From ff15a08bea27674923afa494b303c6e5cb4d513c Mon Sep 17 00:00:00 2001 From: Mark Harfouche Date: Wed, 10 Jul 2024 22:00:09 -0400 Subject: [PATCH] Fix time indexing regression in `convert_calendar` (#9192) * MRC -- Selecting with string for cftime See discussion in #9138 This commit and pull request mostly serves as a staging group for a potential fix. Test with: ``` pytest xarray/tests/test_cftimeindex.py::test_cftime_noleap_with_str ``` * effectively remove fastpath * Add docstring * Revert "effectively remove fastpath" This reverts commit 0f1a5a2271e5522b5dd946d7f4f38f591211286e. * Fix by reassigning coordinate * Update what's new entry * Simplify if condition --------- Co-authored-by: Spencer Clark --- doc/whats-new.rst | 6 ++++++ xarray/coding/calendar_ops.py | 12 +++++++++++- xarray/tests/test_calendar_ops.py | 25 ++++++++++++++++++++++++- 3 files changed, 41 insertions(+), 2 deletions(-) diff --git a/doc/whats-new.rst b/doc/whats-new.rst index 0c401c2348e..f237b406bd5 100644 --- a/doc/whats-new.rst +++ b/doc/whats-new.rst @@ -47,6 +47,12 @@ Bug fixes By `Justus Magin `_. - Promote floating-point numeric datetimes before decoding (:issue:`9179`, :pull:`9182`). By `Justus Magin `_. +- Address regression introduced in :pull:`9002` that prevented objects returned + by py:meth:`DataArray.convert_calendar` to be indexed by a time index in + certain circumstances (:issue:`9138`, :pull:`9192`). By `Mark Harfouche + `_ and `Spencer Clark + `. + - Fiy static typing of tolerance arguments by allowing `str` type (:issue:`8892`, :pull:`9194`). By `Michael Niklas `_. - Dark themes are now properly detected for ``html[data-theme=dark]``-tags (:pull:`9200`). diff --git a/xarray/coding/calendar_ops.py b/xarray/coding/calendar_ops.py index c4fe9e1f4ae..6f492e78bf9 100644 --- a/xarray/coding/calendar_ops.py +++ b/xarray/coding/calendar_ops.py @@ -5,7 +5,10 @@ from xarray.coding.cftime_offsets import date_range_like, get_date_type from xarray.coding.cftimeindex import CFTimeIndex -from xarray.coding.times import _should_cftime_be_used, convert_times +from xarray.coding.times import ( + _should_cftime_be_used, + convert_times, +) from xarray.core.common import _contains_datetime_like_objects, is_np_datetime_like try: @@ -222,6 +225,13 @@ def convert_calendar( # Remove NaN that where put on invalid dates in target calendar out = out.where(out[dim].notnull(), drop=True) + if use_cftime: + # Reassign times to ensure time index of output is a CFTimeIndex + # (previously it was an Index due to the presence of NaN values). + # Note this is not needed in the case that the output time index is + # a DatetimeIndex, since DatetimeIndexes can handle NaN values. + out[dim] = CFTimeIndex(out[dim].data) + if missing is not None: time_target = date_range_like(time, calendar=calendar, use_cftime=use_cftime) out = out.reindex({dim: time_target}, fill_value=missing) diff --git a/xarray/tests/test_calendar_ops.py b/xarray/tests/test_calendar_ops.py index 7d229371808..13e9f7a1030 100644 --- a/xarray/tests/test_calendar_ops.py +++ b/xarray/tests/test_calendar_ops.py @@ -1,9 +1,10 @@ from __future__ import annotations import numpy as np +import pandas as pd import pytest -from xarray import DataArray, infer_freq +from xarray import CFTimeIndex, DataArray, infer_freq from xarray.coding.calendar_ops import convert_calendar, interp_calendar from xarray.coding.cftime_offsets import date_range from xarray.testing import assert_identical @@ -286,3 +287,25 @@ def test_interp_calendar_errors(): ValueError, match="Both 'source.x' and 'target' must contain datetime objects." ): interp_calendar(da1, da2, dim="x") + + +@requires_cftime +@pytest.mark.parametrize( + ("source_calendar", "target_calendar", "expected_index"), + [("standard", "noleap", CFTimeIndex), ("all_leap", "standard", pd.DatetimeIndex)], +) +def test_convert_calendar_produces_time_index( + source_calendar, target_calendar, expected_index +): + # https://github.com/pydata/xarray/issues/9138 + time = date_range("2000-01-01", "2002-01-01", freq="D", calendar=source_calendar) + temperature = np.ones(len(time)) + da = DataArray( + data=temperature, + dims=["time"], + coords=dict( + time=time, + ), + ) + converted = da.convert_calendar(target_calendar) + assert isinstance(converted.indexes["time"], expected_index)