Description
What happened?
scatter plot is slow when the dataset has large (length) coordinates even though those coordinates are not involved in the scatter plot.
What did you expect to happen?
scatter plot speed does not depend on coordinates that are not involved in the scatter plot, which was the case at some point in the past
Minimal Complete Verifiable Example
import numpy as np
import xarray as xr
from matplotlib import pyplot as plt
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
# Define coordinates
month = np.arange(1, 13, dtype=np.int64)
L = np.arange(1, 13, dtype=np.int64)
# Create random values for the variables SP and SE
np.random.seed(0) # For reproducibility
SP_values = np.random.rand(len(L), len(month))
SE_values = SP_values + np.random.rand(len(L), len(month))
# Create the dataset
ds = xr.Dataset(
{
"SP": (["L", "month"], SP_values),
"SE": (["L", "month"], SE_values)
},
coords={
"L": L,
"month": month,
"S": np.arange(250),
"model": np.arange(7),
"M": np.arange(30)
}
)
# slow
ds.plot.scatter(x='SP', y='SE')
ds = xr.Dataset(
{
"SP": (["L", "month"], SP_values),
"SE": (["L", "month"], SE_values)
},
coords={
"L": L,
"month": month
}
)
# fast
ds.plot.scatter(x='SP', y='SE')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
No response
Anything else we need to know?
For me, slow = 25 seconds and fast = instantaneous
Environment
INSTALLED VERSIONS
commit: None
python: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:45:13) [Clang 16.0.6 ]
python-bits: 64
OS: Darwin
OS-release: 23.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.14.3
libnetcdf: 4.9.2
xarray: 2024.6.0
pandas: 2.2.2
numpy: 1.26.4
scipy: 1.13.1
netCDF4: 1.6.5
pydap: installed
h5netcdf: 1.3.0
h5py: 3.11.0
zarr: 2.18.2
cftime: 1.6.4
nc_time_axis: 1.4.1
iris: None
bottleneck: 1.3.8
dask: 2024.6.0
distributed: 2024.6.0
matplotlib: 3.8.4
cartopy: 0.23.0
seaborn: 0.13.2
numbagg: 0.8.1
fsspec: 2024.6.0
cupy: None
pint: 0.24
sparse: 0.15.4
flox: 0.9.8
numpy_groupies: 0.11.1
setuptools: 70.0.0
pip: 24.0
conda: None
pytest: 8.2.2
mypy: None
IPython: 8.17.2
sphinx: None