outline_AMPS_postproc_tozarr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "still-woman",
   "metadata": {},
   "source": [
    "# Post-processing raw wrfout netcdf files\n",
    "\n",
    "Notebook outlining how to read, process, and write wrfout netCDF files to Zarr (using wrf-python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "herbal-justice",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from netCDF4 import Dataset\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xr.set_options(display_style=\"html\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "sound-baseline",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): / ^C\n",
      "\\ "
     ]
    }
   ],
   "source": [
    "# Install a conda package in the current Jupyter kernel\n",
    "import sys\n",
    "!conda install --yes --prefix {sys.prefix} wrf-python netCDF4 salem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "computational-innocent",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'salem'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-65e7990adaa2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwrf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msalem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnetCDF4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'salem'"
     ]
    }
   ],
   "source": [
    "import wrf\n",
    "import salem\n",
    "from netCDF4 import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "second-conflict",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "latest-australian",
   "metadata": {},
   "outputs": [],
   "source": [
    "## A chunk of code to start dask cluster\n",
    "from dask.distributed import Client, progress\n",
    "from dask_gateway import Gateway\n",
    "gateway = Gateway()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "polyphonic-queensland",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gateway' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
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      "\u001b[0;31mNameError\u001b[0m: name 'gateway' is not defined"
     ]
    }
   ],
   "source": [
    "if gateway.list_clusters():\n",
    "    cluster_name = gateway.list_clusters()[0].name\n",
    "    cluster = gateway.connect(cluster_name)\n",
    "    print(f\"\\nConnecting to pre-existing cluster with {len(cluster.scheduler_info['workers'])} workers.\")\n",
    "else:\n",
    "    cluster = gateway.new_cluster() \n",
    "    cluster.adapt(minimum=4, maximum=12)\n",
    "    print(\"\\nStarting up and connecting to new cluster.\")\n",
    "\n",
    "cluster\n",
    "# cluster.scheduler_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "active-guyana",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = cluster.get_client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flush-criminal",
   "metadata": {},
   "source": [
    "## Read in RAW wrfout tiles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alien-divorce",
   "metadata": {},
   "source": [
    "### setup runtime parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "backed-contamination",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gs://ldeo-glaciology/AMPS/WRF_24/wrfout_d03_20190930*\n"
     ]
    }
   ],
   "source": [
    "gcsdir = 'gs://ldeo-glaciology'\n",
    "ampsdir = 'AMPS'\n",
    "amps_ver = 'WRF_24'\n",
    "\n",
    "# domain = 'domain_02'\n",
    "# filepattern = 'wrfout_d02_20191231*'\n",
    "# pattern = os.path.join(gcsdir, ampsdir, amps_ver, domain, filepattern)\n",
    "# print(pattern)\n",
    "\n",
    "domain = 'domain_03'\n",
    "filepattern = 'wrfout_d03_20190930*'\n",
    "pattern = os.path.join(gcsdir, ampsdir, amps_ver, filepattern)\n",
    "print(pattern)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "filled-harassment",
   "metadata": {},
   "source": [
    "### list the netcdf files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fancy-mattress",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gcsfs\n",
    "from tqdm import tqdm\n",
    "# from tqdm.autonotebook import tqdm\n",
    "import fsspec\n",
    "\n",
    "fs = gcsfs.GCSFileSystem(project='ldeo-glaciology', mode='ab', cache_timeout = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "expanded-colleague",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 8 wrf files.\n",
      "\n",
      "ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093000_f003.nc\n"
     ]
    }
   ],
   "source": [
    "NCs = fs.glob(pattern)\n",
    "print(f\"Total of {len(NCs)} wrf files.\\n\")\n",
    "print(NCs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "spread-alarm",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093000_f003.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093000_f006.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093000_f009.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093000_f012.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093012_f003.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093012_f006.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093012_f009.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093012_f012.nc#mode=bytes']\n"
     ]
    }
   ],
   "source": [
    "# NCs_urls = ['gs://ldeo-glaciology' + x for x in NCs]\n",
    "NCs_urls = ['https://storage.googleapis.com/' + x + '#mode=bytes' for x in NCs] #  + '#mode=bytes' needed for open_mfdataset\n",
    "\n",
    "print(NCs_urls)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "wooden-concert",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fifth-massachusetts",
   "metadata": {},
   "source": [
    "## using `salem`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "rising-branch",
   "metadata": {},
   "outputs": [],
   "source": [
    "# wrflist = [Dataset(x) for x in NCs_urls]\n",
    "\n",
    "# ds = salem.open_mf_wrf_dataset(wrflist, \n",
    "#                 chunks={'Time':-1})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "industrial-substance",
   "metadata": {},
   "source": [
    "## using `netCDF4.Dataset`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "widespread-settlement",
   "metadata": {},
   "source": [
    "### get variables needed using wrf-python's `getvar`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animal-uncle",
   "metadata": {},
   "source": [
    "single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "peaceful-illustration",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ncfile = Dataset(NCs_urls[0])\n",
    "# # ncfile.variables.keys()\n",
    "\n",
    "# # T_sfc = getvar(ncfile, \"T_sfc\")\n",
    "# # Td = getvar(ncfile, \"tk\")\n",
    "# da = wrf.getvar(ncfile, \"ALBEDO\", timeidx=wrf.ALL_TIMES)\n",
    "# da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "sporting-drill",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.pcolor(wrf.to_np(da), cmap='Greys_r')\n",
    "# plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlikely-inspiration",
   "metadata": {},
   "source": [
    "mutliple files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abandoned-contrary",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Creating a simple test list with four timesteps\n",
    "# wrflist = [Dataset(NCs_urls[0]),\n",
    "#            Dataset(NCs_urls[1]),\n",
    "#            Dataset(NCs_urls[2]),\n",
    "#            Dataset(NCs_urls[3])]\n",
    "\n",
    "wrflist = [Dataset(x) for x in NCs_urls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sound-invention",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "upper-overview",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_vars_list = ['HFX', 'LH', 'GFX', 'PBLH', 'ACSNOM', 'SWDOWN', 'LWDNB', 'TSK']\n",
    "get_vars_list = ['cloudfrac', 'slp', 'T2', 'theta_e', 'times', \n",
    "                 'uvmet', 'uvmet_wspd_wdir',\n",
    "                 'lat', 'lat', \n",
    "                 '', '', '', '']\n",
    "\n",
    "da = wrf.getvar(wrflist, \n",
    "                'slp', #get_vars_list[1], \n",
    "                timeidx=wrf.ALL_TIMES, \n",
    "                method='cat') #, method=\"join\", squeeze=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "neural-karen",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "attended-louis",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;times&#x27; (Time: 8)&gt;\n",
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       "    description:  model times [np.datetime64]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'times'</div><ul class='xr-dim-list'><li><span>Time</span>: 8</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5de4b760-50e2-4408-9a0d-b70b759377a7' class='xr-array-in' type='checkbox' checked><label for='section-5de4b760-50e2-4408-9a0d-b70b759377a7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(8,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.DataArray 'times' (Time: 8)>\n",
       "dask.array<xarray-<this-array>, shape=(8,), dtype=datetime64[ns], chunksize=(8,), chunktype=numpy.ndarray>\n",
       "Dimensions without coordinates: Time\n",
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       "    description:  model times [np.datetime64]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
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   ],
   "source": [
    "da = da.chunk(chunks={'Time':-1})\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "conventional-bacon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 28.8 ms, sys: 4.91 ms, total: 33.8 ms\n",
      "Wall time: 65 ms\n"
     ]
    },
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       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;times&#x27; ()&gt;\n",
       "array(&#x27;2019-09-30T13:30:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'times'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4d030d47-e6dd-4fc7-875a-1064937ed7a0' class='xr-array-in' type='checkbox' checked><label for='section-4d030d47-e6dd-4fc7-875a-1064937ed7a0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2019-09-30T13:30:00</span></div><div class='xr-array-data'><pre>array(&#x27;2019-09-30T13:30:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-d52a484f-3269-4ee2-9fa8-737199ddb40e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d52a484f-3269-4ee2-9fa8-737199ddb40e' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ff764bc9-5c0b-46b0-91e6-d52a1acb50b8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ff764bc9-5c0b-46b0-91e6-d52a1acb50b8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'times' ()>\n",
       "array('2019-09-30T13:30:00.000000000', dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "da.mean().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "literary-thing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.]),\n",
       " array([18169.0625, 18169.1625, 18169.2625, 18169.3625, 18169.4625,\n",
       "        18169.5625, 18169.6625, 18169.7625, 18169.8625, 18169.9625,\n",
       "        18170.0625]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "da.mean(dim='Time').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "informed-shareware",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fallen-hobby",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (Time: 8, south_north: 1035, west_east: 675)\n",
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       "    T2        (Time, south_north, west_east) float32 dask.array&lt;chunksize=(8, 1035, 675), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-007d114c-f808-4dbe-8216-243a0e7ba214' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-007d114c-f808-4dbe-8216-243a0e7ba214' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 8</li><li><span>south_north</span>: 1035</li><li><span>west_east</span>: 675</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-33f03ee3-e53b-48b5-835d-94c4ddc79d7a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-33f03ee3-e53b-48b5-835d-94c4ddc79d7a' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>XLONG</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1035, 675), meta=np.ndarray&gt;</div><input id='attrs-302c7061-cb97-4be4-b2c2-fa7a000caa18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-302c7061-cb97-4be4-b2c2-fa7a000caa18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ada54cb-059b-4333-b668-c9c3cf6809e8' class='xr-var-data-in' type='checkbox'><label for='data-2ada54cb-059b-4333-b668-c9c3cf6809e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XTIME</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8,), meta=np.ndarray&gt;</div><input id='attrs-c2455996-b69a-4fbc-93ec-e08f8776befc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2455996-b69a-4fbc-93ec-e08f8776befc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca82947f-f324-4553-a71a-442702cdf9fb' class='xr-var-data-in' type='checkbox'><label for='data-ca82947f-f324-4553-a71a-442702cdf9fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 64 B </td> <td> 64 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (8,) </td> <td> (8,) </td></tr>\n",
       "    <tr><th> Count </th><td> 1 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Time</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-09-30T03:00:00 ... 2019-10-01</div><input id='attrs-3da10e4b-08d4-40ad-9ccb-1329555179df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3da10e4b-08d4-40ad-9ccb-1329555179df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e1e511a-6eb2-4121-badd-411a93881db4' class='xr-var-data-in' type='checkbox'><label for='data-2e1e511a-6eb2-4121-badd-411a93881db4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-09-30T03:00:00.000000000&#x27;, &#x27;2019-09-30T06:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-30T09:00:00.000000000&#x27;, &#x27;2019-09-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-30T15:00:00.000000000&#x27;, &#x27;2019-09-30T18:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-30T21:00:00.000000000&#x27;, &#x27;2019-10-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>datetime</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8,), meta=np.ndarray&gt;</div><input id='attrs-502d6fb3-0fed-4c88-9c18-25de28faf7b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-502d6fb3-0fed-4c88-9c18-25de28faf7b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f414c3b-cce8-4c6e-a684-916f6c96e7a9' class='xr-var-data-in' type='checkbox'><label for='data-3f414c3b-cce8-4c6e-a684-916f6c96e7a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 64 B </td> <td> 64 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (8,) </td> <td> (8,) </td></tr>\n",
       "    <tr><th> Count </th><td> 1 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> datetime64[ns] </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a77ed239-c631-41c6-92fb-2d3f4ec46c48' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a77ed239-c631-41c6-92fb-2d3f4ec46c48' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>T2</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8, 1035, 675), meta=np.ndarray&gt;</div><input id='attrs-f0d33a8e-89c5-454a-bae1-c70d57380b79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0d33a8e-89c5-454a-bae1-c70d57380b79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90528b0a-ae3f-4c7d-9c73-07dcf69f7a23' class='xr-var-data-in' type='checkbox'><label for='data-90528b0a-ae3f-4c7d-9c73-07dcf69f7a23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>TEMP at 2 M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>coordinates :</span></dt><dd>XLONG XLAT XTIME</dd><dt><span>projection :</span></dt><dd>PolarStereographic(stand_lon=180.0, moad_cen_lat=-87.39999389648438, truelat1=-71.0, truelat2=None, pole_lat=90.0, pole_lon=0.0)</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 22.36 MB </td> <td> 22.36 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (8, 1035, 675) </td> <td> (8, 1035, 675) </td></tr>\n",
       "    <tr><th> Count </th><td> 1 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (Time: 8, south_north: 1035, west_east: 675)\n",
       "Coordinates:\n",
       "    XLONG     (south_north, west_east) float32 dask.array<chunksize=(1035, 675), meta=np.ndarray>\n",
       "    XLAT      (south_north, west_east) float32 dask.array<chunksize=(1035, 675), meta=np.ndarray>\n",
       "    XTIME     (Time) float64 dask.array<chunksize=(8,), meta=np.ndarray>\n",
       "  * Time      (Time) datetime64[ns] 2019-09-30T03:00:00 ... 2019-10-01\n",
       "    datetime  (Time) datetime64[ns] dask.array<chunksize=(8,), meta=np.ndarray>\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Data variables:\n",
       "    T2        (Time, south_north, west_east) float32 dask.array<chunksize=(8, 1035, 675), meta=np.ndarray>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = da.to_dataset()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "northern-morris",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "standing-playback",
   "metadata": {},
   "source": [
    "# Write to Zarr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "professional-channels",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gcsfs\n",
    "from tqdm import tqdm\n",
    "import fsspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "minus-database",
   "metadata": {},
   "outputs": [],
   "source": [
    "gcsdir = 'gs://ldeo-glaciology'\n",
    "ampsdir = 'AMPS'\n",
    "amps_ver = 'WRF_24'\n",
    "domain = 'domain_03'\n",
    "\n",
    "outdir = os.path.join(gcsdir, ampsdir, amps_ver, domain, 'zarr-postproc/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "continuous-lancaster",
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = gcsfs.GCSFileSystem(project='ldeo-glaciology', mode='ab', cache_timeout = 0)\n",
    "\n",
    "# fs.glob(outdir)\n",
    "fs.ls(outdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "loving-hungary",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('secrets/ldeo-glaciology-bc97b12df06b.json') as token_file:\n",
    "    token = json.load(token_file)\n",
    "# gcs = gcsfs.GCSFileSystem(token=token)\n",
    "\n",
    "amps_mapper = fsspec.get_mapper(outdir + 'test_20161225-T2_gcp.zarr', mode='ab',\n",
    "                            token=token)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floating-crest",
   "metadata": {},
   "source": [
    "## Projections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "noted-learning",
   "metadata": {},
   "source": [
    "### remove them altogether\n",
    "\n",
    "but ideally give the PROJ4 string if appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fewer-tolerance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# del ds.T2.attrs['projection']\n",
    "\n",
    "ds.T2.attrs['projection'] = '+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "encouraging-victim",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds.to_zarr('data', mode='w');\n",
    "\n",
    "ds.to_zarr(amps_mapper,\n",
    "           mode='w',\n",
    "           consolidated=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proved-certificate",
   "metadata": {},
   "source": [
    "## test reading in that zarr file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "contrary-effort",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gcsmap = gcsfs.mapping.GCSMap(outdir + 'test_20161225-T2.zarr', gcs=fs, check=True, create=False)\n",
    "\n",
    "AMPS_gcs = xr.open_zarr(amps_mapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "upset-incidence",
   "metadata": {},
   "outputs": [],
   "source": [
    "AMPS_gcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "mathematical-victor",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_time = 1\n",
    "plot_var = 'T2'\n",
    "\n",
    "import shapely.geometry as sgeom\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "    \n",
    "box = sgeom.box(minx=160, maxx=200, miny=-88, maxy=-76)\n",
    "# box = sgeom.box(minx=-153, maxx=-159, miny=-84.85, maxy=-87.0)\n",
    "# box = sgeom.box(minx=ds.XLONG.min(), maxx=ds.XLONG.max(), miny=ds.XLAT.min(), maxy=ds.XLAT.max())\n",
    "\n",
    "x0, y0, x1, y1 = box.bounds\n",
    "myproj = ccrs.SouthPolarStereo(central_longitude=180)\n",
    "# myproj = ccrs.Orthographic()\n",
    "\n",
    "fig = plt.figure(figsize=(4, 5), facecolor='white', dpi=144)\n",
    "# ax = plt.axes(projection=ccrs.LambertAzimuthalEqualArea())\n",
    "ax = plt.axes(projection=myproj)\n",
    "ax.set_extent([x0, x1, y0, y1], ccrs.PlateCarree())  \n",
    "# ax.stock_img()\n",
    "fig.canvas.draw()\n",
    "\n",
    "pcm1 = ax.pcolormesh(AMPS_gcs.XLONG, AMPS_gcs.XLAT, AMPS_gcs[plot_var].isel(Time=plot_time)[:-1, :-1],\n",
    "                     #vmin=210, vmax=275,\n",
    "                     transform=ccrs.PlateCarree(), \n",
    "                     cmap=plt.get_cmap(\"twilight_shifted\"))#, cmap=plt.get_cmap(\"gist_stern_r\") # \"BuPu\", cmocean.cm.curl)\n",
    "\n",
    "ax.coastlines(resolution='10m')\n",
    "#         ax.add_feature(cartopy.feature.LAND)\n",
    "#         ax.add_feature(cartopy.feature.OCEAN)\n",
    "ax.gridlines(draw_labels=False, alpha=0.3, color='grey')\n",
    "ax.xformatter = LongitudeFormatter\n",
    "ax.yformatter = LatitudeFormatter\n",
    "\n",
    "cb1 = plt.colorbar(pcm1, ax=ax, label='2 m Temp [$^o$C)', \\\n",
    "                   orientation='vertical', extend='both', \\\n",
    "                  shrink=0.4, pad=0.05)\n",
    "\n",
    "# ax.plot(lon2d.isel(south_north=si, west_east=sj), \n",
    "#         lat2d.isel(south_north=si, west_east=sj), \n",
    "#         'ko', markersize=3, transform=ccrs.PlateCarree())\n",
    "# ax.plot(lon2d.isel(south_north=ai, west_east=aj), \n",
    "#         lat2d.isel(south_north=ai, west_east=aj), \n",
    "#         'ko', markersize=3, transform=ccrs.PlateCarree())\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=1.25)\n",
    "# plt.suptitle(f\"{ds.DX[0]/1000} km\", y=0.98)\n",
    "# plt.savefig(pfile, bbox_inches='tight')  # save the figure to file\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "olive-feature",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "surprising-message",
   "metadata": {},
   "outputs": [],
   "source": [
    "import salem\n",
    "\n",
    "salem.open_mf_wrf_dataset(NCs_urls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "occupied-beauty",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "contained-internet",
   "metadata": {},
   "source": [
    "# TODO: add additional variables:\n",
    "- `merge` DataArrays into single DataSet along Time timension (http://xarray.pydata.org/en/stable/combining.html#merge)\n",
    "\n",
    "`>> xr.merge([ds, ds.rename({\"foo\": \"bar\"})])`\n",
    "\n",
    "\n",
    "- OR append each DataSet onto existing zarr (http://xarray.pydata.org/en/stable/io.html#appending-to-existing-zarr-stores)\n",
    "\n",
    "`>> ds2.to_zarr(\"path/to/directory.zarr\", append_dim=\"t\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "august-horse",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "gentle-stock",
   "metadata": {},
   "source": [
    "## Close your cluster, be a good denizen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "electric-finnish",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cluster' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-0a772ce87452>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcluster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'cluster' is not defined"
     ]
    }
   ],
   "source": [
    "cluster.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "billion-rogers",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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