def test_event_based(self):
self.run_calc(ucerf.__file__, 'job.ini')
gmv_uc = view('global_gmfs', self.calc.datastore)
# check the distribution of the events
self.assertEventsByRlz([2, 2, 2, 2, 6, 6, 2, 2, 2, 2, 6, 6, 2, 2, 3,
3, 6, 6, 1, 1, 1, 1, 6, 6, 2, 2, 3, 3, 2, 2,
2, 2, 3, 3, 2, 2, 3, 3, 3, 3, 2, 2, 3, 3,
3, 3, 3, 3])
[fname] = export(('ruptures', 'csv'), self.calc.datastore)
# check that we get the expected number of ruptures
with open(fname) as f:
self.assertEqual(len(f.readlines()), 72)
self.assertEqualFiles('expected/ruptures.csv', fname, lastline=20,
delta=1E-5)
# run a regular event based on top of the UCERF ruptures and
# check the generated hazard maps
self.run_calc(ucerf.__file__, 'job.ini',
calculation_mode='event_based',
hazard_calculation_id=str(self.calc.datastore.calc_id))
# check ucerf_hazard and event_based produces the same GMFs
gmv_eb = view('global_gmfs', self.calc.datastore)
self.assertEqual(gmv_uc, gmv_eb)
# check the mean hazard map
[fname] = [f for f in export(('hmaps', 'csv'), self.calc.datastore)
if 'mean' in f]
self.assertEqualFiles('expected/hazard_map-mean.csv', fname,
delta=1E-5)
def test_case_15(self):
# an example for Japan testing also the XML rupture exporter
self.run_calc(case_15.__file__, 'job.ini')
[fname] = export(('ruptures', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/ruptures.csv', fname)
[fname] = export(('ruptures', 'xml'), self.calc.datastore)
self.assertEqualFiles('expected/ruptures.xml', fname)
def test_case_2(self):
self.run_calc(case_2.__file__, 'job_risk.ini', exports='csv')
[fname] = export(('loss_curves/rlz-0', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_curves.csv', fname)
[fname] = export(('loss_maps-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_maps.csv', fname)
def test_case_1(self):
self.assert_curves_ok(
['hazard_curve-PGA.csv', 'hazard_curve-SA(0.1).csv'],
case_1.__file__)
if parallel.oq_distribute() != 'no':
info = view('job_info', self.calc.datastore)
self.assertIn('task', info)
self.assertIn('sent', info)
self.assertIn('received', info)
# there is a single source
self.assertEqual(len(self.calc.datastore['source_info']), 1)
# check npz export
export(('hcurves', 'npz'), self.calc.datastore)
# check extraction
sitecol = extract(self.calc.datastore, 'sitecol')
self.assertEqual(len(sitecol.array), 1)
# check minimum_magnitude discards the source
with self.assertRaises(RuntimeError) as ctx:
self.run_calc(case_1.__file__, 'job.ini', minimum_magnitude='4.5')
self.assertEqual(str(ctx.exception), 'All sources were filtered away!')
def test_case_1(self):
# case with volcanic multiperil ASH, LAVA, LAHAR, PYRO
self.run_calc(case_1.__file__, 'job.ini')
[fname] = export(('asset_risk', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/asset_risk.csv', fname)
[fname] = export(('agg_risk', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/agg_risk.csv', fname)
# check extract
md = extract(self.calc.datastore, 'exposure_metadata')
ae(md.array, ['number', 'occupants_night', 'value-structural'])
ae(md.multi_risk, ['collapse-structural-ASH_DRY',
'collapse-structural-ASH_WET',
'loss-structural-ASH_DRY',
'loss-structural-ASH_WET',
'loss-structural-LAHAR',
'loss-structural-LAVA',
'loss-structural-PYRO',
'no_damage-structural-ASH_DRY',
'no_damage-structural-ASH_WET',
'number-LAHAR',
'number-LAVA',
'number-PYRO',
'occupants_night-LAHAR',
'occupants_night-LAVA',
'occupants_night-PYRO'])
def test_case_master(self):
self.assert_stats_ok(case_master, 'job.ini', individual_curves='false')
fnames = export(('loss_maps-rlzs', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname)
fname = writetmp(view('portfolio_loss', self.calc.datastore))
self.assertEqualFiles('expected/portfolio_loss.txt', fname, delta=1E-5)
# check rup_data is stored correctly
fname = writetmp(view('ruptures_events', self.calc.datastore))
self.assertEqualFiles('expected/ruptures_events.txt', fname)
# export a specific eid
fnames = export(('all_loss_ratios:0', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname)
self.assertEqualFiles('expected/losses-eid=0.csv', fname)
# export a specific pair (sm_id, eid)
fnames = export(('all_loss_ratios:1:0', 'csv'),
self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname)
def test_case_3(self):
self.run_calc(case_3.__file__, 'job.ini', exports='csv')
[fname] = export(('loss_curves/rlz-0/sid-0', 'csv'),
self.calc.datastore)
self.assertEqualFiles('expected/loss_curves-000.csv', fname)
[fname] = export(('loss_curves/rlz-0/ref-a8', 'csv'),
self.calc.datastore)
self.assertEqualFiles('expected/loss_curves-ref-a8-000.csv', fname)
def test_case_8a(self):
self.run_calc(
case_8a.__file__, 'job_haz.ini,job_risk.ini')
f1, f2 = export(('damages-rlzs', 'csv'), self.calc.datastore)
self.assertEqualFiles(
'expected/damages-rlzs-AkkarBommer2010().csv', f2)
self.assertEqualFiles(
'expected/damages-rlzs-SadighEtAl1997().csv', f1)
[f] = export(('damages-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/damages-stats.csv', f)
def test_case_20(self): # test for Vancouver using the NRCan15SiteTerm
self.run_calc(case_20.__file__, 'job.ini')
[gmf, _, _] = export(('gmf_data', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/gmf-data.csv', gmf)
# run again the GMF calculation, but this time from stored ruptures
hid = str(self.calc.datastore.calc_id)
self.run_calc(case_20.__file__, 'job.ini', hazard_calculation_id=hid)
[gmf, _, _] = export(('gmf_data', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/gmf-data-from-ruptures.csv', gmf)
def test_case_2_correlation(self):
self.run_calc(case_2.__file__, 'job_loss.ini', asset_correlation=1.0)
[fname] = export(('agg_loss_table', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/agg_losses.csv', fname)
# test losses_by_tag with a single realization
[fname] = export(
('aggregate_by/avg_losses?tag=taxonomy&kind=rlz-0', 'csv'),
self.calc.datastore)
self.assertEqualFiles('expected/losses_by_tag.csv', fname)
def test_case_16(self): # sampling
self.assert_curves_ok(
['hazard_curve-mean.csv',
'quantile_curve-0.1.csv',
'quantile_curve-0.9.csv'],
case_16.__file__)
# test that the single realization export fails because
# individual_curves was false
with self.assertRaises(KeyError) as ctx:
export(('hcurves/rlz-3', 'csv'), self.calc.datastore)
self.assertIn("No 'hcurves-rlzs' found", str(ctx.exception))
def assert_curves_ok(self, expected, test_dir, delta=None, **kw):
kind = kw.pop('kind', '')
self.run_calc(test_dir, 'job.ini', **kw)
ds = self.calc.datastore
got = (export(('hcurves/' + kind, 'csv'), ds) +
export(('hmaps/' + kind, 'csv'), ds) +
export(('uhs/' + kind, 'csv'), ds))
self.assertEqual(len(expected), len(got), str(got))
for fname, actual in zip(expected, got):
self.assertEqualFiles('expected/%s' % fname, actual,
delta=delta)
return got
def test_occupants(self):
self.run_calc(occupants.__file__, 'job.ini')
fnames = export(('agg_curves-rlzs', 'csv'), self.calc.datastore)
for fname in fnames:
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)
def test_case_4b(self):
self.run_calc(case_4b.__file__, 'job_haz.ini,job_risk.ini')
[fname] = export(('dmg_by_event', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname)
[fname] = export(('losses_by_event', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname)
fnames = export(('losses_by_asset', 'csv'), self.calc.datastore)
self.assertEqual(len(fnames), 2) # one per realization
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname)
def test_case_4(self):
# Turkey with SHARE logic tree
self.run_calc(case_4.__file__, 'job.ini')
[fname] = export(('avg_losses-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/avg_losses-mean.csv', fname)
fnames = export(('agg_loss_table', 'csv'), self.calc.datastore)
assert fnames, 'No agg_losses exported??'
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname)
# check that individual_curves = false is honored
self.assertFalse('curves-rlzs' in self.calc.datastore)
self.assertTrue('curves-stats' in self.calc.datastore)
def test_case_1(self):
self.run_calc(case_1.__file__, 'job_risk.ini', exports='csv')
# check loss ratios
lrs = self.calc.datastore['risk_model/VF/structural-vulnerability']
got = scientificformat(lrs.mean_loss_ratios, '%.2f')
self.assertEqual(got, '0.05 0.10 0.20 0.40 0.80')
# check loss curves
[fname] = export(('loss_curves/mean', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_curves.csv', fname)
# check loss maps
[fname] = export(('loss_maps-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_maps.csv', fname)
def test_case_master(self):
self.run_calc(case_master.__file__, 'job.ini')
fnames = export(('loss_maps-stats', 'csv'), self.calc.datastore)
assert fnames # sanity check
# FIXME: on macOS the generation of loss maps stats is terribly wrong,
# the number of losses do not match, this must be investigated
if NOT_DARWIN:
for fname in fnames:
self.assertEqualFiles(
'expected/' + strip_calc_id(fname), fname)
# exported the npz, not checking the content
for kind in ('rlzs', 'stats'):
[fname] = export(('loss_maps-' + kind, 'npz'), self.calc.datastore)
print('Generated ' + fname)
def test_event_based(self):
if h5py.__version__ < '2.6.0':
raise unittest.SkipTest # UCERF requires vlen arrays
self.run_calc(ucerf.__file__, 'job.ini')
[fname] = export(('ruptures', 'csv'), self.calc.datastore)
# just check that we get the expected number of ruptures
self.assertEqual(open(fname).read().count('\n'), 918)
def test_case_5(self):
# test with different curve resolution for different taxonomies
self.run_calc(case_5.__file__, 'job_h.ini,job_r.ini')
# check the cutoff in classical.fix_ones
poes = self.calc.datastore['poes/grp-00'].array
num_ones = (poes == 1.).sum()
self.assertEqual(num_ones, 0)
# check mean loss curves
[fname] = export(('loss_curves/mean', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_curves-mean.csv', fname)
# check avg losses
[fname] = export(('avg_losses-stats', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_curves_avg.txt', fname)
def test_case_3(self):
# this is a test with statistics and without conditional_loss_poes
self.run_calc(case_3.__file__, 'job.ini',
exports='xml', individual_curves='false',
concurrent_tasks='4')
[fname] = export(('agg_curve-stats', 'xml'), self.calc.datastore)
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname)
def test_case_8(self):
# case with a shakemap
self.run_calc(case_8.__file__, 'prejob.ini')
self.run_calc(case_8.__file__, 'job.ini',
hazard_calculation_id=str(self.calc.datastore.calc_id))
[fname] = export(('dmg_by_event', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/dmg_by_event.csv', fname)
def test_case_7(self):
# this is a case with three loss types
self.assert_ok(case_7, 'job_h.ini,job_r.ini', exports='csv')
# just run the npz export
[npz] = export(('dmg_by_asset', 'npz'), self.calc.datastore)
self.assertEqual(strip_calc_id(npz), 'dmg_by_asset.npz')
def test_case_13(self):
self.assert_curves_ok(
['hazard_curve-mean_PGA.csv', 'hazard_curve-mean_SA(0.2).csv',
'hazard_map-mean.csv'], case_13.__file__)
# test recomputing the hazard maps
self.run_calc(
case_13.__file__, 'job.ini', exports='csv',
hazard_calculation_id=str(self.calc.datastore.calc_id),
gsim_logic_tree_file='', source_model_logic_tree_file='')
[fname] = export(('hmaps', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/hazard_map-mean.csv', fname,
delta=1E-5)
# test extract/hcurves/rlz-0, used by the npz exports
haz = vars(extract(self.calc.datastore, 'hcurves'))
self.assertEqual(sorted(haz), ['all', 'investigation_time'])
self.assertEqual(
haz['all'].dtype.names, ('lon', 'lat', 'depth', 'mean'))
array = haz['all']['mean']
self.assertEqual(array.dtype.names, ('PGA', 'SA(0.2)'))
self.assertEqual(array['PGA'].dtype.names,
('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'))
def test_case_21(self): # Simple fault dip and MFD enumeration
self.assert_curves_ok([
'hazard_curve-smltp_b1_mfd1_high_dip_dip30-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd1_high_dip_dip45-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd1_high_dip_dip60-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd1_low_dip_dip30-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd1_low_dip_dip45-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd1_low_dip_dip60-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd1_mid_dip_dip30-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd1_mid_dip_dip45-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd1_mid_dip_dip60-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd2_high_dip_dip30-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd2_high_dip_dip45-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd2_high_dip_dip60-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd2_low_dip_dip30-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd2_low_dip_dip45-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd2_low_dip_dip60-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd2_mid_dip_dip30-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd2_mid_dip_dip45-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd2_mid_dip_dip60-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd3_high_dip_dip30-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd3_high_dip_dip45-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd3_high_dip_dip60-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd3_low_dip_dip30-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd3_low_dip_dip45-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd3_low_dip_dip60-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd3_mid_dip_dip30-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd3_mid_dip_dip45-gsimltp_Sad1997.csv',
'hazard_curve-smltp_b1_mfd3_mid_dip_dip60-gsimltp_Sad1997.csv'],
case_21.__file__, delta=1E-7)
[fname] = export(('sourcegroups', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/sourcegroups.csv', fname)
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_15(self): # full enumeration
self.assert_curves_ok('''\
hazard_curve-mean.csv
hazard_curve-smltp_SM1-gsimltp_BA2008_C2003.csv
hazard_curve-smltp_SM1-gsimltp_BA2008_T2002.csv
hazard_curve-smltp_SM1-gsimltp_CB2008_C2003.csv
hazard_curve-smltp_SM1-gsimltp_CB2008_T2002.csv
[email protected]
[email protected]
[email protected]
[email protected]
hazard_uhs-mean.csv
hazard_uhs-smltp_SM1-gsimltp_BA2008_C2003.csv
hazard_uhs-smltp_SM1-gsimltp_BA2008_T2002.csv
hazard_uhs-smltp_SM1-gsimltp_CB2008_C2003.csv
hazard_uhs-smltp_SM1-gsimltp_CB2008_T2002.csv
[email protected]
[email protected]
[email protected]
[email protected]'''.split(),
case_15.__file__, delta=1E-6)
# now some tests on the exact numbers
check_platform('xenial', 'trusty')
# test UHS XML export
fnames = [f for f in export(('uhs', 'xml'), self.calc.datastore)
if 'mean' in f]
self.assertEqualFiles('expected/hazard_uhs-mean-0.01.xml', fnames[0])
self.assertEqualFiles('expected/hazard_uhs-mean-0.1.xml', fnames[1])
self.assertEqualFiles('expected/hazard_uhs-mean-0.2.xml', fnames[2])
# test hmaps geojson export
fnames = [f for f in export(('hmaps', 'geojson'), self.calc.datastore)
if 'mean' in f]
self.assertEqualFiles(
'expected/hazard_map-mean-0.01-PGA.geojson', fnames[0])
self.assertEqualFiles(
'expected/hazard_map-mean-0.01-SA(0.1).geojson', fnames[1])
self.assertEqualFiles(
'expected/hazard_map-mean-0.1-PGA.geojson', fnames[2])
self.assertEqualFiles(
'expected/hazard_map-mean-0.1-SA(0.1).geojson', fnames[3])
self.assertEqualFiles(
'expected/hazard_map-mean-0.2-PGA.geojson', fnames[4])
self.assertEqualFiles(
'expected/hazard_map-mean-0.2-SA(0.1).geojson', fnames[5])
def test_case_1(self):
out = self.run_calc(case_1.__file__, 'job_risk.ini', exports='csv')
[fname] = out['agglosses-rlzs', 'csv']
self.assertEqualFiles('expected/agg.csv', fname)
# check the exported GMFs
[fname] = export(('gmf_data', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/gmf-FromFile-PGA.csv', fname)
def test_case_4(self):
# this is case with number of lon/lat bins different for site 0/site 1
# this exercise sampling
self.run_calc(case_4.__file__, 'job.ini')
# check stats
fnames = export(('disagg-stats', 'csv'), self.calc.datastore)
self.assertEqual(len(fnames), 64) # 2 sid x 8 keys x 2 poe x 2 imt
def test_case_5(self):
out = self.run_calc(case_5.__file__, 'job.ini', exports='csv')
[fname, _, _] = out['gmf_data', 'csv']
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname,
delta=1E-6)
[fname] = export(('ruptures', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/ruptures.csv', fname, delta=1E-6)
def test_case_4(self):
out = self.run_calc(case_4.__file__, 'job_haz.ini,job_risk.ini',
exports='csv,xml')
fnames = export(('loss_maps-rlzs', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/loss_maps-b1,b1.csv', fnames[0])
self.assertEqualFiles('expected/loss_maps-b1,b2.csv', fnames[1])
fnames = out['loss_curves-rlzs', 'csv']
self.assertEqualFiles('expected/loss_curves-000.csv', fnames[0])
self.assertEqualFiles('expected/loss_curves-001.csv', fnames[1])
[fname] = export(('loss_maps-stats', 'xml'), self.calc.datastore)
self.assertEqualFiles('expected/loss_maps-mean-structural.xml', fname)
[fname] = out['loss_curves-stats', 'xml']
self.assertEqualFiles('expected/loss_curves-mean-structural.xml',
fname)
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, [687.92365])
fnames = export(('agg_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(('agg_loss_table', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname,
delta=1E-5)
# extract tot_curves
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)
# 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(rst_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves5.csv', tmp)
aw = extract(self.calc.datastore, 'agg_curves?kind=rlzs&'
'loss_type=structural&absolute=1&policy=A&taxonomy=RC')
tmp = gettemp(rst_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves6.csv', tmp)
aw = extract(self.calc.datastore, 'agg_curves?kind=stats&'
'loss_type=structural&absolute=0&policy=A&taxonomy=RC')
tmp = gettemp(rst_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves7.csv', tmp)
aw = extract(self.calc.datastore, 'agg_curves?kind=rlzs&'
'loss_type=structural&absolute=0&policy=A&taxonomy=RC')
tmp = gettemp(rst_table(aw.to_dframe()))
self.assertEqualFiles('expected/agg_curves8.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_1_eb(self):
# this is a case with insured losses
self.run_calc(case_1.__file__, 'job_eb.ini')
[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'])
self.assertEqual(aw.array, numpy.float32([767.82324]))
fnames = export(('agg_curves-stats', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/eb_%s' % strip_calc_id(fname),
fname)
fnames = export(('agg_losses-stats', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/%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)
# extract agg_curves, no tags
aw = extract(
self.calc.datastore, 'agg_curves?kind=stats&'
'loss_type=structural&absolute=1')
tmp = gettemp(rst_table(aw.to_table()))
self.assertEqualFiles('expected/agg_curves1.csv', tmp)
aw = extract(self.calc.datastore, 'agg_curves?kind=rlzs&'
'loss_type=structural&absolute=1')
tmp = gettemp(rst_table(aw.to_table()))
self.assertEqualFiles('expected/agg_curves2.csv', tmp)
aw = extract(
self.calc.datastore, 'agg_curves?kind=stats&'
'loss_type=structural&absolute=0')
tmp = gettemp(rst_table(aw.to_table()))
self.assertEqualFiles('expected/agg_curves3.csv', tmp)
aw = extract(self.calc.datastore, 'agg_curves?kind=rlzs&'
'loss_type=structural&absolute=0')
tmp = gettemp(rst_table(aw.to_table()))
self.assertEqualFiles('expected/agg_curves4.csv', tmp)
# TODO: fix extract agg_curves for insured types
# extract agg_curves with tags
self.run_calc(case_1.__file__,
'job_eb.ini',
aggregate_by='policy,taxonomy',
hazard_calculation_id=str(self.calc.datastore.calc_id))
aw = extract(
self.calc.datastore, 'agg_curves?kind=stats&'
'loss_type=structural&absolute=1&policy=A&taxonomy=RC')
tmp = gettemp(rst_table(aw.to_table()))
self.assertEqualFiles('expected/agg_curves5.csv', tmp)
aw = extract(
self.calc.datastore, 'agg_curves?kind=rlzs&'
'loss_type=structural&absolute=1&policy=A&taxonomy=RC')
tmp = gettemp(rst_table(aw.to_table()))
self.assertEqualFiles('expected/agg_curves6.csv', tmp)
aw = extract(
self.calc.datastore, 'agg_curves?kind=stats&'
'loss_type=structural&absolute=0&policy=A&taxonomy=RC')
tmp = gettemp(rst_table(aw.to_table()))
self.assertEqualFiles('expected/agg_curves7.csv', tmp)
aw = extract(
self.calc.datastore, 'agg_curves?kind=rlzs&'
'loss_type=structural&absolute=0&policy=A&taxonomy=RC')
tmp = gettemp(rst_table(aw.to_table()))
self.assertEqualFiles('expected/agg_curves8.csv', tmp)
def test_case_15(self):
# this is a case with both splittable and unsplittable sources
self.assert_curves_ok('''\
hazard_curve-max-PGA.csv,
hazard_curve-max-SA(0.1).csv
hazard_curve-mean-PGA.csv
hazard_curve-mean-SA(0.1).csv
hazard_curve-std-PGA.csv
hazard_curve-std-SA(0.1).csv
hazard_uhs-max.csv
hazard_uhs-mean.csv
hazard_uhs-std.csv
'''.split(),
case_15.__file__,
delta=1E-6)
# test UHS XML export
fnames = [
f for f in export(('uhs', 'xml'), self.calc.datastore)
if 'mean' in f
]
self.assertEqualFiles('expected/hazard_uhs-mean-0.01.xml', fnames[0])
self.assertEqualFiles('expected/hazard_uhs-mean-0.1.xml', fnames[1])
self.assertEqualFiles('expected/hazard_uhs-mean-0.2.xml', fnames[2])
# npz exports
[fname] = export(('hmaps', 'npz'), self.calc.datastore)
arr = numpy.load(fname)['all']
self.assertEqual(arr['mean'].dtype.names, ('PGA', 'SA(0.1)'))
[fname] = export(('uhs', 'npz'), self.calc.datastore)
arr = numpy.load(fname)['all']
self.assertEqual(arr['mean'].dtype.names, ('0.01', '0.1', '0.2'))
# here is the size of assoc_by_grp for a complex logic tree
# grp_id gsim_idx rlzis
# 0 0 {0, 1}
# 0 1 {2, 3}
# 1 0 {0, 2}
# 1 1 {1, 3}
# 2 0 {4}
# 2 1 {5}
# 3 0 {6}
# 3 1 {7}
# nbytes = (2 + 2 + 8) * 8 + 4 * 4 + 4 * 2 = 120
# full source model logic tree
cinfo = self.calc.datastore['csm_info']
ra0 = cinfo.get_rlzs_assoc()
self.assertEqual(sorted(ra0.by_grp()),
['grp-00', 'grp-01', 'grp-02', 'grp-03'])
# reduction of the source model logic tree
ra = cinfo.get_rlzs_assoc(sm_lt_path=['SM2', 'a3b1'])
self.assertEqual(len(ra.by_grp()), 1)
numpy.testing.assert_equal(len(ra.by_grp()['grp-02']),
len(ra0.by_grp()['grp-02']))
# more reduction of the source model logic tree
ra = cinfo.get_rlzs_assoc(sm_lt_path=['SM1'])
self.assertEqual(sorted(ra.by_grp()), ['grp-00', 'grp-01'])
numpy.testing.assert_equal(ra.by_grp()['grp-00'],
ra0.by_grp()['grp-00'])
numpy.testing.assert_equal(ra.by_grp()['grp-01'],
ra0.by_grp()['grp-01'])
# reduction of the gsim logic tree
ra = cinfo.get_rlzs_assoc(trts=['Stable Continental Crust'])
self.assertEqual(sorted(ra.by_grp()), ['grp-00', 'grp-01'])
numpy.testing.assert_equal(ra.by_grp()['grp-00'], [[0, 1]])
def test_case_1_ruptures(self):
self.run_calc(case_1.__file__, 'job_ruptures.ini')
self.assertEqual(len(self.calc.datastore['ruptures']), 2)
[fname] = export(('events', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/events.csv', fname)
def test_case_6(self):
# test with international date line
self.run_calc(case_6.__file__, 'job.ini')
fnames = export(('disagg', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname)
def test_case_10(self):
# case with more IMTs in the imported GMFs than required
self.run_calc(case_10.__file__, 'job.ini')
fnames = export(('dmg_by_asset', 'csv'), self.calc.datastore)
for i, fname in enumerate(fnames):
self.assertEqualFiles('expected/dmg_by_asset-%d.csv' % i, fname)
def test_case_2_correlation(self):
self.run_calc(case_2.__file__, 'job_loss.ini', asset_correlation=1.0)
[fname] = export(('agg_loss_table', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/agg_losses.csv', fname)
def test_case_26_liq(self):
# cali liquefaction simplified
self.run_calc(case_26.__file__, 'job_liq.ini')
[fname] = export(('avg_gmf', 'csv'), self.calc.datastore)
self.assertEqualFiles('avg_gmf.csv', fname)
def test_case_9(self):
# case with noDamageLimit==0 that had NaNs in the past
self.run_calc(case_9.__file__, 'job.ini')
fnames = export(('dmg_by_asset', 'csv'), self.calc.datastore)
for i, fname in enumerate(fnames):
self.assertEqualFiles('expected/dmg_by_asset-%d.csv' % i, fname)
def test_case_1g(self):
# vulnerability function with PMF
self.run_calc(case_1g.__file__, 'job_h.ini,job_r.ini')
[fname] = export(('avg_losses-rlzs', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/avg_losses.csv', fname)
os.remove(fname)
def test_continuous(self):
self.run_calc(case_2.__file__, 'job_continuous.ini')
[fname] = export(('damages-rlzs', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/damage_continuous.csv', fname)
def test_discrete(self):
# a test producing NaNs
self.run_calc(case_2.__file__, 'job_discrete.ini')
[fname] = export(('damages-rlzs', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/damage_discrete.csv', fname)
def test_case_14(self):
# test event_based_damage, aggregate_by=NAME_1
self.run_calc(case_14.__file__, 'job.ini')
[f] = export(('aggcurves', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(f), f, delta=5E-5)
def test_case_master(self):
self.run_calc(case_master.__file__, 'job.ini', exports='npz')
# check losses_by_tag
fnames = export(('losses_by_tag-rlzs', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname), fname)
def test_case_24(self):
# This is a test for shift_hypo = true - The expected results are the
# same ones defined for the case_44 of the classical methodology
self.run_calc(case_24.__file__, 'job.ini')
[fname] = export(('hcurves', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/hazard_curve-mean-PGA.csv', fname)
def test_case_15(self):
# an example for Japan testing also the XML rupture exporter
self.run_calc(case_15.__file__, 'job.ini')
[fname] = export(('ruptures', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/ruptures.csv', fname)
def test_case_10(self):
# this is a case with multiple files in the smlt uncertaintyModel
# and with sampling
self.run_calc(case_10.__file__, 'job.ini')
[fname] = export(('realizations', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/realizations.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)
# 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(('aggregate_by/occupancy/avg_losses', 'csv'),
self.calc.datastore)
self.assertEqualFiles('expected/losses_by_occupancy.csv', fnames[0])
self.check_multi_tag(self.calc.datastore)
# ------------------------- ebrisk calculator ---------------------- #
self.run_calc(case_master.__file__,
'job.ini',
hazard_calculation_id=str(self.calc.datastore.calc_id),
calculation_mode='ebrisk',
exports='',
aggregate_by='taxonomy',
insured_losses='false')
# agg_losses-rlzs has shape (L=5, R=9)
# agg_losses-stats has shape (L=5, S=4)
fname = export(('agg_losses-stats', 'csv'), self.calc.datastore)[0]
self.assertEqualFiles('expected/agglosses.csv', fname)
fname = export(('avg_losses', 'csv'), self.calc.datastore)[0]
self.assertEqualFiles('expected/avglosses.csv', fname, delta=1E-5)
def test_case_13a(self):
# test event_based_damage, no aggregate_by
self.run_calc(case_13.__file__, 'job_a.ini')
[f1, f2] = export(('aggcurves', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/' + strip_calc_id(f1), f1, delta=1E-5)
self.assertEqualFiles('expected/' + strip_calc_id(f2), f2, delta=1E-5)
def test_case_57(self):
# AvgPoeGMPE
self.run_calc(case_57.__file__, 'job.ini')
f1, f2 = export(('hcurves/mean', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/hcurve_PGA.csv', f1)
self.assertEqualFiles('expected/hcurve_SA.csv', f2)
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)
# ------------------------- ebrisk calculator ---------------------- #
self.run_calc(case_master.__file__,
'job.ini',
calculation_mode='ebrisk',
exports='',
aggregate_by='id')
# agg_losses-rlzs has shape (L=5, R=9)
# agg_losses-stats has shape (L=5, S=4)
fname = export(('agg_losses-stats', 'csv'), self.calc.datastore)[0]
self.assertEqualFiles('expected/agglosses.csv', fname, delta=1E-5)
fname = export(('agg_curves-stats', 'csv'), self.calc.datastore)[0]
self.assertEqualFiles('expected/aggcurves.csv', fname, delta=1E-5)
fname = export(('agg_maps-stats', 'csv'), self.calc.datastore)[0]
self.assertEqualFiles('expected/aggmaps.csv', fname, delta=1E-5)
fname = export(('avg_losses', 'csv'), self.calc.datastore)[0]
self.assertEqualFiles('expected/avg_losses-mean.csv',
fname,
delta=1E-5)
fname = export(('losses_by_event', 'csv'), self.calc.datastore)[0]
self.assertEqualFiles('expected/elt.csv', fname)
def post_execute(self, result):
result = {str(imt): gmvs for imt, gmvs in result.items()}
out = export('gmf_xml', self.oqparam.export_dir, self.sitecol,
self.etags, result)
return out
def test_case_12(self):
# 1 assets, 2 samples
self.run_calc(case_master.__file__, 'job12.ini', exports='csv')
# alt = extract(self.calc.datastore, 'asset_loss_table')
[fname] = export(('avg_losses', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/avg_loss_12.csv', fname)
def test_case_62(self):
# multisurface with kite faults
self.run_calc(case_62.__file__, 'job.ini')
[f] = export(('hcurves/mean', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/hcurve-mean.csv', f)
def test_case_2(self):
self.run_calc(case_2.__file__, 'job.ini', exports='csv,geojson')
[fname] = export(('hmaps', 'csv'), self.calc.datastore)
self.assertEqualFiles(
'expected/hazard_map-mean.csv', fname, delta=1E-6)
def test_case_5(self):
# this exercise gridded nonparametric sources
self.run_calc(case_5.__file__, 'job.ini')
fnames = export(('disagg', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/%s' % strip_calc_id(fname), fname)
def test_case_59(self):
# test NRCan15SiteTerm
self.run_calc(case_59.__file__, 'job.ini')
[f] = export(('hcurves/mean', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/hcurve-mean.csv', f)
def test_occupants(self):
self.run_calc(occupants.__file__, 'job.ini')
fnames = export(('agg_curves-rlzs', 'csv'), self.calc.datastore)
for fname in fnames:
self.assertEqualFiles('expected/' + strip_calc_id(fname),
fname, delta=1E-5)
def test_case_15(self): # full enumeration
self.assert_curves_ok('''\
hazard_curve-max-PGA.csv,
hazard_curve-max-SA(0.1).csv
hazard_curve-mean-PGA.csv
hazard_curve-mean-SA(0.1).csv
hazard_uhs-max.csv
hazard_uhs-mean.csv
'''.split(), case_15.__file__, delta=1E-6)
# test UHS XML export
fnames = [f for f in export(('uhs', 'xml'), self.calc.datastore)
if 'mean' in f]
self.assertEqualFiles('expected/hazard_uhs-mean-0.01.xml', fnames[0])
self.assertEqualFiles('expected/hazard_uhs-mean-0.1.xml', fnames[1])
self.assertEqualFiles('expected/hazard_uhs-mean-0.2.xml', fnames[2])
# test hmaps geojson export
fnames = [f for f in export(('hmaps', 'geojson'), self.calc.datastore)
if 'mean' in f]
self.assertEqualFiles(
'expected/hazard_map-mean-0.01-PGA.geojson', fnames[0])
self.assertEqualFiles(
'expected/hazard_map-mean-0.01-SA(0.1).geojson', fnames[1])
self.assertEqualFiles(
'expected/hazard_map-mean-0.1-PGA.geojson', fnames[2])
self.assertEqualFiles(
'expected/hazard_map-mean-0.1-SA(0.1).geojson', fnames[3])
self.assertEqualFiles(
'expected/hazard_map-mean-0.2-PGA.geojson', fnames[4])
self.assertEqualFiles(
'expected/hazard_map-mean-0.2-SA(0.1).geojson', fnames[5])
# npz exports
export(('hmaps', 'npz'), self.calc.datastore)
export(('uhs', 'npz'), self.calc.datastore)
# here is the size of assoc_by_grp for a complex logic tree
# grp_id gsim_idx rlzis
# 0 0 {0, 1}
# 0 1 {2, 3}
# 1 0 {0, 2}
# 1 1 {1, 3}
# 2 0 {4}
# 2 1 {5}
# 3 0 {6}
# 3 1 {7}
# nbytes = (2 + 2 + 8) * 8 + 4 * 4 + 4 * 2 = 120
# full source model logic tree
cinfo = self.calc.datastore['csm_info']
ra0 = cinfo.get_rlzs_assoc()
self.assertEqual(
sorted(ra0.by_grp()), ['grp-00', 'grp-01', 'grp-02', 'grp-03'])
# reduction of the source model logic tree
ra = cinfo.get_rlzs_assoc(sm_lt_path=['SM2', 'a3b1'])
self.assertEqual(len(ra.by_grp()), 1)
numpy.testing.assert_equal(
len(ra.by_grp()['grp-02']),
len(ra0.by_grp()['grp-02']))
# more reduction of the source model logic tree
ra = cinfo.get_rlzs_assoc(sm_lt_path=['SM1'])
self.assertEqual(sorted(ra.by_grp()), ['grp-00', 'grp-01'])
numpy.testing.assert_equal(
ra.by_grp()['grp-00'], ra0.by_grp()['grp-00'])
numpy.testing.assert_equal(
ra.by_grp()['grp-01'], ra0.by_grp()['grp-01'])
# reduction of the gsim logic tree
ra = cinfo.get_rlzs_assoc(trts=['Stable Continental Crust'])
self.assertEqual(sorted(ra.by_grp()), ['grp-00', 'grp-01'])
numpy.testing.assert_equal(ra.by_grp()['grp-00'][0], [0, [0, 1]])
def test_interpolation(self):
self.run_calc(case_2.__file__, 'job_interpolation.ini')
[fname] = export(('damages-rlzs', 'csv'), self.calc.datastore)
self.assertEqualFiles('expected/damage_interpolation.csv', fname)
