{
 "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_3484198/391916811.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_3484198/2338418733.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_3484198/2771304440.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_3484198/178454329.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_PAR_exp_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_PAR_exp_slice[monthly_array_PAR_exp_slice == 0 ] = np.nan\n",
    "monthly_array_PAR_exp_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_PAR_exp_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_PAR_exp_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'm$^{-2}$')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_PAR_slicemean[12,:],color='r',linestyle='-',label='Original WY')\n",
    "ax.plot(xticks, monthly_array_PAR_exp_slicemean[12,:],color='k',linestyle='-.',label='WY with CY 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('m$^{-2}$')"
   ]
  },
  {
   "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, 'm$^{-2}$')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_PAR_slicemean[1,:],color='b',linestyle='-',label='Original CY')\n",
    "ax.plot(xticks, monthly_array_PAR_exp_slicemean[1,:],color='k',linestyle='-.',label='CY with WY 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('m$^{-2}$')"
   ]
  },
  {
   "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_3484198/1288103633.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_3484198/3661973807.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_3484198/3312634990.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_3484198/241793216.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_3484198/231329215.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_3484198/3737416097.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slice[monthly_array_silicon_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_nitrate_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 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$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.64834195, 22.38691698, 16.99383033,  7.63730902,  4.96273598,\n",
       "        1.56547187,  1.23107567,  1.5230891 ,  7.95614177, 16.79074658,\n",
       "       19.52305174, 21.62759183])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.67644305, 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 N m$^{-2}$')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_silicon_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 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 N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.06238141, 49.17238747, 41.59249494, 18.0018168 ,  5.46358173,\n",
       "       12.1107789 , 19.87997362, 28.03714477, 36.00806573, 41.96864256,\n",
       "       44.54149611, 47.0336623 ])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.08720697, 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$^{-2}$')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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079/fAIxq1aql2b5ixQoDMDp06GDd1rZt2wxDKEtQ8/TTT9/yWpafTaFChay9vl599dVs15xaVoK1Wz3P69evbwDGgAED0mxPTEy0hgotWrQw4uPjs1xT6p5Ap0+fzrDNvn37DJPJZLi5uRl///13hm2ioqKs7ys///xzlq9vGOaeqYDRrl27NNsvXrxorc8S+GTFsmXLDMAoUaKEcebMmQzbnD592tr7KnUvvuvXrxs+Pj4GYDzxxBMZHjty5MhMe17lRE6CtVu9Hh9++GEDMNq3b5/ptQDjf//7X6bXyc37l6urqwFYe0dnRer376pVq6YLxA3DMJYuXWptc/NzdeLEiQaYe9WlDmAtoqKijICAAAMwunbtmm5/Zv/GbNu27ZZBZVJSktGiRYvbvnZFRCR3NMeaiIjkSFhYmPX/fXx8cn0+f39/pk2bBpjnvwGYPn06/v7+uT53am+99Raenp6cP3+e6dOn5+m5s2rIkCF4eHjk+PiuXbsCsHHjxhwdv3fvXrZv346joyPDhg3LtN2TTz4JwJo1a0hOTs7y+S1zg/3xxx9pjrNMyv/ggw/SqFEjIiIi2LVrV7r9TZs2xcXFJes3lE0DBw60ruKZWq1atShXrhwAf//9d7bPa1nA4ODBg1y8eNG63XJflv2AdZ611IsdnD17lqNHjwLp51e72cSJE6lSpQrR0dFERkZSpUqVu7JiatmyZa3Pi5tZ5nW7+XsXEhLCiRMnAJg2bRpOTk7Zvu4TTzyR6RyOX331FYZh0LVrV2rVqpVhm0KFCvHQQw8BsHr16mxd2/J627JlS5rnc6FChbCzM/8qnXq+u9v58ssvAfM9lS5dOsM2ZcqUsT4HUtf722+/ER4eDsDYsWMzPHbkyJF39PWTFc7OzrzxxhsZ7rMsYHOr15i3tzcvvPBChvty+/5VuHBhIHs/s9SGDRuGq6truu1BQUHW5/a+ffvS7LMsNtK7d29q1qyZ7thChQoxfPhwAFauXElkZGSWarEsVlK2bFmeeeaZdPvt7e0ZM2ZMls4lIiI5p2BNRERyxLgDE2Q/++yz1K1bF4C6detm+EEht3x9fa0fYCZNmpQmILxbmjdvfts2x48f54033qB+/foULlwYe3t760TuXbp0AW4EkNllCeRSUlKoUqUKJUqUyPDRuXNnAGJiYrL1fWrdujX29vZERkayc+dOAOLi4tiyZQseHh40atTIGhqsW7fOepzl/28XKuVW48aNM91XqlQpAGt4kR2p6069MIHl/y1hWur/T90udch2u++Bq6srH374ofXrDz/8MMMP+3mtYcOG1jDpZpl97zZv3gxAiRIlaNCgQY6ue6vXjOX5vHLlykyfyyVKlGDOnDkA1sVGUrt48SLjxo2jadOm+Pr64uDgYH29Va9eHYDY2FiuXr1qPcbV1ZV27doB0LlzZ8aOHcu2bdvSrW6cWb2ff/75Letds2ZNunp37NgBmIOUihUrZnh+Ly8v6tevf8sa7rQaNWpk+seDrLzGGjZsmGkAm9v3L8sK0U899RTDhg1jw4YNxMbGZvneMnv/cHBwoGjRounuLSEhwRoitm/fPtPzdujQwXpfqf/gcCuW54NlcZ2MtGrVKlcL/4iIyO0pWBMRkRwpUqSI9f9zEkJkxrJiqOW/d8LQoUMpUaIEkZGRfPDBB3fsOpnJqLdUaj///DPVq1dn6tSp7Nq1i8jISDw8PChWrBjFixfH29sbINNVDm/n3LlzACQnJ3Px4sVMH1euXLEek50Pnl5eXjzwwAPAjbBs8+bNxMfH07JlSxwcHGjbtm2a/devX7euyHmng7VChQplus/yAfR2KzJmpGLFitZeVZbALDo6ml27duHm5kajRo2sbRs3boyzszN//fWX9XtrOaZixYqULVv2ttdL/Rq5k6+X1HLyvbtw4QJArnqf3uo1Y3k+X7t27ZbPZ8vr5ebn8pYtW6hatSrvvPMOW7duJTw83LqyYvHixdO81938mvvyyy+pU6cOly9f5t1336VJkyYUKlSIFi1aMHny5HTvjYmJidbXVWRk5C3rtaycmbpey4qzmfV0s7jdCs13WlaeJ0lJSZm2ycrPO6fvX5MmTaJNmzZcu3aNjz76iMDAQDw9PWnQoAHjxo3j7Nmzub631K+B8PBwa4+5W/3cUv/MMlpZOCNZeT64uLjg6+ubpfOJiEjOKFgTEZEcqVGjhvX/d+/ebcNKss/Nzc06jGrmzJmEhobe1evb29tnui8sLIynn36a+Ph42rZty/r164mNjbV+CL9w4QI//vhjrq5v+ZBXtWpVDPN8q7d9BAQEZOsaN/dIs/zXEqhZhntu3LiRxMRENm3aRHx8PG5ubrfsUZbfWe7b0vvszz//JDk5maZNm+Lo6Ght5+LiQuPGjUlISLD26LIcc6eDRVvJrEdNVtzqNWN5Pk+cODFLz+XUvQSTkpLo168fERER1K1blxUrVhAVFUV0dLT19bZ161Zr+5t76vr5+bFr1y5WrVrFK6+8Qv369UlJSWHTpk0MHz6cihUrpumVmXpI4g8//JCler/55pt095yb72VBkJWfd07fvwoXLsy6dev4888/GT58OM2bN8fBwYGdO3fyzjvvUKlSJb7//vs7cl+3+rml3pfdn++9/nwQEcnvFKyJiEiOtGnTxjok7Oeff7ZxNdn3/PPPU6lSJeLj4xk3bpyty7GyfLD39vbm119/pXXr1umG+Fl6AOVUiRIlAPNw05z2ersdS4C2adMmEhIS0gVrzs7ONGvWjJiYGLZt22bd36JFizQBVEFjCcX+/fdfzp8/bw1xUs+vZpF6OOjp06c5fvx4mnPcK0qWLAlgnWctr1mezzfPa5UVW7Zs4dSpU9jb27Ns2TKCgoLS9Ui63evNzs6OTp06MWPGDHbs2EF4eDjz58/Hz8+Pq1ev0r9/f+vwUBcXF2vvwpzUa+nJdbth4LfrdVWQ5dX7V4sWLfjwww/ZuHEjERER/PLLL9SqVYvr16/z7LPPppknMTd8fHysQeHp06czbZd6n2VI6e1k5fkQHx9vkykPRETuJwrWREQkR4oXL06vXr0AWLBgAYcPH87ysXdifrbscnBw4L333gNg7ty5/PPPPzk+lyVgzIv7sny4qlKlCm5ubhm2scy9lNNaLPNVJSQk3LFQ1BKQxcbGsmbNGrZv3463t7d1Dj0gzXDQ3PTWysvvf27dPM9aRvOrWaRewCD1/GoZhXAFWbNmzQDzPGaWOaHykuX5vHz5cq5du5atYy2vt6JFi2Y6nO5Wr7eMFCpUiP79+/PVV18B5vtOHaJZ6v3xxx9JSUnJ1rktc9SdPn2aY8eOZdgmKirKOrfhvehOvH+5uLjQvXt3fvrpJ8A8J2ROF4e5mZOTE7Vr1wZg7dq1mbazPM/s7OyoV69els5teT5s2LAh0/e/P/7445bDbkVEJPcUrImISI699957eHh4cP36dR5++OHb9pK4evUqvXr1yvKKZ3faI488QoMGDUhJSWH06NE5Po+npycAERERua7J0pvl8OHD1jmWUtuzZw8LFizIVS0NGjSwzoH21ltvcfny5VvWlJM59Dw8PGjYsCEA77zzDklJSQQGBqaZ+N4SQi1dutQauOQkWMvL739uBQQEWIed/frrr+zatcs67PNmluGh27dvZ8WKFYB5eJulh9e9ok2bNpQvXx4wz294u8n9s+v555/HZDIRERHBm2++ecu2iYmJacI3y+vNMi/Xzc6cOcP//ve/DM91u/tI3dM09dDGgQMHAubX+OTJk295jpiYmDTX6dChg3WOxXfffTfDYyZNmsT169dved6CLDfvX0lJSbcMMzP7meVW3759AVi8eHGGf8S5du0akyZNAqBLly5ZnjOxT58+AISGhvLtt9+m25+SkmL9A5KIiNw5CtZERCTHKleuzLx583BycmL//v3UrVuXDz/8kKNHj1rbJCcns3v3bsaOHUv58uWtPQLyA5PJxMSJEwFzCJJTNWvWBMwfmlKvGpgTHTt2xM7OjvDwcB577DFrWJmQkMCiRYvo2LHjLSfPttSyf/9+69xdNzOZTMyaNQtnZ2dCQ0Np3LgxixcvTjPB99mzZ/nuu+/o0KEDI0aMyNG9WEIyy6IElh5qFo0aNcLDw4OdO3eSlJREoUKFcrSaoeWeV6xYkS+GwFnue9GiRdb51TJa4dDNzY2GDRuSmJjI4sWL0xx7L7G3t+eTTz7BZDKxceNG2rVrx8aNG60BR1RUFOvXr+fxxx/nwIED2T5/3bp1ee211wCYNWsWjzzyCHv27LH24ElOTmbv3r28++67VKhQgT179liPbdGiBe7u7hiGwaOPPmrteZucnMzq1atvudri5s2bqV27NtOmTePgwYPW+zEMg82bNzNo0CDAPCl9rVq1rMf16NGDnj17AjBy5EgGDRqUpsdvQkIC27ZtY8SIEfj7+6eZyN7V1ZUxY8YA8O233/Laa69Zh/lFRUXx7rvv8sEHH1C4cOFsfx8Lity8f505c4ZKlSrx3nvvsXv37jQ9uf7++28ef/xxANzd3WnVqlWe1Txo0CDKlStHYmIiQUFBrFy50vp82bdvH506deLEiRM4OTllKwhr3Lgx3bt3t17jiy++ID4+HjCHbX369GHLli2Z9n4WEZE8YoiIiOTSxo0bjYoVKxqA9eHk5GT4+PgYdnZ21m0mk8no16+fkZCQkOm5WrdubQBG69atc13XnDlzrNe+lQ4dOqSpfc6cOZnWNW7cuHT7NmzYYJhMJgMw7O3tjZIlSxr+/v6Gv7+/tc2JEyes5z9x4sQt6xkxYkSaery8vAxHR0cDMMqVK2fMnz8/0/tKTEw0qlSpYt3v7e1treXHH39M0/a3334zfH19rW3t7e0NX19fw83NLc31BwwYcMt6M7N27do059m/f3+6Np07d7bu79KlS6bneuqppwzAeOqpp9LtO3z4sOHi4mIAhp2dnVG8eHHrPZ8+fdraznKdkJCQTK9zq59zVs2dOzfNfQcHB2fadtSoUWnaLlq0KMvXCQkJydI9ZZW/v3+m3+Nbff8tLK+31M/71L799lvD2dnZWrOzs7NRuHDhNPe/e/fuNMdk9f6SkpKM1157Lc25XFxcDF9fX8PBwSHN9o0bN6Y59rPPPkuz38PDw/p8KlKkiLF06dIMX7upv/+A4ejomO56np6exh9//JGu3piYGKNv375pjnd3dze8vb3TvGcCxpkzZ9Icm5ycbDzxxBPW/XZ2doa3t7dhb29vAEbfvn2z9PPKKsu5Mvu5pjZu3Ljbvn+n/r5ldq2s1J2T96/U78OWY3x8fAwnJ6c0/3bd/F6Z1fdvy2soo39D9u3bZ5QuXTrN89PT0zPN6+Hm62blvFeuXDHq1KmT5nloeV2ZTCZj5syZtzxeRERyTz3WREQk15o3b86hQ4f4/vvveeyxx6hYsSIuLi5ER0fj4+NDixYteOuttzh48CALFizId5PTT5w4MVerqrVq1Yrly5fTvn17vLy8uHjxIqdOneLUqVM5rmfu3Lk0atQIV1dXEhMTqVixIqNHj2b37t2UKlUq02MdHBxYu3YtAwYMICAggJiYGGstN88/1aFDB44ePcqECRNo0aIFXl5eREREYGdnR/Xq1XnuuedYunQpH3/8cY7uo1mzZjg7OwPmOfmqV6+erk3qXmw57a1VqVIlQkJC6N69O0WLFiUsLMx6z7aYW+jm+8hofrWM9plMpntufrXUnnzySQ4dOsRrr71G9erVcXBwICEhgQoVKvDQQw8xb948qlWrlqNz29vbM23aNHbt2sXAgQOpUqUK9vb2REZG4u3tTfPmzRk/fjx79uyxztFl8eKLL7J8+XICAwPx8PAgKSmJ0qVL8/LLL7N37940vc1Sa9iwIYsWLWLQoEHUr1+fIkWKEBkZiYuLC3Xr1mX48OEcPHiQli1bpjvWzc2N77//npCQEJ544gnKly9PSkoK165do1ixYrRt25ZJkyZx5MiRdHO/2dnZMXfuXObOnUuTJk1wdXUlKSmJevXqMWvWrFsOFb+X5OT9q3Tp0ixdupShQ4fSpEkTSpYsybVr13BwcKB69eq89NJL/PPPP/Tu3TvP661Zsyb79+9n/Pjx1K1bFwcHB+Lj46lQoQIvvvgi+/fvz9F1fX192bx5M8HBwVStWhU7OzscHBzo3Lkzv//+O4MHD87zexERkbRMhpEPZvoVEREREREREREpYNRjTUREREREREREJAfyZbC2Z88eunbtip+fH66urvj4+NC0aVO+++67dG137dpF+/bt8fDwoHDhwjz88MMcP37cBlWLiIiIiIiIiMj9JF8GaxEREZQtW5YPPviAFStWMHfuXAICAnjiiSfSrJRz6NAhAgMDrSulff311xw+fJiWLVvedultERERERERERGR3ChQc6w1adKEc+fOERoaCsCjjz5KSEgIx44dw9PTE4BTp05RqVIlhg4dyocffmjLckVERERERERE5B6WL3usZaZIkSI4ODgAkJSUxLJly+jVq5c1VAPw9/enTZs2/Pzzz7YqU0RERERERERE7gP5OlhLSUkhKSmJy5cv8+mnn7J69WpGjBgBwLFjx7h+/Tq1a9dOd1zt2rU5evQocXFxd7tkERERERERERG5TzjYuoBbGTx4MLNnzwbAycmJ//3vf7zwwgsAhIWFAeDj45PuOB8fHwzD4OrVq5QsWTLd/vj4eOLj461fp6SkEB4ejq+vLyaT6U7cioiIiIiIiIiIFBCGYRAdHU2pUqWws8u8X1q+DtZGjx7NgAEDuHTpEr/++itDhgwhJiaGN954w9rmVkFYZvsmTJhAcHBwntcrIiIiIiIiIiL3jtOnT1OmTJlM9+frYM3Pzw8/Pz8AunTpAsCoUaN46qmn8PX1BW70XEstPDwck8lE4cKFMzzvqFGjeP31161fR0ZG4ufnx+nTp9PM1yYiIiIiIiIiIvefqKgoypYtS6FChW7ZLl8Hazdr1KgRs2bN4vjx49SvXx9XV1f27duXrt2+ffuoWLEiLi4uGZ7H2dkZZ2fndNs9PT0VrImIiIiIiIiICHDrkZKQzxcvuFlISAh2dnaUL18eBwcHunXrxk8//UR0dLS1TWhoKCEhITz88MM2rFRERERERERERO51+bLH2sCBA/H09KRRo0YUL16cK1eu8OOPP7Jw4ULefPNNihYtCkBwcDANGzbkwQcfZOTIkcTFxTF27FiKFCnCsGHDbHwXIiIiIiIiIiJyL8uXwVrTpk2ZM2cO3377LREREXh4eFCnTh3mzZvH448/bm1XtWpV1q9fz4gRI+jduzcODg60bduWKVOmWMM3ERERERERERGRO8FkGIZh6yJsLSoqCi8vLyIjIzXHmoiIiIiIiIjIfS6rWVGBmmNNREREREREREQkv1CwJiIiIiIiIiIikgMK1kRERERERERERHJAwZqIiIiIiIiIiEgOKFgTERERERERERHJAQVrIiIiIiIiIiIiOaBgTUREREREREREJAcUrImIiIiIiIiIiOSAgjUREREREREREZEcULAmIiIiIiIiIiKSAwrWREREREREREREckDBmoiIiIiIiIiISA4oWBMREREREREREckBBWsiIiIiIiIiIiI5oGBNREREREREREQkBxSsiYiIiIiIiIiI5ICCNRERERERERERkRxQsCYiIiIiIiIiIpIDCtZERERERERERERyQMGaiIiIiIiIiIhIDihYExERERERERERyQEFayIiIiIiIiIiIjmgYE1ERERERERERCQHFKyJiIiIiIiIiIjkgII1ERERERERERGRHFCwJiIiIiIiIiIikgP5Mlhbt24dzz77LFWrVsXd3Z3SpUvTo0cPdu7cmabd008/jclkSveoWrWqjSoXEREREREREZH7hYOtC8jIZ599RlhYGK+++irVq1fn8uXLTJ06lSZNmrB69Wratm1rbevq6sq6devSHO/q6nq3SxYRERERERERkftMvgzWZs6cSbFixdJs69y5MxUrVuSDDz5IE6zZ2dnRpEmTu12iiIiIiIiIiIjc5/LlUNCbQzUADw8PqlevzunTp21QkYiIiIiIiIiISFr5MljLSGRkJLt27aJGjRpptl+/fp0SJUpgb29PmTJlGDJkCOHh4TaqUkRERERERERE7hf5cihoRl566SViYmJ46623rNvq1KlDnTp1qFmzJgAbNmxg2rRprF27lu3bt+Ph4ZHhueLj44mPj7d+HRUVdWeLFxERERERERGRe06BCNbGjBnD/Pnz+fjjj6lfv751+9ChQ9O069ChAw888AC9e/fmiy++SLffYsKECQQHB9/Rmm3txx8hJGQJLi5XqFnTjwYN/KlYsSxubm62Lk1ERERERERE5J5gMgzDsHURtxIcHMz48eN5//33GT169G3bp6Sk4OnpSdeuXVm4cGGGbTLqsVa2bFkiIyPx9PTMs9ptqXlz2Ly5PbA2zXYHhyJ4ePhRtKg/pUv7Ub68HzVq+PPAA35Ur+5HsWLFMJlMtilaRERERERERCQfiIqKwsvL67ZZUb7usWYJ1caPH5+lUM3CMAzs7DKfPs7Z2RlnZ+e8KDHfatsWLl9uyfnzTsTGhpKScgq4RlLSFSIirhARsYsjR2D9+rTHFSnyMs2a/Q9/fyhePIq9ez+ialV/XnrpaYoVM6HMTURERERERETELN8Ga++++y7jx4/n7bffZty4cVk+bvHixcTGxtKkSZM7WF3+9+678O675u+bYUBYmME//0Swa1coBw6Ecvx4KGfOnOLKlVCio0NJSjoFnOfKlbIsXWo5ywkgGCjKu+8+g6sr+PlBZGRfkpOPU6yYH2XL+lG5sj81a/pRp44f5cr5UaRIEfV6ExEREREREZF7Xr4cCjp16lTeeOMNOnfunGGo1qRJE06dOkX//v3p27cvFStWxGQysWHDBqZPn06FChXYtm0b7u7uWbpeVrv33cuuXYOjRxM4cSKJixfdOHUK9u07wvbtk4mJcSY29mNuPFOqAIczPZednSseHn4UKeJHqVLm4aYPPtiR7t2bcI93FBQRERERERGRe0BWs6J8GawFBgayYcOGTPcbhsHVq1d57rnn2L17NxcvXiQ5ORl/f3969uzJ6NGj8fLyyvL1FKzdXkICnD4Np07Bxo1/c+DACU6eDOXcuVDCw08RGxuKYYQC5zM5w4fAcEqUgKJF/+bIkU6UKlWPYcOW4+8P/v5w4cJmfH1d8fPzw8fHR73eRERERERERMQmCnSwdrcpWMu95GQ4fx6OHIlnz54z7N8fyrFj5uGmly+HEhf3OPHxgf+1Xgr0AOoDO1Kd5UZPOHt7dzw9/ShWzA8/P38qVvSjZk3zAgsBAf6ULl0aR0fHu3mLIiIiIiIiInKfULCWDQrW7jzzPG/mHm+HDl1j585/OXs2kYSEJpw8ad5+9Wpr4BBwKQtnNOHuXoouXSby0EOP4+8PHh6XOXFiK5UrV6B69ep39oZERERERERE5J6lYC0bFKzlD9HR5oDt8OHr/P33GQ4dCuX48VP/DTcN5fr1UMDyiP/vqB+APv/9v7knnJNTA5o1205AgHmIaUjI83h62lOlij+1apnnfPPz86N06dI4OOTb9TtERERERERExEYUrGWDgrWCIT7ePM/b8eMp/PPPZQ4cCCU6uhwXLxbh1CkIDV1BSspYoA7w1X9HGYAbEJfufCaTHR4epf9b3dQ83LRGDT8qVfKjYcOGFCtW7O7dnIiIiIiIiIjkGwrWskHB2r0hORnOnTP3erM8TpxIYsuWb7hwIZSIiFCSk09h7vF2GkjM9FwBAd/zwAN98feHxMQNbNs2hWbNWjJ27HB8fMBkggsXLlC0aFHs7e3v1i2KiIiIiIiIyF2gYC0bFKzdHwwDLl+2BG4p7N9/kYMHT3H8uHl107CwUyQkWIaafg40/O/ImcAQoCfwE+7u4Odn8O+/bkASnp6lKVHCH39/PypX9qNaNT8CAvzw9/fH398fd3d329ywiIiIiIiISB5LSUnh4sWLhIaGWh+nT5/mo48+ws7Oztbl5RkFa9mgYE0soqLMwZtlQYVTp2DfvkP8++8fRESUJiKi638tw4ASQNJtz+nuXpTixQPw9w9g+PBRdOjwAPb2cO3aNQA8PDzu1O2IiIiIiIiI5Mju3bvZuXNnmgDtxIlTnDlzmqSk9CPAKlc+z44dJShUyAbF3gEK1rJBwZpkVVwchIZaer0ls3//BQ4fDuXkyVNcuBBKZGQohmEZbnoKiLzpDJtwdGyGnx/Y28/k8OEh1KnzOG++Oe+/xRYMfvnlMwIC/AkICMDf31/Bm4iIiIiIiOSJlJQULl26RFxcHAEBAQAkJyfTu3dvjh8P5ZNP1hAV5c2ZM/DFF0PYuXNmJmeyA0oDfqker3PwYDGqVr0bd3LnZTUr0pKIItng4gKVK5sfYI/5jaQ00BSApCQ4e/ZGr7dDhyI4cOAUx4+f5Ny5k0REVCUxEY4dAzgHwN69RXj8ccsVLgMvpbmmm1sRSpQw93irXNmfGjUCKFcuwBq8FbpX/hwgIiIiIiIiuRIbG8vp06fT9DI7efIUx46FcupUKBcunCYpKYGAgPa0aPE7Z87AmTP2HD26CbhMq1ahgPd/Z6sPBGEOzfyxBGheXn74+ZWibFlHypQh3eN+ox5rqMea3D2WBRZOnLAEb5GcPJnEhQu+/w0/PU1KyqvAyf8eV297Tjc3X95+ew2BgXUJCIBLl/Zx+vQpatasaf0LhIiIiIiIiBR8Fy5cIDQ0lNq1a+Pi4kJyMkyd+jlffz2b8+dDiYq6koWz2AEtgA2pti0E3PH1bYmfn1eGgVmZMlC6NNwv04hrKGg2KFiT/CIpyRy8nTxpDt8OHYpM0+Pt6tWTGMZJzKHbKSD8vyPPYO45B/b2w0lOnoyf3yt07jyDgAAoUiScBQuep0qVAKpXv9HjLSAgQD3eRERERERE8oHr16/f1NMslJQUDx588M3/epbBW28V4/r1y9SuvYerV+tw7hwkJ08CRqQ6kwepe5hZHj4+fpQu7Ue5cqXx90/f26xUKfMoLTFTsJYNCtakoLAMNbUEb//+G8WBA6eIiKjBqVN2nD4NKSlTgQXAQOCF/47cwY1VTtNydfWxDjW9OXirWrUqjo6Od+XeRERERERE7mUREREcPnyY0NBQjh8P5eDBUI4dC+XMmVAuXQolJuZyBkdVBv5N9XUjzNMKfQ+0BMBkOoKPzyFKlfKjXDk/ypUrTNmypjShWcmS4OR0x2/xnqJgLRsUrMm9IjHxRvBmeZgDuHMcPvx/XL16khvDTE9yo8dbxh5//DQ1a5YhIAAOHvyOCxd2069fD1q3bgWAYRiYTKY7dTsiIiIiIiIFQmJiorVTwvXr8O67H7F79z7at3+LxMSKnDkDISGTOHBgxG3O5M6N3mb+2NlVomzZYamGYqZQtqwdZcpA2bLmbcWLg4Nm0M9zCtayQcGa3C8SE83dhy2hm6XH27FjJzl37hQRESe5EbqdxTzE1P6/o/sCC3F0/IgKFYZSrhy4u+9k+fIOFC9u7uFWpUoA1aqlXVzBy8vr7t+oiIiIiIhIHjEMg0uXLhEaGsrhw6H884/5v6dOneL8+VDCw0Oxt/ehYsVDnDkDYWEADYCdwFKg239nWgS8jmVopr29H97e/hQv7oe/vx+VKvlRoULa3mbFioGdnU1u+76nYC0bFKyJmCUkpA3eUj/2719MePhW4BGg8X9H/B/Q+5bndHX1pkSJAPz8/KlaNYCqVQMYNOhFnJ2d79yNiIiIiIiIZFFcXBx2dnZcv+7EmTOwevUmfvrpK86dCyUsLJRr10JJSYm/zVlcgFjAPKLH0fEzPD3DqVixF1WqVM1wIYAiRUADgPIvBWvZoGBNJGsSEuD06Rth25Ej19m//7h1cYW0Pd5OAhmtSONAtWpxlCtnT0AA7Nz5AhcubGPIkPE8/fRD+PpCZGQEJ0+eJCAggMKFC9+VexMRERERkXuPubfZZf75J5S9e0M5fTqSatWesS4G8MsvHQgPX4Or61KuX7f0LPsB6HfTmUxAKcAPBwc/PD39KFbMjzJl/Chf3o9q1fypXPlGb7PChRWaFXQK1rJBwZpI3oiPTxu8HT58Lc2qppGRJ4EY4NNURzUFtgKLgV64u4OPz8+cPv0wAC4uXtbFFapWNQ83Tb2qqYI3ERERERFZv/4gP/+8laNHbywGEBkZSlxcKIYRl6pl2p5l8DDwMzATGIy3NxQtehTDWPjfCpp+VKniR82apSlXzokyZUCxwf1BwVo2KFgTuTvi4yE0NO0Q07//PsyxY0cJD6/PxYvF/2s5HxgKZLQqTlouLp4ULx5AhQoVWbRoMT4+JkwmuHTpEsWKFbtj9yIiIiIiIra3ZUsoTz31NkeOfAdkFm+YgJI4OPjh4eFH06bf4O/vSpky4OFxDn9/F6pV86ZMGRPu7nexeMnXFKxlg4I1kfwhLi5t8Hb4cAwHD57i+PFTnDt3kqiok6Qdanop1dEBwAkKFYKAADh3rjndujXm66+nauVSEREREZF7zJkz8MYb+1i4sCFgnv/M3T2QIkUqUaKEH35+5sUAatXyp04dc28zFxfb1iwFi4K1bFCwJlIwWIK3Eycsc7zFWoeaXryYQEREj/9aJgBlgMt8990mHnusme2KFhERERGRPHP+PEyYAJ9/DvHxBtCYwoXdmDx5MgMGNLR1eXIPuSPBWlxcHEeOHKFChQq4ubml2bdp0yaaN2+e84ptSMGayL3h+vUbwdvAgd9w+vR1ihYdxNq1UKuWrasTEREREZGcunDBYMCAxaxYMR3DWAUUolUrePPNq3TtWlijVCTPZTUrssvqCbds2ULZsmUJDAykaNGiTJw4Mc3+oKCgnFcrIpIHXF2hShXo3Bn27Hma+vUHcfkytGkD69ZdJi4u7vYnERERERGRfOPyZRg+HMqVS2L58rcwjM2ULTuDtWth/Xp48EFvhWpiU1kO1oYNG8bUqVMJCwtj586d/PTTTzz77LOkpKQA5iVsRUTyCx8fWLMGGjeGsLAwOnRoQ2Bgd2JiYmxdmoiIiIiI3EZYGLzwwmECAhKYPBni4hypXHkqTzwxnv37X6NtW1CeJvlBloeCFi5cmIiICOvX169f55FHHsHJyYkffvgBX19foqOj71Sdd5SGgorcu6KioEWLLezb1wGIoU6dFmzYsAwvLy9blyYiIiIiIje5ehXee+8SH38cTGLibOAj6td/hXfegaAghWly9+T5UFBPT0/Onj1r/drV1ZUlS5bg4uJC586drT3XRETyE09P2Ly5KXXr/g54sXfvRho3bkdYWJitSxMRERERkf9ERsLbb8dSsuR7fPRRBRITPwWSadt2N9u3Q5cuCtUkf8pysNa+fXvmzJmTZpuDgwPz58+nQoUKXL9+Pc+LExHJCx4esHFjUxo1CgGK8O+/O2nYMJALFy7YujQRERERkftadDS8914ypUp9zfvvVyI+fgxwjQoVGrB2bQhr185RoCb5WpaDtVmzZvH666+n224ymfjiiy84efJknhW1bt06nn32WapWrYq7uzulS5emR48e7Ny5M13bXbt20b59ezw8PChcuDAPP/wwx48fz7NaROTe4O4O69c/QPPmG4CSnDjxD/XrtyQ0NNTWpYmIiIiI3HeuXYOJEw1Kl17FmDEPEBv7HHCOokUD+O67BRw+vI22bQNtXabIbWU5WHNycsLNzS3T/X5+fnlSEMBnn33GyZMnefXVV1mxYgUzZszg0qVLNGnShHXr1lnbHTp0iMDAQBISEli0aBFff/01hw8fpmXLlly+fDnP6hGRe4OrK6xZU502bf4E/Dl37igNGrTkyJEjti5NREREROS+EBsLU6dC2bK7GTWqA9HRQcA+3N29mTx5KqdPH+Kxx/phZ5fluELEprK8eEFGxo0bR3BwcF7WA8ClS5coVqxYmm3Xrl2jYsWK1KxZkzVr1gDw6KOPEhISwrFjx6wTyZ06dYpKlSoxdOhQPvzwwyxdT4sXiNxfEhKge/fTrF7dHjiMt3cJ/vjjd2rWrGnr0kRERERE7klxcTB7Nrz3XihXrrwNfAcYODg48fLLL/P226Px8fGxdZkiVnm+eEFGJk+enJvDM3VzqAbg4eFB9erVOX36NABJSUksW7aMXr16pblBf39/2rRpw88//3xHahORgs/JCX79tSzduv0B1Obq1Qs0bdo6w+HmIiIiIiKSc/HxMHMmVKgAr70GV658C8wDDPr27cfhw4f46KMpCtWkwMpVsJaLzm7ZFhkZya5du6hRowYAx44d4/r169SuXTtd29q1a3P06FHi4uLuWn0iUrA4OsJPPxWnV68QoBHXroXTsmVbtm3bZuvSREREREQKvIQEcw+1ChUSGDLkFOfOgZ8ffPzx6/Tq9Qjbt2/n++8XUK5cOVuXKpIruQrWTHdxaY6XXnqJmJgY3nrrLQDCwsIAMky1fXx8MAyDq1evZniu+Ph4oqKi0jxE5P7j4AALF/rQv/8aoDXXrxfjr7/8bV2WiIiIiEiBlZgIX30FlSvDiy/u5OzZ6jg69uSTT1I4fBiGDHFn8eJFNGjQwNaliuSJAjEb4JgxY5g/fz7Tpk2jfv36afbdKtzLbN+ECRPw8vKyPsqWLZun9YpIwWFvD/PmFeLpp1cA63n11RJ8/bWtqxIRERERKViSkuDbb6FqVRgwAE6dgmLF/HBxuYyv73k6dz6Bs7OtqxTJe/k+WAsODua9997j/fffZ8iQIdbtvr6+wI2ea6mFh4djMpkoXLhwhuccNWoUkZGR1odl3jYRuT/Z2cFXX7kxeHBpDAOeew6eeWYe33//va1LExERERHJ15KTYf58qF4dnn76X44ff4dixWDaNDh5sii//76cI0eOUKFCBVuXKnJHOOTm4Ds9x1pwcDDjx49n/PjxjB49Os2+ChUq4Orqyr59+9Idt2/fPipWrIiLi0uG53V2dsZZUbmIpGJnB598Yp57bcaMbXzzzdN8+62Bv78/zZo1s3V5IiIiIiL5SkoKLFoEwcFw6NBFIBj4HEjm888b06NHJwBatGhhyzJF7rhc9Vhr3rx5XtWRzrvvvsv48eN5++23GTduXLr9Dg4OdOvWjZ9++ono6Gjr9tDQUEJCQnj44YfvWG0icm8ymcx/WXvzzYbAQAzjGTZvbmLrskRERERE8o2UFFi8GGrXhn79Yjl06D2gIvAZkEy3bt2oWjXAtkWK3EUm424u7ZlFU6dO5Y033qBz584ZhmpNmpg/6B46dIiGDRtSr149Ro4cSVxcHGPHjiU8PJw9e/ZQtGjRLF0vKioKLy8vIiMj8fT0zNN7EZGCxzBgzBiD999PAeyZMAHefDMZOzu7u7poi4iIiIhIfmEY8MsvMG4c/P13MvANJtNYDOMcAA0bNmTy5Mm0bt3atoWK5JGsZkX5MlgLDAxkw4YNme5PXfLOnTsZMWIEW7ZswcHBgbZt2zJlypRsjd9WsCYiGXn3XRg7FiCJ6tX70q1bRSZMmKBwTURERETuG4YBy5ebfy/evdsAVmFnN5yUlH8ACAgIYMKECTz66KPY2eX7adxFsuyuBmtLlixh/vz5nDp1iri4uLQXMJnYu3dvbi9xRylYE5HMTJwIo0atBLoAMHjwS3z88f/0S4OIiIiI3NMMA1atMvdQ274dYBf29sNJTl4LgLe3N2+//TYvvfSS5jCXe1JWs6JcLV4AMHnyZEaMGEHRokWpWLEi7u7uuT2liEi+MXIkODkFMWzYLGAQn346k9jYGL744gscHHL9FioiIiIikq8YBqxZY+6htnUrQDL29s+SnDyX5GRwcnLilVdeYfTo0Xh7e9u6XBGby/Wnwk8//ZRnn32W2bNnY29vnxc1iYjkK6+/Dk5OL/Dyy+7A03zzzTfExMTw3Xff4eTkZOvyRERERETyREiIOVDbuNH8tasrDB5sz4kTSfz0E/Tv35/333+fgIAAm9Ypkp/kOlgLCwujf//+CtVE5J42ZAg4Oj7Oiy+6A3348ccfiYmJZfHiH3F1dbV1eSIiIiIiOfbnn+ZAbf16gATs7T/lySe788EH5SlRAkJDJzBq1FAaNGhg40pF8p9cTxLUvHlzDh48mBe1iIjkay+8AF991RP4FXBlxYrldO3alWvXrtm6NBERERGRbNuyBTp0gFatzKGakxNUq/YiyclDuX79LUqUMLfz8/NTqCaSiVwHa9OnT2fmzJksXbqUhISEvKhJRCTfevZZmDevEybTKqAQISEhdOjQkYiICFuXJiIiIiKSJX/9BUFB0KyZeT41B4dkXnwRjh6F779/ldKlS9OuXTtblylSIOR6VdDk5GSGDh3KzJkzMZlMuLm5pb2AyURkZGSuirzTtCqoiGTXDz/AY49tJyWlE3CVunUf4LffVlO0aFFblyYiIiIikqFdu8yrfC5bZv7azu4QZcuOpHXrsnz77cfWdomJiTg6OtqoSpH8IatZUa6DtWHDhjFt2jTq1q1LtWrVMpzIe86cObm5xB2nYE1EcuL//g/69Pmb5OQOwCWqVq3G2rVrKFWqlK1LExERERGx2rsXxo+HJUvMX5tMF6lceTxHj35BcnIyLi4unDlzBl9fX1uWKZKv3LVgzdfXl4EDBzJhwoTcnMamFKyJSE4tXQq9eh0mKakdDg4R/PFHCE2bav4JEREREbG9f/6B4GBYvNj8tckUQ61aH3Hs2CRiYszzBHfv3p2JEydSrVo1G1Yqkv9kNSvK9RxrycnJdOjQIbenEREpkLp3h19+qYyT058kJa3i/fcbEBdn66pERERE5H528CD07Qu1a1tCtWQaNfqSIkUq8fffY4mJuUbDhg1Zv349v/zyi0I1kVzIdbDWsWNHtm7dmhe1iIgUSF26wLJlAbi4NGf5cnjoIdi4cQd79+61dWkiIiIich85fBgefxxq1ICFC8EwDFq0WEHFinX466/nuXz5POXKleP7779n69attG7d2tYlixR4uR4Kum/fPvr06cMLL7xA165d8fHxSdcmo235iYaCikheCAmBBx+E2NgDODi0pFAhg/Xr11O7dm1blyYiIiIi97Bjx+Ddd2HePEhJMW9r02YXMTFv8tdf6wDw9vZmzJgxDB48GGdnZxtWK1Iw3LU51uzszJ3eTCZTpm2Sk5Nzc4k7TsGaiOSVjRuhc+cIYmK6UKhQCocO/U6pUoVsXZaIiIiI3INOnoT33oNvvgHLx+5u3eDhh7fwzDPNAHBycuKVV15h9OjReHt726xWkYImq1mRQ24vNHbs2FuGaiIi95MWLWDNmsJ07Pgb0dGJPPJIIVauBGX2IiIiIpJXQkPhgw/gq68gKcm8rXPnFN55x46GDSElpTGffdaISpUq8d577xEQEGDTekXuZbnusXYvUI81EclrO3ZAhw4QEQGNGkFQ0IfUrl2Jhx9+2NaliYiIiEgBdfYsTJgAX3wBCQnmbe3aJVKz5kzWrPmSrVu34uHhAUBcXBwuLi42rFakYLtrq4KKiEh6DRrAunXg6wt//bWC4OCRPProo3z33Xe2Lk1ERERECpjz5+HVV6FCBZg50xyqtW0Lf/4JK1YYLF8+k/379/PVV19Zj1GoJnJ35HooqIiIZOyBB8wLGrRt24krV54hOXkOTz75JDExMbzwwgu2Lk9ERERE8rlLl+DDD+HTTyEuzrytZUt49NHNvPBCQxwdHQEnpk+fzvnz53n66adtWa7IfUk91kRE7qBatWDDBnuKF/8SeBnDMHjxxReZOnWqrUsTERERkXzqyhUYMQLKlYOPPjKHak2bwldfHcLbuwcvv9yczz//3Nq+a9euDBgwAAcH9Z0RudsUrImI3GHVq8Mff9hRqtQMYBQAb7zxBsHBwWiaSxERERGxCA+Ht94yB2qTJkFsrHm+3u+/v0jt2oMYOLAmS5cuxd7ennPnztm6XBFBQ0FFRO6KypXhjz9MtG37AaGhHsBbjB8/nujoaCZPnqzVlUVERETuYxERMG2a+REdbd5Wrx6MHh3DP/9M5fnnJ3Pt2jUAevTowcSJE6latartChYRKwVrIiJ3SYUKsGEDtGkzmpMnPYBXmTp1KjExMcycORM7O3UiFhEREbmfREXBjBkwdSpERpq31akDY8cmExY2h5dfHsv58+cBaNSoEZMnT6ZVq1Y2rFhEbqZgTUTkLgoIgD/+gDZtXuHYMQ9gALNmzSImJoavv/5a82KIiIiI3Aeio+GTT2DyZLh61bytRg0YP97AxWUlI0cOZ//+/QCUK1eOCRMm8Oijj2qUg0g+lKNPcLVr185yW5PJxN69e3NyGRGRe1LZsuaea+3aPcu//7oBTzBv3jxiYmJYsGABzs7Oti5RRERERO6AmBjzCp+TJpkXKACoWhXGj4cKFXYxfPgbhISEAODj48OYMWMYNGiQfj8UycdyFKz5+PjcNim/du0aO3fuVKIuIpKB0qVh/Xpo164vBw64AY/w008/ERISQufOnW1dnoiIiIjkodhYmDULPvwQLl0yb6tUyRyo9ekD9vYwbtwvhISE4OzszCuvvMKoUaPw9va2ad0icns5CtbWr1+f6b6kpCQ+//xz3nnnHUwmE/37989pbSIi97QSJczhWvv23fn77+V4eByldGmFaiIiIiL3irg4+PxzmDABLlwwbytfHsaNg6Cgq1y9ehl7+8oAvPnmm1y+fJkRI0bg7+9vw6pFJDvydKbsH3/8kerVq/Pyyy9Tp04ddu7cybx58/LyEiIi95SiRWHdOnjggfZcu/YibdrAnj1w8eJFwsPDbV2eiIiIiORAfLx5yGfFivDqq+ZQLSAAvvoKDh2CMmXWUaVKBR577DFSUlIA8PDw4NNPP1WoJlLA5Emwtn79eho3bkyfPn3w9PTkt99+Y/Xq1dStWzcvTi8ick/z9YW1a6FRIwgLg8DAcFq06EBgYCAXL160dXkiIiIikkUJCeYeapUqwUsvwdmz5vl1Z8+Gf/+FZ58FR0eoUaMGiYmJXL9+Xb/viRRwuQrW9u3bR5cuXWjXrh1hYWEsWLCAHTt20K5du1wVFR0dzfDhw+nYsSNFixbFZDIxfvz4dO2efvppTCZTukfVqlVzdX0RkbvN2xt++w2aNYPIyMscO3aFc+cuEx0dbevSREREROQ2rl41z6FWpQq88AKcPg2lSsHMmXDkCFSt+gdvvfWmtX3x4sXZuHEje/fupWTJkjasXERyK0dzrJ0+fZq3336bBQsW4OPjw/Tp03nxxRdxdHTMk6LCwsL4/PPPqVOnDg899BBffvllpm1dXV1Zt25dum0iIgWNlxesWgUPPliFP/74g9jYeC5cqEjFirauTERERERuFhsLv/4K338PK1ZAYqJ5e4kSMGoUDBwIJ04c5JFHRvDrr78C0KlTJ9q3bw9AnTp1bFW6iOShHAVrlStXJiEhgc6dOzN8+HAKFSrEvn37Mm1fr169bJ3f39+fq1evYjKZuHLlyi2DNTs7O5o0aZKt84uI5FeFCpl/MevevSLr1kGnTrB8OcTHr6Zs2bJUr17d1iWKiIiI3LcSE+H332HBAliyBGJibuyrVcs81HPgQIiKusBrr43nyy+/JDk5GXt7ewYOHEitWrVsVruI3Bk5Ctbi4+MBWLlyJatWrcq0nWEYmEwmkpOTs3V+k8mUk7JERO4J7u6wbBk89JB5eGinTpswjB54eRVi9erV2f5jhYiIiIjkXEoKbNxo7pn244/mOXEtypWDfv3Mj5o14dq1a0yePJXJkycT81/q1qNHDyZOnKgpi0TuUTkK1ubMmZPXdeTY9evXKVGiBJcvX6ZkyZI89NBDvPPOO/j4+Ni6NBGRHHN1hV9+gd69YfnyqphMtbhyZQdt27ZlxYoVNGvWzNYlioiIiNyzDMO8UvuCBfDDD3DmzI19xYtDnz7mMK1xYzCZICUlha++msPbb7/NhQsXAGjUqBGTJ0+mVatWtrkJEbkrchSsPfXUU3ldR47UqVOHOnXqULNmTQA2bNjAtGnTWLt2Ldu3b8fDwyPD4+Lj46297gCioqLuSr0iItnh4gI//QR9+viyZMlaTKYHiYz8k44dO7J06VLatm1r6xJFRERE7ilHjph7pi1YYF7F08LLCx5+GPr3h8BAcEj1SfrQoUM8//zzbNy4EYDy5cszYcIEHnnkEY3GErkP5ChYyy+GDh2a5usOHTrwwAMP0Lt3b7744ot0+y0mTJhAcHDw3ShRRCRXnJxg0SJ47DFPfvxxFSZTT2JifqNLly4sXryYBx980NYlioiIiBRoZ8/CwoXmQG3HjhvbXVygWzdzz7SgIPPXqaWkpPDee+/x/vvvk5CQgLu7O8HBwQwZMgRnZ+e7exMiYjN2ti4gr/Xs2RN3d3e2bt2aaZtRo0YRGRlpfZw+ffouVigikj2Ojua/mvbv74ZhLAUeIj4+np49e7Jo0SJblyciIiJS4ISHwxdfQJs2ULYsDBtmDtXs7aFzZ5g7Fy5eNP+Bs2fP9KEamBfS27dvHwkJCXTp0oX9+/czbNgwhWoi95kC3WMtM4ZhYGeXeWbo7OysNzsRKVAcHMy/4Dk4ODN37iLgaZKSFtCvXz9iYmJ45plnbF2iiIiISL4WEwNLl5p7pq1aZV7h06JFC3PPtEcegaJFMz9HVFQUiYmJ+Pr6AvC///2PXr160adPHw37FLlP3XPB2uLFi4mNjaVJkya2LkVEJE/Z28OcOeDk5MiXX84F3ElJ+YJnn32WmJgYhgwZYusSRURERPKVhATzKusLFpgXhoqNvbGvTh3znGl9+oC//+3PFRISwhNPPEHz5s1ZuHAhACVLlqRv3753qHoRKQjybbC2cuVKYmJiiI6OBuDAgQMsXrwYgC5dunD58mX69+9P3759qVixIiaTiQ0bNjB9+nRq1KjBgAEDbFm+iMgdYWcHs2eDo6M9n302G/AApvHyyy8THR3NqFGjbF2iiIiIiE2lpMAff5h7pi1ebB72aVGhgrlnWr9+UL169s7r5eXFhQsX2LlzJ1evXsXb2ztvCxeRAslkGIZh6yIyEhAQwKlTpzLcd+LECby8vHjuuefYvXs3Fy9eJDk5GX9/f3r27Mno0aPx8vLK8rWioqLw8vIiMjIST0/PvLoFEZE7xjBg6FCYMcMAxgPvALB06VK6detmy9JERERE7jrDgF27zD3TFi40L0hgUaIE9O1rDtMaNoSsjthMSUlh165dNGjQwLptxYoVtGnTBldX1zy+AxHJb7KaFeUoWAsNDc1Wez8/v+xe4q5SsCYiBZFhwPDhMGUKwGQaNTrIli1f3nKOSREREZF7yb//mnumLVgAR47c2O7lBb17m8O0wEDzlBrZcejQIQYOHMiWLVvYvXs3NWvWzNO6RST/y2pWlKOhoAEBAdmamDE5OTknlxERkVswmWDSJHBygg8+eJO//jKYPNnEiBGQkJCAvb099tn9LVJEREQknztzxtwrbcECcy81C1dX6NbNPG9a586Qk/XqEhIS+PDDD3nvvfdISEjA3d2dgwcPKlgTkUzlKFj7+uuvteKJiEg+YDLBe++Zf3EcN87EyJFw/XoS+/c/hoODA3PnzsXR0dHWZYqIiIjkSliYeb607783z59mGXdlbw+dOpl7pvXoAYUK5fwaW7Zs4fnnn2f//v0ABAUF8dlnn+GflZUNROS+lW/nWLubNBRURO4FEybA6NEA27Cza4m9PWzatImGDRvaujQRERGRbLt2DZYuNfdMW70akpJu7GvZ0twzrXdvKFIkd9eJiopi9OjRfPrppxiGQdGiRZk+fTr9+vVThxKR+9gdHQqamcOHDxMWFkaRIkWoVKlSXp5aRERuY9Qo87DQN95oTErKErp3j6dBA4VqIiIiUnAkJMCqVeaeaUuXQmzsjX0PPGDumdanD+TVNN5Lly5l8ODBnP1vtYOnnnqKqVOn4uvrmzcXEJF7Xp4Eaz/++CNvvPEGZ86csW4rU6YMU6dOpXfv3nlxCRERyYJhw8zh2iuvdGHJEnj9dfjoIzh+/BhFihTJ1orJIiIiIndDcrJ5eOeCBfB//wdXr97YV7GiuWdav35QtWreXfPChQu8/PLLLF68GIDy5csze/Zs2rdvn3cXEZH7Qq6DtRUrVtC3b19q1KjBkCFDKFWqFGfPnuW7776jb9++/PrrrwQFBeVFrSIikgUvvwyOjjBoEEyfDleunGT9+kCKFSvG6tWrKZLb8RIiIiIiuWQYsGOHuWfaDz/A+fM39pUqZe6V1q8fNGhgnlM2L/388888++yzREREYG9vz7Bhwxg3bhxubm55eyERuS/keo615s2b4+npyfLly7Gzs7NuNwyDoKAgoqOj2bRpU64LvZM0x5qI3Iu+/hoGDADD+BsXl/bExV2mevXqrFmzhpIlS9q6PBEREbkPHTxoDtO+/x6OHr2x3dvbPF9av37QqpV5UYI7Zdu2bTRt2pR69erxxRdf8MADD9y5i4lIgZXVrCjXwZq7uzs//PAD3bp1S7dv6dKl9O/fn2vXruXmEnecgjURuVfNmwdPPw0pKYdwc2tPbOxZKlSowNq1a7XClYiIiNwVp0+be6UtWAB79tzY7uYG3bubh3p26mSezuJOSEhI4K+//qJFixbWbSEhIbRs2RIHhzyddlxE7iF3bfECe3t7EhISMtyXmJiYphebiIjcXU88AQ4O8MQTVYmN/RN393YcO3aMli1bsmbNGipXrmzrEkVEROQedOUK/PijuWfan3/e2O7gAJ07m3umde8OHh53to6wsDACAwM5fPgwf//9N1WqVAGgTZs2d/bCInLfyHXq1bBhQyZNmsT169fTbI+Pj2fKlCk0btw4t5cQEZFc6NfP/FdiB4dyxMT8SaFCVTl9+jStWrVi3759ti5PRERE7hHR0fDdd9ClC5QsCYMHm0M1kwlat4ZZs+DCBfj1V3MvtTsdqgH4+PhQpkwZPD09CQ0NvfMXFJH7Tq6Hgm7cuJF27drh4+PDI488QokSJTh//jw//fQTYWFhrFu3jmbNmuVVvXeEhoKKyP3gl1/gkUcgMfESXl6diIzcg4+PD6tXr6ZBgwa2Lk9EREQKoPh4WLXKPMzz118hdX+LevXMAVqfPlCmzN2racWKFTRt2hRvb28Azp07h5OTkxZwEpFsuWtzrAFs2LCBkSNH8tdff2EYBnZ2djRu3JgJEybQqlWr3J7+jlOwJiL3ixUr4OGHIT7+KoULdyEiYiuFChVi+fLltGzZ0tbliYiISAGQnAzr15vDtJ9+goiIG/sqVzb3lu/XD/4bdXnXXLhwgVdffZVFixYxYMAAvvjii7tbgIjcU+7aHGsArVu3ZsuWLcTGxnL16lW8vb21VLGISD7UpQssXQo9engTEfEbPj7dCQ9fT6dOnViyZAkdO3a0dYkiIiKSDxkG/PWXec60hQvNQzotSpeGvn3NYVq9euahn3e3NoOvv/6aN954g4iICOzt7fHx8cEwDEx3uxgRue/kSY+1gk491kTkfrNuHXTrBrGx1/H17U1Y2AqcnJxYvXo1gYGBti5PRERE8okDB8w9077/Ho4fv7Hdxwd69zYP9WzZEmy1Zt3hw4d54YUXWL9+PQD16tXjyy+/5IEHHrBNQSJyz7irPdaWLFnC/PnzOXXqFHFxcWn2mUwm9u7dmxeXERGRPNK2rXk+lC5dXAkL+5kiRR4jICCU+vXr27o0ERERsbFTp8wLH33/PaT+KOfmBj16mMO0jh3Bycl2NSYkJDB58mTeffdd4uPjcXNz45133uHVV1/FwSFPPuaKiGRJrt9xJk+ezIgRIyhatCgVK1bE3d09L+oSEZE7rGVLWL0agoKcuHLle8qXj8EwCtm6LBEREbGBy5fhxx/NvdM2bbqx3dEROnc2D/Ps3h3yw8e9bdu2MWDAAP755x8AOnbsyKxZsyhXrpyNKxOR+1Guh4KWK1eOdu3aMXv2bOzt7fOqrrtKQ0FF5H62fbv5r84REdCokTlsmz37Q5ycnBg6dKityxMREZE7JCoKliwx90z7/XfzogRgniOtdWtzz7RevczDPvOD6Oho3nrrLT755BMMw6BIkSJMmzaNxx57THOpiUieu2tDQcPCwujfv3+BDdVERO53DRua51xr3948KXHjxps5fHgkAE2aNKFp06Y2rlBERETySlwcrFxp7pm2bJn5a4sGDcw90/r0MS9IkJ+cOXOGZs2acfr0aQCeeOIJPvroI4oUKWLjykTkfpfrYK158+YcPHiQtm3b5kU9IiJiAw88ACEh5nDt8OGmlCjxLv37JyhUExERuQckJZn/nf/+e/i//zP3VLOoUsXcM61fP6hUyXY13k7p0qWpWLEiDg4OzJo1SyuZi0i+keuhoP/++y89e/Zk4sSJdO7cGSdbzmCZQxoKKiJiduCAeWGDixehRg1YuxaKF4eYmBhcXV2xs9WSXyIiIpIthgHbtpl7pi1aZP633aJMGejb1xyo1a1rHvqZ3xiGwXfffUePHj2sn9HOnz+Pp6en5vUWkbvirg0FrVixIu3bt6dnz56YTCbc3NzS7DeZTERGRub2MiIichdUrw4bNpjDtf37ITAQli2L5ZlngihfvjxffvmlVtoSERHJx/75x9wz7fvv4cSJG9t9feGRR8w901q0gPz+t7IBAwbw9ddfM3jwYGbOnAlAyZIlbVyViEh6uf50NHz4cD755BPq1q1LtWrVCmSPNRERuaFKlRvh2qFD0KrVFi5e3Myff/5JTEwM8+fP13u9iIhIPpKUBAsXwpQpsGfPje3u7vDQQ+aeaR06mFf4LCgee+wxfvjhBypUqGDrUkREbinXQ0F9fX0ZOHAgEyZMyKua7joNBRURSe/ECXO4dvIkFCv2CxERj5KQkECXLl1YvHgxrq6uti5RRETkvhYbC19/bQ7UTp0yb3N0hC5dzD3TunWDmwYU5Vvbtm3jxIkT9O3b17rtypUrWpxARGwmq1lRrjsAJycn06FDh9yeRkRE8ply5cw91ypUgEuXeuDltQwXF1dWrFhB165diY6OtnWJIiIi96WrV+G998DfH15+2RyqFS0K778PFy7AkiXmlT0LQqh27do1XnvtNZo2bcqAAQM4ZUkIQaGaiBQIuQ7WOnbsyNatW/OiFhERyWf8/MzhWuXKcPlyBzw8VuPuXoiQkBA6dOjA1atXbV2iiIjIfePsWXjjDfO/z2PGwJUrEBAAM2eaw7XRo8HHx9ZVZt2KFSuoUaMGM2bMwDAMevbsqYUJRKTAyXWwNmbMGL777jtmzJjB0aNHCQ8PT/fIrujoaIYPH07Hjh0pWrQoJpOJ8ePHZ9h2165dtG/fHg8PDwoXLszDDz/M8ePHc3lXIiJiUbq0OVyrXh2uXGmJi8tavLx82LZtG23atOHSpUu2LlFEROSe9u+/MGCAuTf51Klw7RrUqgXz58ORIzB4MBSkGRouXrxIv3796Nq1K6GhoQQEBLBq1SrmzZunXmoiUuDkOlirU6cOhw4d4vXXX6dKlSoULVo03SO7wsLC+Pzzz4mPj+ehhx7KtN2hQ4cIDAwkISGBRYsW8fXXX3P48GFatmzJ5cuXc3FXIiKSWokSEBJi/iU+LKwh9vbr8fUtzt69e2ndujVnz561dYkiIiL3nO3boVcvqFYNvvoKEhOhVStYvhz27jUvSlCQFus2DIM5c+ZQrVo1fvjhB+zs7Bg2bBj//PMPnTp1snV5IiI5kuu34bFjx2IymfKiFit/f3+uXr2KyWTiypUrfPnll5le29nZmWXLllknkqtfvz6VKlViypQpfPjhh3lal4jI/axYMXO41qED7N5di8KF/6BEifYcOnSIli1bsnbtWsqVK2frMkVERAo0w4A1a2DiRFi37sb27t1hxAho1sx2teXG0aNHeeGFF1j330098MADfPHFF9SvX9/GlYmI5E6ug7XMhmjmRlaCuqSkJJYtW8aTTz6ZZnUGf39/2rRpw88//6xgTUQkj/n6wtq10KkTbN9eGU/PPylTph0nThyjRYsWrF27lqpVq9q6TBERkQInORl++skcqO3aZd7m4GDulTZ8ONSoYdv6cioxMZGpU6cSHBxMXFwcLi4uvPPOOwwdOhSHgtTdTkQkEwX2nezYsWNcv36d2rVrp9tXu3Ztfv/9d+sbt4iI5B1vb/j9dwgKgi1b/ElJ+ZNy5doTGxuGvb29rcsTEREpUOLiYO5cmDwZjh41b3Nzg+efh9dfNy9UUJD17t2bpUuXAtCuXTtmz55NhQoVbFyViEjeyZNgbcmSJcyfP59Tp04RFxeXZp/JZGLv3r15cZk0wsLCAPDJYNkbHx8fDMPg6tWrlCxZMt3++Ph44uPjrV9HRUXleX0iIvcyLy9YvRq6doU//yyJYWxg1qzLVKpUydaliYiIFAhRUTBrFkybBhcumLf5+MDLL8OQIXCvzOH/4osvsnHjRj766COefPLJPJ9GSETE1nIdrE2ePJkRI0ZQtGhRKlaseNeXR77VG3Nm+yZMmEBwcPCdKklE5L5QqBCsXAndukFISBFefLEIZcpAYCCsWLGCkJAQXnjhBSpWrGjrUkVERPKNixdhxgz49FOIjDRvK1MGhg0zr/zp4WHb+nJr5cqVRERE0K9fPwCCgoI4ceJEmul7RETuJbkO1j799FOeffZZZs+efVeHAPn6+gI3eq6lFh4ejslkonDhwhkeO2rUKF5//XXr11FRUZQtW/aO1Ckici9zd4dly6BnT/jtN+jSBaZM2cmwYb2Ii4ujR48e1mDt5MmT2NnZ4VfQx7SIiIjkwLFjMGUKzJkDlsEz1aqZ50/r3x+cnGxbX15YuXIlXbp0wdPTk1atWlG6dGkAhWoick/LdbAWFhZG//797/q8OhUqVMDV1ZV9+/al27dv3z4qVqyY6fxqzs7OODs73+kSRUTuC25u8Msv0KsXrFgBQ4fWZujQOYSFraVJkybWdpMmTeKzzz6jevXqBAUF0blzZ1q2bKn3YxERuaft2QMffgiLFkFKinlb48YwapS517ednU3Ly1MdO3akWbNmNGnSJNNODiIi95pcv403b96cgwcP5kUt2eLg4EC3bt346aefiI6Otm4PDQ0lJCSEhx9++K7XJCJyv3JxMa9k1qMHJCQ4Mm1aX5KSvuCHHxw4f97c5urVq9jZ2XHgwAGmTp1Khw4d8PX1pXv37nz66aecOHHCtjchIiKSRwwDNmwwL/TzwAPwww/mUK1zZ1i/HrZsMf+bWdBDtWPHjvHMM88QGxsLgL29PevXr2fq1Kl3fYogERFbMRmGYeTmBP/++y89e/Zk4sSJdO7cGac86sO8cuVKYmJiiI6O5tlnn+WRRx7h0UcfBaBLly64ublx6NAhGjZsSL169Rg5ciRxcXGMHTuW8PBw9uzZQ9GiRbN0raioKLy8vIiMjFQ3ZRGRXEhMNA9nWbw47fbq1aFdO2jc+CqJib+zYcNKVq1axQXLbM3/qVKlCp07dyYoKIjWrVtrZWcRESlQUlJg6VJzD7WtW83b7Ozg0UdhxAioW9em5eWZxMREPvroI8aPH09cXByjRo3igw8+sHVZIiJ5KqtZUa6DteTkZIYOHcrMmTMxmUy4ubmlvYDJRKRlVs5sCAgI4NSpUxnuO3HiBAEBAQDs3LmTESNGsGXLFhwcHGjbti1TpkzJ1hLOCtZERPJOSgqsWwe//w5r1sDu3ea/3FvY2UGDBtC2rUG5cnu5eHEla9asYtOmTSQnJ1vbubq68v3339OjRw8b3IWIiEjWJSTAggXmQO3QIfM2Z2d49lnzogTZ+GiS7+3YsYMBAwawd+9eANq1a8fs2bOz9flLRKQguGvB2rBhw5g2bRp169alWrVqGfZYmzNnTm4ucccpWBMRuXPCwszDXtauNT8OH06738UFmjeH5s0jcXVdw5EjK1m9ehVnz57lyJEj1sUPFi9ezIYNG+jfvz9Nmza9+zciIiJyk2vX4MsvYepUOHPGvM3TE156CV59FYoXt219eSkmJoYxY8YwY8YMUlJS8PHxYerUqTz11FOYTCZblycikufuWrDm6+vLwIEDmTBhQm5OY1MK1kRE7p7Tp2+EbGvXYp2DzaJwYWjd2qBGjYM8/nh1qlYFkwl69uzJkiVLeOeddxgzZgwA165d4+zZs1SuXFm/1IuIyF1z5Qp88gl8/DGEh5u3lSgBQ4fCCy+Al5dt68trq1at4sUXX7SOKOrXrx/Tp0+nWLFiNq5MROTOuWvBWuHChfnpp59o27Ztbk5jUwrWRERswzDMQ2YsIVtICNw8e0CpUtC2LRQtupJLl35h+PDB1K5dG4BFixbRp08fypUrZ52brU2bNnh4eNjgbkRE5F4XGmrunfbll/DffP1UrAhvvglPPmnuhX0vuXz5Mq+99hoLFiwAwM/Pj1mzZhEUFGTjykRE7ry7Fqw9+uij1K1bl9GjR+fmNDalYE1EJH9ISoJdu24EbRs3Qnx82jaVK5sXQmjXDv75ZzIffPA2CQkJ1v1OTk60bNmSoKAgOnfuTPXq1dWbTUREcmX/fpg0yTyPWlKSeVu9ejByJDz8MNjb27a+vGYYBnPnzuX1118nPDwcOzs7XnnlFd5991398UpE7ht3LVjbt28fffr04YUXXqBr1674+Pika5PRtvxEwZqISP4UFwebN5tDtjVrYMcO8+IIFiYT1K59jQoV1pOYuJJ9+1Zy8uSJNOfw8/Ojc+fOdO7cmXbt2ul9XkREsmzzZpg4EX799ca2tm3NgVr79uZ/h+5FP/zwA/369QOgTp06fPHFFzRs2NDGVYmI3F13LVizs7Mzn+gW/6qkXuUtP1KwJiJSMEREwIYNN3q0HTiQdr+jo0HdukcoUmQl4eEr2bNnPfGpurw5ODgwevRogoOD727hIiJSYBgGrFxpDtT+/NO8zTzXpzlQux/ypaSkJFq3bk337t15/fXXcXR0tHVJIiJ3XVazIofcXmjs2LEaYiMiIndF4cLQo4f5AXDuHKxbdyNoO33axPbtlYHKwKt4eMRSu/YGXFxWEhq6ilOnjlC2bFnr+Y4dO8YHH3xA9+7d6WE5qYiI3JeSkmDRIvjwQ/j7b/M2R0fz3GlvvglVqti2vjtp586dTJ48mW+//RZnZ2ccHBz4888/rZ0oREQkc7nusXYvUI81EZGCzzDg6NEbIdu6dTdWarPw8TlGYKAPQUHetG8PS5f+j1dffZW2bduydu1aa7tDhw5RpUoV/eFIROQ+cP06zJkDkyfDyZPmbR4e5tU9hw6F0qVtWt4dl5CQQPny5Tl79izvvfceb731lq1LEhHJF+7aUNB7gYI1EZF7T0oK7NlzI2j7888bK7hZlC69E2/v72jZsi7BwU9RtChcvHiREiVKUKJECevcbB06dMj384WKiEj2XL0Kn34KM2bA5cvmbUWKwKuvwuDBcD+97S9atIiff/6Z6dOnU7x4cVuXIyKSLyhYywYFayIi9774eNi2zbwIwtq15v+/eQrQ2rWhUqV1LF/enbi4GOt2Ozs7GjduTFBQEEFBQdSrV0/DY0RECqhz52DaNJg1C65dM2/z9zcP93zmGXBzs219d9rly5d5/fXX6dy5M4899pityxERybcUrGWDgjURkftPdDT88ceNHm2W+XTM4rGz20iJEqtITFzJ5cv70xxbtGhROnXqRFBQEB07dqRIkSJ3tXYREcm+w4fNwz3nzoWEBPO2mjXNCxI8+qh5PrV7mWEYfPfddwwdOpSwsDCKFy/OyZMncXFxsXVpIiL5koK1bFCwJiIily6lXQjhxInUe0NxclqNt/dKIiLWEB8fbd1jMpkICgpi+fLld71mERG5vR07zAsS/N//mefjBGjRwhyodeliXvHzXnf8+HFefPFFfv/9dwBq167Nl19+ScP7YYlTEZEcympWpHEsIiIiQLFi0LcvfPEFHD9ufnzxhXlb0aJ+JCQ8z8WLPxEffwVYj4vLCLy8amMYBvb2ha0f1gzDYNCgQcydO5fr16/b9J5ERO5XhmEe+t++PTRsCIsXm7d16wYbN5rn3eza9d4P1ZKSkpgyZQo1a9bk999/x8XFhQkTJrBjxw6FaiIieUQ91lCPNRERubWUFPjnnxu92TZsuDEvD5wDYvHzq0i7dlC58j5GjaqNq6sr4eHh1iE2oaGhlCpVCgcHB1vdhojIPS85GX7+GSZOhJ07zdvs7aF/fxg+3Dz0836xa9cunn/+eXbt2gVAmzZtmD17NpUqVbJxZSIiBYOGgmaDgjUREcmOxETYvv1G0LZ5s3mbWSgwGx+fWB57bBrt20Pr1tCsWQ3Onz9Phw4dCAoKolOnTpQsWdKGdyEicu+Ij4d582DSJDhyxLzN1RUGDIBhw8yLE9wvYmNjGTduHNOmTSM5ORlvb2+mTJnCM888g+le76InIpKHFKxlg4I1ERHJjZgY89AiS9C2e/eNeXwATKar2NlVIDn5aprj6tatS+fOnQkKCqJp06Y43uszZ4uI5LGoKJg927zK5/nz5m3e3jBkCLz8MhQtatv67rZffvmFoUOHcuK/iUL79OnDjBkzKF68uI0rExEpeBSsZYOCNRERyUthYbB+vTlkW7PG0nsiCdgOrMRkWoVh7ABu/BPs6elJ+/btCQoKonPnzpQpU8YmtYuIFAQXL8L//gczZ0JkpHlb6dLm3mnPPw8eHrat706Li4tj8+bNhIaG8vTTT1u3N2zYkB07dlC2bFk+++wzunbtarsiRUQKOAVr2aBgTURE7qTTp2/0Zlu71tKr4hLwG7AKk2k1hnElzTE1a9Zk7dq1FCtWzAYVi4jkT8ePw9Sp8PXXEBdn3lalCowYAY89Bk5Otq3vToiMjGTXrl24urrSpEkTAE6dOkVAQAAODg5ER0db5/McN24cMTExjBs3jkKFCtmybBGRAk+rgoqIiOQTZcvC00+b5/85exb274f//a8YPXo8jpfXdxjGBWAbEAw0AUwcOxbOihVFOXPGfI4ZM2Ywe/ZsLl26ZLP7EBGxlb17zQsQVKoEn35qDtUaNYKffoIDB+CZZ+6NUC0yMpKQkBAmT55M3759qVSpEoULF6Zt27Z8+OGH1nZ+fn7Ur1+fHj16EGnpsgcEBwczZcqUey5U27p1K4888gglS5bEycmJEiVK0Lt3b7Zs2ZKt84wfPz7H88ytX78ek8nE+vXrc3R8VgUGBhIYGHjLNsnJyXz00UfWHu5ubm5Uq1aNkSNHEhERkeExH3/8MVWrVsXZ2Zly5coRHBxM4o0JYq0uXbrE008/TZEiRXBzc6Np06asXbs2Xbv4+HgmT55MzZo1cXd3p3jx4gQFBbF58+ac3LZIgaYea6jHmoiI2E5SEuzadWPY6KZNEB8fBhwFGgNQqVIKp08XJy7uCkuXrqdbt9YAhIWF4eHhgbOzs+1uQETkDjEM+PNP8wqfK1fe2N6pk7mHWmAgFOS5+C090Xbs2MHOnTvZuXMnR48ezbCtv78/Xbt2ZebMmXe5Stv7+OOPee2112jUqBGDBw/G39+f0NBQZs6cyV9//cWMGTMYMmRIls515swZzpw5Y+35lx1RUVEcOHCA6tWr39HPjJZQ7VYB3rVr1yhVqhT9+vWjQ4cOFClShF27dvHee+9RsmRJduzYgaurq7X9+++/z5gxYxg5ciQdO3Zk+/btvP322zz11FN8/vnn1nbx8fE0aNCAiIgIJk6cSLFixZg5cybLly9nzZo1tG7d2tr2ySefZP78+YwaNYq2bdsSHh7OxIkT2bt3L5s2baJRo0Z5/r0Ruds0FDQbFKyJiEh+cf26eZVRy7DRHTsgJeU68BGwAVhO/fqOtGsHBw8OYe3aObRt25agoCCCgoIoV66cje9ARCR3UlJg2TJzoGbpkGRnB488Yg7UHnjAtvXlREREBC4uLtYhm1OmTOHNN9/MsG1AQAD169e3PurVq0eRIkXuZrn5xqZNm2jVqhVdunTh559/xsHBwbovKSmJnj17smLFCv744w+aN2+e6XliY2Nxc3O7GyXnWlaCteTkZCIiIvD19U2zffHixTzyyCPMmzePxx9/HDD/Ea5MmTI8+eSTzJ4929r2gw8+4O233+aff/6hevXqAHz66ae89NJLbN68maZNmwLm73OdOnXw8PBg27ZtgDmAc3d3p1+/fsybN896zvPnz1OqVCleeeUVZsyYkevvhYitaSioiIhIAeTqCu3awQcfwLZt5oUQlixxZciQt6hW7TfAkZ07YdIk+PXXPcTGxrJs2TJeeuklypcvT5UqVXjttddYs2YNCQkJtr4dEZEsS0yEb7+FWrWgRw9zqObsDC+8AP/+Cz/8UDBCtdjY2DRfd+nSBW9vb9atW2fdVqFCBcAcovXq1YsPPviA1atXc+XKFU6cOMHixYsZNWoUHTt2vG9DNYAJEyZgMpn47LPP0oRqAA4ODnz66aeYTCYmTpxo3W4Z7rlr1y569+6Nt7e39fud0VDQ+Ph4hg0bRokSJXBzc6NVq1bs3LmTgICANAtDZDQU9Omnn8bDw4OjR4/SpUsXPDw8KFu2LMOGDSM+Pj7NdYKDg2ncuDE+Pj54enpSr149vvrqK3LSz8Xe3j5dqAZYe4mdPn3aum3VqlXExcXxzDPPpGn7zDPPYBgGS5YssW77+eefqVKlijVUA/P3+fHHH+evv/7i7NmzANjZ2WFnZ4eXl1eac3p6emJnZ2cNkEXuFw63byIiIiK2Uriw+QNmjx7mr8+dg3XrLENH/+TMmb3AKmAlsInDhw9z+PBhZsyYgYeHJ0FBnejWrRtdunTJ8JdwERFbi4mBL780L0pgyQM8PWHwYHj1VShRwrb13UpERAS7du2yDuXcuXMnFy5cICIiAnt7ewB8fHwAOHz4MF26dAGgU6dOXLly5Y6+LxsG3JTx2ZSbW/aG7iYnJxMSEkKDBg0yXSm7bNmy1K9fn3Xr1pGcnGz9ngM8/PDD9O3blxdffJGYmJhMr/PMM8+wcOFChg8fTtu2bTlw4AA9e/YkKioqS3UmJibSvXt3nnvuOYYNG8Yff/zBu+++i5eXF2PHjrW2O3nyJC+88AJ+fn6Aed64l19+mbNnz6ZplxuW8LZGjRrWbf/88w8AtWrVStO2ZMmSFClSxLrf0rZly5bpzlu7dm0A9u/fT+nSpXF0dGTw4MF89dVXtG/f3joUdPTo0Xh5efH888/nyf2IFBQK1kRERAqQUqXg8cfND8MwcfRoXdasqcvatSNZuzaSiIg1wApgGdeuXeLHH3/kxx9/xM7OjqZNmzFt2kc0bNjQ1rchIkJYGHzyCfzvfxAebt5WvDgMHQovvgg3dYaxuezMiXb06FGqVKkCmIfczZgxI02I5ubmdseHJsbGgofHHb1Etly7Bu7uWW9/5coVYmNjbzvFQbly5fjrr78ICwtLs5L2U089RXBw8C2PPXDgAN9//z0jRoxgwoQJAHTo0IHixYvTr1+/LNWZkJBAcHAwjzzyCADt2rVjx44dLFiwIE1gNmfOHOv/p6SkEBgYiGEYzJgxgzFjxuR4UQWLs2fPMnLkSBo0aMCDDz5o3R4WFoazszPuGXzzfXx8CAsLS9PWEgTf3M6y32LatGl4eXnRq1cvUlJSAPOiGuvWraNixYq5uheRgkbBmoiISAFlMplXyKtUCQYNgpQUL/bs6cXvv/dixYoUNm7cTkrKr8CvpKT8zaZNGxk92os+faBLFzh7djsxMTG0aNEi3RAbEZE75fRp+Ogj+PzzGz2qypeH4cPhqacgP40iO3fuHK+//votQ7Sb50SrX79+mhDN0kNJ7gzLUMqbg6levXrd9tgNGzYA8Oijj6bZ3rt3b5544oksXd9kMtGtW7c022rXrp1m6C+Ye5N98MEHbN++PV1vuEuXLlG8ePEsXS8j4eHhdOnSBcMwWLhwIXZ2aWd8ulVod/O+rLZ9//33mTJlCuPHj6dly5ZERUXxySef0KFDB3777TceKAjjtkXyiH6LFhERuUfY2UG9eubHiBF2REQ05vffG7NixXssW3aKK1dCWLOmMmvWmNt7eU0kMvInnnsumFmzxqJsTUTupAMHzPNDzp9vXhEZzHOmjRgBvXph8/egkJAQZs+eTZ06dRg1ahRgnjNq0aJF1vDmdiFafuLmZu4lll9kt4NekSJFcHNz48SJE7dsd/LkSdzc3NL1tCpZsuRtr2HpgXVzqOXg4JDln6ubm1u6OcWcnZ2Ji4uzfv3XX3/RsWNHAgMD+eKLLyhTpgxOTk4sWbKE999/n+vXr2fpWhm5evUqHTp04OzZs6xbt47y5cun2e/r60tcXFyGCziEh4dTv379NG1T90pL3Q5u9Fw7ePAgY8eOZdKkSbzxxhvWdkFBQVSvXp3XX3+dkJCQHN+TSEGjX6FFRETuUYULm1fRe+QRSEnxZ/fup1m+HFasgL/+gsjIkkARvvqqCz/9BJ06QcmSv7J9+zQefrgb3bp103AOEcm1rVvNK3z+8suNbW3awMiR0KFD9ubdyq2bh3MOGzbMOjz+7NmzLFy4kDNnzliDNQ8PD2bOnEmFChXydYiWEZMpe0Mv8xt7e3vatGnDqlWrOHPmTIbzrJ05c4adO3cSFBSUZn41uHXPKwvLz/PixYuULl3auj0pKSnDgCmnfvjhBxwdHVm2bFmaEC71wgE5cfXqVdq3b8+JEydYu3atdS601Cxzq+3bt4/GjRtbt1+4cIErV65Qs2bNNG337duX7hyWbZa2e/fuxTCMdFNLODo6UqdOHWtPQJH7RYFeFdSyMktGj61bt9q6PBERkXzDzg7q14exY80fci9ehHnzPqFPnwsULlyfq1fNK+5Nm/YzGzeG8Prrr1OpUiXKlavG8OEj2LhxI8nJyba+DREpIAwDVq6EwEBo2vRGqNazp/k9aN066NjxzoZqkZGRhISEMGXKFPr27UulSpUoXLgwbdu2Zfjw4SxcuJCNGzda27dq1YoPPviAd999N815Bg0aRMeOHQtUqHavGDVqFIZhMHjw4HT/BiUnJzNo0CAMw7AGodnVqlUrABYuXJhm++LFi0mydKvMAyaTCQcHhzTh3/Xr15k3b16Oz2kJ1Y4fP37LoZedO3fGxcWFb775Js32b775BpPJxEMPPWTd1rNnTw4dOsS2bdus25KSkvjuu+9o3LgxpUqVArD+9+bP3PHx8ezatSvTxSZE7lX3RI+1Dz74gDZt2qTZljp5FxERkbSKFrUsgmBPUhJs22buyfbzz29z8GBt4FfgD06ePMTkyYeYPHkShQr50qVLF3r37k7Hjh3x9PS09W2ISD6TlAQ//ggffgh795q3OTqa32/efBOqVbtz1960aRNbtmxh586d7NixI0tzorVr18663c/PL8cBjdwZzZs3Z/r06bz22mu0aNGCIUOG4OfnR2hoKDNnzmTbtm1Mnz6dZs2a5ej8NWrUoF+/fkydOhV7e3vatm3L/v37mTp1Kl5eXunmKsuprl278tFHH9G/f38GDhxIWFgYU6ZMwdnZOUfnu379Op06dWL37t1Mnz6dpKSkNCFX0aJFqVChAmAevvn2228zZswYfHx86NixI9u3b2f8+PEMGDCA6tWrW4979tlnmTlzJo888ggTJ06kWLFifPrpp/z777+sscwjAbRo0YKGDRsyfvx4YmNjadWqFZGRkXz88cecOHEiV4GhSEF0TwRrlSpVokmTJrYuQ0REpEBycIDmzc2P998vz9mzr7Fq1WssWRLBmjWriIv7FVhBdHQYCxfOY+HCedjbO9KoUSB9+3bj4Yd76q/TIvep5GT45x/YvNn8CAmBs2fN+9zd4YUXzKt85uVbRHx8PJs3b+bo0aM8//zz1u2jRo3izz//TNPW39+fBg0aFIg50SRjL7/8Mg0bNmTq1KkMGzbMunJlixYt2LhxI02bNs3V+efMmUPJkiX56quvmDZtGnXr1mXRokV07tyZwoUL58k9tG3blq+//poPP/yQbt26Ubp0aZ5//nmKFSvGc889l+3zXbx4ke3btwPw6quvptv/1FNPpemh9tZbb1GoUCFmzpzJlClTKFGiBCNHjuStt95Kc5yzszNr165l+PDhvPzyy8TGxlK3bl1WrlxJ69atre3s7Oz4/fffmTx5Mj/++CNTpkzBw8OD6tWrs2LFCoKCgrJ9TyIFmcmwzMRZAK1fv542bdrw448/0rt37xyfJyoqCi8vLyIjI/XXdxERkVQSEmDjRvj110R++mkToaHmVUbhiLVNy5af8cYbL9K2LTg6xuPo6Jhnf+UXkfwlOto8lHPzZti0yfz/0dFp2/j6wquvwksvwU3zyWebZU40BwcHWrZsCcDly5cpVqyYdb/l9/cJEyawY8cOa5BWr149ihQpkrsC5L60efNmmjdvzvz58+nfv7+tyxERG8lqVnRPBGvFihUjLCwMNzc3mjZtypgxY2jRokWWz6NgTUREJGtOnDDPm7Rw4b9s3vwrSUlLgQVAGZycoHz5Tzl79h0GDRrGhx++aetyRSQXDANOnrzRG23TJti3D1JS0rbz8IAmTcy9Xps1gxYtsr8CJKRfWGDnzp3W4ZwdO3Zk9erV1rYtWrSgRIkSTJ8+XT1mJVd+//13tmzZQv369XF1dWXv3r1MnDgRLy8v/v7773QrforI/eO+CNZ2797Nt99+S2BgIL6+vhw9epTJkydz+PBhli9fTqdOnTI8Lj4+nvj4eOvXUVFRlC1bVsGaiIhINly/DuvXm+dmW77cHLpBT2AJMImKFd+kSxdo0SKMCxd+pGfPB/UBWCQfS0iA3btvhGibN8P58+nbBQTcCNGaNYNateCmBRlv61YhWvrrBdChQwc+//zz7N+UyG1s27aNYcOGceDAAaKjoylSpAidOnViwoQJlCxZ0tbliYgN3RfBWkYiIiKoVasWPj4+7LXMmHqT8ePHExwcnG67gjUREZGcMQz4919YujSBH374k337qpCUZAnR5gFPAlC27AP06tWdxx/vRr169TDdySUBReSWrlyBLVtuhGjbt0NcXNo2Dg5Qr17aIO2/BQGzLDIyEicnJ1xdXQH45JNPePnllzNsm3phAc2JJiIitnTfBmtgXhJ71qxZxMbGWv8BT0091kRERO6s6GhYu9bcm+3//m8J4eGTgK3AjV87PDxK0abNgzz3XHc6dmyb4b/ZIpI3UlLM4Xfq3mj//pu+na/vjQCtWTNo2BCy89K8du0aHh4e1q979uzJkiVLWLx4Mb169QJg1apVBAUFKUQTEZF8LavB2j2xKujNLFlhZn8Fd3Z2zvHSxiIiInJ7hQrBQw+ZH7NnP8Tffz/EokWX+PHH5Rw58ivwG9eunePXXz/n118/x97elVq1OvD449147LEHKVGihI3vQKRgi40190CzhGhbtkB4ePp21ardCNGaN4fKleF2HUlTUlI4deoU//77L4cOHUrzCA8PJzo62vq7dtGiRQH4N1WKFxgYyJUrVxSiiYjIPeGe67F29epVatWqRdGiRdm9e3eWjtHiBSIiIndPeDgsXx7Ht9+uZ9OmpcTF/QqcSdOmefNRTJnyAQ0bZn/uJpH70dmzN0K0TZtgzx5ISkrbxtUVGjW6EaI1aWLuoZaZ+Ph49u/fny5AO3z4MHE3jxlNZc+ePdSpU+e/us7i7Oys1TlFRKTAuS+Ggvbv3x8/Pz8aNGhAkSJFOHLkCFOnTuXYsWOsXLmS9u3bZ+k8CtZERERsIyUFtm83mDNnL8uW/crZs0uBHZjnZXv8v2Fph4mL+x+DBvWkZ892Nq5YxPaSkuDvv9MO6wwNTd+uVClzgGaZH61uXXB0zPic+/bt448//qBKlSrW36H3799PzZo1M2zv5OREpUqVqFq1KlWqVKFq1arW/9fv0yIici+4L4K1iRMnsnDhQk6cOMG1a9fw8fGhRYsWjBo1ioYNG2b5PArWRERE8oeLF+H778/xxx+erFvnQWQkwCRgBNCJZs1W0aULdOkC/v5X8fHxtm3BIndBRARs3XojSNu2DWJi0raxs4M6dW70RmvWDPz8bgzrjIuL4+jRo2l6nn3wwQf4+fkB8M477zBu3DieeeYZvv76a8DcYy0gIICKFStagzNLeBYQEICDwz05q4yIiAhwnwRreUXBmoiISP6TlGSeF+rzzzezYsW3hIe3BB7/b+8ZwJ9ixZrQoUM3Xn65G40aVdcqo1LgGQYcO5a2N9r+/ebtqXl6QtOmN0K0xo3B3d3g8uXL1uAs9RDOEydOcPOv/cuWLaNr166AeUGBzz77jM6dOzNo0KC7dbsiIiL5loK1bFCwJiIikv+dPg0rV5pXGl216gfi4/ul2e/iUp4GDbrxzDPdePzxVjg5ZTLmTSQfiY+HnTtvhGibN8OlS+nbVahwI0Rr1CiRUqUiKF7cvDBAYmIirVu35tChQ1y9ejXTa3l5eaXpedarVy8qVap0p25NRESkQMtqVmR3F2sSERERybGyZWHgQFiyBCIj+zJ/fiht236Ku3tnwIm4uONs3DiD555rj4tLESpV6ssbb8znzJkMlkIUsZGLF83P4TffNAdlnp7m/w4fbt5+6RI4OZkDtCFDrjJhwhb27DnH0aPw7bdQpMj/0aiRG489diNYdnR0JDQ0lKtXr2IymShXrhxBQUG89tprzJo1i/Xr13PhwgWuXr3K1q1b+eabbxg5cqRCNcn31q1bx7PPPkvVqlVxd3endOnS9OjRg507d2bYfteuXbRv3x4PDw8KFy7Mww8/zPHjxzNs+/HHH1O1alWcnZ0pV64cwcHBJCYmpmsXEhJChw4dKFasGB4eHtSuXZv//e9/JCcn5+m9pmYymRg/frz16wMHDjB+/HhOnjyZrm1gYGCmcyHeTlRUFO+//z6BgYGUKFECDw8PatWqxYcffpjhAiWJiYkEBwcTEBCAs7MzVatW5eOPP07Xbv/+/QwePJimTZvi7u6OyWRi/fr1GdYQHR3NK6+8QunSpXF2dqZy5cpMmjQpS9/fkydPYjKZ+Oabb7J76/etgIAAnn766Vu2iY2NZfz48Rn+zMaPH4/JZOLKlSt3psBsyi/1aGIEERERKXCcnaF//7L07z8IGMTevdf4+OPfWbXqV86eXYZhXObo0YVMnbqQqVPt8fZuTqdOj/Pee89ToYKtq5f7RUoKHDiQdrXOY8dubpWMj88pKlc+RJEih7CzO0R4+L8cPnyIzZvNXde8vD6lTh3z8MzSpUuTlJRE6E2rFXz33Xf4+PhQqVIlXF1d78Ldidx5n332GWFhYbz66qtUr16dy5cvM3XqVJo0acLq1atp27atte2hQ//f3n1HRXG9fQD/7rJLb1KkSCh2EHnBSIwFKYIN7KiAGrFEY8WgUVGkKDZExSjGEhCjBrvGEiGiWGJBiLGipvyC2FDB0BQQdu/7B9kJ466KRFzB53POHN07d2bv3JmdnX245Sbc3Nzg6OiInTt3oqysDGFhYXBxccGlS5dgbGzM5V24cCHmzZuH2bNno3v37sjIyEBoaCju3buHDRs2cPlSU1PRo0cPdO3aFRs3boSWlhYOHDiAoKAg/Pnnn1i1alWdHPe5c+dgYWHBvc7KykJkZCTc3NxgbW391t4nJycHsbGxGDFiBIKDg6GtrY3Tp08jIiICR48exdGjR3lDLEycOBFbtmzBggUL4OzsjJSUFAQFBaG4uBhz5szh8mVmZmL//v1wcnJCt27dcPDgQYXvX1lZCS8vL/z2229YsGABWrZsieTkZMyePRt3797F119//crym5mZ4dy5c2hGX+xv1bNnzxAZGQmgKnBLaoARVlhYyACwwsJCZReFEEIIIf9RUVElW7bsLHNyCmFisT0D8M8yigGMtWrF2LRpErZy5WlWUlKh7OKSBqS4mLHUVMbmz2esZ0/G9PQYqxodrfqyhxkbhzIbG19mYWHPVFXVql2j8ouFhQVbs2YN9x5lZWXszp07TCqVKu9ACXlHHj58KJdWXFzMTExMWLdu3XjpgwcPZkZGRrzfdNnZ2UwsFrOZM2dyaXl5eUxdXZ2NGzeOt/3ChQuZQCBg169f59KGDRvG1NTUWElJCS9v9+7dma6u7n86tjexa9cuBoClpaXJrXN1dWVt2rSp1X5LSkrkjo0xxpYtW8YAsNOnT3Np165dYwKBgC1atIiX9/PPP2caGhosPz+fS5NIJDUqe1JSEgPA9uzZw0sfN24cEwqF7ObNm7U6rrr27NkzZReh1qysrNjIkSNfmefx48cMAAsPD5dbFx4ezgCwx48fv/F7S6XSt153/6U8NVHTWBF1BSWEEEJIg6Kjo4IZMzri4sVFKC+/ipSU/6Ffv1VwchoNkQi4dQuIjb2IL790gY5Oc/TrJ8WGDcDdu8ouOalPGANycoCkJGDKFKBdO0BPD/D0BMLCgOTkeygsnAyR6DN4eADz5lWNEejsHIPHj6Pw11+7cffuNTx/Xg41NTW0bdsWgwcPxrx587B161ZkZmaiqKgId+7cwaRJk7j3VVNTg4WFBU3UQT4IjRs3lkvT1taGnZ0d7ty5w6VVVlbi0KFDGDRoEG8cJCsrK7i7u2Pfvn1cWnJyMsrKyjBq1CjefkeNGgXGGPbv38+licViqKqqyrUC1dfXh7q6+ivLHhcXB6FQiEfVBk1cvnw5BAIB7zMtlUrRqFEjTJ8+nUur3hU0MTERgwcPBgC4u7tDIBAo7P6YkZEBFxcXaGpqomnTpliyZAmkUukry6ilpQUtLS259E8++QQAeHW8f/9+MMYU1ltpaSmSk5O5NKGwZmGGM2fOQCAQoFevXrx0Hx8fSKVS3nlTRFFXUFnXwOvXr8Pf3x96enowMTHB6NGjUVg11ThHKpVi9erVcHR0hIaGBvT19fHpp5/iwIEDXB5ra2v4+Phg7969cHJygrq6OteaKzc3F+PHj4eFhQVUVVW5LsWVlZW894mMjESHDh1gYGAAXV1dtGvXDvHx8XITyhw/fhxubm4wNDSEhoYGLC0tMWjQIDx79ozL8/z5c0RFRXHdmI2NjTFq1Cg8fvyYt6+KigrMnDkTpqam0NTURJcuXXDhwoVX1qesTmWtOyMjI7nr7cXuow8fPnxt/QoEAkyePBnr1q2Dra0t1NTUsHnzZgDA77//joCAADRu3BhqamqwtbVFXFyc3PmJiopCq1atuPPj4OCgsKVoTcpTl6grKCGEEEIaLIEA6N7dBt27TwUAFBYCqanA2rW3kZZmAMba48ABIWTP0I0aBaJdOyd8/nkfDBrUFCJ6UiL/qKgALl2q6tJ56lQpfv75Nzx6dAvAzWrLAFhazkOnToC9vQChoXFgTAU//rgRampqAIArV/rDyaktbxIBS0tLqKioKO/gSIP29OnTN95GTU0Non9ugJWVlSgvL4dQKOQFmN5kv4qCN7VVWFiIixcv8rqB/vnnnygtLYWDg4NcfgcHBxw9ehRlZWVQV1fHtWvXAABt27bl5TMzM4ORkRG3HgC++OILJCUlYerUqZgzZw40NTVx8OBB7Nu3D4sXL35lOT09PcEYw7Fjx+DvXzUmYmpqKjQ0NHD06FEuX2ZmJgoKCuDp6alwP97e3li0aBHmzJmDuLg4tGvXDgB43R9zc3MxbNgwTJ8+HeHh4di3bx9CQkJgbm6Ozz777JXlVOT48eMAgDZt2nBp165dg7GxMUxNTXl5ZXVevd5q6vnz5xAKhRCL+ZMN/Xu/vPLG+5QZNGgQhg4dijFjxuDq1asICQkBACQkJHB5AgMDsXXrVowZMwbz58+HqqoqLl68KDeW3cWLF3Hjxg2EhobCxsYGWlpayM3NxSeffAKhUIiwsDA0a9YM586dQ1RUFLKzs7Fp0yZu++zsbIwfPx6WlpYAgPPnz2PKlCm4d+8ewsLCuDze3t5wcXFBQkIC9PX1ce/ePSQnJ+P58+fQ1NSEVCpFv379cPr0acycOROdOnXC7du3ER4eDjc3N2RmZnKf0c8//xzfffcdZsyYAS8vL1y7dg0DBw5EcXHxK+vNzMwMycnJ6NmzJ8aMGYOxY8cCAK8rdU3rF6gKyJ4+fRphYWEwNTVF48aNkZWVhU6dOsHS0hLLly+HqakpUlJSMHXqVOTl5SE8PBwAEB0djYiICISGhqJr166oqKjAzZs3UVBQUKvzXafqpL1cPUNdQQkhhJAPT3l5BTt27DGLimKsY0fGgJu8LnhCYRtmazubhYaeYffvVyq7uOQdy8uTsi1bHrBhw9JYixbfMBWVIAb0ZIA1AwQKu2327DmA214qlbK5c+eyTZs2sdLSUiUeCfnQKbpWX7fs3LmT237nzp0MAHN1deXt18jIqMb7e5uGDRvGRCIRy8zM5NLOnDnDALCkpCS5/IsWLWIA2P379xljVV0X1dTUFO67ZcuWrHv37ry0M2fOMHNzc+5YVFRUWHR0dI3KamFhwUaPHs0YY6y8vJxpaWmxWbNmMQDs9u3bjLGqLqhisZjXJRMvdMN7XVdQACw9PZ2Xbmdnx3r06FGjclZ3+fJlpqGhwQYMGMBL9/LyYq1atVK4jaqqqlzX2pqUPTY2Vq7LKWOMzZs3jwGQOxcv+uuvvxgAtmnTJi5N1jXwxXM0ceJEpq6uznWjP3XqFAPA5s6d+8r3sLKyYioqKuzWrVu89PHjxzNtbW3uPMrExMQwALwuxdVJJBJWUVHB5s+fzwwNDbny7N69mwFgly5demlZXtZ1NiMjgwFga9euZYwxduPGDQaAffnll7x827ZtYwDeSlfQ19UvY1XXsZ6eHnvy5Akvb48ePZiFhYVc/GXy5MlMXV2dy+/j48McHR1fWdY3KU9tUFdQQgghhJBXUFUVwcPDCHPnVrVCunHDAMOGLYeJiRsAFUil13HjxhJERXWGubkZjIwCMXToXpw4UYLX9K4h9YhUKkVW1g2sWbMP8fFSjB0L2NkBRkaBGDHCDNu2ueP33ydAIlkFIBlANgAGHZ1G6NChI0aNGoWlS5fihx9+QFxcDLdfgUCAqKgoBAYGvrbLGCGkZubNm4dt27Zh5cqV+Pjjj+XWv6qLdPV1Nc33yy+/YMCAAfj4449x8OBBHD9+HCEhIQgNDcWCBQteW95u3bohNTUVAHD27Fk8e/YMwcHBMDIy4lqtpaamcrNn1papqSnXfVPGwcEBt2/ffqP9ZGdnw8fHBx999BG+/fZbufU1rbeaGjZsGAwMDDBu3Dikp6ejoKAASUlJ3KQFNe1Sqkjfvn15rx0cHFBWVsZ1zT1y5AgA8LrlvoyDgwNatmzJSzt06BDc3d1hbm6OyspKbpF1az158iSX9/jx4/D09ISenh5UVFQgFosRFhaG/Px8rjyOjo5QVVXFuHHjsHnzZoWz2R46dAj6+vro06cP7z0dHR1hamrKzeKZlpYGoKp+qxsyZAjXEvW/el39ynh4eKBRo0bc67KyMhw7dgwDBgyApqYm7zh69+6NsrIynD9/HkBVl+TLly9j4sSJSElJQVFR0X8uT12hDg6EEEIIIQBatzbG1q3BAIKRl/c34uKOYNeug7h58wgkksfIz9+MnTs3Y+dOVYjF7nBy6oPPPuuDgABLVHtmJO+p/Px83Lx5E7du3YJQqI5mzQJw5gzw889SHD7sCOA5gL8AWP+zhTUAIXR0bGBt3Rrt2rVCx46tYWdX1X3TyMiIxjkj9UJJSckbbyPrigcAAwYMQElJiVyQ48XucnUtMjISUVFRWLhwISZPnsxbZ2hoCKDqc/6iJ0+eQCAQQF9fn8tbVlaGZ8+eQVNTUy5v9YDdpEmTYGJign379nHdtd3d3SEUChEREYFhw4ahadOmLy2zp6cnNm/ejN9//x2pqalwcnJC48aN4eHhgdTUVAQEBODs2bOYO3durerkxeOvTk1NDaWlpTXex+3bt+Hu7g6RSIRjx47BwMBA7j0uXbokt93Tp0/x/Plzufw1YWRkhOTkZIwcORKffvop9z4rVqzAmDFj0KRJkzfeZ/XyVie7pmV18vjxY6ioqMh1bVXEzMxMLu3hw4c4ePCgXDdWmby8PADAhQsX0L17d7i5uWHjxo3ceGz79+/HwoULufI0a9YMqampiI6OxqRJk/D06VM0bdoUU6dORVBQEPeeBQUFUFVVfeV7yj4HLx6bSCRSeK3UxuvqV+bFusvPz0dlZSVWr16N1atXK9y37DhCQkKgpaWFrVu3Yt26dVBRUUHXrl2xdOlStG/fvlblqSsUWCOEEEIIeYGRUSOEhwcgPDwAFRUV2L//NDZuPIizZw/i6dM/UVGRggsXUnDhQgymTv0fOnUSwNsb6N0baNu2amw3ohhjDIwxSKVS7t8X/y97Xf3HMFD1o6KiogLGxsbcQ3NhYSEeP37MbVdZWYns7GzcvHmTC6RlZd3Ekyd51UrRDkDAP/8XAWgHgaASTk5F8PICOncG7O2nw8wshFqbkXrvv45vJhKJFLZyeZvjpr1OZGQkIiIiEBERgTlz5sitb9asGTQ0NHD16lW5dVevXkXz5s25z7JsbLWrV6+iQ4cOXL7c3Fzk5eXB3t6eS7t06RL8/f3lxkB0dnaGVCrFjRs3XhlY69atG4CqVmlHjx6Fl5cXlx4aGopTp06hvLz8peOrvSu3b9+Gm5sbGGM4ceIELCws5PK0bdsW27dvR25uLi9gI6vz6vX2JpydnZGVlYXs7Gw8ffoULVq0wC+//AIA6Nq1a632WRPGxsaQSCTIzc1VGDirTtEfUYyMjODg4ICFCxcq3Mbc3BwAsH37dojFYhw6dIj3fVJ9kgwZFxcXuLi4QCKRIDMzE6tXr8a0adNgYmICPz8/GBkZwdDQkDdRRHU6OjoA/g0y5ebm8oKTlZWVCoPPdenFumvUqBFUVFQwYsSIl7YWtLGxAVB17wkODkZwcDAKCgqQmpqKOXPmoEePHrhz545cYFyZKLBGCCGEEPIKYrEYgwd7YPBgDzC2Alev3kRc3AEcPnwQpaXt8eSJAD//DPz8cwVCQhyhpeWJVq1WQSgEhELg8eM4lJf/D4AUAgH7599//y97XTV8T9X/TUw80bTpcAiFQEXF37hw4QsIhUJ4eCRx+710aTEePjzLbceY9J/9/btfgHHpjEnRvLknevSYD6EQEAgkiI1tD4AhKOgktLX1IBQChw6FIyNjO7dt1fb//r9q4b9u08YFERH7uLINHWqB4uInSEy8DCurFhAKgY0bw5CQECU3C9qr2Nq2xcmTV7j9dunSFX/88RuOHj2NLl26QCgE4uMTMX36tBru0RJAKwDtYGpaFUDr1Ano3PkcnJwAfiMAXcW7IIS8UwsWLOAGMJcNav4ikUiEPn36YO/evYiOjuYCDDk5OUhLS8OXX37J5e3ZsyfU1dWRmJjIC6wlJiZCIBCgf//+XJq5uTkyMzMhkUh4wbVz584BgMIAVHVmZmaws7PDnj178Msvv2DRokUAAC8vL4wfPx4rVqyArq4unJ2dX7mfumx9k5OTAzc3N0gkEpw4cQJWVlYK8/Xr1w+hoaHYvHkzZs2axaUnJiZCQ0MDPXv2/E/lsLa2BlD1x5fly5fD3Nycmw21LvTq1QuLFy/GN998g/nz57/x9j4+Pvjxxx/RrFkzXlfHFwkEAohEIt71U1paii1btrx0GxUVFXTo0AGtW7fGtm3bcPHiRfj5+cHHxwfbt2+HRCLhXbsvcnNzAwBs27aN1wJz586dcjOWKlKX15umpibc3d3x66+/wsHB4aWt716kr68PX19f3Lt3D9OmTUN2djbs7OzeevlqiwJrhBBCCCE1JBAI4OBgi/XrbQHMAmMMOTnAkSPAd9+dwblzWXj6NB8XL1afCn47gJ/f6H1u39bBhQvD/3lVDmAnACHi45Oq5coEcOiN9nvnjiX+GXrlH5cAADNnVn/Qfgjgtzfa77lzhejRo3rKMwCl8PeXVEuTjf1dczduMDRuXD1FFYAYXl7V96OGqiCYEIDgn38tALTmFoGgFdq0aQkXFy0umGZtTS0LCXnfLV++HGFhYejZsye8vb25sZdkZN0HgapWbc7OzvDx8cHs2bNRVlaGsLAwGBkZYfr06Vw+AwMDhIaGYt68eTAwMED37t2RkZGBiIgIjB07lvdj/csvv8TUqVPRp08fjB8/Hpqamjh27BiWL18OT09P/N///d9rj6Fbt25YvXo1NDQ00LlzZwBVLXJsbGzw008/oW/fvq8d90rWGmzDhg3Q0dGBuro6bGxs/nO3vkePHsHd3R0PHjxAfHw8Hj16xBuTysLCggsetmnTBmPGjEF4eDhUVFTg7OyMn376CRs2bEBUVBSvK+izZ8/w448/AgB3zk6ePIm8vDxoaWlx45ABwNy5c9G2bVuYmZkhJycHCQkJSE9Px+HDh3mz0L5tLi4uGDFiBKKiovDw4UP4+PhATU0Nv/76KzQ1NTFlypRXbj9//nwcPXoUnTp1wtSpU9GqVSuUlZUhOzsbP/74I9atWwcLCwt4e3tjxYoVCAgIwLhx45Cfn4+YmBhed2sAWLduHY4fPw5vb29YWlqirKyMm9FS1qLRz88P27ZtQ+/evREUFIRPPvkEYrEYd+/eRVpaGvr164cBAwbA1tYWw4cPR2xsLMRiMTw9PXHt2jXExMRAV/f1fzTS0dGBlZUVfvjhB3Tr1g0GBgYwMjLigp//1apVq9ClSxe4uLhgwoQJsLa2RnFxMf744w9uLEMA6NOnD+zt7dG+fXsYGxvj9u3biI2NhZWVFVq0aPFWyvK2UGCNEEIIIaSWBAIBrKyAL74ARo/uhKNHf8L+/RcwcCAglVYthw7549GjTmBMAIFACH4ASP41YwJYWLRDy5ZV25eW6iI9/WsAQnToAEgkVel//DERf//d+59t/t1W9n+pVMBbJ5UKoKlpAQODqu0lEiHu3j0CQAgjo6rWHRIJUFQ0HaWlw8BY1bZSqYD7f9UigFTKXwdoQST695jLyi5CKgWEQnMwVpVWUTETEslk3rYSyb//l0qFkEqFkEiqv34x8iXfzQv44p/lXzo6wKef/tsirUMHoAa/JQgh75mDBw8CAJKTkxV2f6veArZ169Y4ceIEZs2aBV9fX4hEInh4eCAmJgbGxsa87ebOnQsdHR3ExcUhJiYGpqammD17ttxYZ1OmTEGTJk2wcuVKjB07FqWlpbC2tkZ4eDivFdyreHp6YvXq1ejSpQuvK6Cnpyc2btxYo26gNjY2iI2NxapVq7jWZZs2bUJgYGCNyvAyWVlZ3CD5w4cPl1sfHh6OiIgI7vXatWvRpEkTrF69Grm5ubC2tsaqVavkglCPHj2Sa20m24+VlRVvfL6///4bs2bNQm5uLnR1deHq6or09HSuy25dSkxMRLt27RAfH8+1vLOzs1PY3fhFZmZmyMzMxIIFC7Bs2TLcvXsXOjo6sLGxQc+ePblWbB4eHkhISMDSpUvRp08fNGnSBJ9//jkaN26MMWPGcPtzdHTETz/9hPDwcOTm5kJbWxv29vY4cOAAunfvDqCqJduBAwewatUqbNmyBYsXL4ZIJIKFhQVcXV15dRYfHw8TExMkJibi66+/hqOjI/bs2QM/P78a1U18fDy++uor9O3bF+Xl5Rg5ciQSExPfoHZfzs7ODhcvXsSCBQsQGhqKR48eQV9fHy1atEDv3r25fO7u7tizZw++/fZbFBUVwdTUFF5eXpg3b95Lx7ZTFgF7k/b4DVRRURH09PRQWFhYowguIYQQQgh5N2SBueqLLLio6DVjgIkJ8MKQSIQQQgghb6SmsSJqsUYIIYQQQt5bAkFVkIwCZYQQQgh5Hwlfn4UQQgghhBBCCCGEEPIiCqwRQgghhBBCCCGEEFILFFgjhBBCCCGEEEIIIaQWKLBGCCGEEEIIIYQQQkgtUGCNEEIIIYQQQgghhJBaoMAaIYQQQgghhBBCCCG1QIE1QgghhBBCCCGEEEJqgQJrhBBCCCGEEEIIIYTUAgXWCCGEEEIIIYQQQgipBQqsEUIIIYQQQgghhBBSC/U+sFZSUoJp06bB3Nwc6urqcHR0xPbt25VdLEIIIYQQQgghhBDSwImUXYD/auDAgcjIyMCSJUvQsmVLfP/99/D394dUKkVAQICyi0cIIYQQQgghhBBCGigBY4wpuxC19eOPP8Lb25sLpsl0794d169fR05ODlRUVF67n6KiIujp6aGwsBC6urp1WWRCCCGEEEIIIYQQ8p6raayoXncF3bdvH7S1tTF48GBe+qhRo3D//n2kp6crqWSEEEIIIYQQQgghpKGr14G1a9euwdbWFiIRv0erg4MDt54QQgghhBBCCCGEkLpQr8dYy8/PR9OmTeXSDQwMuPWKlJeXo7y8nHtdWFgIoKqZHyGEEEIIIYQQQgj5sMliRK8bQa1eB9YAQCAQvPG6xYsXIzIyUi79o48+emvlIoQQQgghhBBCCCH1W3FxMfT09F66vl4H1gwNDRW2Snvy5AmAf1uuvSgkJATBwcHca6lUiidPnsDQ0PCVgbr6pKioCB999BHu3LlDEzIoAdW/clH9KxfVv/LROVAuqn/lovpXLqp/5aL6Vy6qf+Wi+leuhlj/jDEUFxfD3Nz8lfnqdWCtbdu2SEpKQmVlJW+ctatXrwIA7O3tFW6npqYGNTU1Xpq+vn6dlVOZdHV1G8xFXR9R/SsX1b9yUf0rH50D5aL6Vy6qf+Wi+lcuqn/lovpXLqp/5Wpo9f+qlmoy9XryggEDBqCkpAR79uzhpW/evBnm5ubo0KGDkkpGCCGEEEIIIYQQQhq6et1irVevXvDy8sKECRNQVFSE5s2bIykpCcnJydi6dStUVFSUXURCCCGEEEIIIYQQ0kDV68AaAOzduxdz585FWFgYnjx5gtatWyMpKQl+fn7KLppSqampITw8XK7LK3k3qP6Vi+pfuaj+lY/OgXJR/SsX1b9yUf0rF9W/clH9KxfVv3J9yPUvYK+bN5QQQgghhBBCCCGEECKnXo+xRgghhBBCCCGEEEKIslBgjRBCCCGEEEIIIYSQWqDAGiGEEEIIIYQQQgghtUCBtXomMTERAoEAmZmZyi7KB0VW74qWGTNm1Hg/gYGB0NbWrsOSNjzV6/7EiRNy6xljaN68OQQCAdzc3N55+T40X3/9NQQCAezt7ZVdlAaPrv33C33/vj/+y7kQCASIiIh4+4Vq4Ojerxzp6ekYMGAALC0toaamBhMTE3Ts2BHTp09XdtE+OOfPn8fgwYNhZmYGVVVVmJqawtfXF+fOnXvjfWVlZSEiIgLZ2dlvv6ANhOw+r66ujtu3b8utd3Nzo/tRHXvx96+6ujpMTU3h7u6OxYsX49GjR8ou4nuFAmuEvIFNmzbh3LlzvGXq1KnKLtYHQUdHB/Hx8XLpJ0+exJ9//gkdHR0llOrDk5CQAAC4fv060tPTlVyaDwNd+4QQZaN7/7t3+PBhdOrUCUVFRYiOjsZPP/2EVatWoXPnztixY4eyi/dBWb16NTp37oy7d+8iOjoaqampiImJwb1799ClSxesWbPmjfaXlZWFyMhICqzVQHl5OUJDQ5VdjA+a7Pfv0aNHERcXB0dHRyxduhS2trZITU1VdvHeGxRYI+QN2Nvb49NPP+UtlpaWyi7WB2Ho0KHYs2cPioqKeOnx8fHo2LHjWz0PpaWlb21fDUlmZiYuX74Mb29vAFAY7Pkvnj179lb311C8y2ufEEJeVNf3fqJYdHQ0bGxskJKSAj8/P7i6usLPzw8xMTHIyclRdvE+GGfOnMG0adPQu3dvnD59GiNGjEDXrl0xfPhwnD59Gr1790ZQUBDOnDmj7KI2SD179sT333+Py5cvK7soHyzZ718XFxcMGjQIK1euxJUrV6ClpYWBAwfi4cOHyi7ie4ECa/VcZmYm/Pz8YG1tDQ0NDVhbW8Pf31+uyaysKWdaWhomTJgAIyMjGBoaYuDAgbh//76SSt+w7NixAx07doSWlha0tbXRo0cP/PrrrwrzXr9+Hd26dYOWlhaMjY0xefJkCiq8hr+/PwAgKSmJSyssLMSePXswevRoufyRkZHo0KEDDAwMoKuri3bt2iE+Ph6MMV4+a2tr+Pj4YO/evXBycoK6ujoiIyPr9mDqKdmPqSVLlqBTp07Yvn0777rNzs6GQCBAdHQ0Fi5cCEtLS6irq6N9+/Y4duwYb18REREQCAS4ePEifH190ahRIzRr1uydHk99URfX/pgxY2BgYKDwvuPh4YE2bdrUwZE0LG5ubgq74AYGBsLa2pp7LftcxMTEYMWKFbCxsYG2tjY6duyI8+fPv7sCN2A1PRekdl537z9x4oTCLuuyaz8xMZGXvnHjRrRs2RJqamqws7PD999/T+dKgfz8fBgZGUEkEsmtEwr5P+Fq8gwqG46EnkHfzOLFiyEQCPDNN9/InQuRSIS1a9dCIBBgyZIlXPrNmzfh7+8PExMTqKmpwdLSEp999hnKy8uRmJiIwYMHAwDc3d25bnYvfk5IlZkzZ8LQ0BCzZs16Zb6ysjKEhITAxsYGqqqqaNKkCSZNmoSCggIuT//+/WFlZQWpVCq3fYcOHdCuXbu3XfwGy9LSEsuXL0dxcTHWr1/PpWdmZqJv374wMDCAuro6nJycsHPnTrnt7927h3HjxuGjjz6CqqoqzM3N4evrW6+DdBRYq+eys7PRqlUrxMbGIiUlBUuXLsWDBw/g7OyMvLw8ufxjx46FWCzG999/j+joaJw4cQLDhw9XQsnrJ4lEgsrKSt4CAIsWLYK/vz/s7Oywc+dObNmyBcXFxXBxcUFWVhZvHxUVFejduze6deuG/fv3Y/LkyVi/fj2GDh2qjEOqN3R1deHr68t1RwGqAg1CoVBh3WVnZ2P8+PHYuXMn9u7di4EDB2LKlClYsGCBXN6LFy/iq6++wtSpU5GcnIxBgwbV6bHUR6WlpUhKSoKzszPs7e0xevRoFBcXY9euXXJ516xZg+TkZMTGxmLr1q0QCoXo1auXwnFIBg4ciObNm2PXrl1Yt27duziUeqcurv2goCD8/fff+P7773nbZmVlIS0tDZMmTaq7A/pAxcXF4ejRo4iNjcW2bdvw9OlT9O7dG4WFhcouGiEv9Sb3/prYsGEDxo0bBwcHB+zduxehoaGIjIxUOI7kh65jx45IT0/H1KlTkZ6ejoqKCoX56Bm07kgkEqSlpaF9+/awsLBQmOejjz7Cxx9/jOPHj0MikeDy5ctwdnbG+fPnMX/+fBw5cgSLFy9GeXk5nj9/Dm9vbyxatAhA1feCbGgZWYtQwqejo4PQ0FCkpKTg+PHjCvMwxtC/f3/ExMRgxIgROHz4MIKDg7F582Z4eHigvLwcADB69Gjk5OTI7efmzZu4cOECRo0aVefH05D07t0bKioqOHXqFAAgLS0NnTt3RkFBAdatW4cffvgBjo6OGDp0KC9wfO/ePTg7O2Pfvn0IDg7GkSNHEBsbCz09Pfz9999KOpq3gJF6ZdOmTQwAy8jIULi+srKSlZSUMC0tLbZq1Sq57SZOnMjLHx0dzQCwBw8e1Gm56ztZ/SlacnJymEgkYlOmTOFtU1xczExNTdmQIUO4tJEjRzIAvHPDGGMLFy5kANjPP//8To6nPql+zaelpTEA7Nq1a4wxxpydnVlgYCBjjLE2bdowV1dXhfuQSCSsoqKCzZ8/nxkaGjKpVMqts7KyYioqKuzWrVt1fiz12XfffccAsHXr1jHGqq5vbW1t5uLiwuX566+/GABmbm7OSktLufSioiJmYGDAPD09ubTw8HAGgIWFhb27g6hn6vrad3V1ZY6Ojrz8EyZMYLq6uqy4uLhuDqoee/H719XVVWG9jxw5kllZWXGvZZ+Ltm3bssrKSi79woULDABLSkqq66I3OLU9F4wxBoCFh4fXfSEbiJrc+2X3p7S0NN62smt/06ZNjLGq+5GpqSnr0KEDL9/t27eZWCyWO1cfury8PNalSxfueVMsFrNOnTqxxYsXc/doegatW7m5uQwA8/Pze2W+oUOHMgDs4cOHzMPDg+nr67NHjx69NP+uXbsUfmbIv6rf58vLy1nTpk1Z+/btuecYV1dX1qZNG8YYY8nJyQwAi46O5u1jx44dDADbsGEDY4yxiooKZmJiwgICAnj5Zs6cyVRVVVleXt47OLL643VxB8YYMzExYba2towxxlq3bs2cnJxYRUUFL4+Pjw8zMzNjEomEMcbY6NGjmVgsZllZWXVXeCWgFmv1XElJCWbNmoXmzZtDJBJBJBJBW1sbT58+xY0bN+Ty9+3bl/fawcEBABTOtkLkfffdd8jIyOAtKSkpqKysxGeffcZryaaurg5XV1eFf4UdNmwY73VAQACAqkg/eTlXV1c0a9YMCQkJuHr1KjIyMhR2hQOA48ePw9PTE3p6elBRUYFYLEZYWBjy8/PlZrFxcHBAy5Yt38Uh1Fvx8fHQ0NCAn58fAEBbWxuDBw/G6dOn8fvvv/PyDhw4EOrq6txrHR0d9OnTB6dOnYJEIuHlpdaBNVMX135QUBAuXbrEjQtTVFSELVu2YOTIkTR7cR3w9vaGiooK95q+f0l98Cb3/te5desWcnNzMWTIEF66paUlOnfu/NbK3FAYGhri9OnTyMjIwJIlS9CvXz/89ttvCAkJQdu2bZGXl0fPoO8J9s9QC6WlpTh58iSGDBkCY2NjJZeq4VBVVUVUVBQyMzMVdiuUtUALDAzkpQ8ePBhaWlrccCQikQjDhw/H3r17udbiEokEW7ZsQb9+/WBoaFi3B9IAya79P/74Azdv3uTuL9XvR71798aDBw9w69YtAMCRI0fg7u4OW1tbpZW7LlBgrZ4LCAjAmjVrMHbsWKSkpODChQvIyMiAsbGxwgHYX7xhqKmpAaDB2mvK1tYW7du35y2yvuDOzs4Qi8W8ZceOHXJdckUikdx5MDU1BVA1ngZ5OYFAgFGjRmHr1q1Yt24dWrZsCRcXF7l8Fy5cQPfu3QFUjeVy5swZZGRkYO7cuQDkr3czM7O6L3w99scff+DUqVPw9vYGYwwFBQUoKCiAr68vAPC6KAL/Xs8vpj1//hwlJSW8dKr7mqmLa79fv36wtrZGXFwcgKqxOJ8+fUrdQOsIff+S+uZN7/2vI3vGMTExkVunKI1Uad++PWbNmoVdu3bh/v37+PLLL5GdnY3o6Gh6Bq1jRkZG0NTUxF9//fXKfNnZ2dDU1IRIJIJEInlpt1FSe35+fmjXrh3mzp0r1y06Pz8fIpFILpgpEAhgamrKu7ZHjx6NsrIybN++HQCQkpKCBw8eUDfQWnj69Cny8/Nhbm7O3YtmzJghdy+aOHEiAHD3o8ePHzfIz4j8aJik3igsLMShQ4cQHh6O2bNnc+nl5eV48uSJEkv2YTEyMgIA7N69G1ZWVq/NX1lZifz8fN6DTW5uLgD5H15EXmBgIMLCwrBu3TosXLhQYZ7t27dDLBbj0KFDvJZT+/fvV5hfIBDURVEbjISEBDDGsHv3buzevVtu/ebNmxEVFcW9ll3P1eXm5kJVVVWuJRTVfc297WtfKBRi0qRJmDNnDpYvX461a9eiW7duaNWqVV0dQoOirq6ucHw0ReObkrpF56Ju1PTeL7vXyMYxknmx/mXPOIoGp1b0vUHkicVihIeHY+XKlbh27Rr69esHgJ5B64qKigrc3d2RnJyMu3fvKgwG3L17F7/88gt69eoFAwMDqKio4O7du0oobcMmEAiwdOlSeHl5YcOGDbx1hoaGqKysxOPHj3nBNcYYcnNz4ezszKXZ2dnhk08+waZNmzB+/Hhs2rQJ5ubm3B8lSc0dPnwYEokEbm5u3O/hkJAQDBw4UGF+2fOlsbFxg/yMUIu1ekwgEIAxxv3VW+bbb7+V625F6k6PHj0gEonw559/yrVmky0v2rZtG++1bABxRbOaEb4mTZrgq6++Qp8+fTBy5EiFeQQCAUQiEa/bVWlpKbZs2fKuitlgSCQSbN68Gc2aNUNaWprcMn36dDx48ABHjhzhttm7dy/Kysq418XFxTh48CBcXFx454S8mbq49seOHQtVVVUMGzYMt27dwuTJk+uk7A2RtbU1fvvtN14wIT8/H2fPnlViqT5MdC7evje598tm87xy5QpvHwcOHOC9btWqFUxNTeW6cuXk5NC5UuDBgwcK02VDvZibm9Mz6DsQEhICxhgmTpwo9/tKIpFgwoQJYIwhJCQEGhoacHV1xa5du14Z2KcWy7Xj6ekJLy8vzJ8/n9cDolu3bgCArVu38vLv2bMHT58+5dbLjBo1Cunp6fj5559x8OBBjBw5kp5P31BOTg5mzJgBPT09jB8/Hq1atUKLFi1w+fLll96LdHR0AAC9evVCWloa1zW0oaAWa/WUQCCArq4uunbtimXLlsHIyAjW1tY4efIk4uPjoa+vr+wifjCsra0xf/58zJ07F//73//Qs2dPNGrUCA8fPsSFCxegpaWFyMhILr+qqiqWL1+OkpISODs74+zZs4iKikKvXr3QpUsXJR5J/VF9SnNFvL29sWLFCgQEBGDcuHHIz89HTEyMXBCavN6RI0dw//59LF26VOFDt729PdasWYP4+HisXLkSQNVfeL28vBAcHAypVIqlS5eiqKiI9zkgtfO2r319fX189tln+Oabb2BlZYU+ffrURbEbFFkryxEjRmD9+vUYPnw4Pv/8c+Tn5yM6Ohq6urpKLuGHg85F3XmTe7+Pjw88PT2xePFiNGrUCFZWVjh27Bj27t3L20YoFCIyMhLjx4+Hr68vRo8ejYKCAkRGRsLMzAxCIf29v7oePXrAwsICffr0QevWrSGVSnHp0iUsX74c2traCAoKomfQd6Bz586IjY3FtGnT0KVLF0yePBmWlpbIyclBXFwc0tPTERsbi06dOgEAVqxYgS5duqBDhw6YPXs2mjdvjocPH+LAgQNYv349dHR0YG9vD6BqllwdHR2oq6vDxsaGWg3WwNKlS/Hxxx/j0aNHaNOmDQDAy8sLPXr0wKxZs1BUVITOnTvjypUrCA8Ph5OTE0aMGMHbh7+/P4KDg+Hv74/y8nK5sdkI37Vr17jx0h49eoTTp09j06ZNUFFRwb59+7hWguvXr0evXr3Qo0cPBAYGokmTJnjy5Alu3LiBixcvcrNJy2bL7dq1K+bMmYO2bduioKAAycnJCA4ORuvWrZV5uLWnrFkTSO3ExcUxAOzq1auMMcbu3r3LBg0axBo1asR0dHRYz5492bVr15iVlRUbOXIkt93LZvV42UxOhK8ms6Ls37+fubu7M11dXaampsasrKyYr68vS01N5fKMHDmSaWlpsStXrjA3NzemoaHBDAwM2IQJE1hJScm7OJR6pyZ1z5j8zIgJCQmsVatWTE1NjTVt2pQtXryYxcfHMwDsr7/+4vJZWVkxb2/vOip9/de/f3+mqqr6ytmt/Pz8mEgkYufPn2cA2NKlS1lkZCSzsLBgqqqqzMnJiaWkpPC2kc0K+vjx47o+hHqrrq99mRMnTjAAbMmSJW/5CBqWF79/GWNs8+bNzNbWlqmrqzM7Ozu2Y8eOl84KumzZMrl9gmaorJXangvGqM5r6k3u/bm5uezBgwfM19eXGRgYMD09PTZ8+HCWmZnJmxVUZsOGDax58+ZMVVWVtWzZkiUkJLB+/foxJyenOj6q+mXHjh0sICCAtWjRgmlrazOxWMwsLS3ZiBEj5GbTo2fQunfu3Dnm6+vLTExMmEgkYo0bN2YDBw5kZ8+elcublZXFBg8ezAwNDZmqqiqztLRkgYGBrKysjMsTGxvLbGxsmIqKisLPyYfuVc9AAQEBDAA3KyhjjJWWlrJZs2YxKysrJhaLmZmZGZswYQL7+++/Fe5fto/OnTvX1SHUe7JzIFtUVVVZ48aNmaurK1u0aJHC74fLly+zIUOGsMaNGzOxWMxMTU2Zh4cHN7O0zJ07d9jo0aOZqakpE4vFzNzcnA0ZMoQ9fPjwXR3eWydg7J+pHEi9EBQUhDVr1qCgoIBrTkkIIe+D7Oxs2NjYYNmyZZgxY4ayi0NqaPr06fjmm29w584d+mv5K9D37/uDzkXDUlBQgJYtW6J///5yYyeRtycwMBC7d++Wm0SIEELIf0ddQeuJX375BRkZGUhISEDfvn3pQZIQQsh/cv78efz2229Yu3Ytxo8fT0G1l6Dv3/cHnYv6Lzc3FwsXLoS7uzsMDQ1x+/ZtrFy5EsXFxQgKClJ28QghhJBaocBaPeHr64vCwkL07dsXX3/9tbKLQwghpJ7r2LEjNDU14ePjw5vVlfDR9+/7g85F/aempobs7GxMnDgRT548gaamJj799FOsW7eOGy+JEEIIqW+oKyghhBBCCCGEEEIIIbVA0+8QQgghhBBCCCGEEFILFFgjhBBCCCGEEEIIIaQWKLBGCCGEEEIIIYQQQkgtUGCNEEIIIYQQQgghhJBaoMAaIYQQQgghhBBCCCG1QIE1QgghhBBCCCGEEEJqgQJrhBBCCCGEEEIIIYTUAgXWCCGEEEIIIYQQQgipBQqsEUIIIYQQQgghhBBSC/8PXBe6c8EQwQ0AAAAASUVORK5CYII=\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$^{-2}$')"
   ]
  },
  {
   "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 N m$^{-2}$')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_silicon_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 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 N m$^{-2}$')"
   ]
  },
  {
   "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_3484198/3403781678.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_3484198/2320522072.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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