{
 "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",
    "# \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",
    "# \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_3223056/391916811.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_Thrml19/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_Thrml19/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_Thrml08/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_Thrml08/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_3223056/2338418733.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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\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY SST with CY Thermal',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([ 6.09901394,  5.98979208,  7.70769097,  9.1698652 , 13.09307022,\n",
       "       16.58461295, 20.71957752, 19.56493846, 15.88781688, 12.09782938,\n",
       "        9.42774859,  5.93728831])"
      ]
     },
     "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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\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY SST with WY Thermal',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([ 6.55805313,  4.6761362 ,  6.77186194, 10.27311911, 13.59493183,\n",
       "       17.42428301, 18.35690238, 18.73194292, 16.01386188, 11.39800505,\n",
       "        8.80717597,  6.78115408])"
      ]
     },
     "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_3223056/2771304440.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_Thrml19/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_Thrml19/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_Thrml08/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_Thrml08/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_3223056/178454329.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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\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY PAR with CY Thermal',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([ 14.21095929,  25.7881738 ,  40.84249474,  60.0591015 ,\n",
       "        84.93951999,  93.86293186, 101.22744323,  80.5323536 ,\n",
       "        61.02210488,  29.35150897,  13.13822587,  10.14679653])"
      ]
     },
     "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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\n",
      "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 Thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY PAR with WY Thermal',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([ 15.32649944,  26.11869233,  53.62521119,  62.5976493 ,\n",
       "        90.68618976, 103.30374366,  93.93976964,  79.87383634,\n",
       "        51.12972378,  31.54723225,  20.31534137,   8.03696216])"
      ]
     },
     "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_3223056/1288103633.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_Thrml19/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_Thrml19/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_Thrml08/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_Thrml08/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_3223056/3661973807.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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\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY Halocline with CY Thermal',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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55982106, 0.33795863, 0.71550134, 1.10226626, 1.76050025,\n",
       "       2.69903492, 2.99644459, 2.0364869 , 1.33602564, 0.72712177,\n",
       "       1.38972946, 0.89137146])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_halocline_strength_SSslicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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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\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY Halocline with WY Thermal',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": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Depth-averaged Nutrients (0-10m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223056/3312634990.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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223056/241793216.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": {},
   "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_Thrml19/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_Thrml19/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_Thrml08/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_Thrml08/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": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223056/231329215.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": {},
   "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_Thrml19/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_Thrml19/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_Thrml08/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_Thrml08/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’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223056/3737416097.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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Hm5sbJ06cSNRqowmVLVu2h+7/6quvqFChAl9//TV79+7l1q1beHl5kT17drJnz46npycAt2/fTtD1z507B1iDz4sXLz7wERUsBAcHx+v8JUuWtAVUUWFZVO+1qNAt6r9R20+cOGELVJI7WMuQIcMD90WthBufwDVLliy2kCYwMDBxxUUT/XsgOb8f4iMhr11Svd+yZMmCg8Oj/wyIS40JuY/IyEg6duxI8+bNmTNnDv7+/oSFhZEpUybb96arqyuQ8O9NERGRlKJgTUREJIVEhRy3bt1i+/btAPz555+AfY+16M+jwpSo/0bv+ZYW+Pj48NZbbwEwcuRIbt26laLXf9gw0IMHD/L2228TGRlJu3bt2Lp1KyEhIVy7do0LFy5w4cIFPvvsM8C+p1R8REREANC4cWOMde7aRz7iw2Kx2ELXqODsUcFa1H+LFClC7ty5E3RfqcXJyYmiRYsCsGvXrlSuJu1JqvdbYoZPJ4WpU6cya9YsHB0dGTp0KEePHiU0NJSrV6/avjfbtm0LJPx7U0REJKUoWBMREUkhzz77rG1utPXr13PgwAEuXbpEoUKFYgQg9/dYi/pvjRo1bHO1pRUffPABmTJl4tKlS4wfPz61y7GZN28eERERFC9enF9//ZXKlSvb5v2KcuHChURdI0eOHED85hGLr/uDs3Xr1tkFbnnz5qVgwYIcPXqUs2fP2told2+15FKvXj3AGqzFd+jsky4l3m8p4ddffwWgR48eDB8+nMKFC8foQZfY700REZGUomBNREQkhbi6ulKtWjXAGo7ENr9alIIFC5I7d24CAgLYv3+/bX61tBiWZMyYkYEDBwIwfvx4Ll26lOBzRQ0DTIpeKmfOnAGgbNmyDxz2tnr16gceH3XMw2qJmuMtICAg2SZZj/qaHz9+nFWrVnHu3DnKlCmDt7e3rU308C3qfZWQ90pc7jm59e7dG4vFQkREBB999FGcj4uMjEzGqtKGlHi/pYSo783y5cvHuv/WrVv8888/KVmSiIhIgilYExERSUFRYcfGjRtZtWoVEHN+tShRgdvYsWNtcyWlxWANoE+fPuTOnZubN28ycuTIBJ8nas6z69evJ7qmqLm09u7dG2tQtHz5cruFJBJSS/PmzcmZMycAffv2tc2D9yDxmcg/StQiBWBdJAJiLmAR9b6YPHky58+fBx78vnqYpHz9E6pkyZK2yfd/+OEHJkyY8MhjFi5cmKj33eMiJd5vKSHqe3PPnj2x7h8xYgQ3b95MyZJEREQSTMGaiIhICooKQG7fvs3vv/8OxN5jLfr2WbNmAdZJwitWrJgCVcafq6srfn5+ALb7SohSpUoBMGPGjEeGBo/SuHFjwLqi5htvvGELGW7fvs2UKVNo27YtWbJkeWQtf//9N4cOHYq1jaurK5MnT8ZisbBz506qV6/OihUrCAsLs7Xx9/dnypQpVKlShcmTJyfoXqLeN1G9eO4P1qKeR+0vXry4bdhgfETd87x58x64kmNK+OKLL6hZsyYA77zzDk2aNGHFihV2k/FfvXqVuXPnUrduXVq1apVmQ6SklFLvt+QW9b353Xff8e2339rqv3DhAu+88w6ffPLJQ783RURE0hIFayIiIimoWrVquLm5AdaJyAsUKEDevHljbRsVrEVNWF6zZk3bKntpUdeuXSlWrFiizvHaa68B8Ntvv5ExY0Zy585N/vz5qVGjRrzPVa9ePTp06ADA119/TZYsWciUKRNeXl689tprFC9e3BYGxqZNmzZkzZqVa9euUbx4cbJmzUr+/PnJnz8/W7ZssbVr2bIl06dPx93dnd27d9O4cWPSp0+Pt7c3rq6uFCxYkNdee41t27bZhrrGV/QgzdHRkeeee85uf44cOexe+4T2bOzZsycWi4VNmzaRNWtWcuXKZbvnlOTi4sLq1at54403cHJyYvny5TRu3Bh3d3cyZsyIh4cHWbJk4cUXX2TdunXkzp2b559/PkVrTC0p8X5Lbv369aNYsWKEh4fTq1cv3NzcyJQpE7ly5WLChAn06tWLZs2apXaZIiIicaJgTUREJAWlS5eOZ5991vb8Qb3VAIoWLWrX6yitDgON4ujoyOjRoxN1jpdeeonp06dTo0YN3N3dOX/+PKdOneLs2bMJOt+MGTOYMGECZcqUwcXFhYiICEqXLs2YMWPYuHEjHh4eDzw2U6ZM/PXXX3To0AEfHx+CgoI4deoUp06dIiQkxK5tp06dOHbsGIMHD6ZSpUp4eHhw/fp1XF1dKVeuHG+++SarV69mwIABCbqP6F/7SpUq2YZsRhc9fEvoe+W5555j6dKl1K9fHy8vLy5evGi755SWLl06vvrqKw4fPszgwYN59tlnyZ49u60nY+HChenQoQO//vorx44do1GjRileY2pJ7vdbcsuYMSObNm3i7bffJn/+/Dg6OuLk5ETt2rWZNWsW33zzTWqXKCIiEmcWozWsRURERERERERE4k091kRERERERERERBIgTQZru3fvpmnTpuTNmxc3NzcyZ87MM888wy+//BKj7c6dO6lfvz4eHh5kzJiR1q1bc+LEiVSoWkREREREREREniZpMli7fv06efLkYfTo0Sxbtoyff/6Z/Pnz8/LLL9stpX7o0CFq165NWFgYc+bM4YcffuDIkSPUrFmTy5cvp+IdiIiIiIiIiIjIk+6xmmOtWrVqnDt3jtOnTwPYVoI6fvy4bRLfU6dOUaRIEd555x0+/vjj1CxXRERERERERESeYGmyx9qDeHt74+TkBEB4eDhLliyhTZs2ditj5cuXjzp16rBgwYLUKlNERERERERERJ4CaTpYi4yMJDw8nMuXLzN58mRWrFhhWzb8+PHjBAcHU6ZMmRjHlSlThmPHjhESEpLSJYuIiIiIiIiIyFPCKbULeJjevXszZcoUANKlS8cXX3xBr169AAgMDAQgc+bMMY7LnDkzxhiuXbtGzpw5Y+wPDQ0lNDTU9jwyMpKrV6+SJUsWLBZLctyKiIiIiIiIiIg8Jowx3Lx5k1y5cuHg8OB+aWk6WBs0aBA9evTg0qVL/P7777z55pvcvn2b9957z9bmYUHYg/aNGTOG4cOHJ3m9IiIiIiIiIiLy5Dhz5gy5c+d+4P40HazlzZuXvHnzAtCkSRMAPvjgA7p06UKWLFmAez3Xort69SoWi4WMGTPGet4PPviAd9991/Y8KCiIvHnzcubMGbv52kRERERERERE5Olz48YN8uTJQ4YMGR7aLk0Ha/erUqUK33zzDSdOnKBixYq4ubmxd+/eGO327t1L4cKFcXV1jfU8Li4uuLi4xNju6empYE1ERERERERERICHj5SENL54wf3WrVuHg4MDBQsWxMnJiebNmzN//nxu3rxpa3P69GnWrVtH69atU7FSERERERERERF50qXJHms9e/bE09OTKlWqkD17dq5cucLcuXOZPXs277//PlmzZgVg+PDhVK5cmWbNmjFw4EBCQkIYOnQo3t7e9OvXL5XvQkREREREREREnmRpMlh75plnmDZtGj/99BPXr1/Hw8ODsmXLMn36dF566SVbu2LFirF+/XoGDBhA27ZtcXJyom7duowbN84WvomIiIiIiIiIiCQHizHGpHYRqe3GjRt4eXkRFBSkOdZERERERERERJ5ycc2KHqs51kRERERERERERNIKBWsiIiIiIiIiIiIJoGBNREREREREREQkARSsiYiIiIiIiIiIJICCNRERERERERERkQRQsCYiIiIiIiIiIpIACtZEREREREREREQSQMGaiIiIiIiIiIhIAihYExERERERERERSQAFayIiIiIiIiIiIgmgYE1ERERERERERCQBFKyJiIiIiIiIiIgkgII1ERERERERERGRBFCwJiIiIiIiIiIikgAK1kRERERERERERBJAwZqIiIiIiIiIiEgCKFgTERERERERERFJAAVrIiIiIiIiIiIiCaBgTUREREREREREJAEUrImIiIiIiIiIiCSAgjUREREREREREZEEULAmIiIiIiIiIiKSAArWREREREREREREEkDBmoiIiIiIiIiISAIoWBMREREREREREUkABWsiIiIiIiIiIiIJkCaDtbVr19K9e3eKFStG+vTp8fHxoUWLFuzYscOuXdeuXbFYLDEexYoVS6XKRURERERERETkaeGU2gXE5uuvvyYwMJC+fftSokQJLl++zPjx46lWrRorVqygbt26trZubm6sXbvW7ng3N7eULllERERERERERJ4yaTJYmzRpEtmyZbPb1rhxYwoXLszo0aPtgjUHBweqVauW0iWKiIiIiIiIiMhTLk0OBb0/VAPw8PCgRIkSnDlzJhUqEhERERERERERsZcmg7XYBAUFsXPnTkqWLGm3PTg4mBw5cuDo6Eju3Ll58803uXr1aipVKSIiIiIiIiIiT4s0ORQ0Nm+88Qa3b9/mww8/tG0rW7YsZcuWpVSpUgD8+eeffP7556xZs4Zt27bh4eER67lCQ0MJDQ21Pb9x40byFi8iIiIiIiIiIk+cxyJYGzJkCDNmzODLL7+kYsWKtu3vvPOOXbsGDRpQvnx52rZty3fffRdjf5QxY8YwfPjwZK1ZRERERERERESebBZjjEntIh5m+PDh+Pn5MWrUKAYNGvTI9pGRkXh6etK0aVNmz54da5vYeqzlyZOHoKAgPD09k6x2ERERERERERF5/Ny4cQMvL69HZkVpusdaVKjm5+cXp1AtijEGB4cHTx/n4uKCi4tLUpQoIiIiIiIiIiJPqTS7eMGIESPw8/Nj8ODBDBs2LM7HzZs3jzt37lCtWrVkrE5ERERERERERJ52abLH2vjx4xk6dCiNGzemadOmbNmyxW5/tWrVOHXqFB07dqRDhw4ULlwYi8XCn3/+yYQJEyhZsiQ9evRIpepFRERERERERORpkCaDtd9//x2AP/74gz/++CPGfmMMnp6eZM+enc8++4yLFy8SERFBvnz56NOnD4MGDSJ9+vQpXbaIiIiIiIiIiDxF0vziBSkhrhPSiYiIiIiIiIjIky+uWVGanWNNREREREREREQkLUuTQ0ElaRw+fBgAHx8fPDw8UrkaEREREREREZEni4K1J9WLL/LusmUsu30bAM906fDJlAmfbNnI5eODT4EC+BQpYv2vjw8+Pj5kz54dR0fHVC5cREREREREROTxoGDtSXXkCC63b5MBuAncCAvjxsWLHLx4EfbujfUQB4uFflWq8EmnTpArF7czZeKLP/4gV9GidH7lFSwWS4regoiIiIiIiIhIWqbFC3hCFy84dgzOnIFz57h54gQBx45x7tQpAs6fJ+DKFQKCggiIiCAACAAuABHAcGDof6c4CJQAvIDrWbJArlyQKxftDh/m3xs38PH2xidnTnzy5cOncGFyFS1q/X8fH3LkyIGzs3Oq3LqIiIiIiIiISGLENStSsMYTGqw9ijEQFATnzsG5c0ScPculI0dwvnwZ7+vX4dw5jp86xYjz53GMjGRqtEPLALH3ebvHAmR3c8MnY0Z8smalQ4MG/O9//4OcOQnLmJGj/v74+PiQMWPG5LpDEREREREREZEEUbAWD09lsBZXxsDVq7YAjnPnOL5vH6ePHyfg7FkCLl3i3LVrBNy+TYAxBADngfD7TvMRMOS//z9gsVDSGDI6OnKtSRNbT7hP/v2XK2Cd983XF5/ixfHJk4ccOXKQLl26lLxrEREREREREXmKxTUr0hxr8nAWC2TJYn2ULg1Aof8ediIj4coVOHeOyIAALh0+TMDRo5w7eZKAc+eofPeutYfchQtcj4wkE5ArIgJ+/912il+IvSecBcjq4oJPhgz4ZMliHX6aJw8+hQpRtV49Slavbq1TRERERERERCQFqcca6rGWoiIi4NIlOH+eu6dP43zpkq0n3NebN3Pk4kUCbtwgIDSUc8A5IOwhpxsJfJguHeTMyaFMmWh29CglsmVjcc+ekDMn5MrFnwEBOGbPjo+vL7l8fHBxcUmZexURERERERGRx5KGgsaDgrU06O5duHSJyLNnCTx8mIBDhwg4cYKAM2c4d/EiAdeuEXDrFq+FhfHCf4esBBoBpbDv+VYK2B/tubeTEz4eHuT6b/43n9y5rcNPixbFp1QpfIoWJYu3t1ZBFREREREREXlKKViLBwVrj7GwMLhwAc6dI+joUf7dsYOIy5ep7ewM58/DuXM0O3yYA3fvcg4IjeNpR2fJwgelSkGuXJxOn54JR45QtHBhXuvc2TYnXLiLC05OGk0tIiIiIiIi8qRRsBYPCtaeAiEhmHPnCDx4kHMHDxJw9CgBp08TcO4cAVeuEBAUREBwMOciI7kM/Ah0+e/QFUBjoDTwb7RTlnJw4Dzg4+aGj6cnPt7e1vnf8uUjV6FC5K9YkRJ16+Lg4JCityoiIiIiIiIiiaNgLR4UrInN7duEnjyJOXcO1/9WQ93/77/8+M8/eIeGMsDJCQIC4PZtMgJBjzhdVmdnGjzzDI26daNh48bkyJEjBW5CRERERERERBJDwVo8KFiTeLt5k2uHDhGwbx8Bhw9b5387e5aAS5ds878dCQvj9n2Hzfj2Wzq++mqqlCwiIiIiIiIicaNgLR4UrElyCDt2jM1DhrBi4UJWhISwEzjq5kbh7t3hrbf4+Z9/mDNnDq+88gqtWrVK7XJFRERERERE5D9xzYo0+ZNIMklXuDC1Zs1idGAgO6ZM4bKvL4WDg2HSJChWjIX9+7N06VL279tnO+b69essWLCAoKBHDTIVERERERERkdSmYE0kubm7Q8+eeB88CKtXQ/PmYLHw0cWLfAy0/uEHa9h26xYrV66kdevWZMmShZo1azJy5Ei2bdtGZGRkat+FiIiIiIiISAyBgYFs3bo1tctINRoKioaCSio4fhy++gp++AFu3LBu8/RkZvXqDD90iCP+/nbNs2TJQoMGDWjUqBENGzYkV65cqVC0iIiIiIiIPM1WrlzJzp07OXz4MEeOHOHw4cMEBgYCEDR5Mp6vvQYWSypXmTQ0x1o8KFiTVHPzJvz0E3zxBRw9at1mseBfrx4rS5VixalTrFmzhhtR4dt/SpUqRaNGjWjUqBE1a9bE1dU1FYoXERERERGRJ9Hly5cZO3Ysly9f5ueff7ZuvH2berVrs3b79hjtcwNrgKIBAfCEdARJlmDt2rVrBAQEUKpUKQD++OMPjh07Rq1atShdunTiq04lCtYk1UVGwh9/WAO2FSvubS9dmru9e/NPkSKsWL+eFStWsH37dqJ/27Zv355ff/0VwLbd8oR8QiAiIiIiIiJJ686dO7beZlH/PXz4MI0aNWLkyJEQHMy1f/4hc506ANxs3BiPw4fB35+PgT2Ab7RHUSB9zpxQsqR1ZJavb6rdW1JK8mBt6dKldOrUibt371KvXj0aNGjAypUriYiIYP369cydO5emTZsm2Q2kJAVrkqYcPGj9YfTjj3DnjnVb5szQsyf07s0VNzdWr17NihUrWLlyJcOHD6dHjx4AHD58mAYNGvDCCy/w5ZdfKmATERERERF5CkVGRnLmzBlbaBb9cebMmViPaZwtG8s9Pa1TFxnDICAv8BLgEdUoWzZrgBb1KFUKSpSw/s36hEnyYK1cuXJ89913ODo6UrlyZVasWEH9+vUBmDNnDuPHj+eff/5JmupTmII1SZOuX4epU60h28mT1m2OjtC6NfTtC88+iwHCw8NxdnYG4Msvv6RPnz7Uq1eP1atX20719ddfU758eSpXroyjo2OK34qIiIiIiIgkvcjISHbs2MHhw4d58cUXSZcuHQCvvvoq33///QOPy+zggG9kpF3Ps1JAkagGWbLYB2hRj6xZk/eG0pAkD9YyZszI9evXAXBzc+P27ds4OFgXFY2IiCBr1qxcvXo18ZWnAgVrkqZFRMDvv8PEibB+/b3tFStCnz7Qvj24uADWLr1//vknLi4u1K1bF4CLFy+SI0cOADJlykT9+vVt87Plzp07pe9GRERERERE4iE8PBx/f39bjzN3d3def/11wDodkKenJ7du3eLAv/9S3NkZ9u1j7DffMHTtWgo7O+N79y6+xtiFaFmiTp4xY+wBWvbsT8wiBAmV5MFajhw5OHv2LE5OTrz00kv88ssvtn137tzBx8eHa9euJb7yVKBgTR4be/bAl1/CjBkQEmLdlj07vPaa9fFfgBbdsWPHGDhwIKtXryYoKMhuX4kSJWwh23PPPYebm1tK3IWIiIiIiIhEY4zhypUrdnOeRT2OHz/O3bt3bW1LlCjB/oULYd8+2L+fJpMnc+fGDSaEhlIuPByAO0A6wCnqoAwZYg/QcuV66gO0B0nyYO1///sfQ4cOpXjx4jH2zZ8/nwkTJvDXX38lvOJUpGBNHjtXrsB338GkSRAQYN3m7GztvdanD1SuHOOQ8PBwtm7dyooVK1ixYgXbtm0jMjLStt/V1ZXnnnvOFrSVKFFCc7SJiIiIiIgkodDQUCwWi23I5oIFC/jkk084fPjwQzsruTk7U9TDA19jKH3rFoP/C9BiSJ/eOufZ/QFanjwK0OIpWVYFfZCLFy8CkD179sSeCoC1a9fyyy+/sGnTJs6cOUPGjBmpVKkSQ4cOpWLFinZtd+7cSf/+/dmyZQtOTk7UrVuXcePGUbBgwThfT8GaPLbu3oX5862riW7adG/7M89Y52Fr3doauMXi6tWrtkUQVqxYQUBUQAd4e3tz8eJF23Dv0NBQXP4bbioiIiIiIiIPZozh/PnzBAQEUDlap4cmTZqwYsUKli5dSuPGjQGYOXMmnTp1srXJmzEjvm5u+IaH43v9unUYJ5AbcIh+EVfX2AO0fPnAwa6lJFCKBmtJrV27dgQGBtKuXTtKlCjB5cuXGT9+PNu3b2fFihW2uaMOHTpElSpVKFeuHAMHDiQkJIShQ4dy7do1du/eTdY4TqqnYE2eCNu2WQO22bOtgRuAjw/07m1dUdTb+4GHGmM4cOCAbaVRHx8fpk6datuXL18+cuXKxaxZsyhQoEBK3I2IiIiIiEiadvv2bbuhm1H/f+TIEW7evImHhwc3btywjQRq06YN8+fP58vevXmzUCHYt4+AXbvYePAgvqGhFAHc779IunRQrJh19c3oAVqBAtbF7STZpEiwNmzYMIYPH57Qwx/o0qVLZMuWzW7brVu3KFy4MKVKlbKtdvjiiy+ybt06jh8/brvJU6dOUaRIEd555x0+/vjjOF1PwZo8US5cgG++ga+/hkuXrNtcXKBTJ2svtjJl4nW6w4cPU6xYMdzc3Lh69Squrq4A/Pzzz4SEhNCoUSPy5cuX1HchIiIiIiKSZpw9e5b58+fbzX129uzZB7Z3dHSkQPbs/PPGG2T294f9+/Hftw/XmzfJAcQYlOnsDEWLxgzQChUCJ6dYriDJLUWCNXd3d+7cuZPQw+Otbt26BAQEcPjwYcLDw/H09KRz58588803du0aNWqEv78/R44cidN5FazJEyk0FObMsa4mumPHve21a1vnYXvhhTh/wnH27Fn2799Po0aNbNtKlizJgQMHAPD19bXNzVa7dm3c3WN8ziIiIiIiIvJYmD59OosWLeJ///sfbdq0AWDjxo3UqFEjRlvvLFkomiMHvhkyWFfevHED34AACt24QbrYTu7oCEWKWEOz6CFakSIPnMZHUkdcs6JExZ4pOYo0KCiInTt32oaBHj9+nODgYMrE0vumTJkyrFq1ipCQEFvvGpGnjosLvPwyvPSSdf61L76A336D9eutj/z54c03oXt3yJTpoafKnTs3uXPntj2PjIykffv2rFixgn/++cf2ic0XX3xBunTpqFmzpi1oK126tBZBEBERERGRVHf37l1OnDgRY9XNI0eOcODAAbJkyQJY53L/7bffyJcvny1YK5YtG61q1rw3/9m1a/ieOkXmwEAIDIx5MQcHa2+z+wO0okWtf6vJE+Ox6bH20ksvMXv2bLZs2ULFihXZtGkT1atXZ9asWXTo0MGu7ZgxYxg0aBDnzp0jZ86cMc4VGhpKaGio7fmNGzfIkyePeqzJk+/MGesQ0W+/vffD390dunSx9mIrVizep7x+/Tpr1qyxLYJw+vRpu/05c+akYcOGNGrUiKZNm+p7TEREREREko0xhsuXL8cIzw4fPsyJEycIf8Bqmps2beKZZ56B69f5e+ZMdqxfTw0HBypduQL79sF/izbGYLFY5zu7P0Dz9QU3t2S8U0luT9RQ0CFDhjBy5Ei+/PJL3nzzTQBbsPbrr7/Svn17u/ZRwdr58+fJkSNHjPP5+fnFOjecgjV5agQHw4wZ1mGi+/bd296woXUetsaNE7SSjDGGw4cP20K29evXExwcbNv/77//Urp0aQCuXLmCl5cXzuruLCIiIiIiCRQWFsb169dt87SfOXOGvHnzPrC9u7s7RYsWxbdgQXwzZsTX0ZGit25R6tIlXA8ehHPnHnyxfPnuBWdRIVrx4tbOCvLEeWKCteHDh+Pn58eoUaMYNGiQbXvUhOqTJk2id+/edse8//77jB8/njt37sQ6FFQ91kT+Y4x1WOjEibB4sfU5WMf3v/UWdO0KGTIk+PQhISFs2LCBFStWsGvXLlatWmUbFtqpUyeWLFnCpEmTeOmllxJ/LyIiIiIi8lSZMWMGr7/+Os2bN2fGjBmAddoaT09PvL298fX1tQZo0eY/8zl9Gof9+62jeR4kd277BQRKlbIGaIn420geP0/EHGtRoZqfn59dqAZQqFAh3Nzc2Lt3b4zj9u7dS+HChR84v5qLiwsuGtMsYu22XKeO9XHiBEyaBFOnwtGj1qGhH35onYPtrbes8wPEk6urK/Xr16d+/fox9u3atYsbN27YfZq0fv165s2bR6NGjahTpw4eHh6Juj0REREREXkynD59msWLF1O5cmWqVq0KQP78+bl58ya7du3CBAdjOXgQh/37ufr666Q7dAj274eVKx980pw5YwZoJUqAl1cK3ZU8CRLVY61+/fqsXr06KeuxGTFiBEOHDmXw4MGMGDEi1jbt27dn/fr1HDt2jAz/JcenT5+mSJEivPPOO4wdOzZO19KqoCLR3LoFP/9sXezg8GHrNosFmjWzDhOtW9f6PJEiIyPZuXMnZcuWtQ0HffPNN5k0aRIAzs7OVK9e3bYIQtmyZXFIwPBUERERERF5/Bhj2LNnD4sWLWLRokXs2rULgF69evHN11+Dvz8RmzaxffFiKvv747BnD9y9G/vJsmWLPUDLnDkF70geNykyFDS5jB8/nvfee4/GjRszbNiwGPurVasGwKFDh6hcuTIVKlRg4MCBhISEMHToUK5evcru3bvJmjVrnK6nYE0kFpGR1k93vvgCli+/t71kSWtvtpdeSvK5BNasWcO8efNYsWIF/v7+dvuyZ89OgwYNaNSoEQ0bNrTNoSAiIiIiIk+Gu3fv8vfff7Nw4UIWL17MqVOnbPssFgvV8+ali5cXPc6fh8uXY54gc2b7BQSiHnHMBkSie6yDtdq1a/Pnn38+cH/0knfs2MGAAQPYvHkzTk5O1K1bl3HjxlEoHsPWFKyJPMLhw/Dll/Djj3D7tnVbpkzw6qvwxhvwkMlBE8IYw7Fjx2yLIKxbt47bUdf9T/ny5WnUqBEdOnSgbNmySXp9ERERERFJGTdv3uSPP/5g0aJFLF26lOvXr9v2uTk60jB9elrcvEkzY7CLx5ydoXx5qFYNnnnG+t98+ZJkdI0IpHCwtnDhQmbMmMGpU6cICQmxv4DFwp49exJ7iWSlYE0kjoKC4IcfrCFbVI8yBwdo1co6TLRGjWT5hywsLIyNGzfagrbdu3fb9k2cOJE+ffoAcP36da5cuULhwoWTvAYREREREUlaCxYsoEOHDoSFhdm2eVssNDeGFkADwDZGJm9ea3gW9ShfHh4wr7pIUkixYO3TTz9lwIABZM2alcKFC5MuXboYbdatW5eYSyQ7BWsi8RQRAUuXWlcTXbv23vby5a3DRDt0SNZ/5C5evMjKlStZsWIFw4YNo0iRIgB8//33vPrqq7Rt25a5c+cm2/VFRERERCR+jh49ytzZsymTIQPN0qWDLVvw//tvCvr7UwRo8d/jGcDRzQ0qV74XolWtCrlype4NyFMnxYK1AgUKUK9ePaZMmYKjo2NiTpVqFKyJJMLevdYebNOnQ1SP1axZ4bXX4PXXrSvtpBA/Pz9Gjx7Nhx9+GOv8jCIiIiIikjIiIiKIPHcO5x07YMsWRvz6K0NPnaI5sDhau2NAoSJFsEQN56xWzTpP2n8LnImklhQL1jw9PVm4cCF169ZNzGlSlYI1kSQQGAjffQeTJsHZs9ZtTk7w4ovWYaJVqqRIGTdv3iQ8PJxMmTIBsH79eiwWC7Vq1UqR64uIiIiIPJVCQ7mzeTMrp09n0dq1LDl9msmRkbT7b/c+YADwopsbXWrWvBeiVakCWbKkYuEisUuxYO3555+nWbNmvPHGG4k5TapSsCaShMLDYcEC6zDRjRvvba9a1RqwtW2bYp8+HTlyhCpVqnDnzh2mTZtGp06dUuS6IiIiIiJPNGPg1CnYsoVLa9fy++rVLDp5klXGEH3W9e7A1NKl7edGK1bMOk+zSBqXYsHa4cOHadWqFWPHjqVx48axzrGW1ilYE0kmO3bAF1/Ar79C1ISkuXJZh4j26pXsy14HBwfTuXNn5s2bB8DIkSMZNGgQFq0UJCIiIiISd7dvw/btsGULbN7MkQ0bWBQYyCJgExA9VMjv5kaL8uVp2aYNNbp1w+m/kSQij5sUC9YiIiJ45513mDRpEhaLBXd3d7v9FouFoKCgxFwi2SlYE0lmFy/ClCnw9ddw4YJ1m4sLdOxoXeygXLlku3RkZCQDBgxg3LhxAPTo0YPJkyfjrDkbRERERERiioyEo0etIVrU499/ORAZyc/AIuDQfYdULFCAFi1a0KJrV0qXKaMPsuWJkGLBWr9+/fj8888pV64cxYsXj7XH2rRp0xJziWSnYE0khYSFwZw51mGi27ff2/7cc9aArUUL67xsyWDy5Mm89dZbREZG0qhRI+bMmaPvdxERERGRa9dg69Z7Ido//8C1a4QA4YDHf82mZspEj2vXAHBydKRO7dq0aNWKF154gTx58qRW9SLJJsWCtSxZstCzZ0/GjBmTmNOkKgVrIinMGOs/2hMnwrx5EBFh3Z43L7z5JvToAcnQZfz333+nQ4cO3LlzhzJlyrB06VJy586d5NcREREREUmTIiJg3z773miH7u9/BsOdnPg0MpKRtWrx9htvQNWqXEqXjj59+tCyZUuef/55vLy8UuEGRFJOigVrGTNmZP78+VoVVEQS5uxZ6xDRKVOsK4sCuLvDyy9be7GVKJGkl9u+fTvNmjXj4sWL+Pj4sGzZMsqUKZOk1xARERERSRMuXrT2QIsK0bZutc6XFs0JYJG3N51q1yZb7dpQrRoT1q/nnffeo23btsydOzdVShdJbSkWrL344ouUK1eOQYMGJeY0qUrBmkgaEBwMs2ZZe7H9+++97Q0aWAO2Jk2SbPWgkydP0qRJEw4ePEiGDBmYN28eDRs2TJJzi4iIiIikirAw2L3bvjeav3+MZsbDgx0lSrAoXToWnjnDvlOnAPj+++955ZVXALh06RJnzpyhQoUKmi9NnlopFqzt3buX9u3b06tXL5o2bUrmzJljtIltW1qiYE0kDTEG/vrLGrAtWmSdPBWgUCF46y3o1g2S4Pv02rVrtG7dmvXr1+Pk5MSUKVPo3r17os8rIiIiIpLsjLGO/PhvlU62bIGdOyE01L6dxQIlShBWuTLrvLxYdOECizdsICAgwNbE0dGRmjVr8u6779K8efMUvhGRtCvFgjWH/3qQPCzFjoiaPymNUrAmkkadPAmTJsH338P169ZtGTJYw7U334QiRRJ1+tDQUHr06MEvv/xCt27dmDp1qj6RExEREZG0584d2LHDvjfauXMx22XJAtWqQbVqXC9ViuWBgSxatYrly5dz48YNW7P06dPTuHFjWrRo8cAOMiJPuxQL1vz8/B75h+iwYcMSc4lkp2BNJI27fRt+/hm++OLe5KoWi3V4aN++UL++9XkCGGP48ccfeemll3B2dk7CokVEREREEsAYOHbMPkTbs+fegl9RHB2hXDlbkEa1atZRHhYLY8aMYejQoYSHh9ua58iRgxdeeIEWLVpQt25dXF1dU/a+RB4zKRasPQkUrIk8JoyBVausw0SXLbu3vXhx6zxsL78M6dMn6hIRERGMGDGCt99+m4wZMyauXhERERGRRwkKsi4qEBWi/fPPvUW9osuZE555xvqoVg0qVAB3d/bu3cuCBQto3749vr6+AMyZM4f27dtTvHhxWrRoQYsWLahSpYptxJmIPJqCtXhQsCbyGDp6FL78EqZNg1u3rNsyZoQePazDRPPlS9Bp33//fcaNG0fFihXZunWrfvkQERERkaQTEQEHD94L0TZvtj6//89yFxeoWNG+N1ru3GCxEB4ejpOTk61p8+bNWbJkCSNGjGDw4MEA3L59m3PnzlEkkVOniDzNFKzFg4I1kcdYUBD8+KM1ZDt+3LrNwQFatoRx46BAgXidbs+ePTRv3pzPP/+cNm3aJHm5IiIiIvIUuXzZ2gMtKkjbuhVu3ozZrmBB+xCtbFlIl862++bNm/zxxx8sWrSIZcuWsWPHDgr893vuL7/8wty5c3n11Vdp1qxZSt2ZyBNPwVo8KFgTeQJERMDy5dZhoqtXW7d5eVl7tLVqFa9T3blzB3d39wc+FxERERGJ4e5d61xo0edGi/rgNzoPD6hS5V6IVrUqZMsWo9n58+dZvHgxixYtYs2aNYSFhdn2ffnll7z55pvJeTciTz0Fa/GgYE3kCbNvH/Tsae1aD9b51z75xNqlPp7OnDlD9erVGThwIL17907iQkVERETkibBiBXTvHvtKncWL2/dGK1nSuvDAfYwxHDhwgEWLFrFo0SK2bt1qt79w4cK2+dKeffZZHGM5h4gknbhmRU4P3CMi8rgqVQr+/BM+/BA+/dS6mujmzTB7dryHhv7000+cOXOGN954A39/fz7++GPNuyYiIiIiViEhMGCA9fdNgEyZ7EO0KlWs8wA/xNatW5kzZw6LFi3i2LFjdvuqVKlCy5YtadGiBcWLF8disSTTjYhIQqnHGuqxJvJEW7IEunSBq1cTNDTUGMOoUaMYMmQIAO3atePnn3/W8uQiIiIiT7u9e6FjR+toCbAuoPXJJ+Dm9tDD7ty5g6urq+3D2l69evHtt98CkC5dOurVq0eLFi1o3rw5uXLlStZbEJEHi2tWpG4XIvJka9YMdu2yLkseFAStW0PfvhAaGqfDLRYLgwcPZvr06Tg7OzN37lzq1avHlStXkrlwEREREUmTIiPh88+hUiVrqJY9OyxbZl1M6xGhWpcuXfD29mbLli22be3bt+ell15i7ty5XLlyhWXLltGrVy+FaiKPiQQNBS1Tpkyc21osFvbs2ZOQy4iIJI28eWMODd20yTo0tGDBOJ3ipZdewsfHh1atWrFp0yaeffZZli1bRuHChZO5eBERERFJM86dg65dYdUq6/PmzeH772NdfODo0aOsWrWK119/3TaE8+7duwQHB7N27VqeffZZAOrWrUvdunVT6g5EJIklaCho7dq1Hzm2+9atW+zYsQOLxUJERESCC0wJGgoq8hS5f2joDz9Ye7HF0YEDB2jSpAmnTp3C29ubxYsX88wzzyRjwSIiIiKSJixYAK++CoGB1p5pn30GvXrBf38bR0ZGsnXrVtviAwcPHgTg33//pXTp0gDs27ePiIgIypQpo/nSRNK4ZF28YP369Q/cFx4ezrfffstHH32ExWKhY8eOCbmEiEjyiBoa2qGDdUGDNm3itWpoiRIl2LJlC82aNWPHjh3UrVuXX375hTZt2qRA8SIiIiKS4m7dgrffhqlTrc8rVIAZM6BYMcA6J++cOXP44IMP8Pf3tx3m5OREnTp1CI02BUmpUqVSsnIRSQFJOsfa3LlzKVGiBG+99RZly5Zlx44dTJ8+PSkvISKSeFFDQ99/3/r8iy+gRg04cSJOh+fIkYP169fTrFkzQkJCaNeuHZ9//jlaC0ZERETkCbN1K5Qvbw3VLBbrCqCbN9tCtW3btlGzZk06dOiAv78/np6edOjQgVmzZnHlyhVWrlxJpUqVUvkmRCQ5JUmwtn79eqpWrUr79u3x9PRk5cqVrFixgnLlyiXF6UVEkp6zs7WX2pIlkDkzbN9u/fRx/vw4He7h4cGCBQvo3bs3xhjeffddpkZ9iikiIiIij7eICBg5Ep59Fo4dgzx5YO1aGDsW0qXj7NmzdO7cmSpVqrBx40bc3d0ZPnw458+fZ9asWXTo0AEvL6/UvgsRSQGJCtb27t1LkyZNqFevHoGBgcycOZPt27dTr169RBV18+ZN+vfvT8OGDcmaNSsWiwU/P78Y7bp27YrFYonxKPbfpwciIo/UtCns3n1v1dA2beK8aqiTkxNfffUVn376KZUrV6ZDhw7JX6+IiIiIJK+TJ6FWLRgyxBqwtW8Pe/ZA7drcuXOH4cOHU7RoUdvorM6dO3PkyBGGDh2Ku7t76tYuIikuQcHamTNn6NKlCxUqVGDHjh1MmDCBgwcPJtkflYGBgXz77beEhobSsmXLh7Z1c3Nj8+bNdo/Zs2cnSR0i8pTIkyfBQ0MtFgvvvfceGzduxMPDA7DOs3H9+vVkLFhEREREkpwx8MsvULYsbNwIGTLAzz/DrFmQKRMAJ06c4KOPPiI4OJjq1auzdetWfvrpJ3x8fFK5eBFJLQlavKBo0aKEhYXRuHFj+vfvT4YMGdi7d+8D21eoUCFe58+XLx/Xrl3DYrFw5coVvv/++we2dXBwoFq1avE6v4hIDFFDQ2vVgs6d7w0NjeOqoc7Ozrb/HzVqFFOnTmXZsmUUL148OasWERERkaRw/Tq8/jr8+qv1efXqMH06FCjAqVOnyJcvH2BdfGDYsGEUK1aMdu3aaWVPEUlYsBa1qsny5cv5448/HtjOGIPFYiEiIiJe59cPJxFJNVFDQ9u3T9Cqobdv3+bnn3/m5MmTbNiwQcGaiIiISFr355/WD1ZPnwZHR/Dzg4EDCYuMpHOHDvz222/s2bOHEiVKADB06NDUrVdE0pQEBWvTpk1L6joSLDg4mBw5cnD58mVy5sxJy5Yt+eijj8icOXNqlyYij6uooaEffgiffmodGrppE8yeDQULPvTQ9OnTs3HjRubNm8err76aQgWLiIiISLyFhcGwYfDxx9ZhoIUKwYwZULUqAOmwdiqJiIhg7dq1tmBNRCQ6izHGpHYRD3PlyhWyZs3KsGHDYixg8PnnnwPW7rgAf/75J59//jl58+Zl27ZttvmO7hcaGmrrdQdw48YN8uTJQ1BQEJ6enslzIyLyeFq61PoJ5tWr4OUV56Gh0QUGBjJnzhxee+019cgVERERSQsOH4ZOnWDHDuvzV14h8rPP+Om332jUqBG5cuUCwN/fn6CgIMqVK5d6tYpIqrhx4wZeXl6PzIoS1GMtrXjnnXfsnjdo0IDy5cvTtm1bvvvuuxj7o4wZM4bhw4enRIki8rhL5NDQ8PBwWrZsyYYNG9i1axeTJ0/Gyemx/tErIiIi8vgyBqZMgXffheBgyJwZvvuOP7Nk4Z3atdm1axddu3a1jdIqUKBAKhcsImldglYFTctatWpF+vTp2bJlywPbfPDBBwQFBdkeZ86cScEKReSxEzU0tH9/6/N4rBrq5ORE+/btcXBw4LvvvqN58+bcvHkzmQsWERERkRguX4YWLayLFAQHQ/36HF+8mDYzZlD7v1DNy8uLMmXKpHalIvIYeeKCNbAumuDg8OBbc3FxwdPT0+4hIvJQzs7W+TeWLLF+shm1auj8+Y889M0332TBggW4ubnxxx9/8Nxzz3Hu3LkUKFpEREREAFi+HEqXht9/h3TpCBo1iv7lylGibl3mz5+Pg4MDvXv35ujRow8c+SQiEpsnLlibN28ed+7coVq1aqldiog8iaKGhj7zDAQFWYeG9u0L0eZtjM0LL7zAn3/+SbZs2di9ezdVq1Zl7969KVOziIiIyNMqONg6jUeTJnDxIuElSvDN++9TZMIEPh03jrCwMBo2bMi///7LpEmTyJo1a2pXLCKPmTS7eMHy5cu5ffs2N2/epHv37rRr144XX3wRgCZNmnD58mU6duxIhw4dKFy4MBaLhT///JMJEyZQqFAh/vnnH9KnTx+na8V1QjoREZu7d2HwYOtcawCVKsVp1VB/f3+aNGnCoUOH8PT05LfffqN+/fopULCIiIjIU2bPHusCBfv3A7CqZUvePXqUff89L1asGOPHj+f555/XAlMiEkNcs6I0G6zlz5+fU6dOxbrP398fLy8vXnnlFXbt2sXFixeJiIggX758tGrVikGDBuHl5RXnaylYE5EES8CqodeuXaNly5b89ddfODk58d1339G1a9eUqVdERETkSRcZCRMmwAcfQFgYR7JkoV+hQizZuhWAzJkz4+fnx2uvvYazs3Pq1ioiaVayBmunT5+OV/u8efPG9xIpSsGaiCTKmTPQoQNs2mR9HodVQ0NDQ+nevTszZ84EYOjQofj5+enTUhEREZHECAiArl1h9Wrr8xdeYFGbNrTs0gUnJyfeeOMNhg4dSubMmVO1TBFJ+5I1WHNwcIjXH38RERHxvUSKUrAmIol2/9DQihVhzpyHDg2NjIxkyJAhjB49GoDOnTvz3XffkS5dupSoWEREROTJ8ttv0LMnd69e5aCLC2UmToSePTHAsGHD6NSpE76+vqldpYg8JpI1WPvxxx/jFax16dIlvpdIUQrWRCTJRB8a6ulpHRraps1DD/nuu+94/fXXKVCgAFu2bCFLliwpVKyIiIjIE+DWLetiUj/8wCmgsasrF11cOHbihHqmiUiCPfZzrKUkBWsikqQSMDR0xYoVFCpUiMKFC6dQkSIiIiJPgH/+sS5QcPw4WCyE9+9PhaVLuXDxIgsWLKB69eqpXaGIPKZSJVg7cuQIgYGBeHt7U6RIkaQ6bbJTsCYiSS4BQ0Oj++WXXyhRogQVKlRIxiJFREREHlPh4TBmDJf9/Pg8MpKhefLg+ssv8NxzHDp0iJw5c8ZrQTsRkfvFNStySIqLzZ07l3z58lG8eHFq1KhBsWLFyJcvH/PmzUuK04uIPH6cneHjj2HJEsicGXbsgPLlrXN/PMK6devo2rUrzz33HIcPH06BYkVEREQeI/7+hNWsyfihQykSGckY4POuXeG55wAoVqyYQjURSTGJDtaWLVtGhw4d8PLyYuzYsfz888+MGTMGLy8vOnTowPLly5OiThGRx1PTprB7Nzz7LNy4AW3bWucACQ194CEVKlSgTp06tGnThqJFi6ZcrSIiIiJpmTGYn35iYYkSlNyyhfeAIKB8+fJUr18/tasTkadUooeCVq9eHU9PT5YuXYqDw72czhjD888/z82bN9m4cWOiC01OGgoqIskunkND7969izHGtkJoSEgI6dKls/s5KyIiIvLUuHaN3e3b8+6qVaz7b1OOrFkZ/fHHdO7cGUdHx1QtT0SePCk2FHT37t307t07xh97FouF3r17s2fPnsReQkTk8RfPoaHOzs62UC0iIoIOHTrQsWNHQkJCUrJqERERkVR3Yf58Xs2Thwr/hWouTk58+MEHHDl+nG7duilUE5FUlehgzdHRkbCwsFj33b17V70rRESii21oaJ8+Dx0aumPHDpYtW8bs2bNp0KABgYGBKVeviIiISCoJuXGDMbVqUaRNG76/fRsDdGjYkMPHjjFy9GgyZMiQ2iWKiCQ+WKtcuTKffPIJwcHBdttDQ0MZN24cVatWTewlRESeLHnywPr10L+/9fmXX0L16nDiRKzNq1Spwh9//IGXlxcbNmzg2Wef5fjx4ylXr4iIiEgKWzRpEsWzZmXQX39xC6iSNSsbV61i1ooV5MuXL7XLExGxSfQcaxs2bKBevXpkzpyZdu3akSNHDs6fP8/8+fMJDAxk7dq1PPvss0lVb7LQHGsikmqWLoXOneHqVfD0hB9+gDZtYm26f/9+mjRpwunTp8maNSuLFy+mWrVqKVywiIiISDIyBr75hk/79KF/eDg+Dg6MfestOn72mUZDiUiKimtWlOhgDeDPP/9k4MCBbN26FWMMDg4OVK1alTFjxvDcf0sep2UK1kQkVZ05Ax06wKZN1udvvQWffgouLjGanj9/nmbNmrFz505cXV2ZOXMmrVq1SuGCRURERJJWQEAAFw8epMLEibBkCaHAV0WK8NqyZaQvXDi1yxORp1CKBmtR7ty5w7Vr18iUKRPu7u5Jddpkp2BNRFJdPFYNvXXrFu3bt2fZsmVYLBY+++wz3n777ZStV0RERCSJrFq1ipbNm5MvPJw9ERE4u7jA2LHWeWjVS01EUkmKrQoanbu7Oz4+Po9VqCYikibEY9VQDw8PFi1axGuvvYYxhnfeeYe+ffsSERGRCoWLiIiIJEJwMJXnzMEtNJTMERFc8fWFbdvg7bcVqonIYyFJeqwtXLiQGTNmcOrUKUJCQuwvYLGwZ8+exF4iWanHmoikKXEcGmqM4dNPP2XAgAEAtGzZkhkzZujDDREREUnTNm/ezKxZs5jYrRuWTp3g4EFOAAX69MHy8cfg6praJYqIpNxQ0Kg/6rJmzUrhwoVJly5djDbr1q1LzCWSnYI1EUlz4jE0dPbs2XTu3JmwsDBatGjBwoULU7ZWERERkTg4deoUAwcO5NdffwVgnqMjbSIiIEcO+OknaNgwlSsUEbknxYK1AgUKUK9ePaZMmYKjo2NiTpVqFKyJSJq1bBm8/PIjVw3dsGEDnTp1Yv78+VSsWDEVChURERGJ3a1btxg7dizjx48nJCQEC/AKMALI0bIlfPcdeHunbpEiIvdJsTnWAgMD6dix42MbqomIpGlNmsDu3fDss3DjBrRta53INzTUrlmNGjU4evSoXagWFBSUwsWKiIiI3BMZGcm0adMoUqQIo0aNIiQkhNpOTuwEvnN3J8d338H8+QrVROSxluhgrXr16hw8eDApahERkdjkyQPr10P//tbnX34J1avDiRN2zaIPxf/nn3/Inz8/s2fPTsFCRURERKz+/PNPKlWqRPfu3blw4QKFMmRgAbA2PJxylSrBrl3QowdYLKldqohIoiQ6WJswYQKTJk1i8eLFhIWFJUVNIiJyv6hVQ5cufeSqoQBTp07l+vXrTJ8+nSRYo0ZEREQkTo4fP06bNm2oXbs2u3btwjN9esZlzsz+mzdpabFg+fBD6wJNRYumdqkiIknCKbEnKFy4MPXr16dVq1ZYLJYYq9FZLBYNRxIRSSpRQ0OjVg1t2zbWVUO//vprfH196dWrFxZ9EiwiIiLJzBjDoEGD+OyzzwgLC8PBwYFeFSowfOdOst6+DfnywfTpULNmapcqIpKkEh2s9e/fn6+++opy5cpRvHjxWFcFFRGRJBQ1NDRq1dAvv7SGbNFWDXV0dKRfv362Q4wxTJ48mc6dO5MhQ4ZUKlxERESeVBaLhStXrhAWFkaDGjX47OZNSm3fbt3ZqRNMmgReXqlbpIhIMkj0qqBZsmShZ8+ejBkzJqlqSnFaFVREHltxXDX0448/ZuDAgZQvX54lS5aQK1euVChWREREniSrVq0if/78FClSBICLFy6wbexYmn7/PZbbt62/m3z9NXTsmMqViojEX4qtChoREUGDBg0SexoREUmIOK4aWrt2bbJmzcquXbuoVq0a+/btS516RURE5IkwevRoGjZsyLvvvmvdcPUq2fv0odnEidZQrWZN+PdfhWoi8sRLdLDWsGFDtmzZkhS1iIhIQkQNDR0wwPo8llVDq1atypYtW/D19eXMmTNUr16dNWvWpE69IiIi8thr06YNbm5uFCpUiPBVq6BMGZg7F5ycYNQoWLfOOq+aiMgTLtHB2pAhQ/jll1+YOHEix44d4+rVqzEe8XXz5k369+9Pw4YNyZo1KxaLBT8/v1jb7ty5k/r16+Ph4UHGjBlp3bo1J6L9MSki8lRwdoaxYx+6amjBggXZtGkTNWvW5MaNGzRu3JiffvopFYsWERGRx8Hdu3f58ssvef/9923bfH19OXPsGBPSpcOpUSMICLCu9Ll5MwwaBI6OqVixiEjKSXSwVrZsWQ4dOsS7776Lr68vWbNmjfGIr8DAQL799ltCQ0Np2bLlA9sdOnSI2rVrExYWxpw5c/jhhx84cuQINWvW5PLly4m4KxGRx1RsQ0Pfess2NDRz5sysXLmSDh06EB4eTteuXRk+fDiJnG5TREREnkDGGJYtW0aZMmXo06cP48ePZ+/evdadBw+SpWlT68rkxkDPnrBzJ1SqlLpFi4iksESvCjp06FAsFktS1GKTL18+rl27ZltZ5vvvv3/gtV1cXFiyZIltIrmKFStSpEgRxo0bx8cff5ykdYmIPBaihoYOGQIffwxffWX99Hj2bChUCFdXV2bMmEH+/PkZO3Ysfn5+nDx5kilTpmhlZxEREQFg//79vPvuu6xcuRKArFmzMmLECIoXKwaTJ0O/fhASAlmywNSp0KJFKlcsIpI6Er0qaHK7cuUKWbNmZdiwYXbDQcPDw/H09KRz58588803dsc0atQIf39/jhw5EqdraFVQEXliLVsGnTtDYGCsq4Z+++239O7dm4iICOrVq8dvv/2Gl5dXKhYsIiIiqeny5csMGzaMKVOmEBkZibOzM2+//TYffvghXiEh8Mor1qknABo1gmnTIGfO1C1aRCQZpNiqoKnl+PHjBAcHU6ZMmRj7ypQpw7FjxwgJCUmFykRE0pAmTWDXrgcODe3Zsye///476dOnZ82aNdSoUYMzZ86kctEiIiKS0sLCwhg/fjxFihTh66+/JjIyktatW3Pw4EE++eQTvDZsgNKlraGaiwtMnGj9AE+hmog85RI9FBRg4cKFzJgxg1OnTsUIsywWC3v27EmKy9gJDAwErPMF3S9z5swYY7h27Ro5Y/lBHxoaSuh/f1SCNYUUEXliPWJo6PPPP8/ff/9N06ZNOXToEEePHiVPnjypXbWIiIikAGMMixYt4v333+fYsWMAlC9fns8//5xatWrBnTvwxhvW4Z9gDddmzoRSpVKxahGRtCPRPdY+/fRTWrduzV9//YWzszNZsmSxe8QWfCWlh83v9qB9Y8aMwcvLy/bQH5Ai8sSLvmpolizWVUMrVIB58wDrL9D//PMPc+bMoW7duqlcrIiIiKSEoKAg6tWrR6tWrTh27Bg5cuTghx9+YNu2bdZQbdcuqFjxXqj2zjuwdatCNRGRaBLdY23y5Ml0796dKVOm4JiCSypnyZIFuNdzLbqrV69isVjImDFjrMd+8MEHvPvuu7bnN27cULgmIk+HqKGhHTrApk3Qrh28+SaMG0eePHnsfhYePnyYjRs30r1791QsWERERJKLp6cnERERuLq60q9fPwYMGECGDBkgMhI++QQGD4a7d63DPX/6CRo0SO2SRUTSnEQHa4GBgXTs2DFFQzWAQoUK4ebmdm+552j27t1L4cKFcXV1jfVYFxcXXFxckrtEEZG06RFDQwGuX7/O888/j7+/P3fv3qVXr16pW7OIiIgkWkhICF999RWvvPIKmTJlwmKx8O233+Lq6kq+fPmsjc6csS58tH699XmrVvDtt+DtnWp1i4ikZYkeClq9enUOHjyYFLXEi5OTE82bN2f+/PncvHnTtv306dOsW7eO1q1bp3hNIiKPjUcMDfXy8qJLly4UKlRIP09FRESeEG3atOH9999n5MiRtm2+vr73QrU5c6BMGWuo5u4O338Pv/2mUE1E5CESHaxNmDCBSZMmsXjxYsLCwpKiJgCWL1/OvHnz+P333wE4cOAA8+bNY968edy5cweA4cOHc+fOHZo1a8by5ctZsGABTZs2xdvbm379+iVZLSIiT6z7Vw1t1w7eegtLWBjDhg1j9+7dZM2a1dZcqy2LiIg8Xowxtv/v06cPPj4+VKpUyb7RjRvQpQu0bw/Xr0PlyrB7N7zyCjxkTmsREQGLif6TNgEiIiJ45513mDRpEhaLBXd3d/sLWCwEBQXF+7z58+fn1KlTse7z9/cnf/78AOzYsYMBAwawefNmnJycqFu3LuPGjaPQf8OZ4uLGjRt4eXkRFBSEp6dnvGsVEXns3b17b2goWCcqjjY0FOCHH37g008/ZenSpRQsWDCVChUREZG4CAgI4IMPPqBs2bJ2nQ5CQkLsp8zZtAleegn8/cHBAQYNgqFDrb3bRUSeYnHNihIdrPXr14/PP/+ccuXKUbx4cdKlSxejzbRp0xJziWSnYE1E5D/LllnnVQkMBE9PmDoV2rYlNDSUEiVKcOLECbJly8bvv/9OlSpVUrtaERERuc+dO3cYN24cH3/8MXfu3MHLy4uzZ8/i4eFh3zA8HEaMgJEjrYsV5M8P06dDjRqpUreISFqTYsFalixZ6NmzJ2PGjEnMaVKVgjURkWjOnLm3aijYVg09FxhI06ZN2b17N25ubsyaNYsWLVqkbq0iIiICQGRkJLNmzWLgwIGcPXsWgGeffZYJEyZQuXJl+8bHj0OnTvDPP9bnL71kXczIyyuFqxYRSbvimhUleo61iIgIGmjZZRGRJ0fUqqEDBliff/UVVK9OruBg/vrrL55//nmCg4Np1aoVX3zxRaqWKiIiIrBu3TqqVKnCSy+9xNmzZ8mXLx+zZ89mw4YN9qGaMTBtGpQrZw3VvLxg1ixrTzWFaiIiCZLoYK1hw4Zs2bIlKWoREZG04gGrhmZYsYLFixfTq1cvjDH07duXd999l8jIyNSuWERE5Klz4MABmjdvTt26ddmxYwcZMmRg9OjRHDp0iBdffBFL9IUHAgOtixR17w63bkGtWvDvv9Ze6iIikmCJHgq6d+9e2rdvT69evWjatCmZM2eO0Sa2bWmJhoKKiDxELENDzaef8snEiQwcOBCA1q1b88svv+Dm5paKhYqIiDwdLly4wLBhw/j++++JjIzEycmJXr16MXToULJlyxbzgDVrrHOonjsHTk7WudXefx8cHVO+eBGRx0SKzbHm4GDt9GZ5yDLMERERiblEslOwJiLyCA9YNfTXbdvo0qULYWFhVKtWjcWLF5M1a9bUrVVEROQJtmnTJho2bMjt27cBaNWqFWPHjqVo0aIxG4eGwocfwvjx1ue+vjBjhvXfcREReagUC9b8/PweGqoBDBs2LDGXSHYK1kRE4iiWVUP/zp6dFi1acO3aNQoVKsSyZcti/+VeREREEi0kJIRixYqRI0cOxo0bR40HreK5f791gYI9e6zPX3sNxo2D9OlTrlgRkcdYigVrTwIFayIi8XDmDPzvf7Bxo/X5m29yuGdPnm/RAn9/f7y9vTl8+HCanwZAREQkrTPGsHz5cqZNm8asWbNwcnIC4MyZM+TOnTv2Dg7GwKRJ1qGeISHg7Q1Tp8ILL6Rw9SIij7cUWxVURESeMnnywLp18N/8anz1Fb5du7Ll11+pUqUK7733ni1U8/f3Z+7cuZw4cSIVCxYREXk83bp1i5dffpl58+bx448/2rbnyZMn9lDtwgVo2hTeessaqjVuDHv3KlQTEUlGCtZERCT+nJ1hzJh7q4bu3Em2Bg346+236d+/v63Z0qVLefHFF+nbt69tmzGGqVOnsnnzZm7dupUa1YuIiKRZ58+fJ2pQUYYMGRgxYgTvvfcebdq0efBBd+5Yh3mWLAnLl4OLC3zxhXUKhxw5UqhyEZGnk1NqFyAiIo+xJk1g1y7b0FCXjh2tq4eOGwcuLnh6elK5cmWqVatmO+TChQv06NEDsC58U7hwYcqWLUu5cuUoW7YsZcuWffDwFhERkSdUUFAQY8eO5fPPP+fXX3+lZcuWAPTu3fvBB4WGwnffwahR1t5qAGXLWhcoKFky+YsWERHNsQaaY01EJNHu3oWhQ2HsWOvzChXgjTegalUoXhwc7nWQPnLkCH379mX37t1ciPoj4D6ZM2e2hWxRgVupUqVsc8uIiIg8KcLCwpgyZQofffQRV65cAaB79+5MnTr1wQfdvQs//QQffWSd+xQgf37w87MuWKB/L0VEEk2LF8SDgjURkSQSfdXQKBkyQOXK1pCtalWoUgVy5gTg0qVL7Nmzh927d7Nnzx727NnDwYMHiYiIiHHqc+fOkfO/4zZu3EhISAgVK1YkY8aMKXFnIiIiScoYw/z58xk4cCDHjh0DoFixYnz88cc0b9489p7bEREwc6Y1QIuav9THB4YMgW7dIF26lLsBEZEnnIK1eFCwJiKShM6eta5GtnkzbN8Ot2/HbJMnz72grWpVqFgR3N0BCAkJ4cCBA7agbc+ePZw/f56DBw/a/sho1qwZS5cu5auvvuKNN97477Jn2bhxI+XKlaNw4cI4Ojqm2C2LiIjEx+bNm3nvvffYtGkTANmyZWP48OH06NEj9t7ZkZHw228wbBgcPMh/B8GgQdCrF7i6pmD1IiJPh7hmReojLCIiSSt3buvCBgDh4XDgAPzzz73H/v3WYStnzsC8edZ2jo5QujRUrYprlSpUqFqVCl262A0hjS5v3rwUKlSIcuXK2batWrWK7t27A+Du7k6pUqXs5m0rU6YMGTJkSM47FxEReahjx47xwQcfMO+/f//c3Nzo168f/fv3j/3fKGNgyRJrj7Q9e6zbMmWCAQPgzTchffoUrF5ERGKjHmuox5qISIq6eRN27LAP286di9nu/iGkVas+dGWz2bNn89lnn7F3716Cg4NjbRMVxkWfvy1v3rxJdWciIiKxCgwMZMSIEUyePJm7d+9isVjo1q0bH330ET4+PjEPMAZWr4bBg2HrVuu2DBmgXz94+23w8krR+kVEnkYaChoPCtZERFLZ2bP2Qdv27XDnTsx2efNa52iLZQhplIiICI4ePWobRho1f9u5WMK7smXLsnv3btvzRYsWkSdPHkqXLo2zs3NS36WIiDyl3njjDSZPngxA48aN+eSTTyhdunTsjf/+2xqo/fWX9bm7O/TpA++9B1mypFDFIiKiYC0eFKyJiKQxDxpCev8/WdGGkNoexYrFOoT08uXLMcK2KlWq8P333/93yXA8PDwIDQ3l2LFjFCpUCIAtW7Zw8+ZNypYtS7Zs2ZL91kVE5PEXGRnJjRs3bAvsnDt3jnbt2uHn50eDBg1iP2jrVuuQz5Urrc9dXOD112HgQMiePWUKFxERGwVr8aBgTUTkMXDzprUnW/Sw7fz5mO3iMYTUGGNbEOHSpUt06NCBEydOcOLECRz+C+defPFF5s6dC0DOnDnthpGWLVuWokWLaqEEERGx2bVrFz169CB37twsWrTo0Qfs2QNDh8LixdbnTk7Qowd8+KF13lKRONqyZQvjx49nw4YNBAYGkjlzZmrUqEG/fv145pln4nwePz8/hg8fTkKigvXr11OnTh3WrVtH7dq14318XEWde/369Q9sExERwcSJE1m5ciX79u3j6tWr5MuXjxYtWjBw4MBYV5b/8ssvmTRpEv7+/uTKlYuuXbsyaNAgu5EMZ8+eZdy4cezatYs9e/YQFBTEtGnT6Nq1a4zzhYWFMXLkSKZPn05AQAA5c+akY8eODB06FDc3t0S+CpLcFKzFg4I1EZHHkDEQEBD3IaRVq94bRhrLENIHeffdd1myZAnHjh2L9RdMV1dXSpcubRe4lSlTRv+eiIg8pQ4dOkSpUqVwd3fn0KFD5MqV60ENrat8zpljfe7gAJ07W0O2AgVSrmB5Inz55Ze8/fbbVKlShd69e5MvXz5Onz7NpEmT2Lp1KxMnTuTNN9+M07nOnj3L2bNnqVatWrzruHHjBgcOHKBEiRLJ+rtQXIK1W7dukStXLv73v//RoEEDvL292blzJyNHjiRnzpxs377dLtwaNWoUQ4YMYeDAgTRs2JBt27YxePBgunTpwrfffmtrt379etq1a0e5cuXImjUrs2bNemCw1qZNG5YtW8bQoUOpXLkymzdvZuTIkTRq1IjFUWG6pFlxzoqMmKCgIAOYoKCg1C5FREQS4+5dY3bvNmbKFGNeecWYUqWMsViMscZw9x6OjsaUK2dMr17G/PCDMfv3GxMR8dBT37x502zatMlMnjzZ9OrVy1SrVs24u7sbIMZjwIABtuOuX79uFixYYPz9/ZP55kVEJDWcP3/e/Pzzz3bbfv31V3Px4sXYDzh+3JjOnY1xcLj371KHDsYcOpQC1cqTaMOGDcbBwcE0a9bM3L17127f3bt3TbNmzYyDg4PZsGHDQ89z+/bt5CwzSdWqVcvUqlXroW3Cw8PNlStXYmyfO3euAcz06dNt265cuWJcXV1Nz5497dqOGjXKWCwWs3//ftu2iGi/M27bts0AZtq0aTGus3nzZgOY8ePH220fPXq0AczKlSsfWr+kvrhmRTEnoREREXlcOTlB2bLQsyd8/z3s3QtBQbB2LYwZAy1bQs6cEBEBu3fDlCnQvTuULAkZM0K9ejBoECxaBBcu2J3aw8ODZ555htdff51vvvmGzZs3c+PGDQ4fPsycOXMYNGgQTZs2JXfu3JQtW9Z23LZt22jVqhUNGza0O9/SpUvZsWMHISEhyf+6iIhIkrt9+zbDhw+ncOHCdO3alX379tn2tW/fPua8nGfOQK9e4OsLP/8MkZHWf5f27IFZs6zbRRJgzJgxWCwWvv76a5ycnOz2OTk5MXnyZCwWC2PHjrVt9/Pzw2KxsHPnTtq2bUumTJls88tG7YsuNDSUfv36kSNHDtzd3XnuuefYsWMH+fPnt+uptX79eiwWi11Psq5du+Lh4cGxY8do0qQJHh4e5MmTh379+hEaGmp3neHDh1O1alUyZ86Mp6cnFSpUYOrUqQkaluro6EiWWBb8qFKlCgBnzpyxbfvjjz8ICQmhW7dudm27deuGMYaFCxfatjnEMpdvbDZu3AhAkyZN7LY3a9YMgN9++y1O55G0z+nRTURERB5jGTJAnTrWB1j7BkStQrp1670hpDdvWgO4tWvvHRs1hDTqUaGC3RBSR0dHihYtStGiRWnXrp1te/Rf/u7evUvZsmUpWbKk3f5OnToRFBSEo6MjxYoVizF3W3ZNVC0ikiZFREQwbdo0hg4dyvn/5vqsUqUK4eHhsR9w4YL1w51vvoGwMOu2xo3ho4+sc4JK6jIm9mkkUou7O9wXaj1MREQE69ato1KlSuR+wJx8efLkoWLFiqxdu5aIiAi7uWFbt25Nhw4deO2117h9+/YDr9OtWzdmz55N//79qVu3LgcOHKBVq1bcuHEjTnXevXuXF154gVdeeYV+/frx119/MWLECLy8vBg6dKit3cmTJ+nVqxd58+YFrPPGvfXWWwQEBNi1S4y1//2uF/13s6hg/P7VenPmzIm3t7ddcB5XYf99v7u4uNhtj3r+77//xvuckjYpWBMRkaeLxQJ58lgfbdtat4WHW1cdjT5f24EDcPq09fHf4gVxXYU0+qe8zz//PM8//7xd2Hbjxg0qVarE7t27CQwMZP/+/ezfv5+ZM2fa2mTPnt0uaCtbtiy+vr4xPokWEZGUYYxh+fLl9O/fn/379wNQoEABxowZw4svvhijhw+BgfDJJ/DllxAcbN1WqxaMHAk1aqRw9fJAd+6Ah0dqV3HPrVuQPn2cm1+5coU7d+5Q4BHz8hUoUICtW7cSGBho15uyS5cuDB8+/KHHHjhwgFmzZjFgwADGjBkDQIMGDciePTv/+9//4lRnWFgYw4cPt30QWa9ePbZv387MmTPtArNp06bZ/j8yMpLatWtjjGHixIkMGTIk5vdZPAUEBDBw4EAqVapk6zkGEBgYiIuLC+ljee0zZ85MYGBgvK9VokQJwNpzLfrXZ8OGDbZrypNBv52LiIhEDSGNGkYKcOMG7NgRcxXS3bvvDSMF8PS8twpp1OIIsaxCGv0XQS8vL1avXo0xhnPnzrF792727NnDnj172L17N0ePHuXixYusXLmSlStX2o6LvsLWoUOHuHz5MmXKlMHLyyuZXhgREQHrSp/vv/8+a9asASBTpkwMGTKE3r17x+iNQlAQfPYZfP65tTc0QLVq1kCtbt149UYSSSpRH/DdH0y1adPmkcf++eefgHWl9Ojatm3Lyy+/HKfrWywWmjdvbretTJkytt5jUdauXcvo0aPZtm1bjN5wly5dSlSP/qtXr9KkSROMMcyePTvGkM6HhXYJCfSef/55ChcuzIABA8iePTuVK1dmy5YtDBo0CEdHxzgPKZW0T8GaiIhIbDw9HzyENOqxY4c1gFuzxvqI8oghpFEsFgs+Pj74+PjQtGlT2/bbt2+zb98+u8Dt33//tZu77fvvv2f8+PG89dZbfPHFFwDcuXOHFStWUK5cOfLnz5/oT3VFRJ52p0+fZvDgwfzyyy8YY0iXLh19+vRh0KBBZMqUyb7xrVvW3mmffgrXrlm3lStnDdSaNFGglla5u1u/dmlFHFctj+Lt7Y27uzv+/v4PbXfy5Enc3d3JnDmz3facOXM+8hpRPavuD7WcnJxincMsNu7u7ri6utptc3FxsZtrduvWrTRs2JDatWvz3XffkTt3btKlS8fChQsZNWoUwVE9PxPg2rVrNGjQgICAANauXUvBggXt9mfJkoWQkBDu3LmD+31fg6tXr1KxYsV4XzNdunQsX76cl19+2TbXbvr06Rk9ejQjRozAx8cnwfcjaYuCNRERkbhIgSGkUdKnT0/VqlWpWrWqbVtkZKTdJ5vu7u7kzZvXLmzbs2cPrVu3BqBIkSK0atWK1q1bU7lyZX0qKiISD2FhYQwbNozPP//cNrl6x44dGTVqFPnz57dvHBxsnT9tzBi4fNm6rUQJ6xxqrVo98Ge9pBEWS7yGXqY1jo6O1KlThz/++IOzZ8/GOs/a2bNn2bFjB88//7zd/GoQt55YUeHZxYsX7cKg8PDwJB3O+Ouvv+Ls7MySJUvsQrjoCwckxLVr16hfvz7+/v6sWbOGMmXKxGgTNbfa3r177X7/unDhAleuXKFUqVIJunbhwoXZvHkzAQEBXL16lUKFChEUFETfvn157rnnEnZDkuY81j/lo1Ycie2xZcuW1C5PRESedNFXIZ06Ffbtg+vXrb3XRo+GFi2sw0JjW4U0UyaoXx8+/BAWL4aLFx96qfuDsY8++ohTp07RvXt327aQkBDKly+Ps7MzR48e5ZNPPqFatWrkyZOHN998kzVr1nD37t1keCFERJ4szs7ObNiwgdDQUGrVqsW2bduYMWOGfagWFgZffw2FC8O771pDtUKF4Jdf4N9/oU0bhWqSIj744AOMMfTu3ZuIiAi7fREREbz++usYY/jggw8SdP6oAGj27Nl22+fNm/fgRTsSwGKx4OTkZBf+BQcHM3369ASfMypUO3HiBCtXrqR8+fKxtmvcuDGurq78+OOPdtt//PFHLBYLLVu2THANAD4+PpQuXRp3d3c+/fRT0qdPzyuvvJKoc0ra8UT0WBs9ejR1oobq/CehibKIiEiieHpa59CpW9f6PBmGkEYX/ZPmOnXqsHPnTm7evMny5ctZsGABS5cu5dy5c0yaNIlJkyaROXNmmjdvTuvWrWnQoAFubm7J8SqIiDxWjDEsWLCAunXrkjFjRiwWCxMnTiQgIIBmzZrZ9+oJD4fp02H4cDh1yrotb14YOhQ6dwZn59S5CXlqVa9enQkTJvD2229To0YN3nzzTfLmzcvp06eZNGkS//zzDxMmTODZZ59N0PlLlizJ//73P8aPH4+joyN169Zl//79jB8/Hi8vryTrFd+0aVM+++wzOnbsSM+ePQkMDGTcuHEx5zGMo+DgYBo1asSuXbuYMGEC4eHhdh1wsmbNSqFChQDrAgWDBw9myJAhZM6cmYYNG7Jt2zb8/Pzo0aOHbSGCKPPmzQPgxIkTAGzfvh2P/xbBaBs1sgH45JNPyJEjB3nz5uXixYvMmTOHhQsXMn36dA0FfZKYx9i6desMYObOnZuo8wQFBRnABAUFJVFlIiIiD3H3rjG7dhnzzTfGdOtmTMmSxlgsxlhjuHsPR0djypc35rXXjPnhB2P27zcmIiJelwoJCTFLly41r7zyivH29jaA7ZE+fXozYMCA5LlHEZHHSLdu3Qxg3n///Qc3iogwZuZMY4oUufdzOkcOY776ypiQkJQrVuQBNm/ebNq2bWuyZ89unJycTLZs2Uzr1q3Npk2bYrQdNmyYAczly5cfuC+6kJAQ8+6775ps2bIZV1dXU61aNbN582bj5eVl3nnnHVu7qL/R161bZ9vWpUsXkz59+jhd54cffjC+vr7GxcXFFCxY0IwZM8ZMnTrVAMbf39/WrlatWqZWrVoPfT38/f3tfu+5/9GlS5cYx0ycONEULVrUpEuXzuTNm9cMGzbMhIWFxWj3sPNGN3z4cFOoUCHj4uJiMmbMaBo3bmz++uuvh9YtaUdcsyKLMf8tD/IYWr9+PXXq1GHu3Ll2qXB83bhxAy8vL4KCgvD09EzCCkVEROLoxg3Yvt2+Z9uFCzHbRV+FNOoRxxWywsPD2bhxIwsWLGD+/PmcOXMGPz8/hg0bBlgXTZg5cyYtWrQgW7ZsSXl3IiJp2tKlS3nxxRcZNGgQH374of1OY2DhQmuPtH37rNu8vWHgQHj99XhPNi/ypNi0aRPVq1dnxowZdOzYMbXLEUlycc2KnohgLVu2bAQGBuLu7s4zzzzDkCFDqFGjRpzPo2BNRETSHGPgzBnYuvVe0LZ9u3WS7PvlywdVqsRrCKkxhh07dpAjRw7bRMe//fYbbdu2pUiRIhw5ciQ57kpEJNUFBgYyYsQIChQoQN++fQHrz8QrV66QNWvWew2NgT/+gCFDrEP4Aby84P33oU8fyJAhFaoXSR2rVq1i8+bNVKxYETc3N/bs2cPYsWPx8vLi33//jbHip8iTIK5Z0WM9x5qXlxd9+/aldu3aZMmShWPHjvHpp59Su3Ztli5dSqNGjWI9LjQ01La6D1hfLBERkTTFYrHO2ZM3r/0qpPv22fdqO3jQOsfPqVP3ViF1crIGbA0bWh+VKlm32Z3eQqVKley2OTs7U6lSJWrXrm3bFhYWRsOGDWnQoAGtW7emePHiyXnXIiLJJiQkhC+++ILRo0cTFBSEl5cX3bp1w9PTE4vFYh+qrVsHgwfDpk3W5x4e8Pbb1kUKMmVKlfpFUpOnpycrV65kwoQJ3Lx5E29vb55//nnGjBmjUE2eeo91j7XYXL9+ndKlS5M5c2b27NkTaxs/Pz+GDx8eY7t6rImIyGMnLkNIM2a0LqYQFbQVKPDQU0ZERNhW5FqxYgWNGze27fP19aV169a0atWKSpUq2U/oLSKSBkVGRjJz5kw+/PBDTp8+DUDZsmX59NNPadCggX3jTZusPdTWrrU+d3WFN9+E/v0hevAmIiJPvKdiKOiDvP7663zzzTfcuXMn1tXOYuuxlidPHgVrIiLy+DMG/P1h9WpYudK66uj16/ZtChW6F7LVqWMd2vQAV69etc3Jtnr1asLCwmz78uTJQ6tWrWjVqhU1atTAyemx7ggvIk+gtWvX8v7777Nz504AcufOzciRI3nppZdsHyAAsHOnNVBbtsz63NkZevWCQYMgZ85UqFxERFLbUx2svfbaa0yZMoXg4OA4dUvVHGsiIvLEioiw9mhbudL62LLFOqQ0iqOj/bDRypVjDBuNcuPGDZYtW8b8+fNZtmwZt2/ftu3z9vamRYsWtGrVivr16+Pi4pLcdyYi8kAHDhygf//+LF26FIAMGTLwwQcf8Pbbb9t/8L5vHwwbBvPnW587OkK3btZhoPnypULlIiKSVjy1wdq1a9coXbo0WbNmZdeuXXE6RsGaiIg8NW7cgPXrrSHbqlVw/yIFXl72w0YLFoz1NMHBwaxevZr58+ezePFirl69atvXoEEDVq5cmYw3ISISu/PnzzNs2DCmTp1KZGQkTk5O9OrVi6FDh9qvdnz0KPj5waxZ1p6+Fgt06mQN2QoXTrX6RUQk7XgqgrWOHTuSN29eKlWqhLe3N0ePHmX8+PEcP36c5cuXU79+/TidR8GaiIg8tU6etAZsq1ZZh49eu2a/v2BB+2GjGTPGOEV4eDh//fUX8+fPZ+HChfTr14933nkHgCtXrtC9e3datWpF165dNSebiCSbjRs30qhRI1tv2latWjF27FiKFi16r9HJkzBiBPz0k7VHL1gXiBk+HEqUSPmiRUQkzXoqgrWxY8cye/Zs/P39uXXrFpkzZ6ZGjRp88MEHVK5cOc7nUbAmIiKC9Y/MHTvu9WbbtMl+2KiDg/2w0SpVYgwbjYyM5O7du7ahoNOmTaN79+6UK1fOrif5tWvXyKSV9UQkCQUHB+Pr60uuXLkYN24cNWrUuLfz3DkYNQq++w7u3rVua9YMPvoIypdPnYJFRCRNeyqCtaSiYE1ERCQWN29ah42uWmUN2w4ftt/v6Xlv2GiDBtZFEe7rkXb8+HFmzpyJj48P3bt3B+DWrVtky5aN0qVL06pVK1q3bm3fo0RE5BGMMSxfvpwff/yRWbNm2RYiOHPmDLlz577XO/bSJfj4Y5g8GUJCrNvq17f2WqtWLZWqFxGRx4GCtXhQsCYiIhIHp07ZDxuNNq8aAAUK3AvZ6taFB/RIW716NQ0bNiT6ryAlSpSgdevWtGrVivLly2vIqIg81I0bN8ifPz/Xrl1j6tSptuDe5to1GDcOJk6EqIVWatSAkSOhVq2UL1hERB47cc2KHFKwJhEREXmc5csHPXrA7NnWXiBbt1qHVtWqBc7O4O8PU6ZY5yvy9oZnnrFOBL5hw72hV0D9+vU5d+4c33zzDY0aNcLJyYkDBw4wcuRIKlasSIECBXjnnXf4+++/iYiaA0lEnnrnz5+3BfKenp6MGDGC9957j1atWt1rdOOGtTdagQIwerQ1VKtUCf74A/76S6GaPJHWrl1L9+7dKVasGOnTp8fHx4cWLVqwY8eOWNvv3LmT+vXr4+HhQcaMGWndujUnTpyI0W7ChAm0bt2aAgUKYLFYqF279gNrWLFiBdWrV8fNzQ0vLy+aN2/O/v37k+oWY2WxWPDz87M9P3DgAH5+fpw8eTJG29q1a1OqVKkEXefGjRuMGjWK2rVrkyNHDjw8PChdujQff/wxIVE9YaO5e/cuw4cPJ3/+/Li4uFCsWDG+/PLLWM994sQJWrduTcaMGfHw8KBBgwbs3Lkz1ho+/PBDihYtiru7Oz4+PrRr1y5Or/G5c+fw8/Nj9+7dMfZ17doVDw+PR78Ij5muXbuSP3/+lLugERMUFGQAExQUlNqliIiIPJ5u3jRmyRJj+vQxplgxY6zr7N17ZMhgTIsWxnz1lTFHjhgTGWk79Nq1a2b69OmmdevWxs3NzQC2R7Zs2cyrr75qVq5cmXr3JiKp6tq1a6Z///7GxcXFLFq0KPZGt28b88knxmTJcu/nTunSxixcaPfzRuRJ1LZtW1OnTh0zefJks379ejN37lxTrVo14+TkZNasWWPX9uDBgyZDhgymZs2aZunSpea3334zJUuWNLly5TKXLl2ya+vr62sqVKhgunfvbrJmzWpq1aoV6/UXLlxoLBaLadmypVm6dKmZOXOm8fX1NZkyZTLHjh1Lrts2mzdvNmfOnLE9nzt3rgHMunXrYrStVauWKVmyZIKus3fvXuPt7W3eeecds2jRIrNmzRrj5+dnXF1dTb169UzkfT9jevToYVxcXMwnn3xi1q1bZwYOHGgsFosZNWqUXbtLly6ZXLlymZIlS5rffvvNLF261NSoUcNkyJDBHDp0yK7tc889Z9zd3c0nn3xi1q5da37++WdTuHBhkyFDBnPy5MmH1r9t2zYDmGnTpsXY16VLF5M+ffoEvS5pWZcuXUy+fPkSfZ64ZkUK1oyCNRERkSR3+rQxU6ca0769/R+6UY/8+Y3p2dOYuXONuXrVdtjt27fN/Pnzzcsvv2wyZsxoC9iaNGlid/o7d+6k9B2JSAoLDQ01EydONFmyZLH9LOjRo4d9o5AQY774wpjs2e/9fPH1NebXX42JiEidwkVS2MWLF2Nsu3nzpsmePbupV6+e3fZ27doZb29vu799T548aZydnU3//v3t2kZE+x4qWbLkA4M1X19fU6ZMGbuA6eTJkyZdunSmY8eOCbmlBEmuYO3WrVvm1q1bMbZ/+umnBjB///23bdu+ffuMxWIxo0ePtmv76quvGjc3NxMYGGjb9v777xtnZ2e7YCwoKMh4e3ubF1980bbt6NGjBjCDBw+2O+emTZsMYD777LOH1p8WgrWU/r0tpYM1DQUVERGRpJcnD3TvDr/+ah02un27dVhW7drWYaMnT8K330K7dtZho9WqwZAhuO/YQatmzfj555+5dOkSK1eu5LXXXqNLly62U586dYosWbLQunVrDRUVeQIZY5g3bx4lS5akb9++BAYGUqxYMRYvXsy3335rbXT3rvVnSJEi0KcPXLxoHf7544+wbx+0b29dyVjkKZAtW7YY2zw8PChRogRnzpyxbQsPD2fJkiW0adPGbr6ofPnyUadOHRYsWGB3Doc4fA8FBgZy+PBhnn/+ebv5UfPly0epUqVYuHDhQ/+tnjRpEg4ODly6dMm2bfz48VgsFt544w3btsjISDJlykS/fv1s26IPBf3xxx9p164dAHXq1MFisWCxWPjxxx/trrdt2zZq1qyJu7s7BQsWZOzYsURGRj70HtOnT0/69OljbK9SpQqA3Wu8cOFCjDF069bNrm23bt0IDg7mjz/+sG1bsGABdevWJV++fLZtnp6etG7dmt9//53w/1Zmd3Z2BsDLy8vunBkzZgTA1dX1gbWvX7+eypUr22qIel2iD6EFOHbsGE2aNMHDw4M8efLQr18/QkND7dqEhYUxcuRIihUrhouLC1mzZqVbt25cvnzZrl3+/Plp1qwZ8+fPp3z58ri6ujJ8+HDWr1+PxWJh5syZDBgwgJw5c+Lh4UHz5s25ePEiN2/epGfPnnh7e+Pt7U23bt24deuW3bknTZrEc889R7Zs2UifPj2lS5fmk08+4W60KUdSg1OqXl1ERESefA4OULGi9fHBB3DrlnWuo5UrrY+DB+Gff6yPkSMhQwaoUwfnBg1o0LAhDSZPtlttdOXKlQQHB3PlyhXbSoBg/QW1atWq5MqVKzXuUkSSwKZNm3jvvffYvHkzANmzZ2f48OG88sorODk5QUQEzJwJfn4QNSdU7twweDB06wbp0qVe8fJYux21yEU8uLi4WN+XWEOr0NBQHBwccHNzS9B5YwtvEiooKIidO3dSt25d27bjx48THBxMmTJlYrQvU6YMq1atIiQk5KFBzf3CwsIA62txPxcXF+7cucPx48cfuPp3/fr1McawZs0a/ve//wHWRY7c3NxYtWqVrd327du5fv069evXj/U8TZs2ZfTo0QwaNIhJkyZRoUIFAAoVKmRrc+HCBTp16kS/fv0YNmwYCxYs4IMPPiBXrlx07tw5zvccZe3atQCULFnStm3fvn1kzZqVHDly2LWNes337dsHQHBwMMePH7efIzJa2+DgYE6cOEHRokXJly8fLVq04PPPP6dixYpUrlyZs2fP0qdPH/LmzUuHDh0eWGOFChWYNm0a3bp1Y/DgwTRt2hSA3Llz29rcvXuXF154gVdeeYV+/frx119/MWLECLy8vBg6dChgDTZbtGjB33//Tf/+/Xn22Wc5deoUw4YNo3bt2mzfvt3ufb9z504OHjzI4MGDKVCgAOnTp7d9LwwaNIg6derw448/cvLkSd577z3+97//4eTkRNmyZZk1axa7du1i0KBBZMiQgS+++MJ23uPHj9OxY0cKFChAunTp2LNnD6NGjeLQoUP88MMPcfiqJZNE9417AmgoqIiISCo6c8aYH34wpkOH2IeN5stnTI8exsyZY0xgoImMjDS7d+82mzZtsp3i8uXLxsHBwQDmmWeeMZ988ok5evRo6t2TiMTL0aNHTZs2bWxDPt3d3c2QIUPMjRs3rA0iIoyZPdt+Dsds2YyZMMGY4ODULV6eCFHvvfg85syZYzt+zpw5BogxXNLb2zvO50tKnTp1Mk5OTmb79u22bRs3bjSAmTVrVoz2o0ePNoA5d+5crOd70FDQiIgIkzlz5hhDTq9du2YyZMhgALt/r2OTO3du0717d2OMdQh4+vTpzYABAwxgTp06ZYwxZtSoUcbZ2dluSCZghg0bZnv+qKGggPnnn3/stpcoUcI0atToofXFZs+ePcbNzc20atXKbnuDBg2Mr69vrMekS5fO9OzZ0xhjTEBAgAHMmDFjYrSbOXNmjNctLCzMvPrqq3bvlzJlyhh/f/9H1vqooaD3v5eNMaZJkyZ29zFr1iwDmN9++y3Wc0+ePNm2LV++fMbR0dEcPnzYru26desMYJo3b263/e233zaA6dOnj932li1bmsyZMz/wviIiIszdu3fNzz//bBwdHc3VaFOLaCioiIiIPF1y57b2NJk1yzpsdMcOGDMG6tSx9j45dQq+/x5efBG8vbFUq0bZuXN55u5d+O+T8gsXLlC1alUANm/eTP/+/SlSpAhlypTBz8+PPXv22FYTFJG048qVK/Tt25fixYvz22+/4eDgwCuvvMLRo0f56KOPyODhAb//DhUqWId3HjoEmTPD2LHWHmt9+0I8eteIPA2GDBnCjBkzbD2c7hd9yGZ89sXGwcGBN954gzVr1jBixAguXbrEsWPHeOmll7hz546tzcPUq1eP1atXA9Zeq3fu3OHdd9/F29vb1mtt9erVPPPMM4nq1ZcjRw7b8M0oZcqU4dSpU/E6z8mTJ2nWrBl58uTh+++/j7E/Pq9vXNu+/vrr/Pbbb3z++ef8+eefzJ49m3Tp0lG3bt141x/bdZo3b2637f7XZcmSJWTMmJHmzZsTHh5ue5QrV44cOXKwfv36GMc/qJdis2bN7J4XL14cwNabLvr2q1ev2g0H3bVrFy+88AJZsmTB0dERZ2dnOnfuTEREBEeOHIn3vScVDQUVERGRtMPBwfoHdIUKMHAg3L59b9joqlWwfz9s3Wp9jBoFHh5Qpw6lGjRg07RpBKRPz6LFi1mwYAHr1q1j79697N27l+HDh1OwYEFatWpF69atqVatWpzmjhGR5DVkyBC++eYbAJ5//nk++eQTSpUqZe2TtnIlDBli/X4H8PSEd9+Fd96x/r9IErp/Lqe4iD78sVWrVty6dSvGvy0nT55MbGnxMnz4cEaOHMmoUaN488037fZlyZIFsM6Ldr+rV69isVhs83bFx9ChQ7l16xYjR460DR1s2rQp3bp14/vvv8fHx+ehx9evX5+ffvqJo0ePsnr1asqXL0+2bNmoW7cuq1evpmPHjmzatIkPP/ww3rVFF3X/0bm4uBAcHBznc5w6dYo6derg5OTEmjVryJw5c4xr7N69O8Zxt2/fJiwszNY+U6ZMWCyWB34tAFvbP/74g6lTpzJ37lzatm1ra9ewYUPy58+Pn58f06ZNi/M93M/d3T3G8F8XFxdCQkJszy9evMj169dJ94Dh9leuXLF7njNnzgde7/7XLOqcD9oeEhKCh4cHp0+fpmbNmvj6+jJx4kTy58+Pq6srW7du5Y033ojX1zGpKVgTERGRtCt9enj+eesDICDAGrBFPS5ftvZm+f13AHzy5qV3gwb0fvVVrn79NUs2bWL+/PmsWLGCEydOMH78eMaPH0+OHDl4+eWX+eT/7d15XFTl/gfwz8BsrCqMwaCyGLnrTZNruARKuIArgYJpknXjZXmza5ZZ/kTMUhGXSnPpInrNXclut1wqMc1cQG8uWXktkdRBNpElGGB4fn+MMzrOqECO08Dn/XqdF5znPHN4znPOnDnz5VmSk214cERNT21tLUpKSoxf3mfOnIlTp04hKSnp5thJBw7ox0w7eFC/7uysn6Bg2jTAwhdjovvhj45vJpVKjeOt3c/91kdSUhJmz56N2bNn48033zTb/vDDD8PJyQmnT58223b69GkEBgbWa3w1A6lUisWLF2POnDm4cOECVCoV1Go1Bg0ahICAAJPxvCwJCwsDoG+V9uWXXyI8PNyYPnPmTBw4cABarfaO46s9KBcvXkRoaCiEENi/f7/F4+ratSs2b96M3Nxck3HWDHXepUsXAICTkxMCAwPveC6cnJzQtm1bADAG6gyTEBg0b94cgYGBxnHbrEmlUsHT09Nk8oVbubm5mazXt+VjXezcuRPl5eVIT083mfDBUiDzQeO/aomIiMh+tGoFxMcDGzYAubnAiRP6LmFhYfpuozk5QGoqMGYMPNq1wzMffICdnTuj4JNPsH3zZowdOxbu7u7Izc1FTk6OcbdCCOzatcvYbYWI7r8TJ04gKCjIZLa8Vq1a4dChQ/ovzMeOAYMGASEh+qCaQgG88oq+y+e8eQyqEd3F22+/jdmzZ2PmzJlITEy0mEcqlWLYsGFIT09HaWmpMT0nJwcZGRmIior6Q2VwdXVF165doVarceLECXz99deYMmXKPV+nVqvRqVMn7NixA8ePHzcG1sLDw5Gfn4/FixfD3d3dLLB0O0MLQmu0XMrJyUFoaCh0Oh327dtnEti51YgRIyCRSLBu3TqT9LVr18LJyQmDBw82po0aNQr79u0zmVW0tLQU6enpGD58uDFQa5iU6ciRIyb7LCwsxLlz5+4ZuLwf9TJ06FAUFhZCp9OhZ8+eZkv79u0bvO+6MgTrbm0pKoTARx99ZPW/fS9ssUZERET2ycEB6N5dv0yfDvz+u2m30TNngKwsICsLLgCecnHBU6GhqEpMxD43N3j+5S/GXZ09exYRERFo0aIFcnNz79jVgYgaTqlU4vvvv8f58+eh0WhudhU6eVLf5fNGy1NIpcDf/ga8+aZ+DEYiuqtFixZh1qxZGDx4MCIjI80CMI8//rjx96SkJAQFBWHo0KF44403UFlZiVmzZkGlUuHVV181eV1WVpaxK2tJSQmEENi+fTsAfespQ3Bp//79yMzMRLdu3SCEwLFjx7BgwQIMHjzYrDvqnYSFheGDDz6Ak5MT+vTpAwAICAhAQEAA9u7daxJouhNDa7DVq1fDzc0NSqUSAQEBFruA1kdeXh769+8PjUaD1NRU5OXlIS8vz7i9devWxuBW586d8dxzzyExMRGOjo4ICgrC3r17sXr1asydO9eku+O0adOwfv16REZGYs6cOVAoFJg/fz4qKysxe/ZsY76oqCjMmjULkyZNwqVLl9CjRw9oNBosXLgQv//++z2Dl4aWihs2bEDHjh3h6uoKHx+fes2iHhsbiw0bNiAiIgJTpkzBX//6V8hkMly6dAkZGRkYMWKExRlO76fw8HDI5XLExcXh9ddfR2VlJVasWIFr165Z9e/WyR+eJqER4KygREREjdDly0KsXSvE00/rZw+8fbbR1q2FmDhRiM2bxd5t24S/v7+IjIw02cVLL70kVq5cKTQajY0Ogsh+XblyRaxfv94kbdOmTSIvL0+/cvasEDExN9+TDg5CxMcL8euvNigtkf0yzHh5p+V2WVlZIiwsTDg7Owt3d3cxcuRIcf78ebN8hhkjLS23zjB56NAh0atXL+Hu7i4UCoXo0qWLSElJEVVVVXU+hk8//VQAEOHh4Sbphpkw33//fbPX4LZZQYUQYunSpSIgIEA4OjqalDMkJER07tzZ4jHea/ZIw2yWd1puL0NVVZVITEwUvr6+Qi6Xi3bt2lksvxBCnD9/XowcOVK4u7sLZ2dnERYWJo4fP26WT6PRiMmTJ4vAwEChVCqFj4+PiIyMFIcPH75r2Q02bdokOnToIGQymUmZJ0yYIFxcXMzyJyYmml071dXVIiUlRfzlL38RSqVSuLq6ig4dOoiEhASTmdj9/PzMnqeEuFmP27ZtM0lPS0sTAERmZqbFMuTn5xvTPvvsM+Pfb9WqlXjttdfErl27zGaDfdCzgkqE4BRZJSUlaNasGa5fvw53DoRKRETU+NTWAqdO6Vuy7d2r72am1d7cLpFAdO+OkpAQNBs+HAgORrZGg4CAgBubJejduzeioqIwatQoYzoRmSsrK0NKSgpSUlJQUVGB06dPo1OnTjcz/PILkJSk79JdWwtIJEBsLJCYCDyA7kRERER1UddYEQNrYGCNiIioyfn9d+Dbb/VBtr17gdsHD3Z2Rn5wMNY4OSH94kUcu237o48+iqioKERFRaFTp05WGaSXyB7U1tYiPz8fGo0GGo0GP/74IxYuXIjc3FwA+i5oq1atQrdu3fRjIM6dC6SlATU1+h2MHAnMmQN07Wq7gyAiIrKAgbV6YGCNiIioidNogK++ujk+29WrJpsveXtjp58f0ktL8c1PP6G2tta47ZFHHjG2ZAsKCoKDA+eGIvun0+lw+fJl5Ofn47HHHjOmL168GBkZGcZA2tWrV6HT6cxe37ZtW8yfPx/R0dGQXL0KvPsusGoVUFWlzzBkiD6g1rPngzokIiKiemFgrR4YWCMiIiIjIfQt2Ayt2Q4cMOk2WgDgMz8/pEul+DInB9rqauO2Hj164Pjx48b1ffv2wcPDA507d4ZMJnuQR0FkUXl5uTEodvvSu3dvJCQkAAByc3OhVqshkUhQVVVlHDQ8Li4OmzdvNtmnRCJBy5YtoVaroVarERERgYSEBMhLSoDkZGDZMsAwG11oqL7V2o3ByYmIiP6sGFirBwbWiIiI6I4qKky7jZ46ZdxUCmCXXI50T098XlSEuGHDsHrrVkAigVarhVKpBAAUFRWhRYsWAID/+7//wxdffAGVSmVcPD09TdZvTbt1WnkiS4QQKC4uNl5jALB161YcPnzYJHCWm5uL0tLSO+5nzJgxxqCZTqeDm5sbVCoVTpw4AZVKBQDYu3cvsrOzjUE0tVoNLy8v09n6iouBxYuBJUuAsjJ92uOP6wNqAwbox1QjIiL6k2NgrR4YWCMiIqI6y8017TZ6YyypSugDbS3VasDHB4UODgg5exbXamrw28CBcFAqAYUCMd9+i+3Z2XX+c65OToh+4gmkzZwJKBSAQoHX3nsPzZo3x5SEBLh5egIKBa4WF6PWwQGeKhXkcrlVDp0eLJ1Oh7y8PLOWZR06dEBMTAwAoKCgAK1atUJ1dbVJy7Knn34aGzdutLhfZ2fnm4ExLy+oH3oI6pYt0b1jRwzq00ffXbO6GkKrhaS6Wr9e1yU/H0hNBa5d0/+x7t31AbUhQxhQIyIiu8LAWj0wsEZEREQNIgRw5oxpt9HKyru+5CyAC9B3KS288bPAwnohAMPIVc8AWHfj90oATjd+LwbQ7MbvCQBW3/jdHYCngwNUjo5QyWT6RS6Hp1IJlUIBlbMzVC4uaOPujrY3AnNQKAC5/Obvty9323a37XJ50wuo6HR1DkQd+u9/cfLcOWjy86EpLISmqAiaa9egKS5GXmkpai08qscEBmLrgAFAVRVqtVrIN2+GTghcDg2Fj4MDUFWFTRoN/ltaCrVEAjUAtRBQ19bCW6eDW1XVzYCZtXTqBLz9tn5yAo47SEREdoiBtXpgYI2IiIjui8pKICsLKCnRj8tmWKqqTNfrsK22shLXKypQWF4OWXU1/IQAtFqUV1bi7evXUVRTg1VCQHJjIoV4AOsB1N6tfLcZCuCzW9a7AnAG8CkA7xtp+wH8D4DqxuJ546cHgFs6/92dTNawgF1DtsnlQG1t/VpZ3aelVKuFpqoKGiGgAeAFoP+NKigBEAwgF4AGgKFN4XgAH9+l6hwAPATog2M3lmAAz92S57cbee5Lp2FDHf6RJSQEGD0acHS8HyUiIiKyibrGiur8PERERERE96BUAn373pddOQBocWO5lQuA+bcm6HSAVou1Wi3WVFSgOD8fBVevoiAvDwV5eSgsLERBQQEKiopQcO0aCq9fR0FJCQpKShDQvj0wYgSg1eL30lKcefddAIDzCy/oW+Nptdj47bf46NdfLZaxhYMDVA4OUEkk+qCbEFDV1uIvtbUYd0u+89XVaF5dDY+yMthr26UL0AcYNbcsubetl9/2mqdwI7Amk8FVJsO5339HDYCrajXaODsDcjkeLylBWXk51Eol1E5OUDs7Q+3iArWrK9Tu7mjp5gapUnnXQFabPxoIMyyOjk2vdSEREdEfxMAaERERkT1zdAScnQFnZzi0aAEPHx94AGhXz93Ia2rwbUQECgoK4DZ8uDHA0m3ZMgzbu1cfnCsoQGFhIYqKigAA12prca22Fv+7bV/Dhg7FuG3bjC3yuvn6oqKyEhf27oW/SgVotVi5dSu+OHwYKldXqFxcoHJy0i8KBVQKBTxvdGFt4eAAh9tb9d2rBaCj4z2DSDVSKa7W1EBTXQ2lUokurVoBcjkqJBLEbtkCTWkpDr7+OhQuLoBcjsRVq7D+wIF71qOriwvU3t5Qe3ujc2iovjukRAIHAF8fOABPT094t2unb8UH4KUbCxEREdkndgUFu4ISERER1UdNTQ2uXbtmEmwz/F5QUICOHTviuef0nRUrKyuhVqtRXFyMkpISuLm5AQCef/55pKam3vNvOTg4wMPDAyqVCv3798eHH35o3LZixQo4OTlh1KhRaNasmfHvXblyxWQmzNsH/9doNMjPz4fhMXjUqFFIT08HoJ9hU6lUoqqqCtnZ2fDz8wMAzJ07F1u2bDGZDdPb29tkXa1Ww9XV9f5VNBEREdkMx1irBwbWiIiIiKyrpqYGjo6OkNxoCXf48GGcPn3aYmDOsH79+nWTfYwYMQI7d+4EoA+AOTk5QavVmgTApk+fjuTk5DqVydHREV5eXhg4cCDS0tKM6evXr4e7uzvCwsIYKCMiImqimswYa2VlZZg5cya2bt2KoqIidOjQAW+88QZiY2NtXTQiIiIiukEqNX3sDA4ORnBw8F1fU1VVhaKiImOw7dYgV01NDWJiYlBYWAiVSmVMLygogFKpNGtJZqmFmUqlgqOFAfbHjx//B4+WiIiImgq7b7E2cOBAZGZmYv78+WjXrh02btyIf/7zn9iwYQPGjh1bp32wxRoRERFR41BdXQ2pVGpsGUdERETUEE2iK+gXX3yByMhIbNy4EXFxccb0gQMH4ocffkBOTo7F/0LejoE1IiIiIiIiIiIyqGusyF5nPAcAfPLJJ3B1dUVMTIxJ+rPPPosrV67g6NGjNioZERERERERERE1dnYdWDtz5gw6duxoNmZHt27djNuJiIiIiIiIiIiswa4nLygsLETbtm3N0j08PIzbLdFqtdBqtcZ1w4xTJSUlViglERERERERERHZE0OM6F4jqNl1YA3AXQemvdO2efPmISkpySy9TZs2961cRERERERERERk30pLS9GsWbM7brfrwJqnp6fFVmlFRUUAbrZcu92MGTMwdepU43ptbS2Kiorg6enZaGaQKikpQZs2bfDbb79xQgYbYP3bFuvftlj/tsdzYFusf9ti/dsW69+2WP+2xfq3Lda/bTXG+hdCoLS0FD4+PnfNZ9eBta5du2LTpk2oqakxGWft9OnTAIAuXbpYfJ1CoYBCoTBJa968udXKaUvu7u6N5qK2R6x/22L92xbr3/Z4DmyL9W9brH/bYv3bFuvftlj/tsX6t63GVv93a6lmYNeTF4waNQplZWXYsWOHSfq6devg4+ODXr162ahkRERERERERETU2Nl1i7UhQ4YgPDwckyZNQklJCQIDA7Fp0ybs3r0bH3/8MRwdHW1dRCIiIiIiIiIiaqTsOrAGAOnp6Xjrrbcwa9YsFBUVoUOHDti0aRNiY2NtXTSbUigUSExMNOvySg8G69+2WP+2xfq3PZ4D22L92xbr37ZY/7bF+rct1r9tsf5tqynXv0Tca95QIiIiIiIiIiIiMmPXY6wRERERERERERHZCgNrREREREREREREDcDAGhERERERERERUQMwsGZn1q5dC4lEgqysLFsXpUkx1LulZdq0aXXeT3x8PFxdXa1Y0sbn1rrfv3+/2XYhBAIDAyGRSBAaGvrAy9fUvP/++5BIJOjSpYuti9Lo8dr/c+Hn75/HHzkXEokEs2fPvv+FauR477eNo0ePYtSoUfD19YVCoYCXlxeCg4Px6quv2rpoTc6RI0cQExMDtVoNuVwOb29vREdH4/Dhw/Xe19mzZzF79mxkZ2ff/4I2Eob7vFKpxMWLF822h4aG8n5kZbd//1UqlfD29kb//v0xb9485OXl2bqIfyoMrBHVQ1paGg4fPmyyvPzyy7YuVpPg5uaG1NRUs/RvvvkGv/zyC9zc3GxQqqZnzZo1AIAffvgBR48etXFpmgZe+0Rka7z3P3iff/45evfujZKSEiQnJ2Pv3r1477330KdPH2zZssXWxWtSPvjgA/Tp0weXLl1CcnIyvvrqK6SkpODy5cvo27cvli1bVq/9nT17FklJSQys1YFWq8XMmTNtXYwmzfD998svv8Ty5cvx6KOPYsGCBejYsSO++uorWxfvT4OBNaJ66NKlCx5//HGTxdfX19bFahLGjBmDHTt2oKSkxCQ9NTUVwcHB9/U8VFRU3Ld9NSZZWVk4efIkIiMjAcBisOeP+P333+/r/hqLB3ntExHdztr3frIsOTkZAQEB2LNnD2JjYxESEoLY2FikpKQgJyfH1sVrMg4dOoRXXnkFEREROHjwIMaPH48nnngC48aNw8GDBxEREYEpU6bg0KFDti5qozR48GBs3LgRJ0+etHVRmizD999+/frhqaeewpIlS3Dq1Cm4uLggKioKV69etXUR/xQYWLNzWVlZiI2Nhb+/P5ycnODv74+4uDizJrOGppwZGRmYNGkSVCoVPD09ERUVhStXrtio9I3Lli1bEBwcDBcXF7i6umLQoEH473//azHvDz/8gLCwMLi4uKBly5aYPHkygwr3EBcXBwDYtGmTMe369evYsWMHJk6caJY/KSkJvXr1goeHB9zd3dGjRw+kpqZCCGGSz9/fH0OHDkV6ejq6d+8OpVKJpKQk6x6MnTJ8mZo/fz569+6NzZs3m1y32dnZkEgkSE5OxjvvvANfX18olUr07NkTX3/9tcm+Zs+eDYlEghMnTiA6OhotWrTAww8//ECPx15Y49p/7rnn4OHhYfG+M2DAAHTu3NkKR9K4hIaGWuyCGx8fD39/f+O64X2RkpKCxYsXIyAgAK6urggODsaRI0ceXIEbsbqeC2qYe9379+/fb7HLuuHaX7t2rUn6Rx99hHbt2kGhUKBTp07YuHEjz5UFhYWFUKlUkEqlZtscHEy/wtXlGdQwHAmfQetn3rx5kEgkWLFihdm5kEql+PDDDyGRSDB//nxj+k8//YS4uDh4eXlBoVDA19cXzzzzDLRaLdauXYuYmBgAQP/+/Y3d7G5/n5De66+/Dk9PT0yfPv2u+SorKzFjxgwEBARALpejVatWeOmll1BcXGzMM3LkSPj5+aG2ttbs9b169UKPHj3ud/EbLV9fXyxatAilpaVYtWqVMT0rKwvDhw+Hh4cHlEolunfvjq1bt5q9/vLly3jhhRfQpk0byOVy+Pj4IDo62q6DdAys2bns7Gy0b98eS5cuxZ49e7BgwQJoNBoEBQWhoKDALP/zzz8PmUyGjRs3Ijk5Gfv378e4ceNsUHL7pNPpUFNTY7IAwLvvvou4uDh06tQJW7duxfr161FaWop+/frh7NmzJvuorq5GREQEwsLCsHPnTkyePBmrVq3CmDFjbHFIdsPd3R3R0dHG7iiAPtDg4OBgse6ys7ORkJCArVu3Ij09HVFRUfj73/+Ot99+2yzviRMn8Nprr+Hll1/G7t278dRTT1n1WOxRRUUFNm3ahKCgIHTp0gUTJ05EaWkptm3bZpZ32bJl2L17N5YuXYqPP/4YDg4OGDJkiMVxSKKiohAYGIht27Zh5cqVD+JQ7I41rv0pU6bg2rVr2Lhxo8lrz549i4yMDLz00kvWO6Amavny5fjyyy+xdOlSbNiwAeXl5YiIiMD169dtXTSiO6rPvb8uVq9ejRdeeAHdunVDeno6Zs6ciaSkJIvjSDZ1wcHBOHr0KF5++WUcPXoU1dXVFvPxGdR6dDodMjIy0LNnT7Ru3dpinjZt2uCxxx7Dvn37oNPpcPLkSQQFBeHIkSOYM2cOdu3ahXnz5kGr1aKqqgqRkZF49913Aeg/FwxDyxhahJIpNzc3zJw5E3v27MG+ffss5hFCYOTIkUhJScH48ePx+eefY+rUqVi3bh0GDBgArVYLAJg4cSJycnLM9vPTTz/h2LFjePbZZ61+PI1JREQEHB0dceDAAQBARkYG+vTpg+LiYqxcuRKffvopHn30UYwZM8YkcHz58mUEBQXhk08+wdSpU7Fr1y4sXboUzZo1w7Vr12x0NPeBILuSlpYmAIjMzEyL22tqakRZWZlwcXER7733ntnrXnzxRZP8ycnJAoDQaDRWLbe9M9SfpSUnJ0dIpVLx97//3eQ1paWlwtvbW4wePdqYNmHCBAHA5NwIIcQ777wjAIhvv/32gRyPPbn1ms/IyBAAxJkzZ4QQQgQFBYn4+HghhBCdO3cWISEhFveh0+lEdXW1mDNnjvD09BS1tbXGbX5+fsLR0VH8/PPPVj8We/avf/1LABArV64UQuivb1dXV9GvXz9jngsXLggAwsfHR1RUVBjTS0pKhIeHh3jyySeNaYmJiQKAmDVr1oM7CDtj7Ws/JCREPProoyb5J02aJNzd3UVpaal1DsqO3f75GxISYrHeJ0yYIPz8/IzrhvdF165dRU1NjTH92LFjAoDYtGmTtYve6DT0XAghBACRmJho/UI2EnW59xvuTxkZGSavNVz7aWlpQgj9/cjb21v06tXLJN/FixeFTCYzO1dNXUFBgejbt6/xeVMmk4nevXuLefPmGe/RfAa1rtzcXAFAxMbG3jXfmDFjBABx9epVMWDAANG8eXORl5d3x/zbtm2z+J6hm269z2u1WtG2bVvRs2dP43NMSEiI6Ny5sxBCiN27dwsAIjk52WQfW7ZsEQDE6tWrhRBCVFdXCy8vLzF27FiTfK+//rqQy+WioKDgARyZ/bhX3EEIIby8vETHjh2FEEJ06NBBdO/eXVRXV5vkGTp0qFCr1UKn0wkhhJg4caKQyWTi7Nmz1iu8DbDFmp0rKyvD9OnTERgYCKlUCqlUCldXV5SXl+PHH380yz98+HCT9W7dugGAxdlWyNy//vUvZGZmmix79uxBTU0NnnnmGZOWbEqlEiEhIRb/C/v000+brI8dOxaAPtJPdxYSEoKHH34Ya9aswenTp5GZmWmxKxwA7Nu3D08++SSaNWsGR0dHyGQyzJo1C4WFhWaz2HTr1g3t2rV7EIdgt1JTU+Hk5ITY2FgAgKurK2JiYnDw4EH873//M8kbFRUFpVJpXHdzc8OwYcNw4MAB6HQ6k7xsHVg31rj2p0yZgu+//944LkxJSQnWr1+PCRMmcPZiK4iMjISjo6NxnZ+/ZA/qc++/l59//hm5ubkYPXq0Sbqvry/69Olz38rcWHh6euLgwYPIzMzE/PnzMWLECJw7dw4zZsxA165dUVBQwGfQPwlxY6iFiooKfPPNNxg9ejRatmxp41I1HnK5HHPnzkVWVpbFboWGFmjx8fEm6TExMXBxcTEORyKVSjFu3Dikp6cbW4vrdDqsX78eI0aMgKenp3UPpBEyXPvnz5/HTz/9ZLy/3Ho/ioiIgEajwc8//wwA2LVrF/r374+OHTvarNzWwMCanRs7diyWLVuG559/Hnv27MGxY8eQmZmJli1bWhyA/fYbhkKhAMDB2uuqY8eO6Nmzp8li6AseFBQEmUxmsmzZssWsS65UKjU7D97e3gD042nQnUkkEjz77LP4+OOPsXLlSrRr1w79+vUzy3fs2DEMHDgQgH4sl0OHDiEzMxNvvfUWAPPrXa1WW7/wduz8+fM4cOAAIiMjIYRAcXExiouLER0dDQAmXRSBm9fz7WlVVVUoKyszSWfd1401rv0RI0bA398fy5cvB6Afi7O8vJzdQK2En79kb+p7778XwzOOl5eX2TZLaaTXs2dPTJ8+Hdu2bcOVK1fwj3/8A9nZ2UhOTuYzqJWpVCo4OzvjwoULd82XnZ0NZ2dnSKVS6HS6O3YbpYaLjY1Fjx498NZbb5l1iy4sLIRUKjULZkokEnh7e5tc2xMnTkRlZSU2b94MANizZw80Gg27gTZAeXk5CgsL4ePjY7wXTZs2zexe9OKLLwKA8X6Un5/fKN8j5qNhkt24fv06/vOf/yAxMRFvvPGGMV2r1aKoqMiGJWtaVCoVAGD79u3w8/O7Z/6amhoUFhaaPNjk5uYCMP/iRebi4+Mxa9YsrFy5Eu+8847FPJs3b4ZMJsN//vMfk5ZTO3futJhfIpFYo6iNxpo1ayCEwPbt27F9+3az7evWrcPcuXON64br+Va5ubmQy+VmLaFY93V3v699BwcHvPTSS3jzzTexaNEifPjhhwgLC0P79u2tdQiNilKptDg+mqXxTcm6eC6so673fsO9xjCOkcHt9W94xrE0OLWlzw0yJ5PJkJiYiCVLluDMmTMYMWIEAD6DWoujoyP69++P3bt349KlSxaDAZcuXcLx48cxZMgQeHh4wNHREZcuXbJBaRs3iUSCBQsWIDw8HKtXrzbZ5unpiZqaGuTn55sE14QQyM3NRVBQkDGtU6dO+Otf/4q0tDQkJCQgLS0NPj4+xn9KUt19/vnn0Ol0CA0NNX4fnjFjBqKioizmNzxftmzZslG+R9hizY5JJBIIIYz/9Tb45z//adbdiqxn0KBBkEql+OWXX8xasxmW223YsMFk3TCAuKVZzchUq1at8Nprr2HYsGGYMGGCxTwSiQRSqdSk21VFRQXWr1//oIrZaOh0Oqxbtw4PP/wwMjIyzJZXX30VGo0Gu3btMr4mPT0dlZWVxvXS0lJ89tln6Nevn8k5ofqxxrX//PPPQy6X4+mnn8bPP/+MyZMnW6XsjZG/vz/OnTtnEkwoLCzEd999Z8NSNU08F/dffe79htk8T506ZbKPf//73ybr7du3h7e3t1lXrpycHJ4rCzQajcV0w1AvPj4+fAZ9AGbMmAEhBF588UWz71c6nQ6TJk2CEAIzZsyAk5MTQkJCsG3btrsG9tliuWGefPJJhIeHY86cOSY9IMLCwgAAH3/8sUn+HTt2oLy83Ljd4Nlnn8XRo0fx7bff4rPPPsOECRP4fFpPOTk5mDZtGpo1a4aEhAS0b98ejzzyCE6ePHnHe5GbmxsAYMiQIcjIyDB2DW0s2GLNTkkkEri7u+OJJ57AwoULoVKp4O/vj2+++Qapqalo3ry5rYvYZPj7+2POnDl466238Ouvv2Lw4MFo0aIFrl69imPHjsHFxQVJSUnG/HK5HIsWLUJZWRmCgoLw3XffYe7cuRgyZAj69u1rwyOxH7dOaW5JZGQkFi9ejLFjx+KFF15AYWEhUlJSzILQdG+7du3ClStXsGDBAosP3V26dMGyZcuQmpqKJUuWAND/hzc8PBxTp05FbW0tFixYgJKSEpP3ATXM/b72mzdvjmeeeQYrVqyAn58fhg0bZo1iNyqGVpbjx4/HqlWrMG7cOPztb39DYWEhkpOT4e7ubuMSNh08F9ZTn3v/0KFD8eSTT2LevHlo0aIF/Pz88PXXXyM9Pd3kNQ4ODkhKSkJCQgKio6MxceJEFBcXIykpCWq1Gg4O/H//rQYNGoTWrVtj2LBh6NChA2pra/H9999j0aJFcHV1xZQpU/gM+gD06dMHS5cuxSuvvIK+ffti8uTJ8PX1RU5ODpYvX46jR49i6dKl6N27NwBg8eLF6Nu3L3r16oU33ngDgYGBuHr1Kv79739j1apVcHNzQ5cuXQDoZ8l1c3ODUqlEQEAAWw3WwYIFC/DYY48hLy8PnTt3BgCEh4dj0KBBmD59OkpKStCnTx+cOnUKiYmJ6N69O8aPH2+yj7i4OEydOhVxcXHQarVmY7ORqTNnzhjHS8vLy8PBgweRlpYGR0dHfPLJJ8ZWgqtWrcKQIUMwaNAgxMfHo1WrVigqKsKPP/6IEydOGGeTNsyW+8QTT+DNN99E165dUVxcjN27d2Pq1Kno0KGDLQ+34Ww1awI1zPLlywUAcfr0aSGEEJcuXRJPPfWUaNGihXBzcxODBw8WZ86cEX5+fmLChAnG191pVo87zeREpuoyK8rOnTtF//79hbu7u1AoFMLPz09ER0eLr776yphnwoQJwsXFRZw6dUqEhoYKJycn4eHhISZNmiTKysoexKHYnbrUvRDmMyOuWbNGtG/fXigUCtG2bVsxb948kZqaKgCICxcuGPP5+fmJyMhIK5Xe/o0cOVLI5fK7zm4VGxsrpFKpOHLkiAAgFixYIJKSkkTr1q2FXC4X3bt3F3v27DF5jWFW0Pz8fGsfgt2y9rVvsH//fgFAzJ8//z4fQeNy++evEEKsW7dOdOzYUSiVStGpUyexZcuWO84KunDhQrN9gjNUNkhDz4UQrPO6qs+9Pzc3V2g0GhEdHS08PDxEs2bNxLhx40RWVpbJrKAGq1evFoGBgUIul4t27dqJNWvWiBEjRoju3btb+ajsy5YtW8TYsWPFI488IlxdXYVMJhO+vr5i/PjxZrPp8RnU+g4fPiyio6OFl5eXkEql4qGHHhJRUVHiu+++M8t79uxZERMTIzw9PYVcLhe+vr4iPj5eVFZWGvMsXbpUBAQECEdHR4vvk6bubs9AY8eOFQCMs4IKIURFRYWYPn268PPzEzKZTKjVajFp0iRx7do1i/s37KNPnz7WOgS7ZzgHhkUul4uHHnpIhISEiHfffdfi58PJkyfF6NGjxUMPPSRkMpnw9vYWAwYMMM4sbfDbb7+JiRMnCm9vbyGTyYSPj48YPXq0uHr16oM6vPtOIsSNqRzILkyZMgXLli1DcXGxsTklEdGfQXZ2NgICArBw4UJMmzbN1sWhOnr11VexYsUK/Pbbb/xv+V3w8/fPg+eicSkuLka7du0wcuRIs7GT6P6Jj4/H9u3bzSYRIiKiP45dQe3E8ePHkZmZiTVr1mD48OF8kCQioj/kyJEjOHfuHD788EMkJCQwqHYH/Pz98+C5sH+5ubl455130L9/f3h6euLixYtYsmQJSktLMWXKFFsXj4iIqEEYWLMT0dHRuH79OoYPH47333/f1sUhIiI7FxwcDGdnZwwdOtRkVlcyxc/fPw+eC/unUCiQnZ2NF198EUVFRXB2dsbjjz+OlStXGsdLIiIisjfsCkpERERERERERNQAnH6HiIiIiIiIiIioARhYIyIiIiIiIiIiagAG1oiIiIiIiIiIiBqAgTUiIiIiIiIiIqIGYGCNiIiIiIiIiIioARhYIyIiIiIiIiIiagAG1oiIiIiIiIiIiBqAgTUiIiIiIiIiIqIGYGCNiIiIiIiIiIioAf4fwfGG9tvwKfsAAAAASUVORK5CYII=\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY Nitrate with CY Thermal',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": {},
   "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([23.70228905, 22.39409214, 18.93542225,  8.06537268,  5.75761214,\n",
       "        1.78122465,  0.81580702,  2.03092249,  8.91424762, 16.00387333,\n",
       "       19.86077911, 21.75788525])"
      ]
     },
     "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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\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY Silicon with CY Thermal',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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.06739732, 49.19871481, 46.38849762, 21.98268465,  7.24247022,\n",
       "       11.15793683, 16.08159249, 15.88039615, 26.06336839, 36.72660236,\n",
       "       42.34819421, 46.02331588])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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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\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY Nitrate with WY Thermal',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([24.61595001, 23.26098825, 20.61280733,  7.68941071,  5.75685816,\n",
       "        1.97729292,  2.72072923,  4.003872  , 11.9685279 , 19.67454109,\n",
       "       23.13616984, 23.96606766])"
      ]
     },
     "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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TZ7cm1pKbLpp4v2TJkhQtWvS+HvN+1KhR446vWWqfM4C7uzs1a9YEbBsTJN5PTKYBFC1alOLFi3PmzBmCgoIAiImJYfv27QDWxOPdvPnmm+TLlw8wk1YpOSejeXt74+/vn+yxxNcWsGlicPDgQevr3bt3b/Lnz5/srUCBAly/fh0wk7DJqVevXoriTPx7up2fn5/1fo0aNe465k6NGI4fP86wYcOoXr06uXPnxtHR0do0oXXr1sDNLwpERESyOiXWREREMpGPj4/1fmoSE3eSWIWTntU4bm5udOrUiY8//pidO3cSHh7O1q1b6dWrFwARERF069aNCxcupNtjJifx+vdbhXfp0iUAa+LsThKrdxLH3y5Xrlx3PDexcuhOHRDvJrnE2a3rp5UsWZLixYtz6NAh62twv+urpVZGPWe4GfuWLVuIi4sDkq6vligx0ZZ4fOfOndZKrpS8Bs7OztY1A7NCpRqk7LUF29f33Llz1vuXL1/m4sWLd7wlruF4a8XbrXx9fdMU560x3mtMcv9GvvvuO8qXL8/UqVPZs2cPYWFheHh44Ovri5+fH3ny5AGw6TwqIiKSlSmxJiIikokqVKhgvZ/dFud2cnKibt26LFy4kDFjxgDmh9/EKZcZLbkpZ+l5Xmqvn1qJiaEjR45w7tw5AgMDOX36NBUqVLBW/NyefEtMrGWFyqvUSnxO169ft1YJbtq0CbCtWLt1O/F5J/55a+Xbw+DWRg8XLlzAMIx73nr37p3stRwdHTMp6qSCg4Pp3bs30dHRNGnShI0bNxIREUFYWBgXL17kwoULLF++3G7xiYiIpIYSayIiIpno1mmJ3333nZ2jSb3EDotAsh0F01OBAgUArGuLpVRiZc7p06fvOi5xylnilMHMUr58efLnzw+YibNbp4EmujWxdvToUWust1d2ZSd169a1rt21ceNGDh48yKVLlyhVqlSStb9ur1hL/LN+/frWtdoeBon/TgD2799vx0jSZvXq1YSHh5MnTx5WrVpFw4YNrVPME2V0BayIiEh6U2JNREQkE/n5+dGpUycAvvjiC44ePZriczNifbbU8vDwsN6/2wLy6aFu3boAXLx48Y7roCUncT22DRs2WKfH3e7w4cOcPXsWuPN6URnp1nXWkqtGu7WBQeLxMmXK2KzFlRKJydys8G/I1dWV2rVrA+bfTXLrqyUqWbIkhQsX5uzZsxw4cMC6vlpGT4XNaipWrIinpydAplWIZoTEJHeZMmWsU3Rvt27duswMSUREJM2UWBMREclkEyZMwMPDg8jISDp27GhN7NxJSEgInTp1IiwsLMNju1s3wVstWrTIer9atWoZGZJ1vTEwOxXGxMSk6Lxu3boBcPbsWT799NNkxyROafXx8aFZs2bpEO39uTVxtnHjRhwcHGwSTIUKFSIgIIATJ07w2WefAalLKiUmZUJDQ9MedDpIfA5bt261dmO9UxVe4usxefJkIiMjbc5/WDg5OdG3b1/A/L93r46Z6bl+Y3pKXOfu6NGjREVFJTm+d+9evvjii8wOS0REJE2UWBMREclkpUuXZsmSJeTIkYMDBw5QtWpV3n33XQIDA61j4uPj+euvvxgzZgwlS5bk22+/zZTYLly4wKOPPkrDhg2ZM2cOR44csVY5xcfHc+TIEQYPHszgwYMBs6FAYgVeRnF0dOTjjz/GYrGwZcsWmjZtypYtW6xVaOHh4WzcuJEePXpw8OBB63k1a9a0xvbyyy/z8ccfWxd0v3DhAs8//7x1Pae3334bV1fXDH0eyUlMEP37779cuHCBRx55xLp4e6LE5Nsff/xhc879qFixImC+Vl9//XVaQk4Xic/hxo0brFq1Cki+Yu3W/V9++SVgLphfvXr1TIgya3nzzTcpVaoUcXFxtGzZkg8++IDLly9bj4eFhbFmzRp69epFgwYN7BjpnT3++OM4ODhw9epVnn76aeuXCjExMXz99dc8/vjjd23uICIikhUpsSYiImIH7du3Z/369fj7+3PlyhVGjhxJQEAALi4u5M2blxw5clCtWjXefvttwsLC+N///kfOnDkzPC4nJycsFgubN2+mf//+lC1blhw5cpA3b15cXFwoW7Ys06ZNIz4+npIlS7J69epMiatVq1YsXLgQFxcXtmzZQoMGDXB3dydPnjx4eXnRuHFjli5dmqSabf78+TRs2JCYmBhefvllvLy88Pb2pmDBgtYqtmHDhtGvX78Mfw7J8ff3p0iRItbt5JoS3J5IS836av7+/jRt2hSArl274unpSfHixSlevDjTpk277+ulVe3ata1ra8XHx1OiRAmKFi2a7NjExFriAv4NGjSw6Uz5sPD29mbt2rVUqVKFGzduMHToUHx9fa3/B3Lnzk2rVq1YvHhxiqs6M1tAQACvvfYaAN9++y2FCxcmd+7ceHh40LVrVzw8PJgxY4adoxQREbk/SqyJiIjYSb169Th8+DBffvklTz/9NP7+/ri6unLt2jW8vb2pX78+b7zxBocOHeKLL77IlMXay5Yty+nTp5k7dy49evSgcuXK5MyZk7CwMFxcXChevDhPPvkkn376KQcPHqR8+fIZHlOinj17cvjwYQYNGkT58uVxcnIiJiaGUqVK0b59e5YsWUK5cuVszvHy8uK3335j/vz5NGrUiFy5cnH9+nXy589Pp06d2LBhA++//36mPYfk3Jo4u1NiLbFjaYUKFaxNGe7XihUrGDx4MKVLlyY2NpZ///2Xf//91y7TQ3PkyGFdOw/uXK0GZoXnrYv3P2zTQG9VokQJdu/ezeLFi2nTpg0FChTgxo0bxMTEUKJECTp06MCCBQusa9FlRZMnT2bx4sXUrFkTNzc3YmNj8ff35/XXX+evv/667/UDRURE7M1iZIVVbEVERERERERERLIZVayJiIiIiIiIiIikQpZOrG3ZsoXWrVuTJ08e3NzcCAgI4O2337YZs2fPHpo1a4aHhwe5c+emY8eOHD9+3E4Ri4iIiIiIiIjIwyLLJta++OILGjZsiJeXF4sXL2b16tWMGDGCW2euHj58mEaNGlk7CS1YsICjR4/SoEEDmy5JIiIiIiIiIiIi6S1LrrF29uxZypQpQ8+ePZk1a9Ydxz311FNs2LCBoKAgPD09AbNdfUBAAIMHD+bdd9/NrJBFREREREREROQhkyUr1j799FNu3LjBiBEj7jgmLi6OH3/8kU6dOlmTagDFihWjcePGfPfdd5kRqoiIiIiIiIiIPKSyZGJt8+bNeHt7c/jwYapWrYqTkxO+vr7069eP8PBwAIKCgoiMjKRy5cpJzq9cuTKBgYFERUVldugiIiIiIiIiIvKQcLJ3AMk5e/YsERERdOnShVGjRjFt2jR27drF2LFj+eeff/j9998JDg4GwNvbO8n53t7eGIZBSEgIBQoUSHI8Ojqa6Oho63ZCQgJXr14lb968WCyWjHtiIiIiIiIiIiKS5RmGwbVr1yhYsCAODneuS8uSibWEhASioqIYO3YsI0eOBKBRo0bkyJGDQYMG8dtvv+Hu7g5w10TYnY5NmjSJ8ePHp3/gIiIiIiIiIiLywDh9+jSFCxe+4/EsmVjLmzcvx44do0WLFjb7W7VqxaBBg9izZw/t2rUDsFau3erq1atYLBZy586d7PVHjRrFkCFDrNthYWEULVqU06dP26zXJiIiIiIiIiIiD5/w8HCKFClCrly57jouSybWKleuzI4dO5LsT2xg6uDgQKlSpXBzc2P//v1Jxu3fvx9/f39cXV2Tvb6LiwsuLi5J9nt6eiqxJiIiIiIiIiIiwN1nSkIWbV7QqVMnAH7++Web/atXrwagdu3aODk50bZtW7799luuXbtmHXPq1Ck2bNhAx44dMy9gERERERERERF56FiMxDKwLObJJ5/k119/ZfTo0dSuXZvdu3czfvx4mjVrxqpVqwA4fPgwNWrUoFq1aowcOZKoqCjGjBnD1atX2bt3L/ny5UvRY4WHh+Pl5UVYWJgq1kREREREREREHnIpzRVl2cRaZGQk48eP54svvuD8+fMULFiQp59+mrFjx9pM4/zzzz8ZMWIE27dvx8nJiSZNmjBlyhRKlSqV4sdSYk1ERERERERERBJl+8RaZlJiTUREREREREREEqU0V5Ql11gTERERERERERHJ6pRYExERERERERERSQUl1kRERERERERERFJBiTUREREREREREZFUUGJNREREREREREQkFZRYExERERERERERSQUl1kRERERERERERFJBiTUREREREREREZFUUGJNREREREREREQkFZRYExERERERERERSQUl1kRERERERERERFJBiTUREREREREREZFUUGJNREREREREREQkFZRYExERERERERERSQUl1kRERERERERERFJBiTUREREREREREZFUUGJNREREREREREQkFZRYExERERERERERSQUl1kRERERERERERFJBiTUREREREREREZFUUGJNREREREREREQkFZRYExERERERERERSQUl1kRERERERERERFJBiTUREREREREREZFUUGJNREREREREREQkFZzsHYBIVhYdDeHhcO7cNS5eDMfZOT8REY6Eh8P+/X8SGLiPsLBwwsLCuH49nIiIcCIjw4mKCicuLpacOYuRJ48/+fKVws+vFEWK+OPn54WXF3h6gpfXzVvidq5c4Oho72cuIiIiIiIiIveSJRNrGzdupHHjxske2759O7Vr17Zu79mzh+HDh7Njxw6cnJxo0qQJU6ZMoWTJkpkVrmQxsbFw7ZqZEAsPhzNngjl9+hJXroQTHBxOcHAYoaHhhIeHEx5+MyEWFRVOVFQYsbHhWCyPEh8/g9jYxKv6ADHAKaDIf/u+BKbeNZbr1+HiRTh8OHFPVeCvW0Z8BOQG2v73p8nDI/mk290Scrffd3FJw4soIiIiIiIiIveUJRNrid55550kCbaKFSta7x8+fJhGjRpRtWpVvv76a6KiohgzZgwNGjRg79695MuXL7NDllSKjzeTYdeuQWhoAhcv3uDSpXAuXQrnypUwgoPDCQkJ/686LJzr18O5cSMMJ6eK5MzZh/BwCAuLJji4OoYRDhwAcv139RHA/PuM6PaslCcQQrFi18mXz6wqCw2tyMWLrcmZ04ucOT3JlcsTT09Pcuf2wtvbkxw5HDh16gSnTwdy/nwQV64Ekj9/KerXh7Aw83lu2jQcw4jC2zuQ69dzExMDMI/r13/h+vVSnD3rD5QC/IHC3M/sbReX1CXkbt328ACL5T5fOhEREREREZGHRJZOrAUEBNhUp91uzJgxuLi48OOPP+Lp6QlA9erVCQgIYMqUKbz77ruZFepDKSEBbtwwq8ISE2KXLpkJscuXzYTY1atmQszRsTDu7rW5dg2uXr3GoUNDiIoKJ2fOr7h2zUJEBMCLwFfANcBIYRTtgT7/3c8BHAHigHBcXXPh6QkxMd5cu+aNs7Mnzs6euLp64ebmSc6cnnh4mAkxLy8v8uTxxNvbk7x5PSlevBD16pkJJg8PiI09jYuLCxabLFPv/24pFxcXh9N//+siIqIYNOgZjh8/zs8/F8XZ2Zx62qPHZlas+CbJuY6OOciTpySenqVwd/fH2bkUFksp4uP9iYkpTnh4DsLCzCo5MK91+bJ5Sy0HB/M1SGuCzilL/6QRERERERERSZ1s+3E3Li6OH3/8kZ49e1qTagDFihWjcePGfPfdd0qsJcMwIDLy5jTJxITY5ctmQiwqygXD8CE8HK5cucY//yznxo0o8uUbYD3n7NkJRERsJT4+DAi/7XanhFhvIDFJ6gB8CsD16/MBj//2x/53jUSOODl54ezsSY4cnri5eeHu7om7e2J1mBcBAZV54gmzgszT08KBA+vIly8ntWr54JF4Wd7775Z6jo6uaTo/kdMtGSZ3d3c++eQTm+MuLjB4cH/q13+UoKAgAgMDCQoK4sSJE8TGxnDlymGuXDl8+2Vp164dK1euBCAuzuDddz/Ex6c41as/QVSUC2FhZpWcWdmX9H5y2/HxZvI0NNS8pYW7e+oScrfed3NT9ZyIiIiIiIhkLVk6sfbSSy/RrVs33N3dqVOnDm+++Sb169cHICgoiMjISCpXrpzkvMqVK7N27VqioqJwdU2fhEh2M2wY/Prrl4SEHLUuph8TY64fZpsIC8O2QmwQ8OF/98OBZwFHoD+QmNXYA6y5y6M74uzs9V91mJkQK106gNatzSRJrlzu/PzzBLy9PenWzRFfX3N/aOh4EhJG4ulpTql0c3O7rULs3ipVanhf47OiunXrUrduXZt98fHxnD592ibZduufpUqVso4NDr7E6NFDsVgsREZGWtdamzx5MocOHcLf35+KFUvh7+9PqVKl8Pb2TvI6JyZg75WAu9cxsxLR/DMiAi5cSP3r4uR0fwm5IkWgXj0l40RERERERDJSSEgIJ06coFq1avYOxS6yZGLNy8uLV199lUaNGpE3b14CAwN5//33adSoET/99BMtWrQgODgYAG9v7yTne3t7YxgGISEhFChQIMnx6OhooqOjrdvh4eFJxmR3O3bA/v2fAutTfI7F4kiJErE89piZoHB1zcMPPzyBh4cnAwfG4e3tTK5ccPBgf65fb4evrxd+fp74+Hji5WUmw7y8vHB1db1HQszC//73RpK9Pj5FkhkrAI6OjhQvXpzixYvTtGlTm2OGYRBjLs4GQGxsLP/73/+4ceMGLrd0MFi9ejW///57kmvnzp2bUqVuJtpuvV+gQAEKFEj5um63i429WR2Z2uRceLhZORcXB8HB5i2leveG+fPNKa0iIiIiIiKSOiEhIQQGBlK9enUc/vuA9cYbbzBnzhyuXr0KmLmVXLly3e0yD6QsmVh75JFHeOSRR6zbDRo0oEOHDlSqVInhw4fTokUL67G7JXDudGzSpEmMHz8+/QLOgkaMgC+/fILg4ABy5/Ykb15zQf18+cybn5+XTTLM09MzmYSYO++++2OSazdq1Dzznojck8VisUmgFS5cmC+++CLJuOHDh9OyZUubSrdz584RGhrKn3/+yZ9//pnknEGDBvHhh2YFY3h4OEuXLiUgIIBmzZqlKDZnZ8ib17yllmGY68alNDkXHm5OXd28GRYuNGOYM0fJNRERERERkbtJTJ4dO3YMV1dXOnbsCJhLcfn6+hIXF8epU6coUuRmUUxiUq1AgQJcuHBBibWsLHfu3LRp04Y5c+YQGRlJ3v8+qQcnU75y9epVLBYLuXPnTvZao0aNYsiQIdbt8PBwm38YD4K2baFt2yH3HigPjTZt2tCmTRubfRERERw/fjzZKaYnT56kRIkS1rGHDx9mwIABFCxYkLNnz1r3jxw5kuvXr9tUupUsWTLdpmFbLOYaerlyQaFCKT/vq6/g6adh3jwzufbxx5oWKiIiIiIiD7dbk2fHjh2z3g8MDLTJr9SsWdOaWHNycqJkyZJcv36dy5cvW/Mnzz//PE899RT+/v7kzJnTLs8nK8g2iTUwp7yBWaFTqlQp3Nzc2L9/f5Jx+/fvx9/f/44f7F1cXGwqfEQeVu7u7lSsWJGKFSsmORYbG0tcXJx129nZmbZt25InTx6bcV9++SWnTp2y2WexWChUqFCy00tLlSqFl5dXxjyhW3TrZk5F7dULZs0yk2sffqjkmoiIiIiIPNhCQ0Nxd3cnR44cgPmZbfr06UmSZ8kpUKAAAQEBSdZL279/v/V6iYoXL56ucWdXFiMxW5XFhYSEUKlSJfLly8dff/0FQNeuXdm4cSOBgYHWcsNTp04REBDA4MGDmTx5coquHR4ejpeXF2FhYTYdRkXk3j777DOOHj1qU+12r3ULP/roIwYOHAjA2bNnWb9+PeXLl6d69erpHt+CBfDss+b9116Dd99Vck1ERERERLK30NBQjh07RkhICI8//rh1f/Xq1dmzZw+///67tfnjJ598wosvvmgdk5g88/f3t/mzVKlSeHh4ZPpzyapSmivKkhVr3bt3p2jRojz66KP4+Phw7Ngxpk6dysWLF1m4cKF13Pjx46lRowZt2rRh5MiRREVFMWbMGHx8fBg6dKj9noDIQ6RPnz4224ZhEBwcnGz30qCgIC5dukTRokWt47dt20bPnj2pU6cO27Zts+4fMGAAnp6eNtVuhQsXti6UmVJ9+5qND158Ed5/36xcmzBByTUREREREcnaQkNDk522eezYMWvlmZ+fHxcuXLCek7hs1q2zih5//HGWL1+u5FkGyZKJtcqVK7Ns2TLmzJnD9evX8fb2pn79+ixZsoQaNWpYx5UtW5aNGzcyYsQIOnfujJOTE02aNGHKlCnky5fPjs9A5OFlsVjw8fHBx8eH2rVrJzl+7do1nJ2drdseHh40btyYqlWrWvfFxsbyySefEB8fb3Oui4sLJUqUsCbabv2zWLFiSUqTE73wAsTEwMsvwzvvQI4cMHZs+jxfERERERGR1DIMw9pEcPfu3cyYMcOaQLty5cpdzy1QoAD+/v7ExsZaP2N99tlneHl52STPihcvrmmbGSjbTAXNSJoKKpK1REZGMnfuXJtKtxMnTtis+XY7BwcHVq1aRevWre845sMPIbFvycSJ8Prr6R25iIiIiIiIrbCwME6ePEmVKlWs+wYOHMiyZcuYNm0aTz/9NAC//PILLVu2tDk3f/781qmat0/fVOVZxsrWU0FF5OHm5ubGoEGDbPbFxcVx+vTpZKeXBgUFERERQaFb2oZ+/fXX/PDDDyxevNg6fXTwYLOhwYgR8MYbZuXasGGZ+cxERERERORBFBYWdsdum5cvXwYgIiICNzc3AKKiorhy5QrHjh2zXqNKlSpMnDjRmjjz9/e3ricvWZcq1lDFmkh2ZxgGFy5cwMfHB2dnZ86ePUupUqWIjo5m0aJF9OzZ02b8hAnw5pvm/WnT4NVXMz9mERERERHJns6dO8eCBQtskmiJybM78fPzY/v27ZQoUQKAI0eOEBERQUBAgCrPsqiU5oqUWEOJNZEH0cKFCzlw4ACTJ0/G0dExyfGxY+Gtt8z7M2fCgAGZHKCIiIiIiGQ54eHh5MyZ0/oZYt68eSxcuJBu3brx8ssvA3Do0CHKly+f5Fw/P787dttUriH70VRQEXmo9e7d+67Hx40zGxpMngwvvWR2C33++UwJTURERERE7Cg8PDxJl83E+5cvX+bIkSOULl0agIsXL7Jt2zbrNkDJkiXp3bu3kmdAeDjs3g27dsG+ffD55/DfSjwPDSXWROSBFxMTw1tvvcWQIUPw9vYGwGIxO4TGxsLUqfDii2Zy7R75OBERERERySYiIyP54YcfkiTR7jVt88SJE9ZEWocOHShdurRN4wEXFxc+++yzDI09K4qONpNnO3eaibSdO+HIEbh1HuSYMVC2rP1itAcl1kTkgde3b1+WLl3KH3/8wc8//4yTk/mjz2KB9983k2szZkDfvuDkBD162DlgERERERG5L2vXrmXp0qVUrFiRYf91KEtISKBbt27Jjvf19U2206a/v79N5VmFChWoUKFCpjyHrCQ+3kya3ZpE27fP/Ox0u2LFoGZNqFEDcufO9FDtTmusoTXWRB50+/fvp06dOty4cYPBgwfzwQcf2Bw3DHM66OzZZtnyl1/CU0/ZKVgREREREbmjkJAQtm3bxtatW+nRo4d1rbNFixbRu3dvGjduzPr1663j27dvT+7cuW2SaLcnzx52hgGnTt1MoO3aZU7vvH496VgfHzOBlphIq1EDfH0zP+bMoDXWRET+U6lSJRYtWkTnzp358MMPqVKlCr169bIet1jg44/Nb18+/RS6dzcr1zp2tGPQIiIiIiIPOcMwOHnyJFu2bGHr1q1s2bKFAwcOWI/ny5fPmlhr2rQpb731FtWqVbO5xsqVKzMz5GwhOPhmEi0xkXbpUtJx7u7w6KO2ibTixc3PT3KTKtZQxZrIw2Ls2LG89dZbuLi4sGnTJmrVqmVzPCHBnA66aJGZWPv2W2jb1k7BioiIiIg8ZOLi4ti3b581ibZlyxbOnz+fZFxAQAD16tWjR48eNG3a1A6RZh83bsCePbbVaMePJx3n5ASVK99MotWsCeXKwX/NUR9KKc0VKbGGEmsiD4uEhAQ6duzI999/T8GCBdm9ezcFChSwGRMfDz17whdfQI4csHIltGpln3hFRERERB4Wr7zyCgsWLODGjRs2+52cnKhevTr16tWjfv361K1bFz8/PztFmbXFxsI//9iui3bggFlAcLvSpW9WodWsCVWqgJtb5seclWkqqIjIbRwcHFiyZAm1a9fm4MGDdOzYkY0bN+Li4mId4+hoVqzFxsLy5dChA6xaBc2b2zFwEREREZEHRExMDMOGDWP79u1s2LABDw8PwEyg3bhxAy8vL+rWrWtNpNWoUQN3d3c7R531GAYEBtom0f76C6Kiko4tVMh2Ouejjz6cTQYyiirWUMWayMMmMDCQGjVqEBoaSp8+fZg/fz6W2xYKiI01GxisXAmurrB6NTRubJ94RURERESym4SEBA4cOMCWLVuIiIhg6NCh1mPFihXj1KlT/PbbbzRp0gSAoKAgIiIiqFChAg4ODvYKO8s6d852OueuXRAamnRc7txm4ixxOmeNGlCwYGZH+2DQVND7oMSayMPn119/pVWrViQkJDBjxgxefvnlJGNiYqBTJ/jxR3PhzjVroEEDOwQrIiIiIpLFRUREsGvXLmujgW3bthEWFgaAt7c3ly9ftibMFi1ahKurK82bN8fb29ueYWdJoaFmV85bE2lnzyYd5+IC1arZVqP5+4PykulDibX7oMSayMPpgw8+YOjQoTg6OvLrr79avy27VXQ0tG9vJtU8POCXX6Bu3cyPVUREREQkK7l06ZK1ycDWrVv5888/iYuLsxmTM2dOateuTf369RkxYgRuWsQriago2LvXtkvn0aNJxzk4QIUKtuuiVawIzs6ZHvJDQ4m1+6DEmsjDyTAMevXqxZIlSyhVqhSHDx/GySnp0pORkfDkk7BuHXh6wtq15i8yEREREZGHzd9//03nzp05duxYkmMFChSgfv361K9fn3r16lGlSpVk318/rOLj4dAh23XR/v4bbstHAlCypG0lWrVqkDNn5sf8MFPzAhGRe7BYLMydO5eEhATGjBlzx1/6bm7w/ffwxBOwcSO0aAG//Wb+chMREREReVD9/PPPzJkzh/r16/Paa68BULhwYWtSrWLFitYmA/Xq1aN48eJJ1i5+WBkGnDxpO53zzz/htqanAPj62ibRatQAH59MD1lSSYk1EXmoubm58fnnn99znLu72R20VSvYssXsErp+vdmWWkREREQkO7t69Srbtm1j69at9O7dmzJlygBw9uxZfvjhB8LCwqyJNW9vb9avX0/VqlXJkyePPcPOUi5ftk2i7dwJV64kHefhYTYXuDWRVrQoKB+ZfSmxJiJyi3Xr1rF//34GDx6c5JiHh9kd9PHHYccOaNbMrGCrUCHz4xQRERERSQ3DMDhx4oR1bbQtW7Zw8OBB6/FChQpZE2vNmzfn/fffp2HDhjbXaNy4cabGnNVcv25Wn92aSDt5Muk4Z2fzi/hb10UrUwYcHTM9ZMlA95VYCwkJ4ezZs1SsWBGANWvWEBgYSMOGDalUqVKGBCgiklkOHjxIixYtSEhIoHLlyjRt2jTJmFy5zEYGzZqZnXqaNjWTa2XLZn68IiIiIiL3EhcXx969e20SaRcuXEgyrnTp0tSvX9/6eR+gWLFiDBs2LDPDzXJiYmD/fttKtEOHICHBdpzFYibNbk2iValidu6UB1uKE2s//fQTTz/9NLGxsTRt2pTmzZvz66+/Eh8fz/Dhw1m+fDlPPPFERsYqIpKhypcvT//+/bl+/Tr16tW74zgvL/j1V2jSxOzg06QJbNoEAQGZF6uIiIiIyL0MGDCARYsWERERYbPf2dmZ6tWrW9dGq1u3Lr6+vnaKMutISIBjx25259y1y3y/Hx2ddGyRIrbTOatXNz8nyMMnxV1Bq1atyrx583B0dKRGjRr88ssvNGvWDICvv/6aqVOn8scff2RosBlFXUFFJFF8fDwODg4pWnT1yhUzqbZ/PxQubCbXSpbMhCBFRERERG4RFRXFa6+9xo4dO9i8eTNubm4AvPzyy3z88cfkzp2bunXrWhNpNWrUsI55WBkGnD1rO51z1y4ID086Nk8e20q0GjUgf/7Mj1kyV0pzRSlOrOXOnZvQ0FDAXOz7xo0bODg4AOYH0Xz58nH16tW0R24HSqyJSHISEhJYtGgRPXv2xPEOCyFcugSNG8PBg+aio5s3Q7FimRyoiIiIiDwU4uPj+eeff9i6dSsxMTEMGjQIMNdNK1iwIBcuXGDz5s00aNAAgMDAQKKioihfvrz18/vDKiTkZvIsMZF2/nzScW5uUK3azSRazZrml+dqLvDwSWmuKMVTQV1dXYmLi8PJyYlOnTrZ/KeMjo4mhfk5EZFso3v37ixbtoxDhw7x3nvvJTvG1xd++w0aNoSjR80k26ZNZmm4iIiIiEhaREREsHPnTuv6aNu2bSP8v5IqX19fXn31VSwWCxaLhXfeeQcPDw+bNdL8/f3tFbpdRUbCX3/ZJtGOHUs6ztERKla0rUarUAGc1OZR7kOK/7k0btyYY8eOUa5cOT7//HObY2vWrFHzAhF54HTo0IFly5bx/vvvU7VqVbp3757suPz5Yf16M7kWFHRzzbWCBTM5YBERERHJ1i5evGhtMLB161b27NlDXFyczRgPDw/q1KlDvXr1iImJweW/1fH79Oljj5CzhPh4WLnSXAd5505zqZb4+KTj/P1tp3M+8gi4u2d6uPKASfFU0Lu5ePEiAH5+fmkOyB40FVRE7uT1119n0qRJuLq6smXLFqpXr37HsadPm8m1EyfMjkAbN2rtBRERERG5tz179tC1a1cCAwOTHCtUqJB1bbT69etTqVIlnFRSBUBsLHzxBbzzjjl75FZ+fjenctasCY8+Ct7e9olTsqd0X2PNnj799FOef/55cubMyfXr122O7dmzh+HDh7Njxw6cnJxo0qQJU6ZMoeR9rCCuxJqI3El8fDzt2rXjp59+onDhwuzevfuuXyKcPGkm106dgvLlzeRavnyZFq6IiIiIZHE//vgjn3zyCY0bN2bw4MEAXLp0CT8/PywWCxUrVrRJpBUtWjRFjbUeJtHRsHAhTJ5svv8GM2nWuzfUq2dWoxUurHXRJG1SmitK0+qFY8eOTcvpKXL27FmGDRtGwWTmVB0+fJhGjRoRExPD119/zYIFCzh69CgNGjTg8uXLGR6biDz4HB0dWbp0KWXKlOHMmTN06tSJmJiYO44vXtycFlqokNnQoFkzCA7OvHhFREREJGsIDg5m1apVjBw50qYS7fTp06xatYrVq1db9/n6+rJ+/XquXr3K33//zaxZs3j66acpVqyYkmq3iIiAGTOgVCno189Mqvn6wnvvmfenToWOHc31jvWySWZJU8Wau7s7ERER6RlPEm3btsViseDt7c2KFStsKtaeeuopNmzYQFBQkDV7+O+//xIQEMDgwYN59913U/QYqlgTkXs5cuQItWrVIiwsjBdeeIG5c+fedfzRo9Cokdlp6JFHzAYHefJkTqwiIiIikvlOnTrFxo0b2bJlC1u2bOHQoUPWY3PmzOHFF18E4Pjx46xcuZKGDRvedZkRuenaNZg920ycXbpk7itUCIYPh+ee0zppkjEyZSqom5sbkZGRqT39nj7//HMGDBjAwYMHGT16tE1iLS4uDk9PT3r27MmcOXNszmvRogUnTpzg6O2TrO9AiTURSYnVq1fTpk0bDMNg9uzZ9OvX767jDx0yk2uXLpnl6GvXgpdX5sQqIiIiIhnv+PHjrFixguXLl7N79+4kx8uUKUP9+vXp2bMnjz32mB0izN5CQ80KtWnTICTE3Fe8OIwaBb16wX99G0QyREpzRWla8TAjS1IvXbrEoEGDmDx5MoULF05yPCgoiMjISCpXrpzkWOXKlVm7di1RUVG4urpmWIwi8nBp3bo1kyZNYuTIkbz88suUL1/+rm+QypUzK9UaNzZbfLdqBb/8ArlyZWLQIiIiIpKuAgMDWb58OcuXL+evv/6y7ndwcKBWrVrUr1+f+vXrU7duXXx8fOwYafZ1+bKZTPv4YwgPN/eVLg2vvw7du4Ozs13DE7GRZVuJDBgwgDJlytC/f/9kjwf/t2iRdzJtPby9vTEMg5CQEAoUKJDkeHR0NNHR0dbt8MT/qSIi9zB8+HD27t3LV199RefOndm1axfFihW74/iKFWHdOjO5tn07tG4Na9ZAzpyZGLSIiIiIpIlhGNbCksmTJzN//nzAXI+3cePGdOnShfbt2+Pr62vPMLO98+dhyhSYM8dcTw3M99OjR0PnzuDoaN/4RJKTpuYFGeWbb75h1apVzJs3755VcXc7fqdjkyZNwsvLy3orUqRImuIVkYeHxWJh/vz5PPLII1y+fJnu3btzrxn1VarcnAa6ZQu0aXPzjYKIiIiIZF1TpkyhYsWK7Ny507qvW7dutGjRgnnz5nHhwgXWrl3LCy+8oKRaGpw6BQMHQokS8MEH5nvl6tXhu+9g3z7o2lVJNcm60pRYS8PybHd0/fp1XnrpJV5++WUKFixIaGgooaGh1i58oaGh3Lhxg7x58wI3K9dudfXqVSwWC7lz5072MUaNGkVYWJj1dvr06XR/HiLy4HJ3d2flypVUr16dGTNmpGhafPXqN6eBbtwI7dpBVFTGxyoiIiIiKWMYBgcPHrT5nPvnn39y4MABli9fbt3XrFkz1qxZw3PPPaepnmkUGGg2HyhVCmbOhOhoqFsXfv7ZXEqlfXtwyJLlQCI3pal5QbNmzVi3bl16xsPJkycpUaLEXce0a9eOFStW4OnpSa9evZg9e7bN8ZYtW3L8+HE1LxCRDHXrlICU2rYNHn8cbtyAli1h5UotuioiIiJiL4ZhsG/fPpYvX86KFSs4evQo+/fvp2LFigBs3bqVwMBAnnzySfKoxXu6OXQI3nkHvvgCEhLMfU2amFM+GzWCDFzOXSTFMqUraEaIiopix44dSfZPnjyZTZs28fPPP+Pj40PFihXp2rUrGzduJDAwkFz/rQZ+6tQpAgICGDx4MJMnT07RYyqxJiJptXv3bq5evcrjjz9+z7GbN5uNDCIioG1bWLECcuTIhCBFREREBMMw2LNnDytWrGDFihUEBgZaj7m4uDB//nyefvppO0b44Nq7FyZOhG++gcRMROvW8MYbZqWaSFaSbRNrd9K7d29WrFjB9evXrfsOHz5MjRo1qFatGiNHjiQqKooxY8Zw9epV9u7dS758+VJ0bSXWRCQttm/fTuPGjXFxcWHXrl2ULl36nuesXw9PPGFOB+3QAZYtU3cjERERkYxiGAa7du2yJtNOnDhhPebq6krr1q3p3LkzTzzxhD4TZoCdO2HCBFi16ua+Dh3MhFr16vaLS+RuUporSpeuoCtXrmTp0qX8+++/RN22aJDFYmHfvn3p8TBJlC1blo0bNzJixAg6d+6Mk5MTTZo0YcqUKSlOqomIpFX16tWpUaMGefLkIX/+/Ck6p0kT+P57ePJJc1HWHj1g6VJwyrK9mkVERESynz179rB06VJWrFjBqVOnrPvd3d154okn6Ny5M61bt8bDw8OOUT64Nm82E2pr15rbDg5mI4LXXze7fYo8CNJcsfb+++8zYsQI8uXLh7+/PzmSmc+0YcOGtDxEhlPFmoikVVhYGLly5cLhPldXXb3aXJQ1Nha6d4fFi9XxSERERCS1Ev5bsCvxPdnQoUP54IMPAMiZMydt27alc+fOtGzZkpw5c9otzgeZYcC6dfD22/D77+Y+R0d45hkYNQpSMLlDJEvItIq1WbNm0bdvX+bOnYujPg2KyEPKy8vLet8wDLZu3Ur9+vXveV7r1uYaa506mYu3OjvDggXqfiQiIiJyv8aNG8cnn3zCokWLaN68OQDdunXjwoULdOnShRYtWuDm5mbnKB9chgE//mhWqO3cae7LkQP69oURI6B4cbuGJ5Jh0vzRLTg4mO7duyupJiKC+S1p7969adCgAcuWLUvROU8+CV99ZX6Tt2gRvPjize5IIiIiIpJUXFwcmzZtslaoAZw/f57z58+zcuVK674aNWqwdOlS2rdvr6RaBklIML8ofuQR833tzp3g5gavvgrHj8Ps2UqqyYMtzYm1evXqcejQofSIRUQk23NwcLCus9anTx/++uuvFJ3XqZO5xpqDA3z6KQwceLNTkoiIiIiYybR169bRr18/ChYsSKNGjdixY4f1+MCBA/nxxx+tUz8lY8XFweefm2uldekC+/aBh4dZnXbiBEybBoUK2TtKkYyX5qmg06ZNo0OHDhQpUoSWLVsmu8aaiMjD5J133uHvv/9mzZo1tG/fnl27duHr63vP87p2Ndda69nT/GYvRw748EOwWDIhaBEREZEsKDY2lvXr17NixQq+++47goODrce8vb05deoUdevWBaBSpUpUqlTJXqE+NGJiYMkSmDQJgoLMfblzwyuvmFVq3t52DU8k06U5sebv70+zZs3o0KEDFosFd3d3m+MWi4WwsLC0PoyISLbh6OjIl19+Sc2aNTl27BhdunRh3bp1ODs73/PcHj3M5FrfvjB9urnm2nvvKbkmIiIiD4+YmBjWrVvHihUrWLlyJSEhIdZjPj4+dOjQgc6dO9O4ceMUvb+S9BEVBfPnw7vvwunT5j4fHxgyBAYMgFuWHBZ5qKQ5sTZ8+HA+/vhjqlatSrly5VSxJiIC5M6dm++//55atWqxefNmBg0axMyZM1N0bp8+ZnLtxRdhyhQzuTZxopJrIiIi8mDbvHkz8+fP5/vvv7cpzvD19aVjx4507tyZhg0b4uSU5o+xch9u3IC5c+H99+HCBXNf/vzw2mvm+1U1V5WHXZp/Ii1cuJARI0YwadKk9IhHROSBUa5cOZYuXUq7du2YNWsWVapU4YUXXkjRuS+8YCbXBg40y+xz5IBx4zI2XhEREZHMFBkZiaOjo7U4Y8OGDSxevBiA/Pnz06lTJ7p06UL9+vXVLM8OwsJg5kz44ANInIFbpAiMHGnOrnB1tW98IllFmpsXxMfHW1sZi4iIrbZt2zJhwgTAXFB3y5YtKT73pZfMNdYAxo83q9ZEREREHgSvvfYavr6+rFq1yrqva9euvPLKK/z++++cPXuWjz/+mIYNGyqplsmCg2HMGChWDN54w9wuVcqcBhoYaE77VFJN5KY0J9Yef/xxm04sIiJia9SoUXTp0oXY2Fg6derE6cRFKVJg0CBzjTWA0aPNEnwRERGR7OTGjRssX76cmJgY6z6LxcL169dZt26ddV/ZsmWZPn069evXx8EhzR9V5T5dvGh29CxeHN5+26xYK1fO7Px5+LBZpaaVn0SSshiGYaTlAvv376dr1668+OKLPPHEE3gn0wIkuX1ZSXh4OF5eXoSFheHp6WnvcETkAXTjxg3q1avHvn37qF69Or///jtubm4pPn/iRDOxBmYV26BBGROniIiISHq4du0aP/30EytWrGD16tVERkayZs0aWrRoAcCJEye4dOkSNWvWxKKFZO3qzBnzy9tPPjEbFABUrWq+9+zQAZTjlIdVSnNFaU6sJX6TcLcfhvHx8Wl5iAynxJqIZIaTJ0/y6KOPEhwcTM+ePVm0aNF9nT9unDklFODjj82poiIiIiJZRXh4OKtWrWLFihWsWbOGqMQsDVCyZEnee+89OnXqZMcI5VYnTpgdPj/7DBKLCWvWhDffhCeeUOMskZTmitLcvGDMmDH6hkFEJAWKFy/OihUr6NatG7169brv88eONd/0TJpkNjVwdjabHIiIiIjYS2hoKKtWrWL58uX88ssvNtM9/f396dKlC126dKFq1ar63JhFHDlivp/8/HNIrIF57DEzoda0qRJqIvcrzRVrDwJVrIlIZoqIiMDd3T1V5xoGDB8OU6aY2wsWQJ8+6RiciIiISAr8/PPPzJw5k19//ZXY2Fjr/jJlyliTaZUqVVIyLQvZvx/eeQeWLTPfUwI8/rjZoOCxx+wbm0hWlGkVayIicn9uTaodOXIEBwcHAgICUnSuxWI2M4iNhenT4dlnzcq1Hj0yKloRERERuHLlCm5ubuTMmROAw4cP89NPPwFQoUIFOnfuTJcuXShfvrySaVnM7t3mer0rV97c17atuYZazZp2C0vkgaHEmoiInWzevJm2bdtSqFAhduzYkeKKWYvFbGAQEwOzZ0OvXmZyrWvXDA5YREREHkoDBw5kzpw5zJs3jz7/lcp36dKF69ev07lzZ8qVK2fnCCU527aZ3T3XrDG3LRbo3Blef91sTiAi6UP9PURE7CQgIIBcuXKRN29eoqOj7+tci8VsYPDcc5CQAE8/Dd98k0GBioiIyEPj4sWLzJ49m+vXr1v35c+fn/j4ePbs2WPdV7hwYd58800l1bIYw4D166FJE6hXz0yqOTrCM8/AgQPw9ddKqomkN62xhtZYExH7CQwMpGjRouTIkSNV5yckQN++sGgRODmZybUnn0znIEVEROSBdv78eb755htWrFjB5s2bMQyDZcuW8dRTTwFmsu3atWv4+/vbOVK5E8OAn3+GCRNg+3Zzn7OzObNh5EgoVcq+8YlkR1pjTUQkG7j9Derp06cpUqRIis93cID588011774wizvX7kSWrdO50BFRETkgXLmzBm+/fZbli9fztatW7m13qJmzZq4urpat/38/PDz87NHmHIPCQnw/fdmQi2xoNDFBZ5/Hl57DYoWtW98Ig8DJdZERLKAuLg4RowYwdy5c9m2bRuVK1dO8bmOjmbFWlycWd7fsSP88IPZ5UlEREQk0alTp6yVadu2bbM5VqdOHTp37kynTp0oVqyYnSKUlIqPN9/3TZxoTvEEcHeH/v1h6FAoUMC+8Yk8TFKVWDt16hQFChTA2dmZU6dO3XN8UaXJRUTuaf/+/dy4cYN27dqxa9cufHx8UnyukxN8/rlZufbdd9CuHfz0k7m+hoiIiDzcvvvuO959913++OMPm/316tWjS5cudOzY8b4q5sV+YmNh6VJ45x04dszc5+kJL78MgwbBfbx9FJF0kqo11hwdHdm+fTs1a9bEwcHhnu2U4+PjUx1gZtAaayKSFVy9epUaNWpw/PhxGjduzC+//IKzs/N9XSMmxpwOumqV+a3lzz/DY49lUMAiIiKSJQUFBZEnTx68vb0BWLBgAc8++ywWi4UGDRrQpUsXOnToQKFChewcqaRUdDR89hlMngz//mvu8/Y2k2kvvwy5c9szOpEHU4ausbZgwQJK/bf64YIFC+6ZWBMRkXvz9vbmhx9+oHbt2mzYsIFhw4Yxffr0+7pGjhywfDm0b292gWrdGn79FerWzZiYRUREJGt5/vnn+fTTT5k2bRqvvvoqAO3atSMqKoqOHTuSP39+O0co9yMiAubNg/feg3PnzH2+vjBsGPTrB7ly2Tc+EVFXUEAVayKStaxcuZIOHToAMH/+fPr27Xvf14iMNLuDrltnvuFatw5q1kzvSEVERCSrmTFjBkOGDOHVV19l6tSp9g5HUunaNZg1C6ZOhcuXzX2FCsHw4fDcc+bMBBHJWCnNFSmxhhJrIpL1vPXWW4wdO5YcOXKwadMmateufd/XiIiAJ56AjRvBywvWr4dq1dI/VhEREbGPmJgYJkyYQOXKlencuTMAYWFhxMbG3tdarZJ1hITAjBkwfbp5H6B4cRg1Cnr1Mjt+ikjmsEti7erVq7z33nv8888/FCpUiFdeeYUKFSqk1+UzjBJrIpLVJCQk0KVLF7799lsKFCjA7t27KViw4H1f5/p1aNUKtmyBPHlgwwaoUiUDAhYREZFM9ddff9G7d2/+/vtvfHx8CAwMxMvLy95hSSpdvgwffggff2xWqwGULg2vvw7du8N9LrsrIukgQxNrw4YN4+uvv7bpCHrjxg0qV67MyZMnSbxkrly52LlzJ2XKlEnFU8g8SqyJSFZ0/fp16tSpwz///EPNmjXZtGkTrq6u932da9egRQvYvt3sFLVhA1SsmAEBi4iISIaLiYlh4sSJvPPOO8TFxeHj48PMmTN56qmn7B2apMK5czBlCsyda842APN92ujRZkMqR0f7xifyMEtprsghNRfftm0b3bp1s9n38ccfc+LECQYNGkRoaCjbtm3Dw8ODyZMn3/f19+7dyxNPPEHRokVxc3PD29ubOnXq8PnnnycZu2fPHpo1a4aHhwe5c+emY8eOHD9+PDVPS0QkS/Hw8OD777/H29ubnTt30q9fP1JTZJwrl9kd9NFH4coVaNoUDh3KgIBFREQkQ+3du5eaNWvy1ltvERcXR6dOnThw4ICSatnQv//CSy9ByZJmpVpEBFSvDt99B/v2QdeuSqqJZBepSqwdP36cRx991GbfqlWryJcvH++99x6enp7Url2bIUOGsHHjxvu+fmhoKEWKFOGdd95h9erVLF68mOLFi/PMM88wYcIE67jDhw/TqFEjYmJi+Prrr1mwYAFHjx6lQYMGXE5c4VFEJBsrWbIkX3/9NY6OjixatOi+u4Qm8vIyu4M+8ghcugRNmsDRo+kcrIiIiGSImJgYxo0bR40aNdi3bx8+Pj4sW7aMFStW4Ovra+/w5D4EBsKzz4K/v9mcIDra7N7+88+wa5fZ2d0hVZ/SRcReUjUV1NXVlbVr19KgQQMA4uLiyJkzJ+3bt2fZsmXWcevXr6d169ZERUWlS7C1a9fm3Llz1imoTz31FBs2bCAoKMhalvfvv/8SEBDA4MGDeffdd1N0XU0FFZGsbvr06bzxxhssXryYjh07pvo6wcHQuDHs3292ltq0CUqVSsdARUREJF3t3buX3r17s2/fPgA6derErFmzlFDLZg4ehHfegS+/hIQEc1+TJuaUz0aNwGKxa3gikowMnQrq5+fH+fPnrdt79uwhNjY2SRWbg4MDLunYtsTHxwcnJyfATOb9+OOPdOrUyeYJFitWjMaNG/Pdd9+l2+OKiNjbK6+8wsGDB9OUVAPImxfWrYPy5eHsWfMN3cmT6ROjiIiIpJ/Y2FjGjx9vrVLLmzcvX331FcuXL1dSLRv56y9zrbSKFWHpUjOp1ro1bN0Kv/1mfuGppJpI9paqxFr16tWZN2+eda2fpUuXYrFYaNq0qc24w4cPU6BAgVQHl5CQQFxcHJcvX2bWrFn88ssvjBgxAoCgoCAiIyOpXLlykvMqV65MYGBgulXKiYjYm8VioWjRotbts2fPci2xZdR98vU138iVKQOnTpnJtdOn0ytSERERSSvDMGjZsiXjxo2zrqV28OBBunbtikVZmGxhxw5o2xaqVYNvvgHDgA4dYPdu+Oknc/qniDwYUpVYGzFiBBs2bKBMmTLUrVuXjz76iPr161OtWjWbcatWraJGjRqpDm7AgAE4Ozvj6+vL4MGDmTFjBi+++CIAwcHBAHh7eyc5z9vbG8MwCAkJSfa60dHRhIeH29xERLKL7du3U716dXr16kVC4lyC+5Q/P6xfb67vceKE+W3p2bPpHKiIiIikisVioXfv3qpSy4Y2bYLmzaFOHfjxR3O9tP/9z1yG49tvzQYFIvJgSVVirVatWnz//fcULFiQa9eu8dxzzyWZennhwgXOnDlDu3btUh3c66+/zq5du/jpp5/o27cvAwcOZMqUKTZj7vaNzZ2OTZo0CS8vL+utSJEiqY5RRCSzWSwWQkJCCAoKuuMXCClRsKCZXCtRAoKCzMq1CxfSMVARERFJsb///psNGzZYt3v06MHRo0dVpZbFxceb1Wnjx0ONGuZ6aevWmR09e/c2O7F/8YU5FVREHkypal5gL/379+fTTz/l3LlzXL16lbJlyzJz5kwGDBhgM+61115j6tSpRERE4OrqmuQ60dHRREdHW7fDw8MpUqSImheISLaxfv16atasiYeHR5qvdfIkNGxoTgstXx42bDCni4qIiEjmWLduHa1btyZv3rwcOHAg2Vk5knWcPQu//GLe1q6FW7/nzJED+vaFESOgeHG7hSgi6SClzQucMjGmNKtZsyZz5szh+PHjVK9eHTc3N/bv359k3P79+/H39082qQbg4uKSrk0VREQyW5MmTWy2IyMjcXNzS9W1ihc3k2mPPWZ2rGrWzNzOmzcdAhUREZF7qlevHiVLlqRcuXLEx8fbOxy5TVQU/P77zWTaP//YHvfyMqd/tmgBbdqYS26IyMMjVVNB7WXDhg04ODhQsmRJnJycaNu2Ld9++63NAt6nTp1iw4YNae6cJyKSHRiGwaRJk6hSpQpXr15N9XVKljSTaQUKmGuANG9u++2riIiIpJ/Y2FjmzZtnTaK5ubmxdetWvv32W/Lly2fn6MQw4PBhmD7d7ODp7Q2PPw5Tp5pJNYsFatWCMWPM7p5XrsDy5fDcc0qqiTyMsuRU0BdeeAFPT09q1qyJn58fV65cYfny5SxbtozXXnuN9957DzC7jtaoUYNq1aoxcuRIoqKiGDNmDFevXmXv3r0p/qWU0vI+EZGsJiwsjEceeYQTJ07QrFkzfv75Z5ycUl+MfPiwOS300iV49FFzjRAvr3QMWERE5CG3f/9+evfuzZ49e3jvvfd47bXX7B2SAGFhZtf0xKq0f/+1PV6woFmR1qKFWd2vyn6RB1+2ngpap04dPvvsMxYtWkRoaCgeHh5UqVKFJUuW0KNHD+u4smXLsnHjRkaMGEHnzp1xcnKiSZMmTJkyRd/0iMhDwcvLi5UrV1KnTh3WrVvHiBEjmDp1aqqvV7as+aaycWOzHXzLluabS33nICIikjaxsbG8++67vPXWW8TGxpInTx41UbOjhAT488+bibTt281GBIly5DCXyUhMplWsaFaqiYjcLktWrGU2VayJSHb3zTff0LlzZwAWLVpEz54903S9ffvMLqFXr0L9+vDzz5AOfRJEREQeSrdWqQG0a9eOOXPmkF/zBjPV+fPw6683mw5cuWJ7vHRp80vFFi3MCv6cOe0Tp4hkDSnNFSmxhhJrIvJgGDNmDG+//TYuLi5s3ryZmjVrpul6e/ZA06YQGmq2jv/pJ3B3T5dQRUREHgpxcXG8++67jB8/3lql9tFHH9G9e3csKn/KcDEx5hpoa9aYybR9+2yP58plTutMrEpTF08RuVWGJtZOnTp1X+OLFi16vw+RqZRYE5EHQUJCAh06dOCHH36gYMGC7N69mwIFCqTpmjt3mo0MwsPNN54//ACpbD4qIiLyUPnnn3/o3bs3f/75JwBPPvkkc+bMSfPvZrm7wMCbibQNG+DGDdvjjz56M5FWuzY4O9snThHJ+jI0sebg4HBf37Bk9ZbRSqyJyIMiPDycOnXqcPDgQWrXrs3GjRtxcXFJ0zW3bTPffF6/bk6P+O47cHVNp4BFREQeMMlVqc2YMYOnn35aVWoZ4No1WL/+5lppx4/bHvfzu5lIa94ctBS3iKRUhjYvWLBggX4piIhkQZ6ennz//ffUqFGDHTt28NJLLzFv3rw0/cyuWxdWrzaTamvWQJcu8M035qK+IiIictOpU6fo1KkTu3fvBqBt27bMnTtXVWrpKCHBnNKZWJW2dSvExd087uxsrg+bmEyrXBkcHOwXr4g8+LTGGqpYE5EHzy+//ELr1q1JSEjgo48+YuDAgWm+5oYN0Lo1REVBhw6wbJmmT4iIiNzqxo0bVKlSheDgYGbMmEGPHj1UkJAOLl262XTg11/N7VuVKnWz6UDjxmq4JCLpwy7NC44ePUpwcDA+Pj4EBASk12UznBJrIvIgmjp1KsOGDcPR0ZG1a9fSuHHjNF9z7Vpo2xaio83KtS++AKdU1T6LiIg8GAIDAylZsiQO/5VF7d27F19fXwoWLGjnyLKv2FjYvv1mVdp/zVStcuY0GywlVqWVKmWfOEXkwZbSXFG6FMUuX76cYsWKUa5cOerXr0/ZsmUpVqwYK1asSI/Li4hIKgwZMoQePXrg4OBw301n7qR5c3ONtRw5YPly6NULsvgymiIiIhlm2rRpVKhQgZkzZ1r3Va1aVUm1VDhxAubMgfbtIW9eaNgQJk26mVSrWhVGjjQr6K9ehe+/hwEDlFQTEftLc53B6tWr6datGxUqVGDgwIEULFiQs2fP8vnnn9OtWzdWrVpFq1at0iNWERG5DxaLhU8++YRXXnmFGjVqpNt1W7WCFSugY0ezYs3ZGRYs0PolIiLy8MmRIwcxMTFs3bqVgQMHatrnfbhxAzZuvFmVduyY7XEfH9umA/nz2yVMEZF7SvNU0Hr16uHp6clPP/1kLX8GMAyDVq1ace3aNbZu3ZrmQDOSpoKKyMMi8edcerzx//ZbeOops2Ltuedg7lwl10RE5MEWFxfH6dOnKVGiBAAJCQmsWbOGVq1aKal2D4YB+/ffTKRt2QIxMTePOzlBnTo310p75BG9rxAR+8rQrqC32rt3L1999ZVNUg3MSokBAwbQvXv3tD6EiIikg3/++Yd27drx7LPP8vrrr6f5eh07wtKl0L07fPqpWbk2cyboc4WIiDyIDh48SJ8+fbh06RL79+/Hw8MDBwcHWrdube/QsqzgYHN91jVrzKYD58/bHi9e/GYirUkTUI2DiGRHaU6sOTo6EnPrVw23iI2NTZJwExER+9ixYwfHjx9nwYIFDBo0CHd39zRfs2tXc4Hhnj1h9mwzuTZtmpJrIiLy4IiLi2Pq1KmMGTOGmJgYvLy82L9/P3Xq1LF3aFlOXBz88cfNqrTdu81KtUTu7tCo0c1kWkCA3jOISPaX5qmgTZs25fr162zcuBE3Nzfr/ujoaBo1aoSHhwdr165Nc6AZSVNBReRhMXPmTLp160bevHnT9boLF0KfPub9oUPh/ff1RllERLK/Q4cO0bt3b3bu3AnAE088wdy5cylUqJCdI8s6Tp0yk2hr1sBvv0FYmO3xSpVuJtLq1wcXF/vEKSJyv1KaK0pzYm3Lli00bdoUb29vunTpQv78+Tl//jzffvstwcHBrF+/nrp166blITKcEmsi8rAyDCPd1oSZNw9eeMG8P3IkvPOOkmsiIpI9JVapjR07lujoaLy8vJg2bRq9evV66NdSi4iAzZtvVqUdPmx73NvbbDbQsiU8/jioQaqIZFeZllgD2LRpEyNHjmTnzp0YhoGDgwO1atVi0qRJPPbYY2m9fIZTYk1EHkbz5s3jxx9/5Ntvv8XR0TFdrjlrFrz0knm/UyeYOhWKFUuXS4uIiGSKQ4cO0adPH/744w8AWrduzSeffPLQVqkZBhw8eLMqbfNmiI6+edzBAWrXvlmVVr06pNPbChERu8rUxFqiiIgIQkJCyJMnT7qs3ZNZlFgTkYfNmTNnKF26NJGRkbz22mu899576Xbtjz6CQYMgIQFcXWHECBg+3FxXRUREJKuKj4+3rqX2sFephYTAunU3mw6cOWN7vEiRm4m0pk0hd267hCkikqHskljLrpRYE5GH0bJly+jWrRsAS5cuTdcuzn//Da++Chs3mttFi8KUKdC5s6aHiohI1nP48GF69+5trVJr1aoVn3zyCYULF7ZzZJkjPh527bpZlbZzp/kFWSJXV2jY8GYyrWxZ/T4XkQdfSnNFae4KCrBy5UqWLl3Kv//+S1RUlM0xi8XCvn370uNhREQkHXXt2pV9+/YxadIknn32WcqUKUP16tXT5dqVK8P69fDNN2Yzg1On4KmnzDflM2aYx0VERLKKn3/+mT/++ANPT0+mTZtG7969H/gqtbNnbybS1q0zq9RuVb68mURr2RIaNIBb+tSJiMgt0lyx9v777zNixAjy5cuHv78/OXLkSDJmw4YNaXmIDKeKNRF5WMXHx9OuXTt++uknChcuzO7du/Hz80vXx4iMNLuETp5s3ndwgBdfhLffhnRuTioiIpJicXFxODmZdQbx8fG8/vrrvPzyyw9slVpUFPz++81k2oEDtsdz54ZmzW42HShSxC5hiohkGZk2FbREiRI0bdqUuXPnptvi15lNiTUReZiFhYVRq1Ytjhw5Qv369fntt9+S/ZIkrU6dgtdeg6+/Nrfz5IG33oJ+/cApXeqnRURE7i0+Pp4PPviAzz//nO3bt2ertaHvh2HAkSM3E2mbNplfcCWyWKBmzZtVaTVq6PexiMitMi2x5unpycqVK2nSpElaLmNXSqyJyMPuyJEj1KxZk/DwcF588UXmzJmTYY+1aZO5/lriKgEVK8L06ZCNf42IiEg2EhYWRoUKFTh79iyzZ8+mX79+9g4p3YSFwW+/3UymnTple7xgwZuJtGbNwNvbPnGKiGQHmZZYa9WqFW3atOGll15Ky2XsSok1ERFYvXo1bdq0wTCMDP+gER8P8+bBG2/A1avmvk6dzAYHxYtn2MOKiMhDKj4+HgcHB+u6ab/88gtnzpyhb9++D8RaalevmssuzJgBERE39+fIAY89drPpQIUKajogIpJSmZZYO3LkCB06dGDy5Mm0bNkyQ6YPZTQl1kRETO+++y4jR47EycmJ3377jcceeyxDH+/qVRg7FmbPNpNtrq7mdNGRI+EBnZkjIiKZ7MiRI/Tp04cXXniB3r172zucdHXtmln1/f77EB5u7gsIgFatzGRaw4b6fSoiklqZlliLj49n8ODBzJw5E4vFkmSNAovFQlhYWFoeIsMpsSYiYjIMg+7du/PVV1+RL18+du/eTdGiRTP8cf/5x5weun69uV24sPkhoWtXfbMuIiKpEx8fz7Rp0xg9ejRRUVEULVqUwMBAnJ2d7R1amkVFmV9KvfMOXLli7qtUCSZOhDZt9LtTRCQ9pDRXlOblKYcPH87HH39M1apVKVeuXLasWBMREZPFYmH+/PkcOXKEvXv38ttvv9GnT58Mf9yKFWHdOvjuOxg6FE6ehP/9D2bNMqe1VK2a4SGIiMgD5OjRo/Tp04dt27YB0Lx5cz799NNsn1SLjYXPPjOb/5w9a+4LCDC3n3rK7LwtIiKZK80Va3nz5uWFF15g0qRJ6RVTplPFmoiIrVOnTrF//36eeOKJTH/syEhzrbVJk8z7Dg7w/PMwYQL4+GR6OCIiko3Ex8czffp03njjDaKiosiVKxdTp07lueeey9ZrqcXHw1dfmcsnBAWZ+4oUMbd79VI3TxGRjJDSXFGav9OIj4+nefPmab2MjfXr19O3b1/Kli1Lzpw5KVSoEO3atePPP/9MMnbPnj00a9YMDw8PcufOTceOHTl+/Hi6xiMi8rApWrSoTVItPj4+0x7bzQ3efBOOHIFu3SAhAebONb+R/+gjiIvLtFBERCQbOXr0KA0bNmTo0KFERUXRvHlz/vnnH55//vlsm1QzDFi50qzc7tHDTKr5+prrqh09Cs8+q6SaiIi9pTmx9vjjj7Njx470iMVq9uzZnDx5kldffZXVq1czffp0Ll26RO3atVmfuAAPcPjwYRo1akRMTAxff/01CxYs4OjRozRo0IDLly+na0wiIg+rkydPUqNGDVavXp2pj1ukCHz5JWzebH6gCA2FV14x7//2W6aGIiIiWVh8fDwffvghVapUYevWrXh4eDB37lx++eWXTFknNCMYBqxdC7VqQYcO5lqkuXOba6oFBZm/D11d7R2liIhAOkwF3b9/P127duXFF1/kiSeewNvbO8mY5PbdzaVLl/D19bXZd/36dfz9/alYsSLr1q0D4KmnnmLDhg0EBQVZy/L+/fdfAgICGDx4MO+++26KHk9TQUVE7mzYsGFMnTqV0qVLc+DAAZzs8NV4fDzMnw+vvw7Bwea+Dh1g6lQoUSLTwxERkSzi2LFj9OnTh61btwLQrFkzPv30U4oVK2bnyFJv61Z44w3YtMnczpnTbPAzbBjkyWPf2EREHiaZ1hXU4b8VMu9WXp1eU4iaNGnC2bNnOXLkCHFxcXh6etKzZ0/mzJljM65FixacOHGCo0ePpui6SqyJiNxZTEwMgwYNYtSoURQpUsSusYSEwLhxMHOmmWxzcYHXXoORI80PHiIi8vCIi4sjICCAkydP4uHhwdSpU7P1tM+//oLRoyGxQDxHDujfH0aNAj8/+8YmIvIwyrSuoGPGjMmUX15hYWHs2bOHJk2aABAUFERkZCSVK1dOMrZy5cqsXbuWqKgoXFUjLSKSJjly5GDWrFn2DgMwv6mfPh1eeAEGDTI7iU6YAAsXwnvvmWuyZdPPUyIicp+cnJz44IMPmDVrVrauUjt8GMaMgeXLzW1HR+jTx1xvNJvOZBUReaikObE2bty4dAjj3l566SVu3LjBG2+8AUDwf3OB7jT11DAMQkJCKFCgQJLj0dHRREdHW7fDw8MzKGoRkQfPt99+y4EDB3jzzTftFkOFCvDrr/D99zBkCJw4Ad27w6xZMGMGPPKI3UITEZEMkpCQwEcffUTBggXp0qULAB06dKB9+/bZskrt5EkYPx4WLzYb9Vgs5hdE48ebDXtERCR7SHPzgszw5ptvsnTpUj788EOqV69uc+xuv0TvdGzSpEl4eXlZb/ae2iQikl38888/dOrUiTFjxrBs2TK7xmKxQPv2cPCgWbXm7g5btkD16mZFm3rYiIg8WBYsWMCgQYPo168fV65cse7Pbkm18+dh4EAoXdqsuE5IgCefhL174YsvlFQTEclu0iWxtnLlSrp06ULNmjWpXLmyza1KlSppuvb48eOZMGECEydOZODAgdb9efPmBW5Wrt3q6tWrWCwWcufOnew1R40aRVhYmPV2+vTpNMUoIvKwqFixIq+99hoAffr04a+//rJzRGZXtDfegCNHzKo1w4B588wPJtOmQWysvSMUEZH00LNnT+rXr8/EiROtnwWyk+BgGDECSpUy1wqNjYVmzWDHDrMCO5kVbkREJBtIc2Lt/fffp2PHjmzevBlnZ2fy5s1rc7vfjqC3Gj9+POPGjWPcuHG8/vrrNsdKlSqFm5sb+/fvT3Le/v378ff3v+P6ai4uLnh6etrcREQkZSZNmkTLli2JjIykffv2XM4ipWGFC8PSpfD77+ZU0LAwGDwYqlSBtWvtHZ2IiNyvwMBAXnjhBWJiYgBzzc/NmzfTr1+/bFWldu0avPUWlCxprgcaGQl16sD69ebvp1q17B2hiIikRZq7gpYoUYKmTZsyd+5cHB0d0ysu3n77bcaMGcPo0aN5++23kx3TtWtXNm7cSGBgILly5QLg1KlTBAQEMHjwYCZPnpyix1JXUBGR+xMSEkKtWrU4duwYjz32GOvWrcPZ2dneYVnFx8OCBfD665A4W6hdO/jgA/ODjYiIZF0JCQl8/PHHjBw5ksjISMaPH8+YMWPsHdZ9i4w01/6cPPnm76IqVczlC554Qs12RESyupTmitKcWPP09GTlypXWbp3pYerUqQwbNoyWLVsyduzYJMdr164NwOHDh6lRowbVqlVj5MiRREVFMWbMGK5evcrevXvJly9fih5PiTURkft36NAhatWqxbVr1xgwYAAzZ860d0hJhIaai0B/9JGZbMuRA4YNg1GjwMPD3tGJiMjtgoKC6Nu3L5s3bwagcePGzJ8/nxIlStg5spSLiTG/3Hn7bTh3ztxXurRZtdalCzhki1WuRUQkpbmiNP9Yr1evHocOHUrrZWysWrUKgDVr1lCnTp0kt0Rly5Zl48aNODs707lzZ3r37o2/vz+bN29OcVJNRERSp1y5cixduhSLxcKsWbP45JNP7B1SErlzw4cfwt9/Q/Pm5oedd96BMmXMaaNp+2pJRETSS2LHz8qVK7N582Zy5szJrFmzWLduXbZJqsXHw5IlUK4c9O9vJtWKFoX58+HAAejaVUk1EZEHUZor1o4cOUKHDh2YPHkyLVu2JEeOHOkVW6ZRxZqISOpNnDiR0aNH4+zszIYNG6hXr569Q0qWYcAPP8CQIXD8uLmvbl2YMcPsJCoiIvZx/Phx+vbty6ZNm4DsV6VmGPDdd/Dmm2anagA/P7OxzgsvgIuLfeMTEZHUybSKNX9/f5o1a0aHDh1wd3dP0hTAy8srrQ8hIiJZ2Ouvv06XLl2IjY2lU6dOWbbTssVirrN24IBZtZYzJ2zbBjVqwPPPw6VL9o5QROThkriWWqVKldi0aRM5c+Zk5syZ2aZKzTDgl1+gZk3o1MlMquXJA5MmQVAQvPyykmoiIg+DNFesDR06lA8//JCqVatSrly5ZCvWPvvss7Q8RIZTxZqISNrcuHGDunXr8vfff1O9enV+//133Nzc7B3WXZ09CyNGmFNCAby8YOxYGDgQslAfBhGRB9LtVWqNGjViwYIF2SKhBrBli1mR9t9ScOTMaXaiHjrUXIZARESyv0xrXpA3b15eeOEFJk2alJbL2JUSayIiaXfixAlq1KhBcHAwS5cupXv37vYOKUW2bYNXXoE//zS3y5aFadOgRQu7hiUi8sDavn07zZs358aNG+TMmZN3332X/v3745ANFiDbswdGj4affza3XVxgwAAYORJ8fe0bm4iIpK+U5oqc0vpA8fHxNG/ePK2XERGRbK5EiRIsX76c06dPW5Nq8fHxxMfHZ+n1N+vWhT/+gIULzW6hhw9Dy5bQti188AH4+9s7QhGRB0u1atUoVqwYvr6+zJ8/n5IlS9o7pHs6dAjGjIEVK8xtJyfo29dcV61wYfvGJiIi9pXmr4Uef/xxduzYkR6xiIhINte4cWN69uxp3f7xxx8pUqQI77//vh2jujdHR3j2WTh61JzK4+QEq1ZBhQpmsu3aNXtHKCKSfSUkJPDVV18RFxcHgIuLC7/99hu//fZblk+qnTgBvXtDxYpmUs1igR49zC9h5s5VUk1ERNIhsfbmm2/y+eefM336dAIDA7l69WqSm4iIPJyWLVvGpUuXuHLlinVfQkIC8fHxdozqznLnNqvU/v7bnAoaEwOTJ0OZMrBkCSQk2DtCEZHs56mnnuJ///sfU6ZMse7Lnz9/lp76ee4cvPSS+fN/0SLz53+HDubvhyVLoFQpe0coIiJZRZp/m1WpUoXDhw8zZMgQypQpQ758+ZLcRETk4bRo0SK++eYbBgwYYN2X2O3t7bff5vz583aM7s7KlTPXz/nhB/PD0/nz0LMn1KsHu3bZOzoRkezlySefxN3dndzZYFX/4GAYPtz82T9rFsTGwuOPw86d8O23ZuWaiIjIrdLcvGDcuHFYLJa7jhk7dmxaHiLDqXmBiEjmeeaZZ/j8888BcHJyol27dvTr148mTZpkyeqF6Gj48EOYMAFu3DCnAfXpA++8A35+9o5ORCTrOXnyJGfPnqVevXoAGIbBuXPnKFSokJ0ju7PwcPNn/dSpN6f/16sHEydCw4b2jU1EROwj07qCPgiUWBMRyTxRUVGsWLGCOXPmsHXrVuv+gIAAXnzxRXr37k3evHntGGHyzp0zu74tWWJue3qaC1m//DJk4d4MIiIZKjY2lkuXLnHp0iUuXrzI33//zVtvvYWnpycHDhwgT5489g7xriIiYOZMePdds1oN4JFHzC9TWrUyv0wREZGHkxJr90GJNRER+9i/fz9z5sxhyZIlXPuvRMDFxYUuXbrQv39/6tSpc8+q6My2fTu88grs3m1ulyljVjm0amXfuERE0oNhGNy4cYO4uDjr1M2wsDBmzJjBxYsXrQm0xD9DQkKSvU6DBg344osvKJxFV/ePiYFPPzUTaImrEpQtC2+9BZ06QRYsoBYRkUymxNp9UGJNRMS+rl+/zpdffsns2bP566+/rPsrVapEv3796NmzJx4eHnaM0FZCAixcaHYMvXTJ3Nemjdn4ICDArqGJiCSRkJDA1atXk02MXbp0ibFjx1oTYG+99RZjx46lf//+zJo1C4CQkBC8vb3veH1HR0d8fX3x9fXFz8+P9u3b8+KLL2bJ6f3x8fD55zBuHJw8ae4rVszc7tHD7AotIiICSqzdFyXWRESyBsMw2LVrF3PmzOGrr74iMjISR0dH/v333yy5Nk9YGLz9NkyfDnFx4OwMgwfD6NGQK5e9oxORB1lMTAyXL1/G0dGR/PnzAxAaGsqECRO4ePGiTfLs8uXLd+3GvHnzZho0aADAzJkzGThwIF27duWrr74CzJ/NL774Ivny5cPPzw8/Pz9rEs3X1xdvb+8smUS7lWGYzQfefBMOHTL35c9v/rx+7jlwcbFvfCIikvUosXYflFgTEcl6QkJCWLJkCf/++y9Tp0617h86dCiVKlWia9euuLm52THCm44cgUGDYM0aczt/fpg8GZ55RtOJRCRlEqdg3poQu72y7L333qN48eIAjB8/nnHjxvHCCy8wd+5cwJyyebfOm97e3jYJscQkWffu3SlZsiQAN27cwDAMcubMmeWm4qeGYcAvv8Abb8CePeY+b28YMQIGDgR3d/vGJyIiWVdKc0UqdhYRkSwpT548vPLKKzb7goKC+OCDD7BYLDz22GPWD4L2VqYMrF4NP/1kVqwFBkLv3jB7NsyYATVr2jtCEbGX8PBwTp8+jbu7OyVKlADMLw6GDx+eJIkWGRl512u99NJL1sSar68vjo6OxMTEWI97enry2muvWSvLbk2e+fj4kCMFnVZy5syZ+iebxfz+O7z+OmzZYm57eMCQIebNy8u+sYmIyINDFWuoYk1EJLu4evUqn3zyCcePH+eTTz6x7h83bhzly5enffv2KfrgmJGio82poW+/Ddevm/t694ZJk8xKNhHJ3qKjo7l8+fJdK8tmz55NqVKlgJtrlj3//PPWn1vXrl2743tOd3f3JFMtE5Nj7dq1o2jRooDZjdPR0THLT8G0hz//NCvUfvnF3HZ1hZdeMqvU8uWzb2wiIpJ9qGJNREQeON7e3owcOdJm35kzZ3j77bdJSEjA19eXZ599lhdeeMFa1ZHZXFxg+HBzGujIkbB4sdno4JtvzLV9Xn0V7Jz7E5FkXLp0icDAQHLnzk358uUBCA4Opl+/fjaJs9DQ0Hte69SpU9bEWv78+fH29sbZ2dl63MPDgwkTJiRbWZbSirFbryemgwfNn7PffmtuOzmZ66eNHg1ZcJlOERF5QKhiDVWsiYhkZ1euXOGjjz5i3rx5nD9/HgCLxUKrVq3o168frVu3xtHR0W7x/fEHvPwy7NplbgcEwLRp0Lq13UISeeAlJCQQHBycpAPm7ZVlixcvpkyZMgBMnDiR0aNH8+yzz/Lpp58CZsfiXMl0InFycrLpgnn7n82bN6dAgQKAuXbag7BWWVZ2/LjZ1fPzz8011SwWs8PnuHGQRVYMEBGRbCjFuSJDjLCwMAMwwsLC7B2KiIikUkxMjPHNN98YzZs3NwDrrUiRIsbbb79tnDt3zm6xxccbxmefGYafn2GYH/sMo3VrwzhyxG4hiWRrCQkJRlRUlHX78uXLRs+ePY169eoZBQoUMBwcHGx+Dtzptm7dOus1Fi1aZJQoUcIYOnSozWPNnDnTWLZsmbFx40bj4MGDRnBwsBEfH59pz1Xu7MwZw+jXzzCcnG7+bO3Y0TD++cfekYnYx/bt243OnTsb+fPnN5ydnQ0/Pz+jU6dOxrZt2+7rOmPHjjVSmyrYsGGDARgbNmxI1fkp1bBhQ6Nhw4Z3HRMXF2dMnTrVaNGihVGoUCHDzc3NKFu2rDFixAgjJCQk2XNmzJhhlClTxsiRI4dRvHhxY9y4cUZMTEyScRcvXjR69epl5M2b13BzczNq165t8zslUVRUlPHee+8ZFSpUMNzd3Q1fX1+jZcuWxtatW1PztCWTpTRXpIo1VLEmIvKgOXbsGJ988gmfffYZwcHBgFlh0q5dO/r370/jxo3tsi5ReDhMmGBWrMXGgrOzOTX0zTdBv35EbMXHx3P69GmCgoKoXr26tdvl7NmzGTFiBN26dbOuWRYREZHsFMq8efPesarMz8+POnXqkE+LbmU7V66YnZdnzoSoKHNfixbmz9dHH7VvbCL28tFHHzFo0CBq1qzJgAEDKFasGKdOnWLmzJns3LmT6dOnM3DgwBRd68yZM5w5c4batWvfdxzh4eEcPHiQ8uXLZ+hn60aNGgGwcePGO465fv06BQsW5H//+x/NmzfHx8eHPXv2MGHCBAoUKMDu3bttOsxPnDiRN998k5EjR/L444+za9cuRo8eTa9evWzW9o2OjubRRx8lNDSUyZMn4+vry8yZM/npp59Yt24dDRs2tI7t2bMnS5cuZdSoUTRp0oSrV68yefJk9u3bx9atW6mpDldZmirW7oMq1kREHkyRkZHGkiVLjHr16tlUqQwYMMCucR05YlasJVZY+PmZFW0qgpGHTUREhPHPP/8Y33//vfHBBx8YL730ktGyZUsjICDAcHZ2tv6f/eWXX6znLFq0yACMJk2a2Fzrww8/NL766itj586dxtmzZ5OtMJDsLTTUMMaMMQwPj5s/P+vXN4xNm+wdmYh9bdmyxXBwcDDatGljxMbG2hyLjY012rRpYzg4OBhbtmy563Vu3LiRkWGmq5RWrF25ciXJ/uXLlxuAsWTJEuu+K1euGK6ursYLL7xgM3bixImGxWIxDhw4YN03c+ZMA7CpBIyNjTXKly9v1KxZ07ovKirKcHR0NHr06GFzzXPnzhmA8corr6TouYr9pDRXpDZCIiLywHJ1daVHjx5s2bKFv//+mwEDBpArVy46dOhgHXP69Gm2bduGkYkF3KVLw08/mbeAALh4Efr0gTp1zDXZRB4UhmEQlVhShFmF1qdPHx577DEKFSqEu7s7FStWpF27dgwZMoSZM2eyZs0ajh07RmxsLDly5KBMmTLExsZar9GmTRsOHjzIjz/+aPNYgwYNomvXrtSoUYOCBQtqcf8HSEQEvPeeuV7aW2+ZHZerVYOff4bNm+Gxx+wdoYh9TZo0CYvFwuzZs3Fysu1P6OTkxKxZs7BYLEyePNm6f9y4cVgsFvbs2UPnzp3JkyePtelK4rFbRUdHM3ToUPLnz4+7uzuPPfYYf/75J8WLF6d3797WcRs3bsRisdhUkvXu3RsPDw8CAwNp3bo1Hh4eFClShKFDhxIdHW3zOOPHj6dWrVp4e3vj6elJtWrVmD9/fqrepzk6OpI3b94k+xOrxE6fPm3dt2bNGqKioujTp4/N2D59+mAYBitXrrTu++677yhTpgx16tSx7nNycqJHjx7s3LmTs2fPAuDg4ICDgwNeXl421/T09MTBwQFXV9f7fk6SNakrqIiIPBQqVarEzJkzeffdd3F3d7funzFjBlOmTGHAgAHMnDkzU2Nq3RqaNYMZM8wPizt3Qu3a0LOnOc3pv7XPRbK0+Ph4zpw5Q1BQEGXKlKHQf+0XV65cSe/evalZsya//vorYH7I+emnn7h8+bL1fC8vL0qVKpXsrVChQkmaj3h7e+Pt7Z15T1DsJiYG5s0zp3heuGDuK1cO3n4bOnY0mxSIpJVhmMnbrMLd/f7+bcfHx7NhwwYeffRRChcunOyYIkWKUL16ddavX098fLzNz9WOHTvSrVs3+vXrx40bN+74OH369GHZsmUMHz6cJk2acPDgQTp06EB4eHiK4oyNjeXJJ5/k2WefZejQoWzevJm3334bLy8vxowZYx138uRJXnzxRYoWLQrAjh07ePnllzl79qzNuLRYv349ABUqVLDu++effwDz/eKtChQogI+Pj/V44tgGDRokuW7lypUBOHDgAIUKFcLZ2ZkBAwYwf/58mjVrZp0K+vrrr+Pl5cXzzz+fLs9H7E+JNREReah4eHjYbBuGgaurKy1btrTuu3DhAufPn+eRRx7J8Hhy5IBhw8wOdqNGwcKFsHgxfPstjB4NgwaBi0uGhyFyV1FRUZw4cYKgoCACAwMJCgqy3k6ePElMTAwAn3zyifWDgre3N2FhYRw/ftzmWpMnT8bNzc2aPPP29lbXTLERF2d2+Bw3Dv7919xXvDiMHw9PPw12bPQsD6CICLjtrYFdXb8OySwZeUdXrlwhIiKCEiVK3HVciRIl2LlzJ8HBwfj6+lr39+rVi/Hjx9/13IMHD/Lll18yYsQIJk2aBEDz5s3x8/Pjf//7X4rijImJYfz48XTp0gWApk2bsnv3br744gubhNlnn31mvZ+QkECjRo0wDIPp06fz5ptvpvn3xdmzZxk5ciSPPvoobdq0se4PDg7GxcUl2fU6vb29rWv2Jo5N7guexH23jv3www/x8vKiU6dOJCQkAFC0aFHWr1+Pv79/mp6LZB1KrImIyENtypQpvPHGGzYLks6aNYu3336bWrVq0a9fP5566imbKreMkD8/fPYZ9O8Pr7xiTgkdORI+/RQ+/BCeeELVGZKxIiIibP6dDxkyhD179hAUFMTZs2fvOg3H2dmZ4sWL21RBPProo/zzzz9JPuz17ds3/YOXB0JCAnzzjdnQ5cgRc1+BAub2s8+aX0SISOok/gy/PTHVqVOne567adMmAJ566imb/Z07d+aZZ55J0eNbLBbatm1rs69y5crW6rFE69ev55133mHXrl1JquEuXbqEn59fih4vOVevXqV169YYhsGyZcuSNLK6W9Lu9mMpHTtx4kSmTJnCuHHjaNCgAeHh4Xz88cc0b96cX3/9NVO+xJWMp8SaiIg89PLkyWOzHR4ejrOzM3/88Qd//PEHgwcPpnfv3rz44ouULVs2Q2OpWRO2bTOrNUaMgMBAaNsWWrY0u4mWKZOhDy8PsISEBM6ePUtQUBAFCxakdOnSAOzcuZMWLVrg6+vLkcRsBvD777+ze/du63auXLnuOGWzSJEiSaZsuru720yzEbkTwzDXS3vjDdi719zn7W1W8Q4YYE6NE8ko7u5mlVhWcb//3n18fHB3d+fEiRN3HXfy5Enc3d2TVFoVSMG6E4kVWLcntZycnJJdwyw57u7uSdYUc3FxsVmHc+fOnTz++OM0atSIefPmUbhwYXLkyMHKlSuZOHEikZGRKXqs5ISEhNC8eXPOnj3L+vXrKVmypM3xvHnzEhUVleRLJjATctWrV7cZe2tV2q3j4Gbl2qFDhxgzZgzvvfcew4YNs45r1aoV5cuXZ8iQIWzYsCHVz0myDiXWREREbjNt2jRGjRrFZ599xty5czl58iTTpk1j2rRpNGrUiP79+9O+fXtyZFD5hIODuc5ahw7m2kIffghr1kDFimY125gxcNs6uCKAubh04pTN228nTpywLhL9xhtvMGHCBAB8fX0JDQ0lIiLCZu2dkSNHEhUVZU2e+fj4aMqmpLtNm8yE2tat5nauXDB0KAweDLcUEotkGIvl/qZeZjWOjo40btyYNWvWcObMmWTXWTtz5gx//vknrVq1SvIlSEp+ricmzy5evGhdRxMgLi4u2QRTan311Vc4Ozvz448/2iThbm0ckBohISE0a9aMEydO8Ntvv1nXQrtV4tpq+/fvp1atWtb9Fy5c4MqVK1SsWNFm7P79+5NcI3Ff4th9+/ZhGAY1atSwGefs7EyVKlWslYCS/WXJrqDXrl1j+PDhPP744+TLlw+LxcK4ceOSHbtnzx6aNWuGh4cHuXPnpmPHjknW8hAREblffn5+jBw5kqCgIH7++WeefPJJHBwc2LhxI127dqVIkSK8/vrrnDx5MsNiyJUL3n0XDhyANm3MdYc++MDsKrpggTltSh4+165ds9meMGECjRs3pmjRori5uVGuXDnatGnDq6++yowZM/jpp584fPgw0dHRODk54e/vb7OGTJEiRdi/fz8hISE2H7g6derE008/Te3ata3vx0TSy65d0KIFNGpkJtVcXeG11+DECRg7Vkk1kfsxatQoDMNgwIABxMfH2xyLj4+nf//+GIbBqFGjUnX9x/5rvbts2TKb/StWrCAuLi51QSfDYrHg5ORk87soMjKSJUuWpPqaiUm148eP33XqZcuWLXF1dWXhwoU2+xcuXIjFYqF9+/bWfR06dODw4cP8cUsr97i4OD7//HNq1apFwYIFAax/7tixw+aa0dHR7Nmz547NJiT7yZIVa8HBwXzyySdUqVKF9u3b8+mnnyY77vDhwzRq1IiqVavy9ddfExUVxZgxY2jQoAF79+4lX758mRy5iIg8aBwcHGjZsiUtW7bk9OnTfPrpp8ybN4/z588zadIkJk+eTKtWrViyZEmGdSoMCIBVq8yqtUGDzLWHnn0WZs82O4re0u1dHgAJCQmcP3+eoKAgPD09qVq1KmBWHFSpUoWIiAhu3LhhXRtm7969bNy40Xq+h4fHXadsOjnZvv1zdHS0+SZeJCMdOGCumfbdd+a2szM8/7xZtfbfZ1ARuU/16tVj2rRpDBo0iPr16zNw4ECKFi3KqVOnmDlzJn/88QfTpk2jbt26qbp+hQoV+N///sfUqVNxdHSkSZMmHDhwgKlTp+Ll5ZVkrbLUeuKJJ/jggw/o3r07L7zwAsHBwUyZMgWXVHZxioyMpEWLFvz1119MmzaNuLg4myRXvnz5KFWqFGBO3xw9ejRvvvkm3t7ePP744+zatYtx48bx3HPPUb58eet5ffv2ZebMmXTp0oXJkyfj6+vLrFmzOHLkCOvWrbOOq1+/PjVq1GDcuHFERETw2GOPERYWxkcffcSJEyfSlDCULMbIghISEoyEhATDMAzj8uXLBmCMHTs2ybguXboYPj4+RlhYmHXfyZMnDWdnZ2P48OEpfrywsDADsLmOiIjIncTExBjffPON0bx5cwMwAgICjPj4eOvxiIiIDHvs6GjDmDLFMHLlMgxzZSLD6NHDMM6ezbCHlAwQFRVlHD582Pjpp5+MGTNmGK+++qrRpk0bo1y5coarq6sBGIDRo0cP6zkxMTGGo6OjARhnb/kLX7t2rbFkyRJj27ZtxsWLF63voUSyksBA82eVxWL+3HJwMIxevQzj+HF7Ryby4Ni+fbvRuXNnw8/Pz3BycjJ8fX2Njh07Gtu2bUsyduzYsQZgXL58+Y7HbhUVFWUMGTLE8PX1NVxdXY3atWsb27dvN7y8vIzBgwdbx23YsMEAjA0bNlj39erVy8iZM2eKHmfBggVGmTJlDBcXF6NkyZLGpEmTjPnz5xuAceLECeu4hg0bGg0bNrzr63HixAnr79Pkbr169UpyzvTp043SpUsbOXLkMIoWLWqMHTvWiImJSTLuwoULRs+ePQ1vb2/r67F27dok40JDQ4033njDKFeunOHu7m74+voajRo1MlavXn3X2CVrSGmuyGIYd2nxlAVcuXKFfPnyMXbsWJvpoHFxcXh6etKzZ0/mzJljc06LFi04ceIER48eTdFjhIeH4+XlRVhYmE1XOBERkXs5duwY586do2HDhoD57WiJEiWoX78+c+fOTfGivvfr4kV4/XVzSiiY68OMHm2uS5TKL3YlnYWHh5MrVy7rFMrZs2ezfPlygoKCOH369F27bDo6OlKsWDHatm3LtGnTrPsPHTpEkSJF8PDwyOjwRdLFmTPmWpHz55vT2QE6d4a33oJy5ewbm4ikzbZt26hXrx5Lly6le/fu9g5HJN2lNFeUJaeCpkRQUBCRkZHJLjxYuXJl1q5dS1RUVJLOIyIiIukpICCAgIAA6/b69eu5ePEiu3btInfu3Nb9sbGxODs7p9vj+vmZH1T79TMbGuzYYXbQ+/RTmDrVXLcoZ05wyra/6bO+Gzdu8O+//3Ly5ElcXFxo2rQpYH75V6hQIS5dusSFCxesXdSCgoJsun+5u7vfccpm0aJFk/33Uk6ZCMkmLl+GSZNg1iz4r2cGrVqZSbZq1ewbm4jcv7Vr17J9+3aqV6+Om5sb+/btY/LkyQQEBNCxY0d7hydiV9n27XZi95Hk1rPx9vbGMAxCQkKSbR8cHR1t7YoFZhZSREQkPTzxxBP8/fffnD171rr4bmxsLKVLl6ZBgwb069ePOnXqpNtC8DVqmAt/L10KI0ZAUBDcsr4uLi7g4WEm2Tw8bt5u3U7N/duaij2QwsPDrYmzxD9vvX/lyhXr2AYNGlgTa05OTtYv9k6ePGlNrD311FNUrlzZmjzz8/NTQwB54ISGmsn9adPg+nVz32OPwcSJUL++PSMTkbTw9PTk119/Zdq0aVy7dg0fHx9atWrFpEmTVMwiD71sm1hLdLc3pHc6NmnSJMaPH59RIYmIyEOuUqVK1rbtAOvWrbMmZZYsWULlypXp168fTz/9dLosQeDgAM88YybU3nkHPv745gfa6Gjz9t/3UenG1fX+knEpGZczp/lcMtO+ffs4ceIETZs2JVeuXABMnTqViRMnEhIScs/zvby8KF68OGXLlrXZv379enx9fa3XBKhZsyY1a9ZM3ycgkgni4+HKFXMKenK3S5ds7ydO+axe3fyZ1Lw5KIcskr3VqlWLLVu22DsMkSwp2ybWEtesCU7mk8LVq1exWCw2U3BuNWrUKIYMGWLdDg8Pp0iRIhkSp4iISMuWLfnjjz+YM2cOX331FX///TcDBgzgtdde4+mnn6Z///7Wzo9pkSuXOfXqnXcgJsZMrl2/DjdupP7+7dsJCeZjRUWZt1uKttKFm1vqE3O2STqDqKgrXL36LxcunOT0abPKzMPDg0mTJlkfr02bNpw5c4YdO3ZQq1YtAJydna1Jtbx581KsWDGKFy9O8eLFrfeLFStGsWLF7vheI7HLmEhWFRNjTte8U7Ls1kTZlSs3/++nRPny5pTP9u2VUBMRkQdftm9e0KtXL2bPnm1zTsuWLTl+/LiaF4iISJYTEhLC4sWLmTNnDocPH7bur1WrFv369aNr1664ubnZMcI7Mwyz+i0tibk73U/du5FQ4Ahw8r/bv7f9GZHkDCenopQv/681AffPP+2JiTlHgwbT8PevS86cYBgXMYwrFClSDB8fjzsm8NzdlTSQrCUyMvkqsuRuKSjItGGxgI+Pub5jcjdf35v3CxXS/w0REcn+UporyraJNYCuXbuyceNGAgMDrVMtTp06RUBAAIMHD2by5Mkpegwl1kREJLMZhsHmzZuZPXs23377LbGxsQDkzp2b3r17079/f0qXLm3nKDOHYZjVb7cm465fN7hxw2JNwP344yxOntzHo48Oxs2tLNev8//27jssiqt/G/i9sEsvCqiASlEjiuWxEWMsgIK9EjRiCdijRjFqYiMisRONGCWxRNGYiA30jeYRoxGjjwVBE0uMpgIWsGCoAlLO+wfZ+bnuqoCs6+L9ua65dM+cmT1zZnZ2+O4pSEpajcTEac/YuwMAZwAu//7bEMC4Kim3TFYWYNPUYs7KSvNiba053dLy1Ri3jipGiLLPw5O6XT6+5ORUbP+GhqoBsacFzezsOBkKERG9WvQ+sHbw4EHk5eUhJycHo0ePxuDBgzFkyBAAQO/evWFmZoarV6/Cw8MDbdq0wezZs1FQUID58+fj/v37+Pnnn1GrVq1yvRcDa0REpEu3b99GVFQU1q9fj+TkZADAqlWrMG3aNJ2WS5tKSkpw8+ZNjZMCpKSkwMDAANeuXZPyd+zYEadOncKuXbswePBgAMD+/fsxadIktS6aym6bdevWhxAmVdqyLi+vbNGGpwXknhaUe3S9pSWDHy87IcoG+H/WWGXKJT+/Yvs3Mnp2oEy51Kz54sc1JCIi0hd6H1hzcXFBSkqKxnV///03XFxcAADnzp3DrFmzcPr0acjlcnTt2hUrVqyo0NgmDKwREdHLoKSkBN9//z2+/PJLbNy4UZr5OjY2FklJSRg/frz0/adPrly5gl27dqkE0W7cuIFi5QjnGhgaGqKgoADyf6NE69atQ1paGvz9/VUmhtCF0lLgwYOnB+ZycoCsLCA7+8lLVlbZ8m9jxSpjZvZ8wTnl/xWKqi1XdVZaWjZByLPGKlP++/BhxfZvbq7e3fJJi5UVu2ESERFVBb0PrL1IDKwREdHLrEOHDjhz5gyWLFmCOXPm6Lo4AIDCwkJcv34d6enp6NSpk5Q+adIkHDhwABEREfDz8wMA7Nu3D4MGDVLbh0KhgJOT0xMnB3Bycnrq7N/VRWHhkwNvzwrMPfq6oKBqy2Vi8nyBOeVibFy15XpRiovLP7j/3btlM2dWhLW15vHJNC3m5to5RiIiInqy8saK2FmAiIjoJSaEwAcffIANGzZg9OjRUvrBgweRlJSEsWPHwsHBocrfNz8/H6mpqWpdNJUtztLS0iCEgFwuR35+vtSyLDMzE9evX8dff/0l7at58+YYN26cWldNe3t7GHJgMRgbA7VqlS3P4+HDspZylQ3MKZcH/875oJz59c6d5yuXkdHzBeaUi4nJ87fEKiws/+D+GiaefyZb26cP6v9omonJ8x0LERERvRzYYg1ssUZERPqna9euiI+Ph1wux4ABAzBx4kR4e3vDoJwDJuXl5SElJQU2Njawt7cHACQmJmLKlClITk7G7du3n7kPU1NTuLi44Pjx47CzswMAXLx4EQ8ePICbmxtq1qxZ+QMknSkuLn9X1qetz82t2nIpFE8PyikDcxYWZe+vKViWlVWx9zQwKAt4PmsWzDp1yvKx+ywR6cLRo0fx9ddf49SpU7h+/Tpq1KiBdu3aYf78+Wjbtq1a/vPnz+PDDz/EmTNnVIZTatCggVreNWvWIDIyEn///TccHR0RFBSEuXPnQvHYDS8+Ph5LlizBhQsX8ODBAzRo0ABjx47F5MmTtfYjmkwmU5nkUDn0RFBQkNrQGV5eXrh37x4uX75c4ffJzs7GmjVrcPjwYVy9ehW5ublwdXXFiBEjEBwcDJPHfikpKirCkiVLEBUVhbS0NLi6umLy5MmYMmWKSr5ffvkFkZGR+Omnn6Tnp/j4eHh5eamVIScnB/PmzUNMTAzu3bsHZ2dnjB07FjNmzHhm/d66dQsbNmzAwIED0apVK5V1QUFB2LNnD3Kr+ktbx4KCgnDs2DFp7OLKYos1IiKiakoIgTFjxuDhw4c4efIkYmJiEBMTg9deew3vvvsuAgMDoVAo1FqbLV++XHr4Gjt2LHbs2IEVK1ZgxowZAMrGNUtISJDex8LCQq2L5qP/t7OzU+uq2bJlyxdXEaQVcnnZoPbPGxctKVFtQVeRwNyj65QzXRYVlbUiq0xLskcpFE/vevnoOltbztZKRC+/L774AhkZGQgODoa7uzvu3r2LlStX4o033sChQ4fQtWtXKe/Vq1fh5eWFVq1aYdeuXdIEgJ07d1abAHDx4sX46KOPMHv2bHTv3h2JiYkICQnBzZs3sWHDBinfkSNH0KNHD3Tp0gUbN26Eubk5vv32WwQHB+PPP//E6tWrtXLcp0+fRr169aTXV65cQVhYGLy8vKp0TNrU1FRERERg5MiRmD59OiwsLHDixAksWLAAhw8fxuHDh1WehyZNmoRt27Zh4cKF8PDwwKFDhxAcHIycnBzMnTtXypeUlIR9+/ahdevW6NatG/bv36/x/YuLi+Hr64vffvsNCxcuROPGjREXF4fZs2fjxo0b+Oyzz55a/lu3biEsLAwuLi5qgTWqGgysERER6RmZTIbhw4dj+PDhuHTpEtavX4+vvvoKv//+O2bMmIGZM2dCU4P04OBg1K9fH0DZJEHW1tZ4+Mgo6m5uboiNjZUCZzVr1nwlxjgj7TA0BGrUKFueR2lpWeu38gblcnLKZkd9UsCsZk0O7k9E1UtkZCRq166tktazZ080atQIS5YsUQmszZ8/H8bGxjhw4IDUAqdt27Z47bXXsGLFCixfvhwAkJGRgUWLFmHcuHFYsmQJgLJWX0VFRQgJCcG0adPg7u4OANiyZQsUCgUOHDgA838HhfTx8cG1a9ewZcsWrQXW3njjDa3s93Gurq5ITk6Wjg0o6zlgbm6ODz74ACdPnpTGm/3ll1+wadMmLF68GB988AGAsnpT1ue7774rTU41cuRIBAYGAgD27NnzxMDanj17kJCQgJiYGGn8Wl9fX+Tm5iIyMhKTJ0+Gm5ub1o6/KuTn58PU1FTXxdAaTrBNRESkx1q0aIG1a9dKzfxbt24tBdVsbW3Rtm1b+Pn5Yfr06SpdBRYuXIjMzEyVyRDMzc0xaNAgtGnTBjY2Ngyq0UvBwKCsm2e9eoC7O/DGG0CPHsDgwcCYMcD77wOhocDKlcDGjcCOHWX/LloETJkCDBkCeHoCTZsCNjYMqhFR9fN4UA0oa3Xu7u6O69evS2nFxcU4cOAA3nrrLZVubc7OzvD29sbevXultLi4OBQUFGDUqFEq+x01ahSEENi3b5+UplAoYGRkpBY4qVGjhlo3ycdFRkbCwMAAdx4Z0HPlypWQyWSYPHmylFZaWoqaNWtKreyBsh8ald1At2zZgsGDBwMAvL29IZPJIJPJsGXLFpX3S0xMROfOnWFmZoYGDRpg2bJlKC0tfWoZzc3NVYJqSq+//joAqNTxvn37IITQWG/5+fmIi4uT0so7fMfJkychk8nQq1cvlfS+ffuitLRU5bw97tixY/Dw8JDKoKwXZb0p/fHHH+jduzcsLCxQv359zJgxA4WFhSp5Hj58iEWLFqFJkyYwNjZGrVq1MGrUKNy9e1cln4uLC/r27YvY2Fi0bt0aJiYmCAsLw7FjxyCTybB9+3bMmjULDg4OsLCwQL9+/XD79m3k5ORg/PjxsLOzg52dHUaNGqXWRTUyMhJdunRB7dq1YW5ujhYtWiA8PBxFVT3FegWxxRoREVE1YGFhgXHjxmHs2LG4efMmrK2tYWlp+cT8yskGiIiIqGzs0YoyNjaWvk+Li4tRWFgIAwMDlQBTRfarKXhTWVlZWTh//rxKa7U///wT+fn5GodtaNmyJQ4fPoyCggKYmJhIY5G1aNFCJZ+DgwPs7OxUxip79913ER0djalTp2Lu3LkwMzPD/v37sXfvXixduvSp5fTx8YEQAj/88AMCAgIAlHUtNTU1xeHDh6V8SUlJyMzMhI+Pj8b99OnTB0uWLMHcuXMRGRmJNm3aAAAaNmwo5UlPT8fw4cMxY8YMhIaGYu/evZgzZw4cHR3xzjvvPLWcmhw9ehQA0KxZMynt8uXLqFWrljR+rZKyziszxtvDhw9hYGCgNq6d8b/Tbl+8ePGJ27Zp0wZRUVEYNWoUQkJC0KdPHwBQ6UJbVFSE/v37Y8yYMZgxYwaOHz+OhQsXwtraGvPnzwdQFtgcMGAATpw4gQ8//BBvvvkmUlJSEBoaCi8vLyQlJalc9+fPn8evv/6KkJAQuLq6wtzcXPoszJ07F97e3tiyZQuSk5Mxc+ZMBAQEQC6X4z//+Q+io6Px008/Ye7cubC0tFTp6vrnn39i2LBhcHV1hZGRES5cuIDFixfj6tWr2Lx5c4XrtsoIEllZWQKAyMrK0nVRiIiIiIiI6AUDUOFl165d0va7du0SAISnp6fKfu3s7Mq9v6o0fPhwIZfLRVJSkpR28uRJAUBER0er5V+yZIkAIG7duiWEEGLcuHHC2NhY474bN24sunfvrpJ28uRJ4ejoKB2LoaGhCA8PL1dZ69WrJ0aPHi2EEKKwsFCYm5uLWbNmCQAiJSVFCCHE4sWLhUKhELm5udJ2AERoaKj0evfu3QKAiI+PV3sPT09PAUAkJCSopLu7u4sePXqUq5yPunDhgjA1NRWDBg1SSff19RVubm4atzEyMhLjx4/XuO5pZY+IiBAAxIkTJ1TSP/roIwFA7Vw8LjExUQAQUVFRausCAwPVrmUhhOjdu7fKcURHRwsAIiYmRuO+P//8cynN2dlZGBoaimvXrqnkjY+PFwBEv379VNKnTZsmAIipU6eqpA8cOFDY2Ng88bhKSkpEUVGR+Oqrr4ShoaG4f/++ynE5Ozs/cdvyKm+siF1BiYiIiIiIiKqJjz76CN988w1WrVqlcVbQpw318Oi68uY7d+4cBg0ahLZt22L//v04evQo5syZg5CQECxcuPCZ5e3WrRuOHDkCADh16hQePHiA6dOnw87OTmq1duTIEXTo0OG5WvXZ29tL3TeVWrZsiZSUlArtJzk5GX379kX9+vXx5Zdfqq0vb72V1/Dhw2FjY4Px48cjISEBmZmZiI6OllpylbdL6dPK1K9fP5W0x+vlwIEDqFGjBvr164fi4mJpadWqFezt7XHs2DG17Rs3bqzx/fr27avyumnTpgAgtaZ7NP3+/fsq3UF/+ukn9O/fH7a2tjA0NIRCocA777yDkpIS/PbbbxU+9qrCfiBERERERET0Snt8LKfyUHbFA4BBgwYhNzdXLciRnJz8vEWrkLCwMCxatAiLFy/Ge++9p7LO1tYWQNnEBI+7f/8+ZDIZavw744ytrS0KCgrw4MEDmJmZqeV9NGA3efJk1KlTB3v37pXGc/X29oaBgQEWLFiA4cOHo0GDBk8ss4+PD7Zu3Yrff/8dR44cQevWrVG7dm107doVR44cwbBhw3Dq1CnMmzevUnXy+PE/ytjYGPn5+eXeR0pKCry9vSGXy/HDDz9IExE8+h4///yz2nZ5eXl4+PChWv7ysLOzQ1xcHAIDA6UJG2xtbfHpp59izJgxqFu3boX3+SgzMzO1sfCMjY1RUFAgvb59+zYyMzNhZGSkcR/37t1Tee3g4PDE93u8DpT7fFJ6QUEBLCwskJqais6dO8PNzQ2rV6+Gi4sLTExMcPbsWUyePLlC57GqMbBGREREREREr7TnHd9MLpdrHL+0KsdNe5awsDAsWLAACxYswNy5c9XWN2zYEKamprh06ZLaukuXLqFRo0ZSgEU5ttqlS5fQvn17KV96ejru3buH5s2bS2k///wzAgICVCZJAgAPDw+Ulpbi119/fWpgrVu3bgDKWqUdPnwYvr6+UnpISAiOHz+OwsLCJ46v9qKkpKTAy8sLQggcO3ZMZZwypRYtWmDHjh1IT09XGWdNWeeP1ltFeHh44MqVK0hOTkZeXh5ee+01nDt3DgDQpUuXSu2zIuzs7GBra6sy+cKjHh/XVxsTYO3btw95eXnSDPZKmgKZLxq7ghIRERERERHpsYULF2LBggUICQlBaGioxjxyuRz9+vVDbGwscnJypPTU1FTEx8fDz89PSuvZsydMTEzUZtXcsmULZDIZBg4cKKU5OjoiKSkJJSUlKnlPnz4NABoDUI9ycHCAu7s7YmJicO7cOSmw5uvri7t37+LTTz+FlZWVNLvlkyhbEGqj5VJqaiq8vLxQUlKCo0ePqgR2HjVgwADIZDJs3bpVJX3Lli0wNTVFz549n6scLi4uaNasGRQKBVauXAlHR0dpNtQnqYp66du3LzIyMlBSUoJ27dqpLW5ubpXed3kpg3WPthQVQmDjxo1af+9nYYs1IiIiIiIiIj21cuVKzJ8/Hz179kSfPn1w5swZlfXK7oNAWas2Dw8P9O3bF7Nnz0ZBQQHmz58POzs7zJgxQ8pnY2ODkJAQfPTRR7CxsUH37t2RmJiIBQsWYOzYsXB3d5fyvv/++5g6dSr69euHCRMmwMzMDD/88ANWrlwJHx8f/Oc//3nmMXTr1g1r1qyBqakpOnbsCABwdXWFq6srvv/+e/Tv3/+ZM5orW4Nt2LABlpaWMDExgaurq8YuoBVx584deHt7Iy0tDZs2bcKdO3dw584daX29evWk4GGzZs0wZswYhIaGwtDQEB4eHvj++++xYcMGLFq0SKW744MHD/Df//4XAKRz9uOPP+LevXswNzdHr169pLzz5s1DixYt4ODggNTUVGzevBkJCQn47rvvVGbj1ETZUvGbb75B06ZNYWFhAUdHRzg6Opa7DoYOHYpvvvkGvXv3RnBwMF5//XUoFArcuHED8fHxGDBgAAYNGlTu/VWGr68vjIyMEBAQgA8//BAFBQX44osv8M8//2j1fcuDgTUiIiIiIiIiPbV//34AQFxcnMauekII6f9NmjTBsWPHMGvWLPj7+0Mul6Nr165YsWIFatWqpbLdvHnzYGlpicjISKxYsQL29vaYPXu22lhnU6ZMQd26dbFq1SqMHTsW+fn5cHFxQWhoKN5///1yHYOPjw/WrFmDTp06qYz35ePjg40bN5arG6irqysiIiKwevVqqXVZVFQUgoKCylWGJ7ly5Qr++usvAMCIESPU1oeGhmLBggXS688//xx169bFmjVrkJ6eDhcXF6xevRpTpkxR2e7OnTtqrc2U+3F2dlYZn++ff/7BrFmzkJ6eDisrK3h6eiIhIUHqsvs0ZmZm2Lx5M8LCwtC9e3cUFRWplflZDA0N8e2332L16tXYtm0bli5dCrlcjnr16sHT07Nc5XheTZo0QUxMDEJCQuDn5wdbW1sMGzYM06dPVwlC6oJMPPope0VlZ2fD2toaWVlZsLKy0nVxiIiIiIiIiIhIh8obK+IYa0RERERERERERJXAwBoREREREREREVElMLBGRERERERERERUCQysERERERERERERVQIDa0RERERERERERJXAwBoREREREREREVElMLBGRERERERERERUCQysERERERERERERVQIDa0RERERERERERJXAwBoREREREREREVElMLBGRERERERERERUCXofWMvNzcW0adPg6OgIExMTtGrVCjt27NB1sYiIiIiIiIiIqJqT67oAz8vPzw+JiYlYtmwZGjdujO3btyMgIAClpaUYNmyYrotHRERERERERETVlEwIIXRdiMr673//iz59+kjBNKXu3bvjl19+QWpqKgwNDZ+5n+zsbFhbWyMrKwtWVlbaLDIREREREREREb3kyhsr0uuuoHv37oWFhQUGDx6skj5q1CjcunULCQkJOioZERERERERERFVd3odWLt8+TKaNm0KuVy1R2vLli2l9URERERERERERNqg12OsZWRkoEGDBmrpNjY20npNCgsLUVhYKL3OysoCUNbMj4iIiIiIiIiIXm3KGNGzRlDT68AaAMhksgqvW7p0KcLCwtTS69evX2XlIiIiIiIiIiIi/ZaTkwNra+snrtfrwJqtra3GVmn3798H8H8t1x43Z84cTJ8+XXpdWlqK+/fvw9bW9qmBOn2SnZ2N+vXr4/r165yQQQdY/7rF+tct1r/u8RzoFutft1j/usX61y3Wv26x/nWL9a9b1bH+hRDIycmBo6PjU/PpdWCtRYsWiI6ORnFxsco4a5cuXQIANG/eXON2xsbGMDY2VkmrUaOG1sqpS1ZWVtXmotZHrH/dYv3rFutf93gOdIv1r1usf91i/esW61+3WP+6xfrXrepW/09rqaak15MXDBo0CLm5uYiJiVFJ37p1KxwdHdG+fXsdlYyIiIiIiIiIiKo7vW6x1qtXL/j6+mLixInIzs5Go0aNEB0djbi4OHz99dcwNDTUdRGJiIiIiIiIiKia0uvAGgDExsZi3rx5mD9/Pu7fv48mTZogOjoaQ4cO1XXRdMrY2BihoaFqXV7pxWD96xbrX7dY/7rHc6BbrH/dYv3rFutft1j/usX61y3Wv269yvUvE8+aN5SIiIiIiIiIiIjU6PUYa0RERERERERERLrCwBoREREREREREVElMLBGRERERERERERUCQys6ZktW7ZAJpMhKSlJ10V5pSjrXdMyc+bMcu8nKCgIFhYWWixp9fNo3R87dkxtvRACjRo1gkwmg5eX1wsv36vms88+g0wmQ/PmzXVdlGqP1/7Lhd+/L4/nORcymQwLFiyo+kJVc7z360ZCQgIGDRoEJycnGBsbo06dOujQoQNmzJih66K9cs6cOYPBgwfDwcEBRkZGsLe3h7+/P06fPl3hfV25cgULFixAcnJy1Re0mlDe501MTJCSkqK23svLi/cjLXv8718TExPY29vD29sbS5cuxZ07d3RdxJcKA2tEFRAVFYXTp0+rLFOnTtV1sV4JlpaW2LRpk1r6jz/+iD///BOWlpY6KNWrZ/PmzQCAX375BQkJCTouzauB1z4R6Rrv/S/ed999hzfffBPZ2dkIDw/H999/j9WrV6Njx47YuXOnrov3SlmzZg06duyIGzduIDw8HEeOHMGKFStw8+ZNdOrUCWvXrq3Q/q5cuYKwsDAG1sqhsLAQISEhui7GK0359+/hw4cRGRmJVq1aYfny5WjatCmOHDmi6+K9NBhYI6qA5s2b44033lBZnJycdF2sV8Lbb7+NmJgYZGdnq6Rv2rQJHTp0qNLzkJ+fX2X7qk6SkpJw4cIF9OnTBwA0Bnuex4MHD6p0f9XFi7z2iYgep+17P2kWHh4OV1dXHDp0CEOHDoWnpyeGDh2KFStWIDU1VdfFe2WcPHkS06ZNQ+/evXHixAmMHDkSXbp0wYgRI3DixAn07t0bwcHBOHnypK6LWi317NkT27dvx4ULF3RdlFeW8u/fzp0746233sKqVatw8eJFmJubw8/PD7dv39Z1EV8KDKzpuaSkJAwdOhQuLi4wNTWFi4sLAgIC1JrMKptyxsfHY+LEibCzs4OtrS38/Pxw69YtHZW+etm5cyc6dOgAc3NzWFhYoEePHvjpp5805v3ll1/QrVs3mJubo1atWnjvvfcYVHiGgIAAAEB0dLSUlpWVhZiYGIwePVotf1hYGNq3bw8bGxtYWVmhTZs22LRpE4QQKvlcXFzQt29fxMbGonXr1jAxMUFYWJh2D0ZPKf+YWrZsGd58803s2LFD5bpNTk6GTCZDeHg4Fi9eDCcnJ5iYmKBdu3b44YcfVPa1YMECyGQynD9/Hv7+/qhZsyYaNmz4Qo9HX2jj2h8zZgxsbGw03ne6du2KZs2aaeFIqhcvLy+NXXCDgoLg4uIivVZ+LlasWIFPP/0Urq6usLCwQIcOHXDmzJkXV+BqrLzngirnWff+Y8eOaeyyrrz2t2zZopK+ceNGNG7cGMbGxnB3d8f27dt5rjTIyMiAnZ0d5HK52joDA9U/4crzDKocjoTPoBWzdOlSyGQyfPHFF2rnQi6X4/PPP4dMJsOyZcuk9KtXryIgIAB16tSBsbExnJyc8M4776CwsBBbtmzB4MGDAQDe3t5SN7vHPydU5sMPP4StrS1mzZr11HwFBQWYM2cOXF1dYWRkhLp162Ly5MnIzMyU8gwcOBDOzs4oLS1V2759+/Zo06ZNVRe/2nJycsLKlSuRk5OD9evXS+lJSUno378/bGxsYGJigtatW2PXrl1q29+8eRPjx49H/fr1YWRkBEdHR/j7++t1kI6BNT2XnJwMNzc3RERE4NChQ1i+fDnS0tLg4eGBe/fuqeUfO3YsFAoFtm/fjvDwcBw7dgwjRozQQcn1U0lJCYqLi1UWAFiyZAkCAgLg7u6OXbt2Ydu2bcjJyUHnzp1x5coVlX0UFRWhd+/e6NatG/bt24f33nsP69evx9tvv62LQ9IbVlZW8Pf3l7qjAGWBBgMDA411l5ycjAkTJmDXrl2IjY2Fn58fpkyZgoULF6rlPX/+PD744ANMnToVcXFxeOutt7R6LPooPz8f0dHR8PDwQPPmzTF69Gjk5ORg9+7dannXrl2LuLg4RERE4Ouvv4aBgQF69eqlcRwSPz8/NGrUCLt378a6detexKHoHW1c+8HBwfjnn3+wfft2lW2vXLmC+Ph4TJ48WXsH9IqKjIzE4cOHERERgW+++QZ5eXno3bs3srKydF00oieqyL2/PDZs2IDx48ejZcuWiI2NRUhICMLCwjSOI/mq69ChAxISEjB16lQkJCSgqKhIYz4+g2pPSUkJ4uPj0a5dO9SrV09jnvr166Nt27Y4evQoSkpKcOHCBXh4eODMmTP4+OOPcfDgQSxduhSFhYV4+PAh+vTpgyVLlgAo+15QDi2jbBFKqiwtLRESEoJDhw7h6NGjGvMIITBw4ECsWLECI0eOxHfffYfp06dj69at6Nq1KwoLCwEAo0ePRmpqqtp+rl69irNnz2LUqFFaP57qpHfv3jA0NMTx48cBAPHx8ejYsSMyMzOxbt06/L//9//QqlUrvP322yqB45s3b8LDwwN79+7F9OnTcfDgQURERMDa2hr//POPjo6mCgjSK1FRUQKASExM1Li+uLhY5ObmCnNzc7F69Wq17SZNmqSSPzw8XAAQaWlpWi23vlPWn6YlNTVVyOVyMWXKFJVtcnJyhL29vRgyZIiUFhgYKAConBshhFi8eLEAIP73v/+9kOPRJ49e8/Hx8QKAuHz5shBCCA8PDxEUFCSEEKJZs2bC09NT4z5KSkpEUVGR+Pjjj4Wtra0oLS2V1jk7OwtDQ0Nx7do1rR+LPvvqq68EALFu3TohRNn1bWFhITp37izl+fvvvwUA4ejoKPLz86X07OxsYWNjI3x8fKS00NBQAUDMnz//xR2EntH2te/p6SlatWqlkn/ixInCyspK5OTkaOeg9Njj37+enp4a6z0wMFA4OztLr5WfixYtWoji4mIp/ezZswKAiI6O1nbRq53KngshhAAgQkNDtV/IaqI8937l/Sk+Pl5lW+W1HxUVJYQoux/Z29uL9u3bq+RLSUkRCoVC7Vy96u7duyc6deokPW8qFArx5ptviqVLl0r3aD6Dald6eroAIIYOHfrUfG+//bYAIG7fvi26du0qatSoIe7cufPE/Lt379b4maH/8+h9vrCwUDRo0EC0a9dOeo7x9PQUzZo1E0IIERcXJwCI8PBwlX3s3LlTABAbNmwQQghRVFQk6tSpI4YNG6aS78MPPxRGRkbi3r17L+DI9Mez4g5CCFGnTh3RtGlTIYQQTZo0Ea1btxZFRUUqefr27SscHBxESUmJEEKI0aNHC4VCIa5cuaK9wusAW6zpudzcXMyaNQuNGjWCXC6HXC6HhYUF8vLy8Ouvv6rl79+/v8rrli1bAoDG2VZI3VdffYXExESV5dChQyguLsY777yj0pLNxMQEnp6eGn+FHT58uMrrYcOGASiL9NOTeXp6omHDhti8eTMuXbqExMREjV3hAODo0aPw8fGBtbU1DA0NoVAoMH/+fGRkZKjNYtOyZUs0btz4RRyC3tq0aRNMTU0xdOhQAICFhQUGDx6MEydO4Pfff1fJ6+fnBxMTE+m1paUl+vXrh+PHj6OkpEQlL1sHlo82rv3g4GD8/PPP0rgw2dnZ2LZtGwIDAzl7sRb06dMHhoaG0mt+/5I+qMi9/1muXbuG9PR0DBkyRCXdyckJHTt2rLIyVxe2trY4ceIEEhMTsWzZMgwYMAC//fYb5syZgxYtWuDevXt8Bn1JiH+HWsjPz8ePP/6IIUOGoFatWjouVfVhZGSERYsWISkpSWO3QmULtKCgIJX0wYMHw9zcXBqORC6XY8SIEYiNjZVai5eUlGDbtm0YMGAAbG1ttXsg1ZDy2v/jjz9w9epV6f7y6P2od+/eSEtLw7Vr1wAABw8ehLe3N5o2baqzcmsDA2t6btiwYVi7di3Gjh2LQ4cO4ezZs0hMTEStWrU0DsD++A3D2NgYAAdrL6+mTZuiXbt2KouyL7iHhwcUCoXKsnPnTrUuuXK5XO082NvbAygbT4OeTCaTYdSoUfj666+xbt06NG7cGJ07d1bLd/bsWXTv3h1A2VguJ0+eRGJiIubNmwdA/Xp3cHDQfuH12B9//IHjx4+jT58+EEIgMzMTmZmZ8Pf3BwCVLorA/13Pj6c9fPgQubm5Kums+/LRxrU/YMAAuLi4IDIyEkDZWJx5eXnsBqol/P4lfVPRe/+zKJ9x6tSpo7ZOUxqVadeuHWbNmoXdu3fj1q1beP/995GcnIzw8HA+g2qZnZ0dzMzM8Pfffz81X3JyMszMzCCXy1FSUvLEbqNUeUOHDkWbNm0wb948tW7RGRkZkMvlasFMmUwGe3t7lWt79OjRKCgowI4dOwAAhw4dQlpaGruBVkJeXh4yMjLg6Ogo3Ytmzpypdi+aNGkSAEj3o7t371bLz4j6aJikN7KysnDgwAGEhoZi9uzZUnphYSHu37+vw5K9Wuzs7AAAe/bsgbOz8zPzFxcXIyMjQ+XBJj09HYD6H16kLigoCPPnz8e6deuwePFijXl27NgBhUKBAwcOqLSc2rdvn8b8MplMG0WtNjZv3gwhBPbs2YM9e/aord+6dSsWLVokvVZez49KT0+HkZGRWkso1n35VfW1b2BggMmTJ2Pu3LlYuXIlPv/8c3Tr1g1ubm7aOoRqxcTEROP4aJrGNyXt4rnQjvLe+5X3GuU4RkqP17/yGUfT4NSavjdInUKhQGhoKFatWoXLly9jwIABAPgMqi2Ghobw9vZGXFwcbty4oTEYcOPGDZw7dw69evWCjY0NDA0NcePGDR2UtnqTyWRYvnw5fH19sWHDBpV1tra2KC4uxt27d1WCa0IIpKenw8PDQ0pzd3fH66+/jqioKEyYMAFRUVFwdHSUfpSk8vvuu+9QUlICLy8v6e/hOXPmwM/PT2N+5fNlrVq1quVnhC3W9JhMJoMQQvrVW+nLL79U625F2tOjRw/I5XL8+eefaq3ZlMvjvvnmG5XXygHENc1qRqrq1q2LDz74AP369UNgYKDGPDKZDHK5XKXbVX5+PrZt2/aiilltlJSUYOvWrWjYsCHi4+PVlhkzZiAtLQ0HDx6UtomNjUVBQYH0OicnB/v370fnzp1VzglVjDau/bFjx8LIyAjDhw/HtWvX8N5772ml7NWRi4sLfvvtN5VgQkZGBk6dOqXDUr2aeC6qXkXu/crZPC9evKiyj2+//VbltZubG+zt7dW6cqWmpvJcaZCWlqYxXTnUi6OjI59BX4A5c+ZACIFJkyap/X1VUlKCiRMnQgiBOXPmwNTUFJ6enti9e/dTA/tssVw5Pj4+8PX1xccff6zSA6Jbt24AgK+//lolf0xMDPLy8qT1SqNGjUJCQgL+97//Yf/+/QgMDOTzaQWlpqZi5syZsLa2xoQJE+Dm5obXXnsNFy5ceOK9yNLSEgDQq1cvxMfHS11Dqwu2WNNTMpkMVlZW6NKlCz755BPY2dnBxcUFP/74IzZt2oQaNWrouoivDBcXF3z88ceYN28e/vrrL/Ts2RM1a9bE7du3cfbsWZibmyMsLEzKb2RkhJUrVyI3NxceHh44deoUFi1ahF69eqFTp046PBL98eiU5pr06dMHn376KYYNG4bx48cjIyMDK1asUAtC07MdPHgQt27dwvLlyzU+dDdv3hxr167Fpk2bsGrVKgBlv/D6+vpi+vTpKC0txfLly5Gdna3yOaDKqeprv0aNGnjnnXfwxRdfwNnZGf369dNGsasVZSvLkSNHYv369RgxYgTGjRuHjIwMhIeHw8rKSsclfHXwXGhPRe79ffv2hY+PD5YuXYqaNWvC2dkZP/zwA2JjY1W2MTAwQFhYGCZMmAB/f3+MHj0amZmZCAsLg4ODAwwM+Hv/o3r06IF69eqhX79+aNKkCUpLS/Hzzz9j5cqVsLCwQHBwMJ9BX4COHTsiIiIC06ZNQ6dOnfDee+/ByckJqampiIyMREJCAiIiIvDmm28CAD799FN06tQJ7du3x+zZs9GoUSPcvn0b3377LdavXw9LS0s0b94cQNksuZaWljAxMYGrqytbDZbD8uXL0bZtW9y5cwfNmjUDAPj6+qJHjx6YNWsWsrOz0bFjR1y8eBGhoaFo3bo1Ro4cqbKPgIAATJ8+HQEBASgsLFQbm41UXb58WRov7c6dOzhx4gSioqJgaGiIvXv3Sq0E169fj169eqFHjx4ICgpC3bp1cf/+ffz66684f/68NJu0crbcLl26YO7cuWjRogUyMzMRFxeH6dOno0mTJro83MrT1awJVDmRkZECgLh06ZIQQogbN26It956S9SsWVNYWlqKnj17isuXLwtnZ2cRGBgobfekWT2eNJMTqSrPrCj79u0T3t7ewsrKShgbGwtnZ2fh7+8vjhw5IuUJDAwU5ubm4uLFi8LLy0uYmpoKGxsbMXHiRJGbm/siDkXvlKfuhVCfGXHz5s3Czc1NGBsbiwYNGoilS5eKTZs2CQDi77//lvI5OzuLPn36aKn0+m/gwIHCyMjoqbNbDR06VMjlcnHmzBkBQCxfvlyEhYWJevXqCSMjI9G6dWtx6NAhlW2Us4LevXtX24egt7R97SsdO3ZMABDLli2r4iOoXh7//hVCiK1bt4qmTZsKExMT4e7uLnbu3PnEWUE/+eQTtX2CM1RWSmXPhRCs8/KqyL0/PT1dpKWlCX9/f2FjYyOsra3FiBEjRFJSksqsoEobNmwQjRo1EkZGRqJx48Zi8+bNYsCAAaJ169ZaPir9snPnTjFs2DDx2muvCQsLC6FQKISTk5MYOXKk2mx6fAbVvtOnTwt/f39Rp04dIZfLRe3atYWfn584deqUWt4rV66IwYMHC1tbW2FkZCScnJxEUFCQKCgokPJEREQIV1dXYWhoqPFz8qp72jPQsGHDBABpVlAhhMjPzxezZs0Szs7OQqFQCAcHBzFx4kTxzz//aNy/ch8dO3bU1iHoPeU5UC5GRkaidu3awtPTUyxZskTj98OFCxfEkCFDRO3atYVCoRD29vaia9eu0szSStevXxejR48W9vb2QqFQCEdHRzFkyBBx+/btF3V4VU4mxL9TOZBeCA4Oxtq1a5GZmSk1pyQiehkkJyfD1dUVn3zyCWbOnKnr4lA5zZgxA1988QWuX7/OX8ufgt+/Lw+ei+olMzMTjRs3xsCBA9XGTqKqExQUhD179qhNIkRERM+PXUH1xLlz55CYmIjNmzejf//+fJAkIqLncubMGfz222/4/PPPMWHCBAbVnoDfvy8Pngv9l56ejsWLF8Pb2xu2trZISUnBqlWrkJOTg+DgYF0Xj4iIqFIYWNMT/v7+yMrKQv/+/fHZZ5/pujhERKTnOnToADMzM/Tt21dlVldSxe/flwfPhf4zNjZGcnIyJk2ahPv378PMzAxvvPEG1q1bJ42XREREpG/YFZSIiIiIiIiIiKgSOP0OERERERERERFRJTCwRkREREREREREVAkMrBEREREREREREVUCA2tERERERERERESVwMAaERERERERERFRJTCwRkREREREREREVAkMrBEREREREREREVUCA2tERERERERERESVwMAaERERERERERFRJfx/vtSVAD51KBgAAAAASUVORK5CYII=\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY Silicon with WY Thermal',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([50.04129223, 51.42741271, 47.97131586, 17.22230011,  7.05583654,\n",
       "       10.13642248, 13.54628137, 15.74654144, 32.40864067, 40.63494107,\n",
       "       46.43542205, 50.00465601])"
      ]
     },
     "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_3223056/3403781678.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_Thrml19/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_Thrml19/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_Thrml08/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_Thrml08/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_3223056/2320522072.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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\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY Diatoms (0-100 m) with CY Thermal',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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\n",
      "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY Diatoms (0-100 m) with WY Thermal',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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.24531845,  0.1722018 , 11.15381515, 35.62884681, 32.77864728,\n",
       "       17.98166513, 15.88818571,  8.06120314,  0.97470466,  0.65459443,\n",
       "        1.07020377,  0.641704  ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_diatoms_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py39)",
   "language": "python",
   "name": "py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}