xarray to and from Iris

.travis.yml

@@ -13,7 +13,7 @@ matrix:
   - python: 2.7
     env: CONDA_ENV=py27-min
   - python: 2.7
-    env: CONDA_ENV=py27-cdat+pynio
+    env: CONDA_ENV=py27-cdat+iris+pynio
   - python: 3.4
     env: CONDA_ENV=py34
   - python: 3.5

ci/requirements-py27-cdat+pynio.yml → ci/requirements-py27-cdat+iris+pynio.yml

@@ -22,6 +22,7 @@ dependencies:
   - seaborn
   - toolz
   - rasterio
+  - iris>=1.10
   - zarr
   - pip:
     - coveralls

doc/api.rst

@@ -448,6 +448,8 @@ DataArray methods
    DataArray.to_index
    DataArray.to_masked_array
    DataArray.to_cdms2
+   DataArray.to_iris
+   DataArray.from_iris
    DataArray.to_dict
    DataArray.from_series
    DataArray.from_cdms2

doc/environment.yml

@@ -17,3 +17,4 @@ dependencies:
   - rasterio=0.36.0
   - sphinx-gallery
   - zarr
+  - iris

doc/io.rst

@@ -338,6 +338,38 @@ supported by netCDF4-python: 'standard', 'gregorian', 'proleptic_gregorian' 'nol
 By default, xarray uses the 'proleptic_gregorian' calendar and units of the smallest time
 difference between values, with a reference time of the first time value.
 
+.. _io.iris:
+
+Iris
+----
+
+The Iris_ tool allows easy reading of common meteorological and climate model formats
+(including GRIB and UK MetOffice PP files) into ``Cube`` objects which are in many ways very
+similar to ``DataArray`` objects, while enforcing a CF-compliant data model. If iris is
+installed xarray can convert a ``DataArray`` into a ``Cube`` using
+:py:meth:`~xarray.DataArray.to_iris`:
+
+.. ipython:: python
+
+    da = xr.DataArray(np.random.rand(4, 5), dims=['x', 'y'],
+                      coords=dict(x=[10, 20, 30, 40],
+                                  y=pd.date_range('2000-01-01', periods=5)))
+
+    cube = da.to_iris()
+    cube
+
+Conversely, we can create a new ``DataArray`` object from a ``Cube`` using
+:py:meth:`~xarray.DataArray.from_iris`:
+
+.. ipython:: python
+
+    da_cube = xr.DataArray.from_iris(cube)
+    da_cube
+
+
+.. _Iris: http://scitools.org.uk/iris
+
+
 OPeNDAP
 -------
 

doc/whats-new.rst

@@ -25,6 +25,9 @@ Enhancements
   1D co-ordinate (e.g. time) and a 2D co-ordinate (e.g. depth as a function of
   time) (:issue:`1737`).
   By `Deepak Cherian <https://github.com/dcherian>`_.
+- Added :py:meth:`DataArray.to_iris <xray.DataArray.to_iris>` and :py:meth:`DataArray.from_iris <xray.DataArray.from_iris>` for
+  converting data arrays to and from Iris_ Cubes with the same data and coordinates (:issue:`621` and :issue:`37`).
+  By `Neil Parley <https://github.com/nparley>`_ and `Duncan Watson-Parris <https://github.com/duncanwp>`_.
 - Use ``pandas.Grouper`` class in xarray resample methods rather than the
   deprecated ``pandas.TimeGrouper`` class (:issue:`1766`).
   By `Joe Hamman <https://github.com/jhamman>`_.
@@ -36,6 +39,7 @@ Enhancements
 
 .. _Zarr: http://zarr.readthedocs.io/
 
+.. _Iris: http://scitools.org.uk/iris
 
 Bug fixes
 ~~~~~~~~~

xarray/convert.py

@@ -7,24 +7,42 @@
 import numpy as np
 
 from .core.dataarray import DataArray
+from .core.pycompat import OrderedDict, range
+from .core.dtypes import get_fill_value
 from .conventions import (
     maybe_encode_timedelta, maybe_encode_datetime, decode_cf)
 
-ignored_attrs = set(['name', 'tileIndex'])
+cdms2_ignored_attrs = {'name', 'tileIndex'}
+iris_forbidden_keys = {'standard_name', 'long_name', 'units', 'bounds', 'axis',
+                       'calendar', 'leap_month', 'leap_year', 'month_lengths',
+                       'coordinates', 'grid_mapping', 'climatology',
+                       'cell_methods', 'formula_terms', 'compress',
+                       'missing_value', 'add_offset', 'scale_factor',
+                       'valid_max', 'valid_min', 'valid_range', '_FillValue'}
+cell_methods_strings = {'point', 'sum', 'maximum', 'median', 'mid_range',
+                        'minimum', 'mean', 'mode', 'standard_deviation',
+                        'variance'}
+
+
+def encode(var):
+    return maybe_encode_timedelta(maybe_encode_datetime(var.variable))
+
+
+def _filter_attrs(attrs, ignored_attrs):
+    """ Return attrs that are not in ignored_attrs
+    """
+    return dict((k, v) for k, v in attrs.items() if k not in ignored_attrs)
 
 
 def from_cdms2(variable):
     """Convert a cdms2 variable into an DataArray
     """
-    def get_cdms2_attrs(var):
-        return dict((k, v) for k, v in var.attributes.items()
-                    if k not in ignored_attrs)
-
     values = np.asarray(variable)
     name = variable.id
-    coords = [(v.id, np.asarray(v), get_cdms2_attrs(v))
+    coords = [(v.id, np.asarray(v),
+               _filter_attrs(v.attributes, cdms2_ignored_attrs))
               for v in variable.getAxisList()]
-    attrs = get_cdms2_attrs(variable)
+    attrs = _filter_attrs(variable.attributes, cdms2_ignored_attrs)
     dataarray = DataArray(values, coords=coords, name=name, attrs=attrs)
     return decode_cf(dataarray.to_dataset())[dataarray.name]
 
@@ -35,9 +53,6 @@ def to_cdms2(dataarray):
     # we don't want cdms2 to be a hard dependency
     import cdms2
 
-    def encode(var):
-        return maybe_encode_timedelta(maybe_encode_datetime(var.variable))
-
     def set_cdms2_attrs(var, attrs):
         for k, v in attrs.items():
             setattr(var, k, v)
@@ -53,3 +68,150 @@ def set_cdms2_attrs(var, attrs):
     cdms2_var = cdms2.createVariable(var.values, axes=axes, id=dataarray.name)
     set_cdms2_attrs(cdms2_var, var.attrs)
     return cdms2_var
+
+
+def _pick_attrs(attrs, keys):
+    """ Return attrs with keys in keys list
+    """
+    return dict((k, v) for k, v in attrs.items() if k in keys)
+
+
+def _get_iris_args(attrs):
+    """ Converts the xarray attrs into args that can be passed into Iris
+    """
+    # iris.unit is deprecated in Iris v1.9
+    import cf_units
+    args = {'attributes': _filter_attrs(attrs, iris_forbidden_keys)}
+    args.update(_pick_attrs(attrs, ('standard_name', 'long_name',)))
+    unit_args = _pick_attrs(attrs, ('calendar',))
+    if 'units' in attrs:
+        args['units'] = cf_units.Unit(attrs['units'], **unit_args)
+    return args
+
+
+# TODO: Add converting bounds from xarray to Iris and back
+def to_iris(dataarray):
+    """ Convert a DataArray into a Iris Cube
+    """
+    # Iris not a hard dependency
+    import iris
+    from iris.fileformats.netcdf import parse_cell_methods
+    from xarray.core.pycompat import dask_array_type
+
+    dim_coords = []
+    aux_coords = []
+
+    for coord_name in dataarray.coords:
+        coord = encode(dataarray.coords[coord_name])
+        coord_args = _get_iris_args(coord.attrs)
+        coord_args['var_name'] = coord_name
+        axis = None
+        if coord.dims:
+            axis = dataarray.get_axis_num(coord.dims)
+        if coord_name in dataarray.dims:
+            iris_coord = iris.coords.DimCoord(coord.values, **coord_args)
+            dim_coords.append((iris_coord, axis))
+        else:
+            iris_coord = iris.coords.AuxCoord(coord.values, **coord_args)
+            aux_coords.append((iris_coord, axis))
+
+    args = _get_iris_args(dataarray.attrs)
+    args['var_name'] = dataarray.name
+    args['dim_coords_and_dims'] = dim_coords
+    args['aux_coords_and_dims'] = aux_coords
+    if 'cell_methods' in dataarray.attrs:
+        args['cell_methods'] = \
+            parse_cell_methods(dataarray.attrs['cell_methods'])
+
+    # Create the right type of masked array (should be easier after #1769)
+    if isinstance(dataarray.data, dask_array_type):
+        from dask.array import ma as dask_ma
+        masked_data = dask_ma.masked_invalid(dataarray)
+    else:
+        masked_data = np.ma.masked_invalid(dataarray)
+
+    cube = iris.cube.Cube(masked_data, **args)
+
+    return cube
+
+
+def _iris_obj_to_attrs(obj):
+    """ Return a dictionary of attrs when given a Iris object
+    """
+    attrs = {'standard_name': obj.standard_name,
+             'long_name': obj.long_name}
+    if obj.units.calendar:
+        attrs['calendar'] = obj.units.calendar
+    if obj.units.origin != '1':
+        attrs['units'] = obj.units.origin
+    attrs.update(obj.attributes)
+    return dict((k, v) for k, v in attrs.items() if v is not None)
+
+
+def _iris_cell_methods_to_str(cell_methods_obj):
+    """ Converts a Iris cell methods into a string
+    """
+    cell_methods = []
+    for cell_method in cell_methods_obj:
+        names = ''.join(['{}: '.format(n) for n in cell_method.coord_names])
+        intervals = ' '.join(['interval: {}'.format(interval)
+                              for interval in cell_method.intervals])
+        comments = ' '.join(['comment: {}'.format(comment)
+                             for comment in cell_method.comments])
+        extra = ' '.join([intervals, comments]).strip()
+        if extra:
+            extra = ' ({})'.format(extra)
+        cell_methods.append(names + cell_method.method + extra)
+    return ' '.join(cell_methods)
+
+
+def from_iris(cube):
+    """ Convert a Iris cube into an DataArray
+    """
+    import iris.exceptions
+    from xarray.core.pycompat import dask_array_type
+
+    name = cube.var_name
+    dims = []
+    for i in range(cube.ndim):
+        try:
+            dim_coord = cube.coord(dim_coords=True, dimensions=(i,))
+            dims.append(dim_coord.var_name)
+        except iris.exceptions.CoordinateNotFoundError:
+            dims.append("dim_{}".format(i))
+
+    coords = OrderedDict()
+
+    for coord in cube.coords():
+        coord_attrs = _iris_obj_to_attrs(coord)
+        coord_dims = [dims[i] for i in cube.coord_dims(coord)]
+        if not coord.var_name:
+            raise ValueError("Coordinate '{}' has no "
+                             "var_name attribute".format(coord.name()))
+        if coord_dims:
+            coords[coord.var_name] = (coord_dims, coord.points, coord_attrs)
+        else:
+            coords[coord.var_name] = ((),
+                                      np.asscalar(coord.points), coord_attrs)
+
+    array_attrs = _iris_obj_to_attrs(cube)
+    cell_methods = _iris_cell_methods_to_str(cube.cell_methods)
+    if cell_methods:
+        array_attrs['cell_methods'] = cell_methods
+
+    # Deal with iris 1.* and 2.*
+    cube_data = cube.core_data() if hasattr(cube, 'core_data') else cube.data
+
+    # Deal with dask and numpy masked arrays
+    if isinstance(cube_data, dask_array_type):
+        from dask.array import ma as dask_ma
+        filled_data = dask_ma.filled(cube_data, get_fill_value(cube.dtype))
+    elif isinstance(cube_data, np.ma.MaskedArray):
+        filled_data = np.ma.filled(cube_data, get_fill_value(cube.dtype))
+    else:
+        filled_data = cube_data
+
+    dataarray = DataArray(filled_data, coords=coords, name=name,
+                          attrs=array_attrs, dims=dims)
+    decoded_ds = decode_cf(dataarray._to_temp_dataset())
+    return dataarray._from_temp_dataset(decoded_ds)

xarray/core/dataarray.py

@@ -1539,6 +1539,19 @@ def from_cdms2(cls, variable):
         from ..convert import from_cdms2
         return from_cdms2(variable)
 
+    def to_iris(self):
+        """Convert this array into a iris.cube.Cube
+        """
+        from ..convert import to_iris
+        return to_iris(self)
+
+    @classmethod
+    def from_iris(cls, cube):
+        """Convert a iris.cube.Cube into an xarray.DataArray
+        """
+        from ..convert import from_iris
+        return from_iris(cube)
+
     def _all_compat(self, other, compat_str):
         """Helper function for equals and identical"""