def check_avg_gmf(self):
# checking avg_gmf with a single site
min_iml = self.calc.oqparam.min_iml
df = self.calc.datastore.read_df('gmf_data', 'sid')
weights = self.calc.datastore['weights'][:]
rlzs = self.calc.datastore['events']['rlz_id']
[(sid, avgstd)] = compute_avg_gmf(df, weights[rlzs], min_iml).items()
avg_gmf = self.calc.datastore['avg_gmf'][:] # 2, N, M
aac(avg_gmf[:, 0], avgstd)
def test_compute_avg_gmf(self):
numpy.random.seed(42)
E = 1000
eids = numpy.arange(E)
min_iml = numpy.array([.05])
gmvs = numpy.random.lognormal(mean=-2.0, sigma=.5, size=E)
ok = gmvs >= min_iml
self.assertEqual(ok.sum(), 983)
gmf_df = pandas.DataFrame(dict(eid=eids[ok], gmv_0=gmvs[ok]),
numpy.zeros(E, int)[ok])
weights = numpy.ones(E)
[(sid, avgstd)] = compute_avg_gmf(gmf_df, weights, min_iml).items()
# aac(avgstd, [[0.13664978], [1.63127694]]) without cutting min_iml
# aac(avgstd, [[0.14734], [1.475266]], atol=1E-6) # cutting at .10
aac(avgstd, [[0.137023], [1.620616]], atol=1E-6)