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Xarray-Dask-Zarr Demo using GMET dataset
Raw
README.md 
$ conda env update -f scipy-demo.yml
$ conda activate scipy-demo
$ jupyter labextension install --clean \
                            @jupyter-widgets/jupyterlab-manager  dask-labextension @pyviz/jupyterlab_pyviz
Display the source blob
Display the rendered blob
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convert-gmet-to-zarr.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"from dask_jobqueue import SLURMCluster\n",
"from distributed import Client\n",
"from glob import glob\n",
"import os\n",
"from pathlib import Path\n",
"import pandas as pd\n",
"from tqdm.auto import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
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"application/vnd.jupyter.widget-view+json": {
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"source": [
"cluster = SLURMCluster(cores=2)\n",
"cluster.adapt(minimum=1)\n",
"client = Client(cluster)\n",
"cluster"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3700,\n",
" ['/glade/p/ral/hap/anewman/conus_v1p2/eighth/v2_landmask/conus_daily_eighth_19800101_19801231_001.nc4',\n",
" '/glade/p/ral/hap/anewman/conus_v1p2/eighth/v2_landmask/conus_daily_eighth_19800101_19801231_002.nc4'])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"files = \"/glade/p/ral/hap/anewman/conus_v1p2/eighth/v2_landmask/*.nc4\"\n",
"files = sorted(glob(files))\n",
"len(files), files[:2]"
]
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"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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" <tr>\n",
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"2 /glade/p/ral/hap/anewman/conus_v1p2/eighth/v2_... 3\n",
"3 /glade/p/ral/hap/anewman/conus_v1p2/eighth/v2_... 4\n",
"4 /glade/p/ral/hap/anewman/conus_v1p2/eighth/v2_... 5"
]
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"def parse_file(file):\n",
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" parts = f.split('_')\n",
" return {'path': file, 'member_id': int(parts[-1])}\n",
"entries = [parse_file(file) for file in files]\n",
"df = pd.DataFrame(entries)\n",
"df.head()"
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"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"path 3700\n",
"member_id 100\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"grouped = df.groupby('member_id')"
]
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{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
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"source": [
"def preprocess(ds):\n",
" return ds.set_coords(['elevation'])\n",
" \n",
"dsets = []\n",
"for key, group in tqdm(grouped):\n",
" files = group.path.tolist()\n",
" ds = xr.open_mfdataset(files, concat_dim=\"time\", combine=\"by_coords\", parallel=True, preprocess=preprocess).expand_dims({'member_id': [key]})\n",
" dsets.append(ds)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (lat: 224, lon: 464, member_id: 100, time: 13515)\n",
"Coordinates:\n",
" * lon (lon) float64 -124.9 -124.8 -124.6 -124.5 ... -67.25 -67.12 -67.0\n",
" * lat (lat) float64 25.12 25.25 25.38 25.5 ... 52.62 52.75 52.88 53.0\n",
" * time (time) datetime64[ns] 1980-01-01 1980-01-02 ... 2016-12-31\n",
" * member_id (member_id) int64 1 2 3 4 5 6 7 8 9 ... 93 94 95 96 97 98 99 100\n",
"Data variables:\n",
" elevation (lat, lon) float64 nan nan nan nan ... 617.5 618.6 611.5 625.4\n",
" pcp (member_id, time, lat, lon) float32 dask.array&lt;chunksize=(1, 366, 224, 464), meta=np.ndarray&gt;\n",
" t_mean (member_id, time, lat, lon) float32 dask.array&lt;chunksize=(1, 366, 224, 464), meta=np.ndarray&gt;\n",
" t_range (member_id, time, lat, lon) float32 dask.array&lt;chunksize=(1, 366, 224, 464), meta=np.ndarray&gt;\n",
"Attributes:\n",
" history: Wed Oct 24 13:59:29 2018: ncks -4 -L 1 conus_daily_eighth_1...\n",
" NCO: netCDF Operators version 4.7.4 (http://nco.sf.net)\n",
" institution: National Center fo Atmospheric Research (NCAR), Boulder, CO...\n",
" title: CONUS daily 12-km gridded ensemble precipitation and temper...\n",
" source: Generated using version 1.1 of CONUS ensemble code base\n",
" references: Newman et al. 2015: Gridded Ensemble Precipitation and Temp...</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-eaf2be36-b199-4aab-a4d2-81301a08de0e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eaf2be36-b199-4aab-a4d2-81301a08de0e' 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'>lat</span>: 224</li><li><span class='xr-has-index'>lon</span>: 464</li><li><span class='xr-has-index'>member_id</span>: 100</li><li><span class='xr-has-index'>time</span>: 13515</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-76c54a8a-40bf-4059-bda2-75283778c80d' class='xr-section-summary-in' type='checkbox' checked><label for='section-76c54a8a-40bf-4059-bda2-75283778c80d' class='xr-section-summary' >Coordinates: <span>(4)</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 class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-124.9 -124.8 ... -67.12 -67.0</div><input id='attrs-32b35a61-9ff9-4d48-a0ee-f703780e0257' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32b35a61-9ff9-4d48-a0ee-f703780e0257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c474a05f-11ea-407a-ab23-21141e1c2253' class='xr-var-data-in' type='checkbox'><label for='data-c474a05f-11ea-407a-ab23-21141e1c2253' 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>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-124.875, -124.75 , -124.625, ..., -67.25 , -67.125, -67. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>25.12 25.25 25.38 ... 52.88 53.0</div><input id='attrs-98b1e891-b707-479d-b748-9f9a59a5aa3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98b1e891-b707-479d-b748-9f9a59a5aa3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b038eaf4-9472-4b03-83f0-07e3330819d8' class='xr-var-data-in' type='checkbox'><label for='data-b038eaf4-9472-4b03-83f0-07e3330819d8' 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([25.125, 25.25 , 25.375, ..., 52.75 , 52.875, 53. ])</pre></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'>1980-01-01 ... 2016-12-31</div><input id='attrs-3bbaf801-a6bc-4244-a381-a973c68d3d2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bbaf801-a6bc-4244-a381-a973c68d3d2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c823f9f-cc1d-489e-bdb9-5187c02174bd' class='xr-var-data-in' type='checkbox'><label for='data-5c823f9f-cc1d-489e-bdb9-5187c02174bd' 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;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-02T00:00:00.000000000&#x27;,\n",
" &#x27;1980-01-03T00:00:00.000000000&#x27;, ..., &#x27;2016-12-29T00:00:00.000000000&#x27;,\n",
" &#x27;2016-12-30T00:00:00.000000000&#x27;, &#x27;2016-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 95 96 97 98 99 100</div><input id='attrs-8bcdf5a9-c882-4976-af7e-103b81125caa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bcdf5a9-c882-4976-af7e-103b81125caa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33af9aa9-dd12-4298-8f2b-d2402f2b7a26' class='xr-var-data-in' type='checkbox'><label for='data-33af9aa9-dd12-4298-8f2b-d2402f2b7a26' 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([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,\n",
" 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
" 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,\n",
" 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
" 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,\n",
" 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,\n",
" 99, 100])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4fa94b61-ac11-4b5c-80f9-ca1c1ef654a5' class='xr-section-summary-in' type='checkbox' checked><label for='section-4fa94b61-ac11-4b5c-80f9-ca1c1ef654a5' class='xr-section-summary' >Data variables: <span>(4)</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>elevation</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 618.6 611.5 625.4</div><input id='attrs-ddf423b8-78d3-4a6a-a0ad-e5de6ff04a8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddf423b8-78d3-4a6a-a0ad-e5de6ff04a8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f0523e4-1628-4ccb-b1b0-dc8c1ea569b1' class='xr-var-data-in' type='checkbox'><label for='data-0f0523e4-1628-4ccb-b1b0-dc8c1ea569b1' 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>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, ..., nan, nan,\n",
" nan],\n",
" [ nan, nan, nan, ..., nan, nan,\n",
" nan],\n",
" [ nan, nan, nan, ..., nan, nan,\n",
" nan],\n",
" ...,\n",
" [1762.729 , 1813.113 , 1535.78 , ..., 600.8774, 614.8537,\n",
" 601.4614],\n",
" [1537.095 , 1588.2531, 1543.651 , ..., 672.7229, 676.0225,\n",
" 611.3737],\n",
" [1146.868 , 1239.931 , 1362.275 , ..., 618.6008, 611.4532,\n",
" 625.3992]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pcp</span></div><div class='xr-var-dims'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 366, 224, 464), meta=np.ndarray&gt;</div><input id='attrs-919acce6-6d98-40c1-bd92-7ee2c0efccf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-919acce6-6d98-40c1-bd92-7ee2c0efccf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b04678f-401c-4972-b0d4-97c368dff33a' class='xr-var-data-in' type='checkbox'><label for='data-4b04678f-401c-4972-b0d4-97c368dff33a' 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>estimated precip in mm/day</dd><dt><span>units :</span></dt><dd>mm</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> 561.88 GB </td> <td> 152.16 MB </td></tr>\n",
" <tr><th> Shape </th><td> (100, 13515, 224, 464) </td> <td> (1, 366, 224, 464) </td></tr>\n",
" <tr><th> Count </th><td> 18500 Tasks </td><td> 3700 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_mean</span></div><div class='xr-var-dims'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 366, 224, 464), meta=np.ndarray&gt;</div><input id='attrs-dfda5215-506b-4495-a616-bac9a044b259' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfda5215-506b-4495-a616-bac9a044b259' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f762866d-f1e6-4668-86f2-9b72ac258012' class='xr-var-data-in' type='checkbox'><label for='data-f762866d-f1e6-4668-86f2-9b72ac258012' 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>estimated daily mean temperature</dd><dt><span>units :</span></dt><dd>deg_C</dd></dl></div><div class='xr-var-data'><table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_range</span></div><div class='xr-var-dims'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 366, 224, 464), meta=np.ndarray&gt;</div><input id='attrs-6dbf70b4-4b20-4bcc-842d-956aff1aa24a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dbf70b4-4b20-4bcc-842d-956aff1aa24a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-306b75f0-adbc-4a1c-8169-58a558ef8016' class='xr-var-data-in' type='checkbox'><label for='data-306b75f0-adbc-4a1c-8169-58a558ef8016' 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>estimated diurnal range</dd><dt><span>units :</span></dt><dd>deg_C</dd></dl></div><div class='xr-var-data'><table>\n",
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" <text x=\"217.791351\" y=\"84.584623\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,217.791351,84.584623)\">224</text>\n",
" <text x=\"120.294118\" y=\"83.286894\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,83.286894)\">13515</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed6d6f15-89a8-44f4-bdcd-5b7fe5631db3' class='xr-section-summary-in' type='checkbox' checked><label for='section-ed6d6f15-89a8-44f4-bdcd-5b7fe5631db3' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>history :</span></dt><dd>Wed Oct 24 13:59:29 2018: ncks -4 -L 1 conus_daily_eighth_19800101_19801231.nc_001 -O conus_daily_eighth_19800101_19801231.nc_001</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.4 (http://nco.sf.net)</dd><dt><span>institution :</span></dt><dd>National Center fo Atmospheric Research (NCAR), Boulder, CO USA</dd><dt><span>title :</span></dt><dd>CONUS daily 12-km gridded ensemble precipitation and temperature dataset for 1980-2016</dd><dt><span>source :</span></dt><dd>Generated using version 1.1 of CONUS ensemble code base</dd><dt><span>references :</span></dt><dd>Newman et al. 2015: Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States. J. Hydrometeorology</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 224, lon: 464, member_id: 100, time: 13515)\n",
"Coordinates:\n",
" * lon (lon) float64 -124.9 -124.8 -124.6 -124.5 ... -67.25 -67.12 -67.0\n",
" * lat (lat) float64 25.12 25.25 25.38 25.5 ... 52.62 52.75 52.88 53.0\n",
" * time (time) datetime64[ns] 1980-01-01 1980-01-02 ... 2016-12-31\n",
" * member_id (member_id) int64 1 2 3 4 5 6 7 8 9 ... 93 94 95 96 97 98 99 100\n",
"Data variables:\n",
" elevation (lat, lon) float64 nan nan nan nan ... 617.5 618.6 611.5 625.4\n",
" pcp (member_id, time, lat, lon) float32 dask.array<chunksize=(1, 366, 224, 464), meta=np.ndarray>\n",
" t_mean (member_id, time, lat, lon) float32 dask.array<chunksize=(1, 366, 224, 464), meta=np.ndarray>\n",
" t_range (member_id, time, lat, lon) float32 dask.array<chunksize=(1, 366, 224, 464), meta=np.ndarray>\n",
"Attributes:\n",
" history: Wed Oct 24 13:59:29 2018: ncks -4 -L 1 conus_daily_eighth_1...\n",
" NCO: netCDF Operators version 4.7.4 (http://nco.sf.net)\n",
" institution: National Center fo Atmospheric Research (NCAR), Boulder, CO...\n",
" title: CONUS daily 12-km gridded ensemble precipitation and temper...\n",
" source: Generated using version 1.1 of CONUS ensemble code base\n",
" references: Newman et al. 2015: Gridded Ensemble Precipitation and Temp..."
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = xr.concat(dsets, dim='member_id').reset_coords()\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3min 23s, sys: 7.31 s, total: 3min 30s\n",
"Wall time: 10min 6s\n"
]
},
{
"data": {
"text/plain": [
"<xarray.backends.zarr.ZarrStore at 0x2af50df0ee90>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"ds.chunk({'member_id': 1, 'time': 366}).to_zarr(\"/glade/scratch/abanihi/data/gmet_v1.zarr\", mode=\"w\", consolidated=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"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",
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},
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}
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