def test_anova():
np_args = [i * np.random.random(size=(30, )) for i in range(4)]
da_args = [da.from_array(x, chunks=10) for x in np_args]
result = dask.array.stats.f_oneway(*da_args)
expected = scipy.stats.f_oneway(*np_args)
assert allclose(result.compute(), expected)
def test_anova():
np_args = [i * np.random.random(size=(30,)) for i in range(4)]
da_args = [da.from_array(x, chunks=10) for x in np_args]
result = dask.array.stats.f_oneway(*da_args)
expected = scipy.stats.f_oneway(*np_args)
assert allclose(result.compute(), expected)
def test_one(kind):
a = np.random.random(size=30, )
a_ = da.from_array(a, 3)
dask_test = getattr(dask.array.stats, kind)
scipy_test = getattr(scipy.stats, kind)
result = dask_test(a_)
expected = scipy_test(a)
assert isinstance(result, Delayed)
assert allclose(result.compute(), expected)
def test_one(kind):
a = np.random.random(size=30,)
a_ = da.from_array(a, 3)
dask_test = getattr(dask.array.stats, kind)
scipy_test = getattr(scipy.stats, kind)
result = dask_test(a_)
expected = scipy_test(a)
assert isinstance(result, Delayed)
assert allclose(result.compute(), expected)
def test_two(kind, kwargs):
a = np.random.random(size=30, )
b = np.random.random(size=30, )
a_ = da.from_array(a, 3)
b_ = da.from_array(b, 3)
dask_test = getattr(dask.array.stats, kind)
scipy_test = getattr(scipy.stats, kind)
result = dask_test(a_, b_, **kwargs)
expected = scipy_test(a, b, **kwargs)
assert isinstance(result, Delayed)
assert allclose(result.compute(), expected)
def test_two(kind, kwargs):
a = np.random.random(size=30)
b = np.random.random(size=30)
a_ = da.from_array(a, 3)
b_ = da.from_array(b, 3)
dask_test = getattr(dask.array.stats, kind)
scipy_test = getattr(scipy.stats, kind)
with pytest.warns(None): # maybe overflow warning (powrer_divergence)
result = dask_test(a_, b_, **kwargs)
expected = scipy_test(a, b, **kwargs)
assert isinstance(result, Delayed)
assert allclose(result.compute(), expected)
def test_two(kind, kwargs):
a = np.random.random(size=30,)
b = np.random.random(size=30,)
a_ = da.from_array(a, 3)
b_ = da.from_array(b, 3)
dask_test = getattr(dask.array.stats, kind)
scipy_test = getattr(scipy.stats, kind)
with pytest.warns(None): # maybe overflow warning (powrer_divergence)
result = dask_test(a_, b_, **kwargs)
expected = scipy_test(a, b, **kwargs)
assert isinstance(result, Delayed)
assert allclose(result.compute(), expected)
def test_two(kind, kwargs):
# The sums of observed and expected frequencies must match
a = np.random.random(size=30)
b = a[::-1]
a_ = da.from_array(a, 3)
b_ = da.from_array(b, 3)
dask_test = getattr(dask.array.stats, kind)
scipy_test = getattr(scipy.stats, kind)
with warnings.catch_warnings(
): # maybe overflow warning (power_divergence)
warnings.simplefilter("ignore", category=RuntimeWarning)
result = dask_test(a_, b_, **kwargs)
expected = scipy_test(a, b, **kwargs)
assert isinstance(result, Delayed)
assert allclose(result.compute(), expected)
