def _verify_loss_map(self, path, expected_data):
namespaces = dict(nrml=xml.NRML_NS, gml=xml.GML_NS)
root = etree.parse(path)
actual_lm_data = []
lm_nodes = root.xpath('.//nrml:LMNode', namespaces=namespaces)
# sanity check: there should be only 3 loss map nodes:
self.assertEqual(3, len(lm_nodes))
for node in lm_nodes:
node_data = dict()
[pos] = node.xpath('.//gml:pos', namespaces=namespaces)
node_data['pos'] = pos.text
[loss] = node.xpath('./nrml:loss', namespaces=namespaces)
node_data['asset'] = loss.get('assetRef')
[mean] = loss.xpath('./nrml:mean', namespaces=namespaces)
[stddev] = loss.xpath('./nrml:stdDev', namespaces=namespaces)
node_data['mean'] = float(mean.text)
node_data['stddev'] = float(stddev.text)
actual_lm_data.append(node_data)
helpers.assertDeepAlmostEqual(self,
sorted(expected_data),
sorted(actual_lm_data),
places=self.LOSSMAP_PRECISION)
def test_bug_932765(self):
# Test to directly address this bug:
# https://bugs.launchpad.net/openquake/+bug/932765
# To make this test work, we basically have to define a list of epsilon
# limits which has a different length from all of the others.
eps_limits = [-0.5, +0.5, +1.5]
subset_name = 'MagDistEpsPMF'
file_name = '%s.dat' % subset_name
target_path = os.path.join(self.tempdir,
'%s_932765.hdf5' % subset_name)
# load in the test data
data = self.read_data_file(
file_name, [self.NMAG - 1, self.NDIST - 1, self.NEPS - 1])
# chop the test data to match what we expected with the shortened
# epsilon limits we've defined (see above)
expected_data = data[:, :, 0:2]
disagg_subsets.extract_subsets(666, self.SITE, self.full_matrix_path,
self.LATITUDE_BIN_LIMITS,
self.LONGITUDE_BIN_LIMITS,
self.MAGNITUDE_BIN_LIMITS, eps_limits,
self.DISTANCE_BIN_LIMITS, target_path,
[subset_name])
actual = h5py.File(target_path, 'r')[subset_name].value
helpers.assertDeepAlmostEqual(self, expected_data, actual)
def _test_pmf(self, name, result_shape):
target_path = os.path.join(self.tempdir, '%s.hdf5' % name)
disagg_subsets.extract_subsets(
111, self.SITE, self.full_matrix_path, self.LATITUDE_BIN_LIMITS,
self.LONGITUDE_BIN_LIMITS, self.MAGNITUDE_BIN_LIMITS,
self.EPSILON_BIN_LIMITS, self.DISTANCE_BIN_LIMITS, target_path,
[name])
expected_result = self.read_data_file('%s.dat' % name, result_shape)
result = h5py.File(target_path, 'r')[name].value
helpers.assertDeepAlmostEqual(self, expected_result, result)
def test_compute_beta_dist(self):
# expected beta distributions provided by Vitor
expected_beta_distributions = [
[1.0000000, 1.0000000, 1.0000000, 1.0000000, 1.0000000],
[0.9895151, 0.9999409, 1.0000000, 1.0000000, 1.0000000],
[0.9175720, 0.9981966, 0.9999997, 1.0000000, 1.0000000],
[0.7764311, 0.9887521, 0.9999922, 1.0000000, 1.0000000],
[0.6033381, 0.9633258, 0.9999305, 1.0000000, 1.0000000],
[0.4364471, 0.9160514, 0.9996459, 1.0000000, 1.0000000],
[0.2975979, 0.8460938, 0.9987356, 1.0000000, 1.0000000],
[0.1931667, 0.7574557, 0.9964704, 1.0000000, 1.0000000],
[0.1202530, 0.6571491, 0.9917729, 0.9999999, 1.0000000],
[0.0722091, 0.5530379, 0.9832939, 0.9999997, 1.0000000],
[0.0420056, 0.4521525, 0.9695756, 0.9999988, 1.0000000],
[0.0130890, 0.2790107, 0.9213254, 0.9999887, 1.0000000],
[0.0037081, 0.1564388, 0.8409617, 0.9999306, 1.0000000],
[0.0009665, 0.0805799, 0.7311262, 0.9996882, 1.0000000],
[0.0002335, 0.0384571, 0.6024948, 0.9988955, 1.0000000],
[0.0000526, 0.0171150, 0.4696314, 0.9967629, 1.0000000],
[0.0000022, 0.0027969, 0.2413923, 0.9820831, 1.0000000],
[0.0000001, 0.0003598, 0.0998227, 0.9364072, 1.0000000],
[0.0000000, 0.0000367, 0.0334502, 0.8381920, 0.9999995],
[0.0000000, 0.0000030, 0.0091150, 0.6821293, 0.9999959],
[0.0000000, 0.0000002, 0.0020162, 0.4909782, 0.9999755],
[0.0000000, 0.0000000, 0.0000509, 0.1617086, 0.9995033],
[0.0000000, 0.0000000, 0.0000005, 0.0256980, 0.9945488],
[0.0000000, 0.0000000, 0.0000000, 0.0016231, 0.9633558],
[0.0000000, 0.0000000, 0.0000000, 0.0000288, 0.8399534],
[0.0000000, 0.0000000, 0.0000000, 0.0000001, 0.5409583],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.3413124],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.1589844],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0421052],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0027925],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000]
]
vuln_function = shapes.VulnerabilityFunction(self.imls,
self.mean_loss_ratios,
self.covs)
# steps = 5
loss_ratios = _generate_loss_ratios(vuln_function, 5)
lrem = numpy.empty((len(loss_ratios), vuln_function.imls.size), float)
for col, _ in enumerate(vuln_function):
for row, loss_ratio in enumerate(loss_ratios):
lrem[row][col] = BetaDistribution.survival_function(
loss_ratio, col=col, vf=vuln_function)
helpers.assertDeepAlmostEqual(self,
expected_beta_distributions,
lrem,
delta=0.0005)
def _test_pmf(self, name, result_shape):
target_path = os.path.join(self.tempdir, '%s.hdf5' % name)
disagg_subsets.extract_subsets(
111, self.SITE, self.full_matrix_path,
self.LATITUDE_BIN_LIMITS, self.LONGITUDE_BIN_LIMITS,
self.MAGNITUDE_BIN_LIMITS, self.EPSILON_BIN_LIMITS,
self.DISTANCE_BIN_LIMITS,
target_path, [name]
)
expected_result = self.read_data_file('%s.dat' % name, result_shape)
result = h5py.File(target_path, 'r')[name].value
helpers.assertDeepAlmostEqual(self, expected_result, result)
def test_compute_bcr_in_the_classical_psha_calculator(self):
self._compute_risk_classical_psha_setup()
helpers.delete_profile(self.job)
bcr_config = helpers.demo_file('benefit_cost_ratio/config.gem')
job_profile, params, sections = engine.import_job_profile(
bcr_config, self.job)
# We need to adjust a few of the parameters for this test:
job_profile.imls = [
0.005, 0.007, 0.0098, 0.0137, 0.0192, 0.0269, 0.0376, 0.0527,
0.0738, 0.103, 0.145, 0.203, 0.284, 0.397, 0.556, 0.778]
params['ASSET_LIFE_EXPECTANCY'] = '50'
job_profile.asset_life_expectancy = 50
params['REGION_VERTEX'] = '0.0, 0.0, 0.0, 2.0, 2.0, 2.0, 2.0, 0.0'
job_profile.region = GEOSGeometry(shapes.polygon_ewkt_from_coords(
params['REGION_VERTEX']))
job_profile.save()
job_ctxt = engine.JobContext(
params, self.job_id, sections=sections, oq_job_profile=job_profile)
calculator = classical_core.ClassicalRiskCalculator(job_ctxt)
[input] = models.inputs4job(self.job.id, input_type="exposure")
emdl = input.model()
if not emdl:
emdl = models.ExposureModel(
owner=self.job.owner, input=input,
description="c-psha test exposure model",
category="c-psha power plants", stco_unit="watt",
stco_type="aggregated", reco_unit="joule",
reco_type="aggregated")
emdl.save()
assets = emdl.exposuredata_set.filter(asset_ref="rubcr")
if not assets:
asset = models.ExposureData(exposure_model=emdl, taxonomy="ID",
asset_ref="rubcr", stco=1, reco=123.45,
site=GEOSGeometry("POINT(1.0 1.0)"))
asset.save()
Block.from_kvs(self.job_id, self.block_id)
calculator.compute_risk(self.block_id)
result_key = kvs.tokens.bcr_block_key(self.job_id, self.block_id)
res = kvs.get_value_json_decoded(result_key)
expected_result = {'bcr': 0.0, 'eal_original': 0.003032,
'eal_retrofitted': 0.003032}
helpers.assertDeepAlmostEqual(
self, res, [[[1, 1], [[expected_result, "rubcr"]]]])
def test_compute_beta_dist(self):
# expected beta distributions provided by Vitor
expected_beta_distributions = [
[1.0000000, 1.0000000, 1.0000000, 1.0000000, 1.0000000],
[0.9895151, 0.9999409, 1.0000000, 1.0000000, 1.0000000],
[0.9175720, 0.9981966, 0.9999997, 1.0000000, 1.0000000],
[0.7764311, 0.9887521, 0.9999922, 1.0000000, 1.0000000],
[0.6033381, 0.9633258, 0.9999305, 1.0000000, 1.0000000],
[0.4364471, 0.9160514, 0.9996459, 1.0000000, 1.0000000],
[0.2975979, 0.8460938, 0.9987356, 1.0000000, 1.0000000],
[0.1931667, 0.7574557, 0.9964704, 1.0000000, 1.0000000],
[0.1202530, 0.6571491, 0.9917729, 0.9999999, 1.0000000],
[0.0722091, 0.5530379, 0.9832939, 0.9999997, 1.0000000],
[0.0420056, 0.4521525, 0.9695756, 0.9999988, 1.0000000],
[0.0130890, 0.2790107, 0.9213254, 0.9999887, 1.0000000],
[0.0037081, 0.1564388, 0.8409617, 0.9999306, 1.0000000],
[0.0009665, 0.0805799, 0.7311262, 0.9996882, 1.0000000],
[0.0002335, 0.0384571, 0.6024948, 0.9988955, 1.0000000],
[0.0000526, 0.0171150, 0.4696314, 0.9967629, 1.0000000],
[0.0000022, 0.0027969, 0.2413923, 0.9820831, 1.0000000],
[0.0000001, 0.0003598, 0.0998227, 0.9364072, 1.0000000],
[0.0000000, 0.0000367, 0.0334502, 0.8381920, 0.9999995],
[0.0000000, 0.0000030, 0.0091150, 0.6821293, 0.9999959],
[0.0000000, 0.0000002, 0.0020162, 0.4909782, 0.9999755],
[0.0000000, 0.0000000, 0.0000509, 0.1617086, 0.9995033],
[0.0000000, 0.0000000, 0.0000005, 0.0256980, 0.9945488],
[0.0000000, 0.0000000, 0.0000000, 0.0016231, 0.9633558],
[0.0000000, 0.0000000, 0.0000000, 0.0000288, 0.8399534],
[0.0000000, 0.0000000, 0.0000000, 0.0000001, 0.5409583],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.3413124],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.1589844],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0421052],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0027925],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000]]
vuln_function = shapes.VulnerabilityFunction(self.imls,
self.mean_loss_ratios, self.covs)
# steps = 5
loss_ratios = _generate_loss_ratios(vuln_function, 5)
lrem = numpy.empty((len(loss_ratios), vuln_function.imls.size), float)
for col, _ in enumerate(vuln_function):
for row, loss_ratio in enumerate(loss_ratios):
lrem[row][col] = BetaDistribution.survival_function(loss_ratio,
col=col, vf=vuln_function)
helpers.assertDeepAlmostEqual(self, expected_beta_distributions,
lrem, delta=0.0005)
def test_compute_bcr(self):
cfg_path = helpers.demo_file(
'probabilistic_event_based_risk/config.gem')
helpers.delete_profile(self.job)
job_profile, params, sections = engine.import_job_profile(
cfg_path, self.job)
job_profile.calc_mode = 'event_based_bcr'
job_profile.interest_rate = 0.05
job_profile.asset_life_expectancy = 50
job_profile.region = GEOSGeometry(shapes.polygon_ewkt_from_coords(
'0.0, 0.0, 0.0, 2.0, 2.0, 2.0, 2.0, 0.0'))
job_profile.region_grid_spacing = 0.1
job_profile.maximum_distance = 200.0
job_profile.gmf_random_seed = None
job_profile.save()
params.update(dict(CALCULATION_MODE='Event Based BCR',
INTEREST_RATE='0.05',
ASSET_LIFE_EXPECTANCY='50',
MAXIMUM_DISTANCE='200.0',
REGION_VERTEX=('0.0, 0.0, 0.0, 2.0, '
'2.0, 2.0, 2.0, 0.0'),
REGION_GRID_SPACING='0.1'))
job_ctxt = engine.JobContext(
params, self.job_id, sections=sections, oq_job_profile=job_profile)
calculator = eb_core.EventBasedRiskCalculator(job_ctxt)
self.block_id = 7
SITE = shapes.Site(1.0, 1.0)
block = Block(self.job_id, self.block_id, (SITE, ))
block.to_kvs()
location = GEOSGeometry(SITE.point.to_wkt())
asset = models.ExposureData(exposure_model=self.emdl, taxonomy="ID",
asset_ref=22.61, stco=1, reco=123.45,
site=location)
asset.save()
calculator.compute_risk(self.block_id)
result_key = kvs.tokens.bcr_block_key(self.job_id, self.block_id)
result = kvs.get_value_json_decoded(result_key)
expected_result = {'bcr': 0.0, 'eal_original': 0.0,
'eal_retrofitted': 0.0}
helpers.assertDeepAlmostEqual(
self, [[[1, 1], [[expected_result, "22.61"]]]], result)
def test_compute_lrem_using_beta_distribution(self):
# expected lrem provided by Vitor
expected_beta_distributions = [
[1.0000000, 1.0000000, 1.0000000, 1.0000000, 1.0000000],
[0.9895151, 0.9999409, 1.0000000, 1.0000000, 1.0000000],
[0.9175720, 0.9981966, 0.9999997, 1.0000000, 1.0000000],
[0.7764311, 0.9887521, 0.9999922, 1.0000000, 1.0000000],
[0.6033381, 0.9633258, 0.9999305, 1.0000000, 1.0000000],
[0.4364471, 0.9160514, 0.9996459, 1.0000000, 1.0000000],
[0.2975979, 0.8460938, 0.9987356, 1.0000000, 1.0000000],
[0.1931667, 0.7574557, 0.9964704, 1.0000000, 1.0000000],
[0.1202530, 0.6571491, 0.9917729, 0.9999999, 1.0000000],
[0.0722091, 0.5530379, 0.9832939, 0.9999997, 1.0000000],
[0.0420056, 0.4521525, 0.9695756, 0.9999988, 1.0000000],
[0.0130890, 0.2790107, 0.9213254, 0.9999887, 1.0000000],
[0.0037081, 0.1564388, 0.8409617, 0.9999306, 1.0000000],
[0.0009665, 0.0805799, 0.7311262, 0.9996882, 1.0000000],
[0.0002335, 0.0384571, 0.6024948, 0.9988955, 1.0000000],
[0.0000526, 0.0171150, 0.4696314, 0.9967629, 1.0000000],
[0.0000022, 0.0027969, 0.2413923, 0.9820831, 1.0000000],
[0.0000001, 0.0003598, 0.0998227, 0.9364072, 1.0000000],
[0.0000000, 0.0000367, 0.0334502, 0.8381920, 0.9999995],
[0.0000000, 0.0000030, 0.0091150, 0.6821293, 0.9999959],
[0.0000000, 0.0000002, 0.0020162, 0.4909782, 0.9999755],
[0.0000000, 0.0000000, 0.0000509, 0.1617086, 0.9995033],
[0.0000000, 0.0000000, 0.0000005, 0.0256980, 0.9945488],
[0.0000000, 0.0000000, 0.0000000, 0.0016231, 0.9633558],
[0.0000000, 0.0000000, 0.0000000, 0.0000288, 0.8399534],
[0.0000000, 0.0000000, 0.0000000, 0.0000001, 0.5409583],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.3413124],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.1589844],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0421052],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0027925],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000]
]
vuln_function = shapes.VulnerabilityFunction(self.imls,
self.mean_loss_ratios,
self.covs, "BT")
lrem = _compute_lrem(vuln_function, 5)
helpers.assertDeepAlmostEqual(self,
expected_beta_distributions,
lrem,
delta=0.0005)
def test_compute_lrem_using_beta_distribution(self):
# expected lrem provided by Vitor
expected_beta_distributions = [
[1.0000000, 1.0000000, 1.0000000, 1.0000000, 1.0000000],
[0.9895151, 0.9999409, 1.0000000, 1.0000000, 1.0000000],
[0.9175720, 0.9981966, 0.9999997, 1.0000000, 1.0000000],
[0.7764311, 0.9887521, 0.9999922, 1.0000000, 1.0000000],
[0.6033381, 0.9633258, 0.9999305, 1.0000000, 1.0000000],
[0.4364471, 0.9160514, 0.9996459, 1.0000000, 1.0000000],
[0.2975979, 0.8460938, 0.9987356, 1.0000000, 1.0000000],
[0.1931667, 0.7574557, 0.9964704, 1.0000000, 1.0000000],
[0.1202530, 0.6571491, 0.9917729, 0.9999999, 1.0000000],
[0.0722091, 0.5530379, 0.9832939, 0.9999997, 1.0000000],
[0.0420056, 0.4521525, 0.9695756, 0.9999988, 1.0000000],
[0.0130890, 0.2790107, 0.9213254, 0.9999887, 1.0000000],
[0.0037081, 0.1564388, 0.8409617, 0.9999306, 1.0000000],
[0.0009665, 0.0805799, 0.7311262, 0.9996882, 1.0000000],
[0.0002335, 0.0384571, 0.6024948, 0.9988955, 1.0000000],
[0.0000526, 0.0171150, 0.4696314, 0.9967629, 1.0000000],
[0.0000022, 0.0027969, 0.2413923, 0.9820831, 1.0000000],
[0.0000001, 0.0003598, 0.0998227, 0.9364072, 1.0000000],
[0.0000000, 0.0000367, 0.0334502, 0.8381920, 0.9999995],
[0.0000000, 0.0000030, 0.0091150, 0.6821293, 0.9999959],
[0.0000000, 0.0000002, 0.0020162, 0.4909782, 0.9999755],
[0.0000000, 0.0000000, 0.0000509, 0.1617086, 0.9995033],
[0.0000000, 0.0000000, 0.0000005, 0.0256980, 0.9945488],
[0.0000000, 0.0000000, 0.0000000, 0.0016231, 0.9633558],
[0.0000000, 0.0000000, 0.0000000, 0.0000288, 0.8399534],
[0.0000000, 0.0000000, 0.0000000, 0.0000001, 0.5409583],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.3413124],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.1589844],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0421052],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0027925],
[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000]]
vuln_function = shapes.VulnerabilityFunction(
self.imls, self.mean_loss_ratios, self.covs, "BT")
lrem = _compute_lrem(vuln_function, 5)
helpers.assertDeepAlmostEqual(
self, expected_beta_distributions, lrem, delta=0.0005)
def test_multiple_matrices(self):
target_path = os.path.join(self.tempdir, 'multiple.hdf5')
pmfs = {
'MagDistEpsPMF': ('MagDistEpsPMF.dat',
[self.NMAG - 1, self.NDIST - 1, self.NEPS - 1]),
'LatLonPMF': ('LatLonPMF.dat', [self.NLAT - 1, self.NLON - 1]),
FULL_DISAGG_MATRIX: (self.FULL_MATRIX_DATA, self.FULL_MATRIX_SHAPE)
}
disagg_subsets.extract_subsets(112, self.SITE, self.full_matrix_path,
self.LATITUDE_BIN_LIMITS,
self.LONGITUDE_BIN_LIMITS,
self.MAGNITUDE_BIN_LIMITS,
self.EPSILON_BIN_LIMITS,
self.DISTANCE_BIN_LIMITS, target_path,
pmfs.keys())
result = h5py.File(target_path, 'r')
for name, (datafile, shape) in pmfs.items():
expected_result = self.read_data_file(datafile, shape)
helpers.assertDeepAlmostEqual(self, expected_result, result[name])
def test_multiple_matrices(self):
target_path = os.path.join(self.tempdir, 'multiple.hdf5')
pmfs = {
'MagDistEpsPMF': ('MagDistEpsPMF.dat',
[self.NMAG - 1, self.NDIST - 1, self.NEPS - 1]),
'LatLonPMF': ('LatLonPMF.dat',
[self.NLAT - 1, self.NLON - 1]),
disagg.FULL_DISAGG_MATRIX: (self.FULL_MATRIX_DATA,
self.FULL_MATRIX_SHAPE)
}
disagg_subsets.extract_subsets(
112, self.SITE, self.full_matrix_path,
self.LATITUDE_BIN_LIMITS, self.LONGITUDE_BIN_LIMITS,
self.MAGNITUDE_BIN_LIMITS, self.EPSILON_BIN_LIMITS,
self.DISTANCE_BIN_LIMITS,
target_path,
pmfs.keys()
)
result = h5py.File(target_path, 'r')
for name, (datafile, shape) in pmfs.items():
expected_result = self.read_data_file(datafile, shape)
helpers.assertDeepAlmostEqual(self, expected_result, result[name])
def test_write_uhs_data(self):
# Test object type to use instead of `UhSpectrumData`;
# this is a little more light-weight
# First, set up all the test data:
Data = namedtuple('Data', 'realization, sa_values, location')
points = [
Point(0.0, 0.0),
Point(1.0, 0.0),
Point(0.0, 1.0),
Point(1.0, 1.0),
]
# A single 2D matrix for each location/point
sa_test_values = [
# each row repsents a realization,
# while the contents of each row is an array of SA values
[[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]],
[[13.0, 14.0, 15.0, 16.0],
[17.0, 18.0, 19.0, 20.0],
[21.0, 22.0, 23.0, 24.0]],
[[25.0, 26.0, 27.0, 28.0],
[29.0, 30.0, 31.0, 32.0],
[33.0, 34.0, 35.0, 36.0]],
[[37.0, 38.0, 39.0, 40.0],
[41.0, 42.0, 43.0, 44.0],
[45.0, 46.0, 47.0, 48.0]],
]
uhs_data = []
for i, pt in enumerate(points):
for j, sa_values in enumerate(sa_test_values[i]):
uhs_data.append(Data(j, sa_values, pt))
# Done setting up the test data.
# Now, create the empty file:
target_dir = tempfile.mkdtemp()
try:
poe = 0.05
n_rlz = 3 # rows
n_periods = 4 # columns
ds_names = [uhs_export._point_to_ds_name(p) for p in points]
# As a robustness test, reverse the order of the ds_names.
# It should not matter when we're creating the file (since the
# structure of the file is basically a dict of 2D matrices).
the_file = uhs_export.touch_result_hdf5_file(
target_dir, poe, ds_names[::-1], n_rlz, n_periods)
# Finally, call the function under test with our list of fake
# `UhSpectrumData` objects.
uhs_export.write_uhs_data(the_file, uhs_data)
# Now read the file and check the contents:
with h5py.File(the_file, 'r') as h5_file:
for i, ds in enumerate(ds_names):
helpers.assertDeepAlmostEqual(
self, sa_test_values[i], h5_file[ds].value)
finally:
shutil.rmtree(target_dir)
def test_write_uhs_data(self):
# Test object type to use instead of `UhSpectrumData`;
# this is a little more light-weight
# First, set up all the test data:
Data = namedtuple('Data', 'realization, sa_values, location')
points = [
Point(0.0, 0.0),
Point(1.0, 0.0),
Point(0.0, 1.0),
Point(1.0, 1.0),
]
# A single 2D matrix for each location/point
sa_test_values = [
# each row repsents a realization,
# while the contents of each row is an array of SA values
[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]],
[[13.0, 14.0, 15.0, 16.0], [17.0, 18.0, 19.0, 20.0],
[21.0, 22.0, 23.0, 24.0]],
[[25.0, 26.0, 27.0, 28.0], [29.0, 30.0, 31.0, 32.0],
[33.0, 34.0, 35.0, 36.0]],
[[37.0, 38.0, 39.0, 40.0], [41.0, 42.0, 43.0, 44.0],
[45.0, 46.0, 47.0, 48.0]],
]
uhs_data = []
for i, pt in enumerate(points):
for j, sa_values in enumerate(sa_test_values[i]):
uhs_data.append(Data(j, sa_values, pt))
# Done setting up the test data.
# Now, create the empty file:
target_dir = tempfile.mkdtemp()
try:
poe = 0.05
n_rlz = 3 # rows
n_periods = 4 # columns
ds_names = [uhs_export._point_to_ds_name(p) for p in points]
# As a robustness test, reverse the order of the ds_names.
# It should not matter when we're creating the file (since the
# structure of the file is basically a dict of 2D matrices).
the_file = uhs_export.touch_result_hdf5_file(
target_dir, poe, ds_names[::-1], n_rlz, n_periods)
# Finally, call the function under test with our list of fake
# `UhSpectrumData` objects.
uhs_export.write_uhs_data(the_file, uhs_data)
# Now read the file and check the contents:
with h5py.File(the_file, 'r') as h5_file:
for i, ds in enumerate(ds_names):
helpers.assertDeepAlmostEqual(self, sa_test_values[i],
h5_file[ds].value)
finally:
shutil.rmtree(target_dir)
