{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from salishsea_tools import viz_tools, places, visualisations\n",
    "from matplotlib import pyplot as plt, dates\n",
    "from datetime import datetime, timedelta\n",
    "from calendar import month_name\n",
    "from scipy.io import loadmat\n",
    "from tqdm.notebook import tqdm\n",
    "from salishsea_tools import nc_tools\n",
    "from dask.diagnostics import ProgressBar\n",
    "import cmocean\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 12, 'axes.titlesize': 'medium'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data for original cold and warm years\n",
    "\n",
    "monthly_array_temp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['votemper']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 original temp      \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)\n",
    "\n",
    "\n",
    "### 2019 original               \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223455/391916811.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_river19/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_river19/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_river08/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",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/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",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/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_3223455/2338418733.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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RFRXF8uXLzSNrLl++TNeuXYmPj8/y+kKFCvHcc88xY8YM/v33XyIjI1mwYAENGzYEIDQ01LxzaW5Jfz8PHDjAhQsXgP9Gm6WHbOl90s+nB21ly5Z9IDtSpsuXL98t29J3ggVuuUNkVjJueJBxdNq9yvh9dj+/56ZOnYphGBiGQVRUFKtXr6ZBgwacOnWKAQMG3NVmC5AWpJlMJk6cOGEOxyDtvUxfxP/GjRKSkpLMO31GRUWZA76sjvQ/A7caLVioUKHb1vfTTz9RrVo1Ll26xLhx46hbty758uWjYcOGjB8//qY1/URERB5XCtJERETyqPQwJiYmhm3btgGwdu1aIPOItIyfpwc36f/NOLLtYeDg4ECLFi34+++/6devH5A2nTQoKChb17u4uNChQwfWrl1rfn+2bdvGrl27HlTJN6lUqRKFCxcG/gvIVq9ejclkMtd046i1jCPWHjWVKlUyf7xz504LVpJzrq6uNG3alGXLllGpUiX++ecfXnrppbu6V6lSpcwB7owZM8zng4KCuHz5MnZ2djeFuhk3CpkzZ4454LvdMW3atCxfP3005q2UKFGCHTt2EBQUxCuvvIK/vz+pqals3LiRN998Ex8fnyx3mxUREXncKEgTERHJo+rXr29e22zNmjX8+++/XLx4kbJly+Lt7Z2p740j0tL/27BhQ/Naaw+b559/3vxx+vpS2WVlZcWzzz5719ffq/QRgatWreLw4cOcOXOGypUrm6fHpgdqGYO2jOcfJV5eXlSsWBGABQsW3DSt8VHg5OTEV199BcD06dPZtGnTXd0nfcTZ77//bh5Blj6ts02bNjetIefg4GAebbd37967es2csLKyIjAwkC+++IJt27YRERHBzJkzKVGiBFeuXKF3796a7ikiIo89BWkiIiJ5lIODA3Xr1gXSgpis1kdLV6ZMGby9vTl79iz79+83r4/2MAc3Li4u5o9vtxnCg7r+XmQMym6c1glQvHhxfHx8OHbsGMuXL+fcuXPAzVNy7yR9AXnAogHWkCFDADhy5Ai//vprtq9LTU19UCXlWEBAgPnPzltvvXVX9+jRowcODg5ERUWxcOFC83/h5mmd6dLXNvv9999z/f3Ily8fvXv3ZsqUKQCEhYXlSqAnIiLyMFOQJiIikoelBzYbN25k+fLlwK3DmPSQ4OOPPyYuLi7T9bkpNDSUw4cP37Hf9OnTzR/7+fmZP963bx9nz5694/UZp9fVqFEjh1Xem/TQLDQ0lKlTp2Y6ly79vR81ahQATzzxRI53i3R1dTV/HBkZebfl3rP03VYhLVRbt27dbfunpKTw3nvvsXjx4twoL9veeecdIG3dwfQ/Tznh6upq3v10xowZ5pFpHh4etG3bNstr0kdeHj58mPHjx9/2/rGxsXc1YuxO1zg6Opo/vtMUURERkbxOQZqIiEgelh7GxMbGmke+ZDUiLeP52bNnA2mjUfz9/XOhysz2799PhQoVaNu2LTNmzDDvVglpi6/v3LmTAQMG8NlnnwFQu3Zt89pTkDYttUyZMvTs2ZPff/+d8+fPm9vi4+PZsGEDHTp0YN68eQB069aNkiVL5s7DXefr62ueXrtlyxasra1v+rqkB2tbtmwB7i7UdHd3p1ixYkDaIvrpm07kNnt7e+bPn0+RIkW4evUqLVq0YMiQIYSEhGRaB+zEiRNMnjyZJ554gnfffTdT28PgySefpFatWsB/AWdOpe/eGRQUxNdffw1Az549zTvS3qhjx4507twZgLfffpsXX3wxU9CcmJjIli1beOuttyhZsiQXL17McU2bNm2iatWqfP755xw4cMA88s0wDDZt2sSLL74IgLe3tzkQFREReVwpSBMREcnD6tatax5NkpKSQunSpSlRokSWfdODnPTwolGjRpl2aswttra2pKamsmTJEvr160fp0qWxt7enQIEC2Nvb4+fnZ15Q3c/Pj/nz52eawmhra0tiYiK//fYbPXr0oGjRojg6OuLh4YGjoyONGjUyh4otW7Y0T1vLbRmDMT8/v5t2nrwxOLvb0YEvvPACAF999RUuLi6UKFGCUqVK5fpupWXLlmXbtm00b96cpKQkJk+eTO3atbGzszN/bUuXLs2QIUM4evQoderUoWrVqrlaY3aMGDECSAs472bEXGBgIIULFyY5OZndu3cDt57Wme7XX381f72+++47ypcvj4uLi/l7um7dunz66aeEh4djMplyXBOkrcE2dOhQKlasiIODA56entjZ2dGgQQP27t2Lq6srs2bN0og0ERF57ClIExERycPs7OyoX7+++fNbjUYDKFeuHF5eXubPLbU+WmBgIEeOHOGLL76ge/fuVKhQAXt7eyIjI3FycsLX15cePXowZ84cQkJCKFq0aKbrBw0axO7du/nkk0/o2LEjPj4+WFtbExUVRb58+ahYsSJ9+/ZlyZIlBAcHZ5r+mJsyvr9Z7cZZuHBh8yL9JpMpx+ujpRsxYgRffPEFNWvWxNbWljNnznDy5EkuXLhwV/e7F0WLFmXFihWsW7eOIUOGULVqVdzd3YmOjsbR0ZFq1arxwgsvsGbNGv755x/KlCmT6zXeSceOHalcuTIAo0ePzvH1NjY2PPXUU+bPfX19zWsZ3oqTkxOzZ89m9erV9OnThzJlypCamkpMTAyFChWiWbNmfPrppxw5csQ8AjEnatWqxW+//caLL76Iv78/np6eREVF4eDgQPXq1XnzzTc5cOAAjRo1yvG9RURE8hqT8ShunSQiIiIiIiIiIpLLNCJNREREREREREQkGywepK1atYpnnnmGJ554AmdnZ4oVK0bHjh3Zvn17pn79+/fHZDLddDzxxBMWqlxERERERERERB4nub+C8A2+/fZbwsPDefXVV6lYsSKXLl1i4sSJ1K1bl+Dg4Exrhjg6OrJq1apM12fcjltERERERERERORBsfgaaRcvXqRQoUKZzsXExODj40PlypVZsWIFkDYi7Y8//iAmJsYSZYqIiIiIiIiIyGPO4lM7bwzRAFxcXKhYsSKnT5+2QEUiIiIiIiIiIiI3s3iQlpWoqCh27NhBpUqVMp2Pi4vDy8sLa2trvL29eemll4iIiLBQlSIiIiIiIiIi8jix+BppWRkyZAixsbG888475nPVqlWjWrVqVK5cGYC1a9fy+eefs3LlSkJCQnBxcbnl/RISEkhISDB/npqaSkREBAUKFMBkMj24BxERERERERERkYeaYRhcvXqVokWLYmV1hzFnxkNm5MiRBmB89dVXd+z7xx9/GIDx2Wef3bbfu+++awA6dOjQoUOHDh06dOjQoUOHDh06dGR5nD59+o5ZlMU3G8ho7NixjBkzhg8++IARI0bcsX9qaiqurq60bduWuXPn3rLfjSPSoqKiKFGiBKdPn8bV1fW+1C4iIiIiIiIiIo+e6OhoihcvTmRkJG5ubrft+9BM7UwP0caMGZOtEC2dYRh3HHZnb2+Pvb39TeddXV0VpImIiIiIiIiISLaW/3ooNhsYN24cY8aMYeTIkbz77rvZvu6PP/7g2rVr1K1b9wFWJyIiIiIiIiIi8hCMSJs4cSKjR4+mVatWtG3bln/++SdTe926dTl58iS9e/emV69e+Pj4YDKZWLt2LZMmTaJSpUo8++yzFqpeREREREREREQeFxYP0hYuXAhAUFAQQUFBN7UbhoGrqyuFCxfms88+IywsjJSUFEqWLMkrr7zCiBEjcHZ2zu2yRURERERERETkMfNQbTaQW6Kjo3FzcyMqKkprpImIiIiIiIiIPMZykhM9FGukiYiIiIiIiIiIPOwUpImIiIiIiIiIiGSDgjQREREREREREZFsUJAmIiIiIiIiIiKSDQrSREREREREREREskFBmoiIiIiIiIiISDYoSBMREREREREREckGBWkiIiIiIiIiIiLZoCBNRERERERERB4K//zzD927d6dIkSLY2dnh5eVFt27d2Lx5c47uM2bMGEwm013VsGbNGkwmE2vWrLmr67OradOmNG3a9LZ92rVrR758+UhOTs50fufOnZhMJooUKXLTNevXr8dkMvHll18yZMgQbG1t2bFjx039EhMTqVKlCj4+PsTGxt7TszxOFKSJiIiIiIiIiMV99dVXNGjQgDNnzvDpp5+yYsUKJkyYwNmzZ2nYsCFff/11tu/17LPP5jh8S+fn58fmzZvx8/O7q+vvp4CAAGJiYti2bVum82vWrMHZ2ZkLFy5w8ODBm9rSrx0/fjylS5emX79+JCYmZuo3ZswY/v33X6ZPn46zs/MDfY68REGaiIiIiIiIiFjUxo0bee2112jTpg3r16+nT58+NG7cmKeffpr169fTpk0bXn31VTZu3Hjb+1y7dg0Ab29v6tate1e1uLq6UrduXVxdXe/q+vspICAA4KbRcWvWrKFjx44UKVKE1atX39Tm6elJ5cqVcXJyYvr06Rw4cIB3333X3CckJIRPP/2UYcOG0aBBgwf+HHmJgjQRERERERERsaiPPvoIk8nEt99+i42NTaY2GxsbJk+ejMlk4uOPPzafT5++uWPHDrp160b+/PkpW7ZspraMEhISeOONN/Dy8sLJyYnGjRuzfft2SpUqRf/+/c39spra2b9/f1xcXDh69Cht2rTBxcWF4sWL88Ybb5CQkJDpdcaOHUudOnXw8PDA1dUVPz8/pkyZgmEYOX5fqlevTv78+TPVkpqayvr162natClNmjTJFKQlJiayefNmmjZtan7+evXq8X//93+MHz+eLVu2kJCQQP/+/alQoQLvvfdejmt63NncuYuIiIiIiIiIPLQMA66PxHooODlBDtYnS0lJYfXq1dSsWRNvb+8s+xQvXhx/f39WrVpFSkoK1tbW5rYuXbrQq1cvXnjhhduu9TVgwADmzp3Lm2++SbNmzfj333/p3Lkz0dHR2aozKSmJDh06MHDgQN544w3WrVvHuHHjcHNzY/To0eZ+J06cYNCgQZQoUQJIW/ft5Zdf5uzZs5n6ZYeVlRWNGzdmxYoVJCcnY2Njw65du7hy5QpNmjQhJSUl00izf/75h7i4OPNItnRjx45lyZIl9O/fn8DAQI4cOcKWLVuwt7fPUT2iIE1ERERERETk0XbtGri4WLqK/8TEQA7W3Lp8+TLXrl2jdOnSt+1XunRptm7dSnh4OIUKFTKf79evH2PHjr3ttf/++y+zZ8/mrbfe4qOPPgLgySefpHDhwjz11FPZqjMxMZGxY8fSvXt3AJo3b862bduYNWtWpoBs6tSp5o9TU1Np2rQphmHwxRdfMGrUqBxvghAQEMCCBQsICQmhXr16rFmzhiJFilCuXDlSUlK4ePEi+/fvp1KlSpnWR8vIzs6OGTNmULt2bb744gvGjRtHjRo1clSHpNHUThERERERERF56KVPjbwxiOratesdr127di0APXr0yHS+W7duN00lvRWTyUT79u0znatatSonT57MdG7VqlW0aNECNzc3rK2tsbW1ZfTo0YSHh3Px4sVsvVZGN66TtmbNGpo0aQJAhQoVKFSokHl655o1ayhcuDAVKlS46T7VqlWjS5cuODo6Mnz48BzXIWkUpImIiIiIiIg8ypyc0kaBPSyHk1OOyvf09MTJyYnQ0NDb9jtx4gROTk54eHhkOl+kSJE7vkZ4eDgAhQsXznTexsaGAgUKZKtOJycnHBwcMp2zt7cnPj7e/PnWrVtp2bIlAD/++CMbN24kJCSEd955B4C4uLhsvVZGVapUwdPTk9WrV5vXR0sP0gAaN27MmjVrSEhIYPPmzTeNRruxXisrq0xTYyVnNLVTRERERERE5FFmMuVoKuXDxtramoCAAIKCgjhz5kyW66SdOXOG7du307p165tCoOxMlUwPy8LCwihWrJj5fHJysjlkux/mzJmDra0tixYtyhS6/fXXX3d9T5PJRJMmTQgKCmLr1q1ERkZmCtKaNGnCmDFj2Lx5M/Hx8bcN0uTeaUSaiIiIiIiIiFjU8OHDMQyDwYMHk5KSkqktJSWFF198EcMw7npKYuPGjQGYO3dupvN//PEHycnJd1d0FkwmEzY2NpnCvri4OH755Zd7um9AQACxsbGMHz+eQoUKZZq62aRJE8LDw/nqq6/MfeXB0Yg0EREREREREbGoBg0aMGnSJF577TUaNmzISy+9RIkSJTh16hTffPMNW7ZsYdKkSdSvX/+u7l+pUiWeeuopJk6ciLW1Nc2aNWP//v1MnDgRNzc3rKzuzzijtm3b8tlnn9G7d2+ef/55wsPDmTBhwj3vjpkejs2fP59u3bplaqtcuTIFChRg/vz5FCtWDF9f33t6Lbk9jUgTEREREREREYt7+eWX2bhxI97e3rzxxhs0a9aMoUOHUqRIETZs2MDLL798T/efOnUqr776KlOmTKF9+/bMmTOH3377DQB3d/f78ATQrFkzfv75Z/bu3Uv79u1555136NatG2+//fY93bdixYp4eXlhGEamaZ2QNgquUaNGGIZB06ZN7+l15M5MRvq2F4+R6Oho3NzciIqKwtXV1dLliIiIiIiIiIgFbNq0iQYNGjBz5kx69+5t6XLEQnKSE2lqp4iIiIiIiIjkecuXL2fz5s34+/vj6OjI7t27+fjjj/H19aVLly6WLk8eEQrSRERERERERCTPc3V1ZdmyZUyaNImrV6/i6elJ69at+eijjzLtsClyOwrSRERERERERCTPq1OnDhs2bLB0GfKI02YDIiIiIiIiIiIi2aAgTUREREREREREJBsUpImIiIiIiIiIiGSDgjQREREREREREZFsUJAmIiIiIiIiIiKSDQrSREREREREREREskFBmoiIiIiIiIiISDYoSBMREREREREREckGBWkiIiIi8shITU1l69atjB41ijpVq1KtXDma1K5Nx9at6fv007zyyiuMHj2aiRMnMmXKFObNm8fKlSu5dOmS+R6GYVjwCURE5EZ//PEHJpOJuXPn3tRWrVo1TCYTwcHBN7WVLVsWPz8//vnnH2xsbHjjjTeyvP+HH36IyWQiKCjovtdeqlQp+vfvb/783LlzjBkzhl27dt3Ut3///ri4uNzT66WmpvLLL7/QokULPD09sbW1pVChQrRr146FCxeSmppKu3btcHd35/Tp0zddHxERQZEiRWjQoAGpqak5fv0TJ05gMpmYNm3aPT3Ho8zG0gWIiIiIiNxOamoqVlZWEB5OzIIFNHj2WZJzGIb91rAh3atUATc35p08Sd8//qBllSr8NXo0uLqCqysvTpwItra4FyqEm4cH7vnz4+7ujpubW6b/uru74+joiMlkekBPLCLyeGnatCkmk4nVq1fTs2dP8/mIiAj27t2Ls7Mzq1evJjAw0Nx25swZjh8/ztChQ6lbty5vvfUWH3/8MZ07d6Zhw4bmfvv27WPs2LEMGjSIVq1a3ffa58+fj6urq/nzc+fOMXbsWEqVKkX16tXv62vFx8fTqVMnli1bRq9evfj222/x8vLi0qVLBAUF0b17d+bOnctPP/1E5cqVefbZZ28KIF966SWuXr3K9OnT0/7fmkNFihRh8+bNlC1b9n491iPH4kHaqlWr+PXXX9m0aROnT5/G3d2dmjVrMnr0aPz9/TP13bFjB2+++aY5bW7WrBkTJkygTJkyFqpeRERERB6UA3v3MvS554g5d471RYpASAiuhkEH0qZVtLO1pWi+fETGxhKZkEAkEAVEXj8yfuy1YQNs2ADXz8cBKTt2QKdO5tebfv18dthYWeHu4MCX3bvzVEAAuLqy5/Jlvly0iApPPMEbr72WFtA5ObFq9WpsbGwyBXH58uW7qx9gRETyIk9PTypXrsyaNWsynV+7di02NjYMHDiQ1atXZ2pL/zwgIACAd999l8WLF9O/f3/27NmDk5MTycnJ9O/fH29vbyZMmPBAaq9Ro8YDuW9Whg4dSnBwMNOnT6dv376Z2rp06cL//d//ERcXh5eXF5MnT6Znz558//33DBo0CEgL/WbPns3kyZPx8fHJ0WunpKSQnJyMvb09devWvW/PlF1xcXE4ODg8HL/EMiysW7duRkBAgDF58mRjzZo1xu+//27UrVvXsLGxMVauXGnud+DAASNfvnxGo0aNjMWLFxvz5s0zKlWqZBQtWtS4ePFijl4zKirKAIyoqKj7/TgiIiIicheSk5ONzZs3Gxv//NMwfvzRMLp2Nc7ny2cABmCcB8MAw6hc2TCGDTOM5csNIy4u4w0M48oVwzh50jD27jWMjRsNY+lSw5g7N+1+EyYYxujRhvHaa0Zs377G8datjRONGhlG3bqGUbGikVqsmPG5vb0xBozXwOgPRicwAsCoAUZpMDzAsLpeT/oxN70uMH6/fq5hhnOGtbVRxGTKdA1gmMBws7MzSrq6GlULFzYaly1rdKhe3ejbpInxcseOxvrvvjOMf/81jDNnjIiTJ40Vy5YZe/bssdjXR0TkQXvllVcMwDh37lymc/Xr1zeCgoIMa2trIzo62tz2zDPPGNbW1kZkZKT53O7duw07OzvjpZdeMgzDMN577z3DysrKWLdu3W1fe9GiRQZgbN261Xzujz/+MACjTZs2mfpWqVLF6NKli/nzkiVLGv369TMMwzBWr15909/3gPHuu+8ahmEY/fr1M5ydnY0jR44YrVu3NpydnQ1vb29j6NChRnx8/G1rPH/+vGFra2sEBgbetl9GvXr1MlxcXIzQ0FDj8uXLRqFChYwnn3zyjteFhoYagPHJJ58Y48aNM0qVKmVYW1sbS5cuNbdNnTrVMAzDmD9/vgEYK1asuOk+kydPNgBj9+7d5nMhISFG+/btjfz58xv29vZG9erVjblz52a6burUqQZgBAcHGwMGDDA8PT0NwIiLizMuXrxoPPfcc4a3t7dhZ2dneHp6GvXr1zeWL1+e7fclKznJiSw+Iu2bb76hUKFCmc61atUKHx8fPvzwQ5o1awbA6NGjsbe3Z9GiReZhk/7+/vj6+jJhwgQ++eSTXK9dRERERO7e1atXWbZoEYumTmXxhg1cioujObDiersXMNXJiTqNG1O4e3cIDIRixbK+mbU1uLunHXfgBJS+4ZwJeA0gNRViYiA6OsvDiIoi5vJlIi9dIio8nGKpqRAfD9HRVLx4kfcvXaJIYiIkJKTdKyWFckA+0kbCXQESSfvJKioxkajExLR7h4VlqqfyggWkT0zaAbQAKltZsbdYMfNU1Fr793MuMRF3BwfcHB1xd3bGPV8+3Fxd06alenjgVqAA7gUL4u7lhXuRIpSpVIlCRYrc8T0SkUdTbGxsjq+xt7fHxiYtGkhOTiYhIQErKyscHR3v6r7Ozs45rgHSRpZ9+eWXrFmzhqeeegpIG3XWrl07GjRogMlkYv369bRp08bc5ufnh5ubm/keVatWZezYsYwYMQIfHx/GjRvH0KFDadSo0W1fu0mTJtja2rJixQpq1aoFwIoVK3B0dGTt2rUkJSVha2vLxYsX2bdvHy+++GKW9/Hz82Pq1KkMGDCAkSNH0rZtWwC8vb3NfZKSkujQoQMDBw7kjTfeYN26dYwbNw43NzdGjx59yxpXr15NUlISnTKMpL6Tb775hrVr1/LMM89QsGBBEhMT+fnnn7N9/Zdffkm5cuWYMGECrq6u+Pr63tSnXbt2FCpUiKlTp9K8efNMbdOmTcPPz4+qVauan6FVq1bUqVOH7777Djc3N+bMmUPPnj25du1aprXmAJ555hnatm3LL7/8QmxsLLa2tvTp04cdO3bwwQcfUK5cOSIjI9mxYwfh4eHZfq57ZfEg7cYQDcDFxYWKFSuaF8ZLTk5m0aJF9O3bN9Pc45IlSxIQEMD8+fMVpImIiIg8AkKPH2fRlCks/OMP1hw5QlKGtc5cgSKAUacOptatITCQ/rVqpYVkucXKyhxUZcVEWiiWDyh+Q1vF6weQNh7t2jWIjmZNVFSmMC4+PJyosLC0MC4igsiICCKjooiKiiIyJobIa9fwt7ZOC+OiorBOSaESUC41FTIsHH0u/YiPh8jIbD3ex8BbLi7g6spOe3sCT5/mCRcX1jVvbn7uj/bsITwlJW1duPz5cff0TAvjChfGzcsL92LFcC9WDBd3d01PFXnI3M1C9r/99hvdu3cH0qb+9ejRgyZNmmSaZlmqVCkuX76crfsZd7mhS5MmTbCysjIHaeHh4ezbt4/x48fj4uKCn58fq1evpk2bNpw+fZrQ0FBz3Rn93//9H3///TevvfYalSpV4v3337/ja7u4uFCnTh1WrFjB8OHDgbQg7aWXXmLixIls3ryZxo0bs2LFCgzDoEWLFlnex9XVlcqVKwNpGyFkNQUyMTGRsWPHmmtv3rw527ZtY9asWbcN0k6dOgVA6dI3/iro1jw8PJgyZYo5fPzll18yhXp34uDgQHBwMLa2tuZzJ06cyNTHxsaGp59+mm+//ZaoqChzsHngwAG2bt3KV199Ze47ePBgKlWqxKpVq8zhbWBgIJcvX2bEiBH07ds30/9Xmjdvzvfff5/p9TZu3Mizzz7Lc889Zz7XsWPHbD/T/WDxIC0rUVFR7Nixwzwa7dixY8TFxZlTzIyqVq3K8uXLiY+Px8HBIcv7JSQkkJCQYP48Ojr6wRQuIiIiIpmkpKTwz/LlLPz+exatXcv+K1cytfsA7Z2dad+4MQ379ME2MBA8PCxT7P1kMoGzc9pxwwgwh+tH4ezcxzBoGh/Pvowj464HcxuPHePKxYtEhocTGRFBVGQkkdHRRF69SlRsLJFxcUTGxxOVlERkUlLaWnGQNuIuJoYI4BJQKDIS5s0zv+SvwL/ZKM0KqOjoSNd69Xhq2DDKt26dnScSEclS/vz5qVatmjnAW7t2LdbW1jRo0ABIC9pWrVoF3Lw+WkbW1ta8++67tGrVihEjRmBvb5+t12/evDkff/wxcXFxXLx4kaNHj9KrVy9WrlzJ8uXLzUFaiRIlshyZlV0mk4n27dtnOle1alXzs91vrVu3pm7duoSHh/P000/n6NoOHTpkCtFu5ZlnnuGzzz5j7ty5PP/88wBMnToVe3t7evfuDcDRo0c5ePCgea265ORk8/Vt2rRh0aJFHDp0iAoVKpjPd+3a9abXql27NtOmTaNAgQK0aNECf3//bNV4Pz2UQdqQIUOIjY3lnXfeATAP0fPI4h9VHh4eGIbBlStXKHKLYeofffQRY8eOfXAFi4iIiMh/UlNh+3b2TJ9Os++/JzzDP5atgYYmE+19fGjXuTPl+/SBSpXSgie5mckEjo5pR+HM0Vup60e2JSRkGhlXNyyMvUePkhIdDfnzmwO6FzZs4HR4eNomDteupYVxiYlEJicTmZJCJGnTU1OBfXFx7Fu1CptVqxhZoQJ07kxKx45Y1ayJSaPVRHJdTExMjq/JGDR17tyZmJiYm0ab3jgK6UEJCAjgs88+49y5c6xevRp/f3/zKLsmTZowceJEoqKiWH19E5eMu3NmlP5MdnZ22X7tFi1aMHbsWDZs2MDJkyfx9PSkRo0atGjRghUrVjBu3DhWrlx5y9Fo2eXk5HTTICB7e3vi4+Nve12JEiUACA0NzfFr2tvb5+i9SHerjOVGlSpVolatWkydOpXnn3+elJQUfv31Vzp27GjOccKuL2EwbNgwhg0bluV9bhz1mNXrz507l/fff5+ffvqJUaNG4eLiQufOnfn000/x8vLKyePdtYcuSBs1ahQzZ87kq6++umnXztvtznC7tuHDhzN06FDz59HR0RQvfuNgfBERERG5W2d27OCPCRPId+wYA48fh8uXKUfaLpj5gdb58tGufn1aPfMM+du1AycnC1f8GLK3h4IF0w7AGaicRbeX73SfxETiL10i/PhxVk6dyrzFi+kWEQEHDsCBA/zx4Ye8aW3NS/Xq8X/vvw8NG+bu9FyRx9jdrk+WzsbGxjzl7n7eN7vSg7Q1a9awZs0a85REwByarVu3jjVr1lCrVq27msp6K3Xq1MHFxYUVK1Zw4sQJmjdvjslkonnz5kycOJGQkBBOnTp1z0Ha3QoICMDW1pa//vqLF154IVdeMyc7ZA4YMIDBgwdz4MABjh8/zvnz5xkwYIC53dPTE0jLZ7p06ZLlPcqXL3/H1/f09GTSpElMmjSJU6dO8ffff/P2229z8eJFgoKCsl3vvXiogrSxY8fy/vvv88EHH/DSSy+ZzxcoUAAgy8XjIiIiMJlMuN9mYVl7e/tsD+cUERERkTtLvnaNhDVrcF63DoKC2LB7N68DFYCBAPny4dC8OVurVqV8nz7Y+PhYtmC5f+zscChWjGLFitG3USP6QtpotsWLYf58/p4/n1MpKURs2ABNm0LBgiS0bctGX18av/oqNrn0A7mIPHoaN26MtbU1f/zxB/v37+fTTz81t7m5uVG9enWmT5/OiRMnzFMG7xdbW1saN27M8uXLOX36NB9//DEAjRo1wsbGhpEjR5qDtdtJzx7i4uLua31eXl48++yzfPvtt8yYMYO+ffve1OfYsWPExsZmuSzWg/bUU08xdOhQpk2bxvHjxylWrBgtW7Y0t5cvXx5fX192797Nhx9+eF9es0SJErz00kusXLmSjRs33pd7ZsdDE6SNHTuWMWPGMGbMGEaMGJGprWzZsjg6OrJ3796brtu7dy8+Pj63XB9NRERERO6PKzt2EPz11yxctoylZ8/yBvDO9bZWQPN8+Whbpw4p77yDdYMGYGtLJQvWK7nIzQ1694bevfkpIoLuEyZQ6eBBWLsWLl1i+bRptAc8R46kY6lSdO3Zk+bDhmF3/RfmIiKQtli/n58ff/31F1ZWVub10dI1adKESZMmAVmvj3avmjdvzhtvvAFgHnnm6OhI/fr1WbZsGVWrVs1yw8SM0vOLmTNnUqFCBVxcXChatChFixa95/o+++wzjh8/Tv/+/QkODqZz584ULlyYy5cvs3z5cqZOncqcOXMsEqS5u7vTuXNnpk2bRmRkJMOGDbtpivD3339P69atCQwMpH///hQrVoyIiAgOHDjAjh07+P3332/7GlFRUQQEBNC7d2+eeOIJ8uXLR0hICEFBQbcc5fYgPBQLF4wbN44xY8YwcuRI3n333ZvabWxsaN++PX/++SdXr141nz916hSrV6/O1TdMRERE5LERE8Ph777js4YNCXB0pKC/P09Nncqss2e5Aqyzs4Onn4Zff8U9LIwV0dG8vnw51k2bQi4v/CsPD0cPDzp9+CG+f/4JFy7AihVcbtaMAlZWXDYMpoSG0ubjjynk6Ukfb28WvPwycRl2IxWRx1tAQACGYVCjRg1cb9hBuUmTJhiGgZ2dHfXr17/vr50envn6+lKyZMmbzmdnWqeTkxM///wz4eHhtGzZklq1avHDDz/cl/ocHBxYvHgx06ZN48KFCwwaNIhmzZoxaNAgTpw4wc8//3zTRga5acCAAVy8eJHExET69+9/U3tAQABbt27F3d2d1157jRYtWvDiiy+yYsWKbL23Dg4O1KlTh19++YX//e9/tG7dmp9++om33nqLH3/88QE8UdZMxt3uTXufTJw4kWHDhtGqVassQ7T07WIPHjxIrVq18PPz4+233yY+Pp7Ro0cTERHBrl27KHh9rYfsiI6Oxs3NjaioqJv+YIqIiIg8tgyDpO3b2fjjjyxcupRFp09z+IYuFZ2caF+zJu369aNenz5YKzCTbEpOTGTdjz/yx5QpzN+zhwspKeY2Z6BtwYJ0aduWtm+9hcsTT1iuUBEReezkJCeyeJDWtGlT1q5de8v2jOVt376dt956i82bN2NjY0OzZs2YMGECZcuWzdFrKkgTERERue7yZVi+nB2zZjF+xQqC4uOJzNBsCzQpVoz2rVvT7qWXKFOtmoUKlbwkNSWFTbNmMe/bb/lz2zZOJSWZ2+yBQHd3hnTuTMu33oIbFp8WERG53x6pIM0SFKSJiIjIYys5GeOffzg4cyZ2GzdSdt8+MAzWAk2vd/G0s6Nt9eq0e/ppWvbti6ubmwULlrzOMAy2LVjAvK++Yt6mTRyNjwfgC+AVgAoVuNq2LfGBgRRs3hxysIuciIhIdihIuwMFaSIiIvJYOXUKgoPTjhUrGB4VxcfAEOBrgCpVSH7ySd67eJFWAwdSp1EjrK2tLVuzPJYMw2DvmjXMmziR52Jj8d64EZKS+BF4AXjRxYWvBw6ELl2gQQPQ96mIiNwHOcmJHppdO0VERETkPomLg3XruPzXXyxZsIBF58/zf0Ct6831nZ2xi4sjrn59mDMHihXDBnjPgiWLAJhMJqoGBFA1fTe+qChYvJjdo0aRevw4xWJi4Isv4IsviC5QgJ98fOgyaBClevcGe3vLFi8iIo8FjUjTiDQRERF51BkGHDyIsXQp++fNY9HWrSxMTmYzkP4PveHFivHhc89Bq1YkVq1KQnIy+fLls2TVIjkSeuAAzlu3Umj1avj7b2ZducL/rrf5W1nRtXJluj7zDOUGDgQXF4vWKiIijxZN7bwDBWkiIiLyyIuMhJUrSViyhLULF7Lo0iUWAidu6FatZEnad+tGtz59qKaNAiSvSEoiaMIEPvn6a9adO0dqhqbKJhNdfXzo2qcPlV94AVPBghYrU0REHg0K0u5AQZqIiIg8clJTYft2CAri0qJFLA4JYaFhsAyIydDN3saG5vXq0a5XL9q1b0/x4sUtVbFIrrh44QILvviCeXPnsjI0lOQMbb5A1xIl6NqjB/4vv4ypRAlLlSkiIg8xBWl3oCBNREREHgnnz8OyZRhBQaQuW4Z1RAQAY4ExGbp55c9Pu44dad+5M82bN8fZ2dkS1YpY3JWICBZ+9x3zZswg+PBhEjL8qFMSGF2qFM8MGgSdO0P58pYrVEREHioK0u5AQZqIiIg8lBITYeNGCApK22Fz924+BL4HPgO6urpC8+bsqFCB5/7+m3ZdutC+fXv8/PywsrKycPEiD5erV6+yZNo05k2ZwuK9e7mWmspUoP/19vO+vhyoXZvGr7yCTa1aYDJZsFoREbEkBWl3oCBNREREHhrHjpmDswsrVrAkLo7/Aen7Dw4tVIjPL17kmTZtmPLXX2Bra8FiRR5NcXFxBM+ZQ5PISPIHB8OqVUxMSmIY0BH4q2TJtFFqnTtDgwZgbW3pkkVEJBcpSLsDBWkiIiJiMTExsHo1BAVhBAWx+/hxFgKLgK3Xuyxv0YIWAwbAk09y4PJljh07RrNmzXBycrJg4SJ5SGQkn774Ip/++SdjDYMhSUkAXAD+z96ero0aEThkCI6tW4O9/e3vJSIijzwFaXegIE1ERERyjWHAnj3mUWdx69ezKjmZRaSFZ2du6F6zZk3GjRtHq1atLFCsyOMlOTmZ5OhoHNavhz//5Ps//uCFa9cAcAba2tjQpXZt2r7wAi6dO4OLi2ULFhGRB0JB2h0oSBMREZEH6vJlWL48LTxbtoxzFy6wGFgIrADiMnR1cnLiySefpF27drRt25YiRYpYpmYRYc/27Uz98EPmLVvG6Zj/9sO1BwKtrOhWtSrtn30W9169oEAByxUqIiL3lYK0O1CQJiIiIvdVcjJs2fLfJgHbtqWNRAMGAT/c0N3b25v27dvTrl07AgICcHR0zPWSReTWDMMgZMsW5n3zDfMWLeJYZKS5zRZoDnQtX56O/fpRsE8f8Pa2VKkiInIfKEi7AwVpIiIics8iImD+fFiyBFauhKgoFgF/Ax8ABatWhcBAxkdF8daPP1K7dm3atWtH+/btqVq1KibtECjySDAMgz27dzPvu++Y9+ef/HvpkrnNCngPeKd27bSNCrp0gXLlLFariIjcHQVpd6AgTURERO7KtWuwcCHMnAlBQUQkJeGR3ubhQY3UVHZFRjJ90iT6vvoqABERESQlJVG4cGGLlS0i98/BgweZ9+OPzJs7l51nzzIP6HK97RCwuHBhunTvTqkBA6BGDVBoLiLy0FOQdgcK0kRERCTbkpPTRpzNnAnz53M+JobfgbnADpOJyyNG4NyhA/j789XkyRw6dIiBAwdSo0YNS1cuIg/Y8ePHKWIy4bhsGcyfz7vLlvGeYdAR+AugZMn/RqrVrw/W1pYtWEREsqQg7Q4UpImIiMhtGUbammezZsHcuVy+eJF5wBxgLZD+jyeTycTKlSsJCAiwXK0i8tCY+/PPfDt+PM+5uPC/f/+Fa9c4BnQGujo707VVKyo98wym5s3B3t7S5YqIyHUK0u5AQZqIiIhk6cCBtPBs1iwijx9nPmkjz1YAKRm61a1bl549e9K9e3eKFStmmVpF5OF27RosW8an48bx1o4d5tPlgK52dnQNCMBv4EBMrVuDi4vl6hQREQVpd6IgTURERMzOnoXZs2HWLK7u3MnfpIVnwUBihm5+fn707NmTHj16UKpUKYuUKiKPnoiICBbOn8+8n35iWUgICSn/xfKlgC7W1nStV4+6AwZg1bEjFChgsVpFRB5XCtLuQEGaiIjIY+7KFZg3L23ds7Vr06ZyAm1NJpZk+KdR5cqV6dmzJz179sTX19dS1YpIHnH16lUWL1zIvJ9+YsmGDVxLSjK3FQU6m0x0rV6dRv36YdO1K3h7W65YEZHHiIK0O1CQJiIi8hiKi4NFi9Kmbi5ZwsLERGYD44FiDRvC//7HlPh4Pv32W3N4VqlSJUtXLSJ51LVr1wgOCmLezz+zcMUKohMSzG2vAZ8D1K6dtlFB585QrpyFKhURyfvue5BmGAaLFi2idOnSVK5cOcs+e/fu5cSJE7Rv3/7uqs5FCtJEREQeEykpsGoVzJxJ6rx5WMXEmJsaOjmx8do1Ph81itfee+969xSsrKwwmUyWqlhEHkMJCQmsXLmSedOm8dfixcwtVYoWBw6AYbAF+Ab4X8mSBPbpkxasVa8O+ntKROS+ue9B2qJFi+jRowd79+6lbNmyWfY5fvw4VapUYerUqfTo0ePuKs8lCtJERETyMMOAbdtg5kxSZs9m7cWLzAEWAvuKFaNAnz7Quze/7t7Njh07GDBgAFWqVLF01SIiACQnJ2MymbC+eBEWLOD1jz5i0qlTPA38cr2PUbIk19q3x7l/f/D3t2C1IiJ5w30P0tq3b0+xYsX47rvvbttv8ODBnDp1ikWLFuWs4lymIE1ERCQPOnwYZs0ideZMNh09yhzgDyAsQ5cpP/7IM88+a6ECRURyLiQkhFk//0xbNzdaHDkCS5eyMy6O+kAnYHj9+lSdMAHq1bNwpSIij66c5EQ22bnh1q1b+f777+/Yr2XLlrzwwgvZq1JERETkXp0/D3PmYMycScj27cwFfgPOZOji4eFB165d6dmzJ02aNLFQoSIid6dWrVrUqlXrvxPXrrHixReJnzGDOcCcTZvoWL8+I2vXpubEidCwocVqFRF5HFhlp9OVK1coWLDgHft5enpy5cqVey5KRERE5JaiomDqVIzmzdlVrBjDhw6l7Pbt1AE+Iy1Ec3V1pW/fvixZsoQLFy7www8/0Lx5c2xssvU7RBGRh5eTE8OmTWPbtm30bNsWE7AAqLV1K60bNWKjvz+sW2fpKkVE8qxsBWlubm5cuHDhjv3CwsI0VVJERETuv/h4+PNP6NYNCheGZ57h1VWrqGEYfAyEAk5OTvTq1Yv58+cTFhbG9OnTad26Nba2tpauXkTkvjKZTPj7+zNn0SL+PXCAvl26YG0yEQQ03LGDgCZNWFWtGsaqVWnrRoqIyH2TrSCtevXqzJ8//4795s+fT/Xq1e+1JhEREZH/dtwcOJBjBQvyYdeuHJ03DxISoGJFGvXqhb2dHV26dGHu3LlcvHiR2bNn06lTJxwcHCxdvYhIrnjiiSeYPm8eh44c4bmnnsLWyoo1QPM9e2jYvDlLq1TBWLFCgZqIyH2SrSCtb9++zJkzh5kzZ96yzy+//MKcOXPo16/ffStOREREHjOGAdu3wxtvQIkS0Lw5/PwzL8fE8A4wq0ED2LUL9u2jw7RpXLx0iXnz5tGjRw+cnZ0tXb2IiMWULVuWH2bN4mhoKC/174+9tTWbgDb791PrySe5UrcuLFumQE1E5B5la9dOwzBo3bo1y5cvp1WrVnTs2JHSpUsDEBoayl9//UVwcDCBgYEsXrwYk8n0wAu/F9q1U0RE5CFz9CjMmsX5GTP4/dgx5gKzgRL580P37swsWJDpW7YweMgQOnXqZOFiRUQefufPn2fi2LF8O2UKfikprDMMTAB168Lo0dCqFTzkP7eJiOSWnORE2QrSABISEnj99deZMmUKSUlJ5rDMMAxsbW159tln+eyzz7C3t7/3J3jAFKSJiIg8BC5cgN9+49K0aczbuZO5wFog/R8mEwYM4I1vv4VH4N8WIiIPq0uXLhF+4ABPzJ8P331HeHw8rYGXy5Thf5MmYdWunQI1EXnsPZAgLV1YWBirV6/m1KlTAJQoUYKAgAAKFy589xXnMgVpIiIiFhIdDfPnc2X6dOavWcNcw2AlkJKhS/06dejZuzfdu3enSJEilqpURCTvuXCBcV27MnrTJqoDOwCTv3/aCLX27RWoichj64EGaXmBgjQREZFclJAAQUFET5vG34sXMzcpiWAgKUMX/6pV6dWnDz169KBEiRKWqlREJM+Ljo7m2/Hjqfzvv7QNCoJr14gBfvH2ZsCECTh07w5W2VpKW0Qkz1CQdgcK0kRERB6w1FRYtw5mzYI//mDylSsMBRIydKlSvjy9+valR48e+Pj4WKpSEZHH16VL8NlnTPjsM/4vMREv4P+KFGHQp5/i3Lu3AjUReWzkJCey+N+MV69e5c0336Rly5YULFgQk8nEmDFjburXv39/TCbTTccTTzyR+0WLiIjIzQwDdu0i/vXX+atgQQ4HBMCPP8KVK/h4eJAAlC9ZktGjRrF//372HDzIiBEjFKKJiFhKwYLw0Ud4TZpEcVdXLgBvnD9PqT59+KhoUaKnTUv7xYiIiJhZPEgLDw/nhx9+ICEh4Y67cDk6OrJ58+ZMx9y5c3OnUBEREcna8ePwwQdQqRLUqMGgSZPoHBHBD3Z2MHAgrFpFs3Pn2LlzJwdCQxn73ntUrFjR0lWLiMh1T7/4IkcvXeKnL76gTP78XAZGhIVRcsAAxhQuTMSPP0JKyh3vIyLyOLB4kFayZEmuXLnC2rVr+eijj27b18rKirp162Y6qlWrlkuVioiIiNnFi6R8+SUrK1Xi+bJlOTByJBw4APb2dK5Xj2IeHniOGgU//QQBAdjY21O9enXzrt8iIvJwsbOzY+Arr3Do4kV++e47nvD0JBIYe/kyJZ9/nrcLFeLi5MmQnGzpUkVELMriQVr6FE0RERF5yMXEkDpjBuvr1OElLy+KvvoqLf79lx+B2aVLw9SpEBZG+/XrOXXpEm+PHGnpikVEJIdsbGx4etAg9l24wG/TplG1cGFigE8iIig1ZAhDCxbk3BdfKFATkcdWjoO0+Ph4oqOjM5377bffePvtt1m5cuV9KywrcXFxeHl5YW1tjbe3Ny+99BIREREP9DVFREQea4mJGH//zdaWLRnq7k6Jfv1ovHUr3xgGFwEPJyee+9//aD1zJvTvD25uWFtbY6UFqkVEHmnW1tZ079ePnefOsWD2bGoWK0Yc8HlkJPVee42U8uXTfoGSlHTHe4mI5CU53rWze/fuODs7M23aNAC+/PJLXnvttbSbmUwsXLiQNm3a3FUxly9fpmDBgrz77rs3bTjw+eefA1C5cmUA1q5dy+eff06JEiUICQnBxcXllvdNSEggIeG/fcKio6MpXry4du0UERHJSmoqxoYN7P7yS+YsXsxv8fGEZmh2tbenc5s29HzuOVq0aIGtra3FShURkdxhGAbL/vqLca+/TsdLl/i/a9cASC1VihODBlHmjTdA/z8QkUdUTnbtzHGQVrJkST755BN69eoFgI+PD/Xr1+frr79m4MCBhIeHs2rVqrsq/HZBWlbmzZtHt27d+Oyzz3j99ddv2W/MmDGMHTv2pvMK0kRERDLYswdmzSLo55959dIlDmdocra1pUOzZvR84QUCW7XCwcHBYmWKiIjlGIZB6tWrWP/wA4wfzx8XL9ITGJwvH19NmJA2OtnOztJliojkSE6CtBzPu7h06RLFihUDIDQ0lOPHj/Pyyy/j6urKwIED2bdv391VfRc6d+6Ms7Mz//zzz237DR8+nKioKPNx+vTpXKpQRETkIXfyJEeHDeN4+fJQrRp88gnu10M0eysrujRsyG9z5nAxMpJZQUF07NRJIZqIyGPMZDJh7eoKw4ZBaCibGjcmFch/9SoMGgQ+PvDtt5BhRpCISF5ik9MLnJyciIqKAmD9+vW4uLhQs2ZNABwcHIiJibm/Fd6BYRh3XIfF3t4ee3v7XKpIRETkIXf5Mvz+O8ycybiNGxkNPAf8YGcHbdtSp3dv5sbH06pDB43cFhGRW3Ny4rO1a/nfxo2UXrsWvv4aTp9m2eDBfDN0KCNffpla770H+gWMiOQhOR6RVqVKFb755hv27t3L5MmTCQgIMO+6eerUKby8vO57kbfyxx9/cO3aNerWrZtrrykiIvJIio3l3Dff8EWlSuzx8oLBg2HjRhoA1iYTMbVrQ1gY/Pknpm7d6PH00wrRREQkW/wbNMBjxAg4fhy++ooP7ez4Oz6e2uPH08rdnY2vvQZxcZYuU0TkvsjxGmmrVq2iXbt2JCQkYGdnx4oVK2jQoAEAPXv2JCUlhT/++CNHRSxdupTY2FiuXr3KM888Q/fu3enRowcAbdq04dKlS/Tu3ZtevXrh4+ODyWRi7dq1TJo0ibJly7JlyxacnZ2z/Xo5mfsqIiLyyEpK4uLvvzNv0iTmbt/OutRUDOBVYJKfH/zvf6R060aEoyMFCxa0dLUiIpJHHNqzhw+ff56ZW7aQcv1cUzs7Rg0cSMD48Zhy8LObiEhueKCbDQCcPHmS7du3U716dcqUKWM+//3331O9enXq1KmTo/uVKlWKkydPZtkWGhqKm5sbAwcOZOfOnYSFhZGSkkLJkiXp3LkzI0aMwM3NLUevpyBNRETyLMMgIjiY+Z98wtyNG1mVlGT+IQagvrc3g4YMoe/bb1usRBEReTwcP3CAj59/nmkbNpB0/Vw9W1tG9e1Lq0mTMLm4WLQ+EZF0DzxIe9QpSBMRkbwmessWFnzwAXNXrGBZXJz5BxaAmoUK0bNnT3q88QYlSpa0WI0iIvJ4OnX0KOOff54fV68mfQsCfxsbRvbuTYevvsJKP5OJiIU90F07ARISEvj+++956qmnePLJJzly5AgACxYs4Pjx43dzSxEREcmps2fZ+/rrdHF3p1DduvRduJDF10O0qvnz80H//hw9eJCQsDCGffmlQjQREbGIEj4+fLVqFaEnT/JGy5Y4mUxsT06m84wZVPfw4I/eveHqVUuXKSKSLTkekXb58mUCAgLYv38/Xl5ehIWFERISgp+fHwMGDMDR0ZHJkyc/qHrvC41IExGRR5Vx7RpXZ8/G9fffYfly/k1NpdL1tvIuLvRq1Yqew4dTwc/PonWKiIjcyqVz55g0aBBfLV7MVcOgDzDDwwOGDoWXXwb9jCYiueyBjkh78803iYyMZNu2bZw6dYqMOVxAQABr167NecUiIiJya4YBGzcS1Lo15fLl44Vnn4XgYEhNpWLDhkzs1o1da9dyIDqaMb//rhBNREQeagWLFuWDhQs5GRbG2C5dGF6yJEREwMiRHPL25ocOHUi8eNHSZYqIZMkmpxcsWrSITz75BD8/P1JSUjK1eXt7c+bMmftWnIiIyOMs5sABYqZNw2v+fDhyBE/gKBBpZUXi8OHYDRgAZcsy1NKFioiI3IX8BQsyet48SEmBuXNh3DjeP3iQXxcuZKu3Nz8NHw6vvQb581u6VBERsxyPSIuOjqbkLdZYSUpKIjk5+Z6LEhEReVylXr3Kqrffpl+RInhVrMg7n34KR46AszP+/frxx5gxhF65gt3770PZspYuV0RE5N5ZW0Pv3rBvH7X796eojQ0vJiXBe+9BqVJEDBtGzMmTlq5SRAS4iyCtdOnSbN68Ocu2rVu3Ur58+XsuSkRE5LGSmsqRX39lZLVqlHZzo/knnzDjwgVigV358mFMmwYXLmCaNo2u776Li9aOERGRvMjampenTuVETAz+v/0GlStDdDQjJ06kVKlSfNi0KdGhoZauUkQeczkO0v73v//xySefsGDBAvP6aCaTiZCQEL744gv69Olz34sUERHJiyJ37uSHdu1o4OREuT59+GDPHk4ZBm5WVjxfsyYb581jW1QUpn79wMXF0uWKiIjkClt7e+jeHXbvJvm331jn4EA48M7atZQsU4Z3GzQg4vBhS5cpIo+pHO/amZSURIcOHQgODiZ//vxcuXIFT09PwsPDadWqFQsXLsTKKsf5XK7Srp0iImIpyRERLH/vPabPnMlfly+TcP28FRBYvDj9nn2WDsOG4ejkZMkyRUREHhrJiYnMffttPvj2Ww7ExwPgAgypXZuhU6ZQqHJlyxYoIo+8nOREOQ7SAAzDYO7cuSxevJiwsDA8PT1p164dvXr1euhDNFCQJiIiuSwlBVat4upPP/HEb79xLkNTZRcX+nXowP/GjaNImTIWK1FERORhl5qSwp/vvMP7X37J7rg4AByBQTVq8H8//0zR6tUtWp+IPLoeeJD2qFOQJiIiueHS5s1s/uwzOvzzD1zf1boJsN/amt516tB/1ChqBAZiMpksW6iIiMgjxEhNZdHYsYybOJGQ2FgA7ICBVarw1k8/UbJ2bcsWKCKPnFwJ0g4ePMjatWu5fPkyAwcOxMvLi3PnzpE/f34cHR3vqvDcoiBNREQemCtXYO5czv74I6V37MAAzgEF8+eHXr043bo1hVu2xM7e3tKVioiIPNKM1FSWf/wx4z7+mA1XrwJgA4ytW5cRv/8O3t6WLVBEHhkPNEhLSUnh+eefZ9q0aRiGYd5owM/Pj/bt21OjRg3ee++9e3qAB01BmoiI3E9GUhI7Jk9m78yZ9N+zBxLSVj6rDaS6uTFl5EiqvfwyKDwTERG574zUVNZ+9hnvv/8+K6Oi+B3oZmcHAwdivPUWppIlLV2iiDzkcpIT5XhBsw8++IBZs2Yxfvx49u3bR8YcrnXr1gQFBeW8YhERkUfQ+dWrGd+kCVWcnKj52mu8EBJCZEICVKkCEyaw8sgRtkVGUm3YMIVoIiIiD4jJyoqmw4ax4soVtn7zDV0aNYLERPj2Wz4vU4buZcuyNzjY0mWKSB6R4yBt2rRpjBo1iqFDh1K+fPlMbaVLlyY0NPS+FSciIvKwiTt9mjn9+9PazQ3vZs14c9069icnYw908vUlevFi2L0b3niDfD4+li5XRETk8WEyUWvwYKzWrYPVq0lq0oTxqan8cfw429u0geeeA/28KiL3KMdB2tmzZ6lXr16WbQ4ODly9PjddREQkrzASE9n48cc8X7o0RUqU4Knp0wmKjiYVqO/hwfcvvsiFsDDmHD5MiTZtQJsHiIiIWFbTptiuWcPyadMY4u3N/1JT4aefwNeXpS1bsmHuXEtXKCKPqBwHaYUKFeL48eNZth06dAhvLegoIiJ5xMklSxhXrx7lnJxoOHw4P544QRRQws6OkYGBHP7nHzaGh/P85Mm4Fypk6XJFRETkBpX79ePr06ex3bABWrYkKSWFwcuX06hXL5p6ebFy+nTucv89EXlM5ThIa9OmDR988AFnz541nzOZTERFRfHll1/Svn37+1qgiIhIrgoLg88+g2rVeLFtW0b/8w9HU1JwNpnoV6kSq376idC4OMYFBeFbp46lqxUREZHsaNAAgoOJXbaMlsWLYwusDQujRf/+NChUiCXff69ATUSyJce7doaFhVGrVi2ioqIICAhg4cKFtGzZkn379mFra8u2bdvw8PB4UPXeF9q1U0REMklIYPVHHzFtyhTGnTtHidRUAH63seE7Dw/6Pf00XUaNwsXd3bJ1ioiIyH1xZuFCPn3lFX48cYL46+f88udn5NixdBwyBCurHI85EZFHWE5yohwHaZAWpr377rssXryYsLAwPD09adeuHe+99x5eXl53XXhuUZAmIiIYBoSEwPTpMHs2AVeusAZ4H3inTh3o1w969oSH/JdDIiIicvcuBAczccgQvj12jNjr5yq7uTFy5Ei6vf461tbWFq1PRHLHAwvS4uPjee+99+jatSv+/v73XKilKEgTEXl8Rf77L3NHjuTXoCD+iIuj8PXzv+XPz8qSJXl+5Ej8u3a1aI0iIiKSuy6vWsWkwYP56tAhoq+fK58vHyPeeoun3nwTW1tbi9YnIg/WAx2R5ujoSHBwMI0bN76nIi1JQZqIyOMl+epVlo0bx/RffmHBhQskXD//mY0Nr/foAf37Q7NmoN86i4iIPNYi16/nqxdeYNK//xJx/dwnlSrx5q+/QvXqlixNRB6gnOREOZ74XaFCBUJDQ++6OBERkVxhGOydMYNhVatS3M2NtuPH89v1EK2ykxPju3Wj1759MHMmPPmkQjQRERHBvVEjRu3fz4l//uGTqlUpCwzcvx9q1IBOnTixcCFxcXGWLlNELCjHI9Lmz5/Pm2++SVBQEGXLln1QdT1QGpEmIpJ3Xdq5k9kjRzJ95Up2JCSYz3taWdG7Zk36jRhBjQ4dMJlMFqxSREREHgWp+/Zh9eGHMGcOhmHQCDhqb8/szz8n4MUXLV2eiNwnD3RqZ4cOHdi+fTuXLl2iatWqFClSJNMPIyaTiQULFtxd5blEQZqISB4TG8uWTz/lo8mTWXz5MsnXT9sC7UqUoN9zz9F62DDsHBwsWaWIiIg8qg4e5OLIkdSaN48LQChQtGlT6NCBLcWK4Vq5Mk9UqKBf1Ik8oh5okFaqVKnb/uVgMpk4fvx4Tm6Z6xSkiYg8+oyUFJJWr8Zu5kz44w8WxcTQ/nqbf7589O/YkV7jxuFZqpQlyxQREZE8JGn/fkKGDaP+8uWQkgJAA2ATUNzFhcD69WnZpw8t2rYlf/78Fq1VRLLvgQZpeYGCNBGRR9ixY/w5fDij//qLjklJfHD9dFKZMowrXpyeI0ZQqWVLi5YoIiIieVxoKPz1F6lLltB25UpWGwYJGZqtgFolSxLYrh0tn3qKOnXqYGNjY6lqReQOFKTdgYI0EZFHS1xYGEm//47rb7/B+vX8BvQEyllZcXDAAEwDBkD9+qDpFCIiIpLbYmK4tnQp66ZNY9n69QRfvcq/N3RxtbOjec2aBPbqRWD79pTSiHmRh8oDDdJOnTp1yzYrKyvc3NzIly9fTm6Z6xSkiYg8/IzkZDZ9/TXTv/mG344eZRgwEsBkIr5ZM34tXZru772HW5EiFq5URERE5DrDgAMHODNrFsvmzWPZoUMsNwwibui2eehQ6g4ZAmXKYBiG1lYTsbAHGqRZWVnd8Q+5r68vw4cPp1+/fjm5da5RkCYi8vA6uWoVM8aMYcbmzRxNTjafb+7kxIpRo+Dpp8Hb24IVioiIiGTT1aukLFvG9l9+Ydnq1QRHR/MvcIG0TZEoV47XnZ3ZlZLC8A8+oGW7dpatV+Qx9UCDtJ9++okPP/wQJycnevToQeHChTl//jy///47cXFxvPjiiyxfvpyVK1fy66+/8tRTT93TwzwICtJERB4uMadPM2/kSKb/9Rero6PN552BbuXL0++VV2gyaBBW1taWK1JERETkXhgG7NtHwsKF2C9bBhs3QnIyvsBRYIGdHR1atoQ2bfjXx4eQc+do2bIlRTT6XuSBe6BB2ujRo9m5cyd///13ppFphmHQvn17qlSpwkcffUTXrl05ffo0W7duvbuneIAUpImIWF5qYiJrJk5k+vffM+/kSWKvnzcBAQUK0K9XL7qMGYOLp6clyxQRERF5MKKiYMUKjs6dy7LgYJ6Ojib9p9ORYN5QqUrp0gR26kTL1q1p1KgRDg4OFipYJO96oEFa8eLF+e6772jbtu1NbQsXLuSFF17g7Nmz/Pnnn/Tt25eYmJicVZ8LFKSJiFjQvn2c/PJLGk+ZwqnUVPNpXzs7+jVrRp9x4yhRs6YFCxQRERHJZYYBe/bAkiWwdCnfb9jAj4bBDiDjD+wOtrY0qV+fwI4dCQwMpEKFClpfTeQ+yElOlOP9dy9fvkxcXFyWbfHx8Vy5cgWAAgUK8BhuCCoiIlmIPHaMA998Q71162D7dopfP+9mMtGzUiX6DR1KvX79MFlZWbROEREREYswmaBatbRj+HAGRUYyaPlyLv35JyuWLmVZVBTBwPmkJILXriV47VoAvAsWpGXbtrRs1YoWLVpQoEAByz6HyGMgxz+xVK9enQ8//NAcmKWLiIjggw8+oHr16gCcPn0aLy+vO97v6tWrvPnmm7Rs2ZKCBQtiMpkYM2ZMln137NhBixYtcHFxwd3dnS5dunD8+PGcPoKIiOSGpCRYsICdzZrh5eNDh88/J3H7drCxwapTJxZPmsT5K1f4fu9e6g8YoBBNREREJJ27O3TvTsHZs3kqIoKp27dzdtw49larxgSTiScBe+DMpUv8PG0avXr14ptnnoGzZwFITEwkKSnJkk8gkmfl+KeW8ePHc/DgQUqWLEmnTp0YNGgQnTp1olSpUhw+fJgJEyYAsHPnTtq3b3/H+4WHh/PDDz+QkJBAp06dbtnv4MGDNG3alMTERH777Td+/vlnDh8+TKNGjbh06VJOH0NERB6QfX/8wdJOnaBYMejUiSqrV+MOFHZw4PSoUXDuHMyfT+VXX8XRzc3C1YqIiIg85KyswM8P08iRVN61izcuX2bZ7Nlc6d2bIDc3hgKVgMC//07b2bxaNf7u2RPP/Pl5/dVXLV29SJ6T4zXSAPbs2cP777/PunXrCA8Pp0CBAjRp0oR33nmHqlWr5uhe6S9vMpm4fPkyBQsW5N13371pVFqPHj1YvXo1x44dM89XPXnyJL6+vrz++ut88skn2X5NrZEmInJ/Xdq/n9kjRzI9OJgdcXEUA04C1oULw9NPc75NG7wCArSGh4iIiMj9lJoKO3aY11ZjyxYwDF4BvgJes7Pj8w4doE0b4ps25c3PP6dFixYEBASQL18+S1cv8tB4oJsNPEi3CtKSk5NxdXWlb9++fPfdd5muCQwMJDQ0lMOHD2f7dRSkiYjcuzNbthA8eTJ/BwezJCyM5OvnbYB2RYvy4/jxePboATY5Xo5TRERERO7G5cuwbBmpixezY/Fi3KKi8L3etBJocf1jG2tr6tevT2CrVgQGBlKjRg2stMyGPMYe6GYDGR06dIjLly9TvXp1nJ2d7+VWt3Xs2DHi4uKyHO1WtWpVli9fTnx8vLYBFhF5gOIjI1n/7bcEz5tH0L597E9IyNTu7+xMvzZteOqDD/D09b3FXURERETkgfH0hN69serdm5opKbBtW9pItSVLKBwSwmAgGDiWksK69etZt34977zzDp4eHjwZGEjLli1p2bIlRYsWtfSTiDy07ipImzFjBiNGjOD8+fMAhISE4OfnR48ePXjyySd57rnn7muR4eHhAHh4eNzU5uHhgWEYXLlyhSJFimR5fUJCAgkZfuCLjo6+r/WJiORJhgFHjpC4aBGdP/6Y1ZcukXHPZhNQ28WFQH9/erzxBpWysS6miIiIiOQSa2uoUyftGDOGyhcv8k1wMCxdyrHFi1kWHU0wsAq4HBHB7NmzmT17NgCVK1cmMDCQNm3a0KxZM4s+hsjDJsdjN3///Xf69++Pn58fX3/9NRlnhvr5+fHbb7/d1wIzut3aOrdr++ijj3BzczMfxYsXfxDliYg88q6FhbHgnXf4omFDKFsWypfH7o03OHM9RCtiZcUAX1/mvPYal44e5Z+rVxm7Zo1CNBEREZGHXaFC0KcPzJpF2YgIXty0ib9GjiTcz491wEigFmm/LN23bx8TJ05k3EsvQYbN/Y4ePcpDtDqUiEXkeETaRx99xIABA5gyZQopKSkMGTLE3FahQgW++uqr+1ogQIECBYD/RqZlFBERgclkwt3d/ZbXDx8+nKFDh5o/j46OVpgmIgKkpqQQvXkz7ps2QVAQJ9evp1NyMvbAc4CTrS00asSXvr54PPkklTt3xqT1M0REREQebdbWUK8e1KuH7bhxNLpwgUbBwYxbsoTwpUtZcfUqy4A6Bw5A4cJQqxaXGjfGd8IEihUrxqFDhx7o8k4iD7McB2kHDhy45Q6ZHh4eWYZd96ps2bI4Ojqyd+/em9r27t2Lj4/PbddHs7e3x97e/r7XJSLyKLp46BDLv/6aoKVLWRYaStPUVOZeb3sCCHBwoJKvL7FvvolTp07g4kITC9YrIiIiIg+Ylxf06wf9+lEgOZme//xDz/SdQHftgq1b2bt1Kw6A54ULOL/wArRuDYGBvDJ2LK6urrRs2ZJ69epha2tr6acReaByHKQ5OTkRFRWVZdvZs2fJnz//PRd1IxsbG9q3b8+ff/7Jp59+at6m99SpU6xevZrXX3/9vr+miEhekRQfzz/TpxM0cybBO3awPTY2U/s/JhNG69aYWrfG1KoVq3x8LFSpiIiIiFicjQ00bJh2fPghnDsHQUE0W7qUK8HBnL16FX79FX79lViTie+BRMPggw8+IF++fAQEBBAYGEhgYCBly5a19NOI3HcmI4cTnDt06EB0dDSrV68mNTUVW1tbtm3bhp+fH61atSJ//vzmBQqza+nSpcTGxnL16lWeeeYZunfvTo8ePQBo06YNTk5OHDx4kFq1auHn58fbb79NfHw8o0ePJiIigl27dlGwYMFsv15OtjUVEXkUnQwJIXjyZIJWrGDl2bNE3/BXfXUHB1pVrUpgjx7Uf+457PR3oYiIiIjcSVISbN4M10erXduzh99J2wl0OXD5hu5lypQh8PpuoM2aNdPP3/LQyklOlOMgbdu2bTRs2JAqVarQu3dvhg0bxvDhw9m9ezcrV65k69atVK5cOUcFlypVipMnT2bZFhoaSqlSpQDYvn07b731Fps3b8bGxoZmzZoxYcKEHKfcCtJEJM9JSoJNm9g5bRq9Z8/mYIadigE8TSZaensT2KIFLYcMwcvf30KFioiIiEieceZM2vTPpUtJXb6cnTExLCMtWNsIJGfoam1tTb169QgMDOTll1/Gzc3NMjWLZOGBBmkAq1evZvDgwRw6dMh8ztfXl++//56mTZvmuODcpiBNRPKC0PXrmf/llxQ7cYKehw7B1atcBgqRtiVzPRcXAv39afX00/j16YOV1ooUERERkQclMRE2bkwL1pYs4er+/awBc7B25Ho3B1tbIk6exLFIEQBCQkIoWrQoxYoVs0zdIuRCkJbu2LFjhIWF4enpSbly5e72NrlOQZqIPIqunDuH9T//4Lp+PQQFMfngQYYAjYG1AAULQmAgK7y9qfnMM7j7+lq2YBERERF5fJ06ZQ7VWLmS0NhYlgFhwGhra6hfH9q0ocqUKew7epSFCxfSrl07S1ctj6lcC9IeVQrSRORRkJKczPY//yRo6lSC//mHfyIj+QZ44Xr7cSsrBru60q5hQ14aMwZq1AArKwtWLCIiIiKShYQEWL/+v2Dt4EEA4oEAYBtw4emnKdC5M7RowcQffyQoKMi8aUHlypUxmUyWfALJ4x5YkHbp0iW+//571q1bx7lz5wAoWrQoAQEBPP/88xQoUODeKs8lCtJE5GF1/vBhln39NUFLlrAsNJSI1NRM7YOdnfnmqaegVSto3hzc3S1TqIiIiIjI3QoNNa+txsqVRMfFYf7J3MaGJs7OrIuKMncvUqQILVu2JDAwkBYtWuRos0GR7HggQdrKlSvp2rUr0dHRWFtb4+npiWEYhIeHk5KSQv78+Zk/fz6NGze+Lw/xIClIE5GHRWJCAhtnzCDo118J3r6d3bGxmdpdgRYFC9KqSRMCBw2iRPPmoN/GiYiIiEheER8P69aZdwLl8GEOkLau2jJgjclEXIbYwmQy4efnR/PmzalVqxa1atWiRIkSGrEm9+S+B2mXLl2iQoUKODs7M3HiRNq0aYOTkxMA165dY9GiRQwbNoz4+HgOHDjw0I9MU5AmIhYVHg7Ll/PNpEm8tXUrsRn/YQD429vTqmpVArt1o87zz2OrUWciIiIi8rg4duy/KaCrVxMfH88G/tu0YE8Wl3h6evLNN9/Qo0cPAFJTU7HSkieSAznJibL1nTVlyhRSUlLYuHEj3bp1M4doAE5OTvTo0YMNGzaQlJTElClT7q16EZG8JiWF5V9+yUu1a7OzSpW0TQGeeooiW7YQaxgUNpno6+3NzP79CduyhZD4eMZt3UrDN99UiCYiIiIij5eyZeGll9KCtIgIHJYsocXLL/Np2bLsBs4B04FBgJ/JhI3JxOXLlym4ezccOgSpqfz2228UK1aMt956y7LPInlStkakNWvWjBo1ajBx4sTb9hs6dCi7du1i1apV963AB0Ej0kTkQTIMg/2rV1PpxAlMwcGwfDk9rlzhd+BdYAxAlSrENGvG0YoVqdqnD1aOjhatWURERETkoXfkyH9TQDduhJgY4oG9QCXACSBfPt5wc+OzM2cYEhjI15MnQ+nSXIuLo2rVqtSoUYOaNWtSq1Yt/Pz8cNcvroUHMLWzSJEiTJ48mc6dO9+23/z58xk8eDDnz5/PWcW5TEGaiNxv4efPs3zyZIL/+ovggwc5n5zMAeCJ6+3znJxYWaQIvbp2pfErr0CxYpYsV0RERETk0ZaaCocPw7ZtEBKS9t+dOyEujlhgF5AfqAiQPz+bfHxoEBJy0218fX3NwVrNmjWpUaMGLi4uufooYnn3PUhzdHRkxYoVNGjQ4Lb9Nm7cSIsWLYiLi8tZxblMQZqI3Kvk5GS2/vUXwVOnErRpEyGRkWT8y9QJmOnrS6f0HTZr1QIbG0uVKyIiIiKS9yUnw4ED/wVr27bB7t2QmEgssBXYBoQA26ysCE1NvekWVlZWVKhQgZo1a1KzZk0GDhyIo2aP5Hn3PUizsrLin3/+oXbt2rftt2XLFurXr09KSkrOKs5lCtJE5G6cOXKE4G++IWjxYlYcP07kDf/jrWJtTWD58rTq1ImGgwdjr1FnIiIiIiKWlZgI+/ZlDtf27oWUFMJJC9YyhmtnM/wb39HRkejoaGyu/0J86tSpJCUl0aZNG7y9vS3xNPKA5CQnyvbwiEOHDpm/eW7l4MGD2b2diMjDzzDSfqMVHEyHjz5i4aVLmZrzA08WLEhgo0YEPv88xZ58ErQ7kIiIiIjIw8PODvz80o5Bg9LOxcXBnj0UCAkhcNs2ArdtS/t3f2oq5/kvXIuNi8PG1zdtdknNmkz89lv2nzjB33//bQ7S9uzZw44dO6hZsyYVKlTA2traUk8quSTbI9JMJtMdb2YYBiaTSSPSROSRlXDxIt8PH87qlSuZm5KC3ZkzALwMTAZq29kRWLkyrbp3p9bzz2Pt4WHRekVERERE5D6IiYFduzKPXDt82NxsAKOALcAvZcrgVbcu1KzJu3v28N60aQA4OTnh5+eXac01Hx8frPTL9ofefZ/aOX369BwV0K9fvxz1z20K0kQkXXRkJEcXL8bv5EkICiJ140a8UlO5BKwBmtjbQ9OmnKtXD/uWLSlQty5k4xcLIiIiIiLyiIuKgh07ModroaGZukwBZgDbrayIzWLNNTc3N/z9/c1rrtWqVYuSJUtma7CS5J77HqTlNQrSRB5fqamp7Fq9mqAffiB4zRo2XbxIQeAskP6/sk88PbF+4gl6Pf883l27gpOTBSsWEREREZGHRnj4f6Fa+nHmDCnAITKvubbLZCI+i8ilV69ezJ49G0ib2XfhwgWKFCmSiw8hN3oga6SJiDyqLp47x7Lvvyd4/nyWHTjAxeTkTO35TCbCWrbEq1MnCAzkrdKlLVOoiIiIiIg83AoUgMDAtCPdhQtYb9tGxetH35AQuHiRJMNgPxnCNZOJPUCFY8fg55+hVi3OubriXaoU3t7eHD9+HFtbWwDi4+NxcHCwwAPKnWhEmkakieQ5SUlJbF6wgOCpUwnatIkdkZGZ2l2A5q6uBNatS2C/fpTp1i1tEVIREREREZF7ZRhw5szNI9ciIogHEoH0JGKFnR2BiYlUKlCAPZ9/nraxQbly1K1fnwsXLmRab83f3x93d3fLPVcepqmdd6AgTSQPSkiADRuI/OsvSk6eTPQN6xPUsLYmsFw5WnXoQL0XXsCuVCnL1CkiIiIiIo8fw0hbXy1jsLZ9O0RHEwtcAMpe75rs4oLrtWvEZbHmmq+vb6b11mrUqIGLi0tuPkmepCDtDhSkieQBYWEc+vtvPvr2WxzCw/nu8mW4dg2AykAY0LJAAVo1bsyTzz6LV2AgaCtqERERERF5WKSmwpEjmcO1HTvg2jWigR38t97aNisrjmcRrJlMJipUqECtWrUYOnQoVatWzeWHyBsUpN2BgjSRR0dMTAy7Vqxg+5IlbN+yhZbW1jx98SKcPcsBoCLgAEQAjl5e0KoV52rXxqtbN6wKFrRs8SIiIiIiIjmRnAwHD2YO13btgoQEwoHtZFhzzcqKMxnCtc0LF1K3XTsAFi5cyMKFC+nYsSNt27a1wIM8WrTZgIg8kmKuXmVncDDbly5le0gI20NDORgTQ8a0PxV4GsBkoly5coy1t6dGzZpYvfAC1KwJJhNFLVO+iIiIiIjIvbGxgcqV047+/dPOJSbC/v0U2LaNltcP9uyB5GQu8N+otWrt20PRolCzJksvXODHrVtxt7MzB2mRkZGMHDnSPDW0QoUKWGvWTo5pRJpGpIlYhmGwe+lSVv3+e1poduIEh2JjyeovpKKAf758+JcpQ9NGjWjSsydUrw5aC0BERERERB5H8fFpYVr6qLWQEPj337TposAaYBkQCDQpWRJq1mSFuztPTplivoWTkxN+fn6Z1lzz8fHBysrKEk9kUZraeQcK0kRyl5Gayro5c9geFMRzBQqQb98+2LGD1yMimHRD32KAv6sr/mXL4l+7Nv7t2uEVEADOzhaoXERERERE5BERG5s2DTRjuHbokLn5X+Bn0kawbTeZiMkiDnJ1dcXf3z/TbqGlSpXCZDLl1lNYhIK0O8hzQdrq1axcvpwTVlb41quHr58fXl5eef4bXR5OUVeusGPhQi5v3053O7u0nWh27KBEVBSnSfvNSJPrfRdYWzPVxQV/Hx/869bFv21bCgcEgIOD5R5AREREREQkr4iOTtvAID1Y27YNjh8nBThMhs0MgJ0mE/E3RERubm5cuXLFnC+EhIRQtGhRihYtmqcyBwVpd5DngrSePenz22/8muGUi5UVPm5u+BYpgq+vL75Vq6aFbP7+FCxYME99w4vlRIaHs+Pvv9m+bBnbd+xg+6lTHI2PB6AAcAlI/04bbGXFBVdX3mralDpt2oCfX9q8f3t7S5UvIiIiIiLy+ImISBvwkDFcO32aZGA/GTYzAArly8eSHj3S1qOuWZMy3bsTeuIEq1atIiAgwKKPcT8pSLuDPBekjRvHZ9OmEXzmDEcSEzlJ2oLst+JqbY2vmxu9/P0Z1q8f+Phg+PgQbhgUKFBAIZtk6cqlS+xYsIDty5ezfedOtp8+zbHrodmNSgL+Hh5M79oVl7p100KzSpXA1jZ3ixYREREREZE7CwvLHK6FhEBYGAb/DY6IB+qYTPwLXOrfH/c33kj7OS8PUJB2B3kuSMsoJoaEAwcI3byZI9u3c+TgQY6cPs2R8HCOJCZyGsyLub8OfHb944tAYcDD2poL3btjW64c+PqyJSEBU4kS+Pr7k9/DwxJPJBYQERZGyr//UjA0FHbsYM2qVQQcOJBl31ImE/4eHviXK4d/gwb4tW+PZ/36abvNiIiIiIiIyKPHMODcucyj1rZtg/Bw4gBHgA0boEEDCxd6fyhIu4M8HaTdTmws8f/+y/GNGzmyYwclY2KoHhEBR4+y9exZ6gAlgJMZLmkCrLv+cQEbm7TpokWL/jddtEEDfGvWxM3dPbefRu6T8HPncDtzBpvdu2HHDt7+6y8+uXCBt4GPrve5DBQESptM+BcogH/58mmhWceOFKhTB7RlsoiIiIiISN5mGHDy5H/h2ujReWZTOAVpd/DYBmm3ExvLtf37Cdu+ndJXr8KRI3D0KN3/+YeN8fGcv8PlBW1s8HV3x7doUZ5q2ZLAzp3B1xc8PUFTRR8al8+cYfuff7J95Up27NnD9nPnOJGYSAhQ83qf74EXgL62tkxv2DBtWqa/P1fKliV/zZrwGG6FLCIiIiIiInmXgrQ7UJCWQ9euEbtvH0c3bODIzp0cOXQobbpoRARHEhMJu6H7F8Ar1z/e7uJC2/h46hcuzJ8DBoCPD/j6ctzGhsIVK+Ls4pLLD/P4uHTyZFpotmoV2/fsYfv585xKSsqy7y9OTjxdvz74+RFdoQIpVaqQv0YNhWYiIiIiIiKS5ylIuwMFafdRXBzRu3dzdONGjuzcydFDh2hnMlHtwgU4fZrZQG+gIbA+w2WlSJtCWtTWNm0kW7Fi+Pr64lO9Or716+NTuzaOTk4WeKBHVEwM7NqFsX07T02axKazZzl9i9DMx8oK/0KF8K9QAf/GjfHr0gX3KlU0clBEREREREQeSwrS7kBBWi6JiyN23z4OrFtHysmT1ElMhCNHSDpyhGKnT3PpDpd729rimz9/2pps5crRsVMnfAMCoHDhxzv0iY6GnTs5uHQpb8+cSXJkJItiY9PmqwP+wI7rXX2trdNCs4oV8W/SBL8uXXCrWPHxfv9EREREREREMlCQdgcK0h4C8fFE7NjBkesj2Y4cPszRM2c4cuUKRxITiczikvlAJwAXF4ILFWJCTAyBlSsz7Omn06aM+viQ7OmJja1tbj7JA3Xh0CG2z5vH9rVr2b5/P4FJSQy+eBGAE0BpwBa4CtgXLQr+/ix3c8O2QgVqdOmCW/nyCs1EREREREREbiMnOZFNLtUkkpmDAx7161Onfn3q3NBkxMURvnMnRzZs4MiuXWlrsp09S2Ubm7Ttd2Ni2BkTwwrAa9UqWLUKgGQgH1DEzi5tJFuxYviWK4dvjRr4NmhAqVq1sLWzy+UHzb7z+/enrWm2bh3b9+9n+8WLnEtJydTHGhgMULw4Jf38+DI5mWqNG2Pduzd4ewPwZK5XLiIiIiIiIvJ4eGRGpK1Zs4aAgIAs2zZv3kzdunWzfS+NSHuEJSRAaCiH16xh09q1lIiNpVl8PBw5wtGTJ/G9zbezNVDazg6f/Pnx9fZOC9mqV6dBp07k8/XN3ZFbly7xz6+/snThQrYfOMD2S5e4cENoBmACnrC1xb9IEfyrVKFBy5bUeuopKFgw92oVERERERERycPy5NTO9CDtww8/vClQq1y5Mi452P1RQVreZMTHc2HrVo5s2pQ2ku3wYY6cPcuRK1c4mpRE3C2u2wlUd3ICHx8WODmxJjmZ1g0b0rJjR/D1hSJF7m33ygsXWPTtt4SsXcvLjo547tsHZ87wDvBhhm5WQAU7O/yLFMGvalX8mzWjepcuuJQocfevLSIiIiIiIiK3laendvr6+uZo9Jk8PkwODhRp3JgijRvT+Ia21Ph4zm/dmrYm265dHDlyxByylU1OhmvXYM8egoDvAKdt22g5aRIAp+3tCQR88+fHx9sb3/Ll06aLNmyIt78/VjZpf4yM1FTObt/O9r/+4uKePTwHsH07nD/PMOAQUBdoDWAy0bxYMc7a2uJfrRr+zZpRrUsXnIsVy423SkRERERERETuwiMXpIncDSsHB4o1bkyxxo1pemNjYiKcOAFHj9L2r79wCgmhmY0NXLkCJ05wOCGBA8CBCxfgwgXYtg1mzgTAAShrb08hR0f2R0Vx8foAT2fgGdKmk2JlRVcPD865uuLZsSN06gTVq9PM1ZVmufL0IiIiIiIiInI/PHJTOwsVKkR4eDhOTk7Uq1ePUaNG0bBhwxzdS1M7JduSkojcs4eQoKC0kWxHj6aNZIuM5HhSEsk3dLcGKjk44O/tzaTnnsO1QQOoXh2cnS1QvIiIiIiIiIjcSZ5cI23nzp1Mnz6dpk2bUqBAAY4ePcr48eM5fPgwixcvJjAw8JbXJiQkkJCQYP48Ojqa4sWLK0iTe5IcF8fJTZs4smkTYSdP8kS9elTt1AnHAgUsXZqIiIiIiIiIZFOeDNKyEhkZSZUqVfDw8GD37t237DdmzBjGjh1703kFaSIiIiIiIiIij7ecBGn3sBWh5bm7u9OuXTv27NlDXNyt9mSE4cOHExUVZT5Onz6di1WKiIiIiIiIiEhe8MhvNpA+oM5kMt2yj729Pfb29rlVkoiIiIiIiIiI5EGP9Ii0K1eusGjRIqpXr46Dg4OlyxERERERERERkTzskRmR1rt3b0qUKEHNmjXx9PTkyJEjTJw4kbCwMKZNm2bp8kREREREREREJI97ZIK0qlWrMnfuXL777jtiYmLw8PCgYcOG/PLLL9SqVcvS5YmIiIiIiIiISB73SO/aebdyshuDiIiIiIiIiIjkXY/Nrp0iIiIiIiIiIiK5RUGaiIiIiIiIiIhINihIExERERERERERyQYFaSIiIiIiIiIiItmgIE1ERERERERERCQbFKSJiIiIiIiIiIhkg4I0ERERERERERGRbFCQJiIiIiIiIiIikg0K0kRERERERERERLJBQZqIiIiIiIiIiEg2KEgTERERERERERHJBgVpIiIiIiIiIiIi2aAgTUREREREREREJBsUpImIiIiIiIiIiGSDgjQREREREREREZFsUJAmIiIiIiIiIiKSDQrSREREREREREREskFBmoiIiIiIiIiISDYoSBMREREREREREckGBWkiIiIiIiIiIiLZoCBNREREREREREQkGxSkiYiIiIiIiIiIZIOCNBERERERERERkWxQkCYiIiIiIiIiIpINCtJERERERERERESyQUGaiIiIiIiIiIhINihIExERERERERERyQYFaSIiIiIiIiIiItmgIE1ERERERERERCQbFKSJiIiIiIiIiIhkg4I0ERERERERERGRbFCQJiIiIiIiIiIikg0K0kRERERERERERLLhkQrSYmJieO211yhatCgODg5Ur16dOXPmWLosERERERERERF5DNhYuoCc6NKlCyEhIXz88ceUK1eOWbNm8dRTT5Gamkrv3r0tXZ6IiIiIiIiIiORhJsMwDEsXkR1Lliyhbdu25vAsXcuWLfn/9u40KqojbwP409LdoGwKKojKokRF0SNGxiAq4jIKuAVBgagg4+hBjWTQxBA9EhwVwY1kXNAZQKMRcUEniUeYGHGJC0KcuBF1khFxAReQzUEiWO8H3+5J262iY3NteH7n9Id7b/U9/6pbXX27+lbVxYsXUVhYCCMjo3qdq6KiApaWligvL4eFhYW+QiYiIiIiIiIiojfcy/QTGczQzr1798LMzAyBgYEa+6dOnYpbt24hJydHosiIiIiIiIiIiKgpMJiOtAsXLsDFxQVyueZo1F69eqmPExERERERERER6YvBzJFWUlKCTp06ae23srJSH3+Wmpoa1NTUqLfLy8sBPHl0j4iIiIiIiIiImi5V/1B9Zj8zmI40AJDJZK90LC4uDrGxsVr7O3bs+FriIiIiIiIiIiIiw1ZZWQlLS8vnpjGYjjRra2udT52VlpYC+O+TabpER0cjKipKvf348WOUlpbC2tr6uR1whqKiogIdO3bE9evXuXiCBFj+0uM1kBbLX1osf2mx/KXF8pcWy19aLH9psfylxfKXVmMsfyEEKisrYWdn98K0BtOR1rNnT6SlpaG2tlZjnrTz588DAFxdXZ/5XmNjYxgbG2vsa9mypV7ilJKFhUWjqcSGiOUvPV4DabH8pcXylxbLX1osf2mx/KXF8pcWy19aLH9pNbbyf9GTaCoGs9jAu+++i6qqKuzZs0dj/5YtW2BnZ4d+/fpJFBkRERERERERETUFBvNEmo+PD4YPH46IiAhUVFTA2dkZaWlpyMzMxLZt22BkZCR1iERERERERERE1IgZTEcaAGRkZGDBggVYtGgRSktL0a1bN6SlpSEoKEjq0CRlbGyMmJgYreGr1DBY/tLjNZAWy19aLH9psfylxfKXFstfWix/abH8pcXyl1ZTL3+ZqM/ankRERERERERERE2cwcyRRkREREREREREJCV2pBEREREREREREdUDO9KIiIiIiIiIiIjqgR1pb7DNmzdDJpMhLy9P6lCaFFW563rNmzev3ucJCwuDmZmZHiNtfH5b9ocPH9Y6LoSAs7MzZDIZBg8e3ODxNTWff/45ZDIZXF1dpQ6l0WPdf7Pw+/fN8b9cC5lMhk8//fT1B9XIse2XRk5ODt59913Y29vD2NgYNjY28PDwwNy5c6UOrUk6deoUAgMD0a5dOyiVStja2iIgIAAnT5586XPl5+fj008/RUFBwesPtBFQtfMmJia4du2a1vHBgwezPdKzp3//mpiYwNbWFt7e3oiLi8OdO3ekDvGNw440omdITU3FyZMnNV5z5syROqwmwdzcHMnJyVr7jxw5gl9++QXm5uYSRNX0pKSkAAAuXryInJwciaNpGlj3iUhqbPsb3v79+9G/f39UVFQgISEB//jHP/DZZ5/B09MT6enpUofX5PzlL3+Bp6cnbty4gYSEBBw8eBArV67EzZs3MWDAAKxdu/alzpefn4/Y2Fh2pL1ATU0NFi5cKHUYTZrq9++3336LdevWoXfv3oiPj4eLiwsOHjwodXhvFHakET2Dq6sr3nnnHY2Xvb291GE1CRMnTsSePXtQUVGhsT85ORkeHh6v9TpUV1e/tnM1Jnl5eTh79iz8/PwAQGfnzv/iP//5z2s9X2PRkHWfiOhp+m77SbeEhAQ4OTkhKysLQUFB8PLyQlBQEFauXInCwkKpw2tSjh8/jg8++AC+vr44duwYJk+ejEGDBmHSpEk4duwYfH19ERkZiePHj0sdaqMzcuRIbN++HWfPnpU6lCZL9ft34MCBGD9+PNasWYNz587B1NQU/v7+uH37ttQhvjHYkWZA8vLyEBQUBEdHRzRv3hyOjo4IDg7WegRW9WhmdnY2IiIi0Lp1a1hbW8Pf3x+3bt2SKPrGJT09HR4eHjA1NYWZmRlGjBiBf/7znzrTXrx4EUOHDoWpqSnatGmD2bNnsxPhBYKDgwEAaWlp6n3l5eXYs2cPwsPDtdLHxsaiX79+sLKygoWFBfr06YPk5GQIITTSOTo6YtSoUcjIyICbmxtMTEwQGxur38wYKNWPp+XLl6N///7YsWOHRr0tKCiATCZDQkICli5dCnt7e5iYmKBv37747rvvNM716aefQiaT4cyZMwgICECrVq3QuXPnBs2PodBH3f/DH/4AKysrne3OkCFD0KNHDz3kpHEZPHiwziG1YWFhcHR0VG+rPhcrV67E6tWr4eTkBDMzM3h4eODUqVMNF3AjVt9rQa/mRW3/4cOHdQ5BV9X9zZs3a+z/61//ii5dusDY2Bjdu3fH9u3bea10KCkpQevWrSGXy7WONWum+XOtPvegqulFeA/68uLi4iCTybBhwwat6yGXy7F+/XrIZDIsX75cvf/SpUsIDg6GjY0NjI2NYW9vjylTpqCmpgabN29GYGAgAMDb21s9dO7pzwoBH330EaytrTF//vznpnv48CGio6Ph5OQEpVKJ9u3bY9asWSgrK1OnGTduHBwcHPD48WOt9/fr1w99+vR53eE3Wvb29li1ahUqKyuxceNG9f68vDyMGTMGVlZWMDExgZubG3bu3Kn1/ps3b2L69Ono2LEjlEol7OzsEBAQYPCdcuxIMyAFBQXo2rUrEhMTkZWVhfj4eBQVFcHd3R337t3TSj9t2jQoFAps374dCQkJOHz4MCZNmiRB5Iaprq4OtbW1Gi8AWLZsGYKDg9G9e3fs3LkTW7duRWVlJQYOHIj8/HyNczx69Ai+vr4YOnQo9u3bh9mzZ2Pjxo2YOHGiFFkyGBYWFggICFAPLwGedCw0a9ZMZ9kVFBRgxowZ2LlzJzIyMuDv74/3338ff/7zn7XSnjlzBh9++CHmzJmDzMxMjB8/Xq95MUTV1dVIS0uDu7s7XF1dER4ejsrKSuzatUsr7dq1a5GZmYnExERs27YNzZo1g4+Pj845RPz9/eHs7Ixdu3YhKSmpIbJicPRR9yMjI3H//n1s375d4735+fnIzs7GrFmz9JehJmrdunX49ttvkZiYiC+//BIPHjyAr68vysvLpQ6N6Jlepu2vj02bNmH69Ono1asXMjIysHDhQsTGxuqcB7Kp8/DwQE5ODubMmYOcnBw8evRIZzreg+pXXV0dsrOz0bdvX3To0EFnmo4dO+Ltt9/GoUOHUFdXh7Nnz8Ld3R2nTp3C4sWLceDAAcTFxaGmpga//vor/Pz8sGzZMgBPvhtU08Wonvqk/zI3N8fChQuRlZWFQ4cO6UwjhMC4ceOwcuVKTJ48Gfv370dUVBS2bNmCIUOGoKamBgAQHh6OwsJCrfNcunQJp0+fxtSpU/Wen8bE19cXRkZGOHr0KAAgOzsbnp6eKCsrQ1JSEv7+97+jd+/emDhxokYn8c2bN+Hu7o69e/ciKioKBw4cQGJiIiwtLXH//n2JcvOaCHpjpaamCgAiNzdX5/Ha2lpRVVUlTE1NxWeffab1vpkzZ2qkT0hIEABEUVGRXuM2dKry0/UqLCwUcrlcvP/++xrvqaysFLa2tmLChAnqfaGhoQKAxrURQoilS5cKAOL7779vkPwYkt/W+ezsbAFAXLhwQQghhLu7uwgLCxNCCNGjRw/h5eWl8xx1dXXi0aNHYvHixcLa2lo8fvxYfczBwUEYGRmJy5cv6z0vhuyLL74QAERSUpIQ4kn9NjMzEwMHDlSnuXr1qgAg7OzsRHV1tXp/RUWFsLKyEsOGDVPvi4mJEQDEokWLGi4TBkbfdd/Ly0v07t1bI31ERISwsLAQlZWV+smUAXv6+9fLy0tnuYeGhgoHBwf1tupz0bNnT1FbW6vef/r0aQFApKWl6Tv0RudVr4UQQgAQMTEx+g+ykahP269qn7KzszXeq6r7qampQogn7ZGtra3o16+fRrpr164JhUKhda2aunv37okBAwao7zcVCoXo37+/iIuLU7fRvAfVv+LiYgFABAUFPTfdxIkTBQBx+/ZtMWTIENGyZUtx586dZ6bftWuXzs8NPfHbdr6mpkZ06tRJ9O3bV30f4+XlJXr06CGEECIzM1MAEAkJCRrnSE9PFwDEpk2bhBBCPHr0SNjY2IiQkBCNdB999JFQKpXi3r17DZAzw/GifgchhLCxsREuLi5CCCG6desm3NzcxKNHjzTSjBo1SrRr107U1dUJIYQIDw8XCoVC5Ofn6y94ifCJNANSVVWF+fPnw9nZGXK5HHK5HGZmZnjw4AF++uknrfRjxozR2O7VqxcA6FwNhbR98cUXyM3N1XhlZWWhtrYWU6ZM0XhSzcTEBF5eXjr/ZX3vvfc0tkNCQgA86cmnZ/Py8kLnzp2RkpKC8+fPIzc3V+fQNgA4dOgQhg0bBktLSxgZGUGhUGDRokUoKSnRWmWmV69e6NKlS0NkwWAlJyejefPmCAoKAgCYmZkhMDAQx44dw7/+9S+NtP7+/jAxMVFvm5ubY/To0Th69Cjq6uo00vLpv/rRR92PjIzEjz/+qJ7TpaKiAlu3bkVoaChXF9YDPz8/GBkZqbf5/UuG4GXa/he5fPkyiouLMWHCBI399vb28PT0fG0xNxbW1tY4duwYcnNzsXz5cowdOxZXrlxBdHQ0evbsiXv37vEe9A0i/n/6hOrqahw5cgQTJkxAmzZtJI6qcVAqlViyZAny8vJ0DhNUPWEWFhamsT8wMBCmpqbq6UXkcjkmTZqEjIwM9dPgdXV12Lp1K8aOHQtra2v9ZqQRUtX7n3/+GZcuXVK3L79tj3x9fVFUVITLly8DAA4cOABvb2+4uLhIFre+sCPNgISEhGDt2rWYNm0asrKycPr0aeTm5qJNmzY6J0x/uoEwNjYGwMnV68vFxQV9+/bVeKnGcru7u0OhUGi80tPTtYbYyuVyretga2sL4Ml8GPRsMpkMU6dOxbZt25CUlIQuXbpg4MCBWulOnz6N3//+9wCezMVy/Phx5ObmYsGCBQC063u7du30H7wB+/nnn3H06FH4+flBCIGysjKUlZUhICAAADSGHAL/rc9P7/v1119RVVWlsZ9lXz/6qPtjx46Fo6Mj1q1bB+DJXJoPHjzgsE494fcvGZqXbftfRHWPY2Njo3VM1z56om/fvpg/fz527dqFW7du4U9/+hMKCgqQkJDAe9AG0Lp1a7Ro0QJXr159brqCggK0aNECcrkcdXV1zxwGSq8mKCgIffr0wYIFC7SGOZeUlEAul2t1XMpkMtja2mrU7fDwcDx8+BA7duwAAGRlZaGoqIjDOl/BgwcPUFJSAjs7O3VbNG/ePK22aObMmQCgbo/u3r3baD8f2jNa0hupvLwc33zzDWJiYvDxxx+r99fU1KC0tFTCyJqW1q1bAwB2794NBweHF6avra1FSUmJxo1McXExAO0fWqQtLCwMixYtQlJSEpYuXaozzY4dO6BQKPDNN99oPBm1b98+nellMpk+Qm00UlJSIITA7t27sXv3bq3jW7ZswZIlS9Tbqvr8W8XFxVAqlVpPOrHs6+911/1mzZph1qxZ+OSTT7Bq1SqsX78eQ4cORdeuXfWVhUbFxMRE5/xmuuYnJf3itdCP+rb9qrZGNQ+RytPlr7rH0TWZtK7vDdKmUCgQExODNWvW4MKFCxg7diwA3oPqk5GREby9vZGZmYkbN27o7AC4ceMGfvjhB/j4+MDKygpGRka4ceOGBNE2XjKZDPHx8Rg+fDg2bdqkccza2hq1tbW4e/euRmeaEALFxcVwd3dX7+vevTt+97vfITU1FTNmzEBqairs7OzUf0JS/e3fvx91dXUYPHiw+vdwdHQ0/P39daZX3V+2adOm0X4++ESagZDJZBBCqP/VVvnb3/6mNXyK9GfEiBGQy+X45ZdftJ5WU72e9uWXX2psqyb81rXqGGlq3749PvzwQ4wePRqhoaE608hkMsjlco1hVNXV1di6dWtDhdlo1NXVYcuWLejcuTOys7O1XnPnzkVRUREOHDigfk9GRgYePnyo3q6srMTXX3+NgQMHalwTejn6qPvTpk2DUqnEe++9h8uXL2P27Nl6ib0xcnR0xJUrVzQ6D0pKSnDixAkJo2qaeC1ev5dp+1WrbZ47d07jHF999ZXGdteuXWFra6s1NKuwsJDXSoeioiKd+1VTt9jZ2fEetIFER0dDCIGZM2dq/caqq6tDREQEhBCIjo5G8+bN4eXlhV27dj23M59PJb+8YcOGYfjw4Vi8eLHGCIehQ4cCALZt26aRfs+ePXjw4IH6uMrUqVORk5OD77//Hl9//TVCQ0N5f/qSCgsLMW/ePFhaWmLGjBno2rUr3nrrLZw9e/aZbZG5uTkAwMfHB9nZ2eqhno0Jn0gzADKZDBYWFhg0aBBWrFiB1q1bw9HREUeOHEFycjJatmwpdYhNhqOjIxYvXowFCxbg3//+N0aOHIlWrVrh9u3bOH36NExNTREbG6tOr1QqsWrVKlRVVcHd3R0nTpzAkiVL4OPjgwEDBkiYE8Px2+XFdfHz88Pq1asREhKC6dOno6SkBCtXrtTqdKYXO3DgAG7duoX4+HidN9murq5Yu3YtkpOTsWbNGgBP/r0dPnw4oqKi8PjxY8THx6OiokLjc0Cv5nXX/ZYtW2LKlCnYsGEDHBwcMHr0aH2E3aionqKcPHkyNm7ciEmTJuGPf/wjSkpKkJCQAAsLC4kjbDp4LfTnZdr+UaNGYdiwYYiLi0OrVq3g4OCA7777DhkZGRrvadasGWJjYzFjxgwEBAQgPDwcZWVliI2NRbt27dCsGf/L/60RI0agQ4cOGD16NLp164bHjx/jxx9/xKpVq2BmZobIyEjegzYQT09PJCYm4oMPPsCAAQMwe/Zs2Nvbo7CwEOvWrUNOTg4SExPRv39/AMDq1asxYMAA9OvXDx9//DGcnZ1x+/ZtfPXVV9i4cSPMzc3h6uoK4MlKtubm5jAxMYGTkxOfDHyB+Ph4vP3227hz5w569OgBABg+fDhGjBiB+fPno6KiAp6enjh37hxiYmLg5uaGyZMna5wjODgYUVFRCA4ORk1NjdbcaqTpwoUL6vnO7ty5g2PHjiE1NRVGRkbYu3ev+inAjRs3wsfHByNGjEBYWBjat2+P0tJS/PTTTzhz5ox6tWfVSraDBg3CJ598gp49e6KsrAyZmZmIiopCt27dpMzu/0aqVQ7oxdatWycAiPPnzwshhLhx44YYP368aNWqlTA3NxcjR44UFy5cEA4ODiI0NFT9vmetuvGslZZIU31WLdm3b5/w9vYWFhYWwtjYWDg4OIiAgABx8OBBdZrQ0FBhamoqzp07JwYPHiyaN28urKysREREhKiqqmqIrBic+pS9ENorF6akpIiuXbsKY2Nj0alTJxEXFyeSk5MFAHH16lV1OgcHB+Hn56en6A3fuHHjhFKpfO7KU0FBQUIul4tTp04JACI+Pl7ExsaKDh06CKVSKdzc3ERWVpbGe1Srdt69e1ffWTBY+q77KocPHxYAxPLly19zDhqXp79/hRBiy5YtwsXFRZiYmIju3buL9PT0Z67auWLFCq1zgitIvpJXvRZCsMzr62Xa/uLiYlFUVCQCAgKElZWVsLS0FJMmTRJ5eXkaq3aqbNq0STg7OwulUim6dOkiUlJSxNixY4Wbm5uec2VY0tPTRUhIiHjrrbeEmZmZUCgUwt7eXkyePFlrtTvegzaMkydPioCAAGFjYyPkcrlo27at8Pf3FydOnNBKm5+fLwIDA4W1tbVQKpXC3t5ehIWFiYcPH6rTJCYmCicnJ2FkZKTzs9KUPe8eKCQkRABQr9ophBDV1dVi/vz5wsHBQSgUCtGuXTsREREh7t+/r/P8qnN4enrqKwsGT3UNVC+lUinatm0rvLy8xLJly3R+P5w9e1ZMmDBBtG3bVigUCmFrayuGDBmiXvlZ5fr16yI8PFzY2toKhUIh7OzsxIQJE8Tt27cbKnt6IRPi/5dfoDdOZGQk1q5di7KyMvXjkUREb4KCggI4OTlhxYoVmDdvntThUD3NnTsXGzZswPXr1/lP+HPw+/fNwWvRuJSVlaFLly4YN26c1txH9PqEhYVh9+7dWov+EBHR68GhnW+gH374Abm5uUhJScGYMWN440hERP+TU6dO4cqVK1i/fj1mzJjBTrRn4Pfvm4PXwvAVFxdj6dKl8Pb2hrW1Na5du4Y1a9agsrISkZGRUodHRET0ytiR9gYKCAhAeXk5xowZg88//1zqcIiIyMB5eHigRYsWGDVqlMaqq6SJ379vDl4Lw2dsbIyCggLMnDkTpaWlaNGiBd555x0kJSWp5zsiIiIyRBzaSUREREREREREVA9cMoeIiIiIiIiIiKge2JFGRERERERERERUD+xIIyIiIiIiIiIiqgd2pBEREREREREREdUDO9KIiIiIiIiIiIjqgR1pRERERERERERE9cCONCIiIiIiIiIionpgRxoREREREREREVE9sCONiIiIiIiIiIioHv4PZ9jZR0cDjzIAAAAASUVORK5CYII=\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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY SST with CY Rivers',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([ 6.68492084,  5.18552462,  7.25557932, 10.16054886, 14.0224531 ,\n",
       "       17.57203394, 20.68068024, 20.043708  , 16.67098008, 11.61331057,\n",
       "        8.74718019,  6.62090044])"
      ]
     },
     "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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY SST with WY Rivers',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([ 6.11334258,  5.87921074,  7.48800428,  9.09396314, 12.75033495,\n",
       "       16.07578677, 17.97372228, 18.18481324, 15.3735647 , 11.97804754,\n",
       "        9.27182311,  6.43142438])"
      ]
     },
     "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_3223455/2771304440.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_river19/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_river19/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_river08/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, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/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",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/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"
   ]
  },
  {
   "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_3223455/178454329.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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY PAR with CY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('uE/m$^{2}$/s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.32141178,  25.46487863,  53.28121643,  62.53450945,\n",
       "        90.96772924, 103.14297376,  94.98511739,  80.55321234,\n",
       "        51.44989112,  31.57708017,  20.31491912,   8.03466877])"
      ]
     },
     "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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY PAR with WY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('uE/m$^{2}$/s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.23691735,  25.43099464,  41.17917068,  59.83653825,\n",
       "        85.16592265,  93.9387496 , 100.45387598,  80.0917271 ,\n",
       "        60.47075832,  29.24338572,  13.14281766,  10.14914155])"
      ]
     },
     "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_3223455/1288103633.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_river19/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_river19/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_river08/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, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/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",
    "# 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(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/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_3223455/3661973807.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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY Halocline with CY Rivers',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([0.51926909, 0.32624015, 0.94315948, 0.79663785, 1.97864725,\n",
       "       2.99150912, 3.4154679 , 1.8896247 , 1.57378358, 0.44097102,\n",
       "       1.00447982, 0.8545834 ])"
      ]
     },
     "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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY Halocline with WY Rivers',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": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Depth-averaged Nutrients (0-10m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223455/3312634990.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": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223455/241793216.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": 31,
   "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_river19/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_river19/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_river08/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, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/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",
    "        \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(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223455/231329215.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": 33,
   "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_river19/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_river19/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_river08/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, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/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(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/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",
    "# # 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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223455/3737416097.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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-3}$')"
      ]
     },
     "execution_count": 35,
     "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[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY Nitrate with CY Rivers',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": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.69352626, 22.4096068 , 17.12926893,  7.66441735,  4.87999298,\n",
       "        1.37387281,  0.99858407,  1.40141887,  8.04740459, 16.94915656,\n",
       "       19.72782593, 22.07596841])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol Si m$^{-3}$')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H8kKR9evXk52dDRTdXy1PXtCW175582bLN/Il2UfL1tbWUjW0rGbr5PV1991388UXX7Bz504SExNZuXKlZZ+t8+fPM2rUqHKvEJi3UXq9evWu6rqzZ88CXDEAcXBwsGyin3f+37m6ul72+rwKsFlZWVc1Nii6z1paWhp//PEHrq6udOzYke7du+Pg4MDvv/9u+Ry6mv3VrkV5PHPe73NCQkKx1VtLo7w+98EcBBqGQW5uLqdOneLTTz/Fw8OD77777h8LXlxJ3qy1vNlpeZYsWUJ8fDz29vZF9r07deqU5eOCgV9xrzwFw8CCatasedmxNWzYkC+//BIXFxciIyN58MEHadiwITVr1uS2225j4cKFhYo3iIiIXI8UrImIiFwH8oKPlJQUy0yodevWAYVnrBV8nxea5P1acOZbVeDg4EBAQACLFi3i3nvvBcxL8JYtW1Yh9y/tTKeSXleWM6lKomD4mpWVxYYNG8jIyKB3797Y2Njg4OBAt27dSElJsYSteVU1y3JmVkXJm02VkZHB/v37K3k0JWcymahVqxbjx49n/vz5mEwmJk2aVGzF1pLIC9aOHDnChg0bLMfzgrbg4GCqVatW6Jq8ZZmOjo6WwO+fXn8P8PPkzdC8nLvuuotjx47x6aefctttt+Hn58e5c+eYO3cuI0aMoE+fPkWq74qIiFxPFKyJiIhcBwou1Vu7di379u3j7NmzNGrUiDp16hQ69+8z1vJ+7dmzp2Wvtqrm4Ycftnx84MCBcr1X3vLPo0ePXtV1eTNzTpw4cdlz0tPTLXt/5S0prCh5AVpqaip//PFHoWWgeQrOalu/fj2ZmZk4OzvTqVOnCh1rWejfv7/l4/nz51fiSErP39+fe+65B8MwePzxx0u1D1nDhg0tyzHzwrSEhAQWL14M5AdvBfn4+ADmWY3F7c1W1jw9PRk/fjw///wzx48f59ChQ0yePBmTycTvv//OSy+9VO5jEBERKS8K1kRERK4DBTeXj4iIKHZ/tTwNGzakTp06xMTEsHfvXsv+auW93O9aFNz8vDz3+gJzSAnmPdBOnz5d4us6duwIwOrVqy97ztq1ay3LLCs6rHJxcbGMcc2aNYUKF+QpWMAgr700gWtesYDKXMbXqVMnywzMDz/8kPPnz5fourJaNlpWpk6dirW1Nfv37+fbb78tVR95e6jNnTuXjIwMy681atRg8ODBRc7v3r27ZUblzz//XPrBl1KjRo2YMWMGd955JwArV66s8DGIiIiUFQVrIiIi14m8YGzDhg2Wb0QvtzwrL3CbOXMmaWlpha6vSNHR0Rw8ePAfzysYKLRv3748h8SYMWNwc3MjOzubiRMnljgcuv322wHzxu8rVqwo0p6dnc1///tfAFq2bEnLli3LbtAllBecLVq0iK1bt1K9enXatGljae/cuTPOzs5ERkaydOlSoHSfF25ubgBcuHDh2gd9DWbNmoW1tTWxsbGMGjWKxMTEK55/8uRJRowYUTGDK6FGjRpZ9kB7+eWXS7W/3tixY7G3tychIYHw8HDLzLXbb7+92NC0Zs2aDB8+HIA333zzH/+OXm1hiTwZGRlXbHd0dAT+eTmpiIhIVaZgTURE5DqRF4CkpqYSFhYGFD9jreDxn376CTBvHt+hQ4cKGGVhe/fu5dZbb2XIkCF89913hZZfZmVlsWPHDu6//37efvttwBz89OzZs1zH5O7uzhtvvAHAnDlzGDlyJDt37rS05y2jGz58eKG9n0aNGmWpbjl27Fh+/PFHSwgSHR3NqFGjLLMD8/qvaHmfI9u2bSM7Oxt/f/9Ce73Z2trSs2dP0tPT2bVrV6FrrkZeaLh3715LldXK0KtXL9577z1MJhO//fYbrVu35pNPPuHkyZOWc7Kysti4cSNPPvkkt9xyC7/99luljfdypkyZgslk4ujRo3z11VdXfb2Hh4elCMiMGTMse60Vtww0z1tvvUX16tVJSkqiZ8+ezJ49u1Awef78eUJDQwkJCeGOO+646jEBPP7444wdO5Z58+YVKuaRkpLCp59+ynfffQdQ7Kw6ERGR64VNZQ9ARERESqZr1644OjqSlpZGTk4ODRo0oG7dusWemxes5e3Z1KtXL0v1xYpka2tLbm4uS5YsYcmSJQDY2dnh4uJCQkJCodli7du3Z/78+ZZlhuVp/PjxxMfH88ILL7Bw4UIWLlyIo6MjNjY2JCcnW84ruGzQ2tqaefPmERgYyN69e7nrrru4//77cXJysszcsrKy4p133mHQoEHl/gzF6dGjB3Z2dmRmZgLFFyXo27cvy5cvB8wzz0oTuPr7+9O0aVMOHDhAjx49qFatmmUW26xZsxg9evQ1PMXVeeyxx6hTpw6PPvoox48fZ8KECUyYMAEHBwccHR25cOGC5fPMxsaG8ePHV9jYSqply5YMGzaMhQsX8uqrr3L//fdf9ZLocePG8euvv7Jt2zYAmjZtesViJQ0bNmTlypWEhIRw9OhRHnjgAR588EE8PDzIysoiJSXFcm5AQECpnisrK4tffvmFX375BTAvV7axsSk007Fnz5783//9X6n6FxERqQo0Y01EROQ6YWdnZ9kfDC4/Ww3glltusWxQDpW3v1pgYCB//fUX7733HmPGjOHWW2/F3t6eCxcu4OTkRJMmTRg7diw///wzW7ZswdfXt8LGNmXKFHbt2sVDDz1E48aNAfOeYU2bNuWOO+4gNDTUEhblqV27Nlu3buXtt9+2BJ0XL17Ez8+Pe+65h23btvHvf/+7wp7h7xwdHS178UHxwVrBY7169SrVMjwbGxtWr17Ngw8+SP369UlNTeXYsWMcO3asUCBTUYYPH86RI0f44osvCAkJoX79+lhbW5OamkrNmjXp378/r776KkeOHOGdd96p8PGVRF64dPLkST777LOrvn7QoEGFCmbk7bt2Je3atWPfvn18+OGHBAQEUKNGDZKTk8nNzaVJkybceeed/Pzzz4SGhl71eABefPFF3n//fUaOHEmzZs2wsbEhJSWFmjVrMmDAAGbPns3atWtxdnYuVf8iIiJVgcmozF1nRURERERERERErlOasSYiIiIiIiIiIlIKVTpYW79+PYMHD6ZatWo4OjrSpEkTXn755ULnbN++nYCAAFxcXPDw8CAkJIQjR45U0ohFRERERERERORmUWWDtR9//JE+ffrg7u7Od999x5IlS5g0aVKhTY6joqLw9/cnMzOTuXPnMnv2bA4ePEivXr04d+5cJY5eRERERERERERudFVyj7WYmBiaNm3KuHHj+Pjjjy973tixY4mIiODw4cOWzYWPHTtGkyZNmDhxIq+//npFDVlERERERERERG4yVXLG2pdffklqaiqTJk267DnZ2dmEh4czatSoQhW76tWrR9++fZk/f35FDFVERERERERERG5SVTJY++233/D09CQqKoq2bdtiY2NDzZo1eeSRR0hKSgLg8OHDpKWl0bp16yLXt27dmkOHDpGenl7RQxcRERERERERkZuETWUPoDgxMTFcvHiRMWPGMGXKFN599122bNnCtGnT+PPPP/n999+Ji4sDwNPTs8j1np6eGIZBQkICtWrVKtKekZFBRkaG5X1ubi7x8fFUr14dk8lUfg8mIiIiIiIiIiJVnmEYJCcn4+vri5XV5eelVclgLTc3l/T0dKZNm8bkyZMB8Pf3x87OjieffJLVq1fj5OQEcMUg7HJtM2bMYPr06WU/cBERERERERERuWGcOHGCOnXqXLa9SgZr1atX56+//iIwMLDQ8UGDBvHkk0+yfft2hg8fDmCZuVZQfHw8JpMJDw+PYvufMmUKTz31lOV9YmIidevW5cSJE4X2axMRERERERERkZtPUlISfn5+uLq6XvG8KhmstW7dmk2bNhU5nlfA1MrKikaNGuHo6MiePXuKnLdnzx4aN26Mg4NDsf3b29tjb29f5Libm5uCNRERERERERERAa68UhKqaPGCUaNGAbB06dJCx5csWQJA165dsbGxYejQoYSGhpKcnGw55/jx40RERBASElJxAxYRERERERERkZuOycibBlbFDBs2jBUrVvDCCy/QtWtXtm7dyvTp0wkICCAsLAyAqKgoOnXqRPv27Zk8eTLp6elMnTqV+Ph4du7ciZeXV4nulZSUhLu7O4mJiZqxJiIiIiIiIiJykytpVlRlg7W0tDSmT5/Ojz/+yOnTp/H19eWuu+5i2rRphZZxbtu2jUmTJhEZGYmNjQ39+vVj1qxZNGrUqMT3UrAmIiIiIiIiIiJ5rvtgrSIpWBMRERERERERkTwlzYqq5B5rIiIiIiIiIiIiVZ2CNRERERERERERkVJQsCYiIiIiIiIiIlIKCtZERERERERERERKQcGaiIiIiIiIiIhIKShYExERERERERERKQUFayIiIiIiIiIiIqWgYE1ERERERERERKQUFKyJiIiIiIiIiIiUgoI1ERERERERERGRUlCwJiIiIiIiIiIiUgoK1kREREREREREREpBwZqIiIiIiIiIiEgpKFgTEREREREREREpBQVrIiIiIiIiIiIipaBgTUREREREREREpBQUrImIiIiIiIiIiJSCgjUREREREREREZFSULAmIiIiIiIiIiJSCgrWRERERERERERESkHBmoiIiIiIiIiISCkoWBMRERERERERESkFBWsiIiIiIiIiIiKloGBNRERERERERESkFBSsiYiIiIiIiIiIlIKCNRERERERERERkVKoksHa2rVrMZlMxb42bdpU6Nzt27cTEBCAi4sLHh4ehISEcOTIkUoauYiIiIiIiIiI3CxsKnsAV/Laa6/Rt2/fQsdatmxp+TgqKgp/f3/atm3L3LlzSU9PZ+rUqfTq1YudO3fi5eVV0UMWEREREREREZGbRJUO1po0aULXrl0v2z516lTs7e0JDw/Hzc0NgA4dOtCkSRNmzZrF66+/XlFDFRERERERERGRm0yVXApaEtnZ2YSHhzNq1ChLqAZQr149+vbty/z58ytxdCIiIiIiIiIicqOr0sHaY489ho2NDW5ubgQGBrJ+/XpL2+HDh0lLS6N169ZFrmvdujWHDh0iPT29IocrIiIiIiIiIiI3kSq5FNTd3Z3//Oc/+Pv7U716dQ4dOsSbb76Jv78/ixcvJjAwkLi4OAA8PT2LXO/p6YlhGCQkJFCrVq0i7RkZGWRkZFjeJyUlld/DiIiIiIiIiIjIDalKBmvt2rWjXbt2lve9evVi5MiRtGrViueee47AwEBLm8lkumw/l2ubMWMG06dPL7sBi4iIiIiIiIjITadKLwUtyMPDg+DgYHbv3k1aWhrVq1cHsMxcKyg+Ph6TyYSHh0exfU2ZMoXExETL68SJE+U5dLnZ5OZCSgqkplb2SERERERERESkHFXJGWuXYxgGYJ6J1qhRIxwdHdmzZ0+R8/bs2UPjxo1xcHAoth97e3vs7e3LdaxyHcjMhNRUjORk0uPjSY2LI/X8eVITErBJT+cWd3dzOJaSwk9//EF8YiJ31KmDZ04OpKSw6OhR5p06RWpmJhezskjNziY1J4fU3FwuYk6te9WoQVD//gx8/HFq9OgBV5hhKSIiIiIiIiLXF5ORl1ZVcQkJCbRq1QovLy927NgBwG233cbatWs5dOgQrq6uABw/fpwmTZowceJEZs6cWaK+k5KScHd3JzExsVCFUakCcnMhLY3M+Hhz6HX+PKlxcVy8cIHU+HhcgTY1alhmiL0TEUFKaipPNmmCa0YGpKYy+/Bh5p85w8XsbFKzsizhVyqQClwE/v6XoAewvsD7WsAZYAfQ9tKx14D/K+FjmICO9vaM7tSJ5/77X+jZE2xtS/mbIiIiIiIiIiLlqaRZUZUM1u68807q1q1Lx44dqVGjBn/99RdvvfUWhw8fZunSpQQEBAAQFRVFp06daN++PZMnTyY9PZ2pU6cSHx/Pzp078fLyKtH9FKxdu5z0dC6ePZsffMXHkxofT2pCAqmJidSyt6dTzZqQmkrGhQu8vGYNqRcv8nqLFtilp0NKCjMPHiQsLq7QzK9Uw+AikH2Z+w4GFhd47wSkAdFA/UvHngPeLOFzOJhMOFtZ0dnNjSVdu4KLCzg788Aff5CYk8OMQYNoUq8eODuz6fRpfjtyBGc3N5w9PCwvJ09PnKtXJ+nUKVZ8/TXLIiPZnZwMQAgwD8DDAwYN4qfq1en96KPUbt78an67RURERERERKQcXdfB2syZM5kzZw7R0dGkpKTg6elJz549mTJlCp06dSp07rZt25g0aRKRkZHY2NjQr18/Zs2aRaNGjUp8v5shWDMMg7SLF0k9d84y8+tiQoI5+EpIIPXCBVKTkmjq4UF3Hx9ISSEhLo7/W72azIwMvmzb1jIrbGJUFMsuXMhf9mgYpP/D/e8E/nfp4yzA7tLH8UC1Sx8/DHzxD/3YAM5WVjhbWeFkY0MfT0++7NQJnJ3BxYVHN20i19qa6YMH4+PjA87O/HH6NH/GxppDL3d3nKtVw7l69fxXtWo4OTnh5OSEtbV1KX53/9mpv/5ixQcf4HfwIP23b4dz5zgENLn0e5Hg74/TiBEwbBhG/fpXLMohIiIiIiIiIuXrug7WKtoNGazddx9jFyxgbXIyFw2Di4ZRZLljcSYAH136+BxQ89LH2UBe5HQbMPcy15sAZy6FX9bWOFlb42xry8CaNflvu3aWGWBPbtqEo5MTzwcF4VqjBjg7s+3MGY4nJxcNv7y8zLPAXF2xs7O7zJ2vIzk58McfbP7sM/7zyy+4pKWxskBzHycnXH19CRo2jKDx42l8yy2VNlQRERERERGRm5GCtatwQwZrPXsStGEDy4tpciA//HKytsb5UvjlbGfHEF9fHmvVCpydybC3Z8auXTi7uPBkYCC2bm7g7Mzuc+dIyM7G2dPT/KpeHacaNXD28sLBzU2zra5SdlQUNkuXQlgYZ9etwzs3t1B7I1dXAnv2JOhf/6JvUBAuLi6VNFIRERERERGRm4OCtatwQwZrGzdy8MABMmxtC4VfTjVqYO3qClZWlT1CKYYRH8/uzz5j2c8/s3zvXtbn5JBVoN3WZKJX06YEjR5N0NixtGzZUkGmiIiIiIiISBlTsHYVbshgTa5/mZkkL19OxOefs2zdOpYlJxP9t1N8XV0J7NePt7/+Go9q1YrtRkRERERERESujoK1q6BgTao8w8D4808Off01y0JDWXbsGBGYK6BWB2Lr1sV62DAYNoyw5GS869ShQ4cO5VaMQURERERERG4iOTlw7hycOkVuTAznDh4k5q+/OHXsGDGnTnHq3DmmmUxY/fkn3CCTPhSsXQUFa3LdOXOG9Pnz+f2774jdto27s8wLRg3Az2QixjBY/txzDJw0CTw9yc3NxUrLf0VERERERKSgAoGZceoUGceP43DuHJw+TU5MDG/s2EFMQgJvpKfjdGk/8IeAL4vp6gzgvXcvNG9ekU9QbhSsXQUFa3Jdu3gRVq+GRYtIWrSIB86eZQNwBHCwtoaePXnOZGJlbCxBw4cTFBREt27dbowKqyIiIiIiIlJUTg6cPQunT8OpU6QfP86pAwc4dfQoMSdPcursWWISEjh18SIxhsEp4BTQDwgr0I0rkAIcBJqYTODtzVTglTNn8HFywtfdndpeXvj6+vLSk0/i3bs3ODpW/POWAwVrV0HBmtwwcnNh61ZyFy7EKjwcdu8GoDWwp8Bprk5O9B8wgMCgIIKCgqhfv35ljFZERERERESuxt8Cs7yZZtt27ybm5EkCs7NxiI2FM2d4LzeXr4AYIL6E3bdzc2P7bbdBrVrg68vklSuxdnPjsccfx7d1a7CxIS0tDVtbW2xsbMrxQSufgrWroGBNblhHj0JYGGd//ZWV69ezLDeX5cC5v53WtEkTggYPJigoiD59+uB4g/yEQURERERE5LqQF5idOgWnT2PExJBw5Ainjhwh5vhxTsXGEhMXx6mUFGKAhsA7BS7Pm1kWBTS9dOwlYHqBcxysrant5oZv9erUrlUL37p18W3YkNpNm+Lr50ft2rWpVasWTk5OFfHEVZ6CtaugYE1uComJsHw5uQsXsnPRIpalpLAM2AjkFDjN3t6ePn368Morr9CpU6dKGqyIiIiIiMgNoGBgdik0u3jsGPZnz2J95gycPs3K6GiWxsdzCvPssrxlmelX6LatjQ072rSxzCzzX76ciyYTXz31FK26dwdfXw7Ex3MsJobatWvj6+uLh4cHJpOpQh77RqBg7SooWJObTlYWbNgAYWEkhoay+uhRlgNLgROXTtn1yCO0fughaNeOTX/8walTp+jfvz/u7u6VOHAREREREZEqIDu7yJLMrJMniT182DzD7PRpYs6fxyU5mfsKxC71gWPAfqDZpWPTMc8uK051R0d8PT2p7e2Nb5065hlmTZrQsHFjBg4cWI4PKArWroKCNbmpGQZERcGiRRiLFhG1cSNrgAmACaBOHe5xcuKHgweZ9PTTzJw1C4CcnBxMJpOqjYqIiIiIyI0jLzC7NLus4K8nDh/mz2PHOHXuHDHJyZwyDMsMsxjgLPD3gKUNsNPKCnx8oFYtmh84wP6UFFaPG0e/nj2hVi0iYmJYsnMntZs0wbdOHcsMs1q1auHg4FDRvwNyiYK1q6BgTaSAs2dhyRJYtAhWrIDUVKYDPwGfOTjQZ/BgGDaMxba2/GviRAYOHEhQUBADBw7Ey8urskcvIiIiIiJSVMHArEBYlnL8ODHR0aTHxtLmwgXzObm5PIB5v7JvgCaXungZmPoPt7GxsqKWuzu+NWtSu3ZtmrVsyatvvw3W1gAcP34cd3d33NzctCyzilOwdhUUrIlcRno6RESYQ7ZFizBOnSLvn/6JwLsFTjWZTHTo0IHAwECCgoLo2rXrDV8lRkREREREKllODsTGFgnMMk+c4MyxY8ScPMmps2eJuXChyB5mMUDypW5aAbvz+rS2poXJxL7sbFZ260ZA69bg68tPJ07wxpo1+Pr64lu3LrUbNSo0w8zX1xcvLy+t6rlBKFi7CgrWRErAMGDHDkvIlrljB5HAskuvnX873d3dnYCAAIKCgggMDMTPz6/ChywiIiIiItcpw4CEhPzALCYGIyaGi8eO4XzunOXY7NOnOWIYjAfyvuN4C3jmKm7l6uBAy/r12fjjj+DrCzVqMH/RInJzc+nVqxc1a9Ys++eTKk/B2lVQsCZSCidOQFiY+bVmDWcyM1mBOWRbYTIR97d/Wpo3b87EiRN58MEHK2W4IiIiIiJSRVy8aAnMjJMnSTxyhNhDh4g9fpwzp04Re/48sRcucCYnh1ggFjhz6dfGwN4CXbUC/gRWVK/OgAYNoHZtvk5M5F9r12JrbY1v9erUrlULXz8/ajdsiO+l2WUFZ5m5urpW/O+BVHklzYq0TktESsfPDyZMML+Sk/FZsYJxixYxbvFicuLi2IY5ZFtuMrHJMNi3bx+pJ05YLj99+jS//PILQUFB3HLLLZX2GCIiIiIiUkaysiA2FuPkSdKPHsUxLg5iYuDUKb7ZupXDsbE8lJlJ3aQkAD4CngYyruIWZxwd4e23zTPLatdm7Jw59ElNxeeRR6BVKwDGpKQwND2d6tWrax8zKXeasYZmrImUqZwciIy0LBnlwAESgFVAV8CvdWsYNoyvDIMHX32VLl26sGnTJsvl6enpqnwjIiIiIlKVGAbG+fOk/PUXZ/btM88uO3qUMydPEnv2LLEJCZxJTiY2I8Mys6wBsK9AF20w72G2DAgEcHbmGxcX7o+NBcDVzg5vNze8q1fHp1YtvOvUwbtBA3zq1MHb2xsfHx+8vb3x9vbG0dGxYp9fbkqasSYilcPaGnr2NL/eeAMOHqRaWBhjFi2C9eth927YvZuaQH87O/rl5EB4OPTvT2JmJrVq1aJLly4EBQURFBRE69at9VMmEREREZFyYBgGGXFxOMTFWZZmfh8WxqHoaP5VvTr1EhPh1Ck+PXGCp3JySLuKvk9bW8Pw4VC7Nvj6MnrLFnpmZ+Nz333Qvz+4ujIiMZE+CQl4e3vj5ORUXo8pUq6uasZaQkICMTExtGzZEoBly5Zx6NAh+vTpQ6tLUy6vR5qxJlJB4uJg6VLzTLZlyyA5Ob/N0ZHFrVsT/McfhS6pVasWgYGBBAYGMmDAAKpXr17BgxYRERERub6kpKRw5vhxYvfvJ/bgQc4cOULsiRPExsZy5vx5YpOSiE1L40xWFnWBqALXtsNcmGwJMOjSse+Aey997GJlhbeDA96urvh4euJdsybetWvjU78+3k2a4N2kCT6+vgrL5LpX5sULFi9ezF133UVWVhb9+/dnwIABrFixgpycHNauXcsvv/zCkCFDyuwBKpKCNZFKkJEB69aZix8sWgTHjwNwiEuVRj08iEhN5WJWluUSk8lE586dLbPZOnXqhLW1deWMX0RERESkAmVkZGBvbw+5uXDuHD988QWH9u3jvpYtqZ+ZCTExfL5pExP37eNibm6J+3UHLri5WfYse/n8eU5bWfHwgAG07dwZfH254OJCvK0t3n5+ODs7l99DilQhZR6stW3bli+++AJra2s6derE8uXLCQgIAGDu3Lm89dZb/PG3mSbXCwVrIpXMMMxLRBctMgdtW7YA5k1M1wPL3N1ZZmPDn3FxhS6rVq0aISEhfPnllxU/ZhERERGRa3Tx4kXzLLIzZ4g9c4bYY8c4c6k6Zuzp05w5d85cHTMlBV9bWw56ecHp05CdTQdgOxAO5E1x+QG459LHToAP4G1vj7ezMz4eHnh7eeHt64tP3bp4N26Mz6234t2yJc7e3hX/8CJVXJkHax4eHly4cAEAR0dHUlNTsbKyAiAnJwcvLy/i4+OvfeSVQMGaSBVz6pR537VFi2D1akhPB+AksNzRkWU1arAqLo4LFy8yfPhwFixYYLl0xowZdO3alZ49e2Jra1s54xcRERGRm1ZmZiZ2dnaW9z/99BN/7d/PPQEBNLCyglOn+Co0lCdDQ0kpsDrjn7gCSXlvTCZednYmxsGBh2+9lfbNm0Pt2lzw8CDO2Rnv5s1xadwYqlcH7VcsUiplHqz5+Phw8uRJbGxsuPvuu/nhhx8sbRcvXqR27dokJCRc+8grgYI1kSosNRVWrTKHbOHhcPYsANnAZisrbNq1o/Pdd8PQoRwGGjdujI2NDXFxcfr7LCIiIiLlxjAM/vrrLyIjI9m4bh2b1q0j+tQpajo6cmjkSIiJgVOn6LRvH1tzclgEDL107f+Auy997MClmWWXXj52dni7u5urY/r44O3nh3eDBng3bYpb48bmJZs+PmCjWoQi5anMq4L27duXv/76i1tvvbVQqAbmIgbXc/ECEanCnJ3N1YSGDzfvJ7F5MyxahM2iRXTfuxe2bTO/Jk4kp1EjxrVsSVrNmrhp7wcRERERKUMpKSls2bKFyMhIItetIzIykriCxbguyU1Ph9mzLe+HYy4I4GtnB35+4OvLEC8vDrq7492oEa4NG2KqXdtcPbNWLdCG/yLXlauqCno5sbGxAHhfp+uyNWNN5Dp15Eh+8YN16yAnJ7/Nxwdmz+ZQkyYcPnyYwMDAyhuniIiIiFyX/vrrL9595x0if/uN3fv3k/O3ogD2QAegG9Ddy4sWrVrh06AB7g0amIOySwUB8PUFDw8tyxS5jpT5UtDK9OWXX/LQQw/h7OxMSkpKobbt27fz3HPPsWnTJmxsbOjXrx+zZs2iYcOGJe5fwZrIDSAhAZYtM4dsS5dCYiLHbWzo5urK+dRUFi1apHBNRERERC5r9+7dLFu2jNYtWxJUrx78/ju7Fy2izdKllnPqYA7RugHdGzSgbUAA9v7+0KuXeTaaiNwwKiRYmzZtGtOnTy/t5SUSExNDixYtcHZ2JjExsVCwFhUVRefOnWnbti2TJ08mPT2dqVOnkpCQwM6dO/Hy8irRPRSsidxgMjLgvvvI+vln7gDmAQ4ODoSFhVmqGYuIiIjIzckwDI4fP05kZCSBgYFUc3GBHTuY9n//x39XreIeOzu+y8wEIAeYDHSysqJbq1b4DRhgDtF69DAXBhCRG1aFBGtOTk5cvHixtJeXyNChQzGZTHh6evLrr78WCtbGjh1LREQEhw8ftjzksWPHaNKkCRMnTuT1118v0T0UrIncgHJy4JFHyPzyS8YCCzFXNF6yZAn+/v6VPDgRERERqSjp6els377dXGRg40YiN27k9JkzACxs3Zphhw7BxYusA97HXGDgPkdH6NbNHKL16gVdu5r3/hWRm0aZFy8oTnmvIv3hhx9Yt24d+/bt44UXXijUlp2dTXh4OOPGjSv0gPXq1aNv377Mnz+/xMGaiNyArK3h88+xc3NjzttvMwpYnJbGkCFDWLZsGb169arsEYqIiIhIOTh58qS5wMClIG3Hjh1kXpqBlscaaAuwe7f5QLVq9OnZkz55QVr79mBnV8EjF5Hr0TUFa6Zy3Hjx7NmzPPnkk8ycOZM6deoUaT98+DBpaWm0bt26SFvr1q1ZuXIl6enpODg4lNsYRaSKM5lg1izs3d35ddo0RgLLLl5k0KBBrFixgu7du1f2CEVERESkjEyYMIGwsDBOnjxZpM2LS/uiXfq1Y61aOOXtjdarFzRvDlZWFTtgEbkhXFOwVp4mTJhA06ZNefTRR4ttj4uLA8DT07NIm6enJ4ZhkJCQQK1atYq0Z2RkkJGRYXmflJRURqMWkSrHZIKpU3FwdSX0qacYBqxKTSUoKIiVK1fSpUuXyh6hiIiIiFyF+Ph4ZsyYwcGDB1kwfz6mv/6C338n5lKoZgW0pnCQ1vCWWzD17p0fpNWvrwqdIlImqmSwNm/ePMLCwtixY8c/zoq7Uvvl2mbMmFHuRRdEpIqZOBFHFxcWPvwwwUBEcjKBgYGsWrWKjh07VvboRERERORvsrKy2L17Nxs3bsTd3Z1x48ZBTg4O+/fz7ttvk52by/GaNal3adLFJOBJoJPJhEu7dvkhWs+e4O1dmY8iIjewKrfHWkpKCo899hhPPPEEvr6+XLhwAcCyJv7ChQvY2tpS/VIFlryZawXFx8djMpnw8PAo9h5TpkzhqaeesrxPSkrCT6WRRW58Dz2Ek6srYXffzeCcHH5LTGTAgAGsWbOGdu3aVfboRERERG5q586dyy8wEBnJli1bSEtLA6Bj7dqM+/FH2LgRp+RkpgO1Afe4OLC3hy5d6J4XpHXrBipKJyIV5JqCtR49epTVOCzOnz9PbGwsb731Fm+99VaR9mrVqjF8+HB+/fVXHB0d2bNnT5Fz9uzZQ+PGjS+7v5q9vT329vZlPnYRuQ7cfjvOLi6EjxrFoMxMNly4QEBAAL///jvNmzev7NGJiIiI3BSys7P5888/CwVphw8fLnKeB9AV6B0TAzEx5oNubjzfo0f+jLROnczhmohIJbimYG3VqlVlNQ4LHx8fIiIiihyfOXMm69atY+nSpdSoUQMbGxuGDh1KaGgob7zxBq6urgAcP36ciIgIJk6cWOZjE5EbRHAwrsuWsSQ4mMCLFzFlZVFb5dNFREREyo1hGJatekJDQxk3bhypqalFzmuOeU+0vFczwMrbOz9E690bWrUyV4AXEakCTEZ5rOcsB/fddx+//vorKSkplmNRUVF06tSJ9u3bM3nyZNLT05k6dSrx8fHs3LkTLy+vEvWdlJSEu7s7iYmJuGnKsMjNY9MmEoOCsEpMxLVdO1i+HEr474aIiIiI/LM333yTr776iueee45/3X8/REez7bvv6Dh9Oq5WVnTNzbWEaF2AagANG5oDtLwwrXFjFRoQkQpX0qyoTIoXLFiwgP/9738cO3aM9PT0Qm0mk4ldu3aVxW2KaNasGWvXrmXSpEmMHj0aGxsb+vXrx6xZs0ocqonITaxrV9zXrYOBA2HHDujdm4/uvpuA0aNp2rRpZY9ORERE5Lpw4cIFNm3aRGRkJJGRkcyZM4dq1apBbi7xUVEcOHCAja+8wr9efBFOnaINsBtonpuLtclknoGWF6L16gW+vpX9SCIiJXbNM9befPNNJk2ahJeXF40bN8bOzq7IOcUt7axKNGNN5CZ34AAEBPDVyZM8CPh6e7Nn3z48PT0re2QiIiIiVUpubi4HDhwotDfavn37Cp2z9F//Iig2FjZsYN+FC0Rj3ietOoCtLXTsmL+ss3t3qFatEp5EROTKSpoVXXOw1qBBA/r3789nn32G9XW6zl3Bmohw7Bjn+valb3Q0t7u48MIff4CKGYiIiMhNLjk5mT/++MMyG23Tpk0kJCQUOa+RgwPdsrLolpPDMKBOXoOzszk8y5uN1rkzODlV5COIiJRKhS0FjYuL484777xuQzUREQDq1cNr40Y29++P07595p+gLl8OHTpU9shEREREKoRhGJw7d46aNWta3jds2JDz588XOs/RxoZOjo50S0mhm2HQDaiZtyVQjRrQs2d+kNauHdiUyQ5EIiJV0jX/C9ejRw/2799Pv379ymI8IiKVx8cHp99/h6Ag2LKFlL59mdC9O69+8QV+fn6VPToRERGRMlWwUueuXbsICAjA2dmZo0ePAmA6cYJOtWuzPyODbiYT3ZKS6Aa0yc7GNjnZ3EnduvnLOnv1gmbNVGhARG4q17wU9MCBA4wcOZKZM2cSFBRU7B5rVZ2WgopIIcnJMHQo49at43ugUa1arNuyhdq1a1f2yERERERKxTAMoqOjLUs6N27cyMCBA5k5cyYAyUlJeFSrhq2VFTEjRlB982Y4fpx0wKFgR82bFy40ULduZTyOiEi5q7CloI0bNyYgIICRI0diMplw+tt6eZPJRGJi4rXeRkSk4ri6wpIlvDpkCOvXruXw6dP07dSJddu2UatWrcoenYiIiMg/SktLY+vWrYWCtLNnzxY6xyE7G2rWhN9/x3X9erbn5nJrbi52v/5qPsHaGof27fNDtJ49zUs9RUTE4ppnrD399NO88847tG3blltvvbXYGWtff/31tdyi3GnGmogUKzOTYyNH0mfJEo4BzWrVYu2OHXh7e1f2yEREREQKSUlJYfny5fz2229s3LiRnTt3kp2dXegcWxsb2vv40M1kovvZs3TLyMgvMgDg4ABdu+Yv6+zaFVxcKvQ5RESqigqrClq9enUefvhhZsyYcS3dVCoFayJyWTk5HLn9dvr8+isngeY+PqzdvRsvL6/KHpmIiIiIxcsvv8zUqVMLHavl6Um3GjXolpFB95MnaZ+TU3hZp4dH4UIDHTrAdbi1j4hIeShpVmR1rTfKyclhwIAB19qNiEjVZG1NwzlziBg3Dl9g35kz9G/dukh1LBEREZGK8vPPP9O/f39CQ0Mtx0YGBtLYx4fHW7XiRz8/jgIx8fHMO3iQZ44do3tODg6+vnDbbfDhh7BrF8TFQVgYPPccdOumUE1EpBSueY+1gQMHsmnTJlUFFZEbl5UVjb/5hjXOzvh/8gl7zpxhQMuWrN67F8/q1St7dCIiInKDO3ToEHXr1rVsu7N9+3bWrFmDt4cHIUlJMH8+LZYt42BmJqYzZ/IvbNIkf1lnr17QoIEqdoqIlLFrDtZefPFFbrvtNpydnRkyZAienp5FzinumIjIdcVkounHH7PG0RH/t99mZ2wsA2+9lVVRUXjo3zgREREpQ4ZhsGfPHkJDQwkNDWXPnj0sWbKEQYMGwalT3G1ri3fjxoxcsAAuzVozATRqBIMHm8O0nj3Bx6cyH0NE5KZwzXusWVmZV5OarvCTj5ycnGu5RbnTHmsicjX+fPFF+r7yCueBzjVqsCIqCnfNXBMREZFrkJubyx9//EFoaCjz58/n8OHDljZra2teDwri6bg42LSp8IVt2sDIkRASAi1bakaaiEgZKWlWdM0z1qZOnXrFUE1E5EbT8uWXWeXoSL//+z82nz/Pfa1bM//IEbC3r+yhiYiIyHUkKyuL3377zRKmnT592tLmYG9PYP36jExNZejJk3guXpx/Ybdu5iBt5EjzLDUREak01zxj7UagGWsiUho73nmHe55+ml8Ng2ZBQTBvHjg5VfawREREpIrbsGEDX3zxBWFhYcTHx1uOuzk7M8TPj5CEBIJiY3HJa7C2hr59zWHa8OHg61sp4xYRuZlU2Iw1EZGbVbuJE9ndvDlWISGwbBkEBUF4OCigFxERkQKSkpKwtrbG2dkZgC1btvDtt98CUMPdnRG+voScOUO/hATso6LMFzk4QGCgeVba0KGgPV1FRKokq8oegIjI9cwqMBBWrAA3N1b9/jvD6tYl7eTJyh6WiIiIVBFPPfUUXl5ezJkzx3wgLY0QV1f+c+utrHVx4UxiIl/s38+ghATs3dzgjjvgl1/g3DlYsADuvVehmohIFaZgTUTkWvXoQcqSJdxhMhGWmMis9u2hwB4pIiIicnM4ceIEH3zwAcnJyZZjnp6eZGZmsumHH2DMGPDyou6DD/Lu/v30SUnBukYNePBBWLIEzp6FH3+E0aPBxeUKdxIRkapCS0FFRMqAS48ehH77LZ+OH8+kc+egVy9YtQrq16/soYmIiEg5OnDgAPPnzyc0NJQtW7YA4O3tzdi+fWHhQh6IiGCUrS23RkTkX+Tnl198oGdP8x5qIiJyXVLxAlS8QETKUHQ0BATAkSMYvr5kL1uGbatWlT0qERERKSOGYbBz505CQ0MJDQ1l3759ljaTyUSPBg2Y4uTE4H37IDc3/8KmTWHUKHOY1qEDmEyVMHoRESkpFS8QEakMDRrA779jBATwn/37OdmxI3PWr8e2U6fKHpmIiIiUUk5ODpGRkZYw7dixY5Y2W1tb+tWvT0hmJsOPHcP7yJH8Czt0MAdpISFw662VMHIRESlvpZqxdvz4cWrVqoWtrS3Hjx//x/Pr1q1bqsFVFM1YE5Gytnf9etr37k2mYTDG1pYfV6/Gplevyh6WiIiIXKVPPvmE6dOnExsbaznmaG/PoPr1CUlJYUhMDB55DSaTeWlnSAiMGKEtIURErmPlOmOtQYMGREZG0rlzZ+rXr4/pH6Yx5+TklOY2IiLXrRY9ezJ/zhxG3HYbv2RlYdO3L98vWYL1wIGVPTQRERG5jIsXL7J8+XK6dOmCr68vAPb29sTGxuLh4sJQPz9Czp9n4LlzOB04YL7I1hb69zeHacOGgbd3JT6BiIhUtFIFa7Nnz6ZRo0aWj/8pWBMRuRkNHjOGeSYTIWPH8lNODjaDBvH1r79iPXJkZQ9NREREijFy5EhWrFjBO++8w5MTJkBEBCPWraOOuzv+iYnY7d9vPtHJCQYNMi/zHDIEPDwqddwiIlJ5VLwALQUVkfI1f+5cxtx+OzmGwX0mE199/z1Wd91V2cMSERG5acXGxrJw4ULmz5/Pt99+S82aNQF4/803eWfWLJ6rV49HDx6ExMT8izw8YOhQ88y0gQPN4ZqIiNywVLxARKSKGDl2LD/l5nLHnXfyjWFgc/fdfJacjNUjj1T20ERERG4aR48eZf78+YSGhrJhwwby5hcs+uknHvT0hPnzeXTpUp5IT8d09qz5Ih8f815pISHg729e9ikiIlJAmQZr8fHxvPHGG/z555/Url2bf//737Ro0aIsbyEicl0ac/vtZOfkcPc99/ClYWD76KN8lJKC6ZlnKntoIiIiNyTDMNi/f7+lkueOHTsKtXeuX5+RtrYEPP00XNoT2hbMFb5DQsyvrl3ByqriBy8iIteNUgVrzzzzDHPnzi1UETQ1NZVOnTpx9OhRy09/fv75ZzZv3kzTpk3LZrQiItexO+66i5zcXMaNG8cngM2zz/JeUhKm6dPNVcRERETkmhiGwdatWwkNDWX+/PkcyCswAFhZWdG7QQNCcnMZER2N39Gj+Re2bGneLy0kBNq00f/LIiJSYqX68cvGjRu5/fbbCx378MMPiY6O5sknn+TChQts3LgRFxcXZs6cedX979y5kyFDhlC3bl0cHR3x9PSkW7du/PDDD0XO3b59OwEBAbi4uODh4UFISAhHjhwpzWOJiJS7u++5h9lff43JZOID4OmXX8aYOBG03aWIiMg1e+yxx+jcuTMzZ87kwIED2NnaMuSWW/jKz48zublEHD7ME9HR+AF07gwzZ8KBA7BnD/z3v9C2rUI1ERG5KqUK1o4cOULHjh0LHQsLC8PLy4s33ngDNzc3unbtylNPPcXatWuvuv8LFy7g5+fHa6+9xpIlS/juu++oX78+99xzD6+88orlvKioKPz9/cnMzGTu3LnMnj2bgwcP0qtXL86dO1eaRxMRKXf33Xcfn3/+OQAfAH++9x489JBlGYqIiIj8s5UrV/LQQw+xb98+y7F+ffvi7OjImGbN+MnHh3NZWYQfPMi/TpzAy8oK+vaFDz6AEyfgjz9g0iS45ZZKfAoREbnelaoqqIODAytXrqRXr14AZGdn4+zszIgRI5gzZ47lvDVr1jB48GDS09PLZLBdu3bl1KlTliWoY8eOJSIigsOHD1sqNBw7dowmTZowceJEXn/99RL1q6qgIlIZPvvsM2rt38+wDz6A3Fy47Tb47juws6vsoYmIiFQ5aWlpODo6Wt4PGzaMsLAwpk+bxlR/fwgNJTM0lJyYGCxn2dmZK3iGhJgretaoURlDFxGR61BJs6JSzVjz9vbm9OnTlvfbt28nKyuryCw2Kysr7O3tS3OLYtWoUQMbG/O2cNnZ2YSHhzNq1KhCD1ivXj369u3L/Pnzy+y+IiLlYfz48Qx7912YOxdsbbkwZ475C/+0tMoemoiISJUQHx/Pt99+y4gRI/D09CQ6OtrckJHBvS1bMqFZMwLfeccyE80uJgZHFxfzD6t+/hnOn4ewMLj/foVqIiJSLkpVvKBDhw588cUXjBkzBpPJxP/+9z9MJhP9+/cvdF5UVBS1atUq9eByc3PJzc0lISGBX375heXLl/Phhx8CcPjwYdLS0mjdunWR61q3bs3KlStJT0/HwcGh1PcXEakQo0Zx5LPP6PfAAzy4eDEvDB4MixaBq2tlj0xERKTCnT59mgULFhAaGkpERAQ5BbZKWP7aazySnAxLljAqOZlReQ3Vq8OwYeYfUAUEgL4HEBGRClKqYG3SpEn06NGDpk2bUqNGDTZt2kSvXr1o3759ofPCwsLo1KlTqQc3YcIEPvvsMwDs7Ox4//33GT9+PABxcXEAeHp6FrnO09MTwzBISEgoNtjLyMggIyPD8j4pKanUYxQRKQvL0tI4Zhh8bzLx5Nq1uAQEwNKlUMy/cSIiIjeaw4cPM3/+fEJDQ4mMjCzU1trPjxBHR0ZGR9Pqyy/zG2rXzq/k2asX2JTqWxsREZFrUqr/fbp06cLChQt58803iYuL48EHHyxS/fPMmTOcPHmS+++/v9SDe/7553nwwQc5e/YsYWFhPP7446SmpvLMM89YzjFdoWrP5dpmzJjB9OnTSz0uEZGyNmHCBKytrRlWrx4ud98NmzeDvz+sWAE+PpU9PBERkTK3b98+fv31V0JDQ9m1a1ehtq716xNibc3I6GganziR39C4MYwaZQ7UOnUCq1LtbCMiIlJmSlW8oLI8+uijfPnll5w6dYr4+HiaNWvGRx99xIQJEwqd9+yzz/LWW29x8eLFYpeCFjdjzc/PT8ULRKRq2LsXAgL468wZmjRpAqtWQd26lT0qERGRa5Kbm4vJZLL88Hv8+PGWKtnW1tb4N2hASHY2w48epXbBC9u2zZ+Z1qIFXOEH6yIiImWlXIsXVJbOnTuTnZ3NkSNHaNSoEY6OjuzZs6fIeXv27KFx48aX3V/N3t4eNze3Qi8RkSqjRQt+eOYZbgU++usv6NkT/vqrskclIiJSas8//zx+fn5s2rTJfMAwGNuhA0NvuYWv69QhNieHVYcOMeHoUWqbTNC9O8yaBYcPw44dMHUqtGypUE1ERKqc6ypYi4iIwMrKioYNG2JjY8PQoUMJDQ0lOTnZcs7x48eJiIggJCSkEkcqInJt9sXFkQM8Dnx+4oR575jduyt7WCIiIv8oPT2d5cuXU3BhzLFjxzh16hQLPvoInn0WGjem//jxLDp4kPtOnqS6jQ0MGACffAIxMbBhAzz9NDRsWIlPIiIi8s+q5FLQhx9+GDc3Nzp37oy3tzfnz5/nl19+Yc6cOTz77LO88cYbgLnqaKdOnWjfvj2TJ08mPT2dqVOnEh8fz86dO/Hy8irR/Uo6vU9EpKIYhmFZ1g7wFfCvatXMBQ26dKncwYmIiPxNXFwcS5YsISwsjKVLl5KSksKuXbtofeutsG4dWz/9lLNr1tA/IQH7vIscHCAoyLzEMzgYqlWrzEcQEREppKRZUZUM1r7++mu+/vpr9u/fz4ULF3BxcaFNmzY8+OCD3H333YXO3bZtG5MmTSIyMhIbGxv69evHrFmzaNSoUYnvp2BNRKoiwzCYOHEi7733Hibga+BeFxcICzMXNhAREakkhmGwb98+wsPDCQsLIzIyktzcXEt7nerV+bxtWwZt3w4JCfkXurnB0KHmPdOCgsDZuRJGLyIi8s+u62CtoilYE5GqyjAMHn/8cT7++GNMwA/AnQ4O8OuvMGRIZQ9PRERuIhkZGfz222+EhYURHh5OdHR0ofY29eox1NGRYdHRdMzIwLIbWs2aMHy4eWZav35gZ1fhYxcREblaJc2KbCpwTCIicpVMJhMffPAB2dnZfP7559wD2KSnM3bECPjf/2Ds2MoeooiI3ASeeeYZPvvsM1JSUizH7O3t6de0KUOB4Kgo/I4dy7+gbl1zkBYSYi5EYG1d8YMWERGpAKUK1o4fP35V59etW7c0txEREcDKyopPPvmE7OxsZs+ezZ0mE9bZ2Yy64w5ISYF//auyhygiIjcIwzDYs2cPixcvZuLEiTg4OADmH/SkpKTgU7MmwU2bEnzxIgG7duFcsLBOkyYwejSMGgXt26uCp4iI3BRKFazVr18f01X8R5mTk1Oa24iIyCVWVlZ88cUXZGdn891333G7ycSvubkMf+ABSEqCJ5+s7CGKiMh1Kjc3FysrK8v7QYMGcerUKdq1a0dQUBCcPcuj7u7c1rkz7bduxers2fyLW7TID9NatlSYJiIiN51SBWuzZ8++qmBNRESunZWVFbNnzyY7O5sff/yRMVZWzM/NZcjEiZCcDC+8oG9oRESkRM6cOWOp4rlr1y7++usvrK2tMZlMjB07lkN//onL8uXw+uvw2280zM2lYd7Fbduag7RRo+DWWyvxKURERCqfiheg4gUicn3Jzs7m7rvvZs6cOUz192f62rXmhmeegTfeULgmIiJFGIbBrl27LIUHNm/eXKg9MjKSrr6+EBpqLpCzcSMU/DahU6f8MK1x4woevYiISMWrlKqgBw8eJC4ujho1atCkSZOy6rbcKVgTketNVlYW8+fPZ8yYMZjeew8mTjQ3jB8PH32kTaJFRIS0tDTWrFlDeHg44eHhnDx5slB7x44dGdqjB8GGQbvISExbthTuoHt3c5AWEgL161fcwEVERKqACg3WfvnlF5555plC/1nXqVOHt956i9GjR19r9+VOwZqIXO8ufvwx+x57jI4Ad94J33wDtraVPCoREalohmHw1VdfsWjRIlatWkVaWpqlzdHRkQEDBjC0UyeGpKRQa8UK2LEj/2KTCXr3zg/TateuhCcQERGpGiosWFuyZAlDhw6lRYsW3HPPPfj6+hITE8MPP/zAvn37CAsLY9CgQddyi3KnYE1ErmepqakMHTqUPzZuZGl2Nr1zcmDYMJgzBy5VcxMRkRuTYRgcOnSo0GqRjh07sm3bNgD8/PwIHjKE4Fat6BsTg+OiRfDnn/kdWFtD377mMG3ECPDxqeAnEBERqZoqLFjr0aMHbm5uLF68uFA1IcMwGDRoEMnJyWzYsOFablHuFKyJyPUsPT2dkSNHsn79epa++CI9p02D9HTo3x8WLAAXl8oeooiIlIPz58/Tpk0bzp07R1xcHK6urgB8/fXXxJw8ydBbbqH17t2Y5s2DAwfyL7S1hYAAc5g2fDjUqFFJTyAiIlJ1lTQrKlVV0IJ27tzJzz//XChUAzCZTEyYMIE777zzWm8hIiJX4ODgQGhoKAcPHqRNmzbQuTMMHQqrV8PAgbBkCXh4VPYwRUTkGpw8eZLFixdz4cIFJk2aBECNGjVwdnYmMTGRP//8k25du8KWLdwfFWUuQHDkSH4H9vbm/xNGjzb/H1GtWiU9iYiIyI3lmoM1a2trMjMzi23LysoqEriJiEjZc3R0NIdqAP7+7PrkEzInTKBTZKR5ic/y5VCzZuUOUkRESiw3N5dt27YRFhZGWFgYO3fuBMDV1ZWJEydiZ2cHQNjChdQ7fRqHX36B22+H48fzO3F0hEGDzGHakCGglRkiIiJl7pqXgvbv35+UlBTWrl2Lo6Oj5XhGRgb+/v64uLiwcuXKax5oedJSUBG5kezdu5fevXuTm5XFaltb2sfHQ9OmsGoV1KlT2cMTEZHLSE1NZeXKlYSHh7N48WLOnDljaTOZTHTt2pWhQ4fy+KOP4rpzJ8ybZ36dPp3fibMzBAebw7RBg8zvRURE5KpV2FLQ6dOn079/fxo2bMiYMWPw8fHh9OnThIaGEhcXx5o1a671FiIichXq1avHrbfeyoYNGxjg4cEab2/aHDgAPXual4c2alTZQxQRkUuOHz9OeHg4YWFhREREkJGRYWlzdXUlMDCQ4OBgBg8YgNfeveYgrWlTOHs2vxM3N3PRmtGjzcs9C/ywW0RERMrXNc9YA1i3bh2TJ09m8+bNGIaBlZUVXbp0YcaMGfTu3bssxlmuNGNNRG40SUlJBAYGsmnTJmp4ehLh4kLL48ehVi1YuRJatKjsIYqI3PSSkpLw9PQkJyfHcqxBgwYMHTqUoUOH0rtrV+x++80cpi1YAPHx+Rd7epoLD4webS5WY29f8Q8gIiJyA6uwqqAFXbx4kYSEBKpVq4aTk1NZdVvuFKyJyI0oMTGRAQMGsGXLFryqV2etpyfN//rL/M3Y8uXQsWNlD1FE5KZx5MgRXn75ZS5cuMD8+fMtx/v160dWVhbBwcEMHTqUWxs0wLRypbn4wKJFkJiY34mXF4wcaQ7T/P3N1T1FRESkXFRKsHa9UrAmIjeqhIQEAgIC2L59O941a7LO25ume/aAqyssXgy9elX2EEVEbkhHjx4lOTmZVq1aAXDixAnq1q2LyWQiNjYWLy8vALKzs7HJyIClS80z08LDISUlv6NatSAkxBym9eoF1taV8TgiIiI3nQrbYw1gwYIF/O9//+PYsWOkp6cXajOZTOzatassbiMiIlepWrVqrFixgv79+7Nr1y76WlmxrnNnmmzeDIGBEBoKQUGVPUwRketeTk4OmzZtsuyXtnfvXoKCgli6dCkAfn5+vP7663Ts2BEPDw9ITobwcGzmzYMlSyAtLb8zPz8YNcocpnXrBlZWlfNQIiIi8o+uOVh78803mTRpEl5eXjRu3BhnVR4SEalSqlevzsqVK+nbty979+6ln7U16/r2pWFEhHmz659+Mn8DJyIiVyUxMZEVK1YQFhbGkiVLiIuLs7RZW1tjGAaGYWAymQB47uGHzcs7R482L8kvUKiABg3Mx0ePhk6d4NI1IiIiUrVd81LQBg0a0L9/fz777DOsr9Op6VoKKiI3g9jYWPr27cv+/fupW7cu61q1ov7ixeaZELNnw733VvYQRUSqvMOHDxMWFkZ4eDjr1q0jOzvb0latWjUGDRpEcHAwQUFBVKtWDeLiYOFC855pq1ZBVlZ+Z7fckh+mtW2rME1ERKQKqbCloHFxcdx5553XbagmInKz8Pb2ZvXq1fj7+3Pw4EH6Wlmx/rbbqD1nDtx3n3lPn8ceq+xhiohUSfv27WPUqFFERUUVOt60aVOGDh1KcHAwPXr0wMbGBmJjYe5cc5gWEQEFqn7SokV+mNaihcI0ERGR69w1B2s9evRg//799OvXryzGIyIi5ahWrVqsWbOGPn360KRJE6rPng0+PvDee/D445CUBFOmVPYwRUQq1YULF1i2bBk2NjaMHj0agHr16hEdHY2NjQ29evWyhGlNmjQxX3TqFHz6qTlM+/13yM3N77BtW3OQNmoUNGtW8Q8kIiIi5eaag7V3332XkSNH4ufnR1BQEHZ2dmUxLhERKSe1a9fm999/x9PTE3t7e3jnHXB3h//+F55/3hyuvfaaZlGIyE0lJyfHsgIjLCyMcePG0aZNG0uw5uzszPLly2nTpo25+ADAsWPmf0N//RU2bizcYadO+WFao0YV+CQiIiJSka45WGvcuDEBAQGMHDkSk8mEk5NToXaTyURiYuK13kZERMpQrVq1LB8bwNtubtz14ov4vPwyzJxpDtc++ECV6ETkhpWVlcWGDRssVTwffPBBnn32WQAGDRpEy5YtCQoKKhS49enTBw4fhs8/N4dpW7YU7rR7d3OYFhIC9epV9COJiIhIJbjmYO25557jww8/pG3bttx6662asSYicp159dVXefHFF/m6RQu2ffgh9k88AR9/DMnJ5qIGNtf8X4WISJUQHx/PsmXLCAsLY9myZVy4cMHStmTJEkuwVqNGDfbs2ZN/YVQUzJtnDtN27sw/bjJB797mMG3kSKhdu2IeRERERKqMa/5u6ZtvvmHSpEnMmDGjLMYjIiIV7Pbbb+ezzz7jsccew/7RR8HTE+65B77/HlJT4ccfwd6+socpInLVDMPgwIEDliqeGzZsIKdAIYEaNWowePBghg4dysCBAwteCHv3moO0X381f5zH2hr69jWHaSNGgLd3xT2QiIiIVDnXHKzl5OQwYMCAshiLxZo1a/jhhx/YuHEjJ06cwMPDg44dOzJ16lQ6dOhQ6Nzt27fz3HPPsWnTJmxsbOjXrx+zZs2iYcOGZTomEZEbVePGjdm/fz8uLi7mA3fcAc7OMGYMhIbC8OHmX/+21F9EpKqKioris88+IywsjMOHDxdqa9mypaXwQJcuXfIr2xsG7NiRH6YdPJh/ka0tBASYw7Rhw6BGjQp8GhEREanKrjlYGzhwIJs2bSrTqqCffPIJcXFx/Oc//6F58+acO3eOt956i65du7J8+XLLvaKiovD396dt27bMnTuX9PR0pk6dSq9evdi5cydeXl5lNiYRkRuZJVQD4uLieGb+fN6ZMwePu+6C5cshMBDCw81FDkREqpizZ8+SnZ2Nr68vADExMbz77rsA2NnZ0bdvX4KDgwkODqZ+/fr5FxoG/PFH/jLP6Oj8Nnt78799o0fD0KGQV7BAREREpACTYRjGtXSwZ88ebrvtNsaPH8+QIUPw9PQsck5xx67k7Nmz1KxZs9CxlJQUGjduTMuWLVm1ahUAY8eOJSIigsOHD+Pm5gbAsWPHaNKkCRMnTuT1118v0f2SkpJwd3cnMTHR0o+IyM1qwIABrFq1is6dO7Pi5ZdxHzsWEhOhfXtzyKaZGiJShbzyyitMnTqVJ598krfffhuAzMxMnnjiCYKCgggICMDV1TX/gtxccwXPefPMrxMn8tscHWHwYHOYNmQIFLxOREREbiolzYquOVizulQxzmQyXfacgntZXIt+/foRExPDgQMHyM7Oxs3NjXHjxvHpp58WOi8wMJDo6GgOFpzCfwUK1kRE8u3evZu+ffsSHx9Pt27dWP7mm7iOHAnnzkHz5rByJVyaFSIiUlEuXrxIREQE4eHhPPLII7Rp0waA0NBQRo0aRUhICPPmzSv+4pwc+P1386y00FA4fTq/zcUFgoPNYVpQkHkpvIiIiNz0SpoVXfNS0KlTp14xVCsriYmJbN++3bIM9PDhw6SlpdG6desi57Zu3ZqVK1eSnp6Og4NDuY9NRORG0rp1a1atWkX//v2JjIxk8OTJLF26FJcRI2DfPujVC1atggYNKnuoInKDO3HiBIsXLyY8PJzVq1eTnp4OgLe3tyVYGzRoECdPnqT23ytyZmXB2rXmMG3+fPMPB/K4u5v3Shs9GgYOBH29KCIiIqV0zcHaSy+9VAbD+GePPfYYqamp/N///R9g3gMIil9m6unpiWEYJCQkUKtWrSLtGRkZZGRkWN4nJSWV06hFRK5P7dq1Y+XKlfTv35/169cz9JlnWLxiBU7BwXDkSH641qxZZQ9VRG4gOTk5bN68mfDwcMLDw9m9e3eh9rp16xIcHFyocJajo2N+qJaeDqtXm5d4LlwI8fH5F3t6mqt4jhoF/fur2rGIiIiUiWsO1irCiy++yP/+9z8++OCDIlVBrzRb7nJtM2bMYPr06WU6RhGRG02HDh1YsWIFAwYMYO3atQx74gnCVqzAcdiw/JlrK1ZAu3aVPVQRuY4lJiayfPlyFi9ezJIlSzh//rylzcrKim7dulkKD7Ro0aLo13cJCbBkCSxYAMuWQUpKfpuXF4SEmMM0f39zdU8RERGRMlQmwdqCBQv43//+x7FjxyxT9POYTCZ27dpV6r6nT5/OK6+8wquvvsrjjz9uOV69enUgf+ZaQfHx8ZhMJjwuU71pypQpPPXUU5b3SUlJ+Pn5lXqMIiI3qs6dO7Ns2TIGDhzI6tWrGTFhAguXL8dhxAjYtg369oXFi6FHj8oeqohcJ/K2980LyN544w1ee+01S7uHhwdBQUEEBwcTFBRk+ZqvkBMnzDPSFi40L/fMzs5vq13bPDNt9GjzDwCsrcvxaURERORmd83B2ptvvsmkSZPw8vKicePGOJfhhq/Tp0/npZde4qWXXuL5558v1NaoUSMcHR3Zs2dPkev27NlD48aNL7u/mr29Pfaa/i8iUiLdunVj6dKlBAUFsWLFCkaNH0/o0qXYjxpl3gx84EDzTJECS7NERIozc+ZMvvrqK9566y2GDRsGQHBwMPPnzyc4OJghQ4bQvXt3bP8+s8ww4M8/zf/WLFxoDvYLatHCHKYNHw4dOsCl4loiIiIi5e2aq4I2aNCA/v3789lnn2Fdhj8RfPnll5k6dSovvPACL7/8crHn3Hbbbaxdu5ZDhw5ZyqgfP36cJk2aMHHiRGbOnFmie6kqqIjIP1u3bh2DBg0iLS2NoUOH8ut332F3xx3mpVd2djB3rvmbWhER4OzZsyxdupQ777zTEpQ98cQTfPjhhzz88MN89tlngHkGW7Hbd+TkwIYN5iBtwQLz/o55TCbzTNm8MK1x4/J/IBEREbmplDQruuZgzc3NjQULFliqdZaFt956i2eeeYagoCCmTZtWpL1r164AREVF0alTJ9q3b8/kyZNJT09n6tSpxMfHs3PnTry8vEp0PwVrIiIls3r1aoKDg0lPT+eJJ57g/TffhLvuMm8Ubm0N335rfi8iNx3DMNi5c6eliufmzZsxDIO1a9fSp08fAHbv3s2hQ4cYMGCA5YeihVy8CCtXmsO0sDAosN8a9vbmGbIjRkBwMNSsWTEPJiIiIjelCgvWBg0aRHBwMI899ti1dFOIv78/69atu2x7wSFv27aNSZMmERkZiY2NDf369WPWrFk0atSoxPdTsCYiUnIrVqzgqaeeYunSpeb9KbOz4cEHzaGayQSffALjx1f2MEWkAly8eJHVq1cTHh7O4sWLiYmJKdTevn17XnvtNQIDAy/fyfnzEB5uDtOWL4e0tPy2atVg6FDzrLSBA8HFpZyeRERERKSwCgvWDhw4wMiRI5k5cyZBQUHY2dldS3eVQsGaiMjVycnJKbz8PzcX/vMf+PBD8/s33oBnn62cwYlIuTp27JhlVlpEREShwlVOTk4MGDCA4OBgBg8ejK+vb/GdREfnL/H8/XfzvyF56tXLX+LZqxfYXBdF7EVEROQGU9Ks6Jq/UmncuDEBAQGMHDkSk8mEk5NToXaTyURiYuK13kZERKqQgqHaL7/8wtKlS/ni88+xdnOD116D556D9evhpZegXbvKG6iIlKlx48bx/fffFzpWv359S+EBf3//4otHGQbs2JEfpu3eXbi9bVtzkDZiBLRpY579KiIiInIduOZg7bnnnuPDDz+kbdu23HrrrdfljDURESmd06dPM27cONLT0+nRowcPvPoquLvDlCmwaJH5NWIETJtm/sZZRK4LhmEwZ84cFi9ezPvvv0+1atUAaNasGVZWVvTo0YMhQ4YQHBxM8+bNiy8+kJUFv/1mDtMWLoTjx/PbrK2hd29zmDZ8ONSvXzEPJiIiIlLGrnkpaPXq1Xn44YeZMWNGWY2pwmkpqIhI6f3yyy+sXLmSTz75JH8mW1QUvPwy/PSTeaYKwMiR5oCtTZvKG6yIFMswDE6fPl1o6WbLli3Zu3cvP/30E7fffjsA8fHxAHh6ehbfUUqKeZ+0BQvM+6ZduJDf5uQEQUHmIG3IEKhevZyeRkREROTaVdgeax4eHoSGhpZpVdCKpmBNRKTs5OTkYGVlZZ7Bsn+/OWD7+ef8gC0kxBywtW5duQMVucllZGTw22+/WfZLO336NOfPn8fR0RGA999/n5iYGO69916aN29++Y5iY80VPBcsgFWrICMjv83Ly1x8YMQICAiAS32LiIiIVHUVFqyNHTuWtm3b8vzzz19LN5VKwZqISNnIzs7m3nvvxdPTk/fffz9/edi+feaAbc6c/IBt1ChzwNaqVeUNWOQmExsby5IlSwgPD2fFihWkpKRY2uzs7Pjtt9/o0qXLP3d08GD+fmmRkfl/rwEaNTIHaSNGQLdu5mWfIiIiIteZCgvW9uzZw2233cb48eMZMmRIsUsDLrtcoIpQsCYiUjYiIiLo378/hmHQsWNHnnrqKUaPHo2tra35hL174b//hV9+yf9GfMwYmDoVWrasvIGL3KAMw2DHjh2Eh4cTHh7Oli1bCrX7+PhY9koLCAjAxcWl+I5yc2HLlvwwbf/+wu0dO+aHac2bq/iAiIiIXPcqLFizsrIyd3SFL6BycnKu5RblTsGaiEjZ+frrr5kwYQLp6ekA1KlThyeeeIKHHnrIsgE6f/6ZH7CB+ZvwvICtRYtKGrnIjcMwDJ544gnmz5/PqVOnCrV17NiR4OBggoODadeuneVruSIyMyEiwhykLVwIp0/nt9nYQN++5iBt2DCoU6fcnkVERESkMlRYsPbSSy9dMVQDmDZt2rXcotwpWBMRKVvnzp3j008/5aOPPiI2NhYAZ2dn7r//fv7zn//QuHFj84l79pgDtl9/Nb83mWDsWHPAdqU9nUSkkKNHjxIZGckdd9xhOTZgwABWrVqFs7MzAwcOJDg4mEGDBlGrVq3Ld5SYCEuXmsO0JUsgOTm/zdUVBg0yh2mDBoGHR3k9joiIiEilq7Bg7UagYE1EpHxkZGTw008/8fbbb7Nnzx7APMN56NChPPXUU/Tu3dv8w5ndu80B27x5XDoJbrvNHLDdemslPoFI1Xf+/Hlq1qyJYRicOnXKEpytXbuWzMxM+vTpg729/eU7iImBRYvMYVpEBGRl5bf5+JireI4YYZ6hdqV+RERERG4gCtaugoI1EZHyZRgGa9as4e2332bJkiWW4+3ateOrr76iXbt25gO7dpkDttBQ83uTCe64A158EZo1q4SRi1QdCQkJLF++nPDwcDIzM5k7d66lrXv37tja2vLhhx/S6p8KghiGeY+0BQvMr7/tu0azZvn7pXXqBJdbKioiIiJyA1OwdhUUrImIVJyoqCjee+89vv32W3Jycjh27Bg+Pj6AOYAzmUywcydMn27+ph/M39jnBWxNm1ba2EUqkmEYREVFWQoPbNiwwbJvrY2NDXFxcZavW7Kzs7Gxsbl8Zzk5sGlTfph26FB+m8kEXbuag7Thw/V3TERERAQFa1dFwZqISMWLi4tj48aNDB061HJs+PDh+Pr68sILL1C7dm3YscMcsC1caD7BygruvNMcsN1ySyWNXKT8ZGRksG7dOsLDw1m8eDFHjhwp1N6iRQtL4YFu3bphbW19+c7S0mD1anOQtmgRnDuX32ZnBwEB5jBt6FDzkk8RERERsVCwdhUUrImIVL6oqChuvfVWrKysOHLkCPXq1ctv3L7dHLAtWmR+b2UFd91lDtiaNKmcAYuUkbS0NH766SfCw8NZsWIFqampljZ7e3v69u1LcHAwQ4YMoX79+lfuLD4eFi82h2nLlsHFi/lt7u4QHGwO0wIDzcUIRERERKRYCtaugoI1EZHKZxgG69atY+vWrTzzzDOW488++ywtW7bk9ttvx/7PP80BW1iYudHKCu6+2xyw5VUaFanicnNziY2NtRQZSE9Pp3r16ly8FILVqlXLMiutf//+ODs7X7nDY8fMszoXLIDffjMv+8xTp07+Es8+fcDWtnweSkREROQGo2DtKihYExGpmg4ePEizZs0wDAMfHx8ee+wxHnnkEWocPQovvWSemQNgbQ333AMvvACNGlXmkEWuaMuWLQwbNoxq1aqxb98+y/HJkyfj5OREcHAw7dq1M+81eDmGYa6ku2CBOVDbsaNwe6tW+WFa+/bmPdRERERE5KooWLsKCtZERKqmCxcu8Pnnn/P+++8TExMDgIODA+PGjePJJ5/k1pQUc8CWV2nU2hrGjTMHbA0bVt7ARYDo6GjCw8Px8vLi9ttvB8yf0zVq1MDR0ZEjR47g5eVVss6ys2H9+vww7ejR/DYrK+jZ0xykDR+ucFlERESkDChYuwoK1kREqrasrCx++eUX3nnnHbZu3Wo5PmjQICZOnEiAqyum//4Xli41N1hbw733mgO2Bg0qadRys8nNzWXz5s3Mnz+f8PBwy4y0rl27EhkZaTlvy5YttG7dGnt7+yt3mJoKK1aYw7TwcPP+aXkcHMz7pA0fbt43raQBnYiIiIiUSEmzIqsKHJOIiEip2Nracuedd7J582Z+//13Ro4ciclkYunSpQwcOJDWDz3E7NGjSV+7FoKCzHtMzZ5trhz64IOFZ/eIlKHs7GwiIiJ44oknqFu3Lt26deONN95g3759WFtb4+/vz5gxYyj4c8xOnTpdPlQ7d878uTtsGNSoASEh8N135lCtenW47z6YPx/OnzcHbvffr1BNRETK1KZNmxgzZgy1atXCzs4OHx8fRo8eXeiHRCXx0ksvXXlrgytYu3YtJpOJtWvXlur6kvL398ff3/+K5+Tk5PD2228TFBREnTp1cHJy4tZbb2Xy5MlcuHCh2Gs++OADmjVrhr29PQ0aNGD69OlkZWUVOufkyZM8+eST9OnTBw8PD0wmE998802x/WVmZjJ16lQaNGiAnZ0d9erVY8qUKaSlpZXiqaWsacYamrEmInI9Onz4MO+//z6zZ88mJSUFgJo1a/LRRx8xunZt8xLRFSvMJ9vYmAOI55+Hf6qqKPIPMjIyWLNmDfPmzWPhwoWcP3/e0ubq6kpwcDDDhw8nMDAQDw+Pf+7w8OH8JZ4bNkBubn5b/frm/dJGjIAePcyfyyIiIuXkgw8+4Mknn6Rz585MmDCBevXqcfz4cT766CM2b97Me++9x+OPP16ivk6ePMnJkyfp2rXrVY8jKSmJffv20bx583L9Hj0vVLtSgJeSkoKvry933HEHAwYMoEaNGmzfvp1XXnmFWrVqsXXrVhwdHS3nv/rqq7z44otMnjyZgQMHsmXLFl544QXuvfdePv/8c8t5a9euZcyYMbRt2xYvLy9++uknvv76a+67774iYxg1ahRLlixh6tSpdOrUicjISF555RUCAwNZtGhRWf12yN+UOCsyxEhMTDQAIzExsbKHIiIiVykhIcF48803DT8/PwMw1q9fb2nL/u03wxgwwDDM270bho2NYTz8sGEcPVqJI5br2d69ew03NzcDsLyqV69u/Otf/zLCw8ON9PT0f+4kN9cwtmwxjP/7P8No2TL/8zPv1b69YUyfbhi7dpnPFRERqQDr1683rKysjODgYCMrK6tQW1ZWlhEcHGxYWVkV+lqrOKmpqeU5zDLVp08fo0+fPlc8Jzs72zh//nyR47/88osBGN9//73l2Pnz5w0HBwfj4YcfLnTuq6++aphMJmPv3r2WYzk5OZaPt2zZYgDG119/XeQ+kZGRBmC89dZbhY6/9tprBmCsWLHiiuOX0itpVqSloCIicl3z8PDgmWee4ciRIyxZsoTu3btb2p6eN4+BwObPP4eAAPMG8J9/Dk2awCOPwPHjlTdwqfKSkpL48ccfCy3LuOWWW7C1taVWrVpMmDCB1atXc+bMGb766iuGDBly+SWemZmwciU89hjUrQudOsGrr8Kff5r3BOzfH95/37xseds2mDoVWrdWRU8REakwM2bMwGQy8cknn2DztxnSNjY2fPzxx5hMJmbOnGk5nrfcc/v27YwePZpq1arR6FIRneKWgmZkZPD000/j4+ODk5MTvXv3Ztu2bdSvX7/QTK3iloLed999uLi4cOjQIQYPHoyLiwt+fn48/fTTZGRkFLrP9OnT6dKlC56enri5udG+fXu++uqrQlszlJS1tTXVq1cvcrxz584AnDhxwnJs2bJlpKenc//99xc69/7778cwDBYsWGA5ZmVVsjhmw4YNAAwePLjQ8eDgYADmzZtXon6k/Gg9gYiI3BBsbGwYNGiQ5X16ejrffPMNiYmJPPvss+ZQ4/ffYfp0WL0aPvvMvJfVAw+Yl4j6+VXi6KWqMAzD8k3A2rVrueuuu/Dz8+Pee+/FZDJhY2PD5s2bqV+//j9/QZyUBMuWmZd4Ll4MiYn5bc7O5v0AR4yAwYPB07P8HkpERMqfYcDFi5U9inxOTlf1w5mcnBwiIiLo2LEjderUKfYcPz8/OnTowJo1a8jJycHa2trSFhISwu23384jjzxCamrqZe9z//33M2fOHJ577jn69evHvn37GDlyJElJSSUaZ1ZWFsOGDeOBBx7g6aef5rfffuPll1/G3d2dqVOnWs47evQo48ePp27duoB537gnnniCmJiYQuddizVr1gDQokULy7E///wTgFatWhU6t1atWtSoUcPSfjUyMzMBivzwLu/97t27r7pPKVsK1kRE5Ibk4ODAjh07+PnnnwkICDAf7NWL1wcMIKV2bR47dAifjRvh00/NAduDD8KUKXCZLyblxhUTE8OCBQuYN28evXv35qWXXgJg4MCBdOzYkcDAQNLT0y37pzRs2PDynUVHm/f2W7jQHOBe+mIYgJo1zUUJRowwz1BzcCi/hxIRkYp18SK4uFT2KPKlpJh/iFNC58+f5+LFizT4h2rqDRo0YPPmzcTFxVGzZk3L8XvvvZfp06df8dp9+/bx008/MWnSJGbMmAHAgAED8Pb25o477ijRODMzM5k+fTpjxowBoH///mzdupUff/yxUGD29ddfWz7Ozc3F398fwzB47733ePHFF0tdVCFPTEwMkydPpmPHjpaZYwBxcXHY29vjXMzvvaenJ3FxcVd9r+bNmwPmmWsF/3zWr19vuadULgVrIiJyw2rQoAFTpkyxvE9NTeX1118nISGBN+zsuCMwkIlxcbTZuhU+/hi+/BIeesgcsNWuXYkjl/IWHR3NvHnzCA0NLVTl7PTp05ZgzcHBgS1btly5o7g4iIiAVavMr8OHC7c3aZJffKBLF/OyTxERketU3lLKvwdTo0aN+sdr161bB8DYsWMLHR89ejT33HNPie5vMpkYOnRooWOtW7e2zB7Ls2bNGl577TW2bNlSZDbc2bNn8fb2LtH9ihMfH8/gwYMxDIM5c+YUmcF+pdCuNIHeoEGDaNy4MZMmTcLb25tOnTqxadMmnn/+eaytrUu8pFTKj4I1ERG5adjb2/P555/z9ttvExkZybfLl/Mt0K9dO57KzGTQ3r1YffQRfPEFPPwwTJ6sgO0Gsn//fkuYtmPHjkJt3bt3JyQkhJCQkCt3kp5urty5apV5efH27eblP3msrc0BWnCwOUxr1kz7pImI3AycnMyzxKoKJ6erOr1GjRo4OTkRHR19xfOOHj2Kk5MTnn/bwqBWrVr/eI+8mVV/D7VsbGyK3cOsOE5OTjj8bca3vb096enplvebN29m4MCB+Pv788UXX1CnTh3s7OxYsGABr776KmlpaSW6V3ESEhIYMGAAMTExrFmzpsgs9urVq5Oens7Fixdx+tufQXx8PB06dLjqe9rZ2bF06VLuueceBg4cCICzszOvvfYaL7/8MrX1tWqlU7AmIiI3DRsbG0aPHs3o0aPZtGkT77zzDvPmzWPNjh2sAZr6+fGkvT3jDh3C6cMPCwdsvr6VPXy5SoZhsGPHDkJDQ5k3bx5RUVGWNmtra/r06cOoUaMYMWIEvpf7883NhR078mekrV9vDtcKatHCXBwjIAB694YrlWMXEZEbk8l0VUsvqxpra2v69u3LsmXLOHnyZLH7rJ08eZJt27YxaNCgQvurQclmYuWFZ7GxsYXCoOzs7DJdzvjzzz9ja2tLeHh4oRCuYOGA0khISCAgIIDo6GhWr15N69ati5yTt7fanj176NKli+X4mTNnOH/+PC1btizVvRs3bkxkZCQxMTHEx8fTqFEjEhMT+c9//kPv3r1L90BSZqrknMHk5GSee+45Bg4ciJeXFyaTybIs4++2b99OQEAALi4ueHh4EBISwpEjRyp2wCIict3p2rUrc+bM4ciRIzzzzDO4ublx4MQJHj10CD9XV56vU4dTGRnwwQfQqBE8+SScPl3Zw5arsGPHDjp06MCrr75KVFQUtra2DB48mK+++oozZ86wevVqJkyYUDRUO3LEXD127Fjw8oKOHc3h6qpV5lDN1xfGjYPvvoOYGHNlz3ffNc9SU6gmIiLXqSlTpmAYBhMmTCAnJ6dQW05ODo8++iiGYRTaZuNq5AVAc+bMKXT8119/JTs7u3SDLkZesaGC4V9aWhrff/99qfvMC9WOHDnCihUraNeuXbHnBQUF4eDgUKiiOMA333yDyWRixIgRpR4DQO3atWnVqhVOTk68+eabODs788ADD1xTn3LtquSMtbi4OD7//HPatGnDiBEj+PLLL4s9LyoqCn9/f9q2bcvcuXNJT09n6tSp9OrVi507d+Ll5VXBIxcRketN3bp1efPNN5k6dSqzZ8/mvffeIzo6mhnJycyysWGSjw8vnzwJ771nriT6yCMwaRL4+FT20KWA/fv38+GHH+Lq6srMmTMBaNeuHc2aNaN58+aMGjWKIUOG4O7uXvTiuDhYs8a8tHPVKnMBgoJcXcHf3zwjbcAALe8UEZEbUo8ePXj33Xd58skn6dmzJ48//jh169bl+PHjfPTRR/zxxx+8++67dO/evVT9t2jRgjvuuIO33noLa2tr+vXrx969e3nrrbdwd3cvs73ChgwZwttvv82dd97Jww8/TFxcHLNmzSpSVbOk0tLSCAwMZMeOHbz77rtkZ2ezadMmS7uXlxeNGjUCzAUKXnjhBV588UU8PT0ZOHAgW7Zs4aWXXuLBBx+0FCLI8+uvvwJYJgdt3boVl0tFMEaPHm0574033sDHx4e6desSGxvL3LlzWbBgAd9//72WglYFRhWUm5tr5ObmGoZhGOfOnTMAY9q0aUXOGzNmjFGjRg0jMTHRcuzo0aOGra2t8dxzz5X4fomJiQZQqB8REbk5ZWdnG6GhoUbPnj0NwPjs008NY8UKw+jWzcgEIwcMw8HBMCZONIzTpyt7uDet9PR04/z585b3ERERBmB4enoamZmZluM5OTlFL7540fxn+txzhtG+vWGYTIZh3inN/LKxMYyePQ1j+nTD2LDBMAr0JyIicqOLjIw0Ro8ebXh7exs2NjZGzZo1jZCQEGPjxo1Fzp02bZoBGOfOnbtsW0Hp6enGU089ZdSsWdNwcHAwunbtakRGRhru7u7GxIkTLefl/b8eERFhOXbvvfcazs7OJbrP7NmzjaZNmxr29vZGw4YNjRkzZhhfffWVARjR0dGW8/r06WP06dPnir8f0dHRBnDZ17333lvkmvfee8+45ZZbDDs7O6Nu3brGtGnTCn19kudK/RY0ffp0o1GjRoa9vb3h4eFhBAUFGb/99tsVxy3XrqRZkckwCu64W/WcP38eLy8vpk2bVmg5aHZ2Nm5ubowbN45PP/200DWBgYFER0dz8ODBEt0jKSkJd3d3EhMTcdMSDhERuWTLli20aNHCvPmsYfDZk08y6/PPmZ6ezp0Ajo7w6KPw3HNwDdWlpGRSU1NZunQpoaGhhIeHc9999/H+++8D5q8LJk6cyODBgxkwYAA2NgUm5efkFN0nLSOjcOctWxbeJ83VtQKfTERE5Oa1ceNGevTowf/+9z/uvPPOyh6OiEVJs6IquRS0JA4fPkxaWlqxGwa2bt2alStXkp6eXqRiiIiISEl16tQp/43JxLdbtnAoPZ2z48fDzp3wxx/w9tvwySfw2GPw7LNQs2aljfdGdOHCBcLDw5k3bx7Lli0rVPXrjz/+sHxsY2PDBx98YH5jGHD4cH6QtmYNxMcX7tjX17ysMyAA+veHElQzExERkWuzcuVKIiMj6dChA46OjuzatYuZM2fSpEmTf67MLVJFXbfBWl7VkL+X+c07ZhgGCQkJxZb9zcjIIKPAT6qTkpLKb6AiInLDWLlyJd9++y133323eUbT8uX89PjjhB0+zMRZs+j08cf5AZv2+Sy1s2fPsnDhQubNm8fq1asLbWjcsGFDQkJCGDVqFJ07d86/6Pz5wvukHT1auFNXV+jbN3+ftKZNtU+aiIhIBXNzc2PFihW8++67JCcnU6NGDQYNGsSMGTM0KUauW9dtsJbnSmV9L9c2Y8YMpk+fXl5DEhGRG5SzszMTJkywvDcCA3nTzY0dwE9Az4sXmfjmmwz/8EOsn3gCnnlGAVsJxcfH87///Y958+bx+++/k5uba2nLKz4watQoWrdubf7/PS0tf0baqlXmpZ4F2dhAt275s9I6dTIfExERkUrTpUsX1q9fX9nDEClT1+1XmNWrVwfyZ64VFB8fj8lkwsPDo9hrp0yZwlNPPWV5n5SUhJ+fX7mMU0REblwmk4mvvvqKd955h59//pn1WVmsBxqkpfGfN97gXx9+iGtewFajRmUPt8rJyMiwVOg6f/48//73vy1tHTp0ICQkhJCQEJo1a2beJ237dpg50xykbdhQdJ+0Vq0K75N2qaqWiIiIiEh5KZt6tpWgUaNGODo6smfPniJte/bsoXHjxpedSmpvb4+bm1uhl4iISGm0a9eO7777jqNHj/L888/j6elJNPAkUOfiRZ55/XWO1a0LU6ZAMT8MuhlFRETQpk0b7rrrLsuxW265hXvuuYe3336b6Ohotm7ZwvNjx9Js7VoYPdo8869zZ3j+efOSz4wMqF0b7rsPfvgBTp+G3bvNe94NHqxQTUREREQqxHUbrNnY2DB06FBCQ0NJTk62HD9+/DgRERHa+FBERCqUr68vr776KidOnOCTTz6hadOmJAFvAY3S0rht5kw2+fnB//3fTRWwGYbB1q1b2bt3r+WYu7s7u3fvZuXKlWRmZlqOf/fWW0z09aX+K69AgwbQpIm56uq8eZCQAG5uMHw4fPghREXBiRPw9ddw113g41MZjyciIiIiNzmTYRhGZQ+iOEuXLiU1NZXk5GT+9a9/MWbMGMaOHQvA4MGDcXJyIioqik6dOtG+fXsmT55Meno6U6dOJT4+np07d+JVwn1tSlpCVUREpKRyc3NZunQp77zzDqtXr7YcHwAsd3HB9J//wFNPQTFFeK53OTk5bNy4kXnz5hEaGsqJEye49957+eabbwBz2PbTTz8R2KsX1ffty98nbefOwh3Z2kL37vnLOzt21D5pIiIiIlIhSpoVVdlgrX79+hw7dqzYtujoaOrXrw/Atm3bmDRpEpGRkdjY2NCvXz9mzZpFo0aNSnwvBWsiIlKedu3axbvvvsuPP/zAIx4evHf+PACGiwspjz6K65QpUK1aJY/y2mRlZREREUFoaCgLFiwgNjbW0ubs7Mxdd93FZx9/DNu25QdpGzZAgRlrALRunR+k9eqlJZ0iIiIiUimu+2CtIilYExGRinDmzBlMhoH3pk3w0kus372bwcDjdna8NmkSTJx4XQVsaWlprFy5knnz5rFo0SIuXLhgaXN3d2fYsGGM6taNgZmZOP72m3lvtALnAFCnTn7lzv79wdu7Qp9BRERERKQ4CtaugoI1ERGpcLm5/HvoUD5YsoSHgM8B3N3hySfNr8tUtq4K/vrrL1544QUWL15Mamqq5XjNmjUZERhIiK8vfc+cwS4iAo4fL3yxuzv07ZsfpjVpAiZTBT+BiIiIiMiVlTQrum6LF4iIiFzXrKx4NyyMpYsX89wHH0DLlpCYyPbp0+lSowY/jx5N1qUlo5UtISGBgwcPWt47OTkxd+5cUlNTqVO7Nv8ePpx1t93GKR8fPvv+ewJffx27b781h2q2tuDvD6+8Aps2wfnzMH8+TJgAt9yiUE1ERKSKWLNmDf/6179o1qwZzs7O1K5dm+HDh7Nt27Ziz9++fTsBAQG4uLjg4eFBSEgIR44cKXLeu+++S0hICA0aNMBkMuHv73/ZMSxfvpwePXrg6OiIu7s7Q4cOLVQAqTyYTCZeeukly/t9+/bx0ksvcfTo0SLn+vv707Jly1LdJykpiVdffRV/f398fHxwcXGhVatW/H979x5XU7r/AfyztduVEipKKNHk3pmMxjQhpElyTZncJszFz+XgYMY0/FQGkVszGJffJA7jkuswMxiXGMclYcZljOuRGCWiGynV9/dHp31se0sa2XY+79drvV72s561ep5nrdZefT2XWbNm4eHDh1r5Hz16hIiICDRo0AAmJiZo0qQJFixYoPPc//73vxEQEIAaNWrAwsICPj4+OHnypM4yTJo0CS4uLqhatSrq1q2LoKCgv9zG+/fvh0KhwP79+//SeQwVA2tERER6UqVKFXTp2hXOo0YBp04BcXH4ukYNHCssRL9Nm9Codm3Mfu89ZDxlztGXYd26dahduzZGjRpVnFBQgLo3bmBBt25IaNUKyWlp+Or779F+/XoYnT5dnOdvfwPGjwd27ChezTM+vng11DZtuPgAERHRK2rx4sVISkrCmDFj8NNPP+Grr75CWloa3nnnHezbt08j7/nz59GhQwfk5+cjLi4Oy5cvx8WLF9GuXTvcvn1bI++SJUtw7do1dOrUqdQFBr///nv4+fmhdu3a2LRpE5YsWYJLly6hXbt2uHLlSoXUGQCOHDmCjz76SP353LlziIiI0BlY+yuSk5MRHR2NVq1aYdmyZdi2bRsCAwMRHh6Obt264cnBhCNGjEBkZCRGjhyJXbt2oXfv3hgzZgxmzJihke/27dto164dLl68iOXLlyMuLg4PHz5Ehw4dcOHCBY283bt3R3R0ND7++GP8+OOPmDlzJn777Td4eHg8dY77smjVqhWOHDmCVq1alfscBk1IMjMzBYBkZmbquyhERPSau5WSIhFBQVLbyEgACAAxB+TvbdrI5V9/rdCffe3aNYmOjpbdu3er0y5euCAAxK1ePXnUo4dI9eoigOZWv77I0KEia9aI3LpVoWUkIiKiinFLx3d4dna22Nraire3t0Z6UFCQ2NjYaPwNnZSUJMbGxvLZZ59p5C0sLFT/u3nz5uLl5aXz5zdu3FhcXV2lqKhI45wqlUr69+9fniqVy4YNGwSAxMfHa+3z8vKS5s2bl+u8OTk5kpOTo5U+e/ZsASAHDx5Up509e1YUCoXMmDFDI+/HH38sZmZmkp6erk779NNPxdjYWJKSktRpmZmZYmNjI3379lWnXbp0SQDI5MmTNc55+PBhASDz5s177jrl5+fLo0ePnvu4F+X+/fsVev6yxorYY42IiOgVUtvODlPi4nAtMxPL/+d/0MLEBPcBLEhIwBtubujdrBkO7typ9b+a5XXp0iXMnDkTb7/9NhwdHTF27Fh8M38+sGYNMHQo3ujcGf8GcPLGDSi3bQMyM4vnSevdG/jmG+DiReDaNSAmBujXD6hd+4WUi4iIiF6u2jq+wy0sLNCsWTNcv35dnVZQUIAffvgBffr00Zh3ytHRER07dsSWLVs0zlGlyrPDDunp6bhw4QL8/PygeGyaCEdHR7Ro0QJbt25FYWHhU49ftGgRqlSpgrS0NHXa3LlzoVAoMHLkSHVaUVERatasifHjx6vTHh8KumLFCgQFBQEAOnbsCIVCAYVCgRUrVmj8vMTERLRr1w5Vq1ZFw4YNMXPmTBQVFZVaR3Nzc5ibm2ulv/322wCg0cZbt26FiGDIkCEaeYcMGYLc3Fzs3LlTnbZlyxZ06tQJjo6O6jRLS0sEBARg+/btKCgoAAAYGxsDKF5g6nE1/jOvr6mpaanlLxnuuWrVKowfPx5169aFiYkJLl++rDUUNDo6GgqFApcvX9Y6z8SJE6FSqXDnsSlP9uzZA29vb1haWqJq1arw9PTE3r17NY4LDw+HQqHAyZMnERgYiJo1a6JRo0YAiofCBgcHw97eHiYmJrC1tYW3tzd+++23Uuv0ojCwRkRE9AoyNTfHkMWLcTonB7u/+AJ+5uYQAFv/+APt/fzgXq8e1sTE4NGjR891XhHB6dOnER4ejpYtW8LFxQWhoaFITEyEAkB7c3P4/vQTMGAAEBsLXL8OJ5WqeMGB6dOBhAQgPR3YvBkYPpyLDxARET3m/v37z72VBD6A4qDV/fv3kZubW+7zvkiZmZk4efIkmjdvrk67cuUKcnNz4erqqpXf1dUVly9f1jlnWGny8/MBACYmJlr7TExM8ODBg1KHg3bu3BkiohGM2bNnD8zMzLB792512vHjx5GRkYHOnTvrPI+/v796qOWiRYtw5MgRHDlyBP7+/uo8qampGDBgAAYOHIht27bBz88PoaGhWL169XPVuUTJMNvH2/js2bOoVasW7OzsNPKWtPnZs2cBFK/QfuXKladei9zcXPW8d46OjujZsyfmz5+P+Ph45OTk4Pz58xg9ejQcHBwQHBxcpvKGhoYiOTkZS5Yswfbt23UGZAcOHAiVSqUVkCwsLMTq1avRvXt32NjYAABWr16N9957D5aWlli5ciXi4uJgZWUFX19freAaAAQEBMDZ2RkbNmzAkiVLAABdu3bFiRMnEBUVhd27d2Px4sVwc3PTWLG+QlVovzkDwaGgRET0yisokHNRUfJJ9epi+p8hogCkXvXqci85udRDi4qKJCEhQT777DNxdnZWHwtAlAqFvKdQyFJAUh8f3vnmmyITJojs3CmiY9gCERERaXv8O7asW1xcnPr4uLg4AaA1XNLGxqbM53uRBgwYIEqlUo4fP65OO3TokACQtWvXauWfMWOGAJCbN2/qPN/ThoIWFhaKlZWV1pDTe/fuSbVq1QSAHD58uNSy1qtXT4YOHSoiInl5eWJubi4TJ04UAHLt2jUREZk+fboYGxtrDMkEIGFhYerPzxoKCkASEhI00ps1aya+vr6llk+XU6dOiZmZmfTu3Vsj3cfHRxo3bqzzGJVKJZ988omIiPz5558CQCIjI7XyrVmzRqvd8vPz5eOPP9a4X1xdXeXq1avPLGt8fLwAkPbt2z913+NtFhAQIPXq1dMYCvzTTz8JANm+fbuIFA/ltLKyku7du2ucr7CwUP72t7/J22+/rU4LCwsTADJlyhSNvHfu3BEAEh0d/cw6PC8OBSUiIqpMjIzQ9NNPsTQ9HdcXL8aX1tawA9AkMxM13NyAmTOBnBytCYMTExPh4OCANm3aICoqCpcvX4YpgJ4AVgJIE8EuEXzi4ADbDz8E1q4F0tKAX38FZs8GfH0BHcMWiIiIqHL73//9X3z33XeYP38+3nrrLa39ilJ6rJe2T5cqVapg5MiR2Lt3L7788kukpaXh8uXLGDhwIB48eKDOUxpvb2/s2bMHAHD48GE8ePAA48aNg42NjbrX2p49e+Dh4aFzSGZZ2dnZqYdvlnB1dX3uyf+TkpLQrVs31K9fH99++63W/udp37LmHT58ODZt2oT58+fjwIEDWL9+PVQqFTp16lTm8vfp06dM+YYMGYIbN26orwkAxMbGws7ODn5+fgCKr9Pdu3cREhKCgoIC9VZUVIQuXbogMTFRqxfmkz/fysoKjRo1wuzZszFv3jz8+uuvzxyW+6IxsEZERGRIjIxg8z//g8mpqUhavhyxDRoUD80MDUWKoyPq29tjRlgYkJoKfPcdnOfPR+qNG7AA8D6AOAC3AWytUQMf9OmDmosXA5cuAUlJwLffAsHBQCkrdhEREdHT5eTkPPfWu3dv9fG9e/dGTk4OduzYoXHepKSkMp/vRYiIiMC0adMwffr0/64M/h/W1tYAiudFe9Ldu3ehUCjU83Y9jylTpuAf//gHpk2bBltbW7zxxhsAoJ5nrG7duqUe37lzZyQnJ+PSpUvYs2cP3NzcULt2bXTq1Al79uxBbm4uDh8+/NRhoGVVUv/HmZiYaA3fLc21a9fQsWNHKJVK7N27F1ZWVlo/Q1f73r9/H/n5+er8NWvWhEKheOq1AKDOu3PnTsTExGDp0qUYO3Ys2rdvj759+2L37t24e/euep65Z6lTp06Z8vn5+aFOnTqIjY0FANy7dw/btm3DBx98ACMjIwDArVu3AACBgYEwNjbW2GbNmgURUdfjaT9foVBg79698PX1RVRUFFq1aoVatWph9OjRyM7OLlNZ/yqueU9ERGSIlEqYDBmCeoMGFfcymzoVuy9fRh6AbdOm4YupUwEANQH8AsDN2Bim7doBnTsXb61aAf95qSEiIqIX46/0hAIApVIJpVL7z/S/et7nERERgfDwcISHh+OLL77Q2t+oUSOYmZnhzJkzWvvOnDkDZ2fnZ06Er4tSqcS8efMwdepUXL16FTY2NqhTpw58fX3h5OSEevXqlXq8t7c3gOJeabt374aPj486ffLkyfjll1+Ql5f3lwNrf9W1a9fQoUMHiAj279+vs14tW7bEunXrkJqaqjHPWkmbt2jRAgBgZmYGZ2fnp14LMzMzNGzYEADUE/m7u7tr5KtRowacnZ3V87Y9S1l7IxoZGWHQoEH4+uuvkZGRgTVr1iAvL09jQYaSedYWLFiAd955R+d5bG1tn/nzHR0dERMTAwC4ePEi4uLiEB4ejvz8fPU8bBWJPdaIiIgMmVIJDBoE/PEHPlixAufr10dwUREKAcDNDfj0U3js2gXTjAxg714gNBRwd2dQjYiIiLR8+eWXCA8Px+TJkxEWFqYzj1KpRPfu3bF582aNHkHJycmIj49HQEDAXyqDhYUFWrZsiTp16uDkyZPYu3cvxowZ88zj6tSpg2bNmmHTpk04ceKEOrDm4+OD27dvY968ebC0tNQKLD2pZAGF5+mBVlbJycno0KEDCgsLsW/fPo2VPB/Xs2dPKBQKrFy5UiN9xYoVMDMzQ5cuXdRpvXv3xr59+zRWFc3OzsbmzZvRo0cPdaDW3t4eAHD06FGNc6anp+PixYvPDFyWx5AhQ/Dw4UOsXbsWK1asgIeHB5o0aaLe7+npiRo1auDcuXNo3bq1zk2lUj3Xz3RxccHkyZPRsmVLnDx58kVXSSf2WCMiIqoMlEogJASNBwxA45MnAScnDukkIiKiMps7dy6mTJmCLl26wN/fXysA83iPooiICLi7u6Nbt274/PPP8fDhQ0yZMgU2NjYYP368xnHHjx9HUlISACArKwsigo0bNwIo7j1VElzav38/EhMT4erqChHBsWPHMGvWLHTp0kVrOOrTeHt7Y8GCBTAzM4OnpycAwMnJCU5OTvj55581Ak1PU9IbbNmyZahWrRpMTU3h5OSkcwjo80hLS0PHjh2RkpKCmJgYpKWlIS0tTb2/Xr166uBW8+bN8eGHHyIsLAxGRkZwd3fHzz//jGXLlmHatGkaQ0cnTJiAVatWwd/fH1OnToWJiQlmzpyJhw8fagzvDAgIwJQpUzB8+HDcuHEDrVq1QkpKCmbPno0HDx6UKXj5vJo0aQIPDw9ERkbi+vXrWLZsmcZ+CwsLLFiwACEhIbh79y4CAwNRu3Zt3L59G6dOncLt27exePHiUn/G6dOnMWrUKAQFBeGNN96ASqXCvn37cPr0aXz++ecvvE46vfBlEwwQVwUlIiIiIiKi11nJipdP2550/Phx8fb2lqpVq4qlpaX06tVLLl++rJUvJCTkqeeMjY1V5zt06JC0adNGLC0txcTERFq0aCFz5syR/Pz8Mtfh+++/FwDi4+OjkV6yEubXX3+tdQyeWBVURCQ6OlqcnJzEyMhIo5xeXl7SvHlznXV0dHQstWwlK2c+bXuyDPn5+RIWFiYODg6iUqnExcVFZ/lFRC5fviy9evUSS0tLqVq1qnh7e8uJEye08qWkpMioUaPE2dlZTE1Nxd7eXvz9/eXIkSOllv3x8m/YsOGp+3StpLps2TIBIGZmZk+NuRw4cED8/f3FyspKjI2NpW7duuLv76/xs0pWBb19+7bGsbdu3ZLBgwdLkyZNxNzcXCwsLMTV1VXmz58vBQUFz6xXacoaK1KIiLyMAN6rLCsrC9WrV0dmZiYsLS31XRwiIiIiIiIiItKjssaKOMcaERERERERERFROTCwRkREREREREREVA4MrBEREREREREREZUDA2tERERERERERETlwMAaERERERERERFROTCwRkREREREREREVA4MrBEREREREREREZUDA2tERERERERERETlwMAaERERERERERFROTCwRkREREREREREVA4MrBEREREREREREZWDwQfWcnJyMHbsWNjb28PU1BRvvvkm1q1bp+9iERERERERERFRJafUdwH+qoCAACQmJmLmzJlwcXHBmjVr0K9fPxQVFaF///76Lh4REREREREREVVSChERfReivH766Sf4+/urg2kl3nvvPfz+++9ITk6GkZHRM8+TlZWF6tWrIzMzE5aWlhVZZCIiIiIiIiIiesWVNVZk0ENBt2zZAgsLCwQFBWmkDxkyBDdv3kRCQoKeSkZERERERERERJWdQQfWzp49i6ZNm0Kp1BzR6urqqt5PRERERERERERUEQx6jrX09HQ0bNhQK93Kykq9X5e8vDzk5eWpP2dmZgIo7uZHRERERERERESvt5IY0bNmUDPowBoAKBSK594XGRmJiIgIrfT69eu/sHIREREREREREZFhy87ORvXq1Z+636ADa9bW1jp7pd29exfAf3uuPSk0NBTjxo1Tfy4qKsLdu3dhbW1daqDOkGRlZaF+/fq4fv06F2TQA7a/frH99Yvtr3+8BvrF9tcvtr9+sf31i+2vX2x//WL761dlbH8RQXZ2Nuzt7UvNZ9CBtZYtW2Lt2rUoKCjQmGftzJkzAIAWLVroPM7ExAQmJiYaaTVq1KiwcuqTpaVlpbmpDRHbX7/Y/vrF9tc/XgP9YvvrF9tfv9j++sX21y+2v36x/fWrsrV/aT3VShj04gW9e/dGTk4ONm3apJG+cuVK2Nvbo02bNnoqGRERERERERERVXYG3WPNz88PPj4+GD58OLKysuDs7Iy1a9di586dWL16NYyMjPRdRCIiIiIiIiIiqqQMOrAGAJs3b8akSZMwZcoU3L17F02aNMHatWsRHBys76LplYmJCcLCwrSGvNLLwfbXL7a/frH99Y/XQL/Y/vrF9tcvtr9+sf31i+2vX2x//Xqd218hz1o3lIiIiIiIiIiIiLQY9BxrRERERERERERE+sLAGhERERERERERUTkwsEZERERERERERFQODKwZmBUrVkChUOD48eP6LsprpaTddW0TJkwo83kGDx4MCwuLCixp5fN42+/fv19rv4jA2dkZCoUCHTp0eOnle918/fXXUCgUaNGihb6LUunx3n+18Pv31fFXroVCoUB4ePiLL1Qlx2e/fiQkJKB3795wcHCAiYkJbG1t4eHhgfHjx+u7aK+do0ePIigoCHXq1IFKpYKdnR0CAwNx5MiR5z7XuXPnEB4ejqSkpBdf0Eqi5DlvamqKa9euae3v0KEDn0cV7Mm/f01NTWFnZ4eOHTsiMjISaWlp+i7iK4WBNaLnEBsbiyNHjmhso0eP1nexXgvVqlVDTEyMVvqBAwdw5coVVKtWTQ+lev0sX74cAPD7778jISFBz6V5PfDeJyJ947P/5fvxxx/x7rvvIisrC1FRUfj555/x1VdfwdPTE+vXr9d38V4rCxYsgKenJ27cuIGoqCjs2bMHc+bMwZ9//om2bdti4cKFz3W+c+fOISIigoG1MsjLy8PkyZP1XYzXWsnfv7t378aiRYvw5ptvYtasWWjatCn27Nmj7+K9MhhYI3oOLVq0wDvvvKOxOTg46LtYr4X3338fmzZtQlZWlkZ6TEwMPDw8Xuh1yM3NfWHnqkyOHz+OU6dOwd/fHwB0Bnv+igcPHrzQ81UWL/PeJyJ6UkU/+0m3qKgoODk5YdeuXQgODoaXlxeCg4MxZ84cJCcn67t4r41Dhw5h7Nix6Nq1Kw4ePIhBgwahffv2GDhwIA4ePIiuXbtizJgxOHTokL6LWil16dIFa9aswalTp/RdlNdWyd+/7dq1Q58+fTB//nycPn0a5ubmCAgIwK1bt/RdxFcCA2sG7vjx4wgODkaDBg1gZmaGBg0aoF+/flpdZku6csbHx2P48OGwsbGBtbU1AgICcPPmTT2VvnJZv349PDw8YG5uDgsLC/j6+uLXX3/Vmff333+Ht7c3zM3NUatWLYwaNYpBhWfo168fAGDt2rXqtMzMTGzatAlDhw7Vyh8REYE2bdrAysoKlpaWaNWqFWJiYiAiGvkaNGiAbt26YfPmzXBzc4OpqSkiIiIqtjIGquSPqZkzZ+Ldd9/FunXrNO7bpKQkKBQKREVFYfr06XBwcICpqSlat26NvXv3apwrPDwcCoUCJ0+eRGBgIGrWrIlGjRq91PoYioq49z/88ENYWVnpfO506tQJzZs3r4CaVC4dOnTQOQR38ODBaNCggfpzye/FnDlzMG/ePDg5OcHCwgIeHh44evToyytwJVbWa0Hl86xn//79+3UOWS+591esWKGR/n//939wcXGBiYkJmjVrhjVr1vBa6ZCeng4bGxsolUqtfVWqaP4JV5Z30JLpSPgO+nwiIyOhUCiwePFirWuhVCrxzTffQKFQYObMmer08+fPo1+/frC1tYWJiQkcHBzwwQcfIC8vDytWrEBQUBAAoGPHjuphdk/+nlCxzz77DNbW1pg4cWKp+R4+fIjQ0FA4OTlBpVKhbt26GDlyJDIyMtR5evXqBUdHRxQVFWkd36ZNG7Rq1epFF7/ScnBwwNy5c5GdnY2lS5eq048fP44ePXrAysoKpqamcHNzQ1xcnNbxf/75Jz755BPUr18fKpUK9vb2CAwMNOggHQNrBi4pKQmNGzdGdHQ0du3ahVmzZiElJQXu7u64c+eOVv6PPvoIxsbGWLNmDaKiorB//34MHDhQDyU3TIWFhSgoKNDYAGDGjBno168fmjVrhri4OKxatQrZ2dlo164dzp07p3GOR48eoWvXrvD29sbWrVsxatQoLF26FO+//74+qmQwLC0tERgYqB6OAhQHGqpUqaKz7ZKSkjBs2DDExcVh8+bNCAgIwN///nd8+eWXWnlPnjyJTz/9FKNHj8bOnTvRp0+fCq2LIcrNzcXatWvh7u6OFi1aYOjQocjOzsaGDRu08i5cuBA7d+5EdHQ0Vq9ejSpVqsDPz0/nPCQBAQFwdnbGhg0bsGTJkpdRFYNTEff+mDFjcO/ePaxZs0bj2HPnziE+Ph4jR46suAq9phYtWoTdu3cjOjoa3333He7fv4+uXbsiMzNT30UjeqrnefaXxbJly/DJJ5/A1dUVmzdvxuTJkxEREaFzHsnXnYeHBxISEjB69GgkJCTg0aNHOvPxHbTiFBYWIj4+Hq1bt0a9evV05qlfvz7eeust7Nu3D4WFhTh16hTc3d1x9OhRTJ06FTt27EBkZCTy8vKQn58Pf39/zJgxA0Dx90LJ1DIlPUJJU7Vq1TB58mTs2rUL+/bt05lHRNCrVy/MmTMHgwYNwo8//ohx48Zh5cqV6NSpE/Ly8gAAQ4cORXJystZ5zp8/j2PHjmHIkCEVXp/KpGvXrjAyMsIvv/wCAIiPj4enpycyMjKwZMkSfP/993jzzTfx/vvvawSO//zzT7i7u2PLli0YN24cduzYgejoaFSvXh337t3TU21eACGDEhsbKwAkMTFR5/6CggLJyckRc3Nz+eqrr7SOGzFihEb+qKgoASApKSkVWm5DV9J+urbk5GRRKpXy97//XeOY7OxssbOzk759+6rTQkJCBIDGtRERmT59ugCQf/3rXy+lPobk8Xs+Pj5eAMjZs2dFRMTd3V0GDx4sIiLNmzcXLy8vnecoLCyUR48eydSpU8Xa2lqKiorU+xwdHcXIyEguXLhQ4XUxZP/85z8FgCxZskREiu9vCwsLadeunTrP1atXBYDY29tLbm6uOj0rK0usrKykc+fO6rSwsDABIFOmTHl5lTAwFX3ve3l5yZtvvqmRf/jw4WJpaSnZ2dkVUykD9uT3r5eXl852DwkJEUdHR/Xnkt+Lli1bSkFBgTr92LFjAkDWrl1b0UWvdMp7LUREAEhYWFjFF7KSKMuzv+T5FB8fr3Fsyb0fGxsrIsXPIzs7O2nTpo1GvmvXromxsbHWtXrd3blzR9q2bat+3zQ2NpZ3331XIiMj1c9ovoNWrNTUVAEgwcHBpeZ7//33BYDcunVLOnXqJDVq1JC0tLSn5t+wYYPO3xn6r8ef83l5edKwYUNp3bq1+j3Gy8tLmjdvLiIiO3fuFAASFRWlcY7169cLAFm2bJmIiDx69EhsbW2lf//+Gvk+++wzUalUcufOnZdQM8PxrLiDiIitra00bdpURESaNGkibm5u8ujRI4083bp1kzp16khhYaGIiAwdOlSMjY3l3LlzFVd4PWCPNQOXk5ODiRMnwtnZGUqlEkqlEhYWFrh//z7++OMPrfw9evTQ+Ozq6goAOldbIW3//Oc/kZiYqLHt2rULBQUF+OCDDzR6spmamsLLy0vn/8IOGDBA43P//v0BFEf66em8vLzQqFEjLF++HGfOnEFiYqLOoXAAsG/fPnTu3BnVq1eHkZERjI2NMWXKFKSnp2utYuPq6goXF5eXUQWDFRMTAzMzMwQHBwMALCwsEBQUhIMHD+LSpUsaeQMCAmBqaqr+XK1aNXTv3h2//PILCgsLNfKyd2DZVMS9P2bMGPz222/qeWGysrKwatUqhISEcPXiCuDv7w8jIyP1Z37/kiF4nmf/s1y4cAGpqano27evRrqDgwM8PT1fWJkrC2traxw8eBCJiYmYOXMmevbsiYsXLyI0NBQtW7bEnTt3+A76ipD/TLWQm5uLAwcOoG/fvqhVq5aeS1V5qFQqTJs2DcePH9c5rLCkB9rgwYM10oOCgmBubq6ejkSpVGLgwIHYvHmzurd4YWEhVq1ahZ49e8La2rpiK1IJldz7ly9fxvnz59XPl8efR127dkVKSgouXLgAANixYwc6duyIpk2b6q3cFYGBNQPXv39/LFy4EB999BF27dqFY8eOITExEbVq1dI5AfuTDwwTExMAnKy9rJo2bYrWrVtrbCVjwd3d3WFsbKyxrV+/XmtIrlKp1LoOdnZ2AIrn06CnUygUGDJkCFavXo0lS5bAxcUF7dq108p37NgxvPfeewCK53I5dOgQEhMTMWnSJADa93udOnUqvvAG7PLly/jll1/g7+8PEUFGRgYyMjIQGBgIABpDFIH/3s9PpuXn5yMnJ0cjnW1fNhVx7/fs2RMNGjTAokWLABTPxXn//n0OA60g/P4lQ/O8z/5nKXnHsbW11dqnK42KtW7dGhMnTsSGDRtw8+ZN/OMf/0BSUhKioqL4DlrBbGxsULVqVVy9erXUfElJSahatSqUSiUKCwufOmyUyi84OBitWrXCpEmTtIZFp6enQ6lUagUzFQoF7OzsNO7toUOH4uHDh1i3bh0AYNeuXUhJSeEw0HK4f/8+0tPTYW9vr34WTZgwQetZNGLECABQP49u375dKX9HtGfDJIORmZmJH374AWFhYfj888/V6Xl5ebh7964eS/Z6sbGxAQBs3LgRjo6Oz8xfUFCA9PR0jReb1NRUANp/eJG2wYMHY8qUKViyZAmmT5+uM8+6detgbGyMH374QaPn1NatW3XmVygUFVHUSmP58uUQEWzcuBEbN27U2r9y5UpMmzZN/bnkfn5camoqVCqVVk8otn3Zveh7v0qVKhg5ciS++OILzJ07F9988w28vb3RuHHjiqpCpWJqaqpzfjRd85tSxeK1qBhlffaXPGtK5jEq8WT7l7zj6JqcWtf3BmkzNjZGWFgY5s+fj7Nnz6Jnz54A+A5aUYyMjNCxY0fs3LkTN27c0BkMuHHjBk6cOAE/Pz9YWVnByMgIN27c0ENpKzeFQoFZs2bBx8cHy5Yt09hnbW2NgoIC3L59WyO4JiJITU2Fu7u7Oq1Zs2Z4++23ERsbi2HDhiE2Nhb29vbq/5Sksvvxxx9RWFiIDh06qP8eDg0NRUBAgM78Je+XtWrVqpS/I+yxZsAUCgVERP2/3iW+/fZbreFWVHF8fX2hVCpx5coVrd5sJduTvvvuO43PJROI61rVjDTVrVsXn376Kbp3746QkBCdeRQKBZRKpcawq9zcXKxateplFbPSKCwsxMqVK9GoUSPEx8drbePHj0dKSgp27NihPmbz5s14+PCh+nN2dja2b9+Odu3aaVwTej4Vce9/9NFHUKlUGDBgAC5cuIBRo0ZVSNkrowYNGuDixYsawYT09HQcPnxYj6V6PfFavHjP8+wvWc3z9OnTGufYtm2bxufGjRvDzs5OayhXcnIyr5UOKSkpOtNLpnqxt7fnO+hLEBoaChHBiBEjtP6+KiwsxPDhwyEiCA0NhZmZGby8vLBhw4ZSA/vssVw+nTt3ho+PD6ZOnaoxAsLb2xsAsHr1ao38mzZtwv3799X7SwwZMgQJCQn417/+he3btyMkJITvp88pOTkZEyZMQPXq1TFs2DA0btwYb7zxBk6dOvXUZ1G1atUAAH5+foiPj1cPDa0s2GPNQCkUClhaWqJ9+/aYPXs2bGxs0KBBAxw4cAAxMTGoUaOGvov42mjQoAGmTp2KSZMm4d///je6dOmCmjVr4tatWzh27BjMzc0RERGhzq9SqTB37lzk5OTA3d0dhw8fxrRp0+Dn54e2bdvqsSaG4/ElzXXx9/fHvHnz0L9/f3zyySdIT0/HnDlztILQ9Gw7duzAzZs3MWvWLJ0v3S1atMDChQsRExOD+fPnAyj+H14fHx+MGzcORUVFmDVrFrKysjR+D6h8XvS9X6NGDXzwwQdYvHgxHB0d0b1794oodqVS0sty0KBBWLp0KQYOHIiPP/4Y6enpiIqKgqWlpZ5L+Prgtag4z/Ps79atGzp37ozIyEjUrFkTjo6O2Lt3LzZv3qxxTJUqVRAREYFhw4YhMDAQQ4cORUZGBiIiIlCnTh1UqcL/73+cr68v6tWrh+7du6NJkyYoKirCb7/9hrlz58LCwgJjxozhO+hL4OnpiejoaIwdOxZt27bFqFGj4ODggOTkZCxatAgJCQmIjo7Gu+++CwCYN28e2rZtizZt2uDzzz+Hs7Mzbt26hW3btmHp0qWoVq0aWrRoAaB4ldxq1arB1NQUTk5O7DVYBrNmzcJbb72FtLQ0NG/eHADg4+MDX19fTJw4EVlZWfD09MTp06cRFhYGNzc3DBo0SOMc/fr1w7hx49CvXz/k5eVpzc1Gms6ePaueLy0tLQ0HDx5EbGwsjIyMsGXLFnUvwaVLl8LPzw++vr4YPHgw6tati7t37+KPP/7AyZMn1atJl6yW2759e3zxxRdo2bIlMjIysHPnTowbNw5NmjTRZ3XLT1+rJlD5LFq0SADImTNnRETkxo0b0qdPH6lZs6ZUq1ZNunTpImfPnhVHR0cJCQlRH/e0VT2etpITaSrLqihbt26Vjh07iqWlpZiYmIijo6MEBgbKnj171HlCQkLE3NxcTp8+LR06dBAzMzOxsrKS4cOHS05OzsuoisEpS9uLaK+MuHz5cmncuLGYmJhIw4YNJTIyUmJiYgSAXL16VZ3P0dFR/P39K6j0hq9Xr16iUqlKXd0qODhYlEqlHD16VADIrFmzJCIiQurVqycqlUrc3Nxk165dGseUrAp6+/btiq6Cwaroe7/E/v37BYDMnDnzBdegcnny+1dEZOXKldK0aVMxNTWVZs2ayfr165+6Kujs2bO1zgmuUFku5b0WImzzsnqeZ39qaqqkpKRIYGCgWFlZSfXq1WXgwIFy/PhxjVVBSyxbtkycnZ1FpVKJi4uLLF++XHr27Clubm4VXCvDsn79eunfv7+88cYbYmFhIcbGxuLg4CCDBg3SWk2P76AV78iRIxIYGCi2traiVCqldu3aEhAQIIcPH9bKe+7cOQkKChJra2tRqVTi4OAggwcPlocPH6rzREdHi5OTkxgZGen8PXndlfYO1L9/fwGgXhVURCQ3N1cmTpwojo6OYmxsLHXq1JHhw4fLvXv3dJ6/5Byenp4VVQWDV3INSjaVSiW1a9cWLy8vmTFjhs7vh1OnTknfvn2ldu3aYmxsLHZ2dtKpUyf1ytIlrl+/LkOHDhU7OzsxNjYWe3t76du3r9y6detlVe+FU4j8ZykHMghjxozBwoULkZGRoe5OSUT0KkhKSoKTkxNmz56NCRMm6Ls4VEbjx4/H4sWLcf36df5veSn4/fvq4LWoXDIyMuDi4oJevXppzZ1EL87gwYOxceNGrUWEiIjor+NQUANx4sQJJCYmYvny5ejRowdfJImI6C85evQoLl68iG+++QbDhg1jUO0p+P376uC1MHypqamYPn06OnbsCGtra1y7dg3z589HdnY2xowZo+/iERERlQsDawYiMDAQmZmZ6NGjB77++mt9F4eIiAych4cHqlatim7dumms6kqa+P376uC1MHwmJiZISkrCiBEjcPfuXVStWhXvvPMOlixZop4viYiIyNBwKCgREREREREREVE5cPkdIiIiIiIiIiKicmBgjYiIiIiIiIiIqBwYWCMiIiIiIiIiIioHBtaIiIiIiIiIiIjKgYE1IiIiIiIiIiKicmBgjYiIiIiIiIiIqBwYWCMiIiIiIiIiIioHBtaIiIiIiIiIiIjKgYE1IiIiIiIiIiKicvh/PnGQTuyBK/YAAAAASUVORK5CYII=\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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY Silicon with CY Rivers',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": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.07595093, 49.1295625 , 42.12771314, 16.72076613,  5.66290843,\n",
       "       13.70585083, 21.09441445, 28.55469541, 36.52751148, 42.10990644,\n",
       "       44.38558449, 46.95928795])"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-3}$')"
      ]
     },
     "execution_count": 41,
     "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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY Nitrate with WY Rivers',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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.74045154, 23.38186726, 21.69020601, 10.37701929,  5.74879707,\n",
       "        2.70849594,  2.85380337,  5.48641413, 11.13281657, 15.64983911,\n",
       "       21.55740055, 23.13323183])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol Si m$^{-3}$')"
      ]
     },
     "execution_count": 43,
     "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[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY Silicon with WY Rivers',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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.13663507, 51.513422  , 50.16886923, 27.00665494,  8.25802888,\n",
       "        7.09006067,  6.40147506,  9.4362604 , 22.08336301, 30.14843401,\n",
       "       41.93311685, 47.46025761])"
      ]
     },
     "execution_count": 44,
     "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": 45,
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223455/3403781678.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": 47,
   "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_river19/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_river19/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_river08/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, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/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(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/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",
    "# # 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": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3223455/2320522072.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": 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[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY Diatoms (0-100 m) with CY Rivers',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": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 50,
     "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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY Diatoms (0-100 m) with WY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (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
}