{
 "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_3220928/391916811.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_NSi/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_NSi/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_NSi/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_NSi/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_3220928/2338418733.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_temp_exp_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_temp_exp_slice[monthly_array_temp_exp_slice == 0 ] = np.nan\n",
    "monthly_array_temp_exp_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_temp_exp_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_temp_exp_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Degrees C')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_temp_slicemean[12,:],color='r',linestyle='-',label='Original WY')\n",
    "ax.plot(xticks, monthly_array_temp_exp_slicemean[12,:],color='k',linestyle='-.',label='WY with CY nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('WY SST with CY Nutrients',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([ 6.66011314,  5.16426706,  7.25914742, 10.17815932, 14.09488876,\n",
       "       17.59587821, 20.67570395, 20.07901062, 16.81303244, 11.56899786,\n",
       "        8.62051358,  6.7005392 ])"
      ]
     },
     "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 nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('CY SST with WY Nutrients',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([ 6.13830953,  5.82549722,  7.48766262,  9.10256158, 12.68796849,\n",
       "       16.20069323, 18.13362829, 18.18589498, 15.38127229, 11.96611296,\n",
       "        9.38018667,  6.34800222])"
      ]
     },
     "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_3220928/2771304440.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_NSi/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_NSi/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_NSi/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_NSi/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3220928/178454329.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, 'm$^{-2}$')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABOEAAAEvCAYAAAAD/9WIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAACRSklEQVR4nOzdd3yNd//H8dfJkCESkiBGgiSCIIgt9qzVltJq71rV0qGt0YGiSRctVaO971a1dN2qN6UtpbX3DoIgSOwdZMnO+f1Bzk8kNCEnJ4n38/E4jzrX9b2u63NdCc155zsMRqPRiIiIiIiIiIiIiJiNlaULEBERERERERERKe4UwomIiIiIiIiIiJiZQjgREREREREREREzUwgnIiIiIiIiIiJiZgrhREREREREREREzEwhnIiIiIiIiIiIiJkphBMRERERERERETEzhXAiIiIiIiIiIiJmphBORERERERERETEzBTCiYiIiBQxJ06cwGAwYDAYOHHixH2dIzg4GIPBQNu2bfO1NrG8QYMGYTAYGDRokKVLERERkdsohBMRESnCYmJisLGxwWAw8Omnn9613aFDh0yhTbVq1e55zi5dumAwGGjRogVXrlzBw8MDg8HAk08++Y/1xMfH4+3tjcFgoFOnThiNxlzdR2YgdOfL3t6eypUr8+ijj/LLL7/84/neeust07HPPvtsrq5dtWrVHK/t5OREzZo1GTx4MNu2bcvVuQqDEydOEBwcTHBwsKVLyWL58uUMHTqU2rVr4+rqiq2tLW5ubjRp0oQRI0awfft2AIxGI506dcJgMODt7U18fPw/nrtv374YDAY8PDyIjo7OVT3r1q3L8vVesmTJPdtnfp+Y47lOnz6d4OBg9u7dm+/nLkz27t1LcHAw06dPt3QpIiIiFqEQTkREpAhzcXGhQYMGAKxdu/au7datW2f684kTJ+7aeyotLY0tW7YA0K5dO9zd3fn6668B+N///sf8+fPvWc/o0aOJiorCxcWFuXPnYjAY8nA3N5UvX970MhgMnD17lj/++IOnnnqK7t27k5ycfNfaf/jhB9P7RYsWcf369Vxf197e3nTdcuXKkZiYyJEjR5g3bx4tWrTgvffey/O9mIutrS01atSgRo0a2NraZtl34sQJQkJCCAkJsVB1WUVERNC4cWO6devG119/TXh4OLGxsTg7OxMTE8POnTuZMWMGzZo1o3379kRHRzN37lxcXFyIiopi9OjR9zz//PnzWbhwIQBff/01bm5u91XnuHHjSE9Pv69jH9T06dMJCQnJtxCuQoUK1KhRgwoVKuTL+fLL3r17CQkJUQgnIiIPLYVwIiIiRVy7du0A2Lhx411DhMwQzsPDI8v7O+3cudPU8yjzvD179mTw4MEAvPLKK5w7dy7HY//66y9mz54NwKxZs6hcuXLebwa4cOGC6ZWQkMCBAwfo1KkTcLM31fjx43M8btmyZVy4cAF/f3/at29PUlIS//3vf3N93aeeesp03YsXL5KUlMTatWupU6cORqORd999l9WrV9/XPeW3SpUqcfjwYQ4fPkylSpUsXc5d7dy5k6ZNm7Jr1y5KlizJ2LFj2bdvH6mpqURHR5OSksLBgwf58MMPKV++PGvXruXMmTNUrlyZmTNnAjB79myWL1+e4/nPnz/P8OHDAXjuuefo2bPnfdd66NAhvvvuu/s+vjCZNGkShw8fZtKkSZYuRURERG6jEE5ERKSIywzLYmNjCQ0NzbHN+vXrAXjzzTeBu/eay9xeokQJWrRoYdo+ffp0qlSpwrVr1xgyZEi2465fv27a3rt3b/r373+fd5OVlZUVtWvX5vfff8fX1xeAr776irS0tGxtv/nmGwAGDBjAgAEDAPj222/v+9q2tra0bduW3377DRsbmyzXkH8WHR1N7969uX79OhUrVmT79u189NFHBAQEmHpIWllZ4e/vz7hx44iMjGTo0KGmfQMGDKBXr14APP/881y7di3bNV544QWuXr1KlSpVHqh3VY8ePQB49913SUpKuu/ziIiIiNyLQjgREZEirlWrVqaQKKcebocOHeLixYvUqFGDfv363bXd7dubNm2Ko6OjabuzszPz5s3DYDCwYsUKU4+3TK+++ipnz56lXLlyfPnllw9+U3ewt7enb9++AMTFxXH48OEs+8+fP8/y5cuxsrLi2Wef5YknnqBkyZLs3r2bffv2PdC1vb298fPzA+DgwYN5Pr5OnToYDAY+//zzbPu2bt1qmpOsT58+2fanpqbi5OSEwWBgzZo1pu13W5ihatWqplAWyDbP3b0m6l+9ejXdu3enbNmy2NvbU6tWLUJCQu47lPrkk084c+YMcHPIaO3ate/Z3tHRka+++oq6deuatn311VeUK1eOc+fO8corr2Rp/80337Bs2TIMBgPz5s2jVKlS91UnwIQJE3BycuLMmTPMmjUrz8dnzhc3b968u7bJabGEzLkQT548CcDgwYOzfc0y3fk1P378OEOHDqVatWrY2dlRtWrVe17rThcuXGDMmDHUq1cPFxcX7O3t8fb25vnnnyc8PDzHY26fRw/g2LFjPPfcc3h6emJnZ0flypV54YUXOHv2bLZjDQaDqUftyZMns93nnXPt/fXXX/Tu3ZvKlStTokQJnJ2d8fb2pnPnzkydOpWrV6/e9d5EREQKK4VwIiIiRZyTkxONGjUCcg7XMre1adOGihUr4uvry6lTp4iKisrSLjU1Nct8cHdq27Ytr732GnBz7rfIyEgAlixZwo8//gjcHDpYtmzZfLmvO90+vDU2NjbLvu+++460tDQ6dOhApUqVcHJy4oknngDyp/da5oIQ9zNnWPv27QGyhGiZbt+2bt26bAtP7Nixg4SEBOzs7GjevPk/Xqts2bKUKVPG9P72+fXKly+Pi4tLjsdNmTKFTp06sXz5ctLS0khJSeHw4cMEBwfTrVu3PN93WloaX331FQAdOnSgdevWuT7Wyur/fzwtW7as6Tzz58/nf//7HwCnTp1i1KhRALz++usPvMJruXLlTHPPTZo0KU9zCT4IJycnypcvb7pnZ2fnbF+znGzZsoX69evz9ddfc+nSpWzzAv6TpUuXUr16dT7++GPCwsJITEzExsaGqKgovvnmGxo0aMD3339/z3OsXbuWBg0aMHfuXGJiYsjIyODs2bPMmTOHJk2aZAviypcvj7OzM3Dza3znfTo5OZnavvfeezzyyCMsXryYs2fPYmtri9FoJCoqipUrV/Lmm28SFhaWp3sWEREpDBTCiYiIFAP3mhcuM4TLDCratGkDZB+Smhn43H6+O02ePJmaNWsSHx/PoEGDuHjxIsOGDQNu9r557LHH8uV+cnJ7jy9XV9cs+zKHnWYOQwUYOHAgAD/99NNdF3PIjcjISI4ePQrc7BWXV5nPct26dWRkZGTZl/k1cHZ2Jjo6Oluvvcz9zZo1w8HB4R+vtXPnTn799VfT+9vn17tw4QIzZszIdsy+ffsYM2YMY8aM4dKlS1y7do3r168zceJEUw15nStt165dxMTEAJiGlN6vxx9/3PS1fOmllzh//jyDBw8mNjaWmjVr5tu8Z6NHj6Zs2bJcu3aNyZMn58s5/8kbb7zBhQsX8PT0BGDGjBnZvmY5GTZsGLVr12bnzp0kJCQQHx/P33//natr7tixgyeeeIL4+HiGDRvGoUOHSExMJD4+npMnT/Lyyy+TkpLCkCFD2LVr113P88QTT9C+fXsOHTpEbGwsCQkJLFiwgFKlSnHu3DnGjh2bpf3t33+enp7Z7vONN94AbvaSy1xUZNSoUZw9e5aEhATi4uK4fv06Gzdu5OWXX36gno8iIiKWohBORESkGMgMeuLi4ti9e3eWfZnzwWWGb5n/vbPXXOZ7e3v7u/a6sre35/vvv8fGxoaNGzfSqFEjLl26hKenp1lXPIyNjeWnn34CbgZwmcNDATZs2MDRo0dxcnKid+/epu3t2rXD09OTq1evsmTJkjxfMzU1lXXr1vHYY4+Z5qDLHE6XF23btsXKyopr165lWf0yOTmZLVu24OjoyNChQ4HsveUy398tFM0P169fZ8KECXz00Ue4u7sDN0PBkJAQ0/P8p1Vx73T7sN3M1XsfxIwZM/D09CQ6OpomTZqwZs0abGxs+P7777G3t3/g8wOUKlXKtOjHzJkzcxxSWVi4ubmxatUqUw9YIMvfiXsZPnw4KSkpTJgwgS+//JKaNWtibW0NgJeXF1988QWvvfYaaWlpfPDBB3c9T/369Vm8eDE1a9YEbs4j+eSTT/Lhhx8CsHDhwhznbvwn27dvJyMjAz8/Pz799FMqVqxo2ufi4kLLli354osvaNiwYZ7PLSIiYmkK4URERIqBoKAg05C023u4hYeHc/HiRapXr276MHu3nnCZ75s3b46dnd1dr9W4cWNTL5czZ86Y5sK621DHB3H9+nVWr15N+/btTauyvv7661mGLGYON+3Tp0+WeewMBoNpgYjcDEldsGABHh4eeHh4UL58eRwcHGjXrh0HDhwA4LXXXuPxxx/P8z2UKVOGevXqAVlDtm3btpGYmEhQUBCPPPJItv3Jycls3boVMG8IZ2dnZ+qFdKfMno15HfoXHR1t+vOdvRbvh4uLC3PnzsVgMJjmmRs3bhyNGzd+4HPf7sUXX6RatWokJiaaemMVRsOHD88yfDO39u3bx86dO7G1tTUNv81JZo/SVatW3XUo8rhx47L8PcyU+T2TmJho6kGaF6VLlwZu/kIhs2euiIhIcaEQTkREpBhwdHSkSZMmQNYebrfPB5fJy8uLqlWrcubMGY4fPw5ASkqKKfDJnMPsXiZMmGCa++2xxx7L1TG5dftk7WXKlKFjx46m3n3PPvss77zzjqltbGwsCxcuBLIORc2UOYxx9erVpsnv7yYpKYmLFy9y8eJFLl26ZAof7Ozs+PXXX5kxY0aWifLzIqd54TL/3L59e1q0aIGdnR0bNmww9R7asmULSUlJODg40LRp0/u6bm7Url37roFOZnCb10nwb5/b7n6f2Z06dOhgCnjKli3LhAkT8uW8tytRogTvv/8+cHOI85EjR/L9GvkhKCjovo7btGkTABkZGdSoUcMUOt/5ygyFExISsgSqt7vb9+TtPdfuZ/GEJk2a4O7uzvnz52natCmff/45hw8fzjZfooiISFGkEE5ERKSYyOwttWnTJlOQc+d8cJnuHJK6Y8cObty4keU892Jra2vqdZbfPeBun6zdy8uLwMBAhgwZwpo1a/jhhx9MQ+cAfv75Z27cuIGXl1eOk/P7+fnRrFkzMjIy7rlyJdwM7IxGI0ajkaSkJPbv38/gwYNJTk7mxRdfvK+VUTPdPmdf5tcms+dh+/btcXBwoFmzZsTFxZnm4crcnxnQmcu95tbKXHU3r8MKM4e1AncNce5H5veao6Ojqbb89swzz1CvXj3S09MZN26cWa7xoMqVK3dfx2X2Jk1PTzcFzjm9rly5Yjom89+FO93t++b2r0tqamqeayxdujTz58+nbNmyHDx4kFdffZVatWpRpkwZHn30UX788cf7Oq+IiEhhoBBORESkmMgMeuLj401Bzp3zwWW6c0hq5n9v71FnKbdP1n7y5El2797NnDlzcgwHM4eZnjp1Cisrqyy96DJf27ZtA2Du3Lm57k1jZ2dHnTp1+Pbbbxk0aBCXLl2iT58+JCYm3tc9tW7dGhsbG+Lj402B5/bt23FxcTHNbXVnb7nbe8oVNbVr1zb9ec+ePRasJO8MBoNpsYdff/2V7du3W7ii7G4PovMis3dnzZo1TYHzP72qVq2aj5XnTseOHYmKiuL7779n4MCBVK9enZiYGP744w/69+9PgwYNCvWcfSIiInejEE5ERKSYuL3H1Lp16wgPD+fSpUv4+PhQuXLlLG3v7AmX+d+WLVua5pYr7A4cOMCOHTty3f7kyZOsXr06z9eZNm0aLi4uHD58+L4XnyhVqpQpbFuzZg2bNm0iJSWF1q1bmwKVzJBxzZo1JCQkmO7NnPPBmUujRo1MvdYWL15s4WryrmvXrqaelWPGjPnH9pm9v5KSku7aJnO1WEvy8PAAbq74W9jnWytZsiT9+/dn3rx5REREcObMGT7++GPs7e1NPeRERESKGoVwIiIixYS9vT3NmjUDbvZsy2k+uEze3t5UrlyZs2fPcvDgwQJZACC/ZfaCCwwMJC4u7p6vzAUVcrNAw53KlCnDiBEjAJg8eTLXrl27r3pvD9luH4qaqVmzZjg6OrJlyxZWr15NamoqTk5OeV584PbJ8i01j5aNjY1pxdfVq1ezYcOGXB+bkZFhrrLyZPLkycDNgHr58uX3bFumTBkATp8+neP+jIwMU+/UnGR+zcz99cqcSy4lJcUi4eiD3GelSpV46623TAtKrFy5Ml9rExERKQgK4URERIqRzKBn8+bNpg+pOc2VBv8fzk2ePNk0zLKohHApKSn8+OOPADz55JM4OTnd8/XUU08BN3tl3U+I9tprr+Hk5ERsbCxTp069r5ozA7etW7eaQp3bQzhbW1uCgoJITEzko48+Am72TMzr3GfOzs6mP1+/fv2+as0Pb731lmmS/qeffvof59RLTEzk5ZdfZv/+/QVR3j9q2rQpvXv3BmDs2LH3DI4yV79dvHhxju2+++4706quOcn8mpn769WoUSMaNGgAwDvvvMPly5fv2f5+Fla4l9zcZ3Jy8j3P4eDgANz/kFwRERFLUggnIiJSjGSGaAkJCfzxxx9Azj3hbt8+f/58IOuQycLut99+M00e37dv339s37NnTxwcHEhOTuann37K8/VcXV158cUXAZg5c2aWietzKygoiBIlSpCUlMS+fftwd3enbt26WdpkhnKZ85DdTyjq5+dHiRIlAJgzZ47FesO5u7uzaNEinJ2dOXfuHE2bNmXcuHEcOHDAVJPRaOTw4cN88skn+Pj48J///KdQrYL50UcfYW1tzb59+zh16tRd2z399NMAHDp0iKFDh5oWo4iNjeWzzz7jxRdfxNXV9a7H16lTB4CFCxfed0/L3DAYDHz55ZfY2dlx6tQpmjZtysKFC7MsvnD27Fl+/PFHOnXqxNtvv52v18+8z9jYWH755Zcc23z88cd07dqVH374IUtwmZyczC+//MKUKVMA6NatW77WJiIiUhAUwomIiBQjzZo1M/UUSU9Pp1q1anh5eeXYNjOEy5ysvVWrVmZbcTK/ZQ4rbdiwId7e3v/YvmTJknTt2jXLsXk1evRo7O3tiY+P5+OPP87z8Y6OjjRt2tT0vl27dhgMhixt7gzd7ieEc3R0pH///sDN3mhOTk5UqVKFqlWr8sYbb+T5fA+iWbNmbNu2jcDAQBISEpg0aRJ169alRIkSuLm5UaJECWrVqsXbb7/N+fPn6dKlC56engVa473UqFGD55577h/bdejQgQEDBgA3g093d3fKlClDmTJlGDVqFEOHDqVnz553PX7o0KEYDAa2bNlC2bJlqVixIlWrVjXLoghNmjThjz/+wM3NjaioKPr27YuzszPu7u6ULFmSypUr079/f1atWpXv1/b19aVDhw4APPXUUzg7O5vuM3O+xYyMDFasWMGAAQPw9PTE0dERNzc3HBwceOqpp4iJiaFWrVpMmzYt3+sTERExN4VwIiIixUiJEiVo0aKF6f3desHBzR5TmRO1Q9EZinr69GnTUNsnn3wy18dltt27dy+hoaF5vq6Hh4cpkPniiy+4cOFCns9x+zPOadXTRo0amYbsOTs7ExgYmOdrZNYXHBxs6nl06tQpTp48eV89+B5UrVq12L17N0uXLmXIkCHUrFnTNLTX2dmZxo0bM3LkSHbv3s2KFStwc3Mr8BrvJTg42BRs38u3337LjBkzqF+/Pg4ODmRkZBAUFMSCBQuYNWvWPY9t3bo1y5Yto2PHjri4uHDx4kVOnjzJyZMn8+s2sujUqRPHjh1j0qRJtGzZEhcXF65fv46VlRX+/v4MGTKE33///R/rvh8LFy5k5MiR+Pn5kZqaarrPzCGqQ4cOZfbs2Tz99NPUqVMHR0dHYmNjKVOmDK1atWL69OmEhoZm+bdLRESkqDAYC1OffxERERERERERkWJIPeFERERERERERETMrFCGcHFxcbz11lt07tyZsmXLYjAYCA4OztZu0KBBGAyGbK+aNWvmeN5Zs2ZRs2ZN7OzsqFatGiEhIaSmppr5bkRERERERERE5GFXKGdfjo6OZvbs2dSrV4/HH3+cOXPm3LWtg4MDa9asybbtTh9++CETJkxgzJgxdO7cmZ07dzJ+/HjOnj3L7Nmz8/0eREREREREREREMhXKEK5KlSpcu3YNg8HAlStX7hnCWVlZ0axZs3ueLzo6mg8++IAXXniBjz76CIC2bduSmprK+PHjGTFiBP7+/vl6DyIiIiIiIiIiIpkK5XDUzGGl+WXFihUkJSUxePDgLNsHDx6M0WhkyZIl+XYtERERERERERGROxXKEC4vEhMT8fDwwNramsqVKzN8+HCuXr2apc2BAwcAqFu3bpbtFSpUwN3d3bRfRERERERERETEHArlcNTcqlevHvXq1aNOnToArF+/ns8++4zVq1ezc+dOnJycgJvDUe3s7ChZsmS2c7i6uhIdHX3XayQnJ5OcnGx6n5GRwdWrV3Fzc8vX3noiIiIiIiIiIlL0GI1G4uLiqFixIlZWd+/vVqRDuJEjR2Z536lTJxo0aECfPn34+uuvs+y/V2B2r32TJk0iJCTkwYsVEREREREREZFi6/Tp01SuXPmu+4t0CJeTXr16UbJkSbZt22ba5ubmRlJSEjdu3MDR0TFL+6tXr9KwYcO7nm/s2LGMGjXK9D4mJgYvLy9Onz6Ns7Nz/t+AiIiIiIiIiIgUGbGxsXh6elKqVKl7tit2IRzc7AZ4e/e/zLng9u/fT9OmTU3bL1y4wJUrV0zDWXNiZ2eHnZ1dtu3Ozs4K4UREREREREREBLj3SEsoBgsz3GnhwoXcuHGDZs2ambY98sgj2NvbM2/evCxt582bh8Fg4PHHHy/YIkVERERERERE5KFSaHvCLV++nISEBOLi4gAIDw9n4cKFAHTr1o3Lly/zzDPP0K9fP3x9fTEYDKxfv57p06dTu3Ztnn/+edO5XF1dGT9+PBMmTMDV1ZXOnTuzc+dOgoODef755/H397fIPYqIiIiIiIiIyMPBYDQajZYuIidVq1bl5MmTOe6LiorCxcWFIUOGsGfPHi5evEh6ejpVqlShV69ejBs3DhcXl2zHzZw5ky+++IITJ07g4eHB4MGDeeedd7C1tc11XbGxsbi4uBATE6PhqCIiIiIiIiIiD7ncZkWFNoQrrBTCiYiIiIiIiIhIptxmRcVuTjgREREREREREZHCRiGciIiIiIiIiIiImSmEExERERERERERMTOFcCIiIiIiIiIiImamEE5ERERERERERMTMFMKJiIiIiIiIiIiYmUI4ERERERERERERM1MIJyIiIiIiIiIiYmYK4URERERERETELLZt20bfvn2pUKECJUqUwMPDgz59+rB169Y8nSc4OBiDwXBfNaxbtw6DwcC6devu6/jcatu2LW3btr1nmx49elCqVCnS0tKybN+zZw8Gg4EKFSpkO2bjxo0YDAZmzpzJK6+8gq2tLaGhodnapaSkULduXXx9fUlISHigexHzUAgnIiIiIiIiIvlu1qxZBAUFcebMGT755BNWrVrF1KlTOXv2LC1btuTzzz/P9bmef/75PAd3mQIDA9m6dSuBgYH3dXx+ateuHfHx8ezatSvL9nXr1lGyZEkuXLjA4cOHs+3LPHbKlClUq1aNgQMHkpKSkqVdcHAw4eHhfPfdd5QsWdKs9yH3RyGciIiIiIiIiOSrzZs3M2LECLp168bGjRvp378/rVu35tlnn2Xjxo1069aN119/nc2bN9/zPDdu3ACgcuXKNGvW7L5qcXZ2plmzZjg7O9/X8fmpXbt2ANl65a1bt47HHnuMChUqsHbt2mz73N3dqVOnDo6Ojnz33XccOnSId99919Rm586dfPLJJ7zxxhsEBQWZ/T7k/iiEExEREREREZF8NWnSJAwGA//5z3+wsbHJss/GxoZ///vfGAwGJk+ebNqeOeQ0NDSUPn36UKZMGXx8fLLsu11ycjKjR4/Gw8MDR0dHWrduze7du6latSqDBg0ytctpOOqgQYNwcnLi2LFjdOvWDScnJzw9PRk9ejTJyclZrhMSEkLTpk1xdXXF2dmZwMBAvvnmG4xGY56fS/369SlTpkyWWjIyMti4cSNt27alTZs2WUK4lJQUtm7dStu2bU3337x5c958802mTJnC9u3bSU5OZtCgQdSqVYv33nsvzzVJwbH55yYiIiIiIiIiUiCMRrjV+6tQcHSEPM7Flp6eztq1a2nUqBGVK1fOsY2npycNGzZkzZo1pKenY21tbdrXu3dv+vXrx4svvnjPuc0GDx7MggULeOutt2jfvj3h4eH06tWL2NjYXNWZmprKo48+ypAhQxg9ejQbNmzg/fffx8XFhYkTJ5ranThxgmHDhuHl5QXcnOfu1Vdf5ezZs1na5YaVlRWtW7dm1apVpKWlYWNjw969e7l27Rpt2rQhPT09Sw+3bdu2kZiYaOpBlykkJIQ///yTQYMG0aVLF44ePcr27duxs7PLUz1SsBTCiYiIiIiIiBQWN26Ak5Olq/h/8fGQx/nFrly5wo0bN6hWrdo921WrVo0dO3YQHR1NuXLlTNsHDhxISEjIPY8NDw9n/vz5vP3220yaNAmATp06Ub58eZ5++ulc1ZmSkkJISAh9+/YFoEOHDuzatYv//ve/WcK1uXPnmv6ckZFB27ZtMRqNzJgxgwkTJuR5wYh27drx22+/sXPnTpo3b866deuoUKECfn5+pKenc+nSJQ4ePEjt2rWzzAd3uxIlSvD999/TpEkTZsyYwfvvv0+DBg3yVIcUPA1HFREREREREZEClzmc884Q64knnvjHY9evXw/Ak08+mWV7nz59sg1/vRuDwUDPnj2zbAsICODkyZNZtq1Zs4aOHTvi4uKCtbU1tra2TJw4kejoaC5dupSra93uznnh1q1bR5s2bQCoVasW5cqVMw1JXbduHeXLl6dWrVrZzlOvXj169+6Ng4MDY8eOzXMdUvAUwomIiIiIiIgUFo6ON3ufFZaXo2Oeb8Hd3R1HR0eioqLu2e7EiRM4Ojri6uqaZXuFChX+8RrR0dEAlC9fPst2Gxsb3NzcclWno6Mj9vb2WbbZ2dmRlJRker9jxw46d+4MwNdff83mzZvZuXMn77zzDgCJiYm5utbt6tati7u7O2vXrjXNB5cZwgG0bt2adevWkZyczNatW7P1gruzXisrqyzDeaXw0nBUERERERERkcLCYMjz8M/Cxtramnbt2rFixQrOnDmT47xwZ86cYffu3XTt2jVbgJSb4Z2ZQdvFixepVKmSaXtaWpopoMsPP//8M7a2tixdujRLYLdkyZL7PqfBYKBNmzasWLGCHTt2cP369SwhXJs2bQgODmbr1q0kJSXdM4STokU94UREREREREQkX40dOxaj0cjLL79Menp6ln3p6em89NJLGI3G+x5G2bp1awAWLFiQZfvChQtJS0u7v6JzYDAYsLGxyRIUJiYm8sMPPzzQedu1a0dCQgJTpkyhXLlyWYabtmnThujoaGbNmmVqK8WDesKJiIiIiIiISL4KCgpi+vTpjBgxgpYtWzJ8+HC8vLw4deoUX3zxBdu3b2f69Om0aNHivs5fu3Ztnn76aT799FOsra1p3749Bw8e5NNPP8XFxQUrq/zpc9S9e3emTZvGM888w9ChQ4mOjmbq1KkPvAppZrC2ePFi+vTpk2VfnTp1cHNzY/HixVSqVInq1as/0LWk8FBPOBERERERERHJd6+++iqbN2+mcuXKjB49mvbt2zNq1CgqVKjApk2bePXVVx/o/HPnzuX111/nm2++oWfPnvz888/88ssvAJQuXTof7gDat2/Pt99+y/79++nZsyfvvPMOffr0YcyYMQ90Xn9/fzw8PDAajVmGosLN3netWrXCaDTStm3bB7qOFC4GY+ZyJJIrsbGxuLi4EBMTg7Ozs6XLEREREREREZFbtmzZQlBQED/99BPPPPOMpcuRh0RusyINRxURERERERGRImflypVs3bqVhg0b4uDgwL59+5g8eTLVq1end+/eli5PJBuFcCIiIiIiIiJS5Dg7O/P3338zffp04uLicHd3p2vXrkyaNCnLSqYihYVCOBEREREREREpcpo2bcqmTZssXYZIrmlhBhERERERERERETNTCCciIiIiIiIiImJmhTKEi4uL46233qJz586ULVsWg8FAcHBwljbp6elMmzaNRx55hMqVK+Po6EitWrUYM2YM169fz3ZOg8GQ42vy5MkFc1MiIiIiIiIiIvLQKpRzwkVHRzN79mzq1avH448/zpw5c7K1SUxMJDg4mKeffprnn38ed3d3QkND+eCDD/jjjz/YtWsXDg4OWY7p06cPo0ePzrLNy8vLrPciIiIiIiIiIiJSKEO4KlWqcO3aNQwGA1euXMkxhHNwcCAqKgo3NzfTtrZt2+Ll5UXfvn1ZtGgRzz77bJZjypcvT7Nmzcxev4iIiIiIiIiIyO0KZQhnMBj+sY21tXWWAC5TkyZNADh9+nS+1yUiIiIiIiIiInI/CuWccA9izZo1ANSuXTvbvv/+9784ODhgZ2dHw4YNmTt3bkGXJyIiIiIiIiIiD6FC2RPufp09e5YxY8bQqFEjevTokWXfM888Q/fu3fH09OTSpUt88803PPfcc0RGRvL+++/f9ZzJyckkJyeb3sfGxpqtfhERERERERERKZ6KTU+4q1ev0q1bN4xGIwsWLMDKKuut/fTTTzzzzDO0atWKJ554gj///JMePXowefJkLl++fNfzTpo0CRcXF9PL09PT3LciIiIiIiIiUmQtXLgQg8HAggULsu2rV68eBoOBv/76K9s+Hx8fAgMD2bZtGzY2NtkWVsz00UcfYTAYWLFiRb7XXrVqVQYNGmR6f+7cOYKDg9m7d2+2toMGDcLJyemBrpeRkcEPP/xAx44dcXd3x9bWlnLlytGjRw/++OMPMjIy6NGjB6VLl85x2q2rV69SoUIFgoKCyMjIeKBa8uLGjRsEBwezbt26PB0XHBycqynIHsT91lYQikUId+3aNTp16sTZs2dZuXIl3t7euTru2WefJS0tjV27dt21zdixY4mJiTG9NNeciIiIiIiIyN21bdsWg8HA2rVrs2y/evUq+/fvp2TJktn2nTlzhsjISNq1a0ezZs14++23mT59Ops2bcrS7sCBA4SEhDBs2DAeeeSRfK998eLFTJgwwfT+3LlzhISE5BjCPaikpCS6devGwIEDKVeuHP/5z39Ys2YNX375JRUrVqRv37788ccfzJkzBxsbG55//vls5xg+fDhxcXF899132TojmdONGzcICQnJc9D1/PPPs3XrVvMUdcv91lYQivxw1GvXrtGxY0eioqJYvXo1AQEBuT7WaDQC3PMb1c7ODjs7uweuU0RERERERORh4O7uTp06dbKFIOvXr8fGxoYhQ4ZkC+Ey37dr1w6Ad999l2XLljFo0CDCwsJwdHQkLS2NQYMGUblyZaZOnWqW2hs0aGCW8+Zk1KhR/PXXX3z33XcMGDAgy77evXvz5ptvkpiYiIeHB//+97956qmn+Oqrrxg2bBhwMzCcP38+//73v/H19S2wuu/HjRs3cHR0pHLlylSuXNnS5VhMke4JlxnARUZG8vfff+f5L8sPP/yAra0tDRs2NFOFIiIiIiIiIg+fdu3aceTIEc6fP2/atm7dOho3bky3bt3YvXs3cXFxWfZZW1vTqlUrAEqUKMH333/P6dOnefvtt4Gb00Xt2bOHefPm3XMY6LJlyzAYDOzcudO0bdGiRRgMBrp3756lbUBAAE888YTp/e3DUTPrBRg8eDAGgwGDwUBwcHCWcxw7doxu3brh5OSEp6cno0ePzjK3fE4uXLjAnDlz6NKlS7YALlP16tVNHY2efPJJ+vXrxxtvvMGJEyeIjo7mxRdfpFOnTrz00kv3vNaJEycwGAxMnTqVadOmUa1aNZycnGjevDnbtm3L0rZt27a0bds22zkGDRpE1apVTecrW7YsACEhIabnkvncMoechoaG0qdPH8qUKYOPj0+WfXdasGABzZs3p2TJkjg5OdGlSxf27NmTrQYnJ6d7Pu9/qu3y5csMHToUT09P7OzsKFu2LEFBQaxateqezzC/FNqecMuXLychIcH0lzI8PJyFCxcC0K1bNwwGg+mLMn36dNLS0rJ885QtW9b0RZ4yZQrh4eF06NCBypUrmxZm+PvvvwkODsbd3b3gb1BERERERETkLhISEvJ8jJ2dHTY2Nz/mp6WlkZycjJWVFQ4ODvd13pIlS+a5hkzt2rVj5syZrFu3jqeffhq42dutR48eBAUFYTAY2LhxI926dTPtCwwMxMXFxXSOgIAAQkJCGDduHL6+vrz//vuMGjXKFNTdTZs2bbC1tWXVqlWmEG3VqlU4ODiwfv16UlNTsbW15dKlSxw4cOCuIVZgYCBz585l8ODBjB8/3hTg3d6TKzU1lUcffZQhQ4YwevRoNmzYwPvvv4+LiwsTJ068a41r164lNTWVxx9//J8f5i1ffPEF69ev57nnnqNs2bKkpKTw7bff5un4mjVrMn36dAAmTJhAt27diIqKyvLc/0mFChVYsWIFjzzyCEOGDDENk80MvzL17t2bfv368eKLL97z++6jjz5i/PjxpueckpLClClTaNWqFTt27MDf39/U9p+e9z/V1r9/f0JDQ/nwww/x8/Pj+vXrhIaGEh0dnev7fyDGQqpKlSpGIMdXVFSUMSoq6q77AePAgQNN5/r999+NLVu2NJYtW9ZoY2NjLFWqlLFVq1bG+fPn57mumJgYI2CMiYnJx7sVERERERER+X/3+rx7t9cvv/xiOv6XX34xAsY2bdpkOa+7u3uuz/cgrl69arSysjIOHTrUaDQajVeuXDEaDAbjihUrjEaj0dikSRPjG2+8YTQajcZTp04ZAeNbb72V7TxpaWnG5s2bGwFj7dq1jUlJSbm6fsuWLY3t27c3vff19TW++eabRisrK+P69euNRqPR+NNPPxkBY0REhKldlSpVsuQJO3fuNALGuXPnZrvGwIEDsz13o9Fo7Natm7FGjRr3rG/y5MlGwPQ8cuvPP/80fX1++OGHXB2TmZ/UrVvXmJaWZtq+Y8cOI5AlG2nTpk227xmj8ea9VqlSxfT+8uXLRsD47rvvZmv77rvvGgHjxIkT77ov06lTp4w2NjbGV199NUu7uLg4o4eHh/HJJ5/MUkNunve9anNycjKOGDEi2/YHldusqND2hDtx4sQ/tjHemtPtn/Ts2ZOePXs+YEUiIiIiIiIikhtlypShXr16pnnh1q9fj7W1NUFBQcDN3mpr1qwBss8Hdztra2veffddHnnkEcaNG5frOds7dOjA5MmTSUxM5NKlSxw7dox+/fqxevVqVq5cSevWrVm1ahVeXl5Ur179vu/TYDBkyxsCAgJM95bfunbtSrNmzYiOjubZZ5/N07Hdu3fH2tra9D5zqOvJkyfztcZMtw/zvZu//vqLtLQ0BgwYQFpammm7vb09bdq0yTZ34IM+7yZNmjBv3jzc3Nzo2LEjDRs2xNbWNlfH5ociPSeciIiIiIiISHEUHx+f51evXr1Mx/fq1Yv4+HiWL1+e5bwnTpzI9fkeVLt27YiIiODcuXOsXbuWhg0bmuZya9OmDXv27CEmJoa1a9diY2NDy5YtczxPZvBWokSJXF+7Y8eOJCcns2nTJlauXIm7uzsNGjSgY8eOpvm/Vq9eTceOHR/oHh0dHbG3t89Wb1JS0j2P8/LyAiAqKirP17Szs8vTs8jk5uaW7TwAiYmJeT5XblSoUOEf21y8eBGAxo0bY2trm+W1YMECrly5kqX9/T7vTAsWLGDgwIHMmTOH5s2b4+rqyoABA7hw4UIu7+rBFNqecCIiIiIiIiIPqweZjw3AxsbGND9cfp43L9q1a8e0adNYt24d69atM83/BpgCtw0bNpgWQLjXYgt51bRpU5ycnFi1ahUnTpygQ4cOGAwGOnTowKeffsrOnTs5derUA4dw96tdu3bY2tqyZMkSXnzxRYvUkBN7e3tiYmKybb8zDMuNnBZguFPmHP0LFy6kSpUqeb5GXrm7uzN9+nSmT5/OqVOn+P333xkzZgyXLl1ixYoVZr++esKJiIiIiIiISL5r3bo11tbWLFy4kIMHD2ZZddPFxYX69evz3XffceLEiRyHoj4IW1tbWrduzcqVK1mzZg2dOnUCoFWrVtjY2DB+/HhTKHcv5uot5uHhwfPPP89ff/3F999/n2Ob48ePExYWlq/X/SdVq1YlIiIiy+qu0dHRbNmyJUu7/HouXbp0wcbGhuPHj9OoUaMcX3mV29q8vLwYPnw4nTp1IjQ09L7qzyv1hBMRERERERGRfOfs7ExgYCBLlizBysrKNB9cpjZt2phW6szvEA5uzgs3evRoAFOPNwcHB1q0aMHff/9NQEAA5cqVu+c5fHx8cHBw4KeffqJWrVo4OTlRsWJFKlas+MD1TZs2jcjISAYNGsRff/1Fr169KF++PFeuXGHlypXMnTuXn3/+2TR3W0Ho378/X331Fc8++ywvvPAC0dHRfPLJJzg7O2dpV6pUKapUqcJvv/1Ghw4dcHV1xd3dnapVq+bpelWrVuW9997jnXfeITIykkceeYQyZcpw8eJFduzYQcmSJQkJCcnTOe9WW5kyZWjXrh3PPPMMNWvWpFSpUuzcuZMVK1bQu3fvPF3jfqknnIiIiIiIiIiYRbt27TAajTRo0CBbkNOmTRuMRiMlSpSgRYsW+X7tzOCtevXqWYY6Zm7PzVBUR0dHvv32W6Kjo+ncuTONGzdm9uzZ+VKfvb09y5YtY968eVy4cIFhw4bRvn17hg0bxokTJ/j2228LfJHJoKAgvvvuOw4ePMhjjz3GBx98wNixY7P0Ysz0zTff4OjoyKOPPkrjxo0JDg6+r2uOHTuWhQsXEhERwcCBA+nSpQtvvfUWJ0+epHXr1vd1zpxqs7e3p2nTpvzwww/861//omvXrsyZM4e3336br7/++r6uk1cGY26XGBUAYmNjcXFxISYmJts/ICIiIiIiIiIi8nDJbVaknnAiIiIiIiIiIiJmphBORERERERERETEzBTCiYiIiIiIiIiImJlCOBERERERERERETNTCCciIiIiIiIiImJmCuFERERERERERETMTCGciIiIiIiIiIiImSmEExERERERERERMTOFcCIiIiIiIiIiImamEE5ERERERERERMTMFMKJiIiIiIiIiIiYmUI4ERERERERERERM1MIJyIiIiIiIiIiYmYK4URERERERERERMxMIZyIiIiIiIiIiIiZKYQTERERERERERExM4VwIiIiIiIiIiIiZqYQTkRERERERERExMwUwomIiIiIiIiIiJhZoQzh4uLieOutt+jcuTNly5bFYDAQHBycY9vQ0FA6duyIk5MTpUuXpnfv3kRGRubYdtasWdSsWRM7OzuqVatGSEgIqampZrwTERERkTukpkJYGAc+/JCZjz7Kn2+9Bdu3w+XLYDRaujoRERERMRMbSxeQk+joaGbPnk29evV4/PHHmTNnTo7tDh8+TNu2balfvz6//PILSUlJTJw4kVatWrF3717Kli1ravvhhx8yYcIExowZQ+fOndm5cyfjx4/n7NmzzJ49u6BuTURERB4i6QkJRCxbRuiKFezetYu+aWk0j4yE5GSOA68Dtf/4g25Tptw8wMmJZ21sSCtZkmoVK+Lt40O1OnXwbtwYz2bNsHV2tuTtiIiIiMgDKJQhXJUqVbh27RoGg4ErV67cNYSbOHEidnZ2LF26FOdbP5Q2bNiQ6tWrM3XqVD7++GPgZqj3wQcf8MILL/DRRx8B0LZtW1JTUxk/fjwjRozA39+/YG5OREREiqXUq1cJ//13Qv/+m9A9ewg9dYq9N25w47Y2pYHmAM7ONPb35/EzZ/A2GsFggLNnMcbH8zsQd/06nD0LO3eajrUGPK2tqebkhHfZslTz9MS7Rg2q1auHf5s2ONeoAVaFcpCDiIiIiFBIQziDwfCPbdLS0li6dCkDBgwwBXBwM8Br164dixcvNoVwK1asICkpicGDB2c5x+DBg3nnnXdYsmSJQjgRERHJvatXCVu0iG0rVhC6bx+hZ84QlpxMcg5NHYH6pUsT6O1NULduMHAgeHtT0cqKxbc3TE7GGBXFdz/9RFR4OJGRkUSdP0/k9etE3Tr3ifR0TsTEsDYmBo4dg7VrAZgLDLKzg6pV2efmxtz4eBrXqcO/+vSBatXA2xvUi05ERETEogplCJcbx48fJzExkYCAgGz7AgICWLlyJUlJSdjb23PgwAEA6tatm6VdhQoVcHd3N+0XERERuVPa6dPsWLCAg5s28YKVFYSGwsmTjAOW3dHW2WAg0NWVwOrVCWzenMAePfBr3Rprm1z8yGVnh1XNmvR6//1suzLS07lw+DBR27YRuWcPUUeOEHnqFFGXLhEZF4cPQHIyHDnCDmAG0C0sjH/997+mc9S0sqKMgwPebm5Uq1wZb19fqtWti3fTplRu3Bhre/v7f0giIiIi8o+KbAgXHR0NgKura7Z9rq6uGI1Grl27RoUKFYiOjsbOzo6SJUvm2DbzXDlJTk4mOfn/f68dGxubD9WLiIhIoWM0ErN/P3t/+430I0dof+0ahIaSfOECrYAMoAdQ4Vbzdm5upNrYEFizJoFBQQQ+9hjVGjXCygxDQq2sralYuzYVa9cmKKcGaWlw5gxERlJ35UreWLuWmhkZN4e5RkVx7fJljmRkQEIC2xIS4NQp2LLFdLgNUMXGhmqlSuFdvjzVvLzwrlmTtl27Uq5hQ3B3v3kuEREREblvRTaEy3Svoau378ttuztNmjSJkJCQ+ytORERECqeMDKJ37CD0998J3bSJ0CNHCL1yhWMZGcDNedsyI6qSVla0sbfHsUwZ4vr3p0KXLlC/PqNLl2a0xW7gDjY2ULUqVK1Ks/btaXbHbqerV9m9ciWRu3cTdegQkSdOEHXhApGxsZxISSEVOJ6WxvFr1+DaNTh8GP7+mzUzZ1IOwMmJ39zc+C4lhW61a/P8Y4/dHOJarRpJHh7YlylT4LcsIiIiUtQU2RDOzc0NIMdebFevXsVgMFC6dGlT26SkJG7cuIGjo2O2tg0bNrzrdcaOHcuoUaNM72NjY/H09MyHOxAREZECkZrKhY0bCf3jD0K3bSM0IoLQa9c4aTTm2NzL1pZqXl4wciQEBkJAAGty6E1flNi6uhL41FMEPvVUtn3pqamc27uXqO3biQwLIyoigsjTp4m6cgVfBwe4eBHi49kRH89iwOP8eVi1CoAYbi42UdHK6uaCEe7uN4e61qhxc6hrs2ZUbNAAq9wMxxUREREp5orsT0Q+Pj44ODiwf//+bPv279+Pr68v9rfmNsmcC27//v00bdrU1O7ChQtcuXKFOnXq3PU6dnZ22NnZ5XP1IiIiYg7GxETOrF5NqaNHKX34MISG8vXevQxNS8uxva+9PYGVKhFYvz6B7dvT4LHHcK9UqYCrtixrW1s8GzfGs3FjWufUICkJTpygz6pVVNywAX+j8ebw16goThw9CjducC4jg3OxsWyOjYXISNiwwXR4CaCqrS3VXFzw9vCgWtWqDOzdm3KBgTcXjdCCESIiIvKQKLIhnI2NDT179uTXX3/lk08+oVSpUgCcOnWKtWvXMnLkSFPbRx55BHt7e+bNm5clhJs3bx4Gg4HHH3+8oMsXERGRB2SMjeXUX39R5fz5m4slhIby+P79/A58C2SuiV4bsAJqOjoS6OVFYGAggZ06Ub9nT1xu9ayXe7C3h5o1aVCzJg2GD8+yKyAjgyvHjhG1dSuRe/YQefgwUSdPEnnpElGxsZxMSyMFiEhNJeLKFbhyBQ4c4ImlS28OcwU+cXRkATC0Vi2GdeoE3t6keHpywtaWKs2aYVfEeyGKiIiIZCq0Idzy5ctJSEggLi4OgPDwcBYuXAhAt27dcHR0JCQkhMaNG9OjRw/GjBlDUlISEydOxN3dndGj/3+WFldXV8aPH8+ECRNwdXWlc+fO7Ny5k+DgYJ5//nn8/f0tco8iIiKSO+mXLhHxxx+ErlpF6J49hJ46xZ7ERGKA64DLrXa+3Pzh5ryvL/TpA4GBNK5bl9hKlSh56xd2kn8MVla4+fnh5udHo4EDs+1PS0ri7K5dRGYOdT12jMgzZ/AsWxZOnIDoaPbfuEEocH33bti9G4AjQABgACpZW+NdqhTVypbF28uLajVq4N2gAdWaNcOjVi2srK0L8I5FRERE7p/BaLzLhCgWVrVqVU6ePJnjvqioKKpWrQrA7t27efvtt9m6dSs2Nja0b9+eqVOn4uPjk+24mTNn8sUXX3DixAk8PDwYPHgw77zzDra2trmuKzY2FhcXF2JiYnDW8AkREZF8l3rqFId+/53QNWsI3beP0LNn2ZucTEIObe2Aja1a0bh9ewgM5Jq3Nw4+Ptg7OBR02XI/YmM5vmED4Vu2UCMjA7/4eIiMZM3+/Tx65kyOX/Pb2QPV7OyoVro03hUq8OG//oWzvz94e5NWuTI2Tk4FcRciIiLykMttVlRoQ7jCSiGciIhIPjEaMZ44gWHPHggNJXrbNrquX09YWhrJOTR3NBio7+pKYPXqBLZoQWCPHvi3bJmnX6ZJ0WFMT+fygQNEbdtG5N69NxeMOHWKqMuXiYyP53R6Oum3tTcASdycgw5gILDMyoopPj4Mbt4cvL2JLluW0ORkvJs2xathQ2w176+IiIjkg9xmRYV2OKqIiIgUI+npcPQo3Arclq9cybgDB6iTns4Pt5qU4eYwxGTA2cqKBu7uNKxVi8CgIAIffRS/Ro2w1tDDh4bB2ppy9epRrl49muawPzU2ltPbthG1cyeRBw5w5fRpSri7Q1QUREYSGR9PdEYGDkeP3vzeA7YCPW8db8XNBSOaenoS1Lw5QX36ULdHD6y1kquIiIiYiXrC5ZF6womIiPyD1FRitm9n79KlhG7eTOiRI4ReuUKI0UifW01WAp2B6kBEgwYQGAiBgazJyMCrTRu8a9fGysrKcvcgRZvRSNyJE0Rt3kzlpCRcL12CyEh+37GDMYcPE5WaSlIOh5UyGGjm7k5QvXoEde9O03/9i1JlyxZ4+SIiIlK0aDiqmSiEExERuU1iItGbNrFn6VJCt21j99GjhF6/zrEcfrx428aGyY0aQWAgsbVqsSYlhQY9e1KlenULFC4PM2NKChd27+bgypVsWbmSzQcOsPX6deLuaGcFtHdxYeULL0CLFhAUBOXK5XRKEREReYgphDMThXAiIvLQiouDvXshNJQv58/n70OHCI2NJedllMDLzo7AypUJrF+fwA4daPzYY5SrWLEgKxbJtfQbNziwaBGbFy9m886dbD53jpMZGfQEfr+tXQt7e6p4ePDpSy9RsXt3qFUL1GtTRETkoaYQzkwUwomIyEMhOhr27CFu61amLljAoZMnWRAfj+HW7r7Awtua+zo6EujlRWBgIIGdO9Oga1fc1WNIijKjkbNbthC/aRM1IiNh82ZOHzyIF2ANxAAlAcqU4atKlThXtixBXbvSrH9/nD08LFq6iIiIFCyFcGaiEE5ERIoVoxHjuXNELl9O6MqVhO7ZQ7nLlxl5/ToAKUCpW/89DnhXrgyBgfxRsiTHHBwIfOQR6nfqhEvp0ha7BZGCknzhApvnzuX4pk28cOMGbN8OiYk0AXbeamMF1HVwIKh6dYLatiXoX//Cq3FjDAbDPc4sIiIiRZlCODNRCCciIsVB7I4dfD18OMv27iU0NZWY2/bVB/YA+PhAYCAhV6/iVqMG/V59FfeaNS1Sr0ihlJoK+/YxZ+pU1m3dyuazZzmRnp6tWSVra4IqVSKoSROCevemXu/e2NjZWaBgERERMQeFcGaiEE5ERIoso5EzCxcy4+23mR0VRextu0oYDASUKUOgnx9NWrViyDvvgIuLxUoVKZKMRs7t3Mnmn35i87p1bD56lD2JidwZyzkC8wIC6Nu7983FHpo2hVKlLFGxiIiI5AOFcGaiEE5ERIqctDTCpk1j6scfM//qVdJuba5VqhTDn3uOoH798G/YEFtbW4uWKVIcJZw/z86ffmLzn3+yOSyMLdHRxAA7gMa32vxgMDDVzo7BjRox4uWXbwZzXl4WrFpERETyQiGcmSiEExGRIiM+nth//5u+777L30lJps1tK1bkjXffpevzz2OlVR1FClRGSgrhv/9OjXPnsN22DTZv5oVTp5gDvAV8fKtdTMWKvGBtTYtGjQh6/HHq9+2LrYODBSsXERGRu1EIZyYK4UREpLAznj+P4fPP4T//wXjtGo25Ocdb39q1eeOzz2jUqZOlSxSR21wIDWXzTz/hd+ECdY8cgb17+Ss9nUdua+MINCldmqDatQnq0oXmAwZQukoVS5UsIiIit1EIZyYK4UREpLBKDQtj1gsv8O3OnWw2GnEB8PVld58+uPbvTzV/f0uXKCK5kZBA5JIl/DxvHpv37WPL5ctcv6OJAahtZ0eQtzdBrVsT1K8f1Vq3xqDerSIiIgVOIZyZKIQTEZFCxWiEDRtg6lQyli6lLhAOfFa1KiOmTYNHHwVra0tXKSIPICMtjUNLl7J54UI2b93K5lOnOJ6Wlq1dLWtrDj72GIagIAgKwli/PgatwioiImJ2CuHMRCGciIgUCmlpHJg+nX9/8glTL1/GEcBgYEnjxlxp145ng4Oxt7e3dJUiYiYXwsLY8uOPbF6zhs2HDxOakEAHYPltbeoaDLg5O/NNv3749OgBLVqAq6ulShYRESm2FMKZiUI4ERGxJGN8PGvHjmXKN9+wIjERgC9sbHh5yBAYNQr8/CxcoYhYQuLVq0SvXUvlo0dh82bOb9pExevXMQDXgcyfWmeUL0946dIEtWxJUL9+eLdvryGsIiIiD0ghnJkohBMREUtIPXOGha++ytQ//iA0PR0AK+AJf3/GzJhBYMeOli1QRAqVjLQ0jvz1FweWLqVvcjJs3gwREQQBW25rV95gIMjDg6DAQFr07Eng009TQj/jioiI5InZQrikpCSOHj2Kj48Pjo6OWfZt3ryZoKCg+6u4iFAIJyIiBSlu926+GT6cz7Zt49StbQ4GA0Nat2bE55/jU6eOResTkSLkyhWWzZzJuhUr2Hz4MLvi4ki9o4k90NjZmaCaNQnq2JEWAwbgWqOGJaoVEREpMswSwm3dupVHH32UjIwMkpKSmDBhAmPGjDHtd3Z2JjY29sEqL+QUwomIiNkZjZxbsoRZb73Fl8eOmVZFLGdjw6t9+vDSjBm4lStnyQpFpBhIiolh1/z5bP7jDzaHhrLl4kWic/hoMLtcOV7o2hWCgkhr2hTr2rUxaMEXERERE7OEcC1atODFF19kwIABHD58mAEDBlCnTh3mzJmDlZUVpUqVIi4uLl9uoLBSCCciImaTns6lefMYM2YMP165Yuqh4leyJG+8/DL9Q0Kwd3CwaIkiUnwZMzI4snIlmxcsYPPmzWw+cYKIlBQ2Ay1utfkJGGUw8Jy3N5Oeew6CgqBxY7hjhIyIiMjDxCwhXOnSpbl+/brpfWJiIn379qVEiRL8/PPPuLm5KYQTERHJq4QEmDcPpk3jRmQkXkA00NLDgzcnTqTHsGFYaeJ0EbGAy0ePUvrQIWy3b4fNm3ll0yb+nZ7OSGDarTYJ1tZ0c3SkeY0aBHXoQIv+/XGrXduSZYuIiBQos4RwXl5ebN26lUqVKpm2paWlMWDAAC5cuMD27dtJSEh4sMoLOYVwIiKSX9LOnWPh8OH8vmwZP6WkYABwdeWXtm3xGjKEZt26WbpEEZEskuPj2b1gAW7Hj1Pj1kqsa86fp8Md7Wra2hLk5UVQixYE9elD9W7dMNjYWKRmERERczNLCPfcc8/h7e3N+PHjs2w3Go0MHTqUb775hoyMjPuvughQCCciIg/syBGYNo2YefPwTEkhDljm4UG38eNh0CAoWdLSFYqI5I7RyMVdu1j29dds3riRzVFRHElOztasrMFAC3d3gurXJ6hHDxr+61/YublZoGAREZH8Z5YQLiUlhbS0tGyromY6deoUXl5eea+2CFEIJyIi98Vo5Pxvv/G/CRN49cCBm73egI8qVyatRQtenjkT9/LlLVqiiEh+uBIVxZbvv2fL33+z+eBBdsbEcGcs5wZcatgQq6FDYcAAsLe3RKkiIiL5wiwhnCiEExGRPEpPJ3zWLKZ+9BE/Xb5MCrAOaPPoo/DGG9CyJRgM/3ASEZGiKzkhgdD//Y/Nv/3G5p072Xz+PHUzMlid2cDDg6VdutB+0iQcK1SwZKkiIiL3JbdZ0QPP8vzuu+8+6ClERESKHWNCAutGjKC7szO1R45k7q0ArkW5ctj+97/w22/QqpUCOBEp9uxKlqT5oEG8sXgxi8+c4WJaGov274dp06ByZQ5duEDP776jWqVKxIwcCRcuWLpkERERs3jgnnCOjo7cuHEjv+op9NQTTkRE7iXt/HkWvfoqU3/7jV1paQAYgF41ajB6yhRa9Oxp2QJFRAqTlBTWjB/P89OnUz81lV8B7Oxg4EBiXnwRlwYNLF2hiIjIPyqwnnCWHM06aNAgDAbDXV/btm27Z7uaNWtarHYRESle4vfuZWZQENUrVaLfokXsSkvD3mDgxZYtObJ3L4sOH1YAJyJypxIlaP/JJ0TExzP7+++heXNITubE7NlUCAxkSLVqHF60yNJVioiI5Isi3RPu+PHjXL58Odv2nj17Ymdnx8mTJ7G2tmbQoEH88ssvrFmzJks7BwcH6tWrl6drqieciIjc7sqff/LZyJH8JyKCa7e2udvYMLxXL16eMYOymt9IRCT3jEbYtInPhg1j1KFDwK3exGXLMubdd2n88ssaxi8iIoVObrMimwKsKd/5+Pjg4+OTZdv69eu5cuUK48ePx9ra2rTdysqKZs2aFXSJIiJSHKWnw++/w5QpXN+6lUmAEfB1dGTUsGEMfP99HEuWtHSVIiJFj8EArVoxMjycZj/8wMfjxvHbmTP8evkyvw4fTvuxYxnz+ut0fPddDDZF+qOMiIg8hB54OGph880332AwGHjuuecsXYqIiBQzxhs32DB6NFM8PKB3b9i6Fd8SJRhfvz6LZszgcGwsL02bpgBORCQfNO/fnyWnT3Pw778ZWLMmNsCauDg6f/ABDZ2c+GXoUNITEixdpoiISK4V6Tnh7hQTE8PChQvp0KED1apVy7IvMTERDw8PrK2tqVy5MsOHD+fq1asWqlRERIqUK1cgJISjlSvTZto0xly5wnFnZxg3Dk6c4L09e+j92mtZemCLiEj+8O/UiXmHDnE8NJTXmzTBEdiTnMxTX39NTRcXZvfuTdLFi5YuU0RE5B89cAgXFBSUH3Xki/nz55OYmMiQIUOybK9Xrx5Tp07lhx9+YMWKFQwaNIi5c+cSFBREfHz8Pc+ZnJxMbGxslpeIiDwcEsLCWNmzJ3h5QXAwfteu0cvRkRdatMBuxw748EPQnG8iIgXCq0EDpm/fzqkTJwju0gVXKyuOpaczbPFiqlWowKmXX4YLFyxdpoiIyF098MIMhUnjxo2Jiori7Nmz2NnZ3bPtokWL6NOnD9OmTWPkyJF3bRccHExISEi27VqYQUSk+Lr45598PmoU/z5yhDggCqjUsCG8+SbG3r0x2NpaukQRkYdewrVrzBk+nE9/+QX3tDR2AwY7Oxg4kOTXXsOudm1LlygiIg+J3C7MUGzmhAsLC2PXrl08++yz/xjAAfTq1YuSJUuybdu2e7YbO3YsMTExptfp06fzq2QRESlMMjI4/MUXDPXwoEr37nxw5AhXAU9HR07OmgU7d8JTTymAExEpJEqWKcPrP/3Esbg4fv3qKwzNm0NyMnGzZ1OtTh1e8vEhbsMGS5cpIiJikm9LCi1ZsoSffvqJkydPkpSUlGWfwWBg3759+XWpHH3zzTcAPP/887k+xmg0YmV17xzSzs4uV6GeiIgUTcYbN9gcHMyUf/+b32+b4LuJuztvjhtHL831JiJSqJWwt6fq0KHwwguwaROLhw/nfFgYayMjKdmmDXTsCGPGQPv2N1dfFRERsZB86Qk3ZcoUevfuzYYNG7C1tcXNzS3Ly9XVNT8uc1fJycn8+OOPNGnShDp16uTqmIULF3Ljxg2aNWtm1tpERKRwSr94kUX9+tHcxYVWU6aYArhHq1dnw+LFbLt0iT4jRyqAExEpKgwGaNWK/nv3sv7bb5nZvj1W1tawahWJHTvytJsb60JCMKalWbpSERF5SOXLnHDVqlWjQ4cOfPXVVxb5sLJgwQL69evH7NmzeeGFF7LsO3nyJM888wz9+vXD19cXg8HA+vXrmT59Oj4+Pmzfvp2SJUvm+lq5HecrIiKF040DB5j3yitM27iR47f+F2gHDGjenFGzZlGzYUPLFigiIvnnxAn49FO+/OorXkpNBaCpvT1jBg/m0alTsXJ0tGx9IiJSLOQ2K8qXEM7Z2ZklS5bQvn37Bz3VfencuTNbtmzh/PnzlCpVKsu+a9euMWTIEPbs2cPFixdJT0+nSpUq9OrVi3HjxuHi4pKnaymEExEporZvh6lTWbZoET1u/a/P1dqalx99lOGzZlG+UiULFygiIuYStWsXU196iW937SJz4pya1ta81asX//r3vylRtqxF6xMRkaKtQEO4rl270qNHD1555ZUHPVWhpxBORKQIycgg4quvOPrFF3Q/ePDmJqBn2bJ07dePwR99REknJ8vWKCIiBeZiZCQzhw3ji9Wribn1MaiywcCodu14YfZsnHx8LFyhiIgURQUawh05coRevXoxefJkHnnkEUqUKPGgpyy0FMKJiBQBSUnwww9sfP992pw+jRtw0sYGx2efhdGjIZfzh4qISPEUe+UKX73yCp/9+ivnb80RVwZ4NTCQV//zH9ybNLFsgSIiUqTkNivKl4UZfH196dixI7169cLR0RFnZ+csr7wO+RQREbkf6ZcucWzkSKhSBYYOpfnp01SzsqK5ry9Xt26FuXMVwImICM7u7ry5YAFRsbF8/corVLe35xrwXmgoXk2b8nr16lxds8bSZYqISDGTLz3hRo8ezWeffUb9+vWpVatWjj3h5s6d+6CXKRTUE05EpPC5cfAg373yCtM2bCDRaCQSKOHpCSNHEt+vH04VKli6RBERKcTS09JYPGkSkz/9lN0xMZQBTgKlOnaEMWOgffubq6+KiIjkoECHo7q5uTF06FAmTZr0oKcq9BTCiYgUHpf//psvRo7ki/BwrtzaVsbamtXvvUeDN98EW1uL1iciIkWL0Whk9Zw5nJ83j/7bt0N6OkZgRLlyPDl8OEHjxoG1taXLFBGRQqZAh6Omp6fTqVOn/DiViIjIvWVkcHT2bF6qWBGvLl0IuRXAVXVwYMYrr3Dq2jUajBunAE5ERPLMYDDQ8YUX6L95Mxw7Bq++yl8lSjDz0iW6TJxITPXqMHv2zblHRURE8ihfQrjOnTuzbdu2/DiViIhIzpKS2DpuHE+ULk2NYcP48vx5koCGbm78/MknHI2N5bXPP8epVClLVyoiIsVB1aowcyZ+mzbxfIMGvGpvj0tUFAwbhrFqVZYNHkxadLSlqxQRkSIkX4aj7t+/n6eeeophw4bRvXt3XF1ds7XJaVtRpOGoIiIFK+PKFX4fMYIpv/zCltRU0/bu3t68MXkybfr0waB5ekRExNzi42HOHPj0UzaeOUNroIrBwBsdOvDcV1/h6O1t6QpFRMRCCnROOCurmx3q7vUhKD09/UEvUygohBMRKSBRUTB9Ov/+8kteSUkBoATwbJMmjJo5k9pNm1q2PhEReTilpLBw1Che+fJLLt36jOMOvN6oEa98+SVlGja0bH0iIlLgCjSECw4O/sdeCO++++6DXqZQUAgnImJeV1at4uKnn1L7778hI4PrQICtLc8+8givfv45Fby8LF2iiIgIiQkJzHvjDabMnUtUcjIATsAwPz9GTp9Opa5dLVugiIgUmAIN4R4mCuFERMwgIwOWL2fZ2LH03b+fOsB2wNC5M7z5Jult22JtY2PpKkVERLJJS03lfx98wOTPPiMsLg4AW2BAxYq8+eGH1Bg4EDRtgohIsVagq6OKiIjcl8RE4v/zH6hbF3r0oPH+/RiBdFdXrm3YAH/9BR07KoATEZFCy8bWlqdDQtgbE8OfX3xB63LlSAW+OXeOWoMH84SrKzsnTYJiMj2PiIjcP/WEyyP1hBMRyaP0dDJOnuTsli1EbNtGxIEDRERFEXH5MocTE/EE1gE4O8OwYRzt2RPfli212IKIiBRZWxYt4uM33+T3qCgADEBUlSpUGTcOBgwAe3vLFigiIvlKw1HNRCGciEgOjEaIjiZuzx4OrFtHxL59RBw7RsT580TExXHUaCTxLofaAkfHjaPKW2+Bi0tBVi0iImJWBzdu5JPhw0k6fJgFtxYZwsODbb170/j997F2dbVsgSIiki8UwpmJQjgReajduAFHj0JEBKuXL2fbnj10TUsj8OxZuHaNRUCfuxxqA/g4OeHn4YGftzd+9erh16IFdVu2xM3dvQBvQkREpGBlxMZi9e238OmnRJ05gy/gbWXFrtdew+Xtt8HDw9IliojIA1AIZyYK4USk2EtLI+34cU5s2kTEjh03h4+ePMnx6Gj+TErC+lazp4GfgU+ANwEMBg54eND16lX83N2pUa0afrVr49ekCX5BQVT18cFGc7uJiMjDLCWFP8eMof/MmTRKT+cvADs7GDiQ1BEjsK1Vy9IViojIfVAIZyYK4USkWDAaMV68yIWtW4nYsoWIsDAiIiOJuHCBiPh4jgOpORwWCVRzcwM/P74xGNiQmMiTjzxC96efBl9fcHAo4BsREREpehLi4riyYAFV5s6FLVs4D9QHnqtZk9enT8ejSxcLVygiInmhEM5MFMKJSJESH0/snj0c2bAB54sXqXH1Khw5QvihQzRNSCD+Hoc6GAxUd3bGr0IF/Hx98atfn0cHDaKMj0+BlS8iIlLsbdrExy+8wJjDhwGwAwZXqsQbH32ET//+oIWKREQKPYVwZqIQTkQKndRUkg4f5viGDUTs2kVEeDjP29riFhUF584xBvgYeAX4/NYhMUBpwBqo5uiIX7ly+FWrhl/duvg1bYpfUBCVPD2xsrKyzD2JiIg8RDIyMlj6+edMev99tl25AoAV0LdMGd4eM4YGo0eDtfW9TyIiIhajEM5MFMKJiEUYjaSfOcPpzZuJ2LqVI/v3ExEVRcSlS0TcuMFJ4PZ/zNcBbW79+dtSpRifnMwzfn5MffZZqFED/PyISE+nao0alChRosBvR0RERLIzGo1s/OUXJr/9NstPnjRt7+LoyJgXX6TNBx9g0NQPIiKFjkI4M1EIJyLmZLx+naQDB3A4eRIiIkg6dIhn/v6biNhYjhmNJN/jWGcrK/xcXPCrVIlRTz5Jwy5doHp1jKVLY9BQFhERkSJl37p1fPLqq/x84AAZt7Y1tbXl7aee4rGZM7EqU8ai9YmIyP9TCGcmCuFE5IGlpBAXFsbRDRvwjImh7LlzcOQI/wsL44WYGFoDv99qagRcgeu33pcAfJ2c8CtfHj8fn5vDR1u0wK9FC8qVL6+wTUREpJiJ3L+fT196iW+3bCHp1ke3mlZW/D1sGJ4TJ4KHh4UrFBERhXBmohBORHIlI4OUqCgiN2wgYscOIg4cIOLkSSIuXyYiKYnzt5p9Bwy49efVQEegprU1h1q2BD8/8PPj5wsXKFOzJn6tWuHl64u15oQRERF56Fw8fZqZL7/MF3/+iUdGBuGAlZ0dDByI8Y03MFSvbukSRUQeWgrhzEQhnIhkce0aRETAkSN8+8sv7I+IIOLCBSLi44kyGkm/x6FlbWx4v0EDhnXrBn5+JHh5ccLWFp969bC3ty+wWxAREZGiI/b6dU5+/z11FyyALVtIBBoCfWrV4u3ZsynZsqWlSxQReegohDMThXAiD6GkJNKPHMH6+HGIiCBi507eWb8eQ1wcv6SkmJrVA8LuOLSkwYBfqVL4VaiAn68vfvXq4RcURPVmzSjj6lqgtyEiIiLFzKZNfDd8OIP27cMLOAbYduwIb78NHTqApqkQESkQCuHMRCGcSDGVkcGNI0c4tn49ETt3EhEeTsSpU0RERxORnMzrwIRbTY8CfoADEA9YVaoEfn58fOMGlx0c8KtTB7+mTfFr3ZoKnp6ap01ERETMJj09ncXTp8Nvv9FnyxZITycVeKNcOV546y3qjBgBmspCRMSsHooQbt26dbRr1y7HfVu3bqVZs2am96Ghobz11lts27YNGxsb2rdvz9SpU/H29s7TNRXCiRQfF9av58vRo9l0+DARCQmcvkfbZ21t+aF+fahRgzQfH2ZGRlKjUSMeGTgQaxeXgipZRERE5O5OnIBp0/jhyy8ZkJoKQE9HR8YMH06LkBDQdBciImbxUIVwH330UbYwrk6dOjg5OQFw+PBhmjRpQv369RkzZgxJSUlMnDiRa9eusXfvXsqWLZvrayqEEynijEbCZs9m2nvvMf/cOVLu2F3GyooaLi74VayIX/Xq+AUG4teyJb6NG1Py1r8pIiIiIoXZvnXr+ODll1l06BCZH/Za2doy5l//outnn2EoXdqS5YmIFDu5zYpsCrAms6levXqWXm93mjhxInZ2dixdutT0MBo2bEj16tWZOnUqH3/8cUGVKiKWkpwM//0vfPYZ8/fv57tbm1u4ujL4mWeo3aYNfq1b41aunEXLFBEREXlQ9dq25X/h4UTs2cOUl1/mu23b2JiaysZ586j7/feM6d6dJ//9b2wqV7Z0qSIiDxUrSxdgbmlpaSxdupQnnngiSxpZpUoV2rVrx+LFiy1YnYiY242TJ/nq0UfZXKECPPcc7N/PcAcH+lWvzraFC9kcHc3zs2bRvE8fBXAiIiJSrPg1aMDXW7cSFRXFG1274mRlxf6MDP71xx9U9/RkvJ8fu4ODMV65YulSRUQeCsUihHvllVewsbHB2dmZLl26sGnTJtO+48ePk5iYSEBAQLbjAgICOHbsGElJSQVZrogUhEOHYNgwgn18ePGPP/jg2jWoXBk++YRKZ88yPyKCpk88YekqRURERMyuUtWqTPnzT05dusSHzz5LWRsbTgAfHj1Ko5AQqpYtS1jTpvDFF3D2rKXLFREptop0COfi4sLrr7/OV199xdq1a5kxYwanT5+mbdu2/PXXXwBER0cD4Orqmu14V1dXjEYj165du+s1kpOTiY2NzfISkULKaGTf7NmEt24N/v4wezYvpqfja2dH1wEDMB4/Dm++CWXKWLpSERERkQJXxs2NcT/8wMnYWH786COeqFULR4OBS4DPjh0wfDhUrswSPz9+HziQxLAwS5csIlKsFOmFGXJy/fp16tati6urK/v27WPLli0EBQXx888/89RTT2VpO2nSJMaNG8f58+fx8PDI8XzBwcGEhIRk266FGUQKj4zERJaPG8e0OXNYEx9Pb2CRwQCPPQajRmEMCsJgVaR/5yAiIiJiFomJiexfvpwmkZGweDFs3Updo5EDwHfAgNq1oVcv6N0b6tcHg8HCFYuIFD65XZih2H0qLV26ND169CAsLIzExETc3NyA/+8Rd7urV69iMBgofY/VgcaOHUtMTIzpdfr0aXOVLiJ5dOPUKb589FH8S5Wix/TprImPxxqw9/Mj/dChmz9ItmqlAE5ERETkLhwcHGjSuze88QZs3kzqiRO0a9uWag4O9LC2hoMH4YMPmBEYyCOOjnzVsSMXliyB9HRLly4iUuQUy0+mmZ37DAYDPj4+ODg4sH///mzt9u/fj6+vL/b29nc9l52dHc7OzlleImJZ5zdsYHxgIJ5VqvDSH39wJD0dZ4OBN1q3JjIsjJ+OHMG6Rg1LlykiIiJS5Nh6eTFz7VqOJyTgevky/PAD9OrFAisr/kpK4sXVq6nYqxdBDg5MbdaM499+Cykpli5bRKRIKHYh3LVr11i6dCn169fH3t4eGxsbevbsya+//kpcXJyp3alTp1i7di29e/e2YLUikmtGI3tnz2Zg5cpUadOGD/fs4SpQrUQJZvTvz5krV5iyfj1edetaulIRERGRIs9gMNycR/fZZ+HXX5m7ezeTnn2WJm5uGIEtqam8uX07vkOGEODgwLt167J3yhSMt33mEhGRrIr0nHDPPPMMXl5eNGrUCHd3d44ePcqnn37K8ePHWb58OR07dgTg8OHDNG7cmMDAQMaMGUNSUhITJ07k6tWr7N27l7Jly+b6mrkd5ysi+SQlhT/HjuXT2bNZEx9v2tzS1ZVRI0fy6JgxWNvYWLBAERERkYfLmagofps2jcWLF7Pu7FluH5ha1WCgV7Vq9HrqKVqMGIF1uXIWq1NEpKDkNisq0iHc5MmTWbBgAVFRUcTHx+Pq6krLli0ZO3YsjRs3ztJ29+7dvP3222zduhUbGxvat2/P1KlT8fHxydM1FcKJFJDoaPjqK/j8cx49f54/AGugb/XqjPzoI5r06WPpCkVEREQeelevXGHpzJksnj+fv44fJ/G2j5crrazo2LbtzUUdHn8cKlWyWJ0iIub0UIRwlqAQTsS8zm3YwBcjRjA0PJwqyckAbHJz4/fatRk+axZeAQEWrlBEREREcpIQH8/fX3/N4nnz2Hz4MIdTUrC9tW8iEOHqyoh+/Wj2+uvg52fJUkVE8pVCODNRCCdiBkYjrFsH06bRdelSVgCjgakNGsCoUfDkk1CihIWLFBEREZHcMhqNGKKiYPFijIsWUW3rVk4CvwK9AGrX5lLnztC9O+XatweDwbIFi4g8AIVwZqIQTiT/ZCQl8ee4cTT6+288Dh4E4E9gkqsrY954g+5jxugHMhEREZEizmg0suPPP1kyaxYT0tJwXL8e0tKYAHwEBNnZ0atZM3q9/DJVn3gCrK0tXbKISJ4ohDMThXAiDy7h9Gm+Hz6c6cuWEZGeznjgfQcHGDQI4+uvY6hRw9IlioiIiIi5XLsGy5bxzNixzD9zJsuu+jY29KpXj8efe466zz2Hwd7eQkWKiOSeQjgzUQgncv/ObdzI5yNG8FVoKFdvbXMxGHi7UyfG/ve/4OZm0fpEREREpGCdOnKEJVOmsHjpUjZcvEjGbft8DAYer1GDXv/6F81few0rff4SkUJKIZyZKIQTySOjkT3ffMNnISH8fOYMqbc2e5cowYgnn2TwjBk4ubpatEQRERERsbwr58/zx6efsnjhQv4+eZLk2/aVBx6rUoWRw4ZRc+hQ/fJWRAoVhXBmohBOJHcykpJY9s47TJs9m3Xx8abtrVxdGfn66zw6bhzWNjYWrFBERERECqv42FhWzJrF4p9+YumRI8Rm3Owjtw1oam0NbdpwoWNHnJ54AiettCoiFqYQzkwUwoncW+rFi3w9dCjTly3jaHo6ANbAk76+jJw0icZ9+li2QBEREREpUlKSk1k7dy4rv/+eTxISsAoLA+B54EdgetWqvPjii9CrFyiQExELUAhnJgrhRO7i6FGYMYOMb7+lRmIix7g539uwli0ZPmsWnvXqWbpCERERESkOIiNh8WJahISwNS6OVUCHW7v2eHuzwdeXXq+/jlfXrmAwWLJSEXlIKIQzE4VwIrcxGgn95hu+/vBDPjtxgsy1q3728uJyq1Y353vTfB0iIiIiYgZGo5ED69ZR89AhbH/7DdasYURaGjNu7W9YogS9GjWi14svUuvppzFoKhQRMROFcGaiEE4ESE2F//2P9E8/pVpoKKeBb4DnuneHUaOgXTv91lFERERECta1a3w3ZgzfLFrEpuhobv+g62dtTa86deg1cCCNX3wRKwcHi5UpIsWPQjgzUQgnD7OEM2eYP2IEA7duxfbcOQCm29iws2pV3p46lYDHHrNwhSIiIiIicOnECX6fMoXFv/3GqrNnSbltX0WDgcd9fenVrx9tRo7EtkwZi9UpIsWDQjgzUQgnD6Ozmzbx+euv81VoKNeA+UC/8uVh+HB48UVwd7d0iSIiIiIiOYqNjmb5Z5+xeMEC/jx+nLjbPgKXAeY2bsxjL78MPXuCplIRkfugEM5MFMLJQ+PWfG/TQkJYcOYMabc2+5QowccvvMATn34KdnYWLVFEREREJC+SExNZ/e9/s/j77/ntwAEuZ2RwEPAHsLZmY716RPr70/Ott3CtW9fC1YpIUaEQzkwUwklxl5GczNJ33mHaV1+xPj7etL11mTKMGjGCHuPGYa1JbUVERESkiEtPS2Pnzz/T9NgxDEuWwL59PAn8DxgDTGrSBHr3hl69wM/PssWKSKGmEM5MFMJJcZVw9izzhg9n+tKlHEu72e/NBnjSx4eRH31EoyeftGyBIiIiIiLmFBnJJ6+9xo9r1zLnxg2a3Nq8DHjPwYFezZrR69VXqfH441qETESyUAhnJgrhpLi5GhrKJ0OHMnv3bq7d2lbaYGBYUBDDP/+cyvXqWbQ+EREREZECd+EC/PYb/PorQ1au5NvbPjbXsrWlV/369HrhBRoOHoxBo0REHnoK4cxEIZwUC0YjbNkC06Zx9ddf8QRuAL4lSjCib18GTp+OkxZbEBERERHhwuHD/PbJJyxetow1ly6Rets+TysrHq9Vi17PPkurV1/FpmRJi9UpIpajEM5MFMJJUZaenMzS8eNZ/cMPzLx40bR9Vq1aeD31FD3eeUfzvYmIiIiI3EXM+fMsmzKFxb/+yvKTJ0m4bZ+rwUDPqlXp1bcvnd94A4eyZS1Wp4gULIVwZqIQToqk69dhzhwuTp+O19mzpABbbW1pNnAgjBgBtWtbuEARERERkaIlMTaWVTNmsPi//+X3I0eIvu2j9UwbG17t2vXmwg49e4KbmwUrFRFzUwhnJgrhpCg5s2ULyyZMYNiOHXBrpdNRDg7YNWzI6//5Dx516li4QhERERGRoi8tJYVNX3/N4m+/ZUlYGJvS0vC8tW+uwcDPZcrwYu/e9Hr3Xahc2aK1ikj+UwhnJgrhpNAzGtn17bd8FhLCL6dPkwbsAwL8/WHUKPjXv8De3tJVioiIiIgUS8aMDAwHD8LixfDrr3Tdt48VwIfAOIAmTbjUqRNHypcn4NFHcalSxbIFi8gDUwhnJgrhpLBKT07mj/HjmfbVV2yMizNtb1OmDJ++9x4NX3lFS6mLiIiIiBSwiNWrWTxtGk9cuIDvnj1gNPIdMOjW/irW1gS4uxPg40O9Jk0I6NQJ3w4dsLazs2DVIpIXCuHMRCGcFDbxZ88yd/hwZixdyvG0NABsgH4+Poz86CMCn3zSsgWKiIiIiMhNFy7Ab78x5z//4b0DBzidnp5jMwegtqMj9SpVIqBOHQJatiSgZ09cq1cv2HpFJFcUwpmJQjgpLM5s2cKs115j9u7dXL+1rYzBwItBQbwyaxaV6te3YHUiIiIiIvJPrp04wf4//iBs40bC9u9n3+nTHEhI4EYObesCYRUrQkAABASw3GjEq0ULanTpgo2DQ0GXLiK3UQhnJgrhxNIiFiwg5M03TfO9AVQvUYKRffsyYPp0Srq7W7Q+ERERERG5f+mpqUSuX0/YX38RtmMHYUePsu/yZVqmpfH9rTZpQCkgCThqY4Nv7doQEMCOMmWIrViRgO7dKadF2EQKjEI4M1EIJxaRlnZzYtdp0wjdto2Gtza3K1OGka+9Rvfx47GysbFoiSIiIiIiYj7p169jfegQhIVxaetWev36K5Hx8Zw1GrG61eZp4Odbfy5vZUVA6dIEVKtGvYYNCWjfnppdu2Knz7Ei+e6hCOHWrFnDjz/+yJYtWzh9+jSlS5emUaNGTJw4kYYNG5raDRo0iO+++y7b8TVq1ODw4cN5uqZCOClI8efOMXf4cKLXrCE4JubmxhIl+KhOHbq+/TYNNN+biIiIiMhDy5iRgeHUKQgLg337GP3DD/xx4gTHUlPJ6YO+DVDTzo4ADw/q+fsT0Lw5gY8+SrmAAC3iJvIAHooQrm/fvkRHR9O3b1/8/f25fPkyn376Kbt27eKvv/6iffv2wM0Q7pdffmHNmjVZjndwcKBevXp5uqZCOCkQJ0/CzJls/s9/aJmYiB1wytWVcq+8Ai+/DB4elq5QREREREQKqYTLlzm4bBn71qwhbN8+wk6eJCw2lus5fPx/C/jYzQ0CArheowaLk5Op1749gU88AZprTiRXHooQ7tKlS5QrVy7Ltvj4eHx9falTpw6rVq0CboZwCxcuJD4+/oGvqRBOzGnn3Lkc/vJL+u/aBRkZGIF/OTvTskcPBs2ciaObm6VLFBERERGRIsiYkcGZXbvYt2wZYdu2EXb4MGEXLjAxNZV+t2KBNUAHwAc4ZmUFfn4QEMD36em4+vsT0LUrnk2bYrCyutelRB46uc2KivQkUncGcABOTk74+/tz+vRpC1Qkknfpycn8Pn480776ik1xcZQEegKlO3bEMGoU/+3SBfQ/OREREREReQAGKys8mzTBs0kTety+IykJwsMhLAzrP/+k3apVVLtxA5KT4fBhjIcP8zpwfdEieP99SgMBzs4EVKlCQL16BLRtS52ePSmZw+dzEcmqSIdwOYmJiSE0NNQ0FDVTYmIiHh4eXL58mQoVKvD444/z3nvv4erqaqFK5WFjzMjg3K5dhK9eTfiOHRw6fJjwc+c4GBfH1Vu/ebIBevv4cOPLLyndsaNlCxYRERERkeLP3h4CAyEwkDaDBrEGwGiECxcgLIzEXbvoPmcOYRcucCgpievAhthYNuzfD/v3w48/YgB8bW0JKFuWAD8/Apo1o3XfvrjWr68OBSK3KdLDUXPy7LPPsmDBArZt22ZanOGzzz4DoM6tJZrXr1/PZ599hpeXFzt37sTJyemu50tOTiY5Odn0PjY2Fk9PTw1HlbtLT4eoKDh0iOSwMF6aO5fwCxc4lJBA7F0OKWMw8FKLFrwyaxYVGzQo0HJFRERERERyIyU+nkPLlxO2ejVhoaGERUYSdu0aFzIysrVdA7RzcoK6ddlevjx77O0J6tqVuo8/DvosLcXMQzEn3J0mTJjABx98wKxZsxg+fPg92y5atIg+ffowbdo0Ro4cedd2wcHBhISEZNuuEE5SExJIj4jAPjISwsNZtWYNb23fjm9yMr/c+p+QESgLRN86xhrwLVECf3d3/L29qRUQgH/r1vhrqXARERERESmiLh08SNiyZYRt3kzYwYOEnTvH32lpuKemAjcXf5gCvAJ8DlC1Kgm1a/NxbCwBjRtTr0sXvNu2xbpECcvdhMgDeOhCuJCQEIKDg/nwww8ZN27cP7bPyMjA2dmZ7t27s2DBgru2U084Sbp2jYiVKwnfuJFD+/YRHhlJ+OXLHE1JYTYw6Fa79UBboBoQaW8PNWqAvz9zk5JwqlYN/zZtqN6hAyVKlrTQnYiIiIiIiBSQ1FQ4ehT27WPujz+ycOdO+qen0+/qVQC2Ac1va+4I1ClZkoDKlQmoU4d6rVtTt2dPylSrZonqRfLkoQrhMgO44OBg3n333Vwdk5GRQalSpXj00UeZP39+rq+l1VGLr/jz5zn899+Eb9pE+P79HDpxgvDoaCLT0sjeufqmt21tmdygAdSqRay3N2tTUvBv147qbduCtXVBli8iIiIiIlL4Xb0KYWEcWLGCzxYvJuzMGQ7cuEHSXZp7WlsT4OZGgI8PAY0a0XvIEErUrg02xW6KeynCHpoQ7v3332fixImMHz+e999/P9fH/fLLLzz11FNMnz6d119/PdfHKYQr+pLOnmX3b79xft8++jg4wKFDEB5OgzNn2HuXY0obDPg7OVGrYkX8a9bEv3FjarVvj2eTJlgpbBMREREREblv6SkpHFu9mrCVK9m3Ywdhx44RduUKJ9PTs7SzB+IBazs7qF2br+3tiS9blsf69MH7kUfA3d0i9Ys8FCHcp59+yhtvvMEjjzySYw+4Zs2acfLkSZ555hn69euHr68vBoOB9evXM336dHx8fNi+fTsl8zA8UCFc0WDMyODi/v0cWrWK8O3b8U5IoOuNGxAeTuSlS/gAdtz8Bzzz9yf/AlZbWVHL2Rn/ypXx9/enVtOm+HfqRPnatTFoVR8REREREZECc/3kSQ4sW0bYhg3sCwsj9coVvr1xAxISAAgA9gN/AD0AKlRgi6cnvwEBDRsS0KEDNbp0ocQ9FmMUyQ8PRQjXtm1b1q9ff9f9RqORa9euMWTIEPbs2cPFixdJT0+nSpUq9OrVi3HjxuHi4pKnayqEK1yM6emc3r6dQ2vWEL5zJ+GHD3Po/HnC4+O5dtu3dj8gc9BxBlDTxgbv0qX5qW9f3AIDwd+fVF9fbMuVs8RtiIiIiIiISG5kZEBUFISF8eHnn7P70CFmlihB5ZMnAQgBgm9rbgvUsrenXoUKBPj7ExAURED37ngEBFigeCmuHooQzhIUwllIejrcWoX01//9j9+2buXQhQscunGD+LscYgC8bW3xd3WlY926vNa/P/j7Q82aoN+EiIiIiIiIFB/x8XDgAH///DO/rV5N2KlThMXGEnuX5h5WVgS6udGwZk1atW9PpyFDoHJlMBgKtGwpHhTCmYlCOPMyJidjOHYMwsMxHjxI/3nzOHDxIisyMvBISQFgDPDxbcfYANXt7PB3d6eWtzf+9erh37o1fh074lCmjCVuQ0RERERERCzMmJHBqa1b2bd8OWHbthF25Ahhly5xNCUly+J7PYHfAdzdMQYG8n5iIv6NG9PjhRewr1FDwZz8I4VwZqIQLn8kRkdzZOVKwjdu5NC+fYRHRRF+5QrlU1JYd1u76sAxYDXQ3t4eatVio5sb64xG/AMD8W/TBt927bB1dLTIfYiIiIiIiEjRcuPKFfb9+iuhq1YRuncvTRMTGXrhAqSlcRaoDFgDcYBD6dIQGMjiUqVI9vKiYc+e+LRrh5VWZ5XbKIQzE4VweRN75gyH/vqLQ1u2EL5/P+EnT3Lo6lWi0tLI6RuvNHDVyQlD7dpQqxaL0tOxq1aNoMcfp0xAAGglUhEREREREclvSUmwfz8nV67kox9+IPbiReYnJMCtEVlNgR23mjoDDVxcCPTxIbBJExp2745f585YlyhhqerFwhTCmYlCuJxFR0QQ/tdf+CUlUf7MGQgPZ+6uXTx3/fpdj3E1GPAvVQr/SpWoVaMG/k2a4N+xI5UaNtRKpCIiIiIiImJZKSlw8CCEhjJq5ky2HD/OvoQEknJo6gjUL1WKhtWqEdi4MYFdu+LfvTs29vYFXbVYgEI4M3mYQzhjRgYX9u0jfOVKTuzdyxAXFwgPh0OHaH/5MmuBecDAW+3XAu2BClZW1HJxwd/TE39/f2o1bYp/586UrVULg8bWi4iIiIiISBGRlpTEoWXLCF2xgt07dhAaFcXeuDgScmj7uY0NrwQGQsOGXK5enTMeHtTu2ZMSWiiw2FEIZyYPQwiXkZbGqW3bOLRmDeE7d3IoIoLwc+cIj48n5rZ2MdzshgvwKvCHtTUTa9XiuS5doFYtkn19uVGlCmWqVi3wexAREREREREpCOkpKUT8/Tehf/5J6Pbt7D5+nD0xMfwFNLvV5hvgeaCTwcDfDRpAw4YQGMjeUqWo2b079qVLW6x+eXAK4cyk2IVw169zYfFi5v78M+FHj3Lo4kUO3bjBjbs0twJ8bW2p5ebGF336UKlpU6hViww/P6xKlSrIykVEREREREQKpYy0NDh+HKu9e2H3bmYsWULIsWMMMRqZcqvNNcAVsAFq29sT6OlJYL16NOzUiXq9e+Po7m6x+iVvFMKZSbEL4UJDOd6wIb53bLYFatjZ4V+2LLV8fPBv0IBaLVvi16kTdsXhvkVEREREREQKkDEjg+SICOwPHoTdu9m7bh2dtm3jSg6xjBVQy86OwEqVCAwIoGGHDtTv3ZtSFSsWfOHyjxTCmUmxC+Fu3CC9ZUueu36dGr6++DdsiH+bNni3basJJEVERERERETMyJiRwZmdOwn97Td2b9pE6JEjhF65wvmMjGxtDUB1W1s29+yJe/Pm0LAh6QEBWLu5FXzhkoVCODMpdiGciIiIiIiIiBQq5/fuJXTJEkI3bGD3oUOEXr7M6fR0SgNXuRnIATwF7LKxYVqTJjzWsyc0bIixQQMMGspaoBTCmYlCOBEREREREREpaJcOHuTE6tU0iY+H0FAIDcU3KorjwCqgw612PwNvW1sTWK4cDf39CWzdmsBevfCoW9dyxRdzCuHMRCGciIiIiIiIiBQGV48fZ8+vv9IkKYlSBw9CaChvHT1qWvzhdhWtrAgsW5aGNWsS2LIlDR9/nIqBgRisrAq87uJGIZyZKIQTERERERERkcIq5tQp9i5ezO41awgNCyP03DkOp6SQU/hTzsqKhm5uPNKgAa+9+CIEBoKXFxgMObSWu1EIZyYK4URERERERESkKIm/cIF9ixcTuno1u/fuJfTsWcKTkki/tf8pbg5jBchwdeVxKytq+fgw/uWXKRUUBN7eCubuQSGcmSiEExEREREREZGiLjE6mrAlSwhduZJq16/zyKVLsH8/x9LSqA7YA3GADYCLC5+4uXGxTBkaNmtGYPfu+HXqhJWNjUXvobBQCGcmCuFEREREREREpFhKTiZ60yZ+nTePKxERjM3IgLAwSEkhANh/W1MnoL6zM4He3jRs0oTArl2p2bUrNnZ2FirechTCmYlCOBERERERERF5aKSmQng4P8yaxY4dO9gdFcXe+HgSc2jqANRzciKwalUCGzWi01NP4dWhA9jaFnTVBUohnJkohBMRERERERGRh1l6cjJH/vqL3cuWEbpjB7sjI9kTG0v8He3mAoPs7CAggKM+PqyxtqZ5jx4E9OoFxajHnEI4M1EIJyIiIiIiIiKSVUZaGkdXriR02TJ2b9tG6PHjzEpLo3b8zWjuc+BVoDuw1N8fDh60ZLn5KrdZkWbQExERERERERGRB2JlY0ONrl2p0bUrT2duzMiAqCjYvZsK8+fTeeNG2t24AQEBlizVYtQTLo/UE05ERERERERE5D4ZjXDjBpQsaelK8k1usyKrAqxJREREREREREQeZgZDsQrg8kIhnIiIiIiIiIiIiJkphBMRERERERERETEzhXAiIiIiIiIiIiJm9lCFcPHx8YwYMYKKFStib29P/fr1+fnnny1dloiIiIiIiIiIFHM2li6gIPXu3ZudO3cyefJk/Pz8+O9//8vTTz9NRkYGzzzzjKXLExERERERERGRYspgNBqNli6iIPz55590797dFLxl6ty5MwcPHuTUqVNYW1v/43lyu+ysiIiIiIiIiIgUf7nNih6a4aiLFy/GycmJvn37Ztk+ePBgzp07x/bt2y1UmYiIiIiIiIiIFHcPTQh34MABatWqhY1N1hG4AQEBpv0iIiIiIiIiIiLm8NDMCRcdHY23t3e27a6urqb9OUlOTiY5Odn0PiYmBrjZ1VBERERERERERB5umRnRP8349tCEcAAGgyHP+yZNmkRISEi27Z6envlWl4iIiIiIiIiIFG1xcXG4uLjcdf9DE8K5ubnl2Nvt6tWrwP/3iLvT2LFjGTVqlOl9RkYGV69exc3N7Z6hXlESGxuLp6cnp0+f1mITFqDnb1l6/pal529Zev6WpedvWXr+lqXnb1l6/panr4Fl6flbVnF8/kajkbi4OCpWrHjPdg9NCFe3bl3mz59PWlpalnnh9u/fD0CdOnVyPM7Ozg47O7ss20qXLm22Oi3J2dm52PwFKIr0/C1Lz9+y9PwtS8/fsvT8LUvP37L0/C1Lz9/y9DWwLD1/yypuz/9ePeAyPTQLM/Tq1Yv4+HgWLVqUZft3331HxYoVadq0qYUqExERERERERGR4u6h6QnXtWtXOnXqxEsvvURsbCy+vr7Mnz+fFStW8OOPP2JtbW3pEkVEREREREREpJh6aEI4gF9//ZV33nmHiRMncvXq1f9r796jmjryOIB/A0lAeaiA8tDyqBRE0SMW1iIi4mMRUHExIFAQZF05qJUu2lKqR8RVEUSlXVBwF9BqVXxQt61H2VpRqQpC3WotVbddkaKAiiLgIoX42z/cpMbEitYQCb/POfeP3Ezu+c3cydybyZ0ZDBkyBLt27UJISIimQ9MoPT09JCUlKQ27ZV2Dy1+zuPw1i8tfs7j8NYvLX7O4/DWLy1+zuPw1j8+BZnH5a1ZPLn8BPW39VMYYY4wxxhhjjDHG2G/SY+aEY4wxxhhjjDHGGGNMU7gTjjHGGGOMMcYYY4wxNeNOOMYYY4wxxhhjjDHG1Iw74bTU1q1bIRAIUFFRoelQehxZ2avalixZ0unjREVFwdDQUI2Rap9Hy/7YsWNK7xMR7O3tIRAIMH78+C6Pr6f58MMPIRAI4OzsrOlQtB7X/ZcHX39fHr/lXAgEAqxYseLFB6XluN3XnLKyMvzhD3+AtbU19PT0YG5uDnd3dyxevFjTofU4paWlCAoKgqWlJcRiMSwsLCCRSHD69OlnPlZlZSVWrFiBqqqqFx+olpC19fr6+rh69arS++PHj+c2SY0e/+2rr68PCwsLeHt7IyUlBTdu3NB0iC8d7oRjTE3y8/Nx+vRphW3RokWaDqtHMDIyQm5urtL+48eP48cff4SRkZEGoup58vLyAADfffcdysrKNBxNz8B1nzGmSdzua8bBgwcxZswYNDU1IS0tDf/85z/xwQcfwMPDAwUFBZoOr0f561//Cg8PD9TU1CAtLQ1HjhxBeno6rl27hrFjxyIzM/OZjldZWYnk5GTuhOuEtrY2LFu2TNNh9Fiy375ffPEFsrKyMHLkSKSmpsLJyQlHjhzRdHgvFe6EY0xNnJ2d8cYbbyhs1tbWmg6rR5g1axb279+PpqYmhf25ublwd3d/oeehtbX1hR1Lm1RUVODcuXPw9/cHAJUdQ7/Ff//73xd6PG3RlXWfMcYepe52nz1ZWloa7OzsUFRUhJCQEHh5eSEkJATp6emorq7WdHg9xsmTJ/H222/Dz88PJSUliIiIwLhx4xAeHo6SkhL4+fkhLi4OJ0+e1HSoWmnKlCnYuXMnzp07p+lQeiTZb19PT0/MnDkTGzduxPnz52FgYIDAwEDU19drOsSXBnfC9RAVFRUICQmBra0tevXqBVtbW4SGhio9sit7nLS4uBixsbEwMzODqakpAgMDcf36dQ1Fr30KCgrg7u4OAwMDGBoawsfHB//6179Upv3uu+8wceJEGBgYoH///li4cCF3QDxFaGgoAGDXrl3yfXfv3sX+/fsRHR2tlD45ORmjR4+GiYkJjI2NMWrUKOTm5oKIFNLZ2tpi6tSpKCwshIuLC/T19ZGcnKzezHRTsh9fa9euxZgxY7B7926FeltVVQWBQIC0tDSsXr0a1tbW0NfXh6urK7788kuFY61YsQICgQBnz56FRCJBv379MHjw4C7NT3ehjrr/xz/+ESYmJirbnQkTJmDYsGFqyIn2GD9+vMohwFFRUbC1tZW/ln0n0tPTsWHDBtjZ2cHQ0BDu7u4oLS3tuoC1WGfPBXs+T2v3jx07pnLIvKzub926VWH/3/72Nzg4OEBPTw9Dhw7Fzp07+Vw9QUNDA8zMzCAUCpXe09FR/LnXmXtQ2ZQofA/6bFJSUiAQCLB582alcyEUCrFp0yYIBAKsXbtWvv/ixYsIDQ2Fubk59PT0YG1tjdmzZ6OtrQ1bt25FUFAQAMDb21s+3O/x7wp76N1334WpqSkSEhJ+Nd39+/eRmJgIOzs7iMViDBw4EAsWLEBjY6M8zYwZM2BjY4MHDx4ofX706NEYNWrUiw5fK1lbW2P9+vVobm5GTk6OfH9FRQWmT58OExMT6Ovrw8XFBXv27FH6/LVr1zBv3jy88sorEIvFsLKygkQi6fYdetwJ10NUVVXB0dERGRkZKCoqQmpqKmpra+Hm5oZbt24ppZ87dy5EIhF27tyJtLQ0HDt2DOHh4RqIvPuSSqXo6OhQ2ABgzZo1CA0NxdChQ7Fnzx5s374dzc3N8PT0RGVlpcIx2tvb4efnh4kTJ+LAgQNYuHAhcnJyMGvWLE1kqdswNjaGRCKRD4sBHnZK6OjoqCy7qqoqxMTEYM+ePSgsLERgYCDeeust/OUvf1FKe/bsWbzzzjtYtGgRDh8+jJkzZ6o1L91Ra2srdu3aBTc3Nzg7OyM6OhrNzc3Yu3evUtrMzEwcPnwYGRkZ2LFjB3R0dODr66ty3pTAwEDY29tj7969yM7O7oqsdDvqqPtxcXG4c+cOdu7cqfDZyspKFBcXY8GCBerLUA+UlZWFL774AhkZGfj4449x7949+Pn54e7du5oOjbEnepZ2vzO2bNmCefPmYcSIESgsLMSyZcuQnJyscs5LBri7u6OsrAyLFi1CWVkZ2tvbVabje1D1kUqlKC4uhqurKwYNGqQyzSuvvILXX38dR48ehVQqxblz5+Dm5obS0lKsXLkShw4dQkpKCtra2vDzzz/D398fa9asAfDw2iCb3kb2tClTZGRkhGXLlqGoqAhHjx5VmYaIMGPGDKSnpyMiIgIHDx5EfHw8tm3bhgkTJqCtrQ0AEB0djerqaqXjXLx4EWfOnMGcOXPUnh9t4efnB11dXZw4cQIAUFxcDA8PDzQ2NiI7Oxv/+Mc/MHLkSMyaNUuhg/natWtwc3PDJ598gvj4eBw6dAgZGRno06cP7ty5o6HcvCDEtFJ+fj4BoPLycpXvd3R0UEtLCxkYGNAHH3yg9Ln58+crpE9LSyMAVFtbq9a4tYGsDFVt1dXVJBQK6a233lL4THNzM1lYWFBwcLB8X2RkJAFQOD9ERKtXryYA9NVXX3VJfrqTR+t9cXExAaALFy4QEZGbmxtFRUUREdGwYcPIy8tL5TGkUim1t7fTypUrydTUlB48eCB/z8bGhnR1denSpUtqz0t39tFHHxEAys7OJqKH9dvQ0JA8PT3laa5cuUIAyMrKilpbW+X7m5qayMTEhCZNmiTfl5SURABo+fLlXZeJbkbddd/Ly4tGjhypkD42NpaMjY2publZPZnqph6//np5eaks88jISLKxsZG/ln0nhg8fTh0dHfL9Z86cIQC0a9cudYeudZ73XBARAaCkpCT1B6klOtPuy9qm4uJihc/K6n5+fj4RPWyLLCwsaPTo0Qrprl69SiKRSOlcMaJbt27R2LFj5febIpGIxowZQykpKfI2mu9B1auuro4AUEhIyK+mmzVrFgGg+vp6mjBhAvXt25du3LjxxPR79+5V+b1hv3i0rW9ra6NXX32VXF1d5fcxXl5eNGzYMCIiOnz4MAGgtLQ0hWMUFBQQANqyZQsREbW3t5O5uTmFhYUppHv33XdJLBbTrVu3uiBn3cPT+h2IiMzNzcnJyYmIiIYMGUIuLi7U3t6ukGbq1KlkaWlJUqmUiIiio6NJJBJRZWWl+oLXEH4SrodoaWlBQkIC7O3tIRQKIRQKYWhoiHv37uH7779XSj99+nSF1yNGjAAAlSvOMNU++ugjlJeXK2xFRUXo6OjA7NmzFZ6Q09fXh5eXl8p/eN98802F12FhYQAe/ovAnszLywuDBw9GXl4evv32W5SXl6scjgcAR48exaRJk9CnTx/o6upCJBJh+fLlaGhoUFrRZ8SIEXBwcOiKLHRbubm56NWrF0JCQgAAhoaGCAoKQklJCf79738rpA0MDIS+vr78tZGREaZNm4YTJ05AKpUqpOWnDjtHHXU/Li4O33zzjXwem6amJmzfvh2RkZG8ivML5u/vD11dXflrvv6y7uBZ2v2nuXTpEurq6hAcHKyw39raGh4eHi8sZm1iamqKkpISlJeXY+3atQgICMDly5eRmJiI4cOH49atW3wP+pKg/0/30NraiuPHjyM4OBj9+/fXcFTaQywWY9WqVaioqFA5vFH2ZFtUVJTC/qCgIBgYGMinRBEKhQgPD0dhYaH8SXSpVIrt27cjICAApqam6s2IlpHV+x9++AEXL16Uty2PtkV+fn6ora3FpUuXAACHDh2Ct7c3nJycNBa3unAnXA8RFhaGzMxMzJ07F0VFRThz5gzKy8vRv39/lRPLP96w6OnpAeBJ6J+Fk5MTXF1dFTbZ+HU3NzeIRCKFraCgQGlosFAoVDoXFhYWAB7O/8GeTCAQYM6cOdixYweys7Ph4OAAT09PpXRnzpzB73//ewAP5585efIkysvLsXTpUgDKdd7S0lL9wXdjP/zwA06cOAF/f38QERobG9HY2AiJRAIACsMkgV/q8+P7fv75Z7S0tCjs57LvHHXU/YCAANja2iIrKwvAw/lD7927x0NR1YCvv6y7edZ2/2lk9zfm5uZK76nax37h6uqKhIQE7N27F9evX8ef//xnVFVVIS0tje9B1czMzAy9e/fGlStXfjVdVVUVevfuDaFQCKlU+sShq+z5hYSEYNSoUVi6dKnS0OyGhgYIhUKljk+BQAALCwuFuh0dHY379+9j9+7dAICioiLU1tbyUNRndO/ePTQ0NMDKykreDi1ZskSpHZo/fz4AyNuimzdvau33Q3n2TqZ17t69i88//xxJSUl477335Pvb2tpw+/ZtDUbW85iZmQEA9u3bBxsbm6em7+joQENDg8JNUF1dHQDlH2pMWVRUFJYvX47s7GysXr1aZZrdu3dDJBLh888/V3gi68CBAyrTCwQCdYSqNfLy8kBE2LdvH/bt26f0/rZt27Bq1Sr5a1l9flRdXR3EYrHSE1Zc9p33ouu+jo4OFixYgPfffx/r16/Hpk2bMHHiRDg6OqorC1pDX19f5XxuquZjZerF50I9Otvuy9oZ2ZxLMo+Xv+z+RtXE26quGUw1kUiEpKQkbNy4ERcuXEBAQAAAvgdVF11dXXh7e+Pw4cOoqalR2XlQU1ODr7/+Gr6+vjAxMYGuri5qamo0EK12EwgESE1NxeTJk7FlyxaF90xNTdHR0YGbN28qdMQREerq6uDm5ibfN3ToUPzud79Dfn4+YmJikJ+fDysrK/kfmKxzDh48CKlUivHjx8t/CycmJiIwMFBletm9Zf/+/bX2+8FPwvUAAoEARCT/N13m73//u9JwL6ZePj4+EAqF+PHHH5WekpNtj/v4448VXssmR1e1whtTNHDgQLzzzjuYNm0aIiMjVaYRCAQQCoUKw79aW1uxffv2rgpTa0ilUmzbtg2DBw9GcXGx0rZ48WLU1tbi0KFD8s8UFhbi/v378tfNzc347LPP4OnpqXBO2LNRR92fO3cuxGIx3nzzTVy6dAkLFy5US+zaxtbWFpcvX1boeGhoaMCpU6c0GFXPxOfixXuWdl+2qun58+cVjvHpp58qvHZ0dISFhYXSULLq6mo+V09QW1urcr9syhkrKyu+B+0CiYmJICLMnz9f6TeWVCpFbGwsiAiJiYno1asXvLy8sHfv3l/9I4Cfhn4+kyZNwuTJk7Fy5UqFkRUTJ04EAOzYsUMh/f79+3Hv3j35+zJz5sxBWVkZvvrqK3z22WeIjIzk+9NnUF1djSVLlqBPnz6IiYmBo6MjXnvtNZw7d+6J7ZCRkREAwNfXF8XFxfLhqdqEn4TTcgKBAMbGxhg3bhzWrVsHMzMz2Nra4vjx48jNzUXfvn01HWKPYmtri5UrV2Lp0qX4z3/+gylTpqBfv36or6/HmTNnYGBggOTkZHl6sViM9evXo6WlBW5ubjh16hRWrVoFX19fjB07VoM56T4eXQZeFX9/f2zYsAFhYWGYN28eGhoakJ6ertRpzZ7u0KFDuH79OlJTU1XeoDs7OyMzMxO5ubnYuHEjgIf/HE+ePBnx8fF48OABUlNT0dTUpPA9YM/nRdf9vn37Yvbs2di8eTNsbGwwbdo0dYStNWRPbkZERCAnJwfh4eH405/+hIaGBqSlpcHY2FjDEfYcfC7U51na/alTp2LSpElISUlBv379YGNjgy+//BKFhYUKn9HR0UFycjJiYmIgkUgQHR2NxsZGJCcnw9LSEjo6/AzB43x8fDBo0CBMmzYNQ4YMwYMHD/DNN99g/fr1MDQ0RFxcHN+DdgEPDw9kZGTg7bffxtixY7Fw4UJYW1ujuroaWVlZKCsrQ0ZGBsaMGQMA2LBhA8aOHYvRo0fjvffeg729Perr6/Hpp58iJycHRkZGcHZ2BvBwxWAjIyPo6+vDzs6On0bshNTUVLz++uu4ceMGhg0bBgCYPHkyfHx8kJCQgKamJnh4eOD8+fNISkqCi4sLIiIiFI4RGhqK+Ph4hIaGoq2tTWkuOfaLCxcuyOd3u3HjBkpKSpCfnw9dXV188skn8icPc3Jy4OvrCx8fH0RFRWHgwIG4ffs2vv/+e5w9e1a+qrZsxeBx48bh/fffx/Dhw9HY2IjDhw8jPj4eQ4YM0WR2fxtNrQjB1CsrK4sA0LfffktERDU1NTRz5kzq168fGRkZ0ZQpU+jChQtkY2NDkZGR8s89aXWTJ61oxZR1ZoWYAwcOkLe3NxkbG5Oenh7Z2NiQRCKhI0eOyNNERkaSgYEBnT9/nsaPH0+9evUiExMTio2NpZaWlq7ISrfTmbInUl4hMi8vjxwdHUlPT49effVVSklJodzcXAJAV65ckaezsbEhf39/NUXf/c2YMYPEYvGvrvIVEhJCQqGQSktLCQClpqZScnIyDRo0iMRiMbm4uFBRUZHCZ2Sro968eVPdWei21F33ZY4dO0YAaO3atS84B9rj8esvEdG2bdvIycmJ9PX1aejQoVRQUPDE1VHXrVundEzwSp3P5XnPBRGXeWc9S7tfV1dHtbW1JJFIyMTEhPr06UPh4eFUUVGhsDqqzJYtW8je3p7EYjE5ODhQXl4eBQQEkIuLi5pz1f0UFBRQWFgYvfbaa2RoaEgikYisra0pIiJCaWVBvgdVv9OnT5NEIiFzc3MSCoU0YMAACgwMpFOnTimlrayspKCgIDI1NSWxWEzW1tYUFRVF9+/fl6fJyMggOzs70tXVVfld6el+7R4oLCyMAMhXRyUiam1tpYSEBLKxsSGRSESWlpYUGxtLd+7cUXl82TE8PDzUlYVuTVb+sk0sFtOAAQPIy8uL1qxZo/L6cO7cOQoODqYBAwaQSCQiCwsLmjBhgnyFbZmffvqJoqOjycLCgkQiEVlZWVFwcDDV19d3VfbUQkD0/6UqmFaJi4tDZmYmGhsb5Y90MsbYy6Kqqgp2dnZYt24dlixZoulwWCctXrwYmzdvxk8//cT/wj8BX39fHnwutEtjYyMcHBwwY8YMpXme2IsVFRWFffv2KS2QxBhj7Lfj4aha5uuvv0Z5eTny8vIwffp0vulkjDH2m5WWluLy5cvYtGkTYmJiuANOBb7+vjz4XHR/dXV1WL16Nby9vWFqaoqrV69i48aNaG5uRlxcnKbDY4wxxp4bd8JpGYlEgrt372L69On48MMPNR0OY4wxLeDu7o7evXtj6tSpCqvbsl/w9fflweei+9PT00NVVRXmz5+P27dvo3fv3njjjTeQnZ0tn9uJMcYY6454OCpjjDHGGGOMMcYYY2rGywsxxhhjjDHGGGOMMaZm3AnHGGOMMcYYY4wxxpiacSccY4wxxhhjjDHGGGNqxp1wjDHGGGOMMcYYY4ypGXfCMcYYY4wxxhhjjDGmZtwJxxhjjDHGGGOMMcaYmnEnHGOMMcYYY4wxxhhjasadcIwxxhhjjDHGGGOMqRl3wjHGGGOMMcYYY4wxpmb/A897IXsgAcedAAAAAElFTkSuQmCC\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 nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('WY PAR with CY Nutrients',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.32157315,  25.46430715,  53.17412975,  62.44887383,\n",
       "        90.84260541, 103.05034021,  94.88347023,  80.51483052,\n",
       "        51.31051136,  31.57238514,  20.31519746,   8.03565263])"
      ]
     },
     "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 Nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('CY PAR with WY Nutrients',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([ 14.23679829,  25.43165744,  41.19107135,  59.75958975,\n",
       "        85.18338548,  94.02408369, 100.44978733,  80.01338611,\n",
       "        60.86782944,  29.30627043,  13.14189992,  10.14616278])"
      ]
     },
     "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_3220928/1288103633.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_NSi/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_NSi/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_NSi/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "            \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_NSi/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_3220928/3661973807.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 nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('WY Halocline with CY Nutrients',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([0.57723351, 0.32422883, 0.78620486, 1.17592297, 1.92554819,\n",
       "       2.39019519, 2.88495737, 2.02522262, 1.49650757, 0.70498795,\n",
       "       1.51586427, 0.98230242])"
      ]
     },
     "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": "iVBORw0KGgoAAAANSUhEUgAABM8AAAEvCAYAAABSa+brAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAACODUlEQVR4nOzdd3QU1cPG8e+m90YKLYSOIr1HWkBQAQtViopEUUSxAiIWBBVRmiAo+AOlKSBIea0gXaV3QcRKbyEJ6SF13j+WXRJSSCDJBng+58whO3Pnzp3JJiRPbjEZhmEgIiIiIiIiIiIiOdjZugEiIiIiIiIiIiKllcIzERERERERERGRPCg8ExERERERERERyYPCMxERERERERERkTwoPBMREREREREREcmDwjMREREREREREZE8KDwTERERERERERHJg8IzERERERERERGRPCg8ExERERERERERyYPCMxEREcnT3LlzMZlMVK5c2dZNAfJvT2lrqy2NHj0ak8lEWFjYNddRuXJlTCYTc+fOLbJ2Sel39OhRTCYTJpOJo0eP2ro5IiIipYLCMxERueVlZGSwZMkS+vfvT82aNfHx8cHJyYnAwEBatWrFyJEjOXjwIAApKSnUqVMHk8lEaGgoGRkZV607NDQUk8lEnTp1SElJKVCbLEFQQX+BtZQdPXp0geqXW9fKlSsZPXo0K1eutMn1v/vuO+v7dffu3XmWmzFjhrVceHh4nuXS09Px9PTEZDLx2muvsWnTJuzt7TGZTHzyySdXbc++fftwcnLCZDIxduzYAt9HWFiYtX2NGjXCMIw8y2b9ei5q+/btY/To0UyZMqXI6y5tpkyZwujRo9m3b5+tmyIiIrcYhWciInJL27ZtG7Vr16Z3794sWLCAv//+m6SkJDw9PYmKimLz5s28//771K1blx49emAymZg/fz6Ojo5s27aN8ePH51v/+PHj2bZtG46OjsyfPx9nZ+cSurNbj7e3N7Vq1aJatWq2borN+fv7U6tWLSpVqpTj2MqVKxkzZozNwrM2bdpgb28PwIYNG/Ist3HjRuvH+ZXbuXMnCQkJALRr1462bdvy4osvAjB8+HD++eefPM9NTU2lf//+pKWl0axZM1599dVC3Mlle/fuZfHixdd07vXat28fY8aMKbLwzNHRkVq1alGrVi0cHR2LpM6iMmXKFMaMGaPwTERESpzCMxERuWV9++23hIWF8ddff1GmTBnGjRvHX3/9RWpqKlFRUaSmprJz505effVVvLy8WL58OUlJSTRq1Ig33ngDMA+P279/f671HzhwwNoT7M0336RRo0YldWu3pG7dunH48GHWrVtn66bY3JAhQzh8+DDz58+3dVNy8PLysn4tZA3IrrRp0yYAypYty7Fjx/LsgWmpw9HRkZYtWwIwduxYateuTVJSEo899liePUTfeustDhw4gKurK/Pnz7eGetfijTfeIC0t7ZrPLy0qVKjA4cOHOXz4MBUqVLB1c0REREoFhWciInJL+vvvv3nkkUdISUmhdu3a7Nu3j1dffZUaNWpYy9jb29OkSRPGjRvHkSNHePDBB63HXnvtNZo2bWrtuZKampqt/rS0NOv+pk2bMnLkyBK7N5HSrl27dgD88ssvuQZbf/zxB+fOnaNWrVo88MADQN69zyz7mzdvjpubGwAuLi7Mnz8fBwcHtmzZwsSJE3Oct23bNiZMmADABx98QK1ata7pXu666y5cXFz477//+PTTT6+pDhERESndFJ6JiMgt6Y033iAuLg4XFxdWrFhBxYoV8y3v5+fHypUr8fb2BsDBwYH58+fj4uLCb7/9lmOusbfffpt9+/ZZe7Q4ODgU161cVWxsLIsXL+bhhx+mbt26+Pn54eLiQkhICP369WPbtm3XVf+///7L4MGDqVGjBq6urtaeRW+//TZxcXH5npuZmcmSJUvo2rUrFSpUwNnZmYCAABo3bsyrr75qnWuuIPJbMODKCfTXrVtHly5dCAgIwMXFhdtvv50xY8Zw8eLFfK8RGxvL2LFjad68Ob6+vjg7OxMcHEzfvn2v6Tnu2rULk8mEg4MDsbGxOY4PGjTIOlfWd999l+P4okWLMJlMOYZn5rZgwMaNGzGZTMybNw+AefPmWeu2bHn1BEtNTWXChAnUr18fd3d3vL29ad++PatWrSr0PcPl8CwuLo49e/bkOG5pR1hYGG3bts22L6u0tDS2bNmSrU6Lxo0bW3uIjho1igMHDliPJScnW3uktW/fniFDhlzTfQBUrFiR5557DoB33nnHOoS0oAqy0EVek/hnnQ/u2LFjOT6fWb8vDRgwAJPJxIABAzAMg9mzZ9OqVSvKlCmTbWGIgi4YsHLlSrp27Ur58uVxcnLC19eXNm3aMHPmzDx74FnmiRs9ejSGYTBr1iyaN2+Ol5cXnp6ehIaG8sUXX+Q4z/J+PnbsGADh4eE57jWrCxcuMGrUKBo1aoSXlxdOTk6ULVuWevXq8fTTT6t3qoiIFJ4hIiJyizl79qxhZ2dnAMYTTzxxXXV9+OGHBmDY29sbW7ZsMQzDMHbu3Gk4ODgYgDFlypRrqnfOnDkGYADGkSNHrlreUvatt97Kceytt96yHgcMDw8Pw9nZ2fraZDIZU6dOzbcdISEhuR7/6quvstXl6emZ7XVwcLBx6NChXM89f/680aZNm2xt8/b2NhwdHa2vH3zwwQK3J79jlmfQtm1bY/z48YbJZDJMJpPh4+NjmEwm6/XatWtnpKen59rebdu2GUFBQday9vb2hqenZ7bn+N577+V6bl4yMjIMHx8fAzD+7//+L8fx6tWrW+t/6aWXchwfOHCgARj9+/fP834tNm/ebAQFBRkuLi4GYLi4uBhBQUHZts2bN1vLh4SEGIAxbdo0o3nz5gZgODo6Gh4eHtnu+bPPPivUPRuGYcTHx1u/RsaPH5/j+EMPPWQAxsKFC42TJ08agFGpUqUc5TZv3mxty/r163McT0tLM5o0aWIARv369Y3U1FTDMAzj+eefNwDDy8vLOHbsWKHbbxiG0bZtWwMwHnvsMSM6Otr6eRwzZkyOslm/nvM6ltfXmGEYxpEjR3L9fhAUFGR4eXkZgGFnZ5fj8zlhwgRr2ccee8z6XunZs6f1HF9fX8POzs6YM2dOvteyiI+PN+67775sX7deXl7Zvo5CQ0ON6OjoPJ/ZG2+8YTz44IMGYDg4OFjvwbKNGjUq23kTJkwwgoKCrN+3vby8ctyrxYkTJ4xKlSpZ67Lco729vXVf1q8LERGRglB4JiIit5xFixZZf4n67rvvrquuzMxMIywszACMGjVqGFFRUcbtt99uDWIyMzOvqd6iDM9mzJhhvPTSS8a2bduMCxcuWNv933//GS+88IJhMpkMe3t7Y8+ePXm2I7df7Hfv3m0Nulq2bGns37/fMAxzIPTNN98Y5cqVMwCjWrVqRnx8fLZz09LSjJYtWxqA4ezsbHzwwQdGRESEYRiGkZ6ebhw5csT49NNPjZEjRxa4PQUJz3x8fAw7Oztj5MiRxvnz5w3DMIzY2Fhj1KhR1meYWxh05MgRazjSs2dPY/fu3UZaWpphGIZx7tw5480337SGQStWrMhxfn4eeOABAzBeeOGFbPtPnDhhDQos4c+VqlWrZgDW4OPK+80tJLCEKI899li+7bKEZ76+vkaFChWMlStXWsOnw4cPGy1atLCGsTExMYW4Y7PQ0FADMDp16pTjmCWkPHXqVLb7/Pfff7OVe/fdd63voeTk5Fyvc+jQIWtg+PrrrxsbNmywBj1z584tdLstsoZnhmEY77//vjVAtryXLYorPCvo+YZx+fPu4eFhODg4GBMnTjRiY2MNwzAHYqdPn77qtQzDMLp27WoARvXq1Y2FCxcacXFxhmEYRnJysvF///d/RtWqVQ3A6Nq1a45zLc/M19fX8Pb2NubOnWskJSUZhmF+v99///3WwOuvv/7Kcb7lPXnl+z2rJ554wgCMypUrG2vXrrWG4enp6cbRo0eNGTNmGCNGjMj3WYmIiFxJ4ZmIiNxy3njjDesvh5Zfzq/H0aNHrQFHxYoVrYHH0aNHr7nOrL9s+/v75+hlceWWX3h2Nc8++6wBuffCy+8X83vvvdf6S3RiYmKO43v27LEGSll7wBiGYcyePdvac+n7778vcFuvNzzL7xl1797dAIwOHTrkOGbpqfPoo4/m2bbJkyfnGXLlZ8qUKQZg1K1bN9v+efPmWT8vFSpUMEwmkxEZGWk9fvz4ces9XfleK8rwzNnZ2fjjjz9yHI+IiLCGUl988UXBb/iS1157zRo2WYJIwzCHXZYw2uLxxx/PNdjs0KGDARhhYWH5XsvyubG3t7eGulf2aiysK8OzpKQko0KFCgZgPP/889nKlqbwDDA++uija7rWd999ZwBG2bJljZMnT+Z6/okTJwx3d3cDMPbu3ZvtmOWZQe49BS9evGiUL1/eAIx33303x/GChGeWP14sXLgwzzIiIiKFpTnPRETklhMVFWX92M/P77rrCwkJ4cMPPwTg5MmTAEyZMoWQkJDrrhsgMjKSc+fO5btdjy5dugDw66+/FvicmJgYVq9eDcDw4cOtE7Vn1bBhQ7p37w6Y5+bK6vPPPwegc+fOdO7c+ZrafS2cnZ0ZNmxYrscsC0L89ttv2fZHR0ezfPlyAF599dU86+7fvz8A+/fvL9TnxDJX18GDBzl//rx1v2Ui/Pbt29OuXTsMw8g2af769esBqFKlSpG913LTs2dPbrvtthz7AwICCA0NBXI+s4KwzMcWHx/P7t27rfst95h1vjbLvGdZ7z+/+c6u9OKLLxIWFkZGRgZnzpwhICCA//3vf4Vuc35cXV2tc4zNnDmTI0eOFGn9RcXX15dBgwZd07mzZ88G4NFHH81zJc6KFStaPx+W7xFXatmyZa6fM2dnZ+655x7g2t5TAD4+PgCcOXPmms4XERHJjcIzERG55RiGUeR1Pv744zRo0ACABg0aWCfxLgpHjhzBMPcWz3O7mv/++49hw4bRuHFjfHx8sLe3t060bQmvLMFfQezZs8d63Q4dOuRZrmPHjoD5F2HLJOLp6ens3LkTgPvvv7/A1ywKd9xxBx4eHrkeK1++PGAOy7LaunUrmZmZgDnIKlu2bK7bHXfcYT3HMrF5QdStWxd/f/8c4diV4RlcDsyyfny14Oh6NW/ePM9jeT2zgmjZsiVOTk5A9sUALB9bArOsH2ctt337dpKSkgDzM8qPyWRiypQp1tevv/46gYGBhW7z1YSHh3PbbbeRmprKqFGjirz+otC0aVPrcy8sS8D+v//9L8+vg7Jly7J27Vog76+D4npPAdx3332AOeh+6qmnWLVq1VUXLhEREbkahWciInLL8ff3t358rb+g5cayEqfl39JixYoV1K5dm0mTJrFnzx5iY2Px8PAgMDCQoKAgfH19AUhMTCxwnREREdaP8+qBAlhXMU1PT7c+66ioKGuQVpw9pnLj6emZ5zHLiqjp6enZ9p8+fdr6cUF7AFpCnYLIuiqmJRD777//OHbsGLfffjtly5a1hkNZwzNLuFbc4VlBnlleqyvmx83NjWbNmgHZQ7Gff/4ZyN7zLCQkhJCQEE6ePMm///6b7Zys9eQn69dlcX2N2tvbM3bsWAAWLlzI/v37i+U61+NaQ8O0tDQiIyMB86qz+X0dWFatzevroLjeU2DuCfvQQw+RlpbGrFmz6NSpEz4+PtStW5fhw4fz119/XVO9IiJya1N4JiIit5ysPYT27t1rw5YUv6ioKAYMGEBKSgrt27dn48aNJCUlWX/5PXv2LEuXLi2RtphMpgLtK20yMjIA87C8q/UAtGxZg5+CuLJnmeVfS2hWuXJlqlSpwp9//snp06f5559/OHHiRLZzb0SWtv/666+kp6dz6NAhzp07R7Vq1XKEslcO3bT8m7UHW2nQvXt3mjdvTmZmJiNHjrR1c3Kwt7e/pvMsXwcAixcvLtDXwdy5c4uo1QXn6OjIV199xb59+xg1ahTt27fHzc2NgwcPMnHiROsfEkRERApD4ZmIiNxy2rVrh52d+b/AFStW2Lg1xeuHH34gLi4OX19fvv32W9q2bYurq2u2MmfPni10vVl7r+Q33NNyzMHBwdrDrUyZMjg6OgJw9OjRQl+7pJUtWxaA5ORk/vnnn2K5hiUk+/vvvzl58mSO8AyyB2yW4zVr1sy3519pZ7mnhIQEdu3aZe1Nllv4mHXoZmpqKlu3bs1WR2nywQcfAPDjjz+yadOmfMtaelpZemvlJjY2tugad41cXFysPfYOHDhg49ZcXf369RkzZgzr1q0jJiaGtWvX0qZNGzIyMhg+fHip7BUoIiKll8IzERG55QQFBdGjRw/APLSqMMN4imO+tOJk6Z1Uq1atXCf1B6zzExVGo0aNrAHkunXr8ixnqbt+/frWwMzBwcE6zO7bb78t9LVL2p133mntIbd48eJiucZtt91GuXLlAHM4tnHjRuzs7LKFSFmHbl7PkE3L5600vJdDQ0NxdnYGzD3JcpvvzCJreLZt2zaSk5OB0hmetW3blk6dOgEwYsSIfMtaQuWIiAhSUlJyLbN9+/Y8zy/Jz2fLli0BWLp0qXUewJJ0rffq4ODAXXfdxffff4+zszOGYVzT9z0REbl1KTwTEZFb0rvvvouHhwfJycl0796dU6dO5Vv+woUL9OjRo1T0ACkMS0+Rv/76K9eeLfv27WPhwoWFrtfHx8e6Kt6ECRNyndto//79LFu2DIC+fftmO/bEE08A5p5xP/zwQ6GvX5ICAwOtK3FOmDDhqmHrtc6jZwmBPvnkE86cOUP9+vWzrQabteeZJWS6luDIy8sLMK+YamsuLi7WFTs3btxo7aWVW88zy1DOU6dOMWvWLAA8PDxo0qRJibW3MMaNG4ednR3bt2+3rtaam/r16wPmQCi3nrDJycnW1XxzU5Kfz6eeegowfz+ZMGFCvmUTExNJTU0t0usX5F7zCiDBvJqnZdjqtQ5fFRGRW5PCMxERuSXVrFmTBQsW4OTkxO+//06DBg344IMPsg3Ly8jIYO/evYwaNYqqVavm+wtwaXX33XdjZ2dHdHQ0Dz/8sDUkTE1NZcmSJdx99935Tt6dn7Fjx+Lo6Mg///zDPffcYx3KlZmZyQ8//EDnzp1JT0+nWrVqDBo0KNu5jz76KK1atcIwDHr06MGECROsk5FnZGRw9OhRPvzww6v22ikpkyZNokyZMsTFxdGqVSs+//zzbEFqZGQky5cvp3v37jmCwoKyBGGWXkZXriBZvnx5atWqxbFjx6xDbQs7txpAnTp1APjll184fPjwNbW1KFnue926dURERFClShWCg4NzLWvpfbZo0SIAWrdubR32WNrUr1+ffv36Afn3sKxYsSKtWrUC4OWXX2bt2rXW+cV2795Nhw4dsi3QcSXL5zMuLo4lS5YUVfNz9eCDD9KtWzfAvJrl4MGDs4XJqampbN++nREjRhASEpJvu6+F5V6//vprLly4kGuZkJAQRo4cybZt27IFaf/88w8PP/wwSUlJ2NnZWcN/ERGRglB4JiIit6yuXbuyfv16qlevTmRkJK+++io1atTA2dmZMmXK4OTkRKNGjXjnnXeIjY2lb9++uLu727rZhVKjRg2GDx8OwPLly6lYsSI+Pj54eHjQu3dvPDw8+Oijj66p7oYNG1oDyF9//ZV69erh7e2Nu7s7Xbp04fTp0wQHB/Ptt9/i4eGR7VwHBwdWrFhB69atuXjxIq+88gqBgYH4+vri6upKlSpVePnll/nzzz+v+xkUhapVq7JmzRoqV67M+fPneeKJJ/D19cXPzw9PT08CAgLo0aMHK1asuObhbFeGZVe+huw9zWrXrk1QUFChr9OjRw8CAgK4cOECt99+OwEBAVSuXJnKlSuzbdu2wjf8OlnuyRIY5RcIWsIzS9nSOGQzq3feeadAixlMmzYNT09Pzpw5Q8eOHfHw8LD2qvv3339ZsGBBnudWr16du+66C4DevXvj5eVl/XxOmTKlqG7F6osvvqBPnz4AzJw5k1q1auHh4YGfnx+urq60aNGC8ePHExUVVeQLgjz11FOYTCa2bNlCQEAA5cuXt96rxblz53j//fcJDQ3Fzc3N2q4aNWqwdOlSTCYTkyZN4vbbby/StomIyM1N4ZmIiNzSWrZsyeHDh1m0aBEPP/ww1atXx8XFhfj4ePz8/GjVqhWvv/46f/zxBwsXLrTO23Ujef/995k/fz7NmjXD1dWVtLQ0qlevzmuvvcbevXspX778Ndfdu3dvfv/9dwYNGkS1atVISUnBwcGBBg0aMGbMGA4ePJjnL6n+/v5s3LiRL774gk6dOhEQEEBiYiK+vr40btyYV199lffee++a21bUGjZsyKFDh5g+fTodOnTA39+f+Ph4MjMzqVGjBv369WPx4sXX3EOxatWqhISEAOZwsXXr1jnK5LaAQGH5+vry888/06dPHypUqEBsbCzHjh3j2LFj+U5aX1yaN2+ebRGL3OY7y+tYaQ/PKleuzNNPP33Vcg0aNGDHjh306dOHwMBAMjMz8ff359lnn2Xfvn3Url073/O//vprXnrpJWrWrElaWpr181kcQznd3NxYtGgRGzZs4NFHH6Vq1apkZmaSkJBAYGAg7du3Z/z48fz9999FvphFmzZt+P777+nQoQPe3t6cO3fOeq8WP/30EyNHjqR169YEBwdb58arXr064eHh7Ny5kxdffLFI2yUiIjc/k1EaZosVEREREREREREphdTzTEREREREREREJA+lPjzbuHEjJpMp180W83KIiIiIiIiIiMito3QuT5SL9957L8e8FpYVd0RERERERERERIrDDROe1ahRgxYtWti6GSIiIiIiIiIicgsp9cM2RUREREREREREbOWGCc+effZZHBwc8PLy4p577uHXX3+1dZNEREREREREROQmZzIMw7B1I/Kzd+9e5s2bR1hYGGXKlOGff/5hwoQJ/PXXX3z//ffcc889uZ6XkpJCSkqK9XVmZibR0dGUKVMGk8lUUs0XEREREREREZFSxjAM4uPjKV++PHZ2+fctK/XhWW5iYmKoW7cufn5+7N+/P9cyo0ePZsyYMSXcMhERERERERERuVGcOHGCihUr5lvmhgzPAAYPHszMmTNJSkrC1dU1x/Ere57FxsZSqVIlTpw4gZeXV0k2VURERERERERESpG4uDiCg4OJiYnB29s737I3zGqbV7JkfnkNwXR2dsbZ2TnHfi8vL4VnIiIiIiIiIiJSoKm9bpgFA7K6cOEC3333HQ0aNMDFxcXWzRERERERERERkZtUqe951q9fPypVqkSTJk3w9/fn77//ZtKkSZw7d465c+faunkiIiIiIiIiInITK/XhWb169fjqq6+YOXMmCQkJ+Pn50apVKxYsWEDTpk1t3TwREREREREREbmJ3bALBhRWXFwc3t7exMbGas4zEREREREREZFbWGFyohtyzjMREREREREREZGSoPBMREREREREREQkDwrPRERERERERERE8qDwTEREREREREREJA8Kz0RERERERERERPKg8ExERERERERERCQPCs9ERERERERERETyoPBMREREREREREQkDwrPRERERERERASAbdu20atXL8qVK4eTkxNly5alZ8+ebN26tVD1jB49GpPJdE1t2LhxIyaTiY0bN17T+QUVFhZGWFhYgcpmZmayYMECOnTogL+/P46OjgQGBnLffffx7bffkpmZyX333YePjw8nTpzIcX50dDTlypWjZcuWZGZmFvGdSHFTeCYiIiIiIiIiTJs2jZYtW3Ly5EnGjx/P2rVrmThxIqdOnaJVq1ZMnz69wHUNHDiw0IGbRaNGjdi6dSuNGjW6pvOL2sWLF+ncuTOPPfYYgYGBzJgxg/Xr1zNz5kzKly9Pr169+Pbbb5k9ezYODg4MHDgwRx1DhgwhPj6eefPmYWenKOZGYzIMw7B1I0pCXFwc3t7exMbG4uXlZevmiIiIiIiIiJQamzdvpk2bNnTu3JkVK1bg4OBgPZaenk63bt344Ycf+Pnnn2nZsmWe9SQlJeHm5lYSTb5ull5nV+vh9swzzzBjxgzmzZtH//79cxz/+++/SU5Opl69eixZsoTevXszc+ZMBg0aBMCKFSvo3r07n3zyCYMHDy7q25BrVJicSHGniIiIiIiIyC1u3LhxmEwmZsyYkS04A3BwcOCTTz7BZDLx/vvvW/dbhmbu2bOHnj174uvrS7Vq1bIdyyolJYWhQ4dStmxZ3NzcaNOmDbt376Zy5coMGDDAWi63YZsDBgzAw8ODf/75h86dO+Ph4UFwcDBDhw4lJSUl23XGjBlD8+bN8fPzw8vLi0aNGvHZZ59xLX2Hzp49y+zZs7nnnntyDc4AatSoQb169QB46KGH6NOnD8OGDePo0aNERUXx9NNP07FjRwVnNzCHqxcRERERERERkdwYBiQl2boV2bm5QWGmG8vIyGDDhg00adKEihUr5lomODiYxo0bs379ejIyMrC3t7ce6969O3369OHpp58mMTExz+uEh4fz1Vdf8corr9C+fXsOHTpEt27diIuLK1A709LSeOCBB3jiiScYOnQoP//8M++88w7e3t6MGjXKWu7o0aMMGjSISpUqAeZ53J577jlOnTqVrVxBbNiwgbS0NLp27Vrgcz7++GM2bdrE448/TkBAAKmpqXz++eeFuq6ULgrPRERERERERK5RUhJ4eNi6FdklJIC7e8HLR0ZGkpSURJUqVfItV6VKFXbs2EFUVBSBgYHW/Y899hhjxozJ99xDhw6xaNEiRowYwbhx4wDo2LEjQUFB9O3bt0DtTE1NZcyYMfTq1QuAu+66i127drFw4cJsodicOXOsH2dmZhIWFoZhGEydOpU333yzUAsZHD9+HOCqzyYrPz8/PvvsMzp37gzAggUL8gwl5cagYZsiIiIiIiIiclWWYY9Xhk89evS46rmbNm0CzMMas+rZs2eOYaJ5MZlM3H///dn21atXj2PHjmXbt379ejp06IC3tzf29vY4OjoyatQooqKiiIiIKNC1rlenTp1o0aIFNWrU4JFHHimRa0rxUc8zERERERERkWvk5mbu6VWaFHa+fn9/f9zc3Dhy5Ei+5Y4ePYqbmxt+fn7Z9pcrV+6q14iKigIgKCgo234HBwfKlClToHa6ubnh4uKSbZ+zszMXL160vt6xYwd33303YWFhzJo1i4oVK+Lk5MTKlSsZO3YsycnJBbqWhWXo59WeTW6cnZ1xcnIq9HlS+ig8ExEREREREblGJlPhhkiWRvb29rRr145Vq1Zx8uTJXIcYnjx5kt27d9OpU6ds851Bzp5oubEEZOfOnaNChQrW/enp6dZgrSgsXrwYR0dHvvvuu2xB28qVK6+pvnbt2uHo6MjKlSt5+umni6iVcqPRsE0RERERERGRW9zIkSMxDINnnnmGjIyMbMcyMjIYPHgwhmEwcuTIa6q/TZs2AHz11VfZ9n/99dekp6dfW6NzYTKZcHBwyBbwJScns2DBgmuqr2zZsgwcOJDVq1czf/78XMv8+++//Pbbb9dUv9wY1PNMRERERERE5BbXsmVLpkyZwosvvkirVq0YMmQIlSpV4vjx43z88cds376dKVOmcOedd15T/XfccQd9+/Zl0qRJ2Nvb0759e37//XcmTZqEt7c3dnZF07enS5cuTJ48mX79+vHUU08RFRXFxIkTcXZ2vuY6J0+ezH///ceAAQNYvXo13bp1IygoiMjISNasWcOcOXNYvHgx9erVK5J7kNJH4ZmIiIiIiIiI8Nxzz9G0aVMmTZrE0KFDiYqKws/Pj1atWvHrr78SGhp6XfXPmTOHcuXK8dlnn/Hhhx/SoEEDlixZwr333ouPj0+R3EP79u35/PPP+eCDD7j//vupUKECTz75JIGBgTzxxBPXVKeLiwvff/89X375JfPmzWPQoEHExcXh6+tLkyZN+Pzzz3MsZCA3F5NhWS7jJhcXF4e3tzexsbF4eXnZujkiIiIiIiIit7wtW7bQsmVLvvzyS/r162fr5sgtpDA5kXqeiYiIiIiIiEixW7NmDVu3bqVx48a4urqyf/9+3n//fWrUqEH37t1t3TyRPCk8ExEREREREZFi5+XlxU8//cSUKVOIj4/H39+fTp06MW7cuGwrY4qUNgrPRERERERERKTYNW/enF9//dXWzRAptKJZzkJEREREREREROQmpPBMREREREREREQkDwrPRERERERERERE8qDwTEREREREREREJA8Kz0RERERERERERPKg8ExERERERERERCQPCs9ERERERERERETyoPBMREREREREREQkDwrPRERERERERASA3377jfDwcKpUqYKLiwseHh40atSI8ePHEx0dzdKlSzGZTEybNi3X85966imcnZ357bffirxtJpOJ0aNHW18fOnSI0aNHc/To0Rxlw8LCqFOnzjVdp06dOtx+++059q9YsQKTyURoaGiOYwsWLMBkMvHNN99w33334ePjw4kTJ3KUi46Oply5crRs2ZLMzMw822AymRgyZEiux77++mtMJhMbN24s+E1dsnHjxms+91rrHz16NCaTqViuV1IUnomIiIiIiIgIs2bNonHjxuzcuZPhw4ezatUqVqxYQa9evZg5cyZPPPEEvXr1ol+/frz66qv8888/2c7/6aefmDVrFmPGjKFevXpF3r6tW7cycOBA6+tDhw4xZsyYXMOz69GuXTsOHz7M2bNns+3fuHEj7u7u7Nq1i/j4+BzH7OzsaNOmDbNnz8bBwSFbWy2GDBlCfHw88+bNw87u1ohkBg4cyNatW23djOtyw32mZs+ejclkwsPDw9ZNEREREREREbkpbN26lcGDB9OhQwd2797NM888Q1hYGB07dmTkyJEcPnyY8PBwAKZPn46Pjw8DBgyw9p6Ki4tj4MCBhIaGMnz48GJpY4sWLahYsWKx1J1Vu3btAHL0ztq4cSMDBw7EZDLx66+/5jjWsGFDfHx8KFu2LJ988gk//fQTn376qbXMihUrWLRoERMmTKB69erFfh+lRcWKFWnRooWtm3Fdbqjw7NSpUwwbNozy5cvbuikiIiIiIiIiN4333nsPk8nE//73P5ydnXMcd3Jy4oEHHgDA19eXzz77jM2bN/Phhx8C8NJLLxEVFcW8efOwt7fP8zoff/wxdnZ2REREWPdNmjQJk8nEs88+a92XmZmJr68vQ4cOte7LOmxz7ty59OrVCzCHXSaTCZPJxNy5c7Ndb+fOnbRu3Ro3NzeqVq3K+++/n+9wSTAP+bxy6GFUVBQHDhygS5cuNG7cmA0bNliPnThxgv/++88augE89NBD9OnTh2HDhnH06FGioqJ4+umn6dixI4MHD873+tdi165d9OnTh8qVK+Pq6krlypXp27cvx44dK9D533zzDaGhobi5ueHp6UnHjh1z7S12+PBh+vbtS1BQEM7OzlSqVIn+/fuTkpKSZ925DdusXLky9913H6tWraJRo0a4urpy22238fnnn+c4/+zZswwaNIiKFSvi5ORElSpVGDNmDOnp6QW6t6LgUGJXKgJPP/00bdq0wc/Pj6+//trWzREREREREREBIDExsdDnODs74+Bg/rU8PT2dlJQU7OzscHV1veZ63d3dC92OjIwM1q9fT+PGjQkODi7QOffeey+DBg3ijTfewM7Ojs8//5zp06dTo0aNfM/r0KEDhmGwbt06+vbtC8DatWtxdXVlzZo11nK7du0iJiaGDh065FpPly5deO+993jttdf4+OOPadSoEQDVqlWzljl79iwPP/wwQ4cO5a233mLFihWMHDmS8uXL079//zzb6OfnR7169bIFZJs2bcLe3p4777yTtm3bsn79eusxS7ms4RmYg8JNmzbx+OOPExAQQGpqaq7hUF4Mw8g1IMot/Dt69Ci1atWiT58++Pn5cebMGWbMmEHTpk05dOgQ/v7+eV5n4cKFPPzww9x9990sWrSIlJQUxo8fT1hYGOvWraNVq1YA7N+/n1atWuHv78/bb79NjRo1OHPmDN988w2pqam5hq752b9/P0OHDuXVV18lKCiI2bNn88QTT1C9enXatGkDmD+HzZo1w87OjlGjRlGtWjW2bt3Ku+++y9GjR5kzZ06hrnnNjBvEggULDE9PT+PEiRPGY489Zri7uxfq/NjYWAMwYmNji6mFIiIiIiIicqsCCr0tWbLEev6SJUsMwGjbtm22ev39/QtV57U4e/asARh9+vQp1Hnx8fFG1apVDcDo0KGDkZmZWaDzKlasaDz++OOGYRhGSkqK4e7ubowYMcIAjGPHjhmGYRhjx441HB0djYSEBOt5gPHWW29ZXy9dutQAjA0bNuS4Rtu2bQ3A2L59e7b9tWvXNu65556rtvHFF180AOP06dOGYRjGc889Z7Ro0cIwDMP44YcfDHt7e2u+EB4ebtjb2xtxcXE56vnhhx+sn5sFCxZc9boWBflc53bfFunp6UZCQoLh7u5uTJ061bp/w4YN2c7NyMgwypcvb9StW9fIyMiwlouPjzcCAwONO++807qvffv2ho+PjxEREZHnda+s3zAM46233srx3gwJCTFcXFysn2/DMIzk5GTDz8/PGDRokHXfoEGDDA8Pj2zlDMMwJk6caADG77//nmdbrqYwOdENMWwzIiKCF198kffff7/A45tTUlKIi4vLtomIiIiIiIhI0fDw8OCVV14BYMyYMQVeUfGuu+5i7dq1AGzZsoWkpCRefvll/P39rb3P1q5dS2ho6DX1pLMoW7YszZo1y7avXr16BRrKeOW8Zxs3biQsLAzA2hPr559/th5r0qQJnp6eOerp1KkTLVq0oEaNGjzyyCOFav9DDz3Ezp07c2wffPBBjrIJCQmMGDGC6tWr4+DggIODAx4eHiQmJvLHH3/keY0///yT06dP8+ijj2ZbwMDDw4MePXqwbds2kpKSSEpKYtOmTTz00EMEBAQU6j7y0qBBAypVqmR97eLiQs2aNbN9fr777jvatWtH+fLlSU9Pt26dOnUCzD0CS8INMWzzmWeeoVatWoUaFzxu3DjGjBlTjK0SERERERERMUtISCj0OVmHuXXr1o2EhIQcKzAW9UqSufH398fNzY0jR44U+lzLPTg5ORX4nA4dOjBv3jz+/vtv1q5dS8OGDQkMDKR9+/asXbuWfv36sWXLFl5//fVCtyerMmXK5Nre5OTkq57btm1b7Ozs2LBhA3fffTcHDx5k/PjxAHh6etKwYUM2btxIvXr1OHLkCL17986zLmdn50I9H4uAgACaNGmSY39u74l+/fqxbt063nzzTZo2bYqXlxcmk4nOnTvne79RUVEAlCtXLsex8uXLk5mZyYULFwDz8N6iXLChIJ+fc+fO8e233+Lo6JhrHZGRkUXWnvyU+vBs2bJlfPvtt+zdu7fAKTbAyJEjefnll62v4+LiCjx2W0RERERERKQwrqeHFGDtLVTU9RaEvb09d911Fz/++CMnT54s9hUt77rrLsDcu2zNmjV07NjRuv+NN97g559/JiUlJc/5zkqCt7e3NSDbuHEjdnZ2tGzZ0nq8bdu2bNiwgbp16wI55zsrSbGxsXz33Xe89dZbvPrqq9b9KSkpREdH53uuJcA6c+ZMjmOnT5/Gzs4OX19fTCYT9vb2nDx5smgbfxX+/v7Uq1ePsWPH5nq8pBaULNXDNhMSEnj22Wd57rnnKF++PDExMcTExJCamgpATExMnpMnOjs74+XllW0TERERERERkZxGjhyJYRg8+eST1t+5s0pLS+Pbb78tkmuVK1eO2rVrs2zZMnbv3m0Nzzp27Mj58+eZPHkyXl5eNG3aNN96LL3eCtKT7Fq0a9eOv//+m4ULF9K4ceNswzLbtm3Lvn37WLlyJY6OjtmCtZJmMpkwDCPHhP2zZ88mIyMj33Nr1apFhQoVWLhwIYZhWPcnJiaybNky6wqcrq6utG3blqVLl5ZYby+A++67j4MHD1KtWjWaNGmSY1N4hrn73blz55g0aRK+vr7WbdGiRSQmJuLr68vDDz9s62aKiIiIiIiI3NBCQ0OZMWMGa9eupXHjxnzyySds2rSJtWvXMmHCBGrXrl2olSKv5q677mLdunU4OTlZg6cqVapQpUoVfvrpJ8LCwnLtiZdVnTp1APjf//7Hr7/+yq5du6zDEIuCpTfZihUraNu2bbZjrVu3BuD//u//aNasWYn0EMyLl5cXbdq0YcKECcyePZu1a9fy5ptvMnbsWHx8fPI9187OjvHjx7Nv3z7uu+8+vvnmG5YuXUq7du2IiYnh/ffft5adPHkyaWlpNG/enFmzZrFhwwYWL15Mv379iI+PL5Z7e/vtt3F0dOTOO+9kxowZrF+/nh9++IFPPvmE++67r8R6wpXqYZtly5bNtjSsxfvvv8+mTZv48ccf811uVUREREREREQK5sknn6RZs2Z8+OGHfPDBB5w9exZHR0dq1qxJv379GDJkSJFdq0OHDkybNo1WrVrh4uKSbf+sWbMKNGSzSpUqTJkyhalTpxIWFkZGRgZz5sxhwIABRdLG1q1b4+DgQHp6eo7wzMfHh3r16rFv3z7rQgK2tHDhQl544QVeeeUV0tPTadmyJWvWrKFLly5XPbdfv364u7szbtw4evfujb29PS1atGDDhg3ceeed1nL169dnx44dvPXWW4wcOZL4+HjKli1L+/btr2lOt4IoV64cu3bt4p133mHChAmcPHkST09PqlSpwr333ouvr2+xXPdKJiNrv7wbxIABA/j6668LNSFjXFwc3t7exMbGaginiIiIiIiIiMgtrDA5UaketikiIiIiIiIiImJLN2R4Nnfu3GtaBlhERERERERERKQwbsjwTEREREREREREpCQoPBMREREREREREcmDwjMREREREREREZE8KDwTERERERERERHJg8IzERERERERERGRPCg8ExERERERERERyYPCMxERERERERERkTwoPBMREREREREREcmDwjMREREREREREZE8KDwTERERERERERHJg8IzERERERERERGRPCg8ExERERERERERyYPCMxERERERERERkTwoPBMREREREREREcmDwjMREREREREREZE8KDwTERERERERERHJg8IzERERERERERGRPCg8ExERERERERERyYPCMxERERERERERkTwoPBMREREREREREcmDwjMREREREREREZE8KDwTERERERERERHJg8IzERERERERERGRPCg8ExERERERERERyYPCMxERERERERERkTwoPBMREREREREREclDkYVnmZmZzJ8/v6iqExERERERERERsbkiC8/S0tIIDw8vqupERERERERERERszqEwhd9+++08j6WlpV13Y0REREREREREREqTQoVn7777Lt26dcPLyyvHsYyMjCJrlIiIiIiIiIiISGlQqPCsbt269O/fny5duuQ4dvHiRebOnVtU7RIREREREREREbG5Qs159uSTT+bZw8zR0ZG33nqrSBolIiIiIiIiIiJSGpgMwzBs3YiSEBcXh7e3N7GxsbkOOxURERERERERkVtDYXKi615ts7h7m+3bt48uXbpQqVIlXF1d8fPzIzQ0lC+++KJYrysiIiIiIiIiInLd4dmECROKoh15iomJITg4mPfee48ffviB+fPnU7lyZR599FHefffdYr22iIiIiIiIiIjc2q572KarqyvJyclF1Z4Ca9GiBadPn+b48eMFKq9hmyIiIiIiIiIiAiU8bNNkMl1vFdfE398fB4dCLRYqIiIiIiIiIiJSKDdM+pSZmUlmZiYXLlxg6dKlrF69munTp9u6WSIiIiIiIiIichO7YcKzZ555hk8//RQAJycnPvroIwYNGpRn+ZSUFFJSUqyv4+Liir2NIiIiIiIiIiJyc7nuYZvXOWVagb322mvs3LmT77//nscff5whQ4YwceLEPMuPGzcOb29v6xYcHFwi7RQRERERERERkZvHdS8Y0KFDB9auXVtU7SmwwYMHM3v2bE6fPk1AQECO47n1PAsODtaCASIiIiIiIiIit7gSXTDAFsEZQLNmzUhPT+e///7L9bizszNeXl7ZNhERERERERERkcK47vDMVjZs2ICdnR1Vq1a1dVNEREREREREROQmVeoXDHjqqafw8vKiWbNmBAUFERkZydKlS/nqq68YPnx4rkM2RUREREREREREikKRhWcrV67kyy+/5NixY1y8eDHbMZPJxP79+6+p3tDQUObMmcO8efOIiYnBw8OD+vXrs2DBAh555JGiaLqIiIiIiIiIiEiurnvBAIAJEyYwYsQIAgICqF69Ok5OTjnKbNiw4Xovc10KMxGciIiIiIiIiIjcvAqTExVJz7NPPvmExx9/nE8//RR7e/uiqFJERERERERERMTmimTBgKioKPr166fgTEREREREREREbipFEp61bNmSP/74oyiqEhERERERERERKTWKZNjmlClT6NatG8HBwdx77725znkmIiIiIiIiIiJyoymS8Kx69ep06NCBbt26YTKZcHNzy3bcZDIRGxtbFJcSEREREREREREpMUUSnr3yyitMnz6dBg0acPvtt6vnmYiIiIiIiIiI3BSKJDybO3cuI0aMYNy4cUVRnYiIiIiIiIiISKlQJAsGZGRk0LFjx6KoSkREREREREREpNQokvDs7rvvZtu2bUVRlYiIiIiIiIiISKlRJMM233zzTXr37o27uztdunTBz88vR5nc9omIiIiIiIiIiJRmJsMwjOutxM7O3IHNZDLlWSYjI+N6L3Nd4uLi8Pb2JjY2Fi8vL5u2RUREREREREREbKcwOVGR9DwbNWpUvsGZiIiIiIiIiIjIjahIep7dCNTzTEREREREREREoHA5UZEsGCAiIiIiIiIiInIzUngmIiIiIiIiIiKSB4VnIiIiIiIiIiIieVB4JiIiIiIiIiIikgeFZyIiIiIiIiIiInlQeCYiIiIiIiIiIpIHB1s3QERERORGYBhw7Bhs2QKentCpEzjoJykRERGRm16R/MhnZ2eHyWTK9ZjJZMLHx4cmTZowYsQI2rVrVxSXFBERESlWGRnw++/wyy/w66/m7eTJy8dDQuC552DgQPD2tl07RURERKR4mQzDMK63ktGjRzN//nzi4+O5//77CQoK4syZM3z//fd4enrywAMPsHbtWg4fPsyPP/5Ix44di6LthRIXF4e3tzexsbF4eXmV+PVFRESkdLt4EXbuNIdkv/xi7mEWG5u9jL19JjVqbOHMGQdiY1sA4OEBTzwBzz8PVavaoOEiIiIiUmiFyYmKpOeZn58fZcuW5cCBA7i7u1v3JyQk0LFjRypUqMC+ffvo2LEjY8eOtUl4JiIiIpJVdLQ5ILOEZbt2QWpq9jIeHhAaCnXqHCEqai4bN87j8OFjPPbYE7Rq1YIPP4RDh2DqVJg2Dbp2hZdfhjvvhDw65YuIiIjIDaZIep5Vr16d8ePH07179xzHli1bxrBhwzhy5AhfffUVTz75JHFxcdd7yUJTzzMREZFb2/Hj2YdgHjyYs0xQELRuDa1aQZMmifz559csWDCXjRs3Wss4Ozvzyy+/0LRpUwwDZszYx8SJizly5FkgGICmTeGll6BnT3B0LJn7ExEREZGCK/GeZydPnsQxj58MHRwcOHv2LADlypUjLS2tKC4pIiIikqfMTPN8ZZZeZb/+CidO5CxXs+blsKxVK6ha1WDLls3MmTOHN95YQkJCAmCew7Vjx46Eh4fTtWtXXFxcLu2H7ds/5MiR+XTpcpJy5b5gwQLz8M9+/eCVV8zzoj35JPj6luQTEBEREZGiUiThWa1atZg6dSqdOnXCIcuyU+np6UydOpVatWoBcObMGQICAorikiIiIiJWKSmX5yv79VfYvBliYrKXsbeHRo3MIVnr1tCyJQQGmo+dOHGC+fPnM3fuXP755x/rOdWrV2fAgAH079+f4ODgXK/dq1cvjh8/zqhRz9OsGYwdC2PH/s3cuds5efIhRoxw4u23ITwcXngBqlcvpocgIiIiIsWiSIZt/t///R89evSgYsWKdO3alaCgIM6dO8fKlSs5deoUy5Yt44EHHmDgwIHEx8fz1VdfFUXbC0XDNkVERG4eMTHm+cosvcp27jQHaFm5u5vnK7P0KmvRwrzvSjt37qR58+ZYfiRyd3fnoYceIjw8nFatWuW5onh+Bg8ezMyZM/H2Louz8zNERAwCAjGZ4IEHzPOitW6tedFEREREbKUwOVGRhGcAq1atYtSoUezevRvDMDCZTDRp0oS3336be+65pygucV0UnomIiNy4Tpy43Kvsl1/M85Vd+RNMYGD2IZgNGoDDFX3sDcNg586dnDp1im7dugGQkZFBlSpVqFKlCuHh4fTs2RMPD4/rau/kyZOZOHEiZ86cAcDR0ZnAwL6cOvUC0AAw94J76SV46CFwcrquy4mIiIhIIZV4eBYZGYm/vz8ASUlJXLhwAV9fX9zc3ADYs2cPjRo1ut7LXBeFZyIiIjeGzEzzCpZZw7Ljx3OWq1Hj8hDMVq3MwyGv1pNr9erV3HvvvQQFBXHixAnrnK1xcXFF/vNBamoqX3/9NVOnTmXHjh3W/eXKtSUy8gXS0h4A7ClfHoYMgUGDwM+vSJsgIiIiInko8fAsNDSUDRs2WCfPzer3338nLCyM8+fPX+9lrovCMxERkdIpJQV27748BHPzZrhwIXsZe3to2PByr7JWrcwrY+YnNTWV7777jpSUFPr27QtAWloaNWrUoFWrVkyZMsX6x7/itm3bNqZOncrXX39Neno6AD4+lcnIGEJ8/BOAD25u8Nhj8OKL5oUMRERERKT4lHh4VrVqVRo2bMiyZcuy7f/nn39o06YN1atX5+eff77ey1wXhWciIiKlQ0wMbN16uVfZjh055ytzczPPUWbpVda8OXh6Fqz+/fv3M2fOHL788ksiIyOpVKkSR44cwc7ODjAvaORw5XjOEnLy5Ek++eQT/ve//xEVFQWAs7M7np4DiIycDJjHb953n3letLAwzYsmIiIiUhxKPDw7fPgwLVu25LHHHmPy5MkAHD9+nNatWxMQEMCGDRvwLOhPvMVE4ZmIiIhtnDp1uVfZr7/Cb7/lnK8sIOByj7LWrc3zlV0aUVkgUVFRfPnll8yZM4d9+/ZZ95ctW5b+/fszatQo3HNbLcBGkpOT+fLLL5k6dSoHDx6kRYsWjBu3lcmT4dtvL5erX988L1qfPuDsbLv2ioiIiNxsbLJgwKZNm7jnnnsYP348vXv3plWrVjg7O/Pzzz/jVwom8FB4JiIiUvwyM+Hw4exh2dGjOctVr559CGbNmoXvYZWens7q1auZM2cO33zzDWlpaQA4Ojry4IMPEh4ezt13322zXmYFYRgGGzZswMHBgTZt2gCwY0c09913NzExT5CW9hRgT9my8Oyz8PTTUEIjTUVERERuajYJzwC++OILwsPDqVixIg4ODvzyyy+ULVv2uupcv349X3zxBVu2bOHEiRP4+PjQpEkTRo0aRePGjQtcj8IzERGRopeaap6vzDIEc/NmiI7OXsbOztyTzDIEs2VLKFfu2q/5xx9/MHfuXBYsWGBdzRKgYcOGhIeH069fP8qUKXPtF7CxiRMnMnz4cOrUqU+/fnuZPt3E6dPmYy4u0L+/eV6022+3aTNFREREbmglEp5FX/mT8SXvvvsuX375JatWrSIkJMS6/1p7n/Xq1YuoqCh69epF7dq1OX/+PJMmTWLXrl2sXr2a9u3bF6gehWciIiLXLy4Otmy53Kts+3a4eDF7GVdX83xlliGYLVoUfL6yqzEMgxo1avDvv/8C4O/vz8MPP0x4eDj169cvmovYWFxcHHPnziU4OJhu3bqRmgrz58czYsRgoqMHAa0AE506mYd0duigedFERERECqtEwjM7OztMefykZhhGjmMZGRnXchkiIiIIDAzMti8hIYHq1atTp04d1q5dW6B6FJ6JiIgU3unTl3uVWeYry8zMXsbfP/sQzEaNCjdfWV4yMzNZv349ixcv5uOPP8b50qRfH3zwAZs3byY8PJwuXbrg5OR0/Rcr5aZNm8bzzz8PgLd3Q2JjXwR6A87UrWvuidavn7lnmoiIiIhcXYmEZ6NHj84zPMvNW2+9dS2XyVP79u05deoUf/75Z4HKKzwTERHJn2GY5yuz9Cr75Rc4ciRnuapVLw/BbNUKatUqnp5PGRkZVK5cmZMnT7JkyRJ69epV9Be5QRw6dIgPP/yQL774gouXuvq5uQWRlvY0aWlPA2UJDIRnnoHBg+GKvzuKiIiIyBVsNudZSYmNjSUkJIT27duzfPnyAp2j8ExERCS71FTYs+dyWPbrrxAVlb2MnZ15xUfLEMyWLaF8+aJvS0JCAkuXLuWbb75hyZIlOF7qujZ+/HiOHj3KkCFDqF27dtFf+AYTGRnJrFmz+Pjjjzl16hQA9vaOODv3ISnpBaAxzs7wyCPmIZ133GHb9oqIiIiUViUSnk2dOpUePXpQsWLFa2rk9XjkkUf46quv2LZtW56LBqSkpJCSkmJ9HRcXR3BwsMIzERG5ZcXFwbZtl4dgbt8OycnZy7i4XJ6vrFUrCA2F4vpv0zAMfvnlF+bMmcPSpUtJTEwE4JtvvuH+++8vnoveJNLS0li2bBlTp05l27Zt1v0eHq1ISHgB6Ao4cPfd5hDtnns0L5qIiIhIViUSngUFBREZGUmTJk3o2bMn3bt3p1q1atfU4MJ48803effdd5k2bRpDhgzJs9zo0aMZM2ZMjv0Kz0RE5FZx5kz2IZj79+ecr8zP73KvMst8ZcU9hdjx48eZP38+c+fOtU78D1C9enXCw8MJDw+n3PUsx3mL2bFjB1OnTmXJkiWkp6cD4OZWieTk7zGMOgDUrm2eF+2RR8wLOoiIiIjc6kokPMvMzGTTpk0sW7aMFStWcPbsWerWrWsN0opjaMWYMWMYPXo0Y8eO5bXXXsu3rHqeiYjIrcQw4M8/sw/BzJJLWVWpcrlXWevW5vnK7OyKv33JycmsWLGCOXPmsG7dOiw/fnh4eNC7d28GDBhAy5YtCzWfqmR3+vRpZsyYwcyZMzGZTPz883E+/dSFzz6D+PgEwAN/f/OcaM88A2XL2rrFIiIiIrZjkznPNm/ezNdff82KFSs4ceIENWvWpEePHvTo0YOGDRted/2W4Gz06NHXtPiA5jwTEZGbSVoa7N17eQjmr79CZGT2MibT5fnKLFuFCiXbzv379zNjxgwWL15MbGysdX9YWBjh4eH06NEDd3f3km3UTe7ixYv88ccf1p+/LlzIpFat24mPr8LFi58CITg5mVfnfOklqFfPtu0VERERsQWbLxiwY8cOli1bxvLly/nvv/8ICQmhZ8+ejB8//prqe+eddxg1ahRvvPEG77zzzjXVofBMRERuBn/+Ca++Cj/9BElJ2Y85O0Pz5peHYIaGgre3bdppMXnyZIYOHQpASEgIjz32GI899hhVq1a1bcNuIbt27aJZs2Z4eXkxbdpJZszwYOtWAAMwcddd5hCtU6eS6YUoIiIiUhrYPDzLat++fdYg7ffffy/0+ZMmTWLYsGHce++9ufY4a9GiRYHqUXgmIiI3svh4ePdd+PBDc68zAF/f7EMwGzUyB2i2snbtWqZOnUrv3r155JFHAIiIiGDo0KEMGDCAdu3aYad0xib+++8/Dh48yAMPPADA1q0GDzzQlqioZhjGEKAytWqZ50Xr3x/c3GzaXBEREZFiV6rCs+sVFhbGpk2b8jxe0OYrPBMRkRuRYcDixTBsGJw+bd7XpYs5SKtXz/Y9hQzDsM5TNnbsWN544w3atm3Lxo0bbdswydcvv/xCmzZtADCZ7LC3f5D09BeANvj5mXj6aXj2WShf3rbtFBERESkuhcmJiuRHbjs7O+zt7XPdHBwc8Pf3p1OnTmzYsKHQdW/cuBHDMPLcREREblYHDkBYmHluqtOnoWpV+PZb+O47aNDAdsFZZGQkU6dOpUGDBixdutS6v3///rz66qvMnDnTNg2TAmvZsiXfffcdHTt2xDAySU9fAYTh5NSI6Oi5vPfeRSpXNvdC27vX1q0VERERsa0i6Xk2evRo5s+fT3x8PPfffz9BQUGcOXOG77//Hk9PTx544AHWrl3L4cOH+fHHH+nYsWNRtL1Q1PNMRERuFDExMHo0TJ8OGRng6gqvvWbufebiYps2paen8+OPPzJ37ly+/fZb0i6NHe3SpQvfffedbRolReLQoUN89NFHzJ8/n+TkZAAcHQNIS3saGAyUIyzMPC/afffZvrejiIiISFEo8WGbH330EYsXL2bNmjXZVsxKSEigY8eOdO/enZdeeunSXzcNmwzlUHgmIiKlXWYmzJ8PI0ZARIR5X48eMGkShITYpk2HDh1izpw5LFiwgHPnzln3N27cmPDwcPr27Yufn59tGidFKjo6mlmzZjF9+nROnjwJgJ2dI4bRG8N4AWhCjRrwwgswYABokVQRERG5kZV4eFa9enXGjx9P9+7dcxxbtmwZw4YN48iRI3z11Vc8+eSTxMXFXe8lC03hmYiIlGa7d8OQIbBtm/l1rVowbRrYoLM2MTExLF68mDlz5rBjxw7r/oCAAB555BHCw8OpW7duyTdMSkR6ejorVqxgypQpbNmyxbrf1XUqycnPA+DjA4MGmd+zFSvaqKEiIiIi16HE5zw7efIkjo6OuR5zcHDg7NmzAJQrV846zENEREQgKgqefhqaNjUHZx4eMH48/PZbyQdnx48fp1+/fpQrV47BgwezY8cOHBwcePDBB1mxYgUnT55k8uTJCs5ucg4ODvTq1YvNmzezc+dOHnnkEVxcXNi58wGmT4fq1SEm5ggffBBFlSrw8MOwa5etWy0iIiJSfIqk51n9+vUJCAhg1apVODg4WPenp6dz9913Ex0dzb59+/jqq68YPnw4x48fv95LFpp6nomISGmSkQGzZsHrr0N0tHlfv37m4KxChZJrR3JyMq6urgBcuHCBcuXKkZKSQp06dQgPD+fhhx8mKCio5BokpVJsbCze3t6A+b17550PsmvXGjIzPwP6AtC6tXletAceAHt7GzZWREREpAAKkxM55Hu0gN5++2169OhB9erV6dq1K0FBQZw7d46VK1dy6tQpli1bBsCaNWsIDQ0tikuKiIjcsLZuNQ9327PH/LpuXfPiAG3alFwbfv/9dwYPHkxKSgrbt28HwNfXlxkzZlC3bl0aN26MyWQquQZJqWYJzgDS01PIyDhFZmYyy5Y1YsUKWLwYfvklhl9+8aJqVTteeAHCw8HT04aNFhERESkiRdLzDGDVqlWMGjWK3bt3YxgGJpOJJk2a8Pbbb3PPPfcUxSWui3qeiYiIrZ07B6++CnPnml97e8M778DgweBQJH/OypthGERFReHv7w9AREQEFSpUIDMzk//++48QW61IIDckwzA4cOAA9erVA+DUKejYsQ9//rmHzMzngAF4e3vy5JPw3HNQqZJt2ysiIiJypRJfMCCrpKQkLly4gK+vL25ubkVZ9XVReCYiIraSng4ffwyjRoFlzZzwcHj/fQgMLN5rHz9+nHnz5jF37lwqVqzIpk2brMe+/vprQkNDqVCS40TlppSUlERISAiRkZEA2Nl5kZn5OPAc9vZV6dnTPKSzeXPbtlNERETEwqbhWWml8ExERGxh0ybzEM2DB82vGzc2D9Fs0aL4rpmcnMzy5cuZO3cu69atw/JfvZeXF0eOHMHPz6/4Li63rISEBBYsWMDUqVP5888/L+01AQ8ALwBh3HmniZdegq5di7+3pYiIiEh+FJ7lQuGZiIiUpFOnYPhwWLTI/NrPD8aNgyeeKJ7J1A3DYPv27cyZM4fFixcTZ+niBrRr147w8HC6d++Ou7t70V9cJIvMzEx++uknpk6dyqpVq7IcqQc8D/QjJMSVF14wfz3oxzIRERGxBYVnuVB4JiIiJSE1FaZMgbffhsREMJng6afNc5uVKVP01zt9+jQLFixg7ty5HD582Lq/cuXKDBgwgP79+1OlSpWiv7BIARw+fJiPPvqIefPmkZSUBIDJ5I9hDAKewdOzPAMHwvPPQ+XKNm2qiIiI3GIUnuVC4ZmIiBS3n34yT47+11/m16Gh5iGajRoVz/X69OnD0qVLyczMBMDV1ZWePXsSHh5O27ZtsbOzK54LixTShQsX+Oyzz5g+fTrHjh0DwMOjCwkJ3wFgZwfdu5vnRQsNNYfOIiIiIsWpMDmRfqoWERG5TkePmn/xv+cec3AWGGheUfPXX4s2OMs6FBPAzc2NzMxM7rzzTmbNmsXZs2eZP38+7dq1U3AmpYqvry/Dhg3jn3/+YdmyZbRp04YlS57jxx+hY0fIzDzF118vpmXLNFq0gK++Mi+0ISIiIlIaqOeZiIjINbp4ESZMgPfeM39sb2/ueTZ6NHh7F911DMPgpZdeYtasWfz66680bNgQgH///ZeMjAxq1qxZdBcTsYGBA1/js8/GYWfXk8zMpQAEB5uHcw4cCD4+tm2fiIiI3HzU80xERKQYGQZ8+y3ccQeMGmUOzsLCYN8++PDDog3OAEwmExERESQlJbF06VLr/mrVqik4k5tCnTqBBAUF8dlnDzN6NAQEwIkTUQwffpCKFeGFF+Dff23dShEREblVqeeZiIhIIfz9N7z4Ivzwg/l1hQowaRI89FDRzNOUmZnJ999/z6RJk5g5cya33XYbAH/88QdnzpyhXbt2mDQhlNyEUlJScHBwwN7enosXoW/fd1i5chTQHngB6EK3bva89BK0aqV50UREROT6FCYnciihNomIiNzQEhPNwzMnTjSvqOnoCEOHwuuvg4fH9defkpLCF198waRJk/jjjz8A+PDDD/n0008BuP3227n99tuv/0IipZSzs7P1YxcXqFDhHHZ2dmRmrgfWA1VZseJ5VqwIp0IFL7y8zOWcnc3/Xrnltf9ajjk7m4dli4iIyK1JPc9ERETyYRiwbBm8/DKcOGHed/fd8NFHUKvW9dd/4cIFZsyYwbRp0zh79iwAXl5eDBo0iOeff56KFSte/0VEblDHjh3j448/ZtasWcTExFza6wl0AfwAj0uvLf92AspfKhcNRAFlLpW9Po6OJRPU5XXMyUm97URERIpSYXIihWciIiJ5OHTIPGH5unXm1yEhMGUKPPjg9f8Se+zYMT788ENmz55NYmIiABUrVuTFF1/kySef1P9VIlkkJiayYMECPvroI2vPzNwMHbqJ4OA2XLwImzZN58cfn6NGjV506LCEixchOTmTlSsrYW/vjp2dBybT5eDNMDzIzPQkM9OT9HQP0tPN+8yhXHOg7KWrXLy0eVDSgziuFroVdYh35X4HjVkREZGbiIZtioiIXIe4OHj7bZg6FdLTzb9AvvoqjBgBrq7XV/eePXuYMGECS5cuJSMjA4C6desyfPhwevfujZOTUxHcgcjNxd3dnaeffppBgwaxfv169uzZQ0JCAvHx8dn+feqpsljW0HBzM/j1V0/atPHik0/M+xISkli8+FShr//FF98QFnY/Fy/CkiVLee21/jRt2pFx434iJcW8aMjIka3IzDRwdPTE0dEDBwdP7O09sLf3xGS6HNQZxuWgzsGhFhkZ/tZgLyXFREqKiYsXsW5Z5bavJNnbm0M0d3cIDDRvAQGXP77ydUCAeQEV9ZgTEZEbncIzERGRSwwDvvwShg+HSyMoeeAB8wqaVateX93x8fF07dqV9evXW/d16NCB4cOH07FjRy0CIFIAJpOJu+66i7vuuuuqZZ977jmee+65bPtcXFzYs2dPjtAtv3/j4+OpUSOIChXMdfj6mnuKBgd7YmmGYRj06rWVzMzMQt3P/PnzefTRRwH49tvv6dq1K23btrV+nzAM6N27LwkJibi6euLi4oGLiydOTh44OXni5OSJg4M5qLOz88DOzhzUubiUx87OK1sIZ9ksYV9B96elXW5vRoZ5/sfERIiIKNg9OjoWLGSzfOzuXqhHKCIiUiIUnomIiAD798OQIfDrr+bX1aube5517nztdRqGYQ3FPDw8SE5Oxt7ent69ezNs2DAaNmxYBC0XkYJycHC47q+7p59+mscff5y0LKmSYRisXr06z/Atr2DO39/fWkd8fDyZmZnZgnSTCdat+4no6OhCtXHatGkMGTIEgC1bttCtWzfq16/PTz/9ZC0zfPhwoqOj8fDwwNPTM89/3dw8cHb2vNSjzouMDEfi4uD8eXOAZvn3yo8jIiA+3hy+nTpl3grC1bXgvdoCAsw94URERIqbwjMREbmlXbgAo0bBJ59AZia4ucEbb5gXCMiy+F+hpKWlMWXKFD777DO2bNmCn58fJpOJGTNm4OPjQ0hISNHehIiUKCcnp2xDrO3s7OjQocN11dmjRw/CwsJy7J8xYwaxsbEF7iWXkJCAt7e39fyYmBgiIiJyBHDLly/nv//+K1Qb33nnHd544w0ADh8+zMsvP0TlypX55ptvrGU++eQTzp8/j6enJ66uXhiGFxkZXqSleXHxoheJiZ4kJHgRE+NJZKSDNXQ7d45Lw1fh2DHzVhBeXnn3Yrvytb+/5m0TEZFro/8+RETklpSZCXPmmOcyi4w073voIZg4EYKDr69uBwcHvvzyS/78808+//xzhg0bBkD9+vWvs9UicrNydnamfPnyOfY/9NBD11VvmzZt2L9/P3Z2dtn2v/7660RERBS4l1xycjIeHh7W86Oiojhw4ADJycnZ6p09ezZ79+4tUNvc3Nzw8vLihRdeYMSIV0lMhMOHz/Pmmy/j6FiGrl2nWHux/fbbBqKiYomP9yI21hy+pad7ERfnRVycG//+W7Ch735+BR9G6ucHVzw2ERG5RSk8ExGRW87OnfDss+Z/AWrXhmnToH37a6vvt99+Y/r06UyaNAlPT09MJhNjx44lIiKCfv36FV3DRUQKycPDg3r16uXY//jjjxeqnoyMjGxzut1xxx2sWbMmx3yNffr0oXnz5sTFxVm3+Pj4bK9TUlIASEpKIikpiZSUFEwm8PAAV9cIVq36An9/f775Zoq13nbt3mbfvo25ts3Ozg5XVy+cnMxDS+3svAAvvLy64uT09KXebRcxjI+JjvYkOnoghw9bUrETQDrgdWlzzFKvubeaFkcQERGFZyIicsuIjITXXoPZs80TcXt6wpgx5rnOHB2vfn5WhmGwbt06JkyYYJ1HqHbt2rz44osAdOnSpYhbLyJiO/b29tjb21tf+/j45DpU9ZVXXrlqXampqdZALT4+njJlyliPBQQEMHHixBw95erUqcPFixdzBHGGYZCZmUliYgyJiTHZzunbtzaTJ5s/Pn48ipCQYTg4OPDTT09y/rx5uOi0ac/z558rreeYTC6AF4bhSWamFxER5s0crHle+rcZ0OPSGQawDvDEwaExQUEOWhxBROQmpPBMRERuehkZ8Omn5rnMLlww73v0UfjgAyhXrnB1paWlsXTpUiZMmMC+ffsAc6+Hnj170rZt26JtuIjITcjJyYkyZcpkC80sAgMDGTp0aI7906ZNy7HPMAySkpLy7OVWq1atLNe055FHHiEjI4N27S53D9uwwZ7jx12tw08N4yJwEch7OdGaNcO57bYeRETAuXOJHDnSEYD09AROnXK4tDjCQGAJl3u0Zd3MIZyDgxceHl54e3vh6+tF+fJVqFOnhTVkc3GJIiTEk/LlnbQ4goiIjZkMwzBs3YiSEBcXh7e3N7GxsXh5edm6OSIiUkI2bzb3LLuUc1G/PkyfDq1aFa6e+Ph4Zs+ezZQpUzh+/Dhgnq/n8ccf56WXXqJq1apF23ARESkx6enpOXq15fW6adOm9OnTBzDP/dauXTvi4xPYtOlfzp83EREBo0b1ZNeuZYVsRTdg+aWPDcxDSDOAk0AFvLzA0XEiGRmrqVq1Hs2bN+S++xrSsWMtHB3VJ0JEpLAKkxMpPBMRkZvSmTMwYgQsWGB+7eMDY8fCoEGQZeRRAeo5w0cffcTMmTOJiYkBzD0jnnvuOQYPHpxrzwkREbm1RUdHExUVlWsQFxsbR3R0POfOxREZGceFC3HExsZTvnwb6tUbRUQEnDmTxPr15nGd9vZxZGR4Xqr5CeDzK67mipdXPapVa0izZuZA7a676uLqqq5qIiL5UXiWC4VnIiK3hrQ0c8+yt96C+Hjz5M0DB5qDs4CAgtcTGRnJiBEj+OKLL0hNTQWgZs2aDB06lP79++Oi8TMiIlKMMjIySEhIwNPTi9hYE+fPw+bN+/jll93s2rWXI0f2kpCwH0jM5Wx7PD1vp0qVhtx/fzdeeKFbof4PFBG5FSg8y4XCMxGRm9/69fDcc3DokPl1s2bmIK1p08LXdfHiRUJCQoiIiKBly5YMHz6c+++/P8ck1iIiIraSnJzBjz/+w48/7mXHjr38999eEhL2ApFZSr0CfEClSlCvXhRHjjxF8+aNmDTpNXx8tDyoiNy6FJ7lQuGZiMjN68QJGDYMliwxv/b3h/ffh/BwKEjWlZ6ezvLly1m+fDkLFy60BmTLli2jfPnyhIaGFmPrRUREik5iosHataf4/vu9bN++l5iYdhw/3vrS0bVAR6Aa8A81a5r/wHTmzAgqVLDn7rsb0qJFQ6pWrao/FonITU/hWS4UnomI3HxSUmDyZHj3XUhKMgdlzzwDb78Nvr4Fryc+Pp7g4GBiY2NZsWIFXbt2LbY2i4iIlLS4ONi9G1avPsqPPy7j5ElHoqOfv3TUAHyBWGt5R0dPQkIa0KRJQ+66qyFNmzakdu3aODo62qL5IiLF4qYKz+Lj43nnnXfYt28fe/fuJTIykrfeeovRo0cXqh6FZyIiN5dVq+D55+Hvv82vW7UyD9GsX//q5547d47Fixfz/PPPYzKZh6yMHz+epKQknn32WQI0MYyIiNzkzp+HXbtg+/Y0li+fzT//7CU5eS9wAEjJUd7Ozong4Do0btyQsLCGdOv2IBUrVizxdouIFJWbKjw7evQoDRo0oH79+tSsWZPZs2crPBMRuYUdOQIvvQT/93/m12XLwsSJ0K+feXGA/Pz5559MnjyZefPmkZKSwk8//UTHjh2Lv9EiIiKlnGHAqVOwbVsaq1cfZuvWvfzzz15SUvYC+8jaMw2gbt31dOzYjqZNwc5uM0ePbqFduzCaXstEoyIiNlCYnMihhNp0zUJCQrhw4QImk4nIyEhmz55t6yaJiIgNJCfDBx+Y5zJLSQEHB3jhBRg1Cq72N5HNmzczYcIEvvnmGyx/M2rWrBnOzs4l0HIREZHSz2SCihWhZ09HevasC9TFMPrz33+wY4fB2rVHrIFaWtpeDhxowIEDlrNXAJMIDn6WRx9tStOmULt2Il9+OYFGjRrSsGFDgoODrb29RURuNKU+PNM3WBGRW5thmHuZvfQSHD1q3nfXXfDRR1C7dt7nZWRk8M033zBhwgS2bt1q3X///fczfPhwWrVqpf9jRERE8mEyQbVqUK2aib59qwJVycjoweHDsHOnedu1C3bvbkxGRk9OnGjHe+9Zzv4NGGOty8urDA0aNKBZM3OY1rBhQ2rWrIm9vb0N7kxEpHBKfXgmIiK3rr/+MvcuW7XK/Do42LxAQI8eeQ/RTE5OZt68eUyePJm/L02I5uTkRP/+/Xn55Ze5/fbbS6j1IiIiNx97e7jjDvM2YIB5X2pqXw4c6GsN03buhIMH3cnMfAzYCxwiLi6Kn39ex88/r7PW5eLiRt269WjS5HKg1qhRI630KSKlzk0bnqWkpJCScnmiy7i4OBu2RkRECiMhAcaOhUmTIC0NnJxg+HAYORLc3XM/58KFC0ybNo3p06dz/vx5AHx9fRk8eDDPPfccZcuWLcE7EBERuXU4OUHjxubNIimpHnv3zmXXLti27SJbtvzO8eN7MYdpe4H9XLyYxM6d29i5cxsAjo6urF8fT+PG4OoKq1evxtXVlcaNG+Oe1w8AIiIl4KYNz8aNG8eYMWOuXlBEREoNw4AlS2DoUPOkxQCdO8PUqVC9ev7nxsbG8vbbb5ORkUHlypV56aWXePzxx/Hw8Cj+houIiEg2bm7QsqV5e+EFF6AxsbGN2b3b3DNtx44Mtm79mzNnLgdqaWlOtG5tj7091KkDx48P5cKF35ky5VueeeY+HB3Ni//8999/NGzYUH8YE5ESU+pX28wqMjKSgICAAq22mVvPs+DgYK22KSJSSh08CM89Bxs3ml9XqWIOze67L/chmjt27ODnn39m2LBh1n2jR4/mtttuo2fPnjg43LR/HxIREblpRERcHupp+ffcOQAD6AnsBLbi7FyBBg0gM/Mtdu58G4CyZctah3tahnxWqVJFc5qKSIEUZrXNmzY8u1JhHoqIiJSc2FgYM8a8AEBGBri4wGuvmYdpurjkfs6xY8eoUqUKAH/88Qe1atUqwRaLiIhIcTEMOHkye5i2axfExFhKTAJmA39iDtiy8/b2pkGDBtlCtdtuuw1HR8cSuwcRuTEUJifSn+VFRMQmMjPhiy/glVcsf2GG7t3N85xVrpy97MWLF9myZQvt27cHICQkhB49euDh4YGzs3PJNlxERESKjclkXiAoONj8cwGYA7V//rGEaUPZuXMou3cnkpz8G7CHy/OoHSQ2NpZNmzaxadMma53dunVj+fLll+oy2LlzJ3Xq1MHNza2kb09EblA3RHj2448/kpiYSHx8PACHDh3i66+/BqBz5876picicoPZuxeGDIEtW8yva9Uy9zy7++7s5S5cuMCMGTP46KOPiIyM5N9//yUkJASAJUuWaFiGiIjILcBkgho1zFvfvuZ96enuHD4cys6doezcae6htm9fGunph7gcpu0F9rFmTT0efBCaNoXKlU/x6KPNcXJyIi4uzvpHuL/++gt/f3/8/PxsdJciUprdEMM2K1euzLFjx3I9duTIESpf2UUhFxq2KSJie9HR8MYbMHOm+a/I7u4wahS8+KJ5pS6Lo0eP8uGHH/LZZ5+RmJgIQHBwMPPnzycsLMwmbRcREZHSLSUFDhzAGqbt2gUHD2ZiGCmA66VS24EHcXQMoFu3AzRtag7Vhg5tzu7dOwgJCck25LNhw4ZUqFBBf7ATuQndtHOeXQ+FZyIitpORAZ99Zp7LLCrKvK9vX5gwASpUuFxuz549TJgwgaVLl5KRkQFA/fr1GTZsGL1799Z8JSIiIlIoiYnmHu9Z51D7+2+AeMDzUikDqAv8nmsd/v7+1iCtbt26lC1bloCAAKpWrYqnp2eu54hI6afwLBcKz0REbGP7dvMQzV27zK/r1IHp06FtW/NrwzBYtWoVEydOZP369dbzOnbsyPDhw+nQoYP+2isiIiJF5sIF2L37cpi2cyecOAEQA+wj+7DPP4CMXOtZtGgRffr0AWDt2rW8+eabtG7dmvHjx1vLLFu2DDc3NwICAggICMDf3x93d/divDsRKSgtGCAiIjYXEQEjR8Lnn5tfe3nBO+/AM8+AgwOkpqayaNEiJk6cyMGDBwGwt7enT58+DBs2jAYNGtiu8SIiInLT8vWFDh3Mm8W5c7Brlw87d4Zd2uD8eYBk4CCXw7TD2NmdB87zxhuBzJwJ3t4QEXGEbdu2ERXlT2CgeZ+3N/Tv/ygpKcnZru/q6moN07Ju/v7+BAQE0L59e6pWrQpARkYGdnZ2+kOiiI2p55mIiBSp9HSYMQPefBNiY837wsNh3DgICrpcbu3atXTs2BEADw8PnnrqKV544QUqVapkg1aLiIiIXGYY5t5olp5plmGfcXF5nXEc2A34AZe615MGdAHOA5GX/k256rV79FjMnXf2xscH/vhjOVOm9KFFi3v47LNv8fExh3Jjx47C0dEx1wDOz88Pe3v763sAIrcADdvMhcIzEZHi9/PP5iGaBw6YXzdqBB9/DC1awIkTJzh48CCdOnUCzMM1O3fuTFhYGIMGDcLHx8d2DRcRERG5isxMcw+1mBjzHwhjY7N/fPV9BoaRgDlEy28bAzS5dNVPgaeBB4D/u7TPwLwAQu5BnMlkh6urH56eAfj4BODraw7WevYMp2XLZnh7Q2bmBc6cOU65cuUIDAws+oclcgNQeJYLhWciIsXn9Gl45RX48kvzaz8/eO89GDgQ7O1h165dhIaG4unpyfHjx/Hw8LBtg0VERERKmGFAQkLhgrfo6FSios4RH2+QmFjpUs+3dOAtcg/eLuTTgiVAr0sfLwN64uh4J9WqbbYOMz148EEcHcHT0x9f3wDKlAkgMDCAsmUDKF8+gODgACpXDqBcOTe8vMw/54ncqDTnmYiIlIjUVPjoIxgzxvzDoMkEgwbBO+8YJCWdwN7ePASzYcOGVKlShYoVK3L+/HmFZyIiInLLMZnA09O8VaxY0LOcgGDrq4wMiI93IDZ2bK6hW3R0GmfPRhERcZ7IyPNcuHCeuLhI4uPPY2dXn8RE8wqk5gAukLS0chw+bKndAFYBqQVolxsQgJ1dAL6+owkK6oKPDzg5HSMxcS1BQVWpXbuddZipl5eBj4/J+tqyeXqCnV1Bn4WI7ajnmYiIXJM1a+D557H+wNWiBUyZksY//yxh4sSJnDlzhqNHj+Li4gJATEyMhmaKiIiI2Fh6unnutthYuHDBIC7OdOnjTH79dSUREeeJjj5PTMx54uLOk5h4nosXz5Oaep709POY53LLainQM8vHDwGtgF+ylKmMOZQLyLG5uATg4RGAl5d5mGmZMgH4+/vi62ufLWizBG9+fpc3Hx/1fpNrp55nIsXAMODIkcsThu7fD46OEBiY9xYQAM7Otm65SOGlp0NkpHnFzPPnzf9m3f75BzZuNJcNDITRo+NJTp7NQw9N4fjx4wC4ubmxZ88e7rzzTgAFZyIiIiKlgIPD5fCpSpWsq3jaMWBA93zPNQyD+Ph4Tp06z9Gj5zl+/DyVKzfB0dEcxm3dWobvvuuMn18dmjUz94iLiTFYufI0hpEGnMlR58WL5i0yMuteO+AzYMCl1weBT4B6mOeAs/gBd3cTXl7u+Ph44OfnTpkyHgQEuBMY6E5AgCN+fuYVVrOGbn5+4ORUyAcntzT1PBPJw5kzOVfXiYrKWiIFc/6c/586vL3zD9iybn5+6rYsxcMw4MKF3IMwy5b1WPb3eu7s7WHAgNN4eHzE3Lkzib20tGZgYCDPP/88gwcPxs/Pr5jvTERERERKM8MwOHHiBOfPn8+2nTlznjNnIjl71vw6Ksrc2y0xMQaAnj2XUb58d2Jj4fffv2LXrj64u7fF338j0dEQHw8QBETkc3UnwB3wuPSvO/Aq0BN3d/D0/Ju0tI/w8QmhQYNh1mAtIuJH3NxS8ff3IDDQHMSVK+dB+fLuBAZ64Obmislkyue6ciPQggG5UHgm+blwwRyOZQ3LTp3KWuI8sBc7u734+OwjM3MvsbF/YRgGLi4+ODuXISTkUcqWfYuICDh37iJnz47FMPyA57jcyfMs5r+i+AKOOdphZ2furVbQsM3d3Tx3gtyaEhMLFoRZXqenF65+OzsoU+by+83fPw1v7xg8PGJxcDjLf/99zrfffkFamrnrfq1atRg2bBiPPPKIdaimiIiIiEhhpKWlERkZiZeXF+7u7gD89ttvLFu2jLJlyzJ48OBL5eDuu+8hIuI8CQmJJCYmkpycwMWLCWRmZuRzhVnAwEsf/wTcA9QH9mUpUwP4J586TJhMbjg4eODo6I6zswdNmjxP27ZP4OcHhnGSH354l/Lly/Lqq6Px8wMvL1izZjXx8fF4eHjg7u6Ou7t7to/d3d1xdMz5e6IUD4VnuVB4JhZJSbBnT/YeZX//fWWpdcAmPDz2kpm5l6SkU7nUlN3LL7/MpEmTADh16hQVK1bE3t6egwfTOH/eREQEvPtuD/btWw6Ag4Mn9vZ+gB8ZGX6kp5s/Nm++WT6uAdTJ9ZqurgUP2gICzMNMpfRKTc099MorIEtOLuwVLuLpGYOfX8ylECwGV9dYnJxiKFPGlfvu6299v7z7bjhHj/7J//73KXXr1gVg0qRJDBs2LEetrVq1Yvjw4dx3333YqeukiIiIiNiQYRikpqaSmGgO1BISErL9e8cddfH2rkR0NOzd+yfLli3AySmA5s1fIDoaoqNhxYrexMQcIzU1kbS0BNLTEzGMRCApnytPACw/K28HWgAhwFHA/Idpk6kpGRm78m2/g4MTrq7mUM3Dw50nn3yS4cNfBiAyMpJXXnkFHx8fJk+ebD1nzZo1REZG5gjjLB97eHjg4uKi3nJX0JxnIpekpcGBA7Bjx+Ww7PffITPTUiIV+ArYT5UqH9CsmT1Nm8Lq1XNYs+ZLEhIu11WjRg0aNGhAw4YNadiwIfXq1cPBwYGoqCiio6MJDAy0lnV0dOTZZ58lNTWV224zcdtt5v1z56ayf78JwzBIT48nPT0eOJbvPdSpM4CmTecQEQFnziSxd2+ZSz3a/iY52Y1jx+DYsbnA71wO3CxbmSwfu+Praypw2ObjoyGk1ysjw/yfb0GCsPPnzXNC5M0AEjEPFy5j3evo+DWenhFUqtSb8uXLEBgIUVGL+fPPOaSnx5CSEsPFi7EkJMSQkpJCfLyli3t2tWrVYsGC/tbXhw7t4bfffuPs2bPW8MwyZ5mnpyfe3t60aNGCoUOH0qJFi+t8UiIiIiIiRcNkMuHs7Iyzs3O+U4j4+kK1arXo2fPdHMcmT/4qxz7DgPj4TE6dSuLUqQTOnk3k7NkEIiMTiYxMxN6+OpmZ5p//T58ux5EjY7h40Y20NHMHDvPvoI0AF8w/1ydc+tfysbm3XHp6KvHxqcTHXwDglVcu8O675uGkbm6RHDo0B0dHH5KTJ1uHmX7++XgOHVqb73Oxs7PDzc3NGqg9/PDDjBkzBoCkpCQGDRqEh4cHH330kbX327p16zh58uSlIM+DWrVqUbly5Xyvc7NSzzO5aWRmwp9/Zh96uW8fpKSA+RvSAWAv4Ei5cgNp2hQaN85g3DhvLl5M5NChQ9x+++0ALFy4kHXr1lnDsvr16+Pp6Vkk7czIyCA2Npbo6OgCbQ8++CAjRowA4OTJkwQHB+Pg4EB0dKq1R9tLL3Vn27YVV7myI9mDtU7A65eOGcBMzD3eugIuODiAv38KgYGOBAXZXTVsc3MrksdTqhmGeWWi/IKwrPsjI7MGtZlAPOb5Fizz5B3A3D08xrqZTDE4OcXg6BiDyRSLYcSQnh5DamoMmZkZ1KrVgrlzt1qf+x13hHD8+HF27NhB06ZNAZg4cSLDhw/P9R5MJhPe3t74+Pjg4+Nj/TgkJISpU6day/34449cvHiRli1bWoPhtLQ0TCYTDg76u4uIiIiISEFdvGieKsjSsy3nZhAZmUpkZCJRUQlERycSE5NIYmICEAxUu1TTeWA25qmARmS5wivAbi4HclmDudyHq4SGDmHAgGmY88Wz9OpVDpPJRGxsBh4eJkwm6NmzJ8uWLbOe89577zFy5MiifDQ2pZ5nctMzDDh2LHtQtnu3pUdNJOZAYi+WecoyM//CHF5A9eq38fffljHu9pw92x8HBwecsyyL2a9fP/r161csbbe3t8fPz++aJlIvW7Ys//33H7GxsXh6mvD0hKpV4dlnu9OyZdVcw7eoqChSU1MxLyl97tIGLVrUpGNHc8hz+nQS3377DABeXvHExZnnxzp79hnOnp1L9mGkuW/OzuZ7CgoqT3BwpXyDNn9/8yo/pUFycsGCsHPnMoiIiCUtLQaIJWvgZd6u3FcTGA+Y/xoUE1OGzMwY7rnnL6pWrUFgIGzbtpjVq9/L1h7DMAe+5tA3J3v7OLJ29LrnnnuIjo62zgcB0KlTJ4KCgrKFZJagzNPTs0BDKzt16pRjn+ZfEBEREREpPBcXKFfOvOXOBDhf2i7/npiebh6dcjlkCyA6emSO8O3ChfE59l3+I34G5uGm2YO1rVsD2brVUsYNmIBhpOLlZcLR0fw7TEZGQ3x8ErCzMwdxmzZVpGFDuPfeon0+NwL1PJMbwrlz2YOynTstSxmfBzZjCcpMpr0Yxslc6wgKCqJhw4Y0adKEt99++5YZ720YBsnJyTlCtQoVKtC8eXMALly4wMCBA4mNjWXNmjWkppo4fx769+/Ohg1X69F2pa6A5RwDuA3wBH4EAi7tX4Wb2yG8vc2BW2CgH2XL+lGhgh+VKvlSoYJztrDN27vgCyOkp5vfG1cGYWfPpnLyZCxnzsQQERFDZGQMMTGxXLzoB7TP0t7+wAVgAebQEOBl4MNCPYW6dZuzevU2/P3Nc81VqlSJEydOZOsh9sUXX7BgwYIcAVd+r93c3G6Z966IiIiIiBReZqa5Y0nePd1ybhcuQFSUeR7m/IwaBZdGe97wtGBALhSe3ThiY3OufHniBMBfwA6gOVADR0eoUOFLjh59JEcd1atXzzY/WcOGDSlbtmzJ3shNICUlhQsXLlh7sOU1tDQiIprz580fN2x4Hw8+OI2ICDh1KpGZMz0AuP32BCIj3YmMBMN4HJiTz5XdydqrzWTyw9OzBdWqDbMGavHx35GYmICnZ08iIx2IiIATJ+aQmPgTufcEy2t2/XtxdPzRWu9vv3mRkRHP44//Ta1a1QkIgFWrXmfJEnMPMTc3t3zDLcu+SpUqZeu9FRkZaZ2oU0REREREpLQxDPPInPxCtg4dzNvNQOFZLhSelU7JybB3b/ag7K+/koDfMM8HNRDzMsDg4dGd+PgVdO8+mREjXqJePThy5A/69euXLSirX7++PselRHp6Ort27SIqKorOnTtjMpnIyIApU2axfv1Ga+AWGxtNYmI0yckXMPf+yk03YPmljw3MXZrTMPc+9L+0/1ngk3zb5OLihYeHOeDy9fUhNLQ5U6dOsPZs+/TTT7G3t6dHjx74+pp7nsXExJCeno63t7eGLoqIiIiIiNwEFJ7lQuGZ7aWlwcGD2YOyAweiyMw0D7k0b/uAP7HMTzZy5DHuvrsSjRvDrFmTWbFiBU899RSPPvqoze5Dik9mZmaOxRTOno3m+PFo3N0rcdtt91+aoy2Fjz9uj6OjEwMHLqRGjXIEBsK//67jzJkDVKjgg59fzt5hXl5e2NvbX70hIiIiIiIiclNTeJYLhWclKzMT/vrrcki2Y4fBvn3HSUmxBGSWsOxErudb5iebPHmydQVMEREREREREZGioNU2pUQZBhw/fjko2749nV27/iQxMRVoeKlUFFA51/OrVauWbW4yzU8mIiIiIiIiIqWFwjMptIiIy0HZtm1JbN9+gJiYEMASeH0ODMLO7m5atFhN06bQtKk/I0dWpUwZL81PJiIiIiIiIiI3DIVnkq/YWNi92xyUbd4czfbte4mIyDo/2WEgEzu7mdSvP4imTcHfvwFTpnjQoYMr//d/l+vq0+cvzTclIiIiIiIiIjcUhWdilZwM+/aZ5yfbtOkE27fv5fTprEHZ8VzP8/UN5PXXUxg61Pw6M7MJ77wTi52dXbZyCs5ERERERERE5Eaj8OwWlZYGv/8O27dnsGHDUf78sxoHD0J6OkAHYH2u55UrV5WmTRvSrJl52GWDBg0oV64cJpPJWubK0ExERERERERE5Eal8OwWkJkJf/8Nmzcns2VLDIcOlWPvXrh48RxQBUgDEgBnAgPB2bkqp079TEhIbZo1a0ho6OX5yby9vW17MyIiIiIiIiIiJUjh2U3GMODECdiw4QKrVu1l1669HDu2j7S0vZjnJ7sfWAGAp2cgycmumEx2TJx4hK5dbyM4GKKj38fDYzrOzs62vBUREREREREREZtTeHaDi4gw+PHHk6xatZfdu81BWWrqXuBYruUDA08waRI0bQo1apg4fXo/5cqVyzYfWZkyZUqo9SIiIiIiIiIipZvCsxvU3LkwbNgcoqKGA1G5lvH0rEL16g1p0aIBd9/dkKZNG1K+fHmyTE9GxYoVS6S9IiIiIiIiIiI3IoVnNygnJ4iK8sYcnNnj41ObGjXM85N16tSAFi0a4OPjY+NWioiIiIiIiIjc2BSe3aA6doSVK9vj5bWT0NA6uLi42LpJIiIiIiIiIiI3HYVnN6iAAHjwQR+gia2bIiIiIiIiIiJy07KzdQNERERERERERERKK4VnIiIiIiIiIiIiebghwrOEhARefPFFypcvj4uLCw0aNGDx4sW2bpaIiIiIiIiIiNzkbog5z7p3787OnTt5//33qVmzJgsXLqRv375kZmbSr18/WzdPRERERERERERuUibDMAxbNyI/P/zwA126dLEGZhZ33303v//+O8ePH8fe3v6q9cTFxeHt7U1sbCxeXl7F2WQRERERERERESnFCpMTlfphmytWrMDDw4NevXpl2x8eHs7p06fZvn27jVomIiIiIiIiIiI3u1Ifnh08eJDbb78dB4fsI0zr1atnPS4iIiIiIiIiIlIcSv2cZ1FRUVStWjXHfj8/P+vx3KSkpJCSkmJ9HRsbC5i75YmIiIiIiIiIyK3Lkg8VZDazUh+eAZhMpkIfGzduHGPGjMmxPzg4uMjaJSIiIiIiIiIiN674+Hi8vb3zLVPqw7MyZcrk2rssOjoauNwD7UojR47k5Zdftr7OzMwkOjqaMmXK5BvG3Uji4uIIDg7mxIkTWgTBBvT8bUvP37b0/G1Lz9+29PxtS8/ftvT8bUvP37b0/G1Lz9+2bsbnbxgG8fHxlC9f/qplS314VrduXRYtWkR6enq2ec8OHDgAQJ06dXI9z9nZGWdn52z7fHx8iq2dtuTl5XXTvHlvRHr+tqXnb1t6/ral529bev62pedvW3r+tqXnb1t6/ral529bN9vzv1qPM4tSv2BAt27dSEhIYNmyZdn2z5s3j/Lly9O8eXMbtUxERERERERERG52pb7nWadOnejYsSODBw8mLi6O6tWrs2jRIlatWsUXX3yBvb29rZsoIiIiIiIiIiI3qVIfngEsX76c119/nVGjRhEdHc1tt93GokWL6NOnj62bZlPOzs689dZbOYanSsnQ87ctPX/b0vO3LT1/29Lzty09f9vS87ctPX/b0vO3LT1/27rVn7/JKMianCIiIiIiIiIiIregUj/nmYiIiIiIiIiIiK0oPBMREREREREREcmDwjMREREREREREZE8KDwrZebOnYvJZGLXrl22bsotx/Lsc9uGDRtW4HoGDBiAh4dHMbb05pP12W/cuDHHccMwqF69OiaTibCwsBJv363mo48+wmQyUadOHVs35aam933pov9/S4/r+VyYTCZGjx5d9I26yen7vu1s376dbt26UalSJZydnQkKCiI0NJShQ4faumm3nG3bttGrVy/KlSuHk5MTZcuWpWfPnmzdurXQdR06dIjRo0dz9OjRom/oTcLyvd7FxYVjx47lOB4WFqbvScXoyt99XVxcKFu2LO3atWPcuHFERETYuomljsIzkSvMmTOHrVu3Ztuef/55WzfrluDp6clnn32WY/+mTZv4999/8fT0tEGrbj2ff/45AL///jvbt2+3cWtufnrfi4it6fu+bXz//ffceeedxMXFMX78eH766SemTp1Ky5Yt+eqrr2zdvFvKtGnTaNmyJSdPnmT8+PGsXbuWiRMncurUKVq1asX06dMLVd+hQ4cYM2aMwrMCSElJ4Y033rB1M25Zlt9916xZw8cff0yDBg344IMPuP3221m7dq2tm1eqKDwTuUKdOnVo0aJFtq1SpUq2btYtoXfv3ixbtoy4uLhs+z/77DNCQ0OL9POQnJxcZHXdTHbt2sX+/fvp0qULQK6hzvVISkoq0vpuBiX5vhcRuVJxf9+XvI0fP54qVaqwevVq+vTpQ9u2benTpw8TJ07k+PHjtm7eLWPz5s28+OKLdO7cmV9++YVHH32UNm3a8Mgjj/DLL7/QuXNnXnjhBTZv3mzrpt6U7r33XhYuXMj+/ftt3ZRbkuV339atW9OjRw8+/PBDfvvtN9zd3enevTvnzp2zdRNLDYVnpdyuXbvo06cPlStXxtXVlcqVK9O3b98cXVst3S43bNjA4MGD8ff3p0yZMnTv3p3Tp0/bqPU3n6+++orQ0FDc3d3x8PDgnnvuYe/evbmW/f3337nrrrtwd3cnICCAIUOGKDi4ir59+wKwaNEi677Y2FiWLVvG448/nqP8mDFjaN68OX5+fnh5edGoUSM+++wzDMPIVq5y5crcd999LF++nIYNG+Li4sKYMWOK92ZuUJZfmt5//33uvPNOFi9enO19e/ToUUwmE+PHj2fs2LFUqlQJFxcXmjRpwrp167LVNXr0aEwmE3v27KFnz574+vpSrVq1Er2fG0FxvO+feOIJ/Pz8cv2e0759e+64445iuJObS1hYWK7DZQcMGEDlypWtry1fExMnTmTy5MlUqVIFDw8PQkND2bZtW8k1+CZW0M+FXJurfd/fuHFjrsPLLe/9uXPnZts/a9YsatasibOzM7Vr12bhwoX6XOUhKioKf39/HBwcchyzs8v+a1pBfga1TB2in0ELZ9y4cZhMJmbMmJHjc+Hg4MAnn3yCyWTi/ffft+4/fPgwffv2JSgoCGdnZypVqkT//v1JSUlh7ty59OrVC4B27dpZh8Vd+bUiZq+88gplypRhxIgR+Za7ePEiI0eOpEqVKjg5OVGhQgWeffZZYmJirGW6du1KSEgImZmZOc5v3rw5jRo1Kurm35QqVarEpEmTiI+P59NPP7Xu37VrFw888AB+fn64uLjQsGFDlixZkuP8U6dO8dRTTxEcHIyTkxPly5enZ8+eN3wQp/CslDt69Ci1atViypQprF69mg8++IAzZ87QtGlTIiMjc5QfOHAgjo6OLFy4kPHjx7Nx40YeeeQRG7T8xpWRkUF6enq2DeC9996jb9++1K5dmyVLlrBgwQLi4+Np3bo1hw4dylZHWloanTt35q677mLlypUMGTKETz/9lN69e9vilm4YXl5e9OzZ0zp8BMyBgp2dXa7P7ujRowwaNIglS5awfPlyunfvznPPPcc777yTo+yePXsYPnw4zz//PKtWraJHjx7Fei83ouTkZBYtWkTTpk2pU6cOjz/+OPHx8SxdujRH2enTp7Nq1SqmTJnCF198gZ2dHZ06dcp1XpDu3btTvXp1li5dysyZM0viVm4oxfG+f+GFF7hw4QILFy7Mdu6hQ4fYsGEDzz77bPHd0C3q448/Zs2aNUyZMoUvv/ySxMREOnfuTGxsrK2bJpKnwnzfL4j//e9/PPXUU9SrV4/ly5fzxhtvMGbMmFzndRQIDQ1l+/btPP/882zfvp20tLRcy+ln0OKTkZHBhg0baNKkCRUrVsy1THBwMI0bN2b9+vVkZGSwf/9+mjZtyrZt23j77bf58ccfGTduHCkpKaSmptKlSxfee+89wPx/g2UaGEvvTsnO09OTN954g9WrV7N+/fpcyxiGQdeuXZk4cSKPPvoo33//PS+//DLz5s2jffv2pKSkAPD4449z/PjxHPUcPnyYHTt2EB4eXuz3c7Po3Lkz9vb2/PzzzwBs2LCBli1bEhMTw8yZM/m///s/GjRoQO/evbMFw6dOnaJp06asWLGCl19+mR9//JEpU6bg7e3NhQsXbHQ3RcSQUmXOnDkGYOzcuTPX4+np6UZCQoLh7u5uTJ06Ncd5zzzzTLby48ePNwDjzJkzxdrum4HlGea2HT9+3HBwcDCee+65bOfEx8cbZcuWNR566CHrvscee8yA/2/v3mOavN44gH9rLyACKkVuGoo3EG8Zm8xNwIrCkCFiHDDEC0jcCMyETdkQTSQQnQJDzeIFzYB4WSaROaMuQrKNKc4p6OKF6GBuIhIpah0iBBmU5/eHv77zta2is0Lx+ST94317+uac95ye9/T0XCDKHyKi9evXEwA6efLkS0mPJXm03JeXlxMAqq6uJiIiX19fio+PJyKiCRMmkFqtNnoNnU5HnZ2dlJWVRUqlkrq7u4X3VCoVSaVSqqmpMXtaLNmePXsIAOXn5xPRw/Jta2tLAQEBQphr164RAHJzc6P29nbhfEtLCzk4OFBQUJBwLiMjgwDQ2rVrX14iLIi5y71arabXXntNFD4pKYns7e3p/v375kmUBXv8+atWq43e97i4OFKpVMKx/jsxadIk6urqEs5XVlYSAPrmm2/MHfV+53nzgogIAGVkZJg/kv1ET+p9ff1UXl4u+qy+7BcVFRHRw/rIxcWFpk6dKgp3/fp1ksvlBnnFiO7cuUP+/v5Ce1Mul9O0adNow4YNQj3NbVDz0mg0BIBiYmKeGO79998nANTU1EQzZ86kIUOG0K1bt0yGP3DggNHvDfvXo3V9R0cHjRo1iqZMmSK0ZdRqNU2YMIGIiEpLSwkA5eTkiK5RXFxMAGjXrl1ERNTZ2UnOzs4UGxsrCvfZZ5+RQqGgO3fuvISUWYan9TsQETk7O5O3tzcREY0bN458fHyos7NTFGbOnDnk6upKOp2OiIgSEhJILpfT5cuXzRf5XsIjz/q41tZWpKWlYcyYMZDJZJDJZLC1tUVbWxuuXLliEH7u3Lmi48mTJwOA0R1MmHF79uxBVVWV6FVWVoauri4sWbJENCLN2toaarXa6D+qCxcuFB3HxsYCeNhrz0xTq9UYPXo0CgsLcenSJVRVVRmdugYAP/30E4KCgjB48GBIpVLI5XKsXbsWWq3WYIeYyZMnw9PT82UkwWIVFBRg4MCBiImJAQDY2toiKioKFRUV+OOPP0Rh58+fD2tra+HYzs4O4eHhOHHiBHQ6nSgsj/J7OnOU+5SUFJw/f15Yo6WlpQV79+5FXFwc7whsBmFhYZBKpcIxP3+ZJXiWev9pampqoNFoEB0dLTrv7u4OPz+/Fxbn/kSpVKKiogJVVVXYuHEjIiIiUFtbi/T0dEyaNAl37tzhNmgfQf9fGqG9vR3Hjx9HdHQ0hg0b1sux6j8UCgXWrVuHs2fPGp0GqB9JFh8fLzofFRWFQYMGCUuHyGQyLFq0CAcPHhRGfut0OuzduxcRERFQKpXmTUg/oy/3V69exe+//y7ULY/WRe+++y4aGxtRU1MDADh27BgCAwPh7e3da/E2F+486+NiY2OxdetWLFu2DGVlZaisrERVVRWGDRtmdMHzxysEKysrALw4+rPw9vbGlClTRC/9/GxfX1/I5XLRq7i42GAKrUwmM8gLFxcXAA/Xt2CmSSQSLF26FPv27UN+fj48PT0REBBgEK6yshLvvPMOgIfrq/zyyy+oqqrCmjVrABiWeVdXV/NH3oJdvXoVJ06cQFhYGIgIzc3NaG5uRmRkJACIphQC/5bnx8/9888/aG1tFZ3ne/905ij3ERER8PDwwLZt2wA8XBuzra2Np2yaCT9/maV51nr/afTtG2dnZ4P3jJ1j/5oyZQrS0tJw4MAB3Lx5E5988gnq6uqQk5PDbVAzc3R0hI2NDa5du/bEcHV1dbCxsYFMJoNOpzM5xZM9v5iYGLz++utYs2aNwRRmrVYLmUxm0GEpkUjg4uIiKtsJCQl48OAB9u/fDwAoKytDY2MjT9l8Rm1tbdBqtXBzcxPqodTUVIN6KDk5GQCEuuj27dv99vthuDol6zPu3buHo0ePIiMjA6tWrRLOd3R04O7du70Ys1ePo6MjAKCkpAQqleqp4bu6uqDVakWNF41GA8DwBxYzFB8fj7Vr1yI/Px/r1683Gmb//v2Qy+U4evSoaATUoUOHjIaXSCTmiGq/UVhYCCJCSUkJSkpKDN7fvXs31q1bJxzry/OjNBoNFAqFwagmvvc986LL/YABA/DRRx9h9erVyMvLw/bt2zFr1ix4eXmZKwn9irW1tdH1yoytN8rMi/PCPHpa7+vrGv2aQnqP3399+8bYgtDGnhnMOLlcjoyMDGzevBnV1dWIiIgAwG1Qc5FKpQgMDERpaSkaGhqM/uhvaGjAuXPnEBoaCgcHB0ilUjQ0NPRCbPs3iUSC7OxsBAcHY9euXaL3lEolurq6cPv2bVEHGhFBo9HA19dXODd+/Hi8+eabKCoqQmJiIoqKiuDm5ib8+ch65vvvv4dOp8OMGTOE38Lp6emYP3++0fD69uWwYcP67feDR571YRKJBEQk/Hut99VXXxlMi2LmFRISAplMhj///NNgVJr+9bivv/5adKxfuNvYjmFMbPjw4fj0008RHh6OuLg4o2EkEglkMplomlR7ezv27t37sqLZb+h0OuzevRujR49GeXm5wWvlypVobGzEsWPHhM8cPHgQDx48EI7v37+PI0eOICAgQJQnrOfMUe6XLVsGhUKBhQsXoqamBsuXLzdL3PsjDw8P1NbWijoMtFotTp061YuxejVxXrx4z1Lv63fJvHjxougahw8fFh17eXnBxcXFYMpVfX0955UJjY2NRs/rl2Zxc3PjNuhLkJ6eDiJCcnKywW8snU6HpKQkEBHS09MxcOBAqNVqHDhw4Ikd+Dz6+PkEBQUhODgYWVlZopkMs2bNAgDs27dPFP7bb79FW1ub8L7e0qVLcebMGZw8eRJHjhxBXFwct0+fQX19PVJTUzF48GAkJibCy8sLY8eOxYULF0zWQ3Z2dgCA0NBQlJeXC9M4+xMeedZHSSQS2NvbY/r06cjNzYWjoyM8PDxw/PhxFBQUYMiQIb0dxVeKh4cHsrKysGbNGvz111+YPXs2hg4diqamJlRWVmLQoEHIzMwUwisUCuTl5aG1tRW+vr44deoU1q1bh9DQUPj7+/diSizHo9uBGxMWFoZNmzYhNjYWH374IbRaLb744guDzmb2dMeOHcPNmzeRnZ1ttGE9ceJEbN26FQUFBdi8eTOAh//UBgcHY8WKFeju7kZ2djZaWlpE3wP27F50uR8yZAiWLFmCHTt2QKVSITw83BzR7lf0IyUXL16MnTt3YtGiRfjggw+g1WqRk5MDe3v7Xo7hq4Pzwnyepd6fM2cOgoKCsGHDBgwdOhQqlQo//vgjDh48KPrMgAEDkJmZicTERERGRiIhIQHNzc3IzMyEq6srBgzg/+wfFxISghEjRiA8PBzjxo1Dd3c3zp8/j7y8PNja2iIlJYXboC+Bn58ftmzZgo8//hj+/v5Yvnw53N3dUV9fj23btuHMmTPYsmULpk2bBgDYtGkT/P39MXXqVKxatQpjxoxBU1MTDh8+jJ07d8LOzg4TJ04E8HAHWjs7O1hbW2PkyJE8+q8HsrOz8cYbb+DWrVuYMGECACA4OBghISFIS0tDS0sL/Pz8cPHiRWRkZMDHxweLFy8WXWPBggVYsWIFFixYgI6ODoO10ti/qqurhfXLbt26hYqKChQVFUEqleK7774TRvrt3LkToaGhCAkJQXx8PIYPH467d+/iypUr+O2334RdmvU70E6fPh2rV6/GpEmT0NzcjNLSUqxYsQLjxo3rzeT+N721UwEzbtu2bQSALl26REREDQ0N9N5779HQoUPJzs6OZs+eTdXV1aRSqSguLk74nKndMkztkMQM9WTHkUOHDlFgYCDZ29uTlZUVqVQqioyMpB9++EEIExcXR4MGDaKLFy/SjBkzaODAgeTg4EBJSUnU2tr6MpJicXpy74kMdx0sLCwkLy8vsrKyolGjRtGGDRuooKCAANC1a9eEcCqVisLCwswUe8s3b948UigUT9w1KiYmhmQyGZ0+fZoAUHZ2NmVmZtKIESNIoVCQj48PlZWViT6j323z9u3b5k6CRTJ3udf7+eefCQBt3LjxBaegf3n8+UtEtHv3bvL29iZra2saP348FRcXm9xtMzc31+Ca4J0fn8vz5gUR3/OeepZ6X6PRUGNjI0VGRpKDgwMNHjyYFi1aRGfPnhXttqm3a9cuGjNmDCkUCvL09KTCwkKKiIggHx8fM6fK8hQXF1NsbCyNHTuWbG1tSS6Xk7u7Oy1evNhgpzpug5rfr7/+SpGRkeTs7EwymYycnJxo/vz5dOrUKYOwly9fpqioKFIqlaRQKMjd3Z3i4+PpwYMHQpgtW7bQyJEjSSqVGv2uvOqe1A6KjY0lAMJum0RE7e3tlJaWRiqViuRyObm6ulJSUhL9/fffRq+vv4afn5+5kmDR9Pdf/1IoFOTk5ERqtZo+//xzo8+HCxcuUHR0NDk5OZFcLicXFxeaOXOmsGOz3o0bNyghIYFcXFxILpeTm5sbRUdHU1NT08tKnllIiP6/hQLrE1JSUrB161Y0NzcLQx8ZY6yvqKurw8iRI5Gbm4vU1NTejg7rgZUrV2LHjh24ceMG/+P9BPz87Ts4L/qX5uZmeHp6Yt68eQbrGLEXKz4+HiUlJQYb9zDGGPvveNpmH3Hu3DlUVVWhsLAQc+fO5cYiY4yx/+T06dOora3F9u3bkZiYyB1nJvDzt+/gvLB8Go0G69evR2BgIJRKJa5fv47Nmzfj/v37SElJ6e3oMcYYY8+NO8/6iMjISNy7dw9z587Fl19+2dvRYYwxZuHefvtt2NjYYM6cOaKdUpkYP3/7Ds4Ly2dlZYW6ujokJyfj7t27sLGxwVtvvYX8/Hxh7SLGGGPMEvG0TcYYY4wxxhhjjDHGTOBtbxhjjDHGGGOMMcYYM4E7zxhjjDHGGGOMMcYYM4E7zxhjjDHGGGOMMcYYM4E7zxhjjDHGGGOMMcYYM4E7zxhjjDHGGGOMMcYYM4E7zxhjjDHGGGOMMcYYM4E7zxhjjDHGGGOMMcYYM4E7zxhjjDHGGGOMMcYYM4E7zxhjjDHGGGOMMcYYM+F/WlsJQYucXQoAAAAASUVORK5CYII=\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 Halocline')\n",
    "\n",
    "\n",
    "ax.set_title('CY Halocline with WY Nutrients',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_3220928/3312634990.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_3220928/241793216.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_NSi/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_NSi/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_NSi/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_NSi/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_3220928/231329215.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_NSi/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_NSi/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_NSi/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_NSi/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "         \n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3220928/3737416097.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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('WY Nitrate with CY Nutrients',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([24.47601856, 23.37162061, 17.92793601,  8.21108767,  5.33221614,\n",
       "        1.58238226,  1.21867822,  1.79531768,  8.10286171, 17.52341884,\n",
       "       20.56683092, 23.22281989])"
      ]
     },
     "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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\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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('WY Silicon with CY Nutrients',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([50.36258288, 51.49776725, 43.67255925, 19.35702357,  5.51602533,\n",
       "       10.55762737, 16.99747485, 25.10753505, 34.81283984, 42.2860491 ,\n",
       "       45.66187067, 48.57553256])"
      ]
     },
     "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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('CY Nitrate with WY nutrients',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([23.9644255 , 22.29828934, 20.45139818,  8.97187906,  5.34151849,\n",
       "        1.96037127,  1.81394895,  4.30505968, 11.52311063, 16.41310313,\n",
       "       21.46072197, 22.33267238])"
      ]
     },
     "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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('CY Silicon with WY Nutrients',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([47.7632301 , 49.11254302, 47.48290684, 23.36581641,  8.2090725 ,\n",
       "       11.07120201, 11.99591242, 12.30988367, 25.86081355, 32.83781192,\n",
       "       41.9107794 , 46.15827035])"
      ]
     },
     "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_3220928/3403781678.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_NSi/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_NSi/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_NSi/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_NSi/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n",
    "        \n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3220928/2320522072.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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('WY Diatoms (0-100 m) with CY N and Si',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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('CY Diatoms (0-100 m) with WY N and Si',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
}