Add use_cftime option to open_dataset

doc/whats-new.rst

@@ -68,7 +68,15 @@ Enhancements
 - :py:meth:`pandas.Series.dropna` is now supported for a
   :py:class:`pandas.Series` indexed by a :py:class:`~xarray.CFTimeIndex`
   (:issue:`2688`). By `Spencer Clark <https://github.com/spencerkclark>`_.
-
+- :py:meth:`~xarray.open_dataset` now accepts a ``use_cftime`` argument, which
+  can be used to require that ``cftime.datetime`` objects are always used, or
+  never used when decoding dates encoded with a standard calendar.  This can be
+  used to ensure consistent date types are returned when using
+  :py:meth:`~xarray.open_mfdataset` (:issue:`1263`) and/or to silence
+  serialization warnings raised if dates from a standard calendar are found to
+  be outside the :py:class:`pandas.Timestamp`-valid range (:issue:`2754`).  By
+  `Spencer Clark <https://github.com/spencerkclark>`_. 
+
 Bug fixes
 ~~~~~~~~~
 

xarray/backends/api.py

@@ -161,7 +161,7 @@ def open_dataset(filename_or_obj, group=None, decode_cf=True,
                  mask_and_scale=None, decode_times=True, autoclose=None,
                  concat_characters=True, decode_coords=True, engine=None,
                  chunks=None, lock=None, cache=None, drop_variables=None,
-                 backend_kwargs=None):
+                 backend_kwargs=None, use_cftime=None):
     """Load and decode a dataset from a file or file-like object.
 
     Parameters
@@ -231,6 +231,16 @@ def open_dataset(filename_or_obj, group=None, decode_cf=True,
         A dictionary of keyword arguments to pass on to the backend. This
         may be useful when backend options would improve performance or
         allow user control of dataset processing.
+    use_cftime: bool, optional
+        Only relevant if encoded dates come from a standard calendar
+        (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not
+        specified).  If None (default), attempt to decode times to
+        ``np.datetime64[ns]`` objects; if this is not possible, decode times to
+        ``cftime.datetime`` objects. If True, always decode times to
+        ``cftime.datetime`` objects, regardless of whether or not they can be
+        represented using ``np.datetime64[ns]`` objects.  If False, always
+        decode times to ``np.datetime64[ns]`` objects; if this is not possible
+        raise an error.
 
     Returns
     -------
@@ -269,7 +279,7 @@ def maybe_decode_store(store, lock=False):
         ds = conventions.decode_cf(
             store, mask_and_scale=mask_and_scale, decode_times=decode_times,
             concat_characters=concat_characters, decode_coords=decode_coords,
-            drop_variables=drop_variables)
+            drop_variables=drop_variables, use_cftime=use_cftime)
 
         _protect_dataset_variables_inplace(ds, cache)
 
@@ -284,7 +294,8 @@ def maybe_decode_store(store, lock=False):
                 mtime = None
             token = tokenize(filename_or_obj, mtime, group, decode_cf,
                              mask_and_scale, decode_times, concat_characters,
-                             decode_coords, engine, chunks, drop_variables)
+                             decode_coords, engine, chunks, drop_variables,
+                             use_cftime)
             name_prefix = 'open_dataset-%s' % token
             ds2 = ds.chunk(chunks, name_prefix=name_prefix, token=token)
             ds2._file_obj = ds._file_obj
@@ -360,7 +371,7 @@ def open_dataarray(filename_or_obj, group=None, decode_cf=True,
                    mask_and_scale=None, decode_times=True, autoclose=None,
                    concat_characters=True, decode_coords=True, engine=None,
                    chunks=None, lock=None, cache=None, drop_variables=None,
-                   backend_kwargs=None):
+                   backend_kwargs=None, use_cftime=None):
     """Open an DataArray from a netCDF file containing a single data variable.
 
     This is designed to read netCDF files with only one data variable. If
@@ -428,6 +439,16 @@ def open_dataarray(filename_or_obj, group=None, decode_cf=True,
         A dictionary of keyword arguments to pass on to the backend. This
         may be useful when backend options would improve performance or
         allow user control of dataset processing.
+    use_cftime: bool, optional
+        Only relevant if encoded dates come from a standard calendar
+        (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not
+        specified).  If None (default), attempt to decode times to
+        ``np.datetime64[ns]`` objects; if this is not possible, decode times to
+        ``cftime.datetime`` objects. If True, always decode times to
+        ``cftime.datetime`` objects, regardless of whether or not they can be
+        represented using ``np.datetime64[ns]`` objects.  If False, always
+        decode times to ``np.datetime64[ns]`` objects; if this is not possible
+        raise an error.
 
     Notes
     -----
@@ -450,7 +471,8 @@ def open_dataarray(filename_or_obj, group=None, decode_cf=True,
                            decode_coords=decode_coords, engine=engine,
                            chunks=chunks, lock=lock, cache=cache,
                            drop_variables=drop_variables,
-                           backend_kwargs=backend_kwargs)
+                           backend_kwargs=backend_kwargs,
+                           use_cftime=use_cftime)
 
     if len(dataset.data_vars) != 1:
         raise ValueError('Given file dataset contains more than one data '

xarray/coding/times.py

@@ -80,32 +80,7 @@ def _unpack_netcdf_time_units(units):
     return delta_units, ref_date
 
 
-def _decode_datetime_with_cftime(num_dates, units, calendar):
-    cftime = _import_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 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 calendar in _STANDARD_CALENDARS:
-            dates = cftime_to_nptime(dates)
-    return dates
-
-
-def _decode_cf_datetime_dtype(data, units, calendar):
+def _decode_cf_datetime_dtype(data, units, calendar, use_cftime):
     # 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.
@@ -115,7 +90,8 @@ def _decode_cf_datetime_dtype(data, units, calendar):
                                     last_item(values) or [0]])
 
     try:
-        result = decode_cf_datetime(example_value, units, calendar)
+        result = decode_cf_datetime(example_value, units, calendar,
+                                    use_cftime)
     except Exception:
         calendar_msg = ('the default calendar' if calendar is None
                         else 'calendar %r' % calendar)
@@ -129,7 +105,52 @@ def _decode_cf_datetime_dtype(data, units, calendar):
     return dtype
 
 
-def decode_cf_datetime(num_dates, units, calendar=None):
+def _decode_datetime_with_cftime(num_dates, units, calendar):
+    cftime = _import_cftime()
+
+    if cftime.__name__ == 'cftime':
+        return 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.
+        return np.asarray(cftime.num2date(num_dates, units, calendar))
+
+
+def _decode_datetime_with_pandas(flat_num_dates, units, calendar):
+    if calendar not in _STANDARD_CALENDARS:
+        raise OutOfBoundsDatetime(
+            'Cannot decode times from a non-standard calendar, {!r}, using '
+            'pandas.'.format(calendar))
+
+    delta, ref_date = _unpack_netcdf_time_units(units)
+    delta = _netcdf_to_numpy_timeunit(delta)
+    try:
+        ref_date = pd.Timestamp(ref_date)
+    except ValueError:
+        # ValueError is raised by pd.Timestamp for non-ISO timestamp
+        # strings, in which case we fall back to using cftime
+        raise OutOfBoundsDatetime
+
+    # fixes: https://github.com/pydata/pandas/issues/14068
+    # these lines check if the the lowest or the highest value in dates
+    # cause an OutOfBoundsDatetime (Overflow) error
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', 'invalid value encountered',
+                                RuntimeWarning)
+        pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
+        pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
+
+    # Cast input dates to integers of nanoseconds because `pd.to_datetime`
+    # works much faster when dealing with integers
+    # make _NS_PER_TIME_DELTA an array to ensure type upcasting
+    flat_num_dates_ns_int = (flat_num_dates.astype(np.float64) *
+                             _NS_PER_TIME_DELTA[delta]).astype(np.int64)
+
+    return (pd.to_timedelta(flat_num_dates_ns_int, 'ns') + ref_date).values
+
+
+def decode_cf_datetime(num_dates, units, calendar=None, use_cftime=None):
     """Given an array of numeric dates in netCDF format, convert it into a
     numpy array of date time objects.
 
@@ -149,41 +170,30 @@ def decode_cf_datetime(num_dates, units, calendar=None):
     if calendar is None:
         calendar = 'standard'
 
-    delta, ref_date = _unpack_netcdf_time_units(units)
-
-    try:
-        if calendar not in _STANDARD_CALENDARS:
-            raise OutOfBoundsDatetime
-
-        delta = _netcdf_to_numpy_timeunit(delta)
+    if use_cftime is None:
         try:
-            ref_date = pd.Timestamp(ref_date)
-        except ValueError:
-            # ValueError is raised by pd.Timestamp for non-ISO timestamp
-            # strings, in which case we fall back to using cftime
-            raise OutOfBoundsDatetime
-
-        # fixes: https://github.com/pydata/pandas/issues/14068
-        # these lines check if the the lowest or the highest value in dates
-        # cause an OutOfBoundsDatetime (Overflow) error
-        with warnings.catch_warnings():
-            warnings.filterwarnings('ignore', 'invalid value encountered',
-                                    RuntimeWarning)
-            pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
-            pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
-
-        # Cast input dates to integers of nanoseconds because `pd.to_datetime`
-        # works much faster when dealing with integers
-        # make _NS_PER_TIME_DELTA an array to ensure type upcasting
-        flat_num_dates_ns_int = (flat_num_dates.astype(np.float64) *
-                                 _NS_PER_TIME_DELTA[delta]).astype(np.int64)
-
-        dates = (pd.to_timedelta(flat_num_dates_ns_int, 'ns') +
-                 ref_date).values
-
-    except (OutOfBoundsDatetime, OverflowError):
+            dates = _decode_datetime_with_pandas(flat_num_dates, units,
+                                                 calendar)
+        except (OutOfBoundsDatetime, OverflowError):
+            dates = _decode_datetime_with_cftime(
+                flat_num_dates.astype(np.float), units, calendar)
+
+            if (dates[np.nanargmin(num_dates)].year < 1678 or
+               dates[np.nanargmax(num_dates)].year >= 2262):
+                if calendar in _STANDARD_CALENDARS:
+                    warnings.warn(
+                        'Unable to decode time axis into full '
+                        'numpy.datetime64 objects, continuing using '
+                        'cftime.datetime objects instead, reason: dates out '
+                        'of range', SerializationWarning, stacklevel=3)
+            else:
+                if calendar in _STANDARD_CALENDARS:
+                    dates = cftime_to_nptime(dates)
+    elif use_cftime:
         dates = _decode_datetime_with_cftime(
             flat_num_dates.astype(np.float), units, calendar)
+    else:
+        dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
 
     return dates.reshape(num_dates.shape)
 
@@ -397,15 +407,17 @@ def encode(self, variable, name=None):
 
         return Variable(dims, data, attrs, encoding)
 
-    def decode(self, variable, name=None):
+    def decode(self, variable, name=None, use_cftime=None):
         dims, data, attrs, encoding = unpack_for_decoding(variable)
 
         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)
+            dtype = _decode_cf_datetime_dtype(data, units, calendar,
+                                              use_cftime)
             transform = partial(
-                decode_cf_datetime, units=units, calendar=calendar)
+                decode_cf_datetime, units=units, calendar=calendar,
+                use_cftime=use_cftime)
             data = lazy_elemwise_func(data, transform, dtype)
 
         return Variable(dims, data, attrs, encoding)

xarray/conventions.py

@@ -240,7 +240,7 @@ def encode_cf_variable(var, needs_copy=True, name=None):
 
 def decode_cf_variable(name, var, concat_characters=True, mask_and_scale=True,
                        decode_times=True, decode_endianness=True,
-                       stack_char_dim=True):
+                       stack_char_dim=True, use_cftime=None):
     """
     Decodes a variable which may hold CF encoded information.
 
@@ -270,6 +270,16 @@ def decode_cf_variable(name, var, concat_characters=True, mask_and_scale=True,
         Whether to stack characters into bytes along the last dimension of this
         array. Passed as an argument because we need to look at the full
         dataset to figure out if this is appropriate.
+    use_cftime: bool, optional
+        Only relevant if encoded dates come from a standard calendar
+        (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not
+        specified).  If None (default), attempt to decode times to
+        ``np.datetime64[ns]`` objects; if this is not possible, decode times to
+        ``cftime.datetime`` objects. If True, always decode times to
+        ``cftime.datetime`` objects, regardless of whether or not they can be
+        represented using ``np.datetime64[ns]`` objects.  If False, always
+        decode times to ``np.datetime64[ns]`` objects; if this is not possible
+        raise an error.
 
     Returns
     -------
@@ -291,9 +301,10 @@ def decode_cf_variable(name, var, concat_characters=True, mask_and_scale=True,
             var = coder.decode(var, name=name)
 
     if decode_times:
-        for coder in [times.CFTimedeltaCoder(),
-                      times.CFDatetimeCoder()]:
-            var = coder.decode(var, name=name)
+        timedelta_coder = times.CFTimedeltaCoder()
+        datetime_coder = times.CFDatetimeCoder()
+        var = timedelta_coder.decode(var, name=name)
+        var = datetime_coder.decode(var, name=name, use_cftime=use_cftime)
 
     dimensions, data, attributes, encoding = (
         variables.unpack_for_decoding(var))
@@ -346,7 +357,8 @@ def _update_bounds_attributes(variables):
 
 def decode_cf_variables(variables, attributes, concat_characters=True,
                         mask_and_scale=True, decode_times=True,
-                        decode_coords=True, drop_variables=None):
+                        decode_coords=True, drop_variables=None,
+                        use_cftime=None):
     """
     Decode several CF encoded variables.
 
@@ -387,7 +399,7 @@ def stackable(dim):
         new_vars[k] = decode_cf_variable(
             k, v, concat_characters=concat_characters,
             mask_and_scale=mask_and_scale, decode_times=decode_times,
-            stack_char_dim=stack_char_dim)
+            stack_char_dim=stack_char_dim, use_cftime=use_cftime)
         if decode_coords:
             var_attrs = new_vars[k].attrs
             if 'coordinates' in var_attrs:
@@ -406,7 +418,8 @@ def stackable(dim):
 
 
 def decode_cf(obj, concat_characters=True, mask_and_scale=True,
-              decode_times=True, decode_coords=True, drop_variables=None):
+              decode_times=True, decode_coords=True, drop_variables=None,
+              use_cftime=None):
     """Decode the given Dataset or Datastore according to CF conventions into
     a new Dataset.
 
@@ -430,6 +443,16 @@ def decode_cf(obj, concat_characters=True, mask_and_scale=True,
         A variable or list of variables to exclude from being parsed from the
         dataset. This may be useful to drop variables with problems or
         inconsistent values.
+    use_cftime: bool, optional
+        Only relevant if encoded dates come from a standard calendar
+        (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not
+        specified).  If None (default), attempt to decode times to
+        ``np.datetime64[ns]`` objects; if this is not possible, decode times to
+        ``cftime.datetime`` objects. If True, always decode times to
+        ``cftime.datetime`` objects, regardless of whether or not they can be
+        represented using ``np.datetime64[ns]`` objects.  If False, always
+        decode times to ``np.datetime64[ns]`` objects; if this is not possible
+        raise an error.
 
     Returns
     -------
@@ -454,7 +477,7 @@ def decode_cf(obj, concat_characters=True, mask_and_scale=True,
 
     vars, attrs, coord_names = decode_cf_variables(
         vars, attrs, concat_characters, mask_and_scale, decode_times,
-        decode_coords, drop_variables=drop_variables)
+        decode_coords, drop_variables=drop_variables, use_cftime=use_cftime)
     ds = Dataset(vars, attrs=attrs)
     ds = ds.set_coords(coord_names.union(extra_coords).intersection(vars))
     ds._file_obj = file_obj