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xarray.ipynb.txt
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xarray.ipynb.txt
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Xarray with Dask Arrays\n",
"\n",
"<img src=\"images/dataset-diagram-logo.png\" \n",
" align=\"right\"\n",
" width=\"66%\"\n",
" alt=\"Xarray Dataset\">\n",
" \n",
"**[Xarray](http://xarray.pydata.org/en/stable/)** is an open source project and Python package that extends the labeled data functionality of [Pandas](https://pandas.pydata.org/) to N-dimensional array-like datasets. It shares a similar API to [NumPy](http://www.numpy.org/) and [Pandas](https://pandas.pydata.org/) and supports both [Dask](https://dask.org/) and [NumPy](http://www.numpy.org/) arrays under the hood."
]
},
{
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"shell.execute_reply": "2022-07-27T19:14:16.609763Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from dask.distributed import Client\n",
"import xarray as xr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start Dask Client for Dashboard\n",
"\n",
"Starting the Dask Client is optional. It will provide a dashboard which \n",
"is useful to gain insight on the computation. \n",
"\n",
"The link to the dashboard will become visible when you create the client below. We recommend having it open on one side of your screen while using your notebook on the other side. This can take some effort to arrange your windows, but seeing them both at the same is very useful when learning."
]
},
{
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" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-4eea680b-0de0-11ed-9d1a-000d3a8f7959</p>\n",
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" <tr>\n",
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" \n",
" </tr>\n",
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" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
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" \n",
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" </table>\n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
" <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
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" <strong>Total threads:</strong> 4\n",
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" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Started:</strong> Just now\n",
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" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 1.86 GiB\n",
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"\n",
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" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:34819\n",
" </td>\n",
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" <strong>Total threads: </strong> 2\n",
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" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:39963/status\" target=\"_blank\">http://127.0.0.1:39963/status</a>\n",
" </td>\n",
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" <strong>Memory: </strong> 0.93 GiB\n",
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" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:38683\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /home/runner/work/dask-examples/dask-examples/dask-worker-space/worker-kg42o2xu\n",
" </td>\n",
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"\n",
" \n",
"\n",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:43051/status\" target=\"_blank\">http://127.0.0.1:43051/status</a>\n",
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" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 0.93 GiB\n",
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" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:43647\n",
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" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /home/runner/work/dask-examples/dask-examples/dask-worker-space/worker-q4dslyjg\n",
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"\n",
" \n",
"\n",
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"\n",
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"\n",
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"</div>\n",
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" \n",
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],
"text/plain": [
"<Client: 'tcp://127.0.0.1:36327' processes=2 threads=4, memory=1.86 GiB>"
]
},
"execution_count": 2,
"metadata": {},
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}
],
"source": [
"client = Client(n_workers=2, threads_per_worker=2, memory_limit='1GB')\n",
"client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Open a sample dataset\n",
"\n",
"We will use some of xarray's tutorial data for this example. By specifying the chunk shape, xarray will automatically create Dask arrays for each data variable in the `Dataset`. In xarray, `Datasets` are dict-like container of labeled arrays, analogous to the `pandas.DataFrame`. Note that we're taking advantage of xarray's dimension labels when specifying chunk shapes."
]
},
{
"cell_type": "code",
"execution_count": 3,
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
"Dimensions: (lat: 25, time: 2920, lon: 53)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 dask.array<chunksize=(2920, 25, 25), meta=np.ndarray>\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...</pre><div class='xr-wrap' style='display:none'><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-b8276ada-b471-4d87-a627-d8a615b0e97a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b8276ada-b471-4d87-a627-d8a615b0e97a' 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>: 25</li><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-549170cd-8db2-4171-a1cb-94167a5eabdb' class='xr-section-summary-in' type='checkbox' checked><label for='section-549170cd-8db2-4171-a1cb-94167a5eabdb' class='xr-section-summary' >Coordinates: <span>(3)</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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-9a8b5778-14d7-4092-b555-5ad69d3eb840' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a8b5778-14d7-4092-b555-5ad69d3eb840' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ea5cee9-7af7-4667-b6b6-3bc68ae4e3a0' class='xr-var-data-in' type='checkbox'><label for='data-7ea5cee9-7af7-4667-b6b6-3bc68ae4e3a0' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
" 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
" 15. ], dtype=float32)</pre></div></li><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'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-70421f4f-37a1-406a-b420-341554b63054' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70421f4f-37a1-406a-b420-341554b63054' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa3a2fef-a167-4b8d-9642-73b57afbb2de' class='xr-var-data-in' type='checkbox'><label for='data-aa3a2fef-a167-4b8d-9642-73b57afbb2de' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
" 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
" 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
" 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
" 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
" 325. , 327.5, 330. ], dtype=float32)</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'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-1b5ca778-7fcd-4eb2-8bda-f78603544e9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b5ca778-7fcd-4eb2-8bda-f78603544e9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd8b2cb2-870a-4b70-bb5d-637c36d2edde' class='xr-var-data-in' type='checkbox'><label for='data-fd8b2cb2-870a-4b70-bb5d-637c36d2edde' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n",
" '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n",
" '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n",
" dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db68c9de-36fc-450e-ae7e-47aa98280c00' class='xr-section-summary-in' type='checkbox' checked><label for='section-db68c9de-36fc-450e-ae7e-47aa98280c00' 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>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(2920, 25, 25), meta=np.ndarray></div><input id='attrs-b1de8560-6d35-40e9-b2f1-f01524033712' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1de8560-6d35-40e9-b2f1-f01524033712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6dce934-0073-4d5d-9b73-3481cbf4be39' class='xr-var-data-in' type='checkbox'><label for='data-b6dce934-0073-4d5d-9b73-3481cbf4be39' 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>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
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" <th> Chunk </th>\n",
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" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 14.76 MiB </td>\n",
" <td> 6.96 MiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2920, 25, 53) </td>\n",
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" <tr>\n",
" <th> Count </th>\n",
" <td> 4 Tasks </td>\n",
" <td> 3 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float32 </td>\n",
" <td> numpy.ndarray </td>\n",
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" </tbody>\n",
" </table>\n",
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"(4x/day). These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, time: 2920, lon: 53)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
"Data variables:\n",
" air (time, lat, lon) float32 dask.array<chunksize=(2920, 25, 25), meta=np.ndarray>\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
]
},
"execution_count": 3,
"metadata": {},
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}
],
"source": [
"ds = xr.tutorial.open_dataset('air_temperature',\n",
" chunks={'lat': 25, 'lon': 25, 'time': -1})\n",
"ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Quickly inspecting the `Dataset` above, we'll note that this `Dataset` has three _dimensions_ akin to axes in NumPy (`lat`, `lon`, and `time`), three _coordinate variables_ akin to `pandas.Index` objects (also named `lat`, `lon`, and `time`), and one data variable (`air`). Xarray also holds Dataset specific metadata as _attributes_."
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