Switch enable_cftimeindex to True by default

doc/api-hidden.rst

@@ -153,3 +153,4 @@
    plot.FacetGrid.map
 
    CFTimeIndex.shift
+   CFTimeIndex.to_datetimeindex

doc/time-series.rst

@@ -71,10 +71,11 @@ 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`
-can be used for indexing.  The :py:class:`~xarray.CFTimeIndex` enables only a subset of
-the indexing functionality of a :py:class:`pandas.DatetimeIndex` and is only enabled
-when using the standalone version of ``cftime`` (not the version packaged with
-earlier versions ``netCDF4``).  See :ref:`CFTimeIndex` for more information.
+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. 
 
 Datetime indexing
 -----------------
@@ -221,18 +222,28 @@ Non-standard calendars and dates outside the Timestamp-valid range
 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 or dates using a standard calendar, but
-outside the `Timestamp-valid range`_ (approximately between years 1678 and
-2262).  This behavior has not yet been turned on by default; to take advantage
-of this functionality, you must have the ``enable_cftimeindex`` option set to
-``True`` within your context (see :py:func:`~xarray.set_options` for more
-information).  It is expected that this will become the default behavior in
-xarray version 0.11.
-
-For instance, you can create a DataArray indexed by a time
-coordinate with a no-leap calendar within a context manager setting the
-``enable_cftimeindex`` option, and the time index will be cast to a
-:py:class:`~xarray.CFTimeIndex`:
+dates from non-standard calendars commonly used in climate science or dates
+using a standard calendar, but outside the `Timestamp-valid range`_
+(approximately between years 1678 and 2262).  
+
+.. note::
+
+   As of xarray version 0.11, by default, :py:class:`cftime.datetime` objects
+   will be used to represent times (either in indexes, as a
+   :py:class:`~xarray.CFTimeIndex`, or in data arrays with dtype object) if 
+   any of the following are true: 
+
+   - The dates are from a non-standard calendar
+   - Any dates are outside the Timestamp-valid range.
+
+   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.
+
+For example, you can create a DataArray indexed by a time
+coordinate with dates from a no-leap calendar and a
+:py:class:`~xarray.CFTimeIndex` will automatically be used:
 
 .. ipython:: python
 
@@ -241,27 +252,11 @@ coordinate with a no-leap calendar within a context manager setting the
 
    dates = [DatetimeNoLeap(year, month, 1) for year, month in
             product(range(1, 3), range(1, 13))]
-   with xr.set_options(enable_cftimeindex=True):
-       da = xr.DataArray(np.arange(24), coords=[dates], dims=['time'],
-                         name='foo')
+   da = xr.DataArray(np.arange(24), coords=[dates], dims=['time'], name='foo')
 
-.. note::
-
-   With the ``enable_cftimeindex`` option activated, a :py:class:`~xarray.CFTimeIndex`
-   will be used for time indexing if any of the following are true:
-
-   - The dates are from a non-standard calendar
-   - Any dates are outside the Timestamp-valid range
-
-   Otherwise a :py:class:`pandas.DatetimeIndex` will be used.  In addition, if any
-   variable (not just an index variable) is encoded using a non-standard
-   calendar, its times will be decoded into :py:class:`cftime.datetime` objects,
-   regardless of whether or not they can be represented using
-   ``np.datetime64[ns]`` objects.
-
 xarray also includes a :py:func:`~xarray.cftime_range` function, which enables
-creating a :py:class:`~xarray.CFTimeIndex` with regularly-spaced dates.  For instance, we can
-create the same dates and DataArray we created above using:
+creating a :py:class:`~xarray.CFTimeIndex` with regularly-spaced dates.  For
+instance, we can create the same dates and DataArray we created above using:
 
 .. ipython:: python
 
@@ -317,13 +312,43 @@ For data indexed by a :py:class:`~xarray.CFTimeIndex` xarray currently supports:
 
 .. ipython:: python
 
-   da.to_netcdf('example.nc')
-   xr.open_dataset('example.nc')
+   da.to_netcdf('example-no-leap.nc')
+   xr.open_dataset('example-no-leap.nc')
 
 .. note::
 
-   Currently resampling along the time dimension for data indexed by a
-   :py:class:`~xarray.CFTimeIndex` is not supported.
+   While much of the time series functionality that is possible for standard
+   dates has been implemented for dates from non-standard calendars, there are
+   still some remaining important features that have yet to be implemented,
+   for example:
+
+   - Resampling along the time dimension for data indexed by a
+     :py:class:`~xarray.CFTimeIndex` (:issue:`2191`, :issue:`2458`)
+   - Built-in plotting of data with :py:class:`cftime.datetime` coordinate axes
+     (:issue:`2164`).   
+
+   For some use-cases it may still be useful to convert from
+   a :py:class:`~xarray.CFTimeIndex` to a :py:class:`pandas.DatetimeIndex`,
+   despite the difference in calendar types (e.g. to allow the use of some
+   forms resample with non-standard calendars).  The recommended way of doing
+   this is to use the built-in :py:meth:`~xarray.CFTimeIndex.to_datetimeindex`
+   method:
+
+   .. ipython:: python
+
+       modern_times = xr.cftime_range('2000', periods=24, freq='MS', calendar='noleap')
+       da = xr.DataArray(range(24), [('time', modern_times)])
+       da
+       datetimeindex = da.indexes['time'].to_datetimeindex()
+       da['time'] = datetimeindex
+       da.resample(time='Y').mean('time')
+
+   However in this case one should
+   use caution to only perform operations which do not depend on differences
+   between dates (e.g. differentiation, interpolation, or upsampling with
+   resample), as these could introduce subtle and silent errors due to the
+   difference in calendar types between the dates encoded in your data and the
+   dates stored in memory.  
 
 .. _Timestamp-valid range: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#timestamp-limitations
 .. _ISO 8601-format: https://en.wikipedia.org/wiki/ISO_8601

doc/whats-new.rst

@@ -33,6 +33,17 @@ v0.11.0 (unreleased)
 Breaking changes
 ~~~~~~~~~~~~~~~~
 
+- For non-standard calendars commonly used in climate science, xarray will now
+  always use :py:class:`cftime.datetime` objects, rather than by default try to
+  coerce them to ``np.datetime64[ns]`` objects.  A
+  :py:class:`~xarray.CFTimeIndex` will be used for indexing along time
+  coordinates in these cases. A new method,
+  :py:meth:`~xarray.CFTimeIndex.to_datetimeindex`, has been added 
+  to aid in converting from a  :py:class:`~xarray.CFTimeIndex` to a
+  :py:class:`pandas.DatetimeIndex` for the remaining use-cases where
+  using a :py:class:`~xarray.CFTimeIndex` is still a limitation (e.g. for
+  resample or plotting).  Setting the ``enable_cftimeindex`` option is now a
+  no-op and emits a ``FutureWarning``. 
 - ``Dataset.T`` has been removed as a shortcut for :py:meth:`Dataset.transpose`.
   Call :py:meth:`Dataset.transpose` directly instead.
 - Iterating over a ``Dataset`` now includes only data variables, not coordinates.

xarray/coding/cftimeindex.py

@@ -42,6 +42,7 @@
 from __future__ import absolute_import
 
 import re
+import warnings
 from datetime import timedelta
 
 import numpy as np
@@ -50,6 +51,8 @@
 from xarray.core import pycompat
 from xarray.core.utils import is_scalar
 
+from .times import cftime_to_nptime, infer_calendar_name, _STANDARD_CALENDARS
+
 
 def named(name, pattern):
     return '(?P<' + name + '>' + pattern + ')'
@@ -381,6 +384,50 @@ def _add_delta(self, deltas):
         # pandas.  No longer used as of pandas 0.23.
         return self + deltas
 
+    def to_datetimeindex(self):
+        """If possible, convert this index to a pandas.DatetimeIndex.
+
+        Returns
+        -------
+        pandas.DatetimeIndex
+
+        Raises
+        ------
+        ValueError
+            If the CFTimeIndex contains dates that are not possible in the
+            standard calendar or outside the pandas.Timestamp-valid range.
+
+        Warns
+        -----
+        RuntimeWarning
+            If converting from a non-standard calendar to a DatetimeIndex.
+
+        Warnings
+        --------
+        Note that for non-standard calendars, this will change the calendar
+        type of the index.  In that case the result of this method should be
+        used with caution.
+
+        Examples
+        --------
+        >>> import xarray as xr
+        >>> times = xr.cftime_range('2000', periods=2, calendar='gregorian')
+        >>> times
+        CFTimeIndex([2000-01-01 00:00:00, 2000-01-02 00:00:00], dtype='object')
+        >>> times.to_datetimeindex()
+        DatetimeIndex(['2000-01-01', '2000-01-02'], dtype='datetime64[ns]', freq=None)
+        """  # noqa: E501
+        nptimes = cftime_to_nptime(self)
+        calendar = infer_calendar_name(self)
+        if calendar not in _STANDARD_CALENDARS:
+            warnings.warn(
+                'Converting a CFTimeIndex with dates from a non-standard '
+                'calendar, {!r}, to a pandas.DatetimeIndex, which uses dates '
+                'from the standard calendar.  This may lead to subtle errors '
+                'in operations that depend on the length of time between '
+                'dates.'.format(calendar), RuntimeWarning)
+        return pd.DatetimeIndex(nptimes)
+
 
 def _parse_iso8601_without_reso(date_type, datetime_str):
     date, _ = _parse_iso8601_with_reso(date_type, datetime_str)

xarray/coding/times.py

@@ -12,7 +12,6 @@
 from ..core import indexing
 from ..core.common import contains_cftime_datetimes
 from ..core.formatting import first_n_items, format_timestamp, last_item
-from ..core.options import OPTIONS
 from ..core.pycompat import PY3
 from ..core.variable import Variable
 from .variables import (
@@ -61,8 +60,9 @@ def _require_standalone_cftime():
     try:
         import cftime  # noqa: F401
     except ImportError:
-        raise ImportError('Using a CFTimeIndex requires the standalone '
-                          'version of the cftime library.')
+        raise ImportError('Decoding times with non-standard calendars '
+                          'or outside the pandas.Timestamp-valid range '
+                          'requires the standalone cftime package.')
 
 
 def _netcdf_to_numpy_timeunit(units):
@@ -84,41 +84,32 @@ def _unpack_netcdf_time_units(units):
     return delta_units, ref_date
 
 
-def _decode_datetime_with_cftime(num_dates, units, calendar,
-                                 enable_cftimeindex):
+def _decode_datetime_with_cftime(num_dates, units, calendar):
     cftime = _import_cftime()
-    if enable_cftimeindex:
-        _require_standalone_cftime()
+
+    if cftime.__name__ == 'cftime':
         dates = np.asarray(cftime.num2date(num_dates, units, calendar,
                                            only_use_cftime_datetimes=True))
     else:
+        # Must be using num2date from an old version of netCDF4 which
+        # does not have the only_use_cftime_datetimes option.
         dates = np.asarray(cftime.num2date(num_dates, units, calendar))
 
     if (dates[np.nanargmin(num_dates)].year < 1678 or
             dates[np.nanargmax(num_dates)].year >= 2262):
-        if not enable_cftimeindex or calendar in _STANDARD_CALENDARS:
+        if calendar in _STANDARD_CALENDARS:
             warnings.warn(
                 'Unable to decode time axis into full '
                 'numpy.datetime64 objects, continuing using dummy '
                 'cftime.datetime objects instead, reason: dates out '
                 'of range', SerializationWarning, stacklevel=3)
     else:
-        if enable_cftimeindex:
-            if calendar in _STANDARD_CALENDARS:
-                dates = cftime_to_nptime(dates)
-        else:
-            try:
-                dates = cftime_to_nptime(dates)
-            except ValueError as e:
-                warnings.warn(
-                    'Unable to decode time axis into full '
-                    'numpy.datetime64 objects, continuing using '
-                    'dummy cftime.datetime objects instead, reason:'
-                    '{0}'.format(e), SerializationWarning, stacklevel=3)
+        if calendar in _STANDARD_CALENDARS:
+            dates = cftime_to_nptime(dates)
     return dates
 
 
-def _decode_cf_datetime_dtype(data, units, calendar, enable_cftimeindex):
+def _decode_cf_datetime_dtype(data, units, calendar):
     # Verify that at least the first and last date can be decoded
     # successfully. Otherwise, tracebacks end up swallowed by
     # Dataset.__repr__ when users try to view their lazily decoded array.
@@ -128,8 +119,7 @@ def _decode_cf_datetime_dtype(data, units, calendar, enable_cftimeindex):
                                     last_item(values) or [0]])
 
     try:
-        result = decode_cf_datetime(example_value, units, calendar,
-                                    enable_cftimeindex)
+        result = decode_cf_datetime(example_value, units, calendar)
     except Exception:
         calendar_msg = ('the default calendar' if calendar is None
                         else 'calendar %r' % calendar)
@@ -145,8 +135,7 @@ def _decode_cf_datetime_dtype(data, units, calendar, enable_cftimeindex):
     return dtype
 
 
-def decode_cf_datetime(num_dates, units, calendar=None,
-                       enable_cftimeindex=False):
+def decode_cf_datetime(num_dates, units, calendar=None):
     """Given an array of numeric dates in netCDF format, convert it into a
     numpy array of date time objects.
 
@@ -200,8 +189,7 @@ def decode_cf_datetime(num_dates, units, calendar=None,
 
     except (OutOfBoundsDatetime, OverflowError):
         dates = _decode_datetime_with_cftime(
-            flat_num_dates.astype(np.float), units, calendar,
-            enable_cftimeindex)
+            flat_num_dates.astype(np.float), units, calendar)
 
     return dates.reshape(num_dates.shape)
 
@@ -291,7 +279,12 @@ def cftime_to_nptime(times):
     times = np.asarray(times)
     new = np.empty(times.shape, dtype='M8[ns]')
     for i, t in np.ndenumerate(times):
-        dt = datetime(t.year, t.month, t.day, t.hour, t.minute, t.second)
+        try:
+            dt = pd.Timestamp(t.year, t.month, t.day, t.hour, t.minute,
+                              t.second)
+        except ValueError as e:
+            raise ValueError('Cannot convert date {} to a date in the '
+                             'standard calendar.  Reason: {}.'.format(t, e))
         new[i] = np.datetime64(dt)
     return new
 
@@ -399,15 +392,12 @@ def encode(self, variable, name=None):
     def decode(self, variable, name=None):
         dims, data, attrs, encoding = unpack_for_decoding(variable)
 
-        enable_cftimeindex = OPTIONS['enable_cftimeindex']
         if 'units' in attrs and 'since' in attrs['units']:
             units = pop_to(attrs, encoding, 'units')
             calendar = pop_to(attrs, encoding, 'calendar')
-            dtype = _decode_cf_datetime_dtype(
-                data, units, calendar, enable_cftimeindex)
+            dtype = _decode_cf_datetime_dtype(data, units, calendar)
             transform = partial(
-                decode_cf_datetime, units=units, calendar=calendar,
-                enable_cftimeindex=enable_cftimeindex)
+                decode_cf_datetime, units=units, calendar=calendar)
             data = lazy_elemwise_func(data, transform, dtype)
 
         return Variable(dims, data, attrs, encoding)