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)