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CMIP6_exploration_demo.ipynb
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"import intake"
]
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"## Loading a catalog \n",
"\n",
"1. Use intake to open a catalog of data, search and build a dataset dictionary this way it is genenric.\n",
"2. Catalog used here is specific to data on isilon but should be able to replace it with others when on say mistral or even pangeo's google cloud based catalog "
]
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"\n",
"--> The keys in the returned dictionary of datasets are constructed as follows:\n",
"\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (bnds: 2, depth: 46, member_id: 1, ncells: 830305, time: 180, vertices: 16)\n",
"Coordinates:\n",
" * time (time) float64 15.5 45.0 74.5 ... 3.576e+03 3.607e+03 3.638e+03\n",
" * depth (depth) float64 -0.0 10.0 20.0 30.0 ... 5.4e+03 5.65e+03 5.9e+03\n",
" lat (ncells) float64 dask.array&lt;chunksize=(830305,), meta=np.ndarray&gt;\n",
" lon (ncells) float64 dask.array&lt;chunksize=(830305,), meta=np.ndarray&gt;\n",
" * member_id (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
"Dimensions without coordinates: bnds, ncells, vertices\n",
"Data variables:\n",
" time_bnds (time, bnds) float64 dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
" thetao (member_id, time, depth, ncells) float32 dask.array&lt;chunksize=(1, 1, 46, 830305), meta=np.ndarray&gt;\n",
" lat_bnds (ncells, vertices) float64 dask.array&lt;chunksize=(830305, 16), meta=np.ndarray&gt;\n",
" lon_bnds (ncells, vertices) float64 dask.array&lt;chunksize=(830305, 16), meta=np.ndarray&gt;\n",
"Attributes: (12/41)\n",
" parent_variant_label: r1i1p1f1\n",
" parent_experiment_id: piControl\n",
" creation_date: 2018-12-18T12:00:00Z\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" branch_time_in_parent: 54421.0\n",
" grid_label: gn\n",
" ... ...\n",
" nominal_resolution: 25 km\n",
" table_id: Omon\n",
" variant_label: r1i1p1f1\n",
" tracking_id: hdl:21.14100/43ae4684-b1d7-4f1b-ada6-33217f355d0...\n",
" realization_index: 1\n",
" intake_esm_dataset_key: CMIP.AWI.AWI-CM-1-1-MR.historical.Omon.gn</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-2e5492d2-8aed-4a96-8754-5579ae947b2c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2e5492d2-8aed-4a96-8754-5579ae947b2c' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>depth</span>: 46</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span>ncells</span>: 830305</li><li><span class='xr-has-index'>time</span>: 180</li><li><span>vertices</span>: 16</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c55f80da-c4d2-480d-87f8-c5c330559b90' class='xr-section-summary-in' type='checkbox' checked><label for='section-c55f80da-c4d2-480d-87f8-c5c330559b90' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 ... 3.607e+03 3.638e+03</div><input id='attrs-de17b76b-bcd8-4757-a6f3-70a8232df88d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de17b76b-bcd8-4757-a6f3-70a8232df88d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ea77c9f-5d50-4a78-a6ac-39381b45a4c4' class='xr-var-data-in' type='checkbox'><label for='data-8ea77c9f-5d50-4a78-a6ac-39381b45a4c4' 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><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>calendar :</span></dt><dd>standard</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5, 45. , 74.5, 105. , 135.5, 166. , 196.5, 227.5, 258. ,\n",
" 288.5, 319. , 349.5, 15.5, 45. , 74.5, 105. , 135.5, 166. ,\n",
" 196.5, 227.5, 258. , 288.5, 319. , 349.5, 380.5, 410.5, 440.5,\n",
" 471. , 501.5, 532. , 562.5, 593.5, 624. , 654.5, 685. , 715.5,\n",
" 746.5, 776. , 805.5, 836. , 866.5, 897. , 927.5, 958.5, 989. ,\n",
" 1019.5, 1050. , 1080.5, 1111.5, 1141. , 1170.5, 1201. , 1231.5, 1262. ,\n",
" 1292.5, 1323.5, 1354. , 1384.5, 1415. , 1445.5, 15.5, 45. , 74.5,\n",
" 105. , 135.5, 166. , 196.5, 227.5, 258. , 288.5, 319. , 349.5,\n",
" 380.5, 410.5, 440.5, 471. , 501.5, 532. , 562.5, 593.5, 624. ,\n",
" 654.5, 685. , 715.5, 746.5, 776. , 805.5, 836. , 866.5, 897. ,\n",
" 927.5, 958.5, 989. , 1019.5, 1050. , 1080.5, 1111.5, 1141. , 1170.5,\n",
" 1201. , 1231.5, 1262. , 1292.5, 1323.5, 1354. , 1384.5, 1415. , 1445.5,\n",
" 1476.5, 1506. , 1535.5, 1566. , 1596.5, 1627. , 1657.5, 1688.5, 1719. ,\n",
" 1749.5, 1780. , 1810.5, 1841.5, 1871.5, 1901.5, 1932. , 1962.5, 1993. ,\n",
" 2023.5, 2054.5, 2085. , 2115.5, 2146. , 2176.5, 2207.5, 2237. , 2266.5,\n",
" 2297. , 2327.5, 2358. , 2388.5, 2419.5, 2450. , 2480.5, 2511. , 2541.5,\n",
" 2572.5, 2602. , 2631.5, 2662. , 2692.5, 2723. , 2753.5, 2784.5, 2815. ,\n",
" 2845.5, 2876. , 2906.5, 2937.5, 2967. , 2996.5, 3027. , 3057.5, 3088. ,\n",
" 3118.5, 3149.5, 3180. , 3210.5, 3241. , 3271.5, 3302.5, 3332.5, 3362.5,\n",
" 3393. , 3423.5, 3454. , 3484.5, 3515.5, 3546. , 3576.5, 3607. , 3637.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 10.0 20.0 ... 5.65e+03 5.9e+03</div><input id='attrs-fc05c44e-1aed-4695-8252-eacbba9feec8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc05c44e-1aed-4695-8252-eacbba9feec8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2ecc7d0-bdfc-4ec1-bd94-17bd4ca5a0a2' class='xr-var-data-in' type='checkbox'><label for='data-b2ecc7d0-bdfc-4ec1-bd94-17bd4ca5a0a2' 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>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([ -0., 10., 20., 30., 40., 50., 60., 70., 80., 90.,\n",
" 100., 115., 135., 160., 190., 230., 280., 340., 410., 490.,\n",
" 580., 680., 790., 910., 1040., 1180., 1330., 1500., 1700., 1920.,\n",
" 2150., 2400., 2650., 2900., 3150., 3400., 3650., 3900., 4150., 4400.,\n",
" 4650., 4900., 5150., 5400., 5650., 5900.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(830305,), meta=np.ndarray&gt;</div><input id='attrs-a1902180-7987-44eb-8485-a1414a1af4b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1902180-7987-44eb-8485-a1414a1af4b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5a0ff83-b6ae-4385-8e14-dc9f213d55f1' class='xr-var-data-in' type='checkbox'><label for='data-a5a0ff83-b6ae-4385-8e14-dc9f213d55f1' 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>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</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>lon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(830305,), meta=np.ndarray&gt;</div><input id='attrs-c58083dc-700a-4cdb-a760-35469611c434' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c58083dc-700a-4cdb-a760-35469611c434' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-431cb38f-f810-48cc-93f7-e8d5bb62df53' class='xr-var-data-in' type='checkbox'><label for='data-431cb38f-f810-48cc-93f7-e8d5bb62df53' 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>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</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 class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-d18eb413-4e32-4fcd-9632-d142bbeb564f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d18eb413-4e32-4fcd-9632-d142bbeb564f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f53dca58-4998-49e4-8a12-3827e45e0094' class='xr-var-data-in' type='checkbox'><label for='data-f53dca58-4998-49e4-8a12-3827e45e0094' 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;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36679135-b1c7-48d2-8788-c552b41c3add' class='xr-section-summary-in' type='checkbox' checked><label for='section-36679135-b1c7-48d2-8788-c552b41c3add' 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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-2c29ec2e-f4da-4ad0-9a92-04a849160a2f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c29ec2e-f4da-4ad0-9a92-04a849160a2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4a413f1-4bcb-4e13-9db3-0777e18254b7' class='xr-var-data-in' type='checkbox'><label for='data-c4a413f1-4bcb-4e13-9db3-0777e18254b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(member_id, time, depth, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 46, 830305), meta=np.ndarray&gt;</div><input id='attrs-6a4f1e52-b957-4670-b368-8c95e088209c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a4f1e52-b957-4670-b368-8c95e088209c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8294368a-184c-45b1-b382-0b6187f518ce' class='xr-var-data-in' type='checkbox'><label for='data-8294368a-184c-45b1-b382-0b6187f518ce' 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>degC</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>description :</span></dt><dd>Diagnostic should be contributed even for models using conservative temperature as prognostic field.</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd></dl></div><div class='xr-var-data'><table>\n",
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"hdl:21.14100/c949ee91-f922-4348-99a8-ad95406f29d0\n",
"hdl:21.14100/0ce6fbbb-1944-4489-9822-1b7c73730da1</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.AWI.AWI-CM-1-1-MR.historical.Omon.gn</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (bnds: 2, depth: 46, member_id: 1, ncells: 830305, time: 180, vertices: 16)\n",
"Coordinates:\n",
" * time (time) float64 15.5 45.0 74.5 ... 3.576e+03 3.607e+03 3.638e+03\n",
" * depth (depth) float64 -0.0 10.0 20.0 30.0 ... 5.4e+03 5.65e+03 5.9e+03\n",
" lat (ncells) float64 dask.array<chunksize=(830305,), meta=np.ndarray>\n",
" lon (ncells) float64 dask.array<chunksize=(830305,), meta=np.ndarray>\n",
" * member_id (member_id) <U8 'r1i1p1f1'\n",
"Dimensions without coordinates: bnds, ncells, vertices\n",
"Data variables:\n",
" time_bnds (time, bnds) float64 dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
" thetao (member_id, time, depth, ncells) float32 dask.array<chunksize=(1, 1, 46, 830305), meta=np.ndarray>\n",
" lat_bnds (ncells, vertices) float64 dask.array<chunksize=(830305, 16), meta=np.ndarray>\n",
" lon_bnds (ncells, vertices) float64 dask.array<chunksize=(830305, 16), meta=np.ndarray>\n",
"Attributes: (12/41)\n",
" parent_variant_label: r1i1p1f1\n",
" parent_experiment_id: piControl\n",
" creation_date: 2018-12-18T12:00:00Z\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" branch_time_in_parent: 54421.0\n",
" grid_label: gn\n",
" ... ...\n",
" nominal_resolution: 25 km\n",
" table_id: Omon\n",
" variant_label: r1i1p1f1\n",
" tracking_id: hdl:21.14100/43ae4684-b1d7-4f1b-ada6-33217f355d0...\n",
" realization_index: 1\n",
" intake_esm_dataset_key: CMIP.AWI.AWI-CM-1-1-MR.historical.Omon.gn"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"col = intake.open_esm_datastore('awi-cmip6.json')\n",
"cat = col.search(activity_id='CMIP', experiment_id='historical',source_id='AWI-CM-1-1-MR',table_id='Omon',variable_id='thetao', member_id='r1i1p1f1')\n",
"ddict = cat.to_dataset_dict(aggregate=True,cdf_kwargs={'decode_times':False, 'chunks':{'time':1}})\n",
"\n",
"# example dataset\n",
"thetao=ddict[list(ddict.keys())[0]]\n",
"thetao"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use our tools to harmonize loading of cmip datasets, selections across different models uniformly"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from cmprep import model_data \n",
"from cmprep import expeditions "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Models: ['CMIP.AWI.AWI-CM-1-1-MR.historical.Omon.gn']"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cmm=model_data.CMIP6DatasetCollection(ddict) # dictionary from intake results\n",
"cmm"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;thetao&#x27; (member_id: 1, time: 180, depth: 46, ncells: 830305)&gt;\n",
"dask.array&lt;broadcast_to, shape=(1, 180, 46, 830305), dtype=float32, chunksize=(1, 1, 46, 830305), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * time (time) float64 15.5 45.0 74.5 ... 3.576e+03 3.607e+03 3.638e+03\n",
" * depth (depth) float64 -0.0 10.0 20.0 30.0 ... 5.4e+03 5.65e+03 5.9e+03\n",
" lat (ncells) float64 dask.array&lt;chunksize=(830305,), meta=np.ndarray&gt;\n",
" lon (ncells) float64 dask.array&lt;chunksize=(830305,), meta=np.ndarray&gt;\n",
" * member_id (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
"Dimensions without coordinates: ncells\n",
"Attributes:\n",
" units: degC\n",
" CDI_grid_type: unstructured\n",
" description: Diagnostic should be contributed even for models using co...\n",
" standard_name: sea_water_potential_temperature\n",
" cell_methods: area: mean where sea time: mean\n",
" cell_measures: area: areacello volume: volcello</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'thetao'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 180</li><li><span class='xr-has-index'>depth</span>: 46</li><li><span>ncells</span>: 830305</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3c67a301-a92e-4844-8d22-85dc790d0792' class='xr-array-in' type='checkbox' checked><label for='section-3c67a301-a92e-4844-8d22-85dc790d0792' 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, 1, 46, 830305), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
"<tr>\n",
"<td>\n",
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" <tr><th> Bytes </th><td> 27.50 GB </td> <td> 152.78 MB </td></tr>\n",
" <tr><th> Shape </th><td> (1, 180, 46, 830305) </td> <td> (1, 1, 46, 830305) </td></tr>\n",
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" 1749.5, 1780. , 1810.5, 1841.5, 1871.5, 1901.5, 1932. , 1962.5, 1993. ,\n",
" 2023.5, 2054.5, 2085. , 2115.5, 2146. , 2176.5, 2207.5, 2237. , 2266.5,\n",
" 2297. , 2327.5, 2358. , 2388.5, 2419.5, 2450. , 2480.5, 2511. , 2541.5,\n",
" 2572.5, 2602. , 2631.5, 2662. , 2692.5, 2723. , 2753.5, 2784.5, 2815. ,\n",
" 2845.5, 2876. , 2906.5, 2937.5, 2967. , 2996.5, 3027. , 3057.5, 3088. ,\n",
" 3118.5, 3149.5, 3180. , 3210.5, 3241. , 3271.5, 3302.5, 3332.5, 3362.5,\n",
" 3393. , 3423.5, 3454. , 3484.5, 3515.5, 3546. , 3576.5, 3607. , 3637.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 10.0 20.0 ... 5.65e+03 5.9e+03</div><input id='attrs-c26d2f95-55d6-4625-8075-9bc05778fe62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c26d2f95-55d6-4625-8075-9bc05778fe62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cab140c-fb92-4ac3-8bd1-c3be6b5b5fae' class='xr-var-data-in' type='checkbox'><label for='data-0cab140c-fb92-4ac3-8bd1-c3be6b5b5fae' 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>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([ -0., 10., 20., 30., 40., 50., 60., 70., 80., 90.,\n",
" 100., 115., 135., 160., 190., 230., 280., 340., 410., 490.,\n",
" 580., 680., 790., 910., 1040., 1180., 1330., 1500., 1700., 1920.,\n",
" 2150., 2400., 2650., 2900., 3150., 3400., 3650., 3900., 4150., 4400.,\n",
" 4650., 4900., 5150., 5400., 5650., 5900.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(830305,), meta=np.ndarray&gt;</div><input id='attrs-d7a6615d-1aad-4de7-a627-9d32a24c155d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7a6615d-1aad-4de7-a627-9d32a24c155d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b49e270c-7a5b-4652-a04f-f0bddfed48d7' class='xr-var-data-in' type='checkbox'><label for='data-b49e270c-7a5b-4652-a04f-f0bddfed48d7' 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>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</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> 6.64 MB </td> <td> 6.64 MB </td></tr>\n",
" <tr><th> Shape </th><td> (830305,) </td> <td> (830305,) </td></tr>\n",
" <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(830305,), meta=np.ndarray&gt;</div><input id='attrs-440570dc-172b-40dc-8c5a-49822d2c2b05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-440570dc-172b-40dc-8c5a-49822d2c2b05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-320ca142-ea58-4814-ba93-7b99118eea4b' class='xr-var-data-in' type='checkbox'><label for='data-320ca142-ea58-4814-ba93-7b99118eea4b' 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>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</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> 6.64 MB </td> <td> 6.64 MB </td></tr>\n",
" <tr><th> Shape </th><td> (830305,) </td> <td> (830305,) </td></tr>\n",
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" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
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"\n",
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"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-22fa7931-d8fa-4780-beaa-2750ece65f97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22fa7931-d8fa-4780-beaa-2750ece65f97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc9bee26-6225-41eb-9d16-b43619365f15' class='xr-var-data-in' type='checkbox'><label for='data-bc9bee26-6225-41eb-9d16-b43619365f15' 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;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f7b6ae1-6c66-4d59-b484-535ec0c6a0cb' class='xr-section-summary-in' type='checkbox' checked><label for='section-0f7b6ae1-6c66-4d59-b484-535ec0c6a0cb' 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>units :</span></dt><dd>degC</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>description :</span></dt><dd>Diagnostic should be contributed even for models using conservative temperature as prognostic field.</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'thetao' (member_id: 1, time: 180, depth: 46, ncells: 830305)>\n",
"dask.array<broadcast_to, shape=(1, 180, 46, 830305), dtype=float32, chunksize=(1, 1, 46, 830305), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * time (time) float64 15.5 45.0 74.5 ... 3.576e+03 3.607e+03 3.638e+03\n",
" * depth (depth) float64 -0.0 10.0 20.0 30.0 ... 5.4e+03 5.65e+03 5.9e+03\n",
" lat (ncells) float64 dask.array<chunksize=(830305,), meta=np.ndarray>\n",
" lon (ncells) float64 dask.array<chunksize=(830305,), meta=np.ndarray>\n",
" * member_id (member_id) <U8 'r1i1p1f1'\n",
"Dimensions without coordinates: ncells\n",
"Attributes:\n",
" units: degC\n",
" CDI_grid_type: unstructured\n",
" description: Diagnostic should be contributed even for models using co...\n",
" standard_name: sea_water_potential_temperature\n",
" cell_methods: area: mean where sea time: mean\n",
" cell_measures: area: areacello volume: volcello"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cmm['CMIP.AWI.AWI-CM-1-1-MR.historical.Omon.gn'].thetao"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## demo of selection and plotting accessor \n",
"**.sel method has diffrent ways of selections like box,path etc that does model-agnostic selection in a uniform way.**"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"arctic_s1=expeditions.arctic_path_S1"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7fbcc5e33ed0>"
]
},
"execution_count": 17,
"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": [
"cmm['CMIP.AWI.AWI-CM-1-1-MR.historical.Omon.gn'].thetao.isel(time=0).cmip6.sel(path=arctic_s1).plot(x='ncells', y='lev', \n",
" ylim=(3000,0), # sey y limits, descending\n",
" vmin=-2, vmax=2, # set value limits\n",
" cmap='Spectral_r') # set colormap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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