Relax nanosecond datetime restriction in CF time decoding

doc/internals/index.rst

@@ -26,3 +26,4 @@ The pages in this section are intended for:
    how-to-add-new-backend
    how-to-create-custom-index
    zarr-encoding-spec
+   time-coding

doc/user-guide/time-series.rst

@@ -21,20 +21,31 @@ core functionality.
 Creating datetime64 data
 ------------------------
 
-Xarray uses the numpy dtypes ``datetime64[ns]`` and ``timedelta64[ns]`` to
-represent datetime data, which offer vectorized (if sometimes buggy) operations
-with numpy and smooth integration with pandas.
+Xarray uses the numpy dtypes ``datetime64[unit]`` and ``timedelta64[unit]``
+(where unit is anything of "s", "ms", "us" and "ns") to represent datetime
+data, which offer vectorized operations with numpy and smooth integration with pandas.
 
 To convert to or create regular arrays of ``datetime64`` data, we recommend
 using :py:func:`pandas.to_datetime` and :py:func:`pandas.date_range`:
 
 .. ipython:: python
 
     pd.to_datetime(["2000-01-01", "2000-02-02"])
+    pd.DatetimeIndex(
+        ["2000-01-01 00:00:00", "2000-02-02 00:00:00"], dtype="datetime64[s]"
+    )
     pd.date_range("2000-01-01", periods=365)
+    pd.date_range("2000-01-01", periods=365, unit="s")
+
+.. note::
+    Care has to be taken to create the output with the wanted resolution.
+    For :py:func:`pandas.date_range` the ``unit``-kwarg has to be specified
+    and for :py:func:`pandas.to_datetime` the selection of the resolution
+    isn't possible at all. For that :py:class:`pd.DatetimeIndex` can be used
+    directly.
 
 Alternatively, you can supply arrays of Python ``datetime`` objects. These get
-converted automatically when used as arguments in xarray objects:
+converted automatically when used as arguments in xarray objects (with us-resolution):
 
 .. ipython:: python
 
@@ -51,7 +62,7 @@ attribute like ``'days since 2000-01-01'``).
 .. note::
 
    When decoding/encoding datetimes for non-standard calendars or for dates
-   before year 1678 or after year 2262, xarray uses the `cftime`_ library.
+   before 1582-10-15, xarray uses the `cftime`_ library.
    It was previously packaged with the ``netcdf4-python`` package under the
    name ``netcdftime`` but is now distributed separately. ``cftime`` is an
    :ref:`optional dependency<installing>` of xarray.
@@ -68,15 +79,9 @@ You can manual decode arrays in this form by passing a dataset to
     ds = xr.Dataset({"time": ("time", [0, 1, 2, 3], attrs)})
     xr.decode_cf(ds)
 
-One unfortunate limitation of using ``datetime64[ns]`` is that it limits the
-native representation of dates to those that fall between the years 1678 and
-2262. When a netCDF file contains dates outside of these bounds, dates will be
-returned as arrays of :py:class:`cftime.datetime` objects and a :py:class:`~xarray.CFTimeIndex`
-will be used for indexing.  :py:class:`~xarray.CFTimeIndex` enables a subset of
-the indexing functionality of a :py:class:`pandas.DatetimeIndex` and is only
-fully compatible with the standalone version of ``cftime`` (not the version
-packaged with earlier versions ``netCDF4``).  See :ref:`CFTimeIndex` for more
-information.
+From xarray 2024.11 the resolution of the dates can be tuned between "s", "ms", "us" and "ns". One limitation of using ``datetime64[ns]`` is that it limits the native representation of dates to those that fall between the years 1678 and 2262, which gets increased significantly with lower resolutions. When a netCDF file contains dates outside of these bounds (or dates < 1582-10-15), dates will be returned as arrays of :py:class:`cftime.datetime` objects and a :py:class:`~xarray.CFTimeIndex` will be used for indexing.
+:py:class:`~xarray.CFTimeIndex` enables a subset of the indexing functionality of a :py:class:`pandas.DatetimeIndex`.
+See :ref:`CFTimeIndex` for more information.
 
 Datetime indexing
 -----------------

doc/user-guide/weather-climate.rst

@@ -10,7 +10,7 @@ Weather and climate data
 
     import xarray as xr
 
-Xarray can leverage metadata that follows the `Climate and Forecast (CF) conventions`_ if present. Examples include :ref:`automatic labelling of plots<plotting>` with descriptive names and units if proper metadata is present and support for non-standard calendars used in climate science through the ``cftime`` module(Explained in the :ref:`CFTimeIndex` section). There are also a number of :ref:`geosciences-focused projects that build on xarray<ecosystem>`.
+Xarray can leverage metadata that follows the `Climate and Forecast (CF) conventions`_ if present. Examples include :ref:`automatic labelling of plots<plotting>` with descriptive names and units if proper metadata is present and support for non-standard calendars used in climate science through the ``cftime`` module (explained in the :ref:`CFTimeIndex` section). There are also a number of :ref:`geosciences-focused projects that build on xarray<ecosystem>`.
 
 .. _Climate and Forecast (CF) conventions: https://cfconventions.org
 
@@ -64,8 +64,7 @@ Through the standalone ``cftime`` library and a custom subclass of
 :py:class:`pandas.Index`, xarray supports a subset of the indexing
 functionality enabled through the standard :py:class:`pandas.DatetimeIndex` for
 dates from non-standard calendars commonly used in climate science or dates
-using a standard calendar, but outside the `nanosecond-precision range`_
-(approximately between years 1678 and 2262).
+using a standard calendar, but outside the `precision range`_ and dates prior 1582-10-15.
 
 .. note::
 
@@ -75,18 +74,14 @@ using a standard calendar, but outside the `nanosecond-precision range`_
    any of the following are true:
 
    - The dates are from a non-standard calendar
-   - Any dates are outside the nanosecond-precision range.
+   - Any dates are outside the nanosecond-precision range (prior xarray version 2024.11)
+   - Any dates are outside the time span limited by the resolution (from xarray version v2024.11)
 
    Otherwise pandas-compatible dates from a standard calendar will be
-   represented with the ``np.datetime64[ns]`` data type, enabling the use of a
-   :py:class:`pandas.DatetimeIndex` or arrays with dtype ``np.datetime64[ns]``
-   and their full set of associated features.
+   represented with the ``np.datetime64[unit]`` data type (where unit can be any of ["s", "ms", "us", "ns"], enabling the use of a :py:class:`pandas.DatetimeIndex` or arrays with dtype ``np.datetime64[unit]`` and their full set of associated features.
 
    As of pandas version 2.0.0, pandas supports non-nanosecond precision datetime
-   values.  For the time being, xarray still automatically casts datetime values
-   to nanosecond-precision for backwards compatibility with older pandas
-   versions; however, this is something we would like to relax going forward.
-   See :issue:`7493` for more discussion.
+   values. From xarray version 2024.11 the relaxed non-nanosecond precision datetime values will be used.
 
 For example, you can create a DataArray indexed by a time
 coordinate with dates from a no-leap calendar and a
@@ -115,7 +110,7 @@ instance, we can create the same dates and DataArray we created above using:
 Mirroring pandas' method with the same name, :py:meth:`~xarray.infer_freq` allows one to
 infer the sampling frequency of a :py:class:`~xarray.CFTimeIndex` or a 1-D
 :py:class:`~xarray.DataArray` containing cftime objects. It also works transparently with
-``np.datetime64[ns]`` and ``np.timedelta64[ns]`` data.
+``np.datetime64`` and ``np.timedelta64`` data (with "s", "ms", "us" or "ns" resolution).
 
 .. ipython:: python
 
@@ -137,6 +132,7 @@ Conversion between non-standard calendar and to/from pandas DatetimeIndexes is
 facilitated with the :py:meth:`xarray.Dataset.convert_calendar` method (also available as
 :py:meth:`xarray.DataArray.convert_calendar`). Here, like elsewhere in xarray, the ``use_cftime``
 argument controls which datetime backend is used in the output. The default (``None``) is to
+use ``pandas`` when possible, i.e. when the calendar is standard and dates starting with 1582-10-15.
 use ``pandas`` when possible, i.e. when the calendar is standard and dates are within 1678 and 2262.
 
 .. ipython:: python
@@ -241,6 +237,6 @@ For data indexed by a :py:class:`~xarray.CFTimeIndex` xarray currently supports:
 
     da.resample(time="81min", closed="right", label="right", offset="3min").mean()
 
-.. _nanosecond-precision range: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timestamp-limitations
+.. _precision range: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timestamp-limitations
 .. _ISO 8601 standard: https://en.wikipedia.org/wiki/ISO_8601
 .. _partial datetime string indexing: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#partial-string-indexing

xarray/coding/cftime_offsets.py

@@ -64,7 +64,7 @@
 from xarray.core.pdcompat import (
     NoDefault,
     count_not_none,
-    nanosecond_precision_timestamp,
+    default_precision_timestamp,
     no_default,
 )
 from xarray.core.utils import attempt_import, emit_user_level_warning
@@ -77,14 +77,6 @@
 T_FreqStr = TypeVar("T_FreqStr", str, None)
 
 
-def _nanosecond_precision_timestamp(*args, **kwargs):
-    # As of pandas version 3.0, pd.to_datetime(Timestamp(...)) will try to
-    # infer the appropriate datetime precision. Until xarray supports
-    # non-nanosecond precision times, we will use this constructor wrapper to
-    # explicitly create nanosecond-precision Timestamp objects.
-    return pd.Timestamp(*args, **kwargs).as_unit("ns")
-
-
 def get_date_type(calendar, use_cftime=True):
     """Return the cftime date type for a given calendar name."""
     if TYPE_CHECKING:
@@ -93,7 +85,7 @@ def get_date_type(calendar, use_cftime=True):
         cftime = attempt_import("cftime")
 
     if _is_standard_calendar(calendar) and not use_cftime:
-        return _nanosecond_precision_timestamp
+        return default_precision_timestamp
 
     calendars = {
         "noleap": cftime.DatetimeNoLeap,
@@ -1480,10 +1472,8 @@ def date_range_like(source, calendar, use_cftime=None):
     if is_np_datetime_like(source.dtype):
         # We want to use datetime fields (datetime64 object don't have them)
         source_calendar = "standard"
-        # TODO: the strict enforcement of nanosecond precision Timestamps can be
-        # relaxed when addressing GitHub issue #7493.
-        source_start = nanosecond_precision_timestamp(source_start)
-        source_end = nanosecond_precision_timestamp(source_end)
+        source_start = default_precision_timestamp(source_start)
+        source_end = default_precision_timestamp(source_end)
     else:
         if isinstance(source, CFTimeIndex):
             source_calendar = source.calendar

xarray/coding/cftimeindex.py

@@ -644,13 +644,14 @@ def to_datetimeindex(self, unsafe=False):
         CFTimeIndex([2000-01-01 00:00:00, 2000-01-02 00:00:00],
                     dtype='object', length=2, calendar='standard', freq=None)
         >>> times.to_datetimeindex()
-        DatetimeIndex(['2000-01-01', '2000-01-02'], dtype='datetime64[ns]', freq=None)
+        DatetimeIndex(['2000-01-01', '2000-01-02'], dtype='datetime64[us]', freq=None)
         """
 
         if not self._data.size:
             return pd.DatetimeIndex([])
 
-        nptimes = cftime_to_nptime(self)
+        # transform to us-resolution is needed for DatetimeIndex
+        nptimes = cftime_to_nptime(self).astype("=M8[us]")
         calendar = infer_calendar_name(self)
         if calendar not in _STANDARD_CALENDARS and not unsafe:
             warnings.warn(