diff --git a/astropy_xarray/accessors.py b/astropy_xarray/accessors.py index 7f798cb1..5beeb5a0 100644 --- a/astropy_xarray/accessors.py +++ b/astropy_xarray/accessors.py @@ -87,7 +87,7 @@ def either_dict_or_kwargs(positional, keywords, method_name): return keywords -def _decide_unit(unit, registry, unit_attribute): +def _decide_unit(unit, unit_attribute): if unit is _default and unit_attribute in (None, _default): # or warn and return None? raise ValueError("no units given") @@ -123,7 +123,7 @@ def __getitem__(self, indexers): # convert the indexes to the indexer's units try: - converted = conversion.convert_units(self.ds, indexer_units) + converted = conversion.convert_units(self.ds, indexer_units, None) except ValueError as e: raise KeyError(*e.args) from e @@ -155,7 +155,7 @@ def __getitem__(self, indexers): # convert the indexes to the indexer's units try: - converted = conversion.convert_units(self.da, indexer_units) + converted = conversion.convert_units(self.da, indexer_units, None) except ValueError as e: raise KeyError(*e.args) from e @@ -180,7 +180,7 @@ def __setitem__(self, indexers, values): # convert the indexers to the index units try: - converted = conversion.convert_indexer_units(indexers, index_units) + converted = conversion.convert_indexer_units(indexers, index_units, None) except ValueError as e: raise KeyError(*e.args) from e @@ -200,12 +200,12 @@ class AstropyDataArrayAccessor: def __init__(self, da): self.da = da - def quantify(self, units=_default, unit_registry=None, **unit_kwargs): + def quantify(self, units=_default, **unit_kwargs): """ Attach units to the DataArray. Units can be specified as a astropy.units.Unit or as a string, which will be - parsed by the given unit registry. If no units are specified then the + parsed by the astropy unit registry. If no units are specified then the units will be parsed from the `'units'` entry of the DataArray's `.attrs`. Will raise a ValueError if the DataArray already contains a unit-aware array with a different unit. @@ -232,9 +232,6 @@ def quantify(self, units=_default, unit_registry=None, **unit_kwargs): ``DataArray.attrs['units']`` using astropy's parser. The ``"units"`` attribute will be removed from all variables except from dimension coordinates. - unit_registry : optional - Unit registry to be used for the units attached to this DataArray. - If not given then a default registry will be created. **unit_kwargs Keyword argument form of units. @@ -297,8 +294,6 @@ def quantify(self, units=_default, unit_registry=None, **unit_kwargs): units = either_dict_or_kwargs(units, unit_kwargs, "quantify") - registry = astropy.units - unit_attrs = conversion.extract_unit_attributes(self.da) possible_new_units = zip_mappings(units, unit_attrs, fill_value=_default) @@ -307,7 +302,7 @@ def quantify(self, units=_default, unit_registry=None, **unit_kwargs): for name, (unit, attr) in possible_new_units.items(): if unit not in (_default, None) or attr not in (_default, None): try: - new_units[name] = _decide_unit(unit, registry, attr) + new_units[name] = _decide_unit(unit, attr) except (ValueError, AttributeError) as e: if unit not in (_default, None): type = "parameter" @@ -439,16 +434,22 @@ def physical_type(self): """get the dimensionality of the data or :py:obj:`None` if not a quantity.""" return getattr(self.da.data, "physical_type", None) - def to(self, units=None, **unit_kwargs): + def to(self, units=None, equivalencies=None, **unit_kwargs): """convert the quantities in a DataArray Parameters ---------- units : unit-like or mapping of hashable to unit-like, optional The units to convert to. If a unit name or ``astropy.units.Unit`` - object, convert the DataArray's data. If a dict-like, it - has to map a variable name to a unit name or ``astropy.units.Unit`` + object, convert the DataArray's data. If a dict-like, it has to map + a variable name to a unit name or :py:class:`astropy.units.Unit` object. + equivalencies : :class:`list` + A list of equivalence pairs to try if the units are not + directly convertible. See :py:doc:`astropy:units/equivalencies`. + This list is in addition to possible global defaults set by, + e.g., :py:func:`astropy.units.set_enabled_equivalencies`. + Use None to turn off all equivalencies. **unit_kwargs The kwargs form of ``units``. Can only be used for variable names that are strings and valid python identifiers. @@ -541,7 +542,7 @@ def to(self, units=None, **unit_kwargs): units = either_dict_or_kwargs(units, unit_kwargs, "to") - return conversion.convert_units(self.da, units) + return conversion.convert_units(self.da, units, equivalencies) def chunk(self, chunks, name_prefix="xarray-", token=None, lock=False): """unit-aware version of chunk @@ -573,6 +574,7 @@ def reindex( tolerance=None, copy=True, fill_value=NA, + equivalencies=None, **indexers_kwargs, ): """unit-aware version of reindex @@ -602,7 +604,7 @@ def reindex( # TODO: handle fill_value # convert the indexes to the indexer's units - converted = conversion.convert_units(self.da, indexer_units) + converted = conversion.convert_units(self.da, indexer_units, equivalencies) converted_units = conversion.extract_units(converted) stripped = conversion.strip_units(converted) @@ -618,7 +620,7 @@ def reindex( return conversion.attach_units(indexed, converted_units) def reindex_like( - self, other, method=None, tolerance=None, copy=True, fill_value=NA + self, other, method=None, tolerance=None, copy=True, fill_value=NA, equivalencies=None, ): """unit-aware version of reindex_like @@ -637,7 +639,7 @@ def reindex_like( """ indexer_units = conversion.extract_units(other) - converted = conversion.convert_units(self.da, indexer_units) + converted = conversion.convert_units(self.da, indexer_units, equivalencies) units = conversion.extract_units(converted) stripped = conversion.strip_units(converted) stripped_other = conversion.strip_units(other) @@ -659,6 +661,7 @@ def interp( coords=None, method="linear", assume_sorted=False, + equivalencies=None, kwargs=None, **coords_kwargs, ): @@ -686,7 +689,7 @@ def interp( } # convert the indexes to the indexer's units - converted = conversion.convert_units(self.da, indexer_units) + converted = conversion.convert_units(self.da, indexer_units, equivalencies) units = conversion.extract_units(converted) stripped = conversion.strip_units(converted) @@ -700,7 +703,7 @@ def interp( ) return conversion.attach_units(interpolated, units) - def interp_like(self, other, method="linear", assume_sorted=False, kwargs=None): + def interp_like(self, other, method="linear", assume_sorted=False, equivalencies=None, kwargs=None): """unit-aware version of interp_like Like :py:meth:`xarray.DataArray.interp_like`, except the object's indexes are converted @@ -718,7 +721,7 @@ def interp_like(self, other, method="linear", assume_sorted=False, kwargs=None): """ indexer_units = conversion.extract_units(other) - converted = conversion.convert_units(self.da, indexer_units) + converted = conversion.convert_units(self.da, indexer_units, equivalencies) units = conversion.extract_units(converted) stripped = conversion.strip_units(converted) stripped_other = conversion.strip_units(other) @@ -731,7 +734,7 @@ def interp_like(self, other, method="linear", assume_sorted=False, kwargs=None): return conversion.attach_units(interpolated, units) def sel( - self, indexers=None, method=None, tolerance=None, drop=False, **indexers_kwargs + self, indexers=None, method=None, tolerance=None, drop=False, equivalencies=None, **indexers_kwargs ): """unit-aware version of sel @@ -760,7 +763,7 @@ def sel( # convert the indexes to the indexer's units try: - converted = conversion.convert_units(self.da, indexer_units) + converted = conversion.convert_units(self.da, indexer_units, equivalencies) except ValueError as e: raise KeyError(*e.args) from e @@ -791,7 +794,7 @@ def loc(self): """ return DataArrayLocIndexer(self.da) - def drop_sel(self, labels=None, *, errors="raise", **labels_kwargs): + def drop_sel(self, labels=None, *, equivalencies=None, errors="raise", **labels_kwargs): """unit-aware version of drop_sel Just like :py:meth:`xarray.DataArray.drop_sel`, except the indexers are converted @@ -813,7 +816,7 @@ def drop_sel(self, labels=None, *, errors="raise", **labels_kwargs): # convert the indexers to the indexes units try: - converted_indexers = conversion.convert_indexer_units(indexers, index_units) + converted_indexers = conversion.convert_indexer_units(indexers, index_units, equivalencies) except ValueError as e: raise KeyError(*e.args) from e @@ -911,12 +914,12 @@ class AstropyDatasetAccessor: def __init__(self, ds): self.ds = ds - def quantify(self, units=_default, unit_registry=None, **unit_kwargs): + def quantify(self, units=_default, **unit_kwargs): """ Attach units to the variables of the Dataset. Units can be specified as a ``astropy.units.Unit`` or as a - string, which will be parsed by the given unit registry. If no + string, which will be parsed by the astropy unit registry. If no units are specified then the units will be parsed from the ``"units"`` entry of the Dataset variable's ``.attrs``. Will raise a ValueError if any of the variables already contain a @@ -942,10 +945,6 @@ def quantify(self, units=_default, unit_registry=None, **unit_kwargs): will try to read them from ``Dataset[var].attrs['units']`` using astropy's parser. The ``"units"`` attribute will be removed from all variables except from dimension coordinates. - unit_registry : optional - Unit registry to be used for the units attached to each - DataArray in this Dataset. If not given then a default - registry will be created. **unit_kwargs Keyword argument form of ``units``. @@ -1021,7 +1020,6 @@ def quantify(self, units=_default, unit_registry=None, **unit_kwargs): b (x) int64 24B 5 -2 1 """ units = either_dict_or_kwargs(units, unit_kwargs, "quantify") - registry = astropy.units unit_attrs = conversion.extract_unit_attributes(self.ds) @@ -1031,7 +1029,7 @@ def quantify(self, units=_default, unit_registry=None, **unit_kwargs): for name, (unit, attr) in possible_new_units.items(): if unit is not _default or attr not in (None, _default): try: - new_units[name] = _decide_unit(unit, registry, attr) + new_units[name] = _decide_unit(unit, attr) except (ValueError, AttributeError) as e: if unit is not _default: type = "parameter" @@ -1096,7 +1094,7 @@ def dequantify(self, format=None): See Also -------- - :std:doc:`astropy:units/format` + :doc:`astropy:units/format` astropy's string formatting guide Examples @@ -1165,7 +1163,7 @@ def dequantify(self, format=None): .pipe(conversion.attach_unit_attributes, units) ) - def to(self, units=None, **unit_kwargs): + def to(self, units=None, equivalencies=None, **unit_kwargs): """convert the quantities in a Dataset Parameters @@ -1173,8 +1171,14 @@ def to(self, units=None, **unit_kwargs): units : unit-like or mapping of hashable to unit-like, optional The units to convert to. If a unit name or ``astropy.units.Unit`` object, convert all the object's data variables. If a dict-like, it - maps variable names to unit names or ``astropy.units.Unit`` + maps variable names to unit names or :py:class:`astropy.units.Unit` objects. + equivalencies : :class:`list` + A list of equivalence pairs to try if the units are not + directly convertible. See :py:doc:`astropy:units/equivalencies`. + This list is in addition to possible global defaults set by, + e.g., :py:func:`astropy.units.set_enabled_equivalencies`. + Use None to turn off all equivalencies. **unit_kwargs The kwargs form of ``units``. Can only be used for variable names that are strings and valid python identifiers. @@ -1307,7 +1311,7 @@ def to(self, units=None, **unit_kwargs): units = either_dict_or_kwargs(units, unit_kwargs, "to") - return conversion.convert_units(self.ds, units) + return conversion.convert_units(self.ds, units, equivalencies) def chunk(self, chunks, name_prefix="xarray-", token=None, lock=False): """unit-aware version of chunk @@ -1339,6 +1343,7 @@ def reindex( tolerance=None, copy=True, fill_value=NA, + equivalencies=None, **indexers_kwargs, ): """unit-aware version of reindex @@ -1368,7 +1373,7 @@ def reindex( # TODO: handle fill_value # convert the indexes to the indexer's units - converted = conversion.convert_units(self.ds, indexer_units) + converted = conversion.convert_units(self.ds, indexer_units, equivalencies) converted_units = conversion.extract_units(converted) stripped = conversion.strip_units(converted) @@ -1384,7 +1389,7 @@ def reindex( return conversion.attach_units(indexed, converted_units) def reindex_like( - self, other, method=None, tolerance=None, copy=True, fill_value=NA + self, other, method=None, tolerance=None, copy=True, fill_value=NA, equivalencies=None ): """unit-aware version of reindex_like @@ -1403,7 +1408,7 @@ def reindex_like( """ indexer_units = conversion.extract_units(other) - converted = conversion.convert_units(self.ds, indexer_units) + converted = conversion.convert_units(self.ds, indexer_units, equivalencies) units = conversion.extract_units(converted) stripped = conversion.strip_units(converted) stripped_other = conversion.strip_units(other) @@ -1425,6 +1430,7 @@ def interp( coords=None, method="linear", assume_sorted=False, + equivalencies=None, kwargs=None, **coords_kwargs, ): @@ -1452,7 +1458,7 @@ def interp( } # convert the indexes to the indexer's units - converted = conversion.convert_units(self.ds, indexer_units) + converted = conversion.convert_units(self.ds, indexer_units, equivalencies) units = conversion.extract_units(converted) stripped = conversion.strip_units(converted) @@ -1466,7 +1472,7 @@ def interp( ) return conversion.attach_units(interpolated, units) - def interp_like(self, other, method="linear", assume_sorted=False, kwargs=None): + def interp_like(self, other, method="linear", assume_sorted=False, equivalencies=None, kwargs=None): """unit-aware version of interp_like Like :py:meth:`xarray.Dataset.interp_like`, except the object's indexes are @@ -1484,7 +1490,7 @@ def interp_like(self, other, method="linear", assume_sorted=False, kwargs=None): """ indexer_units = conversion.extract_units(other) - converted = conversion.convert_units(self.ds, indexer_units) + converted = conversion.convert_units(self.ds, indexer_units, equivalencies) units = conversion.extract_units(converted) stripped = conversion.strip_units(converted) stripped_other = conversion.strip_units(other) @@ -1497,7 +1503,7 @@ def interp_like(self, other, method="linear", assume_sorted=False, kwargs=None): return conversion.attach_units(interpolated, units) def sel( - self, indexers=None, method=None, tolerance=None, drop=False, **indexers_kwargs + self, indexers=None, method=None, tolerance=None, drop=False, equivalencies=None, **indexers_kwargs ): """unit-aware version of sel @@ -1526,7 +1532,7 @@ def sel( # convert the indexes to the indexer's units try: - converted = conversion.convert_units(self.ds, indexer_units) + converted = conversion.convert_units(self.ds, indexer_units, equivalencies) except ValueError as e: raise KeyError(*e.args) from e @@ -1559,7 +1565,7 @@ def loc(self): """ return DatasetLocIndexer(self.ds) - def drop_sel(self, labels=None, *, errors="raise", **labels_kwargs): + def drop_sel(self, labels=None, *, equivalencies=None, errors="raise", **labels_kwargs): """unit-aware version of drop_sel Just like :py:meth:`xarray.Dataset.drop_sel`, except the indexers are converted @@ -1581,7 +1587,7 @@ def drop_sel(self, labels=None, *, errors="raise", **labels_kwargs): # convert the indexers to the indexes units try: - converted_indexers = conversion.convert_indexer_units(indexers, index_units) + converted_indexers = conversion.convert_indexer_units(indexers, index_units, equivalencies) except ValueError as e: raise KeyError(*e.args) from e diff --git a/astropy_xarray/conversion.py b/astropy_xarray/conversion.py index 13620d64..371a5a2a 100644 --- a/astropy_xarray/conversion.py +++ b/astropy_xarray/conversion.py @@ -70,7 +70,7 @@ def array_attach_unit(data, unit) -> astropy.units.Quantity: return astropy.units.Quantity(data, unit) -def array_convert_unit(data, unit) -> astropy.units.Quantity: +def array_convert_unit(data, unit, equivalencies) -> astropy.units.Quantity: """convert the unit of an array This is roughly the same as ``data.to(unit)``. @@ -80,8 +80,14 @@ def array_convert_unit(data, unit) -> astropy.units.Quantity: data : quantity or array-like The data to convert. If it is not a quantity, it is assumed to be dimensionless. - unit : str or astropy.units.Unit + unit : str or astropy.units.UnitBase The unit to convert to. If a string ``data`` has to be a quantity. + equivalencies : list | None + A list of equivalence pairs to try if the units are not + directly convertible. See :py:doc:`astropy:units/equivalencies`. + This list is in addition to possible global defaults set by, + e.g., :py:func:`astropy.units.set_enabled_equivalencies`. + Use None to turn off all equivalencies. Returns ------- @@ -95,7 +101,7 @@ def array_convert_unit(data, unit) -> astropy.units.Quantity: raise ValueError(f"cannot convert a non-quantity using {unit!r} as unit") data = ( - data.to(unit) + data.to(unit, equivalencies) if isinstance(data, astropy.units.Quantity) else astropy.units.Quantity(data, unit) ) @@ -239,24 +245,24 @@ def attach_unit_attributes(obj, units, attr="units"): return new_obj -def convert_units_variable(variable, units): +def convert_unit_variable(variable, unit, equivalencies): if isinstance(variable, IndexVariable): if variable.level_names: # don't try to convert MultiIndexes return variable - if units is not None: + if unit is not None: quantity = array_attach_unit( variable.data, variable.attrs.get(unit_attribute_name) ) - converted = array_convert_unit(quantity, units) + converted = array_convert_unit(quantity, unit, equivalencies) new_obj = variable.copy(data=array_strip_unit(converted)) new_obj.attrs[unit_attribute_name] = array_extract_unit(converted) else: new_obj = variable elif isinstance(variable, Variable): - converted = array_convert_unit(variable.data, units) + converted = array_convert_unit(variable.data, unit, equivalencies) new_obj = variable.copy(data=converted) else: raise ValueError(f"unknown type: {variable}") @@ -264,7 +270,7 @@ def convert_units_variable(variable, units): return new_obj -def convert_units_index(index, index_vars, units): +def convert_units_index(index, index_vars, units, equivalencies): if not isinstance(index, AstropyIndex): raise ValueError("cannot convert non-quantified index") @@ -273,7 +279,7 @@ def convert_units_index(index, index_vars, units): for name, var in index_vars.items(): unit = units.get(name) try: - converted = convert_units_variable(var, unit) + converted = convert_unit_variable(var, unit, equivalencies) converted_vars[name] = strip_units_variable(converted) except (ValueError, astropy.units.core.UnitConversionError) as e: failed[name] = e @@ -287,7 +293,7 @@ def convert_units_index(index, index_vars, units): return AstropyIndex(index=converted_index, units=units) -def convert_units_dataset(obj, units): +def convert_units_dataset(obj, units, equivalencies): converted = {} failed = {} indexed_variables = obj.xindexes.variables @@ -297,7 +303,7 @@ def convert_units_dataset(obj, units): unit = units.get(name) try: - converted[name] = convert_units_variable(var, unit) + converted[name] = convert_unit_variable(var, unit, equivalencies) except (ValueError, astropy.units.core.UnitConversionError) as e: failed[name] = e @@ -308,7 +314,7 @@ def convert_units_dataset(obj, units): continue try: - converted_index = convert_units_index(idx, idx_vars, idx_units) + converted_index = convert_units_index(idx, idx_vars, idx_units, equivalencies) indexes.update({k: converted_index for k in idx_vars}) index_vars.update(converted_index.create_variables()) except (ValueError, astropy.units.core.UnitConversionError) as e: @@ -324,7 +330,7 @@ def convert_units_dataset(obj, units): return dataset_from_variables(reordered, obj._coord_names, indexes, obj.attrs) -def convert_units(obj, units): +def convert_units(obj, units, equivalencies): if not isinstance(obj, (DataArray, Dataset)): raise ValueError(f"cannot convert object: {obj!r}: unknown type") @@ -335,7 +341,7 @@ def convert_units(obj, units): try: new_obj = call_on_dataset( - convert_units_dataset, obj, name=temporary_name, units=units + convert_units_dataset, obj, name=temporary_name, units=units, equivalencies=equivalencies ) except ValueError as e: (failed,) = e.args @@ -464,10 +470,10 @@ def slice_extract_units(indexer): return astropy.units.Quantity(1, units_).si.unit -def convert_units_slice(indexer, units): +def convert_units_slice(indexer, units, equivalencies): attrs = {name: getattr(indexer, name) for name in slice_attributes} converted = { - name: array_convert_unit(value, units) if value is not None else None + name: array_convert_unit(value, units, equivalencies) if value is not None else None for name, value in attrs.items() } args = [converted[name] for name in slice_attributes] @@ -475,16 +481,16 @@ def convert_units_slice(indexer, units): return slice(*args) -def convert_indexer_units(indexers, units): +def convert_indexer_units(indexers, units, equivalencies): def convert(indexer, units): if isinstance(indexer, slice): - return convert_units_slice(indexer, units) + return convert_units_slice(indexer, units, equivalencies) elif isinstance(indexer, DataArray): - return convert_units(indexer, {None: units}) + return convert_units(indexer, {None: units}, equivalencies) elif isinstance(indexer, Variable): - return convert_units_variable(indexer, units) + return convert_unit_variable(indexer, units, equivalencies) else: - return array_convert_unit(indexer, units) + return array_convert_unit(indexer, units, equivalencies) converted = {} invalid = {} diff --git a/astropy_xarray/index.py b/astropy_xarray/index.py index af0712d8..31909ab4 100644 --- a/astropy_xarray/index.py +++ b/astropy_xarray/index.py @@ -67,8 +67,8 @@ def stack(cls, variables, dim): def unstack(self): raise NotImplementedError() - def sel(self, labels, **options): - converted_labels = conversion.convert_indexer_units(labels, self.units) + def sel(self, labels, equivalencies=None, **options): + converted_labels = conversion.convert_indexer_units(labels, self.units, equivalencies) stripped_labels = conversion.strip_indexer_units(converted_labels) return self.index.sel(stripped_labels, **options) diff --git a/astropy_xarray/tests/test_accessors.py b/astropy_xarray/tests/test_accessors.py index 3dd1a284..592dafe2 100644 --- a/astropy_xarray/tests/test_accessors.py +++ b/astropy_xarray/tests/test_accessors.py @@ -19,7 +19,7 @@ # make sure scalars are converted to 0d arrays so quantities can # always be treated like ndarrays -import astropy.units as unit_registry +import astropy.units as u from astropy.units import Quantity nan = np.nan @@ -41,11 +41,11 @@ def assert_all_str_or_none(mapping): def example_unitless_da(): array = np.linspace(0, 10, 20) x = np.arange(20) - u = np.linspace(0, 1, 20) + y = np.linspace(0, 1, 20) da = xr.DataArray( data=array, dims="x", - coords={"x": ("x", x), "u": ("x", u, {"units": "hour"})}, + coords={"x": ("x", x), "u": ("x", y, {"units": "hour"})}, attrs={"units": "m"}, ) return da @@ -53,10 +53,10 @@ def example_unitless_da(): @pytest.fixture() def example_quantity_da(): - array = np.linspace(0, 10, 20) * unit_registry.m + array = np.linspace(0, 10, 20) * u.m x = np.arange(20) - u = np.linspace(0, 1, 20) * unit_registry.hour - return xr.DataArray(data=array, dims="x", coords={"x": ("x", x), "u": ("x", u)}) + y = np.linspace(0, 1, 20) * u.hour + return xr.DataArray(data=array, dims="x", coords={"x": ("x", x), "u": ("x", y)}) class TestQuantifyDataArray: @@ -64,15 +64,14 @@ def test_attach_units_from_str(self, example_unitless_da: xr.DataArray): orig = example_unitless_da result = orig.astropy.quantify("s") assert_array_equal(result.data.value, orig.data) - # TODO better comparisons for when you can't access the unit_registry? + # TODO better comparisons for when you can't access the u? assert str(result.data.unit) == "s" def test_attach_units_given_registry(self, example_unitless_da): orig = example_unitless_da - ureg = unit_registry - result = orig.astropy.quantify("m", unit_registry=ureg) + result = orig.astropy.quantify("m") assert_array_equal(result.data.value, orig.data) - assert result.data.unit == ureg.Unit("m") + assert result.data.unit == u.Unit("m") def test_attach_units_from_attrs(self, example_unitless_da): orig = example_unitless_da @@ -92,10 +91,9 @@ def test_attach_units_from_str_attr_no_unit(self, example_unitless_da): def test_attach_units_given_unit_objs(self, example_unitless_da): orig = example_unitless_da - ureg = unit_registry - result = orig.astropy.quantify(ureg.Unit("m"), unit_registry=ureg) + result = orig.astropy.quantify(u.Unit("m")) assert_array_equal(result.data.value, orig.data) - assert result.data.unit == ureg.Unit("m") + assert result.data.unit == u.Unit("m") @pytest.mark.parametrize("no_unit_value", conversion.no_unit_values) def test_override_units(self, example_unitless_da, no_unit_value): @@ -119,13 +117,13 @@ def test_attach_no_units(self): assert_units_equal(quantified, arr) def test_attach_no_new_units(self): - da = xr.DataArray(unit_registry.Quantity([1, 2, 3], "m"), dims="x") + da = xr.DataArray(u.Quantity([1, 2, 3], "m"), dims="x") quantified = da.astropy.quantify() assert_identical(quantified, da) assert_units_equal(quantified, da) def test_attach_same_units(self): - da = xr.DataArray(unit_registry.Quantity([1, 2, 3], "m"), dims="x") + da = xr.DataArray(u.Quantity([1, 2, 3], "m"), dims="x") quantified = da.astropy.quantify("m") assert_identical(quantified, da) assert_units_equal(quantified, da) @@ -134,7 +132,7 @@ def test_error_when_changing_units_dimension_coordinates(self): arr = xr.DataArray( [1, 2, 3], dims="x", - coords={"x": ("x", [-1, 0, 1], {"units": unit_registry.Unit("m")})}, + coords={"x": ("x", [-1, 0, 1], {"units": u.Unit("m")})}, ) with pytest.raises(ValueError, match="already has units"): arr.astropy.quantify({"x": "s"}) @@ -149,14 +147,14 @@ def test_dimension_coordinate_array(self): assert isinstance(q.attrs["units"], UnitBase) def test_dimension_coordinate_array_already_quantified(self): - ds = xr.Dataset(coords={"x": ("x", [10], {"units": unit_registry.Unit("m")})}) + ds = xr.Dataset(coords={"x": ("x", [10], {"units": u.Unit("m")})}) arr = ds.x with pytest.raises(ValueError): arr.astropy.quantify({"x": "s"}) def test_dimension_coordinate_array_already_quantified_same_units(self): - x = unit_registry.Quantity([10], "m") + x = u.Quantity([10], "m") coords = xr.Coordinates( {"x": x}, indexes={ @@ -203,7 +201,7 @@ def test_units_to_str_or_none(unit_attrs, formatters): import astropy.units.imperial astropy.units.imperial.enable() - units = {key: unit_registry.Unit(value) for key, value in unit_attrs.items()} + units = {key: u.Unit(value) for key, value in unit_attrs.items()} for formatter in formatters: unit_format = f"{{:{formatter}}}" @@ -211,7 +209,7 @@ def test_units_to_str_or_none(unit_attrs, formatters): actual = accessors.units_to_str_or_none(units, unit_format) assert expected == actual - assert units == {key: unit_registry.Unit(value) for key, value in actual.items()} + assert units == {key: u.Unit(value) for key, value in actual.items()} expected = {None: None} assert expected == accessors.units_to_str_or_none(expected, unit_format) @@ -264,8 +262,8 @@ def test_value_getattr_unitless(self, example_unitless_da): def test_units_getattr(self, example_quantity_da): da = example_quantity_da actual = da.astropy.unit - assert isinstance(actual, unit_registry.UnitBase) - assert actual == unit_registry.m + assert isinstance(actual, u.UnitBase) + assert actual == u.m def test_units_setattr(self, example_quantity_da): da = example_quantity_da @@ -278,8 +276,8 @@ def test_units_getattr_unitless(self, example_unitless_da): def test_units_setattr_unitless(self, example_unitless_da): da = example_unitless_da - da.astropy.unit = unit_registry.s - assert da.astropy.unit == unit_registry.s + da.astropy.unit = u.s + assert da.astropy.unit == u.s @pytest.fixture() @@ -297,8 +295,8 @@ def example_unitless_ds(): @pytest.fixture() def example_quantity_ds(): - users = np.linspace(0, 10, 20) * unit_registry.dimensionless_unscaled - funds = np.logspace(0, 10, 20) * unit_registry.gram + users = np.linspace(0, 10, 20) * u.dimensionless_unscaled + funds = np.logspace(0, 10, 20) * u.gram t = np.arange(20) ds = xr.Dataset( data_vars={"users": (["t"], users), "funds": (["t"], funds)}, coords={"t": t} @@ -317,7 +315,7 @@ def test_attach_units_given_registry(self, example_unitless_ds): orig = example_unitless_ds orig["users"].attrs.clear() result = orig.astropy.quantify( - {"users": ""}, unit_registry=unit_registry + {"users": ""}, ) assert_array_equal(result["users"].data.value, orig["users"].data) assert str(result["users"].data.unit) == "" @@ -335,7 +333,7 @@ def test_attach_units_from_attrs(self, example_unitless_ds): def test_attach_units_given_unit_objs(self, example_unitless_ds): orig = example_unitless_ds orig["users"].attrs.clear() - dimensionless = unit_registry.Unit("") + dimensionless = u.Unit("") result = orig.astropy.quantify({"users": dimensionless}) assert_array_equal(result["users"].data.value, orig["users"].data) assert str(result["users"].data.unit) == "" @@ -366,14 +364,14 @@ def test_attach_no_units(self): assert_units_equal(quantified, ds) def test_attach_no_new_units(self): - ds = xr.Dataset({"a": ("x", unit_registry.Quantity([1, 2, 3], "m"))}) + ds = xr.Dataset({"a": ("x", u.Quantity([1, 2, 3], "m"))}) quantified = ds.astropy.quantify() assert_identical(quantified, ds) assert_units_equal(quantified, ds) def test_attach_same_units(self): - ds = xr.Dataset({"a": ("x", unit_registry.Quantity([1, 2, 3], "m"))}) + ds = xr.Dataset({"a": ("x", u.Quantity([1, 2, 3], "m"))}) quantified = ds.astropy.quantify({"a": "m"}) assert_identical(quantified, ds) @@ -381,7 +379,7 @@ def test_attach_same_units(self): def test_error_when_changing_units_dimension_coordinates(self): ds = xr.Dataset( - coords={"x": ("x", [-1, 0, 1], {"units": unit_registry.Unit("m")})}, + coords={"x": ("x", [-1, 0, 1], {"units": u.Unit("m")})}, ) with pytest.raises(ValueError, match="already has units"): ds.astropy.quantify({"x": "s"}) @@ -406,14 +404,14 @@ def test_error_indicates_problematic_variable(self, example_unitless_ds): def test_existing_units(self, example_quantity_ds): ds = example_quantity_ds.copy() - ds.t.attrs["units"] = unit_registry.Unit("m") + ds.t.attrs["units"] = u.Unit("m") with pytest.raises(ValueError, match="Cannot attach"): ds.astropy.quantify({"funds": "kg"}) def test_existing_units_dimension(self, example_quantity_ds): ds = example_quantity_ds.copy() - ds.t.attrs["units"] = unit_registry.Unit("m") + ds.t.attrs["units"] = u.Unit("m") with pytest.raises(ValueError, match="Cannot attach"): ds.astropy.quantify({"t": "s"}) @@ -553,6 +551,21 @@ def test_roundtrip_data(self, example_unitless_ds): None, id="DataArray-compatible units-dims-no index", ), + # pytest.param( + # xr.DataArray( + # [0, 1], + # dims="x", + # coords=xr.Coordinates({"x": Quantity([2, 4], "s")}, indexes={}), + # ), + # {"x": "ms"}, + # xr.DataArray( + # [0, 1], + # dims="x", + # coords=xr.Coordinates({"x": Quantity([2000, 4000], "ms")}, indexes={}), + # ), + # None, + # id="DataArray-compatible units-dims-no index", + # ), pytest.param( xr.DataArray( [0, 1], @@ -578,20 +591,21 @@ def test_to(obj, units, expected, error): @pytest.mark.parametrize( - ["obj", "indexers", "expected", "error"], + ["obj", "indexers", "equivalencies", "expected", "error"], ( pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, + None, xr.Dataset( { - "x": ("x", [10, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 30], {"units": u.Unit("dm")}), + "y": ("y", [60], {"units": u.Unit("s")}), } ), None, @@ -600,15 +614,16 @@ def test_to(obj, units, expected, error): pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([1, 3], "m"), "y": Quantity([1], "min")}, + None, xr.Dataset( { - "x": ("x", [1, 3], {"units": unit_registry.Unit("m")}), - "y": ("y", [1], {"units": unit_registry.Unit("min")}), + "x": ("x", [1, 3], {"units": u.Unit("m")}), + "y": ("y", [1], {"units": u.Unit("min")}), } ), None, @@ -617,12 +632,31 @@ def test_to(obj, units, expected, error): pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [1, 2, 3], {"units": u.Unit("arcsec")}), + "y": ("y", [2, 3], {"units": u.Unit("k")}), + } + ), + {"x": Quantity([1, 0.5], "pc"), "y": Quantity([300], "1/m")}, + u.parallax(), + xr.Dataset( + { + "x": ("x", [1, 0.5], {"units": u.Unit("pc")}), + "y": ("y", [300], {"units": u.Unit("1/m")}), + } + ), + None, + id="Dataset-equivalent units", + ), + pytest.param( + xr.Dataset( + { + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([1, 3], "s"), "y": Quantity([1], "m")}, None, + None, KeyError, id="Dataset-incompatible units", ), @@ -631,17 +665,18 @@ def test_to(obj, units, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, + None, xr.DataArray( [[0], [4]], dims=("x", "y"), coords={ - "x": ("x", [10, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 30], {"units": u.Unit("dm")}), + "y": ("y", [60], {"units": u.Unit("s")}), }, ), None, @@ -652,17 +687,18 @@ def test_to(obj, units, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([1, 3], "m"), "y": Quantity([1], "min")}, + None, xr.DataArray( [[0], [4]], dims=("x", "y"), coords={ - "x": ("x", [1, 3], {"units": unit_registry.Unit("m")}), - "y": ("y", [1], {"units": unit_registry.Unit("min")}), + "x": ("x", [1, 3], {"units": u.Unit("m")}), + "y": ("y", [1], {"units": u.Unit("min")}), }, ), None, @@ -673,27 +709,50 @@ def test_to(obj, units, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [1, 2, 3], {"units": u.Unit("arcsec")}), + "y": ("y", [2, 3], {"units": u.Unit("k")}), + } + ), + {"x": Quantity([1, 0.5], "pc"), "y": Quantity([300], "1/m")}, + u.parallax(), + xr.DataArray( + [[1], [3]], + dims=("x", "y"), + coords={ + "x": ("x", [1, 0.5], {"units": u.Unit("pc")}), + "y": ("y", [300], {"units": u.Unit("1/m")}), + }, + ), + None, + id="DataArray-equivalent units", + ), + pytest.param( + xr.DataArray( + [[0, 1], [2, 3], [4, 5]], + dims=("x", "y"), + coords={ + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "s"), "y": Quantity([60], "m")}, None, + None, KeyError, id="DataArray-incompatible units", ), ), ) -def test_sel(obj, indexers, expected, error): +def test_sel(obj, indexers, equivalencies, expected, error): obj_ = obj.astropy.quantify() if error is not None: with pytest.raises(error): - obj_.astropy.sel(indexers) + obj_.astropy.sel(indexers, equivalencies=equivalencies) else: expected_ = expected.astropy.quantify() - actual = obj_.astropy.sel(indexers) + actual = obj_.astropy.sel(indexers, equivalencies=equivalencies) assert_units_equal(actual, expected_) assert_identical(actual, expected_) @@ -704,15 +763,15 @@ def test_sel(obj, indexers, expected, error): pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, xr.Dataset( { - "x": ("x", [10, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 30], {"units": u.Unit("dm")}), + "y": ("y", [60], {"units": u.Unit("s")}), } ), None, @@ -721,15 +780,15 @@ def test_sel(obj, indexers, expected, error): pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([1, 3], "m"), "y": Quantity([1], "min")}, xr.Dataset( { - "x": ("x", [1, 3], {"units": unit_registry.Unit("m")}), - "y": ("y", [1], {"units": unit_registry.Unit("min")}), + "x": ("x", [1, 3], {"units": u.Unit("m")}), + "y": ("y", [1], {"units": u.Unit("min")}), } ), None, @@ -738,8 +797,8 @@ def test_sel(obj, indexers, expected, error): pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([1, 3], "s"), "y": Quantity([1], "m")}, @@ -752,8 +811,8 @@ def test_sel(obj, indexers, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, @@ -761,8 +820,8 @@ def test_sel(obj, indexers, expected, error): [[0], [4]], dims=("x", "y"), coords={ - "x": ("x", [10, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 30], {"units": u.Unit("dm")}), + "y": ("y", [60], {"units": u.Unit("s")}), }, ), None, @@ -773,8 +832,8 @@ def test_sel(obj, indexers, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([1, 3], "m"), "y": Quantity([1], "min")}, @@ -782,8 +841,8 @@ def test_sel(obj, indexers, expected, error): [[0], [4]], dims=("x", "y"), coords={ - "x": ("x", [1, 3], {"units": unit_registry.Unit("m")}), - "y": ("y", [1], {"units": unit_registry.Unit("min")}), + "x": ("x", [1, 3], {"units": u.Unit("m")}), + "y": ("y", [1], {"units": u.Unit("min")}), }, ), None, @@ -794,8 +853,8 @@ def test_sel(obj, indexers, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "s"), "y": Quantity([60], "m")}, @@ -827,8 +886,8 @@ def test_loc(obj, indexers, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, @@ -837,8 +896,8 @@ def test_loc(obj, indexers, expected, error): [[-1, 1], [2, 3], [-2, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), None, @@ -849,8 +908,8 @@ def test_loc(obj, indexers, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([1, 3], "m"), "y": Quantity([1], "min")}, @@ -859,8 +918,8 @@ def test_loc(obj, indexers, expected, error): [[-1, 1], [2, 3], [-2, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), None, @@ -871,8 +930,8 @@ def test_loc(obj, indexers, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([1, 3], "s"), "y": Quantity([1], "m")}, @@ -886,8 +945,8 @@ def test_loc(obj, indexers, expected, error): Quantity([[0, 1], [2, 3], [4, 5]], "m"), dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, @@ -896,8 +955,8 @@ def test_loc(obj, indexers, expected, error): Quantity([[-1, 1], [2, 3], [-2, 5]], "m"), dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), None, @@ -908,8 +967,8 @@ def test_loc(obj, indexers, expected, error): Quantity([[0, 1], [2, 3], [4, 5]], "m"), dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, @@ -918,8 +977,8 @@ def test_loc(obj, indexers, expected, error): Quantity([[-1000, 1], [2, 3], [-2000, 5]], "m"), dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), None, @@ -930,8 +989,8 @@ def test_loc(obj, indexers, expected, error): Quantity([[0, 1], [2, 3], [4, 5]], "m"), dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, @@ -958,15 +1017,15 @@ def test_loc_setitem(obj, indexers, values, expected, error): pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, xr.Dataset( { - "x": ("x", [20], {"units": unit_registry.Unit("dm")}), - "y": ("y", [120], {"units": unit_registry.Unit("s")}), + "x": ("x", [20], {"units": u.Unit("dm")}), + "y": ("y", [120], {"units": u.Unit("s")}), } ), None, @@ -975,15 +1034,15 @@ def test_loc_setitem(obj, indexers, values, expected, error): pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([1, 3], "m"), "y": Quantity([1], "min")}, xr.Dataset( { - "x": ("x", [20], {"units": unit_registry.Unit("dm")}), - "y": ("y", [120], {"units": unit_registry.Unit("s")}), + "x": ("x", [20], {"units": u.Unit("dm")}), + "y": ("y", [120], {"units": u.Unit("s")}), } ), None, @@ -992,8 +1051,8 @@ def test_loc_setitem(obj, indexers, values, expected, error): pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([1, 3], "s"), "y": Quantity([1], "m")}, @@ -1004,8 +1063,8 @@ def test_loc_setitem(obj, indexers, values, expected, error): pytest.param( xr.Dataset( { - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), } ), {"x": Quantity([10, 30], "m"), "y": Quantity([60], "min")}, @@ -1018,8 +1077,8 @@ def test_loc_setitem(obj, indexers, values, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "dm"), "y": Quantity([60], "s")}, @@ -1027,8 +1086,8 @@ def test_loc_setitem(obj, indexers, values, expected, error): [[3]], dims=("x", "y"), coords={ - "x": ("x", [20], {"units": unit_registry.Unit("dm")}), - "y": ("y", [120], {"units": unit_registry.Unit("s")}), + "x": ("x", [20], {"units": u.Unit("dm")}), + "y": ("y", [120], {"units": u.Unit("s")}), }, ), None, @@ -1039,8 +1098,8 @@ def test_loc_setitem(obj, indexers, values, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([1, 3], "m"), "y": Quantity([1], "min")}, @@ -1048,8 +1107,8 @@ def test_loc_setitem(obj, indexers, values, expected, error): [[3]], dims=("x", "y"), coords={ - "x": ("x", [20], {"units": unit_registry.Unit("dm")}), - "y": ("y", [120], {"units": unit_registry.Unit("s")}), + "x": ("x", [20], {"units": u.Unit("dm")}), + "y": ("y", [120], {"units": u.Unit("s")}), }, ), None, @@ -1060,8 +1119,8 @@ def test_loc_setitem(obj, indexers, values, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "s"), "y": Quantity([60], "m")}, @@ -1074,8 +1133,8 @@ def test_loc_setitem(obj, indexers, values, expected, error): [[0, 1], [2, 3], [4, 5]], dims=("x", "y"), coords={ - "x": ("x", [10, 20, 30], {"units": unit_registry.Unit("dm")}), - "y": ("y", [60, 120], {"units": unit_registry.Unit("s")}), + "x": ("x", [10, 20, 30], {"units": u.Unit("dm")}), + "y": ("y", [60, 120], {"units": u.Unit("s")}), }, ), {"x": Quantity([10, 30], "m"), "y": Quantity([60], "min")}, @@ -1155,7 +1214,7 @@ def test_chunk(obj): actual = obj.astropy.chunk({"x": 2}) expected = ( - obj.astropy.dequantify().chunk({"x": 2}).astropy.quantify(unit_registry=unit_registry) + obj.astropy.dequantify().chunk({"x": 2}).astropy.quantify() ) assert_units_equal(actual, expected) @@ -1163,11 +1222,12 @@ def test_chunk(obj): @pytest.mark.parametrize( - ["obj", "units", "indexers", "expected", "expected_units", "error"], + ["obj", "units", "equivalencies", "indexers", "expected", "expected_units", "error"], ( pytest.param( xr.Dataset({"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}), {"x": "dm", "y": "s"}, + None, {"x": Quantity([10, 30, 50], "dm"), "y": Quantity([0, 120, 240], "s")}, xr.Dataset({"x": ("x", [10, 30, 50]), "y": ("y", [0, 120, 240])}), {"x": "dm", "y": "s"}, @@ -1177,15 +1237,27 @@ def test_chunk(obj): pytest.param( xr.Dataset({"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}), {"x": "dm", "y": "s"}, + None, {"x": Quantity([0, 1, 3, 5], "m"), "y": Quantity([0, 2, 4], "min")}, xr.Dataset({"x": ("x", [0, 1, 3, 5]), "y": ("y", [0, 2, 4])}), {"x": "m", "y": "min"}, None, id="Dataset-compatible units", ), + pytest.param( + xr.Dataset({"x": ("x", [1, 2, 3]), "y": ("y", [60, 120])}), + {"x": "k", "y": "s"}, + u.parallax(), + {"x": Quantity([0, 100, 300, 500], "1/m"), "y": Quantity([0, 2, 4], "min")}, + xr.Dataset({"x": ("x", [0, 100, 300, 500]), "y": ("y", [0, 2, 4])}), + {"x": "1/m", "y": "min"}, + None, + id="Dataset-equivalent units", + ), pytest.param( xr.Dataset({"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}), {"x": "dm", "y": "s"}, + None, {"x": Quantity([1, 3], "s"), "y": Quantity([1], "m")}, None, {}, @@ -1201,6 +1273,7 @@ def test_chunk(obj): } ), {"a": "kg"}, + None, { "x": [15, 25], "y": [75, 105], @@ -1223,6 +1296,7 @@ def test_chunk(obj): coords={"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}, ), {"x": "dm", "y": "s"}, + None, {"x": Quantity([10, 30, 50], "dm"), "y": Quantity([0, 240], "s")}, xr.DataArray( [[np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan]], @@ -1240,6 +1314,7 @@ def test_chunk(obj): coords={"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}, ), {"x": "dm", "y": "s"}, + None, {"x": Quantity([1, 3, 5], "m"), "y": Quantity([0, 2], "min")}, xr.DataArray( [[np.nan, 1], [np.nan, 5], [np.nan, np.nan]], @@ -1250,6 +1325,24 @@ def test_chunk(obj): None, id="DataArray-compatible units", ), + pytest.param( + xr.DataArray( + [[0, 1], [2, 3], [4, 5]], + dims=("x", "y"), + coords={"x": ("x", [1, 2, 3]), "y": ("y", [60, 120])}, + ), + {"x": "k", "y": "s"}, + u.parallax(), + {"x": Quantity([100, 300, 500], "1/m"), "y": Quantity([0, 2], "min")}, + xr.DataArray( + [[np.nan, 1], [np.nan, 5], [np.nan, np.nan]], + dims=("x", "y"), + coords={"x": ("x", [100, 300, 500]), "y": ("y", [0, 2])}, + ), + {"x": "1/m", "y": "min"}, + None, + id="DataArray-equivalent units", + ), pytest.param( xr.DataArray( [[0, 1], [2, 3], [4, 5]], @@ -1257,6 +1350,7 @@ def test_chunk(obj): coords={"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}, ), {"x": "dm", "y": "s"}, + None, {"x": Quantity([10, 30], "s"), "y": Quantity([60], "m")}, None, {}, @@ -1270,6 +1364,7 @@ def test_chunk(obj): coords={"x": [10, 20, 30], "y": [60, 120]}, ), {None: "kg"}, + None, {"x": [15, 25], "y": [75, 105]}, xr.DataArray( [[np.nan, np.nan], [np.nan, np.nan]], @@ -1282,26 +1377,27 @@ def test_chunk(obj): ), ), ) -def test_reindex(obj, units, indexers, expected, expected_units, error): +def test_reindex(obj, units, equivalencies, indexers, expected, expected_units, error): obj_ = obj.astropy.quantify(units) if error is not None: with pytest.raises(error): obj.astropy.reindex(indexers) else: - expected_ = expected.astropy.quantify(expected_units) + expected_ = expected.astropy.quantify(units=expected_units) - actual = obj_.astropy.reindex(indexers) + actual = obj_.astropy.reindex(indexers, equivalencies=equivalencies) assert_units_equal(actual, expected_) assert_identical(actual, expected_) @pytest.mark.parametrize( - ["obj", "units", "other", "other_units", "expected", "expected_units", "error"], + ["obj", "units", "equivalencies", "other", "other_units", "expected", "expected_units", "error"], ( pytest.param( xr.Dataset({"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}), {"x": "dm", "y": "s"}, + None, xr.Dataset({"x": ("x", [10, 30, 50]), "y": ("y", [0, 120, 240])}), {"x": "dm", "y": "s"}, xr.Dataset({"x": ("x", [10, 30, 50]), "y": ("y", [0, 120, 240])}), @@ -1312,6 +1408,7 @@ def test_reindex(obj, units, indexers, expected, expected_units, error): pytest.param( xr.Dataset({"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}), {"x": "dm", "y": "s"}, + None, xr.Dataset({"x": ("x", [0, 1, 3, 5]), "y": ("y", [0, 2, 4])}), {"x": "m", "y": "min"}, xr.Dataset({"x": ("x", [0, 1, 3, 5]), "y": ("y", [0, 2, 4])}), @@ -1319,9 +1416,21 @@ def test_reindex(obj, units, indexers, expected, expected_units, error): None, id="Dataset-compatible units", ), + pytest.param( + xr.Dataset({"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}), + {"x": "k", "y": "s"}, + u.parallax(), + xr.Dataset({"x": ("x", [0, 1000, 3000, 5000]), "y": ("y", [0, 2, 4])}), + {"x": "1/m", "y": "min"}, + xr.Dataset({"x": ("x", [0, 1000, 3000, 5000]), "y": ("y", [0, 2, 4])}), + {"x": "1/m", "y": "min"}, + None, + id="Dataset-equivalent units", + ), pytest.param( xr.Dataset({"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}), {"x": "dm", "y": "s"}, + None, xr.Dataset({"x": ("x", [1, 3]), "y": ("y", [1])}), {"x": "s", "y": "m"}, None, @@ -1336,6 +1445,7 @@ def test_reindex(obj, units, indexers, expected, expected_units, error): coords={"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}, ), {"x": "dm", "y": "s"}, + None, xr.Dataset({"x": ("x", [10, 30, 50]), "y": ("y", [0, 240])}), {"x": "dm", "y": "s"}, xr.DataArray( @@ -1356,6 +1466,7 @@ def test_reindex(obj, units, indexers, expected, expected_units, error): } ), {"a": "kg"}, + None, xr.Dataset({"x": [15, 25], "y": [75, 105]}), {}, xr.Dataset( @@ -1376,6 +1487,7 @@ def test_reindex(obj, units, indexers, expected, expected_units, error): coords={"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}, ), {"x": "dm", "y": "s"}, + None, xr.Dataset({"x": ("x", [1, 3, 5]), "y": ("y", [0, 2])}), {"x": "m", "y": "min"}, xr.DataArray( @@ -1387,6 +1499,25 @@ def test_reindex(obj, units, indexers, expected, expected_units, error): None, id="DataArray-compatible units", ), + pytest.param( + xr.DataArray( + [[0, 1], [2, 3], [4, 5]], + dims=("x", "y"), + coords={"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}, + ), + {"x": "k", "y": "s"}, + u.parallax(), + xr.Dataset({"x": ("x", [1000, 3000, 5000]), "y": ("y", [0, 2])}), + {"x": "1/m", "y": "min"}, + xr.DataArray( + [[np.nan, 1], [np.nan, 5], [np.nan, np.nan]], + dims=("x", "y"), + coords={"x": ("x", [1000, 3000, 5000]), "y": ("y", [0, 2])}, + ), + {"x": "1/m", "y": "min"}, + None, + id="DataArray-equivalent units", + ), pytest.param( xr.DataArray( [[0, 1], [2, 3], [4, 5]], @@ -1394,6 +1525,7 @@ def test_reindex(obj, units, indexers, expected, expected_units, error): coords={"x": ("x", [10, 20, 30]), "y": ("y", [60, 120])}, ), {"x": "dm", "y": "s"}, + None, xr.Dataset({"x": ("x", [10, 30]), "y": ("y", [60])}), {"x": "s", "y": "m"}, None, @@ -1408,6 +1540,7 @@ def test_reindex(obj, units, indexers, expected, expected_units, error): coords={"x": [10, 20, 30], "y": [60, 120]}, ), {"a": "kg"}, + None, xr.Dataset({"x": [15, 25], "y": [75, 105]}), {}, xr.DataArray( @@ -1421,13 +1554,13 @@ def test_reindex(obj, units, indexers, expected, expected_units, error): ), ), ) -def test_reindex_like(obj, units, other, other_units, expected, expected_units, error): +def test_reindex_like(obj, units, equivalencies, other, other_units, expected, expected_units, error): obj_ = obj.astropy.quantify(units) other_ = other.astropy.quantify(other_units) if error is not None: with pytest.raises(error): - obj_.astropy.reindex_like(other_) + obj_.astropy.reindex_like(other_, equivalencies=equivalencies) else: expected_ = expected.astropy.quantify(expected_units) diff --git a/astropy_xarray/tests/test_conversion.py b/astropy_xarray/tests/test_conversion.py index afc7ac57..2cbb25ed 100644 --- a/astropy_xarray/tests/test_conversion.py +++ b/astropy_xarray/tests/test_conversion.py @@ -16,7 +16,7 @@ assert_indexers_equal, ) -unit_registry = astropy.units +u = astropy.units Quantity = astropy.units.Quantity Unit = astropy.units.Unit @@ -32,14 +32,14 @@ def to_quantity(v, u): return Quantity(v, u) -def convert_quantity(q, u): +def convert_quantity(q, u, equivalencies): if u is None: return q if not isinstance(q, Quantity): q = Quantity(q) - return q.to(u) + return q.to(u, equivalencies=equivalencies) def strip_quantity(q): @@ -104,13 +104,14 @@ def test_array_attach_units(self, data, unit, expected, match): actual = conversion.array_attach_unit(data, unit) assert_array_units_equal(expected, actual) - assert_array_equal(expected, actual) + assert_array_equal(actual, expected) @pytest.mark.parametrize( - ["unit", "data", "expected", "error", "match"], + ["unit", "equivalencies", "data", "expected", "error", "match"], ( pytest.param( 1.2, + None, np.array([0, 1, 2]), None, ValueError, @@ -119,6 +120,7 @@ def test_array_attach_units(self, data, unit, expected, match): ), pytest.param( 1, + None, np.array([0, 1, 2]), None, ValueError, @@ -126,6 +128,7 @@ def test_array_attach_units(self, data, unit, expected, match): id="no unit (1)-ndarray", ), pytest.param( + None, None, np.array([0, 1, 2]), np.array([0, 1, 2]), @@ -135,6 +138,7 @@ def test_array_attach_units(self, data, unit, expected, match): ), pytest.param( "mm", + None, np.array([0, 1, 2]), None, ValueError, @@ -143,6 +147,7 @@ def test_array_attach_units(self, data, unit, expected, match): ), pytest.param( Unit("deg"), + None, Quantity(np.array([0, np.pi / 2, np.pi]), "rad"), Quantity([0, 90, 180], "deg"), None, @@ -151,7 +156,8 @@ def test_array_attach_units(self, data, unit, expected, match): ), pytest.param( Unit("mm"), - Quantity(np.array([0, np.pi / 2, np.pi]), "rad"), + None, + Quantity(np.array([0, np.pi / 2, np.pi])), None, astropy.units.core.UnitConversionError, None, @@ -159,6 +165,7 @@ def test_array_attach_units(self, data, unit, expected, match): ), pytest.param( "mm", + None, Quantity([0, 1, 2], "m"), Quantity([0, 1000, 2000], "mm"), None, @@ -166,7 +173,8 @@ def test_array_attach_units(self, data, unit, expected, match): id="string-quantity", ), pytest.param( - unit_registry.mm, + u.mm, + None, Quantity([0, 1, 2], "m"), Quantity([0, 1000, 2000], "mm"), None, @@ -175,23 +183,52 @@ def test_array_attach_units(self, data, unit, expected, match): ), pytest.param( "s", + None, Quantity([0, 1, 2], "m"), None, astropy.units.core.UnitConversionError, None, id="quantity-incompatible unit", ), + pytest.param( + "arcsec", + u.parallax(), + Quantity([0, 1, 2], "parsec"), + Quantity([np.inf, 1.0, 0.5], "arcsec"), + None, + None, + id="parallax-equivalent unit", + ), + pytest.param( + "", + u.dimensionless_angles(), + Quantity([0, 1, 2], "rad"), + Quantity([0, 1, 2]), + None, + None, + id="dimensionless-angles-equivalent unit", + ), + pytest.param( + "rad", + u.dimensionless_angles(), + Quantity([0, 1, 2], ""), + Quantity([0, 1, 2], "rad"), + None, + None, + id="dimensionless-angles-equivalent unit", + ), ), ) - def test_array_convert_units(self, data, unit, expected, error, match): + def test_array_convert_units(self, data, equivalencies, unit, expected, error, match): if error is not None: with pytest.raises(error, match=match): - conversion.array_convert_unit(data, unit) + conversion.array_convert_unit(data, unit, equivalencies) return - actual = conversion.array_convert_unit(data, unit) - assert_array_equal(expected, actual) + actual = conversion.array_convert_unit(data, unit, equivalencies) + assert_array_equal(actual, expected) + assert_array_units_equal(actual, expected) @pytest.mark.parametrize( ["data", "expected"], @@ -215,7 +252,7 @@ def test_array_extract_units(self, data, expected): def test_array_strip_units(self, data, expected): actual = conversion.array_strip_unit(data) - assert_array_equal(expected, actual) + assert_array_equal(actual, expected) class TestXarrayFunctions: @@ -226,15 +263,15 @@ class TestXarrayFunctions: pytest.param({}, id="empty units"), pytest.param({"a": None, "b": None, "u": None, "x": None}, id="no units"), pytest.param( - {"a": unit_registry.m, "b": unit_registry.m, "u": None, "x": None}, + {"a": u.m, "b": u.m, "u": None, "x": None}, id="data units", ), pytest.param( - {"a": None, "b": None, "u": unit_registry.s, "x": None}, + {"a": None, "b": None, "u": u.s, "x": None}, id="coord units", ), pytest.param( - {"a": None, "b": None, "u": None, "x": unit_registry.m}, id="dim units" + {"a": None, "b": None, "u": None, "x": u.m}, id="dim units" ), ), ) @@ -296,27 +333,38 @@ def test_attach_unit_attributes(self, type): @pytest.mark.parametrize("type", ("DataArray", "Dataset")) @pytest.mark.parametrize( - ["variant", "units", "error", "match"], + ["variant", "units", "equivalencies", "error", "match"], ( - pytest.param("none", {}, None, None, id="none-no units"), + pytest.param("none", {}, None, None, None, id="none-no units"), pytest.param( "none", {"a": Unit("g"), "b": Unit("Pa"), "u": Unit("ms"), "x": Unit("mm")}, + None, ValueError, "(?s)Cannot convert variables:.+'u'", id="none-with units", ), - pytest.param("data", {}, None, None, id="data-no units"), + pytest.param("data", {}, None, None, None, id="data-no units"), pytest.param( "data", {"a": Unit("g"), "b": Unit("Pa")}, None, None, + None, id="data-compatible units", ), + pytest.param( + "data", + {"a": Unit("eV"), "b": Unit("Pa")}, + u.mass_energy(), + None, + None, + id="data-equivalent units", + ), pytest.param( "data", {"a": Unit("s"), "b": Unit("m")}, + None, ValueError, "(?s)Cannot convert variables:.+'a'", id="data-incompatible units", @@ -326,6 +374,7 @@ def test_attach_unit_attributes(self, type): {}, None, None, + None, id="dims-no units", ), pytest.param( @@ -333,11 +382,21 @@ def test_attach_unit_attributes(self, type): {"x": Unit("mm")}, None, None, + None, id="dims-compatible units", ), + pytest.param( + "dims", + {"x": Unit("deg")}, + u.parallax(), + None, + None, + id="dims-equivalent units", + ), pytest.param( "dims", {"x": Unit("ms")}, + None, ValueError, "(?s)Cannot convert variables:.+'x'", id="dims-incompatible units", @@ -347,6 +406,7 @@ def test_attach_unit_attributes(self, type): {}, None, None, + None, id="coords-no units", ), pytest.param( @@ -354,22 +414,32 @@ def test_attach_unit_attributes(self, type): {"u": Unit("ms")}, None, None, + None, id="coords-compatible units", ), + pytest.param( + "coords", + {"x": Unit("pc")}, + u.parallax(), + None, + None, + id="coords-equivalent units", + ), pytest.param( "coords", {"u": Unit("mm")}, + None, ValueError, "(?s)Cannot convert variables:.+'u'", id="coords-incompatible units", ), ), ) - def test_convert_units(self, type, variant, units, error, match): + def test_convert_units(self, type, variant, units, equivalencies, error, match): variants = { "none": {"a": None, "b": None, "u": None, "x": None}, "data": {"a": Unit("kg"), "b": Unit("hPa"), "u": None, "x": None}, - "coords": {"a": None, "b": None, "u": Unit("s"), "x": None}, + "coords": {"a": None, "b": None, "u": Unit("s"), "x": Unit("arcsec")}, "dims": {"a": None, "b": None, "u": None, "x": Unit("m")}, } @@ -403,14 +473,14 @@ def test_convert_units(self, type, variant, units, error, match): if error is not None: with pytest.raises(error, match=match): - conversion.convert_units(obj, units) + conversion.convert_units(obj, units, equivalencies) return - expected_a = convert_quantity(q_a, units.get("a", original_units.get("a"))) - expected_b = convert_quantity(q_b, units.get("b", original_units.get("b"))) - expected_u = convert_quantity(q_u, units.get("u", original_units.get("u"))) - expected_x = convert_quantity(q_x, units.get("x")) + expected_a = convert_quantity(q_a, units.get("a", original_units.get("a")), equivalencies) + expected_b = convert_quantity(q_b, units.get("b", original_units.get("b")), equivalencies) + expected_u = convert_quantity(q_u, units.get("u", original_units.get("u")), equivalencies) + expected_x = convert_quantity(q_x, units.get("x"), equivalencies) expected_index = PandasIndex(pd.Index(strip_quantity(expected_x)), "x") if units.get("x") is not None: expected_index = AstropyIndex( @@ -431,7 +501,7 @@ def test_convert_units(self, type, variant, units, error, match): if type == "DataArray": expected = expected["a"] - actual = conversion.convert_units(obj, units) + actual = conversion.convert_units(obj, units, equivalencies) assert conversion.extract_units(actual) == conversion.extract_units(expected) assert_identical(actual, expected) @@ -606,15 +676,22 @@ def test_strip_unit_attributes(self, obj, expected): class TestIndexerFunctions: @pytest.mark.parametrize( - ["indexers", "units", "expected", "error", "match"], + ["indexers", "units", "equivalencies", "expected", "error", "match"], ( pytest.param( - {"x": 1}, {"x": None}, {"x": 1}, None, None, id="scalar-no units" + {"x": 1}, + {"x": None}, + None, + {"x": 1}, + None, + None, + id="scalar-no units" ), pytest.param( {"x": 1}, {"x": "dimensionless"}, None, + None, ValueError, "(?s)Cannot convert indexers:.+'x'", id="scalar-dimensionless", @@ -622,6 +699,7 @@ class TestIndexerFunctions: pytest.param( {"x": Quantity(1, "m")}, {"x": Unit("dm")}, + None, {"x": Quantity(10, "dm")}, None, None, @@ -630,6 +708,7 @@ class TestIndexerFunctions: pytest.param( {"x": np.array([1, 2])}, {"x": None}, + None, {"x": np.array([1, 2])}, None, None, @@ -638,6 +717,7 @@ class TestIndexerFunctions: pytest.param( {"x": Quantity([1, 2], "m")}, {"x": Unit("dm")}, + None, {"x": Quantity([10, 20], "dm")}, None, None, @@ -646,6 +726,7 @@ class TestIndexerFunctions: pytest.param( {"x": Variable("x", [1, 2])}, {"x": None}, + None, {"x": Variable("x", [1, 2])}, None, None, @@ -654,6 +735,7 @@ class TestIndexerFunctions: pytest.param( {"x": Variable("x", Quantity([1, 2], "m"))}, {"x": Unit("dm")}, + None, {"x": Variable("x", Quantity([10, 20], "dm"))}, None, None, @@ -662,6 +744,7 @@ class TestIndexerFunctions: pytest.param( {"x": DataArray([1, 2], dims="x")}, {"x": None}, + None, {"x": DataArray([1, 2], dims="x")}, None, None, @@ -670,6 +753,7 @@ class TestIndexerFunctions: pytest.param( {"x": DataArray(Quantity([1, 2], "m"), dims="x")}, {"x": Unit("dm")}, + None, {"x": DataArray(Quantity([10, 20], "dm"), dims="x")}, None, None, @@ -678,6 +762,7 @@ class TestIndexerFunctions: pytest.param( {"x": slice(None)}, {"x": None}, + None, {"x": slice(None)}, None, None, @@ -686,6 +771,7 @@ class TestIndexerFunctions: pytest.param( {"x": slice(1, None)}, {"x": None}, + None, {"x": slice(1, None)}, None, None, @@ -694,6 +780,7 @@ class TestIndexerFunctions: pytest.param( {"x": slice(Quantity(1, "m"), Quantity(2, "m"))}, {"x": Unit("m")}, + None, {"x": slice(Quantity(1, "m"), Quantity(2, "m"))}, None, None, @@ -702,6 +789,7 @@ class TestIndexerFunctions: pytest.param( {"x": slice(Quantity(1, "m"), Quantity(2000, "mm"))}, {"x": Unit("dm")}, + None, {"x": slice(Quantity(10, "dm"), Quantity(20, "dm"))}, None, None, @@ -711,6 +799,7 @@ class TestIndexerFunctions: {"x": slice(Quantity(1, "m"), Quantity(2, "m"))}, {"x": Unit("ms")}, None, + None, ValueError, "(?s)Cannot convert indexers:.+'x'", id="slice-incompatible units", @@ -719,18 +808,28 @@ class TestIndexerFunctions: {"x": slice(1000, Quantity(2000, "ms"))}, {"x": Unit("s")}, None, + None, ValueError, "(?s)Cannot convert indexers:.+'x'", id="slice-incompatible units-mixed", ), + pytest.param( + {"x": slice(Quantity(1, "pc"), Quantity(2000, "mpc"))}, + {"x": Unit("arcsec")}, + u.parallax(), + {"x": slice(Quantity(1, "arcsec"), Quantity(0.5, "arcsec"))}, + None, + None, + id="slice-parallax-equivalent units", + ), ), ) - def test_convert_indexer_units(self, indexers, units, expected, error, match): + def test_convert_indexer_units(self, indexers, units, equivalencies, expected, error, match): if error is not None: with pytest.raises(error, match=match): - conversion.convert_indexer_units(indexers, units) + conversion.convert_indexer_units(indexers, units, equivalencies) else: - actual = conversion.convert_indexer_units(indexers, units) + actual = conversion.convert_indexer_units(indexers, units, equivalencies) assert_indexers_equal(actual, expected) assert_indexer_units_equal(actual, expected) diff --git a/docs/examples/plotting.ipynb b/docs/examples/plotting.ipynb index 5ea69382..353af715 100644 --- a/docs/examples/plotting.ipynb +++ b/docs/examples/plotting.ipynb @@ -10,17 +10,17 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 3, "id": "1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 4, "id": "3", "metadata": {}, "outputs": [ @@ -449,26 +449,26 @@ " level_desc: Surface\n", " statistic: Individual Obs\n", " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • " ], "text/plain": [ " Size: 31MB\n", @@ -502,7 +502,7 @@ " actual_range: [185.16 322.1 ]" ] }, - "execution_count": 33, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -533,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 5, "id": "6", "metadata": {}, "outputs": [ @@ -929,7 +929,7 @@ " level_desc: Surface\n", " statistic: Individual Obs\n", " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]
  • long_name :
    4xDaily Air temperature at sigma level 995
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • " ], "text/plain": [ " Size: 31MB\n", @@ -1015,7 +1015,7 @@ " actual_range: [185.16 322.1 ]" ] }, - "execution_count": 34, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1035,7 +1035,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 15, "id": "8", "metadata": {}, "outputs": [ @@ -1413,7 +1413,7 @@ " fill: currentColor;\n", "}\n", "
    <xarray.DataArray 'air' (month: 12, lat: 25, lon: 53)> Size: 127kB\n",
-       "[K] 244.5 244.7 244.7 244.5 244.2 243.8 ... 298.0 297.9 297.9 297.5 297.4 297.4\n",
+       "[°C] -28.68 -28.49 -28.48 -28.67 -28.99 -29.32 ... 24.73 24.77 24.35 24.26 24.22\n",
        "Coordinates:\n",
        "  * lat      (lat) float32 100B [°] 75.0 72.5 70.0 67.5 ... 22.5 20.0 17.5 15.0\n",
        "  * lon      (lon) float32 212B [°] 200.0 202.5 205.0 ... 325.0 327.5 330.0\n",
@@ -1431,58 +1431,58 @@
        "    level_desc:    Surface\n",
        "    statistic:     Individual Obs\n",
        "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    • lat
      AstropyIndex(PandasIndex)
      <astropy_xarray.index.AstropyIndex object at 0x7f2b5a0a52e0>
    • lon
      AstropyIndex(PandasIndex)
      <astropy_xarray.index.AstropyIndex object at 0x7f2b5a0c2120>
    • month
      PandasIndex
      PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • " ], "text/plain": [ " Size: 127kB\n", - "[K] 244.5 244.7 244.7 244.5 244.2 243.8 ... 298.0 297.9 297.9 297.5 297.4 297.4\n", + "[°C] -28.68 -28.49 -28.48 -28.67 -28.99 -29.32 ... 24.73 24.77 24.35 24.26 24.22\n", "Coordinates:\n", " * lat (lat) float32 100B [°] 75.0 72.5 70.0 67.5 ... 22.5 20.0 17.5 15.0\n", " * lon (lon) float32 212B [°] 200.0 202.5 205.0 ... 325.0 327.5 330.0\n", @@ -1503,16 +1503,18 @@ " actual_range: [185.16 322.1 ]" ] }, - "execution_count": 36, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "monthly_means = quantified.astropy\\\n", - " .sel(time=\"2013\")\\\n", - " .groupby(\"time.month\")\\\n", + "monthly_means = (\n", + " quantified.astropy.to(\"deg_C\", equivalencies=u.temperature())\n", + " .sel(time=\"2013\")\n", + " .groupby(\"time.month\")\n", " .mean()\n", + ")\n", "monthly_means" ] }, @@ -1526,7 +1528,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 7, "id": "10", "metadata": {}, "outputs": [ @@ -1919,9 +1921,9 @@ " level_desc: Surface\n", " statistic: Individual Obs\n", " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]
    • lat
      ()
      float32
      [°] np.float32(72.5)
      standard_name :
      latitude
      long_name :
      Latitude
      axis :
      Y
      <Quantity 72.5 deg>
    • lon
      ()
      float32
      [°] np.float32(200.0)
      standard_name :
      longitude
      long_name :
      Longitude
      axis :
      X
      <Quantity 200. deg>
    • month
      (month)
      int64
      1 2 3 4 5 6 7 8 9 10 11 12
      array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])
    • month
      PandasIndex
      PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • " ], "text/plain": [ " Size: 96B\n", @@ -1943,7 +1945,7 @@ " actual_range: [185.16 322.1 ]" ] }, - "execution_count": 39, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1967,23 +1969,23 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 8, "id": "12", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 42, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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    " ] @@ -2006,6 +2008,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -2015,7 +2022,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.12.1" } }, "nbformat": 4, diff --git a/docs/whats-new.rst b/docs/whats-new.rst index 2bdbb268..7e5f8e8a 100644 --- a/docs/whats-new.rst +++ b/docs/whats-new.rst @@ -4,6 +4,9 @@ What's new ========== 0.1 (*unreleased*) ------------------ + +- Added `equivalencies` parameter to :py:meth:`DataArray.astropy.to` and :py:meth:`Dataset.astropy.to`. +- Removed `registry` parameter from :py:meth:`DataArray.astropy.quantify` and :py:meth:`Dataset.astropy.quantify`. - Migrated ``pint.Quantity`` usage to :py:class:`astropy.units.Quantity`, ``pint.UnitRegistry`` to :py:mod:`astropy.units`, ``pint.UnitRegistry.formatter`` to :py:mod:`astropy.units.format` (:pull:`1`) Notable behavioural differences include: