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_items(self):
data = np.random.random((10, 11))
v = Variable(['x', 'y'], data)
# test slicing
self.assertVariableIdentical(v, v[:])
self.assertVariableIdentical(v, v[...])
self.assertVariableIdentical(Variable(['y'], data[0]), v[0])
self.assertVariableIdentical(Variable(['x'], data[:, 0]), v[:, 0])
self.assertVariableIdentical(Variable(['x', 'y'], data[:3, :2]),
v[:3, :2])
# test array indexing
x = Variable(['x'], np.arange(10))
y = Variable(['y'], np.arange(11))
self.assertVariableIdentical(v, v[x.values])
self.assertVariableIdentical(v, v[x])
self.assertVariableIdentical(v[:3], v[x < 3])
self.assertVariableIdentical(v[:, 3:], v[:, y >= 3])
self.assertVariableIdentical(v[:3, 3:], v[x < 3, y >= 3])
self.assertVariableIdentical(v[:3, :2], v[x[:3], y[:2]])
self.assertVariableIdentical(v[:3, :2], v[range(3), range(2)])
# test iteration
for n, item in enumerate(v):
self.assertVariableIdentical(Variable(['y'], data[n]), item)
with self.assertRaisesRegexp(TypeError, 'iteration over a 0-d'):
iter(Variable([], 0))
# test setting
v.values[:] = 0
self.assertTrue(np.all(v.values == 0))
# test orthogonal setting
v[range(10), range(11)] = 1
self.assertArrayEqual(v.values, np.ones((10, 11)))
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_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 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_numpy_same_methods(self):
v = Variable([], np.float32(0.0))
self.assertEqual(v.item(), 0)
self.assertIs(type(v.item()), float)
v = Coordinate('x', np.arange(5))
self.assertEqual(2, v.searchsorted(2))
def test_items(self):
data = np.random.random((10, 11))
v = Variable(['x', 'y'], data)
# test slicing
self.assertVariableIdentical(v, v[:])
self.assertVariableIdentical(v, v[...])
self.assertVariableIdentical(Variable(['y'], data[0]), v[0])
self.assertVariableIdentical(Variable(['x'], data[:, 0]), v[:, 0])
self.assertVariableIdentical(Variable(['x', 'y'], data[:3, :2]),
v[:3, :2])
# test array indexing
x = Variable(['x'], np.arange(10))
y = Variable(['y'], np.arange(11))
self.assertVariableIdentical(v, v[x.values])
self.assertVariableIdentical(v, v[x])
self.assertVariableIdentical(v[:3], v[x < 3])
self.assertVariableIdentical(v[:, 3:], v[:, y >= 3])
self.assertVariableIdentical(v[:3, 3:], v[x < 3, y >= 3])
self.assertVariableIdentical(v[:3, :2], v[x[:3], y[:2]])
self.assertVariableIdentical(v[:3, :2], v[range(3), range(2)])
# test iteration
for n, item in enumerate(v):
self.assertVariableIdentical(Variable(['y'], data[n]), item)
with self.assertRaisesRegexp(TypeError, 'iteration over a 0-d'):
iter(Variable([], 0))
# test setting
v.values[:] = 0
self.assertTrue(np.all(v.values == 0))
# test orthogonal setting
v[range(10), range(11)] = 1
self.assertArrayEqual(v.values, np.ones((10, 11)))
def test_concat(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u, Variable.concat([v[:2], v[2:]], 'x'))
self.assertLazyAndIdentical(u[:2], Variable.concat([v[0], v[1]], 'x'))
self.assertLazyAndIdentical(
u[:3], Variable.concat([v[[0, 2]], v[[1]]], 'x', positions=[[0, 2], [1]]))
def test_index_0d_numpy_string(self):
# regression test to verify our work around for indexing 0d strings
v = Variable([], np.string_('asdf'))
self.assertVariableIdentical(v[()], v)
v = Variable([], np.unicode_(u'asdf'))
self.assertVariableIdentical(v[()], v)
def test_roll_consistency(self):
v = Variable(("x", "y"), np.random.randn(5, 6))
for axis, dim in [(0, "x"), (1, "y")]:
for shift in [-3, 0, 1, 7, 11]:
expected = np.roll(v.values, shift, axis=axis)
actual = v.roll(**{dim: shift}).values
self.assertArrayEqual(expected, actual)
def test_roll_consistency(self):
v = Variable(('x', 'y'), np.random.randn(5, 6))
for axis, dim in [(0, 'x'), (1, 'y')]:
for shift in [-3, 0, 1, 7, 11]:
expected = np.roll(v.values, shift, axis=axis)
actual = v.roll(**{dim: shift}).values
self.assertArrayEqual(expected, actual)
def test_0d_str(self):
v = Variable([], u'foo')
self.assertEqual(v.dtype, np.dtype('U3'))
self.assertEqual(v.values, 'foo')
v = Variable([], np.string_('foo'))
self.assertEqual(v.dtype, np.dtype('S3'))
self.assertEqual(v.values, bytes('foo', 'ascii') if PY3 else 'foo')
def test_decode_cf_with_multiple_missing_values(self):
original = Variable(['t'], [0, 1, 2],
{'missing_value': np.array([0, 1])})
expected = Variable(['t'], [np.nan, np.nan, 2], {})
with warnings.catch_warnings(record=True) as w:
actual = conventions.decode_cf_variable(original)
self.assertDatasetIdentical(expected, actual)
self.assertIn('variable has multiple fill', str(w[0].message))
def test_broadcasting_failures(self):
a = Variable(['x'], np.arange(10))
b = Variable(['x'], np.arange(5))
c = Variable(['x', 'x'], np.arange(100).reshape(10, 10))
with self.assertRaisesRegexp(ValueError, 'mismatched lengths'):
a + b
with self.assertRaisesRegexp(ValueError, 'duplicate dimensions'):
a + c
def test_concat_number_strings(self):
# regression test for #305
a = self.cls('x', ['0', '1', '2'])
b = self.cls('x', ['3', '4'])
actual = Variable.concat([a, b], dim='x')
expected = Variable('x', np.arange(5).astype(str).astype(object))
self.assertVariableIdentical(expected, actual)
self.assertEqual(expected.dtype, object)
self.assertEqual(type(expected.values[0]), str)
def test_concat_fixed_len_str(self):
# regression test for #217
for kind in ['S', 'U']:
x = self.cls('animal', np.array(['horse'], dtype=kind))
y = self.cls('animal', np.array(['aardvark'], dtype=kind))
actual = Variable.concat([x, y], 'animal')
expected = Variable('animal',
np.array(['horse', 'aardvark'], dtype=kind))
self.assertVariableEqual(expected, actual)
def test_item_math(self):
self.ds['x'] = ('x', np.array(list('abcdefghij')))
self.assertVariableEqual(self.dv + self.dv[0, 0],
self.dv + self.dv[0, 0].values)
new_data = self.x[0][None, :] + self.x[:, 0][:, None]
self.assertVariableEqual(self.dv[:, 0] + self.dv[0],
Variable(['x', 'y'], new_data))
self.assertVariableEqual(self.dv[0] + self.dv[:, 0],
Variable(['y', 'x'], new_data.T))
def test_data(self):
v = Variable(['time', 'x'], self.d)
self.assertArrayEqual(v.values, self.d)
self.assertIs(source_ndarray(v.values), self.d)
with self.assertRaises(ValueError):
# wrong size
v.values = np.random.random(5)
d2 = np.random.random((10, 3))
v.values = d2
self.assertIs(source_ndarray(v.values), d2)
def test_incompatible_attributes(self):
invalid_vars = [
Variable(['t'], pd.date_range('2000-01-01', periods=3),
{'units': 'foobar'}),
Variable(['t'], [0, 1, 2], {'add_offset': 0}, {'add_offset': 2}),
Variable(['t'], [0, 1, 2], {'_FillValue': 0}, {'_FillValue': 2}),
]
for var in invalid_vars:
with self.assertRaises(ValueError):
conventions.encode_cf_variable(var)
def test_data(self):
v = Variable(['time', 'x'], self.d)
self.assertArrayEqual(v.values, self.d)
self.assertIs(source_ndarray(v.values), self.d)
with self.assertRaises(ValueError):
# wrong size
v.values = np.random.random(5)
d2 = np.random.random((10, 3))
v.values = d2
self.assertIs(source_ndarray(v.values), d2)
def test_broadcasting_math(self):
x = np.random.randn(2, 3)
v = Variable(['a', 'b'], x)
# 1d to 2d broadcasting
self.assertVariableIdentical(
v * v, Variable(['a', 'b'], np.einsum('ab,ab->ab', x, x)))
self.assertVariableIdentical(
v * v[0], Variable(['a', 'b'], np.einsum('ab,b->ab', x, x[0])))
self.assertVariableIdentical(
v[0] * v, Variable(['b', 'a'], np.einsum('b,ab->ba', x[0], x)))
self.assertVariableIdentical(
v[0] * v[:, 0],
Variable(['b', 'a'], np.einsum('b,a->ba', x[0], x[:, 0])))
# higher dim broadcasting
y = np.random.randn(3, 4, 5)
w = Variable(['b', 'c', 'd'], y)
self.assertVariableIdentical(
v * w,
Variable(['a', 'b', 'c', 'd'], np.einsum('ab,bcd->abcd', x, y)))
self.assertVariableIdentical(
w * v,
Variable(['b', 'c', 'd', 'a'], np.einsum('bcd,ab->bcda', y, x)))
self.assertVariableIdentical(
v * w[0],
Variable(['a', 'b', 'c', 'd'], np.einsum('ab,cd->abcd', x, y[0])))
def test_concat_attrs(self):
# different or conflicting attributes should be removed
v = self.cls("a", np.arange(5), {"foo": "bar"})
w = self.cls("a", np.ones(5))
expected = self.cls("a", np.concatenate([np.arange(5), np.ones(5)]))
self.assertVariableIdentical(expected, Variable.concat([v, w], "a"))
w.attrs["foo"] = 2
self.assertVariableIdentical(expected, Variable.concat([v, w], "a"))
w.attrs["foo"] = "bar"
expected.attrs["foo"] = "bar"
self.assertVariableIdentical(expected, Variable.concat([v, w], "a"))
def test_concat_attrs(self):
# different or conflicting attributes should be removed
v = self.cls('a', np.arange(5), {'foo': 'bar'})
w = self.cls('a', np.ones(5))
expected = self.cls('a', np.concatenate([np.arange(5), np.ones(5)]))
self.assertVariableIdentical(expected, Variable.concat([v, w], 'a'))
w.attrs['foo'] = 2
self.assertVariableIdentical(expected, Variable.concat([v, w], 'a'))
w.attrs['foo'] = 'bar'
expected.attrs['foo'] = 'bar'
self.assertVariableIdentical(expected, Variable.concat([v, w], 'a'))
def test_concat_attrs(self):
# different or conflicting attributes should be removed
v = self.cls('a', np.arange(5), {'foo': 'bar'})
w = self.cls('a', np.ones(5))
expected = self.cls('a', np.concatenate([np.arange(5), np.ones(5)]))
self.assertVariableIdentical(expected, Variable.concat([v, w], 'a'))
w.attrs['foo'] = 2
self.assertVariableIdentical(expected, Variable.concat([v, w], 'a'))
w.attrs['foo'] = 'bar'
expected.attrs['foo'] = 'bar'
self.assertVariableIdentical(expected, Variable.concat([v, w], 'a'))
def test_concat(self):
self.ds['bar'] = Variable(['x', 'y'], np.random.randn(10, 20))
foo = self.ds['foo'].select()
bar = self.ds['bar'].rename('foo').select()
# from dataset array:
self.assertVariableEqual(
Variable(['w', 'x', 'y'], np.array([foo.values, bar.values])),
DataArray.concat([foo, bar], 'w'))
# from iteration:
grouped = [g for _, g in foo.groupby('x')]
stacked = DataArray.concat(grouped, self.ds['x'])
self.assertDataArrayIdentical(foo.select(), stacked)
def test_inplace_math(self):
x = np.arange(5)
v = Variable(['x'], x)
v2 = v
v2 += 1
self.assertIs(v, v2)
# since we provided an ndarray for data, it is also modified in-place
self.assertIs(source_ndarray(v.values), x)
self.assertArrayEqual(v.values, np.arange(5) + 1)
with self.assertRaisesRegexp(ValueError, 'dimensions cannot change'):
v += Variable('y', np.arange(5))
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_missing_values(self):
values = np.array([0, 1, np.nan, 3])
data = da.from_array(values, chunks=(2,))
eager_var = Variable('x', values)
lazy_var = Variable('x', data)
self.assertLazyAndIdentical(eager_var, lazy_var.fillna(lazy_var))
self.assertLazyAndIdentical(Variable('x', range(4)), lazy_var.fillna(2))
self.assertLazyAndIdentical(eager_var.count(), lazy_var.count())
def test_transpose(self):
v = Variable(['time', 'x'], self.d)
v2 = Variable(['x', 'time'], self.d.T)
self.assertVariableIdentical(v, v2.transpose())
self.assertVariableIdentical(v.transpose(), v.T)
x = np.random.randn(2, 3, 4, 5)
w = Variable(['a', 'b', 'c', 'd'], x)
w2 = Variable(['d', 'b', 'c', 'a'], np.einsum('abcd->dbca', x))
self.assertEqual(w2.shape, (5, 3, 4, 2))
self.assertVariableIdentical(w2, w.transpose('d', 'b', 'c', 'a'))
self.assertVariableIdentical(w, w2.transpose('a', 'b', 'c', 'd'))
w3 = Variable(['b', 'c', 'd', 'a'], np.einsum('abcd->bcda', x))
self.assertVariableIdentical(w, w3.transpose('a', 'b', 'c', 'd'))
def test_transpose(self):
v = Variable(["time", "x"], self.d)
v2 = Variable(["x", "time"], self.d.T)
self.assertVariableIdentical(v, v2.transpose())
self.assertVariableIdentical(v.transpose(), v.T)
x = np.random.randn(2, 3, 4, 5)
w = Variable(["a", "b", "c", "d"], x)
w2 = Variable(["d", "b", "c", "a"], np.einsum("abcd->dbca", x))
self.assertEqual(w2.shape, (5, 3, 4, 2))
self.assertVariableIdentical(w2, w.transpose("d", "b", "c", "a"))
self.assertVariableIdentical(w, w2.transpose("a", "b", "c", "d"))
w3 = Variable(["b", "c", "d", "a"], np.einsum("abcd->bcda", x))
self.assertVariableIdentical(w, w3.transpose("a", "b", "c", "d"))
def test_reduce_keep_attrs(self):
_attrs = {'units': 'test', 'long_name': 'testing'}
v = Variable(['x', 'y'], self.d, _attrs)
# Test dropped attrs
vm = v.mean()
self.assertEqual(len(vm.attrs), 0)
self.assertEqual(vm.attrs, OrderedDict())
# Test kept attrs
vm = v.mean(keep_attrs=True)
self.assertEqual(len(vm.attrs), len(_attrs))
self.assertEqual(vm.attrs, _attrs)
def test_reduce_keep_attrs(self):
_attrs = {"units": "test", "long_name": "testing"}
v = Variable(["x", "y"], self.d, _attrs)
# Test dropped attrs
vm = v.mean()
self.assertEqual(len(vm.attrs), 0)
self.assertEqual(vm.attrs, OrderedDict())
# Test kept attrs
vm = v.mean(keep_attrs=True)
self.assertEqual(len(vm.attrs), len(_attrs))
self.assertEqual(vm.attrs, _attrs)
def test_reduce_keep_attrs(self):
_attrs = {'units': 'test', 'long_name': 'testing'}
v = Variable(['x', 'y'], self.d, _attrs)
# Test dropped attrs
vm = v.mean()
self.assertEqual(len(vm.attrs), 0)
self.assertEqual(vm.attrs, OrderedDict())
# Test kept attrs
vm = v.mean(keep_attrs=True)
self.assertEqual(len(vm.attrs), len(_attrs))
self.assertEqual(vm.attrs, _attrs)
def test_data_and_values(self):
v = Variable(["time", "x"], self.d)
self.assertArrayEqual(v.data, self.d)
self.assertArrayEqual(v.values, self.d)
self.assertIs(source_ndarray(v.values), self.d)
with self.assertRaises(ValueError):
# wrong size
v.values = np.random.random(5)
d2 = np.random.random((10, 3))
v.values = d2
self.assertIs(source_ndarray(v.values), d2)
d3 = np.random.random((10, 3))
v.data = d3
self.assertIs(source_ndarray(v.data), d3)
def test_isel(self):
v = Variable(['time', 'x'], self.d)
self.assertVariableIdentical(v.isel(time=slice(None)), v)
self.assertVariableIdentical(v.isel(time=0), v[0])
self.assertVariableIdentical(v.isel(time=slice(0, 3)), v[:3])
self.assertVariableIdentical(v.isel(x=0), v[:, 0])
with self.assertRaisesRegexp(ValueError, 'do not exist'):
v.isel(not_a_dim=0)
def test_get_axis_num(self):
v = Variable(['x', 'y', 'z'], np.random.randn(2, 3, 4))
self.assertEqual(v.get_axis_num('x'), 0)
self.assertEqual(v.get_axis_num(['x']), (0, ))
self.assertEqual(v.get_axis_num(['x', 'y']), (0, 1))
self.assertEqual(v.get_axis_num(['z', 'y', 'x']), (2, 1, 0))
with self.assertRaisesRegexp(ValueError, 'not found in array dim'):
v.get_axis_num('foobar')
def test_rename(self):
data = create_test_data()
newnames = {'var1': 'renamed_var1', 'dim2': 'renamed_dim2'}
renamed = data.rename(newnames)
variables = OrderedDict(data.variables)
for k, v in iteritems(newnames):
variables[v] = variables.pop(k)
for k, v in iteritems(variables):
dims = list(v.dimensions)
for name, newname in iteritems(newnames):
if name in dims:
dims[dims.index(name)] = newname
self.assertVariableEqual(Variable(dims, v.values, v.attrs),
renamed.variables[k])
self.assertEqual(v.encoding, renamed.variables[k].encoding)
self.assertEqual(type(v), type(renamed.variables[k]))
self.assertTrue('var1' not in renamed.variables)
self.assertTrue('dim2' not in renamed.variables)
with self.assertRaisesRegexp(ValueError, "cannot rename 'not_a_var'"):
data.rename({'not_a_var': 'nada'})
# verify that we can rename a variable without accessing the data
var1 = data['var1']
data['var1'] = (var1.dimensions, InaccessibleArray(var1.values))
renamed = data.rename(newnames)
with self.assertRaises(UnexpectedDataAccess):
renamed['renamed_var1'].values
def test_broadcast_equals(self):
v1 = Variable((), np.nan)
v2 = Variable(('x'), [np.nan, np.nan])
self.assertTrue(v1.broadcast_equals(v2))
self.assertFalse(v1.equals(v2))
self.assertFalse(v1.identical(v2))
v3 = Variable(('x'), [np.nan])
self.assertTrue(v1.broadcast_equals(v3))
self.assertFalse(v1.equals(v3))
self.assertFalse(v1.identical(v3))
self.assertFalse(v1.broadcast_equals(None))
v4 = Variable(('x'), [np.nan] * 3)
self.assertFalse(v2.broadcast_equals(v4))
def test_1d_reduce(self):
x = np.arange(5)
v = self.cls(['x'], x)
actual = v.sum()
expected = Variable((), 10)
self.assertVariableIdentical(expected, actual)
self.assertIs(type(actual), Variable)
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_count(self):
expected = Variable([], 3)
actual = Variable(["x"], [1, 2, 3, np.nan]).count()
self.assertVariableIdentical(expected, actual)
v = Variable(["x"], np.array(["1", "2", "3", np.nan], dtype=object))
actual = v.count()
self.assertVariableIdentical(expected, actual)
actual = Variable(["x"], [True, False, True]).count()
self.assertVariableIdentical(expected, actual)
self.assertEqual(actual.dtype, int)
expected = Variable(["x"], [2, 3])
actual = Variable(["x", "y"], [[1, 0, np.nan], [1, 1, 1]]).count("y")
self.assertVariableIdentical(expected, actual)
def test_concat(self):
self.ds['bar'] = Variable(['x', 'y'], np.random.randn(10, 20))
foo = self.ds['foo']
bar = self.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, self.ds['x'])
self.assertDataArrayIdentical(foo, stacked)
# with an index as the 'dim' argument
stacked = concat(grouped, self.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], compat='identical')
def test_count(self):
expected = Variable([], 3)
actual = Variable(['x'], [1, 2, 3, np.nan]).count()
self.assertVariableIdentical(expected, actual)
v = Variable(['x'], np.array(['1', '2', '3', np.nan], dtype=object))
actual = v.count()
self.assertVariableIdentical(expected, actual)
actual = Variable(['x'], [True, False, True]).count()
self.assertVariableIdentical(expected, actual)
self.assertEqual(actual.dtype, int)
expected = Variable(['x'], [2, 3])
actual = Variable(['x', 'y'], [[1, 0, np.nan], [1, 1, 1]]).count('y')
self.assertVariableIdentical(expected, actual)
def test_roll(self):
v = Variable("x", [1, 2, 3, 4, 5])
self.assertVariableIdentical(v, v.roll(x=0))
self.assertIsNot(v, v.roll(x=0))
expected = Variable("x", [5, 1, 2, 3, 4])
self.assertVariableIdentical(expected, v.roll(x=1))
self.assertVariableIdentical(expected, v.roll(x=-4))
self.assertVariableIdentical(expected, v.roll(x=6))
expected = Variable("x", [4, 5, 1, 2, 3])
self.assertVariableIdentical(expected, v.roll(x=2))
self.assertVariableIdentical(expected, v.roll(x=-3))
with self.assertRaisesRegexp(ValueError, "dimension"):
v.roll(z=0)
def null_wrap(ds):
"""
Given a data store this wraps each variable in a NullWrapper so that
it appears to be out of memory.
"""
variables = dict((k, Variable(v.dims, NullWrapper(v.values), v.attrs))
for k, v in iteritems(ds))
return InMemoryDataStore(variables=variables, attributes=ds.attrs)
def test_decode_cf_with_conflicting_fill_missing_value(self):
var = Variable(['t'], np.arange(10), {
'units': 'foobar',
'missing_value': 0,
'_FillValue': 1
})
self.assertRaisesRegexp(ValueError, "_FillValue and missing_value",
lambda: conventions.decode_cf_variable(var))
def test_concat_fixed_len_str(self):
# regression test for #217
for kind in ["S", "U"]:
x = self.cls("animal", np.array(["horse"], dtype=kind))
y = self.cls("animal", np.array(["aardvark"], dtype=kind))
actual = Variable.concat([x, y], "animal")
expected = Variable("animal", np.array(["horse", "aardvark"], dtype=kind))
self.assertVariableEqual(expected, actual)
def test_concat_number_strings(self):
# regression test for #305
a = self.cls("x", ["0", "1", "2"])
b = self.cls("x", ["3", "4"])
actual = Variable.concat([a, b], dim="x")
expected = Variable("x", np.arange(5).astype(str).astype(object))
self.assertVariableIdentical(expected, actual)
self.assertEqual(expected.dtype, object)
self.assertEqual(type(expected.values[0]), str)
def test_concat_fixed_len_str(self):
# regression test for #217
for kind in ['S', 'U']:
x = self.cls('animal', np.array(['horse'], dtype=kind))
y = self.cls('animal', np.array(['aardvark'], dtype=kind))
actual = Variable.concat([x, y], 'animal')
expected = Variable(
'animal', np.array(['horse', 'aardvark'], dtype=kind))
self.assertVariableEqual(expected, actual)
def test_concat_number_strings(self):
# regression test for #305
a = self.cls('x', ['0', '1', '2'])
b = self.cls('x', ['3', '4'])
actual = Variable.concat([a, b], dim='x')
expected = Variable('x', np.arange(5).astype(str).astype(object))
self.assertVariableIdentical(expected, actual)
self.assertEqual(expected.dtype, object)
self.assertEqual(type(expected.values[0]), str)
def test_get_axis_num(self):
v = Variable(["x", "y", "z"], np.random.randn(2, 3, 4))
self.assertEqual(v.get_axis_num("x"), 0)
self.assertEqual(v.get_axis_num(["x"]), (0,))
self.assertEqual(v.get_axis_num(["x", "y"]), (0, 1))
self.assertEqual(v.get_axis_num(["z", "y", "x"]), (2, 1, 0))
with self.assertRaisesRegexp(ValueError, "not found in array dim"):
v.get_axis_num("foobar")
def test_get_axis_num(self):
v = Variable(['x', 'y', 'z'], np.random.randn(2, 3, 4))
self.assertEqual(v.get_axis_num('x'), 0)
self.assertEqual(v.get_axis_num(['x']), (0,))
self.assertEqual(v.get_axis_num(['x', 'y']), (0, 1))
self.assertEqual(v.get_axis_num(['z', 'y', 'x']), (2, 1, 0))
with self.assertRaisesRegexp(ValueError, 'not found in array dim'):
v.get_axis_num('foobar')
def test_isel(self):
v = Variable(['time', 'x'], self.d)
self.assertVariableIdentical(v.isel(time=slice(None)), v)
self.assertVariableIdentical(v.isel(time=0), v[0])
self.assertVariableIdentical(v.isel(time=slice(0, 3)), v[:3])
self.assertVariableIdentical(v.isel(x=0), v[:, 0])
with self.assertRaisesRegexp(ValueError, 'do not exist'):
v.isel(not_a_dim=0)
def test_index_and_concat_datetime(self):
# regression test for #125
date_range = pd.date_range('2011-09-01', periods=10)
for dates in [date_range, date_range.values,
date_range.to_pydatetime()]:
expected = self.cls('t', dates)
for times in [[expected[i] for i in range(10)],
[expected[i:(i + 1)] for i in range(10)],
[expected[[i]] for i in range(10)]]:
actual = Variable.concat(times, 't')
self.assertEqual(expected.dtype, actual.dtype)
self.assertArrayEqual(expected, actual)
def test_missing_values(self):
values = np.array([0, 1, np.nan, 3])
data = da.from_array(values, chunks=(2,))
eager_var = Variable("x", values)
lazy_var = Variable("x", data)
self.assertLazyAndIdentical(eager_var, lazy_var.fillna(lazy_var))
self.assertLazyAndIdentical(Variable("x", range(4)), lazy_var.fillna(2))
self.assertLazyAndIdentical(eager_var.count(), lazy_var.count())
def test_expand_dims(self):
v = Variable(["x"], [0, 1])
actual = v.expand_dims(["x", "y"])
expected = Variable(["x", "y"], [[0], [1]])
self.assertVariableIdentical(actual, expected)
actual = v.expand_dims(["y", "x"])
self.assertVariableIdentical(actual, expected.T)
actual = v.expand_dims(OrderedDict([("x", 2), ("y", 2)]))
expected = Variable(["x", "y"], [[0, 0], [1, 1]])
self.assertVariableIdentical(actual, expected)
v = Variable(["foo"], [0, 1])
actual = v.expand_dims("foo")
expected = v
self.assertVariableIdentical(actual, expected)
with self.assertRaisesRegexp(ValueError, "must be a superset"):
v.expand_dims(["z"])
def test_expand_dims(self):
v = Variable(['x'], [0, 1])
actual = v.expand_dims(['x', 'y'])
expected = Variable(['x', 'y'], [[0], [1]])
self.assertVariableIdentical(actual, expected)
actual = v.expand_dims(['y', 'x'])
self.assertVariableIdentical(actual, expected.T)
actual = v.expand_dims(OrderedDict([('x', 2), ('y', 2)]))
expected = Variable(['x', 'y'], [[0, 0], [1, 1]])
self.assertVariableIdentical(actual, expected)
v = Variable(['foo'], [0, 1])
actual = v.expand_dims('foo')
expected = v
self.assertVariableIdentical(actual, expected)
with self.assertRaisesRegexp(ValueError, 'must be a superset'):
v.expand_dims(['z'])
def test_missing_fillvalue(self):
v = Variable(['x'], np.array([np.nan, 1, 2, 3]))
v.encoding = {'dtype': 'int16'}
with self.assertWarns('floating point data as an integer'):
conventions.encode_cf_variable(v)
def test_reduce_funcs(self):
v = Variable('x', np.array([1, np.nan, 2, 3]))
self.assertVariableIdentical(v.mean(), Variable([], 2))
self.assertVariableIdentical(v.mean(skipna=True), Variable([], 2))
self.assertVariableIdentical(v.mean(skipna=False), Variable([], np.nan))
self.assertVariableIdentical(np.mean(v), Variable([], 2))
self.assertVariableIdentical(v.prod(), Variable([], 6))
self.assertVariableIdentical(v.var(), Variable([], 2.0 / 3))
if LooseVersion(np.__version__) < '1.9':
with self.assertRaises(NotImplementedError):
v.median()
else:
self.assertVariableIdentical(v.median(), Variable([], 2))
v = Variable('x', [True, False, False])
self.assertVariableIdentical(v.any(), Variable([], True))
self.assertVariableIdentical(v.all(dim='x'), Variable([], False))
v = Variable('t', pd.date_range('2000-01-01', periods=3))
with self.assertRaises(NotImplementedError):
v.max(skipna=True)
self.assertVariableIdentical(
v.max(), Variable([], pd.Timestamp('2000-01-03')))
