/usr/lib/python3/dist-packages/xarray/tests/test_variable.py is in python3-xarray 0.10.2-1.
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print_function
from collections import namedtuple
from copy import copy, deepcopy
from datetime import datetime, timedelta
from distutils.version import LooseVersion
from textwrap import dedent
import numpy as np
import pandas as pd
import pytest
import pytz
from xarray import Coordinate, Dataset, IndexVariable, Variable
from xarray.core import indexing
from xarray.core.common import full_like, ones_like, zeros_like
from xarray.core.indexing import (
BasicIndexer, CopyOnWriteArray, DaskIndexingAdapter,
LazilyOuterIndexedArray, MemoryCachedArray, NumpyIndexingAdapter,
OuterIndexer, PandasIndexAdapter, VectorizedIndexer)
from xarray.core.pycompat import PY3, OrderedDict
from xarray.core.utils import NDArrayMixin
from xarray.core.variable import as_compatible_data, as_variable
from xarray.tests import requires_bottleneck
from . import (
TestCase, assert_allclose, assert_array_equal, assert_equal,
assert_identical, raises_regex, requires_dask, source_ndarray)
class VariableSubclassTestCases(object):
def test_properties(self):
data = 0.5 * np.arange(10)
v = self.cls(['time'], data, {'foo': 'bar'})
assert v.dims == ('time',)
assert_array_equal(v.values, data)
assert v.dtype == float
assert v.shape == (10,)
assert v.size == 10
assert v.sizes == {'time': 10}
assert v.nbytes == 80
assert v.ndim == 1
assert len(v) == 10
assert v.attrs == {'foo': u'bar'}
def test_attrs(self):
v = self.cls(['time'], 0.5 * np.arange(10))
assert v.attrs == {}
attrs = {'foo': 'bar'}
v.attrs = attrs
assert v.attrs == attrs
assert isinstance(v.attrs, OrderedDict)
v.attrs['foo'] = 'baz'
assert v.attrs['foo'] == 'baz'
def test_getitem_dict(self):
v = self.cls(['x'], np.random.randn(5))
actual = v[{'x': 0}]
expected = v[0]
assert_identical(expected, actual)
def test_getitem_1d(self):
data = np.array([0, 1, 2])
v = self.cls(['x'], data)
v_new = v[dict(x=[0, 1])]
assert v_new.dims == ('x', )
assert_array_equal(v_new, data[[0, 1]])
v_new = v[dict(x=slice(None))]
assert v_new.dims == ('x', )
assert_array_equal(v_new, data)
v_new = v[dict(x=Variable('a', [0, 1]))]
assert v_new.dims == ('a', )
assert_array_equal(v_new, data[[0, 1]])
v_new = v[dict(x=1)]
assert v_new.dims == ()
assert_array_equal(v_new, data[1])
# tuple argument
v_new = v[slice(None)]
assert v_new.dims == ('x', )
assert_array_equal(v_new, data)
def test_getitem_1d_fancy(self):
v = self.cls(['x'], [0, 1, 2])
# 1d-variable should be indexable by multi-dimensional Variable
ind = Variable(('a', 'b'), [[0, 1], [0, 1]])
v_new = v[ind]
assert v_new.dims == ('a', 'b')
expected = np.array(v._data)[([0, 1], [0, 1]), ]
assert_array_equal(v_new, expected)
# boolean indexing
ind = Variable(('x', ), [True, False, True])
v_new = v[ind]
assert_identical(v[[0, 2]], v_new)
v_new = v[[True, False, True]]
assert_identical(v[[0, 2]], v_new)
with raises_regex(IndexError, "Boolean indexer should"):
ind = Variable(('a', ), [True, False, True])
v[ind]
def test_getitem_with_mask(self):
v = self.cls(['x'], [0, 1, 2])
assert_identical(v._getitem_with_mask(-1), Variable((), np.nan))
assert_identical(v._getitem_with_mask([0, -1, 1]),
self.cls(['x'], [0, np.nan, 1]))
assert_identical(v._getitem_with_mask(slice(2)),
self.cls(['x'], [0, 1]))
assert_identical(v._getitem_with_mask([0, -1, 1], fill_value=-99),
self.cls(['x'], [0, -99, 1]))
def test_getitem_with_mask_size_zero(self):
v = self.cls(['x'], [])
assert_identical(v._getitem_with_mask(-1), Variable((), np.nan))
assert_identical(v._getitem_with_mask([-1, -1, -1]),
self.cls(['x'], [np.nan, np.nan, np.nan]))
def test_getitem_with_mask_nd_indexer(self):
v = self.cls(['x'], [0, 1, 2])
indexer = Variable(('x', 'y'), [[0, -1], [-1, 2]])
assert_identical(v._getitem_with_mask(indexer, fill_value=-1), indexer)
def _assertIndexedLikeNDArray(self, variable, expected_value0,
expected_dtype=None):
"""Given a 1-dimensional variable, verify that the variable is indexed
like a numpy.ndarray.
"""
assert variable[0].shape == ()
assert variable[0].ndim == 0
assert variable[0].size == 1
# test identity
assert variable.equals(variable.copy())
assert variable.identical(variable.copy())
# check value is equal for both ndarray and Variable
assert variable.values[0] == expected_value0
assert variable[0].values == expected_value0
# check type or dtype is consistent for both ndarray and Variable
if expected_dtype is None:
# check output type instead of array dtype
assert type(variable.values[0]) == type(expected_value0)
assert type(variable[0].values) == type(expected_value0)
elif expected_dtype is not False:
assert variable.values[0].dtype == expected_dtype
assert variable[0].values.dtype == expected_dtype
def test_index_0d_int(self):
for value, dtype in [(0, np.int_),
(np.int32(0), np.int32)]:
x = self.cls(['x'], [value])
self._assertIndexedLikeNDArray(x, value, dtype)
def test_index_0d_float(self):
for value, dtype in [(0.5, np.float_),
(np.float32(0.5), np.float32)]:
x = self.cls(['x'], [value])
self._assertIndexedLikeNDArray(x, value, dtype)
def test_index_0d_string(self):
for value, dtype in [('foo', np.dtype('U3' if PY3 else 'S3')),
(u'foo', np.dtype('U3'))]:
x = self.cls(['x'], [value])
self._assertIndexedLikeNDArray(x, value, dtype)
def test_index_0d_datetime(self):
d = datetime(2000, 1, 1)
x = self.cls(['x'], [d])
self._assertIndexedLikeNDArray(x, np.datetime64(d))
x = self.cls(['x'], [np.datetime64(d)])
self._assertIndexedLikeNDArray(x, np.datetime64(d), 'datetime64[ns]')
x = self.cls(['x'], pd.DatetimeIndex([d]))
self._assertIndexedLikeNDArray(x, np.datetime64(d), 'datetime64[ns]')
def test_index_0d_timedelta64(self):
td = timedelta(hours=1)
x = self.cls(['x'], [np.timedelta64(td)])
self._assertIndexedLikeNDArray(
x, np.timedelta64(td), 'timedelta64[ns]')
x = self.cls(['x'], pd.to_timedelta([td]))
self._assertIndexedLikeNDArray(
x, np.timedelta64(td), 'timedelta64[ns]')
def test_index_0d_not_a_time(self):
d = np.datetime64('NaT', 'ns')
x = self.cls(['x'], [d])
self._assertIndexedLikeNDArray(x, d)
def test_index_0d_object(self):
class HashableItemWrapper(object):
def __init__(self, item):
self.item = item
def __eq__(self, other):
return self.item == other.item
def __hash__(self):
return hash(self.item)
def __repr__(self):
return '%s(item=%r)' % (type(self).__name__, self.item)
item = HashableItemWrapper((1, 2, 3))
x = self.cls('x', [item])
self._assertIndexedLikeNDArray(x, item, expected_dtype=False)
def test_0d_object_array_with_list(self):
listarray = np.empty((1,), dtype=object)
listarray[0] = [1, 2, 3]
x = self.cls('x', listarray)
assert_array_equal(x.data, listarray)
assert_array_equal(x[0].data, listarray.squeeze())
assert_array_equal(x.squeeze().data, listarray.squeeze())
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')
assert expected.dtype == actual.dtype
assert_array_equal(expected, actual)
def test_0d_time_data(self):
# regression test for #105
x = self.cls('time', pd.date_range('2000-01-01', periods=5))
expected = np.datetime64('2000-01-01', 'ns')
assert x[0].values == expected
def test_datetime64_conversion(self):
times = pd.date_range('2000-01-01', periods=3)
for values, preserve_source in [
(times, True),
(times.values, True),
(times.values.astype('datetime64[s]'), False),
(times.to_pydatetime(), False),
]:
v = self.cls(['t'], values)
assert v.dtype == np.dtype('datetime64[ns]')
assert_array_equal(v.values, times.values)
assert v.values.dtype == np.dtype('datetime64[ns]')
same_source = source_ndarray(v.values) is source_ndarray(values)
assert preserve_source == same_source
def test_timedelta64_conversion(self):
times = pd.timedelta_range(start=0, periods=3)
for values, preserve_source in [
(times, True),
(times.values, True),
(times.values.astype('timedelta64[s]'), False),
(times.to_pytimedelta(), False),
]:
v = self.cls(['t'], values)
assert v.dtype == np.dtype('timedelta64[ns]')
assert_array_equal(v.values, times.values)
assert v.values.dtype == np.dtype('timedelta64[ns]')
same_source = source_ndarray(v.values) is source_ndarray(values)
assert preserve_source == same_source
def test_object_conversion(self):
data = np.arange(5).astype(str).astype(object)
actual = self.cls('x', data)
assert actual.dtype == data.dtype
def test_pandas_data(self):
v = self.cls(['x'], pd.Series([0, 1, 2], index=[3, 2, 1]))
assert_identical(v, v[[0, 1, 2]])
v = self.cls(['x'], pd.Index([0, 1, 2]))
assert v[0].values == v.values[0]
def test_pandas_period_index(self):
v = self.cls(['x'], pd.period_range(start='2000', periods=20,
freq='B'))
v = v.load() # for dask-based Variable
assert v[0] == pd.Period('2000', freq='B')
assert "Period('2000-01-03', 'B')" in repr(v)
def test_1d_math(self):
x = 1.0 * np.arange(5)
y = np.ones(5)
# should we need `.to_base_variable()`?
# probably a break that `+v` changes type?
v = self.cls(['x'], x)
base_v = v.to_base_variable()
# unary ops
assert_identical(base_v, +v)
assert_identical(base_v, abs(v))
assert_array_equal((-v).values, -x)
# binary ops with numbers
assert_identical(base_v, v + 0)
assert_identical(base_v, 0 + v)
assert_identical(base_v, v * 1)
# binary ops with numpy arrays
assert_array_equal((v * x).values, x ** 2)
assert_array_equal((x * v).values, x ** 2)
assert_array_equal(v - y, v - 1)
assert_array_equal(y - v, 1 - v)
# verify attributes are dropped
v2 = self.cls(['x'], x, {'units': 'meters'})
assert_identical(base_v, +v2)
# binary ops with all variables
assert_array_equal(v + v, 2 * v)
w = self.cls(['x'], y, {'foo': 'bar'})
assert_identical(v + w, self.cls(['x'], x + y).to_base_variable())
assert_array_equal((v * w).values, x * y)
# something complicated
assert_array_equal((v ** 2 * w - 1 + x).values, x ** 2 * y - 1 + x)
# make sure dtype is preserved (for Index objects)
assert float == (+v).dtype
assert float == (+v).values.dtype
assert float == (0 + v).dtype
assert float == (0 + v).values.dtype
# check types of returned data
assert isinstance(+v, Variable)
assert not isinstance(+v, IndexVariable)
assert isinstance(0 + v, Variable)
assert not isinstance(0 + v, IndexVariable)
def test_1d_reduce(self):
x = np.arange(5)
v = self.cls(['x'], x)
actual = v.sum()
expected = Variable((), 10)
assert_identical(expected, actual)
assert type(actual) is Variable
def test_array_interface(self):
x = np.arange(5)
v = self.cls(['x'], x)
assert_array_equal(np.asarray(v), x)
# test patched in methods
assert_array_equal(v.astype(float), x.astype(float))
# think this is a break, that argsort changes the type
assert_identical(v.argsort(), v.to_base_variable())
assert_identical(v.clip(2, 3),
self.cls('x', x.clip(2, 3)).to_base_variable())
# test ufuncs
assert_identical(np.sin(v),
self.cls(['x'], np.sin(x)).to_base_variable())
assert isinstance(np.sin(v), Variable)
assert not isinstance(np.sin(v), IndexVariable)
def example_1d_objects(self):
for data in [range(3),
0.5 * np.arange(3),
0.5 * np.arange(3, dtype=np.float32),
pd.date_range('2000-01-01', periods=3),
np.array(['a', 'b', 'c'], dtype=object)]:
yield (self.cls('x', data), data)
def test___array__(self):
for v, data in self.example_1d_objects():
assert_array_equal(v.values, np.asarray(data))
assert_array_equal(np.asarray(v), np.asarray(data))
assert v[0].values == np.asarray(data)[0]
assert np.asarray(v[0]) == np.asarray(data)[0]
def test_equals_all_dtypes(self):
for v, _ in self.example_1d_objects():
v2 = v.copy()
assert v.equals(v2)
assert v.identical(v2)
assert v.no_conflicts(v2)
assert v[0].equals(v2[0])
assert v[0].identical(v2[0])
assert v[0].no_conflicts(v2[0])
assert v[:2].equals(v2[:2])
assert v[:2].identical(v2[:2])
assert v[:2].no_conflicts(v2[:2])
def test_eq_all_dtypes(self):
# ensure that we don't choke on comparisons for which numpy returns
# scalars
expected = Variable('x', 3 * [False])
for v, _ in self.example_1d_objects():
actual = 'z' == v
assert_identical(expected, actual)
actual = ~('z' != v)
assert_identical(expected, actual)
def test_encoding_preserved(self):
expected = self.cls('x', range(3), {'foo': 1}, {'bar': 2})
for actual in [expected.T,
expected[...],
expected.squeeze(),
expected.isel(x=slice(None)),
expected.set_dims({'x': 3}),
expected.copy(deep=True),
expected.copy(deep=False)]:
assert_identical(expected.to_base_variable(),
actual.to_base_variable())
assert expected.encoding == actual.encoding
def test_concat(self):
x = np.arange(5)
y = np.arange(5, 10)
v = self.cls(['a'], x)
w = self.cls(['a'], y)
assert_identical(Variable(['b', 'a'], np.array([x, y])),
Variable.concat([v, w], 'b'))
assert_identical(Variable(['b', 'a'], np.array([x, y])),
Variable.concat((v, w), 'b'))
assert_identical(Variable(['b', 'a'], np.array([x, y])),
Variable.concat((v, w), 'b'))
with raises_regex(ValueError, 'inconsistent dimensions'):
Variable.concat([v, Variable(['c'], y)], 'b')
# test indexers
actual = Variable.concat(
[v, w],
positions=[np.arange(0, 10, 2), np.arange(1, 10, 2)],
dim='a')
expected = Variable('a', np.array([x, y]).ravel(order='F'))
assert_identical(expected, actual)
# test concatenating along a dimension
v = Variable(['time', 'x'], np.random.random((10, 8)))
assert_identical(v, Variable.concat([v[:5], v[5:]], 'time'))
assert_identical(v, Variable.concat([v[:5], v[5:6], v[6:]], 'time'))
assert_identical(v, Variable.concat([v[:1], v[1:]], 'time'))
# test dimension order
assert_identical(v, Variable.concat([v[:, :5], v[:, 5:]], 'x'))
with raises_regex(ValueError, 'all input arrays must have'):
Variable.concat([v[:, 0], v[:, 1:]], 'x')
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)])).to_base_variable()
assert_identical(expected, Variable.concat([v, w], 'a'))
w.attrs['foo'] = 2
assert_identical(expected, Variable.concat([v, w], 'a'))
w.attrs['foo'] = 'bar'
expected.attrs['foo'] = 'bar'
assert_identical(expected, Variable.concat([v, w], 'a'))
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))
assert_equal(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))
assert_identical(expected, actual)
assert actual.dtype.kind == expected.dtype.kind
def test_concat_mixed_dtypes(self):
a = self.cls('x', [0, 1])
b = self.cls('x', ['two'])
actual = Variable.concat([a, b], dim='x')
expected = Variable('x', np.array([0, 1, 'two'], dtype=object))
assert_identical(expected, actual)
assert actual.dtype == object
def test_copy(self):
v = self.cls('x', 0.5 * np.arange(10), {'foo': 'bar'})
for deep in [True, False]:
w = v.copy(deep=deep)
assert type(v) is type(w)
assert_identical(v, w)
assert v.dtype == w.dtype
if self.cls is Variable:
if deep:
assert source_ndarray(v.values) is not \
source_ndarray(w.values)
else:
assert source_ndarray(v.values) is \
source_ndarray(w.values)
assert_identical(v, copy(v))
def test_copy_index(self):
midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2], [-1, -2]],
names=('one', 'two', 'three'))
v = self.cls('x', midx)
for deep in [True, False]:
w = v.copy(deep=deep)
assert isinstance(w._data, PandasIndexAdapter)
assert isinstance(w.to_index(), pd.MultiIndex)
assert_array_equal(v._data.array, w._data.array)
def test_real_and_imag(self):
v = self.cls('x', np.arange(3) - 1j * np.arange(3), {'foo': 'bar'})
expected_re = self.cls('x', np.arange(3), {'foo': 'bar'})
assert_identical(v.real, expected_re)
expected_im = self.cls('x', -np.arange(3), {'foo': 'bar'})
assert_identical(v.imag, expected_im)
expected_abs = self.cls(
'x', np.sqrt(2 * np.arange(3) ** 2)).to_base_variable()
assert_allclose(abs(v), expected_abs)
def test_aggregate_complex(self):
# should skip NaNs
v = self.cls('x', [1, 2j, np.nan])
expected = Variable((), 0.5 + 1j)
assert_allclose(v.mean(), expected)
def test_pandas_cateogrical_dtype(self):
data = pd.Categorical(np.arange(10, dtype='int64'))
v = self.cls('x', data)
print(v) # should not error
assert v.dtype == 'int64'
def test_pandas_datetime64_with_tz(self):
data = pd.date_range(start='2000-01-01',
tz=pytz.timezone('America/New_York'),
periods=10, freq='1h')
v = self.cls('x', data)
print(v) # should not error
if 'America/New_York' in str(data.dtype):
# pandas is new enough that it has datetime64 with timezone dtype
assert v.dtype == 'object'
def test_multiindex(self):
idx = pd.MultiIndex.from_product([list('abc'), [0, 1]])
v = self.cls('x', idx)
assert_identical(Variable((), ('a', 0)), v[0])
assert_identical(v, v[:])
def test_load(self):
array = self.cls('x', np.arange(5))
orig_data = array._data
copied = array.copy(deep=True)
if array.chunks is None:
array.load()
assert type(array._data) is type(orig_data)
assert type(copied._data) is type(orig_data)
assert_identical(array, copied)
def test_getitem_advanced(self):
v = self.cls(['x', 'y'], [[0, 1, 2], [3, 4, 5]])
v_data = v.compute().data
# orthogonal indexing
v_new = v[([0, 1], [1, 0])]
assert v_new.dims == ('x', 'y')
assert_array_equal(v_new, v_data[[0, 1]][:, [1, 0]])
v_new = v[[0, 1]]
assert v_new.dims == ('x', 'y')
assert_array_equal(v_new, v_data[[0, 1]])
# with mixed arguments
ind = Variable(['a'], [0, 1])
v_new = v[dict(x=[0, 1], y=ind)]
assert v_new.dims == ('x', 'a')
assert_array_equal(v_new, v_data[[0, 1]][:, [0, 1]])
# boolean indexing
v_new = v[dict(x=[True, False], y=[False, True, False])]
assert v_new.dims == ('x', 'y')
assert_array_equal(v_new, v_data[0][1])
# with scalar variable
ind = Variable((), 2)
v_new = v[dict(y=ind)]
expected = v[dict(y=2)]
assert_array_equal(v_new, expected)
# with boolean variable with wrong shape
ind = np.array([True, False])
with raises_regex(IndexError, 'Boolean array size 2 is '):
v[Variable(('a', 'b'), [[0, 1]]), ind]
# boolean indexing with different dimension
ind = Variable(['a'], [True, False, False])
with raises_regex(IndexError, 'Boolean indexer should be'):
v[dict(y=ind)]
def test_getitem_uint_1d(self):
# regression test for #1405
v = self.cls(['x'], [0, 1, 2])
v_data = v.compute().data
v_new = v[np.array([0])]
assert_array_equal(v_new, v_data[0])
v_new = v[np.array([0], dtype="uint64")]
assert_array_equal(v_new, v_data[0])
def test_getitem_uint(self):
# regression test for #1405
v = self.cls(['x', 'y'], [[0, 1, 2], [3, 4, 5]])
v_data = v.compute().data
v_new = v[np.array([0])]
assert_array_equal(v_new, v_data[[0], :])
v_new = v[np.array([0], dtype="uint64")]
assert_array_equal(v_new, v_data[[0], :])
v_new = v[np.uint64(0)]
assert_array_equal(v_new, v_data[0, :])
def test_getitem_0d_array(self):
# make sure 0d-np.array can be used as an indexer
v = self.cls(['x'], [0, 1, 2])
v_data = v.compute().data
v_new = v[np.array([0])[0]]
assert_array_equal(v_new, v_data[0])
v_new = v[np.array(0)]
assert_array_equal(v_new, v_data[0])
v_new = v[Variable((), np.array(0))]
assert_array_equal(v_new, v_data[0])
def test_getitem_fancy(self):
v = self.cls(['x', 'y'], [[0, 1, 2], [3, 4, 5]])
v_data = v.compute().data
ind = Variable(['a', 'b'], [[0, 1, 1], [1, 1, 0]])
v_new = v[ind]
assert v_new.dims == ('a', 'b', 'y')
assert_array_equal(v_new, v_data[[[0, 1, 1], [1, 1, 0]], :])
# It would be ok if indexed with the multi-dimensional array including
# the same name
ind = Variable(['x', 'b'], [[0, 1, 1], [1, 1, 0]])
v_new = v[ind]
assert v_new.dims == ('x', 'b', 'y')
assert_array_equal(v_new, v_data[[[0, 1, 1], [1, 1, 0]], :])
ind = Variable(['a', 'b'], [[0, 1, 2], [2, 1, 0]])
v_new = v[dict(y=ind)]
assert v_new.dims == ('x', 'a', 'b')
assert_array_equal(v_new, v_data[:, ([0, 1, 2], [2, 1, 0])])
ind = Variable(['a', 'b'], [[0, 0], [1, 1]])
v_new = v[dict(x=[1, 0], y=ind)]
assert v_new.dims == ('x', 'a', 'b')
assert_array_equal(v_new, v_data[[1, 0]][:, ind])
# along diagonal
ind = Variable(['a'], [0, 1])
v_new = v[ind, ind]
assert v_new.dims == ('a',)
assert_array_equal(v_new, v_data[[0, 1], [0, 1]])
# with integer
ind = Variable(['a', 'b'], [[0, 0], [1, 1]])
v_new = v[dict(x=0, y=ind)]
assert v_new.dims == ('a', 'b')
assert_array_equal(v_new[0], v_data[0][[0, 0]])
assert_array_equal(v_new[1], v_data[0][[1, 1]])
# with slice
ind = Variable(['a', 'b'], [[0, 0], [1, 1]])
v_new = v[dict(x=slice(None), y=ind)]
assert v_new.dims == ('x', 'a', 'b')
assert_array_equal(v_new, v_data[:, [[0, 0], [1, 1]]])
ind = Variable(['a', 'b'], [[0, 0], [1, 1]])
v_new = v[dict(x=ind, y=slice(None))]
assert v_new.dims == ('a', 'b', 'y')
assert_array_equal(v_new, v_data[[[0, 0], [1, 1]], :])
ind = Variable(['a', 'b'], [[0, 0], [1, 1]])
v_new = v[dict(x=ind, y=slice(None, 1))]
assert v_new.dims == ('a', 'b', 'y')
assert_array_equal(v_new, v_data[[[0, 0], [1, 1]], slice(None, 1)])
# slice matches explicit dimension
ind = Variable(['y'], [0, 1])
v_new = v[ind, :2]
assert v_new.dims == ('y',)
assert_array_equal(v_new, v_data[[0, 1], [0, 1]])
# with multiple slices
v = self.cls(['x', 'y', 'z'], [[[1, 2, 3], [4, 5, 6]]])
ind = Variable(['a', 'b'], [[0]])
v_new = v[ind, :, :]
expected = Variable(['a', 'b', 'y', 'z'], v.data[np.newaxis, ...])
assert_identical(v_new, expected)
v = Variable(['w', 'x', 'y', 'z'], [[[[1, 2, 3], [4, 5, 6]]]])
ind = Variable(['y'], [0])
v_new = v[ind, :, 1:2, 2]
expected = Variable(['y', 'x'], [[6]])
assert_identical(v_new, expected)
# slice and vector mixed indexing resulting in the same dimension
v = Variable(['x', 'y', 'z'], np.arange(60).reshape(3, 4, 5))
ind = Variable(['x'], [0, 1, 2])
v_new = v[:, ind]
expected = Variable(('x', 'z'), np.zeros((3, 5)))
expected[0] = v.data[0, 0]
expected[1] = v.data[1, 1]
expected[2] = v.data[2, 2]
assert_identical(v_new, expected)
v_new = v[:, ind.data]
assert v_new.shape == (3, 3, 5)
def test_getitem_error(self):
v = self.cls(['x', 'y'], [[0, 1, 2], [3, 4, 5]])
with raises_regex(IndexError, "labeled multi-"):
v[[[0, 1], [1, 2]]]
ind_x = Variable(['a'], [0, 1, 1])
ind_y = Variable(['a'], [0, 1])
with raises_regex(IndexError, "Dimensions of indexers "):
v[ind_x, ind_y]
ind = Variable(['a', 'b'], [[True, False], [False, True]])
with raises_regex(IndexError, '2-dimensional boolean'):
v[dict(x=ind)]
v = Variable(['x', 'y', 'z'], np.arange(60).reshape(3, 4, 5))
ind = Variable(['x'], [0, 1])
with raises_regex(IndexError, 'Dimensions of indexers mis'):
v[:, ind]
def test_pad(self):
data = np.arange(4 * 3 * 2).reshape(4, 3, 2)
v = self.cls(['x', 'y', 'z'], data)
xr_args = [{'x': (2, 1)}, {'y': (0, 3)}, {'x': (3, 1), 'z': (2, 0)}]
np_args = [((2, 1), (0, 0), (0, 0)), ((0, 0), (0, 3), (0, 0)),
((3, 1), (0, 0), (2, 0))]
for xr_arg, np_arg in zip(xr_args, np_args):
actual = v.pad_with_fill_value(**xr_arg)
expected = np.pad(np.array(v.data.astype(float)), np_arg,
mode='constant', constant_values=np.nan)
assert_array_equal(actual, expected)
assert isinstance(actual._data, type(v._data))
# for the boolean array, we pad False
data = np.full_like(data, False, dtype=bool).reshape(4, 3, 2)
v = self.cls(['x', 'y', 'z'], data)
for xr_arg, np_arg in zip(xr_args, np_args):
actual = v.pad_with_fill_value(fill_value=False, **xr_arg)
expected = np.pad(np.array(v.data), np_arg,
mode='constant', constant_values=False)
assert_array_equal(actual, expected)
def test_rolling_window(self):
# Just a working test. See test_nputils for the algorithm validation
v = self.cls(['x', 'y', 'z'],
np.arange(40 * 30 * 2).reshape(40, 30, 2))
for (d, w) in [('x', 3), ('y', 5)]:
v_rolling = v.rolling_window(d, w, d + '_window')
assert v_rolling.dims == ('x', 'y', 'z', d + '_window')
assert v_rolling.shape == v.shape + (w, )
v_rolling = v.rolling_window(d, w, d + '_window', center=True)
assert v_rolling.dims == ('x', 'y', 'z', d + '_window')
assert v_rolling.shape == v.shape + (w, )
# dask and numpy result should be the same
v_loaded = v.load().rolling_window(d, w, d + '_window',
center=True)
assert_array_equal(v_rolling, v_loaded)
# numpy backend should not be over-written
if isinstance(v._data, np.ndarray):
with pytest.raises(ValueError):
v_loaded[0] = 1.0
class TestVariable(TestCase, VariableSubclassTestCases):
cls = staticmethod(Variable)
def setUp(self):
self.d = np.random.random((10, 3)).astype(np.float64)
def test_data_and_values(self):
v = Variable(['time', 'x'], self.d)
assert_array_equal(v.data, self.d)
assert_array_equal(v.values, self.d)
assert source_ndarray(v.values) is self.d
with pytest.raises(ValueError):
# wrong size
v.values = np.random.random(5)
d2 = np.random.random((10, 3))
v.values = d2
assert source_ndarray(v.values) is d2
d3 = np.random.random((10, 3))
v.data = d3
assert source_ndarray(v.data) is d3
def test_numpy_same_methods(self):
v = Variable([], np.float32(0.0))
assert v.item() == 0
assert type(v.item()) is float
v = IndexVariable('x', np.arange(5))
assert 2 == v.searchsorted(2)
def test_datetime64_conversion_scalar(self):
expected = np.datetime64('2000-01-01', 'ns')
for values in [
np.datetime64('2000-01-01'),
pd.Timestamp('2000-01-01T00'),
datetime(2000, 1, 1),
]:
v = Variable([], values)
assert v.dtype == np.dtype('datetime64[ns]')
assert v.values == expected
assert v.values.dtype == np.dtype('datetime64[ns]')
def test_timedelta64_conversion_scalar(self):
expected = np.timedelta64(24 * 60 * 60 * 10 ** 9, 'ns')
for values in [
np.timedelta64(1, 'D'),
pd.Timedelta('1 day'),
timedelta(days=1),
]:
v = Variable([], values)
assert v.dtype == np.dtype('timedelta64[ns]')
assert v.values == expected
assert v.values.dtype == np.dtype('timedelta64[ns]')
def test_0d_str(self):
v = Variable([], u'foo')
assert v.dtype == np.dtype('U3')
assert v.values == 'foo'
v = Variable([], np.string_('foo'))
assert v.dtype == np.dtype('S3')
assert v.values == bytes('foo', 'ascii') if PY3 else 'foo'
def test_0d_datetime(self):
v = Variable([], pd.Timestamp('2000-01-01'))
assert v.dtype == np.dtype('datetime64[ns]')
assert v.values == np.datetime64('2000-01-01', 'ns')
def test_0d_timedelta(self):
for td in [pd.to_timedelta('1s'), np.timedelta64(1, 's')]:
v = Variable([], td)
assert v.dtype == np.dtype('timedelta64[ns]')
assert v.values == np.timedelta64(10 ** 9, 'ns')
def test_equals_and_identical(self):
d = np.random.rand(10, 3)
d[0, 0] = np.nan
v1 = Variable(('dim1', 'dim2'), data=d,
attrs={'att1': 3, 'att2': [1, 2, 3]})
v2 = Variable(('dim1', 'dim2'), data=d,
attrs={'att1': 3, 'att2': [1, 2, 3]})
assert v1.equals(v2)
assert v1.identical(v2)
v3 = Variable(('dim1', 'dim3'), data=d)
assert not v1.equals(v3)
v4 = Variable(('dim1', 'dim2'), data=d)
assert v1.equals(v4)
assert not v1.identical(v4)
v5 = deepcopy(v1)
v5.values[:] = np.random.rand(10, 3)
assert not v1.equals(v5)
assert not v1.equals(None)
assert not v1.equals(d)
assert not v1.identical(None)
assert not v1.identical(d)
def test_broadcast_equals(self):
v1 = Variable((), np.nan)
v2 = Variable(('x'), [np.nan, np.nan])
assert v1.broadcast_equals(v2)
assert not v1.equals(v2)
assert not v1.identical(v2)
v3 = Variable(('x'), [np.nan])
assert v1.broadcast_equals(v3)
assert not v1.equals(v3)
assert not v1.identical(v3)
assert not v1.broadcast_equals(None)
v4 = Variable(('x'), [np.nan] * 3)
assert not v2.broadcast_equals(v4)
def test_no_conflicts(self):
v1 = Variable(('x'), [1, 2, np.nan, np.nan])
v2 = Variable(('x'), [np.nan, 2, 3, np.nan])
assert v1.no_conflicts(v2)
assert not v1.equals(v2)
assert not v1.broadcast_equals(v2)
assert not v1.identical(v2)
assert not v1.no_conflicts(None)
v3 = Variable(('y'), [np.nan, 2, 3, np.nan])
assert not v3.no_conflicts(v1)
d = np.array([1, 2, np.nan, np.nan])
assert not v1.no_conflicts(d)
assert not v2.no_conflicts(d)
v4 = Variable(('w', 'x'), [d])
assert v1.no_conflicts(v4)
def test_as_variable(self):
data = np.arange(10)
expected = Variable('x', data)
expected_extra = Variable('x', data, attrs={'myattr': 'val'},
encoding={'scale_factor': 1})
assert_identical(expected, as_variable(expected))
ds = Dataset({'x': expected})
var = as_variable(ds['x']).to_base_variable()
assert_identical(expected, var)
assert not isinstance(ds['x'], Variable)
assert isinstance(as_variable(ds['x']), Variable)
FakeVariable = namedtuple('FakeVariable', 'values dims')
fake_xarray = FakeVariable(expected.values, expected.dims)
assert_identical(expected, as_variable(fake_xarray))
FakeVariable = namedtuple('FakeVariable', 'data dims')
fake_xarray = FakeVariable(expected.data, expected.dims)
assert_identical(expected, as_variable(fake_xarray))
FakeVariable = namedtuple('FakeVariable',
'data values dims attrs encoding')
fake_xarray = FakeVariable(expected_extra.data, expected_extra.values,
expected_extra.dims, expected_extra.attrs,
expected_extra.encoding)
assert_identical(expected_extra, as_variable(fake_xarray))
xarray_tuple = (expected_extra.dims, expected_extra.values,
expected_extra.attrs, expected_extra.encoding)
assert_identical(expected_extra, as_variable(xarray_tuple))
with raises_regex(TypeError, 'tuples to convert'):
as_variable(tuple(data))
with raises_regex(
TypeError, 'without an explicit list of dimensions'):
as_variable(data)
actual = as_variable(data, name='x')
assert_identical(expected.to_index_variable(), actual)
actual = as_variable(0)
expected = Variable([], 0)
assert_identical(expected, actual)
data = np.arange(9).reshape((3, 3))
expected = Variable(('x', 'y'), data)
with raises_regex(
ValueError, 'without explicit dimension names'):
as_variable(data, name='x')
with raises_regex(
ValueError, 'has more than 1-dimension'):
as_variable(expected, name='x')
def test_repr(self):
v = Variable(['time', 'x'], [[1, 2, 3], [4, 5, 6]], {'foo': 'bar'})
expected = dedent("""
<xarray.Variable (time: 2, x: 3)>
array([[1, 2, 3],
[4, 5, 6]])
Attributes:
foo: bar
""").strip()
assert expected == repr(v)
def test_repr_lazy_data(self):
v = Variable('x', LazilyOuterIndexedArray(np.arange(2e5)))
assert '200000 values with dtype' in repr(v)
assert isinstance(v._data, LazilyOuterIndexedArray)
def test_detect_indexer_type(self):
""" Tests indexer type was correctly detected. """
data = np.random.random((10, 11))
v = Variable(['x', 'y'], data)
_, ind, _ = v._broadcast_indexes((0, 1))
assert type(ind) == indexing.BasicIndexer
_, ind, _ = v._broadcast_indexes((0, slice(0, 8, 2)))
assert type(ind) == indexing.BasicIndexer
_, ind, _ = v._broadcast_indexes((0, [0, 1]))
assert type(ind) == indexing.OuterIndexer
_, ind, _ = v._broadcast_indexes(([0, 1], 1))
assert type(ind) == indexing.OuterIndexer
_, ind, _ = v._broadcast_indexes(([0, 1], [1, 2]))
assert type(ind) == indexing.OuterIndexer
_, ind, _ = v._broadcast_indexes(([0, 1], slice(0, 8, 2)))
assert type(ind) == indexing.OuterIndexer
vind = Variable(('a', ), [0, 1])
_, ind, _ = v._broadcast_indexes((vind, slice(0, 8, 2)))
assert type(ind) == indexing.OuterIndexer
vind = Variable(('y', ), [0, 1])
_, ind, _ = v._broadcast_indexes((vind, 3))
assert type(ind) == indexing.OuterIndexer
vind = Variable(('a', ), [0, 1])
_, ind, _ = v._broadcast_indexes((vind, vind))
assert type(ind) == indexing.VectorizedIndexer
vind = Variable(('a', 'b'), [[0, 2], [1, 3]])
_, ind, _ = v._broadcast_indexes((vind, 3))
assert type(ind) == indexing.VectorizedIndexer
def test_indexer_type(self):
# GH:issue:1688. Wrong indexer type induces NotImplementedError
data = np.random.random((10, 11))
v = Variable(['x', 'y'], data)
def assert_indexer_type(key, object_type):
dims, index_tuple, new_order = v._broadcast_indexes(key)
assert isinstance(index_tuple, object_type)
# should return BasicIndexer
assert_indexer_type((0, 1), BasicIndexer)
assert_indexer_type((0, slice(None, None)), BasicIndexer)
assert_indexer_type((Variable([], 3), slice(None, None)), BasicIndexer)
assert_indexer_type((Variable([], 3), (Variable([], 6))), BasicIndexer)
# should return OuterIndexer
assert_indexer_type(([0, 1], 1), OuterIndexer)
assert_indexer_type(([0, 1], [1, 2]), OuterIndexer)
assert_indexer_type((Variable(('x'), [0, 1]), 1), OuterIndexer)
assert_indexer_type((Variable(('x'), [0, 1]), slice(None, None)),
OuterIndexer)
assert_indexer_type((Variable(('x'), [0, 1]), Variable(('y'), [0, 1])),
OuterIndexer)
# should return VectorizedIndexer
assert_indexer_type((Variable(('y'), [0, 1]), [0, 1]),
VectorizedIndexer)
assert_indexer_type((Variable(('z'), [0, 1]), Variable(('z'), [0, 1])),
VectorizedIndexer)
assert_indexer_type((Variable(('a', 'b'), [[0, 1], [1, 2]]),
Variable(('a', 'b'), [[0, 1], [1, 2]])),
VectorizedIndexer)
def test_items(self):
data = np.random.random((10, 11))
v = Variable(['x', 'y'], data)
# test slicing
assert_identical(v, v[:])
assert_identical(v, v[...])
assert_identical(Variable(['y'], data[0]), v[0])
assert_identical(Variable(['x'], data[:, 0]), v[:, 0])
assert_identical(Variable(['x', 'y'], data[:3, :2]),
v[:3, :2])
# test array indexing
x = Variable(['x'], np.arange(10))
y = Variable(['y'], np.arange(11))
assert_identical(v, v[x.values])
assert_identical(v, v[x])
assert_identical(v[:3], v[x < 3])
assert_identical(v[:, 3:], v[:, y >= 3])
assert_identical(v[:3, 3:], v[x < 3, y >= 3])
assert_identical(v[:3, :2], v[x[:3], y[:2]])
assert_identical(v[:3, :2], v[range(3), range(2)])
# test iteration
for n, item in enumerate(v):
assert_identical(Variable(['y'], data[n]), item)
with raises_regex(TypeError, 'iteration over a 0-d'):
iter(Variable([], 0))
# test setting
v.values[:] = 0
assert np.all(v.values == 0)
# test orthogonal setting
v[range(10), range(11)] = 1
assert_array_equal(v.values, np.ones((10, 11)))
def test_getitem_basic(self):
v = self.cls(['x', 'y'], [[0, 1, 2], [3, 4, 5]])
v_new = v[dict(x=0)]
assert v_new.dims == ('y', )
assert_array_equal(v_new, v._data[0])
v_new = v[dict(x=0, y=slice(None))]
assert v_new.dims == ('y', )
assert_array_equal(v_new, v._data[0])
v_new = v[dict(x=0, y=1)]
assert v_new.dims == ()
assert_array_equal(v_new, v._data[0, 1])
v_new = v[dict(y=1)]
assert v_new.dims == ('x', )
assert_array_equal(v_new, v._data[:, 1])
# tuple argument
v_new = v[(slice(None), 1)]
assert v_new.dims == ('x', )
assert_array_equal(v_new, v._data[:, 1])
def test_getitem_with_mask_2d_input(self):
v = Variable(('x', 'y'), [[0, 1, 2], [3, 4, 5]])
assert_identical(v._getitem_with_mask(([-1, 0], [1, -1])),
Variable(('x', 'y'), [[np.nan, np.nan], [1, np.nan]]))
assert_identical(v._getitem_with_mask((slice(2), [0, 1, 2])), v)
def test_isel(self):
v = Variable(['time', 'x'], self.d)
assert_identical(v.isel(time=slice(None)), v)
assert_identical(v.isel(time=0), v[0])
assert_identical(v.isel(time=slice(0, 3)), v[:3])
assert_identical(v.isel(x=0), v[:, 0])
with raises_regex(ValueError, 'do not exist'):
v.isel(not_a_dim=0)
def test_index_0d_numpy_string(self):
# regression test to verify our work around for indexing 0d strings
v = Variable([], np.string_('asdf'))
assert_identical(v[()], v)
v = Variable([], np.unicode_(u'asdf'))
assert_identical(v[()], v)
def test_indexing_0d_unicode(self):
# regression test for GH568
actual = Variable(('x'), [u'tmax'])[0][()]
expected = Variable((), u'tmax')
assert_identical(actual, expected)
def test_shift(self):
v = Variable('x', [1, 2, 3, 4, 5])
assert_identical(v, v.shift(x=0))
assert v is not v.shift(x=0)
expected = Variable('x', [np.nan, 1, 2, 3, 4])
assert_identical(expected, v.shift(x=1))
expected = Variable('x', [np.nan, np.nan, 1, 2, 3])
assert_identical(expected, v.shift(x=2))
expected = Variable('x', [2, 3, 4, 5, np.nan])
assert_identical(expected, v.shift(x=-1))
expected = Variable('x', [np.nan] * 5)
assert_identical(expected, v.shift(x=5))
assert_identical(expected, v.shift(x=6))
with raises_regex(ValueError, 'dimension'):
v.shift(z=0)
v = Variable('x', [1, 2, 3, 4, 5], {'foo': 'bar'})
assert_identical(v, v.shift(x=0))
expected = Variable('x', [np.nan, 1, 2, 3, 4], {'foo': 'bar'})
assert_identical(expected, v.shift(x=1))
def test_shift2d(self):
v = Variable(('x', 'y'), [[1, 2], [3, 4]])
expected = Variable(('x', 'y'), [[np.nan, np.nan], [np.nan, 1]])
assert_identical(expected, v.shift(x=1, y=1))
def test_roll(self):
v = Variable('x', [1, 2, 3, 4, 5])
assert_identical(v, v.roll(x=0))
assert v is not v.roll(x=0)
expected = Variable('x', [5, 1, 2, 3, 4])
assert_identical(expected, v.roll(x=1))
assert_identical(expected, v.roll(x=-4))
assert_identical(expected, v.roll(x=6))
expected = Variable('x', [4, 5, 1, 2, 3])
assert_identical(expected, v.roll(x=2))
assert_identical(expected, v.roll(x=-3))
with raises_regex(ValueError, 'dimension'):
v.roll(z=0)
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
assert_array_equal(expected, actual)
def test_transpose(self):
v = Variable(['time', 'x'], self.d)
v2 = Variable(['x', 'time'], self.d.T)
assert_identical(v, v2.transpose())
assert_identical(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))
assert w2.shape == (5, 3, 4, 2)
assert_identical(w2, w.transpose('d', 'b', 'c', 'a'))
assert_identical(w, w2.transpose('a', 'b', 'c', 'd'))
w3 = Variable(['b', 'c', 'd', 'a'], np.einsum('abcd->bcda', x))
assert_identical(w, w3.transpose('a', 'b', 'c', 'd'))
def test_transpose_0d(self):
for value in [
3.5,
('a', 1),
np.datetime64('2000-01-01'),
np.timedelta64(1, 'h'),
None,
object(),
]:
variable = Variable([], value)
actual = variable.transpose()
assert actual.identical(variable)
def test_squeeze(self):
v = Variable(['x', 'y'], [[1]])
assert_identical(Variable([], 1), v.squeeze())
assert_identical(Variable(['y'], [1]), v.squeeze('x'))
assert_identical(Variable(['y'], [1]), v.squeeze(['x']))
assert_identical(Variable(['x'], [1]), v.squeeze('y'))
assert_identical(Variable([], 1), v.squeeze(['x', 'y']))
v = Variable(['x', 'y'], [[1, 2]])
assert_identical(Variable(['y'], [1, 2]), v.squeeze())
assert_identical(Variable(['y'], [1, 2]), v.squeeze('x'))
with raises_regex(ValueError, 'cannot select a dimension'):
v.squeeze('y')
def test_get_axis_num(self):
v = Variable(['x', 'y', 'z'], np.random.randn(2, 3, 4))
assert v.get_axis_num('x') == 0
assert v.get_axis_num(['x']) == (0,)
assert v.get_axis_num(['x', 'y']) == (0, 1)
assert v.get_axis_num(['z', 'y', 'x']) == (2, 1, 0)
with raises_regex(ValueError, 'not found in array dim'):
v.get_axis_num('foobar')
def test_set_dims(self):
v = Variable(['x'], [0, 1])
actual = v.set_dims(['x', 'y'])
expected = Variable(['x', 'y'], [[0], [1]])
assert_identical(actual, expected)
actual = v.set_dims(['y', 'x'])
assert_identical(actual, expected.T)
actual = v.set_dims(OrderedDict([('x', 2), ('y', 2)]))
expected = Variable(['x', 'y'], [[0, 0], [1, 1]])
assert_identical(actual, expected)
v = Variable(['foo'], [0, 1])
actual = v.set_dims('foo')
expected = v
assert_identical(actual, expected)
with raises_regex(ValueError, 'must be a superset'):
v.set_dims(['z'])
def test_set_dims_object_dtype(self):
v = Variable([], ('a', 1))
actual = v.set_dims(('x',), (3,))
exp_values = np.empty((3,), dtype=object)
for i in range(3):
exp_values[i] = ('a', 1)
expected = Variable(['x'], exp_values)
assert actual.identical(expected)
def test_stack(self):
v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'})
actual = v.stack(z=('x', 'y'))
expected = Variable('z', [0, 1, 2, 3], v.attrs)
assert_identical(actual, expected)
actual = v.stack(z=('x',))
expected = Variable(('y', 'z'), v.data.T, v.attrs)
assert_identical(actual, expected)
actual = v.stack(z=(),)
assert_identical(actual, v)
actual = v.stack(X=('x',), Y=('y',)).transpose('X', 'Y')
expected = Variable(('X', 'Y'), v.data, v.attrs)
assert_identical(actual, expected)
def test_stack_errors(self):
v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'})
with raises_regex(ValueError, 'invalid existing dim'):
v.stack(z=('x1',))
with raises_regex(ValueError, 'cannot create a new dim'):
v.stack(x=('x',))
def test_unstack(self):
v = Variable('z', [0, 1, 2, 3], {'foo': 'bar'})
actual = v.unstack(z=OrderedDict([('x', 2), ('y', 2)]))
expected = Variable(('x', 'y'), [[0, 1], [2, 3]], v.attrs)
assert_identical(actual, expected)
actual = v.unstack(z=OrderedDict([('x', 4), ('y', 1)]))
expected = Variable(('x', 'y'), [[0], [1], [2], [3]], v.attrs)
assert_identical(actual, expected)
actual = v.unstack(z=OrderedDict([('x', 4)]))
expected = Variable('x', [0, 1, 2, 3], v.attrs)
assert_identical(actual, expected)
def test_unstack_errors(self):
v = Variable('z', [0, 1, 2, 3])
with raises_regex(ValueError, 'invalid existing dim'):
v.unstack(foo={'x': 4})
with raises_regex(ValueError, 'cannot create a new dim'):
v.stack(z=('z',))
with raises_regex(ValueError, 'the product of the new dim'):
v.unstack(z={'x': 5})
def test_unstack_2d(self):
v = Variable(['x', 'y'], [[0, 1], [2, 3]])
actual = v.unstack(y={'z': 2})
expected = Variable(['x', 'z'], v.data)
assert_identical(actual, expected)
actual = v.unstack(x={'z': 2})
expected = Variable(['y', 'z'], v.data.T)
assert_identical(actual, expected)
def test_stack_unstack_consistency(self):
v = Variable(['x', 'y'], [[0, 1], [2, 3]])
actual = (v.stack(z=('x', 'y'))
.unstack(z=OrderedDict([('x', 2), ('y', 2)])))
assert_identical(actual, v)
def test_broadcasting_math(self):
x = np.random.randn(2, 3)
v = Variable(['a', 'b'], x)
# 1d to 2d broadcasting
assert_identical(
v * v,
Variable(['a', 'b'], np.einsum('ab,ab->ab', x, x)))
assert_identical(
v * v[0],
Variable(['a', 'b'], np.einsum('ab,b->ab', x, x[0])))
assert_identical(
v[0] * v,
Variable(['b', 'a'], np.einsum('b,ab->ba', x[0], x)))
assert_identical(
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)
assert_identical(
v * w, Variable(['a', 'b', 'c', 'd'],
np.einsum('ab,bcd->abcd', x, y)))
assert_identical(
w * v, Variable(['b', 'c', 'd', 'a'],
np.einsum('bcd,ab->bcda', y, x)))
assert_identical(
v * w[0], Variable(['a', 'b', 'c', 'd'],
np.einsum('ab,cd->abcd', x, y[0])))
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 raises_regex(ValueError, 'mismatched lengths'):
a + b
with raises_regex(ValueError, 'duplicate dimensions'):
a + c
def test_inplace_math(self):
x = np.arange(5)
v = Variable(['x'], x)
v2 = v
v2 += 1
assert v is v2
# since we provided an ndarray for data, it is also modified in-place
assert source_ndarray(v.values) is x
assert_array_equal(v.values, np.arange(5) + 1)
with raises_regex(ValueError, 'dimensions cannot change'):
v += Variable('y', np.arange(5))
def test_reduce(self):
v = Variable(['x', 'y'], self.d, {'ignored': 'attributes'})
assert_identical(v.reduce(np.std, 'x'),
Variable(['y'], self.d.std(axis=0)))
assert_identical(v.reduce(np.std, axis=0),
v.reduce(np.std, dim='x'))
assert_identical(v.reduce(np.std, ['y', 'x']),
Variable([], self.d.std(axis=(0, 1))))
assert_identical(v.reduce(np.std),
Variable([], self.d.std()))
assert_identical(
v.reduce(np.mean, 'x').reduce(np.std, 'y'),
Variable([], self.d.mean(axis=0).std()))
assert_allclose(v.mean('x'), v.reduce(np.mean, 'x'))
with raises_regex(ValueError, 'cannot supply both'):
v.mean(dim='x', axis=0)
@pytest.mark.skipif(LooseVersion(np.__version__) < LooseVersion('1.10.0'),
reason='requires numpy version 1.10.0 or later')
def test_quantile(self):
v = Variable(['x', 'y'], self.d)
for q in [0.25, [0.50], [0.25, 0.75]]:
for axis, dim in zip([None, 0, [0], [0, 1]],
[None, 'x', ['x'], ['x', 'y']]):
actual = v.quantile(q, dim=dim)
expected = np.nanpercentile(self.d, np.array(q) * 100,
axis=axis)
np.testing.assert_allclose(actual.values, expected)
@requires_dask
def test_quantile_dask_raises(self):
# regression for GH1524
v = Variable(['x', 'y'], self.d).chunk(2)
with raises_regex(TypeError, 'arrays stored as dask'):
v.quantile(0.5, dim='x')
@requires_dask
@requires_bottleneck
def test_rank_dask_raises(self):
v = Variable(['x'], [3.0, 1.0, np.nan, 2.0, 4.0]).chunk(2)
with raises_regex(TypeError, 'arrays stored as dask'):
v.rank('x')
@requires_bottleneck
def test_rank(self):
import bottleneck as bn
# floats
v = Variable(['x', 'y'], [[3, 4, np.nan, 1]])
expect_0 = bn.nanrankdata(v.data, axis=0)
expect_1 = bn.nanrankdata(v.data, axis=1)
np.testing.assert_allclose(v.rank('x').values, expect_0)
np.testing.assert_allclose(v.rank('y').values, expect_1)
# int
v = Variable(['x'], [3, 2, 1])
expect = bn.rankdata(v.data, axis=0)
np.testing.assert_allclose(v.rank('x').values, expect)
# str
v = Variable(['x'], ['c', 'b', 'a'])
expect = bn.rankdata(v.data, axis=0)
np.testing.assert_allclose(v.rank('x').values, expect)
# pct
v = Variable(['x'], [3.0, 1.0, np.nan, 2.0, 4.0])
v_expect = Variable(['x'], [0.75, 0.25, np.nan, 0.5, 1.0])
assert_equal(v.rank('x', pct=True), v_expect)
# invalid dim
with raises_regex(ValueError, 'not found'):
v.rank('y')
def test_big_endian_reduce(self):
# regression test for GH489
data = np.ones(5, dtype='>f4')
v = Variable(['x'], data)
expected = Variable([], 5)
assert_identical(expected, v.sum())
def test_reduce_funcs(self):
v = Variable('x', np.array([1, np.nan, 2, 3]))
assert_identical(v.mean(), Variable([], 2))
assert_identical(v.mean(skipna=True), Variable([], 2))
assert_identical(v.mean(skipna=False), Variable([], np.nan))
assert_identical(np.mean(v), Variable([], 2))
assert_identical(v.prod(), Variable([], 6))
assert_identical(v.cumsum(axis=0),
Variable('x', np.array([1, 1, 3, 6])))
assert_identical(v.cumprod(axis=0),
Variable('x', np.array([1, 1, 2, 6])))
assert_identical(v.var(), Variable([], 2.0 / 3))
if LooseVersion(np.__version__) < '1.9':
with pytest.raises(NotImplementedError):
v.median()
else:
assert_identical(v.median(), Variable([], 2))
v = Variable('x', [True, False, False])
assert_identical(v.any(), Variable([], True))
assert_identical(v.all(dim='x'), Variable([], False))
v = Variable('t', pd.date_range('2000-01-01', periods=3))
with pytest.raises(NotImplementedError):
v.argmax(skipna=True)
assert_identical(
v.max(), Variable([], pd.Timestamp('2000-01-03')))
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()
assert len(vm.attrs) == 0
assert vm.attrs == OrderedDict()
# Test kept attrs
vm = v.mean(keep_attrs=True)
assert len(vm.attrs) == len(_attrs)
assert vm.attrs == _attrs
def test_count(self):
expected = Variable([], 3)
actual = Variable(['x'], [1, 2, 3, np.nan]).count()
assert_identical(expected, actual)
v = Variable(['x'], np.array(['1', '2', '3', np.nan], dtype=object))
actual = v.count()
assert_identical(expected, actual)
actual = Variable(['x'], [True, False, True]).count()
assert_identical(expected, actual)
assert actual.dtype == int
expected = Variable(['x'], [2, 3])
actual = Variable(['x', 'y'], [[1, 0, np.nan], [1, 1, 1]]).count('y')
assert_identical(expected, actual)
def test_setitem(self):
v = Variable(['x', 'y'], [[0, 3, 2], [3, 4, 5]])
v[0, 1] = 1
assert v[0, 1] == 1
v = Variable(['x', 'y'], [[0, 3, 2], [3, 4, 5]])
v[dict(x=[0, 1])] = 1
assert_array_equal(v[[0, 1]], np.ones_like(v[[0, 1]]))
# boolean indexing
v = Variable(['x', 'y'], [[0, 3, 2], [3, 4, 5]])
v[dict(x=[True, False])] = 1
assert_array_equal(v[0], np.ones_like(v[0]))
v = Variable(['x', 'y'], [[0, 3, 2], [3, 4, 5]])
v[dict(x=[True, False], y=[False, True, False])] = 1
assert v[0, 1] == 1
def test_setitem_fancy(self):
# assignment which should work as np.ndarray does
def assert_assigned_2d(array, key_x, key_y, values):
expected = array.copy()
expected[key_x, key_y] = values
v = Variable(['x', 'y'], array)
v[dict(x=key_x, y=key_y)] = values
assert_array_equal(expected, v)
# 1d vectorized indexing
assert_assigned_2d(np.random.randn(4, 3),
key_x=Variable(['a'], [0, 1]),
key_y=Variable(['a'], [0, 1]),
values=0)
assert_assigned_2d(np.random.randn(4, 3),
key_x=Variable(['a'], [0, 1]),
key_y=Variable(['a'], [0, 1]),
values=Variable((), 0))
assert_assigned_2d(np.random.randn(4, 3),
key_x=Variable(['a'], [0, 1]),
key_y=Variable(['a'], [0, 1]),
values=Variable(('a'), [3, 2]))
assert_assigned_2d(np.random.randn(4, 3),
key_x=slice(None),
key_y=Variable(['a'], [0, 1]),
values=Variable(('a'), [3, 2]))
# 2d-vectorized indexing
assert_assigned_2d(np.random.randn(4, 3),
key_x=Variable(['a', 'b'], [[0, 1]]),
key_y=Variable(['a', 'b'], [[1, 0]]),
values=0)
assert_assigned_2d(np.random.randn(4, 3),
key_x=Variable(['a', 'b'], [[0, 1]]),
key_y=Variable(['a', 'b'], [[1, 0]]),
values=[0])
assert_assigned_2d(np.random.randn(5, 4),
key_x=Variable(['a', 'b'], [[0, 1], [2, 3]]),
key_y=Variable(['a', 'b'], [[1, 0], [3, 3]]),
values=[2, 3])
# vindex with slice
v = Variable(['x', 'y', 'z'], np.ones((4, 3, 2)))
ind = Variable(['a'], [0, 1])
v[dict(x=ind, z=ind)] = 0
expected = Variable(['x', 'y', 'z'], np.ones((4, 3, 2)))
expected[0, :, 0] = 0
expected[1, :, 1] = 0
assert_identical(expected, v)
# dimension broadcast
v = Variable(['x', 'y'], np.ones((3, 2)))
ind = Variable(['a', 'b'], [[0, 1]])
v[ind, :] = 0
expected = Variable(['x', 'y'], [[0, 0], [0, 0], [1, 1]])
assert_identical(expected, v)
with raises_regex(ValueError, "shape mismatch"):
v[ind, ind] = np.zeros((1, 2, 1))
v = Variable(['x', 'y'], [[0, 3, 2], [3, 4, 5]])
ind = Variable(['a'], [0, 1])
v[dict(x=ind)] = Variable(['a', 'y'], np.ones((2, 3), dtype=int) * 10)
assert_array_equal(v[0], np.ones_like(v[0]) * 10)
assert_array_equal(v[1], np.ones_like(v[1]) * 10)
assert v.dims == ('x', 'y') # dimension should not change
# increment
v = Variable(['x', 'y'], np.arange(6).reshape(3, 2))
ind = Variable(['a'], [0, 1])
v[dict(x=ind)] += 1
expected = Variable(['x', 'y'], [[1, 2], [3, 4], [4, 5]])
assert_identical(v, expected)
ind = Variable(['a'], [0, 0])
v[dict(x=ind)] += 1
expected = Variable(['x', 'y'], [[2, 3], [3, 4], [4, 5]])
assert_identical(v, expected)
@requires_dask
class TestVariableWithDask(TestCase, VariableSubclassTestCases):
cls = staticmethod(lambda *args: Variable(*args).chunk())
@pytest.mark.xfail
def test_0d_object_array_with_list(self):
super(TestVariableWithDask, self).test_0d_object_array_with_list()
@pytest.mark.xfail
def test_array_interface(self):
# dask array does not have `argsort`
super(TestVariableWithDask, self).test_array_interface()
@pytest.mark.xfail
def test_copy_index(self):
super(TestVariableWithDask, self).test_copy_index()
@pytest.mark.xfail
def test_eq_all_dtypes(self):
super(TestVariableWithDask, self).test_eq_all_dtypes()
def test_getitem_fancy(self):
import dask
if LooseVersion(dask.__version__) <= LooseVersion('0.15.1'):
pytest.xfail("vindex from latest dask is required")
super(TestVariableWithDask, self).test_getitem_fancy()
def test_getitem_1d_fancy(self):
import dask
if LooseVersion(dask.__version__) <= LooseVersion('0.15.1'):
pytest.xfail("vindex from latest dask is required")
super(TestVariableWithDask, self).test_getitem_1d_fancy()
def test_getitem_with_mask_nd_indexer(self):
import dask.array as da
v = Variable(['x'], da.arange(3, chunks=3))
indexer = Variable(('x', 'y'), [[0, -1], [-1, 2]])
assert_identical(v._getitem_with_mask(indexer, fill_value=-1),
self.cls(('x', 'y'), [[0, -1], [-1, 2]]))
class TestIndexVariable(TestCase, VariableSubclassTestCases):
cls = staticmethod(IndexVariable)
def test_init(self):
with raises_regex(ValueError, 'must be 1-dimensional'):
IndexVariable((), 0)
def test_to_index(self):
data = 0.5 * np.arange(10)
v = IndexVariable(['time'], data, {'foo': 'bar'})
assert pd.Index(data, name='time').identical(v.to_index())
def test_multiindex_default_level_names(self):
midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2]])
v = IndexVariable(['x'], midx, {'foo': 'bar'})
assert v.to_index().names == ('x_level_0', 'x_level_1')
def test_data(self):
x = IndexVariable('x', np.arange(3.0))
assert isinstance(x._data, PandasIndexAdapter)
assert isinstance(x.data, np.ndarray)
assert float == x.dtype
assert_array_equal(np.arange(3), x)
assert float == x.values.dtype
with raises_regex(TypeError, 'cannot be modified'):
x[:] = 0
def test_name(self):
coord = IndexVariable('x', [10.0])
assert coord.name == 'x'
with pytest.raises(AttributeError):
coord.name = 'y'
def test_level_names(self):
midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2]],
names=['level_1', 'level_2'])
x = IndexVariable('x', midx)
assert x.level_names == midx.names
assert IndexVariable('y', [10.0]).level_names is None
def test_get_level_variable(self):
midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2]],
names=['level_1', 'level_2'])
x = IndexVariable('x', midx)
level_1 = IndexVariable('x', midx.get_level_values('level_1'))
assert_identical(x.get_level_variable('level_1'), level_1)
with raises_regex(ValueError, 'has no MultiIndex'):
IndexVariable('y', [10.0]).get_level_variable('level')
def test_concat_periods(self):
periods = pd.period_range('2000-01-01', periods=10)
coords = [IndexVariable('t', periods[:5]),
IndexVariable('t', periods[5:])]
expected = IndexVariable('t', periods)
actual = IndexVariable.concat(coords, dim='t')
assert actual.identical(expected)
assert isinstance(actual.to_index(), pd.PeriodIndex)
positions = [list(range(5)), list(range(5, 10))]
actual = IndexVariable.concat(coords, dim='t', positions=positions)
assert actual.identical(expected)
assert isinstance(actual.to_index(), pd.PeriodIndex)
def test_concat_multiindex(self):
idx = pd.MultiIndex.from_product([[0, 1, 2], ['a', 'b']])
coords = [IndexVariable('x', idx[:2]), IndexVariable('x', idx[2:])]
expected = IndexVariable('x', idx)
actual = IndexVariable.concat(coords, dim='x')
assert actual.identical(expected)
assert isinstance(actual.to_index(), pd.MultiIndex)
def test_coordinate_alias(self):
with pytest.warns(Warning, match='deprecated'):
x = Coordinate('x', [1, 2, 3])
assert isinstance(x, IndexVariable)
def test_datetime64(self):
# GH:1932 Make sure indexing keeps precision
t = np.array([1518418799999986560, 1518418799999996560],
dtype='datetime64[ns]')
v = IndexVariable('t', t)
assert v[0].data == t[0]
# These tests make use of multi-dimensional variables, which are not valid
# IndexVariable objects:
@pytest.mark.xfail
def test_getitem_error(self):
super(TestIndexVariable, self).test_getitem_error()
@pytest.mark.xfail
def test_getitem_advanced(self):
super(TestIndexVariable, self).test_getitem_advanced()
@pytest.mark.xfail
def test_getitem_fancy(self):
super(TestIndexVariable, self).test_getitem_fancy()
@pytest.mark.xfail
def test_getitem_uint(self):
super(TestIndexVariable, self).test_getitem_fancy()
@pytest.mark.xfail
def test_pad(self):
super(TestIndexVariable, self).test_rolling_window()
@pytest.mark.xfail
def test_rolling_window(self):
super(TestIndexVariable, self).test_rolling_window()
class TestAsCompatibleData(TestCase):
def test_unchanged_types(self):
types = (np.asarray, PandasIndexAdapter, LazilyOuterIndexedArray)
for t in types:
for data in [np.arange(3),
pd.date_range('2000-01-01', periods=3),
pd.date_range('2000-01-01', periods=3).values]:
x = t(data)
assert source_ndarray(x) is \
source_ndarray(as_compatible_data(x))
def test_converted_types(self):
for input_array in [[[0, 1, 2]], pd.DataFrame([[0, 1, 2]])]:
actual = as_compatible_data(input_array)
assert_array_equal(np.asarray(input_array), actual)
assert np.ndarray == type(actual)
assert np.asarray(input_array).dtype == actual.dtype
def test_masked_array(self):
original = np.ma.MaskedArray(np.arange(5))
expected = np.arange(5)
actual = as_compatible_data(original)
assert_array_equal(expected, actual)
assert np.dtype(int) == actual.dtype
original = np.ma.MaskedArray(np.arange(5), mask=4 * [False] + [True])
expected = np.arange(5.0)
expected[-1] = np.nan
actual = as_compatible_data(original)
assert_array_equal(expected, actual)
assert np.dtype(float) == actual.dtype
def test_datetime(self):
expected = np.datetime64('2000-01-01')
actual = as_compatible_data(expected)
assert expected == actual
assert np.ndarray == type(actual)
assert np.dtype('datetime64[ns]') == actual.dtype
expected = np.array([np.datetime64('2000-01-01')])
actual = as_compatible_data(expected)
assert np.asarray(expected) == actual
assert np.ndarray == type(actual)
assert np.dtype('datetime64[ns]') == actual.dtype
expected = np.array([np.datetime64('2000-01-01', 'ns')])
actual = as_compatible_data(expected)
assert np.asarray(expected) == actual
assert np.ndarray == type(actual)
assert np.dtype('datetime64[ns]') == actual.dtype
assert expected is source_ndarray(np.asarray(actual))
expected = np.datetime64('2000-01-01', 'ns')
actual = as_compatible_data(datetime(2000, 1, 1))
assert np.asarray(expected) == actual
assert np.ndarray == type(actual)
assert np.dtype('datetime64[ns]') == actual.dtype
def test_full_like(self):
# For more thorough tests, see test_variable.py
orig = Variable(dims=('x', 'y'), data=[[1.5, 2.0], [3.1, 4.3]],
attrs={'foo': 'bar'})
expect = orig.copy(deep=True)
expect.values = [[2.0, 2.0], [2.0, 2.0]]
assert_identical(expect, full_like(orig, 2))
# override dtype
expect.values = [[True, True], [True, True]]
assert expect.dtype == bool
assert_identical(expect, full_like(orig, True, dtype=bool))
@requires_dask
def test_full_like_dask(self):
orig = Variable(dims=('x', 'y'), data=[[1.5, 2.0], [3.1, 4.3]],
attrs={'foo': 'bar'}).chunk(((1, 1), (2,)))
def check(actual, expect_dtype, expect_values):
assert actual.dtype == expect_dtype
assert actual.shape == orig.shape
assert actual.dims == orig.dims
assert actual.attrs == orig.attrs
assert actual.chunks == orig.chunks
assert_array_equal(actual.values, expect_values)
check(full_like(orig, 2),
orig.dtype, np.full_like(orig.values, 2))
# override dtype
check(full_like(orig, True, dtype=bool),
bool, np.full_like(orig.values, True, dtype=bool))
# Check that there's no array stored inside dask
# (e.g. we didn't create a numpy array and then we chunked it!)
dsk = full_like(orig, 1).data.dask
for v in dsk.values():
if isinstance(v, tuple):
for vi in v:
assert not isinstance(vi, np.ndarray)
else:
assert not isinstance(v, np.ndarray)
def test_zeros_like(self):
orig = Variable(dims=('x', 'y'), data=[[1.5, 2.0], [3.1, 4.3]],
attrs={'foo': 'bar'})
assert_identical(zeros_like(orig),
full_like(orig, 0))
assert_identical(zeros_like(orig, dtype=int),
full_like(orig, 0, dtype=int))
def test_ones_like(self):
orig = Variable(dims=('x', 'y'), data=[[1.5, 2.0], [3.1, 4.3]],
attrs={'foo': 'bar'})
assert_identical(ones_like(orig),
full_like(orig, 1))
assert_identical(ones_like(orig, dtype=int),
full_like(orig, 1, dtype=int))
def test_unsupported_type(self):
# Non indexable type
class CustomArray(NDArrayMixin):
def __init__(self, array):
self.array = array
class CustomIndexable(CustomArray, indexing.ExplicitlyIndexed):
pass
array = CustomArray(np.arange(3))
orig = Variable(dims=('x'), data=array, attrs={'foo': 'bar'})
assert isinstance(orig._data, np.ndarray) # should not be CustomArray
array = CustomIndexable(np.arange(3))
orig = Variable(dims=('x'), data=array, attrs={'foo': 'bar'})
assert isinstance(orig._data, CustomIndexable)
def test_raise_no_warning_for_nan_in_binary_ops():
with pytest.warns(None) as record:
Variable('x', [1, 2, np.NaN]) > 0
assert len(record) == 0
class TestBackendIndexing(TestCase):
""" Make sure all the array wrappers can be indexed. """
def setUp(self):
self.d = np.random.random((10, 3)).astype(np.float64)
def check_orthogonal_indexing(self, v):
assert np.allclose(v.isel(x=[8, 3], y=[2, 1]),
self.d[[8, 3]][:, [2, 1]])
def check_vectorized_indexing(self, v):
ind_x = Variable('z', [0, 2])
ind_y = Variable('z', [2, 1])
assert np.allclose(v.isel(x=ind_x, y=ind_y), self.d[ind_x, ind_y])
def test_NumpyIndexingAdapter(self):
v = Variable(dims=('x', 'y'), data=NumpyIndexingAdapter(self.d))
self.check_orthogonal_indexing(v)
self.check_vectorized_indexing(v)
# could not doubly wrapping
with raises_regex(TypeError, 'NumpyIndexingAdapter only wraps '):
v = Variable(dims=('x', 'y'), data=NumpyIndexingAdapter(
NumpyIndexingAdapter(self.d)))
def test_LazilyOuterIndexedArray(self):
v = Variable(dims=('x', 'y'), data=LazilyOuterIndexedArray(self.d))
self.check_orthogonal_indexing(v)
self.check_vectorized_indexing(v)
# doubly wrapping
v = Variable(
dims=('x', 'y'),
data=LazilyOuterIndexedArray(LazilyOuterIndexedArray(self.d)))
self.check_orthogonal_indexing(v)
# hierarchical wrapping
v = Variable(
dims=('x', 'y'),
data=LazilyOuterIndexedArray(NumpyIndexingAdapter(self.d)))
self.check_orthogonal_indexing(v)
def test_CopyOnWriteArray(self):
v = Variable(dims=('x', 'y'), data=CopyOnWriteArray(self.d))
self.check_orthogonal_indexing(v)
self.check_vectorized_indexing(v)
# doubly wrapping
v = Variable(
dims=('x', 'y'),
data=CopyOnWriteArray(LazilyOuterIndexedArray(self.d)))
self.check_orthogonal_indexing(v)
self.check_vectorized_indexing(v)
def test_MemoryCachedArray(self):
v = Variable(dims=('x', 'y'), data=MemoryCachedArray(self.d))
self.check_orthogonal_indexing(v)
self.check_vectorized_indexing(v)
# doubly wrapping
v = Variable(dims=('x', 'y'),
data=CopyOnWriteArray(MemoryCachedArray(self.d)))
self.check_orthogonal_indexing(v)
self.check_vectorized_indexing(v)
@requires_dask
def test_DaskIndexingAdapter(self):
import dask.array as da
da = da.asarray(self.d)
v = Variable(dims=('x', 'y'), data=DaskIndexingAdapter(da))
self.check_orthogonal_indexing(v)
self.check_vectorized_indexing(v)
# doubly wrapping
v = Variable(dims=('x', 'y'),
data=CopyOnWriteArray(DaskIndexingAdapter(da)))
self.check_orthogonal_indexing(v)
self.check_vectorized_indexing(v)
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