LOCAL_test_AMPS_tozarr.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AMPS to Zarr \n",
    "\n",
    "The Plan (outline)\n",
    "- read in (some, many?) NetCDF files\n",
    "- strip unneccessary variables\n",
    "- squeeze time dimension from non-varying 2D fields\n",
    "- write (and later append) to zarr in GCS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client, progress, LocalCluster\n",
    "import numcodecs\n",
    "\n",
    "cluster = LocalCluster()\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client(cluster)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numcodecs.blosc.use_threads = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dirs etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/dporter/data/Antarctic/AMPS/WRF_24/wrfout_d03_20161222_week/wrfout_*\n"
     ]
    }
   ],
   "source": [
    "gcsdir = '/Users/dporter/data/Antarctic/'\n",
    "ampsdir = 'AMPS'\n",
    "amps_ver = 'WRF_24'\n",
    "domain = 'wrfout_d03_20161222_week'\n",
    "filepattern = 'wrfout_*'\n",
    "\n",
    "pattern = os.path.join(gcsdir, ampsdir, amps_ver, domain, filepattern)\n",
    "print(pattern)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 56 wrf files.\n",
      "\n",
      "/Users/dporter/data/Antarctic/AMPS/WRF_24/wrfout_d03_20161222_week/wrfout_d03_2016122512_f006.nc\n"
     ]
    }
   ],
   "source": [
    "NCs = glob(pattern)\n",
    "print(f\"Total of {len(NCs)} wrf files.\\n\")\n",
    "print(NCs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "NCs_urls = NCs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(NCs_urls[0], engine='netcdf4', chunks={'south_north': 500, 'west_east': 270})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dask.array.core.Array'>\n",
      "((1,), (500, 325), (270, 267))\n"
     ]
    }
   ],
   "source": [
    "print(type(ds.T2.data))\n",
    "print(ds.T2.chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    stagger:      </pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'T2'</div><ul class='xr-dim-list'><li><span>Time</span>: 1</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-d5966ed8-fa08-4826-9478-5a64444a6747' class='xr-array-in' type='checkbox' checked><label for='section-d5966ed8-fa08-4826-9478-5a64444a6747' 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, 500, 270), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "    <tr><th> Count </th><td> 5 Tasks </td><td> 4 Chunks </td></tr>\n",
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      ],
      "text/plain": [
       "<xarray.DataArray 'T2' (Time: 1, south_north: 825, west_east: 537)>\n",
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       "Attributes:\n",
       "    FieldType:    104\n",
       "    MemoryOrder:  XY \n",
       "    description:  TEMP at 2 M\n",
       "    units:        K\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ds.T2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fea617bd190>"
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     },
     "execution_count": 8,
     "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['T2'].mean(dim='Time').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 55/55 [00:47<00:00,  1.16it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;T2&#x27; (Time: 56, south_north: 825, west_east: 537)&gt;\n",
       "dask.array&lt;concatenate, shape=(56, 825, 537), dtype=float32, chunksize=(1, 825, 537), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    XLAT     (Time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    XLONG    (Time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    XTIME    (Time) datetime64[ns] dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: Time, south_north, west_east\n",
       "Attributes:\n",
       "    FieldType:    [104]\n",
       "    MemoryOrder:  XY \n",
       "    description:  TEMP at 2 M\n",
       "    units:        K\n",
       "    stagger:      </pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'T2'</div><ul class='xr-dim-list'><li><span>Time</span>: 56</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-b557d6f1-445a-4bbf-8dbd-fa36acee52f4' class='xr-array-in' type='checkbox' checked><label for='section-b557d6f1-445a-4bbf-8dbd-fa36acee52f4' 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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      ],
      "text/plain": [
       "<xarray.DataArray 'T2' (Time: 56, south_north: 825, west_east: 537)>\n",
       "dask.array<concatenate, shape=(56, 825, 537), dtype=float32, chunksize=(1, 825, 537), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    XLAT     (Time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    XLONG    (Time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    XTIME    (Time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "Dimensions without coordinates: Time, south_north, west_east\n",
       "Attributes:\n",
       "    FieldType:    [104]\n",
       "    MemoryOrder:  XY \n",
       "    description:  TEMP at 2 M\n",
       "    units:        K\n",
       "    stagger:      "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with  fsspec.open(NCs_urls[0], mode='rb')  as openfile:  \n",
    "    ds = xr.open_dataset(openfile, engine='h5netcdf', \n",
    "                         chunks={'south_north': -1, 'west_east': -1, 'Time': 1})  # , decode_coords=False\n",
    "    \n",
    "for i in tqdm(range(1, len(NCs_urls))):  \n",
    "    with  fsspec.open(NCs_urls[i], mode='rb')  as openfile:  \n",
    "        temp = xr.open_dataset(openfile, engine='h5netcdf', \n",
    "                               chunks={'south_north': -1, 'west_east': -1, 'Time': 1})  \n",
    "    ds = xr.concat([ds,temp],'Time')\n",
    "    \n",
    "ds.T2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.89125268\n"
     ]
    }
   ],
   "source": [
    "print(ds.nbytes / 1e9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "# from tqdm.autonotebook import tqdm\n",
    "import fsspec\n",
    "\n",
    "import xarray as xr\n",
    "\n",
    "xr.set_options(display_style=\"html\");\n",
    "# xr.show_versions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = gcsfs.GCSFileSystem(project='ldeo-glaciology', mode='ab', cache_timeout = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a single URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "openfile = fs.open(f'gs://{NCs[0]}', mode='rb') \n",
    "ds = xr.open_dataset(openfile, engine='h5netcdf', chunks={'south_north': -1, 'west_east': -1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(type(ds))\n",
    "# print(ds.T2.chunks)\n",
    "\n",
    "# print(ds.coords)\n",
    "\n",
    "# # ds.T2.mean().compute()\n",
    "# # ds.T2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loop through list of URLs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gs://ldeo-glaciology/AMPS/WRF_24/wrfout_d03_2019093000_f003.nc#mode=bytes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:17<00:00,  2.55s/it]\n"
     ]
    }
   ],
   "source": [
    "NCs_urls = ['gs://' + x + '#mode=bytes' for x in NCs]\n",
    "print(NCs_urls[0])\n",
    "\n",
    "\n",
    "openfile = fs.open(NCs_urls[0], mode='rb') \n",
    "ds = xr.open_dataset(openfile, engine='h5netcdf',chunks={'south_north': -1, \n",
    "                                                               'west_east': -1,\n",
    "                                                               'Time': -1})\n",
    "for i in tqdm(range(1, len(NCs_urls))):\n",
    "    openfile = fs.open(NCs_urls[i], mode='rb') \n",
    "    temp = xr.open_dataset(openfile, engine='h5netcdf',chunks={'south_north': -1, \n",
    "                                                               'west_east': -1,\n",
    "                                                               'Time': -1})\n",
    "    ds = xr.concat([ds,temp],'Time')\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total size for 2.67 km output: 21.443 Gb\n",
      "\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;T2&#x27; (Time: 8, south_north: 1035, west_east: 675)&gt;\n",
       "dask.array&lt;concatenate, shape=(8, 1035, 675), dtype=float32, chunksize=(1, 1035, 675), chunktype=numpy.ndarray&gt;\n",
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       "    XTIME    (Time) datetime64[ns] dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: Time, south_north, west_east\n",
       "Attributes:\n",
       "    FieldType:    [104]\n",
       "    MemoryOrder:  XY \n",
       "    description:  TEMP at 2 M\n",
       "    units:        K\n",
       "    stagger:      </pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'T2'</div><ul class='xr-dim-list'><li><span>Time</span>: 8</li><li><span>south_north</span>: 1035</li><li><span>west_east</span>: 675</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b23db8e9-a701-4435-aaa3-8ec0024c1635' class='xr-array-in' type='checkbox' checked><label for='section-b23db8e9-a701-4435-aaa3-8ec0024c1635' 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, 1035, 675), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"39\" />\n",
       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"39\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.793935054828374 0.0,39.793935054828374\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"59.793935\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >8</text>\n",
       "  <text x=\"140.000000\" y=\"19.896968\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.896968)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee359954-eed8-4319-875c-db8d554c8a84' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ee359954-eed8-4319-875c-db8d554c8a84' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'T2' (Time: 8, south_north: 1035, west_east: 675)>\n",
       "dask.array<concatenate, shape=(8, 1035, 675), dtype=float32, chunksize=(1, 1035, 675), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    XLAT     (Time, south_north, west_east) float32 dask.array<chunksize=(1, 1035, 675), meta=np.ndarray>\n",
       "    XLONG    (Time, south_north, west_east) float32 dask.array<chunksize=(1, 1035, 675), meta=np.ndarray>\n",
       "    XTIME    (Time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "Dimensions without coordinates: Time, south_north, west_east\n",
       "Attributes:\n",
       "    FieldType:    [104]\n",
       "    MemoryOrder:  XY \n",
       "    description:  TEMP at 2 M\n",
       "    units:        K\n",
       "    stagger:      "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Total size for {ds.DX[0]/1000:.2f} km output: {ds.nbytes / 1e9:.3f} Gb\\n\")\n",
    "ds.T2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fd99226bc10>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14,4))\n",
    "ds.coords['XLONG'].isel(Time=0).plot(ax=ax1)\n",
    "ds.coords['XLAT'].isel(Time=0).plot(ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
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       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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       "\n",
       ".xr-header {\n",
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       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
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       "\n",
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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       "\n",
       ".xr-section-item {\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;XTIME&#x27; (Time: 8)&gt;\n",
       "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;)\n",
       "Coordinates:\n",
       "    XTIME    (Time) datetime64[ns] 2019-09-30T03:00:00 ... 2019-10-01\n",
       "Dimensions without coordinates: Time\n",
       "Attributes:\n",
       "    FieldType:    [104]\n",
       "    MemoryOrder:  0  \n",
       "    description:  minutes since 2019-09-30 00:00:00\n",
       "    stagger:      </pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'XTIME'</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-45a852c5-c35a-45c1-9bbd-0347a0a1e733' class='xr-array-in' type='checkbox' checked><label for='section-45a852c5-c35a-45c1-9bbd-0347a0a1e733' 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>2019-09-30T03:00:00 2019-09-30T06:00:00 ... 2019-10-01</span></div><div class='xr-array-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></div></li><li class='xr-section-item'><input id='section-586f365f-bb9a-4a57-a0d3-c8a4862c0b8e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-586f365f-bb9a-4a57-a0d3-c8a4862c0b8e' class='xr-section-summary' >Coordinates: <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>XTIME</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-bee2e6cf-3a18-46a1-b700-680ea040fc0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bee2e6cf-3a18-46a1-b700-680ea040fc0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb0f2c4-304e-4b93-a308-b2cfff978b28' class='xr-var-data-in' type='checkbox'><label for='data-5cb0f2c4-304e-4b93-a308-b2cfff978b28' 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>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-09-30 00:00:00</dd><dt><span>stagger :</span></dt><dd></dd></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></ul></div></li><li class='xr-section-item'><input id='section-356a827c-9661-41cb-9d7d-4a62c5feaa27' class='xr-section-summary-in' type='checkbox'  checked><label for='section-356a827c-9661-41cb-9d7d-4a62c5feaa27' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>[104]</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-09-30 00:00:00</dd><dt><span>stagger :</span></dt><dd></dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'XTIME' (Time: 8)>\n",
       "array(['2019-09-30T03:00:00.000000000', '2019-09-30T06:00:00.000000000',\n",
       "       '2019-09-30T09:00:00.000000000', '2019-09-30T12:00:00.000000000',\n",
       "       '2019-09-30T15:00:00.000000000', '2019-09-30T18:00:00.000000000',\n",
       "       '2019-09-30T21:00:00.000000000', '2019-10-01T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "    XTIME    (Time) datetime64[ns] 2019-09-30T03:00:00 ... 2019-10-01\n",
       "Dimensions without coordinates: Time\n",
       "Attributes:\n",
       "    FieldType:    [104]\n",
       "    MemoryOrder:  0  \n",
       "    description:  minutes since 2019-09-30 00:00:00\n",
       "    stagger:      "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.coords['XTIME'].compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## REchunk\n",
    "\n",
    "this looks cool but can be very inefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
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       "\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
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       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
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       "  list-style: none;\n",
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       "\n",
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       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;T2&#x27; (Time: 8, south_north: 1035, west_east: 675)&gt;\n",
       "dask.array&lt;rechunk-merge, shape=(8, 1035, 675), dtype=float32, chunksize=(8, 1035, 675), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    XLAT     (Time, south_north, west_east) float32 dask.array&lt;chunksize=(8, 1035, 675), meta=np.ndarray&gt;\n",
       "    XLONG    (Time, south_north, west_east) float32 dask.array&lt;chunksize=(8, 1035, 675), meta=np.ndarray&gt;\n",
       "    XTIME    (Time) datetime64[ns] dask.array&lt;chunksize=(8,), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: Time, south_north, west_east\n",
       "Attributes:\n",
       "    FieldType:    [104]\n",
       "    MemoryOrder:  XY \n",
       "    description:  TEMP at 2 M\n",
       "    units:        K\n",
       "    stagger:      </pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'T2'</div><ul class='xr-dim-list'><li><span>Time</span>: 8</li><li><span>south_north</span>: 1035</li><li><span>west_east</span>: 675</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ac3f2b0e-3bb7-45b2-8bda-f4890d1e3bee' class='xr-array-in' type='checkbox' checked><label for='section-ac3f2b0e-3bb7-45b2-8bda-f4890d1e3bee' 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, 1035, 675), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
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       "  <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> 52 Tasks </td><td> 1 Chunks </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG</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-5b102b8c-6a6a-424c-a08d-24e4aeee7954' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b102b8c-6a6a-424c-a08d-24e4aeee7954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52cda26b-3dca-4d6d-8e55-f5b9bd1c19d2' class='xr-var-data-in' type='checkbox'><label for='data-52cda26b-3dca-4d6d-8e55-f5b9bd1c19d2' 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>LONGITUDE, WEST IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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      "text/plain": [
       "<xarray.DataArray 'T2' (Time: 8, south_north: 1035, west_east: 675)>\n",
       "dask.array<rechunk-merge, shape=(8, 1035, 675), dtype=float32, chunksize=(8, 1035, 675), chunktype=numpy.ndarray>\n",
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       "    XLONG    (Time, south_north, west_east) float32 dask.array<chunksize=(8, 1035, 675), meta=np.ndarray>\n",
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       "Dimensions without coordinates: Time, south_north, west_east\n",
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       "    description:  TEMP at 2 M\n",
       "    units:        K\n",
       "    stagger:      "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = ds.chunk({\"Time\": -1})\n",
    "ds.T2\n",
    "\n",
    "# ds.T2.mean(dim='Time').plot()\n",
    "# ds.PSFC.mean().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some pretty plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternatively using `open_mfdatasets`\n",
    "\n",
    "Build list of URLs to public data in the bucket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pre-process to deal with time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "### GENERIC\n",
    "def rename_timecoord(ds): \n",
    "#     .assign_coords(lat=ds.coords['XLAT'].squeeze('XTIME'), lon=ds.coords['XLONG'].squeeze('XTIME'))\n",
    "    return ds.swap_dims({'Time':'XTIME'}) # .assign_coords(lat=ds_geo.coords['XLAT'].squeeze('Time'), lon=ds_geo.coords['XLONG'].squeeze('Time'))\n",
    "    \n",
    "### DROP NON TIME COORDS\n",
    "# def non_time_coords(ds):\n",
    "#     return [v for v in ds.data_vars\n",
    "#             if 'Time' not in ds[v].dims]\n",
    "\n",
    "# def drop_non_essential_vars_pop(ds):\n",
    "#     return ds.drop(non_time_coords(ds))   \n",
    "\n",
    "# ### DROP SOME COORDS\n",
    "# def drop_coords(ds):\n",
    "#     ds = ds.drop(['reference_time','feature_id', 'crs'])\n",
    "#     return ds.reset_coords(drop=True)\n",
    "\n",
    "# ### TEST ADDING TIME\n",
    "# def add_time_dim(xda):\n",
    "#     from datetime import datetime\n",
    "    \n",
    "#     xda = xda.expand_dims(Time = [datetime.now()])\n",
    "#     return xda"
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   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.66 s, sys: 3.48 s, total: 11.1 s\n",
      "Wall time: 3min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "NCs_urls = ['https://storage.googleapis.com/' + x + '#mode=bytes' for x in NCs] # needed for open_mfdataset\n",
    "\n",
    "ds = xr.open_mfdataset(NCs_urls, engine='netcdf4', parallel=True,  \n",
    "                       preprocess = rename_timecoord,\n",
    "                       decode_cf=False,\n",
    "#                        chunks={'XTIME': -1},\n",
    "                       ).swap_dims({'XTIME':'Time'}).chunk({\"Time\": -1})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # this runs almost instantly  (from RABERNAT)\n",
    "# ds = xr.open_mfdataset(NCs_urls, decode_times=False, chunks={'Time': 1},\n",
    "#                        preprocess=drop_non_essential_vars_pop, decode_cf=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 486 ms, sys: 11.3 ms, total: 497 ms\n",
      "Wall time: 51.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ds = xr.open_mfdataset(NCs_urls, engine='netcdf4', parallel=True,   \n",
    "                       concat_dim='Time', data_vars='minimal', coords='minimal', compat='override',\n",
    "#                        combine='by_coords', \n",
    "#                        chunks={'Time': -1},\n",
    "                       preprocess = rename_timecoord,\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(ds.U)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 204 ms, sys: 136 ms, total: 340 ms\n",
      "Wall time: 4.5 s\n"
     ]
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       ".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;T2&#x27; ()&gt;\n",
       "array(239.92863, dtype=float32)\n",
       "Coordinates:\n",
       "    XTIME    datetime64[ns] 2019-09-30T03:00:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'T2'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5441ca95-f523-425e-b9e4-a13840160f96' class='xr-array-in' type='checkbox' checked><label for='section-5441ca95-f523-425e-b9e4-a13840160f96' 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>239.9</span></div><div class='xr-array-data'><pre>array(239.92863, dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-d45b4663-08e3-4543-942a-f4d6d947ff78' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d45b4663-08e3-4543-942a-f4d6d947ff78' class='xr-section-summary' >Coordinates: <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>XTIME</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-09-30T03:00:00</div><input id='attrs-8992d267-125d-4a8c-a818-3a7f76ca7727' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8992d267-125d-4a8c-a818-3a7f76ca7727' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee8e7d12-a356-4e32-8f0f-4a77d26c810b' class='xr-var-data-in' type='checkbox'><label for='data-ee8e7d12-a356-4e32-8f0f-4a77d26c810b' 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>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-09-30 00:00:00</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2019-09-30T03:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5edd41ce-f6d4-4a9a-8dcf-1ea6b9fc0c49' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5edd41ce-f6d4-4a9a-8dcf-1ea6b9fc0c49' 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 'T2' ()>\n",
       "array(239.92863, dtype=float32)\n",
       "Coordinates:\n",
       "    XTIME    datetime64[ns] 2019-09-30T03:00:00"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# ds.T2.chunk({\"XTIME\": -1}).mean().compute()\n",
    "ds.T2.mean().compute()\n",
    "\n",
    "# print(ds['T2'].shape)\n",
    "# print(ds.var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test write to zarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To Do before writing to Zarr\n",
    "- squeeze \"Time\" from all 2D fields\n",
    "- trim fields\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.coords['XLAT_U'].nbytes/1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds.coords['XLAT'] = ds.XLAT.isel(Time=0)\n",
    "ds.coords['XLAT'] = ds.XLAT.isel(Time=0)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f06757c4ca0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.GLW.mean(dim='Time').plot()\n",
    "# ds.U.nbytes/1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# U 34 0 x-wind component (m s-1)\n",
    "# V 34 0 y-wind component (m s-1)\n",
    "# W 34 0 z-wind component (m s-1)\n",
    "# PH 34 0 perturbation geopotential (m2 s-2)\n",
    "# PHB 34 0 base-state geopotential (m2 s-2)\n",
    "# T 34 0 perturbation potential temperature (theta-t0) (K)\n",
    "\n",
    "# P 34 0 perturbation pressure (Pa)\n",
    "# PB 34 0 BASE STATE PRESSURE (Pa)\n",
    "# P_HYD 34 0 hydrostatic pressure (Pa)\n",
    "# Q2 1 0 QV at 2 M (kg kg-1)\n",
    "# T2 1 0 TEMP at 2 M (K)\n",
    "\n",
    "# PSFC 1 0 SFC PRESSURE (Pa)\n",
    "# U10 1 0 U at 10 M (m s-1)\n",
    "# V10 1 0 V at 10 M (m s-1)\n",
    "# QVAPOR 34 0 Water vapor mixing ratio (kg kg-1)\n",
    "# QCLOUD 34 0 Cloud water mixing ratio (kg kg-1)\n",
    "# QRAIN 34 0 Rain water mixing ratio (kg kg-1)\n",
    "# QICE 34 0 Ice mixing ratio (kg kg-1)\n",
    "# QSNOW 34 0 Snow mixing ratio (kg kg-1)\n",
    "# QGRAUP 34 0 Graupel mixing ratio (kg kg-1)\n",
    "# QNICE 34 0 Ice Number concentration ( kg-1)\n",
    "# QNSNOW 34 0 Snow Number concentration ( kg(-1))\n",
    "# QNRAIN 34 0 Rain Number concentration ( kg(-1))\n",
    "# QNGRAUPEL 34 0 Graupel Number concentration ( kg(-1))\n",
    "# LANDMASK 1 0 LAND MASK (1 FOR LAND, 0 FOR WATER) (-)\n",
    "\n",
    "# SST 1 0 SEA SURFACE TEMPERATURE (K)\n",
    "# SSTSK 1 0 SKIN SEA SURFACE TEMPERATURE (K)\n",
    "\n",
    "# TSK 1 0 SURFACE SKIN TEMPERATURE (K)\n",
    "# RAINC 1 0 ACCUMULATED TOTAL CUMULUS PRECIPITATION (mm)\n",
    "# RAINSH 1 0 ACCUMULATED SHALLOW CUMULUS PRECIPITATION (mm)\n",
    "# RAINNC 1 0 ACCUMULATED TOTAL GRID SCALE PRECIPITATION (mm)\n",
    "\n",
    "# QFX 1 0 UPWARD MOISTURE FLUX AT THE SURFACE (kg m-2 s-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "vars_save = ['U', 'V', 'W', 'PH', 'PHB', 'T', 'P', 'PB',\n",
    "             'P_HYD', 'Q2', 'T2', 'PSFC', 'U10', 'V10',\n",
    "             'QVAPOR', 'QICE', 'QCLOUD', 'QRAIN', 'QSNOW', 'QGRAUP', \n",
    "             'LANDMASK', 'HGT',\n",
    "             'SST', 'SSTSK', 'TSK',\n",
    "             'RAINC', 'RAINSH', 'RAINNC', \n",
    "             'QFX', 'HFX', 'LH', 'PBLH', 'ACSNOM', 'SWDOWN', 'LWDNB', 'GLW',\n",
    "            'SNOW', 'SNOWH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'fsspec' has no attribute 'ls'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-54d3546192d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moutdir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgcsdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mampsdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamps_ver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdomain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'zarr-raw/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# fs.glob(outdir)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfsspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'fsspec' has no attribute 'ls'"
     ]
    }
   ],
   "source": [
    "outdir = os.path.join(gcsdir, ampsdir, amps_ver, domain, 'zarr-raw/')\n",
    "# fs.glob(outdir)\n",
    "fsspec.ls(outdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 + 'wrfout_d03_201909.zarr', mode='ab',\n",
    "                            token=token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "ds.to_zarr(amps_mapper, mode='w');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CPU times: user 6.48 s, sys: 594 ms, total: 7.07 s\n",
    "Wall time: 8min 56s"
   ]
  },
  {
   "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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "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
}