{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from salishsea_tools import viz_tools, places, visualisations\n",
    "from matplotlib import pyplot as plt, dates\n",
    "from datetime import datetime, timedelta\n",
    "from calendar import month_name\n",
    "from scipy.io import loadmat\n",
    "from tqdm.notebook import tqdm\n",
    "from salishsea_tools import nc_tools\n",
    "from dask.diagnostics import ProgressBar\n",
    "import cmocean\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 12, 'axes.titlesize': 'medium'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data for original cold and warm years\n",
    "\n",
    "monthly_array_temp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['votemper']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 original temp      \n",
    "# \n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)\n",
    "\n",
    "\n",
    "### 2019 original               \n",
    "# \n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/391916811.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_temp_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_temp_slice[monthly_array_temp_slice == 0 ] = np.nan\n",
    "monthly_array_temp_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_temp_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_temp_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data for Experiments 1 and 2\n",
    "\n",
    "\n",
    "monthly_array_temp_exp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['votemper']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "          \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "                \n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "                \n",
    "\n",
    "###             \n",
    "## Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/2338418733.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_temp_exp_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_temp_exp_slice[monthly_array_temp_exp_slice == 0 ] = np.nan\n",
    "monthly_array_temp_exp_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_temp_exp_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_temp_exp_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Degrees C')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_temp_slicemean[12,:],color='r',linestyle='-',label='Original WY')\n",
    "ax.plot(xticks, monthly_array_temp_exp_slicemean[12,:],color='k',linestyle='-.',label='WY with CY thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY SST with CY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,25)\n",
    "ax.set_ylabel('Degrees C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.09901394,  5.98979208,  7.70769097,  9.1698652 , 13.09307022,\n",
       "       16.58461295, 20.71957752, 19.56493846, 15.88781688, 12.09782938,\n",
       "        9.42774859,  5.93728831])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_temp_exp_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Degrees C')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_temp_slicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_temp_exp_slicemean[1,:],color='k',linestyle='-.',label='CY with WY thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY SST with WY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,25)\n",
    "ax.set_ylabel('Degrees C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.55805313,  4.6761362 ,  6.77186194, 10.27311911, 13.59493183,\n",
       "       17.42428301, 18.35690238, 18.73194292, 16.01386188, 11.39800505,\n",
       "        8.80717597,  6.78115408])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_temp_exp_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Surface PAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PAR data for original years\n",
    "\n",
    "monthly_array_PAR_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['PAR']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 original temp      \n",
    "\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)\n",
    "\n",
    "\n",
    "### 2019 original               \n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/2771304440.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_PAR_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_PAR_slice[monthly_array_PAR_slice == 0 ] = np.nan\n",
    "monthly_array_PAR_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_PAR_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_PAR_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PAR data for experiments 1 and 2\n",
    "\n",
    "monthly_array_PAR_exp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['PAR']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "            \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "                \n",
    "###               \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_carp_T.nc') as ds:\n",
    "            q = ds.PAR.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_PAR_exp_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['PAR']:\n",
    "                data[var].append(ds.PAR.isel(deptht=0, **slc).values)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/178454329.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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY PAR with CY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.21095929,  25.7881738 ,  40.84249474,  60.0591015 ,\n",
       "        84.93951999,  93.86293186, 101.22744323,  80.5323536 ,\n",
       "        61.02210488,  29.35150897,  13.13822587,  10.14679653])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '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 Thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY PAR with WY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.32649944,  26.11869233,  53.62521119,  62.5976493 ,\n",
       "        90.68618976, 103.30374366,  93.93976964,  79.87383634,\n",
       "        51.12972378,  31.54723225,  20.31534137,   8.03696216])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Halocline Strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Halocline Strength data for original years\n",
    "\n",
    "\n",
    "monthly_array_halocline_depth_orig_SSslice = np.zeros([14,12,50,50])\n",
    "monthly_array_halocline_strength_orig_SSslice = np.zeros([14,12,50,50])\n",
    "\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask,depth = [mask[var].isel(**slc).values for var in ('e3t_0', 'tmask','gdept_0')]\n",
    "years, variables = range(2007, 2021), ['halocline','strength']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "         # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_orig_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "          \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "         # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_orig_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_orig_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/1288103633.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_halocline_strength_orig_SSslice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_halocline_strength_orig_SSslice[monthly_array_halocline_strength_orig_SSslice == 0 ] = np.nan\n",
    "monthly_array_halocline_strength_orig_SSslicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_halocline_strength_orig_SSslice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_halocline_strength_orig_SSslicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data for Experiments 1 and 2\n",
    "\n",
    "monthly_array_halocline_depth_SSslice = np.zeros([14,12,50,50])\n",
    "monthly_array_halocline_strength_SSslice = np.zeros([14,12,50,50])\n",
    "\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask,depth = [mask[var].isel(**slc).values for var in ('e3t_0', 'tmask','gdept_0')]\n",
    "years, variables = range(2007, 2021), ['halocline','strength']\n",
    "\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "            \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "       # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    " \n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "            \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "       # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.vosaline.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            \n",
    "            sal=ds.vosaline.isel(**slc).values\n",
    "                      \n",
    "             #get the gradient in salinity\n",
    "            sal_grad = np.zeros_like(sal)\n",
    "\n",
    "            for i in range(0, (np.shape(sal_grad)[1]-1)):\n",
    "                sal_grad[:,i,:,:] =(sal[:,i,:,:]-sal[:,i+1,:,:])/(depth[:,i,:,:]-depth[:,i+1,:,:])\n",
    "\n",
    "            #print(sal_grad)\n",
    "\n",
    "            loc_max = np.argmax(sal_grad,axis=1)\n",
    "            depths=np.tile(depth,[np.shape(sal)[0],1,1,1])\n",
    "            h1=np.take_along_axis(depths, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            h2=np.take_along_axis(depths, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "            \n",
    "            sals=np.tile(sal,[np.shape(sal)[0],1,1,1])\n",
    "            s1=np.take_along_axis(sals, np.expand_dims(loc_max, axis=1), axis=1)\n",
    "            s2=np.take_along_axis(sals, np.expand_dims(loc_max+1, axis=1), axis=1)\n",
    "\n",
    "            #halocline is halfway between the two cells\n",
    "            halocline = 0.5*(h1+h2)\n",
    "            strength = (s2-s1)/(h2-h1)\n",
    "        \n",
    "            data['halocline'].append(halocline)\n",
    "            data['strength'].append(strength)\n",
    "                        \n",
    "            monthly_array_halocline_depth_SSslice[year-2007,month-1,:,:]=halocline\n",
    "            monthly_array_halocline_strength_SSslice[year-2007,month-1,:,:]=strength\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "        \n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/3661973807.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_halocline_strength_SSslice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_halocline_strength_SSslice[monthly_array_halocline_strength_SSslice == 0 ] = np.nan\n",
    "monthly_array_halocline_strength_SSslicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_halocline_strength_SSslice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_halocline_strength_SSslicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'g/kg m$^{-1}$')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_halocline_strength_orig_SSslicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_halocline_strength_SSslicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY Halocline with CY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,5)\n",
    "ax.set_ylabel('g/kg m$^{-1}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55982106, 0.33795863, 0.71550134, 1.10226626, 1.76050025,\n",
       "       2.69903492, 2.99644459, 2.0364869 , 1.33602564, 0.72712177,\n",
       "       1.38972946, 0.89137146])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_halocline_strength_SSslicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'g/kg m$^{-1}$')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_halocline_strength_orig_SSslicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_halocline_strength_SSslicemean[1,:],color='k',linestyle='-.',label='CY with WY thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY Halocline with WY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,5)\n",
    "ax.set_ylabel('g/kg m$^{-1}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Depth-averaged Nutrients (0-10m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for original cold and warm years\n",
    "\n",
    "\n",
    "monthly_array_nitrate_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "          \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/3312634990.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_nitrate_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slice[monthly_array_nitrate_orig_slice == 0 ] = np.nan\n",
    "monthly_array_nitrate_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_nitrate_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_nitrate_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Silicon data for original cold and warm years\n",
    "\n",
    "monthly_array_silicon_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['silicon']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "   \n",
    " ### \n",
    "## Experimental Year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "       \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/241793216.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slice[monthly_array_silicon_orig_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for Experiments 1 and 2\n",
    "\n",
    "monthly_array_nitrate_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)                \n",
    "\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "     \n",
    "        # # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/231329215.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slice[monthly_array_nitrate_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_nitrate_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_nitrate_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Silicon data for Experiments 1 and 2\n",
    "\n",
    "\n",
    "monthly_array_silicon_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['silicon']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    " # Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "        \n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "         \n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/3737416097.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slice[monthly_array_silicon_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 34,
     "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY Nitrate with CY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.64834195, 22.38691698, 16.99383033,  7.63730902,  4.96273598,\n",
       "        1.56547187,  1.23107567,  1.5230891 ,  7.95614177, 16.79074658,\n",
       "       19.52305174, 21.62759183])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.70228905, 22.39409214, 18.93542225,  8.06537268,  5.75761214,\n",
       "        1.78122465,  0.81580702,  2.03092249,  8.91424762, 16.00387333,\n",
       "       19.86077911, 21.75788525])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CNRERkSwQG4ps3bqVqKgoIPH6arFig7bY9t27d1urWZKzjpa9vb1119D0rNZxd3fn+eefZ8aMGRw8eJCAgADWrl1rXWfr1q1bdO3albCwsHS7Z1KuXbsGQPHixVN03Y0bNwAoXLjwPc9xcnIiT548Cc6/W86cOe95fWxl0b12SLyfu4Oz0NBQdu3aRc6cOalZsyb169fHycmJv//+2/o1lJL11dIiI15z7OfZz88vyd1bUyOjvvbTIjmfO0j4+bty5Yr145s3b3L9+vV7PmI/d/Er3uLLly9fmsYZf4wPOiepr4FFixZRoUIFvvjiC/bv309AQACurq7ky5eP/PnzkytXLoAEO4+KiIhkZwrWREREskBs8BEcHGythNq8eTOQsGIt/vPY0CT2z/iVb9mBk5MTLVq0YOnSpfTt2xcwq05Wr16dKfdPbaVTcq9Lz0qq5IgfvkZGRrJt2zbCw8Np3LgxdnZ2ODk5Ua9ePYKDg61h665du4DkVTJmNxUrVgQgPDycY8eOZfFospf4m5xcu3YNwzAe+OjXr1+SfcVWYGYFX19f+vXrR3h4ON7e3mzatIk7d+4QEBDA9evXuXbtGn/++WeWjU9ERCQ1FKyJiIhkgfhT9TZt2sTRo0e5ceMGpUuXTrT+0d0Va7F/NmzY0LpWW3YzcOBA68dJ7TiYnmKnf547dy5F18VW7ly8ePGe54SFhVnX/oqdUphZYgO0kJAQdu3alWAaaKz4VW1bt24lIiICFxeXe07Ry86aN29u/XjRokVZOJLsp0CBAtaP77XW38Ng5cqVBAYGkitXLpYtW0aTJk1wdnZOcE5sBaqIiMjDQsGaiIhIFoi/uPzGjRuTXF8tVqlSpShSpAiXL1/myJEj1vXVMnq6X1q4urpaP87Itb7ADCnBXAPt6tWryb6uZs2aAKxfv/6e52zatMk6zTKzwypXV1frGDds2JBg44JY8TcwiG1PTeAau1lA7DpnWaFWrVrWCswpU6Zw69atZF2XXtNGs7Mnn3wSNzc3AH7//fcsHk3qxYbYZcuWtU7Rvdu6desyc0giIiJppmBNREQki8QGY9u2bWPt2rVA4vXVYsUGbhMmTCA0NDTB9Znp7NmznDhx4oHn/fzzz9aPq1evnpFDonv37ri5uREVFcWwYcOSHQ49++yzAOzYsYM1a9Ykao+KiuLDDz8EzGDjySefTL9BJ1NscLZ06VL27t1L7ty5qVKlirW9du3auLi4sGPHDlatWgWk7usiNrTx9/dP+6DTYOLEidja2nL9+nW6du1KQEDAfc+/dOkSnTp1ypzBZSE7OzsGDBgAmP+2HrRjZko3jsgssevcnThxIsm1Fw8ePMjcuXMze1giIiJpomBNREQki8QGICEhISxbtgxIumIt/vHffvsNMBcNr1GjRiaMMqEjR45Qvnx52rVrx+zZsxNMv4yMjOTAgQP079+fL7/8EjCDn4YNG2bomNzd3fnss88AmDdvHp07d+bgwYPWdj8/P1asWEHHjh0JDAy0Hu/atat1d8sePXowd+5c62LrZ8+epWvXrtbqwNj+M1vs18i+ffuIioqiadOmCdZ6s7e3p2HDhoSFhfHPP/8kuCYlYkPDI0eOWHdZzQqNGjVi0qRJWCwWtmzZQuXKlfn2228T7BAZGRnJ9u3beeONN3jiiSfYsmVLlo03M73//vuULl2aqKgoWrduzZdffsnNmzet7QEBAaxevZq+ffvSqFGjLBzpvbVs2RIbGxtu377Nc889x+XLlwFzp+M//viDli1b3ndzBxERkexIwZqIiEgWqVu3rnV9oejoaEqWLEmxYsWSPDc2WItdxLxRo0YJdufLLPb29sTExLBy5Ur69u1LyZIlcXR0JHfu3Dg6OlK9enVmzZoFmJVqixYtsk4zzEiDBg3ik08+wcbGhiVLllCtWjVy5MiBm5sbnp6etG/fnqVLlyaYNmhra8uCBQuoWLEiAQEBPPfcc7i6upIrVy5KlSrF0qVLsbGxYdKkSbRp0ybDX0NSGjRogIODg/V5UpsSxA/S3NzcUhW4Nm3alLJlyxIdHU2DBg3w9PSkRIkSlChRgvnz56du8Kk0ZMgQFi1aRMGCBblw4QKvvPIKRYsWxdnZGU9PTxwdHWnQoAGTJk0iMjKS/v37Z+r4soqnpydr166lSpUqhISE8Oabb5IvXz5y5cqFu7s7Hh4etGnThtmzZxMREZHVw02Sl5cXb7/9NgALFy6kSJEieHh44OrqyjPPPIOrqyvffPNNFo9SREQkZRSsiYiIZBEHBwfr+mBw72o1gCeeeCLBAuZZtb5aq1atOHnyJJMmTaJ79+6UL18eR0dH/P39yZEjB15eXvTo0YPff/+dPXv2UKhQoUwb26hRo/jnn3946aWXKFOmDGCuGVa2bFl69uzJwoULrVMeYxUuXJi9e/fy5ZdfWoPOO3fuULRoUXr37s2+fft47bXXMu013M3Z2dm6Fh8kHazFP9aoUaNU7fpoZ2fH+vXrefHFFylRogQhISGcP3+e8+fPExwcnLrBp0HHjh05c+YMM2bMoEuXLpQoUQJbW1tCQkLIly8fzZs35+OPP+bMmTN89dVXmT6+rFKyZEn27t3L7Nmzad++PQULFiQkJISIiAhKlixJ586d+emnn6yVltnRhAkTmD17NrVr18bZ2ZnIyEjKlCnDu+++y4EDBzL1e4aIiEh6sBhZuUqtiIiIiIiIiIjIQ0oVayIiIiIiIiIiIqmQrYO1rVu30rZtW3LlyoWzszNeXl7873//S3DO/v37adGiBa6urnh4eNClSxfOnDmTRSMWEREREREREZHHRbYN1ubOnUuTJk1wd3dn9uzZrFy5khEjRhB/5qqPjw9Nmza17iT0008/ceLECRo1apRglyQREREREREREZH0li3XWLt8+TJly5alT58+TJs27Z7n9ejRg40bN3L69GnrYsTnz5/Hy8uLYcOG8emnn2bWkEVERERERERE5DGTLSvWfvjhB0JCQhgxYsQ9z4mKimL58uV07do1wQ5fxYsXp1mzZixatCgzhioiIiIiIiIiIo+pbBmsbdmyBU9PT3x8fKhatSp2dnbky5ePwYMHExgYCMDp06cJDQ2lcuXKia6vXLkyp06dIiwsLLOHLiIiIiIiIiIijwm7rB5AUi5fvsydO3fo3r07o0aN4uuvv2bPnj2MHTuWw4cP8/fff+Pr6wuAp6dnous9PT0xDAM/Pz8KFiyYqD08PJzw8HDr85iYGG7fvk3u3LmxWCwZ98JERERERERERCTbMwyDoKAgChUqhI3NvevSsmWwFhMTQ1hYGGPHjmXkyJEANG3aFAcHB9544w3Wr19Pjhw5AO4bhN2rbfz48YwbNy79By4iIiIiIiIiIo+MixcvUqRIkXu2Z8tgLXfu3Jw8eZJWrVolON6mTRveeOMN9u/fT8eOHQGslWvx3b59G4vFgoeHR5L9jxo1iuHDh1ufBwQEUKxYMS5evJhgvTYREREREREREXn8BAYGUrRoUXLmzHnf87JlsFa5cmV27tyZ6HjsBqY2NjaULl0aZ2dnDh06lOi8Q4cOUaZMGZycnJLs39HREUdHx0TH3dzcFKyJiIiIiIiIiAhw/5mSkE03L+jatSsAq1atSnB85cqVANStWxc7Ozs6dOjAwoULCQoKsp5z4cIFNm7cSJcuXTJvwCIiIiIiIiIi8tixGLFlYNnM008/zZo1axg9ejR169Zl7969jBs3jhYtWrBs2TIAfHx8qFWrFtWrV2fkyJGEhYUxZswYbt++zcGDB8mbN2+y7hUYGIi7uzsBAQGqWBMRERERERERecwlNyvKtsFaaGgo48aNY+7cuVy9epVChQrx3HPPMXbs2ATTOPft28eIESPYsWMHdnZ2eHt7M3HiREqXLp3seylYExERERERERGRWA99sJaZFKyJiIiIiIiIiEis5GZF2XKNNRERERERERERkexOwZqIiIiIiIiIiEgqKFgTERERERERERFJBQVrIiIiIiIiIiIiqaBgTUREREREREREJBUUrImIiIiIiIiIiKSCgjUREREREREREZFUULAmIiIiIiIiIiKSCgrWREREREREREREUkHBmoiIiIiIiIiISCooWBMREREREREREUkFBWsiIiIiIiIiIiKpoGBNREREREREREQkFRSsiYiIiIiIiIiIpIKCNRERERERERERkVRQsCYiIiIiIiIiIpIKCtZERERERERERERSQcGaiIiIiIiIiIhIKihYExERERERERERSQUFayIiIiIiIiIiIqmgYE1ERERERERERCQVFKyJiIiIiIiIiIikgoI1ERERERERERGRVFCwJiIiIiIiIiIikgoK1kRERERERERERFJBwZqIiIiIiIiIiEgqZMtgbdOmTVgsliQfO3fuTHDu/v37adGiBa6urnh4eNClSxfOnDmTRSMXEREREREREZHHhV1WD+B+PvnkE5o1a5bg2JNPPmn92MfHh6ZNm1K1alX++OMPwsLCGDNmDI0aNeLgwYPkzZs3s4csIiIiIiIiIiKPiWwdrHl5eVG3bt17to8ZMwZHR0eWL1+Om5sbADVq1MDLy4uJEyfy6aefZtZQRURERERERETkMZMtp4ImR1RUFMuXL6dr167WUA2gePHiNGvWjEWLFmXh6ERERERERERE5FGXrYO1IUOGYGdnh5ubG61atWLr1q3WttOnTxMaGkrlypUTXVe5cmVOnTpFWFhYZg5XREREREREREQeI9lyKqi7uzuvv/46TZs2JXfu3Jw6dYrPP/+cpk2bsmLFClq1aoWvry8Anp6eia739PTEMAz8/PwoWLBgovbw8HDCw8OtzwMDAzPuxYiIiIiIiIiIyCMpWwZr1apVo1q1atbnjRo1onPnzlSqVIl33nmHVq1aWdssFss9+7lX2/jx4xk3blz6DVhERERERERERB472XoqaHweHh60b9+ef//9l9DQUHLnzg1grVyL7/bt21gsFjw8PJLsa9SoUQQEBFgfFy9ezMihy2MiOjqawMBArly6xMmDBzm2bx8BAQFZPSwRERERERERySDZsmLtXgzDAMxKtNKlS+Ps7MyhQ4cSnXfo0CHKlCmDk5NTkv04Ojri6OiYoWOV7CsqKoqQkBBC/PwIvnmTkFu3CPb1NZ/fvk2wvz+5HRzoUK4chIRAcDBvLFrEzcBAPq9enUIAwcF8fewYX58/T0h0NMExMYT99/V5t5yOjhQtUoQipUtTqVIlJk6caG27ePEiuXLlwtXVNXNevIiIiIiIiIikm4cmWPPz82P58uVUrVrVGph16NCBhQsX8tlnn5EzZ04ALly4wMaNGxk2bFhWDlfSQVRUFCFBQQTfukXIzZsExw/A/PwICQigoLMzrUqXhuBgjOBgBi1YQHBoKNNq1sQjMhJCQvjf0aP8ePUqwdHRBEdHE/7gW9MQ6BDv+R/AVeBtHx8zWAPuAOeTuNYGcAUsQAAQFB7O0dOnOXr6NLcOHoR27aBhQ7C3p3nz5pw8eZLNmzfTuHFjAP7++2+2bNlCkSJFKFq0KEWLFqVIkSI4Ozun7hMpIiIiIiIiIhkiWwZrvXr1olixYtSsWZM8efJw8uRJvvjiC65fv86sWbOs540bN45atWrRvn17Ro4cSVhYGGPGjCFPnjy8+eabWfcCHjORkZGE+PsTfP06Ib6+ZhB2+7YZfvn5ERwQQImcOWlWtCiEhBDq58drK1YQHBrK7Fq1sA8NheBg3jl6lHm3bhEcHU1ITEyyArB2QOyKexZgDhAKfHLyJB7/HQ/i/gGYK+BiY4OrrS2udna42NvzpJsbVKoErq7g4sK7p08TaW9PgRo1oGBBcHGhd1gYzcPDcc2VC5fcuXHNnRvXvHlx9PTE4uoKfn6ELFzIpQULuLhjBxcjI8lx4wZ4e4OHB0br1gTdvAlA4cKFreNas2YNH330UaLx5smTxxqyxQZusY8SJUpQrFix5Px1iYiIiIiIiEg6sRjGPeavZaEJEyYwb948zp49S3BwMJ6enjRs2JBRo0ZRq1atBOfu27ePESNGsGPHDuzs7PD29mbixImULl062fcLDAzE3d2dgIAA3Nzc0vvlZBuREREE37hhTn28edMMv3x9zQDM35/ggADKenjQoGBBCA7m9q1bvLNuHRHh4cyuXh2CgyEkhMFHj7LS35+QmBiCY2KISMa9n8MMvQAigNiJuH5gDcAGAd8nca0t/wVgFos1AHOxs8PVwYHauXLxQeXK4OICrq584eODraMjfevVI1fevODiwtk7d7gZEYGLp6cZfuXOjUv8AMzBIfWf1OQKDoZ162DpUli+HP4L1AACbW1xadIE26efhqefZtHBgyxbtoyLFy9aH3fu3Llv9zVq1GDv3r3W50OGDMHW1pZ33nmHIkWKAHDnzh0cHByws8uWebqIiIiIiIhItpHcrChbBmuZ7ZEM1vr14/klS9gSFERwTAzBhkFkMi57BZj638c3gXz/fRyFGXABPIM5NfJudvxX/WWxmOGXrS2u9va42NvTLF8+3o5XAfbJv/+Sw8WFlxo3xiV3bnBx4VRgIH4xMbjkymWGX3ny4Jo3Lw6enlhy5ACbh2avjfuLjoZdu2DZMjNoO3o0YfuTT8LTT0OHDlC7NobFgp+fH5cuXUoQtsU+Ll26RM2aNfntt98Acy3CHDlyEBYWxqlTp6wh8+jRoxk/fjwFCxZMUO12dwVc/vz5sbW1vXvUIiIiIiIiIo8NBWsp8EgGaw0b0nrbNv5Koik2AHO1sTHDr9gAzMGBtoUKMaRSJXBxIdzRkYmHD+Pq6sqQFi2wc3MDFxd8/PwIAjP88vTENW9eXPPlwyFXrsyp/nrUnDplhmzLlsGWLWbwFit/fmjf3gzaWrSAHDke2F1UVBTTp0/n4sWLjBs3zrpRR//+/RNMpb4XOzs7ChcuTJEiRWjatGmCaamHDh0iX7585MuXD4vFkuKXKiIiIiIiIvIwULCWAo9ksLZ9O0eOHCHU1jYuAMuXD5e8eXHw8Hh0qr8eNX5+sGqVWcm2ahUEBsa1OTmZ4drTT5thW8GCKeo6JiaG69evJ6h0u7v67cqVK8TExFiv6dSpE4sWLQISVsKdPn2aUqVKATB//nz27duXqPotd+7cCt9ERERERETkoaRgLQUeyWBNHn4REfD332bItnQpnDuXsL1WLTNke/ppc6OFdAixoqKiuHbtmjVoy5MnD97e3gAEBARQvnx5bty4QUhIiLUSrm/fvsyePTtRX87Oztag7e7ppk888QRlypRJ83hFREREREREMoKCtRRQsCbZnmHAkSNxIduuXQnbixc312R7+mlo0iRDp+RGRkZib29vfT5v3jy2bduWoPLtxo0b9+2jc+fOLFy4EDAr4dq3b0+BAgX48ssvcXd3B+DWrVs4OjqSM2fODHstIiIiIiIikgzR0eYmfFeuwNWr9/7z0CHIlSurR5suFKylgII1eehcu2buLrpsGaxdC6GhcW05c0KbNmbQ1rYteHpm+vDCwsK4fPnyPTdcaN++vXXttps3b5IvXz7rdXdXwrm7uyeodkuqAs7Z2TnTX6OIiIiIiMhDL6nALKnQ7Pr1hOuB38uRI1ChQsaPOxMoWEsBBWvyULtzB9avNyvZli83Q7dYtrbQsGFcNZuXV9aN8x6CgoJYsGABvr6+vPnmm9bj7du3Z8WKFcnqI3fu3BQtWpQePXowatSojBqqiIiIiIjIwyE6Gm7cuHd1WezHyQ3MwFx+KH9+c73vQoXi/oz/ccWK8IgUPihYSwEFa/LIiImBvXvNkG3ZMvj334Tt5crFrctWt64ZvGVjQUFB991s4eLFi4SEhFjPf/311/n6668BCA8PZ+LEibz11lvWKjgREREREZGH2t2B2b2mZF67Zv5+mBw2NpAvX8KALKnQLF8+sLPL2NeXjShYSwEFa/LIOnfODNiWLoVNmyAqKq4tTx5o184M2Vq2BFfXrBplqhmGQUBAgDVkK1y4MFWqVAHgmWee4Y8//qBbt278+eefWTxSERERERGR+4gNzO5XXRY7JTMlgVlSFWZ3h2YpCMwiIyM5d+4cJ0+e5MSJE9bH2bNn8fHxSbAe98NOwVoKKFiTx0JAAPz1lxmyrVgB/v5xbQ4O4O1thmwdOkCRIlk2zPSyfv16a7gWu7OpiIiIiIhIpoofmN1v4f/UBGZJhWXxQ7O8eVNVYWYYBpcuXeLEiRM0bNjQOgNo9OjRfPrpp0TFL9iI58SJE3hlw+WHUkvBWgooWJPHTmQkbNtmVrMtWQKnTydsr149bl22atXMufQPoaCgIO0qKiIiIiIi6S8qKnlTMlMamBUocO+wLP6UzDQu62MYBrdu3bJWnFksFvr162dt9/T0xM/Pj3///ZdKlSoBMHHiRN5++22cnZ154okn8PLy4oknnrA+qlWrhpOTU5rGlZ0oWEsBBWvyWDMM8PGJW5dt+3bzWKwiRcyQrUMHaNYMHtJvlEePHmX06NH8/PPPCttERERERCRpsYHZg6Zk3riR+sDsXqFZOgRmdwsMDEwwbTP+xwEBAdbzvLy8OHHihPV5o0aNuHHjBj/99BMNGjQA4NatW4SFhVGoUCFsbGzSdZzZkYK1FFCwJhLPjRuwcqUZtK1ZA/E2B8DFBVq1MivZ2rY1S4sfAtHR0Tz55JP4+PhQt25dVq9ejbu7e1YPS0REREREMkv8wOx+UzJTEpjZ2iaekplUaJY3b4ZuHBcWFoaNjQ0ODg4ArFixgk8//ZQTJ05w/fr1e15nsVgoWrQoTzzxBBUqVGDSpEnWNsMwsDykM5fSi4K1FFCwJnIPYWGwcaMZsi1dav5HE8tigfr143YZLVs2W08Z3bdvH0899RR+fn7UrFmTNWvWkCtXrqweloiIiIiIpEV0tDnd8u7ALKkpmcmNP2xtk56SeXdolsGBWXxRUVGcP3+eK1eu0KhRI+vxdu3asWrVKpYuXUr79u0B+PPPP+nRo4f1nHz58iWYshk7hbN06dI4OztnyvgfRgrWUkDBmkgyGAYcOBAXsh04kLC9TJm4kK1Bg2y5DfM///xDixYtuHXrFlWrVmXdunXkzp07q4clIiIiIiJ3Mwzw84sLyS5fTvhn7MfXrqWswqxAgQcv+p8nT6YFZvEZhsGVK1cSTdk8efIkp0+fJjIyEicnJ0JCQqxTMXv06MGff/7J119/zeuvvw7AlStX2LRpkzVE02yd1FGwlgIK1kRS4eJFc022ZctgwwaIiIhry5XLnCr69NPQujVko39Xhw8fpnnz5ty4cYNKlSqxbt068uXLl9XDEhERERF5fNy5c+/ALH5wFhaWvP7iV5gVLnzv0CyLArN7OXPmDD/99FOCAO3OnTv3PN/JyQkvLy82btxoLRC4cOECTk5O5M2b97GfupneFKylgII1kTQKCjLXY1u6FFasAF/fuDZ7e2ja1AzZOnSA4sWzbJixjh07hre3N9euXaNChQqsX7+eAgUKZPWwREREREQebpGR5pTLu6vK7v7Y3z/5febObYZihQsn/DP+xxmw6H9aBQcH4+LiYg27Jk2axG+//cagQYPo378/ALt27aJu3boJrrO1taVUqVKJdtz08vKiSJEij8WmAdlFcrOi7DdXS0QePjlzQteu5iM6GnbsiJsyevw4rF1rPoYOhcqV46aM1qhh7pCTycqXL8/mzZvx9vbm6NGjNGnShA0bNlC4cOFMH4uIiIiISLZnGOab50mFZPGPpWQdMxeXpEOy+McKFgQnp4x9bWkQHh7OmTNnktxx8+rVq1y9etX6Bv6lS5fYtWsXderUsV5ftmxZBg8enGDds5IlS2Jvb59VL0lSQRVrqGJNJEOdOGFOF126FLZuTbj+QYECZhXb009D8+aQyQtnnj59Gm9vby5cuEDp0qXZuHEjRYsWzdQxiIiIiIhkqeDgBwdmV64kXPrlfuzs4oKx+1Wa5cyZrTc/iy80NJQtW7YkCM5OnDjB+fPnibnP+m5bt26lQYMGABw8eJBTp05RrVo1SpcunVlDlzTQVNAUULAmkkl8fWHVKjNkW73anEIay9kZnnrKDNnatze3rc4E586dw9vbm7Nnz1KyZEk2bNhAiRIlMuXeIiIiIiIZJiIi4Q6Z91r8P/7P5A+SL9+DA7M8ebJkVkp62bFjB4sWLaJixYr07dsXgGvXrlGwYMEkz8+ZM2eCirPYj728vMiVK1dmDl3SWYYEa2FhYZw8eZLSpUuTI0eOBG3btm2zJrEPGwVrIlkgPBw2b46rZrtwIa7NYoHateOmjFasmKHvZl28eBFvb29OnTpF7969mT17dobdS0REREQkTWJi4ObNBwdmN28mv083t3uvXxb7cYEC4OCQca8rE9y+fTvRbpsnTpxgxowZ1KhRA4ApU6YwdOhQOnbsyOLFiwFzt866detSuHDhRAFa/vz5tWnAIyrdg7UdO3bw9NNPExMTQ1hYGO+//z4jR460tru5uREYGJj2kWcBBWsiWcww4N9/zYBt2TLYsydhe8mScVNGGzc2N0RIZ1euXGH06NFMnjwZFxeXdO9fREREROS+DAMCA++94H/sx1evQlRU8vp0cLh3YBb/mKtrxr62TBQSEsLJkyeTDNB842+yFs8vv/zC888/D8C+ffuYPXs29erV49lnn83MoUs2k+7BWv369Rk8eDB9+vTBx8eHPn368OSTT/LDDz9gY2NDzpw5CUpJCWk2omBNJJu5cgWWLzeDtvXrE26z7e4ObdqYQVubNpBB5dWGYXDz5k3y5cuXIf2LiIiIyGMkLMwMxB60lllISPL6s1jMpVMeFJjlzv3QrGOWGoGBgVy7do0nnngCgJiYGFxdXQkNDb3nNUlVndWpU4f8mbQUjTw80j1Y8/DwwD/elrihoaF0794dBwcHfv/9d3Lnzq1gTUTSX0gIrFtnhmzLl8ONG3FttrZmBdvTT5tBWzouAjpu3DimTp3K+vXrqVSpUrr1KyIiIiKPoKAg2LULTp1KOjC7R6VUkjw8HhyYFShgbhLwGDEMg8jISBz+m466cuVK2rdvT40aNdgTb8ZLpUqVuHr1apLrnpUpUwbXR6g6TzJWugdrxYoVY8eOHRQuXNh6LCoqij59+nDt2jV27dpFSHLT9WxGwZrIQyImBnbvNkO2pUvhyJGE7RUqxK3LVru2GbylQmhoKA0aNODAgQNMnTqVV155JR0GLyIiIiKPjJs3zR3vt2yBv/+GgwchOvr+1zg53XvB/9g/CxaEu9Yzf1zduXOHvXv3smPHDrZv386OHTsYPny4dUmqCxcuULx4cby8vDh69Ch2/wWNd+7cSbQmvEhqpHuwNmDAAEqVKsXo0aMTHDcMg4EDB/Ljjz/ed5vZ7EzBmshD6syZuM0PNm9O+MNMgQLw00/mdNFU8PPzY+nSpdadgERERETkMWUYcP68GaDFPnx8Ep9XogRUqZI4PIv908PjkZ6WmVYXL15k+/bt1hDtwIEDRN21ltzdGwpcv36dAgUKZMFo5XGQ7sFaREQEUVFR90x+L1y4QLFixVI32gf44YcfeOmll3BxcSE4ODhB2/79+3nnnXfYuXMndnZ2eHt7M3HiREqVKpXs/hWsiTwC/Pxg9WozZFu1CgICzE0Ofv0VundPc/f+/v6cPXuWatWqpcNgRURERCTbiomBY8fiQrQtW+DSpcTnVawIjRrFPYoWzfyxPsSOHj3KmjVrrGHa5cuXE51TsGBB6tevb31Uq1YNR0fHLBitPI7SPVjLKpcvX6ZixYq4uLgQEBCQIFjz8fGhdu3aVK1alZEjRxIWFsaYMWPw8/Pj4MGD5M2bN1n3ULAm8ogJD4d+/eD338HGBn74Afr3T3V3gYGBtGzZkqNHj7Jq1SoaNGiQfmMVERERkawVGQkHDsRN69y6FW7fTniOnR1Ur26u79uoETRoYG4MIMly/fp1duzYgbe3t/V37nfffZfx48dbz7G1taVq1arUq1fPGqQVK1YMi6r8JIskNytK02qHY8eOZdy4cWnp4oEGDx5M48aN8fT0ZP78+QnaxowZg6OjI8uXL7e+yBo1auDl5cXEiRP59NNPM3RsIpJNOTrCnDnmtuE//AADBpgLyr72Wqq6s7W1xdnZmaCgIFq1asXKlStp3LhxOg9aRERERDLFnTuwc2dcRdqOHeax+JydoV69uGq0unXBxSVrxvuQiY6O5vz58wlmkTVq1IiTJ0+ycuVK2vy3VIu3tzeHDh2yBmm1atXCRZ9jeQilqWItR44c3Ln7G1A6mjNnDq+88gpHjx5l9OjRzJ8/31qxFhUVhZubG3369OG7775LcF2rVq04e/YsJ06cSNZ9VLEm8ogyDHjrLfjyS/P5Rx/Bu++mam2LO3fu0LFjR9atW4ezszPLly/H29s7nQcsIiIiIunu9m3Yti1uWue+fXDX2l3kygUNG8YFadWrw3+7T8r9+fv7s3PnTuuUzl27dlmP2/63mdiAAQPYs2cPH330ER07dszK4YokW6ZUrGXkLNIbN27wxhtvMGHCBIoUKZKo/fTp04SGhlK5cuVEbZUrV2bt2rWEhYXh5OSUYWMUkWzOYoGJE8HdHcaOhdGjzbXXPv00xeFajhw5WLZsGV26dGHVqlW0a9eOJUuW0LJlywwavIiIiIikyqVLCTcaOHw48TmFC8dN62zUyNxd3sYm88f6kDEMg+PHj1s3GNi+fTtHjx5NdF7OnDkTVK398MMP2OjzK4+oNAVrGTnX+ZVXXqFs2bK8/PLLSbb7+voC4OnpmajN09MTwzDw8/OjYMGCidrDw8MJDw+3Pg8MDEynUYtItmOxwJgxkDMnDB8On39uTgudOjXFPzw5OTmxaNEiunfvzrJly+jQoQMLFy6kXbt2GTR4EREREbkvw4ATJxIGaWfPJj6vbNmEGw2UKKEdOpPJ39+fqVOnsn37dnbu3Mntu9efA7y8vBKsjVahQgVrtRqgUE0eaWkK1jLKggULWLZsGQcOHHhgeHe/9nu1jR8/PsPXhhORbGbYMHPNtUGD4LvvIDgYZs40F6JNAUdHR+bPn8+zzz7LokWL6Ny5M3/++adK2kVEREQyQ3Q0/PNPwiDtxo2E59jYQNWqcSFaw4aQP3+WDPdhc+3aNdavX4+zszNdunQBwM7OjjFjxhATEwOYbzbXrl2b+vXrU69ePerVq5fsjQNFHkXZLlgLDg5myJAhDB06lEKFCuHv7w9AREQEYKbl9vb25P5vB5bYyrX4bt++jcViwcPDI8l7jBo1iuHDh1ufBwYGUlRbI4s8+l56yaxc693b3NwgONjcOTSFW3Y7ODgwb948nn/+ef744w+6devG77//TteuXTNo4CIiIiKPqbAw2L07LkTbvt2cfRCfoyPUqRMXpNWrB1o7+4HCwsLYt28fxYsXty6/tHbtWvr06UO9evWswZqrqytvvvkmRYoUoX79+lSpUgV7e/usHLpItpKmzQucnZ0JDQ1Nz/Fw7tw5SpYsed9zOnbsyPz583Fzc6Nv3758++23Cdpbt27NmTNntHmBiCRt+XLo1g3Cw+Gpp2DRolTt8hQVFUXfvn2ZO3cutra2zJkzh2effTYDBiwiIiLymAgIMMOz2CBt9274r8jCys0NGjSIC9Jq1UrxG6WPoytXrlg3GNixYwf79u0jMjKSiRMn8uabbwJw5swZevXqRdOmTZkwYUIWj1gka2XK5gUNGjRIy+VJKlCgABs3bkx0fMKECWzevJlVq1aRJ08e7OzsrOsbffbZZ+TMmROACxcusHHjRoYNG5buYxORR0T79rBqFXToAGvXQsuWsGIF3KPK9V7s7OyYPXs29vb2/Pzzzzz33HPY2trSvXv3jBm3iIiIyKPm+vW4EG3LFvj3X/hvyqFV/vxxIVrjxlCpEsRbv0sSi4yM5J9//rFuMLB9+3YuXLiQ6Lz8+fMTHR1tfV6qVCl27tyZmUMVeeilqWItM/Xr14/58+cTHBxsPebj40OtWrWoXr06I0eOJCwsjDFjxnD79m0OHjyY7HneqlgTeUzt3Alt2oC/P1SrBn/9BalYHyImJoZBgwaxevVqNm/ebN39SERERETiMQxzY4EtW+LCtJMnE59XqlTCHTvLlNFGA8k0Y8YMfv31V3bv3p1odpmNjQ2VK1e2ro1Wv359SpYsmaGbEoo8zDKlYi2rlStXjk2bNjFixAi6deuGnZ0d3t7eTJw4UYsnisiD1a0LmzaZFWsHDpg/wK1bZ26/ngI2NjZMnz6dGzduUKBAgYwZq4iIiMjDJiYGDh9OuNHAlSsJz7FYzAq0+Dt2FiqUNeN9iMTExPDDDz+wfft2vvnmG+sv/SdOnGDz5s0A5MqVy7q5QP369alVq5Z1ppeIpJ90qVhbvHgxv/76K+fPnycsLCzhDSwW/vnnn7TeIkOpYk3kMXf8OLRoAZcuQcmSZriWxqqzZcuWcf78eV599dV0GqSIiIhINhcRAfv2xU3r3LbNnBkQn7091KwZN62zfn3IlStLhvuwCAgIYNeuXVy/fp3evXtbj5csWZJz586xdu1aWrRoAcC+ffv4559/qFevHmXLlsXGxiarhi3y0Mu0irXPP/+cESNGkDdvXsqUKYNLKhYAFxHJUmXLwtatZrh26pS5Jfu6dVChQqq68/HxoVu3bkRERFCyZEnatWuXzgMWERERyQaCg2HHjrhqtF274O7N7VxczPAsthqtdm3IkSNrxvsQMAyDkydPJlgb7ciRIxiGgYeHB88995w1LBs4cCB37tyhWLFi1utr1KhBjRo1smr4Io+lNFeslSxZkubNmzN9+nRsH9IFJFWxJiIAXLtm7hJ6+DDkzm2uuZaKH0wMw2D06NEcP36c33//HTu7h3rWvYiIiIjp1i3zzcjYIG3/foi38D0AefKYb1LGBmnVqoF+FrqnO3fusGfPHmuQtmPHDm7dupXovJIlS1K/fn2mTp2Ku7t7FoxU5PGT3KwozcGam5sbixcvxtvbOy3dZCkFayJidfs2tG4Ne/aYW7mvWGH+cJhChmEQExNjfcPBMAwtDCsiIiIPlwsXEu7YeexY4nOKFYub1tmoEZQrp40GksHHx4fevXtz8OBBoqKiErQ5OjpSs2ZN6yYD9erV0zq+Ilkg06aCNmjQgGPHjj3UwZqIiJWnJ6xfDx06wObN5sYGixebf6aAxWKxhmoxMTEMHDiQggUL8uGHHypgExERkezHMMzgLP5GAxcuJD6vQoWEGw3Em4YoSVu7di3ff/89tWvX5u233wagQIEC7N27F4BChQpRv359a5BWrVo1HB0ds3LIIpICaQ7Wvv76azp37kzRokVp3bo1Dg4O6TEuEZGskzMnrFwJ3brBqlVmyPb779C5c6q627BhAz/++CMAkZGRjB8/XuGaiIiIZK2oKHNX9NgQbetWc6pnfLa2UL16XIjWsKE51VOSdOvWLf7++2+2b9/OgAEDKF++PAAXL15k/vz53LhxwxqseXh4sGzZMipXrkzRokX1s6HIQyzNwVqZMmVo0aIFnTt3xmKxkOOuhSgtFgsBAQFpvY2ISObKkcOsVHv+efjzT+jeHWbOhHg7MSVXixYt+Prrr3njjTf49NNPiYyMZOLEifoBSkRERDJPaKi5uUDstM4dOyAkJOE5Tk5Qt27ctM66dcHVNWvG+xDZtWsXkydP5o8//iAyMhKAYsWKWYO15s2bM378eBo3bpzguvbt22f6WEUk/aU5WHvnnXeYMmUKVatWpXz58qpYE5FHh4MD/Pab+QPlzJnQpw8EBcErr6S4q9dffx17e3uGDBnCl19+SUREBN98843CNREREckYfn6wbVtcRdrevfBf6GPl4ZFwo4EaNcyff+SBwsPD+fPPP/nmm2/Ys2eP9XjFihVp2LAhVapUsR4rXrw4I0eOzIphikgmSHOwNmvWLEaMGMH48ePTYzwiItmLrS388IM5PfSbb2DIEDNcGzEixV298sorODg4MHDgQKZMmUJkZCTTpk2zbpkuIiIikmrBweaO5hs3mkHaoUPmumnxFSqUcH20J58E/RySIleuXGH69Ol899133LhxAwAHBwd69uzJ0KFDqZGKHeVF5OGW5mAtOjqap556Kj3GIiKSPdnYwNdfm7uEfvQRjBwJgYHmxymsOHvxxRext7enf//+TJ8+nYiICGbMmGHd6EBEREQk2Xx9YdkyWLTIDNXCwxO2e3nFTets1AhKltSOnalgGAY7d+5k8uTJ/Pnnn9ZdPAsXLszLL7/MSy+9RL58+bJ4lCKSVdIcrLVs2ZKdO3dqV1ARebRZLPC//5mVayNGwCefmOHapEkpfqe3b9++2NnZ0adPH2bOnElUVBQzZ85UuCYiIiIPduWKuQ7swoWwaRNER8e1lS4NbduaYVrDhlCgQFaN8pHy999/06RJE+vzhg0bMnToUDp37oy9vX0WjkxEsoM0B2vvv/8+zzzzDC4uLrRr1w5PT89E5yR1TETkofTOO2bl2iuvwJQp5rTQH34Au5R9O33uueewt7enV69e/PLLL0RGRvLLL79gl8J+RERE5DFw6pRZlbZwIezcmbCtShVz5/IuXcypnapIS7PLly9z9OhR68yshg0bUrlyZWrUqMHQoUOpVq1aFo9QRLITi2HcPfE+ZWLXBrrfAtzR8d9FyYYCAwNxd3cnICAANze3rB6OiDwMfv0V+vY13yXu2tV87uiY4m4WLVrEM888Q2RkJF27duW3337TO58iIiKPO8Mw10hbuNB8HDqUsL1ePTNI69zZrFKTdLNr1y4aNGhArly5uHjxIk5OToD5O61mF4g8XpKbFaW5NGLMmDHa1U5EHj/PPWfuFtqjByxYYG5Xv2AB5MiRom46d+7MggUL6NatG8uXL+fff//VorciIiKPo5gY2LXLDNIWLYLTp+PabG2hWTMzTOvY0dyEQNJFWFgYJ06coHLlygDUqFGDwoULU6JECa5du0aJEiUAFKqJyD2luWLtUaCKNRFJtbVroVMnuHPHXBR4+XJzqmgKrVq1CsMwaNu2bfqPUURERLKnyEjYsiUuTLt6Na7NyQlatTKr0jp0AC2vk64uXrzIt99+y4wZM3BwcODcuXPWWQN+fn7kypUri0coIlktuVmRgjUUrIlIGm3bZi4UHBgINWvC6tWQO3eaujxz5gwFCxbE2dk5nQYpIiIi2UJoqPnG3MKFsHQp+PnFtbm5Qbt2ZmVa69ZmdbykG8Mw2Lp1K9988w2LFi2yLllUrFgx1qxZQ9myZbN4hCKSnWTaVFARkcdegwbmrlwtW8LevdCkifkDc8GCqeru5MmTNGnShPLly7N06VJcXFzSd7wiIiKSuQIDYcUKM0xbtcpcQiJWnjxm9XuXLuDtnao1W+X+QkNDmTt3LpMnT+aff/6xHm/WrBlDhw6lQ4cO2kBKRFJN3z1ERNJDtWrmVI6nnoIjR8xpoevWwX/rcqTEzZs3CQoK4saNG4SGhipYExEReRjdvAlLlphTPNetg4iIuLaiReM2H2jY0FxDTdLdhQsXmDZtGjNmzOD27dsAODs707t3b1599VUqVaqUxSMUkUeBpoKiqaAiko7OnoUWLeDMGShc2PxBuly5FHezZ88eihcvTr58+TJgkCIiIpIhLl40g7SFC+Hvv80NCWKVLWvuJN65M9SoAdoALsPs3LmTzz//nMWLFxPz399BiRIlGDJkCAMGDMBT69WJSDJoKqiISFYoWdL8Qfqpp+DoUWjcGNasgapVU9RNrVq1EjxfsGABzZs3x8PDI/3GKiIiIml3/LgZpC1caC4JEV+NGmaQ1qULlC+fNeN7DG3dupWFCxcC0Lx5c4YOHUr79u21s6eIZAgFayIi6a1QIdi82dzJa/9+aNrUXE+lXr1UdffLL7/Qp08fatSowZo1a/Quq4iISFYyDDhwIG4nz6NH49osFnNqZ5cu5rppqVgSQlLm3LlzTJs2jXr16tG5c2cAXnjhBc6ePcsrr7xCxYoVs3iEIvKo01RQNBVURDJIQAC0bw9bt4KLi7nOSvPmKe7m33//pXnz5ty6dYsqVaqwbt068uTJkwEDFhERkSRFR8P27XHTPM+fj2uztzf/f+/SBZ5+GvLnz7pxPoY+/PBDxo4dS926ddmxY0dWD0dEHiHJzYpSFaxVrlw52edaLJYEO69kRwrWRCTD3LljTgFZswYcHODPP80fulPoyJEjNG/enOvXr/Pkk0+ybt068usHdxERkYwTEQEbN5pB2uLFcONGXFuOHNCmjfl/fLt2oKUaMkVISAi//PILTzzxBN7e3gBcv36d/v378/LLL9O+fXssWrtORNJJhgZrTZs2feA3rODgYPbt24fFYiE6Ojqlt8hUCtZEJEOFh0OvXuYP5ra28Msv0LNnirs5fvw43t7eXLlyhXLlyrFhwwYKFiyYAQMWERF5TIWEwF9/mf9nL19uVp/H8vCADh3MyrSWLc1wTTLFmTNnmDp1Kj/99BP+/v40a9aMDRs2ZPWwROQRl6GbF2zatOmebVFRUXz//fd8+OGHWCwWevXqlZpbiIg8OhwdYd48GDDADNWeew6CgmDgwBR1U7ZsWTZv3kyzZs3w8fGhSZMmbNiwgSJFimTQwEVERB4Dfn5miLZoEaxeDaGhcW0FCphrpXXpYq6Zam+fVaN87BiGwbp165g8eTLLly8nth6kdOnSdOzYEcMwVJ0mItlCum5e8Oeff/Lee+9x+vRpWrRowaeffkrVFO6EJyLySLKzg1mzIGdOmDYNBg0yw7U330xRN2XKlGHLli00a9aMkydPWsO14sWLZ8y4RUREHkXXrplrny5cCBs2QFRUXFvJkmaQ1qUL1K0LNjZZN87HUHBwMLNnz2bKlCkcO3bMerxVq1YMHTqUNm3aYKO/ExHJRtIlWNu0aRMjRoxgz549VK9enTVr1tA8FQt0i4g80mxsYMoUcHODCRPgrbcgMBA++MDcRSyZSpYsyebNm/H29ubMmTPWcK1UqVIZN3YREZGH3dmzcZsPbN9u7u4Z68knzfXSunSBKlVS9P+ypI9Tp05Zp3sGBgYC4OrqSr9+/Xj11VcpW7ZsFo9QRCRpaYr6Dx06RNu2bWnevDm+vr7MnTuXvXv3pjlUO3jwIO3ataNYsWI4Ozvj6elJvXr1mDNnTqJz9+/fT4sWLXB1dcXDw4MuXbpw5syZNN1fRCTDWCwwfjx88on5/MMPYfjwhD/cJ0Px4sXZvHkzXl5enD9/niZNmnDq1KkMGLCIiMhDyjDgyBH43/+gWjUoVcqsFN+2zWyrXdt8o+v4cTh0yPw/uWpVhWqZzDAMunXrxhNPPMHXX39NYGAgXl5eTJo0icuXLzN58mSFaiKSraWqYu3ixYuMHj2auXPn4unpyddff83gwYOxT6c1B/z9/SlatCg9e/akcOHChISE8Ouvv9K7d2/OnTvH6NGjAfDx8aFp06ZUrVqVP/74g7CwMMaMGUOjRo04ePAgefPmTZfxiIiku1GjzMq1V1+Fr782p4VOn25ubpBMRYoUsVau+fj40LhxY44cOUKuXLkybtwiIiLZmWHAnj1xlWknTsS12dhAkyZmVVqnTqA1SrPMnTt3yPHf5g8Wi4WcOXNiGAZt2rThtddeo2XLlpruKSIPjVTtCurs7ExERAStW7fmnXfeIWfOnPc9v3r16qkeYHx169blypUrXLhwAYAePXqwceNGTp8+bd2h4fz583h5eTFs2DA+/fTTZPWrXUFFJMv8/LO5qUFMDDzzDMyeDQ4OKeri+vXrtGjRgp49e/Luu+9m0EBFRESyqago2LrVDNIWLYJLl+LaHBzMHTy7dDF39MyTJ+vGKRiGwdtvv83333/P33//TZUqVQA4e/YskZGRPPHEE1k8QhGROBm6K2h4eDgAq1atYvXq1fc8L3anlujo6NTcJpE8efJw48YNwNx9dPny5fTp0yfBCyxevDjNmjVj0aJFyQ7WRESyTN++4OoKPXuaO4cGB8Off4Kzc7K7yJ8/P7t27bK+8ysiIvLICw+HdevMMG3JEvD1jWtzdYV27cw109q2NTcOkiwTf/dOi8XCxYsXCQoK4vfff7cGayVLlszKIYqIpEmqgrWZM2em9ziSFBMTQ0xMDH5+fvz555/89ddfTJkyBYDTp08TGhpK5cqVE11XuXJl1q5dS1hYGE5OTpkyVhGRVOvaFZYuNd9NX7HC/CVg6dIU/SIQP1QLCgriueee44MPPki3imEREZEsFxQEq1aZYdrKlebzWLlzw9NPm/+XtmgB+h0gywUGBjJr1iymTZvGkiVLrOukvffee7zwwgu0aNEii0coIpI+UhWs9e3bN73HkaRXXnmF6dOnA+Dg4MA333zDoEGDAPD9710pT0/PRNd5enpiGAZ+fn4ULFgwUXt4eLi16g6w7jojIpJlWreGv/4y32HftMn8pWDVKkjie9yDvPfeeyxbtowjR47g4+OTbutfioiIZDpfX1i2zAzT1qwxK9ViFS4ct5Nno0Zgl6pfbSSdHT9+nClTpjBr1iyCg4MB+O677/jqq68AswgiqeIIEZGHVbb+3+fdd9/lxRdf5MaNGyxbtoxXX32VkJAQ3nrrLes5lvvs2nOvtvHjxzNu3Lh0H6+ISJo0agQbNpgh2+7d0LSp+UtEgQIp6uajjz7i9OnTjBs3TqGaiIg8fC5fhsWLzTBt82aIv6xMmTJmpXfnzlCrlrkhgWS5mJgYVq1axTfffMOaNWusx8uXL8/QoUPp3bt3Fo5ORCRjZetgrVixYhQrVgyAtm3bAjBq1Cj69u1L7ty5gbjKtfhu376NxWLBw8MjyX5HjRrF8OHDrc8DAwMpWrRoOo9eRCQVatY0f4lo0QIOHYLGjc01ZP77Xpgcbm5urFixIsExTY0XEZFs7dQpM0hbuBB27UrYVrVqXGVaxYpwnzfWJXMFBAQwc+ZMpkyZwunTpwGzuKFDhw4MHTqU5s2b37cQQkTkUZCtg7W71a5dm++++44zZ85Qo0YNnJ2dOXToUKLzDh06RJkyZe75S6SjoyOOjo4ZPVwRkdSpWBH+/tsM106ehIYNYf168PJKVXe7d++mU6dOzJ07l6ZNm6bvWEVERFLDMODff+N28oz/M73FAvXqmUFa585QqlTWjVOSdOzYMSZPnszs2bMJCQkBwMPDgxdeeIFXXnmFUvo7E5HHyEMVrG3cuBEbGxtKlSqFnZ0dHTp0YOHChXz22Wfk/G+R7wsXLrBx40aGDRuWxaMVEUmDMmVg61YzXDt+3JwmumYNpGJNki+++IKrV6/Stm1bli5dqsWCRUQka8TEmNVosZVpZ87EtdnZQbNmZpjWsSMksU6yZA9Hjx6lYsWK1ucVK1Zk6NChPP/887i4uGThyEREsobFMAwjqwdxt4EDB+Lm5kbt2rXJnz8/t27d4s8//2TevHm8/fbbfPbZZwD4+PhQq1YtqlevzsiRIwkLC2PMmDHcvn2bgwcPkjdv3mTdLzAwEHd3dwICAnBzc8vIlyYikjI3bkCrVnDwIOTKZW5oUKdOiroICwuja9eurFy5EkdHRxYvXkzr1q0zZrwiIiLxRUaaSxwsXGium3b1alybk5O5rmiXLtC+vfn/nGQ7fn5+7Nu3z/rGnGEY1KtXj4IFC/Laa6/RtGlTTfcUkUdScrOibBmszZw5k5kzZ3Ls2DH8/f1xdXWlSpUqvPjiizz//PMJzt23bx8jRoxgx44d2NnZ4e3tzcSJEyldunSy76dgTUSyNX9/aNsWduwAV1dzd7QUTukMDw/nmWeeYcmSJTg4ODB//nw6dOiQIcMVEZHHXGioWWW9cKH5f5afX1ybmxt06GBO8WzdGlThlK2dOHGCatWqYbFYuHz5Mu7u7gBERETg4OCQxaMTEclYGRqsXbhwIUXnF0vBottZQcGaiGR7wcHQqZO51pqTE8yfD+3apaiLyMhIevXqxfz587Gzs+OPP/6gc+fOGTNeERF5vAQFwYoVsGABrFwJd+7EteXLZ07v7NIFvL1BgUy2FR0djY+Pj3Wqp2EYVKpUCRsbG3799VcqVaqUxSMUEck8GRqs2djYpKjcNzr+FtnZkII1EXkohIXBM8/A0qXmWjS//go9eqSoi6ioKPr06cNvv/2Gra0tc+fOpUcK+xAREQHMiuply8wwbfVqCA+PaytWzAzSunSB+vXB1jbLhikPdvv2bX788UemTZuGn58fly5dwtXVFYAbN26QN29eTfcUkcdOcrOiVG1e8NNPP+kbq4hIZoutVOvXD+bOhZ49zUq2AQOS3YWdnR2//PKL9c+ePXsSGRnJc889l3HjFhGRR4evLyxZYoZpa9eaa6jF8vKCbt2ga1eoXt3c3VOytUOHDjF58mTmzJlDaGgoALlz5+bIkSPU+W9N13z58mXlEEVEsr1UBWv9+vVL52GIiEiy2NvD7NnmWmvffw8vvACBgfDGG8nuwtbWlpkzZ2Jvb89PP/1E7969iYqKom/fvhk3bhEReXjduAGLFplh2oYNEH82SsWKcWHak08qTHsIREVFsXTpUiZPnsymTZusx6tWrcrQoUPp2bMnzs7OWTdAEZGHTKqCtXs5ceIEvr6+5MmTBy8vr/TsWkREYtnawnffmQtAT5wIw4aZa9uMHp3sX2hsbW2ZMWMG9vb2TJ8+nf79+xMREcFLL72UwYMXEZGHwpUr5uYDCxbAli0QExPXVrWqGaR17Qrly2fZECVlfH19+eGHH5g2bZp1zWxbW1u6dOnCa6+9RoMGDTQrSUQkFdIlWPvzzz956623uHTpkvVYkSJF+OKLL+jWrVt63EJEROKzWOCzz8xwbcwY8xEYaB5L5g/FNjY2fPvttzg4ODB58mT++usvXnzxRf1QLSLyuLpwwQzT5s+H7dsh/lLMtWrFhWllymTdGCXFjh49ypdffsmvv/5KWFgYAHny5GHgwIEMHjyYokWLZvEIRUQebqnavCC+lStX0qFDBypWrEjv3r0pVKgQly9fZs6cORw9epRly5bRpk2b9BpvhtDmBSLyUPv6a7NqDWDQIJg6NUWLRBuGwaxZs3juuedw0E5tIiKPl9Onzaq0BQtg9+6EbfXrm0Faly5QokSWDE/S7ocffrBWpFerVo3XXnuNZ599FicnpywemYhI9pahu4LG16BBA9zc3FixYgU2NjbW44Zh0KZNG4KCgti2bVtabpHhFKyJyEPvxx/hpZfM6oJevWDWLHM9tlSIiYlh3bp1tGzZMn3HKCIi2cPx42ZV2oIFcOBA3HGLBRo3jgvTChfOujFKqty6dYsZM2ZQvHhxevXqBcCdO3cYMmQIL774IvXr11dluohIMmVasObi4sLvv/9Ohw4dErUtXbqUXr16ERwcnJZbZDgFayLySJg3D55/HqKi4OmnzecpfDfaMAyGDh3K1KlT+eSTTxg1alQGDVZERDKNYcCRI3Fh2uHDcW22ttCsmRmmdeoEBQpk2TAl7aZNm8aQIUMoV64cR44cSVD4ICIiKZPcrCjNa6zZ2toSERGRZFtkZKS+mYuIZJZnnjF3C+3WDZYuhfbtYfFi81gyWSwW8ufPj8VioVChQhk3VhERyViGAQcPmkHa/PlmlVose3to0cIM0zp2hDx5smyYknqBgYHMmTOHQoUK0alTJwCef/555s2bxwsvvEAa6ydERCSZ0lyx1rx5c4KDg9m0aVOCbZnDw8Np2rQprq6urF27Ns0DzUiqWBORR8qmTdChAwQHQ716sHIleHikqIt///2XypUrZ8jwREQkgxgG7NkTF6adORPX5ugILVuab7506AC5cmXdOCVNDhw4wLfffsvcuXMJCQmhWrVq7Nu3T1M8RUTSWaZVrI0bN47mzZtTqlQpunfvToECBbh69SoLFy7E19eXDRs2pPUWIiKSEk2bwrp10KYN7NhhTvH56y/Ily/ZXcQP1a5du8Yvv/zCW2+9pR/aRUSym5gY83t97AYEFy7EtTk7m/8XdOsG7dqZO0nLQ+nOnTv88ccffPvtt+yOt8lE+fLl6devHzExMdimYOMiERFJP2kO1ho2bMiaNWsYOXIkU6dOxTAMbGxsqFOnDr/99hv169dPj3GKiEhK1KljVq61bGlOBWrc2AzbihRJUTfh4eG0aNGCI0eOcPnyZb766iuFayIiWS06Gv7+Oy5Mu3o1rs3FxVwKoFs3M1Rzccm6cUqa+fj4MH36dGbNmoW/vz8A9vb2dO3alcGDB9O4cWP9vywiksXSPBU0vjt37uDn50euXLnIkSNHenWb4TQVVEQeWSdOmOvoXLwIxYvD+vVQunSKupg+fTqDBw8G4OWXX2bKlClaP1NEJLNFRppvmCxYAIsWwY0bcW1ubuamNd26mW+oxFueRR4+ERERLF68mO+++46NGzdaj5coUYJBgwbRv39/8ufPn4UjFBF5PGTaVND4cuTI8VAFaiIij7wnnoCtW81w7eRJaNQI1q6FihWT3cWgQYOwt7fnxRdf5NtvvyUyMpLp06crXBMRyWgREWa18YIF5mY0t2/HtXl6mhsPdOsGzZuba6jJQ88wDKpWrcqxY8cAsLGxoV27drz88su0bNlS0z1FRLKhdAnWFi9ezK+//sr58+cJCwtL0GaxWPjnn3/S4zYiIpIaxYrBli1mFcOhQ+a00L/+gpo1k93FgAEDsLe3p1+/fvzwww9ERkby448/6gd8EZH0FhYGa9aYmw8sXQoBAXFtefNC585mmNa0qbm7pzzUoqOj2bBhAy1atMBisWCxWGjVqhV+fn68+OKLvPTSSxQrViyrhykiIveR5qmgn3/+OSNGjCBv3ryUKVMGBweHROfEL2HOjjQVVEQeC7dvm+vt7N4NOXPCihVmBVsK/Pbbb/Tu3Zvo6Gh69erFzz//jJ1duhY/i4g8fkJCYNUqszJt+XJzV+dYBQtCly5mmNaoEegNjUdGTEwMVapU4fDhw2zcuJGmTZsCEBAQQI4cObBXcCoikqUybSrotGnTGDBgANOnT1flgohIdubpaU4pevppc52eVq1g4UJo3TrZXfTs2RN7e3t69uzJ3LlziYqKYs6cOfrhX0QkpYKCzBBtwQJYuRJCQ+PaihaFrl3NMK1ePdDU+0eCYRjs3LmTunXrYrFYsLGxoUGDBly+fJlLly5Zz3N3d8/CUYqISEqluWLNzc2NxYsX4+3tnV5jynSqWBORx0poKHTvblas2dvDb7+Zv8ClwJIlS+jevTuRkZF07tyZ33//PcmKZRERicff35zeuWCBOSU/PDyurWRJM0jr1g1q1QLt9PjI8PPz4+eff+a7777j+PHj7Ny5kzp16gBw69YtXFxccNaGEyIi2U5ys6I0v/3VoEED6+KaIiLyEHB2NivVevQwd5nr0QN+/jlFXXTs2JFFixbh4ODAokWL6NatG+Hxf0EUERGTry/89BO0bQv58kHfvma4Fh5ubjDz7ruwfz+cPg2ffQa1aytUewQYhsGuXbvo378/hQoVYtiwYRw/fhxXV1eOHz9uPS9PnjwK1UREHnJprlg7fvw4nTt3ZsKECbRu3fqhrFhQxZqIPJaio2HgQPMXPoApU2DIkBR18ddff9GpUyfCwsJ49dVXmTx5cgYMVETkIXP9urmL5/z5sHGj+f02VsWKcZVpFSsqRHvEBAcHM3fuXL777jsOHDhgPV65cmVefvllnnvuOXLmzJmFIxQRkeTKtDXWypQpQ4sWLejcuTMWi4UcOXIkaLdYLATE381IRESyB1tbmDHD3Mhg0iR49VUIDIRRo5LdRatWrVixYgVvv/027733XgYOVkQkm7tyxawGnj8f/v4bYmLi2qpWNYO0rl2hXLksG6JknMOHD/Ptt9/yyy+/EBQUBICjoyM9evTg5Zdftq6rJiIij540B2vvvPMOU6ZMoWrVqpQvX/6hrFgTEXls2djAV1+Buzt8+KE5JSkwED75JNlVFN7e3uzZswebeItrx8TEJHguIvJIOn8+Lkzbvj1hW61acWFa6dJZMz7JcHv37uWNN95g27Zt1mNeXl4MHjyYvn37kjt37iwcnYiIZIY0TwXNnTs3AwcOZPz48ek1pkynqaAiIsDEifD22+bHr7wCkyenaie6n3/+mZ9++oklS5bg4eGRvmMUEclqp0+bmw/Mnw979iRsq1/fDNO6dIHixbNmfJLhoqKisLMz6xOOHz9OuXLlsLW1pVOnTgwePBhvb2+9uSQi8gjItKmg0dHRPPXUU2ntRkREstpbb4GbGwweDNOmQVCQuf6aXfL/q/D392f48OHcvn2bFStW8Nxzz2XggEVEMomPT1yYdvBg3HGLBRo3NsO0zp2hcOEsG6JkvAMHDjBixAhy5crFvHnzAChbtiyzZs3iqaeeolChQlk8QhERyQppDtZatmzJzp078fb2To/xiIhIVho40FxzrXdv+OUXCAmBuXPB0TFZl3t4eLB27Vq++eYbevbsaT0eFBSkxZpF5OFhGHDkiBmkzZ9vfhzL1haaNTPDtE6dIH/+LBumZDzDMKxro9nY2LB27VocHBzw9/e3VmX37ds3C0coIiJZLc01yu+//z5z5sxh0qRJnDp1itu3byd6pNSGDRsYMGAA5cqVw8XFhcKFC9OxY0f27duX6Nz9+/fTokULXF1d8fDwoEuXLpw5cyatL0tE5PHVs6e5ZpCDg/lnx45w506yL69evTqzZs2yToMJCgqifPnyvPjii9y8eTOjRi0ikjaGAQcOwHvvmRsMVKoE48aZoZq9PbRpAz/+CNeuwdq1MGiQQrVHVExMDH/99RedO3fmxRdftB6vUqUKkydPxsfHR0sdiIiIVZrXWIv9xel+u9xEx99iPBm6d++Or68v3bt3p0KFCty8eZMvvviCvXv38tdff1mr43x8fKhduzZVq1Zl5MiRhIWFMWbMGPz8/Dh48CB58+ZN1v20xpqISBLWrYsL1Ro2hOXLzU0OUuj333+3Vq95eHjw4Ycf8vLLL1vXpxERyTKGAbt3x03zPHs2rs3REVq1MivTOnQABSmPvJs3bzJz5kymT59ufaPeycmJ69ev63cEEZHHUHKzojQHax988MEDt44eO3Zsivq8ceMG+fLlS3AsODiYMmXK8OSTT7Ju3ToAevTowcaNGzl9+rT1RZ4/fx4vLy+GDRvGp59+mqz7KVgTEbmH7duhbVsICIDq1eGvvyBPnhR3s23bNl599VUO/rc2UaVKlZg8eTJNmjRJ5wGLiDxATIz5vW3BAvNx8WJcm7Oz+T2vWzdo186cGi+PNMMw2LZtG99++y3z588nIiICAHd3d/r27cugQYOoUKFCFo9SRESyQqYFa5nJ29uby5cvc/z4caKionBzc6NPnz589913Cc5r1aoVZ8+e5cSJE8nqV8GaiMh9HDwILVvCzZtQoYI5BSoVCzRHR0czY8YM3nvvPesyAc888wwTJ06kSJEi6TxoEZF4oqPh77/NqrSFC+Hq1bg2V1do394M01q3BheXrBunZJqAgADmzJnDd999x+HDh63Ha9asycsvv8wzzzyDi74WREQea8nNih6afaADAgLYv38/FStWBOD06dOEhoZSuXLlROdWrlyZU6dOERYWltnDFBF59FStClu2QJEicPQoNGqUcLpUMtna2jJ48GBOnDjByy+/jI2NDfPmzaNs2bKMHz+e8PDw9B+7iDy+IiPj1kIrWNDccGDqVDNUc3c3N2lZssR80+C336BrV4Vqj4H9+/czcOBAChcuzKuvvsrhw4dxdnbmhRdeYM+ePezZs4cBAwYoVBMRkWRLlwVuFi9ezK+//sr58+cThVkWi4V//vknzfcYMmQIISEhvPfeewD4+voC4OnpmehcT09PDMPAz8+PggULJmoPDw9P8AtcYGBgmscnIvJIK1fOrPZo3hzOnDHDtXXrzOMplDt3bqZNm8ZLL73E0KFD2bZtG++++y4//fQTkyZNom3bthnwAkTksRAWBuvXm1M8lyyB+JtoeXqau3h27Wp+L0vmbsfyaBkxYoR1WZkKFSowePBgevfurc0IREQk1dJcsfb555/TpUsXtmzZgr29Pblz507wSCr4Sqn333+fX3/9la+++ooaNWokaLvf+m73ahs/fjzu7u7WR9GiRdM8RhGRR16JEma4VqECXL5shmsHDqS6u2rVqvH333/zyy+/ULBgQU6dOkW7du3o169fug1ZRB4Dfn7w66/QvTvkzWtO65w50wzV8uY1K9bWrDF38/zxR3MNNYVqj4Vjx47xxhtvcDXe1N9XX32VZ599ls2bN3P48GGGDh2qUE1ERNIkzRVr06ZNY8CAAUyfPh1bW9v0GFMC48aN46OPPuLjjz/m1VdftR7PnTs3EFe5Ft/t27exWCz3/E9y1KhRDB8+3Po8MDBQ4ZqISHIUKgSbN5vrEO3bZ06tWrECGjRIVXcWi4Xnn3+ejh078r///Y+vvvqKevXqpfOgReSRc/GiWZG2ZAls2gRRUXFthQublWnduplvAGTAz6fycHjppZfYtm0befLkYfTo0QB07NiRjh07ZvHIRETkUZLmYM3X15devXplWKj2wQcf8MEHH/Duu+8maCtdujTOzs4cOnQo0XWHDh2iTJkyODk5Jdmvo6MjjnqnUkQkdfLkgQ0bzKqQv/82NzZYvBieeirVXebMmZPPPvuMl156iVKlSlmPr169moCAAHr06PHAHahF5BFmGHD4sPm9ZskSM9iPr2JFM0zr2BFq1ACbh2YZYUkn586dY8aMGQwbNow8/+1ePWTIEHLnzk2jRo2yeHQiIvIoS3Ow1qBBA44dO4a3t3d6jMfqf//7Hx988AGjR49m7Nixidrt7Ozo0KEDCxcu5LPPPiPnf9uhX7hwgY0bNzJs2LB0HY+IiMTj5garV5trFa1ebYZsf/xh/lKbBl5eXtaPQ0NDGTx4MOfPnycwMJCXXnopraMWkYdJdDRs22YGaYsXm+s7xrJYzErZ2DCtTJmsGqVkoejoaFatWsW3337LqlWrMAwDT09P3nzzTQB69uxJz549s3iUIiLyqLMYhmGkpYPjx4/TuXNnJkyYQOvWrXFwcEjzoL744gveeustWrdunWSoVrduXQB8fHyoVasW1atXZ+TIkYSFhTFmzBhu377NwYMHyZs3b7Lul9wtVEVE5C7h4fDcc+ZC4ba28PPP5vN0EBYWxmeffcbcuXPZv38/OXLkSJd+RSQbu3PH3MlzyRJYtgxu3Yprc3Q0K2Q7dTLD/Hz5smyYkrWuXbvGjz/+yPfff8+FCxesx1u0aME777zDU2mooBYREYmV3KwozcFadHQ0w4YNY+rUqVgslkS/+FgsFgICAlLUZ9OmTdm8efM92+MPed++fYwYMYIdO3ZgZ2eHt7c3EydOpHTp0sm+n4I1EZE0iIqCF180QzWLBb791lwsPN26j8LOziywjomJoUOHDnTt2pV+/fpho+leIg+/W7dg+XIzTPvrLwgNjWvLlQs6dDCr0lq2BFfXrBunZCnDMNi4cSPffvstixcvJuq/dfU8PT3p378/gwYNSlD1LCIiklaZFqy9+eabfPXVV1StWpXy5csnWbE2c+bMtNwiwylYExFJo5gYeP11mDLFfP7ZZ/D22+l+m99++41evXoBULt2bSZPnkzt2rXT/T4iksHOno2b4vn33+b3kFjFi8dN8WzUCOzSvHKJPMRu377Nzz//zHfffceJEyesx+vXr8/gwYPp3r37PddVFhERSYtMC9Zy587NwIEDGT9+fFq6yVIK1kRE0oFhwOjR8Mkn5vOnn4YPPoBq1dLtFpGRkUyePJkPPviAoKAgAAYMGMD48ePJp2lhItmXYcCBA3Fh2r//JmyvWtUM0jp1gipVzOpXeezt3buXRo0aERYWBoCrqyu9e/dm0KBBVKlSJYtHJyIij7pMC9Y8PDxYuHBhum9ekJkUrImIpKPPPoNRo+IqUDp1grFjzV+c08m1a9cYOXIkP//8MwDu7u6MGzeOIUOGWKeNikgWi4yELVvMMG3JEoi3Fha2ttC4sRmmdewIJUpk2TAl+wgODubEiRNUr14dMN9MKV68OPny5ePll1+mV69e1g3LREREMlqmBWs9evSgatWqvPvuu2npJkspWBMRSWc+PvC//8Fvv5mVKgCdO5sBWzpWGezYsYNXX32V/fv3A/Dkk08yefJkmjZtmm73EJEUCA4210lbvNhcN83fP64tRw5o3doM0tq1g9y5s2qUkg3t2bOH5s2b4+7uztmzZ61vkly9epUCBQpgURWjiIhkskwL1g4dOsQzzzzDoEGDaNeuHZ6enonOSepYdqJgTUQkgxw7ZgZsv/8eF7B16WIGbJUrp8stoqOj+fHHH3n33Xfx9fUFzDd9Jk6cSNGiRdPlHiJyH9evmzt4Ll4M69aZuwXHypvX3HygUydo0QKcnbNqlJLNhIWFcfbsWcqXLw9AeHg4RYoUIVeuXKxevZpSpUpl8QhFRORxl2nBWuyObPd7Fyk6Ojott8hwCtZERDLY0aNmwDZvXlzA1rWrGbBVqpQut7h9+zbvv/8+3333HTExMeTIkYOxY8fyzjvvpEv/IhLPiRNx66Xt2BH37xqgdGkzSOvUCerVM6d9ivzn5MmTTJ8+nZkzZ5IrVy5OnDhh/X3izJkzlCxZUtVpIiKSLSQ3K0rzQjRjxozRf34iInJ/FSqY00JHj4YPP4Q//4QFC8xH9+4wZgw8+WSabuHp6cnUqVMZOHAgr776Klu3buXatWvp9AJEHnMxMbBnT1yYduxYwvaaNePCtAoVtPmAJBAZGcmyZcv49ttvWbdunfW4i4sLFy5coMR/a+ypSk1ERB5Gaa5YexSoYk1EJJMdPhwXsIH5S3hswFaxYpq7NwyDefPm0aZNG9zd3QE4fvw4NjY2eHl5pbl/kcdCRARs3GgGaUuWwNWrcW12dtCsmRmkPf00FCmSVaOUbOzixYv88MMPzJgxg6v/ff1YLBbatGnD4MGDadu2LbaqaBQRkWwq06aCPgoUrImIZJFDh8yAbf5887nFAj16mAFbhQrpdhvDMGjSpAm7du3i559/5tlnn023vkUeKQEBsGqVGaatXAlBQXFtOXNCmzZmmNamDXh4ZNEgE7t9+zYODg64uroCcODAAcaPH0+pUqWYMGGC9bx33nmHW7duYWNjk6yHra0trVu3pkmTJoC5kP4PP/yAu7s7r732mrXfuXPncv369UTX3q/v8uXLU61aNQBCQ0NZs2YNdnZ2tGvXztrvwYMH8fX1TfZ4bWxsyJUrF0X+CzpjYmI4d+4cNjY2FCtWzDrlMiAggPDw8Ae+9tiPUzI7JSYmhjVr1vDdd9+xbNkyYv7bITpfvny88MILDBw40FqhJiIikp0lOysyxAgICDAAIyAgIKuHIiLyePrnH8Po2tUwzJWaDMNiMYxnnzWMo0fTpfuAgACjdevWhrOzs3H+/Pl06VPkkXHpkmFMm2YYLVsahr193L9DMIwCBQxj0CDDWLXKMMLCsnqkhmEYxo0bN4zVq1cbH3/8sdG1a1ejRIkSBmDMnj3bes6KFSsMwKhRo0aCa2PPTcljwoQJ1uv37NljAEbRokUT9Fu7du0U9/vmm29arz937pwBGE5OTgn6bdeuXYr77d27t/X6O3fuWI8HBQVZj/fr1y/F/bZp0ybB2PLly2e4uroaZ86csR778MMPDXd39wTXNW3a1Pj999+N8PDwlPw1i4iIZLnkZkVpXmNNREQkzSpXNqvW/vnHrGBbuNDcSXTePOjZE95/H8qVS3X3bm5urFy5ktOnT1OsWDHr8QkTJtC2bVsqp9MOpSIPBcMw10hbvNh87NmTsL1cubj10mrVgv+qnLLCtWvX2L9/P/v27bP+efHixSTPPXPmjPXjChUqMGXKFPLkyZPgnJEjR+Lv7090dDQxMTEPfERHR1OzZk3r9Xny5GHQoEF43FWt16pVK8qUKZOsPmMf8ael29vbU69ePRwcHBL0W6JECSpVqpTkuO7Vb+z0dzCrdV1cXIiJiUlQdWakYsLK3VVrwcHB3LlzJ8HxwMBAAgIC8PDwoG/fvgwaNMi666eIiMijSlNB0VRQEZFs5+BBGDfO/KUfzF/sYwO2smXT5RZbtmyhSZMm2NjY8Morr/Dhhx+SK1eudOlbJNuJjoadO+PCtFOn4tosFqhb1wzSOnZMt39jaTF06FAWLlzIlStXkmx/4oknqFGjBtWrV6dGjRpUq1YtUdgl92cYBoZhJDsItLe3T/A98ty5c8TExFC0aFHs7e0BMwi9efMmZcqUwdnZOatemoiISLrQGmspoGBNRCSbOnDADNiWLDGf29hAr15mwPbEE2nq+sKFC7z55pvM/299tzx58jB+/HgGDBhgXYdI5KEWGgrr15tB2tKlcPNmXJuDA7RoYYZpHTpAgQJZMsTbt2/z/PPP4+Pjw8mTJ60L2ffu3Zs5c+ZgsVgoV64cNWrUsAZpVatW1c9rIiIikuEUrKWAgjURkWxu/34zYFu61HxuYwPPPWcGbGnc5XP9+vUMHTqUY8eOAVCzZk2mTJlCnTp10jpqkcx3+zasWGGGaatXw507cW3u7tC+vRmmtWplbkaQwQzD4MyZMwmmcz7xxBNMmTIFgOjoaNzd3QkJCeHIkSNU+G/TkoMHDxISEkLVqlVxcXHJ8HGKiIiI3E3BWgooWBMReUjs22cGbMuWmc9tbOD5582ArUyZVHcbGRnJlClT+OCDDwgMDASgX79+TJgwgfz586fHyEUyzvnzZlXn4sWwZYs57TNWkSJxUzybNIH/puxlhJiYGE6dOpVgPbT9+/cTEBCQ4Lxy5cpZg2yAhQsXUrhwYapWrYqjo2OGjU9EREQkJRSspYCCNRGRh8zevfDBB2ZlDoCtLfTuDaNHQ+nSqe722rVrjBo1ilmzZgHmpgfjxo1jyJAh1jWERLKcYcC//5pB2pIl5pTp+CpVigvTqlc311DLIHv37mXu3Lns27ePAwcOEBQUlOgcBwcHKleubF0PLfYhIiIikp0pWEsBBWsiIg+pPXvMgG3lSvO5rS306WMGbKVKpbrbHTt2MHToUPbt2weYOwxOnjwZb2/vdBi0SCpERcHWrXFh2rlzcW02NtCwoRmkdeyYpnD5fhYtWsTGjRvp378/1apVA2DOnDn07t3beo6TkxNVqlRJsLFAhQoVEu12KSIiIpLdKVhLAQVrIiIPuV27zCmiq1aZz21toW9fM2ArWTJVXUZHR/PTTz8xatQofH19adu2LStiK+REMkNICKxZY4Zpy5eb66fFcnIy10nr2NFcNy1v3nS5ZUREBEePHmX//v0cOXKEiRMnYvmv4q1Lly4sWrSIL7/8kmHDhgFw9uxZvvrqK2uQVr58eezs7NJlLCIiIiJZKblZkbY9ExGRh1+dOmbV2o4d0Lq1ucbUTz+ZO4e++GLC6p5ksrW15aWXXuLkyZMMHTqUr7/+2toWEBBAWFhY+o1fJNbNm+bX7tNPQ5480KULzJ5thmq5c0O/frBoEdy6ZQZu/funOlQLDw9n7969fP/99wwaNIhatWqRM2dOqlWrxgsvvMCXX37JhQsXrOd36dKFN954g1q1almPlSxZkm+++Ya+fftSqVIlhWoiIllo586ddO/enYIFC+Lg4ECBAgXo1q0bO3bsSFE/H3zwgfVNlZTatGkTFouFTZs2per65GratClNmza97znR0dF8+eWXtG7dmiJFipAjRw7Kly/PyJEj8ff3T/KayZMnU65cORwdHSlZsiTjxo0jMjIywTmXLl3ijTfeoEmTJnh4eGCxWKzLiNwtIiKCMWPGULJkSRwcHChevDijRo0iNDQ0Fa9asitVrKGKNRGRR86OHeYU0TVrzOd2dmYA8e67UKJEmrt/8cUX2bhxIz/++OMDf6gTeaDTp+OmeG7bBjExcW0lSpjrpXXqBA0amF/LqRAZGZlgQ4F9+/Zx+PBhoqKiEp3r4eFB9erVqV69Oq+//jpFihRJ1T1FRCTzTJ48mTfeeIPatWvzyiuvULx4cS5cuMDUqVPZvXs3kyZN4tVXX01WX5cuXeLSpUvUrVs3xeMIDAzk6NGjVKhQIUN/t479+et+AV5wcDCFChWiZ8+ePPXUU+TJk4f9+/fz0UcfUbBgQfbu3Yuzs7P1/I8//pj333+fkSNH0rJlS/bs2cPo0aPp27cv33//vfW8TZs20b17d6pWrUrevHn57bffmDlzJv369Us0hq5du7Jy5UrGjBlDrVq12LFjBx999BGtWrViaexu95JtaSpoCihYExF5RG3fbgZsa9eaz+3sYMAAM2ArXjxVXQYFBVGhQgUuXbrE1q1badCgQfqNVx4PhmHucBsbph0+nLC9enVzimenTuZGBCmsGrhz5w4HDx7EYrFQr149AG7dukXeJCrbPD09E6yHVr16dUqVKpXqSgUREcl827Zto3HjxrRt25ZFixYlqB6Oioqic+fOrFy5ki1bttz355Y7d+6QI0eOzBhymiUnWIuOjsbf35/cuXMnOD5//ny6d+/OL7/8wvPPPw+Ar68vRYoUoU+fPkyfPt167ieffMLo0aM5fPgwFSpUAMxdsG1szMl/e/fupVatWkkGazt37qRevXp88cUXDB8+3Hp8/PjxvPvuu6xZs4annnoqtZ8CyQSaCioiIlK/vlm1tnUrtGhhLgD//ffg5QWDB0O8aW7JlTNnTo4dO8a8efMS/HC6bt06goOD03P08iiJiDAD3iFDoFgxqFULPv7YDNVsbaF5c/jmG3Pa8r59MGYMVK78wFAtMDCQLVu2cPXqVeuxOXPm0KBBA8aOHWs9lidPHmrWrEnr1q157733WLBgAefOnePWrVusWbOGCRMm0L17d0qXLq1QTUTkITN+/HgsFgvffvttoin5dnZ2TJs2DYvFwoQJE6zHY6d77t+/n27dupErVy5K/7f5TVJTQcPDw3nzzTcpUKAAOXLkoHHjxuzbt48SJUokCJSSmgrar18/XF1dOXXqFG3btsXV1ZWiRYvy5ptvEh4enuA+48aNo06dOnh6euLm5kb16tX58ccfSU09kK2tbaJQDaB27doAXLx40Xps9erVhIWF0b9//wTn9u/fH8MwWLx4sfVYbKj2INu2bQOgbdu2CY63b98egAULFiSrH8n+tBCGiIg8+ho0MEONv/82NzlYvx6mTzfXsnrhBbOCrWjRZHfn6upKjx49rM/PnTtHhw4d8PT05PPPP6dnz54KJwQCA2H1arMqbcUKCAiIa3NxMdcD7NQJ2rYFT88Hdufv78/+/fsTTOk8ceIEAN999x2DBg0CoHr16hQsWJACBQokuH7Pnj3p9tJERB4ZhgF37mT1KOLkyJGiSuXo6Gg2btxIzZo17zl1v2jRotSoUYMNGzYQHR2Nra2tta1Lly48++yzDB48mJCQkHvep3///sybN4933nkHb29vjh49SufOnQkMDEzWOCMjI3n66ad54YUXePPNN9myZQv/+9//cHd3Z8yYMdbzzp07x6BBgyhWrBhgVn0NHTqUy5cvJzgvLTZs2ABAxYoVrccO/1c9XqlSpQTnFixYkDx58ljbUyIiIgIAR0fHBMdjn//7778p7lOyJwVrIiLy+GjUCNatgy1bzCmiGzfCd9+ZAduLL8KoUZCK9aSuX79OoUKFOHPmDM899xzTp09n8uTJVK5cOf1fg2RvZ8+aVZJLlpgB7n8/VAOQL5+5KUGnTmaFmpPTPbvx9fVNFKKdPn06yXOLFi1KdHS09XmNGjW4cuVKer0iEZFH25074Oqa1aOIExxsvvmSTLdu3eLOnTuUfMAu6CVLlmT37t34+vqSL18+6/G+ffsybty4+1579OhRfvvtN0aMGMH48eMBeOqpp8ifPz89e/ZM1jgjIiIYN24c3bt3B6B58+bs3buXuXPnJgjMZs6caf04JiaGpk2bYhgGkyZN4v3330/zG5eXL19m5MiR1KxZ01o5Bub/u46Ojrgk8bn39PTE19c3xfeKnTq6bdu2BH8/W7dutd5THg0K1kRE5PHTuDFs2ACbN5sB26ZNMG0a/PADvPSSGbAVLpzs7urUqcORI0f44osv+Pjjj9myZQvVqlXj5Zdf5sMPP8QzGdVI8pDy9TUD2nXrzMfd4ZeXV9zmA3XqmNM+7+Ln50euXLmsz59++mmWLVuW5O1KlCiRaE20u9dOU7WkiIjcLXYq5d3/R3Tt2vWB127evBkgQbU+QLdu3ejdu3ey7m+xWOjQoUOCY5UrV7ZWj8XasGEDn3zyCXv27ElUDXfjxg3y58+frPsl5fbt27Rt2xbDMJg3b16iKZ33+/8zNf+3tmnThjJlyjBixAjy589PrVq12LlzJ++++y62trbJnlIq2Z+CNREReXw1aWKGIps2wdixZiXb1KkwYwYMHAgjRyY7YHNycuK9996jd+/evPXWW/z5559MnTqV33//nfHjxzNgwIAEUy/kIRUWZu7cuW6dOb14/35zGlEsW1szQGvf3gzTypVLMKUnNDTUugOZr68vVapU4caNGwQFBVmnhhQsWBCAMmXKJAjQqlevrpBWRCS95chhVollFyncPCBPnjzkyJGDs2fP3ve8c+fOkSNHjkT/j8T+n3M/sZVVd4dadnZ2Sa5hlpQcOXLgdFeltqOjI2FhYdbnu3fvpmXLljRt2pQZM2ZQpEgRHBwcWLx4MR9//DGhoaHJuldS/Pz8eOqpp7h8+TIbNmygVKlSCdpz585NWFhYkhs43L59mxo1aqT4ng4ODqxatYrevXvTsmVLAFxcXPjkk0/43//+R+EUvIkr2ZuCNRERkaZNzeq1jRvNgO3vv2HKlIQBW6FCyeqqWLFi/PHHH2zYsIHXXnuNI0eOMHDgQKZPn86UKVNStXW9ZKGYGDhwIK4ibetWM1yLr2JFc3OMFi3Makg3NwzD4NKlS+xbsiTBdM569eqxcOFCwJxaEhYWRlRUFMePH7dOHf7ggw/49NNP8fDwyOQXKyLyGLJYUjT1MruxtbWlWbNmrF69mkuXLiW5ztqlS5fYt28fbdq0SfQmX3IqsWLDs+vXrycIg6KiotJ1OuPvv/+Ovb09y5cvTxDCxd84IDX8/Pxo0aIFZ8+eZf369Uku1RG7ttqhQ4eoU6eO9fi1a9e4desWTz75ZKruXaZMGXbs2MHly5e5ffs2pUuXJiAggNdff53GjRun7gVJtpMtaw+DgoJ45513aNmyJXnz5sVisfDBBx8kee7+/ftp0aIFrq6ueHh40KVLF86cOZO5AxYRkUdDs2ZmwLZ+PTRsCOHhMHkylC4Nb7wB8XZefBBvb28OHDjA119/jZubG/v27aNevXr069ePa9euZdxrkLQ7c8bcPbZHD8ibF2rWNMPVdevMUK1QIejTB2bPxrh0ievr17O5c2emX77MsLFjadWqFfnz56dYsWJ07tyZ//3vf6xcuZJr165x8OBB621id00LCAhI8EN+wYIFFaqJiEiyjRo1CsMweOWVVxKsuQnm5gYvv/wyhmEwatSoVPUfGwDNmzcvwfH58+cTFRWVukEnwWKxYGdnlyD8Cw0N5Zdffkl1n7Gh2pkzZ1izZg3VqlVL8rzWrVvj5OTErFmzEhyfNWsWFouFTp06pXoMAIULF6ZSpUrkyJGDzz//HBcXF1544YU09SnZR7asWPP19eX777+nSpUqdOrUiR9++CHJ83x8fGjatClVq1bljz/+ICwsjDFjxtCoUSMOHjyYaM0RERGRB7JYwNvbDNk2bDAr2LZtg0mTzJ1EBw+GESPgrh0Xk2Jvb8/rr7/Os88+y6hRo5g5cyY///wzx48fZ8eOHZnwYiRZfH3Nv+u1a83w7O7pNDlzEtW4MWerVqVAp07krFEDLBZmzZrF8EqV8PPzS7JbW1tbKlasmGBNtLvfJU/tO+AiIiKxGjRowNdff80bb7xBw4YNefXVVylWrBgXLlxg6tSp7Nq1i6+//pr69eunqv+KFSvSs2dPvvjiC2xtbfH29rauLevu7p5ua4W1a9eOL7/8kl69ejFw4EB8fX2ZOHFiol01kys0NJRWrVpZ3+iMiopi586d1va8efNSunRpwKwiHz16NO+//z6enp60bNmSPXv28MEHH/Diiy9aNyKINX/+fABrUc/evXtx/W8TjG7dulnP++yzzyhQoADFihXj+vXr/PHHHyxevJhffvlFU0EfJUY2FBMTY8TExBiGYRg3b940AGPs2LGJzuvevbuRJ08eIyAgwHrs3Llzhr29vfHOO+8k+34BAQEGkKAfERERwzAMIybGMNasMYx69QzDXE3LMJycDGPYMMO4ejVFXe3cudOoVauWsXbt2njdx6T3iOVB7twx/07feccwqlc3DIvF+ncbCMYeW1tjSfnyhjFunGFs22YYERFGrVq1DMBYsmSJtZsFCxYYgGGxWIxSpUoZbdu2NYYPH258//33xu7du43Q0NAsfJEiIvK42bFjh9GtWzcjf/78hp2dnZEvXz6jS5cuxvbt2xOdO3bsWAMwbt68ec+2+MLCwozhw4cb+fLlM5ycnIy6desaO3bsMNzd3Y1hw4ZZz9u4caMBGBs3brQe69u3r+Hi4pKs+/z0009G2bJlDUdHR6NUqVLG+PHjjR9//NEAjLNnz1rPa9KkidGkSZP7fj7Onj1rAPd89O3bN9E1kyZNMp544gnDwcHBKFasmDF27FgjIiIi0Xn36ze+cePGGaVLlzYcHR0NDw8Po3Xr1saWLVvuO27JPpKbFVkMI/6Ku9nPrVu3yJs3L2PHjk0wHTQqKgo3Nzf69OnDd999l+CaVq1acfbsWU6cOJGsewQGBuLu7k5AQABubm7pOXwREXlUGIZZ0TR2LMS+2+nsDC+/DO+8A8ncpcowjATrmXz++efs2rWLL774guLFi2fEyCU6OsE6acbff3MlIgIfsD6OubjgY7Fw+b8FrB0dHQkJCbFOR+nVqxeLFy9mypQpDBgwAAB/f38uXLiAl5eXdUMCERGRx8X27dtp0KABv/76K7169crq4Yiku+RmRdlyKmhynD59mtDQ0CQXHqxcuTJr164lLCws0c4jIiIiqWKxQMuW8NRTsGaNGbDt2gVffgnffgtDhsDbb0O+fA/oJi5UCwkJ4ZNPPsHf35/27dvTr1+/DH4RjwnDMNdJ+y9Iu7B2LXMCAswADTNIS7T/W0iI9cMCBQpQrlw5/P39rQs2T58+nTlz5iSY7uLh4aG10ERE5LGwdu1aduzYQY0aNXB2duaff/5hwoQJeHl50aVLl6wenkiWemiDtdjdR5Ladt7T0xPDMPDz80ty++Dw8HDCw8OtzwMDAzNuoCIi8mixWKBVKzNk++svM2DbvRsmToRp0+ICtmSs8+ni4sKWLVuYMWMGffr0sR6/evUqBQoUSNZOXfKfW7dgwwZmT5vG/N27eTY0lNj3zq8D7911uq2tLaVLl6ZcuXKUL1+ecuXKUa5cOcqWLUuuXLkSdZ8zZ86MfgUiIiLZlpubG2vWrOHrr78mKCiIPHny0KZNG8aPH69iFnnsPbTBWqz7/dJxr7bx48czbty4jBqSiIg8DiwWaN3aDNlWrYIPPoA9e+Dzz2HqVHj1VXjrrQcGbJUqVeKbb76xPg8ODqZmzZpUqlSJSZMmUbZs2Qx+IQ+HmJgYLly4gI+Pj/k4fJhju3fjc/o0B0uVouDhwwAcBZYBxSwWejVsCE89Rbl69eg9axblKlSwBmmlS5fGwcEhS1+TiIjIw6JOnTps3bo1q4chki09tMFa7NSM2Mq1+G7fvo3FYrnn9IxRo0YxfPhw6/PAwECKFi2aIeMUEZFHnMUCbdtCmzawcqUZsO3dC599ljBgy5MnWd1t27aNW7du8ddff1GpUiXeeOMN3n///cemYio0NJQTJ07EBWg+Phw7dowTJ04QGhqa5DU+hw9TEKBSJTpVrEjRXLmo16sXNGwIQE5gdosWmfYaREREROTx8dAGa6VLl8bZ2ZlDhw4lajt06BBlypS5Z0mqo6NjqrfsFRERSZLFAu3amSHbihVmwLZvH3z6KUyZAkOHmgHbf28M3UurVq04fPgwb7zxBitXruTzzz9nzpw5fP755/Tq1euRmR7q7+/Pv//+i6OjI3Xq1AHMDYvy5cvHvfZVcgC8gHKxj1y5KN+gARW6dDGDzQIFqAvUzaTXICIiIiJi8+BTsic7Ozs6dOjAwoULCQoKsh6/cOECGzdu1AKKIiKSNSwWaN/enBa6dClUq2YujD9hApQoAe+9B0lUW8fn5eXFihUrWLZsGaVLl+bq1as8//zzNG7cmIMHD2bKy0gPUVFRnDx5kmXLlvH5559z+vRpa9vcuXNp0qQJH3/8sfVY7pgYPHLkIJeDA/UdHRkAfAYsBU4CITlzcrhjR+ZPmcJHPj487+tLjWXLcO7fHwoUyOyXJyIiIiKCxbjX28JZbNWqVYSEhBAUFMSAAQPo3r07PXr0AKBt27bkyJEDHx8fatWqRfXq1Rk5ciRhYWGMGTOG27dvc/DgQfImY+FoSP4WqiIiIilmGGbA9sEHEBuK5cwJr70Gw4dDEpvwxBcWFsaXX37Jxx9/zJ07d7CxsWHQoEF89NFHSW7gkxWCgoI4fvx4gqmbPj4+nDx5ksjISOt5s2bNom/fvgBs2rSJAf3706J8eb6vWNHcwfPgQfwBd8ACYG8P9etDixbmo2ZNsHtoi+1FRERE5CGS3Kwo2wZrJUqU4Pz580m2nT17lhIlSgCwb98+RowYwY4dO7Czs8Pb25uJEydSunTpZN9LwZqIiGQ4w4AlS8yA7Z9/zGM5c8Lrr5sBWxI7UcZ38eJF3nrrLf744w/AXGv0448/5sUXX8TW1jaDB5/YO++8w/79+/Hx8eHy5cv3PM/Z2ZmyZctSrlw5XujXjxa5cpkh2rp1sG0bREQkvKBy5bggrVEjcHXN4FciIiIiIpLYQx+sZSYFayIikmliYuICtn//NY+5uZkB27BhDwzYNm7cyGuvvcbh/3bBbNGiBWvWrEm3tdciIiKIiIjA9b9A6+DBgwwcOBAnJye2bNliPa9q1ar8ExsQAvnz56dcuXLWR/ny/2/vzuOiqv7/gb8GZgaQTQEVUVnUBFz4qElFaoCKC4gogQFqLC1+XZJ+LplLLO7ihuX+yS1zF/ST9UlygbRMBS1zSS0NiVxQlE0BZTi/P/jM5HVGBRRH8PV8PObx4J577p1zz71z586bs7jCxdkZzUtLYbB/f0Ugbf9+IC9P+obNmgE+PhWBtB49gMaNn8pxEBERERE9CQbWqoCBNSIieubKy4GdOysCbOqJeCwtgQ8/rHg9ZGZroGLssqVLlyImJgYzZszAyJEjq/z2eXl5Wl03z549iwsXLiA2NhaffPIJAOCPP/7ASy+9BGNjYxQVFWlax23YsAF3796Fq6srnJ2d0UAdEMzJAfbt+6dVWlaW9I0tLQFv73+CaS+9VDEuHRERERHRc6SysaJaO3kBERFRrWZgAAQGVoy7tn070K4dkJ8PxMdXTHIQH1+xrINcLsfo0aNx/vx5DBs2TJP+3//+F/Pnz5eMa5adnY2UlBQsWrQI//d//wcvLy/Y2tqiQYMG8PDwQGRkJBISEvDVV1/h/PnzUKlUkkkGnJyckJSUhOPHj0taxQ0ePBiRkZF4rX17NDhypGLG0w4dKlqchYUBq1dXBNUUCsDLC5g+HTh8GLhxA9ixAxgxAmjdmkE1IiKiJ7R//35ERUXBxcUFpqamaNq0KQICAnDs2DGd+Y8fP46ePXvCzMwM9evXR2BgIC5evKiVLzExEYGBgXBycoJMJoOXl9dDy5CSkoIuXbrAxMQElpaW8Pf3x+nTp5/WIeokk8kQFxenWT5z5gzi4uKQmZmpldfLywvt2rWr1vsUFBRgxowZmmcoMzMztG/fHnPmzEFJSYlW/nv37iE+Ph6Ojo4wMjKCi4sLPvvsM537vnjxIgIDA1G/fn2YmZnBx8cHx48f11mGyZMno3Xr1qhXrx6aNm2K4ODgStXx5cuXERcXp3MCrIiICE0vhbokIiJCM3zYs8ARgImIiPTJwAB4801g4EAgKakioHb6dEVLtsTEiu6h0dEVLb0e0KhRI83fJSUl+OCDD3Dx4kWUl5dj/PjxAIDhw4fj66+/1vnWzZo1k3TfVHfhbNKkiSaPoaGhdKbtsjLg2LF/WqQdOqQ9Ttq//iUdJ83UtNrVQ0RERI+2bNky5ObmIjo6Gm3atMH169cxf/58vPbaa0hJSUH37t01ec+ePQsvLy906NABW7du1UwA2K1bN60JAJcvXw5TU1N0794du3bteuj7/+c//8HAgQMREBCApKQk5OfnIz4+Ht26dUN6enqVxj+vip9++gnNmjXTLJ85cwbx8fHw8vJ6qkGVrKwsJCYmYujQoRgzZgzMzMxw8OBBxMXFYc+ePdizZ4/kn48jRozA+vXrMW3aNLi7uyMlJQXR0dEoLCzEpEmTNPmuX7+Obt26oUGDBli9ejWMjY0xa9YseHl5IT09Hc7Ozpq8/v7+yMjIQFxcHDp37ozs7GxMnToVHh4eOHnyJBwcHB5a/suXL2sCfR06dHhq9UL3ESTy8/MFAJGfn6/vohAR0YtOpRJiyxYh2rQRomLKAyHq1xdi6lQhHvE9pVKpxOeffy46duwoCgsLNemTJ08Wbdq0EYGBgWLy5Mli/fr1Ij09XRQUFFSuPOXlQpw7J8TixUIMGCCEpeU/5VK/mjcXIipKiI0bhbh27QkrgIiIiKrimo7v3sLCQtG4cWPRo0cPSXpwcLCwsbGR/PbNzMwUCoVCfPTRR5K8KpVK83fbtm2Fp6enzvd3dnYWbm5uory8XLJPpVIpwsLCqnNI1bJt2zYBQKSmpmqt8/T0FG3btq3WfouKikRRUZFW+ty5cwUAcfDgQU3aqVOnhEwmEzNnzpTkfe+994SJiYnIzc3VpI0fP14oFAqRmZmpScvPzxc2NjZi0KBBmrTff/9dABBTpkyR7PPQoUMCgFiwYMEjy5+eni4AiDVr1mitCw8PF6ampo/c/mm4c+dOjb/H/cLDw4WDg8MT76eysSJ2BSUiInqeGBgAgwZVTGyweTPg6lox4H9MTEUX0RkzgIICHZsZ4J133sGxY8ckTfqnT5+O06dPIykpCdOnT8eQIUPQuXNnmJubP7wM164BGzcCUVGAgwPg7AyMGlUxJlx+fkXruYEDgaVLgfPngUuXgFWrgNBQ4L5WdERERFTzGun47jUzM0ObNm3w119/adLKysrw9ddf480335SMF+Xg4ABvb2/s2LFDsg8Dg8eHC3Jzc3Hu3Dn07dtX0mrLwcEB7dq1w86dO6FSqR66/ZIlS2BgYICcnBxN2vz58yGTySRjyJaXl6NBgwYYO3asJu3+rqBr165FcHAwAMDb2xsymQwymQxr166VvF96ejq6deuGevXqoUWLFpg9ezbKy8sfeYympqYw1dH6/pVXXgEASR3v3LkTQghERkZK8kZGRqK4uBi7d+/WpO3YsQPdu3eXtDazsLBAYGAgdu3ahbKyMgCAQqEAAFg+0Huh/v/G4zU2Nn5o2dPS0uDu7q4pg7pe7u9CC1SMqevr6wszMzM0b94cY8eORWlpqSTP3bt3MX36dLi4uMDIyAgNGzZEZGQkrl+/Lsnn6OiIfv36ITk5GR07doSxsTHi4+ORlpYGmUyGjRs3YsKECWjSpAnMzMzg7++Pa9euobCwEO+//z5sbGxgY2ODyMhIFBUVSfa9ZMkSvPHGG2jUqBFMTU3Rvn17JCQkSIZB0Qd2BSUiInoeGRoCb70FBAUB27ZVdBE9exaYMgVYsAAYOxb44APggQBZtWYHvX0bOHDgn+6d6tlK1ZRKoEuXf7p3vvxyRfmIiIjqiNu3b1d5GyMjI8jlFT+py8rKUFpaCgMDA5iYmFRrv7qCN9WVn5+P48ePS7qBXrhwAcXFxXBzc9PK7+bmhj179qCkpOSRgZoH3f3fcBBGRkZa64yMjHDnzh1cuHABrVu31rl9z549IYTAvn37EBoaCgDYu3cvTExMsGfPHk2+jIwM5OXloWfPnjr34+fnh5kzZ2LSpElYsmQJOnXqBACSbqhXr17F4MGDMXbsWMTGxmLHjh2YOHEi7Ozs8Pbbb1f6mNX2798PAGjbtq0m7dSpU2jYsCFsbW0ledV1rp7Vvbi4GBcuXMDAgQO19uvm5obi4mJcvHgRrVu3hoODAwICArBw4UK8/PLLcHd3R3Z2NkaPHg17e3uEhIQ8tIydOnXCmjVrEBkZiSlTpsDPzw8AJF1o7927h/79++Odd97B2LFjceDAAUybNg2WlpaIiYkBUBHYDAgIwMGDB/HRRx/h9ddfx6VLlxAbGwsvLy9kZGRIrvvjx4/jt99+w5QpU+Dk5ARTU1PNZ2HSpEnw9vbG2rVrkZmZiXHjxiE0NBRyuRz/+te/sGnTJvz888+YNGkSzM3N8emnn2r2e+HCBYSFhcHJyQlKpRInTpzAjBkzcPbsWaxevboSZ62GPHHbuDqAXUGJiOi5V1YmxIYNQjg7/9MF08pKiJkzhahst061e/eE+OknIaZNE+KNN4RQKLS7d3boIMS4cULs3i2Eju4PREREdQmAKr+2bt2q2X7r1q0CgFZ3SRsbm0rv72kaPHiwkMvlIiMjQ5P2448/CgBi06ZNWvlnzpwpAIjLly/r3N/DuoKqVCphZWWl1eX01q1bwtzcXAAQhw4demRZmzVrJqKiooQQQpSWlgpTU1MxYcIEAUBcunRJCCHEjBkzhEKhkHTJBCBiY2M1y4/rCgpAHDlyRJLepk0b0bt370eWT5cTJ04IExMTMXDgQEm6j4+PcHZ21rmNUqkU77//vhBCiL///lsAELNmzdLKt3HjRq16u3v3rnjvvfck14ubm5v4888/H1vWx3UFffBaFkIIX19fyXFs2rRJABBJSUk697106VJNmoODgzA0NBTnzp2T5E1NTRUAhL+/vyT9ww8/FADE6NGjJekDBgwQVlZWDz0ulUol7t27J7744gthaGgobt68KTkudgUlIiIiKUPDitk2T58GvvyyYkbNmzeBSZMAJydg9mzggebyGkIA584BixcDAwYA1taAhwfwyScVLdXu3QPs7YF33gE2bQJycoCffwbmzgV69+bkA0RERLXIJ598gg0bNmhaOD3oUa3bq9ry3cDAACNHjsS+ffswbdo05OTk4I8//sCQIUNw584dTZ5H6dGjB/bu3QsAOHToEO7cuYMxY8bAxsZG02pt79698PDweKJWfba2tprum2pubm64dOlSlfaTmZmJfv36oXnz5vj888+11lelfiubd/jw4UhKSsLChQvx/fffY8uWLVAqlejevXuVy6/rffz9/SVpD9bL119/jfr168Pf3x9lZWWaV4cOHWBra4u0tDSt7R/WSrFfv36SZVdXVwDQtKa7P/3mzZuS7qA///wz+vfvD2traxgaGkKhUODtt9+GSqXC+fPnq3zsTwu7ghIREdUmhobA4MEV3UQ3bwamTgV+/x2YOBGYPx8YPx4YMaIiyLZv3z/dO7OzpfupXx/o0eOf7p0tWwLV6UZKRERUBzw4llNl3N/9ceDAgSgqKtIKImVmZj5p0aokPj4e06dPx4wZMzBq1CjJOmtrawAV46I96ObNm5DJZJpxu6oiJiYGRUVFmD59uqbroJ+fHyIjI/H555+jadOmj9y+Z8+eWLduHX7//Xfs3bsXHTt2RKNGjdC9e3fs3bsXYWFhOHToECZPnlzlst1Pffz3MzIyQnFxcaX3cenSJXh7e0Mul2Pfvn2wsrLSeo9ffvlFa7vbt2/j7t27mvwNGjSATCZ76LkAoMm7e/durFq1Ctu2bUNQUJAmX69eveDo6Ii4uDisWbOm0sfwoHr16ml1/zUyMkJJSYlm+dq1a8jLy4NSqdS5jxs3bkiW759h/kEP1pl6nw9LLykpgZmZGbKystCtWzc4Oztj0aJFcHR0hLGxMY4ePYqRI0dW6Tw+bQysERER1UZyOTBkCBASUtHKbOpU4I8/gAkTKsZj+99/iTWUSqBr138CaZ06cZw0IiKi/3nS8c3kcrlmvLWnud+qiI+PR1xcHOLi4jBp0iSt9S1btoSJiQlOnjypte7kyZNo1apVlcZXU5PL5ViwYAGmTp2KP//8EzY2NmjSpAl69+4NJycnyXheuvTo0QNARau0PXv2wMfHR5M+ZcoUHDhwAKWlpQ8dX+1ZuXTpEry8vCCEQFpams7jat++PTZv3oyrV69KxllT13m7du0AACYmJmjVqtVDz4WJiQlatGgBAJpAnXoSArX69eujVatWmnHbapKNjQ2sra0lky/c78FJsao15u9j7Ny5E7dv30ZycrJkwgddgcxnjV1BiYiIajO5HBg6FPjtN2Dt2oqWZ+qgWseOFS3YUlKAW7cqWrBNnAi4uzOoRkREVIdMmzYNcXFxmDJlCmJjY3Xmkcvl8Pf3R3JyMgoLCzXpWVlZSE1NRWBg4BOVwczMDO3bt0eTJk1w/Phx7Nu3D9HR0Y/drkmTJmjTpg2SkpJw7NgxTWDNx8cH169fx4IFC2BhYaEVWHqQugVhTbRcysrKgpeXF1QqFfbv3y8J7NwvICAAMpkM69atk6SvXbsWJiYm6NOnjyZt4MCB2L9/v2RW0cLCQiQnJ6N///6aQK2dnR0A4PDhw5J95ubm4vz5848NXD6NeunXrx9yc3OhUqnQuXNnrZezs3O1911Z6mDd/S1FhRD497//XePv/ThssUZERFQXyOVAeHhFN9HjxyvGXWvYUN+lIiIioho2f/58xMTEoE+fPvDz89MKwLz22muav+Pj4+Hu7o5+/frh448/RklJCWJiYmBjY4OxY8dKtsvIyNB0ZS0oKIAQAtu3bwdQ0XpKHVxKS0tDeno63NzcIITA0aNHMWfOHPTp00erO+rD9OjRA5999hlMTEzQpUsXAICTkxOcnJzw3XffSQJND6NuDbZy5UqYm5vD2NgYTk5OOruAVkVOTg68vb1x5coVrFq1Cjk5OcjJydGsb9asmSa41bZtW7zzzjuIjY2FoaEh3N3d8d1332HlypWYPn26pLvjuHHjsH79evj5+WHq1KkwMjLC7NmzUVJSgri4OE2+wMBAxMTEYPjw4cjOzkanTp1w5coVzJ07F3fu3Hls8FLdUnHDhg1wdXWFmZkZ7OzsNAG7yggJCcGGDRvg6+uL6OhovPLKK1AoFMjOzkZqaioCAgJ0znD6NPn4+ECpVCI0NBQfffQRSkpKsGzZMty6datG37cy2GKNiIioLpHLgVdeYVCNiIjoBbFr1y4AFWNxeXh4aL3u5+LigrS0NCgUCgQFBSEiIgKtWrXCgQMH0PCBZ4fFixcjODgYwcHB+Ouvv3DmzBnNcmpqqiafUqlEUlISBg0ahAEDBmDbtm2YOnUqduzYAcNKtpBXd/Ps2rWrpDuqOr0y3UCdnJyQmJiIEydOwMvLC+7u7pq6eRJnzpzBxYsXUVpaiiFDhmjV74MTGCxduhQff/wxPvvsM/Tq1Qvbt2/HokWLtLrnNmzYEAcPHkTLli0RHh6OoKAgKBQKpKWlwcXFRZPPzMwMhw8fxuDBg7F8+XL4+vpi/PjxaNq0KX744Qd4eXk9svz16tXD6tWrkZubi169esHd3R0rV66sUh0YGhriq6++wqRJk5CcnIyBAwdiwIABmD17NoyNjdG+ffsq7a86XFxckJSUhFu3biEwMBAffPABOnTogE8//bTG3/txZEIIoe9C6FtBQQEsLS2Rn58PCwsLfReHiIiIiIiIiIj0qLKxIrZYIyIiIiIiIiIiqgYG1oiIiIiIiIiIiKqBgTUiIiIiIiIiIqJqYGCNiIiIiIiIiIioGhhYIyIiIiIiIiIiqgYG1oiIiIiIiIiIiKqBgTUiIiIiIiIiIqJqYGCNiIiIiIiIiIioGhhYIyIiIiIiIiIiqgYG1oiIiIiIiIiIiKqBgTUiIiIiIiIiIqJqqPWBtaKiInz44Yews7ODsbExOnTogM2bN+u7WEREREREREREVMfJ9V2AJxUYGIj09HTMnj0brVu3xsaNGxEaGory8nKEhYXpu3hERERERERERFRHyYQQQt+FqK7//ve/8PPz0wTT1Hr16oXTp08jKysLhoaGj91PQUEBLC0tkZ+fDwsLi5osMhERERERERERPecqGyuq1V1Bd+zYATMzMwQHB0vSIyMjcfnyZRw5ckRPJSMiIiIiIiIiorquVgfWTp06BVdXV8jl0h6tbm5umvVEREREREREREQ1oVaPsZabm4sWLVpopVtZWWnW61JaWorS0lLNcn5+PoCKZn5ERERERERERPRiU8eIHjeCWq0OrAGATCar8rpZs2YhPj5eK7158+ZPrVxERERERERERFS7FRYWwtLS8qHra3VgzdraWmertJs3bwL4p+XagyZOnIgxY8ZolsvLy3Hz5k1YW1s/MlBXmxQUFKB58+b466+/OCGDHrD+9Yv1r1+sf/3jOdAv1r9+sf71i/WvX6x//WL96xfrX7/qYv0LIVBYWAg7O7tH5qvVgbX27dtj06ZNKCsrk4yzdvLkSQBAu3btdG5nZGQEIyMjSVr9+vVrrJz6ZGFhUWcu6tqI9a9frH/9Yv3rH8+BfrH+9Yv1r1+sf/1i/esX61+/WP/6Vdfq/1Et1dRq9eQFAwcORFFREZKSkiTp69atg52dHV599VU9lYyIiIiIiIiIiOq6Wt1irW/fvvDx8cHw4cNRUFCAVq1aYdOmTdi9eze+/PJLGBoa6ruIRERERERERERUR9XqwBoAJCcnY/LkyYiJicHNmzfh4uKCTZs2ISQkRN9F0ysjIyPExsZqdXmlZ4P1r1+sf/1i/esfz4F+sf71i/WvX6x//WL96xfrX79Y//r1Ite/TDxu3lAiIiIiIiIiIiLSUqvHWCMiIiIiIiIiItIXBtaIiIiIiIiIiIiqgYE1IiIiIiIiIiKiamBgrZZZu3YtZDIZMjIy9F2UF4q63nW9xo0bV+n9REREwMzMrAZLWvfcX/dpaWla64UQaNWqFWQyGby8vJ55+V40n376KWQyGdq1a6fvotR5vPafL/z+fX48ybmQyWSIi4t7+oWq43jv148jR45g4MCBsLe3h5GRERo3bgwPDw+MHTtW30V74Rw+fBjBwcFo0qQJlEolbG1tERQUhJ9++qnK+zpz5gzi4uKQmZn59AtaR6jv88bGxrh06ZLWei8vL96PatiDv3+NjY1ha2sLb29vzJo1Czk5Ofou4nOFgTWiKlizZg1++uknyWv06NH6LtYLwdzcHKtWrdJK//7773HhwgWYm5vroVQvntWrVwMATp8+jSNHjui5NC8GXvtEpG+89z9733zzDV5//XUUFBQgISEB3333HRYtWoQuXbpgy5Yt+i7eC+Wzzz5Dly5dkJ2djYSEBOzduxfz5s3D33//ja5du2Lx4sVV2t+ZM2cQHx/PwFollJaWYsqUKfouxgtN/ft3z549WLJkCTp06IA5c+bA1dUVe/fu1XfxnhsMrBFVQbt27fDaa69JXvb29vou1gvhrbfeQlJSEgoKCiTpq1atgoeHx1M9D8XFxU9tX3VJRkYGTpw4AT8/PwDQGex5Enfu3Hmq+6srnuW1T0T0oJq+95NuCQkJcHJyQkpKCkJCQuDp6YmQkBDMmzcPWVlZ+i7eC+PHH3/Ehx9+CF9fXxw8eBBDhw7FG2+8gSFDhuDgwYPw9fVFdHQ0fvzxR30XtU7q06cPNm7ciBMnTui7KC8s9e/fbt264c0338TChQvx66+/wtTUFIGBgbh27Zq+i/hcYGCtlsvIyEBISAgcHR1hYmICR0dHhIaGajWZVTflTE1NxfDhw2FjYwNra2sEBgbi8uXLeip93bJlyxZ4eHjA1NQUZmZm6N27N37++WedeU+fPo0ePXrA1NQUDRs2xKhRoxhUeIzQ0FAAwKZNmzRp+fn5SEpKQlRUlFb++Ph4vPrqq7CysoKFhQU6deqEVatWQQghyefo6Ih+/fohOTkZHTt2hLGxMeLj42v2YGop9Y+p2bNn4/XXX8fmzZsl121mZiZkMhkSEhIwY8YM2Nvbw9jYGJ07d8a+ffsk+4qLi4NMJsPx48cRFBSEBg0aoGXLls/0eGqLmrj233nnHVhZWem873Tv3h1t27atgSOpW7y8vHR2wY2IiICjo6NmWf25mDdvHhYsWAAnJyeYmZnBw8MDhw8ffnYFrsMqey6oeh53709LS9PZZV197a9du1aS/u9//xutW7eGkZER2rRpg40bN/Jc6ZCbmwsbGxvI5XKtdQYG0p9wlXkGVQ9HwmfQqpk1axZkMhmWLVumdS7kcjmWLl0KmUyG2bNna9LPnj2L0NBQNG7cGEZGRrC3t8fbb7+N0tJSrF27FsHBwQAAb29vTTe7Bz8nVOGjjz6CtbU1JkyY8Mh8JSUlmDhxIpycnKBUKtG0aVOMHDkSeXl5mjwDBgyAg4MDysvLtbZ/9dVX0alTp6dd/DrL3t4e8+fPR2FhIVasWKFJz8jIQP/+/WFlZQVjY2N07NgRW7du1dr+77//xvvvv4/mzZtDqVTCzs4OQUFBtTpIx8BaLZeZmQlnZ2ckJiYiJSUFc+bMwZUrV+Du7o4bN25o5X/33XehUCiwceNGJCQkIC0tDUOGDNFDyWsnlUqFsrIyyQsAZs6cidDQULRp0wZbt27F+vXrUVhYiG7duuHMmTOSfdy7dw++vr7o0aMHdu7ciVGjRmHFihV466239HFItYaFhQWCgoI03VGAikCDgYGBzrrLzMzEsGHDsHXrViQnJyMwMBAffPABpk2bppX3+PHjGD9+PEaPHo3du3fjzTffrNFjqY2Ki4uxadMmuLu7o127doiKikJhYSG2bdumlXfx4sXYvXs3EhMT8eWXX8LAwAB9+/bVOQ5JYGAgWrVqhW3btmH58uXP4lBqnZq49qOjo3Hr1i1s3LhRsu2ZM2eQmpqKkSNH1twBvaCWLFmCPXv2IDExERs2bMDt27fh6+uL/Px8fReN6KGqcu+vjJUrV+L999+Hm5sbkpOTMWXKFMTHx+scR/JF5+HhgSNHjmD06NE4cuQI7t27pzMfn0FrjkqlQmpqKjp37oxmzZrpzNO8eXO8/PLL2L9/P1QqFU6cOAF3d3ccPnwYU6dOxbfffotZs2ahtLQUd+/ehZ+fH2bOnAmg4ntBPbSMukUoSZmbm2PKlClISUnB/v37deYRQmDAgAGYN28ehg4dim+++QZjxozBunXr0L17d5SWlgIAoqKikJWVpbWfs2fP4ujRo4iMjKzx46lLfH19YWhoiAMHDgAAUlNT0aVLF+Tl5WH58uX4z3/+gw4dOuCtt96SBI7//vtvuLu7Y8eOHRgzZgy+/fZbJCYmwtLSErdu3dLT0TwFgmqVNWvWCAAiPT1d5/qysjJRVFQkTE1NxaJFi7S2GzFihCR/QkKCACCuXLlSo+Wu7dT1p+uVlZUl5HK5+OCDDyTbFBYWCltbWzFo0CBNWnh4uAAgOTdCCDFjxgwBQPzwww/P5Hhqk/uv+dTUVAFAnDp1SgghhLu7u4iIiBBCCNG2bVvh6empcx8qlUrcu3dPTJ06VVhbW4vy8nLNOgcHB2FoaCjOnTtX48dSm33xxRcCgFi+fLkQouL6NjMzE926ddPk+fPPPwUAYWdnJ4qLizXpBQUFwsrKSvTs2VOTFhsbKwCImJiYZ3cQtUxNX/uenp6iQ4cOkvzDhw8XFhYWorCwsGYOqhZ78PvX09NTZ72Hh4cLBwcHzbL6c9G+fXtRVlamST969KgAIDZt2lTTRa9zqnsuhBACgIiNja35QtYRlbn3q+9Pqampkm3V1/6aNWuEEBX3I1tbW/Hqq69K8l26dEkoFAqtc/Wiu3HjhujatavmeVOhUIjXX39dzJo1S3OP5jNozbp69aoAIEJCQh6Z76233hIAxLVr10T37t1F/fr1RU5OzkPzb9u2Tednhv5x/32+tLRUtGjRQnTu3FnzHOPp6Snatm0rhBBi9+7dAoBISEiQ7GPLli0CgFi5cqUQQoh79+6Jxo0bi7CwMEm+jz76SCiVSnHjxo1ncGS1x+PiDkII0bhxY+Hq6iqEEMLFxUV07NhR3Lt3T5KnX79+okmTJkKlUgkhhIiKihIKhUKcOXOm5gqvB2yxVssVFRVhwoQJaNWqFeRyOeRyOczMzHD79m389ttvWvn79+8vWXZzcwMAnbOtkLYvvvgC6enpkldKSgrKysrw9ttvS1qyGRsbw9PTU+d/YQcPHixZDgsLA1AR6aeH8/T0RMuWLbF69WqcPHkS6enpOrvCAcD+/fvRs2dPWFpawtDQEAqFAjExMcjNzdWaxcbNzQ2tW7d+FodQa61atQomJiYICQkBAJiZmSE4OBgHDx7E77//LskbGBgIY2NjzbK5uTn8/f1x4MABqFQqSV62Dqycmrj2o6Oj8csvv2jGhSkoKMD69esRHh7O2YtrgJ+fHwwNDTXL/P6l2qAq9/7HOXfuHK5evYpBgwZJ0u3t7dGlS5enVua6wtraGgcPHkR6ejpmz56NgIAAnD9/HhMnTkT79u1x48YNPoM+J8T/hlooLi7G999/j0GDBqFhw4Z6LlXdoVQqMX36dGRkZOjsVqhugRYRESFJDw4OhqmpqWY4ErlcjiFDhiA5OVnTWlylUmH9+vUICAiAtbV1zR5IHaS+9v/44w+cPXtWc3+5/37k6+uLK1eu4Ny5cwCAb7/9Ft7e3nB1ddVbuWsCA2u1XFhYGBYvXox3330XKSkpOHr0KNLT09GwYUOdA7A/eMMwMjICwMHaK8vV1RWdO3eWvNR9wd3d3aFQKCSvLVu2aHXJlcvlWufB1tYWQMV4GvRwMpkMkZGR+PLLL7F8+XK0bt0a3bp108p39OhR9OrVC0DFWC4//vgj0tPTMXnyZADa13uTJk1qvvC12B9//IEDBw7Az88PQgjk5eUhLy8PQUFBACDpogj8cz0/mHb37l0UFRVJ0ln3lVMT135AQAAcHR2xZMkSABVjcd6+fZvdQGsIv3+ptqnqvf9x1M84jRs31lqnK40qdO7cGRMmTMC2bdtw+fJl/L//9/+QmZmJhIQEPoPWMBsbG9SrVw9//vnnI/NlZmaiXr16kMvlUKlUD+02StUXEhKCTp06YfLkyVrdonNzcyGXy7WCmTKZDLa2tpJrOyoqCiUlJdi8eTMAICUlBVeuXGE30Gq4ffs2cnNzYWdnp7kXjRs3TuteNGLECADQ3I+uX79eJz8j2qNhUq2Rn5+Pr7/+GrGxsfj444816aWlpbh586YeS/ZisbGxAQBs374dDg4Oj81fVlaG3NxcyYPN1atXAWj/8CJtERERiImJwfLlyzFjxgydeTZv3gyFQoGvv/5a0nJq586dOvPLZLKaKGqdsXr1agghsH37dmzfvl1r/bp16zB9+nTNsvp6vt/Vq1ehVCq1WkKx7ivvaV/7BgYGGDlyJCZNmoT58+dj6dKl6NGjB5ydnWvqEOoUY2NjneOj6RrflGoWz0XNqOy9X32vUY9jpPZg/aufcXQNTq3re4O0KRQKxMbGYuHChTh16hQCAgIA8Bm0phgaGsLb2xu7d+9Gdna2zmBAdnY2jh07hr59+8LKygqGhobIzs7WQ2nrNplMhjlz5sDHxwcrV66UrLO2tkZZWRmuX78uCa4JIXD16lW4u7tr0tq0aYNXXnkFa9aswbBhw7BmzRrY2dlp/ilJlffNN99ApVLBy8tL83t44sSJCAwM1Jlf/XzZsGHDOvkZYYu1Wkwmk0EIofmvt9rnn3+u1d2Kak7v3r0hl8tx4cIFrdZs6teDNmzYIFlWDyCua1YzkmratCnGjx8Pf39/hIeH68wjk8kgl8sl3a6Ki4uxfv36Z1XMOkOlUmHdunVo2bIlUlNTtV5jx47FlStX8O2332q2SU5ORklJiWa5sLAQu3btQrdu3STnhKqmJq79d999F0qlEoMHD8a5c+cwatSoGil7XeTo6Ijz589Lggm5ubk4dOiQHkv1YuK5ePqqcu9Xz+b566+/Svbx1VdfSZadnZ1ha2ur1ZUrKyuL50qHK1eu6ExXD/ViZ2fHZ9BnYOLEiRBCYMSIEVq/r1QqFYYPHw4hBCZOnAgTExN4enpi27Ztjwzss8Vy9fTs2RM+Pj6YOnWqpAdEjx49AABffvmlJH9SUhJu376tWa8WGRmJI0eO4IcffsCuXbsQHh7O59MqysrKwrhx42BpaYlhw4bB2dkZL730Ek6cOPHQe5G5uTkAoG/fvkhNTdV0Da0r2GKtlpLJZLCwsMAbb7yBuXPnwsbGBo6Ojvj++++xatUq1K9fX99FfGE4Ojpi6tSpmDx5Mi5evIg+ffqgQYMGuHbtGo4ePQpTU1PEx8dr8iuVSsyfPx9FRUVwd3fHoUOHMH36dPTt2xddu3bV45HUHvdPaa6Ln58fFixYgLCwMLz//vvIzc3FvHnztILQ9HjffvstLl++jDlz5uh86G7Xrh0WL16MVatWYeHChQAq/sPr4+ODMWPGoLy8HHPmzEFBQYHkc0DV87Sv/fr16+Ptt9/GsmXL4ODgAH9//5oodp2ibmU5dOhQrFixAkOGDMF7772H3NxcJCQkwMLCQs8lfHHwXNScqtz7+/Xrh549e2LWrFlo0KABHBwcsG/fPiQnJ0u2MTAwQHx8PIYNG4agoCBERUUhLy8P8fHxaNKkCQwM+P/++/Xu3RvNmjWDv78/XFxcUF5ejl9++QXz58+HmZkZoqOj+Qz6DHTp0gWJiYn48MMP0bVrV4waNQr29vbIysrCkiVLcOTIESQmJuL1118HACxYsABdu3bFq6++io8//hitWrXCtWvX8NVXX2HFihUwNzdHu3btAFTMkmtubg5jY2M4OTmx1WAlzJkzBy+//DJycnLQtm1bAICPjw969+6NCRMmoKCgAF26dMGvv/6K2NhYdOzYEUOHDpXsIzQ0FGPGjEFoaChKS0u1xmYjqVOnTmnGS8vJycHBgwexZs0aGBoaYseOHZpWgitWrEDfvn3Ru3dvREREoGnTprh58yZ+++03HD9+XDObtHq23DfeeAOTJk1C+/btkZeXh927d2PMmDFwcXHR5+FWn75mTaDqWbJkiQAgTp48KYQQIjs7W7z55puiQYMGwtzcXPTp00ecOnVKODg4iPDwcM12D5vV42EzOZFUZWZF2blzp/D29hYWFhbCyMhIODg4iKCgILF3715NnvDwcGFqaip+/fVX4eXlJUxMTISVlZUYPny4KCoqehaHUutUpu6F0J4ZcfXq1cLZ2VkYGRmJFi1aiFmzZolVq1YJAOLPP//U5HNwcBB+fn41VPrab8CAAUKpVD5ydquQkBAhl8vF4cOHBQAxZ84cER8fL5o1ayaUSqXo2LGjSElJkWyjnhX0+vXrNX0ItVZNX/tqaWlpAoCYPXv2Uz6CuuXB718hhFi3bp1wdXUVxsbGok2bNmLLli0PnRV07ty5WvsEZ6isluqeCyFY55VVlXv/1atXxZUrV0RQUJCwsrISlpaWYsiQISIjI0MyK6jaypUrRatWrYRSqRStW7cWq1evFgEBAaJjx441fFS1y5YtW0RYWJh46aWXhJmZmVAoFMLe3l4MHTpUazY9PoPWvJ9++kkEBQWJxo0bC7lcLho1aiQCAwPFoUOHtPKeOXNGBAcHC2tra6FUKoW9vb2IiIgQJSUlmjyJiYnCyclJGBoa6vycvOge9QwUFhYmAGhmBRVCiOLiYjFhwgTh4OAgFAqFaNKkiRg+fLi4deuWzv2r99GlS5eaOoRaT30O1C+lUikaNWokPD09xcyZM3V+P5w4cUIMGjRINGrUSCgUCmFrayu6d++umVla7a+//hJRUVHC1tZWKBQKYWdnJwYNGiSuXbv2rA7vqZMJ8b+pHKhWiI6OxuLFi5GXl6dpTklE9DzIzMyEk5MT5s6di3Hjxum7OFRJY8eOxbJly/DXX3/xv+WPwO/f5wfPRd2Sl5eH1q1bY8CAAVpjJ9HTExERge3bt2tNIkRERE+OXUFriWPHjiE9PR2rV69G//79+SBJRERP5PDhwzh//jyWLl2KYcOGMaj2EPz+fX7wXNR+V69exYwZM+Dt7Q1ra2tcunQJCxcuRGFhIaKjo/VdPCIiomphYK2WCAoKQn5+Pvr3749PP/1U38UhIqJazsPDA/Xq1UO/fv0ks7qSFL9/nx88F7WfkZERMjMzMWLECNy8eRP16tXDa6+9huXLl2vGSyIiIqpt2BWUiIiIiIiIiIioGjj9DhERERERERERUTUwsEZERERERERERFQNDKwRERERERERERFVAwNrRERERERERERE1cDAGhERERERERERUTUwsEZERERERERERFQNDKwRERERERERERFVAwNrRERERERERERE1cDAGhERERERERERUTX8f++4PU0HNMzaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_silicon_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY Silicon with CY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.06238141, 49.17238747, 41.59249494, 18.0018168 ,  5.46358173,\n",
       "       12.1107789 , 19.87997362, 28.03714477, 36.00806573, 41.96864256,\n",
       "       44.54149611, 47.0336623 ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.06739732, 49.19871481, 46.38849762, 21.98268465,  7.24247022,\n",
       "       11.15793683, 16.08159249, 15.88039615, 26.06336839, 36.72660236,\n",
       "       42.34819421, 46.02331588])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_nitrate_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY Nitrate with WY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.61595001, 23.26098825, 20.61280733,  7.68941071,  5.75685816,\n",
       "        1.97729292,  2.72072923,  4.003872  , 11.9685279 , 19.67454109,\n",
       "       23.13616984, 23.96606766])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 42,
     "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 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY Silicon with WY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.04129223, 51.42741271, 47.97131586, 17.22230011,  7.05583654,\n",
       "       10.13642248, 13.54628137, 15.74654144, 32.40864067, 40.63494107,\n",
       "       46.43542205, 50.00465601])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Depth-integrated 0-100 m Diatoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Diatom data for original years\n",
    "\n",
    "monthly_array_diatoms_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 using higher temperature threshold       \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "                \n",
    "### 2019 using higher temperature threshold                \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/3403781678.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_orig_slice[monthly_array_diatoms_orig_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#years, months, data\n",
    "monthly_array_diatoms_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul08_Thrml19/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(7, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jul19_Thrml08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n",
    "        \n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3487216/2320522072.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_depthint_slice[monthly_array_diatoms_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('WY Diatoms (0-100 m) with CY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 thermal')\n",
    "\n",
    "\n",
    "ax.set_title('CY Diatoms (0-100 m) with WY Thermal',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.24531845,  0.1722018 , 11.15381515, 35.62884681, 32.77864728,\n",
       "       17.98166513, 15.88818571,  8.06120314,  0.97470466,  0.65459443,\n",
       "        1.07020377,  0.641704  ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_diatoms_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py39)",
   "language": "python",
   "name": "py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}