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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7edf6e3b-2f59-4016-a36a-33b8be18d531",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import cosima_cookbook\n",
"import xarray"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0ade27de-5c7a-4781-9e50-788d1a29a17b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"start_time = \"2003-01-01 00:00:00\"\n",
"end_time = \"2003-01-05 00:00:00\"\n",
"date_range = [start_time, end_time]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b196bcf2-5383-4f2a-bb34-ddb654d68d72",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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".xr-header {\n",
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" padding-bottom: 6px;\n",
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" border-bottom: solid 1px var(--xr-border-color);\n",
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".xr-header > div,\n",
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"\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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".xr-section-item {\n",
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" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
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".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
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".xr-section-summary-in:disabled + label {\n",
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"}\n",
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".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
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".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
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".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
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"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
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".xr-array-wrap > label {\n",
" grid-column: 1;\n",
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".xr-preview {\n",
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".xr-array-in:checked ~ .xr-array-preview {\n",
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" background-color: var(--xr-background-color-row-odd);\n",
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".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 59GB\n",
"Dimensions: (time: 5, st_ocean: 75, yu_ocean: 2700, xu_ocean: 3600,\n",
" yt_ocean: 2700, xt_ocean: 3600)\n",
"Coordinates:\n",
" * xt_ocean (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
" * yt_ocean (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.89 89.94 89.98\n",
" * st_ocean (st_ocean) float64 600B 0.5413 1.681 2.94 ... 5.511e+03 5.709e+03\n",
" * time (time) object 40B 2170-04-01 12:00:00 ... 2170-04-05 12:00:00\n",
" * xu_ocean (xu_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.8 79.9 80.0\n",
" * yu_ocean (yu_ocean) float64 22kB -81.09 -81.05 -81.0 ... 89.92 89.96 90.0\n",
"Data variables:\n",
" u (time, st_ocean, yu_ocean, xu_ocean) float32 15GB dask.array&lt;chunksize=(2, 14, 600, 800), meta=np.ndarray&gt;\n",
" v (time, st_ocean, yu_ocean, xu_ocean) float32 15GB dask.array&lt;chunksize=(2, 14, 600, 800), meta=np.ndarray&gt;\n",
" salt (time, st_ocean, yt_ocean, xt_ocean) float32 15GB dask.array&lt;chunksize=(2, 14, 600, 800), meta=np.ndarray&gt;\n",
" temp (time, st_ocean, yt_ocean, xt_ocean) float32 15GB dask.array&lt;chunksize=(2, 14, 600, 800), meta=np.ndarray&gt;\n",
" eta_t (time, yt_ocean, xt_ocean) float32 194MB dask.array&lt;chunksize=(3, 2025, 2700), meta=np.ndarray&gt;\n",
"Attributes:\n",
" filename: ocean_daily_3d_dzt.nc\n",
" title: ACCESS-OM2-01\n",
" grid_type: mosaic\n",
" grid_tile: 1</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-58a58f89-d00a-4f74-b7d3-ac479da7b777' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-58a58f89-d00a-4f74-b7d3-ac479da7b777' 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'>time</span>: 5</li><li><span class='xr-has-index'>st_ocean</span>: 75</li><li><span class='xr-has-index'>yu_ocean</span>: 2700</li><li><span class='xr-has-index'>xu_ocean</span>: 3600</li><li><span class='xr-has-index'>yt_ocean</span>: 2700</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2e3b5c90-dfa8-49de-aaf1-239141c1afb0' class='xr-section-summary-in' type='checkbox' checked><label for='section-2e3b5c90-dfa8-49de-aaf1-239141c1afb0' class='xr-section-summary' >Coordinates: <span>(6)</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'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-1196ed8f-58bc-4b7a-8a71-2c5cc048ad87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1196ed8f-58bc-4b7a-8a71-2c5cc048ad87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f4a2fcb-b74b-42f9-aa52-8f69b5bbf1c4' class='xr-var-data-in' type='checkbox'><label for='data-0f4a2fcb-b74b-42f9-aa52-8f69b5bbf1c4' 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>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.95, -279.85, -279.75, ..., 79.75, 79.85, 79.95])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.11 -81.07 ... 89.94 89.98</div><input id='attrs-6e8c4e7e-e2be-4db9-aca0-6063454061a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e8c4e7e-e2be-4db9-aca0-6063454061a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd2d659b-f60c-4527-a69e-21747c0f1904' class='xr-var-data-in' type='checkbox'><label for='data-cd2d659b-f60c-4527-a69e-21747c0f1904' 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>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.108632, -81.066392, -81.024153, ..., 89.894417, 89.936657,\n",
" 89.978896])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>st_ocean</span></div><div class='xr-var-dims'>(st_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5413 1.681 ... 5.709e+03</div><input id='attrs-f66570e9-9559-42e0-b056-f6095e59123a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f66570e9-9559-42e0-b056-f6095e59123a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13b07864-dcfe-4458-bb0c-182319c36c91' class='xr-var-data-in' type='checkbox'><label for='data-13b07864-dcfe-4458-bb0c-182319c36c91' 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>tcell zstar depth</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>edges :</span></dt><dd>st_edges_ocean</dd></dl></div><div class='xr-var-data'><pre>array([5.412808e-01, 1.680735e+00, 2.939953e+00, 4.331521e+00, 5.869350e+00,\n",
" 7.568810e+00, 9.446885e+00, 1.152234e+01, 1.381593e+01, 1.635055e+01,\n",
" 1.915154e+01, 2.224687e+01, 2.566746e+01, 2.944746e+01, 3.362460e+01,\n",
" 3.824057e+01, 4.334140e+01, 4.897796e+01, 5.520640e+01, 6.208874e+01,\n",
" 6.969342e+01, 7.809601e+01, 8.737988e+01, 9.763700e+01, 1.089687e+02,\n",
" 1.214869e+02, 1.353144e+02, 1.505868e+02, 1.674530e+02, 1.860765e+02,\n",
" 2.066365e+02, 2.293296e+02, 2.543701e+02, 2.819920e+02, 3.124492e+02,\n",
" 3.460166e+02, 3.829906e+02, 4.236883e+02, 4.684475e+02, 5.176242e+02,\n",
" 5.715899e+02, 6.307275e+02, 6.954248e+02, 7.660668e+02, 8.430255e+02,\n",
" 9.266482e+02, 1.017244e+03, 1.115068e+03, 1.220309e+03, 1.333076e+03,\n",
" 1.453384e+03, 1.581154e+03, 1.716205e+03, 1.858264e+03, 2.006975e+03,\n",
" 2.161913e+03, 2.322601e+03, 2.488533e+03, 2.659189e+03, 2.834054e+03,\n",
" 3.012631e+03, 3.194453e+03, 3.379089e+03, 3.566145e+03, 3.755274e+03,\n",
" 3.946166e+03, 4.138551e+03, 4.332197e+03, 4.526903e+03, 4.722497e+03,\n",
" 4.918835e+03, 5.115794e+03, 5.313270e+03, 5.511177e+03, 5.709443e+03])</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'>object</div><div class='xr-var-preview xr-preview'>2170-04-01 12:00:00 ... 2170-04-...</div><input id='attrs-b6a44200-eebc-4a2b-86c2-16201cd4b419' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6a44200-eebc-4a2b-86c2-16201cd4b419' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ad92202-1092-4ac3-b333-65f5e9a64c70' class='xr-var-data-in' type='checkbox'><label for='data-3ad92202-1092-4ac3-b333-65f5e9a64c70' 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>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2170, 4, 1, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(2170, 4, 2, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(2170, 4, 3, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(2170, 4, 4, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(2170, 4, 5, 12, 0, 0, 0, has_year_zero=True)],\n",
" dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xu_ocean</span></div><div class='xr-var-dims'>(xu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 -279.7 ... 79.9 80.0</div><input id='attrs-5aa99d7c-730e-4695-a363-bb8bf1286627' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5aa99d7c-730e-4695-a363-bb8bf1286627' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12c990b5-e4d7-4e7e-b382-e20c2bc8da57' class='xr-var-data-in' type='checkbox'><label for='data-12c990b5-e4d7-4e7e-b382-e20c2bc8da57' 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>ucell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.9, -279.8, -279.7, ..., 79.8, 79.9, 80. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yu_ocean</span></div><div class='xr-var-dims'>(yu_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.09 -81.05 -81.0 ... 89.96 90.0</div><input id='attrs-c16b99ca-3398-4fda-baee-de17f4e63827' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c16b99ca-3398-4fda-baee-de17f4e63827' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e135ec4-0bb3-4d3e-aec6-0ac4b128ac09' class='xr-var-data-in' type='checkbox'><label for='data-6e135ec4-0bb3-4d3e-aec6-0ac4b128ac09' 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>ucell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.087512, -81.045273, -81.003033, ..., 89.915537, 89.957776,\n",
" 90. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d19aff9-5d9f-46f0-9872-8168d20fc941' class='xr-section-summary-in' type='checkbox' checked><label for='section-8d19aff9-5d9f-46f0-9872-8168d20fc941' class='xr-section-summary' >Data variables: <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>u</span></div><div class='xr-var-dims'>(time, st_ocean, yu_ocean, xu_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 14, 600, 800), meta=np.ndarray&gt;</div><input id='attrs-ed2f8829-e941-4be3-8624-e554279a893d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2f8829-e941-4be3-8624-e554279a893d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7445070-3f67-45ed-8fb7-f10080d13599' class='xr-var-data-in' type='checkbox'><label for='data-b7445070-3f67-45ed-8fb7-f10080d13599' 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>i-current</dd><dt><span>units :</span></dt><dd>m/sec</dd><dt><span>valid_range :</span></dt><dd>[-10. 10.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>sea_water_x_velocity</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 13.58 GiB </td>\n",
" <td> 51.27 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (5, 75, 2700, 3600) </td>\n",
" <td> (2, 14, 600, 800) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 450 chunks in 3 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, st_ocean, yu_ocean, xu_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 14, 600, 800), meta=np.ndarray&gt;</div><input id='attrs-9b7290fc-2308-422b-9b20-6da11d48ccfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b7290fc-2308-422b-9b20-6da11d48ccfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-046fce8c-0bc2-4eb2-b7a2-b5e12ccbb126' class='xr-var-data-in' type='checkbox'><label for='data-046fce8c-0bc2-4eb2-b7a2-b5e12ccbb126' 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>j-current</dd><dt><span>units :</span></dt><dd>m/sec</dd><dt><span>valid_range :</span></dt><dd>[-10. 10.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>sea_water_y_velocity</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
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" <thead>\n",
" <tr>\n",
" <td> </td>\n",
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" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 13.58 GiB </td>\n",
" <td> 51.27 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (5, 75, 2700, 3600) </td>\n",
" <td> (2, 14, 600, 800) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 450 chunks in 3 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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" <text x=\"7.474299\" y=\"117.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,117.474299)\">5</text>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b20bad7-e59b-4fa0-a33b-ae1044e52760' class='xr-section-summary-in' type='checkbox' ><label for='section-6b20bad7-e59b-4fa0-a33b-ae1044e52760' class='xr-section-summary' >Indexes: <span>(6)</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-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10b3c078-a383-4620-900b-d58612a989b0' class='xr-index-data-in' type='checkbox'/><label for='index-10b3c078-a383-4620-900b-d58612a989b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -279.95, -279.84999999999997, -279.74999999999994,\n",
" -279.6499999999999, -279.5499999999999, -279.4499999999999,\n",
" -279.34999999999985, -279.24999999999983, -279.1499999999998,\n",
" -279.0499999999998,\n",
" ...\n",
" 79.04999999999492, 79.14999999999495, 79.24999999999491,\n",
" 79.34999999999494, 79.4499999999949, 79.54999999999492,\n",
" 79.64999999999489, 79.74999999999491, 79.84999999999488,\n",
" 79.9499999999949],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d96b5856-eb1e-4b8a-85b5-1e9ec011a7dd' class='xr-index-data-in' type='checkbox'/><label for='index-d96b5856-eb1e-4b8a-85b5-1e9ec011a7dd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-81.10863167835483, -81.06639232397434, -81.02415296959386,\n",
" -80.98191361521337, -80.93967426083289, -80.8974349064524,\n",
" -80.85519555207192, -80.81295619769143, -80.77071684331095,\n",
" -80.72847748893047,\n",
" ...\n",
" 89.59874190883218, 89.6409812632127, 89.6832206175932,\n",
" 89.72545997197363, 89.76769932635409, 89.80993868073463,\n",
" 89.85217803511516, 89.89441738949557, 89.936656743876,\n",
" 89.97889609825653],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=2700))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>st_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-858a54ff-8a64-45cd-bb53-8a91b6a36501' class='xr-index-data-in' type='checkbox'/><label for='index-858a54ff-8a64-45cd-bb53-8a91b6a36501' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5412807653916101, 1.680734679831433, 2.939952648914465,\n",
" 4.331521485149508, 5.8693504240540255, 7.5688099200502155,\n",
" 9.446884959648713, 11.522344392803275, 13.815927932333222,\n",
" 16.350552632935347, 19.151540835805992, 22.24687175218868,\n",
" 25.667459058670023, 29.447457087455206, 33.62459841385048,\n",
" 38.24056586677453, 43.341402211041604, 48.97796096317406,\n",
" 55.20640199365842, 62.088735720175855, 69.69341978534455,\n",
" 78.09601210263148, 87.37988400213499, 97.63699685168024,\n",
" 108.96874488209758, 121.48686590184016, 135.31441999260392,\n",
" 150.5868339523135, 167.45300594349283, 186.07646019149857,\n",
" 206.63653528423634, 229.3295811897483, 254.3701289430592,\n",
" 281.99198245469614, 312.44916351368306, 346.0166182176396,\n",
" 382.9905653992972, 423.6883357450478, 468.44751540395634,\n",
" 517.6241725363747, 571.5899153226626, 630.7275131613291,\n",
" 695.4248214962124, 766.0667992097763, 843.0255127651847,\n",
" 926.648198508397, 1017.2437076839874, 1115.0679749837072,\n",
" 1220.3094951527164, 1333.07609257785, 1453.384439814726,\n",
" 1581.1537374282095, 1716.2046509593001, 1858.264029242824,\n",
" 2006.9751980469482, 2161.912882458598, 2322.601234136574,\n",
" 2488.5331512272987, 2659.189113483145, 2834.054068130656,\n",
" 3012.6313812062995, 3194.453386592603, 3379.088520492851,\n",
" 3566.145362412768, 3755.2740976592395, 3946.1659902666333,\n",
" 4138.551439998412, 4332.197126591926, 4526.902649987421,\n",
" 4722.496976663542, 4918.834912408163, 5115.793747063695,\n",
" 5313.270158218243, 5511.177417673949, 5709.442914225761],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;st_ocean&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a2900d1-041d-4200-9deb-e95191617492' class='xr-index-data-in' type='checkbox'/><label for='index-8a2900d1-041d-4200-9deb-e95191617492' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2170-04-01 12:00:00, 2170-04-02 12:00:00, 2170-04-03 12:00:00,\n",
" 2170-04-04 12:00:00, 2170-04-05 12:00:00],\n",
" dtype=&#x27;object&#x27;, length=5, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f994ce68-41d5-41f0-912d-1b751fae3ff0' class='xr-index-data-in' type='checkbox'/><label for='index-f994ce68-41d5-41f0-912d-1b751fae3ff0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -279.9, -279.79999999999995, -279.69999999999993,\n",
" -279.5999999999999, -279.4999999999999, -279.39999999999986,\n",
" -279.29999999999984, -279.1999999999998, -279.0999999999998,\n",
" -278.9999999999998,\n",
" ...\n",
" 79.09999999999494, 79.19999999999493, 79.29999999999492,\n",
" 79.39999999999492, 79.49999999999491, 79.5999999999949,\n",
" 79.6999999999949, 79.7999999999949, 79.89999999999489,\n",
" 80.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;xu_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yu_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eaa07f98-cd37-4aa7-af50-1041666ccb55' class='xr-index-data-in' type='checkbox'/><label for='index-eaa07f98-cd37-4aa7-af50-1041666ccb55' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-81.08751200116458, -81.0452726467841, -81.00303329240361,\n",
" -80.96079393802313, -80.91855458364265, -80.87631522926216,\n",
" -80.83407587488168, -80.79183652050119, -80.74959716612071,\n",
" -80.70735781174022,\n",
" ...\n",
" 89.61986158602242, 89.66210094040294, 89.70434029478342,\n",
" 89.74657964916385, 89.78881900354438, 89.83105835792489,\n",
" 89.87329771230527, 89.91553706668577, 89.9577764210665,\n",
" 90.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;yu_ocean&#x27;, length=2700))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97060fbc-0955-4643-8e0b-09994f198cb8' class='xr-section-summary-in' type='checkbox' checked><label for='section-97060fbc-0955-4643-8e0b-09994f198cb8' 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>filename :</span></dt><dd>ocean_daily_3d_dzt.nc</dd><dt><span>title :</span></dt><dd>ACCESS-OM2-01</dd><dt><span>grid_type :</span></dt><dd>mosaic</dd><dt><span>grid_tile :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 59GB\n",
"Dimensions: (time: 5, st_ocean: 75, yu_ocean: 2700, xu_ocean: 3600,\n",
" yt_ocean: 2700, xt_ocean: 3600)\n",
"Coordinates:\n",
" * xt_ocean (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
" * yt_ocean (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.89 89.94 89.98\n",
" * st_ocean (st_ocean) float64 600B 0.5413 1.681 2.94 ... 5.511e+03 5.709e+03\n",
" * time (time) object 40B 2170-04-01 12:00:00 ... 2170-04-05 12:00:00\n",
" * xu_ocean (xu_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.8 79.9 80.0\n",
" * yu_ocean (yu_ocean) float64 22kB -81.09 -81.05 -81.0 ... 89.92 89.96 90.0\n",
"Data variables:\n",
" u (time, st_ocean, yu_ocean, xu_ocean) float32 15GB dask.array<chunksize=(2, 14, 600, 800), meta=np.ndarray>\n",
" v (time, st_ocean, yu_ocean, xu_ocean) float32 15GB dask.array<chunksize=(2, 14, 600, 800), meta=np.ndarray>\n",
" salt (time, st_ocean, yt_ocean, xt_ocean) float32 15GB dask.array<chunksize=(2, 14, 600, 800), meta=np.ndarray>\n",
" temp (time, st_ocean, yt_ocean, xt_ocean) float32 15GB dask.array<chunksize=(2, 14, 600, 800), meta=np.ndarray>\n",
" eta_t (time, yt_ocean, xt_ocean) float32 194MB dask.array<chunksize=(3, 2025, 2700), meta=np.ndarray>\n",
"Attributes:\n",
" filename: ocean_daily_3d_dzt.nc\n",
" title: ACCESS-OM2-01\n",
" grid_type: mosaic\n",
" grid_tile: 1"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input_v1 = xarray.open_mfdataset(\n",
" f\"/g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output1077/ocean/ocean_daily*\",\n",
" parallel=True, chunks='auto')[[\"u\", \"v\", \"salt\", \"temp\", \"eta_t\"]].isel(time = slice(0, 5))\n",
"\n",
"input_v1"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "39056a55-d0fd-483e-a53a-9a5eff424f6c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"session = cosima_cookbook.database.create_session()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8dca010a-0b45-4eb2-aba1-08d092dafc40",
"metadata": {
"tags": []
},
"outputs": [
{
"ename": "VariableNotFoundError",
"evalue": "No files were found containing 'u' in the '01deg_jra55v13_ryf9091' experiment",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mVariableNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m input_v2 \u001b[38;5;241m=\u001b[39m \u001b[43mcosima_cookbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquerying\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetvar\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m01deg_jra55v13_ryf9091\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mu\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msession\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mfrequency\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1 daily\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mstart_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mend_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_time\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m input_v2\n",
"File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/cosima_cookbook/querying.py:334\u001b[0m, in \u001b[0;36mgetvar\u001b[0;34m(expt, variable, session, ncfile, start_time, end_time, n, frequency, attrs, attrs_unique, return_dataset, **kwargs)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attrs_unique \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 332\u001b[0m attrs_unique \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcell_methods\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime: mean\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m--> 334\u001b[0m ncfiles \u001b[38;5;241m=\u001b[39m \u001b[43m_ncfiles_for_variable\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[43m \u001b[49m\u001b[43mexpt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 336\u001b[0m \u001b[43m \u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 337\u001b[0m \u001b[43m \u001b[49m\u001b[43msession\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 338\u001b[0m \u001b[43m \u001b[49m\u001b[43mncfile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[43m \u001b[49m\u001b[43mstart_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43mend_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 342\u001b[0m \u001b[43m \u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 343\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 344\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs_unique\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 345\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 347\u001b[0m variables \u001b[38;5;241m=\u001b[39m [variable]\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_dataset:\n\u001b[1;32m 349\u001b[0m \u001b[38;5;66;03m# we know at least one variable was returned, so we can index ncfiles\u001b[39;00m\n\u001b[1;32m 350\u001b[0m \u001b[38;5;66;03m# ask for the extra variables associated with cell_methods, etc.\u001b[39;00m\n",
"File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/cosima_cookbook/querying.py:505\u001b[0m, in \u001b[0;36m_ncfiles_for_variable\u001b[0;34m(expt, variable, session, ncfile, start_time, end_time, n, frequency, attrs, attrs_unique)\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[38;5;66;03m# ensure we actually got a result\u001b[39;00m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ncfiles:\n\u001b[0;32m--> 505\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m VariableNotFoundError(\n\u001b[1;32m 506\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo files were found containing \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m in the \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m experiment\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 507\u001b[0m variable, expt\n\u001b[1;32m 508\u001b[0m )\n\u001b[1;32m 509\u001b[0m )\n\u001b[1;32m 511\u001b[0m \u001b[38;5;66;03m# check whether the results are unique\u001b[39;00m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m attrs_unique:\n",
"\u001b[0;31mVariableNotFoundError\u001b[0m: No files were found containing 'u' in the '01deg_jra55v13_ryf9091' experiment"
]
}
],
"source": [
"input_v2 = cosima_cookbook.querying.getvar('01deg_jra55v13_ryf9091', 'u', session,\n",
" frequency=\"1 daily\",\n",
" start_time=start_time,\n",
" end_time=end_time)\n",
"input_v2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27e6158a-4ae4-4c9f-92aa-bd93ec7bc4af",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:analysis3-24.01]",
"language": "python",
"name": "conda-env-analysis3-24.01-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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