enable internal plotting with cftime datetime (#2665)

enable internal plotting with cftime datetime

Author: Julius Busecke

ci/requirements-py37-windows.yml

@@ -4,6 +4,7 @@ channels:
 dependencies:
   - python=3.7
   - cftime
+  - nc-time-axis
   - dask
   - distributed
   - h5py

ci/requirements-py37.yml

@@ -4,6 +4,7 @@ channels:
 dependencies:
   - python=3.7
   - cftime
+  - nc-time-axis
   - dask
   - distributed
   - h5py

doc/installing.rst

@@ -61,6 +61,8 @@ For plotting
   :ref:`plot-maps`
 - `seaborn <https://stanford.edu/~mwaskom/software/seaborn/>`__: for better
   color palettes
+- `nc-time-axis <https://github.com/SciTools/nc-time-axis>`__: for plotting
+  cftime.datetime objects (1.2.0 or later)
 
 
 Instructions
@@ -109,4 +111,4 @@ To run these benchmark tests in a local machine, first install
 - `airspeed-velocity <https://asv.readthedocs.io/en/latest/>`__: a tool for benchmarking Python packages over their lifetime.
 
 and run
-``asv run  # this will install some conda environments in ./.asv/envs``
+``asv run  # this will install some conda environments in ./.asv/envs``

doc/plotting.rst

@@ -23,6 +23,11 @@ Matplotlib syntax and function names were copied as much as possible, which
 makes for an easy transition between the two.
 Matplotlib must be installed before xarray can plot.
 
+To use xarray's plotting capabilities with time coordinates containing
+``cftime.datetime`` objects
+`nc-time-axis <https://github.com/SciTools/nc-time-axis>`_ v1.2.0 or later
+needs to be installed.
+
 For more extensive plotting applications consider the following projects:
 
 - `Seaborn <http://seaborn.pydata.org/>`_: "provides
@@ -226,7 +231,7 @@ Step plots
 ~~~~~~~~~~
 
 As an alternative, also a step plot similar to matplotlib's ``plt.step`` can be
-made using 1D data. 
+made using 1D data.
 
 .. ipython:: python
 
@@ -248,7 +253,7 @@ when plotting data grouped with :py:func:`xarray.Dataset.groupby_bins`.
     plt.ylim(-20,30)
     @savefig plotting_example_step_groupby.png width=4in
     plt.title('Zonal mean temperature')
-    
+
 In this case, the actual boundaries of the bins are used and the ``where`` argument
 is ignored.
 

doc/time-series.rst

@@ -74,7 +74,7 @@ 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. 
+information.
 
 Datetime indexing
 -----------------
@@ -215,7 +215,7 @@ For more examples of using grouped operations on a time dimension, see
 
 
 .. _CFTimeIndex:
-     
+
 Non-standard calendars and dates outside the Timestamp-valid range
 ------------------------------------------------------------------
 
@@ -224,14 +224,14 @@ Through the standalone ``cftime`` library and a custom subclass of
 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 `Timestamp-valid range`_
-(approximately between years 1678 and 2262).  
+(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: 
+   :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.
@@ -252,7 +252,7 @@ coordinate with dates from a no-leap calendar and a
    dates = [DatetimeNoLeap(year, month, 1) for year, month in
             product(range(1, 3), range(1, 13))]
    da = xr.DataArray(np.arange(24), coords=[dates], dims=['time'], name='foo')
-                         
+
 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:
@@ -261,20 +261,20 @@ instance, we can create the same dates and DataArray we created above using:
 
    dates = xr.cftime_range(start='0001', periods=24, freq='MS', calendar='noleap')
    da = xr.DataArray(np.arange(24), coords=[dates], dims=['time'], name='foo')
-   
+
 For data indexed by a :py:class:`~xarray.CFTimeIndex` xarray currently supports:
 
 - `Partial datetime string indexing`_ using strictly `ISO 8601-format`_ partial
   datetime strings:
-  
+
 .. ipython:: python
 
    da.sel(time='0001')
    da.sel(time=slice('0001-05', '0002-02'))
 
 - Access of basic datetime components via the ``dt`` accessor (in this case
   just "year", "month", "day", "hour", "minute", "second", "microsecond",
-  "season", "dayofyear", and "dayofweek"): 
+  "season", "dayofyear", and "dayofweek"):
 
 .. ipython:: python
 
@@ -323,14 +323,7 @@ For data indexed by a :py:class:`~xarray.CFTimeIndex` xarray currently supports:
     da.resample(time='81T', closed='right', label='right', base=3).mean()
 
 .. note::
-   
-   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:
-
-   - 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`,
@@ -351,8 +344,8 @@ For data indexed by a :py:class:`~xarray.CFTimeIndex` xarray currently supports:
    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.  
-  
+   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
 .. _partial datetime string indexing: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#partial-string-indexing

doc/whats-new.rst

@@ -24,7 +24,7 @@ Breaking changes
 - Remove support for Python 2. This is the first version of xarray that is
   Python 3 only. (:issue:`1876`).
   By `Joe Hamman <https://github.com/jhamman>`_.
-- The `compat` argument to `Dataset` and the `encoding` argument to 
+- The `compat` argument to `Dataset` and the `encoding` argument to
   `DataArray` are deprecated and will be removed in a future release.
   (:issue:`1188`)
   By `Maximilian Roos <https://github.com/max-sixty>`_.
@@ -34,6 +34,10 @@ Breaking changes
 Enhancements
 ~~~~~~~~~~~~
 
+- Internal plotting now supports ``cftime.datetime`` objects as time series.
+  (:issue:`2164`)
+  By `Julius Busecke <https://github.com/jbusecke>`_ and
+  `Spencer Clark <https://github.com/spencerkclark>`_.
 - Add ``data=False`` option to ``to_dict()`` methods. (:issue:`2656`)
   By `Ryan Abernathey <https://github.com/rabernat>`_
 - :py:meth:`~xarray.DataArray.coarsen` and

xarray/core/common.py

@@ -713,7 +713,7 @@ def resample(self, indexer=None, skipna=None, closed=None, label=None,
         array([ 0.      ,  0.032258,  0.064516, ..., 10.935484, 10.967742, 11.      ])
         Coordinates:
           * time     (time) datetime64[ns] 1999-12-15 1999-12-16 1999-12-17 ...
-        
+
         Limit scope of upsampling method
         >>> da.resample(time='1D').nearest(tolerance='1D')
         <xarray.DataArray (time: 337)>

xarray/plot/plot.py

@@ -10,8 +10,6 @@
 import numpy as np
 import pandas as pd
 
-from xarray.core.common import contains_cftime_datetimes
-
 from .facetgrid import _easy_facetgrid
 from .utils import (
     _add_colorbar, _ensure_plottable, _infer_interval_breaks, _infer_xy_labels,
@@ -139,15 +137,6 @@ def plot(darray, row=None, col=None, col_wrap=None, ax=None, hue=None,
     """
     darray = darray.squeeze()
 
-    if contains_cftime_datetimes(darray):
-        raise NotImplementedError(
-            'Built-in plotting of arrays of cftime.datetime objects or arrays '
-            'indexed by cftime.datetime objects is currently not implemented '
-            'within xarray. A possible workaround is to use the '
-            'nc-time-axis package '
-            '(https://github.com/SciTools/nc-time-axis) to convert the dates '
-            'to a plottable type and plot your data directly with matplotlib.')
-
     plot_dims = set(darray.dims)
     plot_dims.discard(row)
     plot_dims.discard(col)

xarray/plot/utils.py

@@ -10,6 +10,16 @@
 
 from ..core.options import OPTIONS
 from ..core.utils import is_scalar
+from distutils.version import LooseVersion
+
+try:
+    import nc_time_axis
+    if LooseVersion(nc_time_axis.__version__) < LooseVersion('1.2.0'):
+        nc_time_axis_available = False
+    else:
+        nc_time_axis_available = True
+except ImportError:
+    nc_time_axis_available = False
 
 ROBUST_PERCENTILE = 2.0
 
@@ -471,16 +481,29 @@ def _ensure_plottable(*args):
     """
     numpy_types = [np.floating, np.integer, np.timedelta64, np.datetime64]
     other_types = [datetime]
-
+    try:
+        import cftime
+        cftime_datetime = [cftime.datetime]
+    except ImportError:
+        cftime_datetime = []
+    other_types = other_types + cftime_datetime
     for x in args:
         if not (_valid_numpy_subdtype(np.array(x), numpy_types)
                 or _valid_other_type(np.array(x), other_types)):
             raise TypeError('Plotting requires coordinates to be numeric '
-                            'or dates of type np.datetime64 or '
-                            'datetime.datetime or pd.Interval.')
-
-
-def _ensure_numeric(arr):
+                            'or dates of type np.datetime64, '
+                            'datetime.datetime, cftime.datetime or '
+                            'pd.Interval.')
+        if (_valid_other_type(np.array(x), cftime_datetime)
+                and not nc_time_axis_available):
+            raise ImportError('Plotting of arrays of cftime.datetime '
+                              'objects or arrays indexed by '
+                              'cftime.datetime objects requires the '
+                              'optional `nc-time-axis` (v1.2.0 or later) '
+                              'package.')
+
+
+def _numeric(arr):
     numpy_types = [np.floating, np.integer]
     return _valid_numpy_subdtype(arr, numpy_types)
 

xarray/tests/__init__.py

@@ -63,6 +63,8 @@ def LooseVersion(vstring):
 has_pynio, requires_pynio = _importorskip('Nio')
 has_pseudonetcdf, requires_pseudonetcdf = _importorskip('PseudoNetCDF')
 has_cftime, requires_cftime = _importorskip('cftime')
+has_nc_time_axis, requires_nc_time_axis = _importorskip('nc_time_axis',
+                                                        minversion='1.2.0')
 has_cftime_1_0_2_1, requires_cftime_1_0_2_1 = _importorskip(
     'cftime', minversion='1.0.2.1')
 has_dask, requires_dask = _importorskip('dask')