{
 "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_2066066/391916811.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_W19/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_W19/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_W08/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_W08/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_2066066/2338418733.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 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY SST with CY Winds',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([ 6.94768824,  5.03149186,  6.92260036, 10.4741395 , 13.69086769,\n",
       "       17.51762955, 18.32682018, 18.80116005, 16.19822578, 10.88716039,\n",
       "        8.96325978,  6.76106302])"
      ]
     },
     "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 winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY SST with WY Winds',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([ 5.59662968,  5.90259706,  7.60633249,  9.04497093, 13.09915475,\n",
       "       16.51518926, 20.73906041, 19.60519318, 16.00876067, 11.95839157,\n",
       "        9.37915939,  6.13304258])"
      ]
     },
     "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_2066066/2771304440.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_W19/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_W19/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_W08/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_W08/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_2066066/178454329.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 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY PAR with CY Winds',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([ 15.33108848,  25.43487056,  53.40414242,  62.65396427,\n",
       "        90.50300235, 103.02960905,  93.25852068,  79.68039158,\n",
       "        51.27869687,  27.85621855,  20.32929689,   8.03467567])"
      ]
     },
     "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 winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY PAR with WY Winds',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([ 14.22929443,  25.46042814,  40.97263571,  59.96603881,\n",
       "        84.80457427,  94.07934195, 101.42659579,  80.66420632,\n",
       "        60.70089652,  29.308822  ,  13.13657237,  10.14639563])"
      ]
     },
     "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_2066066/1288103633.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_W19/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_W19/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_W08/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_W08/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_2066066/3661973807.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 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY Halocline with CY Winds',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([0.27275877, 0.43238446, 0.42162363, 1.29093306, 1.82034273,\n",
       "       2.08958861, 1.36382405, 1.45949038, 1.19413448, 1.26065329,\n",
       "       0.80995226, 0.6541769 ])"
      ]
     },
     "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 winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY Halocline with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,5)\n",
    "ax.set_ylabel('g/kg m$^{-1}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Depth-averaged Nutrients (0-10m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for original cold and warm years\n",
    "\n",
    "\n",
    "monthly_array_nitrate_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "          \n",
    "\n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_nitrate_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['nitrate']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2066066/3312634990.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_nitrate_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slice[monthly_array_nitrate_orig_slice == 0 ] = np.nan\n",
    "monthly_array_nitrate_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_nitrate_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_nitrate_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Silicon data for original cold and warm years\n",
    "\n",
    "monthly_array_silicon_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['silicon']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "   \n",
    " ### \n",
    "## Experimental Year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "        \n",
    "        \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_silicon_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['silicon']:\n",
    "                data[var].append((ds[var].isel(deptht=slice(None, 10),**slc)*e3t*tmask).sum(axis=1)/((e3t*tmask).sum(axis=1)).data)\n",
    "    \n",
    "    # Concatenate months\n",
    "    for var in variables: aggregates[var][year] = np.concatenate(data[var]).mean(axis=0)        \n",
    "\n",
    "\n",
    "       \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2066066/241793216.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slice[monthly_array_silicon_orig_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Nitrate data for Experiments 1 and 2\n",
    "\n",
    "monthly_array_nitrate_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['nitrate']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_W19/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_W19/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_W08/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_W08/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": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2066066/231329215.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slice[monthly_array_nitrate_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_nitrate_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_nitrate_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_nitrate_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Silicon data for Experiments 1 and 2\n",
    "\n",
    "\n",
    "monthly_array_silicon_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)} \n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 10),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['silicon']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "\n",
    "        \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_W19/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_W19/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_W08/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_W08/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’]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2066066/3737416097.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slice[monthly_array_silicon_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_silicon_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_silicon_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_silicon_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_nitrate_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY Nitrate with CY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.64834195, 22.38691698, 16.99383033,  7.63730902,  4.96273598,\n",
       "        1.56547187,  1.23107567,  1.5230891 ,  7.95614177, 16.79074658,\n",
       "       19.52305174, 21.62759183])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.80685063, 22.63941779, 18.68073904,  6.54336744,  5.49402545,\n",
       "        1.67369885,  2.81440205,  3.81694601, 11.47654414, 19.18559774,\n",
       "       21.74671523, 22.24619177])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\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 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY Silicon with CY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48.06238141, 49.17238747, 41.59249494, 18.0018168 ,  5.46358173,\n",
       "       12.1107789 , 19.87997362, 28.03714477, 36.00806573, 41.96864256,\n",
       "       44.54149611, 47.0336623 ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_orig_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([47.79922893, 49.19336588, 44.03685091, 16.95528149,  8.04410933,\n",
       "       11.85676838, 23.22130531, 29.99828923, 38.12645109, 42.5020271 ,\n",
       "       45.51880128, 47.52354357])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[12,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_nitrate_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_nitrate_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY Nitrate with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,30)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.13891096, 23.43445999, 20.16081944,  9.53873272,  5.7397893 ,\n",
       "        2.06754755,  0.95348431,  2.25127683,  8.106326  , 16.05059715,\n",
       "       20.61628598, 23.32419151])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_nitrate_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "ax.plot(xticks, monthly_array_silicon_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_silicon_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY Silicon with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=4)\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.06105751, 51.39125156, 49.65665279, 23.79865366,  8.11240424,\n",
       "       10.9729251 , 13.07850414, 11.59949357, 21.74708727, 35.04296807,\n",
       "       42.83359454, 47.61766334])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_array_silicon_depthint_slicemean[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Depth-integrated 0-100 m Diatoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### Diatom data for original years\n",
    "\n",
    "monthly_array_diatoms_orig_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "### 2008 using higher temperature threshold       \n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "                \n",
    "### 2019 using higher temperature threshold                \n",
    "# Add experiment year\n",
    "for year in [2019]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 13):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/v201905r/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_orig_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2066066/3403781678.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_orig_slice[monthly_array_diatoms_orig_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_orig_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_orig_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_orig_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#years, months, data\n",
    "monthly_array_diatoms_depthint_slice = np.zeros([14,12,50,50])\n",
    "# Load monthly averages\n",
    "mask = xr.open_dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')\n",
    "slc = {'y': slice(450,500), 'x': slice(250,300)}\n",
    "e3t, tmask = [mask[var].isel(z=slice(None, 27),**slc).values for var in ('e3t_0', 'tmask')]\n",
    "years, variables = range(2007, 2021), ['diatoms']\n",
    "# Temporary list dict\n",
    "data = {}\n",
    "# Permanent aggregate dict\n",
    "aggregates = {var: {} for var in variables}\n",
    "monthlydat = {var: {} for var in variables}\n",
    "\n",
    "# Add experiment year\n",
    "for year in [2008]:\n",
    "    # Initialize lists\n",
    "    for var in variables: data[var] = []\n",
    "    # Load monthly averages\n",
    "    for month in range(1, 7):\n",
    "        datestr = f'{year}{month:02d}'\n",
    "        prefix = f'/data/sallen/results/MEOPAR/Karyn/01jan08_W19/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_W19/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_W08/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_W08/SalishSea_1m_{datestr}_{datestr}'\n",
    "    \n",
    "        # Load grazing variables\n",
    "        with xr.open_dataset(prefix + '_ptrc_T.nc') as ds:\n",
    "            q = np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data\n",
    "            q2 = q[0,:,:]\n",
    "            monthly_array_diatoms_depthint_slice[year-2007,month-1,:,:] = q2 #year2015 is index 0 along 1st dimension\n",
    "            for var in ['diatoms']:\n",
    "                data[var].append(np.ma.masked_where(tmask == 0, ds[var].isel(deptht=slice(None, 27), **slc).values * e3t).sum(axis=1).data)\n",
    "\n",
    "\n",
    "        \n",
    "# # Calculate 5 year mean and anomalies\n",
    "# for var in variables:\n",
    "#     aggregates[var][‘mean’] = np.concatenate([aggregates[var][year][None, ...] for year in years]).mean(axis=0)\n",
    "#     for year in years: aggregates[var][year] = aggregates[var][year] - aggregates[var][‘mean’]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2066066/2320522072.py:3: RuntimeWarning: Mean of empty slice\n",
      "  np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n"
     ]
    }
   ],
   "source": [
    "monthly_array_diatoms_depthint_slice[monthly_array_diatoms_depthint_slice == 0 ] = np.nan\n",
    "monthly_array_diatoms_depthint_slicemean = \\\n",
    "np.nanmean(np.nanmean(monthly_array_diatoms_depthint_slice, axis = 2),axis = 2)\n",
    "print(np.shape(monthly_array_diatoms_depthint_slicemean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[12,:],color='r',linestyle='-',label='Original 2019')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[12,:],color='k',linestyle='-.',label='2019 with 2008 winds')\n",
    "\n",
    "\n",
    "ax.set_title('WY Diatoms (0-100 m) with CY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mmol N m$^{-2}$')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "bbox = {'boxstyle': 'round', 'facecolor': 'w', 'alpha': 0.9}\n",
    "cmap = plt.get_cmap('tab10')\n",
    "palette = [cmap(0), cmap(0.2), 'k', cmap(0.1), cmap(0.3)]\n",
    "xticks=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov',\"Dec\"]\n",
    "\n",
    "\n",
    "ax.plot(xticks, monthly_array_diatoms_orig_slicemean[1,:],color='b',linestyle='-',label='Original 2008')\n",
    "ax.plot(xticks, monthly_array_diatoms_depthint_slicemean[1,:],color='k',linestyle='-.',label='2008 with 2019 winds')\n",
    "\n",
    "\n",
    "ax.set_title('CY Diatoms (0-100 m) with WY Winds',fontsize=18)\n",
    "ax.legend(frameon=False,loc=1)\n",
    "ax.set_ylim(0,50)\n",
    "ax.set_ylabel('mmol N m$^{-2}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py39)",
   "language": "python",
   "name": "py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}