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asford/sel_fill_repro.ipynb

Created March 28, 2024 01:13
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sel_fill_repro.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xa\n",
"import numpy as np\n",
"import pandas as pd\n",
"from typing import Any"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def sel_fill(\n",
" left: xa.DataArray,\n",
" dim: str,\n",
" right_coord: xa.DataArray,\n",
" fill_value: Any | dict[str, Any] = xa.core.dtypes.NA, # type: ignore\n",
") -> xa.DataArray:\n",
" \"\"\".sel but return nan/_fill for missing values.\n",
"\n",
" This is a \"right join\" when indexing a dim with a coord array.\n",
" For example, for \"data\" with a dim matching a coord of \"samples\":\n",
"\n",
" `data.sel(name=samples.sample_name)` is a strict join...\n",
" ...if a value in sample_name isn't present in data.name it's an error.\n",
" However, you may want to select-and-fill-missing-values...\n",
" ...if a value in present in data.name, then select data.\n",
" ...otherwise return a nan or filled value.\n",
"\n",
" This uses the same filling logic as xa.align,\n",
" provide a dictionary of names to fill with specific values.\n",
"\n",
" \"\"\"\n",
" assert dim in left.indexes\n",
" index: pd.Index = left.indexes[dim]\n",
"\n",
" coord_index = pd.Index(right_coord.values.ravel())\n",
"\n",
" missing_values = coord_index.difference(index)\n",
"\n",
" if missing_values.empty:\n",
" left_data = left\n",
" else:\n",
" left_data = xa.concat(\n",
" [\n",
" left,\n",
" left.reindex({dim: missing_values}, fill_value=fill_value),\n",
" ],\n",
" dim=dim,\n",
" )\n",
"\n",
" return left_data.sel({dim: right_coord})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"query = xa.DataArray([[\"a\", \"b\", \"c\"], [\"d\", \"e\", \"f\"]])\n",
"dat = xa.DataArray(np.arange(4), dims=\"letter\").assign_coords(letter=list(\"abcd\"))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (letter: 4)&gt;\n",
"array([0, 1, 2, 3])\n",
"Coordinates:\n",
" * letter (letter) &lt;U1 &#x27;a&#x27; &#x27;b&#x27; &#x27;c&#x27; &#x27;d&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>letter</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-17870a4f-a499-4b62-bb42-ff2e8e0f540a' class='xr-array-in' type='checkbox' checked><label for='section-17870a4f-a499-4b62-bb42-ff2e8e0f540a' 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>0 1 2 3</span></div><div class='xr-array-data'><pre>array([0, 1, 2, 3])</pre></div></div></li><li class='xr-section-item'><input id='section-5e128d79-2bc6-417a-8d71-5bc97955a330' class='xr-section-summary-in' type='checkbox' checked><label for='section-5e128d79-2bc6-417a-8d71-5bc97955a330' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>letter</span></div><div class='xr-var-dims'>(letter)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;a&#x27; &#x27;b&#x27; &#x27;c&#x27; &#x27;d&#x27;</div><input id='attrs-f437ac78-5990-4488-8879-04e777012c00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f437ac78-5990-4488-8879-04e777012c00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b7069ca-b694-491a-8eb1-a6244dd4bdf3' class='xr-var-data-in' type='checkbox'><label for='data-4b7069ca-b694-491a-8eb1-a6244dd4bdf3' 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;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-abfc951d-da5e-4768-96c8-76ff0ca35246' class='xr-section-summary-in' type='checkbox' ><label for='section-abfc951d-da5e-4768-96c8-76ff0ca35246' class='xr-section-summary' >Indexes: <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-index-name'><div>letter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e646e8db-0dae-49b9-a299-2c06c26da8c8' class='xr-index-data-in' type='checkbox'/><label for='index-e646e8db-0dae-49b9-a299-2c06c26da8c8' 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([&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;object&#x27;, name=&#x27;letter&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45126b78-4664-4276-98c5-e53579204e95' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45126b78-4664-4276-98c5-e53579204e95' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
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"array([0, 1, 2, 3])\n",
"Coordinates:\n",
" * letter (letter) <U1 'a' 'b' 'c' 'd'"
]
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (dim_0: 2, dim_1: 3)&gt;\n",
"array([[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;],\n",
" [&#x27;d&#x27;, &#x27;e&#x27;, &#x27;f&#x27;]], dtype=&#x27;&lt;U1&#x27;)\n",
"Dimensions without coordinates: dim_0, dim_1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span>dim_0</span>: 2</li><li><span>dim_1</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cb276979-fcaf-467e-804f-149c9bc9ae40' class='xr-array-in' type='checkbox' checked><label for='section-cb276979-fcaf-467e-804f-149c9bc9ae40' 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>&#x27;a&#x27; &#x27;b&#x27; &#x27;c&#x27; &#x27;d&#x27; &#x27;e&#x27; &#x27;f&#x27;</span></div><div class='xr-array-data'><pre>array([[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;],\n",
" [&#x27;d&#x27;, &#x27;e&#x27;, &#x27;f&#x27;]], dtype=&#x27;&lt;U1&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-8fb8cb56-90e0-4460-a053-07dba3ae2b83' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8fb8cb56-90e0-4460-a053-07dba3ae2b83' 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-d8108c41-6c22-40ef-bac7-93fed60aa68d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d8108c41-6c22-40ef-bac7-93fed60aa68d' class='xr-section-summary' title='Expand/collapse section'>Indexes: <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-0992dffe-c4a1-4b68-84ae-abf72a231f1e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0992dffe-c4a1-4b68-84ae-abf72a231f1e' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray (dim_0: 2, dim_1: 3)>\n",
"array([['a', 'b', 'c'],\n",
" ['d', 'e', 'f']], dtype='<U1')\n",
"Dimensions without coordinates: dim_0, dim_1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "\"not all values found in index 'letter'\"",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mletter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/xarray/core/dataarray.py:1531\u001b[0m, in \u001b[0;36mDataArray.sel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 1421\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msel\u001b[39m(\n\u001b[1;32m 1422\u001b[0m \u001b[38;5;28mself\u001b[39m: T_DataArray,\n\u001b[1;32m 1423\u001b[0m indexers: Mapping[Any, Any] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1427\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mindexers_kwargs: Any,\n\u001b[1;32m 1428\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T_DataArray:\n\u001b[1;32m 1429\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a new DataArray whose data is given by selecting index\u001b[39;00m\n\u001b[1;32m 1430\u001b[0m \u001b[38;5;124;03m labels along the specified dimension(s).\u001b[39;00m\n\u001b[1;32m 1431\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;124;03m Dimensions without coordinates: points\u001b[39;00m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1531\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_temp_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1532\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1533\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1534\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1535\u001b[0m \u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1536\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mindexers_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1537\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n",
"File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/xarray/core/dataset.py:2580\u001b[0m, in \u001b[0;36mDataset.sel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 2519\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Returns a new dataset with each array indexed by tick labels\u001b[39;00m\n\u001b[1;32m 2520\u001b[0m \u001b[38;5;124;03malong the specified dimension(s).\u001b[39;00m\n\u001b[1;32m 2521\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2577\u001b[0m \u001b[38;5;124;03mDataArray.sel\u001b[39;00m\n\u001b[1;32m 2578\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2579\u001b[0m indexers \u001b[38;5;241m=\u001b[39m either_dict_or_kwargs(indexers, indexers_kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msel\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2580\u001b[0m query_results \u001b[38;5;241m=\u001b[39m \u001b[43mmap_index_queries\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2581\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\n\u001b[1;32m 2582\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m drop:\n\u001b[1;32m 2585\u001b[0m no_scalar_variables \u001b[38;5;241m=\u001b[39m {}\n",
"File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/xarray/core/indexing.py:189\u001b[0m, in \u001b[0;36mmap_index_queries\u001b[0;34m(obj, indexers, method, tolerance, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 187\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(IndexSelResult(labels))\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 189\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 191\u001b[0m merged \u001b[38;5;241m=\u001b[39m merge_sel_results(results)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;66;03m# drop dimension coordinates found in dimension indexers\u001b[39;00m\n\u001b[1;32m 194\u001b[0m \u001b[38;5;66;03m# (also drop multi-index if any)\u001b[39;00m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;66;03m# (.sel() already ensures alignment)\u001b[39;00m\n",
"File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/xarray/core/indexes.py:498\u001b[0m, in \u001b[0;36mPandasIndex.sel\u001b[0;34m(self, labels, method, tolerance)\u001b[0m\n\u001b[1;32m 496\u001b[0m indexer \u001b[38;5;241m=\u001b[39m get_indexer_nd(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, label_array, method, tolerance)\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(indexer \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[0;32m--> 498\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnot all values found in index \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# attach dimension names and/or coordinates to positional indexer\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, Variable):\n",
"\u001b[0;31mKeyError\u001b[0m: \"not all values found in index 'letter'\""
]
}
],
"source": [
"dat.sel(letter=query)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Indexer has dimensions ('dim_0', 'dim_1') that are different from that to be indexed along 'letter'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mletter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/xarray/core/dataarray.py:2041\u001b[0m, in \u001b[0;36mDataArray.reindex\u001b[0;34m(self, indexers, method, tolerance, copy, fill_value, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 1968\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Conform this object onto the indexes of another object, filling in\u001b[39;00m\n\u001b[1;32m 1969\u001b[0m \u001b[38;5;124;03mmissing values with ``fill_value``. The default fill value is NaN.\u001b[39;00m\n\u001b[1;32m 1970\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2038\u001b[0m \u001b[38;5;124;03malign\u001b[39;00m\n\u001b[1;32m 2039\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2040\u001b[0m indexers \u001b[38;5;241m=\u001b[39m utils\u001b[38;5;241m.\u001b[39meither_dict_or_kwargs(indexers, indexers_kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreindex\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2041\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43malignment\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2042\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2043\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2044\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2045\u001b[0m \u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2046\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2047\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2048\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/xarray/core/alignment.py:893\u001b[0m, in \u001b[0;36mreindex\u001b[0;34m(obj, indexers, method, tolerance, copy, fill_value, sparse, exclude_vars)\u001b[0m\n\u001b[1;32m 878\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Re-index either a Dataset or a DataArray.\u001b[39;00m\n\u001b[1;32m 879\u001b[0m \n\u001b[1;32m 880\u001b[0m \u001b[38;5;124;03mNot public API.\u001b[39;00m\n\u001b[1;32m 881\u001b[0m \n\u001b[1;32m 882\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 884\u001b[0m \u001b[38;5;66;03m# TODO: (benbovy - explicit indexes): uncomment?\u001b[39;00m\n\u001b[1;32m 885\u001b[0m \u001b[38;5;66;03m# --> from reindex docstrings: \"any mis-matched dimension is simply ignored\"\u001b[39;00m\n\u001b[1;32m 886\u001b[0m \u001b[38;5;66;03m# bad_keys = [k for k in indexers if k not in obj._indexes and k not in obj.dims]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 890\u001b[0m \u001b[38;5;66;03m# \"or unindexed dimension in the object to reindex\"\u001b[39;00m\n\u001b[1;32m 891\u001b[0m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[0;32m--> 893\u001b[0m aligner \u001b[38;5;241m=\u001b[39m \u001b[43mAligner\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 894\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 895\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 896\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 897\u001b[0m \u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 898\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 899\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 900\u001b[0m \u001b[43m \u001b[49m\u001b[43msparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 901\u001b[0m \u001b[43m \u001b[49m\u001b[43mexclude_vars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 902\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 903\u001b[0m aligner\u001b[38;5;241m.\u001b[39malign()\n\u001b[1;32m 904\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m aligner\u001b[38;5;241m.\u001b[39mresults[\u001b[38;5;241m0\u001b[39m]\n",
"File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/xarray/core/alignment.py:164\u001b[0m, in \u001b[0;36mAligner.__init__\u001b[0;34m(self, objects, join, indexes, exclude_dims, exclude_vars, method, tolerance, copy, fill_value, sparse)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m indexes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 163\u001b[0m indexes \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 164\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindexes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex_vars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_normalize_indexes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mall_indexes \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mall_index_vars \u001b[38;5;241m=\u001b[39m {}\n",
"File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/xarray/core/alignment.py:195\u001b[0m, in \u001b[0;36mAligner._normalize_indexes\u001b[0;34m(self, indexes)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(idx, Index):\n\u001b[1;32m 194\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(idx, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdims\u001b[39m\u001b[38;5;124m\"\u001b[39m, (k,)) \u001b[38;5;241m!=\u001b[39m (k,):\n\u001b[0;32m--> 195\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 196\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndexer has dimensions \u001b[39m\u001b[38;5;132;01m{\u001b[39;00midx\u001b[38;5;241m.\u001b[39mdims\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m that are different \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrom that to be indexed along \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 198\u001b[0m )\n\u001b[1;32m 199\u001b[0m data \u001b[38;5;241m=\u001b[39m as_compatible_data(idx)\n\u001b[1;32m 200\u001b[0m pd_idx \u001b[38;5;241m=\u001b[39m safe_cast_to_index(data)\n",
"\u001b[0;31mValueError\u001b[0m: Indexer has dimensions ('dim_0', 'dim_1') that are different from that to be indexed along 'letter'"
]
}
],
"source": [
"dat.reindex(dict(letter=query))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
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" [ 3, 1663, 1663]])\n",
"Coordinates:\n",
" letter (dim_0, dim_1) object &#x27;a&#x27; &#x27;b&#x27; &#x27;c&#x27; &#x27;d&#x27; &#x27;e&#x27; &#x27;f&#x27;\n",
"Dimensions without coordinates: dim_0, dim_1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span>dim_0</span>: 2</li><li><span>dim_1</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2cbc1927-4f51-4f54-83d0-fadb1e2abddd' class='xr-array-in' type='checkbox' checked><label for='section-2cbc1927-4f51-4f54-83d0-fadb1e2abddd' 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>0 1 2 3 1663 1663</span></div><div class='xr-array-data'><pre>array([[ 0, 1, 2],\n",
" [ 3, 1663, 1663]])</pre></div></div></li><li class='xr-section-item'><input id='section-31eb9811-c62a-4ffa-8228-624c1fc2e736' class='xr-section-summary-in' type='checkbox' checked><label for='section-31eb9811-c62a-4ffa-8228-624c1fc2e736' class='xr-section-summary' >Coordinates: <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>letter</span></div><div class='xr-var-dims'>(dim_0, dim_1)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;a&#x27; &#x27;b&#x27; &#x27;c&#x27; &#x27;d&#x27; &#x27;e&#x27; &#x27;f&#x27;</div><input id='attrs-ff1d7fb5-b240-49e2-804b-e2a3696c4c34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff1d7fb5-b240-49e2-804b-e2a3696c4c34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a11fd3b-edd1-46c7-89f2-3ff74505b50c' class='xr-var-data-in' type='checkbox'><label for='data-6a11fd3b-edd1-46c7-89f2-3ff74505b50c' 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;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;],\n",
" [&#x27;d&#x27;, &#x27;e&#x27;, &#x27;f&#x27;]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dd822035-661b-4d01-8d5f-4e9b401674a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dd822035-661b-4d01-8d5f-4e9b401674a6' class='xr-section-summary' title='Expand/collapse section'>Indexes: <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-ea308f6c-131e-4f57-9ac8-619840b3803e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ea308f6c-131e-4f57-9ac8-619840b3803e' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray (dim_0: 2, dim_1: 3)>\n",
"array([[ 0, 1, 2],\n",
" [ 3, 1663, 1663]])\n",
"Coordinates:\n",
" letter (dim_0, dim_1) object 'a' 'b' 'c' 'd' 'e' 'f'\n",
"Dimensions without coordinates: dim_0, dim_1"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sel_fill(dat, \"letter\", query, fill_value=1663)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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