infer_freq() doesn't recognize monthly output if the time dimension is the middle of each month

[mnlevy1981]

What happened?

CESM used to write the time dimension of its output files at the end of the averaging period, so for monthly output the following would hold:

• January averages would have a time dimension of midnight on February 1
• February averages would have a time dimension of midnight on March 1
• etc

The version currently being developed uses the middle of the averaging period, so

• January averages now have a time dimension of noon on January 16 (15.5 days into a 31 day month)
• February averages now have a time dimension of midnight on February 15 (14 days into a 28 day month)
• etc

Some of our diagnostic packages (https://geocat-comp.readthedocs.io/en/latest/user_api/generated/geocat.comp.climatologies.climatology_average.html) require uniformly spaced data and rely on xr.infer_freq() to enforce that. infer_freq() recognizes Feb 1, March 1, April 1, ... as monthly but does not do the same for January 16 (12:00), Feb 15, March 16 (12:00), April 16, ...

What did you expect to happen?

It would be great if infer_freq() could recognize a time dimension of monthly mid-points as having a monthly frequency

Minimal Complete Verifiable Example

import numpy as np
import xarray as xr

month_bounds = np.array([0., 31., 59., 90., 120., 151., 181., 212., 243., 273., 304., 334., 365.])
mid_month = xr.decode_cf(xr.DataArray(0.5*(month_bounds[:-1] + month_bounds[1:]), attrs={'units': 'days since 0001-01-01 00:00:00', 'calendar': 'noleap'}).to_dataset(name='time'))['time']
end_month = xr.decode_cf(xr.DataArray(month_bounds[1:], attrs={'units': 'days since 0001-01-01 00:00:00', 'calendar': 'noleap'}).to_dataset(name='time'))['time']

print(f'infer_freq(mid_month) = {xr.infer_freq(mid_month)}') # None
print(f'infer_freq(end_month) = {xr.infer_freq(end_month)}') # 'MS'

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

>>> print(f'infer_freq(mid_month) = {xr.infer_freq(mid_month)}') # None
infer_freq(mid_month) = None
>>> print(f'infer_freq(end_month) = {xr.infer_freq(end_month)}') # 'MS'
infer_freq(end_month) = MS

Anything else we need to know?

I'm not familiar enough with xarray to be able to offer up a solution, but I figured logging the issue was a good first step. Sorry I can't do more!

Environment

Details

INSTALLED VERSIONS

commit: None
python: 3.12.8 | packaged by conda-forge | (main, Dec 5 2024, 14:24:40) [GCC 13.3.0]
python-bits: 64
OS: Linux
OS-release: 5.14.21-150400.24.18-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: None
libnetcdf: None

xarray: 2024.11.0
pandas: 2.2.3
numpy: 2.2.0
scipy: None
netCDF4: None
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: 75.6.0
pip: 24.3.1
conda: None
pytest: None
mypy: None
IPython: None
sphinx: None