def test_case_1(self):
out = self.assert_curves_ok(
['poe-0.02-rlz-0-PGA-10.1-40.1_Mag.csv',
'poe-0.02-rlz-0-PGA-10.1-40.1_Mag_Dist.csv',
'poe-0.02-rlz-0-PGA-10.1-40.1_Lon_Lat.csv',
'poe-0.02-rlz-0-SA(0.025)-10.1-40.1_Mag.csv',
'poe-0.02-rlz-0-SA(0.025)-10.1-40.1_Mag_Dist.csv',
'poe-0.02-rlz-0-SA(0.025)-10.1-40.1_Lon_Lat.csv',
'poe-0.1-rlz-0-PGA-10.1-40.1_Mag.csv',
'poe-0.1-rlz-0-PGA-10.1-40.1_Mag_Dist.csv',
'poe-0.1-rlz-0-PGA-10.1-40.1_Lon_Lat.csv',
'poe-0.1-rlz-0-SA(0.025)-10.1-40.1_Mag.csv',
'poe-0.1-rlz-0-SA(0.025)-10.1-40.1_Mag_Dist.csv',
'poe-0.1-rlz-0-SA(0.025)-10.1-40.1_Lon_Lat.csv'],
case_1.__file__,
fmt='csv')
# check disagg_by_src, poe=0.02, 0.1, imt=PGA, SA(0.025)
self.assertEqual(len(out['disagg_by_src', 'csv']), 4)
for fname in out['disagg_by_src', 'csv']:
self.assertEqualFiles('expected_output/%s' % strip_calc_id(fname),
fname)
# disaggregation by source group
rlzs_assoc = self.calc.datastore['csm_info'].get_rlzs_assoc()
pgetter = getters.PmapGetter(self.calc.datastore, rlzs_assoc)
pgetter.init()
pmaps = []
for grp in sorted(pgetter.dstore['poes']):
pmaps.append(pgetter.get_mean(grp))
# make sure that the combination of the contributions is okay
pmap = pgetter.get_mean() # total mean map
cmap = combine(pmaps) # combination of the mean maps per source group
for sid in pmap:
numpy.testing.assert_almost_equal(pmap[sid].array, cmap[sid].array)
def test_case_1(self):
self.assert_curves_ok([
'poe-0.02-rlz-0-PGA-10.1-40.1_Mag.csv',
'poe-0.02-rlz-0-PGA-10.1-40.1_Mag_Dist.csv',
'poe-0.02-rlz-0-PGA-10.1-40.1_Lon_Lat.csv',
'poe-0.02-rlz-0-SA(0.025)-10.1-40.1_Mag.csv',
'poe-0.02-rlz-0-SA(0.025)-10.1-40.1_Mag_Dist.csv',
'poe-0.02-rlz-0-SA(0.025)-10.1-40.1_Lon_Lat.csv',
'poe-0.1-rlz-0-PGA-10.1-40.1_Mag.csv',
'poe-0.1-rlz-0-PGA-10.1-40.1_Mag_Dist.csv',
'poe-0.1-rlz-0-PGA-10.1-40.1_Lon_Lat.csv',
'poe-0.1-rlz-0-SA(0.025)-10.1-40.1_Mag.csv',
'poe-0.1-rlz-0-SA(0.025)-10.1-40.1_Mag_Dist.csv',
'poe-0.1-rlz-0-SA(0.025)-10.1-40.1_Lon_Lat.csv'
],
case_1.__file__,
fmt='csv')
# disaggregation by source group
pgetter = getters.PmapGetter(self.calc.datastore)
pgetter.init()
pmaps = []
for grp in sorted(pgetter.dstore['poes']):
pmaps.append(pgetter.get_mean(grp))
# make sure that the combination of the contributions is okay
pmap = pgetter.get_mean() # total mean map
cmap = combine(pmaps) # combination of the mean maps per source group
for sid in pmap:
numpy.testing.assert_almost_equal(pmap[sid].array, cmap[sid].array)
def test_case_1(self):
self.assert_curves_ok(
['rlz-0-PGA-sid-0-poe-0_Lon_Lat.csv',
'rlz-0-PGA-sid-0-poe-0_Mag.csv',
'rlz-0-PGA-sid-0-poe-0_Mag_Dist.csv',
'rlz-0-PGA-sid-0-poe-1_Lon_Lat.csv',
'rlz-0-PGA-sid-0-poe-1_Mag.csv',
'rlz-0-PGA-sid-0-poe-1_Mag_Dist.csv',
'rlz-0-SA(0.025)-sid-0-poe-0_Lon_Lat.csv',
'rlz-0-SA(0.025)-sid-0-poe-0_Mag.csv',
'rlz-0-SA(0.025)-sid-0-poe-0_Mag_Dist.csv',
'rlz-0-SA(0.025)-sid-0-poe-1_Lon_Lat.csv',
'rlz-0-SA(0.025)-sid-0-poe-1_Mag.csv',
'rlz-0-SA(0.025)-sid-0-poe-1_Mag_Dist.csv'],
case_1.__file__,
fmt='csv')
# disaggregation by source group
rlzs = self.calc.datastore['full_lt'].get_realizations()
ws = [rlz.weight for rlz in rlzs]
pgetter = getters.PmapGetter(self.calc.datastore, ws)
pgetter.init()
pmaps = []
for grp in sorted(pgetter.dstore['poes']):
pmaps.append(pgetter.get_mean(grp))
# make sure that the combination of the contributions is okay
pmap = pgetter.get_mean() # total mean map
cmap = combine(pmaps) # combination of the mean maps per source group
for sid in pmap:
numpy.testing.assert_almost_equal(pmap[sid].array, cmap[sid].array)
check_disagg_by_src(self.calc.datastore)
def test_case_1(self):
out = self.assert_curves_ok(
['poe-0.02-rlz-0-PGA-10.1-40.1_Mag.csv',
'poe-0.02-rlz-0-PGA-10.1-40.1_Mag_Dist.csv',
'poe-0.02-rlz-0-PGA-10.1-40.1_Lon_Lat.csv',
'poe-0.02-rlz-0-SA(0.025)-10.1-40.1_Mag.csv',
'poe-0.02-rlz-0-SA(0.025)-10.1-40.1_Mag_Dist.csv',
'poe-0.02-rlz-0-SA(0.025)-10.1-40.1_Lon_Lat.csv',
'poe-0.1-rlz-0-PGA-10.1-40.1_Mag.csv',
'poe-0.1-rlz-0-PGA-10.1-40.1_Mag_Dist.csv',
'poe-0.1-rlz-0-PGA-10.1-40.1_Lon_Lat.csv',
'poe-0.1-rlz-0-SA(0.025)-10.1-40.1_Mag.csv',
'poe-0.1-rlz-0-SA(0.025)-10.1-40.1_Mag_Dist.csv',
'poe-0.1-rlz-0-SA(0.025)-10.1-40.1_Lon_Lat.csv'],
case_1.__file__,
fmt='csv')
# check disagg_by_src, poe=0.02, 0.1, imt=PGA, SA(0.025)
self.assertEqual(len(out['disagg_by_src', 'csv']), 4)
for fname in out['disagg_by_src', 'csv']:
self.assertEqualFiles('expected_output/%s' % strip_calc_id(fname),
fname)
# disaggregation by source group
rlzs_assoc = self.calc.datastore['csm_info'].get_rlzs_assoc()
pgetter = getters.PmapGetter(self.calc.datastore, rlzs_assoc)
pgetter.init()
pmaps = []
for grp in sorted(pgetter.dstore['poes']):
pmaps.append(pgetter.get_mean(grp))
# make sure that the combination of the contributions is okay
pmap = pgetter.get_mean() # total mean map
cmap = combine(pmaps) # combination of the mean maps per source group
for sid in pmap:
numpy.testing.assert_almost_equal(pmap[sid].array, cmap[sid].array)
def test_case_1(self):
out = self.assert_curves_ok([
'rlz-0-PGA-sid-0-poe-0_Lon_Lat.csv',
'rlz-0-PGA-sid-0-poe-0_Mag.csv',
'rlz-0-PGA-sid-0-poe-0_Mag_Dist.csv',
'rlz-0-PGA-sid-0-poe-1_Lon_Lat.csv',
'rlz-0-PGA-sid-0-poe-1_Mag.csv',
'rlz-0-PGA-sid-0-poe-1_Mag_Dist.csv',
'rlz-0-SA(0.025)-sid-0-poe-0_Lon_Lat.csv',
'rlz-0-SA(0.025)-sid-0-poe-0_Mag.csv',
'rlz-0-SA(0.025)-sid-0-poe-0_Mag_Dist.csv',
'rlz-0-SA(0.025)-sid-0-poe-1_Lon_Lat.csv',
'rlz-0-SA(0.025)-sid-0-poe-1_Mag.csv',
'rlz-0-SA(0.025)-sid-0-poe-1_Mag_Dist.csv'
],
case_1.__file__,
fmt='csv')
# check disagg_by_src, poe=0.02, 0.1, imt=PGA, SA(0.025)
#self.assertEqual(len(out['disagg_by_src', 'csv']), 4)
#for fname in out['disagg_by_src', 'csv']:
# self.assertEqualFiles('expected_output/%s' % strip_calc_id(fname),
# fname)
# disaggregation by source group
rlzs = self.calc.datastore['full_lt'].get_realizations()
ws = [rlz.weight for rlz in rlzs]
pgetter = getters.PmapGetter(self.calc.datastore, ws)
pgetter.init()
pmaps = []
for grp in sorted(pgetter.dstore['poes']):
pmaps.append(pgetter.get_mean(grp))
# make sure that the combination of the contributions is okay
pmap = pgetter.get_mean() # total mean map
cmap = combine(pmaps) # combination of the mean maps per source group
for sid in pmap:
numpy.testing.assert_almost_equal(pmap[sid].array, cmap[sid].array)
