def test_3d(self):
a = numpy.array([x * 3 for x in range(24)]).reshape((3, 2, 4))
expected = [
((0, 0, 0), 0),
((0, 0, 1), 3),
((0, 0, 2), 6),
((0, 0, 3), 9),
((0, 1, 0), 12),
((0, 1, 1), 15),
((0, 1, 2), 18),
((0, 1, 3), 21),
((1, 0, 0), 24),
((1, 0, 1), 27),
((1, 0, 2), 30),
((1, 0, 3), 33),
((1, 1, 0), 36),
((1, 1, 1), 39),
((1, 1, 2), 42),
((1, 1, 3), 45),
((2, 0, 0), 48),
((2, 0, 1), 51),
((2, 0, 2), 54),
((2, 0, 3), 57),
((2, 1, 0), 60),
((2, 1, 1), 63),
((2, 1, 2), 66),
((2, 1, 3), 69),
]
actual = list(utils.ndenumerate(a))
self.assertEqual(expected, actual)
def test_3d(self):
a = numpy.array([x * 3 for x in range(24)]).reshape((3, 2, 4))
expected = [
((0, 0, 0), 0),
((0, 0, 1), 3),
((0, 0, 2), 6),
((0, 0, 3), 9),
((0, 1, 0), 12),
((0, 1, 1), 15),
((0, 1, 2), 18),
((0, 1, 3), 21),
((1, 0, 0), 24),
((1, 0, 1), 27),
((1, 0, 2), 30),
((1, 0, 3), 33),
((1, 1, 0), 36),
((1, 1, 1), 39),
((1, 1, 2), 42),
((1, 1, 3), 45),
((2, 0, 0), 48),
((2, 0, 1), 51),
((2, 0, 2), 54),
((2, 0, 3), 57),
((2, 1, 0), 60),
((2, 1, 1), 63),
((2, 1, 2), 66),
((2, 1, 3), 69),
]
actual = list(utils.ndenumerate(a))
self.assertEqual(expected, actual)
def serialize(self, data):
"""
:param data:
A sequence of data where each datum has the following attributes:
* matrix: N-dimensional numpy array containing the disaggregation
histogram.
* dim_labels: A list of strings which label the dimensions of a
given histogram. For example, for a Magnitude-Distance-Epsilon
histogram, we would expect `dim_labels` to be
``['Mag', 'Dist', 'Eps']``.
* poe: The disaggregation Probability of Exceedance level for which
these results were produced.
* iml: Intensity measure level, interpolated from the source hazard
curve at the given ``poe``.
"""
with open(self.path, 'w') as fh:
root = etree.Element('nrml',
nsmap=openquake.nrmllib.SERIALIZE_NS_MAP)
diss_matrices = etree.SubElement(root, 'disaggMatrices')
_set_metadata(diss_matrices, self.metadata, _ATTR_MAP)
transform = lambda val: ', '.join([str(x) for x in val])
_set_metadata(diss_matrices, self.metadata, self.BIN_EDGE_ATTR_MAP,
transform=transform)
for result in data:
diss_matrix = etree.SubElement(diss_matrices, 'disaggMatrix')
# Check that we have bin edges defined for each dimension label
# (mag, dist, lon, lat, eps, TRT)
for label in result.dim_labels:
bin_edge_attr = self.DIM_LABEL_TO_BIN_EDGE_MAP.get(label)
assert self.metadata.get(bin_edge_attr) is not None, (
"Writer is missing '%s' metadata" % bin_edge_attr
)
result_type = ','.join(result.dim_labels)
diss_matrix.set('type', result_type)
dims = ','.join([str(x) for x in result.matrix.shape])
diss_matrix.set('dims', dims)
diss_matrix.set('poE', str(result.poe))
diss_matrix.set('iml', str(result.iml))
for idxs, value in utils.ndenumerate(result.matrix):
prob = etree.SubElement(diss_matrix, 'prob')
index = ','.join([str(x) for x in idxs])
prob.set('index', index)
prob.set('value', str(value))
fh.write(etree.tostring(
root, pretty_print=True, xml_declaration=True,
encoding='UTF-8'))
def test_1d(self):
a = numpy.array([x * 3 for x in range(4)])
expected = [
((0, ), 0),
((1, ), 3),
((2, ), 6),
((3, ), 9),
]
actual = list(utils.ndenumerate(a))
self.assertEqual(expected, actual)
def test_1d(self):
a = numpy.array([x * 3 for x in range(4)])
expected = [
((0,), 0),
((1,), 3),
((2,), 6),
((3,), 9),
]
actual = list(utils.ndenumerate(a))
self.assertEqual(expected, actual)
def test_4d(self):
a = numpy.array([x * 3 for x in range(48)]).reshape((2, 2, 3, 4))
expected = [
((0, 0, 0, 0), 0),
((0, 0, 0, 1), 3),
((0, 0, 0, 2), 6),
((0, 0, 0, 3), 9),
((0, 0, 1, 0), 12),
((0, 0, 1, 1), 15),
((0, 0, 1, 2), 18),
((0, 0, 1, 3), 21),
((0, 0, 2, 0), 24),
((0, 0, 2, 1), 27),
((0, 0, 2, 2), 30),
((0, 0, 2, 3), 33),
((0, 1, 0, 0), 36),
((0, 1, 0, 1), 39),
((0, 1, 0, 2), 42),
((0, 1, 0, 3), 45),
((0, 1, 1, 0), 48),
((0, 1, 1, 1), 51),
((0, 1, 1, 2), 54),
((0, 1, 1, 3), 57),
((0, 1, 2, 0), 60),
((0, 1, 2, 1), 63),
((0, 1, 2, 2), 66),
((0, 1, 2, 3), 69),
((1, 0, 0, 0), 72),
((1, 0, 0, 1), 75),
((1, 0, 0, 2), 78),
((1, 0, 0, 3), 81),
((1, 0, 1, 0), 84),
((1, 0, 1, 1), 87),
((1, 0, 1, 2), 90),
((1, 0, 1, 3), 93),
((1, 0, 2, 0), 96),
((1, 0, 2, 1), 99),
((1, 0, 2, 2), 102),
((1, 0, 2, 3), 105),
((1, 1, 0, 0), 108),
((1, 1, 0, 1), 111),
((1, 1, 0, 2), 114),
((1, 1, 0, 3), 117),
((1, 1, 1, 0), 120),
((1, 1, 1, 1), 123),
((1, 1, 1, 2), 126),
((1, 1, 1, 3), 129),
((1, 1, 2, 0), 132),
((1, 1, 2, 1), 135),
((1, 1, 2, 2), 138),
((1, 1, 2, 3), 141),
]
actual = list(utils.ndenumerate(a))
self.assertEqual(expected, actual)
def test_4d(self):
a = numpy.array([x * 3 for x in range(48)]).reshape((2, 2, 3, 4))
expected = [
((0, 0, 0, 0), 0),
((0, 0, 0, 1), 3),
((0, 0, 0, 2), 6),
((0, 0, 0, 3), 9),
((0, 0, 1, 0), 12),
((0, 0, 1, 1), 15),
((0, 0, 1, 2), 18),
((0, 0, 1, 3), 21),
((0, 0, 2, 0), 24),
((0, 0, 2, 1), 27),
((0, 0, 2, 2), 30),
((0, 0, 2, 3), 33),
((0, 1, 0, 0), 36),
((0, 1, 0, 1), 39),
((0, 1, 0, 2), 42),
((0, 1, 0, 3), 45),
((0, 1, 1, 0), 48),
((0, 1, 1, 1), 51),
((0, 1, 1, 2), 54),
((0, 1, 1, 3), 57),
((0, 1, 2, 0), 60),
((0, 1, 2, 1), 63),
((0, 1, 2, 2), 66),
((0, 1, 2, 3), 69),
((1, 0, 0, 0), 72),
((1, 0, 0, 1), 75),
((1, 0, 0, 2), 78),
((1, 0, 0, 3), 81),
((1, 0, 1, 0), 84),
((1, 0, 1, 1), 87),
((1, 0, 1, 2), 90),
((1, 0, 1, 3), 93),
((1, 0, 2, 0), 96),
((1, 0, 2, 1), 99),
((1, 0, 2, 2), 102),
((1, 0, 2, 3), 105),
((1, 1, 0, 0), 108),
((1, 1, 0, 1), 111),
((1, 1, 0, 2), 114),
((1, 1, 0, 3), 117),
((1, 1, 1, 0), 120),
((1, 1, 1, 1), 123),
((1, 1, 1, 2), 126),
((1, 1, 1, 3), 129),
((1, 1, 2, 0), 132),
((1, 1, 2, 1), 135),
((1, 1, 2, 2), 138),
((1, 1, 2, 3), 141),
]
actual = list(utils.ndenumerate(a))
self.assertEqual(expected, actual)
def serialize(self, data):
"""
:param data:
A sequence of data where each datum has the following attributes:
* matrix: N-dimensional numpy array containing the disaggregation
histogram.
* dim_labels: A list of strings which label the dimensions of a
given histogram. For example, for a Magnitude-Distance-Epsilon
histogram, we would expect `dim_labels` to be
``['Mag', 'Dist', 'Eps']``.
* poe: The disaggregation Probability of Exceedance level for which
these results were produced.
* iml: Intensity measure level, interpolated from the source hazard
curve at the given ``poe``.
"""
with open(self.path, 'w') as fh:
root = etree.Element('nrml',
nsmap=openquake.nrmllib.SERIALIZE_NS_MAP)
diss_matrices = etree.SubElement(root, 'disaggMatrices')
_set_metadata(diss_matrices, self.metadata, _ATTR_MAP)
transform = lambda val: ', '.join([str(x) for x in val])
_set_metadata(diss_matrices,
self.metadata,
self.BIN_EDGE_ATTR_MAP,
transform=transform)
for result in data:
diss_matrix = etree.SubElement(diss_matrices, 'disaggMatrix')
# Check that we have bin edges defined for each dimension label
# (mag, dist, lon, lat, eps, TRT)
for label in result.dim_labels:
bin_edge_attr = self.DIM_LABEL_TO_BIN_EDGE_MAP.get(label)
assert self.metadata.get(bin_edge_attr) is not None, (
"Writer is missing '%s' metadata" % bin_edge_attr)
result_type = ','.join(result.dim_labels)
diss_matrix.set('type', result_type)
dims = ','.join([str(x) for x in result.matrix.shape])
diss_matrix.set('dims', dims)
diss_matrix.set('poE', str(result.poe))
diss_matrix.set('iml', str(result.iml))
for idxs, value in utils.ndenumerate(result.matrix):
prob = etree.SubElement(diss_matrix, 'prob')
index = ','.join([str(x) for x in idxs])
prob.set('index', index)
prob.set('value', str(value))
fh.write(
etree.tostring(root,
pretty_print=True,
xml_declaration=True,
encoding='UTF-8'))
