pydata / pydata/xarray

Selecting dates with .sel() doesn't work when time index is in cftime

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enhancement topic-cftime
Dominant language
Python
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Description

What happened?

When I try to select a subset of the data in a dataset/array with a list containing dates it fails when the time index is in cftime, and I get the following error message:

KeyError: "not all values found in index 'time'"

What did you expect to happen?

I expect selecting a set of dates with a list to work the same way as when the time index is in datetime64.

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

ds = xr.open_dataset("https://thredds.met.no/thredds/dodsC/osisaf/met.no/ice/index/v2p1/nh/osisaf_nh_sie_daily.nc")

# Time coordinates are in datetime64, and selecting dates with a list works.
print(ds.time)
print(ds.sel(time=["2023-01-01", "2023-01-02"]))

# Converting the calendar to all_leap changes the time coordinates to use cftime instead of datetime64.
ds = ds.convert_calendar("all_leap", missing=np.nan).interpolate_na()

# Time coordinates are in cftime, and selecting dates with a list fails.
print(ds.time)
print(ds.sel(time=["2023-01-01", "2023-01-02"]))
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.
Relevant log output
(geoscience) [michael@localhost ~]$ python minimal.py 
<xarray.DataArray 'time' (time: 16107)>
array(['1979-01-01T00:00:00.000000000', '1979-01-02T00:00:00.000000000',
       '1979-01-03T00:00:00.000000000', ..., '2023-02-03T00:00:00.000000000',
       '2023-02-04T00:00:00.000000000', '2023-02-05T00:00:00.000000000'],
      dtype='datetime64[ns]')
Coordinates:
  * time           (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2023-02-05
    sic_threshold  float32 ...
    lat            float32 ...
    lon            float32 ...
Attributes:
    standard_name:          time
    long_name:              time of the observation (centered)
    coverage_content_type:  auxiliaryInformation
    axis:                   T
<xarray.Dataset>
Dimensions:        (time: 2, nv: 2)
Coordinates:
  * time           (time) datetime64[ns] 2023-01-01 2023-01-02
    sic_threshold  float32 ...
    lat            float32 ...
    lon            float32 ...
Dimensions without coordinates: nv
Data variables:
    lat_bounds     (nv) float32 ...
    lon_bounds     (nv) float32 ...
    area           |S64 ...
    sie            (time) float64 ...
    source         (time) float64 ...
Attributes: (12/35)
    title:                   Daily Northern Hemisphere Sea Ice Extent from EU...
    product_id:              OSI-420
    product_name:            OSI SAF Sea Ice Index
    product_status:          demonstration
    version:                 v2p1
    summary:                 Time series of Daily Sea Ice Extent (SIE) for No...
    ...                      ...
    distribution_statement:  Free
    copyright_statement:     Copyright 2023 EUMETSAT
    references:              Product User Manual for OSI-420, Lavergne et al....
    featureType:             timeSeries
    DODS.strlen:             2
    DODS.dimName:            nchar
<xarray.DataArray 'time' (time: 16140)>
array([cftime.DatetimeAllLeap(1979, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeAllLeap(1979, 1, 2, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeAllLeap(1979, 1, 3, 0, 0, 0, 0, has_year_zero=True), ...,
       cftime.DatetimeAllLeap(2023, 2, 3, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeAllLeap(2023, 2, 4, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeAllLeap(2023, 2, 5, 0, 0, 0, 0, has_year_zero=True)],
      dtype=object)
Coordinates:
  * time           (time) object 1979-01-01 00:00:00 ... 2023-02-05 00:00:00
    lat            float32 90.0
    lon            float32 0.0
    sic_threshold  float32 0.15
Attributes:
    standard_name:          time
    long_name:              time of the observation (centered)
    coverage_content_type:  auxiliaryInformation
    axis:                   T
Traceback (most recent call last):
  File "/var/home/michael/minimal.py", line 15, in <module>
    print(ds.sel(time=["2023-01-01", "2023-01-02"]))
  File "/var/home/michael/mambaforge/envs/geoscience/lib/python3.10/site-packages/xarray/core/dataset.py", line 2554, in sel
    query_results = map_index_queries(
  File "/var/home/michael/mambaforge/envs/geoscience/lib/python3.10/site-packages/xarray/core/indexing.py", line 183, in map_index_queries
    results.append(index.sel(labels, **options))  # type: ignore[call-arg]
  File "/var/home/michael/mambaforge/envs/geoscience/lib/python3.10/site-packages/xarray/core/indexes.py", line 480, in sel
    raise KeyError(f"not all values found in index {coord_name!r}")
KeyError: "not all values found in index 'time'"
Anything else we need to know?

No response

Environment

/var/home/michael/mambaforge/envs/geoscience/lib/python3.10/site-packages/_distutils_hack/init.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")

INSTALLED VERSIONS

commit: None
python: 3.10.9 | packaged by conda-forge | (main, Feb 2 2023, 20:20:04) [GCC 11.3.0]
python-bits: 64
OS: Linux
OS-release: 6.1.9-200.fc37.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: ('en_GB', 'UTF-8')
libhdf5: 1.12.2
libnetcdf: 4.8.1

xarray: 2022.11.0
pandas: 1.5.1
numpy: 1.23.4
scipy: 1.9.3
netCDF4: 1.6.1
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.6.2
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.3.6
dask: None
distributed: None
matplotlib: 3.6.2
cartopy: 0.21.0
seaborn: 0.12.1
numbagg: None
fsspec: None
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 65.5.1
pip: 22.3.1
conda: None
pytest: None
IPython: 8.6.0
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

Start by running the minimal example with xarray 2022.11.0, focusing on Dataset.sel after convert_calendar("all_leap", missing=np.nan).interpolate_na(). Trace the time-index selection path and compare list-of-string behavior for datetime64 and cftime; done means both selections return the requested two dates without the KeyError.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data
Issue type
Bug
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
52/100

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