def test_system_additional_dimension(self):
"""Test with a randomly named dimension with no metadata definition."""
arr = np.random.rand(2, 3, 4, 5, 6)
src_names = ['time', 'flood', 'x', 'y', 'heat_wave']
dst_and_field_names = ['realization', 'time', 'level', 'y', 'x']
extra = [dn for dn in src_names if dn not in dst_and_field_names]
extra = {e: {'index': src_names.index(e)} for e in extra}
for v in list(extra.values()):
v['size'] = arr.shape[v['index']]
src_names_extra_removed = [dn for dn in src_names if dn not in extra]
itr = itertools.product(*[
zip_longest(
[v['index']], list(range(v['size'])), fillvalue=v['index'])
for v in list(extra.values())
])
for indices in itr:
slc = [slice(None)] * arr.ndim
for ii in indices:
slc[ii[0]] = ii[1]
extras_removed = arr.__getitem__(slc)
carr = broadcast_array_by_dimension_names(extras_removed,
src_names_extra_removed,
dst_and_field_names)
self.assertEqual(carr.shape, (1, 2, 1, 5, 4))
self.assertNumpyMayShareMemory(arr, carr)
def test_system_additional_dimension(self):
"""Test with a randomly named dimension with no metadata definition."""
arr = np.random.rand(2, 3, 4, 5, 6)
src_names = ['time', 'flood', 'x', 'y', 'heat_wave']
dst_and_field_names = ['realization', 'time', 'level', 'y', 'x']
extra = [dn for dn in src_names if dn not in dst_and_field_names]
extra = {e: {'index': src_names.index(e)} for e in extra}
for v in list(extra.values()):
v['size'] = arr.shape[v['index']]
src_names_extra_removed = [dn for dn in src_names if dn not in extra]
itr = itertools.product(
*[zip_longest([v['index']], list(range(v['size'])), fillvalue=v['index']) for v in
list(extra.values())])
for indices in itr:
slc = [slice(None)] * arr.ndim
for ii in indices:
slc[ii[0]] = ii[1]
extras_removed = arr.__getitem__(slc)
carr = broadcast_array_by_dimension_names(extras_removed, src_names_extra_removed, dst_and_field_names)
self.assertEqual(carr.shape, (1, 2, 1, 5, 4))
self.assertNumpyMayShareMemory(arr, carr)
def _iter_conformed_arrays_(self, crosswalk, variable_shape, arr, arr_fill,
arr_fill_sample_size):
# Allow sample size array to be set to None.
if arr_fill_sample_size is None:
calc_sample_size = False
else:
calc_sample_size = self.calc_sample_size
itr_extra_indices, src_names_extra_removed = self._get_extra_indices_itr_and_src_names_(
crosswalk, variable_shape)
# Loop for the extra dimensions.
for indices in itr_extra_indices:
# Slice for the extra dimensions.
slc = [slice(None)] * arr.ndim
for ii in indices:
slc[ii[0]] = ii[1]
extras_removed = arr.__getitem__(slc)
extras_removed_fill = arr_fill.__getitem__(slc)
if calc_sample_size:
extras_removed_fill_sample_size = arr_fill_sample_size.__getitem__(
slc)
# Swap axes for the calculation values, the fill array for the calculation result, and (potentially) the
# sample size.
carr, carr_fill = [
broadcast_array_by_dimension_names(t, src_names_extra_removed,
STANDARD_DIMENSIONS)
for t in [extras_removed, extras_removed_fill]
]
if calc_sample_size:
carr_fill_sample_size = broadcast_array_by_dimension_names(
extras_removed_fill_sample_size, src_names_extra_removed,
STANDARD_DIMENSIONS)
if not calc_sample_size:
yld = (carr, carr_fill)
else:
yld = (carr, carr_fill, carr_fill_sample_size)
yield yld
def test(self):
arr = np.random.rand(2, 3, 4)
arr = np.ma.array(arr, mask=False)
src_names = ['time', 'x', 'y']
dst_names = ['realization', 'time', 'level', 'y', 'x']
carr = broadcast_array_by_dimension_names(arr, src_names, dst_names)
self.assertEqual(carr.shape, (1, 2, 1, 4, 3))
self.assertTrue(np.may_share_memory(arr, carr))
self.assertFalse(np.any(arr.mask))
carr.mask[:] = True
self.assertTrue(np.all(carr.mask))
self.assertTrue(np.all(arr.mask))
self.assertTrue(np.may_share_memory(arr.mask, carr.mask))
carr.mask[:] = False
carr2 = broadcast_array_by_dimension_names(carr, dst_names, src_names)
self.assertNumpyAll(arr, carr2)
self.assertTrue(np.may_share_memory(arr, carr2))
# self.assertTrue(np.may_share_memory(arr.mask, carr2.mask))
carr2[:] = 10
self.assertEqual(arr.mean(), 10)
def test(self):
arr = np.random.rand(2, 3, 4)
arr = np.ma.array(arr, mask=False)
src_names = ['time', 'x', 'y']
dst_names = ['realization', 'time', 'level', 'y', 'x']
carr = broadcast_array_by_dimension_names(arr, src_names, dst_names)
self.assertEqual(carr.shape, (1, 2, 1, 4, 3))
self.assertTrue(np.may_share_memory(arr, carr))
self.assertFalse(np.any(arr.mask))
carr.mask[:] = True
self.assertTrue(np.all(carr.mask))
self.assertTrue(np.all(arr.mask))
self.assertTrue(np.may_share_memory(arr.mask, carr.mask))
carr.mask[:] = False
carr2 = broadcast_array_by_dimension_names(carr, dst_names, src_names)
self.assertNumpyAll(arr, carr2)
self.assertTrue(np.may_share_memory(arr, carr2))
# self.assertTrue(np.may_share_memory(arr.mask, carr2.mask))
carr2[:] = 10
self.assertEqual(arr.mean(), 10)
def _iter_conformed_arrays_(self, crosswalk, variable_shape, arr, arr_fill, arr_fill_sample_size):
# Allow sample size array to be set to None.
if arr_fill_sample_size is None:
calc_sample_size = False
else:
calc_sample_size = self.calc_sample_size
itr_extra_indices, src_names_extra_removed = self._get_extra_indices_itr_and_src_names_(crosswalk,
variable_shape)
# Loop for the extra dimensions.
for indices in itr_extra_indices:
# Slice for the extra dimensions.
slc = [slice(None)] * arr.ndim
for ii in indices:
slc[ii[0]] = ii[1]
extras_removed = arr.__getitem__(slc)
extras_removed_fill = arr_fill.__getitem__(slc)
if calc_sample_size:
extras_removed_fill_sample_size = arr_fill_sample_size.__getitem__(slc)
# Swap axes for the calculation values, the fill array for the calculation result, and (potentially) the
# sample size.
carr, carr_fill = [broadcast_array_by_dimension_names(t, src_names_extra_removed, STANDARD_DIMENSIONS)
for t in [extras_removed, extras_removed_fill]]
if calc_sample_size:
carr_fill_sample_size = broadcast_array_by_dimension_names(extras_removed_fill_sample_size,
src_names_extra_removed,
STANDARD_DIMENSIONS)
if not calc_sample_size:
yld = (carr, carr_fill)
else:
yld = (carr, carr_fill, carr_fill_sample_size)
yield yld