{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Nutrient Limitation\n",
    "\n",
    "Compute biomass-weighted-mean limitation terms in the upper ocean (i.e., top 150 m).\n",
    "\n",
    "Make 3 panel plot: maps of most limiting nutrient for each phytoplankton taxa (diatom, small phyto, diazotrophs)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "\n",
    "import cftime\n",
    "\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "import intake\n",
    "import intake_esm\n",
    "import ann_avg_utils as aau\n",
    "\n",
    "import ncar_jobqueue\n",
    "from dask.distributed import Client\n",
    "\n",
    "import xpersist as xp\n",
    "# Set up xperist cache\n",
    "cache_dir = os.path.join(os.path.sep, 'glade', 'p', 'cgd', 'oce', 'projects', 'cesm2-marbl', 'xpersist_cache')\n",
    "if (os.path.isdir(cache_dir)):\n",
    "    xp.settings['cache_dir'] = cache_dir\n",
    "xp_dir = 'nutrient_limitation'\n",
    "os.makedirs(os.path.join(xp.settings['cache_dir'], xp_dir), exist_ok=True)\n",
    "\n",
    "import utils\n",
    "\n",
    "# MCL: commenting this out, getting type error\n",
    "#%load_ext watermark\n",
    "#%watermark -a \"Mike Levy\" -d -iv -m -g -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spin up a Dask Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/u/home/mgrover/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/distributed/node.py:164: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 33872 instead\n",
      "  expected, actual\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://10.12.206.51:40790</li>\n",
       "  <li><b>Dashboard: </b><a href='https://jupyterhub.hpc.ucar.edu/stable/user/mgrover/proxy/33872/status' target='_blank'>https://jupyterhub.hpc.ucar.edu/stable/user/mgrover/proxy/33872/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>0</li>\n",
       "  <li><b>Cores: </b>0</li>\n",
       "  <li><b>Memory: </b>0 B</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://10.12.206.51:40790' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster, client = utils.get_ClusterClient()\n",
    "cluster.scale(12) #adapt(minimum_jobs=0, maximum_jobs=24)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup and Apply the Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_vars = aau.global_vars()\n",
    "\n",
    "exp = 'cesm2_hist'\n",
    "time_slices = global_vars['time_slices']\n",
    "experiment_dict = global_vars['experiment_dict']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "assuming cache is correct\n",
      "reading cached file: /glade/p/cgd/oce/projects/cesm2-marbl/xpersist_cache/nutrient_limitation/sp_lim.nc\n",
      "assuming cache is correct\n",
      "reading cached file: /glade/p/cgd/oce/projects/cesm2-marbl/xpersist_cache/nutrient_limitation/diat_lim.nc\n",
      "assuming cache is correct\n",
      "reading cached file: /glade/p/cgd/oce/projects/cesm2-marbl/xpersist_cache/nutrient_limitation/diaz_lim.nc\n",
      "CPU times: user 48.5 ms, sys: 14.4 ms, total: 62.9 ms\n",
      "Wall time: 211 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'sp': <xarray.Dataset>\n",
       " Dimensions:   (nlat: 384, nlon: 320, nutrient: 3)\n",
       " Coordinates:\n",
       "     TLONG     (nlat, nlon) float64 320.6 321.7 322.8 323.9 ... 318.9 319.4 319.8\n",
       "     TLAT      (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19\n",
       "   * nutrient  (nutrient) object 'P' 'Fe' 'N'\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables:\n",
       "     sp_lim    (nutrient, nlat, nlon) float32 nan nan nan nan ... nan nan nan nan,\n",
       " 'diat': <xarray.Dataset>\n",
       " Dimensions:   (nlat: 384, nlon: 320, nutrient: 4)\n",
       " Coordinates:\n",
       "     TLONG     (nlat, nlon) float64 320.6 321.7 322.8 323.9 ... 318.9 319.4 319.8\n",
       "     TLAT      (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19\n",
       "   * nutrient  (nutrient) object 'P' 'Fe' 'N' 'SiO3'\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables:\n",
       "     diat_lim  (nutrient, nlat, nlon) float32 nan nan nan nan ... nan nan nan nan,\n",
       " 'diaz': <xarray.Dataset>\n",
       " Dimensions:   (nlat: 384, nlon: 320, nutrient: 3)\n",
       " Coordinates:\n",
       "     TLONG     (nlat, nlon) float64 320.6 321.7 322.8 323.9 ... 318.9 319.4 319.8\n",
       "     TLAT      (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19\n",
       "   * nutrient  (nutrient) object 'P' 'Fe' 'TEMP'\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables:\n",
       "     diaz_lim  (nutrient, nlat, nlon) float64 nan nan nan nan ... 0.0 0.0 0.0 0.0}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "temp_thres_diaz = 15.\n",
    "\n",
    "def _get_all_limitation_vars(autotroph):\n",
    "    catalog = intake.open_esm_datastore('data/campaign-cesm2-cmip6-timeseries.json', \n",
    "                                        sep=':')\n",
    "\n",
    "    darrays = []\n",
    "    nutrient_dim = []\n",
    "    for nutrient in nutrients:\n",
    "        if (autotroph, nutrient) not in omit:\n",
    "            if nutrient == 'TEMP':\n",
    "                var = 'TEMP'\n",
    "                da = catalog.search(\n",
    "                        experiment=experiment_dict[exp][1], \n",
    "                        variable=var\n",
    "                    ).to_dataset_dict(\n",
    "                        cdf_kwargs={'chunks':{'time' : 180}}\n",
    "                    )['ocn:historical:pop.h'].drop(['ULAT', 'ULONG']).sel(\n",
    "                        time=time_slices[exp],\n",
    "                    ).isel(\n",
    "                        z_t=0,\n",
    "                        drop=True,\n",
    "                    )[var].mean(['time', 'member_id'])\n",
    "\n",
    "                da = xr.where(da > temp_thres_diaz, 1., 0.)\n",
    "                darrays.append(da)                \n",
    "            else:\n",
    "                var = f'{autotroph}_{nutrient}_lim_Cweight_avg_100m'\n",
    "                print(f'Reading data for {var}')\n",
    "                # Looking at historical run\n",
    "                darrays.append(\n",
    "                    catalog.search(\n",
    "                        experiment=experiment_dict[exp][1], \n",
    "                        variable=var\n",
    "                    ).to_dataset_dict(\n",
    "                        cdf_kwargs={'chunks':{'time' : 180}}\n",
    "                    )['ocn:historical:pop.h'].drop(['ULAT', 'ULONG']).sel(\n",
    "                        time=time_slices[exp]\n",
    "                    )[var].mean(['time', 'member_id'])\n",
    "                )\n",
    "            variables.append(var)\n",
    "            nutrient_dim.append(nutrient)\n",
    "        else:\n",
    "            print(f'Will not pair {autotroph} and {nutrient}')\n",
    "    datasets = xr.concat([da.to_dataset(name=f'{autotroph}_lim') for da in darrays], dim='nutrient')\n",
    "    datasets['nutrient'] = nutrient_dim\n",
    "    \n",
    "    return(datasets.compute())\n",
    "\n",
    "variables = []\n",
    "nutrients = ['P', 'Fe', 'N', 'SiO3', 'TEMP']\n",
    "omit = [('sp', 'SiO3'), ('diaz', 'SiO3'), ('diaz', 'N'), ('sp', 'TEMP'), ('diat', 'TEMP')]\n",
    "darrays = dict()\n",
    "datasets = dict()\n",
    "dsets_plot = dict()\n",
    "for autotroph in ['sp', 'diat', 'diaz']:\n",
    "    xp_func = xp.persist_ds(_get_all_limitation_vars, name=f'{xp_dir}/{autotroph}_lim', trust_cache=True)\n",
    "    datasets[autotroph] = xp_func(autotroph)\n",
    "    dsets_plot[autotroph] = utils.pop_add_cyclic(datasets[autotroph])\n",
    "datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sp': <xarray.Dataset>\n",
       " Dimensions:   (nlat: 384, nlon: 321, nutrient: 5)\n",
       " Coordinates:\n",
       "   * nutrient  (nutrient) object 'Fe' 'N' 'P' 'SiO3' 'TEMP'\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables:\n",
       "     TLAT      (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 80.31 80.31 80.31\n",
       "     TLONG     (nlat, nlon) float64 -220.6 -219.4 -218.3 ... -39.29 -39.57 -39.86\n",
       "     sp_lim    (nutrient, nlat, nlon) float32 nan nan nan ... 100.0 100.0 100.0,\n",
       " 'diat': <xarray.Dataset>\n",
       " Dimensions:   (nlat: 384, nlon: 321, nutrient: 5)\n",
       " Coordinates:\n",
       "   * nutrient  (nutrient) object 'Fe' 'N' 'P' 'SiO3' 'TEMP'\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables:\n",
       "     TLAT      (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 80.31 80.31 80.31\n",
       "     TLONG     (nlat, nlon) float64 -220.6 -219.4 -218.3 ... -39.29 -39.57 -39.86\n",
       "     diat_lim  (nutrient, nlat, nlon) float32 nan nan nan ... 100.0 100.0 100.0,\n",
       " 'diaz': <xarray.Dataset>\n",
       " Dimensions:   (nlat: 384, nlon: 321, nutrient: 5)\n",
       " Coordinates:\n",
       "   * nutrient  (nutrient) object 'Fe' 'N' 'P' 'SiO3' 'TEMP'\n",
       " Dimensions without coordinates: nlat, nlon\n",
       " Data variables:\n",
       "     TLAT      (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 80.31 80.31 80.31\n",
       "     TLONG     (nlat, nlon) float64 -220.6 -219.4 -218.3 ... -39.29 -39.57 -39.86\n",
       "     diaz_lim  (nutrient, nlat, nlon) float64 nan nan nan nan ... 0.0 0.0 0.0 0.0}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsets_aligned = xr.align(*[ds for ds in dsets_plot.values()], join='outer', fill_value=100.)\n",
    "dsets_plot = {k: ds for k, ds in zip(['sp', 'diat', 'diaz'], dsets_aligned)}\n",
    "dsets_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the Output Using Colorlblind Friendly Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "current_palette = sns.color_palette('colorblind', 5)\n",
    "cmap = ListedColormap(current_palette.as_hex())\n",
    "sns.palplot(current_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x252 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,3.5))\n",
    "gs = gridspec.GridSpec(nrows=1, ncols=4, width_ratios=(1, 1, 1, 0.02))\n",
    "\n",
    "prj = ccrs.Robinson(central_longitude=305.0)\n",
    "\n",
    "ocn_mask = np.where(np.isnan(dsets_plot['sp']['sp_lim'].isel(nutrient=0).data), False, True)\n",
    "levels = np.arange(6)\n",
    "norm = colors.BoundaryNorm(levels, ncolors=5)\n",
    "\n",
    "\n",
    "current_palette = sns.color_palette('colorblind', 5)\n",
    "cmap = ListedColormap(current_palette.as_hex())\n",
    "\n",
    "autotroph_names = dict(\n",
    "    sp='Small phytoplankton',\n",
    "    diat='Diatoms',\n",
    "    diaz='Diazotrophs',\n",
    ")\n",
    "\n",
    "maps = []\n",
    "for n, autotroph in enumerate(['sp', 'diat', 'diaz']):\n",
    "    ds = dsets_plot[autotroph]\n",
    "    da = ds[f'{autotroph}_lim']\n",
    "    ax = fig.add_subplot(gs[0, n], projection=prj)\n",
    "    maps.append(ax)\n",
    "    ax.set_title(autotroph_names[autotroph], fontsize=12)\n",
    "    \n",
    "    pc = ax.contourf(ds['TLONG'].data,\n",
    "                     ds['TLAT'].data,\n",
    "                     da.argmin(dim='nutrient', skipna=False).where(ocn_mask).data+0.5,\n",
    "                     levels=levels,\n",
    "                     norm=norm,\n",
    "                     cmap=cmap,\n",
    "                     transform=ccrs.PlateCarree())\n",
    "    \n",
    "    land = ax.add_feature(\n",
    "        cartopy.feature.NaturalEarthFeature(\n",
    "            'physical','land','110m',\n",
    "            edgecolor='face',\n",
    "            facecolor='lightgray'\n",
    "        )\n",
    "    )        \n",
    "\n",
    "# add colorbar\n",
    "gs.update(left=0.05, right=0.95, hspace=0.05, wspace=0.05)\n",
    "cax_vert_shrink = 0.7\n",
    "cbar_ax = plt.subplot(gs[0, -1])\n",
    "p0 = cbar_ax.get_position()\n",
    "shift_up = p0.height * (1. - cax_vert_shrink) / 2\n",
    "cbar_ax.set_position([p0.x0, p0.y0 + shift_up, p0.width, p0.height * cax_vert_shrink])\n",
    "\n",
    "cbar = fig.colorbar(pc, cax=cbar_ax, ticks=levels+.5, orientation='vertical')\n",
    "\n",
    "cbar.ax.set_yticklabels([f'{nutrient}' for nutrient in dsets_plot['sp'].nutrient.values]);\n",
    "cbar.ax.tick_params(length=0);\n",
    "\n",
    "utils.label_plots(fig, maps, xoff=0.02, yoff=0)   \n",
    "utils.savefig('nutrient-limitation-maps.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:miniconda3-cesm2-marbl]",
   "language": "python",
   "name": "conda-env-miniconda3-cesm2-marbl-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}