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)