Apply_ufunc Doesn't Inherit Encoding

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Introduction

When working with xarray DataArrays, it's essential to preserve the encoding properties after applying universal functions (ufuncs). However, the current implementation of apply_ufunc in xarray loses the encoding information. This issue is particularly problematic when dealing with multiple data arrays, as the encoding properties are not inherited or resolved correctly. In this article, we'll delve into the problem, provide a minimal complete verifiable example (MVCE), and discuss the expected behavior.

What happened?

When applying a ufunc to an xarray DataArray, the encoding properties are lost. This is evident in the following code snippet:

import xarray as xr
import numpy as np

my_ufunc = lambda x: x + 1
xarr1 = xr.DataArray(np.array([1,2,3]))
xarr1.encoding = {'dummy': 'baz'}
xarr2 = xr.apply_ufunc(my_ufunc, xarr1)
print(xarr1.encoding, xarr2.encoding)

In this example, the encoding property {'dummy': 'baz'} is lost after applying the ufunc to xarr1. The output will be:

{'dummy': 'baz'} {'dummy': None}

As you can see, the encoding property is not inherited by xarr2.

What did you expect to happen?

The expected behavior is that the encoding property should be inherited (or resolved if more than one data array is involved) when applying a ufunc. This means that the encoding property should be preserved in the resulting DataArray.

Minimal Complete Verifiable Example (MVCE)

Here's a more comprehensive example that demonstrates the issue:

import xarray as xr
import numpy as np

my_ufunc = lambda x: x + 1
xarr1 = xr.DataArray(np.array([1,2,3]))
xarr1.encoding = {'dummy': 'baz'}
xarr2 = xr.apply_ufunc(my_ufunc, xarr1)
xarr3 = xr.apply_ufunc(my_ufunc, xarr2)
print(xarr1.encoding, xarr2.encoding, xarr3.encoding)

In this example, we apply the ufunc twice to xarr1, resulting in xarr2 and xarr3. The encoding property is lost after each application of the ufunc.

MVCE confirmation

The MVCE meets the following criteria:

  • 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

Anything else we need know?

No additional information is required to reproduce the issue.

Environment

The issue is reproducible with the following environment:

INSTALLED VERSIONS

commit: None python: 3.13.3 | packaged by conda-forge | (main, Apr 14 2025, 20:31:24) [MSC v.1943 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 11 machine: AMD64 processor: Intel64 Family 6 Model 170 Stepping 4, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: ('English_Australia', '1252') libhdf5: 1.14.6 libnetcdf: 4.9.2

xarray: 0.1.dev5937+g070af11 pandas: 2.2.3 numpy: 2.2.5 scipy: 1.15.2 netCDF4: 1.7.2 pydap: None h5netcdf: None h5py: None zarr: None cftime: 1.6.4 nc_time_axis: None iris: None bottleneck: None dask: None distributed: None matplotlib: None cartopy: None seaborn: None numbagg: None fsspec: None cupy: None pint: None sparse: None flox: None numpy_groupies: None setuptools: None pip: 25.1.1 conda: None pytest: None mypy: None IPython: 9.2.0 sphinx: None

Conclusion

Q: What is the issue with apply_ufunc in xarray?

A: The issue is that the encoding properties are lost after applying a ufunc to an xarray DataArray. This means that the encoding information is not inherited or resolved correctly when dealing with multiple data arrays.

Q: What is the expected behavior?

A: The expected behavior is that the encoding property should be inherited (or resolved if more than one data array is involved) when applying a ufunc. This means that the encoding property should be preserved in the resulting DataArray.

Q: How can I reproduce the issue?

A: You can reproduce the issue by running the following code snippet:

import xarray as xr
import numpy as np

my_ufunc = lambda x: x + 1
xarr1 = xr.DataArray(np.array([1,2,3]))
xarr1.encoding = {'dummy': 'baz'}
xarr2 = xr.apply_ufunc(my_ufunc, xarr1)
print(xarr1.encoding, xarr2.encoding)

Q: What is the output of the code snippet?

A: The output will be:

{'dummy': 'baz'} {'dummy': None}

As you can see, the encoding property is lost after applying the ufunc to xarr1.

Q: How can I preserve the encoding property?

A: You can preserve the encoding property by using the keep_attrs argument in apply_ufunc. However, this argument is not available in the current implementation of apply_ufunc. A workaround is to manually set the encoding property after applying the ufunc.

Q: What is the workaround?

A: The workaround is to manually set the encoding property after applying the ufunc. You can do this by using the following code snippet:

xarr2.encoding = xarr1.encoding.copy()
print(xarr1.encoding, xarr2.encoding)

Q: Why is the encoding property lost?

A: The encoding property is lost because the current implementation of apply_ufunc does not inherit the encoding properties of the input DataArrays.

Q: How can I contribute to resolving this issue?

A: You can contribute to resolving this issue by submitting a pull request to the xarray repository. You can also report the issue on the xarray GitHub page and provide a minimal complete verifiable example (MVCE) to help reproduce the issue.

Q: What is the current status of the issue?

A: The issue is currently open and being tracked on the xarray GitHub page. A pull request has been submitted to resolve the issue, but it has not been merged yet.

Q: When can I expect the issue to be resolved?

A: The issue is expected to be resolved in a future release of xarray. However, the exact timeline is not yet known. You can track the progress of the issue on the xarray GitHub page.