{
 "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_3499910/391916811.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_temp_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_temp_slice[monthly_array_temp_slice == 0 ] = np.nan\n",
    "monthly_array_temp_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_temp_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_temp_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data for Experiments 1 and 2\n",
    "\n",
    "\n",
    "monthly_array_temp_exp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['votemper']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "          \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "                \n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "                \n",
    "\n",
    "###             \n",
    "## Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/2338418733.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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY SST with CY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,25)\n",
    "ax.set_ylabel('Degrees C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.68492084,  5.18552462,  7.25557932, 10.16054886, 14.0224531 ,\n",
       "       17.57203394, 20.68068024, 20.043708  , 16.67098008, 11.61331057,\n",
       "        8.74718019,  6.62090044])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_temp_exp_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Degrees C')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_temp_slicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_temp_exp_slicemean[1,:],color='k',linestyle='-.',label='CY with WY rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY SST with WY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,25)\n",
    "ax.set_ylabel('Degrees C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.11334258,  5.87921074,  7.48800428,  9.09396314, 12.75033495,\n",
       "       16.07578677, 17.97372228, 18.18481324, 15.3735647 , 11.97804754,\n",
       "        9.27182311,  6.43142438])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_temp_exp_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Surface PAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PAR data for original years\n",
    "\n",
    "monthly_array_PAR_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['PAR']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 original temp      \n",
    "\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)\n",
    "\n",
    "\n",
    "### 2019 original               \n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/2771304440.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_PAR_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_PAR_slice[monthly_array_PAR_slice == 0 ] = np.nan\n",
    "monthly_array_PAR_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_PAR_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_PAR_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PAR data for experiments 1 and 2\n",
    "\n",
    "monthly_array_PAR_exp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['PAR']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "            \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "                \n",
    "###               \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/178454329.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": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_PAR_slicemean[12,:],color='r',linestyle='-',label='Original WY')\n",
    "ax.plot(xticks, monthly_array_PAR_exp_slicemean[12,:],color='k',linestyle='-.',label='WY with CY rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY PAR with CY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.32141178,  25.46487863,  53.28121643,  62.53450945,\n",
       "        90.96772924, 103.14297376,  94.98511739,  80.55321234,\n",
       "        51.44989112,  31.57708017,  20.31491912,   8.03466877])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'm$^{-2}$')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_PAR_slicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_PAR_exp_slicemean[1,:],color='k',linestyle='-.',label='CY with WY rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY PAR with WY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.23691735,  25.43099464,  41.17917068,  59.83653825,\n",
       "        85.16592265,  93.9387496 , 100.45387598,  80.0917271 ,\n",
       "        60.47075832,  29.24338572,  13.14281766,  10.14914155])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Halocline Strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Halocline Strength data for original years\n",
    "\n",
    "\n",
    "monthly_array_halocline_depth_orig_SSslice = np.zeros([14,12,50,50])\n",
    "monthly_array_halocline_strength_orig_SSslice = np.zeros([14,12,50,50])\n",
    "\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask,depth = [mask[var].isel(**slc).values for var in ('e3t_0', 'tmask','gdept_0')]\n",
    "years, variables = range(2007, 2021), ['halocline','strength']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "         # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_orig_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "          \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "         # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_orig_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/1288103633.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_halocline_strength_orig_SSslice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_halocline_strength_orig_SSslice[monthly_array_halocline_strength_orig_SSslice == 0 ] = np.nan\n",
    "monthly_array_halocline_strength_orig_SSslicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_halocline_strength_orig_SSslice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_halocline_strength_orig_SSslicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data for Experiments 1 and 2\n",
    "\n",
    "monthly_array_halocline_depth_SSslice = np.zeros([14,12,50,50])\n",
    "monthly_array_halocline_strength_SSslice = np.zeros([14,12,50,50])\n",
    "\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask,depth = [mask[var].isel(**slc).values for var in ('e3t_0', 'tmask','gdept_0')]\n",
    "years, variables = range(2007, 2021), ['halocline','strength']\n",
    "\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "            \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "       # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    " \n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "            \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "       # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "       # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "        \n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/3661973807.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_halocline_strength_SSslice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_halocline_strength_SSslice[monthly_array_halocline_strength_SSslice == 0 ] = np.nan\n",
    "monthly_array_halocline_strength_SSslicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_halocline_strength_SSslice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_halocline_strength_SSslicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'g/kg m$^{-1}$')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_halocline_strength_orig_SSslicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_halocline_strength_SSslicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY Halocline with CY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,5)\n",
    "ax.set_ylabel('g/kg m$^{-1}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.51926909, 0.32624015, 0.94315948, 0.79663785, 1.97864725,\n",
       "       2.99150912, 3.4154679 , 1.8896247 , 1.57378358, 0.44097102,\n",
       "       1.00447982, 0.8545834 ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_halocline_strength_SSslicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'g/kg m$^{-1}$')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_halocline_strength_orig_SSslicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_halocline_strength_SSslicemean[1,:],color='k',linestyle='-.',label='CY with WY rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY Halocline with WY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,5)\n",
    "ax.set_ylabel('g/kg m$^{-1}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Depth-averaged Nutrients (0-10m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for original cold and warm years\n",
    "\n",
    "\n",
    "monthly_array_nitrate_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "          \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/3312634990.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_3499910/241793216.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slice[monthly_array_silicon_orig_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for Experiments 1 and 2\n",
    "\n",
    "monthly_array_nitrate_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)                \n",
    "\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "     \n",
    "        # # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/231329215.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slice[monthly_array_nitrate_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_nitrate_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_nitrate_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Silicon data for Experiments 1 and 2\n",
    "\n",
    "\n",
    "monthly_array_silicon_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['silicon']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    " # Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "         \n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/3737416097.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$^{-2}$')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_nitrate_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY Nitrate with CY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.64834195, 22.38691698, 16.99383033,  7.63730902,  4.96273598,\n",
       "        1.56547187,  1.23107567,  1.5230891 ,  7.95614177, 16.79074658,\n",
       "       19.52305174, 21.62759183])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.69352626, 22.4096068 , 17.12926893,  7.66441735,  4.87999298,\n",
       "        1.37387281,  0.99858407,  1.40141887,  8.04740459, 16.94915656,\n",
       "       19.72782593, 22.07596841])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "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 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY Silicon with CY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.06238141, 49.17238747, 41.59249494, 18.0018168 ,  5.46358173,\n",
       "       12.1107789 , 19.87997362, 28.03714477, 36.00806573, 41.96864256,\n",
       "       44.54149611, 47.0336623 ])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.07595093, 49.1295625 , 42.12771314, 16.72076613,  5.66290843,\n",
       "       13.70585083, 21.09441445, 28.55469541, 36.52751148, 42.10990644,\n",
       "       44.38558449, 46.95928795])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bqXqfatWqha2tLeHh4ezcuROAqKgoNm/ejJubGxUrVrQER/eOakvN+mrpUalSpfuey5UrFwBXr15Ndb/31n3vqLV711dLlNI6a/eGbP/1Hjg7O/PJJ59YPv7kk0+SraGXVlFRUVy8eJGLFy9y6dIlSzjq6OjIwoUL+eKLL9I0ijQxqN24cSOnTp1Kcm7p0qVcv34dR0dH2rRpk+Rc4ufrgQMH7vu56uvry4cffghg2Uzk3woWLJhioJoocefgAQMG0L17d1asWEFERESqn1NERCSzUbAmIiKSCXl7e1t+n5YQ4n4SdwxN/PVR6N27N76+voSHhzN69OhHdp/7edAP9wCLFi0iMDCQ8ePHs2vXLsLDw3FzcyNHjhz4+PiQLVs2wBx4pcU///wDmBegTwxQUnrdu8tiaqbNenh4WKYHJ4ZlmzZtIjo6mho1amBnZ5dsuujt27ctO3I+6mAtS5Ys9z2XuPNs4oYRqVGwYEHLzriJgdmNGzfYtWsXLi4uVKxY0dK2UqVKODo6sm3bNst7m3hNwYIFyZMnz3/e796vkYz8euncuTOGYWAYBlFRUezfv5+uXbsSHR3NG2+8cd8dQf9L9erVyZcvH4ZhMHv27CTnEkexvfDCC5aRY4kSP19v3779wM/XxBDsfp+r//V198EHH9CmTRtiY2P59ttvady4MVmzZqVkyZJ88MEHHD16NC2PLSIiYnUK1kRERDKh4sWLW37/XzvxZTYuLi4MGzYMgMmTJ3P69OnHev+UdqpMFBYWRpcuXYiOjqZOnTqsW7eOyMhIwsPDuXjxIhcuXLCsBZZWiSOQihYtaglQ/usVEBCQqnv8e0Ra4q+JgVridM+///7bsltodHQ0Li4uDxxRltklPnfi6LO//vqL+Ph4qlSpkmT3VycnJypVqkRMTIxlba/Eax51sJgajo6OlChRgh9++IEuXbpw6dIlWrVqxe3bt1Pdl8lk4pVXXgGSTgcNCwtj+fLlwN1RbfdK/Hx94403HupzNSQkJMX7P+jrDsw76c6bN489e/YwbNgw6tSpg4uLCwcOHGDcuHGWsFtERORJo2BNREQkE0pc+B3MI6yeNK+//jqFChUiOjqa4cOHW7sci+XLlxMREUG2bNlYsmQJtWrVSjbF78KFC+m6R+LC7ydPnkzzqLf/khigbdy4kZiYmGTBmqOjI1WrVuXWrVts3brVcr569epJAqgnTWIoduTIEc6fP28ZhXbv+mqJ7p0OeubMGU6ePJmkj8zm888/x8PDg+Dg4DSvT5gYnB07dowtW7YA5jXdYmNjyZ49u2X68b0SP1/379+ftsJTqXTp0owcOZLVq1dz/fp1Vq1aRc2aNYmPj+eDDz5g7969j6UOERGRjKJgTUREJBPy8fHhpZdeAmDOnDmpmib1KNZnSy07OzvLbpczZ860LMyeFokBY0Y815kzZwAoUqQILi4uKbZZtWpVumpJXOMtJibmkYWiiQFZZGQkq1atYvv27WTLls2yhh4k3T00PaO1MvL9T69/r7OW0vpqie7dwODe9dVSCuEyg2zZsvHee+8B5s0Trl27luo+ChYsSJUqVYC7o9YSf23Xrp1lKu69Ej9ft2zZct/10x4VOzs76taty7Jly3B0dMQwjAd+/YmIiGRGCtZEREQyqY8++gg3Nzdu375Ny5YtOXfu3APbX7t2jZdeeonw8PDHVOGDtW7dmueee46EhAQGDRqU5n7c3d0BuH79erprSlwr6+jRo0RFRSU7v2fPHubMmZOuWp577jnLGmiDBw/m8uXLD6wpLWvoubm5UaFCBQA+/PBD4uLiCAoKsoRgcDeE+u2339ixY0eSY6mRke9/egUEBFimzS5ZsoRdu3ZZpn3+W+L00O3bt1umQhYtWpScOXM+zpJT5d1338XNzY2IiAjGjRuXpj4SR63NmzePQ4cOWUaupTQNFMy7hDo7OxMfH89bb731wB1qExIS0vx5EB0dfd9zjo6Olqmk/zWlVEREJLNRsCYiIpJJFS5cmFmzZuHg4MDBgwcpU6YMn3zyCcePH7e0iY+PZ/fu3QwbNoz8+fOzcOFCK1aclMlkYuzYsYA5BEmrEiVKALBgwYI0jeK5V4MGDbCxseHq1at06NDBElbGxMQwf/58GjRo8MDF9xNrOXjwoGXtrn8zmUxMmTIFR0dHTp8+TaVKlViwYEGSRd/PnTvH7NmzqV+/Pv3790/TsySGZImbEiSOUEtUsWJF3Nzc2LlzJ3FxcWTJkoXy5cun+j6Jz7x8+fL/DHcfh8Tnnj9/vmV9NQcHh2TtXFxcqFChArGxsSxYsCDJtZmVp6cnb7zxBgBffvllkg0uHlbbtm1xcHAgLCyMzp07A1CsWLH7/tn7+vpavk6XLVtG/fr12bhxoyVgMwyD4OBgPv/8c0qUKMHSpUvT8mj4+/szcOBAtmzZkiRkO378OB06dCAyMhIbGxsaNmyYpv5FRESsRcGaiIhIJvbiiy+yZs0aChYsyJUrVxgwYACFChXC0dERLy8vHBwcKFeuHKNGjSI8PJx27drh6upq7bIt6tatS/369dPVR/fu3TGZTGzatIns2bOTK1euJCOXUqNQoUJ88MEHACxcuBA/Pz+yZs2Km5sbbdu2xc3NjS+//PK+1wcFBVGkSBHi4+OpVq0anp6elloSwxswh1pLlizBy8uLU6dO0bp1a9zd3fH29sbV1RU/Pz86duyYrmlv/w7S/v2xnZ0d1atXt3ycuGNoanXu3BknJyeOHz9O3rx58fX1tTzz2bNn01Z8OiSGY4nBz4OmdiZOB01sm9mDNYA+ffrg5OTEzZs3+eSTT1J9fbZs2WjWrBmAZaTi/UarJXr33Xf59NNPsbW1Ze3atVSvXh0XFxe8vb1xdHSkWLFi9OnTh8OHD2MymVL/UMDFixcZO3YsVapUwcXFBU9PT5ydnSlUqBC//PILJpOJ8ePHU6xYsTT1LyIiYi0K1kRERDK5atWqERwczM8//0yHDh0oWLAgTk5O3LhxA09PT6pXr87gwYM5fPgwc+bMyXSL048dOzbNP4wD1KxZk2XLllGvXj08PDy4ePEioaGhaV4PauzYscycOZOKFSvi7OxMbGwsBQsWZNCgQezevZtcuXLd91o7OztWr15Nt27dCAgI4NatW5Zabt68maRt/fr1OX78OGPGjKF69ep4eHhw/fp1bGxsCAwM5LXXXuO3335j0qRJaXqOqlWr4ujoCJjX5AsMDEzW5t6wLa2hUqFChVi7di0vvPAC2bNnJywszPLMcXFxaeozPf79HCmtr5bSOZPJlGnXV7uXr68vr776KmDeVTctm2ncG6TZ2NhYdgt9kA8++IDg4GB69+5NqVKlcHJy4vr165Zpx/369WPTpk20b98+1fUA/PnnnwwcOJAaNWqQJ08ey86nBQsWpGvXrmzfvt2yxpyIiMiTxGRkhpVoRUREREREREREnjAasSYiIiIiIiIiIpIGmTJY27NnD02bNiVv3rw4Ozvj6elJlSpVmD17drK2u3btol69eri5uZE1a1ZatmzJyZMnrVC1iIiIiIiIiIg8SzJlsHb9+nXy5MnD6NGjWb58OTNnziQgIICOHTvy0UcfWdoFBwcTFBRk2cnrhx9+4OjRo9SoUeM/t7YXERERERERERFJjydqjbXKlSvzzz//cPr0aQDatGnD2rVrOXHiBO7u7gCEhoZSqFAhevfunaadlERERERERERERB5Gphyxdj/e3t6WbeLj4uJYunQpL730kiVUA/D396d27dosWrTIWmWKiIiIiIiIiMgzIFMHawkJCcTFxXH58mW+/vpr/vjjD/r37w/AiRMnuH37NqVKlUp2XalSpTh+/DhRUVGPu2QREREREREREXlG2Fm7gAfp2bMnU6dOBcDBwYEvv/ySHj16ABAWFgaAp6dnsus8PT0xDINr166RM2fOZOejo6OJjo62fJyQkMDVq1fx8vLCZDI9ikcREREREREREZEnhGEY3Lhxg1y5cmFjc/9xaZk6WBs0aBDdunXj0qVLLFmyhLfffptbt27Rt29fS5sHBWH3OzdmzBhGjhyZ4fWKiIiIiIiIiMjT48yZM/j5+d33fKYO1vLmzUvevHkBaNKkCQADBw6kc+fOeHl5AXdHrt3r6tWrmEwmsmbNmmK/AwcO5P3337d8HB4eTt68eTlz5kyS9dpEREREREREROTZExERQZ48eciSJcsD22XqYO3fKlasyJQpUzh58iTly5fH2dmZ/fv3J2u3f/9+ChYsiJOTU4r9ODo64ujomOy4u7u7gjUREREREREREQEePFMSMvnmBf+2du1abGxsyJ8/P3Z2djz//PMsXLiQGzduWNqcPn2atWvX0rJlSytWKiIiIiIiIiIiT7tMOWKte/fuuLu7U7FiRXx8fLhy5Qq//PIL8+bN44MPPiB79uwAjBw5kgoVKtCsWTMGDBhAVFQUw4YNw9vbmz59+lj5KURERERERERE5GmWKYO1KlWq8OOPPzJjxgyuX7+Om5sbpUuXZtasWbzyyiuWdkWLFmXdunX079+fVq1aYWdnR506dRg3bpwlfBMREREREREREXkUTIZhGNYuwtoiIiLw8PAgPDxca6yJiIiIiIiIiDzjHjYreqLWWBMREREREREREcksFKyJiIiIiIiIiIikgYI1ERERERERERGRNFCwJiIiIiIiIiIikgYK1kRERERERERERNJAwZqIiIiIiIiIiEgaKFgTERERERERERFJAwVrIiIiIiIiIiIiaaBgTUREREREREREJA0UrImIiIiIiIiIiKSBgjUREREREREREZE0ULAmIiIiIiIiIiKSBgrWRERERERERERE0kDBmoiIiIiIiIiISBooWBMREREREREREUkDBWsiIiIiIiIiIiJpoGBNREREREREREQkDRSsiYiIiIiIiIiIpIGCNRERERERERERkTRQsCYiIiIiIiIiIpIGCtZERERERERERETSQMGaiIiIiIiIiIhIGihYExERERERERERSQMFayIiIiIiIiIiImmgYE1ERERERERERCQNFKyJiIiIiIiIiIikQaYM1tasWcOrr75K0aJFcXV1JXfu3DRv3pydO3cmadelSxdMJlOyV9GiRa1UuYiIiIiIiIiIPCvsrF1ASr755hvCwsLo1asXgYGBXL58mfHjx1O5cmX++OMP6tSpY2nr7OzMmjVrklzv7Oz8uEsWEREREREREZFnTKYM1iZPnkyOHDmSHGvUqBEFCxZk9OjRSYI1GxsbKleu/LhLFBERERERERGRZ1ymnAr671ANwM3NjcDAQM6cOWOFikRERERERERERJLKlMFaSsLDw9m1axfFixdPcvz27dv4+vpia2uLn58fb7/9NlevXrVSlSIiIiIiIiIi8qzIlFNBU/LWW29x69YtBg8ebDlWunRpSpcuTYkSJQBYv349EyZMYPXq1Wzfvh03N7cU+4qOjiY6OtrycURExKMtXkREREREREREnjpPRLA2dOhQfvrpJyZNmkT58uUtx3v37p2kXf369SlbtiytWrXi22+/TXY+0ZgxYxg5cuQjrdnafvkFjhw5Rp48NpQrl5tChZxwcrJ2VSIiIiIiIiIiTw+TYRiGtYt4kJEjRzJixAg+/vhjBg0a9J/tExIScHd3p2nTpsybNy/FNimNWMuTJw/h4eG4u7tnWO3WVK0abNrUDFh254gXdnZ+uLr64eXlR86cfgQE+FG4cG5KlfLjuef88PPLYs2SRUREREREREQyhYiICDw8PP4zK8rUI9YSQ7URI0Y8VKiWyDAMbGzuv3yco6Mjjo6OGVFiplWnDhw/bsvly84Yxm0gjLi4MMLD9xIeDidPwsaN/77KHR+fXlSq9CH+/pAr121OnZpFsWJ+tGvXmBw5TJhMVngYEREREREREZFMKNMGa6NGjWLEiBEMGTKE4cOHP/R1CxYsIDIyksqVKz/C6jK/UaNg1KhfMQyDa9euc/DgWfbsOcvhw+c4efIsZ8+e5fLls0REnCU6+iyGEQ5EcPGiHb/9lthLKNADcKd373CcnSFvXrh1qwfx8cfw8fEjTx4/ChbMTfHifpQs6Ye/vx/Zs2d/YLApIiIiIiIiIvI0yJRTQcePH0/fvn1p1KhRiqFa5cqVCQ0NpX379rz88ssULFgQk8nE+vXrmThxIgUKFGDr1q24uro+1P0ednjf0+zChZvs3HmOa9eycPNmLkJDYd++YDZv/oDbtx2Ijv4fdz9TSgP77tuXyWSPq2tusmXLja+vOWxr1qwxL79cF0dH83TdhIQE7Owyba4rIiIiIiIiIs+wh82KMmWwFhQUxPr16+973jwK6xqvvfYau3fv5uLFi8THx+Pv70+LFi0YNGgQHh4eD30/BWv/LSYGzpyB0FBYvXozwcGnCA09y4ULZ7l27Sy3b5/FMM4CF4CUPqU+BIbi6ws+PofZt68Enp5F+PDDQ/j7g78/7N69ADu7WPz8/PDz8yNXrlxP/ZRdEREREREREcl8nuhg7XFTsJZ+8fFw/jycOBHLvn0XOHToLMePm6ecXrp0llu3nic6OuhO65VAA6A4cOCeXkoB+5P06+aWg+zZc+Pn50fBgn4UKGCefurn50fu3LnJkycPLi4uj+MRRUREREREROQZoWAtFRSsPXqGAWFh5hFvp04lcPDgJU6ejCAiojAhIebj1671BA4B54CzQNR/9luv3ii6dh2Cvz/Y2ITw/fcfU6RIYT744ANLm8jISJydnTFp5wUREREREREReQgK1lJBwVrmcOOGOWALDYWQEIMjR65y5MhZQkPPcv68eaOFu6HbWeAMMAnocqeHP4GG2NuXoFq1/QQEmKeYfvddScLCTpErlx958+Ymb14/y3TTe1/e3t4K30REREREREREwVpqKFh7MkRHm9d5SxzhZg7gEjh92obQUDhz5ijx8XMBD6DXPVdmA67/Z/8ODo53ppf60bt3b1588UUAwsPDOXLkCHny5CFnzpwZ/lwiIiIiIiIikrkoWEsFBWtPh/h4+Oefu6Fb4uvkyUhOnDjHuXNniYn596i3xNfFJH35+0+nXLnO+PvD7dt/MnVqQwoWLMmWLfvw9ASTCXr37g2QbORbzpw5cXBweLwPLyIiIiIiIiIZRsFaKihYezYYBly+nDx4M6/7FkNo6Pk7003PApWAgDtX/ga8BZQFfsPV1TzF9OjRrMTFhSe7j8lkIkeOHOTJk4f8+fNToEABy6+lS5fG09PzMT2xiIiIiIiIiKSFgrVUULAmiSIiEqeYJg/fQkIMLl1KXIMtAfiK5CPfzgEx9+1//Pgf6dWrC7a2sGfPHr7//nsqVqxIx44dH+2DiYiIiIiIiKSDYUB4OJw9e//X5s2QJYu1K80YCtZSQcGaPKyoKDh9OuVRbyEhcPasQULCFcwh22ngBHDyzq8ngB+xt69G3rxgbz+N4OAeFC7chGHDlhEQAAEB0KBBcTw9PS2j3BJf+fPnJ3v27NpgQURERERERDKUYcDVq0lDsjNn/v2xQWSkAdjcuWoHMAM4hfnn350cPmxP0aLWeoqMpWAtFRSsSUaJi4Nz55KOegsJufs6fdrcxmwbsAgoDHS9c+wacP+poq6ubhQsWCDJ9NICBQpQoUIFsmbN+oieSkRERERERJ5UCQnmZZEeNNLs7FnzQBIIB0Iwr0Pe4J5eXgGWANPx9GyBnx/Y2v6P3btbWVrMmHGCli3z4+b2+J7tUXrYrMjuMdYk8tSzszOvv+bvDzVrJj+fuMHCqVMQElLxzutu8BYa6kZCwjbujnC7d7TbOW7dusnevXvZu3dvkn4HD15D06a1CQiAvXv/5NdfF1G3bl1atWqFiIiIiIiIPJ3i4+HixQcHZufOQYxlxaKbmIOzU3d+/ffvrwFgMtnTo0cUefLY4OcHP/4Yx7p1EYwdG0L//uaeTpwow3ffDSBfvnwEBARQrZoPrq6P68kzD41YQyPWJPOIizMHbyEhieHb3dfJk1GcPRtCQsK/A7eTwO9AXgBsbQcTHz8aP783adLkawICIEeOCD74oCD58xegSJH8SaaXFihQgJw5c2qKqYiIiIiISCYSFwfnzz94euY//5jDtbtuAaGAF+Bz59hm4F1MplMYRth/3tfb25t8+fLxxx9/kC1bNgCOHDlCQkICAQEBODs7Z+hzZlaaCpoKCtbkSZE41TSl4C0kxPyXbELCOmA1UBloeufKPZh3NU2Zg4MzuXPno1ChAhQrVoACBcyBW82aNXF7WsbxioiIiIiIZBLR0eZQ7EEjzS5cME/jTOo25uAs5M4rFJPpY3LnNo8sO3u2DWfP/sILL0ygffv38PODq1d38MILFSw9eHp6EhAQQEBAgGW0WeLv/f399TPgHZoKKvIUuneqaa1ayc/HxsK5c0GEhARZwrZTp+DkyWIcP76bixdPYBj/nmYaSkzMbU6dOsSpU4f488+7/bVvf4JSpdwICICDB2dw4sRfdOjwEk2aNAbAMAyNdBMREREREbnH7dvmARH3C8zOnIFLl+53dTTmjQBCgBBMplM4O4dga3uK2NgQoqIuJLsiJOQd8ubNBUC/fgFMnepO1arRtG1rPn/zZlEWL15sCc48PDwy/qGfYRqxhkasybMjNtb8F3nSKaaxBAeHEhp6kitX7g3eQjBvsGB/5+pXgJ+wsxtLwYL9yZcPsmTZz5IlNcmZswD58xegWLH8lCxZwLLBQu7cubG1tbXGo4qIiIiIiGS4mzeThmb/npp59iyE/fdsS2A/dnbb8PEJpGDBKvj5gZ3dLmbOfI7/imnc3NzIly+fZbTZgAEDyJkzJwBxcXHY2WkMVUbQVNBUULAmYhYTkzx4S3wdPvwHV65sARoDFe9csRhocd/+bGwc8PQMIE+eAhQsaA7dSpUqQOPGjXBwcHikzyIiIiIiIpIa4eH/vXPm9ev/1UsccA4Hh1NkyxZyZ7RZCDExp3jrrfkEBvrg5wffftuXb74ZT+/evfn8888BuHTpEj4+Pri4uKQ4TTPx956enpo59BhoKqiIpJqDA+TPb34l15CYmIacOXM3bDt+vBEHDuznxIkT/PPPCcLD791UIYSEhBiuXDnKlStH2b0bfvkFwESxYlHkywcBAXDq1FgiI4Pp0OE1WrSogZcXGEYCJpNJ/1iIiIiIiEi6GQZcu/bfodmNGw/Xn6trNN7e28iS5RT29iEYRgi3b4dw/foprlw5Q3x8PDEx5t0671Wz5kmqVDFvKFCjRnlOnWpEkSJFLOezZ8/OxYsXyZ49u34WeoJoxBoasSaSUaKjsQRvJ0/Gs3//WQ4fPkFIyEkuXjzBzZsngEhg6T1X1QD+Bn4GXsbVFby8lnHu3MtkzVqAnDnzkz9/AQIDC1C+fH7KlCmAv39e7O3tU6hARERERESeRefOwa5dKU/NPHvWvO7Zw8iaNQE/P8iTx7wZQHT0Kk6dmkvZshXo0aMHfn4QGXnBMvUyJQ4ODvj7+ycbbVavXj2yZ8+eQU8sj5pGrInIY+foCAULml9gC/jfedUBzMHb6dNJp5j+/Xc/QkIaERVVgUuX4NYtuHXrBHCTsLC9hIXt5cAB+O23e+9ki4tLXry8CpA7t3maaenSBXn11ZZkywb6zx0RERERkWfD6dPw8cfwww8QF/fgtt7ekDu3QY4cl3BzuzvaLDLyFOHhIVy8GMLp06F8++06KleuDMCkSYeZOfN7sme/RmBgDwCyZPEhMDCQnDlzpjhVM2fOnNjY2DzqR5dMQiPW0Ig1kcwiKsr8D+PRo9Hs2hXCgQMnOHnyBOfOneDatZNERyfuZhqVwtW5gHNkyWKeYhod3ZfRo9vy0ksVUmgrIiIiIiJPsrNnYcwY+PZb8yZtAKVKmZe18fMzv3x9Y9i8eSI3boQQFhZCaOgpQkNDuf0fw9d+/vlnXn75ZQD27NnDb7/9Rvny5WnatOmjfizJRLR5QSooWBN5MkRFQUhIAjt3nmf37pN3ppme4OLFE0RGunP79pR7WufFZLrK/PkbaNWqnNVqFhERERGRjHP+vDlQmzbNPCMGoG5dKFHiS1atmkb58uWZMWMGAAkJCbi4uBCd2PAOk8mEn5/ffTcI8PPz086a8mimgkZFRXHs2DEKFCiAi4tLknMbN26kWrVqaatWROQhODlB0aI2FC2amw4dcmNen+2u27fNI96OHYulXbui3Lxp8OabJShSBEqWtE7NIiIiIiKSfhcvwiefwDffmP/DHaBGjXhGjbKlVi1Yvbo4X3xxECcnJ8s1NjY2vPXWWzg7OycJzvLkyYODg4OVnkSeNg89Ym3z5s288MILJCQkEBUVxdChQxkwYIDlvLu7OxEREY+s0EdJI9ZEnj4XLsTQuHEMe/a44eUFf/5pUK6cFl8TEREREXmSXL4Mn30GX32VuAGBQWDgahwdx9CwYSXGjBkNgGEYrF69mnz58lGgQAGr1ixPhwyfClq1alXeeOMNOnXqRHBwMJ06daJEiRJ899132NjYkCVLFm487N60mYyCNZGn0/Xr0KgRbN1q4ODQmzZtHJg58xNtXS0iIiIiksmFhcH48fDll+YNziCBQoV+xWQaw9Gj2wHw8vLi3LlzODo6WrVWeTpleLCWNWtWrl+/bvn49u3btG7dGgcHB+bOnYuXl5eCNRHJdCIioHr1LezfXwWAJk06s3jxt9jb21u5MhERERER+bdr12DCBJg4EcwRQywBAXOIj/+EM2cOA+Dk5ES3bt3o27cv/v7+1ixXnmIPmxU99P6v7u7unDt3zvKxs7MzixcvxsnJiUaNGpGQkJC+ikVEHgF3d9i0qTKFC/8A2LJ8+Qxq1nyRW+b/9hIRERERkUwgPBw+/BDy5YNRo+DGjdvkzv0V2bMXJCSkC2fOHMbDw4NBgwYRGhrKpEmTFKpJpvDQwVq9evX48ccfkxyzs7Pjp59+okCBAv+5Xa2IiLW4ucGuXV0pXXox4MyWLcupWLEeYWFh1i5NREREROSZduMGjB5tDtSGD4fw8OvkyDEad3d/zp17h8uXT5MjRw7GjBlDaGgoH3/8MTly5LB22SIWDx2sTZkyhffffz/ZcZPJxLfffktISEiGFbVmzRpeffVVihYtiqurK7lz56Z58+bs3LkzWdtdu3ZRr1493NzcyJo1Ky1btuTkyZMZVouIPB1cXWHz5mZUrLgKyMahQ1soW7Y6p0+ftnZpIiIiIiLPnJs3zbt85ssHgwebp4D6+EzC2dmfS5cGExFxmYCAACZPnkxISAgDBgzAw8PD2mWLJPPQwZqDgwMuLi73PZ83b94MKQjgm2++ISQkhF69erF8+XK++OILLl26ROXKlVmzZo2lXXBwMEFBQcTExDB//nx++OEHjh49So0aNbh8+XKG1SMiTwdnZ1i/viq1av0N+HHmTDDly1fj4MGD1i5NREREROSZEBlp3pQgf34YMMC8SUHhwjBnDnz4oTO3b0cQGBjIrFmzOHr0KD179sTZ2dnaZYvc10NvXpCS4cOHM3LkyIysB4BLly4lG9p58+ZNChYsSIkSJVi1ahUAbdq0Ye3atZw4ccKykFxoaCiFChWid+/efPLJJw91P21eIPJsiYmBF144wx9/NAQO4+aWjT/+WErVqlWtXZqIiIiIyFMpKgqmToUxY+DiRTB/Hz6Gtm2DmDLlVezsIDo6mj///JOmTZtiY/PQ44BEHokM37wgJZ999ll6Lr+vlOZLu7m5ERgYyJkzZwCIi4tj6dKlvPTSS0ke0N/fn9q1a7No0aJHUpuIPPkcHGDJkjw0b/43UIWbN69Ru3Y9lixZYu3SRERERESeKtHRMHkyFCgA771nDtUCAuDll//g5s1ZrFs3Ghsb82aIjo6OPP/88wrV5ImSrs/WdAx2S7Xw8HB27dpF8eLFAThx4gS3b9+mVKlSydqWKlWK48ePExUV9djqE5Eni709LFjgSZs2q4CmxMTc5sUXWzBjxgxrlyYiIiIi8sSLiTGPUCtYEN5+2+Cff1aRI8capk2DI0fgu+9ep2PHjsydO1dBmjzR7NJzsclkyqg6/tNbb73FrVu3GDx4MIBlNz9PT89kbT09PTEMg2vXrpEzZ85k56Ojo4mOjrZ8HBER8YiqFpHMzM4O5sxxwdFxEbNmvU5Cwmz27vWydlkiIiIiIk+s2FiYORNGjYLQ0ATgV+ztxxAbu50cOUrSrdteTCYTDg6uzJw509rliqTbExELDx06lJ9++okJEyZQvnz5JOceFO7d79yYMWPw8PCwvPLkyZOh9YrIk8PWFqZPt+e1134EtjBxYjN++MHaVYmIiIiIPFni4mDGDChaFLp1iyU0dAZ2diWAlsTGbsfZ2ZnatYO4ffu2lSsVyViZPlgbOXIkH330ER9//DFvv/225biXl3lUSeLItXtdvXoVk8lE1qxZU+xz4MCBhIeHW16J67aJyLPJxgamTTPRs+dzGAa89hp89NEJ3nnnHWJiYqxdnoiIiIhIphUfDz/9BIGB0KVLJCdPTsLGpiDQhbi4w3h4eDB48GBCQkL48ssvcXFxsXbJIhkqXVNBH/UaayNHjmTEiBGMGDGCQYMGJTlXoEABnJ2d2b9/f7Lr9u/fT8GCBXFyckqxX0dHRxwdHR9JzSLyZLKxga++Mq+99sUXcQwd+jxwGBsbG7744gtrlyciIiIikqkkJMD8+TByJAQHXwcmYzJ9gWFcJiEBfHx86N27N2+++eYDd1QUedKla8RatWrVMqqOZEaNGsWIESMYMmQIw4cPT3bezs6O559/noULF3Ljxg3L8dOnT7N27Vpatmz5yGoTkaeTyQQTJkC/fnbARKAU2bINtHJVIiIiIiKZR0ICLFgApUpBu3YXCQ4eAOQFhmAYlwkICODrr7/m1KlT9O/fX6GaPPVMxuPc2vMhjR8/nr59+9KoUaMUQ7XKlSsDEBwcTIUKFShXrhwDBgwgKiqKYcOGcfXqVfbs2UP27Nkf6n4RERF4eHgQHh6uL3oRwTBg2DD46KN4wJYxY2DAALh58yZubm7WLk9ERERE5LEzDPj1Vxg+HPbtMx9zdHyf6OgJAJQoUYIBAwbQtm1b7OzSNTlOJFN42KwoUwZrQUFBrF+//r7n7y15586d9O/fn82bN2NnZ0edOnUYN24cBQoUeOj7KVgTkZSMGmUO2ABatpzNli39Wb58OaVLl7ZuYSIiIiIij4lhwLJl5u+Ld+8+CJhwdw+kd29o1eoMPXt2oG/fvjRr1gwbm0y/jLvIQ3uswdrixYv56aefCA0NJSoqKukNTCb27t2b3ls8UgrWROR+xo6FgQPjgYrALtzd3fntt9+oVauWtUsTEREREXlkDANWrDCPUNu+HWAy8DYFCzZj69YleHpauUCRR+xhs6J0x8mfffYZLVu2ZMOGDdjb2+Pl5ZXk5amvNhF5gg0YAOPH2wKrgRpERETQsGFDFi5caO3SREREREQynGHAypVQpYpBkyY32b4dXFzg9dfrY2dnR5kyTmTJEmvtMkUyjXSPWMuXLx9169Zl6tSp2NraZlRdj5VGrInIf/nqK3jnndtAe2AxNjY2fP311/To0cPapYmIiIiIZIi1a2Ho0AQ2blwMjMbWthDvvfcz/fpBjhxw/vx5cubMae0yRR6LxzZiLSwsjPbt2z+xoZqIyMN4+22YMsUZ+AXoTkJCAm+88QYffvghmXCpShERERGRh/bXX1CrVix16kxn48biwEvATpycljJ48DVy5DC3U6gmkly6g7Vq1apx+PDhjKhFRCRT69EDvv/eDpgCDAVg+PDhvPXWW8THx1u1NhERERGR1Nq8GerUiaRmzUls2FAA6AoE4+7uwZAhQzh16iTZsmWzdpkimVq698CdOHEiLVq0IE+ePDRq1AgHB4eMqEtEJFN69VVwcDDRufOHJCT4Am/zzTffcOnSJWbPno2Tk5O1SxQREREReaBt22DQoOusXj0Z+AK4DED27D707fs+b7zxhpZJEnlI6Q7WChYsSL169WjRogUmkwkXF5ck500mE+Hh4em9jYhIpvHKK2BnB6+80pP4+BzY2HTgf//7H2FhYSxevBgPDw9rlygiIiIiksyuXdC//wVWrZoAfAPcACBPnnwMHPgBXbt21X8Ui6RSuoO1fv368dVXX1GmTBmKFSumEWsi8kx4+WWwt4eXX25FXJwndnYvsm7dOoKCgti8ebO+IRERERGRTGPvXhgxAhYvngz0AaIBKFy4BMOGDaBt27bY2aU7HhB5JqX7K2f69On079+fMWPGZEQ9IiJPjJdegv/9D1q1qkNs7HocHRvz4osvKVQTERERkUzhwAEYMcLgf/8zAWAyFcUwoilTpjIjRw6kWbNm2Nike+l1kWdauoO1+Ph46tevnxG1iIg8cV54ARYvhpYtyxIdvZ9t27yJigInJzAMA5PJZO0SRUREROQZc/gwvPfeXv78cxhQHJNpNC+/DEOH1iEiYgsVK1bU96kiGSTd0XSDBg3YsmVLRtQiIvJEatIEliwBJ6fsLF9u4sUX4fLlm9SrV49Vq1ZZuzwREREReUYcPWpeD7h4cfjzzxDgN+ztv2H79tvMmQPFipmoVKmSQjWRDJTuEWtDhw6lbdu2uLq60rRpUzw9PZO1SemYiMjTpH59WL4cmjWDP/6AihU/ISRkDYcPH+bEiRM4Oztbu0QREREReUodO5ZA9+6LWL/+FobRCYAXX3web+9B9O3biSJF9L2oyKNiMgzDSE8HifOxH5R4x8fHp+cWj1xERAQeHh6Eh4drS2ERSZe//4bGjeHmzWh8fN5gzpw3qFOnkrXLEhEREZGn0NGjMXTr9hN//fUJcATwpnHjUD76yIVy5axdnciT7WGzonSPWBs2bJiGkYqI3FG9OqxcCQ0bOnLx4o8MHQrPPQfu7nDs2DEKFiyovzNFREREJF2Cg2/x2mvfsWnTeOAMAHZ2Wenc+U0mTIgnSxbr1ifyLEn3iLWngUasiUhG27HDPD30+nWoWBE++WQnzz8fRJs2bZg6daq2MxcRERGRVDt48BqvvTaZrVu/AK4A4ODgS/fufRg9ugdZlKiJZJiHzYq0r66IyCPw3HOwZg14ecG2bdClyyEiIyP54YcfaNmyJZGRkdYuUURERESeEHv2nKd8+X6UKOHP1q1DgSs4OeWnb98phIefYtKkvgrVRKxEwZqIyCNStiysXQvZs0NoaEfy5FmIk5MTS5YsoUGDBly7ds3aJYqIiIhIJrZ9+ylKl36TsmXzsWvXZ8ANXFxKMmTIHG7cOMJnn/XAycnJ2mWKPNMUrImIPEIlS8K6deDrC6GhzfHx+RN3dw82btxIjRo1OHfunLVLFBEREZFM5soV6N8fqlX7jX37pgDRZMlSlVGjlnDjxl5GjWqnpUVEMgkFayIij1hgIKxfD7lzQ2hoDTw9/8LXNxcHDx6katWqBAcHW7tEEREREckEVqzYxCuvrCNfPvj0U4iN7YanZ2s++2w916//zZAhzbCx0UZYIpmJgjURkcegcGFzuJY3L4SElMTRcRMFChTh9OnTVKtWja1bt1q7RBERERGxkuvXoXnz6TRuXI2ffnqHmzcTKFcOli515cqV+fTtW1OBmkgmpWBNROQxKVDAHK4FBEBoqD+xsX9TunRFrl69Sp06dfj999+tXaKIiIiIPCbx8fEcPXqeUaPM3x/+9tuLgDeenhX5+edIduyApk3BpDxNJFNTsCYi8hgFBMCGDeaQ7fRpb8LCVlOjRkMiIyN54YUXmDVrlrVLFBEREZFHKCYmhq+//gFf30ACA1swbJhBeDgUL56Vn34K5fLl73n5ZTcFaiJPiDStdliqVKmHbmsymdi7d29abiMi8lTKk8c8cq1uXThyxI2EhCW88MKr/PbbbDp16oSPjw8NGjSwdpkiIiIikoFu3brF5MnfMnr0eMLDz945mo0CBc7w8cd5ad0abGxcrFqjiKRemoI1T09PTP8Rn9+8eZOdO3f+ZzsRkWdR7tzm3ULr1oVDh+zZunUGnTvnIDz8JHXq1LF2eSIiIiKSQa5du8aECV8xfvwXREaG3TmaE2/vPowd250uXbJga2vVEkUkHdIUrK1bt+6+5+Li4pg2bRoffvghJpOJ9u3bp7U2EZGnmq+vOVyrVw/27bNh+fLx/PFHnGXr9Li4OAzDwN7e3rqFioiIiEiqnT9/nk8//ZxvvplCdPTNO0cL4O3dn7FjO9G5syN2afqJXEQykwxdY+2XX34hMDCQd955h9KlS7Nz506tFyQi8gDZs8OaNVC2LFy+DPXr27FnDxiGQffu3XnhhRe4efPmf/YjIiIiIpnDiRMn6NatB3nzBjBx4rg7oVopvL1/Ztq0YP7553Vee02hmsjTIkOCtXXr1lGpUiXatm2Lu7s7f/75J3/88QdlypTJiO5FRJ5qXl6wejVUrAhhYVCnDixYEMzcuXNZuXIl27Zts3aJIiIiIvIQpkz5jkKFCvP999OIi4sBquHtvYwpU/Zw7tzLvP66HZqMIPJ0SVewtn//fpo0aULdunUJCwtjzpw57Nixg7p166arqBs3btCvXz8aNGhA9uzZMZlMjBgxIlm7Ll26YDKZkr2KFi2arvuLiDxu2bLBn39C1apw7Rp061aML79cw48//qg110REREQyqXPnznH06FGuXYMpU+Cjj2phGACN8fLawOTJf3P2bBN69DDh4GDtakXkUUjT4NMzZ84wZMgQ5syZg6enJxMnTuSNN97IsHWAwsLCmDZtGqVLl+bFF1/ku+++u29bZ2dn1qxZk+yYiMiTxsMDVqyAZs1gwwbo3bsyv/9e2XL+1KlT3Lx5k5IlS1qxShEREREBmDRpKr16vYmvbwuuXPkfsbEAhcie/SRDhvjTvTs4OVm7ShF51NIUrBUuXJiYmBgaNWpEv379yJIlC/v3779v+3LlyqWqf39/f65du4bJZOLKlSsPDNZsbGyoXLnyfc+LiDxJsmSB5cvhhRfMa681bAjLlkFg4CUaNGjA5cuXWbJkCTVq1LB2qSIiIiLPjLi4OFauXIm3d04uXy7DnDmwcGEVDMPg/PlwIIGSJW149VXo3t0fFxdrVywij0uagrXo6GgAfv/9d1asWHHfdoZhYDKZiI+PT1X/JpMpLWWJiDwVXF1h6VJ48UXz9NAmTWD2bHt8fHw4fvw4DRo0YO7cuTRv3tzapYqIiIg8tQzDYNeuXcycOYtZs37m2rVLODh0ICZm9p0WpciTJ5SOHfPSrh2UKGHVckXEStIUrP34448ZXUea3b59G19fXy5fvkzOnDl58cUX+fDDD/H09LR2aSIiaebsDL/+Cq1amUestW+fjblzV+Ll9TK//fYbLVu2ZOrUqXTr1s3apYqIiIg8VUJDQ/nppzl8990sTp06fM8Zb2JicuPjA23bQrt2UKlSXjQuROTZlqZgrXPnzhldR5qULl2a0qVLU+LOfw2sX7+eCRMmsHr1arZv346bm1uK10VHR1tG3QFEREQ8lnpFRFLDyQkWLjR/47Z4MbRt68zcuf/D27sHP/zwA6+//joXL15k0KBBGukrIiIikg7h4eEsWLCAadNmsW3b+nvOOALNcXHpSOvWDXnlFXuCgsAuTT9Ji8jT6In+66B3795JPq5fvz5ly5alVatWfPvtt8nOJxozZgwjR458HCWKiKSLgwPMnw8dOsAvv0CbNnb8/PN3+Pr6Mnr0aIYMGcKFCxf44osvsLFJ10bPIiIiIs+U2NhYVqxYwbRps1ix4jfi4qLvORuEnV1HmjV7iU6dPGjcWBsRiEjKnrqfwlq0aIGrqytbtmy5b5uBAwcSHh5ueZ05c+YxVigikjr29jBnDrRvD3Fx8PLLJkqV+pgvv/wSk8nEV199Rbt27ZKMxBURERGR+7t6FSZMuMQLLzRn6dJf7oRqgZhMY6hVK5SZM9cSFvYqixZ50KKFQjURub8nesTa/RiG8cCRG46Ojjg6Oj7GikRE0sfODmbOvPtr+/YwY8Y7zJmTnU6dOjF//nzCwsJYuHAh7u7u1i5XREREJFO5dOkSkyZNZfPmc7i4TGHFCoiNzQ20A3JQpkxHunUrS5s2JrJnt3a1IvIkeeqCtQULFhAZGUnlypWtXYqISIaytYUffzRPD/3uO+jUCb7//mWWL/emRYsWrF69mtq1a7N8+XJ8fHysXa6IiIiIVRmGQWysiT//hKlTb7B06TDMk7aGAzkpXRrat/+Jtm3B39/KxYrIEyvTBmu///47t27d4saNGwAcOnSIBQsWANCkSRMuX75M+/btefnllylYsCAmk4n169czceJEihcvrp3yROSpZGMDU6eap4d+8w28+ipMnVqPtWvX0qRJE3bt2kWfPn2YPXv2f3cmIiIi8pSJjo5m6dLlfPHFLM6ds+f69XlcvQpQAHiPHDnK0rWrB506QWCglYsVkaeCyTAMw9pFpCQgIIDQ0NAUz506dQoPDw9ee+01du/ezcWLF4mPj8ff358WLVowaNAgPDw8HvpeEREReHh4EB4erilUIvJEMAzo3Ru++ML88aRJ0LDhMXr37s3MmTPx9PS0boEiIiIij4lhGGzcuIkJE2axbNl8oqOv3TljD1zE1zcbL78M7dpBhQqgzdRF5GE8bFaUpmDt9OnTqWqfN2/e1N7isVKwJiJPIsOAfv1g3Djzx59/bg7b7hUSEkJAQMBjr01ERETkUTt27BgTJ85izpzZXL9+6p4zuXFw6EDTpq/w1lslCQoyL6khIpIaD5sVpWkqaEBAAKZUxPzx8fFpuY2IiDyAyQSffmpec230aHj/fYiJgf79zee//vprevfuzezZs2ndurV1ixURERHJAFeuXGHKlLl8++1sTp/ees8ZN2xtX6J69Y68+24QTZvaov3qRORxSFOw9sMPP6QqWBMRkUfDZIKPPgJHRxg+HAYMMIdrQ4YYrFu3jpiYGA4fPmztMkVERETS5coVg4YN27B792IMI+7OUVugAWXKdOTtt5vTpo0LWbJYs0oReRalKVjr0qVLBpchIiJpZTLBsGHmDQ0GDTL/PibGxJw5P9OyZUvatm1r7RJFREREUiUhIYG//97F2bPPMWcO/PGHibi4OCAOKE/+/B3p0eNlXn3VB29va1crIs+yDN0V9OjRo4SFheHt7U2hQoUysmsREfkPAweap4X27WsexRYTY8vYsS9bFui9desWX3/9Ne+//z62WmhEREREMqGYGPjttyi6dAnk1q1TwHHMO3pC0aKjaNp0NO++W4xMvoy3iDxDMiRY++WXX+jbty9nz561HPPz82P8+PG0atUqI24hIiIPoU8fc7j27rvm9ddiYsybGoBBu3btWLJkCZs3b2bOnDk4OTlZu1wRERERzp27wLRpf/HPP6353//g2jUnoBBwlZw5D/L66wVo1w6KFi1h7VJFRJJJ066g91q+fDnPP/88xYsXp2PHjuTKlYtz584xe/ZsDh06xJIlS2jcuHFG1ftIaFdQEXnaTJkCb75p/n3PnjBpEixevJB27doRExNDzZo1+fXXX8maNatV6xQREZFn082bt5gwYTE//DCbkJA/AQM4DfiRKxc0bnyaLl1yUK2aE1reW0Ss4WGzonQHa9WqVcPd3Z1ly5ZhY2NjOW4YBo0bN+bGjRts3LgxPbd45BSsicjT6IcfoFs3MAx4/XVz2LZhwzqaN29OREQEJUuWZMWKFeTKlcvapYqIiMgzID4+nunT1/Dll7PZv38hhnHTcs7WtjIvvDCZd94pR82aoFUrRMTaHjYrSvdU0D179jB37twkoRqAyWSiZ8+etG/fPr23EBGRNHj1VfOGBl26wLffQmwsfPddEBs2bKBRo0bs37+fqlWr8ueff1K4cGFrlysiIiJPqT//3MfHH89i8+Y5xMb+YzluMuUnMPAV3nnnFbp2LYSDgxWLFBFJo3QHa7a2tsTExKR4LjY2NlngJiIij0/HjmBnZ/51+nTzmmszZpRm06ZNNGjQgOPHj1OtWjWWLVtGxYoVrV2uiIiIPCX27z/H8OFz+OOPWURG7r/nTDb8/dvSpUtH+vSpQpYsmucpIk+2dE8FrVu3Ljdv3mTdunU4OztbjkdHRxMUFISbmxsrV65Md6GPkqaCisjTbsECaNcO4uKgTRuYPRuuXbtEkyZN2LlzJ66urvzvf/+jYcOG1i5VREREnlA3bsCvv8KcObBiRUcMY/adMw54ez9Pq1avMGxYE3Lm1NA0Ecn8Htsaa3///Td169bF09OT1q1b4+vry/nz51m4cCFhYWGsWbOGqlWrpucWj5yCNRF5Fvz6K7RubZ4S2qIFzJ0L0dE3aNmyJatWrcLOzo7p06fToUMHa5cqIiIiT4joaBg7diU//PAjFy8OIDq61J0zK3FzG0WjRq8wfHhrSpTIZtU6RURS67EFawDr169nwIABbNu2DcMwsLGxoVKlSowZM4aaNWumt/tHTsGaiDwrli+Hli3N3wQ3a2YeyWYyxdC5c2fmzp0LwOeff07v3r2tXKmIiIhkVnFxBuvXm5gzBxYuhOvXXwIWAn0oXHgc7dqZR8oXKWLtSkVE0u6xBmuJIiMjuXbtGtmyZcPFxSWjun3kFKyJyLPkzz+heXOIioJGjczfEDs6JtC7d2++/PJLcubMycGDB8mWTf+zLCIiImaGAb/+Gsqnn/7Etm2ziY9fBJiTMy+vP8ibdxl9+nShfftymLRsmog8BawSrD2pFKyJyLNmzRp4/nmIjIS6deG338DZ2eDzzz+nfv36lCpV6r87ERERkafeli3XGTVqAWvWzCIqaoPluJPTUDp1+pD27aFGDdCedSLytHmswdrixYv56aefCA0NJSoqKukNTCb27t2b3ls8UgrWRORZ9Ndf0KQJ3LwJtWrB0qXg5pa0zcaNGylbtuwTNQpZRERE0ufYsRg++mgFv/46i/DwJUD0nTMmcuQI4uWXX2HIkJfInt3DmmWKiDxSD5sV2aX3Rp999hn9+/cne/bsFCxYEFdX1/R2KSIij0GNGvDHH9C4MaxfDw0bwu+/Q+K/GX/99RcNGjSgbNmy/P7773h46JtnERGRp9WlSwaffbaV2bNnceHCPCDMcs7NLZBmzToyYkQHihTJY70iRUQyoXQHa19//TWvvvoqU6dOxdbWNiNqEhGRx6RqVVi1Cho0gE2boH59c9iWNSvY2Njg7OxM9uzZ9Z8mIiIiT6GICFi8GL78cjk7d74HHLOcs7f3oVat9gwe3JFatcpg0sJpIiIpSnewFhYWRvv27RWqiYg8oSpUMK+5Vq8ebNtmXnNt5UqoVq0aW7ZsIU+ePNjZpfufCxEREckEoqJg/vyrzJ8fxerVuTCv5JMVOIaNjQtlyrTg/fc70rZtXf37LyLyENK9xGS1atU4fPhwRtQiIiJWUrYsrF0L2bPDrl1Qpw5cvgyFCxfG2dkZAMMw6NOnD5s2bbJytSIiIpIacXHm/zR79VXIlm0SnTv7smzZh0RFQZEiMGJEFSZOnEd4+EV27pxNhw4NFaqJiDykdG9ecOTIEVq0aMHYsWNp1KgRDg4OGVXbY6PNC0REzA4dModqFy9C8eKwejX4+JjPTZkyhTfffBNnZ2fmz59Ps2bNrFusiIiI3JdhwJYtBuPHb2TdulyEheW/c2Y1UI88eWqzePFqypY1oVmeIiLJPWxWlO4RawULFqRevXq0aNECFxcX3N3dk7y02LWIyJMjMNC8kUGuXHDwIAQFwT//mM917NiRpk2bcvv2bV588UWmT59uzVJFREQkBQcOwBtvHMXTcyhVq+bnf/+rQVjYJLy84I03YM2aIPbu3c/p02soV06hmohIeqV7fG+/fv346quvKFOmDMWKFXsiR6yJiMhdRYqYw7U6dSA4GGrVMq/BliePK4sWLeL1119nxowZdO3alYsXL9KvXz8taCwiImJFcXEwY8ZNRoyYydmzM4BtlnN2dm40b27Pzz+DvT2ALVDCSpWKiDx90j0V1MvLi+7duzNmzJiMqumx01RQEZHkTp0yh2shIZAvnzlcCwgwr7U2YMAAPv30UwDee+89xo8fj41NugdBi4iISCpERsKECef59NOviIj4BrgGgMlkS5kyDenVqyOtW7+Ai4uLdQsVEXkCPWxWlO4Ra/Hx8dSvXz+93YiISCaTL9/dkWsnTphHrq1dC/nzm/jkk0/w8fGhT58+TJw4kUuXLvHjjz9q1LKIiMhjcO0aDBt2kG+/HU909E9ADABeXgV5//23eO21dvgkLpIqIiKPVLqHFzRo0IAtW7ZkRC0iIpLJ5M1rDtcKF4bTp6FmTTh2zHzu/fffZ9asWdjZ2TFnzhyef/55bty4Yd2CRUREnmLnzsE770SQPXsTvvqqBNHRPwIx5M9fjblzF3HxYjCDBr2nUE1E5DFKd7A2dOhQZs+ezRdffMHx48e5evVqsldq3bhxg379+tGgQQOyZ8+OyWRixIgRKbbdtWsX9erVw83NjaxZs9KyZUtOnjyZzqcSEZFEuXObw7XAQPM39LVqweHD5nOvvPIKS5YswdXVlT///JM6depw+fJl6xYsIiLylAkONujWzTya/KuvshAffwmwoWLFVvz112ZOnPibtm1fxNbW1tqliog8c9IdrJUuXZrg4GDef/99ihQpQvbs2ZO9UissLIxp06YRHR3Niy++eN92wcHBBAUFERMTw/z58/nhhx84evQoNWrU0A92IiIZyNfXPA20ZEk4f968W+iBA+ZzjRo1Ys2aNXh5ebFjxw6qVatGSEiINcsVERF5KmzaFEOpUuMpVqwk338fTmws1KxpYuLEKRw7dpStW3+hevXK1i5TROSZlu411oYNG5bhu8H5+/tz7do1TCYTV65c4bvvvrvvvR0dHVm6dKllIbny5ctTqFAhxo0bxyeffJKhdYmIPMty5DCHa/Xrw+7d5nBt1SooUwYqVqzIxo0badCgAaGhoZw6dYqAgAArVywiIvLkMQzzv69jx8KaNXbAD8AhSpSYztSpvahaFeA56xYpIiIW6Q7W7jdFMz0eJqiLi4tj6dKldOrUKcnuDP7+/tSuXZtFixYpWBMRyWBeXrB6NTRsCNu3mzc2+PNPeO45KFKkCJs2bWLv3r3Url0bMO8gOnDgQBo2bEitWrW0c6iIiMh9xMfD+PG7+fTTbwgLmwi4YGdnQ/XqH1O7dhj9+nXAycnaVYqIyL89sT/hnDhxgtu3b1OqVKlk50qVKsXx48eJioqyQmUiIk+3bNlg5UqoUsW8K1ndupC4h03u3Llp0qSJpe327dv55JNPaNq0KTdv3rRSxSIiIpnX7dsG7767gixZ6tG/fznCwr7F3n4mvXqZd+Veu/ZFhg17DSelaiIimVK6R6wBLF68mJ9++onQ0NBkYZbJZGLv3r0ZcZskwsLCAPD09Ex2ztPTE8MwuHbtGjlz5kx2Pjo6mujoaMvHERERGV6fiMjTzMMD/vgDmjaFv/6CBg1g+XKoXj1pu6xZs/L6669jZ2eXZHRxt27dqFixIm3atCFr1qyPt3gREZFM4PLlaN5662cWLRpPXNydhUuxpUSJNkyaVIWgIGtWJyIiDyvdI9Y+++wzWrZsyYYNG7C3t8fLyyvJK6XgKyM9aNro/c6NGTMGDw8PyytPnjyPqjwRkadWlizw++9QuzbcuAGNGsG6dUnbFC5cmGnTpvH1119bjh04cIDvv/+eHj164Ovry8svv8zvv/9OXFzc430AERERKzhy5BpBQWPw8Qngl1+6Ehd3AJPJjVq1enPw4An2759DUFBpa5cpIiIPKd3B2tdff82rr77KP//8w8aNG1m7dm2y16Pg5eUF3B25dq+rV69iMpnuOwpi4MCBhIeHW15nzpx5JDWKiDztXF1h6VLziLVbt6BJE/OCyw/i6+vLZ599RokSJYiOjmbevHk0adKEPHny8MEHH3AgcbtRERGRp8i6dacoWbIXRYvmYf36QRjGBezsctOmzadcuHCGdes+JzDQ39pliohIKqU7WAsLC6N9+/bY2tpmRD0PrUCBAjg7O7N///5k5/bv30/BggXvuw6Bo6Mj7u7uSV4iIpI2Li7w66/mUO32bWjWDFasuH97b29v+vbty759+9i5cyfvvvsu3t7eXLhwgXHjxlGyZEnKly/Pl19+yeXLlx/fg4iIiDwCK1eeJ2/ettSuXZADB74EbuHiUopevWZy48ZJ5s37gBw5slq7TBERSaN0B2vVqlXj8OHDGVFLqtjZ2fH888+zcOFCbty4YTl++vRp1q5dS8uWLR97TSIizyonJ1i4EJo3h+ho869du8Ls2XD+fMrXmEwmypUrxxdffMG5c+dYvHgxLVq0wN7enl27dtGrVy9y5cpF8+bNWfGgpE5ERCSTMQxYvx4aN4YGDTw4c2Y1kICXVwPGjfuTGzf2MHFiR5ycHKxdqoiIpJPJMAwjPR0cOXKEFi1aMHbsWBo1aoSDQ8b84/D7779z69Ytbty4wauvvkrr1q1p06YNAE2aNMHFxYXg4GAqVKhAuXLlGDBgAFFRUQwbNoyrV6+yZ88esmfP/lD3ioiIwMPDg/DwcI1eExFJh9hYaN8eFixIejww0Lx7aN26EBRk3vzgfq5cucLcuXOZMWMGO3bsAODdd9/liy++ACDxn60HrbEpIiJiDZGRUfTtO5O5c3/n2rWFgAkbG6hadRHvvFOANm1KWbtEERF5SA+bFaU7WIuPj6d3795MnjwZk8mEi4tL0huYTISHh6e634CAAEJDQ1M8d+rUKQICAgDYuXMn/fv3Z/PmzdjZ2VGnTh3GjRtHgQIFHvpeCtZERDJOQgKsWQMrV5rXW9u92/w/94lsbOC558whW716ULWqecRbSg4dOsSMGTNo3749pUubF3Jeu3Ytb7/9Nm+99RY9e/Z8DE8kIiLyYDExMGcOjB4dzrFjeYAb2Nkt5/XXG9OnD6TiRxMREckkHluw1qdPHyZMmECZMmUoVqxYiiPWfvzxx/Tc4pFTsCYi8uiEhZl3C1292vw6ejTpeScnqFbt7oi28uXhQct2vvbaa/zwww+8/vrrTJs2DTCPYouKisLZ2fnRPYiIiMi/7NlzjMGDf2Hv3oGcO2ceSe3o+CnVqzswdeprFCiQxcoViohIWj22YM3Ly4vu3bszZsyY9HRjVQrWREQenzNn7oZsq1cnX4Mta1bzdNHEoK1oUbh31md4eDi//PILzz33HGXKlAFgy5YtNGjQgDZt2tC5c2eqV6+uqaIiIvJIGIbB8uWb6Nt3HMHBvwIGsB5f35r07g09ejx4yQMREXkyPLZgLWvWrCxcuJA6deqkpxurUrAmImIdhgHBwXdDtrVr4d+rB+TKBXXq3A3a8uRJ3s/AgQMZO3as5eP8+fPTqVMnOnXqRL58+R7xU4iIyLMgPj6eb79dzMiR47hwYYvluItLM95/fxSDB5e579IGIiLy5HlswVqbNm0oU6YMgwYNSk83VqVgTUQkc4iLg1277gZtf/9t3mX0XoUL3w3ZatcGT09ISEjgr7/+YsaMGfzyyy/cvHnT0r5mzZp07tyZVq1a6e94ERFJtVu3bjF69I98+eUEbt48eeeoA15enRg8+H3efbfYA5cwEBGRJ9NjC9b2799P27Zt6dGjB02bNsXT0zNZm5SOZSYK1kREMqeoKNi0yRyyrVoFO3aYN0dIZDJB2bJ3N0KoXh0M4xaLFi1ixowZrF692rKLqLOzMy1atKBz587UrVsXW/0UJCIiD3DhwgUGDPiKOXO+Jjb22p2jngQE9GTs2Ldo08YXrTogIvL0emzBmo2NjbmjB/yrEh8fn55bPHIK1kREngzXr8P69XdHtB06lPS8gwNUqXJ3RJuv7xnmzZvNjBkzOHLkiKVd7ty5+f7772nYsOHjfQAREcn0QkNP0737h6xcOQvDiLlztAClS7/Pl192pmZNV6vWJyIij8djC9ZGjBjxnwtEDx8+PD23eOQUrImIPJn++QfWrLkbtJ05k/R8lixQsybUrWuQI8c2/v57BvPmzeXatWsEBwdTpEgRAEJCQnB3d8/0I6xFROTRiYuD+fPhww+Pc+RIYcDAZKpCnTp9+fLL5gQGaqSziMiz5LEFa08DBWsiIk8+w4Djx++GbGvWwNWrSdvkyAG1akXj57eBd9+tT0CA+XibNm349ddfmTJlCl27dn3stYuIiHXExsYyZ84C5s49RHDwKEJCzMcdHMbRsmVVxo2rSu7cVi1RRESsRMFaKihYExF5+iQkwJ49d4O2v/6CyMikbfLnhzp1Eli3rirHj29l9+7dlClTBoDg4GBu375NmTJl/nNktoiIPHmuXYPhww8yaVIJwAY4hrd3fnr1gp49zZvjiIjIs0vBWiooWBMRefpFR8PWreZNEFavNv8+6RKghyhVKtCyEcLs2Z35+eeZlCxZks6dO9OhQwd8fX2tVb6IiGSAc+fOsWTJRk6caMOUKWDeRLoTHh6FGDz4bd56KxsuLtauUkREMgMFa6mgYE1E5Nlz4wZs2HB3RNu+fUnPm0xdgTmWhattbW1p2LAhnTt35oUXXsDJyenxFy0iImmyd+9ehg8fz2+//YxhmIAQIBclSsCAAdCmDdjbW7lIERHJVBSspYKCNRERuXQp6UYIp04BXAXmATOArZa2WbJkpV27tnTp0pnKlStrqqiISCZkGAYrV65k2LBxbN268p4ztShbdjKjRhWnSRPQX+EiIpISBWupoGBNRET+7dSpuyHb6tVw+fIRzAHbLOCspV2OHIXp0KETvXp1xN8/r7XKFRGRO2JiYvj557mMHDmOU6f23zlqA7SmRo0+jBlTgWrVrFmhiIg8CRSspYKCNREReZCEBDhwwBywrVoVz5o164iKmgH8D0jcEcGWl1/+h2bNclCnDuTMacWCRUSeQdevX+ebb6by2Wdfcu3aP3eOumIydaN5816MGpWPEiWsWqKIiDxBFKylgoI1ERFJjdhY2L4dli+/wYIF/+Po0RkYhi2wytLGx+dzqlYtS6dOtahd2wYPD+vVKyLyNDtz5gyfffY506Z9R3T0zTtHc2Jn14uuXbszeHA2/P2tWqKIiDyBFKylgoI1ERFJj1u3YO3aGDZscGD1ati16wKQG0gAjmJjU4gKFaBuXfOralXQ3gciIukXEQG9ei1l+vTn7xwpgYtLX957rx3vvedA9uxWLU9ERJ5gCtZSQcGaiIhkpH37ztC370cEB5/Gyel3jh1LPDMcyI2jYxtq1MhqCdrKlQNbWysWLCLyhEhISGD58uWEhFzj/PmOTJ4M4eEJQHe8vFozaFADunc34eZm7UpFRORJp2AtFRSsiYjIo2AYBiaTiTNn4Ndfr9KrV04SEmIAR+BFoDNQn6xZ7QgKujuirWhR7VInIpKSqVN/5Y03XgS8gdOAM0WKQP/+0KEDODhYtz4REXl6PGxWZPMYaxIREXmmmO6kY3nyQMeOtowd+xHFixcHooF5QBNMpjxcv/4Bixcf4J13IDAQ/PygUyeYMQPOnn3QHUREnm5Xrlxh06ZN7N0L7dvDm282BUoDXSlXLpqFC+HQIejaVaGaSEbZsmULrVu3JmfOnDg4OODr60urVq3YvHlzqvoZMWKE5Xuh1Fq3bh0mk4l169al6fqHFRQURFBQ0APbxMfH8/nnn9OoUSP8/PxwcXGhWLFiDBgwgOvXr6d4zaRJkyhatCiOjo7ky5ePkSNHEhsbm6zdpUuX6NKlC97e3ri4uFClShVWr16drF10dDSfffYZJUqUwNXVFR8fHxo3bsymTZvS8tiSwTRiDY1YExGRx8cwDHbt2sWMGTOYM2cOYWFhlnNZspQjMrIz8fHtgLsLAxUubB7JVq8eBAWBp+fjr1tE5HE6fvw4n38+gR9++BHwJDr6JGBOzho0SGDAABuCgjS6VySjTZo0iffee4+KFSvSs2dP/P39OX36NJMnT2bbtm188cUXvP322w/V19mzZzl79iyVK1dOdR0REREcOnSIwMDAR/ozemKo9qAA7+bNm+TKlYt27dpRv359vL292bVrFx999BE5c+Zkx44dODs7W9p//PHHDB06lAEDBtCgQQO2b9/OkCFD6Ny5M9OmTbO0i46O5rnnnuP69euMHTuWHDlyMHnyZJYtW8aqVauoVauWpW2nTp346aefGDhwIHXq1OHq1auMHTuWvXv3snHjRipWrJjh741oKmiqKFgTERFriImJ4ffff2fGjBksXbrU8j+ZtrZ25M/fBHiNEydeICHh7jUmk3lNtsRpo9Wrg4uLdeoXEclomzZtYty48SxevIi7P6aUw2T6H23aBNC/P5Qta9USRZ5aGzdupGbNmjRp0oRFixZhZ2dnORcXF0eLFi1Yvnw5GzZsoFq1avftJzIyEpcn5JuThwnW4uPjuX79Ol5eXkmOL1iwgNatWzNr1ixeeeUVAMLCwvDz86NTp05MnTrV0nb06NEMGTKEAwcOEBgYCMDXX3/NW2+9xaZNm6hSpQpgfp9Lly6Nm5sbW7duBcwBnKurK+3atWPWrFmWPs+fP0+uXLl49913+eKLL9L9XkhymgoqIiKSyTk4ONC8eXMWLlzIP//8w6RJk3juueeIj4/j2LHfKFLkW8LCYPFiePttKFYMDAN27oRPP4WGDSFbNvMoto8+gs2bIS7O2k8lIpI68fHxLFy4kCpVqlKtWjUWLVp4J1Rrgr39Grp338HRowHMnatQTeRRGjNmDCaTiW+++SZJqAZgZ2fH119/jclkYuzYsZbjidM9d+3aRatWrciWLRsFChRIcu5e0dHR9OnTB19fX1xcXKhZsyY7d+4kICCALl26WNqlNBW0S5cuuLm5cfz4cZo0aYKbmxt58uShT58+REdHJ7nPyJEjqVSpEp6enri7u1OuXDm+//570jKuyNbWNlmoBlhGiZ05c8ZybMWKFURFRdG1a9ckbbt27YphGCxevNhybNGiRRQpUsQSqoH5fX7llVfYtm0b586dA8DGxgYbGxs8PDyS9Onu7o6NjQ1O2mre6uz+u4mIiIg8at7e3rz99tu8/fbbHDx4kJkzZxIUFETWrNC8OZQuHcKaNU0ZMqQLhQv3Zc0aE6tXw5kzsH69+TV0KGTJArVqQaNG0KQJ5Mtn7ScTEUlZZGQk06dPZ/z4zzl58sSdow5AR1xd3+eddwLp1Qt8fa1ZpcjDMQyIjLR2FXe5uKRuqnR8fDxr167lueeew8/PL8U2efLkoXz58qxZs4b4+Hhs79nSvGXLlrz88su88cYb3Lp167736dq1K/PmzaNfv37UqVOHQ4cO0aJFCyIiIh6qztjYWF544QVee+01+vTpw4YNGxg1ahQeHh4MGzbM0i4kJIQePXqQN29ewLxu3DvvvMO5c+eStEuPNWvWANxZP9fswIEDAJQsWTJJ25w5c+Lt7W05n9i2Ro0ayfotVaoUAAcPHiR37tzY29vTs2dPvv/+e+rVq2eZCjpo0CA8PDx4/fXXM+R5JO0UrImIiGQyxYsX55NPPklybNasWRw6dIicOf9g1KgP6NjR/E384cOxrF9vz+rVsHYtXL0KS5eaX2Ae5dakiflVvboW9xYR67t48SKTJ09m8uSvuXo1cZ3JbEBPvL3fpm9fX954A/41OEMkU4uMBDc3a1dx182b4Or68O2vXLlCZGQk+f7jf+Ty5cvHtm3bCAsLI0eOHJbjnTt3ZuTIkQ+89tChQ/z888/079+fMWPGAFC/fn18fHxo167dQ9UZExPDyJEjad26NQB169Zlx44dzJkzJ0lg9uOPP1p+n5CQQFBQEIZh8MUXXzB06NA0b6qQ6Ny5cwwYMIDnnnuOZs2aWY6HhYXh6OiIawpvvqenZ5K1dcPCwvBMYeHcxGP3tp0wYQIeHh689NJLJNxZIyRv3rysWbOGggULputZJP00FVREROQJ8O677/Ltt9/Sr18/y7Hz5/+henUfdu7sRq9ef3HpksHOnTB2rHnUmq0tHD4M48eb12Pz8oKWLeG77+Cff6z4MCLyTJs4cTqjRo26E6rlAyYREHCGKVM+4swZX/r3V6gmklklTqX8dzD10ksv/ee169evB6BNmzZJjrdq1SrZ1NP7MZlMPP/880mOlSpVitDQ0CTH1qxZQ7169fDw8MDW1hZ7e3uGDRtGWFgYly5deqh73c/Vq1dp0qQJhmEwb948bGySxioPCu3+fe5h23788ceMGzeOESNGsHbtWn799VeKFClC/fr12b17dxqfRDKKRqyJiIg8ATw8POjWrVuSY4sWLeLatWt8//33fP/99+TPn59OnTrRqVMn+vfPx/XrsHIlLF8Ov/8OFy/CokXmF0CZMndHs1WqBA/5Pa2IyEMzDIMNGzZgY2ODl1cNPv0UZs/uAfwJvEmZMi0YMMCWl17S30HyZHNxMY8SyyxSu3eAt7c3Li4unDp16oHtQkJCcHFxSTbSKmfOnP95j8QRWD4+PkmO29nZpbiGWUpcXFySrSnm6OhIVFSU5eNt27bRoEEDgoKC+Pbbb/Hz88PBwYHFixfz8ccfc/v27Ye6V0quXbtG/fr1OXfuHGvWrCF//vxJznt5eREVFZXiBg5Xr16lfPnySdreOyrt3nZwd+Ta4cOHGTZsGJ9++il9+/a1tGvcuDGBgYG8//77rF27Ns3PJOmnf75ERESeUG+++SYlSpRgxowZ/PLLL5w8eZIRI0YwYsQIAgMDqVmzJjVr1uSjj2ry/fe52b0bli0zB23btsGePebX6NHmTRAaNoSmTc2/Zs9u7acTkafBN998w1tvvUXWrBW4fn0rYAKyUrv2agYMgPr1U7cOlEhmZTKlbuplZmNra0vt2rVZsWIFZ8+eTXGdtbNnz7Jz504aN26cZH01ePDIq0SJ4dnFixfJnTu35XhcXFyKAVNazZ07F3t7e5YuXZokhLt344C0uHbtGvXq1ePUqVOsXr3ashbavRLXVtu/fz+VKlWyHL9w4QJXrlyhRIkSSdru378/WR+JxxLb7t27F8MwqFChQpJ29vb2lC5d2jISUKzniZ4KmrhTSEqvLVu2WLs8ERGRR8rGxoZatWrxww8/cOHCBWbPnk39+vUxmUwcOnSIKVOm0L59e/z8/ChcuCCTJ79K2bJL2LLFPHpt1ixo184cql27BnPnQseO4OMDlSvDhx/Cjh1wZykPEZH/dOPGDU6ePIlhmEfKzp7dCsjG9evlgWhatIAtW2DNGmjQQKGaSGYycOBADMOgZ8+exMfHJzkXHx/Pm2++iWEYDBw4ME3916xZE4B58+YlOb5gwQLiMnBbc5PJhJ2dXZLw7/bt28yaNSvNfSaGaidPnuTPP/+k7H22KG7UqBFOTk5Mnz49yfHp06djMpl48cUXLcdatGhBcHAwW7dutRyLi4tj9uzZVKpUiVy5cgFYfv13xhEdHc2uXbvuu9mEPD5PxYi10aNHU7t27STH7k2CRUREnnaurq506NCBDh06EBYWxt9//8369evZsGEDu3fv5sSJE5w4cQJXV1eef/55smeHVq2iiIycyaBBNbl+vQi//25i+XLzKLatW82v4cPNQVvjxuYpo/XrQ9as1n5aEckMDMPg1KlTbNu2jW3btrF9+3Z27txJvnyVsbdfw969ADmwsztHx47OfPCBeUMVEcmcqlWrxsSJE3nvvfeoXr06b7/9Nnnz5uX06dNMnjyZrVu3MnHiRKpWrZqm/osXL067du0YP348tra21KlTh4MHDzJ+/Hg8PDySrVWWVk2bNuXzzz+nffv2dO/enbCwMMaNG4ejo2Oa+rt9+zYNGzZk9+7dTJw4kbi4uCQhV/bs2SlQoABgnr45ZMgQhg4diqenJw0aNGD79u2MGDGCbt26ERgYaLnu1VdfZfLkybRu3ZqxY8eSI0cOvv76a44cOcKqVass7apXr06FChUYMWIEkZGR1KxZk/DwcCZNmsSpU6fSFRhKxngqgrVChQpRuXJla5chIiKSKXh5edG8eXOaN28OQHh4OJs2bWLDhg00aNDA0m779u306NEDHx8fzp8/T/Xq8PHHsG3bafbu9WPFChtWrjSPbps+3fyytYVq1cwhW9OmULy4RpyIPCsuX77M9u3bLUFa4s6A/3bo0DngOq6uWenRA3r3dkYDKkSeDO+88w4VKlRg/Pjx9OnTx7JzZfXq1fn777+pUqVKuvr/8ccfyZkzJ99//z0TJkygTJkyzJ8/n0aNGpE1g/7nrk6dOvzwww988sknPP/88+TOnZvXX3+dHDly8Nprr6W6v4sXL7J9+3YAevXqlex8586dk4xQGzx4MFmyZGHy5MmMGzcOX19fBgwYwODBg5Nc5+joyOrVq+nXrx/vvPMOkZGRlClTht9//51atWpZ2tnY2LBy5Uo+++wzfvnlF8aNG4ebmxuBgYEsX76cxo0bp/qZJGOZjMRtPZ5A69ato3bt2vzyyy+0atUqzf1ERETg4eFBeHg47u7uGVihiIhI5rVhwwaGDRtG3rx5mTlzJmAegZIzZ05iYmKoUaMGVavWJEuWmhw/XpYVK+w4fDhpH3ny3N0AoU4dcHOzwoOIyCOzd+9exo4dy7Zt2zh58mQKLeyBMkBFy8vTszDvvWfDW2/Bv9Y3FxFJZtOmTVSrVo2ffvqJ9u3bW7scEYuHzYqeimAtR44chIWF4eLiQpUqVRg6dCjVq1d/6H4UrImIyLPMMAzLosPnzp2jSJEi3Lp1K0kbNzc3qlWrRsmStUhIqMmhQxVYt86BezbhwsEBatUyj2Rr0gQKFXqcTyEi6bVixQoWLVpEkyZNaN68OYYBv/66nRYtKt7Tqgj3hmhQGjc3RypXNo9mrVoVqldP/Y6EIvJsWLlyJZs3b6Z8+fI4OztbwnsPDw/27duXbMdPEWt6JoK13bt3M2PGDIKCgvDy8uL48eN89tlnHD16lGXLltGwYcMUr4uOjiY6OtrycUREBHny5FGwJiIiAsTGxrJ79242bNjA+vXr+euvvwgPD0/SxsnJiUqVquDnV4tbt2qyZ081QkIckrQpWPDuaLZatUDfK4tYn2EYnD592jKVc+DAgXjeGVbWr99APvtsLJUrv07u3NPYtAnOn48GxmMO0Z4DshIQcDdEq1oVSpY0TxMXEfkvW7dupU+fPhw6dIgbN27g7e1Nw4YNGTNmDDlz5rR2eSJJPBPBWkquX79OyZIl8fT0ZK95xdRkRowYwciRI5MdV7AmIiKSXHx8PAcOHLBshrBhwwYuX76cpM358xe4ft2H5cvhf/87xbZt3sTFZbGcd3GBunXvBm158z7upxB5Nl29epUdO3awdetWS5h26dIly/kRI1Zw+3ZDNm2CrVv/IiZmKdAQqAOAnR2UK5c0SLuzQZ2IiMhT7ZkN1gDefPNNpkyZQmRkJM7OzsnOa8SaiIhI2hmGQXBwsGVE24ULF1izZo3lfP369VmzZg3vvfcTN268zPLlcO6cAdzd5aB48bsbIFStCvb2VngQkadMVFQUe/bsSbK5wLFjx5K1M5nscHAoRXR0RaAH5jXSzLy87gZoVatChQqQwrfTIiIiT72HDdaeil1B/y0xKzTdZ5syR0fHNG+1KyIi8qwzmUwUK1aMYsWK0aNHjyTnDMPg4sWLJCQk0KVLcUqWBMOAESO+Z9q0LzGZanHhQk0OHqzJwYM+fPYZuLtDgwbmoK1xY/D1tdKDiTxB4uPjuXDhArlz5wbMX3sBAQFcvHgxWVtX10LExlYkJsa8LpphlCY62pyWFSt2N0SrVg0KF9ZOvyIiIqnx1AVr165dY+nSpZQpU0YLH4qIiDxmJpOJffv2cebMGcsP/CYTnDixjgsX9gP7ga8AcHcvQkxMTSIiarFgQU0WLMgDQPnyd6eMVqigtZtEDMMgNjYWBwfzOoa7d++mVq1aeHp6EhISAsA//5jw9S1DRMRuHBwqceNGRRISzOui3bplXkPN2RkqVrwbolWubB6hJiIiImn3RE8Fbd++PXnz5uW5557D29ubY8eOMX78eE6cOMHvv/9OvXr1Hqof7QoqIiLyaF26dMmyPtuGDRvYt28f//4WxMEhgJiYWkBNoBaQHy8vE40amUO2hg0VAsiz4fr16+zYsYNt27ZZ1kZr374948ePB+Dq1Qi8vbNib+9Cs2Zn2bEjK6dPA9wEXEmcdp0rlzlAS1wfrUwZTbsWERF5WM/EGmtjx45l3rx5nDp1ips3b+Lp6Un16tUZOHAgFSpUeOh+FKyJiIg8XlevXmXjxo2Wddp27dpFfHx8kjYODu8TEzP+zkcJmEwmqlQxWUazlSmjKWvy5IuOjmbv3r1J1kU7cuRIsnYlStSgRYsNbNwIW7fCrVtHgAIkTkCxsYHSpe+ORqta1bxJiL5GRERE0uaZCNYyioI1ERER67px4wabNm2yjGjbtm0b3377A/nydWD5cvjll62cONEMeBH4FoCcOc1rsjVpAvXrm9dqE3kSbNiwgfnz57Nt2zb27NlDbGxssjbZs+fH3b0it25V5MKFikBZwMVy3t0dqlS5G6JVqgRubo/vGURERJ52CtZSQcGaiIhI5nL79m0Ay+7en332Gf369aNMmRfx91/EqlVw65YBdAZKYGtbi+rVy9GsmT1NmpgXZNdIHckMli1bxsaNG+nRowf+/v4AfP755/Tp08fSJmtWb3x9zRsLnD9fkfDwCoB3kn4KFLgbolWrBoGB5lFqIiIi8mg8bFakf45FREQk03F2draEagC9evVi06ZNTJs2iMWLISwMZs4MAWYB/YmPr8z69dn44IMGFC/+EblybaBHjyiWLYPISCs9hDxTwsPDWbNmDdOmTUtyfPTo0YwZM4a//voLgIsXwWSqS/ny71O48Fzs7U9y/folgoOXERw8nPDwxjg4eFO1KvTtCwsXwoULcPw4zJgBPXpAiRIK1UQk461Zs4ZXX32VokWL4urqSu7cuWnevDk7d+5Msf2uXbuoV68ebm5uZM2alZYtW3Ly5MkU206aNImiRYvi6OhIvnz5GDlyZIqjddeuXUv9+vXJkSMHbm5ulCpVii+//DLZchEZyWQyMWLECMvHhw4dYsSIEZbNYe4VFBREiRIl0nSfiIgIPv74Y4KCgvD19cXNzY2SJUvyySefEBUVlax9bGwsI0eOJCAgAEdHR4oWLcqkSZOStTt48CA9e/akSpUquLq6YjKZWLduXYo13Lhxg3fffZfcuXPj6OhI4cKF+fTTT9P9/q5bt+6B933aPXW7goqIiMjTx8HBgSpVqlg+dnSERo3c+Pzzz1m/fj3r1//F9etXgZXASi5cgGnTHJk2rRK2tjUpW7YWbdtWoUULVwoUsNpjyFMiJiaGffv2JVkXLTg4GMMwMJlMtGvXjixZspCQAFWrvoTJFMhPP+VjxAg4cQKgNDDe0l/27ElHo5UrB9rcXkQet2+++YawsDB69epFYGAgly9fZvz48VSuXJk//viDOnXqWNoGBwcTFBREmTJlmD9/PlFRUQwbNowaNWqwZ88esmfPbmn78ccfM3ToUAYMGECDBg3Yvn07Q4YM4dy5c0n+M2LVqlU0bNiQmjVr8u233+Lq6spvv/1Gr169OHHiBF988cUjee7Nmzfj5+dn+fjQoUOMHDmSoKAgAgICMuw+p0+fZuLEiXTs2JH3338fNzc3/vrrL0aMGMHKlStZuXIlpnuG2/fs2ZNZs2YxatQoKlSowB9//EGvXr24ceMGgwYNsrTbsWMHixcvpmzZstStW5clS5akeP+4uDjq16/P0aNHGTVqFIULF2bFihUMGDCAs2fP8uWXX6b52cqVK8fmzZsJDAxMcx9PNEOM8PBwAzDCw8OtXYqIiIikQXx8vLF//37jq6++Ml56qY2RNauPAfzrZWdAZaNQoZtG796GsXKlYURFWbtyyezi4+ONI0eOGLNmzTLeeecdo1KlSoaDg0MKn18Y/v4BRq1abYy+fc8ajRoZhoeHYUDSl8lkGCVKGEb37oYxY4ZhHDtmGAkJ1n5KERHDuHjxYrJjN27cMHx8fIy6desmOd66dWvD29s7yc/QISEhhr29vdGvXz/LsStXrhhOTk5G9+7dk1z/8ccfGyaTyTh48KDlWIcOHQxHR0fj5s2bSdo2aNDAcHd3T9ezpcYvv/xiAMbatWuTnatVq5ZRvHjxNPV78+bNZM9mGIbx2WefGYDx119/WY4dOHDAMJlMxujRo5O0ff311w1nZ2cjLCzMciw+Pv6hav/5558NwPjf//6X5Hj37t0NGxsbIzg4ONXPFBMTY8TGxqb6uoxy69atR9r/w2ZFGkQuIiIiTzwbGxtKlCjBW2+9xYIF87h69TxHjhxh2rRvef75V8iaNQ8QB1zk2DFXJkwwb3jg5tabAgXeY9SoY5w9a+2nkMwgJibG8vvQ0FC8vLwoUqQIHTt2ZNKkSWzdupWYmBg8PT2pVasRLVsOo1mzpZQocZEzZ06xfv08xo3LzYoVEB4Orq5Qpw4MHQq//w5Xr8L+/TB1KnTqBAULaj1AEckccuTIkeyYm5sbgYGBnDlzxnIsLi6OpUuX8tJLLyVZd8rf35/atWuzaNEiy7EVK1YQFRVF165dk/TbtWtXDMNg8eLFlmP29vY4ODgkWQoCIGvWrDj9xzDeyZMnY2Njw6VLlyzHxo8fj8lk4q233rIcS0hIIFu2bEnWubx3Kuj06dNp3bo1ALVr18ZkMmEymZg+fXqS+23fvp0aNWrg4uJC/vz5GTt2LAkJCQ+s0dXVFVdX12THK1asCJDkPV68eDGGYaT4vt2+fZsVK1ZYjtk85NoAGzduxGQy0bhx4yTHmzVrRkJCQpI/t5QkTvecNWsWffr0sUwnPX78eLKpoBMnTsRkMnH8+PFk/fTv3x8HBweuXLliObZq1Srq1q2Lu7s7Li4uVKtWjdWrVye5bsSIEZhMJnbt2kWrVq3Ili0bBe5MQzh58iQvv/wyuXLlwtHRER8fH+rWrcuePXse6r1JL00FFRERkaeOyWSicOHCFC5cmNdf7waYQ5Lg4LPcvAnLl8OyZfFcvPgDJ09GMGxYR4YNg1KloHTpLeTPH8prr9UkT56cVn4SeVzmz59P3759qVWrFrNmzQLAz8+PuLg4nJycKFu2HP7+FbG3r8iVKxXZuzc/69cnT8Ty5r07pbNqVfPnlJ2+4xZ5Zty6dSvV1zg6OmJ35y+KuLg4oqOjsbGxSRIwpabflMKbtAoPD2fXrl1JpoGeOHGC27dvU6pUqWTtS5UqxcqVK4mKisLJyYkDBw4AULJkySTtcubMibe3t+U8wBtvvMHPP//Mu+++y6BBg3BxcWHJkiUsWrSIMWPGPLDOevXqYRgGq1evpl27doA5rHF2dmblypWWdjt27OD69evUq1cvxX6aNm3K6NGjGTRoEJMnT6ZcuXIAlgAH4MKFC3To0IE+ffowfPhwFi1axMCBA8mVKxedOnV6YJ0pWbNmDQDFixe3HDtw4ADZs2fH19c3SdvE9/ze9+1hxcTEYGNjg729fZLjjo6OAOzbt++h+hk4cCBVqlRhypQp2NjYkCNHDi5cuJCkzSuvvEL//v2ZPn06H330keV4fHw8s2fP5vnnn8fb27xJz+zZs+nUqRPNmzdnxowZ2NvbM3XqVBo2bMgff/xB3bp1k/TdsmVLXn75Zd544w3L10WTJk2Ij4/n008/JW/evFy5coVNmzZx/fr1VL1HaaV/5kVEROSZ4O/vb9mV8aWXICoqnokTp/HrrxuBMmzdCvv2wb5904AfGTkS3NwKUa5cTdq0qUWzZjUt18uTJzY2lv379ydZF23MmDE8//zzAGTJkuX/7d13WBTX3gfw77LLglJEQCkqxS6WK0ZCEBVQrFgJFlADGq/Gnpjcq0YiYFQUG8YS9V5Er4ld9E0TDYolFkS9sZGY3CSIRrBQpAioy3n/IDtx3VWBiOvi9/M8++jMnDmcOTM7O/vbU3Dt2jWkpqYCKG9ZdvKkHCNHnsPlyy44c8YYJ09q5imXA+7ufwbROnUCHhmmh4heQebm5pXeZ8eOHVIrqT179mDo0KHw8fHRGAjexcVFo4XP0wghKl2GJ5k0aRKKioowe/ZsaV12djYAwNraWiu9tbU1hBDIzc2Fg4MDsrOzYWJiojPYZ21tLeUFAJ6enjh06BCGDBmC1atXAwDkcjmio6M1Wpjp0qJFCzRs2BBJSUkIDg7G/fv3cezYMUydOhWLFi1CRkYGnJyckJSUBGNjY3Tt2lVnPvXq1UOzZs0AAG5ubnjjjTe00mRnZ+Obb76RWpr5+/vj8OHD2LJlS6UDaxcuXEBMTAwGDx6sEajMzs7WWb9mZmZQKpUa9VZRbm5uUKlUOHXqFDp37iyt/+6776S/WRFNmjTBzp07n5rG1tYW/fr1w6ZNmzB37lypVd2BAwdw48YNqSXevXv3MG3aNPTr10+jxVzfvn3RoUMHfPjhh0hJSdHIOzQ0FFFRUdJydnY2rly5gtjYWIwcOVJaHxgYWKHjeR4YWCMiIqJXkqmpEjNnDsPMmcMAAHfuAAcOAIsXN8fFi+5Qqb5HYeHPOHr0Zxw9GofJk4E6dZzQqZMPBg3qCl/frmjWrJnGQMP0chBC4JdfftEIov33v//VmnXt1KlT6N+/P4QA6tf3xowZh3HjRge4uQE//KBO1UxKX7funwG0Tp0AD4/yrp5ERDXRRx99hM8//xwrV67Ea6+9prX9aZ9/j26raLqzZ89i8ODB8PT0xLp162BmZoZDhw4hPDwcJSUl+Oijj55a3u7duyMpKQkAcOLECdy7dw/Tp09HXFwcvv32W7z99ttISkqSZs+sKnt7eymoptauXbtKdztMT09Hv3790KhRI/z73//W2l7RequoESNGYO7cuRg3bhzi4+PRokUL7Nu3T5q0oKJdSt98880KpRs9ejQSEhKQlJSEnj17AgDi4+Nhb28vdUc9ceIEcnJyEBoaiocPH2rs37t3b8TExKCoqEjjfD3+962trdGkSRMsXrwYKpUKfn5++Nvf/lbh43keGFgjIiIiAmBrC4SEACEhM6FSzcShQ3nYsOE4kpOP4ubNIwDO4O7dDOzbtxn79pV3FbSysoevb1dMmTJeo5uMvgkhoFKpnvgyNzdH7dq1AZT/Wvz777/D2NhYY/azS5cuoaio6Kn5PP5q06YN2rRpA6D8F+Rdu3bB1NQUoaGhUr5btmzB1atXK5Wvn5+f1LUnNzcX77zzDgBg+/btUr4ff/wxjh49itLSUly6dAm5ubla9WJlZYXXX38d7u6vw8rqdRQWemLAAODECSA72xKAj0b6Fi3+DKJ5e5cvv8DndCIyQIWFhZXeR90VDwAGDx6MwsJCraBAenr6Xy1apURFRWHevHmYP38+Jk+erLHNxsYGgO4WTjk5OZDJZLCyspLSlpSU4N69e9LnzqNpHw3YTZo0CXZ2dtizZw/kcjmA8nHOjIyMEBkZiREjRqBx48ZPLLO/vz82bdqEn3/+GUlJSXB3d0f9+vXRrVs3JCUlISQkBCdOnNBofVcV6uN/lImJCYqLiyucx9WrV+Hn5weFQoGDBw9qtU6zsbHRGagrKiqSxvmsLFtbWyQmJiI0NFRqiWdjY4Nly5bh7bffRoMGDSqUj4NDxYbJ6NOnDxwcHBAfH4+ePXsiNzdXmuVVfX5v3rwJAAgKCnpiPjk5ORqBtcf/vkwmw8GDBzF37lzExMTg/fffh7W1NUaMGIH58+fDwsKiQuX9KxhYIyIiInqMXA706GGFHj0CAAQgMxPYu7cQW7eeQkrKEdy/fxRACvLysrB37w5cvtwHY8YAffsCSuUVLFgwH6+//rr0ZaSsrAyDBw+uVDBJpVJh+fLl0q+8X375JSZOnAgvLy/s2LFDKquLiwtu376tsd+zugGtWbMGEyZMAFDeaqt79+5o3bq1xpgtQUFBuHLlSqXqLSoqSgqs3bhxA++88w7q1aunEVhbu3Ytjh07Vql8lUqlFFi7f/++dPyPBtbOnz8vtVRQ7+Pu7g5PT080b/46yspex6+/NsXJkzIsWwY8eKD5N0xNy1ugqYNoXl7lwVYiosr4q+ObKRQKaby155lvZURFRSEyMhKRkZH48MMPtbY3adIEtWrVwsWLF7W2Xbx4EU2bNpUmG1CPrXbx4kV4enpK6bKysnDnzh3pMwMAvv/+ewQHB0tBFzUPDw+UlZXhhx9+eGpgTT0WV1JSEr799lv06NFDWh8eHi79+PKk8dVelKtXr8LX1xdCCBw+fBgNdYwh0LZtW2zbtg1ZWVka46yp6/zReqsMDw8PpKWlIT09HUVFRWjWrBnOnj0LAE/sHvu4iraWk8vlGDVqFD755BPk5eVhy5YtKC0t1ZiQQT3O2sqVK3V2uwUAOzu7Z/59Z2dnxMXFAQB++ukn7NixA5GRkbh//z7Wrl1bofL+FQysERERET2DgwMwYYI5Jkzwx4MH/jh+HPjiixLs2XMa6elH8fPP3TFrFjBrFmBpuQ/5+Zvx1VfF2Lx5MuTy8ofAEye+qPTfnTs3D5s3l7eSysgowfXr13Hs2E288075OiMj4ObNIpSU3KtUvgcOlKG4uHz/335TwtTUEkVF5li37s98jYwawta2FEZGcsjlchgZyTX+r+vfwkJnHDpUHpi8fdsKXboMgoWFFVJSytcZGQHt2/eFjU0zyOVyKBRyyOVGkMvl0nL5uj//r1DI0aGDB3Jzy/eXySyxePEKKBRylJYKyOUyyOXAlClTMGjQIBgZKQA0xZ077XD6tBJffAHoauhhb//n2Gje3uVjpSmVlT5FREQ1yscff4zIyEiEh4cjIiJCZxqFQoH+/fsjISEBMTExUougjIwMJCcn47333pPS9u7dG6ampti4caNGYG3jxo2QyWQYNGiQtM7R0RFnzpyBSqXSCK6d/GOAS10BqEc5ODjAzc0Nu3fvxtmzZ7FgwQIAQI8ePTB+/HgsW7YMlpaW8PDweGo+6haElWmBVlEZGRnw9fWFSqXC4cOHnzh268CBAxEeHo5NmzZhxowZ0vqNGzeiVq1a6N27918qh7qFuhACS5cuhaOjozTO3/M0evRoxMTEYOvWrdi4cSO8vLzQsmVLabu3tzesrKyQlpam1TKyqpo3b47w8HDs3r0b586dey55PgsDa0RERESVYGwM+PoCvr6mWLasK65e7Yp9+8pnGj14EMjPbwlgMXJzm+P0afVeMgDrARgBkFf4dfx4Gxw/rs7DH8AZZGVZYt26R0uk/iMVzdcIe/fKsHevev/OAO4iPR34o4flH5JQWefPA4sXq5caASgfiPibbx5NNbPS+WqqBWAqAOCR724AfKRummVlmnsYGQFt22rO1uniAnB4PCKiPy1duhRz5sxB7969ERAQgFOnTmlsf7RFUVRUFDw8PNCvXz/MnDkTJSUlmDNnDmxtbTUmGrC2tkZ4eDg++ugjWFtbo2fPnkhNTUVkZCTGjh0LNzc3Ke17772HqVOnon///hg/fjxq166NgwcPYunSpfD398ff/va3Zx5D9+7dsXLlStSqVQve3t4AAFdXV7i6uuLAgQMYMGCAzhaBj1K3Blu/fj0sLCxgamoKV1dXnV1AK+PWrVvw8/NDZmYm4uLicOvWLdy6dUva3rBhQyl42Lp1a7z99tuIiIiAXC6Hh4cHDhw4gPXr12PevHkaXUHv3buHb/74oFWfsyNHjuDOnTswMzOTxjMDgNmzZ6Nt27ZwcHBARkYGNmzYgJSUFHz99dcas9A+Ly1btoSXlxeio6Nx7do1rF+/XmO7ubk5Vq5cidDQUOTk5CAoKAj169fH7du3cf78edy+fRuffvrpU//GhQsXMHnyZAwZMgTNmjWDUqnEoUOHcOHCBcyc+VefOSqGgTUiIiKiv8DZuTwg9c47QEkJcPp0bxQV9UZZGTReKtXfdax7+rLmurooK3tNRxrXZ+xXkbwrnuZF512Zye3UATULC+CNN/4Monl6ApaW1XP+iYhqii+//BIAkJiYiMTERK3tjw4z0LJlSxw+fBgzZsxAUFAQFAoFunXrhiVLlqBevXoa+82ePRsWFhZYvXo1lixZAnt7e8ycOVNrrLMpU6agQYMGWL58OcaOHYvi4mK4uLggIiJCoxXc0/j7+2PlypXo3Lmz1B1Vvf5f//pXhbqBurq6IjY2FitWrJBal8XHxyMsLKxCZXiStLQ0/PrrrwCgMXulWkREBCIjI6XlNWvWoEGDBli5ciWysrLg4uKCFStWYMqUKRr73bp1S6u1mTofZ2dnjfH5cnNzMWPGDGRlZcHS0hI+Pj5ISUmRuuxWh9GjR2PcuHGoVasWhg0bprV95MiRcHJyQkxMDMaPH4+CggLUr18f7du3r1Cd29vbo0mTJlizZg2uXbsGmUyGxo0bY+nSpVp1VV1k4nnOxWug8vPzUadOHdy9exeWfOoiIiIiemkIUbmgnRCAnV1511MiIiKiqqporIgt1oiIiIjopSWTlQfJGCgjIiKilxEnDCciIiIiIiIiIqoCBtaIiIiIiIiIiIiqgIE1IiIiIiIiIiKiKmBgjYiIiIiIiIiIqAoYWCMiIiIiIiIiIqoCBtaIiIiIiIiIiIiqgIE1IiIiIiIiIiKiKmBgjYiIiIiIiIiIqAoYWCMiIiIiIiIiIqoCBtaIiIiIiIiIiIiqwOADa4WFhXj33Xfh6OgIU1NTtG/fHtu2bdN3sYiIiIiIiIiIqIZT6LsAf1VgYCBSU1OxcOFCNG/eHFu2bEFwcDDKysoQEhKi7+IREREREREREVENJRNCCH0Xoqq++eYbBAQESME0tZ49e+Ly5cvIyMiAXC5/Zj75+fmoU6cO7t69C0tLy+osMhERERERERERveQqGisy6K6ge/bsgbm5OYYMGaKxfvTo0bhx4wZSUlL0VDIiIiIiIiIiIqrpDDqwdunSJbRq1QoKhWaP1nbt2knbiYiIiIiIiIiIqoNBj7GWnZ2Nxo0ba623traWtutSWlqK0tJSafnu3bsAypv5ERERERERERHRq00dI3rWCGoGHVgDAJlMVult0dHRiIqK0lrfqFGj51YuIiIiIiIiIiIybAUFBahTp84Ttxt0YM3GxkZnq7ScnBwAf7Zce9ysWbMwffp0abmsrAw5OTmwsbF5aqDOkOTn56NRo0a4du0aJ2TQA9a/frH+9Yv1r388B/rF+tcv1r9+sf71i/WvX6x//WL961dNrH8hBAoKCuDo6PjUdAYdWGvbti22bt2Khw8faoyzdvHiRQBAmzZtdO5nYmICExMTjXVWVlbVVk59srS0rDEXtSFi/esX61+/WP/6x3OgX6x//WL96xfrX79Y//rF+tcv1r9+1bT6f1pLNTWDnrxg8ODBKCwsxO7duzXWb9q0CY6OjvD09NRTyYiIiIiIiIiIqKYz6BZrffr0QY8ePTBhwgTk5+ejadOm2Lp1KxITE/HZZ59BLpfru4hERERERERERFRDGXRgDQASEhIwe/ZszJkzBzk5OWjZsiW2bt2K4cOH67toemViYoKIiAitLq/0YrD+9Yv1r1+sf/3jOdAv1r9+sf71i/WvX6x//WL96xfrX79e5fqXiWfNG0pERERERERERERaDHqMNSIiIiIiIiIiIn1hYI2IiIiIiIiIiKgKGFgjIiIiIiIiIiKqAgbWDMzGjRshk8lw5swZfRfllaKud12vDz74oML5hIWFwdzcvBpLWvM8WveHDx/W2i6EQNOmTSGTyeDr6/vCy/eq+eSTTyCTydCmTRt9F6XG47X/cuHn78vjr5wLmUyGyMjI51+oGo73fv1ISUnB4MGD4eTkBBMTE9jZ2cHLywvvv/++vov2yjl16hSGDBkCBwcHKJVK2NvbIygoCCdPnqx0XmlpaYiMjER6evrzL2gNob7Pm5qa4urVq1rbfX19eT+qZo9//zU1NYW9vT38/PwQHR2NW7du6buILxUG1ogqIT4+HidPntR4TZ06Vd/FeiVYWFggLi5Oa/2RI0fwyy+/wMLCQg+levVs2LABAHD58mWkpKTouTSvBl77RKRvvPe/eF9//TU6deqE/Px8xMTE4MCBA1ixYgW8vb2xfft2fRfvlbJy5Up4e3vj+vXriImJQVJSEpYsWYLff/8dnTt3xqpVqyqVX1paGqKiohhYq4DS0lKEh4fruxivNPX332+//RarV69G+/btsWjRIrRq1QpJSUn6Lt5Lg4E1okpo06YN3njjDY2Xk5OTvov1Shg2bBh2796N/Px8jfVxcXHw8vJ6ruehuLj4ueVVk5w5cwbnz59HQEAAAOgM9vwV9+7de6751RQv8tonInpcdd/7SbeYmBi4urpi//79GD58OHx8fDB8+HAsWbIEGRkZ+i7eK+P48eN499130bdvXxw7dgyjRo1C165dMXLkSBw7dgx9+/bFtGnTcPz4cX0XtUbq3bs3tmzZgvPnz+u7KK8s9fffLl264M0338Ty5ctx4cIFmJmZITAwEDdv3tR3EV8KDKwZuDNnzmD48OFwcXFBrVq14OLiguDgYK0ms+qmnMnJyZgwYQJsbW1hY2ODwMBA3LhxQ0+lr1m2b98OLy8vmJmZwdzcHL169cJ///tfnWkvX76M7t27w8zMDPXq1cPkyZMZVHiG4OBgAMDWrVuldXfv3sXu3bsxZswYrfRRUVHw9PSEtbU1LC0t0aFDB8TFxUEIoZHOxcUF/fr1Q0JCAtzd3WFqaoqoqKjqPRgDpf4ytXDhQnTq1Anbtm3TuG7T09Mhk8kQExOD+fPnw8nJCaampujYsSMOHjyokVdkZCRkMhnOnTuHoKAg1K1bF02aNHmhx2MoquPaf/vtt2Ftba3zvtOtWze0bt26Go6kZvH19dXZBTcsLAwuLi7Ssvp9sWTJEixbtgyurq4wNzeHl5cXTp069eIKXINV9FxQ1Tzr3n/48GGdXdbV1/7GjRs11v/rX/9C8+bNYWJiAjc3N2zZsoXnSofs7GzY2tpCoVBobTMy0vwKV5FnUPVwJHwGrZzo6GjIZDJ8+umnWudCoVBgzZo1kMlkWLhwobT+xx9/RHBwMOzs7GBiYgInJye89dZbKC0txcaNGzFkyBAAgJ+fn9TN7vH3CZX75z//CRsbG8yYMeOp6UpKSjBr1iy4urpCqVSiQYMGmDRpEvLy8qQ0gwYNgrOzM8rKyrT29/T0RIcOHZ538WssJycnLF26FAUFBVi3bp20/syZMxgwYACsra1hamoKd3d37NixQ2v/33//HePGjUOjRo2gVCrh6OiIoKAggw7SMbBm4NLT09GiRQvExsZi//79WLRoETIzM+Hh4YE7d+5opR87diyMjY2xZcsWxMTE4PDhwxg5cqQeSm6YVCoVHj58qPECgAULFiA4OBhubm7YsWMHNm/ejIKCAnTp0gVpaWkaeTx48AB9+/ZF9+7dsXfvXkyePBnr1q3DsGHD9HFIBsPS0hJBQUFSdxSgPNBgZGSks+7S09Mxfvx47NixAwkJCQgMDMSUKVPw8ccfa6U9d+4c/vGPf2Dq1KlITEzEm2++Wa3HYoiKi4uxdetWeHh4oE2bNhgzZgwKCgqwc+dOrbSrVq1CYmIiYmNj8dlnn8HIyAh9+vTROQ5JYGAgmjZtip07d2Lt2rUv4lAMTnVc+9OmTUNubi62bNmisW9aWhqSk5MxadKk6jugV9Tq1avx7bffIjY2Fp9//jmKiorQt29f3L17V99FI3qiytz7K2L9+vUYN24c2rVrh4SEBISHhyMqKkrnOJKvOi8vL6SkpGDq1KlISUnBgwcPdKbjM2j1UalUSE5ORseOHdGwYUOdaRo1aoTXXnsNhw4dgkqlwvnz5+Hh4YFTp05h7ty52LdvH6Kjo1FaWor79+8jICAACxYsAFD+uaAeWkbdIpQ0WVhYIDw8HPv378ehQ4d0phFCYNCgQViyZAlGjRqFr7/+GtOnT8emTZvQrVs3lJaWAgDGjBmDjIwMrXx+/PFHnD59GqNHj67246lJ+vbtC7lcjqNHjwIAkpOT4e3tjby8PKxduxb/93//h/bt22PYsGEagePff/8dHh4e2LNnD6ZPn459+/YhNjYWderUQW5urp6O5jkQZFDi4+MFAJGamqpz+8OHD0VhYaEwMzMTK1as0Npv4sSJGuljYmIEAJGZmVmt5TZ06vrT9crIyBAKhUJMmTJFY5+CggJhb28vhg4dKq0LDQ0VADTOjRBCzJ8/XwAQ33333Qs5HkPy6DWfnJwsAIhLly4JIYTw8PAQYWFhQgghWrduLXx8fHTmoVKpxIMHD8TcuXOFjY2NKCsrk7Y5OzsLuVwurly5Uu3HYsj+85//CABi7dq1Qojy69vc3Fx06dJFSvPbb78JAMLR0VEUFxdL6/Pz84W1tbXw9/eX1kVERAgAYs6cOS/uIAxMdV/7Pj4+on379hrpJ0yYICwtLUVBQUH1HJQBe/zz18fHR2e9h4aGCmdnZ2lZ/b5o27atePjwobT+9OnTAoDYunVrdRe9xqnquRBCCAAiIiKi+gtZQ1Tk3q++PyUnJ2vsq7724+PjhRDl9yN7e3vh6empke7q1avC2NhY61y96u7cuSM6d+4sPW8aGxuLTp06iejoaOkezWfQ6pWVlSUAiOHDhz813bBhwwQAcfPmTdGtWzdhZWUlbt269cT0O3fu1PmeoT89ep8vLS0VjRs3Fh07dpSeY3x8fETr1q2FEEIkJiYKACImJkYjj+3btwsAYv369UIIIR48eCDs7OxESEiIRrp//vOfQqlUijt37ryAIzMcz4o7CCGEnZ2daNWqlRBCiJYtWwp3d3fx4MEDjTT9+vUTDg4OQqVSCSGEGDNmjDA2NhZpaWnVV3g9YIs1A1dYWIgZM2agadOmUCgUUCgUMDc3R1FREX744Qet9AMGDNBYbteuHQDonG2FtP3nP/9Bamqqxmv//v14+PAh3nrrLY2WbKampvDx8dH5K+yIESM0lkNCQgCUR/rpyXx8fNCkSRNs2LABFy9eRGpqqs6ucABw6NAh+Pv7o06dOpDL5TA2NsacOXOQnZ2tNYtNu3bt0Lx58xdxCAYrLi4OtWrVwvDhwwEA5ubmGDJkCI4dO4aff/5ZI21gYCBMTU2lZQsLC/Tv3x9Hjx6FSqXSSMvWgRVTHdf+tGnT8P3330vjwuTn52Pz5s0IDQ3l7MXVICAgAHK5XFrm5y8Zgsrc+5/lypUryMrKwtChQzXWOzk5wdvb+7mVuaawsbHBsWPHkJqaioULF2LgwIH46aefMGvWLLRt2xZ37tzhM+hLQvwx1EJxcTGOHDmCoUOHol69enouVc2hVCoxb948nDlzRme3QnULtLCwMI31Q4YMgZmZmTQciUKhwMiRI5GQkCC1FlepVNi8eTMGDhwIGxub6j2QGkh97f/vf//Djz/+KN1fHr0f9e3bF5mZmbhy5QoAYN++ffDz80OrVq30Vu7qwMCagQsJCcGqVaswduxY7N+/H6dPn0Zqairq1auncwD2x28YJiYmADhYe0W1atUKHTt21Hip+4J7eHjA2NhY47V9+3atLrkKhULrPNjb2wMoH0+Dnkwmk2H06NH47LPPsHbtWjRv3hxdunTRSnf69Gn07NkTQPlYLsePH0dqaipmz54NQPt6d3BwqP7CG7D//e9/OHr0KAICAiCEQF5eHvLy8hAUFAQAGl0UgT+v58fX3b9/H4WFhRrrWfcVUx3X/sCBA+Hi4oLVq1cDKB+Ls6ioiN1Aqwk/f8nQVPbe/yzqZxw7OzutbbrWUbmOHTtixowZ2LlzJ27cuIH33nsP6enpiImJ4TNoNbO1tUXt2rXx22+/PTVdeno6ateuDYVCAZVK9cRuo1R1w4cPR4cOHTB79mytbtHZ2dlQKBRawUyZTAZ7e3uNa3vMmDEoKSnBtm3bAAD79+9HZmYmu4FWQVFREbKzs+Ho6Cjdiz744AOte9HEiRMBQLof3b59u0a+R7RHwySDcffuXXz11VeIiIjAzJkzpfWlpaXIycnRY8leLba2tgCAXbt2wdnZ+ZnpHz58iOzsbI0Hm6ysLADaX7xIW1hYGObMmYO1a9di/vz5OtNs27YNxsbG+OqrrzRaTu3du1dneplMVh1FrTE2bNgAIQR27dqFXbt2aW3ftGkT5s2bJy2rr+dHZWVlQalUarWEYt1X3PO+9o2MjDBp0iR8+OGHWLp0KdasWYPu3bujRYsW1XUINYqpqanO8dF0jW9K1YvnonpU9N6vvteoxzFSe7z+1c84ugan1vW5QdqMjY0RERGB5cuX49KlSxg4cCAAPoNWF7lcDj8/PyQmJuL69es6gwHXr1/H2bNn0adPH1hbW0Mul+P69et6KG3NJpPJsGjRIvTo0QPr16/X2GZjY4OHDx/i9u3bGsE1IQSysrLg4eEhrXNzc8Prr7+O+Ph4jB8/HvHx8XB0dJR+lKSK+/rrr6FSqeDr6yt9H541axYCAwN1plc/X9arV69GvkfYYs2AyWQyCCGkX73V/v3vf2t1t6Lq06tXLygUCvzyyy9ardnUr8d9/vnnGsvqAcR1zWpGmho0aIB//OMf6N+/P0JDQ3WmkclkUCgUGt2uiouLsXnz5hdVzBpDpVJh06ZNaNKkCZKTk7Ve77//PjIzM7Fv3z5pn4SEBJSUlEjLBQUF+PLLL9GlSxeNc0KVUx3X/tixY6FUKjFixAhcuXIFkydPrpay10QuLi746aefNIIJ2dnZOHHihB5L9WriuXj+KnPvV8/meeHCBY08vvjiC43lFi1awN7eXqsrV0ZGBs+VDpmZmTrXq4d6cXR05DPoCzBr1iwIITBx4kSt71cqlQoTJkyAEAKzZs1CrVq14OPjg507dz41sM8Wy1Xj7++PHj16YO7cuRo9ILp37w4A+OyzzzTS7969G0VFRdJ2tdGjRyMlJQXfffcdvvzyS4SGhvL5tJIyMjLwwQcfoE6dOhg/fjxatGiBZs2a4fz580+8F1lYWAAA+vTpg+TkZKlraE3BFmsGSiaTwdLSEl27dsXixYtha2sLFxcXHDlyBHFxcbCystJ3EV8ZLi4umDt3LmbPno1ff/0VvXv3Rt26dXHz5k2cPn0aZmZmiIqKktIrlUosXboUhYWF8PDwwIkTJzBv3jz06dMHnTt31uORGI5HpzTXJSAgAMuWLUNISAjGjRuH7OxsLFmyRCsITc+2b98+3LhxA4sWLdL50N2mTRusWrUKcXFxWL58OYDyX3h79OiB6dOno6ysDIsWLUJ+fr7G+4Cq5nlf+1ZWVnjrrbfw6aefwtnZGf3796+OYtco6laWo0aNwrp16zBy5Ej8/e9/R3Z2NmJiYmBpaannEr46eC6qT2Xu/f369YO/vz+io6NRt25dODs74+DBg0hISNDYx8jICFFRURg/fjyCgoIwZswY5OXlISoqCg4ODjAy4u/9j+rVqxcaNmyI/v37o2XLligrK8P333+PpUuXwtzcHNOmTeMz6Avg7e2N2NhYvPvuu+jcuTMmT54MJycnZGRkYPXq1UhJSUFsbCw6deoEAFi2bBk6d+4MT09PzJw5E02bNsXNmzfxxRdfYN26dbCwsECbNm0AlM+Sa2FhAVNTU7i6urLVYAUsWrQIr732Gm7duoXWrVsDAHr06IFevXphxowZyM/Ph7e3Ny5cuICIiAi4u7tj1KhRGnkEBwdj+vTpCA4ORmlpqdbYbKTp0qVL0nhpt27dwrFjxxAfHw+5XI49e/ZIrQTXrVuHPn36oFevXggLC0ODBg2Qk5ODH374AefOnZNmk1bPltu1a1d8+OGHaNu2LfLy8pCYmIjp06ejZcuW+jzcqtPXrAlUNatXrxYAxMWLF4UQQly/fl28+eabom7dusLCwkL07t1bXLp0STg7O4vQ0FBpvyfN6vGkmZxIU0VmRdm7d6/w8/MTlpaWwsTERDg7O4ugoCCRlJQkpQkNDRVmZmbiwoULwtfXV9SqVUtYW1uLCRMmiMLCwhdxKAanInUvhPbMiBs2bBAtWrQQJiYmonHjxiI6OlrExcUJAOK3336T0jk7O4uAgIBqKr3hGzRokFAqlU+d3Wr48OFCoVCIU6dOCQBi0aJFIioqSjRs2FAolUrh7u4u9u/fr7GPelbQ27dvV/chGKzqvvbVDh8+LACIhQsXPucjqFke//wVQohNmzaJVq1aCVNTU+Hm5ia2b9/+xFlBFy9erJUnOENllVT1XAjBOq+oytz7s7KyRGZmpggKChLW1taiTp06YuTIkeLMmTMas4KqrV+/XjRt2lQolUrRvHlzsWHDBjFw4EDh7u5ezUdlWLZv3y5CQkJEs2bNhLm5uTA2NhZOTk5i1KhRWrPp8Rm0+p08eVIEBQUJOzs7oVAoRP369UVgYKA4ceKEVtq0tDQxZMgQYWNjI5RKpXBychJhYWGipKREShMbGytcXV2FXC7X+T551T3tGSgkJEQAkGYFFUKI4uJiMWPGDOHs7CyMjY2Fg4ODmDBhgsjNzdWZvzoPb2/v6joEg6c+B+qXUqkU9evXFz4+PmLBggU6Px/Onz8vhg4dKurXry+MjY2Fvb296NatmzSztNq1a9fEmDFjhL29vTA2NhaOjo5i6NCh4ubNmy/q8J47mRB/TOVABmHatGlYtWoV8vLypOaUREQvg/T0dLi6umLx4sX44IMP9F0cqqD3338fn376Ka5du8Zfy5+Cn78vD56LmiUvLw/NmzfHoEGDtMZOoucnLCwMu3bt0ppEiIiI/jp2BTUQZ8+eRWpqKjZs2IABAwbwQZKIiP6SU6dO4aeffsKaNWswfvx4BtWegJ+/Lw+eC8OXlZWF+fPnw8/PDzY2Nrh69SqWL1+OgoICTJs2Td/FIyIiqhIG1gxEUFAQ7t69iwEDBuCTTz7Rd3GIiMjAeXl5oXbt2ujXr5/GrK6kiZ+/Lw+eC8NnYmKC9PR0TJw4ETk5OahduzbeeOMNrF27VhoviYiIyNCwKygREREREREREVEVcPodIiIiIiIiIiKiKmBgjYiIiIiIiIiIqAoYWCMiIiIiIiIiIqoCBtaIiIiIiIiIiIiqgIE1IiIiIiIiIiKiKmBgjYiIiIiIiIiIqAoYWCMiIiIiIiIiIqoCBtaIiIiIiIiIiIiqgIE1IiIiIiIiIiKiKvh/hb+XSm8GwQgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_nitrate_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY Nitrate with WY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.74045154, 23.38186726, 21.69020601, 10.37701929,  5.74879707,\n",
       "        2.70849594,  2.85380337,  5.48641413, 11.13281657, 15.64983911,\n",
       "       21.55740055, 23.13323183])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_silicon_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY Silicon with WY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.13663507, 51.513422  , 50.16886923, 27.00665494,  8.25802888,\n",
       "        7.09006067,  6.40147506,  9.4362604 , 22.08336301, 30.14843401,\n",
       "       41.93311685, 47.46025761])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Depth-integrated 0-100 m Diatoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Diatom data for original years\n",
    "\n",
    "monthly_array_diatoms_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 using higher temperature threshold       \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "                \n",
    "### 2019 using higher temperature threshold                \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/3403781678.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_orig_slice[monthly_array_diatoms_orig_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#years, months, data\n",
    "monthly_array_diatoms_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_river19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 10):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(10, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/11oct19_river08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "        \n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3499910/2320522072.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_depthint_slice[monthly_array_diatoms_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('WY Diatoms (0-100 m) with CY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 rivers')\n",
    "\n",
    "\n",
    "ax.set_title('CY Diatoms (0-100 m) with WY Rivers',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py39)",
   "language": "python",
   "name": "py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}