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Hoeze/test_xr_boolean.ipynb

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Boolean indexing of xarray Dataset and DataArrays over multiple dimensions
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test_xr_boolean.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from typing import List, Dict, Tuple\n",
"\n",
"import xarray as xr\n",
"import numpy as np\n",
"import dask.array"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# import pandas as pd\n",
"# def core_dim_locs_from_multiindex(multi_index, coords: Dict[str, pd.Index], new_dim_name, core_dims=None) -> List[\n",
"# Tuple[str, xr.DataArray]]:\n",
"# if core_dims is None:\n",
"# core_dims = np.asarray(multi_index.names)\n",
"#\n",
"# core_dim_locs = []\n",
"# for dim in core_dims:\n",
"# core_dim_locs.append(\n",
"# pd.Index(coords[dim]).get_indexer(multi_index.get_level_values(dim))\n",
"# )\n",
"#\n",
"# core_dim_locs_xr = []\n",
"# for i, dim in enumerate(core_dims):\n",
"# labels = multi_index.get_level_values(dim)\n",
"# locs = pd.Index(coords[dim]).get_indexer(labels)\n",
"# core_dim_locs_xr.append((\n",
"# dim,\n",
"# xr.DataArray(\n",
"# locs,\n",
"# coords={\n",
"# dim: ((new_dim_name,), labels)\n",
"# },\n",
"# dims=(new_dim_name,),\n",
"# name=dim\n",
"# )\n",
"# ))\n",
"# return core_dim_locs_xr"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def core_dim_locs_from_cond(cond, new_dim_name, core_dims=None) -> List[Tuple[str, xr.DataArray]]:\n",
" if core_dims is None:\n",
" core_dims = cond.dims\n",
"\n",
" core_dim_locs = np.argwhere(cond.data)\n",
" if isinstance(core_dim_locs, dask.array.core.Array):\n",
" core_dim_locs = core_dim_locs.persist().compute_chunk_sizes()\n",
"\n",
" core_dim_locs_xr = []\n",
" for i, dim in enumerate(core_dims):\n",
" locs = core_dim_locs[:, i]\n",
" labels = np.asanyarray(cond[dim])\n",
" core_dim_locs_xr.append((\n",
" dim,\n",
" xr.DataArray(\n",
" locs,\n",
" coords={\n",
" dim: ((new_dim_name,), dask.array.asanyarray(labels)[locs])\n",
" },\n",
" dims=(new_dim_name,),\n",
" name=dim\n",
" )\n",
" ))\n",
" return core_dim_locs_xr"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def subset_variable(variable, core_dim_locs, new_dim_name, mask=None):\n",
" core_dims = np.array([dim for dim, locs in core_dim_locs])\n",
" variable_core_dims = core_dims[np.isin(core_dims, variable.dims)]\n",
" if len(variable_core_dims) < len(core_dims):\n",
" return None\n",
"\n",
" other_dims = np.asarray(variable.dims)\n",
" other_dims = other_dims[~np.isin(variable.dims, core_dims)]\n",
"\n",
" variable_dim_order = np.concatenate([core_dims, other_dims])\n",
"\n",
" variable = variable.transpose(*variable_dim_order)\n",
"\n",
" if mask is None:\n",
" subset = dask.array.asanyarray(variable.data).vindex[tuple(idx.values for dim, idx in core_dim_locs)]\n",
" else:\n",
" subset = dask.array.asanyarray(variable.data)[mask]\n",
" # force-set chunk size from known chunks\n",
" chunk_sizes = core_dim_locs[0][1].chunks[0]\n",
" subset._chunks = (chunk_sizes, *subset._chunks[1:])\n",
"\n",
" subset_xr = xr.DataArray(subset, dims=(new_dim_name, *other_dims), coords={\n",
" **{dim: idx.coords[dim] for dim, idx in core_dim_locs},\n",
" **{dim: variable[dim] for dim in other_dims},\n",
" })\n",
"\n",
" return subset_xr"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def dataset_masked_indexing(ds: xr.Dataset, mask: xr.DataArray, new_dim_name: str):\n",
" mask.data = dask.array.asanyarray(mask.data)\n",
" core_dim_locs = core_dim_locs_from_cond(mask, new_dim_name=new_dim_name)\n",
"\n",
" new_variables = {}\n",
" for name, variable in ds.items():\n",
" subset = subset_variable(variable, core_dim_locs, new_dim_name=new_dim_name, mask=mask.data)\n",
" if subset is not None:\n",
" new_variables[name] = subset\n",
" else:\n",
" new_variables[name] = variable\n",
"\n",
" return xr.Dataset(new_variables)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (dim_0: 100, dim_1: 100, dim_2: 100)\n",
"Dimensions without coordinates: dim_0, dim_1, dim_2\n",
"Data variables:\n",
" x (dim_0, dim_1, dim_2) int64 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
" y (dim_0, dim_1) int64 dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;\n",
" missing (dim_0, dim_1, dim_2) bool dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</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-b16c5958-051b-457b-bc0a-2baf8cce6897' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b16c5958-051b-457b-bc0a-2baf8cce6897' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 100</li><li><span>dim_1</span>: 100</li><li><span>dim_2</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bb81e47f-d5e5-4c1c-a712-859f8799f469' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bb81e47f-d5e5-4c1c-a712-859f8799f469' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-3beabe1e-765e-4f1d-8b3e-952aea28ea5b' class='xr-section-summary-in' type='checkbox' checked><label for='section-3beabe1e-765e-4f1d-8b3e-952aea28ea5b' class='xr-section-summary' >Data variables: <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>x</span></div><div class='xr-var-dims'>(dim_0, dim_1, dim_2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-35532f48-f46e-427d-8814-0b95626b76ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35532f48-f46e-427d-8814-0b95626b76ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebdcefc6-0403-4300-9163-2e0680ed9af1' class='xr-var-data-in' type='checkbox'><label for='data-ebdcefc6-0403-4300-9163-2e0680ed9af1' 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",
"<tr>\n",
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" <tbody>\n",
" <tr><th> Bytes </th><td> 8.00 MB </td> <td> 8.00 MB </td></tr>\n",
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],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (dim_0: 100, dim_1: 100, dim_2: 100)\n",
"Dimensions without coordinates: dim_0, dim_1, dim_2\n",
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]
},
"execution_count": 10,
"metadata": {},
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}
],
"source": [
"test_ds = xr.Dataset({\n",
" \"x\": xr.DataArray(dask.array.random.randint(0, 1000, size=[100, 100, 100])),\n",
" \"y\": xr.DataArray(dask.array.random.randint(0, 1000, size=[100, 100])),\n",
" \"missing\": xr.DataArray(dask.array.random.randint(0, 2, size=[100, 100, 100], dtype=bool)),\n",
"})\n",
"test_ds"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
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"Coordinates:\n",
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" dim_2 (newdim) int64 dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;\n",
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],
"text/plain": [
"<xarray.DataArray 'getitem-26a77a50a1e3b23480e914c53c2beb87' (newdim: 499263)>\n",
"dask.array<getitem, shape=(499263,), dtype=bool, chunksize=(499263,), chunktype=numpy.ndarray>\n",
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]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# test single array indexing\n",
"data = test_ds[\"missing\"]\n",
"mask = test_ds[\"missing\"]\n",
"new_dim_name = \"newdim\"\n",
"\n",
"core_dim_locs = core_dim_locs_from_cond(mask, new_dim_name=new_dim_name)\n",
"indexed_test_da = subset_variable(data, core_dim_locs, new_dim_name=new_dim_name, mask=mask.data)\n",
"indexed_test_da"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"assert indexed_test_da.all().compute().item()\n",
"assert indexed_test_da.sum().compute().item() == test_ds[\"missing\"].sum().compute().item()\n",
"assert indexed_test_da.dims == (\"newdim\",)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
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" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (dim_0: 100, dim_1: 100, newdim: 499263)\n",
"Coordinates:\n",
" dim_0 (newdim) int64 dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;\n",
" dim_1 (newdim) int64 dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;\n",
" dim_2 (newdim) int64 dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;\n",
"Dimensions without coordinates: newdim\n",
"Data variables:\n",
" x (newdim) int64 dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;\n",
" y (dim_0, dim_1) int64 dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;\n",
" missing (newdim) bool dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;</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-6154eefd-dd5c-4541-b2a2-942e10b6408c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6154eefd-dd5c-4541-b2a2-942e10b6408c' 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'>dim_0</span>: 100</li><li><span class='xr-has-index'>dim_1</span>: 100</li><li><span>newdim</span>: 499263</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-abf19b31-99ba-41b1-8f3b-d9e079872794' class='xr-section-summary-in' type='checkbox' checked><label for='section-abf19b31-99ba-41b1-8f3b-d9e079872794' 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>dim_0</span></div><div class='xr-var-dims'>(newdim)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;</div><input id='attrs-c1dbd59b-084c-4b11-b06f-d9f6596cd000' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1dbd59b-084c-4b11-b06f-d9f6596cd000' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6172a49-52b1-42fa-8b57-a22783e27f39' class='xr-var-data-in' type='checkbox'><label for='data-e6172a49-52b1-42fa-8b57-a22783e27f39' 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",
"<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> 3.99 MB </td> <td> 3.99 MB </td></tr>\n",
" <tr><th> Shape </th><td> (499263,) </td> <td> (499263,) </td></tr>\n",
" <tr><th> Count </th><td> 26 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> int64 </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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"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >499263</text>\n",
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dim_1</span></div><div class='xr-var-dims'>(newdim)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;</div><input id='attrs-2639c443-6330-4693-a077-af4b4c7a3c72' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2639c443-6330-4693-a077-af4b4c7a3c72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6942df62-eefe-4ea2-889a-9fb02c72664d' class='xr-var-data-in' type='checkbox'><label for='data-6942df62-eefe-4ea2-889a-9fb02c72664d' 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",
"<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> 3.99 MB </td> <td> 3.99 MB </td></tr>\n",
" <tr><th> Shape </th><td> (499263,) </td> <td> (499263,) </td></tr>\n",
" <tr><th> Count </th><td> 26 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> int64 </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",
" <!-- Horizontal lines -->\n",
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" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
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" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dim_2</span></div><div class='xr-var-dims'>(newdim)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;</div><input id='attrs-d8304c0a-8397-4c26-9311-9c4d13ba44e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8304c0a-8397-4c26-9311-9c4d13ba44e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bc348cf-0bbd-4845-9a90-f09869316873' class='xr-var-data-in' type='checkbox'><label for='data-6bc348cf-0bbd-4845-9a90-f09869316873' 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",
"<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> 3.99 MB </td> <td> 3.99 MB </td></tr>\n",
" <tr><th> Shape </th><td> (499263,) </td> <td> (499263,) </td></tr>\n",
" <tr><th> Count </th><td> 26 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> int64 </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",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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"\n",
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"\n",
" <!-- Text -->\n",
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"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb03f7fc-d4d9-4b6f-adae-90ccce5c36b8' class='xr-section-summary-in' type='checkbox' checked><label for='section-cb03f7fc-d4d9-4b6f-adae-90ccce5c36b8' class='xr-section-summary' >Data variables: <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>x</span></div><div class='xr-var-dims'>(newdim)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(499263,), meta=np.ndarray&gt;</div><input id='attrs-0f3f07ff-20c7-4d7c-b0c8-343f76807f4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f3f07ff-20c7-4d7c-b0c8-343f76807f4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de1e44ae-d194-4bf2-9882-21aeefebc24a' class='xr-var-data-in' type='checkbox'><label for='data-de1e44ae-d194-4bf2-9882-21aeefebc24a' 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",
"<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> 3.99 MB </td> <td> 3.99 MB </td></tr>\n",
" <tr><th> Shape </th><td> (499263,) </td> <td> (499263,) </td></tr>\n",
" <tr><th> Count </th><td> 6 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> int64 </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",
" <!-- Horizontal lines -->\n",
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"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (dim_0: 100, dim_1: 100, newdim: 499263)\n",
"Coordinates:\n",
" dim_0 (newdim) int64 dask.array<chunksize=(499263,), meta=np.ndarray>\n",
" dim_1 (newdim) int64 dask.array<chunksize=(499263,), meta=np.ndarray>\n",
" dim_2 (newdim) int64 dask.array<chunksize=(499263,), meta=np.ndarray>\n",
"Dimensions without coordinates: newdim\n",
"Data variables:\n",
" x (newdim) int64 dask.array<chunksize=(499263,), meta=np.ndarray>\n",
" y (dim_0, dim_1) int64 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
" missing (newdim) bool dask.array<chunksize=(499263,), meta=np.ndarray>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"indexed_test_ds = dataset_masked_indexing(test_ds, test_ds[\"missing\"], \"newdim\")\n",
"indexed_test_ds"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"assert indexed_test_ds[\"missing\"].all().compute().item()\n",
"assert indexed_test_ds[\"missing\"].sum().compute().item() == test_ds[\"missing\"].sum().compute().item()\n",
"assert indexed_test_ds[\"x\"].dims == (\"newdim\",)\n",
"assert indexed_test_ds[\"y\"].dims == (\"dim_0\", \"dim_1\",)\n",
"assert indexed_test_ds[\"missing\"].dims == (\"newdim\",)"
]
},
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\" 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\" 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\" 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}
}
},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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