pydata / pydata/xarray

explicit_indexing_adapter fails for empty list as key

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bug topic-lazy array
Dominant language
Python
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Description

What happened?

I am implementing my own lazy loadable backend based on https://docs.xarray.dev/en/latest/internals/how-to-add-new-backend.html#how-to-support-lazy-loading using the xr.core.indexing.explicit_indexing_adapter.

I noticed that when you use data[[]] the method crashes, whilst a "normal" data array just returns an empty list.

What did you expect to happen?

Same result when using a normal data array, not an exception.

Minimal Complete Verifiable Example
import xarray as xr
import numpy as np


def raw_indexing_method(key):
    assert False


class MyBackend(xr.backends.BackendArray):
    def __init__(self, array):
        self.shape = array.shape
        self.dtype = array.dtype

    def __getitem__(self, key):
        return xr.core.indexing.explicit_indexing_adapter(
            key,
            self.shape,
            xr.core.indexing.IndexingSupport.BASIC,
            raw_indexing_method,
        )


data1 = xr.DataArray(np.random.randn(2, 3), dims=("x", "y"), coords={"x": [10, 20]})
backend_array = MyBackend(np.random.randn(2, 3))
data = xr.core.indexing.LazilyIndexedArray(backend_array)

data2 = xr.DataArray(
    data,
    dims=("x", "y"),
    coords={"x": [10, 20]},
)

idx = []

print(data1[idx].values) # Works
print(data2[idx].values) # Crashes somewhere in numpy
MVCE confirmation
  • Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
  • Complete example — the example is self-contained, including all data and the text of any traceback.
  • Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
  • New issue — a search of GitHub Issues suggests this is not a duplicate.
  • Recent environment — the issue occurs with the latest version of xarray and its dependencies.
Relevant log output
Traceback (most recent call last):
  File "/tmp/test123.py", line 36, in <module>
    print(data2[idx].values)
          ^^^^^^^^^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/xarray/core/dataarray.py", line 785, in values
    return self.variable.values
           ^^^^^^^^^^^^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/xarray/core/variable.py", line 540, in values
    return _as_array_or_item(self._data)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/xarray/core/variable.py", line 338, in _as_array_or_item
    data = np.asarray(data)
           ^^^^^^^^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/xarray/core/indexing.py", line 524, in __array__
    return np.asarray(self.get_duck_array(), dtype=dtype)
                      ^^^^^^^^^^^^^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/xarray/core/indexing.py", line 647, in get_duck_array
    array = self.array[self.key]
            ~~~~~~~~~~^^^^^^^^^^
  File "/tmp/test123.py", line 15, in __getitem__
    return xr.core.indexing.explicit_indexing_adapter(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/xarray/core/indexing.py", line 1010, in explicit_indexing_adapter
    raw_key, numpy_indices = decompose_indexer(key, shape, indexing_support)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/xarray/core/indexing.py", line 1045, in decompose_indexer
    return _decompose_outer_indexer(indexer, shape, indexing_support)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/xarray/core/indexing.py", line 1290, in _decompose_outer_indexer
    backend_indexer.append(slice(np.min(k), np.max(k) + 1))
                                 ^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/numpy/core/fromnumeric.py", line 2953, in min
    return _wrapreduction(a, np.minimum, 'min', axis, None, out,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/.../.pyenv/lib/python3.11/site-packages/numpy/core/fromnumeric.py", line 88, in _wrapreduction
    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: zero-size array to reduction operation minimum which has no identity
Anything else we need to know?

No response

Environment
INSTALLED VERSIONS ------------------ commit: None python: 3.11.9 (main, May 10 2024, 17:39:01) [GCC 13.2.1 20240210] python-bits: 64 OS: Linux OS-release: 6.6.30-gentoo-dist machine: x86_64 processor: Intel(R) Core(TM) i7-1065G7 CPU @ 1.30GHz byteorder: little LC_ALL: None LANG: de_DE.utf8 LOCALE: ('de_DE', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.9.3-development

xarray: 2024.5.0
pandas: 2.2.0
numpy: 1.26.4
scipy: 1.12.0
netCDF4: 1.6.5
pydap: None
h5netcdf: None
h5py: None
zarr: None
cftime: 1.6.3
nc_time_axis: None
iris: None
bottleneck: None
dask: 2024.2.0
distributed: None
matplotlib: 3.8.2
cartopy: None
seaborn: None
numbagg: None
fsspec: 2024.2.0
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 69.0.3
pip: 24.0
conda: None
pytest: 7.4.4
mypy: None
IPython: 8.20.0
sphinx: None

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Reproduce the MVCE first, then inspect xarray/core/indexing.py at explicit_indexing_adapter and _decompose_outer_indexer, where the traceback shows the empty index reaches np.min. Add regression coverage for data[[]] and verify that the lazy backend matches a normal DataArray by returning an empty result without the reduction error.

Written by the indexing model from the issue text.

Assessment

Tech stack
numpy, python
Domain
data
Issue type
Bug
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Clearly specified
Newbie friendliness
45/100

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