{
 "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_3469130/391916811.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_NSi/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_NSi/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_NSi/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_NSi/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_3469130/2338418733.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 nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('WY SST with CY Nutrients',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([ 6.66011314,  5.16426706,  7.25914742, 10.17815932, 14.09488876,\n",
       "       17.59587821, 20.67570395, 20.07901062, 16.81303244, 11.56899786,\n",
       "        8.62051358,  6.7005392 ])"
      ]
     },
     "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 nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('CY SST with WY Nutrients',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([ 6.13830953,  5.82549722,  7.48766262,  9.10256158, 12.68796849,\n",
       "       16.20069323, 18.13362829, 18.18589498, 15.38127229, 11.96611296,\n",
       "        9.38018667,  6.34800222])"
      ]
     },
     "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_3469130/2771304440.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_NSi/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_NSi/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_NSi/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_NSi/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_3469130/178454329.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 nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('WY PAR with CY Nutrients',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([ 15.32157315,  25.46430715,  53.17412975,  62.44887383,\n",
       "        90.84260541, 103.05034021,  94.88347023,  80.51483052,\n",
       "        51.31051136,  31.57238514,  20.31519746,   8.03565263])"
      ]
     },
     "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 Nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('CY PAR with WY Nutrients',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([ 14.23679829,  25.43165744,  41.19107135,  59.75958975,\n",
       "        85.18338548,  94.02408369, 100.44978733,  80.01338611,\n",
       "        60.86782944,  29.30627043,  13.14189992,  10.14616278])"
      ]
     },
     "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_3469130/1288103633.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_NSi/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_NSi/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_NSi/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_NSi/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_3469130/3661973807.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 nutrients')\n",
    "\n",
    "\n",
    "ax.set_title('WY Halocline with CY Nutrients',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([0.57723351, 0.32422883, 0.78620486, 1.17592297, 1.92554819,\n",
       "       2.39019519, 2.88495737, 2.02522262, 1.49650757, 0.70498795,\n",
       "       1.51586427, 0.98230242])"
      ]
     },
     "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 Halocline')\n",
    "\n",
    "\n",
    "ax.set_title('CY Halocline with WY Nutrients',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_3469130/3312634990.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_3469130/241793216.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_NSi/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_NSi/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_NSi/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_NSi/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_3469130/231329215.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_NSi/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_NSi/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_NSi/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_NSi/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_3469130/3737416097.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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('WY Nitrate with CY Nutrients',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([24.47601856, 23.37162061, 17.92793601,  8.21108767,  5.33221614,\n",
       "        1.58238226,  1.21867822,  1.79531768,  8.10286171, 17.52341884,\n",
       "       20.56683092, 23.22281989])"
      ]
     },
     "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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('WY Silicon with CY Nutrients',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([50.36258288, 51.49776725, 43.67255925, 19.35702357,  5.51602533,\n",
       "       10.55762737, 16.99747485, 25.10753505, 34.81283984, 42.2860491 ,\n",
       "       45.66187067, 48.57553256])"
      ]
     },
     "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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\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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('CY Nitrate with WY nutrients',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([23.9644255 , 22.29828934, 20.45139818,  8.97187906,  5.34151849,\n",
       "        1.96037127,  1.81394895,  4.30505968, 11.52311063, 16.41310313,\n",
       "       21.46072197, 22.33267238])"
      ]
     },
     "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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('CY Silicon with WY Nutrients',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([47.7632301 , 49.11254302, 47.48290684, 23.36581641,  8.2090725 ,\n",
       "       11.07120201, 11.99591242, 12.30988367, 25.86081355, 32.83781192,\n",
       "       41.9107794 , 46.15827035])"
      ]
     },
     "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_3469130/3403781678.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_NSi/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_NSi/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_NSi/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_NSi/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_3469130/2320522072.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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('WY Diatoms (0-100 m) with CY N and Si',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 N and Si')\n",
    "\n",
    "\n",
    "ax.set_title('CY Diatoms (0-100 m) with WY N and Si',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
}