test_lazyload_AMPS_OOI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook emulating plots from our WHAFT proposal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "import os\n",
    "\n",
    "import gcsfs\n",
    "from tqdm import tqdm\n",
    "import fsspec\n",
    "\n",
    "xr.set_options(display_style=\"html\");\n",
    "# xr.show_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dask Startup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting up and connecting to new cluster.\n"
     ]
    },
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       "VBox(children=(HTML(value='<h2>GatewayCluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n<style scoped>\\n    …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if gateway.list_clusters():\n",
    "    cluster_name = gateway.list_clusters()[0].name\n",
    "    cluster = gateway.connect(cluster_name)\n",
    "    print(f\"Connecting to pre-existing cluster with {len(cluster.scheduler_info['workers'])} workers.\")\n",
    "else:\n",
    "    cluster = gateway.new_cluster() \n",
    "    cluster.adapt(minimum=2, maximum=12)\n",
    "    print(\"Starting up and connecting to new cluster.\")\n",
    "\n",
    "cluster\n",
    "# cluster.scheduler_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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>gateway://traefik-gcp-uscentral1b-prod-dask-gateway.prod:80/prod.c7c4906b0dfe421fbfc61a40aee79406</li>\n",
       "  <li><b>Dashboard: </b><a href='/services/dask-gateway/clusters/prod.c7c4906b0dfe421fbfc61a40aee79406/status' target='_blank'>/services/dask-gateway/clusters/prod.c7c4906b0dfe421fbfc61a40aee79406/status</a></li>\n",
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       "</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",
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       "</td>\n",
       "</tr>\n",
       "</table>"
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      "text/plain": [
       "<Client: 'tls://10.36.27.144:8786' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = cluster.get_client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### setup runtime parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gcsdir = 'gs://ldeo-glaciology'\n",
    "ampsdir = 'AMPS'\n",
    "amps_ver = 'WRF_24'\n",
    "domain = 'domain_03'\n",
    "filepattern = 'wrf-20161225*'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gs://ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-20161225*\n"
     ]
    }
   ],
   "source": [
    "# pattern = 'gs://ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-20161225*'\n",
    "pattern = os.path.join(gcsdir, ampsdir, amps_ver, domain, filepattern)\n",
    "print(pattern)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### list the netcdf files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = gcsfs.GCSFileSystem(project='ldeo-glaciology', mode='ab', cache_timeout = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 8 wrf files.\n",
      "ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122500_f003-cf.nc\n"
     ]
    }
   ],
   "source": [
    "NCs = fs.glob(pattern)\n",
    "print(f\"Total of {len(NCs)} wrf files.\")\n",
    "print(NCs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# url = 'gs://' + NCs[0]\n",
    "# with  fsspec.open(url, mode='rb')  as openfile:  \n",
    "#     ds = xr.open_dataset(openfile, engine='h5netcdf', \n",
    "#                          chunks={'south_north': 200, 'west_east': 200, 'time': 1})  # , decode_coords=False\n",
    "    \n",
    "# for i in tqdm(range(1, len(NCs)-1)):  \n",
    "#     url = 'gs://' + NCs[i]\n",
    "#     with  fsspec.open(url, mode='rb')  as openfile:  \n",
    "#         temp = xr.open_dataset(openfile, engine='h5netcdf', \n",
    "#                                chunks={'south_north': 200, 'west_east': 200, 'time': 1})  \n",
    "#     ds = xr.concat([ds,temp],'time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternatively using `open_mfdatasets`\n",
    "\n",
    "Build list of URLs to public data in the bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# url = 'https://storage.googleapis.com/ldeo-glaciology/bedmachine/BedMachineAntarctica_2019-11-05_v01.nc#mode=bytes'  \n",
    "# ds = xr.open_dataset(url, engine='netcdf4', chunks=3000)\n",
    "# ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122500_f003-cf.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122500_f006-cf.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122500_f009-cf.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122500_f012-cf.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122512_f003-cf.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122512_f006-cf.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122512_f009-cf.nc#mode=bytes', 'https://storage.googleapis.com/ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122512_f012-cf.nc#mode=bytes']\n"
     ]
    }
   ],
   "source": [
    "NCs_urls = ['https://storage.googleapis.com/' + x + '#mode=bytes' for x in NCs]\n",
    "print(NCs_urls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# xr.open_mfdataset(NCs_urls, parallel=True)\n",
    "# ds = xr.open_mfdataset(NCs_urls, engine='netcdf4', chunks={'time': 1})\n",
    "ds = xr.open_mfdataset(NCs_urls, engine='netcdf4', parallel=True,\n",
    "                       combine = 'by_coords', chunks={'time': -1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dask.array.core.Array'>\n",
      "((1, 1, 1, 1, 1, 1, 1, 1), (825,), (537,))\n"
     ]
    }
   ],
   "source": [
    "print(type(ds.T_sfc.data))\n",
    "print(ds.T_sfc.chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;T_sfc&#x27; (time: 8, south_north: 825, west_east: 537)&gt;\n",
       "dask.array&lt;concatenate, shape=(8, 825, 537), dtype=float32, chunksize=(1, 825, 537), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2016-12-25T03:00:00 ... 2016-12-26\n",
       "    lat      (south_north, west_east) float32 dask.array&lt;chunksize=(825, 537), meta=np.ndarray&gt;\n",
       "    lon      (south_north, west_east) float32 dask.array&lt;chunksize=(825, 537), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Attributes:\n",
       "    long_name:      Temperature at the Surface\n",
       "    standard_name:  surface_temperature\n",
       "    units:          K</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'T_sfc'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 8</li><li><span>south_north</span>: 825</li><li><span>west_east</span>: 537</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ab30e606-5e11-4179-99ac-fb4d920efe82' class='xr-array-in' type='checkbox' checked><label for='section-ab30e606-5e11-4179-99ac-fb4d920efe82' 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=(1, 825, 537), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "       &#x27;2016-12-25T09:00:00.000000000&#x27;, &#x27;2016-12-25T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-25T15:00:00.000000000&#x27;, &#x27;2016-12-25T18:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-25T21:00:00.000000000&#x27;, &#x27;2016-12-26T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</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=(825, 537), meta=np.ndarray&gt;</div><input id='attrs-e5b92940-120f-4a33-967c-96230cbdfb8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5b92940-120f-4a33-967c-96230cbdfb8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d446ac4-667e-4500-884f-eec30d9c0810' class='xr-var-data-in' type='checkbox'><label for='data-6d446ac4-667e-4500-884f-eec30d9c0810' 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>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.DataArray 'T_sfc' (time: 8, south_north: 825, west_east: 537)>\n",
       "dask.array<concatenate, shape=(8, 825, 537), dtype=float32, chunksize=(1, 825, 537), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2016-12-25T03:00:00 ... 2016-12-26\n",
       "    lat      (south_north, west_east) float32 dask.array<chunksize=(825, 537), meta=np.ndarray>\n",
       "    lon      (south_north, west_east) float32 dask.array<chunksize=(825, 537), meta=np.ndarray>\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Attributes:\n",
       "    long_name:      Temperature at the Surface\n",
       "    standard_name:  surface_temperature\n",
       "    units:          K"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.T_sfc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick overview plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3c283e7520>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['T_sfc'].mean(dim='time').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test write to zarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ldeo-glaciology/AMPS/WRF_24/domain_03/zarr-cf/test_20161225-cf.zarr']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outdir = os.path.join(gcsdir, ampsdir, amps_ver, domain, 'zarr-cf/')\n",
    "# fs.glob(outdir)\n",
    "fs.ls(outdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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-cf.zarr', mode='ab',\n",
    "                            token=token)\n",
    "# ds.to_zarr(amps_mapper, mode='w');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ldeo-glaciology/AMPS/WRF_24/domain_03/zarr-cf/test_20161225-cf.zarr']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close your cluster, be a good denizen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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-2-0a772ce87452>\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,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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