjvvVCEhIerUqVMXTb9y5coS3zPivKLtvNVqVTfffLNq1qyZ5zomISFB1a9fXyml1IYNGxSgpk+f7pXHihUrFKDee+89pZRShYWFKiIiQvXv398r3fPPP68MBoM6c+bMNdgz//F3cQellIqIiFB169ZVSilVp04d1aRJE1VYWOiV5p577lHVqlVTdrtdKaXUkCFDVEBAgEpOTi67wvuA9Fjzc9nZ2YwePZpatWqh1+vR6/WYzWZycnL47bffiqW/9957vZYbNWoEUOLTVkRx//3vf9m5c6fXtHHjRmw2Gw899JBXTzaTyURCQkKJv8I++OCDXsv9+/cHnJF+cXEJCQnUrFmTBQsWsGfPHnbu3FniUDiAb775hg4dOlCpUiV0Oh0BAQGMGzeO9PT0Yk+xadSoEfHx8ddiF/zW/PnzqVChAn379gXAbDbTq1cvtmzZwsGDB73S9uzZE5PJ5FkOCgqie/fufPfdd9jtdq+00jvw8pTFuT9y5Eh+/fVXz31hMjMzWbJkCYMGDZKnF5eBbt26odPpPMvy+Sv8QWna/r9z4MAB0tLS6N27t9f66OhoWrdufdXKXF6Eh4ezZcsWdu7cydSpU+nRowe///47Y8eOpWHDhpw5c0auQa8TynWrhby8PL799lt69+5NlSpVfFyq8sNgMDBp0iR27dpV4rBCdw+0wYMHe63v1asXgYGBntuR6PV6BgwYwOrVqz29xe12O0uWLKFHjx6Eh4eX7Y6UQ+5z/9ChQ+zfv9/TvhRtj7p27UpqaioHDhwAYP369bRv3566dev6rNxlQQJrfq5///7Mnj2boUOHsnHjRnbs2MHOnTupUqVKiTdgv7DBMBqNgNys/XLVrVuXZs2aeU3useDNmzcnICDAa1qxYkWxIbl6vb7YcYiMjASc99MQF6fRaHj44YdZunQpc+fOJT4+nrZt2xZLt2PHDjp16gQ47+Xy/fffs3PnTl588UWg+PlerVq1si+8Hzt06BDfffcd3bp1QymFxWLBYrGQmJgI4DVEEc6fzxeuKygoIDs722u91P3lKYtzv0ePHsTGxjJnzhzAeS/OnJwcGQZaRuTzV/ib0rb9f8d9jRMREVHsbyWtE07NmjVj9OjRrFy5khMnTvDPf/6TlJQUpk+fLtegZaxy5cpUrFiRw4cPXzJdSkoKFStWRK/XY7fbLzpsVFy5vn370rRpU1588cViw6LT09PR6/XFgpkajYbIyEivc3vIkCHk5+fzwQcfALBx40ZSU1NlGOgVyMnJIT09naioKE9b9OyzzxZri5544gkAT3t0+vTpcvkeKX43TOE3zp07x2effcb48eMZM2aMZ73VaiUjI8OHJbuxVK5cGYBVq1YRExPzt+ltNhvp6eleFzZpaWlA8S9eorjBgwczbtw45s6dy+TJk0tM88EHHxAQEMBnn33m1XNqzZo1JabXaDRlUdRyY8GCBSilWLVqFatWrSr298WLFzNp0iTPsvt8LiotLQ2DwVCsJ5TU/eW72ue+VqvlySef5IUXXuCNN97gnXfe4a677qJ27dpltQvlislkKvH+aCXd31SULTkWZeNy2353W+O+j5HbhfXvvsYp6ebUJX1uiOICAgIYP348M2fOZO/evfTo0QOQa9CyotPpaN++PRs2bOD48eMlBgOOHz/OTz/9RJcuXQgLC0On03H8+HEflLZ802g0TJs2jY4dO/Lee+95/S08PBybzcbp06e9gmtKKdLS0mjevLlnXb169WjRogULFy7k8ccfZ+HChURFRXl+lBSXb926ddjtdtq1a+f5Pjx27Fh69uxZYnr39WWVKlXK5XtEeqz5MY1Gg1LK86u323/+859iw61E2encuTN6vZ4//vijWG8293Sh999/32vZfQPxkp5qJrxVr16d5557ju7duzNo0KAS02g0GvR6vdewq7y8PJYsWXKtillu2O12Fi9eTM2aNdm0aVOx6ZlnniE1NZX169d7tlm9ejX5+fme5aysLD799FPatm3rdUxE6ZTFuT906FAMBgMPPvggBw4cYPjw4WVS9vIoNjaW33//3SuYkJ6ezg8//ODDUt2Y5FhcfaVp+91P80xKSvLKY+3atV7LtWvXJjIysthQrqNHj8qxKkFqamqJ6923eomKipJr0Gtg7NixKKV44oknin2/stvtDBs2DKUUY8eOpUKFCiQkJLBy5cpLBvalx/KV6dChAx07dmTixIleIyDuuusuAJYuXeqV/qOPPiInJ8fzd7eHH36Y7du3s3XrVj799FMGDRok16eldPToUZ599lkqVarE448/Tu3atbnlllvYvXv3RduioKAgALp06cKmTZs8Q0PLC+mx5qc0Gg3BwcHccccdvP7661SuXJnY2Fi+/fZb5s+fT0hIiK+LeMOIjY1l4sSJvPjii/z555/cfffdhIaGcvLkSXbs2EFgYCATJkzwpDcYDLzxxhtkZ2fTvHlzfvjhByZNmkSXLl1o06aND/fEfxR9pHlJunXrxptvvkn//v157LHHSE9PZ8aMGcWC0OLvrV+/nhMnTjBt2rQSL7obNGjA7NmzmT9/PjNnzgScv/B27NiRUaNG4XA4mDZtGpmZmV7vA3Flrva5HxISwkMPPcS7775LTEwM3bt3L4tilyvuXpYDBw5k3rx5DBgwgEcffZT09HSmT59OcHCwj0t445BjUXZK0/bfc889dOjQgSlTphAaGkpMTAxff/01q1ev9tpGq9UyYcIEHn/8cRITExkyZAgWi4UJEyZQrVo1tFr5vb+ozp07U6NGDbp3706dOnVwOBz8+uuvvPHGG5jNZkaOHCnXoNdA69atmTVrFk8//TRt2rRh+PDhREdHc/ToUebMmcP27duZNWsWt99+OwBvvvkmbdq0oWXLlowZM4ZatWpx8uRJ1q5dy7x58wgKCqJBgwaA8ym5QUFBmEwm4uLipNfgZZg2bRq33norp06don79+gB07NiRzp07M3r0aDIzM2ndujVJSUmMHz+eJk2aMHDgQK88+vXrx6hRo+jXrx9Wq7XYvdmEt71793rul3bq1Cm2bNnCwoUL0el0fPzxx55egvPmzaNLly507tyZwYMHU716dTIyMvjtt9/4+eefPU+Tdj8t94477uCFF16gYcOGWCwWNmzYwKhRo6hTp44vd/fK+eqpCeLKzJkzRwFqz549Simljh8/rh544AEVGhqqgoKC1N1336327t2rYmJi1KBBgzzbXeypHhd7kpPwdjlPRVmzZo1q3769Cg4OVkajUcXExKjExET11VdfedIMGjRIBQYGqqSkJNWuXTtVoUIFFRYWpoYNG6ays7Ovxa74ncupe6WKPxlxwYIFqnbt2spoNKqbb75ZTZkyRc2fP18B6vDhw550MTExqlu3bmVUev933333KYPBcMmnW/Xt21fp9Xr1448/KkBNmzZNTZgwQdWoUUMZDAbVpEkTtXHjRq9t3E8FPX36dFnvgt8q63PfbfPmzQpQU6dOvcp7UL5c+PmrlFKLFy9WdevWVSaTSdWrV0+tWLHiok8Fff3114vliTyh8opc6bFQSur8cpWm7U9LS1OpqakqMTFRhYWFqUqVKqkBAwaoXbt2eT0V1O29995TtWrVUgaDQcXHx6sFCxaoHj16qCZNmpTxXvmXFStWqP79+6tbbrlFmc1mFRAQoKKjo9XAgQOLPU1PrkHL3rZt21RiYqKKiIhQer1eVa1aVfXs2VP98MMPxdImJyerXr16qfDwcGUwGFR0dLQaPHiwys/P96SZNWuWiouLUzqdrsT3yY3uUtdA/fv3V4DnqaBKKZWXl6dGjx6tYmJiVEBAgKpWrZoaNmyYOnv2bIn5u/No3bp1We2C33MfA/dkMBhU1apVVUJCgnrttddK/HzYvXu36t27t6pataoKCAhQkZGR6s477/Q8Wdrt2LFjasiQISoyMlIFBASoqKgo1bt3b3Xy5MlrtXtXnUYp16MchF8YOXIks2fPxmKxeLpTCiHE9SAlJYW4uDhef/11nn32WV8XR1ymZ555hnfffZdjx47Jr+WXIJ+/1w85FuWLxWIhPj6e++67r9i9k8TVM3jwYFatWlXsIUJCCCH+dzIU1E/89NNP7Ny5kwULFnDvvffKhaQQQoj/yY8//sjvv//OO++8w+OPPy5BtYuQz9/rhxwL/5eWlsbkyZNp37494eHhHDlyhJkzZ5KVlcXIkSN9XTwhhBDiikhgzU8kJiZy7tw57r33Xt5++21fF0cIIYSfa9WqFRUrVuSee+7xeqqr8Cafv9cPORb+z2g0kpKSwhNPPEFGRgYVK1bktttuY+7cuZ77JQkhhBD+RoaCCiGEEEIIIYQQQghxBeTxO0IIIYQQQgghhBBCXAEJrAkhhBBCCCGEEEIIcQUksCaEEEIIIYQQQgghxBWQwJoQQgghhBBCCCGEEFdAAmtCCCGEEEIIIYQQQlwBCawJIYQQQgghhBBCCHEFJLAmhBBCCCGEEEIIIcQVkMCaEEIIIYQQQgghhBBXQAJrQgghhBBCCCGEEEJcgf8HFl7DVLeRvxQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY Diatoms (0-100 m) with CY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY Diatoms (0-100 m) with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py311)",
   "language": "python",
   "name": "py311"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}