{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from salishsea_tools import viz_tools, places, visualisations\n",
    "from matplotlib import pyplot as plt, dates\n",
    "from datetime import datetime, timedelta\n",
    "from calendar import month_name\n",
    "from scipy.io import loadmat\n",
    "from tqdm.notebook import tqdm\n",
    "from salishsea_tools import nc_tools\n",
    "from dask.diagnostics import ProgressBar\n",
    "import cmocean\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 12, 'axes.titlesize': 'medium'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data for original cold and warm years\n",
    "\n",
    "monthly_array_temp_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['votemper']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 original temp      \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)\n",
    "\n",
    "\n",
    "### 2019 original               \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_grid_T.nc') as ds:\n",
    "            q = ds.votemper.isel(deptht=0, **slc).values\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_temp_slice[year-2007,month-1,:,:] = q2 #year2007 is index 0 along 1st dimension\n",
    "            for var in ['votemper']:\n",
    "                data[var].append(ds.votemper.isel(deptht=0, **slc).values)\n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3221156/391916811.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_tsc/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_tsc/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_tsc/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_tsc/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_3221156/2338418733.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 increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('WY SST with CY Increased Threshold',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([ 6.67567996,  5.16967481,  7.29056174, 10.17487312, 14.0136752 ,\n",
       "       17.62500597, 20.65200325, 20.13192256, 16.657524  , 11.60955676,\n",
       "        8.68565119,  6.48617353])"
      ]
     },
     "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 increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('CY SST with WY Increased Threshold',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([ 6.15109604,  5.8556036 ,  7.47975049,  9.10203945, 12.70656031,\n",
       "       16.08675342, 18.27222176, 18.2109748 , 15.41443395, 11.9993017 ,\n",
       "        9.32771591,  6.42471353])"
      ]
     },
     "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_3221156/2771304440.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_tsc/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_tsc/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_tsc/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_tsc/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_3221156/178454329.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_PAR_exp_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slice[monthly_array_PAR_exp_slice == 0 ] = np.nan\n",
    "monthly_array_PAR_exp_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_PAR_exp_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_PAR_exp_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'uE/m$^{2}$/s')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_PAR_slicemean[12,:],color='r',linestyle='-',label='Original WY')\n",
    "ax.plot(xticks, monthly_array_PAR_exp_slicemean[12,:],color='k',linestyle='-.',label='WY with CY increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('WY PAR with CY Increased Threshold',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('uE/m$^{2}$/s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.32172431,  25.46445341,  53.22065648,  62.60315202,\n",
       "        90.71975931, 103.29945894,  95.21548723,  81.05727718,\n",
       "        51.34878977,  31.48643708,  20.30787763,   8.03411984])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'uE/m$^{2}$/s')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_PAR_slicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_PAR_exp_slicemean[1,:],color='k',linestyle='-.',label='CY with WY increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('CY PAR with WY Increased Threshold',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,150)\n",
    "ax.set_ylabel('uE/m$^{2}$/s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.23694213,  25.42996031,  41.18085163,  59.76610693,\n",
       "        84.90226449,  94.06975087, 100.70847597,  79.87635294,\n",
       "        60.07499117,  29.22549197,  13.13997812,  10.14688224])"
      ]
     },
     "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_3221156/1288103633.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_tsc/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_tsc/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_tsc/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_tsc/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_3221156/3661973807.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 increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('WY Halocline with CY Increased Threshold',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([0.5430552 , 0.31373746, 0.71585734, 1.15099531, 1.94217265,\n",
       "       2.61285887, 2.93682089, 2.14007747, 1.58125968, 0.66524009,\n",
       "       1.22184692, 0.94281413])"
      ]
     },
     "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 increase thresh')\n",
    "\n",
    "\n",
    "ax.set_title('CY Halocline with WY Increased Threshold',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,5)\n",
    "ax.set_ylabel('g/kg m$^{-1}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Depth-averaged Nutrients (0-10m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for original cold and warm years\n",
    "\n",
    "\n",
    "monthly_array_nitrate_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "          \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3221156/3312634990.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_nitrate_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slice[monthly_array_nitrate_orig_slice == 0 ] = np.nan\n",
    "monthly_array_nitrate_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_nitrate_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_nitrate_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Silicon data for original cold and warm years\n",
    "\n",
    "monthly_array_silicon_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['silicon']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "   \n",
    " ### \n",
    "## Experimental Year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "       \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3221156/241793216.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slice[monthly_array_silicon_orig_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for Experiments 1 and 2\n",
    "\n",
    "monthly_array_nitrate_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_tsc/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_tsc/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_tsc/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_tsc/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "     \n",
    "        # # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3221156/231329215.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slice[monthly_array_nitrate_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_nitrate_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_nitrate_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Silicon data for Experiments 1 and 2\n",
    "\n",
    "\n",
    "monthly_array_silicon_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['silicon']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_tsc/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_tsc/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_tsc/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_tsc/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "         \n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3221156/3737416097.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slice[monthly_array_silicon_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-3}$')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o1KNHj1Qfdy/vvfeeNdS29PpK68/hbqkJ1u71OlavXt0AjO7du9tsv3PnjjVUaNCggRETE5PqNiXuCZTSa75//37DZDIZbm5uxr59+5ItExkZaf3cuDvQvZ8vv/zSAIxmzZrZbL98+bK1fZbAJzWWLVtmAEahQoWM8+fPJ1vm3Llz1t5XiXvxRUdHG97e3sl+lloMHjzY2q7sCtZSei8ahmF06NDBAIzmzZuneC7A+Prrr1M8T82aNQ3AGDZsWIplLO+RZ5991ma7pdft5s2b73s9Fhn9fBg9erT1DxWJA1iLyMhIo3jx4gZgtGnTJsn+lD7Dtm3bds+gMi4uzmjQoMF937siIpIxmmNNRETSrX79+jg6OgK2CxMcOnSIy5cvU6ZMGYoUKQJAo0aNkpRL/Lxu3bo4OzuneK6aNWsyZMgQwDw/jmW+HU9Pz8y7IODzzz/H3t6ePXv28OOPP2Zq3an14YcfYm9vn+7j27RpA8DGjRvTdfyKFSsICQmhYMGC1pVZk2OZ52zVqlVpqt+y8MXatWtttlvuhbfeeouiRYty4MABQkNDk+y3HP+gDBgwAFdX1yTbn376aZycnADYv39/qusLDw+3znvl7e2dKW0cPXo05cqVIyoqioiICMqVK2edJ/BBKlasWJL57Sws87rt27fPZvu6des4deoUAOPHj7e+hmnx6quvprji74wZMzAMgzZt2vDEE08kWyZ37tw899xzQNrvV8v7acuWLTYrIOfOnRs7O/P/Siee7+5+vvvuO8B8TT4+PsmWKVq0qPU+T9zeP/74g/DwcACGDx+e7LGDBw/GxcUl1e15EJydnfnggw+S3ffss88CSe+TxPLkycNbb72V7L69e/eyfft2HB0dGTBgQIp1WO7TP//80+bn5uXlBaTtZ5ZYej4fLIuNdOzYkcqVKyc5Nnfu3AwcOBAwf/4mN79kciyLlRQrVizZz2p7e3ubeRpFROTBULAmIiLp5ubmRq1atQDbhQks/7aEaQC+vr4UL16c8+fPc+LECQBiY2PZsmULAE2bNr3v+YYNG0b+/PkB85ez1ByTVuXLl7d+QRk2bNh9JxB/EOrXr3/fMnv37qVXr174+/vj4eGBnZ2ddaL3Xr16AeYAMj0sgdy1a9coXLgwhQoVSvbRo0cPAOviAKll+bkdPHiQy5cvA3D69GlOnz5NuXLlKFKkCE2aNMEwDJsgNquCtdq1aye73cHBwXr/WcKN1LCEasA9F0xIC1dXV7744gvr8y+++CLZL/uZrWbNmtYw6W6WEP3u12bz5s0AFCpUKMVFK+7nXu8Jy/26YsWKFO/VQoUKMWvWLCD5+/Xy5cuMGDGCunXrkjdvXhwcHKzvp4oVKwJw69Ytrl27Zj3G1dWVZs2aAdCqVSuGDx/Otm3biI2Nvee1WNr77bff3rO9f/75Z5L27tixAzAHKaVLl062fk9PT6pXr37PNjxolSpVwt3dPdl9Kd0nidWsWTPFANby+iUkJFCuXLkUX79WrVoBcPPmTcLCwqzHt23bFoCuXbsyYMAA/vrrL27dupXqa0vr50NsbKw1RGzevHmK9bZo0cJ6XSmttH03y/1gWbwlOU8++aR1oSEREXkwFKyJiEiGWEKOjRs3EhcXB/wXrCVeyRL+C9os+4ODg61faFITljg6OlpXDc3snmqJBQYG4urqysmTJzO02mh6FShQ4J77J0+eTLVq1fjf//7H/v37uXHjBp6enhQsWJCCBQvi4eEBkOIqiPdz4cIFwPyF8PLlyyk+LCFDdHR0muqvVKmS9QuoJSyz9F6zhG6W/1q2nzx50howPOhgLXfu3Cnus3xBTUvgalnRELD5gp9Rid8DD/L9kFh6XptLly4B4Ofnl+7z3us9Yblfb9y4cc/71fJ+uDtE2bJlC+XLl+eTTz5h69athIeHW1dWLFiwIPny5bOWvfs99d1331GlShVCQ0MZOXIkderUIXfu3DRo0IAvv/wySXh0584drl69CphXiLxXey0rZyZu75UrVwBS7OlmkVLvvqySmvvE8vsiOan5ecfHx9/z9bO8zmD7Go4ZM4YmTZpw48YNvvrqKxo3boyHhwc1atRgxIgRhISEZPjaEr8HwsPDrT3m7vVzS/wzs/yc7yc194OLiwt58+ZNVX0iIpI+CtZERCRDLCHHjRs3rH89/+uvvwDbHmuJn1vCFMt/E/d8ywl8fHx49913Afj000+5ceNGlp7/XsNADx8+TL9+/UhISKBTp04EBwdz+/Ztrl27xqVLl7h06RJfffUVYNtTKi0sXwJbtWqFYZ6P9b6PtDCZTNbQ1RKc3S9Ys/y3TJky2R4apJWDgwNly5YFYPfu3dncmuyTkd5693pPWO7X0aNHp+peTdy7Ni4ujpdeeonr168TEBDA77//TmRkJFFRUVy+fJlLly6xdetWa/m773VfX1927drFypUree+996hevToJCQls2rSJgQMHUrp0aZshz4mHJP7000+pau/s2bOTXHNm9XzMqVLz8y5fvnyqP5+KFy9uPd7Ly4u1a9fy999/M3DgQOrXr4+DgwM7d+7kk08+oUyZMg9sGoB7/dwS70vrz/dRvx9ERHI6BWsiIpIh9erVs86Ntn79eg4dOsSVK1coVapUkgDk7h5rlv82aNDAOldbTjFkyBDy5MnDlStXGDduXHY3x2rRokXEx8dToUIFfvrpp2SHTFl6CKVXoUKFgLTNI5ZWdwdn69atswncfH19KVmyJMeOHeP8+fPWcg+6t9qDYhkyuHv37jQPnX3YFS5cGMA6z1pmy8j9umXLFs6cOYO9vT3Lli3j6aefTtIj6X7vJzs7O5566ikmTpzIjh07CA8PZ/78+fj6+nLt2jW6dOliHR7q4uJi7V2YnvZaenLdb5j3/XpdPcwsP++TJ0+mu1cumH/vfPHFF2zcuJHr16/zf//3fzzxxBNER0fzxhtvWIepZ5S3t7c1KDx37lyK5RLvs/TovZ/U3A8xMTGZ2lNWRESSUrAmIiIZ4uLiQp06dQBzOJLc/GoWJUuWpGjRooSEhHDw4EHr/Go5MSzx8vJi8ODBAIwbNy7VQ3OSY+lNkN4eZIlZvnxVqVIlxbmuLHMzJcdyzL3aYpnPKiQkJN0LINyP5Wd+4sQJVq9ezYULF/D397cZdpc4fLPcV+m5V1JzzQ9ar169MJlMxMfH88knn6T6uISEhAfYqqxRr149wDyPmaVXa2ay3K/Lly9Pc+9Sy/spf/78KQ6nu9f7KTm5c+emS5cuzJgxAzBfd+IQzdLehQsXpvnna5mj7ty5c9a5Ku8WGRnJzp0701Tvw8Ty+sXGxrJkyZJMqdPFxYVnnnmGxYsXA3D79u1M++xzcnLC398fgDVr1qRYznKf2dnZUa1atVTVbbkf/vrrrxQ/3zZs2HDPYbciIpJxCtZERCTDLGHHpk2bWL16NZB0fjULS+A2evRo69xcOTFYA3jvvfcoWrQoUVFRfPrpp+muxzLn2fXr1zPcpsS9XZL7IrVixQqboW7paUu7du2svYz69u1734m90zKRv4VlkQLAumrd3YtRWO6LqVOnWlfwS+m+upfMfP3Tq1KlStbFHmbOnMmECRPue8zSpUszdN/lFE2aNKFkyZIA9O/f/76T+6dVjx49MJlMXL9+nQ8//PCeZe/cuWMTvlneT5Z5ue52/vx5vv7662Trut91JF5MIvHQxp49ewJw9OhRvvzyy3vWcfPmTZvztGjRgjx58gAwcuTIZI8ZM2ZMmuc9fJjUqFGDqlWrAvDxxx/brBycnMSfT3FxcfcMM1P6mWVU586dAXOP4wMHDiTZf+PGDcaMGQNA69atUz1n4osvvgjA2bNnmTNnTpL9CQkJj8RniIhITqdgTUREMswSgNy8eZPffvsNSL7HWuLtljlscufOne0r2KXExcWFwMBAAOt1pUflypUBmD9/fppWn0uOZaW7gwcP0rt3b+uXxps3b/LNN9/QsWPHe05UbWnL33//zZEjR5It4+LiwtSpUzGZTOzatYv69euzatUqmy/4p06d4ptvvqFWrVpMnTo1XddiuW+2bdsGJA3WLM8t+ytUqGAdBpYWlmtetGiRzaqOWe3rr7+mYcOGgDlgat26NatWrbIJQcLDw1m4cCFNmzalffv26Qotcxp7e3smT56MyWRi48aNNGvWjI0bN1oDjsjISNavX88rr7zCoUOH0lx/QEAA/fr1A2DatGl06tSJPXv2WIPn+Ph49u7dy8iRIylVqhR79uyxHtugQQNy5cqFYRi88MILHD161HrMqlWr7rna4ubNm/H392f8+PEcPnzYej2GYbB582beeecdwDwp/RNPPGE97tlnn6V9+/YADB48mHfeecd6XjAHdtu2bWPQoEH4+fnZ9JZ1dXW1BtFz5syhX79+1mF+kZGRjBw5ks8//xwvL680v44PC5PJxLRp03B2dubs2bPUrl2bRYsW2Xy2hoSE8P3339OiRQsGDRpk3X7+/HnKlCnDp59+yu7du216cu3bt49XXnkFgFy5cvHkk09mWpvfeecdSpQowZ07d3j66adZsWKF9X7Zv38/Tz31FKdOncLJySlNQVjt2rV55plnrOeYPn06MTExgDlse/HFF9myZYt10R8REXlADBERkQyKiYkxXF1dDcAAjBIlSqRY9p9//rGWA4zWrVun6Vx+fn4GYHTt2jWDrTaMESNGGIDh5+eXYpm4uDijfPnyNm1et25diu2aNWtWkn3z5s2zHuvo6Gj4+PgYfn5+Rv369a1l1q1bZy1zP507d7Zpj5eXl2Fvb28ARvXq1Y1JkyaleF3h4eFG/vz5rcfmy5fP8PPzM/z8/IwtW7bYlP3+++8NNzc3a1kHBwcjb968hrOzs835P/300/u2OTkzZsyw1mFvb29EREQkKZP4te/Vq1eKdTVq1MgAjBEjRiTZ99dffxkmk8l6nsKFC1uv2eLUqVPW85w6dSrF89zr55waMTExRu/evQ0HBweb19DT09PIlSuXzbaiRYsaK1euTLGuxPdMcvdkWt3rvdW1a9f7vu9mzZp1z/fTnDlzbO4dZ2dnw8vLy+aad+/ebXNMaq8vLi7O6Nevn01dLi4uRt68eZO81hs3brQ59n//+5/Nfnd3d8PFxcX6/vj111+TvTcSv/6W9/bd5/Pw8DA2bNiQpL03b95M8j7OlSuXkSdPHsPOzs5m+/nz522OjY+PN1599VXrfjs7OyNPnjzWz4DOnTun6ueVWpa67vU5aWH5TG3UqFGKZe71WZeWdv/xxx9G3rx5bT5D8ubNa/OZBRjdu3e3HpP4fW45xtvb23BycrJuc3JyMhYuXGhzrsz4fNi/f7/h4+Njc396eHjYvB/uPm9q6r169apRpUoVm/vQ8r4ymUzGlClTMvy5JSIi96YeayIikmFOTk7WeZQg5d5qAGXLlrXpdZRTh4Fa2Nvb8/nnn2eojldeeYV58+bRoEED3NzcuHjxImfOnLnvBOQpmT9/PhMmTMDf3x9nZ2fi4+N54oknGDVqFJs2bcLd3T3FY/PkycOGDRvo3LkzPj4+REREcObMGc6cOcPt27dtyr788sscP36coUOHUqNGDdzd3bl+/TouLi4EBATQp08f/vzzT5seIWmR+Gdfo0YN65DNxBL3YkvvvfLkk0+yfPlymjdvjqenJ5cvX7Zec1ZzcnJi8uTJ/PPPPwwdOpR69epRsGBBa2+b0qVL07lzZ3766SeOHz/OU089leVtfFBee+01jhw5Qr9+/ahYsSIODg7ExsZSqlQpnnvuOebNm0eFChXSVbe9vT3jx49n165d9OzZk3LlymFvb09ERAR58uShfv36BAYGsmfPHuscXRZvv/02y5cvp3Hjxri7uxMXF2ddGXjv3r02vc0Sq1mzJgsWLOCdd96hevXq5MuXj4iICOv7Y+DAgRw+fNjaSzExNzc3fvzxR9atW8err75KyZIlSUhI4MaNGxQoUICmTZsyZswYjh07lmTuNzs7O+bOncvcuXOpU6cOrq6uxMXFUa1aNaZNm8YPP/yQrtfwYdOiRQuOHz/OqFGjaNCgAZ6enly/fh07OzsqVqzIm2++ya+//sqkSZOsx/j4+PDrr7/Sv39/6tSpQ+HChblx4wYODg5UrFiR3r17c+DAATp27Jjp7a1cuTIHDx4kMDCQgIAAHBwciImJoVSpUrz99tscPHgwXefNmzcvmzdvJigoiPLly2NnZ4eDgwOtWrVi9erV9OrVK9OvRUREbJkMIxtn8hUREREREREREXlIqceaiIiIiIiIiIhIOuTIYG3Pnj20adMGX19fXF1d8fb2pm7dunz//fdJyu7atYvmzZvj7u6Ol5cXHTp04OTJk9nQahEREREREREReZzkyGDt+vXrFCtWjM8//5zff/+duXPnUrx4cV599VWblXKOHDlC48aNiY2NZcGCBcycOZOjR4/SsGHD+y69LSIiIiIiIiIikhEP1RxrderU4cKFC5w9exaAF154gXXr1nHixAnrhMdnzpyhTJky9O/fny+++CI7mysiIiIiIiIiIo+wHNljLSX58uXDwcEBgLi4OJYtW8bzzz9vs4qYn58fTZo0YcmSJdnVTBEREREREREReQzk6GAtISGBuLg4QkNDmTp1KqtWrWLQoEEAnDhxgujoaPz9/ZMc5+/vz/Hjx7l9+3ZWN1lERERERERERB4TDtndgHvp1asX33zzDQBOTk58/fXXvPXWWwCEhYUB4O3tneQ4b29vDMPg2rVrFC5cOMn+mJgYYmJirM8TEhIIDw8nb968mEymB3EpIiIiIiIiIiLykDAMg6ioKIoUKYKdXcr90nJ0sPbRRx/RvXt3rly5wm+//UafPn24efMmH3zwgbXMvYKwlPaNGjWKoKCgTG+viIiIiIiIiIg8Os6dO0fRokVT3J+jgzVfX198fX0BaN26NQBDhgyha9eu5M2bF/iv51pi4eHhmEwmvLy8kq13yJAhvP/++9bnERER+Pr6cu7cOZv52kRERERERERE5PETGRlJsWLFyJ079z3L5ehg7W61atVi2rRpnDx5kurVq+Pq6sr+/fuTlNu/fz+lS5fGxcUl2XqcnZ1xdnZOst3Dw0PBmoiIiIiIiIiIAPceKQk5fPGCu61btw47OztKliyJg4MD7dq1Y/HixURFRVnLnD17lnXr1tGhQ4dsbKmIiIiIiIiIiDzqcmSPtZ49e+Lh4UGtWrUoWLAgV69eZeHChfz88898+OGH5M+fH4CgoCBq1qxJ27ZtGTx4MLdv32b48OHky5ePAQMGZPNViIiIiIiIiIjIoyxHBmt169Zl1qxZzJkzh+vXr+Pu7k6VKlWYN28er7zyirVc+fLlWb9+PYMGDaJjx444ODjQtGlTxo4daw3fREREREREREREHgSTYRhGdjciu0VGRuLp6UlERITmWBMRERERERERecylNit6qOZYExERERERERERySkUrImIiIiIiIiIiKSDgjUREREREREREZF0ULAmIiIiIiIiIiKSDgrWRERERERERERE0kHBmoiIiIiIiIiISDooWBMREREREREREUkHBWsiIiIiIiIiIiLpoGBNREREREREREQkHRSsiYiIiIiIiIiIpIOCNRERERERERERkXRQsCYiIiIiIiIiIpIOCtZERERERERERETSQcGaiIiIiIiIiIhIOihYExERERERERERSQcFayIiIiIiIiIiIumgYE1ERERERERERCQdFKyJiIiIiIiIiIikg4I1ERERERERERGRdFCwJiIiIiIiIiIikg4K1kRERERERERERNJBwZqIiIiIiIiIiEg6KFgTERERERERERFJBwVrIiIiIiIiIiIi6aBgTUREREREREREJB0UrImIiIiIiIiIiKRDjgzW1q5dyxtvvEH58uXJlSsXPj4+PPvss+zcudOm3Ouvv47JZEryKF++fDa1XEREREREREREHhcO2d2A5Pzvf/8jLCyMvn37UrFiRUJDQxk3bhx16tRh1apVNG3a1FrW1dWVtWvX2hzv6uqa1U0WEREREREREZHHTI4M1qZMmUKBAgVstrVq1YrSpUvz+eef2wRrdnZ21KlTJ6ubKCIiIiIiIiIij7kcORT07lANwN3dnYoVK3Lu3LlsaJGIiIiIiIiIiIitHBmsJSciIoJdu3ZRqVIlm+3R0dEUKlQIe3t7ihYtSp8+fQgPD8+mVoqIiIiIiIiIyOMiRw4FTU7v3r25efMmH3/8sXVblSpVqFKlCpUrVwbgr7/+Yvz48axZs4bt27fj7u6ebF0xMTHExMRYn0dGRj7YxouIiIiIiIiIyCPnoQjWhg0bxvz585k0aRLVq1e3bu/fv79NuRYtWlC1alU6duzI9OnTk+y3GDVqFEFBQQ+0zSIiIiIiIiIi8mgzGYZhZHcj7iUoKIjAwEA+++wzPvroo/uWT0hIwMPDgzZt2vDzzz8nWya5HmvFihUjIiICDw+PTGu7iIiIiIiIiIg8fCIjI/H09LxvVpSje6xZQrXAwMBUhWoWhmFgZ5fy9HHOzs44OztnRhNFREREREREROQxlWMXLxg5ciSBgYEMHTqUESNGpPq4RYsWcevWLerUqfMAWyciIiIiIiIiIo+7HNljbdy4cQwfPpxWrVrRpk0btm7darO/Tp06nDlzhi5dutC5c2dKly6NyWTir7/+YsKECVSqVInu3btnU+tFRERERERERORxkCODtd9++w2AlStXsnLlyiT7DcPAw8ODggUL8tVXX3H58mXi4+Px8/Pjvffe46OPPiJXrlxZ3WwREREREREREXmM5PjFC7JCaiekExERERERERGRR19qs6IcO8eaiIiIiIiIiIhITqZg7REWGxuLOiSKiIiIiIiIiDwYOXKONckEL7zACytW8Gd0ND5ubvjkyYNP/vwU8fHBp0QJfMqWxadSJYqUKEHhwoVxdHTM7haLiIiIiIiIiDxUFKw9qo4e5cKNG9wEjkZFcTQqCs6ehZ07kxQ1AQWcnPBxd+e1qlXp++yzUKQIsfnzs/bUKXwqV6ZytWqYTKYsvwwRERERERERkZxKixfwiC5ecPw4N48e5cLhw4QcO8aFM2cIuXCBkKtXCYmI4MKtW4QYBheAO4kOGwqM/PffJ4DSgCtw09sbk48PFCnCRxcvcjIuDp9ChfDx9cWndGmKlC+PT+XKFPHzw8XFJYsvVkREREREREQk86Q2K1KwxiMarN2PYUBEBAnnz3P18GEuHDlCyIkTlIiLo2JMDFy4wIHTp3n54kVcDINtiQ6tASTt9/afvPb2FHFzw8fTE5/8+fEpUoSm9evTqGVLKFwYI39+DHt77Ow0xZ+IiIiIiIiI5DwK1tLgsQzWUsswIDwcLlywPn5bu5Zjp05x4fJlQq5dI+TGDUJiYggBYlKoZhjwyb//PmYyUckwKOPiwsEWLaBIEShShMWXLhGdK5d5/rfKlSlSuTK5cufOmusUEREREREREfmXgrU0ULCWCRISMEJDuXbkCCEHDxJy7Bghp05x4cIFQq5c4VknJ56+eRMuXWJ9QgJNgDLA0URVVAd23VWtp8mEj4sLPu7u+OTNS5GCBfEpVgyfkiXxKV+ekjVq4F26NGj+NxERERERERHJJArW0kDBWhaKjyfuwgUu7NtH1OnTVHJ0tPaE67t6NfuvXuVCdDQh8fHcSEV1w4EgJycoXJgzefPS79IlyhQqxJhOnaBwYShShPN2duQuXRqPYsUwafipiIiIiIiIiNyHgrU0ULCWA925Q+SJE4Ts20fIkSNcOHGCkHPnCLl0iZCwMC7cuEHI7dsEJSTw5r+HrAOaAuWAI4mqqgbsBnIBPo6OtvO/+fjgU7w4RUqXxqdSJQpXqYKjt7d6wImIiIiIiIg8xhSspYGCtYdYbCxcugQXLnBu3z5++/NPnG7coHuBAnDxIly4QNlDhziWkJCq6kxAAZOJwHz5eLtiRShShFBPT34LD6d4mTI0TTQnHLlyPdhrExEREREREZFsoWAtDRSsPfpuhoURsmcPFw4dMs//dvo0IRcucCE0lJCICEJu3uRCXBxx/5b/Fujx77/XAs2A8sDhRHU+4+DAdUdHfHLn/m/+N19ffEqVwqd8eYpUr45zqVJZeJUiIiIiIiIikhkUrKWBgjUBSEhI4OqZM4Ts24cPUOD2bbhwga07dhC0bh2+wDe5c0NICNy8SX7g6j3qcwJeKFiQPgMGUPv998HePkuuQ0REREREREQyRsFaGihYkzSLimLTsmWcP3yYC6dOEXL2LCGXLxMSHk5IVBQXYmK4neitVdPZmT4dOvDCV1/hUqhQNjZcRERERERERO5HwVoaKFiTzGYYBjuWLWPKRx/x44EDxP67fYuLC3XefBPefRfKlcvWNoqIiIiIiIhI8lKbFdllYZtEHhsmk4ma7doxe/9+zp8+zefPPUf73Lmpffs2TJkC5cszvXJl1n3xBUZ8fHY3V0RERERERETSQT3WUI81ySKGAWvXwsSJXP/tN3yAW8AmX1/qDRwIXbuCu3t2t1JERERERETksaceayI5jckEzZrBr78Ss3UrXf39qW1vT92zZ6FPH/DxYekzz3B0zZrsbqmIiIiIiIhI6kRGwtatMHu2uUPJY0Y91lCPNck+RmQkprlz4euvuXnsGD5ABNCqQAH69O1Lq4EDsXdwyO5mioiIiIiIyOPu5k04dAgOHrR9nD37X5mQEChSJPvamIkeyOIF165dIyQkhMqVKwOwcuVKjh8/TqNGjXjiiScy3upsomBNsl1CAufmzaPXhx+yPDQUy5uypJMTvZ59lm7jx+Pt45OtTRQREREREZHHQHQ0HD6cNEA7dcqmWAJwHAgGDubKxed16mCaMuWRWagv04O15cuX8/LLL3Pnzh2aNWtGixYt+OOPP4iPj2f9+vUsXLiQNm3aZNoFZCUFa5KTnPzjD/43aBAz9uzh2r/bXIGXq1ShzxdfUOWpp7KzeSIiIiIiIvIoiImBf/6BAwdsA7QTJ1Ie0lmgAFSqxMeRkUw5dIiI6GjrrnPnzlG0aNEsavyDl+nBWkBAANOnT8fe3p6aNWuyatUqmjdvDsCCBQsYN24c27Zty5zWZzEFa5IT3bpwgR/69WPy0qXsvXPHur1hvnz0efdd2g8ejKOTUza2UERERERERHK82Fg4dixpgHb8OMTHJ39M3rxQqRIJFSvy4rZtbD1/nu1r11Lo3xGMgYGBBAUF4eLiQrVq1ahZsyYDBgygWLFiWXhhD1amB2teXl5cv34dAFdXV27evImdnXntg/j4ePLnz094eHjGW54NFKxJTmbExbFpzBgmT5jAL6GhxP27vbCjI5++9hpvTJkCzs7Z2kYRERERERHJZnFx5rDs7gDt6FHzvuR4eRFToQJ7CxYk2N6e4IgIDE9P5i1caF6AD6hcuTIHDx7k119/pV27dgCcOXOG8PBwKleujKOjY1ZdYZbK9GCtUKFCnD9/HgcHB1555RW+//57675bt27h4+PDtWvX7lFDzqVgTR4WIX/8wbeDB/PN7t1cBr4D3ixYEN5+m/gePbArUgTTvx9+IiIiIiIi8giKj4eTJ5MGaP/8Y+6dlpzcua090I7ky8f2hASCQ0MJPniQvXv3cifRKClXV1ciIiKsgdnKlStxc3OjRo0auLm5ZcUV5giZHqy99NJLDB8+nAoVKiTZt3jxYiZMmMCGDRvS3+JspGBNHjaxFy7wy/vv8+yGDbhdvAjAFDs7Znp5MXzIEJ794INsbqGIiIiIiIhkSEICnD5tDs0Sh2hHjsDt28kfkysXVKwIlSpBpUpc8/VlbXg4wSdPsn3HDnbs2EFUVFSSw/Lly0etWrWoVasWNWvWpEWLFo9sT7TUeiCrgqbk8uXLABQsWDCjVQGwdu1avv/+ezZv3sy5c+fw8vKiRo0aDB8+nOrVq9uU3bVrFwMHDmTr1q04ODjQtGlTxo4dS8mSJVN9PgVr8tC6cwcWL4avv6ba5s3sBiYBferWhb59oUMHeMw/DEVERERERHI0w4CzZ/8Lziwh2uHDcOtW8se4uNgEaFSqxLWiRdl+6RIFCxemSpUqAGzevJn69evbHOrm5kb16tVtgrTixYtr9NNdsjRYy2ydOnUiLCyMTp06UbFiRUJDQxk3bhw7duxg1apVNG3aFIAjR45Qq1YtAgICGDx4MLdv32b48OFcu3aNPXv2kD9//lSdT8GaPArCVq9m1uDB9Ny3D49/x8/P8fJicaFC9Bk5kubPP68PShERERERkexiGBASkjRAO3QIbtxI/hgnJyhfHipXtgnRogsVYve+fQQEBFiHZ77//vuMHz+e3r17M3nyZACio6Np2LChTZBWoUIFHBwcsuqqH1pZEqyNGDGCoKCg9B6eoitXrlCgQAGbbTdu3KB06dJUrlyZP//8E4AXXniBdevWceLECetFnjlzhjJlytC/f3+++OKLVJ1PwZo8Ui5dgmnT4H//o/qVK+z6d3M5T0/6vPMOrw0ZovtcRERERETkQTEM8/ey5AK0iIjkj3F0hLJlkwRolCpFHHDo0CG2b99OcHAwwcHB7N+/n/j4eNauXUuTJk0A+Omnnxg2bBgvv/wygYGBWXa5j6osCdbc3Ny4lVK3xAegadOmhISE8M8//xAXF4eHhwevvfYa06ZNsyn31FNPcerUKY4ePZqqehWsySMpJobD48cz9auvmB0aiuXvH+729nRt2ZLeY8ZQ4d+lkkVERERERCQdrlyxXUDAEqKltLijvT2UKWMOzRKHaGXKgKMjhmFw+vRpa4C2fft2du7cmWz2UrBgQSZPnkzHjh0BMAxDo5QyUWqzogz1/cvKUaQRERHs2rXLOgz0xIkTREdH4+/vn6Ssv78/q1ev5vbt27i4uGRZG0VyFGdnKgwezKRBg/hs9WrmDR7M5N27ORIfz5QVK5iyYgXNy5Shz4gRtO3cGXt7++xusYiIiIiISM4UFmYboFlCtKtXky9vZwelSiUN0MqWBWfnZA/p3Lkzf/75J2FhYUn25c6dmxo1aljnRKtVqxZFixa1CdIUqmWPDAVrWflD6927Nzdv3uTjjz8GsN5o3t7eScp6e3tjGAbXrl2jcOHCSfbHxMQQExNjfR4ZGfmAWi2SA5hMeLRsSe+WLel19ixrBg1i8i+/8NudO/x57Bh/vvIKfu+8wzs9etD9o4/ImzdvdrdYREREREQke1y/nnyA9u+ijUmYTFCiRNIArVw5cHVN9pAzZ84wcOBAwsLCrFNdgXlarLCwMBwdHQkICLAGaLVq1aJcuXLY2dk9gAuWjHooZqsbNmwY8+fPZ9KkSUlWBb1XuJfSvlGjRj2QueFEcjqTry/Nf/yR5tHRnJ44kWnjxjH96lXOREUx+KuvmDB5MucWLcKhTRvzX1hEREREREQeRZGR5jnP7g7QLlxI+Rg/v/+CM0uIVqEC/Lt4QGJ37txh/65d1nnRAgICePfddwFz77MFCxYAEB4ebu0w9Nlnn2Fvb0+VKlVwTqFXm+Q8OT5YCwoK4tNPP+Wzzz6jT58+1u2WXjXJdZEMDw/HZDLh5eWVbJ1Dhgzh/ffftz6PjIykWLFimdtwkZzM1ZXigwczetAgRqxaxc8ffcSk3btpFBuLwzPPQJkyGH368H/589P6+edxcnLK7haLiIiIiIik3c2byQdo586lfEzRorYLCFSubA7QcudOtrhhGBw/ftw6J1pwcDC7d+/m9u3b1jJNmza1Bmve3t5MnjyZ8uXLkytXLmuZunXrZs41S5bK0XOsBQUFERgYSGBgIB999JHNvlKlSuHq6sr+/fuTHLd//35Kly6d4vxqzs7OSn9FAEwmXFu14vVWreh64gSxX38Nc+bAsWP81bcv7YFSnp78ExyMfdmy2d1aERERERGR5N2+nTRAO3gQTp1K+ZjChZMGaBUrgqfnPU8VGhrKli1brEHa9u3buZbMYgVeXl7W4ZwNGjSw2de7d+90XabkPBkK1urXr59Z7Uhi5MiRBAYGMnToUEaMGJFkv4ODA+3atWPx4sWMGTOG3P8mx2fPnmXdunX079//gbVN5FFkKlUK54kT4bPPYO5crn72GYUvXKBFRAT25ctD27YY773HTk9PqteooYkxRUREREQkexiGOTDbuvW/x549cOdO8uULFEg+QEtmzva7RUZGsnPnTvz9/a0j52bMmMGQIUNsyjk7O1O1alXrnGi1atWiVKlSmhftMWAysnJpz1QaN24cH3zwAa1atUo2VKtTpw4AR44coWbNmlSrVo3Bgwdz+/Zthg8fTnh4OHv27CF//vypOl9ql1AVeawkJHDn99+5MXEief6dUHMbUAeoUrQofYYMocvrr+OWzHwCIiIiIiIimSYqCrZvtw3SQkOTlvP2tl1AwPJIZTYQExPDsWPHqFy5snVbrVq12L59Oz/99BMvvvgiAOvXr+fdd9+1WVygcuXKmkLnEZParChHBmuNGzfmr7/+SnF/4ibv3LmTQYMGsWXLFhwcHGjatCljx46lVKlSqT6fgjWR+/jnH5g0iZnTp9MnNpbofzd7OTvz5muv0WvwYEqWLJmtTRQRERERkUdAQoL5+0fiEO3AAfP2xBwdoWpVqFMH6tY1/9fPz7xKZ6pOk8DRo0cJDg62Duncs2cPCQkJREZG4vrvip69e/dm+fLlBAUF0bVr18y+WsnBsjRYW7p0KfPnz+fMmTM2k/OBeWXOvXv3ZvQUD5SCNZFUiojg2uTJzPzqK6aEh2OZrcAEtKlXjz7DhtGiZUt1dxYRERERkdQJD4dt2/4L0bZtg4iIpOV8fc3hmeVRtSqkMK/63QzDICQkxLqwQHBwMDt27CAyMjJJ2bx58/L3339ToUIFAOLi4nBwyPHrPsoDkGXB2pdffsmgQYPInz8/pUuXTrbr47p16zJyigdOwZpIGsXHE//rr6wMDGTyvn2sTLSrTMGC9P7gA17v0QPP+0z6KSIiIiIij5G4ONi/37Y32tGjScu5ukLNmv+FaLVrQ5EiaT5daGgoPXr0IDg4mIsXLyZzGleqV69OrVq1rMM6S5QoofmkBcjCYK1EiRI0a9aMb775Bnt7+4xUlW0UrIlkwP79HB05kqmLFzMrPh7L33xyOTryWpcuDP7kE3x9fbO1iSIiIiIikg0uXfovQNuyBXbsgFu3kpYrW9a2N1rlyuahnmmwdu1aZs6cSYkSJRg5ciQAd+7cwcPDg9u3b2Nvb0/lypWtc6LVrFmTSpUqqTeapCjLgjUPDw+WLl1K06ZNM1JNtlKwJpIJwsK4MXky30+YwOTr1zn47+bDbdpQfvhwqFUrW5snIiIiIiIPUEwM7N5t2xvtzJmk5Tw9zT3QLCFarVrw72qbaXH58mXs7Oysixb+8ssvdOzYkcqVK7N//35ruR9++AE/Pz+qVq2qhdckTbIsWHv66adp27YtvXv3zkg12UrBmkgmiovDWLyY9Z98wt8HDzLcsr12bd739iZf3bq81auXdalqERERERF5yBiGOTRLHKLt3g2xsbblTCZz77PEvdHKl4d0zsl86dIlFi9ezMKFC9mwYQPDhg0jMDAQgFu3bjF27Fjq1KlDy5YtM3iBIlkYrP3zzz+0b9+e0aNH06pVq4dyeVkFayIPyM6d8PXX8NNPXIiNxQ+IA3b36kVAYGCql70WEREREZFsdPOmeRinZUjn1q1w+XLScvnz24ZoNWtC7twZOvWlS5f45ZdfrGFa4gjjpZde4ocffshQ/SIpybJgLT4+nv79+zNlyhRMJlOSrpUmk4mI5Fb0yEEUrIk8YJcvEzNlCgu+/prNERH8D8DZGbp0YbS7O0Vr1aJTp044Oztnd0tFRERERB5vCQlw7Jhtb7R9+8zbE3NwMK/MmThIK1HC3Estgy5evGgN0/7++2+bMK1mzZp06tSJjh07UqJEiQyfSyQlWRasDRgwgPHjxxMQEECFChWS7bE2a9asjJzigVOwJpJFYmNhwQKYOBF27OAqUBSIAfJ7eNCzd2/e7tWLokWLZnNDRUREREQeE9euQXDwfyHatm3mbXcrWvS/AK1uXXOo5uqaac24evUqP/30U7JhWq1//xDfsWNHihcvnmnnFLmXLAvW8ubNS8+ePRk1alRGqslWCtZEsphhwNatRHz5JVOWLuV/hsH5f3fZ29nRvm1b+rz/Pk8++aSWuhYRERERySzx8XDggG1vtCNHkpZzcYEaNf4L0mrXNgdrmSwhIQG7f+db27JlC/Xq1bPuq1OnDp06deL555/Hz88v088tcj9ZFqx5eXmxePFirQoqIulz/jxxU6bwf5MnM/nGDdYn2lW5TBn6DBjAK6+8Qq5cubKrhSIiIiIiD6fLl8090CwhWnCweb60u5UubTuk098fHB0fWLPWrl3L8OHDqVy5MtOmTQPMIdvzzz/Pk08+yfPPP4+vr+8DO79IamRZsPbCCy8QEBDARx99lJFqspWCNZEcIDoafvyRA198wZSjR5kL3Pp3l2euXLzRowe9evemdOnS2dlKEREREZGcKTYW9uyx7Y126lTScrlzm3ugWUK0WrUe+KJi586dw2QyWad8+fPPP2nRogX58+fn4sWL2NvbP9Dzi6RHlgVr+/fv58UXX+Stt96iTZs2eHt7JymT3LacRMGaSA5iGLBhA9e//JLZv//OFMPgeKLdHw0YwGdjx2Zb80REREREsp1hwPnztqt07toFMTG25UwmqFjRtjdahQqQBUHW2bNnWbRoEQsXLmTr1q3069eP8ePHAxAXF8e0adN47rnnNL+y5FhZFqxZxkPfax6k+Pj4jJzigVOwJpJDnT5NwuTJ/DFtGpNv3uR3YJGLCx169oQ+fYgoUICEhATy5MmT3S0VEREREXlwbt2CnTtte6NduJC0XN68tiFazZrg6ZllzTxz5ow1TNu2bZt1u8lk4sUXX+THH3/MsraIZFSWBWuBgYH3nVx8xIgRGTnFA6dgTSSHu3kT5s7lxNix+J08iQOAycSnZcow6swZPvn0UwZ88EF2t1JEREREJOMMA44ftw3R9u41LzyQmL09BATYBmmlSpl7qWWh06dPW8O04OBg63aTyUTDhg3p1KkTHTp0oEiRIlnaLpGMyrJg7VGgYE3kIWEYsHo1TJwIv/9Oa2AF8H2RIrw8bBi8+iq37e2xt7fH8QFOtioiIiIikmkiIsyLClhCtG3bICwsabnChaFuXfOjTh2oVg3c3LK+vUBoaCizZ89m4cKFbN++3bo9cZj2/PPPU7hw4Wxpn0hmULCWBgrWRB5Cx45hfP01f8+YQe3oaJwBvLwYV6UK4//5h7d796Z///5aTVREREREco74eDh8+L8QbcsW8/O7v5Y7O0P16ra90YoWzfLeaInFxsbi5OQEwMGDB6lcuTJgDtOefPJJa880hWnyqFCwlgYK1kQeYhERMHs2TJoEJ05QD9jy767KZcuyZPlyrSQqIiIiItkjNNTcA80SpAUHQ1RU0nIlS9qGaFWqwL8hVnZbv349H3zwAWXKlLGZI61bt27UrFmTDh06UKhQoWxsociDoWAtDRSsiTwC4uNhxQpixo9n0dq1fABcAjzd3Ji/YAFt2rTJ7haKiIiIyKPszh3zXGiJ50Y7cSJpOXd3qFXrvxCtdm0oUCDr25uCE/+2uVSpUgAEBwdTu3ZtPD09uXLlirXXmsijTsFaGihYE3nEHDjAhddfp9POnWwGTEDgsGEMDQy0rmQsIiIiIpJpVq2CN95IfqXOChVse6NVqmReeCAHOX78OAsXLmThwoXs3r2bN954gxkzZgBgGAZz5syhdevWFMhBAaDIg6ZgLQ0UrIk8gu7cIXbwYPp/9RVT/93UtmlT5v3yC15eXtnZMhERERF5VNy+DYMGwddfm5/nyWMbotWqBTn0/z2PHTtmDdP27Nlj3W5vb8/zzz/Pzz//nH2NE8kBFKylgYI1kUfYsmXM7tyZt2/eJAYoXbgwS/74wzrZqoiIiIhIuuzfD126wIED5ud9+sCYMeDqmr3tuod//vnHGqbt27fPut3e3p6mTZvSqVMn2rdvT758+bKxlSI5g4K1NFCwJvKIO3uWnW3b8vz+/ZwB3BwcmDlrFi++8kp2t0xEREREHjYJCTBxIgweDLGxULAgzJoFTz+d3S1LVmhoKN98802SMM3BwYFmzZrRqVMnnn32WYVpInd5oMGav79/qsuaTCb27t2b1lNkKQVrIo+BO3e42r8/L02Zwp//bhrZvz9Dv/oqW5slIiIiIg+RCxfg9ddh9Wrz83bt4LvvctTiAwC3bt3Czc0NgHPnzuHr6wuYw7TmzZtbw7S8efNmZzNFcrTUZkUO6anc29sbk8l0zzI3btxg586d9y0nIpIlHB3JN3kyK1u04OPOnRl3+zYNv/0WGjSADh2yu3UiIiIiktMtWQI9ekBYmHm451dfwVtvQQ76zvv333/Tq1cvihUrxu+//w5AsWLFeP/996lcuTLPPvss3t7e2dxKkUdLpg8FjYuL49tvv+WTTz4hNDSULl26MG/evMw8RaZTjzWRx8zZs5x87jlK7t5tfv7ee9wYPhx3/cVORERERO524wb06wf/rpJJtWowfz6UL5+tzQI4dOgQCQkJ1vmD//nnH8qXL0+uXLm4cuWKtdeaiKRdarMiu8w86cKFC6lYsSLvvvsuVapUYefOnTk+VBORx5CvLyW3bYMPPwTg8NdfU6JQIWaMHp3NDRMRERGRHCU4GKpWNYdqJpN5BdAtW7ItVDMMgwMHDjBixAgqVqxIpUqVCAoKsu4vV64cv/zyC+fOnVOoJpJF0jUU9G7r169n0KBBbN++nWrVqvHHH3/QrFmzzKhaROTBcHQ0r9rUqBHTn3+eqzExfD90KK+XKoV9p07Z3ToRERERyU7x8TBqFAQGmv9drBjMnQuNG2d5UyxhmmU1zyNHjlj3OTk5YW9vj2EY1mmYOmiaE5EslaEea/v376d169Y0a9aMsLAwfvjhB3bs2JHhUC0qKoqBAwfSsmVL8ufPj8lkIjAwMEm5119/HZPJlORRPgd0yRWRh0SbNoz95x/GFy/Oz/Hx2L/wAvTtCzEx2d0yEREREckOp09Do0YwbJg5VHvxRdi7N0tDNcMw2LdvH8OGDaNChQr4+/szcuRIjhw5gpOTE+3atWPu3LlcuXKFn376SXObi2SjdPVYO3fuHEOHDuWHH37A29ubCRMm8Pbbb+Po6JgpjQoLC+Pbb7+lSpUqPPfcc3z33XcplnV1dWXt2rVJtomIpJadnx/9jh6Fjz+GL7+Er79m+MKFNB07lsZdumR380REREQkKxiGee603r0hMhJy54YpU+CVV7JsgYLQ0FAmTpzIwoULOXr0qHW7k5MTrVq1olOnTrRr1w5PT88saY+I3F+6grWyZcsSGxtLq1atGDhwILlz52b//v0plq9WrVqa6vfz8+PatWuYTCauXr16z2DNzs6OOnXqpKl+EZEkEg0NXdK5MyMvXuTzl1/my9Wr6Tdzpv4KKCIiIvIou34d3nkHfvrJ/Lx+fZg3D0qUeKCnNQyD69evkydPHgBMJhOjR48mPj4eZ2dnmzBNC+2J5EzpCtZi/h0itWLFClauXJliOcs47/j4+DTVry+wIpJt2rThqZ07eaV+fb6/epX3Z88mePNmvtuyhVxamlxERETk0fPXX/Daa3D2LNjbm+dVGzwYHDJlSvIUbd68ma5du1KwYEE2btwIQL58+Rg+fDilS5embdu2CtNEHgLp+qSYNWtWZrcj3aKjoylUqBChoaEULlyY5557jk8++QRvfQEWkXRyK1uWuSEh1GrdmvfXrOGno0c5ULQoS5Yto3TTptndPBERERHJDLGxMGIEfPGFeRhoqVLmoaC1a2f6qQzDYPfu3cTHx1OzZk0AfH19OX78OBcuXOD69et4eXkBMHz48Ew/v4g8OOkK1rp27ZrZ7UiXKlWqUKVKFSpXrgzAX3/9xfjx41mzZg3bt2/H3d092eNiYmKsve4AIiMjs6S9IvLwMDk58e6ffxLwxRd0GjKEA9HR1GjWjPkffUSbzz7L7uaJiIiISEb88w+8/DLs3Gl+/uabMGECpPAdMj0Mw2DXrl3W1TxPnjxJy5YtWbVqFQBFixZl1apV1KtXL8XvriKS8z3Yvq0PWP/+/W2et2jRgqpVq9KxY0emT5+eZL/FqFGjCAoKyoomishDruGgQexq2pSOTZuy5cYN2n7+OYFr1jBs3TrstFCKiIiIyMPFMOCbb+D99yE6Gry9Yfp06NAhk6o32LFjBwsXLmTRokWcOnXKus/V1RVvb28SEhKws7MDoGXLlplyXhHJPnbZ3YDM1r59e3LlysXWrVtTLDNkyBAiIiKsj3PnzmVhC0XkYVOkZk3WX7pEr6pVAQjcto1nCxfm+p492dswEREREUm90FB49lnzIgXR0dC8Oezblymh2q1btwgMDKRkyZLUqlWLL7/8klOnTuHm5kanTp34+eefCQ0N5ccff7SGaiLyaHioe6ylxDCMe35YOTs74+zsnIUtEpGHnVOuXEzZtYua/fvz9oQJLIuIoGb16iweO5YnUugdKyIiIiI5xIoV0K0bXL4MTk4wejT07QuZEHKtX7+eHj16cPz4cQDc3Nxo27YtnTp14umnnyZXrlwZPoeI5FyPXFS+aNEibt26RZ06dbK7KSLyCHp9/Hg2LVuGr5MTxxMSaPn++0T37g2J5m0UERERkRwiOhreew9atzaHapUqQXAw9O+f4VDNMAx69epFkyZNOH78OEWLFmX+/PmEhoby888/07FjR4VqIo+BHNtjbcWKFdy8eZOoqCgADh06xKJFiwBo3bo1oaGhdOnShc6dO1O6dGlMJhN//fUXEyZMoFKlSnTv3j07my8ij7Dqbdqw8/RputSrxzunT+M6dar5f9B+/hlKlszu5omIiIgIwN695gUKDh40P3/vPXNPtUyaJ9dkMuHgYP5K/dZbbzFmzBg8PDwypW4ReXiYDMMwsrsRySlevDhnzpxJdt+pU6fw9PTkzTffZPfu3Vy+fJn4+Hj8/Pxo3749H330EZ6enqk+V2RkJJ6enkREROiDUERSzTAMTL//Dq+9BuHhbM+VC79JkyjQrVt2N01ERETk8ZWQYF7hc8gQiI2FggVh9mxo1SrDVV+9epVbt27h6+sLwI0bN9i1axdPPvlkhusWkZwltVlRuoK1s2fPpqm85UMnp1KwJiIZcu4c59q3p/rOnTgDf776KuWmTwfN5SgiIiKStUJC4PXX4c8/zc+feQa++w7y589w1evXr+eFF16gYsWKrF27VosQiDziUpsVpWsoaPHixTGZTKkuHx8fn57TiIg8HIoVI3rOHLyffBLX8HB8582DQ4dgwQINDRURERHJKr/8Aj17Qni4ebjn+PHm52n47novfn5+3Lx5k6tXr3LlyhUKFSqUKfWKyMMtXcHazJkz0xSsiYg86spWqkTwqVNELlmC6/vvw86dJAQEcOfbb3Hu3Dm7myciIiLy6Lpxw7zC58yZ5ufVq8P8+VCuXIaqNQyDzZs3U79+fQBKlCjBmjVrqFq1Ks4amSAi/8qxc6xlJQ0FFZFMde4cdO7MyM2bWQYs6taNYv/7n4aGioiIiGS2bdvMCxScOGHumTZ4MAQGgpNThqo9ffo0PXv2ZPXq1axbt47GjRtnSnNF5OGR2qwoUweFHz16lC1btnDs2LHMrFZE5OFSrBjXlixhoqsrwUD1WbNY7+8PJ09md8tEREREHg1xcTByJNSvbw7VfH1h/Xr4/PMMhWoJCQl8/fXXVK5cmdWrV+Pi4sKpU6cyr90i8sjJlGBt4cKF+Pn5UaFCBRo0aED58uXx8/Nj0aJFmVG9iMhDJ0+BAgQfOECVEiUIBZofPcpXFSti6HNRREREJGNOnYJGjWD4cIiPh5degr17IYMrcx45coSGDRvSt29fbt68yZNPPsm+ffvophXfReQeMhys/f7773Tu3BlPT09Gjx7N3LlzGTVqFJ6ennTu3JkVK1ZkRjtFRB46JUuWZPOBA7zSvj3xwICYGLp06sTNXr0gJia7myciIiLycDEMmDsXqlSBzZvBwwO+/x5++AG8vNJd7Z07dxg1ahQBAQFs3rwZd3d3pk6dyrp16yhTpkzmtV9EHkkZnmOtfv36eHh4sHz5cpvlhg3D4OmnnyYqKopNmzZluKEPkuZYE5EHyTAMJk+cyPsDBhCXkEBlYEmlSpT+9VetGioiIiKSGteuwdtvm1ddB2jQAObNg+LFM1Tt7t27efPNN9m9ezcATz/9NNOmTcPX1zeDDRaRh12WzbG2Z88eevXqZROqAZhMJnr16sXevXszegoRkYeayWTi3X79WPfXXxTKk4cDQI2DB1leubJ5WXgRERERSdn69eDvbw7VHBzg00/N2zIQqt2+fZuPP/6YmjVrsnv3bry9vZk3bx7Lly9XqCYiaZLhYM3e3p7Y2Nhk9925cydJ4CYi8rhq0KABOw8coF716kQAbaOjCerYkYR339XQUBEREZG7xcbCoEHQtCmcPw9lypiHgH78Mdjbp7vaPXv2ULVqVT7//HPi4+Pp1KkThw4d4pVXXsFkMmXiBYjI4yDDqVfNmjUZM2YM0dHRNttjYmIYO3YstWvXzugpREQeGUWKFGHd5s30evttAAKBZydP5nrt2lo1VERERMTiyBGoWxfGjDHPrda9O+zaBTVrZrjqXLlycfr0aQoVKsTixYtZsGABBQsWzIRGi8jjKMNzrG3cuJFmzZrh7e1Np06dKFSoEBcvXmTx4sWEhYWxdu1a6tWrl1ntfSA0x5qIZIfZs2fzds+eGHfusAmo4eEBM2fC889nd9NEREREsodhwLRpMGAAREdD3rwwfTq0b5+hao8dO2azEMHKlSupXbs2efLkyWiLReQRldqsKMPBGsBff/3F4MGDCQ4OxjAM7OzsqF27NqNGjeLJDC55nBUUrIlIdtm1axf/bN7MSz/+aB7aAPDuu/Dll+DsnL2NExEREclKV67Am2/CsmXm5y1awOzZUKRIuquMj4/nrbfeYtasWWzYsIH69etnTltF5JGXZYsXADRq1IgtW7YQFRXFuXPniIyMZNOmTQ9FqCYikp2qVavGS336mCfgHTiQ3cCgSZOIq1dPQ0NFRETk8fH77/DEE+ZQzdkZxo+HlSszFKqBeU7w+Ph4DMNg06ZNmdRYEZH/ZEqPtYedeqyJSE5w+/ZtKhYvzqnLlxkKjNTQUBEREXnURUfDhx/ClCnm55Urww8/mEO2dLpy5Qrx8fEULlwYgGvXrnHo0CH1VhORNMnSHmtLly6lU6dO1KpVC39/f5tHlSpVMuMUIiKPPBcXF76cMoWASpV4v1YtiIyEjh3hvfe0aqiIiIg8evbsgerV/wvV+vaF7dvTHaoZhsH8+fOpWLEi3bt3x9KHJE+ePArVROSByXCw9uWXX9KhQwc2bNiAo6MjefPmtXl4e3tnRjtFRB4Lzz//PDv37SPPxo0wcCAG8NekSVC/voaGioiIyKMhIQHGjoVateDwYShUCFatggkTwMUlXVWeO3eOdu3a8corrxAWFkZISAjXrl3L3HaLiCQjw0NBS5QoQbNmzfjmm2+wt7fPrHZlKQ0FFZGcalqfPrwzZQo9gEm5c+M8a5aGhoqIiMjD6/x56NoV1q41P3/uOfOqn/nypau6hIQEpk+fzocffkhUVBROTk4MHz6cgQMH4ujomHntFpHHTpYNBQ0LC6NLly4PbagmIpKTRfn5YTKZmA48GRXFeQ0NFRERkYfVokXg728O1dzczIHa4sXpDtWOHz9Os2bNePvtt4mKiqJOnTrs3r2bjz/+WKGaiGSZDAdr9evX5/Dhw5nRFhERucuHH37IihUryJMnD8FANWC9hoaKiIjIwyQqCrp1g06d4No1qFEDdu+G7t3BZEpzdfHx8YwbNw5/f3/Wr1+Pm5sbEyZMYOPGjVSsWPEBXICISMoyHKxNmDCBKVOm8OuvvxIbG5sZbRIRkUSeeuopdu7cSUBAAKFAc+CrnTsxAgLgl1+yuXUiIiIi97BlCwQEwOzZ5hDt449h82YoWzZd1R04cIB69erxwQcfEB0dTbNmzdi/fz99+/bVKCoRyRYZDtZKly5N8+bNad++PW5ubnh4eNg8PD09M6OdIiKPtRIlSrBp0yZeffVV4oEBQJeoKG5qaKiIiIjkRHFxEBQEDRuae9n7+cFff8Gnn0I6hmkahkFQUBDVqlUjODgYDw8Ppk+fzurVqylZsuQDuAARkdRxyGgFAwcOZPLkyQQEBFChQgWcnJwyo10iInIXNzc35syZQ61atejfvz8/xcVxAFgyaRKlN2+GBQtA/2MpIiIi2e3kSXjlFXNvNYCXX4YpUyADnS5MJhP//PMPd+7c4ZlnnmHq1Kn4+PhkUoNFRNIvw6uC5s2bl549ezJq1KjMalOW06qgIvKw2bhxI506deLSpUt4mkzMNwzaeHjAzJlaNVRERESyh2HAnDnw7rtw4wZ4eMD//gdduqSruujoaKKjo/H29gYgNDSUdevW0alTJ0zpmJtNRCQtsmxV0Pj4eFq0aJHRakREJA0aNGjAzp07qVevHhGGQVtgXGQkaGioiIiIZIfwcHjxRfMiBTdumIeA7tuX7lAtODgYf39/unfvjqUvSP78+XnhhRcUqolIjpLhYK1ly5Zs3bo1M9oiIiJpUKRIEdatW0fv3r0xmUxU7tTJvEOrhoqIiEhWWrsW/P1h4UJwcIDPPoN168zzqqWTs7Mzp0+fJjg4mMuXL2diY0VEMleGg7Vhw4bx/fffM3HiRI4fP054eHiSR1pFRUUxcOBAWrZsSf78+TGZTAQGBiZbdteuXTRv3hx3d3e8vLzo0KEDJ/VlUkQeE05OTkyePJl9+/bx1IIFsHw5eHsTs3MnVK2qVUNFRETkwYmJgYEDoXlzCAkxr/S5ZQt89BGkY4XO48ePW/9dpUoVfvnlFw4ePEihQoUys9UiIpkqw8FalSpVOHLkCO+//z7lypUjf/78SR5pFRYWxrfffktMTAzPPfdciuWOHDlC48aNiY2NZcGCBcycOZOjR4/SsGFDQkNDM3BVIiIPl8qVK5v/0bo1J5YupZSTEz9Zhoa++66GhoqIiEjmOnwY6tSBL780z63Wsyfs2gU1aqS5qrCwMF577TUqVKjAnj17rNufeeYZPDOw4IGISFbI8Kqgw4cPz/Qx7n5+fly7dg2TycTVq1f57rvvUjy3s7Mzy5Yts04kV716dcqUKcPYsWP54osvMrVdIiIPg4kLFxISG8vXRYrQ6cIF7CdPNv/1+OefoVSp7G6eiIiIPMwMw7wgwYABcPs25M0LM2bAs8+moyqDRYsW0adPH65cuYKdnR1///03AQEBmd9uEZEHJMPBWkpDNDMiNUFdXFwcy5Yt47XXXrNZncHPz48mTZqwZMkSBWsi8lgaP348+fPn54033sB+71547TXYuROqVdOqoSIiIpJ+ly/Dm2+ap54AeOopmDULChdOc1UXL16kV69eLF26FICKFSsyc+ZMateunYkNFhF58DI8FDS7nDhxgujoaPz9/ZPs8/f35/jx49y+fTsbWiYikr3s7e0ZNmwYPj4+0Lo17N7NGD8/gjU0VERERNJr+XJ44gnzf52dYeJE+P33NIdqhmEwa9YsKlasyNKlS3FwcGDYsGHs2rVLoZqIPJQy3GMNYOnSpcyfP58zZ84kCbNMJhN79+7NjNPYCAsLA8Db2zvJPm9vbwzD4Nq1axRO5oM+JiaGmERfKiMjIzO9fSIiOcVve/Yw6MwZnOztmRwfTw8NDRUREZHUunULPvwQpk41P3/iCfjhB7DM75oGp0+fpmfPnqxevRowT+Mzc+bMZDtLiIg8LDLcY+3LL7+kQ4cObNiwAUdHR/LmzWvzSC74ykz3Gjaa0r5Ro0bh6elpfRQrVuxBNU9EJNs1atSI9u3bExsfT0+gh7OzedXQatVg0aLsbp6IiIjkVLt3Q/Xq/4Vq/ftDcHCaQ7WEhAQmTZpE5cqVWb16NS4uLnzxxRds3bpVoZqIPPQy3GNt6tSpvPHGG3zzzTfYp2NJ5fTKmzcv8F/PtcTCw8MxmUx4eXkle+yQIUN4//33rc8jIyMVronII8vDw4NffvmF0aNH8/HHH/NdTAz73N1ZFBlJsU6doE8fGDvWPKxDREREJCHB/P8GQ4fCnTvm4Z5z5kCLFmmu6ujRo7zxxhts2rQJgIYNG/Ldd99RtmzZzG61iEi2yHCPtbCwMLp06ZKloRpAqVKlcHV1Zf/+/Un27d+/n9KlS+Pi4pLssc7Oznh4eNg8REQeZSaTiSFDhrBy5Uq8vb0JvnGD6m5urAOYPBnq14cTJ7K7mSIiIpLdzp2DZs1g0CBzqNa+Pezbl65QDcydGLZs2YK7uztTpkxh/fr1CtVE5JGS4WCtfv36HD58ODPakiYODg60a9eOxYsXExUVZd1+9uxZ1q1bR4cOHbK8TSIiOV3Lli3ZsWMHAQEBhN66RQs7O75yc8PQ0FARERFZsAD8/WH9enBzg+++g19+gXz50lRN4lFFNWrUYObMmRw4cIBevXphZ/fQrp8nIpKsDH+qTZgwgSlTpvDrr78SGxubGW0CYMWKFSxatIjffvsNgEOHDrFo0SIWLVrErVu3AAgKCuLWrVu0bduWFStWsGTJEtq0aUO+fPkYMGBAprVFRORRUqJECTZt2sSrr75KfEICA27d4qW8ebkZGQmdOmnVUBERkcdNZCR07QovvgjXr0PNmrBnD7z5JtxjTuu7xcfHM3ToUPz8/Dh06JB1e9euXfHz88v8douI5AAmwzCMjFQQHx9P//79mTJlCiaTCTc3N9sTmExERESkud7ixYtz5syZZPedOnWK4sWLA7Bz504GDRrEli1bcHBwoGnTpowdO5ZSaVjpLjIyEk9PTyIiIjQsVEQeG4ZhMHXqVPr160dcXByV8+VjydWrlAbzRMVaNVREROTRt3kzvPIKnDoFdnbw0UcwfDg4Oqa5KsMweOaZZ1i2bBnDhw8nKCjoATRYRCRrpDYrynCwNmDAAMaPH09AQAAVKlTAyckpSZlZs2Zl5BQPnII1EXmcbdq0iY4dO3Lp0iWK5M3LccPANTwcPDxgxgzo2DG7mygiIiKZLS4ORo6ETz81L1ZQvDjMmwcNGqSpmps3bxIXF4enpycAISEhbN26leeff/4BNFpEJOtkWbCWN29eevbsyahRozJSTbZSsCYij7sLFy7QqVMnevfuTZeGDaFzZ/NfsEGrhoqIiDxqTpyAl1+GbdvMz195xbyY0b/hWGr9+eef9OjRgyeffJI5c+Y8gIaKiGSf1GZFGZ5jLT4+nhbpXCFGRERyhiJFirBhwwa6dOkCxYrB+vXsf+MNroFWDRUREXlUGAbMmgUBAeZQzdMTfvzR3FMtDaHa9evX6d69Oy1atOD06dOsX7+ea9euPbh2i4jkYBkO1lq2bMnWrVszoy0iIpKN7O3trf++FBZGq5UrqVm4MMe9vECrhoqIiDzcwsLMixS98QbcuAGNGsG+feZe6mnw66+/UqlSJWbMmAFA7969OXDgAHny5HkQrRYRyfEyHKwNGzaM77//nokTJ3L8+HHCw8OTPERE5OESFhaGk5MTTl5eFNy8GerVM68YplVDRUREHj5r1oC/P/zyCzg4wKhR5m2+vqmu4sqVK3Tu3Jlnn32WCxcuUKZMGTZs2MDkyZPJnTv3A2y8iEjOluE51uzszNmc6R7LMMfHx2fkFA+c5lgTEUkqLCyMiIgISpYsCXfuYAwdSvyYMTiAVg0VERF5GMTEwMcfw7hx5uflysH8+ebf46lkGAY//PADffv2JSwsDHt7ez744ANGjBiBq6vrA2q4iEj2S21W5JDREw0fPvyeoZqIiDyc8ubNS968ec1PHB35qkABlj3xBD+fP08By9BQrRoqIiKSMx08aF6gYO9e8/O33zYvRpQrV6qrOH/+PG+//TbLly8HwN/fn5kzZ1I9DcGciMijLsM91h4F6rEmInJv169fp2TJkly7do2ihQuzKH9+au/bZ96pVUNFRERyDsOAKVPgww/h9m3Il8/8h7BnnklDFQbTp0/nww8/JDIyEicnJ4YNG8bAgQNxcnJ6gI0XEck5smxVUBERefR5eXmxefNmypcvz/mLF3nyyBGmt2pl3jl5snkONq0aKiIikr0uXYI2bczzod6+Da1awf79aQrVLH755RciIyOpU6cOu3fvZujQoQrVRESSoWBNRERSpXz58mzbto327dsTGxtLz5Ur6fHUU8R4e8OuXVo1VEREJLvcumXuPV6pEqxYYe5F/vXX8PvvUKhQqqqIj4/n1q1bgHn+7G+++YYJEyawceNGKlas+CBbLyLyUFOwJiIiqebh4cEvv/zCqFGjsLOz47tVq2hYrBjnatTQqqEiIiJZLSbG3HO8VCnz0M/wcKhSBXbuNP8+TuVc2IcOHaJevXr069fPuq148eL07dsXe3v7B9R4EZFHg4I1ERFJE5PJxODBg1mxYgXe3t5s37uX6mfOsO6ll8wFLENDZ840T5yckJC9DRYREXnU3LkD330HZcqYA7RLl6B4cZg9G3bsMPdcS4OrV68SHBzMggULuHLlygNpsojIo0qLF6DFC0RE0uv06dN06NCB3bt3Y29vzxevv877S5ZgCg//r1Du3FCzJtSubX7UqgWFC2dfo0VERB5W8fHwww8QGAgnT5q3+fjAsGHQrRukYQ608PBwvL29rc+nT59O69at8fHxyeRGi4g8nFKbFSlYQ8GaiEhGREdH89ZbbzFv3jwAXmjblhlly+K+c6f5r+Y3byY9qFix/4K22rWhenVwc8vilouIiDwkEhLgl19gxAg4fNi8rUAB+OgjeOstcHFJdVXR0dGMGDGCadOmsWfPHkqWLPmAGi0i8nBTsJYGCtZERDLGMAymTp1qnZtlw4YN1K1bF+LiiN61C9e9e2HbNvPj4EG4+1ePvT088cR/Pdpq14YKFcBOMxaIiMhjzDBg2TJzj7S9e83b8uSBQYOgTx/IlStN1W3YsIHu3btz7NgxAEaPHs2gQYMyu9UiIo8EBWtpoGBNRCRzbNq0iSNHjvDmm28C5sAtT548eHt7s379enx9fSEqipsbN+K2dy+m4GBz2HbhQtLK7h5CWrt2qlc2ExEReagZBvz5JwwdCsHB5m25c8OAAdCvH3h6pqm6qKgoBg8ezNSpUwEoUqQI06ZNo127dpnccBGRR4eCtTRQsCYi8mCcPXsWPz8/nJycuHHjBo6OjgB069aNJUuW4O/vT0BAAFV8fQlISKDSpUu4WIaQ3rqVtEJf3/96tGkIqYiIPIr+/tscqG3YYH7u5gbvvQcffAB586a5upUrV9KzZ0/OnTsHQI8ePRgzZgxeXl6Z2GgRkUePgrU0ULAmIvLghIeHc/z4cWrVqmXdVrNmTXbs2JGkrL29PeXKlSPA358qhQoRYBhUCQ2l4L599x9CanmUL68hpCIi8vAJDjYP+fzjD/NzZ2d45x0YPBgKFkxzdeHh4fTv35+5c+cCUKJECb777juaNm2ama0WEXlkpTYr0jcPERF5oLy9vW1CNTAPGd27dy9z5szh/fffp2nTpuTNm5f4+HgOHTrEDz/9xKAJE3hq4kQK/fADha9eZerYsbB2LYwaRfyzzxJfqJB5dbQ9e+Cbb+CNN6BSJfDygmbNzBM6/9//waVL2XLdIiIiqbJ3Lzz7rPmPQ3/8AQ4O8PbbcPw4jB+frlBt0aJFVKhQgblz52IymejXrx/79+9/aEO1rVu30qlTJwoXLoyTkxOFChWiY8eObNmyJU31BAYGYjKZ0tWG9evXYzKZWL9+fbqOT63GjRvTuHHje5aJj4/nq6++olWrVhQtWhQ3NzcqVKjA4MGDuX79erLHTJo0ifLly+Ps7EyJEiUICgrizp07NmXOnz9Pv379aNSoEV5eXphMJmbPnp1sfbGxsQwfPpwSJUrg5OSEn58fQ4YMITo6Oh1XLfJwc8juBoiIyOPHyckJf39//P39rdsMwyAkJIS9e/eyZ88e9u7dy969ezl27BiXLl3CxcsLmjSBJk3YtnkzzZo1o0Xz5vz69tvWhRFitm/HOSrKHMCtXfvfCX19bRdG0BBSERHJbkeOmFf5XLDA/NzODl57DYYPhxIl0lXlpUuX6N27N4sXLwagQoUKzJw5kzp16mRWq7PcpEmT6NevH7Vq1WLMmDH4+flx9uxZpkyZQoMGDZg4cSJ9+vRJVV3du3enVatW6WpHtWrV2LJlCxUrVkzX8ZkpOjqawMBAXnrpJbp3706+fPnYtWsXn376Kb/99hs7duzA1dXVWv6zzz5j2LBhDB48mJYtW7J9+3aGDh1KSEgI3377rbXc8ePHmT9/PgEBAbRu3Zoff/wxxTa89NJL/P777wwfPpyaNWuyZcsWPv30Uw4ePMivv/76QK9fJMcxxIiIiDAAIyIiIrubIiIid4mKijK2bNliXLp0ybrtf//7nwEYTz/9tE3ZwoULGyWLFjU6VK1qBFWrZiz19TVOg5FgHkT638Pe3jACAgzjrbcMY+ZMwzh40DDi47P60kRE5HF04oRhvPaaYdjZ/fd7qXNnwzhyJMNVr1692gAMBwcHY+jQocbt27czocHZZ+PGjYadnZ3Rtm1b486dOzb77ty5Y7Rt29aws7MzNm7ceM96bt68+SCbmakaNWpkNGrU6J5l4uLijKtXrybZvnDhQgMw5s2bZ9129epVw8XFxejZs6dN2c8++8wwmUzGwYMHrdviE/2/0Pbt2w3AmDVrVpLzbNmyxQCMcePG2Wz//PPPDcD4448/7tl+kYdFarMiDQUVEZEczd3dnTp16lAw0VCYnj17cvToUcaMGWPdFhoaysWLFzl5/jyLd+9mxK5dPHf2LMUBb3d3GpcoQd+SJZnp6cmu+HhuawipiIhkpXPn4K23oFw5mDsXEhLguefMQ0F//NG8PQ0iIyOZOnUqw4YNs25r3rw5o0aNYseOHYwcORJnZ+dMvoisNWrUKEwmE//73/9wcLAdbOXg4MDUqVMxmUyMHj3aut0y3HPXrl107NiRPHnyUKpUKZt9icXExDBgwAAKFSqEm5sbTz75JDt37qR48eK8/vrr1nLJDQV9/fXXcXd35/jx47Ru3Rp3d3eKFSvGgAEDiImJsTlPUFAQtWvXxtvbGw8PD6pVq8aMGTMw0jHlub29PXmTWcjCMvWGZaEKMC9ecfv2bbp162ZTtlu3bhiGwdKlS63b7FI5R+2mTZsAaN26tc32tm3bAvDLL7+kqh6RR4WGgoqIyEPHzs6OMmXK2GzLnz8/V69etQ4htQwnPXToENdv3OCvGzf4K1F5ezs7ynt7M9/XlypHjkBUFPFr12Kf3BBSy6NaNQ0hFRGRtLl0CUaNgmnTIDbWvK1VK/jkE6hZM93Vnjhxgt69e+Pk5ETfvn3Jly8fAIMHDzYXMIzkV9jOLm5ukIb5zeLj41m3bh01atSgaNGiyZYpVqwY1atXZ+3atcTHx2Nvb2/d16FDBzp37szbb7/NzZs3UzxPt27d+Pnnnxk4cCBNmzbl0KFDtG/fnsjIyFS1886dOzzzzDO8+eabDBgwgA0bNjBy5Eg8PT0ZPny4tdzp06d566238PX1Bczzxr377ruEhITYlMuItf/+P0ylSpWs2w4cOADAE088YVO2cOHC5MuXz7o/LWL/vY/vDm4tz/ft25fmOkUeZgrWRETkkZE3b16aNm1qMzlzbGwshw8ftgZtlv+Gh4dz8OpV8u/dCwUKwMGDBH38Md+tXcuHHh70v3IFzp4l4exZjIULsQetQioiIqkXFgZjxsCkSWCZ0L1RI/j0U2jQII1VhTFv3jwiIyOtIUzVqlXp0qULNWvWTL5n2q1b4O6e0avIPDduQK5cqS5+9epVbt26RYn7zDdXokQJgoODCQsLo0CBAtbtXbt2JSgo6J7HHjp0iB9//JFBgwYxatQoAFq0aEHBggV56aWXUtXO2NhYgoKC6NSpEwDNmjVjx44d/PDDDzaB2axZs6z/TkhIoHHjxhiGwcSJExk2bFi6F1WwCAkJYfDgwdSoUcPacwzM946zszO5knntvb29CQsLS/O5LPPMbdq0yebns3HjRus5RR4nCtZEROSR5uTkRJUqVahSpYp1m/HvQgn79u2jcOHC5r+gV6nCHjs7LkZHYz96NLz+Ouzcyf6lS6k7ZQpP2NlR5c4dAvbsocqePfh/8w25ATw8zD0OEi+OUKhQdl2uiIhkt4gI+Oor84qeUVHmbXXqmAO1pk1T3WvLMAzWr1/P9OnTWbx4MTExMbi6utK3b188PT0BmD9//oO6ioeGZSjl3cHU888/f99j//rL3Jf9hRdesNnesWNHXn311VSd32Qy0a5dO5tt/v7+1t5jFmvXruXzzz9n+/btSXrDXblyxWbKi7QKDw+ndevWGIbBzz//nGRI571Cu/QEek8//TSlS5dm0KBBFCxYkJo1a7J161Y++ugj7O3tUz2kVORRoWBNREQeOyaTiaJFiyYZWvLDDz+wf/9+8zANDw9o0oS9584RHR9PcHw8wXfVU8pkIiAykipr1hCwZg1VgGKASUNIRUQePzdumHunffklXLtm3hYQYA7UWrdOdaB25coVZs+ezXfffcexY8es2wMCAujRo0eSucZS5OZmblNOkcbfg/ny5cPNzY1Tp07ds9zp06dxc3PD29vbZnvhwoXvew5Lz6q7Qy0HB4dk5zBLjpubGy4uLjbbnJ2duX37tvV5cHAwLVu2pHHjxkyfPp2iRYvi5OTE0qVL+eyzz4i29GhMh2vXrtGiRQtCQkJYu3YtJUuWtNmfN29ebt++za1bt3C762cQHh5O9erV03xOJycnVqxYwauvvkrLli0ByJUrF59//jkjR47Ex8cn3dcj8jBSsCYiIvIvd3d36tata7Pt5Zdfpk6dOtYhpJbhpCEhIZwwDE4AiafozQPUOHuWVWfPYlq4EIAEOzvs/P01hFRE5FEUHW2eP23UKAgNNW+rWNE8h1r79qn6rE9ISODPP//k22+/5f/+7/+Ii4sDzL+XunTpQo8ePahevXraeheZTGkaepnT2Nvb06RJE1auXMn58+eTnWft/Pnz7Ny5k6efftpmfjVIXU8sS3h2+fJlmzAoLi4uU4cz/vTTTzg6OrJs2TKbEC7xwgHpce3aNZo3b86pU6dYs2YN/v7+ScpY5lbbv38/tWvXtm6/dOkSV69epXLlyuk6d+nSpdmyZQshISGEh4dTqlQpIiIi6Nu3L08++WT6LkjkIfVQB2vr16+nSZMmye7bsmULderUyeIWiYjIo8be3p6yZctStmxZm6EiKS2UcC0ujoslSmDq0QO2bYNt22h46RKRe/YwY88ean3zDQAJuXNjZxk6anlkYBiIiIhksdhYmDHD3CPtwgXztlKlICgIOnc2z8t5HyEhIcyaNYsZM2Zw+vRp6/batWvTo0cPXnzxRdxz0jxpWWzIkCGsWLGCXr16sWTJEpvwLD4+nnfeeQfDMBgyZEi66rcEQD///DPVqlWzbl+0aJE13MwMJpMJBwcHm/ZHR0czb968dNdpCdVOnjzJ6tWrqVq1arLlWrVqhYuLC7Nnz7YJ1mbPno3JZOK5555LdxsAfHx8rKHk0KFDyZUrF2+++WaG6hR52DzUwZrF559/niRgS2/yLiIikhr58uWjWbNmNGvWzLotJiaGw4cPExUVBQ0bAhB35w67cufmdkwMed58E44ehZ07GRMVxaREQ0gDgCqFC1O6fn3s69TREFIRkZwqLg7mzTMHaGfOmLf5+sLw4fDaa+DomOqqunbtypo1awDw8vLilVdeoUePHsn2PHoc1a9fnwkTJtCvXz8aNGhAnz598PX15ezZs0yZMoVt27YxYcIE6tWrl676K1WqxEsvvcS4ceOwt7enadOmHDx4kHHjxuHp6Zlpc4W1adOGr776ii5dutCzZ0/CwsIYO3Zs8otOpEJ0dDRPPfUUu3fvZsKECcTFxbF161br/vz581OqVCnAvEDB0KFDGTZsGN7e3rRs2ZLt27cTGBhI9+7drQsRWCxatAiAkydPArBjxw5ruNuxY0druTFjxlCoUCF8fX25fPkyCxYsYOnSpcybN09DQeWx80gEa2XKlFHvNBERyXbOzs4EBATYbLN3cODosWPs2bOHUm3amIcExcWxt21bLqxaxQXgd0vhixdxW7SIJxYtModtJhNVSpfG/8knca9fX0NIRUSyU0IC/PwzjBgBlrnPChWCoUOhe3e4T0hy5swZZsyYQa9evSj07yI33bt3JzY2lh49etCxY0dcXV0f9FU8dN59911q1qzJuHHjGDBgAGFhYXh7e9OgQQM2btyYZAqHtJo1axaFCxdmxowZjB8/noCAABYsWECrVq3w8vLKlGto2rQpM2fO5IsvvqBdu3b4+PjQo0cPChQokK7eXZcvX2b79u0A9O3bN8n+rl27Mnv2bOvzjz/+mNy5czNlyhTGjh1LoUKFGDx4MB9//HGSYy2rm1pMmTKFKVOmAP8tFAFw+/ZtPvnkE86fP4+rqyt16tRh/fr1NPz3D4sijxOTkfjd8ZCxDAVduHChTXqeVpGRkXh6ehIREYGHh0cmtlBERCR5UVFR7N+//7+hpLt2sW//fqJjYpKUNQGlgK7A0ESrkBq1amGqU0dDSEVEHiTDgKVLzT3SDhwwb8uXDwYPhnfeSXXP4vr167N582ZGjx7NoEGD/q3aSNeqjPJgbd68mfr16zN//ny6dOmS3c0RkWyS2qzokeix1rt3bzp37oybmxt169Zl2LBhNGjQILubJSIikqLcuXNTr149m+Er8fHxHD9+3By07dnD3m3b2LNnDxeuXeM4EOngAJGRsGYNYWvWUBbzENJVvr44/DtPm1GrFqbq1TWEVEQkowwDVq6EYcNg507zNk9P+PBDeO89yJ07xUOPHTvGjBkzGDhwoHW1yh49euDq6mozzFOhWvZbvXo1W7ZsoXr16ri6urJ3715Gjx5NmTJl6NChQ3Y3T0QeAg91j7Xdu3czZ84cGjduTN68eTl+/DhffvklR48eZfny5Tz11FPJHhcTE0NMoh4BkZGRFCtWTD3WREQkRwoNDWXv3r34FCxIhfh42LaNtUuW0GzVKkoCJxKVbQlcBpoVKULbFi1o8OabONWtCw6PxN/SRESyxrp15iGemzebn7u7Q79+8P77kCdPsofcvn2bJUuW8O2337J+/XoAJk6cyHvvvQeod1pOtW3bNgYMGMChQ4eIiooiX758PPXUU4waNYrChQtnd/NEJBultsfaQx2sJef69es88cQTeHt7s3fv3mTLBAYGEhQUlGS7gjUREXlYxMbGcujQIcLPnaNprlywbRvG1q14//Yb1xP9avcAWjo60tbfn6dffJECHTtCiRLZ13ARkZxs82ZzD7W1a83PXVygTx8YOBDy50/2kEOHDjF9+nTmzp1LeHg4YO6J9vTTT/PBBx8kWWRNREQeDo9tsAbwzjvvMG3aNG7dupXsBKDqsSYiIo8iwzA4d/YswcuW8fsPP7B8506uJPp9ZwJqA229vWnbrBn+L72EqWlT89AmEZHH2a5d5kDt93+Xk3F0hLfego8+gmR6Ld26dYuFCxfy7bffstnSqw0oVqwYb7zxBm+88Qa+vr5Z1XoREXkAHutg7e233+abb74hOjoaFxeX+5bX4gUiIvIoSkhIYMe2bSz77juWrVjB7osXbfYXBdqYTARWr06htm2hZUvzwggaNioij4sDB8yrfC5ebH5ubw/dupmHgfr5JSm+Z88epk+fzvz584mIiPj3EHvatWtHjx49eOqpp7C3t8/KKxARkQfksQ3Wrl27xhNPPEH+/PnZvXt3qo5RsCYiIo+DkJAQfv/lF5bNn8/qXbuIjovDCbgKWKbg3pErFwUbNKDYc8+Zg7aSJbOvwSIiD8qxYxAYCD/+aF6kwGSCl182h2ylSyd7yB9//GEzh3OJEiXo3r073bp101xcIiKPoMciWOvSpQu+vr7UqFGDfPnycezYMcaNG8eJEydYsWIFzZs3T1U9CtZERORxEx0dzfr16zm+bRvv+vjA6tXw559Uv3aNXcAvQAcwB2stW5ofTZqAl1e2tltEJENOn4aRI2HOHIiPN2/r2BGCgqBiRWsxwzAIDg4mNDSUtm3bAua5LUuVKkW9evXo0aMHTZs2xc7OLhsuQkREssJjEayNHj2an3/+mVOnTnHjxg28vb1p0KABQ4YMoWbNmqmuR8GaiIgIxNy6RfN69di6fz8htWpRYMcOiIvjO+BvoK3JRMsaNfBs3doctNWqpWGjIvJwuHABPvsMpk+HO3fM29q2hU8+gapVkxRfunQp7du3x8/Pj5MnT1oDtJiYGJydnbOy5SIikk0ei2AtsyhYExER+U9ERASenp4QFQXr19O0Vy/WnT8PgAPQEGgLtM2Vi7ItWphDthYtoFQp83AqEZGc4soV+OILmDoVbt82b2ve3NxrrU4dwNw7bdOmTURFRfH0008D5l69pUqVolmzZkyaNAkv9dYVEXnsKFhLAwVrIiIiKduwYQO//vory5Ys4Z+TJ232leHfkA1oULw4Tk89ZQ7ZmjaFPHmyo7kiInDtGowdCxMnws2b5m0NGsCnn0KjRgBcvXqVuXPn8t1333H48GHKli3LkSNHMP37B4LY2FicnJyy6wpERCSbpTYr0qQAIiIick9PPvkkY8eO5ciJExw7dowJEybQvFkzHB0cOAaMB5oB+U+f5oVvvmFOx46E580LdeuaJwLfuPG/oVciIg9SZKS5N1qJEvD55+ZQrUYNWLkSNmwgoWFD1qxZQ+fOnfHx8WHAgAEcPnwYNzc3GjRowE1LCAcK1XKAtWvX8sYbb1C+fHly5cqFj48Pzz77LDt37ky2/K5du2jevDnu7u54eXnRoUMHTt71ByGACRMm0KFDB0qUKIHJZKJx48YptmHVqlXUr18fV1dXPD09adeuHQcPHsysS0yWyWQiMDDQ+vzQoUMEBgZy+vTpJGUbN25M5cqV03WeyMhIPvvsMxo3bkyhQoVwd3fniSee4IsvvuC2pYdnInfu3CEoKIjixYvj7OxM+fLlmTRpUrJ1nzx5kg4dOuDl5YW7uzstWrRg165dybbh448/pmzZsri5ueHj40OnTp1S9RqfPn0ak8nE7Nmz03ztj6vixYvz+uuv37PMrVu3CAwMZP369Un2BQYGYjKZuHr16oNpYBrllPYoWBMREZFUK126NH379mX1n39yNSyMRYsW0a1bNwrkz08ksBB4HdhhGLB1K3zyCdENG2J4e8Nzz8GUKebV+NRhXkQy061b8OWX5gVXhg+HiAh44glYuhSCg7lUpQqjRo+mbNmyNG/enJ9//pnY2FiqV6/OtGnTuHjxIjNmzMDd3T27r0QS+d///sfp06fp27cvv//+OxMnTuTKlSvUqVOHtWvX2pQ9cuQIjRs3JjY2lgULFjBz5kyOHj1Kw4YNCQ0NtSk7bdo0zpw5Q9OmTcmfP3+K5/+///s/nn76aQoUKMAvv/zCtGnTOHbsGA0bNuTEiRMP5JoBtmzZQvfu3a3PDx06RFBQULLBWkacPXuWCRMmUK1aNb799lt+/fVXOnbsSGBgIG3btuXuwW29evVi1KhR9O7dm1WrVtG+fXv69u3L559/blMuNDSUhg0bcvToUWbOnMmCBQu4ffs2jRs35p9//rEp265dOyZMmECPHj1Yvnw5o0ePZs+ePdStW5czZ87cs/2FCxdmy5YttGnTJnNeEAHMwVpQUFCywZqkwBAjIiLCAIyIiIjsboqIiMhDKT4+3ti2bZsxbNgwo379+sbtY8cMY8YMw3jxRWOAi4tRFIzZ5jjN/Che3DB69jSMhQsNIzw8u5svIg+r27cN4+uvDaNgwf8+X8qVM4yffjLiYmON33//3Wjfvr3h4OBgAAZg5M6d23jnnXeMXbt2ZXfr5T4uX76cZFtUVJRRsGBBo1mzZjbbO3XqZOTLl8/mO93p06cNR0dHY+DAgTZl4+Pjrf+uVKmS0ahRo2TPX65cOcPf399ISEiwqdPJycno0qVLei4pXRYuXGgAxrp165Lsa9SokVGpUqV01Xvjxg3jxo0bSbZ/+eWXBmD8/fff1m0HDhwwTCaT8fnnn9uU7dGjh+Hq6mqEhYVZt3344YeGo6Ojcfr0aeu2iIgII1++fMYLL7xg3Xbs2DEDMIYOHWpT5+bNmw3A+Oqrr9J1XQ/arVu3srsJ6ebn52d07dr1nmVCQ0MNwBgxYkSSfSNGjDAAIzQ0NM3nTkhIyPTXLiPtSY3UZkXqsSYiIiIZZmdnR61atfjkk0/YuHEjzqVLwxtvwE8/sbZCBc4Dbi+9BI0bg6MjR0+f5ptvv+V8p06QL595EvFhw+DvvzVsVETu784d+PZbKFMG3nsPLl82D/+cPRsOHIAXX6Rho0a0bt2aJUuWEBcXR926dZk5cyYXL15k6tSpVE1mNVDJWQoUKJBkm7u7OxUrVuTcuXPWbXFxcSxbtoznn3/eZh4kPz8/mjRpwpIlS2zqsKzyei9hYWH8888/PP3009Z59yx1Vq5cmaVLlxIfH5/i8VOmTMHOzo4rV65Yt40bNw6TyUTv3r2t2xISEsiTJw8DBgywbks8FHT27Nl06tQJgCZNmmAymZId/rh9+3YaNmyIm5sbJUuWZPTo0SQkJNzzGnPlykWuXLmSbK9VqxaAzWu8dOlSDMOgW7duNmW7detGdHQ0K1eutG5bsmQJTZs2xc/Pz7rNw8ODDh068NtvvxEXFweAo6MjgHnBpEQsi4W4uLjcs/3JDQW1DA08ePAgL730Ep6enhQsWJA33niDiIgIm+MTEhKYNGkSAQEBuLq64uXlRZ06dfj111+tZYoXL07btm1ZvHgxVatWxcXFhaCgIAAuXbrEW2+9RdGiRXFycqJEiRIEBQVZr88iKCiI2rVr4+3tjYeHB9WqVWPGjBlJegSuXbuWxo0bkzdvXlxdXfH19eX555/n1q1b1jKxsbF8+umnlC9fHmdnZ/Lnz0+3bt2S9Mq8c+cOAwcOpFChQtah7sHBwfd8PS2vqaUXZ1BQkPV+u3v46OXLl+/7+ppMJvr06cO0adOoUKECzs7OzJkzB4Bjx47RpUsXChQogLOzMxUqVGDKlClJfj6ffvop5cqVs/58/P39mThxYpJ2p6Y9D5JDlp1JREREHkubNm1i/fr11K9fHzw84MYNfn7nHYZ//z0AVRISaLttG223baPmp59inzs3NGliXgShZUvzF2etNioiAPHx8MMPEBgIlrmzihYlbsgQVhYuTKt27XBwMH/Fadq0KUeOHOG1116je/fu6Z6H6mGXeN641HJ2dra+jnFxccTExGBnZ4erq2u66k0uvEmviP9v787jasr/P4C/bndp34tKWkhZ+2I0RpZKJZStSWIYZIafZTAYpmFKTbaEZjCDEYOxL7PI2CLL11pjGNlm+EqWspRUtN7evz+ae8btXlTkKu/n43Ee3M/5nHM/53POPffcd5/l0SOcOXMGXbt2FdKuXbuGgoICuLq6quR3dXXF/v37UVhY+MJAzdOKi4sBlNdFRdra2njy5AmuXbsGZ2dntdv7+PiAiHDgwAEMHDgQAJCYmAhdXV3s379fyJeSkoKcnBz4+Pio3Y+/vz9mz56NL774AkuXLkXbtm0BAI0bNxbyZGZm4oMPPsDkyZMRERGBn376CWFhYbCxscGHH35Y6WNWUHSzbdGihZCWmpoKS0tLWFlZKeVV1HlqaiqA8hl1r127hn79+qns19XVFQUFBfjf//4HZ2dn2Nvbo0+fPli0aBHeeecduLm54datWxg/fjzs7OwQEhJS5bIrvP/++xgwYABGjBiB8+fPIywsDACwatUqIc+wYcPw448/YsSIEYiKioJMJsOZM2dUutyeOXMGly5dwowZM+Do6Ah9fX1kZmbi3XffhZaWFsLDw9G4cWOcOHEC0dHRSEtLw+rVq4Xt09LSMGrUKNjZ2QEATp48iU8++QS3b99GeHi4kMff3x+dO3fGqlWrYGJigtu3b2PPnj0oLi6Gnp4eysrK0KdPHxw9ehRTp06Fu7s7bty4gYiICHh6eiIlJUX4jH788cdYu3YtpkyZAl9fX6SmpiIwMBB5eXnPrTdra2vs2bMH3bt3x4gRI4QuyRW7TFemfoHygOzRo0cRHh4OKysr1KtXDxcvXoS7uzvs7OywYMECWFlZYe/evRg/fjwePHiAiIgIAEBMTAxmzpyJGTNmoEuXLigpKcHly5eRk5NTrfNdo2qkvVwtw11BGWOMsddr7dq15O7uTiKRSOieBYAsRSL6EKAtAOUounXZ2xN99BHRli1ET3U1YYy9ReRyos2biZo2/bfLZ716RHFxVPbkCf3nP/8hAPTrr78Km+Tk5FBBQYEGC/1mePoeW9lly5YtwvZbtmwhACrdJS0sLCq9v1fpgw8+IIlEQikpKULasWPHCABt3LhRJf/s2bMJAN25c0ft/p7VFVQul5OZmZlKl9OHDx+SoaEhAaDjx48/t6y2trYUGhpKRERFRUWkr69P06ZNIwB048YNIiKaNWsWSaVSpS6ZqNAN70VdQQHQqVOnlNKbN29Ofn5+zy2fOufOnSNdXV3q16+fUrqvry+5uLio3UYmk9HIkSOJiOj27dsEgObMmaOSb8OGDSr1VlxcTB9//LHS9eLq6krXr19/YVmvX79OAGj16tVCmqJrYExMjFLeMWPGkI6OjtCt98iRIwSApk+f/tz3sLe3J7FYTFeuXFFKHzVqFBkYGAjnUSE2NpYA0IULF9TuTy6XU0lJCUVFRZG5ublQnm3bthEAOnv27DPLsnHjRgJA27dvV0pPTk4mAPTtt98SEdGlS5cIAH366adK+davX08AXklX0BfVL1H5dWxsbEzZFYb88PPzI1tbW5X4y7hx40hHR0fIHxAQQK1bt35uWatSnurgrqCMMcYYe2MNGTIEx44dw71797B27VoMGDAAxsbGuE+EtQCCAViIRPAWibDoxg38tXIlEBxc3m20fXtgxgzgyBHgnxYFjLE6igjYuRNo2xYYMAC4fBnFpqb4ZcgQlF29CkyYAJGuLnx8fGBpaYns7GxhU2Nj4yq1UGJvvi+//BLr168XWjhVJHpO6+bnrVNHS0sLY8eOxYEDB/DVV1/h3r17uHr1KgYPHix0zXtRl1Jvb28kJiYCAI4fP44nT55g0qRJsLCwEFqtJSYmokOHDi/Vqs/Kykrovqng6ur6wsH/K0pLS0NAQAAaNmyIlStXqqyvSv1WNu/o0aOxfft2LFq0CIcPH8bmzZshk8nQtWvXKpf/ab1791Z67erqisLCQqFr7u7duwFAqVvus7i6uqq0TExISICXlxdsbGxQWloqLD169AAAHD58WMh78OBB+Pj4wNjYGGKxGFKpFOHh4cjKyhLK07p1a8hkMowcORJr1qxRO5ttQkICTExM0KtXL6X3bN26NaysrITJBpKSkgAAH3zwgdL2wcHBQkvUl/Wi+lXo2rUrTE1NhdeFhYU4cOAA+vXrBz09PaXj6NmzJwoLC3Hy5EkA5V2Sz507hzFjxmDv3r3Izc196fLUFA6sMcYYY0xjLCwsMGTIEGzatAn3799HUlISJk+eDBcXF5QS4SARJgFwATC3Xr3yH9mnTwOzZgEeHoC5OdC7N7B4MXDlCs82ylhdQQTs21c+/mLv3sC5c/hLXx+fubvDVixG33XrcPDUKSH7l19+iVu3bmHo0KEaLPSbKT8/v8rL0934+vXrh/z8fCEQoZCWllbp/b0KkZGRiI6OxqxZszBu3Dildebm5gDKx0WrKDs7GyKRSBi3qyrCw8Px6aefIjo6GvXr10eTJk0AQBhnrEGDBs/d3sfHB+np6fj777+RmJiINm3aoF69eujatSsSExNRUFCA48ePP7MbaGUpjv9p2traKCgoqPQ+bty4AS8vL0gkEhw4cABmZmYq76Gufh8/fozi4mIhv6mpKUQi0TPPBQAh7549exAfH4/ly5dj4sSJ6NKlC4KDg7F//35kZ2cL48xVR8U6UXTpVdTJ/fv3IRaLVbq2qmNtba2SdvfuXezcuRNSqVRpUXSfffDgAQDg9OnT6NatGwDg+++/x7Fjx5CcnIzp06crladx48ZITExEvXr1MHbsWDRu3BiNGzdWGk/s7t27yMnJgUwmU3nfzMxM4T0VdV/x2CQSidprpTpeVL8KFesuKysLpaWlWLx4scox9OzZE8C/dRcWFobY2FicPHkSPXr0gLm5Oby9vZGSklLt8tQUHmONMcYYY28EqVQKT09PeHp6IjY2Fn///Td27dqFXbt24fDhw+i0fXv54OT79yNx3TqsOHYMg/Lz0XfnzvIWLQBgZ/fv2Gze3uWBN8ZY7XLkSHmr1KNHUQhgu0yGFfXr48jNm8Dx4wDKf6w9/cO94uDn7F8vO76ZRCJR28rlVY6b9iKRkZGYOXMmZs6ciS+++EJlfePGjaGrq4vz58+rrDt//jycnJyq1XpRIpFg4cKFiIqKwvXr12FhYQFra2v4+fnB0dERtra2z93e29sbQHmrtP3798PX11dInzFjBo4cOYKioqKXDqy9rBs3bsDT0xNEhEOHDqk9rlatWmHTpk3IzMxUCtgo6lwxhqGuri6cnJyeeS50dXXRqFEjAMDZs2cBAG5ubkr5TExM4OTkJIzbVhMsLS0hl8uRmZmpNnD2NHWt7ywsLODq6opZs2ap3cbGxgYAsGnTJkilUiQkJChdgz///LPKNp07d0bnzp0hl8uRkpKCxYsXY+LEiahfvz5CQkJgYWEBc3NzpYkinmZoaAjg3yBTZmamUvC3tLRUbcCzJlWsO1NTU4jFYgwZMuSZrQUdHR0BlH/+Jk2ahEmTJiEnJweJiYn44osv4Ofnh5s3b0JPT6/Gy19Z3GKNMcYYY2+kJk2aYOLEidi/fz8ePHiADh06AA0aAMOGYYeLC7YWFWFf//7A3LmAtzdKpVKcS08HxceXdxmztATc3IDp04HDh7nbKGNvutOnAT8/wMMDqUePYoJYDBttbQwuLsaRmzehpaUFf39//Pzzz0hPT8eAAQM0XWL2Gnz11VfCAOaKQc0rkkgk6NWrF3bs2KE0OHt6ejqSkpIQGBj4UmUwMDBAq1atYG1tjTNnzuDAgQOYMGHCC7eztrZG8+bNsX37dvz+++9CYM3X1xf379/HwoULYWRkpBJYqqgmW9+kp6fD09MTcrkcBw8eVJrJ82l9+vSBSCQSZnVU+OGHH6Crq4vu3bsLaf369cPBgweVZhXNy8vDjh070Lt3byFQqwg+Kbr+KWRlZeGvv/56YeDyZSi6bH733XfV2j4gIACpqalo3Lgx2rVrp7Iojk0kEkEikUAsFgvbFhQUYN26dc/ct1gsRvv27YVZMs+cOSO8Z1ZWFuRyudr3dHFxAQB4enoCANavX6+03y1btqjMWKpOTV5venp68PLywh9//AFXV1e1x6GuVZ2JiQmCgoIwduxYZGdnq0wwoWncYo0xxhhjbzwjIyOl1x9//DEsLCzKu1d06gRMm4aTiYno7OsLWwMDBMhkCMjORteUFOimpACzZwP6+oCnZ3lrtm7dABcXnm2UsTfBuXPAl1/i8c6d2AzgewAngfIZQOVy2NnZYcSIEQgNDa3RH9rszbNgwQKEh4eje/fu8Pf3VwnAvPfee8L/IyMj4ebmhoCAAHz++ecoLCxEeHg4LCwsMHnyZKXtUlJShB/mubm5ICJs27YNQHnrKUVw6dChQ0hOToarqyuICKdPn8a8efPQvXt3le6oz+Lt7Y3FixdDV1e3fHZslLfIcXR0xL59+5QCTc+iaA22YsUKGBoaQkdHB46Oji/dre/evXvw8vJCRkYG4uPjce/ePaUxqWxtbYXPXIsWLTBixAhERERALBbDzc0N+/btw4oVKxAdHa3UdXTKlClYt24d/P39ERUVBW1tbcydOxeFhYVK3TsDAwMRHh6O0aNH49atW2jbti0yMjIwf/58PHnypFLBy+rq3LkzhgwZgujoaNy9excBAQHQ1tbGH3/8AT09PXzyySfP3T4qKgr79++Hu7s7xo8fDxcXFxQWFiItLQ2//fYbli1bBltbW/j7+2PhwoUYNGgQRo4ciaysLMTGxqrMNrts2TIcPHgQ/v7+sLOzQ2FhoTCjpaJFY0hICNavX4+ePXtiwoQJePfddyGVSnHr1i0kJSWhT58+6NevH5o1a4bBgwcjLi4OUqkUPj4+SE1NRWxsrMrzlDqGhoawt7fHL7/8Am9vb5iZmcHCwgIODg7Vq+wKvv76a3Tq1AmdO3fG6NGj4eDggLy8PFy9ehU7d+4UZqXt1asXWrZsiXbt2sHS0hI3btxAXFwc7O3thW7Zb4yXmiKhjuBZQRljjLHab/Xq1aSrq6s0s5iuTEYBDRrQMkNDuqmYSVCx2NoShYYSbdpEdP++povP2Nvn4kWi/v2JAEoAyPCpz65EIqHAwEDavXs3lZaWarqkTEMUM14+a6koJSWFvL29SU9Pj4yMjKhv37509epVlXxDhw595j6fnmHy2LFj1L59ezIyMiJtbW1q2bIlxcbGUnFxcaWP4ZdffiEA5Ovrq5SumAnzm2++UdkGamZkjIuLI0dHRxKLxUrl9PDwoBYtWqg9Rnt7++eWLSkp6bn1W7EMxcXFFBERQXZ2diSTycjZ2Vlt+YmIrl69Sn379iUjIyPS09Mjb29v+v3331XyZWRk0Lhx48jJyYl0dHTIxsaG/P396cSJE88tO9HzZwW9X+F7ffXq1QRAabZRuVxOixYtopYtW5JMJiNjY2Pq0KED7dy5U8hjb29P/v7+at///v37NH78eHJ0dCSpVEpmZmb0zjvv0PTp05VmeV21ahW5uLiQtrY2NWrUiObMmUPx8fFK5Tlx4gT169eP7O3tSVtbm8zNzcnDw0NppmMiopKSEoqNjaX//Oc/pKOjQwYGBtS0aVMaNWoU/f3330K+oqIimjx5MtWrV490dHTovffeoxMnTpC9vf0LZwUlIkpMTKQ2bdqQtra20kyiValfADR27Fi1+79+/TqFhoZSgwYNSCqVkqWlJbm7u1N0dLSQZ8GCBeTu7k4WFhYkk8nIzs6ORowYQWlpaUKeqpSnOiobKxIR8Si/ubm5MDY2xqNHjyoVwWWMMcbYm6mgoACHDh1CQkICdu7cqdQNBQBa29jAXyZDwO3bcCspgdAxQyQqn3VQ0ZqtQwegwl+TGWOvyLVryJ0+HQ+2bEEjIkAkws1eveCQkABHR0d89NFHGDZsWKUGFWeMMcZqSmVjRRxYAwfWGGOMsbqIiJCamoqEhAQkJCTgxIkTePqxx9LYGBNatsT03Fyg4iDLenrK3UabNuVuo4y9rPR0IDoa2+Pj8WFZGboA2N23LxAVBbRqhbNnz8LV1RVaWjwMNGOMMc3jwFoVcGCNMcYYq/sePHiA3bt3IyEhAXv27EFubi6ioqLw5ZdfAhkZePTrr1j1/fcISEtDk4qzZjVo8G+Qzdu7fGIExlilZF+6hEdz58Jx0yaguBjXADgBaNGoEVIuXKjWbI2MMcZYTePAWhVwYI0xxhh7u5SUlODYsWNwdHQUBqneunUrgoOD4eLigstbtgD79gH79qHs8GFoVZxR9Oluo+7u3G2UsQqICEd+/RXfh4Vh26VL6AlgB1DeEjQ6Gn8aGqJVq1YQcUtQxhhjb6jKxop4VlDGGGOMvXWkUqkwHb2CsbExfHx84ObmBri6Aq6uKBw3Dk6NG8PdyQkB+vroceMGLC9eBM6cKV/mzi3vNurhUR5k8/UFmjfnbqPsrVRaWorMzExsWr0aK7/+Gleeavl5U18fJdu3Q9qtGyASwVWD5WSMMcZeJW6xBm6xxhhjjDH19u3bBz8/P+G1SCTCe++8gwBHRwQUFKBVcjJEd+8qb2RtDdjYlLdiUywymfLrmlonkXBQj1UeEVBaChQXA8XFoKIi5GVnw0AqhZZcDhQX4/dz53D+8mW0srPDO46OQHExrt+8iYnx8XiYl4ecx4/x8PFj5BQUIL+oSGn3BgAGmpvj4xkz0G78eIh47DTGGGO1CHcFrQIOrDHGGGNMnbKyMqSkpAgTIPzxxx9K6xs2bIiADh3gb2CArjduQPfYMaCwUEOlRXlQ7XUG8iqzXiZ7+4J9/wSlNLGUFRVBq6QEKC5GzpMnOPHoEXKKi/GwuBg5JSV4WFqKnNJS5MjleEiEHAAPAeT8s5QBuAug3j+H8gmAJQCmA4j+J+1vAM7POXw3ACOtrTFg/nwYDhwIcECNMcZYLcSBtSrgwBpjjDHGKuPWrVv47bffkJCQgMTERBQUFAjrdHV14e3lhdCOHdGvdWugqAgoKsL5q1ehDaCRoSEkpaVAURGosBCi4uLyPIp/1S2VWSeXa6w+Kk0qfX2BPpkMKCvTWGALxcXl719NcgCPAJWAl7r/rwKgGN1vHICVAGYBmPxP2mkA7atRhsv6+nDR0QFkMnxfVITthYUINjFBaP36gEyGx2IxNmRnw0RHBya6ujDV04OJvj5M9PVhrK8PadeuQHAwIBZX490ZY4yxNwMH1qqAA2uMMcYYq6qCggIkJSUJrdlu3rwJAOjbty9++uknIZ9EIoFcLsft27dhY2MDAJgwYQK+++476OrqVnpp1qwZxo8fL+x37dq1KCsrQ5+AAJjq6QFFRbh1/Tru3rkDXbEYuiIRdEUi6ADQBSArKysP5r1sIK8y60pKXuepeKMQgMf4NwD2CEAnqbQ84CeTYaNcjtNlZehrZAQPY2NAJsPxoiIMvH4dOaWlyK1CoDRzxgzUNzcHZDJ8sm0bliQlYXrfvogeMgSQyZCWnY3A6GiYGhnBxMgIpiYmMPlnMTUzg4mZGUwtLGDyz2JqYQETU1Po6urWSN0wxhhjtQkH1qqAA2uMMcYYexlEhPPnzyMhIQFisRjTpk0DUD6Ye8OGDVFQUIDr16/D1NQUADBy5Eh8//33VXoPLy8vHDx4UHhtYWGBrKwsXLhwAc2bNwcAREREICoqSu32Wlpa0NHRURuw27Rpk5AvLCwMDx48wGeffQZn5/IOf2fPnsWRI0deGPwT9q+tDV2xGGLF+F01HciruE4sFgJZ1V2yS0pwv6gIlmZmMDMzA2QyXMvKwtZTp/DwyRPkFBQg58kTPMzPR05+fvl4Y3l5yMnNRWlpqVLdFxYWQvufmWMHDx6M9evXY/78+ZgyZQoA4PTp02jfXrltmb6+/r9BMFNTpX8V/w8NDYWxsTEAIDMzE4WFhbC0tIS+vn6Vri3GGGOMqeJZQRljjDHGXhORSARXV1e4uirPdSiRSJCRkaGSPzY2Fl9++SUKCgoqvdjb2yvtw8/PDw8fPhSCdUB5MMbW1lZpO8XfUMvKyvDkyRM8efJEaT/iCt31tm7dimvXrmH48OFCYC0pKQmTJk2qUp1YWVkpHfuAAQNw/vx5LF68GN7e3gCAo0ePYml8/LODdebmKml6enro2LGjsN+cnBwAgIGBASQSiXCsubm5yMnJwcOHD5GTk6P0f3VphYWFSElJEfY7tFcvJCQkYOXKlRgxaBAA4OrevQj7v/+r1PFLJBIhEPb48WMhsBYQEABbW9vy2Wf/0aJFC5w6dUrIb2xsDJlMVuX6Zowxxtjrx4E1xhhjjLHXzMjI6KVbya9fv14lberUqZg6darwmohQXFz83IBdxW5/U6dOxb179+Dg4CCkNWnSBMHBwZUKAJb80w204n6vXbuGS5cuoeipmSOvXr2KzZs3V+m4dXR0lMa2Gzx4MHbt2oX4+HiEhoYCALZt24YBAwZUab8AUFxcLAS0TE1NYWxsrNT6zNHREcOHD1fbeqzi//X09CBSM2lDSEgIQkJClNL09fXx7rvvVrm8jDHGGNO8Wh9Yy8/Px4wZM7BlyxZkZ2ejadOm+Pzzz1UeWBhjjDHG3jYikQja2trQ1taGiYlJpbYZOXKkSlpAQAACAgIqtb1cLlcKsCnEx8cjOztbqVVf+/bt8fXXX1ep5V7FllyF/8zCqqOjI6QpjlVXV7dSQTDFv1pPzV65Zs0alcCYs7MzVq1aVal6YIwxxtjbodaPsdatWzckJydj7ty5cHZ2xoYNG7By5UqsX78eg/5ptv8iPMYaY4wxxljtpGiVp6WlBalUCgAoKSlBWVmZ0P2SMcYYY6yq3orJC3777Tf4+/tjw4YNGDhwoJDerVs3XLhwAenp6SrjhqjDgTXGGGOMMcYYY4wxplDZWJHWM9fUAj/99BMMDAzQv39/pfThw4fjzp07OHXqlIZKxhhjjDHGGGOMMcbqulodWEtNTUWzZs2EGaAUFGN3pKamaqJYjDHGGGOMMcYYY+wtUKsnL8jKykKjRo1U0s3MzIT16hQVFSnNSPXo0SMA5c38GGOMMcYYY4wxxtjbTREjetEIarU6sAZA7TTmL1o3Z84cREZGqqQ3bNjwlZWLMcYYY4wxxhhjjNVueXl5MDY2fub6Wh1YMzc3V9sqLTs7G8C/LdcqCgsLw6RJk4TXZWVlyM7Ohrm5+XMDdbVJbm4uGjZsiJs3b/KEDBrA9a9ZXP+axfWveXwONIvrX7O4/jWL61+zuP41i+tfs7j+Nasu1j8RIS8vDzY2Ns/NV6sDa61atcLGjRtRWlqqNM7a+fPnAQAtW7ZUu522trbK9OsmJiY1Vk5NMjIyqjMXdW3E9a9ZXP+axfWveXwONIvrX7O4/jWL61+zuP41i+tfs7j+Nauu1f/zWqop1OrJC/r164f8/Hxs375dKX3NmjWwsbFB+/btNVQyxhhjjDHGGGOMMVbX1eoWaz169ICvry9Gjx6N3NxcODk5YePGjdizZw9+/PFHiMViTReRMcYYY4wxxhhjjNVRtTqwBgA7duzA9OnTER4ejuzsbDRt2hQbN25ESEiIpoumUdra2oiIiFDp8speD65/zeL61yyuf83jc6BZXP+axfWvWVz/msX1r1lc/5rF9a9Zb3P9i+hF84YyxhhjjDHGGGOMMcZU1Oox1hhjjDHGGGOMMcYY0xQOrDHGGGOMMcYYY4wxVg0cWGOMMcYYY4wxxhhjrBo4sFbL/PDDDxCJREhJSdF0Ud4qinpXt0yZMqXS+xk2bBgMDAxqsKR1z9N1f+jQIZX1RAQnJyeIRCJ4enq+9vK9bb755huIRCK0bNlS00Wp8/jaf7Pw9++b42XOhUgkwsyZM199oeo4vvdrxqlTp9CvXz/Y2dlBW1sb9evXR4cOHTB58mRNF+2tc/LkSfTv3x/W1taQyWSwsrJCUFAQTpw4UeV9Xbx4ETNnzkRaWtqrL2gdobjP6+jo4MaNGyrrPT09+X5Uwyr+/tXR0YGVlRW8vLwwZ84c3Lt3T9NFfKNwYI2xKli9ejVOnDihtIwfP17TxXorGBoaIj4+XiX98OHDuHbtGgwNDTVQqrfPqlWrAAAXLlzAqVOnNFyatwNf+4wxTeN7/+u3a9cuuLu7Izc3FzExMdi3bx++/vprdOzYEZs3b9Z08d4qixcvRseOHXHr1i3ExMQgMTERsbGxuH37Njp16oQlS5ZUaX8XL15EZGQkB9YqoaioCDNmzNB0Md5qit+/+/fvx9KlS9G6dWvMmzcPzZo1Q2JioqaL98bgwBpjVdCyZUu89957SoudnZ2mi/VWGDBgALZv347c3Fyl9Pj4eHTo0OGVnoeCgoJXtq+6JCUlBefOnYO/vz8AqA32vIwnT5680v3VFa/z2meMsYpq+t7P1IuJiYGjoyP27t2LkJAQeHh4ICQkBLGxsUhPT9d08d4ax44dw8SJE9GzZ08cPXoUQ4YMQZcuXTB48GAcPXoUPXv2xIQJE3Ds2DFNF7VO6t69OzZs2IBz585puihvLcXv386dO+P999/HokWL8Oeff0JfXx+BgYG4e/eupov4RuDAWi2XkpKCkJAQODg4QFdXFw4ODhg4cKBKk1lFU86kpCSMHj0aFhYWMDc3R2BgIO7cuaOh0tctmzdvRocOHaCvrw8DAwP4+fnhjz/+UJv3woUL8Pb2hr6+PiwtLTFu3DgOKrzAwIEDAQAbN24U0h49eoTt27cjNDRUJX9kZCTat28PMzMzGBkZoW3btoiPjwcRKeVzcHBAQEAAduzYgTZt2kBHRweRkZE1ezC1lOLH1Ny5c+Hu7o5NmzYpXbdpaWkQiUSIiYnBrFmzYGdnBx0dHbRr1w4HDhxQ2tfMmTMhEolw5swZBAUFwdTUFI0bN36tx1Nb1MS1P2LECJiZmam973Tt2hUtWrSogSOpWzw9PdV2wR02bBgcHByE14rPRWxsLBYuXAhHR0cYGBigQ4cOOHny5OsrcB1W2XPBqudF9/5Dhw6p7bKuuPZ/+OEHpfTvv/8ezs7O0NbWRvPmzbFhwwY+V2pkZWXBwsICEolEZZ2WlvJPuMo8gyqGI+Fn0KqZM2cORCIRvvvuO5VzIZFI8O2330IkEmHu3LlC+uXLlzFw4EDUr18f2trasLOzw4cffoiioiL88MMP6N+/PwDAy8tL6GZX8XPCyk2dOhXm5uaYNm3ac/MVFhYiLCwMjo6OkMlkaNCgAcaOHYucnBwhT9++fWFvb4+ysjKV7du3b4+2bdu+6uLXWXZ2dliwYAHy8vKwfPlyIT0lJQW9e/eGmZkZdHR00KZNG2zZskVl+9u3b2PkyJFo2LAhZDIZbGxsEBQUVKuDdBxYq+XS0tLg4uKCuLg47N27F/PmzUNGRgbc3Nzw4MEDlfwfffQRpFIpNmzYgJiYGBw6dAiDBw/WQMlrJ7lcjtLSUqUFAGbPno2BAweiefPm2LJlC9atW4e8vDx07twZFy9eVNpHSUkJevbsCW9vb/z8888YN24cli9fjgEDBmjikGoNIyMjBAUFCd1RgPJAg5aWltq6S0tLw6hRo7Blyxbs2LEDgYGB+OSTT/DVV1+p5D1z5gw+++wzjB8/Hnv27MH7779fo8dSGxUUFGDjxo1wc3NDy5YtERoairy8PGzdulUl75IlS7Bnzx7ExcXhxx9/hJaWFnr06KF2HJLAwEA4OTlh69atWLZs2es4lFqnJq79CRMm4OHDh9iwYYPSthcvXkRSUhLGjh1bcwf0llq6dCn279+PuLg4rF+/Ho8fP0bPnj3x6NEjTReNsWeqyr2/MlasWIGRI0fC1dUVO3bswIwZMxAZGal2HMm3XYcOHXDq1CmMHz8ep06dQklJidp8/Axac+RyOZKSktCuXTvY2tqqzdOwYUO88847OHjwIORyOc6dOwc3NzecPHkSUVFR2L17N+bMmYOioiIUFxfD398fs2fPBlD+vaAYWkbRIpQpMzQ0xIwZM7B3714cPHhQbR4iQt++fREbG4shQ4Zg165dmDRpEtasWYOuXbuiqKgIABAaGor09HSV/Vy+fBmnT5/G8OHDa/x46pKePXtCLBbjyJEjAICkpCR07NgROTk5WLZsGX755Re0bt0aAwYMUAoc3759G25ubvjpp58wadIk7N69G3FxcTA2NsbDhw81dDSvALFaZfXq1QSAkpOT1a4vLS2l/Px80tfXp6+//lpluzFjxijlj4mJIQCUkZFRo+Wu7RT1p25JT08niURCn3zyidI2eXl5ZGVlRcHBwULa0KFDCYDSuSEimjVrFgGg//73v6/leGqTp6/5pKQkAkCpqalEROTm5kbDhg0jIqIWLVqQh4eH2n3I5XIqKSmhqKgoMjc3p7KyMmGdvb09icViunLlSo0fS222du1aAkDLli0jovLr28DAgDp37izkuX79OgEgGxsbKigoENJzc3PJzMyMfHx8hLSIiAgCQOHh4a/vIGqZmr72PTw8qHXr1kr5R48eTUZGRpSXl1czB1WLVfz+9fDwUFvvQ4cOJXt7e+G14nPRqlUrKi0tFdJPnz5NAGjjxo01XfQ6p7rngogIAEVERNR8IeuIytz7FfenpKQkpW0V1/7q1auJqPx+ZGVlRe3bt1fKd+PGDZJKpSrn6m334MED6tSpk/C8KZVKyd3dnebMmSPco/kZtGZlZmYSAAoJCXluvgEDBhAAunv3LnXt2pVMTEzo3r17z8y/detWtZ8Z9q+n7/NFRUXUqFEjateunfAc4+HhQS1atCAioj179hAAiomJUdrH5s2bCQCtWLGCiIhKSkqofv36NGjQIKV8U6dOJZlMRg8ePHgNR1Z7vCjuQERUv359atasGRERNW3alNq0aUMlJSVKeQICAsja2prkcjkREYWGhpJUKqWLFy/WXOE1gFus1XL5+fmYNm0anJycIJFIIJFIYGBggMePH+PSpUsq+Xv37q302tXVFQDUzrbCVK1duxbJyclKy969e1FaWooPP/xQqSWbjo4OPDw81P4V9oMPPlB6PWjQIADlkX72bB4eHmjcuDFWrVqF8+fPIzk5WW1XOAA4ePAgfHx8YGxsDLFYDKlUivDwcGRlZanMYuPq6gpnZ+fXcQi1Vnx8PHR1dRESEgIAMDAwQP/+/XH06FH8/fffSnkDAwOho6MjvDY0NESvXr1w5MgRyOVypbzcOrByauLanzBhAs6ePSuMC5Obm4t169Zh6NChPHtxDfD394dYLBZe8/cvqw2qcu9/kStXriAzMxPBwcFK6XZ2dujYseMrK3NdYW5ujqNHjyI5ORlz585Fnz598NdffyEsLAytWrXCgwcP+Bn0DUH/DLVQUFCAw4cPIzg4GJaWlhouVd0hk8kQHR2NlJQUtd0KFS3Qhg0bppTev39/6OvrC8ORSCQSDB48GDt27BBai8vlcqxbtw59+vSBubl5zR5IHaS49q9evYrLly8L95en70c9e/ZERkYGrly5AgDYvXs3vLy80KxZM42VuyZwYK2WGzRoEJYsWYKPPvoIe/fuxenTp5GcnAxLS0u1A7BXvGFoa2sD4MHaK6tZs2Zo166d0qLoC+7m5gapVKq0bN68WaVLrkQiUTkPVlZWAMrH02DPJhKJMHz4cPz4449YtmwZnJ2d0blzZ5V8p0+fRrdu3QCUj+Vy7NgxJCcnY/r06QBUr3dra+uaL3wtdvXqVRw5cgT+/v4gIuTk5CAnJwdBQUEAoNRFEfj3eq6YVlxcjPz8fKV0rvvKqYlrv0+fPnBwcMDSpUsBlI/F+fjxY+4GWkP4+5fVNlW997+I4hmnfv36KuvUpbFy7dq1w7Rp07B161bcuXMHn376KdLS0hATE8PPoDXMwsICenp6uH79+nPzpaWlQU9PDxKJBHK5/JndRln1hYSEoG3btpg+fbpKt+isrCxIJBKVYKZIJIKVlZXStR0aGorCwkJs2rQJALB3715kZGRwN9BqePz4MbKysmBjYyPci6ZMmaJyLxozZgwACPej+/fv18nPiOpomKzWePToERISEhAREYHPP/9cSC8qKkJ2drYGS/Z2sbCwAABs27YN9vb2L8xfWlqKrKwspQebzMxMAKo/vJiqYcOGITw8HMuWLcOsWbPU5tm0aROkUikSEhKUWk79/PPPavOLRKKaKGqdsWrVKhARtm3bhm3btqmsX7NmDaKjo4XXiuv5aZmZmZDJZCotobjuK+9VX/taWloYO3YsvvjiCyxYsADffvstvL294eLiUlOHUKfo6OioHR9N3fimrGbxuagZlb33K+41inGMFCrWv+IZR93g1Oq+N5gqqVSKiIgILFq0CKmpqejTpw8AfgatKWKxGF5eXtizZw9u3bqlNhhw69Yt/P777+jRowfMzMwgFotx69YtDZS2bhOJRJg3bx58fX2xYsUKpXXm5uYoLS3F/fv3lYJrRITMzEy4ubkJac2bN8e7776L1atXY9SoUVi9ejVsbGyEP0qyytu1axfkcjk8PT2F38NhYWEIDAxUm1/xfGlpaVknPyPcYq0WE4lEICLhr94KK1euVOluxWqOn58fJBIJrl27ptKaTbFUtH79eqXXigHE1c1qxpQ1aNAAn332GXr16oWhQ4eqzSMSiSCRSJS6XRUUFGDdunWvq5h1hlwux5o1a9C4cWMkJSWpLJMnT0ZGRgZ2794tbLNjxw4UFhYKr/Py8rBz50507txZ6ZywqqmJa/+jjz6CTCbDBx98gCtXrmDcuHE1Uva6yMHBAX/99ZdSMCErKwvHjx/XYKneTnwuXr2q3PsVs3n++eefSvv49ddflV67uLjAyspKpStXeno6nys1MjIy1KYrhnqxsbHhZ9DXICwsDESEMWPGqPy+ksvlGD16NIgIYWFh0NXVhYeHB7Zu3frcwD63WK4eHx8f+Pr6IioqSqkHhLe3NwDgxx9/VMq/fft2PH78WFivMHz4cJw6dQr//e9/sXPnTgwdOpSfT6soPT0dU6ZMgbGxMUaNGgUXFxc0adIE586de+a9yNDQEADQo0cPJCUlCV1D6wpusVZLiUQiGBkZoUuXLpg/fz4sLCzg4OCAw4cPIz4+HiYmJpou4lvDwcEBUVFRmD59Ov73v/+he/fuMDU1xd27d3H69Gno6+sjMjJSyC+TybBgwQLk5+fDzc0Nx48fR3R0NHr06IFOnTpp8Ehqj6enNFfH398fCxcuxKBBgzBy5EhkZWUhNjZWJQjNXmz37t24c+cO5s2bp/ahu2XLlliyZAni4+OxaNEiAOV/4fX19cWkSZNQVlaGefPmITc3V+lzwKrnVV/7JiYm+PDDD/Hdd9/B3t4evXr1qoli1ymKVpZDhgzB8uXLMXjwYHz88cfIyspCTEwMjIyMNFzCtwefi5pTlXt/QEAAfHx8MGfOHJiamsLe3h4HDhzAjh07lLbR0tJCZGQkRo0ahaCgIISGhiInJweRkZGwtraGlhb/vf9pfn5+sLW1Ra9evdC0aVOUlZXh7NmzWLBgAQwMDDBhwgR+Bn0NOnbsiLi4OEycOBGdOnXCuHHjYGdnh/T0dCxduhSnTp1CXFwc3N3dAQALFy5Ep06d0L59e3z++edwcnLC3bt38euvv2L58uUwNDREy5YtAZTPkmtoaAgdHR04Ojpyq8FKmDdvHt555x3cu3cPLVq0AAD4+vrCz88P06ZNQ25uLjp27Ig///wTERERaNOmDYYMGaK0j4EDB2LSpEkYOHAgioqKVMZmY8pSU1OF8dLu3buHo0ePYvXq1RCLxfjpp5+EVoLLly9Hjx494Ofnh2HDhqFBgwbIzs7GpUuXcObMGWE2acVsuV26dMEXX3yBVq1aIScnB3v27MGkSZPQtGlTTR5u9Wlq1gRWPUuXLiUAdP78eSIiunXrFr3//vtkampKhoaG1L17d0pNTSV7e3saOnSosN2zZvV41kxOTFllZkX5+eefycvLi4yMjEhbW5vs7e0pKCiIEhMThTxDhw4lfX19+vPPP8nT05N0dXXJzMyMRo8eTfn5+a/jUGqdytQ9kerMiKtWrSIXFxfS1tamRo0a0Zw5cyg+Pp4A0PXr14V89vb25O/vX0Olr/369u1LMpnsubNbhYSEkEQioZMnTxIAmjdvHkVGRpKtrS3JZDJq06YN7d27V2kbxayg9+/fr+lDqLVq+tpXOHToEAGguXPnvuIjqFsqfv8SEa1Zs4aaNWtGOjo61Lx5c9q8efMzZwWdP3++yj7BM1RWS3XPBRHXeWVV5d6fmZlJGRkZFBQURGZmZmRsbEyDBw+mlJQUpVlBFVasWEFOTk4kk8nI2dmZVq1aRX369KE2bdrU8FHVLps3b6ZBgwZRkyZNyMDAgKRSKdnZ2dGQIUNUZtPjZ9Cad+LECQoKCqL69euTRCKhevXqUWBgIB0/flwl78WLF6l///5kbm5OMpmM7OzsaNiwYVRYWCjkiYuLI0dHRxKLxWo/J2+75z0DDRo0iAAIs4ISERUUFNC0adPI3t6epFIpWVtb0+jRo+nhw4dq96/YR8eOHWvqEGo9xTlQLDKZjOrVq0ceHh40e/Zstd8P586do+DgYKpXrx5JpVKysrKirl27CjNLK9y8eZNCQ0PJysqKpFIp2djYUHBwMN29e/d1Hd4rJyL6ZyoHVitMmDABS5YsQU5OjtCckjHG3gRpaWlwdHTE/PnzMWXKFE0Xh1XS5MmT8d133+HmzZv81/Ln4O/fNwefi7olJycHzs7O6Nu3r8rYSezVGTZsGLZt26YyiRBjjLGXx11Ba4nff/8dycnJWLVqFXr37s0Pkowxxl7KyZMn8ddff+Hbb7/FqFGjOKj2DPz9++bgc1H7ZWZmYtasWfDy8oK5uTlu3LiBRYsWIS8vDxMmTNB08RhjjLFq4cBaLREUFIRHjx6hd+/e+OabbzRdHMYYY7Vchw4doKenh4CAAKVZXZky/v59c/C5qP20tbWRlpaGMWPGIDs7G3p6enjvvfewbNkyYbwkxhhjrLbhrqCMMcYYY4wxxhhjjFUDT7/DGGOMMcYYY4wxxlg1cGCNMcYYY4wxxhhjjLFq4MAaY4wxxhhjjDHGGGPVwIE1xhhjjDHGGGOMMcaqgQNrjDHGGGOMMcYYY4xVAwfWGGOMMcYYY4wxxhirBg6sMcYYY4wxxhhjjDFWDRxYY4wxxhhjjDHGGGOsGjiwxhhjjDHGGGOMMcZYNfw//sz1ArBlHcMAAAAASUVORK5CYII=\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 increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('WY Nitrate with CY Increased Threshold',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-3}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.64834195, 22.38691698, 16.99383033,  7.63730902,  4.96273598,\n",
       "        1.56547187,  1.23107567,  1.5230891 ,  7.95614177, 16.79074658,\n",
       "       19.52305174, 21.62759183])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.67644305, 22.37273059, 17.09940305,  7.25002158,  4.93789725,\n",
       "        1.24039805,  0.80265601,  1.60089322,  6.53129732, 14.65755052,\n",
       "       18.79863427, 21.41577999])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol Si m$^{-3}$')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_silicon_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('WY Silicon with CY Increased Threshold',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol Si m$^{-3}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.06238141, 49.17238747, 41.59249494, 18.0018168 ,  5.46358173,\n",
       "       12.1107789 , 19.87997362, 28.03714477, 36.00806573, 41.96864256,\n",
       "       44.54149611, 47.0336623 ])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.08720697, 49.17021756, 41.74573903, 17.27394181,  5.18894447,\n",
       "        8.35421668,  9.00828493,  6.48977421, 10.8493533 , 26.04362057,\n",
       "       36.47614259, 43.36377397])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-3}$')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_nitrate_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('CY Nitrate with WY Increased Threshold',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-3}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.77422039, 23.29999955, 21.7438187 ,  9.87203474,  6.32937779,\n",
       "        2.56494218,  2.649082  ,  4.58757776,  7.08316269, 15.02847707,\n",
       "       21.88930665, 23.35885963])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol Si m$^{-3}$')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_silicon_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('CY Silicon with WY Increased Threshold',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol Si m$^{-3}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.13888235, 51.47213852, 50.01017314, 25.05295036,  9.56076795,\n",
       "        6.89618133,  3.9125294 ,  4.51631545,  7.42875662, 19.71116328,\n",
       "       38.33231021, 45.14306696])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Depth-integrated 0-100 m Diatoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Diatom data for original years\n",
    "\n",
    "monthly_array_diatoms_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 using higher temperature threshold       \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "                \n",
    "### 2019 using higher temperature threshold                \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3221156/3403781678.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_orig_slice[monthly_array_diatoms_orig_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#years, months, data\n",
    "monthly_array_diatoms_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_tsc/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_tsc/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_tsc/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_tsc/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n",
    "        \n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3221156/2320522072.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_depthint_slice[monthly_array_diatoms_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('WY Diatoms (0-100 m) with CY Increased Threshold',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 increased thresh')\n",
    "\n",
    "\n",
    "ax.set_title('CY Diatoms (0-100 m) with WY Increased Threshold',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
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