Closed
Description
What happened?
I am interpolating on a single dimension in a somewhat large dataset (a few 100s MB). Prior to updating xarray to 2025.1, I was using DataArray.interp() on a lazy-loaded dataset (open with open_dataset()) with a given interpolation time. Since upgrading to 2025.1, the interpolation time has massively increased, destroying the performance of my application.
What did you expect to happen?
I would expect the interpolation time to remain similar.
Minimal Complete Verifiable Example
This self-contained example behaves similarly to my use case (the hit is not as bad as in my actual use case, but it is quite representative).
import timeit
import xarray as xr
import numpy as np
ds = xr.Dataset(
{
"sigma_a": xr.DataArray(
data=np.ones((4, 2, 2, 16, 8, 36811)),
dims=["x_H2O", "x_CO2", "x_O3", "p", "t", "w"],
coords={"w": np.linspace(0, 30000, 36811)},
)
}
)
ds.to_netcdf("komodo.nc")
ds.close()
print(ds["w"].values)
del ds
w = 15000.5
ds = xr.load_dataset("komodo.nc")
duration = timeit.timeit(lambda: ds["sigma_a"].interp(w=w), number=10)
print(f"load_dataset(): {duration} s")
with xr.open_dataset("komodo.nc") as ds:
duration = timeit.timeit(lambda: ds["sigma_a"].interp(w=w), number=10)
print(f"open_dataset(): {duration} s")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
Console output with xarray 2024.11:
load_dataset(): 3.032241018023342 s
open_dataset(): 0.17071856604889035 s
Console output with xarray 2025.1:
load_dataset(): 3.0386892098467797 s
open_dataset(): 1.0940946410410106 s
Anything else we need to know?
No response
Environment
Tested with Python 3.10 to 3.12, in an environment with SciPy and NetCDF4 installed, with NumPy 1 and 2.
2024 env example
INSTALLED VERSIONS
------------------
commit: None
python: 3.12.10 | packaged by conda-forge | (main, Apr 10 2025, 22:21:13) [GCC 13.3.0]
python-bits: 64
OS: Linux
OS-release: 6.8.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: ('en_GB', 'UTF-8')
libhdf5: 1.14.2
libnetcdf: 4.9.4-development
xarray: 2024.11.0
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.post1
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: None
conda: None
pytest: None
mypy: None
IPython: None
sphinx: None
2025 env example
INSTALLED VERSIONS
------------------
commit: None
python: 3.12.10 | packaged by conda-forge | (main, Apr 10 2025, 22:21:13) [GCC 13.3.0]
python-bits: 64
OS: Linux
OS-release: 6.8.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: ('en_GB', 'UTF-8')
libhdf5: 1.14.2
libnetcdf: 4.9.4-development
xarray: 2025.1.2
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.post1
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: None
conda: None
pytest: None
mypy: None
IPython: None
sphinx: None