def test_unit_weighted(self):
# unit weights should be the same as no weights
data = np.array([5, 2, 6, 4], dtype=np.float64)
weights = np.ones_like(data)
rms = RMS.aggregate(data, 0, weights=weights)
expected_rms = 4.5
self.assertAlmostEqual(rms, expected_rms)
def test_2d_weighted(self):
# 2-dimensional input with weights
data = np.array([[4, 7, 10, 8], [14, 16, 20, 8]], dtype=np.float64)
weights = np.array([[1, 4, 3, 2], [2, 1, 1.5, 0.5]], dtype=np.float64)
expected_rms = np.array([8.0, 16.0], dtype=np.float64)
rms = RMS.aggregate(data, 1, weights=weights)
self.assertArrayAlmostEqual(rms, expected_rms)
def test_1d(self):
# 1-dimensional input.
data = as_lazy_data(np.array([5, 2, 6, 4], dtype=np.float64),
chunks=-1)
rms = RMS.lazy_aggregate(data, 0)
expected_rms = 4.5
self.assertAlmostEqual(rms, expected_rms)
def test_2d(self):
# 2-dimensional input.
data = as_lazy_data(
np.array([[5, 2, 6, 4], [12, 4, 10, 8]], dtype=np.float64))
expected_rms = np.array([4.5, 9.0], dtype=np.float64)
rms = RMS.lazy_aggregate(data, 1)
self.assertArrayAlmostEqual(rms, expected_rms)
def test_1d_weighted(self):
# 1-dimensional input with weights
data = np.array([4, 7, 10, 8], dtype=np.float64)
weights = np.array([1, 4, 3, 2], dtype=np.float64)
expected_rms = 8.0
rms = RMS.aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_1d(self):
# 1-dimensional input.
data = as_lazy_data(np.array([5, 2, 6, 4], dtype=np.float64),
chunks=-1)
rms = RMS.lazy_aggregate(data, 0)
expected_rms = 4.5
self.assertAlmostEqual(rms, expected_rms)
def test_2d_weighted(self):
# 2-dimensional input with weights
data = np.array([[4, 7, 10, 8], [14, 16, 20, 8]], dtype=np.float64)
weights = np.array([[1, 4, 3, 2], [2, 1, 1.5, 0.5]], dtype=np.float64)
expected_rms = np.array([8.0, 16.0], dtype=np.float64)
rms = RMS.aggregate(data, 1, weights=weights)
self.assertArrayAlmostEqual(rms, expected_rms)
def test_unit_weighted(self):
# unit weights should be the same as no weights
data = np.array([5, 2, 6, 4], dtype=np.float64)
weights = np.ones_like(data)
rms = RMS.aggregate(data, 0, weights=weights)
expected_rms = 4.5
self.assertAlmostEqual(rms, expected_rms)
def test_1d_weighted(self):
# 1-dimensional input with weights
data = np.array([4, 7, 10, 8], dtype=np.float64)
weights = np.array([1, 4, 3, 2], dtype=np.float64)
expected_rms = 8.0
rms = RMS.aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_masked_weighted(self):
# weights should work properly with masked arrays
data = ma.array([4, 7, 18, 10, 11, 8], mask=[False, False, True, False, True, False], dtype=np.float64)
weights = np.array([1, 4, 5, 3, 8, 2], dtype=np.float64)
expected_rms = 8.0
rms = RMS.aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_2d(self):
# 2-dimensional input.
data = as_lazy_data(np.array([[5, 2, 6, 4], [12, 4, 10, 8]],
dtype=np.float64),
chunks=-1)
expected_rms = np.array([4.5, 9.0], dtype=np.float64)
rms = RMS.lazy_aggregate(data, 1)
self.assertArrayAlmostEqual(rms, expected_rms)
def test_masked(self):
# masked entries should be completely ignored
data = ma.array([5, 10, 2, 11, 6, 4],
mask=[False, True, False, True, False, False],
dtype=np.float64)
expected_rms = 4.5
rms = RMS.aggregate(data, 0)
self.assertAlmostEqual(rms, expected_rms)
def test_masked(self):
# masked entries should be completely ignored
data = ma.array([5, 10, 2, 11, 6, 4],
mask=[False, True, False, True, False, False],
dtype=np.float64)
expected_rms = 4.5
rms = RMS.aggregate(data, 0)
self.assertAlmostEqual(rms, expected_rms)
def test_masked(self):
# Masked entries should be completely ignored.
data = as_lazy_data(ma.array([5, 10, 2, 11, 6, 4],
mask=[False, True, False, True, False, False],
dtype=np.float64),
chunks=-1)
expected_rms = 4.5
rms = RMS.lazy_aggregate(data, 0)
self.assertAlmostEqual(rms, expected_rms)
def test_1d_weighted(self):
# 1-dimensional input with weights.
data = as_lazy_data(np.array([4, 7, 10, 8], dtype=np.float64))
weights = np.array([1, 4, 3, 2], dtype=np.float64)
expected_rms = 8.0
# https://github.com/dask/dask/issues/3846.
with self.assertRaisesRegex(TypeError, "unexpected keyword argument"):
rms = RMS.lazy_aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_unit_weighted(self):
# Unit weights should be the same as no weights.
data = as_lazy_data(np.array([5, 2, 6, 4], dtype=np.float64))
weights = np.ones_like(data)
expected_rms = 4.5
# https://github.com/dask/dask/issues/3846.
with self.assertRaisesRegex(TypeError, "unexpected keyword argument"):
rms = RMS.lazy_aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_masked_weighted(self):
# weights should work properly with masked arrays
data = ma.array([4, 7, 18, 10, 11, 8],
mask=[False, False, True, False, True, False],
dtype=np.float64)
weights = np.array([1, 4, 5, 3, 8, 2], dtype=np.float64)
expected_rms = 8.0
rms = RMS.aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_1d_weighted(self):
# 1-dimensional input with weights.
data = as_lazy_data(np.array([4, 7, 10, 8], dtype=np.float64),
chunks=-1)
weights = np.array([1, 4, 3, 2], dtype=np.float64)
expected_rms = 8.0
# https://github.com/dask/dask/issues/3846.
with self.assertRaisesRegexp(TypeError, 'unexpected keyword argument'):
rms = RMS.lazy_aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_unit_weighted(self):
# Unit weights should be the same as no weights.
data = as_lazy_data(np.array([5, 2, 6, 4], dtype=np.float64),
chunks=-1)
weights = np.ones_like(data)
expected_rms = 4.5
# https://github.com/dask/dask/issues/3846.
with self.assertRaisesRegexp(TypeError, 'unexpected keyword argument'):
rms = RMS.lazy_aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_masked(self):
# Masked entries should be completely ignored.
data = as_lazy_data(ma.array(
[5, 10, 2, 11, 6, 4],
mask=[False, True, False, True, False, False],
dtype=np.float64),
chunks=-1)
expected_rms = 4.5
rms = RMS.lazy_aggregate(data, 0)
self.assertAlmostEqual(rms, expected_rms)
def test_2d_weighted(self):
# 2-dimensional input with weights.
data = as_lazy_data(
np.array([[4, 7, 10, 8], [14, 16, 20, 8]], dtype=np.float64))
weights = np.array([[1, 4, 3, 2], [2, 1, 1.5, 0.5]], dtype=np.float64)
expected_rms = np.array([8.0, 16.0], dtype=np.float64)
# https://github.com/dask/dask/issues/3846.
with self.assertRaisesRegex(TypeError, "unexpected keyword argument"):
rms = RMS.lazy_aggregate(data, 1, weights=weights)
self.assertArrayAlmostEqual(rms, expected_rms)
def test_masked_weighted(self):
# Weights should work properly with masked arrays, but currently don't
# (see https://github.com/dask/dask/issues/3846).
# For now, masked weights are simply not supported.
data = as_lazy_data(ma.array([4, 7, 18, 10, 11, 8],
mask=[False, False, True, False, True, False],
dtype=np.float64))
weights = np.array([1, 4, 5, 3, 8, 2])
expected_rms = 8.0
with self.assertRaisesRegex(TypeError, 'unexpected keyword argument'):
rms = RMS.lazy_aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_masked_weighted(self):
# Weights should work properly with masked arrays, but currently don't
# (see https://github.com/dask/dask/issues/3846).
# For now, masked weights are simply not supported.
data = as_lazy_data(ma.array([4, 7, 18, 10, 11, 8],
mask=[False, False, True, False, True, False],
dtype=np.float64),
chunks=-1)
weights = np.array([1, 4, 5, 3, 8, 2])
expected_rms = 8.0
with self.assertRaisesRegexp(TypeError, 'unexpected keyword argument'):
rms = RMS.lazy_aggregate(data, 0, weights=weights)
self.assertAlmostEqual(rms, expected_rms)
def test_1d(self):
# 1-dimensional input
data = np.array([5, 2, 6, 4], dtype=np.float64)
rms = RMS.aggregate(data, 0)
expected_rms = 4.5
self.assertAlmostEqual(rms, expected_rms)
def test(self):
shape = ()
kwargs = dict()
self.assertTupleEqual(RMS.aggregate_shape(**kwargs), shape)
kwargs = dict(tom='jerry', calvin='hobbes')
self.assertTupleEqual(RMS.aggregate_shape(**kwargs), shape)
def test(self):
shape = ()
kwargs = dict()
self.assertTupleEqual(RMS.aggregate_shape(**kwargs), shape)
kwargs = dict(tom='jerry', calvin='hobbes')
self.assertTupleEqual(RMS.aggregate_shape(**kwargs), shape)
def test(self):
self.assertEqual(RMS.name(), 'root_mean_square')
def test(self):
self.assertEqual(RMS.name(), 'root_mean_square')
def test_1d(self):
# 1-dimensional input
data = np.array([5, 2, 6, 4], dtype=np.float64)
rms = RMS.aggregate(data, 0)
expected_rms = 4.5
self.assertAlmostEqual(rms, expected_rms)