def test_encoding_kwarg(self):
ds = Dataset({'x': ('y', np.arange(10.0))})
kwargs = dict(encoding={'x': {'dtype': 'f4'}})
with self.roundtrip(ds, save_kwargs=kwargs) as actual:
self.assertEqual(actual.x.encoding['dtype'], 'f4')
self.assertEqual(ds.x.encoding, {})
kwargs = dict(encoding={'x': {'foo': 'bar'}})
with self.assertRaisesRegexp(ValueError, 'unexpected encoding'):
with self.roundtrip(ds, save_kwargs=kwargs) as actual:
pass
kwargs = dict(encoding={'x': 'foo'})
with self.assertRaisesRegexp(ValueError, 'must be castable'):
with self.roundtrip(ds, save_kwargs=kwargs) as actual:
pass
kwargs = dict(encoding={'invalid': {}})
with self.assertRaises(KeyError):
with self.roundtrip(ds, save_kwargs=kwargs) as actual:
pass
ds = Dataset({'t': pd.date_range('2000-01-01', periods=3)})
units = 'days since 1900-01-01'
kwargs = dict(encoding={'t': {'units': units}})
with self.roundtrip(ds, save_kwargs=kwargs) as actual:
self.assertEqual(actual.t.encoding['units'], units)
self.assertDatasetIdentical(actual, ds)
def test_roundtrip_strings_with_fill_value(self):
values = np.array(['ab', 'cdef', np.nan], dtype=object)
encoding = {'_FillValue': np.string_('X'), 'dtype': np.dtype('S1')}
original = Dataset({'x': ('t', values, {}, encoding)})
expected = original.copy(deep=True)
expected['x'][:2] = values[:2].astype('S')
with self.roundtrip(original) as actual:
self.assertDatasetIdentical(expected, actual)
original = Dataset({'x': ('t', values, {}, {'_FillValue': '\x00'})})
if not isinstance(self, Only32BitTypes):
# these stores can save unicode strings
expected = original.copy(deep=True)
if isinstance(self, BaseNetCDF4Test):
# netCDF4 can't keep track of an empty _FillValue for VLEN
# variables
expected['x'][-1] = ''
elif (isinstance(
self,
(NetCDF3ViaNetCDF4DataTest, NetCDF4ClassicViaNetCDF4DataTest))
or (has_netCDF4 and type(self) is GenericNetCDFDataTest)):
# netCDF4 can't keep track of an empty _FillValue for nc3, either:
# https://github.com/Unidata/netcdf4-python/issues/273
expected['x'][-1] = np.string_('')
with self.roundtrip(original) as actual:
self.assertDatasetIdentical(expected, actual)
def test_to_and_from_dataframe(self):
x = np.random.randn(10)
y = np.random.randn(10)
t = list('abcdefghij')
ds = Dataset(
OrderedDict([
('a', ('t', x)),
('b', ('t', y)),
('t', ('t', t)),
]))
expected = pd.DataFrame(np.array([x, y]).T,
columns=['a', 'b'],
index=pd.Index(t, name='t'))
actual = ds.to_dataframe()
# use the .equals method to check all DataFrame metadata
assert expected.equals(actual), (expected, actual)
# check roundtrip
self.assertDatasetIdentical(ds, Dataset.from_dataframe(actual))
# test a case with a MultiIndex
w = np.random.randn(2, 3)
ds = Dataset({'w': (('x', 'y'), w)})
ds['y'] = ('y', list('abc'))
exp_index = pd.MultiIndex.from_arrays(
[[0, 0, 0, 1, 1, 1], ['a', 'b', 'c', 'a', 'b', 'c']],
names=['x', 'y'])
expected = pd.DataFrame(w.reshape(-1), columns=['w'], index=exp_index)
actual = ds.to_dataframe()
self.assertTrue(expected.equals(actual))
# check roundtrip
self.assertDatasetIdentical(ds, Dataset.from_dataframe(actual))
def test_coordinate(self):
a = Dataset()
vec = np.random.random((10, ))
attributes = {'foo': 'bar'}
a['x'] = ('x', vec, attributes)
self.assertTrue('x' in a.coordinates)
self.assertIsInstance(a.coordinates['x'].as_index, pd.Index)
self.assertVariableEqual(a.coordinates['x'], a.variables['x'])
b = Dataset()
b['x'] = ('x', vec, attributes)
self.assertVariableEqual(a['x'], b['x'])
self.assertEqual(a.dimensions, b.dimensions)
# this should work
a['x'] = ('x', vec[:5])
a['z'] = ('x', np.arange(5))
with self.assertRaises(ValueError):
# now it shouldn't, since there is a conflicting length
a['x'] = ('x', vec[:4])
arr = np.random.random((
10,
1,
))
scal = np.array(0)
with self.assertRaises(ValueError):
a['y'] = ('y', arr)
with self.assertRaises(ValueError):
a['y'] = ('y', scal)
self.assertTrue('y' not in a.dimensions)
def test_groupby_sum(self):
array = self.make_groupby_example_array()
grouped = array.groupby('abc')
expected_sum_all = Dataset(
{'foo': Variable(['abc'], np.array([self.x[:, :9].sum(),
self.x[:, 10:].sum(),
self.x[:, 9:10].sum()]).T),
'abc': Variable(['abc'], np.array(['a', 'b', 'c']))})['foo']
self.assertDataArrayAllClose(expected_sum_all, grouped.reduce(np.sum))
self.assertDataArrayAllClose(expected_sum_all, grouped.sum())
expected = DataArray([array['y'].values[idx].sum() for idx
in [slice(9), slice(10, None), slice(9, 10)]],
[['a', 'b', 'c']], ['abc'])
actual = array['y'].groupby('abc').apply(np.sum)
self.assertDataArrayAllClose(expected, actual)
actual = array['y'].groupby('abc').sum()
self.assertDataArrayAllClose(expected, actual)
expected_sum_axis1 = Dataset(
{'foo': (['x', 'abc'], np.array([self.x[:, :9].sum(1),
self.x[:, 10:].sum(1),
self.x[:, 9:10].sum(1)]).T),
'x': self.ds['x'],
'abc': Variable(['abc'], np.array(['a', 'b', 'c']))})['foo']
self.assertDataArrayAllClose(expected_sum_axis1,
grouped.reduce(np.sum, 'y'))
self.assertDataArrayAllClose(expected_sum_axis1, grouped.sum('y'))
def test_decode_cf_with_drop_variables(self):
original = Dataset({
't': ('t', [0, 1, 2], {
'units': 'days since 2000-01-01'
}),
'x': ("x", [9, 8, 7], {
'units': 'km'
}),
'foo': (('t', 'x'), [[0, 0, 0], [1, 1, 1], [2, 2, 2]], {
'units': 'bar'
}),
'y': ('t', [5, 10, -999], {
'_FillValue': -999
})
})
expected = Dataset({
't':
pd.date_range('2000-01-01', periods=3),
'x': ("x", [0, 1, 2]),
'foo': (('t', 'x'), [[0, 0, 0], [1, 1, 1], [2, 2, 2]], {
'units': 'bar'
}),
'y': ('t', [5, 10, np.nan])
})
actual = conventions.decode_cf(original, drop_variables=("x", ))
actual2 = conventions.decode_cf(original, drop_variables="x")
self.assertDatasetIdentical(expected, actual)
self.assertDatasetIdentical(expected, actual2)
def test_pipe(self):
df = Dataset({'A': ('x', [1, 2, 3])})
f = lambda x, y: x**y
result = df.pipe(f, 2)
expected = Dataset({'A': ('x', [1, 4, 9])})
self.assertDatasetIdentical(result, expected)
result = df.A.pipe(f, 2)
self.assertDataArrayIdentical(result, expected.A)
def test_concat_constant_index(self):
# GH425
ds1 = Dataset({'foo': 1.5}, {'y': 1})
ds2 = Dataset({'foo': 2.5}, {'y': 1})
expected = Dataset({'foo': ('y', [1.5, 2.5]), 'y': [1, 1]})
for mode in ['different', 'all', ['foo']]:
actual = concat([ds1, ds2], 'y', data_vars=mode)
self.assertDatasetIdentical(expected, actual)
with self.assertRaisesRegexp(ValueError, 'not equal across datasets'):
concat([ds1, ds2], 'y', data_vars='minimal')
def test_dataset_repr_with_netcdf4_datetimes(self):
# regression test for #347
attrs = {'units': 'days since 0001-01-01', 'calendar': 'noleap'}
with warnings.catch_warnings():
warnings.filterwarnings('ignore', 'unable to decode time')
ds = decode_cf(Dataset({'time': ('time', [0, 1], attrs)}))
self.assertIn('(time) object', repr(ds))
attrs = {'units': 'days since 1900-01-01'}
ds = decode_cf(Dataset({'time': ('time', [0, 1], attrs)}))
self.assertIn('(time) datetime64[ns]', repr(ds))
def test_dataset(self):
original = Dataset({
't': ('t', [0, 1, 2], {'units': 'days since 2000-01-01'}),
'foo': ('t', [0, 0, 0], {'coordinates': 'y', 'units': 'bar'}),
'y': ('t', [5, 10, -999], {'_FillValue': -999})
})
expected = Dataset({'foo': ('t', [0, 0, 0], {'units': 'bar'})},
{'t': pd.date_range('2000-01-01', periods=3),
'y': ('t', [5.0, 10.0, np.nan])})
actual = conventions.decode_cf(original)
self.assertDatasetIdentical(expected, actual)
def test_init(self):
var1 = Variable('x', 2 * np.arange(100))
var2 = Variable('x', np.arange(1000))
var3 = Variable(['x', 'y'], np.arange(1000).reshape(100, 10))
with self.assertRaisesRegexp(ValueError, 'but already exists'):
Dataset({'a': var1, 'b': var2})
with self.assertRaisesRegexp(ValueError, 'must be defined with 1-d'):
Dataset({'a': var1, 'x': var3})
# verify handling of DataArrays
expected = Dataset({'x': var1, 'z': var3})
actual = Dataset({'z': expected['z']})
self.assertDatasetIdentical(expected, actual)
def test_reset_coords(self):
data = DataArray(np.zeros((3, 4)), {
'bar': ('x', ['a', 'b', 'c']),
'baz': ('y', range(4))
},
dims=['x', 'y'],
name='foo')
actual = data.reset_coords()
expected = Dataset({
'foo': (['x', 'y'], np.zeros((3, 4))),
'bar': ('x', ['a', 'b', 'c']),
'baz': ('y', range(4))
})
self.assertDatasetIdentical(actual, expected)
actual = data.reset_coords(['bar', 'baz'])
self.assertDatasetIdentical(actual, expected)
actual = data.reset_coords('bar')
expected = Dataset(
{
'foo': (['x', 'y'], np.zeros((3, 4))),
'bar': ('x', ['a', 'b', 'c'])
}, {'baz': ('y', range(4))})
self.assertDatasetIdentical(actual, expected)
actual = data.reset_coords(['bar'])
self.assertDatasetIdentical(actual, expected)
actual = data.reset_coords(drop=True)
expected = DataArray(np.zeros((3, 4)), dims=['x', 'y'], name='foo')
self.assertDataArrayIdentical(actual, expected)
actual = data.copy()
actual.reset_coords(drop=True, inplace=True)
self.assertDataArrayIdentical(actual, expected)
actual = data.reset_coords('bar', drop=True)
expected = DataArray(np.zeros((3, 4)), {'baz': ('y', range(4))},
dims=['x', 'y'],
name='foo')
self.assertDataArrayIdentical(actual, expected)
with self.assertRaisesRegexp(ValueError, 'cannot reset coord'):
data.reset_coords(inplace=True)
with self.assertRaises(KeyError):
data.reset_coords('foo', drop=True)
with self.assertRaisesRegexp(ValueError, 'cannot be found'):
data.reset_coords('not_found')
with self.assertRaisesRegexp(ValueError, 'cannot remove index'):
data.reset_coords('y')
def test_dataset_math(self):
# more comprehensive tests with multiple dataset variables
obs = Dataset(
{
'tmin': ('x', np.arange(5)),
'tmax': ('x', 10 + np.arange(5))
}, {
'x': ('x', 0.5 * np.arange(5)),
'loc': ('x', range(-2, 3))
})
actual = 2 * obs['tmax']
expected = DataArray(2 * (10 + np.arange(5)), obs.coords, name='tmax')
self.assertDataArrayIdentical(actual, expected)
actual = obs['tmax'] - obs['tmin']
expected = DataArray(10 * np.ones(5), obs.coords)
self.assertDataArrayIdentical(actual, expected)
sim = Dataset({
'tmin': ('x', 1 + np.arange(5)),
'tmax': ('x', 11 + np.arange(5)),
# does *not* include 'loc' as a coordinate
'x': ('x', 0.5 * np.arange(5))
})
actual = sim['tmin'] - obs['tmin']
expected = DataArray(np.ones(5), obs.coords, name='tmin')
self.assertDataArrayIdentical(actual, expected)
actual = -obs['tmin'] + sim['tmin']
self.assertDataArrayIdentical(actual, expected)
actual = sim['tmin'].copy()
actual -= obs['tmin']
self.assertDataArrayIdentical(actual, expected)
actual = sim.copy()
actual['tmin'] = sim['tmin'] - obs['tmin']
expected = Dataset(
{
'tmin': ('x', np.ones(5)),
'tmax': ('x', sim['tmax'].values)
}, obs.coords)
self.assertDatasetIdentical(actual, expected)
actual = sim.copy()
actual['tmin'] -= obs['tmin']
self.assertDatasetIdentical(actual, expected)
def test_squeeze(self):
data = Dataset({'foo': (['x', 'y', 'z'], [[[1], [2]]])})
for args in [[], [['x']], [['x', 'z']]]:
def get_args(v):
return [set(args[0]) & set(v.dimensions)] if args else []
expected = Dataset({
k: v.squeeze(*get_args(v))
for k, v in iteritems(data.variables)
})
self.assertDatasetIdentical(expected, data.squeeze(*args))
# invalid squeeze
with self.assertRaisesRegexp(ValueError, 'cannot select a dimension'):
data.squeeze('y')
def test_unselect(self):
data = create_test_data()
self.assertEqual(data, data.unselect())
expected = Dataset({k: data[k] for k in data if k != 'time'})
actual = data.unselect('time')
self.assertEqual(expected, actual)
expected = Dataset({k: data[k] for k in ['dim2', 'dim3', 'time']})
actual = data.unselect('dim1')
self.assertEqual(expected, actual)
with self.assertRaisesRegexp(ValueError, 'does not exist in this'):
data.unselect('not_found_here')
def test_concat_errors(self):
data = create_test_data()
split_data = [data.isel(dim1=slice(3)), data.isel(dim1=slice(3, None))]
with self.assertRaisesRegexp(ValueError, 'must supply at least one'):
concat([], 'dim1')
with self.assertRaisesRegexp(ValueError, 'are not coordinates'):
concat([data, data], 'new_dim', coords=['not_found'])
with self.assertRaisesRegexp(ValueError, 'global attributes not'):
data0, data1 = deepcopy(split_data)
data1.attrs['foo'] = 'bar'
concat([data0, data1], 'dim1', compat='identical')
self.assertDatasetIdentical(
data, concat([data0, data1], 'dim1', compat='equals'))
with self.assertRaisesRegexp(ValueError, 'encountered unexpected'):
data0, data1 = deepcopy(split_data)
data1['foo'] = ('bar', np.random.randn(10))
concat([data0, data1], 'dim1')
with self.assertRaisesRegexp(ValueError, 'not equal across datasets'):
data0, data1 = deepcopy(split_data)
data1['dim2'] = 2 * data1['dim2']
concat([data0, data1], 'dim1', coords='minimal')
with self.assertRaisesRegexp(ValueError, 'it is not 1-dimensional'):
concat([data0, data1], 'dim1')
with self.assertRaisesRegexp(ValueError, 'compat.* invalid'):
concat(split_data, 'dim1', compat='foobar')
with self.assertRaisesRegexp(ValueError, 'unexpected value for'):
concat([data, data], 'new_dim', coords='foobar')
with self.assertRaisesRegexp(
ValueError, 'coordinate in some datasets but not others'):
concat([Dataset({'x': 0}), Dataset({'x': [1]})], dim='z')
with self.assertRaisesRegexp(
ValueError, 'coordinate in some datasets but not others'):
concat([Dataset({'x': 0}), Dataset({}, {'x': 1})], dim='z')
with self.assertRaisesRegexp(ValueError, 'no longer a valid'):
concat([data, data], 'new_dim', mode='different')
with self.assertRaisesRegexp(ValueError, 'no longer a valid'):
concat([data, data], 'new_dim', concat_over='different')
def test_repr(self):
data = create_test_data()
expected = dedent("""
<xray.Dataset>
Dimensions: (dim1: 100, dim2: 50, dim3: 10, time: 20)
Coordinates:
dim1 X
dim2 X
dim3 X
time X
Noncoordinates:
var1 0 1
var2 0 1
var3 1 0
Attributes:
Empty
""").strip()
actual = '\n'.join(x.rstrip() for x in repr(data).split('\n'))
self.assertEqual(expected, actual)
expected = dedent("""
<xray.Dataset>
Dimensions: ()
Coordinates:
None
Noncoordinates:
None
Attributes:
Empty
""").strip()
actual = '\n'.join(x.rstrip() for x in repr(Dataset()).split('\n'))
self.assertEqual(expected, actual)
def rectify_dim_order(dataset):
# return a new dataset with all variable dimensions tranposed into
# the order in which they are found in `data`
return Dataset(dict((k, v.transpose(*data[k].dims))
for k, v in iteritems(dataset.data_vars)),
dataset.coords,
attrs=dataset.attrs)
def test_groupby(self):
data = Dataset({
'x': ('x', list('abc')),
'c': ('x', [0, 1, 0]),
'z': (['x', 'y'], np.random.randn(3, 5))
})
groupby = data.groupby('x')
self.assertEqual(len(groupby), 3)
expected_groups = {'a': 0, 'b': 1, 'c': 2}
self.assertEqual(groupby.groups, expected_groups)
expected_items = [('a', data.indexed(x=0)), ('b', data.indexed(x=1)),
('c', data.indexed(x=2))]
self.assertEqual(list(groupby), expected_items)
identity = lambda x: x
for k in ['x', 'c', 'y']:
actual = data.groupby(k, squeeze=False).apply(identity)
self.assertEqual(data, actual)
data = create_test_data()
for n, (t, sub) in enumerate(list(data.groupby('dim1'))[:3]):
self.assertEqual(data['dim1'][n], t)
self.assertVariableEqual(data['var1'][n], sub['var1'])
self.assertVariableEqual(data['var2'][n], sub['var2'])
self.assertVariableEqual(data['var3'][:, n], sub['var3'])
# TODO: test the other edge cases
with self.assertRaisesRegexp(ValueError, 'must be 1 dimensional'):
data.groupby('var1')
with self.assertRaisesRegexp(ValueError, 'length does not match'):
data.groupby(data['dim1'][:3])
def test_concat(self):
ds = Dataset({
'foo': (['x', 'y'], np.random.random((10, 20))),
'bar': (['x', 'y'], np.random.random((10, 20)))
})
foo = ds['foo']
bar = ds['bar']
# from dataset array:
expected = DataArray(np.array([foo.values, bar.values]),
dims=['w', 'x', 'y'])
actual = concat([foo, bar], 'w')
self.assertDataArrayEqual(expected, actual)
# from iteration:
grouped = [g for _, g in foo.groupby('x')]
stacked = concat(grouped, ds['x'])
self.assertDataArrayIdentical(foo, stacked)
# with an index as the 'dim' argument
stacked = concat(grouped, ds.indexes['x'])
self.assertDataArrayIdentical(foo, stacked)
actual = concat([foo[0], foo[1]],
pd.Index([0, 1])).reset_coords(drop=True)
expected = foo[:2].rename({'x': 'concat_dim'})
self.assertDataArrayIdentical(expected, actual)
actual = concat([foo[0], foo[1]], [0, 1]).reset_coords(drop=True)
expected = foo[:2].rename({'x': 'concat_dim'})
self.assertDataArrayIdentical(expected, actual)
with self.assertRaisesRegexp(ValueError, 'not identical'):
concat([foo, bar], dim='w', compat='identical')
with self.assertRaisesRegexp(ValueError, 'not a valid argument'):
concat([foo, bar], dim='w', data_vars='minimal')
def test_roundtrip_coordinates(self):
original = Dataset({'foo': ('x', [0, 1])}, {
'x': [2, 3],
'y': ('a', [42]),
'z': ('x', [4, 5])
})
with self.roundtrip(original) as actual:
self.assertDatasetIdentical(original, actual)
expected = original.drop('foo')
with self.roundtrip(expected) as actual:
self.assertDatasetIdentical(expected, actual)
expected = original.copy()
expected.attrs['coordinates'] = 'something random'
with self.assertRaisesRegexp(ValueError, 'cannot serialize'):
with self.roundtrip(expected):
pass
expected = original.copy(deep=True)
expected['foo'].attrs['coordinates'] = 'something random'
with self.assertRaisesRegexp(ValueError, 'cannot serialize'):
with self.roundtrip(expected):
pass
def test_open_and_do_math(self):
original = Dataset({'foo': ('x', np.random.randn(10))})
with create_tmp_file() as tmp:
original.to_netcdf(tmp)
with open_mfdataset(tmp) as ds:
actual = 1.0 * ds
self.assertDatasetAllClose(original, actual)
def test_read_byte_attrs_as_unicode(self):
with create_tmp_file() as tmp_file:
with nc4.Dataset(tmp_file, 'w') as nc:
nc.foo = b'bar'
actual = open_dataset(tmp_file)
expected = Dataset(attrs={'foo': 'bar'})
self.assertDatasetIdentical(expected, actual)
def create_encoded_masked_and_scaled_data():
attributes = {
'_FillValue': -1,
'add_offset': 10,
'scale_factor': np.float32(0.1)
}
return Dataset({'x': ('t', [-1, -1, 0, 1, 2], attributes)})
def test_open_encodings(self):
# Create a netCDF file with explicit time units
# and make sure it makes it into the encodings
# and survives a round trip
with create_tmp_file() as tmp_file:
with nc4.Dataset(tmp_file, 'w') as ds:
ds.createDimension('time', size=10)
ds.createVariable('time', np.int32, dimensions=('time', ))
units = 'days since 1999-01-01'
ds.variables['time'].setncattr('units', units)
ds.variables['time'][:] = np.arange(10) + 4
expected = Dataset()
time = pd.date_range('1999-01-05', periods=10)
encoding = {'units': units, 'dtype': np.dtype('int32')}
expected['time'] = ('time', time, {}, encoding)
with open_dataset(tmp_file) as actual:
self.assertVariableEqual(actual['time'], expected['time'])
actual_encoding = dict(
(k, v) for k, v in iteritems(actual['time'].encoding)
if k in expected['time'].encoding)
self.assertDictEqual(actual_encoding,
expected['time'].encoding)
def test_as_variable(self):
data = np.arange(10)
expected = Variable('x', data)
self.assertVariableIdentical(expected, as_variable(expected))
ds = Dataset({'x': expected})
self.assertVariableIdentical(expected, as_variable(ds['x']))
self.assertNotIsInstance(ds['x'], Variable)
self.assertIsInstance(as_variable(ds['x']), Variable)
self.assertIsInstance(as_variable(ds['x'], strict=False), DataArray)
FakeVariable = namedtuple('FakeVariable', 'values dims')
fake_xarray = FakeVariable(expected.values, expected.dims)
self.assertVariableIdentical(expected, as_variable(fake_xarray))
xarray_tuple = (expected.dims, expected.values)
self.assertVariableIdentical(expected, as_variable(xarray_tuple))
with self.assertRaisesRegexp(TypeError, 'cannot convert arg'):
as_variable(tuple(data))
with self.assertRaisesRegexp(TypeError, 'cannot infer .+ dimensions'):
as_variable(data)
actual = as_variable(data, key='x')
self.assertVariableIdentical(expected, actual)
actual = as_variable(0)
expected = Variable([], 0)
self.assertVariableIdentical(expected, actual)
def setUp(self):
self.attrs = {'attr1': 'value1', 'attr2': 2929}
self.x = np.random.random((10, 20))
self.v = Variable(['x', 'y'], self.x)
self.va = Variable(['x', 'y'], self.x, self.attrs)
self.ds = Dataset({'foo': self.v})
self.dv = self.ds['foo']
def test_remap_label_indexers(self):
# TODO: fill in more tests!
data = Dataset({'x': ('x', [1, 2, 3])})
test_indexer = lambda x: indexing.remap_label_indexers(data, {'x': x})
self.assertEqual({'x': 0}, test_indexer(1))
self.assertEqual({'x': 0}, test_indexer(np.int32(1)))
self.assertEqual({'x': 0}, test_indexer(Variable([], 1)))
def test_coordinates_encoding(self):
def equals_latlon(obj):
return obj == 'lat lon' or obj == 'lon lat'
original = Dataset({
'temp': ('x', [0, 1]),
'precip': ('x', [0, -1])
}, {
'lat': ('x', [2, 3]),
'lon': ('x', [4, 5])
})
with self.roundtrip(original) as actual:
self.assertDatasetIdentical(actual, original)
with create_tmp_file() as tmp_file:
original.to_netcdf(tmp_file)
with open_dataset(tmp_file, decode_coords=False) as ds:
self.assertTrue(equals_latlon(ds['temp'].attrs['coordinates']))
self.assertTrue(
equals_latlon(ds['precip'].attrs['coordinates']))
self.assertNotIn('coordinates', ds.attrs)
self.assertNotIn('coordinates', ds['lat'].attrs)
self.assertNotIn('coordinates', ds['lon'].attrs)
modified = original.drop(['temp', 'precip'])
with self.roundtrip(modified) as actual:
self.assertDatasetIdentical(actual, modified)
with create_tmp_file() as tmp_file:
modified.to_netcdf(tmp_file)
with open_dataset(tmp_file, decode_coords=False) as ds:
self.assertTrue(equals_latlon(ds.attrs['coordinates']))
self.assertNotIn('coordinates', ds['lat'].attrs)
self.assertNotIn('coordinates', ds['lon'].attrs)
def test_roundtrip_object_dtype(self):
floats = np.array([0.0, 0.0, 1.0, 2.0, 3.0], dtype=object)
floats_nans = np.array([np.nan, np.nan, 1.0, 2.0, 3.0], dtype=object)
letters = np.array(['ab', 'cdef', 'g'], dtype=object)
letters_nans = np.array(['ab', 'cdef', np.nan], dtype=object)
all_nans = np.array([np.nan, np.nan], dtype=object)
original = Dataset({
'floats': ('a', floats),
'floats_nans': ('a', floats_nans),
'letters': ('b', letters),
'letters_nans': ('b', letters_nans),
'all_nans': ('c', all_nans),
'nan': ([], np.nan)
})
expected = original.copy(deep=True)
if isinstance(self, Only32BitTypes):
# for netCDF3 tests, expect the results to come back as characters
expected['letters_nans'] = expected['letters_nans'].astype('S')
expected['letters'] = expected['letters'].astype('S')
with self.roundtrip(original) as actual:
try:
self.assertDatasetIdentical(expected, actual)
except AssertionError:
# Most stores use '' for nans in strings, but some don't
# first try the ideal case (where the store returns exactly)
# the original Dataset), then try a more realistic case.
# ScipyDataTest, NetCDF3ViaNetCDF4DataTest and NetCDF4DataTest
# all end up using this case.
expected['letters_nans'][-1] = ''
self.assertDatasetIdentical(expected, actual)
