def test_wrong_sites_csv(self):
sites_csv = general.gettemp(
'site_id,lon,lat\n1,1.0,2.1\n2,3.0,4.1\n3,5.0,6.1')
source = general.gettemp("""
[general]
calculation_mode = scenario
[geometry]
sites_csv = %s
[misc]
maximum_distance=1
truncation_level=3
random_seed=5
[site_params]
reference_vs30_type = measured
reference_vs30_value = 600.0
reference_depth_to_2pt5km_per_sec = 5.0
reference_depth_to_1pt0km_per_sec = 100.0
intensity_measure_types_and_levels = {'PGA': [0.1, 0.2]}
investigation_time = 50.
export_dir = %s
""" % (os.path.basename(sites_csv), TMP))
oq = readinput.get_oqparam(source)
with self.assertRaises(InvalidFile) as ctx:
readinput.get_mesh(oq)
self.assertIn('expected site_id=0, got 1', str(ctx.exception))
os.unlink(sites_csv)
def _assert_equal_sources(nodes):
if hasattr(nodes[0], 'source_id'):
n0 = nodes[0]
for n in nodes[1:]:
n.assert_equal(n0, ignore=('id', 'src_group_id'))
else: # assume source nodes
n0 = nodes[0].to_str()
for n in nodes[1:]:
eq = n.to_str() == n0
if not eq:
f0 = gettemp(n0)
f1 = gettemp(n.to_str())
assert eq, 'different parameters for source %s, run meld %s %s' % (
n['id'], f0, f1)
def test_lr_eq_0_cov_gt_0(self):
# If a vulnerability function loss ratio is 0 and its corresponding CoV
# is > 0, a ValueError should be raised
vuln_content = gettemp(u"""\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml">
<vulnerabilityModel>
<discreteVulnerabilitySet vulnerabilitySetID="PAGER"
assetCategory="population"
lossCategory="occupants">
<IML IMT="PGV">0.005 0.007 0.0098 0.0137</IML>
<discreteVulnerability vulnerabilityFunctionID="A"
probabilisticDistribution="LN">
<lossRatio>0.00 0.06 0.18 0.36</lossRatio>
<coefficientsVariation>0.30 0.30 0.30 0.30
</coefficientsVariation>
</discreteVulnerability>
</discreteVulnerabilitySet>
</vulnerabilityModel>
</nrml>
""")
with self.assertRaises(ValueError) as ar:
nrml.to_python(vuln_content)
self.assertIn('It is not valid to define a loss ratio = 0.0 with a '
'corresponding coeff. of variation > 0.0',
str(ar.exception))
def test_case_miriam(self):
# this is a case with a grid and asset-hazard association
self.run_calc(case_miriam.__file__, 'job.ini')
# check minimum_magnitude >= 5.2
minmag = self.calc.datastore['ruptures']['mag'].min()
self.assertGreaterEqual(minmag, 5.2)
# check asset_loss_table
tot = self.calc.datastore['asset_loss_table'].value.sum()
self.assertEqual(tot, 15787827.0)
[fname] = export(('agg_loss_table', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/agg_losses-rlz000-structural.csv',
fname, delta=1E-5)
fname = gettemp(view('portfolio_losses', self.calc.datastore))
self.assertEqualFiles(
'expected/portfolio_losses.txt', fname, delta=1E-5)
# this is a case with exposure and region_grid_spacing=1
self.run_calc(case_miriam.__file__, 'job2.ini')
hcurves = dict(extract(self.calc.datastore, 'hcurves'))['all']
sitecol = self.calc.datastore['sitecol'] # filtered sitecol
self.assertEqual(len(hcurves), len(sitecol))
assetcol = self.calc.datastore['assetcol']
self.assertEqual(len(sitecol), 15)
self.assertGreater(sitecol.vs30.sum(), 0)
self.assertEqual(len(assetcol), 548)
def test_case_16(self):
# an example with site model raising warnings and autogridded exposure
self.run_calc(case_16.__file__, 'job.ini',
ground_motion_fields='false')
hid = str(self.calc.datastore.calc_id)
self.run_calc(case_16.__file__, 'job.ini', hazard_calculation_id=hid)
tmp = gettemp(view('global_gmfs', self.calc.datastore))
self.assertEqualFiles('expected/global_gmfs.txt', tmp)
def test_get_oqparam_with_sites_csv(self):
sites_csv = general.gettemp('1.0,2.1\n3.0,4.1\n5.0,6.1')
try:
source = general.gettemp("""
[general]
calculation_mode = scenario
[geometry]
sites_csv = %s
[misc]
maximum_distance=1
truncation_level=3
random_seed=5
[site_params]
reference_vs30_type = measured
reference_vs30_value = 600.0
reference_depth_to_2pt5km_per_sec = 5.0
reference_depth_to_1pt0km_per_sec = 100.0
intensity_measure_types_and_levels = {'PGA': [0.1, 0.2]}
investigation_time = 50.
export_dir = %s
""" % (os.path.basename(sites_csv), TMP))
exp_base_path = os.path.dirname(
os.path.join(os.path.abspath('.'), source))
expected_params = {
'export_dir': TMP,
'base_path': exp_base_path,
'calculation_mode': 'scenario',
'truncation_level': 3.0,
'random_seed': 5,
'maximum_distance': {'default': 1.0},
'inputs': {'job_ini': source,
'sites': sites_csv},
'reference_depth_to_1pt0km_per_sec': 100.0,
'reference_depth_to_2pt5km_per_sec': 5.0,
'reference_vs30_type': 'measured',
'reference_vs30_value': 600.0,
'hazard_imtls': {'PGA': [0.1, 0.2]},
'investigation_time': 50.0,
'risk_investigation_time': 50.0,
}
params = getparams(readinput.get_oqparam(source))
self.assertEqual(expected_params, params)
finally:
os.unlink(sites_csv)
def get_path(fname_or_fileobject):
if isinstance(fname_or_fileobject, str):
return fname_or_fileobject
elif hasattr(fname_or_fileobject, 'getvalue'):
return gettemp(fname_or_fileobject.getvalue())
elif hasattr(fname_or_fileobject, 'name'):
return fname_or_fileobject.name
else:
return TypeError(fname_or_fileobject)
def test_case_16(self):
# an example with site model raising warnings and autogridded exposure
self.run_calc(case_16.__file__,
'job.ini',
ground_motion_fields='false')
hid = str(self.calc.datastore.calc_id)
self.run_calc(case_16.__file__, 'job.ini', hazard_calculation_id=hid)
tmp = gettemp(view('global_gmfs', self.calc.datastore))
self.assertEqualFiles('expected/global_gmfs.txt', tmp)
def test_event_based_sampling(self):
self.run_calc(ucerf.__file__, 'job_ebh.ini')
# check the distribution of the events
self.assertEventsByRlz([29, 25])
# check the mean hazard map
got = gettemp(view('hmap', self.calc.datastore))
self.assertEqualFiles('expected/hmap.rst', got)
def test(self):
fname = general.gettemp('''\
<?xml version="1.0" encoding="utf-8"?>
<nrml xmlns:gml="http://www.opengis.net/gml"
xmlns="http://openquake.org/xmlns/nrml/0.4">
<!-- Spectral Acceleration (SA) example -->
<hazardCurves sourceModelTreePath="b1_b2_b4" gsimTreePath="b1_b2" investigationTime="50.0" IMT="SA" saPeriod="0.025" saDamping="5.0">
<IMLs>5.0000e-03 7.0000e-03 1.3700e-02</IMLs>
<hazardCurve>
<gml:Point>
<gml:pos>-122.5000 37.5000</gml:pos>
</gml:Point>
<poEs>9.8728e-01 9.8266e-01 9.4957e-01</poEs>
</hazardCurve>
<hazardCurve>
<gml:Point>
<gml:pos>-123.5000 37.5000</gml:pos>
</gml:Point>
<poEs>9.8727e-02 9.8265e-02 9.4956e-02</poEs>
</hazardCurve>
</hazardCurves>
<!-- Basic example, using PGA as IMT -->
<hazardCurves sourceModelTreePath="b1_b2_b3" gsimTreePath="b1_b7" investigationTime="50.0" IMT="PGA">
<IMLs>5.0000e-03 7.0000e-03 1.3700e-02 3.3700e-02</IMLs>
<hazardCurve>
<gml:Point>
<gml:pos>-122.5000 37.5000</gml:pos>
</gml:Point>
<poEs>9.8728e-01 9.8226e-01 9.4947e-01 9.2947e-01</poEs>
</hazardCurve>
<hazardCurve>
<gml:Point>
<gml:pos>-123.5000 37.5000</gml:pos>
</gml:Point>
<poEs>9.8728e-02 9.8216e-02 9.4945e-02 9.2947e-02</poEs>
</hazardCurve>
</hazardCurves>
</nrml>
''', suffix='.xml')
oqparam = object.__new__(oqvalidation.OqParam)
oqparam.inputs = dict(hazard_curves=fname)
sitecol = readinput.get_site_collection(oqparam)
self.assertEqual(len(sitecol), 2)
self.assertEqual(sorted(oqparam.hazard_imtls.items()),
[('PGA', [0.005, 0.007, 0.0137, 0.0337]),
('SA(0.025)', [0.005, 0.007, 0.0137])])
hcurves = readinput.pmap.convert(oqparam.imtls, 2)
assert_allclose(hcurves['PGA'], numpy.array(
[[0.098728, 0.098216, 0.094945, 0.092947],
[0.98728, 0.98226, 0.94947, 0.92947]]))
assert_allclose(hcurves['SA(0.025)'], numpy.array(
[[0.098727, 0.098265, 0.094956],
[0.98728, 0.98266, 0.94957]]))
def test_case_4(self):
# this test is sensitive to the ordering of the epsilons
# in openquake.riskinput.make_eps
out = self.run_calc(case_4.__file__, 'job.ini', exports='csv')
fname = gettemp(view('totlosses', self.calc.datastore))
self.assertEqualFiles('expected/totlosses.txt', fname)
[fname] = out['agglosses', 'csv']
self.assertEqualFiles('expected/agglosses.csv', fname, delta=1E-6)
def test_case_1(self):
self.run_calc(case_1.__file__, 'job.ini')
ekeys = [('agg_curves-stats', 'csv')]
for ekey in ekeys:
for fname in export(ekey, self.calc.datastore):
self.assertEqualFiles(
'expected/%s' % strip_calc_id(fname), fname)
# make sure the agg_curves-stats has the right attrs
self.check_attr('return_periods', [30, 60, 120, 240, 480, 960])
self.check_attr('units', [b'EUR', b'EUR'])
self.check_attr('nbytes', 96)
# test the loss curves exporter
[f1] = export(('loss_curves/rlz-0', 'csv'), self.calc.datastore)
[f2] = export(('loss_curves/rlz-1', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_curves-rlz-000.csv', f1)
self.assertEqualFiles('expected/loss_curves-rlz-001.csv', f2)
[f] = export(('loss_curves/mean', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_curves-mean.csv', f)
# test the loss maps exporter
fnames = export(('loss_maps-stats', 'csv'), self.calc.datastore)
assert fnames
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname, delta=1E-5)
# test portfolio loss
tmp = gettemp(view('portfolio_loss', self.calc.datastore))
self.assertEqualFiles('expected/portfolio_loss.txt', tmp)
# test the rup_loss_table exporter
fnames = export(('rup_loss_table', 'xml'), self.calc.datastore)
self.assertEqual(len(fnames), 2)
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname)
# test the src_loss_table extractor
arr = extract(self.calc.datastore, 'src_loss_table/structural')
tmp = gettemp(rst_table(arr))
self.assertEqualFiles('expected/src_loss_table.txt', tmp)
def test_case_1_eb(self):
# this is a case with no insured losses, no tags
self.run_calc(case_1.__file__, 'job_eb.ini', concurrent_tasks='4')
[fname] = export(('avg_losses-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname)
aw = extract(self.calc.datastore, 'agg_losses/structural')
self.assertEqual(aw.stats, ['mean'])
numpy.testing.assert_allclose(aw.array, [662.6701])
fnames = export(('tot_curves-stats', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/eb_%s' % strip_calc_id(fname),
fname,
delta=1E-5)
[fname] = export(('losses_by_event', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/%s' % strip_calc_id(fname),
fname,
delta=1E-5)
# extract tot_curves, no tags
aw = extract(
self.calc.datastore, 'tot_curves?kind=stats&'
'loss_type=structural&absolute=1')
tmp = gettemp(rst_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves1.csv', tmp)
aw = extract(self.calc.datastore, 'tot_curves?kind=rlzs&'
'loss_type=structural&absolute=1')
tmp = gettemp(rst_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves2.csv', tmp)
aw = extract(
self.calc.datastore, 'tot_curves?kind=stats&'
'loss_type=structural&absolute=0')
tmp = gettemp(rst_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves3.csv', tmp)
aw = extract(self.calc.datastore, 'tot_curves?kind=rlzs&'
'loss_type=structural&absolute=0')
tmp = gettemp(rst_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves4.csv', tmp)
def test_case_1(self):
self.run_calc(case_1.__file__, 'job.ini')
ekeys = [('agg_curves-stats', 'csv')]
for ekey in ekeys:
for fname in export(ekey, self.calc.datastore):
self.assertEqualFiles('expected/%s' % strip_calc_id(fname),
fname)
# make sure the agg_curves-stats has the right attrs
self.check_attr('return_periods', [30, 60, 120, 240, 480, 960])
self.check_attr('units', [b'EUR', b'EUR'])
self.check_attr('nbytes', 96)
# test the loss curves exporter
[f1] = export(('loss_curves/rlz-0', 'csv'), self.calc.datastore)
[f2] = export(('loss_curves/rlz-1', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_curves-rlz-000.csv', f1)
self.assertEqualFiles('expected/loss_curves-rlz-001.csv', f2)
[f] = export(('loss_curves/mean', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_curves-mean.csv', f)
# test the loss maps exporter
fnames = export(('loss_maps-stats', 'csv'), self.calc.datastore)
assert fnames
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname,
delta=1E-5)
# test portfolio loss
tmp = gettemp(view('portfolio_loss', self.calc.datastore))
self.assertEqualFiles('expected/portfolio_loss.txt', tmp)
# test the rup_loss_table exporter
fnames = export(('rup_loss_table', 'xml'), self.calc.datastore)
self.assertEqual(len(fnames), 2)
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname)
# test the src_loss_table extractor
arr = extract(self.calc.datastore, 'src_loss_table/structural')
tmp = gettemp(rst_table(arr))
self.assertEqualFiles('expected/src_loss_table.txt', tmp)
def test_case_master(self):
if sys.platform == 'darwin':
raise unittest.SkipTest('MacOSX')
self.run_calc(case_master.__file__, 'job.ini', exports='csv')
fnames = export(('avg_losses-stats', 'csv'), self.calc.datastore)
assert fnames, 'avg_losses-stats not exported?'
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname,
delta=1E-5)
# extract loss_curves/rlz-1 (with the first asset having zero losses)
[fname] = export(('loss_curves/rlz-1', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname,
delta=1E-5)
fnames = export(('loss_maps-rlzs', 'csv'), self.calc.datastore)
assert fnames, 'loss_maps-rlzs not exported?'
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname,
delta=1E-5)
# extract curves by tag
tags = 'taxonomy=tax1&state=01&cresta=0.11'
a = extract(self.calc.datastore, 'agg_curves/structural?' + tags)
self.assertEqual(a.array.shape, (4, 3)) # 4 stats, 3 return periods
fname = gettemp(view('portfolio_losses', self.calc.datastore))
self.assertEqualFiles('expected/portfolio_losses.txt',
fname,
delta=1E-5)
os.remove(fname)
# check ruptures are stored correctly
fname = gettemp(view('ruptures_events', self.calc.datastore))
self.assertEqualFiles('expected/ruptures_events.txt', fname)
os.remove(fname)
# check losses_by_tag
fnames = export(('losses_by_tag/occupancy', 'csv'),
self.calc.datastore)
self.assertEqualFiles('expected/losses_by_occupancy.csv', fnames[0])
def test_missing_cost_types(self):
job_ini = general.gettemp('''\
[general]
description = Exposure with missing cost_types
calculation_mode = scenario
exposure_file = %s''' % os.path.basename(self.exposure4))
oqparam = readinput.get_oqparam(job_ini)
with self.assertRaises(InvalidFile) as ctx:
readinput.get_sitecol_assetcol(oqparam, cost_types=['structural'])
self.assertIn("is missing", str(ctx.exception))
def __init__(self, sitecol, integration_distance, hdf5path=None):
if rtree is None:
raise ImportError('rtree')
super().__init__(sitecol, integration_distance, hdf5path)
self.indexpath = gettemp()
lonlats = zip(sitecol.lons, sitecol.lats)
index = rtree.index.Index(self.indexpath)
for i, (lon, lat) in enumerate(lonlats):
index.insert(i, (lon, lat, lon, lat))
index.close()
def test_invalid_magnitude_distance_filter(self):
source = general.gettemp("""
[general]
maximum_distance=[(200, 8)]
""")
with self.assertRaises(ValueError) as ctx:
readinput.get_oqparam(source)
self.assertIn('magnitude 200.0 is bigger than the maximum (11): '
'could not convert to maximum_distance:',
str(ctx.exception))
def get(cls, path, **data):
resp = cls.c.get('/v1/calc/%s' % path, data,
HTTP_HOST='127.0.0.1')
assert resp.content, 'No content from /v1/calc/%s' % path
try:
return json.loads(resp.content.decode('utf8'))
except Exception:
print('Invalid JSON, see %s' % gettemp(resp.content),
file=sys.stderr)
return {}
def test_missing_cost_types(self):
job_ini = general.gettemp('''\
[general]
description = Exposure with missing cost_types
calculation_mode = scenario
exposure_file = %s''' % os.path.basename(self.exposure4))
oqparam = readinput.get_oqparam(job_ini)
with self.assertRaises(InvalidFile) as ctx:
readinput.get_sitecol_assetcol(oqparam, cost_types=['structural'])
self.assertIn("is missing", str(ctx.exception))
def test_no_nrml(self):
fname = gettemp('''\
<?xml version="1.0" encoding="UTF-8"?>
<fragilityModel id="Ethiopia" assetCategory="buildings"
lossCategory="structural" />
''')
with self.assertRaises(ValueError) as ctx:
read(fname)
self.assertIn('expected a node of kind nrml, got fragilityModel',
str(ctx.exception))
def test_invalid_magnitude_distance_filter(self):
source = general.gettemp("""
[general]
maximum_distance=[(200, 8)]
""")
with self.assertRaises(ValueError) as ctx:
readinput.get_oqparam(source)
self.assertIn(
'magnitude 200.0 is bigger than the maximum (11): '
'could not convert to maximum_distance:', str(ctx.exception))
def test_case_23(self):
# case with implicit grid and site model on a larger grid
out = self.run_calc(case_23.__file__, 'job.ini', exports='csv')
[fname] = out['ruptures', 'csv']
self.assertEqualFiles('expected/%s' % strip_calc_id(fname),
fname,
delta=1E-6)
arr = self.calc.datastore.getitem('sitecol')
tmp = gettemp(write_csv(io.BytesIO(), arr).decode('utf8'))
self.assertEqualFiles('expected/sitecol.csv', tmp)
def test_no_nrml(self):
fname = gettemp('''\
<?xml version="1.0" encoding="UTF-8"?>
<fragilityModel id="Ethiopia" assetCategory="buildings"
lossCategory="structural" />
''')
with self.assertRaises(ValueError) as ctx:
read(fname)
self.assertIn('expected a node of kind nrml, got fragilityModel',
str(ctx.exception))
def test_long_code(self):
fname = gettemp(long_ampl_code)
with self.assertRaises(InvalidFile) as ctx:
read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
self.assertIn("line 3: ampcode='long_code' has length 9 > 4",
str(ctx.exception))
def test_multipoint(self):
smodel = self.check_round_trip(MULTIPOINT)
# test toml round trip
temp = general.gettemp(suffix='.toml')
with open(temp, 'w') as f:
tomldump(smodel, f)
with open(temp, 'r') as f:
sm = toml.load(f)['sourceModel']
self.assertEqual(smodel.name, sm['_name'])
def get(cls, path, **data):
resp = cls.c.get('/v1/calc/%s' % path, data,
HTTP_HOST='127.0.0.1')
assert resp.content, 'No content from /v1/calc/%s' % path
try:
return json.loads(resp.content.decode('utf8'))
except Exception:
print('Invalid JSON, see %s' % gettemp(resp.content),
file=sys.stderr)
return {}
def test_event_based_risk(self):
# the fast calculator ucerf_risk
self.run_calc(ucerf.__file__, 'job_ebr.ini')
fname = gettemp(view('portfolio_loss', self.calc.datastore))
self.assertEqualFiles('expected/portfolio_loss.txt', fname, delta=1E-5)
# check the mean losses_by_period
[fname] = export(('agg_curves-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/losses_by_period-mean.csv', fname)
def test_case_master(self):
if sys.platform == 'darwin':
raise unittest.SkipTest('MacOSX')
self.run_calc(case_master.__file__, 'job.ini', exports='csv')
fnames = export(('avg_losses-stats', 'csv'), self.calc.datastore)
assert fnames, 'avg_losses-stats not exported?'
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname,
delta=1E-5)
# check event loss table
[fname] = export(('losses_by_event', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname,
delta=1E-5)
# extract loss_curves/rlz-1 (with the first asset having zero losses)
[fname] = export(('loss_curves/rlz-1', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname,
delta=1E-5)
fnames = export(('loss_maps-rlzs', 'csv'), self.calc.datastore)
assert fnames, 'loss_maps-rlzs not exported?'
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname, delta=1E-5)
fname = gettemp(view('portfolio_losses', self.calc.datastore))
self.assertEqualFiles(
'expected/portfolio_losses.txt', fname, delta=1E-5)
os.remove(fname)
# check ruptures are stored correctly
fname = gettemp(view('ruptures_events', self.calc.datastore))
self.assertEqualFiles('expected/ruptures_events.txt', fname)
os.remove(fname)
# check losses_by_tag
fnames = export(
('aggregate_by/avg_losses?tag=occupancy&kind=mean', 'csv'),
self.calc.datastore)
self.assertEqualFiles('expected/losses_by_occupancy.csv', fnames[0])
self.check_multi_tag(self.calc.datastore)
def test_case_23(self):
# case with implicit grid and site model on a larger grid
out = self.run_calc(case_23.__file__, 'job.ini', exports='csv')
[fname] = out['ruptures', 'csv']
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname,
delta=1E-4)
sio = io.StringIO()
write_csv(sio, self.calc.datastore['sitecol'].array)
tmp = gettemp(sio.getvalue())
self.assertEqualFiles('expected/sitecol.csv', tmp)
def test_dupl(self):
fname = gettemp(dupl_ampl_func)
df = read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
with self.assertRaises(ValueError) as ctx:
Amplifier(self.imtls, df, self.soil_levels)
self.assertEqual(str(ctx.exception), "Found duplicates for b'A'")
def test(self):
npd = PMF([(0.5, NodalPlane(1, 20, 3)),
(0.5, NodalPlane(2, 2, 4))])
hd = PMF([(1, 14)])
mesh = Mesh(numpy.array([0, 1]), numpy.array([0.5, 1]))
mmfd = MultiMFD('incrementalMFD',
size=2,
min_mag=[4.5],
bin_width=[2.0],
occurRates=[[.3, .1], [.4, .2, .1]])
mps = MultiPointSource('mp1', 'multi point source',
'Active Shallow Crust',
mmfd, PeerMSR(), 1.0,
10, 20, npd, hd, mesh)
# test the splitting
splits = list(mps)
self.assertEqual(len(splits), 2)
for split in splits:
self.assertEqual(split.src_group_id, mps.src_group_id)
got = obj_to_node(mps).to_str()
print(got)
exp = '''\
multiPointSource{id='mp1', name='multi point source', tectonicRegion='Active Shallow Crust'}
multiPointGeometry
gml:posList [0.0, 0.5, 1.0, 1.0]
upperSeismoDepth 10
lowerSeismoDepth 20
magScaleRel 'PeerMSR'
ruptAspectRatio 1.0
multiMFD{kind='incrementalMFD', size=2}
bin_width [2.0]
min_mag [4.5]
occurRates [0.3, 0.1, 0.4, 0.2, 0.1]
lengths [2, 3]
nodalPlaneDist
nodalPlane{dip=20, probability=0.5, rake=3, strike=1}
nodalPlane{dip=2, probability=0.5, rake=4, strike=2}
hypoDepthDist
hypoDepth{depth=14, probability=1.0}
'''
self.assertEqual(got, exp)
# test serialization to and from hdf5
tmp = general.gettemp(suffix='.hdf5')
with hdf5.File(tmp, 'w') as f:
f[mps.source_id] = mps
with hdf5.File(tmp, 'r') as f:
f[mps.source_id]
# test the bounding box
bbox = mps.get_bounding_box(maxdist=100)
numpy.testing.assert_almost_equal(
(-0.8994569916564479, -0.39932, 1.8994569916564479, 1.89932),
bbox)
def test(self):
npd = PMF([(0.5, NodalPlane(1, 20, 3)),
(0.5, NodalPlane(2, 2, 4))])
hd = PMF([(1, 14)])
mesh = Mesh(numpy.array([0, 1]), numpy.array([0.5, 1]))
mmfd = MultiMFD('incrementalMFD',
size=2,
min_mag=[4.5],
bin_width=[2.0],
occurRates=[[.3, .1], [.4, .2, .1]])
mps = MultiPointSource('mp1', 'multi point source',
'Active Shallow Crust',
mmfd, PeerMSR(), 1.0,
10, 20, npd, hd, mesh)
# test the splitting
splits = list(mps)
self.assertEqual(len(splits), 2)
for split in splits:
self.assertEqual(split.grp_id, mps.grp_id)
got = obj_to_node(mps).to_str()
print(got)
exp = '''\
multiPointSource{id='mp1', name='multi point source'}
multiPointGeometry
gml:posList [0.0, 0.5, 1.0, 1.0]
upperSeismoDepth 10
lowerSeismoDepth 20
magScaleRel 'PeerMSR'
ruptAspectRatio 1.0
multiMFD{kind='incrementalMFD', size=2}
bin_width [2.0]
min_mag [4.5]
occurRates [0.3, 0.1, 0.4, 0.2, 0.1]
lengths [2, 3]
nodalPlaneDist
nodalPlane{dip=20, probability=0.5, rake=3, strike=1}
nodalPlane{dip=2, probability=0.5, rake=4, strike=2}
hypoDepthDist
hypoDepth{depth=14, probability=1.0}
'''
self.assertEqual(got, exp)
# test serialization to and from hdf5
tmp = general.gettemp(suffix='.hdf5')
with hdf5.File(tmp, 'w') as f:
f[mps.source_id] = mps
with hdf5.File(tmp, 'r') as f:
f[mps.source_id]
# test the bounding box
bbox = mps.get_bounding_box(maxdist=100)
numpy.testing.assert_almost_equal(
(-0.8994569916564479, -0.39932, 1.8994569916564479, 1.89932),
bbox)
def test_case_master(self):
if sys.platform == 'darwin':
raise unittest.SkipTest('MacOSX')
self.run_calc(case_master.__file__, 'job.ini', exports='csv')
fnames = export(('avg_losses-stats', 'csv'), self.calc.datastore)
assert fnames, 'avg_losses-stats not exported?'
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname,
delta=1E-5)
# check event loss table
[fname] = export(('losses_by_event', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname,
delta=1E-5)
fname = gettemp(view('portfolio_losses', self.calc.datastore))
self.assertEqualFiles('expected/portfolio_losses.txt',
fname,
delta=1E-5)
os.remove(fname)
# check ruptures are stored correctly
fname = gettemp(view('ruptures_events', self.calc.datastore))
self.assertEqualFiles('expected/ruptures_events.txt', fname)
os.remove(fname)
# check losses_by_tag
fnames = export(
('aggregate_by/avg_losses?tag=occupancy&kind=mean', 'csv'),
self.calc.datastore)
self.assertEqualFiles('expected/losses_by_occupancy.csv', fnames[0])
self.check_multi_tag(self.calc.datastore)
# check curves-rlzs and curves-stats are readable
df1 = self.calc.datastore.read_df('curves-rlzs', 'assets')
aae(df1.columns, ['rlzs', 'return_periods', 'loss_types', 'value'])
df2 = self.calc.datastore.read_df('curves-stats', 'assets')
aae(df2.columns, ['stats', 'return_periods', 'loss_types', 'value'])
def test_case_master(self):
if sys.platform == 'darwin':
raise unittest.SkipTest('MacOSX')
self.run_calc(case_master.__file__, 'job.ini', exports='csv')
fnames = export(('avg_losses-stats', 'csv'), self.calc.datastore)
assert fnames, 'avg_losses-stats not exported?'
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname,
delta=1E-5)
# extract loss_curves/rlz-1 (with the first asset having zero losses)
[fname] = export(('loss_curves/rlz-1', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname,
delta=1E-5)
fnames = export(('loss_maps-rlzs', 'csv'), self.calc.datastore)
assert fnames, 'loss_maps-rlzs not exported?'
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname,
delta=1E-5)
# extract curves by tag
tags = 'taxonomy=tax1&state=01&cresta=0.11'
a = extract(self.calc.datastore, 'aggcurves/structural?' + tags)
self.assertEqual(a.array.shape, (4, 3)) # 4 stats, 3 return periods
fname = gettemp(view('portfolio_loss', self.calc.datastore))
self.assertEqualFiles('expected/portfolio_loss.txt', fname, delta=1E-5)
os.remove(fname)
# check ruptures are stored correctly
fname = gettemp(view('ruptures_events', self.calc.datastore))
self.assertEqualFiles('expected/ruptures_events.txt', fname)
os.remove(fname)
# check max_gmf_size
exp = self.calc.datastore.get_attr('events', 'max_gmf_size')
got = self.calc.datastore['gmf_data/data'].value.nbytes
self.assertGreater(exp, got) # there is minimum_intensity
def test_case_master(self):
# this tests exercise the case of a complex logic tree; it also
# prints the warning on poe_agg very different from the expected poe
self.run_calc(case_master.__file__, 'job.ini')
fname = gettemp(view('mean_disagg', self.calc.datastore))
self.assertEqualFiles('expected/mean_disagg.rst', fname)
os.remove(fname)
# check stats
fnames = export(('disagg-stats', 'csv'), self.calc.datastore)
self.assertEqual(len(fnames), 192) # 2 sid x 8 keys x 2 poe x 2 imt
def test_case_master(self):
# this tests exercise the case of a complex logic tree; it also
# prints the warning on poe_agg very different from the expected poe
self.run_calc(case_master.__file__, 'job.ini')
fname = gettemp(view('mean_disagg', self.calc.datastore))
self.assertEqualFiles('expected/mean_disagg.rst', fname)
os.remove(fname)
# check stats
fnames = export(('disagg-stats', 'csv'), self.calc.datastore)
self.assertEqual(len(fnames), 192) # 2 sid x 8 keys x 2 poe x 2 imt
def test_event_based_risk(self):
# the fast calculator ucerf_risk
raise unittest.SkipTest('ucerf_risk has been removed')
self.run_calc(ucerf.__file__, 'job_ebr.ini')
fname = gettemp(view('portfolio_loss', self.calc.datastore))
self.assertEqualFiles('expected/portfolio_loss.txt', fname, delta=1E-5)
# check the mean losses_by_period
[fname] = export(('agg_curves-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/losses_by_period-mean.csv', fname)
def test_multipoint(self):
smodel = self.check_round_trip(MULTIPOINT)
# test hdf5 round trip
temp = general.gettemp(suffix='.hdf5')
with hdf5.File(temp, 'w') as f:
f['/'] = smodel
with hdf5.File(temp, 'r') as f:
sm = f['/']
self.assertEqual(smodel.name, sm.name)
self.assertEqual(len(smodel.src_groups), len(sm.src_groups))
def test_case_6a(self):
# case with two gsims
self.run_calc(case_6a.__file__, 'job_haz.ini,job_risk.ini',
exports='csv')
[f] = export(('agglosses', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/agg_structural.csv', f)
# testing the totlosses view
dstore = self.calc.datastore
fname = gettemp(view('totlosses', dstore))
self.assertEqualFiles('expected/totlosses.txt', fname)
# testing portfolio_losses
fname = gettemp(view('portfolio_losses', dstore))
self.assertEqualFiles('expected/portfolio_losses.txt', fname)
# two equal gsims
with self.assertRaises(InvalidLogicTree):
self.run_calc(case_6a.__file__, 'job_haz.ini',
gsim_logic_tree_file='wrong_gmpe_logic_tree.xml')
def __init__(self, sitecol, integration_distance):
if rtree is None:
raise ImportError('rtree')
self.integration_distance = integration_distance
self.distribute = 'processpool'
self.indexpath = gettemp()
lonlats = zip(sitecol.lons, sitecol.lats)
index = rtree.index.Index(self.indexpath)
for i, (lon, lat) in enumerate(lonlats):
index.insert(i, (lon, lat, lon, lat))
index.close()
def test_case_1(self):
self.run_calc(case_1.__file__, 'job.ini')
ekeys = [('agg_curves-stats', 'csv')]
for ekey in ekeys:
for fname in export(ekey, self.calc.datastore):
self.assertEqualFiles('expected/%s' % strip_calc_id(fname),
fname)
# make sure the agg_curves-stats has the right attrs
self.check_attr('return_periods', [30, 60, 120, 240, 480, 960])
self.check_attr('units', [b'EUR', b'EUR'])
# test portfolio loss
tmp = gettemp(view('portfolio_loss', self.calc.datastore))
self.assertEqualFiles('expected/portfolio_loss.txt', tmp)
# test the src_loss_table extractor
arr = extract(self.calc.datastore, 'src_loss_table/structural')
tmp = gettemp(rst_table(arr))
self.assertEqualFiles('expected/src_loss_table.txt', tmp)
def test_case_1_eb(self):
# this is a case with insured losses and tags
self.run_calc(case_1.__file__, 'job_eb.ini', concurrent_tasks='4')
[fname] = export(('avg_losses-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname,
delta=1E-5)
aw = extract(self.calc.datastore, 'agg_losses/structural')
self.assertEqual(aw.stats, ['mean'])
numpy.testing.assert_allclose(aw.array, [685.5015], atol=.001)
[fname] = export(('aggrisk', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/%s' % strip_calc_id(fname),
fname, delta=1E-5)
fnames = export(('aggcurves', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/eb_%s' % strip_calc_id(fname),
fname, delta=1E-5)
[fname] = export(('risk_by_event', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname,
delta=1E-5)
# extract agg_curves with tags
aw = extract(self.calc.datastore, 'agg_curves?kind=stats&'
'loss_type=structural&absolute=1&policy=A&taxonomy=RC')
tmp = gettemp(text_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves5.csv', tmp)
aw = extract(self.calc.datastore, 'agg_curves?kind=stats&'
'loss_type=structural&absolute=0&policy=A&taxonomy=RC')
tmp = gettemp(text_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves7.csv', tmp)
# test ct_independence
loss4 = view('portfolio_losses', self.calc.datastore)
self.run_calc(case_1.__file__, 'job_eb.ini', concurrent_tasks='0')
loss0 = view('portfolio_losses', self.calc.datastore)
self.assertEqual(loss0, loss4)
def test_case_master1(self):
# needs a large tolerance: https://github.com/gem/oq-engine/issues/5825
# it looks like the cholesky decomposition is OS-dependent, so
# the GMFs are different of macOS/Ubuntu20/Ubuntu18
self.run_calc(case_master.__file__, 'job.ini', exports='csv')
fnames = export(('avg_losses-stats', 'csv'), self.calc.datastore)
assert fnames, 'avg_losses-stats not exported?'
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname,
delta=1E-4)
# check event loss table
[fname] = export(('losses_by_event', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname,
delta=1E-4)
fname = gettemp(view('portfolio_losses', self.calc.datastore))
self.assertEqualFiles(
'expected/portfolio_losses.txt', fname, delta=1E-4)
os.remove(fname)
# check ruptures are stored correctly
fname = gettemp(view('ruptures_events', self.calc.datastore))
self.assertEqualFiles('expected/ruptures_events.txt', fname)
os.remove(fname)
# check losses_by_tag
fnames = export(
('aggregate_by/avg_losses?tag=occupancy&kind=mean', 'csv'),
self.calc.datastore)
self.assertEqualFiles('expected/losses_by_occupancy.csv', fnames[0],
delta=1E-4)
self.check_multi_tag(self.calc.datastore)
# check curves-rlzs and curves-stats are readable
df1 = self.calc.datastore.read_df('curves-rlzs', 'assets')
aae(df1.columns, ['rlzs', 'return_periods', 'loss_types', 'value'])
df2 = self.calc.datastore.read_df('curves-stats', 'assets')
aae(df2.columns, ['stats', 'return_periods', 'loss_types', 'value'])
def test_international_date_line_2(self):
# from a bug affecting a calculation in New Zealand
fname = gettemp(characteric_source)
[[src]] = nrml.to_python(fname)
os.remove(fname)
maxdist = IntegrationDistance({'default': 200})
sitecol = SiteCollection([
Site(location=Point(176.919, -39.489),
vs30=760, vs30measured=True, z1pt0=100, z2pt5=5)])
srcfilter = SourceFilter(sitecol, maxdist)
sites = srcfilter.get_close_sites(src)
self.assertIsNotNone(sites)
def test(self):
# make sure the src_group_id is transferred also for single split
# sources, since this caused hard to track bugs
fname = gettemp(characteric_source)
[[char]] = nrml.to_python(fname)
char.id = 1
char.src_group_id = 1
os.remove(fname)
[src], _ = split_sources([char])
self.assertEqual(char.id, src.id)
self.assertEqual(char.source_id, src.source_id)
self.assertEqual(char.src_group_id, src.src_group_id)
def validate_nrml(request):
"""
Leverage oq-risklib to check if a given XML text is a valid NRML
:param request:
a `django.http.HttpRequest` object containing the mandatory
parameter 'xml_text': the text of the XML to be validated as NRML
:returns: a JSON object, containing:
* 'valid': a boolean indicating if the provided text is a valid NRML
* 'error_msg': the error message, if any error was found
(None otherwise)
* 'error_line': line of the given XML where the error was found
(None if no error was found or if it was not a
validation error)
"""
xml_text = request.POST.get('xml_text')
if not xml_text:
return HttpResponseBadRequest(
'Please provide the "xml_text" parameter')
xml_file = gettemp(xml_text, suffix='.xml')
try:
nrml.to_python(xml_file)
except ExpatError as exc:
return _make_response(error_msg=str(exc),
error_line=exc.lineno,
valid=False)
except Exception as exc:
# get the exception message
exc_msg = exc.args[0]
if isinstance(exc_msg, bytes):
exc_msg = exc_msg.decode('utf-8') # make it a unicode object
elif isinstance(exc_msg, str):
pass
else:
# if it is another kind of object, it is not obvious a priori how
# to extract the error line from it
return _make_response(error_msg=str(exc_msg),
error_line=None,
valid=False)
# if the line is not mentioned, the whole message is taken
error_msg = exc_msg.split(', line')[0]
# check if the exc_msg contains a line number indication
search_match = re.search(r'line \d+', exc_msg)
if search_match:
error_line = int(search_match.group(0).split()[1])
else:
error_line = None
return _make_response(error_msg=error_msg,
error_line=error_line,
valid=False)
else:
return _make_response(error_msg=None, error_line=None, valid=True)
def test_international_date_line_2(self):
# from a bug affecting a calculation in New Zealand
fname = gettemp(characteric_source)
[[src]] = nrml.to_python(fname)
os.remove(fname)
maxdist = IntegrationDistance({'default': 200})
sitecol = SiteCollection([
Site(location=Point(176.919, -39.489),
vs30=760, vs30measured=True, z1pt0=100, z2pt5=5)])
srcfilter = SourceFilter(sitecol, maxdist)
sites = srcfilter.get_close_sites(src)
self.assertIsNotNone(sites)
def test_no_absolute_path(self):
temp_dir = tempfile.mkdtemp()
site_model_input = general.gettemp(dir=temp_dir, content="foo")
job_config = general.gettemp(dir=temp_dir, content="""
[general]
calculation_mode = event_based
[foo]
bar = baz
[site]
sites = 0 0
site_model_file = %s
maximum_distance=1
truncation_level=0
random_seed=0
intensity_measure_types = PGA
investigation_time = 50
export_dir = %s
""" % (site_model_input, TMP))
with self.assertRaises(ValueError) as ctx:
readinput.get_params([job_config])
self.assertIn('is an absolute path', str(ctx.exception))
def test_no_absolute_path(self):
temp_dir = tempfile.mkdtemp()
site_model_input = general.gettemp(dir=temp_dir, content="foo")
job_config = general.gettemp(dir=temp_dir, content="""
[general]
calculation_mode = event_based
[foo]
bar = baz
[site]
sites = 0 0
site_model_file = %s
maximum_distance=1
truncation_level=0
random_seed=0
intensity_measure_types = PGA
investigation_time = 50
export_dir = %s
""" % (site_model_input, TMP))
with self.assertRaises(ValueError) as ctx:
readinput.get_params([job_config])
self.assertIn('is an absolute path', str(ctx.exception))
def validate_nrml(request):
"""
Leverage oq-risklib to check if a given XML text is a valid NRML
:param request:
a `django.http.HttpRequest` object containing the mandatory
parameter 'xml_text': the text of the XML to be validated as NRML
:returns: a JSON object, containing:
* 'valid': a boolean indicating if the provided text is a valid NRML
* 'error_msg': the error message, if any error was found
(None otherwise)
* 'error_line': line of the given XML where the error was found
(None if no error was found or if it was not a
validation error)
"""
xml_text = request.POST.get('xml_text')
if not xml_text:
return HttpResponseBadRequest(
'Please provide the "xml_text" parameter')
xml_file = gettemp(xml_text, suffix='.xml')
try:
nrml.to_python(xml_file)
except ExpatError as exc:
return _make_response(error_msg=str(exc),
error_line=exc.lineno,
valid=False)
except Exception as exc:
# get the exception message
exc_msg = exc.args[0]
if isinstance(exc_msg, bytes):
exc_msg = exc_msg.decode('utf-8') # make it a unicode object
elif isinstance(exc_msg, str):
pass
else:
# if it is another kind of object, it is not obvious a priori how
# to extract the error line from it
return _make_response(
error_msg=str(exc_msg), error_line=None, valid=False)
# if the line is not mentioned, the whole message is taken
error_msg = exc_msg.split(', line')[0]
# check if the exc_msg contains a line number indication
search_match = re.search(r'line \d+', exc_msg)
if search_match:
error_line = int(search_match.group(0).split()[1])
else:
error_line = None
return _make_response(
error_msg=error_msg, error_line=error_line, valid=False)
else:
return _make_response(error_msg=None, error_line=None, valid=True)
def test_case_master(self):
self.run_calc(case_master.__file__, 'job.ini', insured_losses='false')
calc0 = self.calc.datastore # single file event_based_risk
self.run_calc(case_master.__file__, 'job.ini', insured_losses='false',
calculation_mode='event_based')
calc1 = self.calc.datastore # event_based
self.run_calc(case_master.__file__, 'job.ini', insured_losses='false',
hazard_calculation_id=str(calc1.calc_id),
source_model_logic_tree_file='',
gsim_logic_tree_file='')
calc2 = self.calc.datastore # two files event_based_risk
check_csm_info(calc0, calc1) # the csm_info arrays must be equal
check_csm_info(calc0, calc2) # the csm_info arrays must be equal
if sys.platform == 'darwin':
raise unittest.SkipTest('MacOSX')
# compare the event loss table generated by a event_based_risk
# case_master calculation from ruptures
f0 = gettemp(view('elt', calc0))
self.assertEqualFiles('expected/elt.txt', f0, delta=1E-5)
f2 = gettemp(view('elt', calc2))
self.assertEqualFiles('expected/elt.txt', f2, delta=1E-5)
def test_pga(self):
imt_dt = numpy.dtype([('PGA', F32)])
array = get_shakemap_array(gettemp(example_pga))
n = 3 # number of sites
self.assertEqual(len(array), n)
self.assertEqual(array.dtype.names,
('lon', 'lat', 'vs30', 'val', 'std'))
dec = 4 # four digits
aae(array['lon'], [13.580, 13.5883, 13.5967], dec)
aae(array['lat'], [39.3103, 39.3103, 39.3103], dec)
aae(array['vs30'], [603, 603, 603], dec)
val = numpy.zeros(n, imt_dt)
std = numpy.array([(0.51,), (0.51,), (0.51,)], imt_dt)
for imt in imt_dt.names:
aae(array['val'][imt], val[imt])
aae(array['std'][imt], std[imt])
def test_case_6a(self):
# case with two gsims
self.run_calc(case_6a.__file__, 'job_haz.ini,job_risk.ini',
exports='csv')
[f] = export(('agglosses', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/agg_structural.csv', f)
# testing the totlosses view
dstore = self.calc.datastore
fname = gettemp(view('totlosses', dstore))
self.assertEqualFiles('expected/totlosses.txt', fname)
# two equal gsims
with self.assertRaises(InvalidLogicTree):
self.run_calc(case_6a.__file__, 'job_haz.ini',
gsim_logic_tree_file='wrong_gmpe_logic_tree.xml')
def test_splittable_events(self):
# split the events in two blocks and check that the ratios are
# same: there is no randomness in VulnerabilityFunction.sample
vuln_model = gettemp("""\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml">
<vulnerabilityModel>
<discreteVulnerabilitySet vulnerabilitySetID="PAGER"
assetCategory="Category"
lossCategory="structural">
<IML IMT="PGA">0.005 0.007 0.0098 0.0137</IML>
<discreteVulnerability vulnerabilityFunctionID="RC/A"
probabilisticDistribution="LN">
<lossRatio>0.01 0.06 0.18 0.36</lossRatio>
<coefficientsVariation>0.30 0.30 0.30 0.30
</coefficientsVariation>
</discreteVulnerability>
</discreteVulnerabilitySet>
</vulnerabilityModel>
</nrml>""")
vfs = {('structural', 'vulnerability'):
nrml.to_python(vuln_model)['PGA', 'RC/A']}
rm = riskmodels.RiskModel('event_based_risk', "RC/A", vfs,
ignore_covs=False)
assets = [0, 1]
eids = numpy.array([1, 2, 3, 4, 5])
gmvs = numpy.array([.1, .2, .3, .4, .5])
epsilons = numpy.array(
[[.01, .02, .03, .04, .05], [.001, .002, .003, .004, .005]])
# compute the ratios by considering all the events
ratios = rm('structural', assets, gmvs, eids, epsilons)
numpy.testing.assert_allclose(ratios, self.expected_ratios)
# split the events in two blocks
eids1 = numpy.array([1, 2])
eids2 = numpy.array([3, 4, 5])
gmvs1 = numpy.array([.1, .2])
gmvs2 = numpy.array([.3, .4, .5])
eps1 = numpy.array([[.01, .02], [.001, .002]])
eps2 = numpy.array([[.03, .04, .05], [.003, .004, .005]])
ratios1 = rm('structural', assets, gmvs1, eids1, eps1)
ratios2 = rm('structural', assets, gmvs2, eids2, eps2)
numpy.testing.assert_allclose(ratios1, self.expected_ratios[:, :2])
numpy.testing.assert_allclose(ratios2, self.expected_ratios[:, 2:])
def test_invalid(self):
fname = gettemp('''\
<?xml version="1.0" encoding="utf-8"?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml">
<gmfCollection gsimTreePath="" sourceModelTreePath="">
<gmfSet stochasticEventSetId="1">
<gmf IMT="PGA" ruptureId="0">
<node gmv="0.012646" lon="12.12477995" lat="43.5812"/>
<node gmv="-0.012492" lon="12.12478193" lat="43.5812"/>
</gmf>
</gmfSet>
</gmfCollection>
</nrml>''', suffix='.xml')
with Print.patch() as p:
tidy([fname])
self.assertIn('Could not convert gmv->positivefloat: '
'float -0.012492 < 0, line 8', str(p))
def test_two_nodes_on_the_same_point(self):
# after rounding of the coordinates two points can collide
fname = general.gettemp('''\
<?xml version="1.0" encoding="utf-8"?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml">
<gmfCollection gsimTreePath="" sourceModelTreePath="">
<gmfSet stochasticEventSetId="1">
<gmf IMT="PGA" ruptureId="0">
<node gmv="0.0126515007046" lon="12.12477995" lat="43.5812"/>
<node gmv="0.0124056290492" lon="12.12478193" lat="43.5812"/>
</gmf>
</gmfSet>
</gmfCollection>
</nrml>''')
self.oqparam.imtls = {'PGA': None}
with self.assertRaises(readinput.InvalidFile) as ctx:
readinput.get_scenario_from_nrml(self.oqparam, fname)
self.assertIn("Expected 1 sites, got 2 nodes in", str(ctx.exception))
def test_event_based_risk_sampling(self):
# the fast calculator ucerf_risk
raise unittest.SkipTest('ucerf_risk has been removed')
self.run_calc(ucerf.__file__, 'job_ebr.ini',
number_of_logic_tree_samples='2')
# check the right number of events was stored
self.assertEqual(len(self.calc.datastore['events']), 79)
fname = gettemp(view('portfolio_loss', self.calc.datastore))
self.assertEqualFiles(
'expected/portfolio_loss2.txt', fname, delta=1E-5)
# check the mean losses_by_period
[fname] = export(('agg_curves-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/losses_by_period2-mean.csv', fname)
# make sure this runs
view('fullreport', self.calc.datastore)
def submit_job(job_ini, username, hazard_job_id=None):
"""
Create a job object from the given job.ini file in the job directory
and run it in a new process. Returns the job ID and PID.
"""
job_id = logs.init('job')
oq = engine.job_from_file(
job_ini, job_id, username, hazard_calculation_id=hazard_job_id)
pik = pickle.dumps(oq, protocol=0) # human readable protocol
code = RUNCALC % dict(job_id=job_id, hazard_job_id=hazard_job_id, pik=pik,
username=username)
tmp_py = gettemp(code, suffix='.py')
# print(code, tmp_py) # useful when debugging
devnull = subprocess.DEVNULL
popen = subprocess.Popen([sys.executable, tmp_py],
stdin=devnull, stdout=devnull, stderr=devnull)
threading.Thread(target=popen.wait).start()
logs.dbcmd('update_job', job_id, {'pid': popen.pid})
return job_id, popen.pid
def test_case_master(self):
# a case with two GSIMs
self.run_calc(case_master.__file__, 'job.ini', exports='npz')
# check realizations
[fname] = export(('realizations', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/realizations.csv', fname)
# check losses by taxonomy
agglosses = extract(self.calc.datastore, 'agg_losses/structural?'
'taxonomy=*').array # shape (T, R) = (3, 2)
self.assertEqualFiles('expected/agglosses_taxo.txt',
gettemp(str(agglosses)))
# extract agglosses with a * and a selection
obj = extract(self.calc.datastore, 'agg_losses/structural?'
'state=*&cresta=0.11')
self.assertEqual(obj.selected, [b'state=*', b'cresta=0.11'])
self.assertEqual(obj.tags, [b'state=01'])
aac(obj.array, [[2493.7097, 2943.6640]])
