def test_6_rlzs(self):
# test with 2x3 realizations and TRTA, TRTB
# rlzs_by_g = 012, 345, 03, 14, 25
inp = read_input(PARAM,
source_model_file=os.path.join(
CWD, 'data', 'sm02.xml'))
R = inp.gsim_lt.get_num_paths()
# compute the contributions by trt
tot = AccumDict() # g_ -> key -> array
for src_group in inp.groups:
cmaker = inp.cmakerdict[src_group.trt]
ctxs = cmaker.from_srcs(src_group, inp.sitecol)
tot += cmaker.get_cs_contrib(ctxs, imti, imls)
# compose the contributions by rlz, 0+2, 0+3, 0+4, 1+2, 1+3, 1+4
rlzs_by_g = inp.gsim_lt.get_rlzs_by_g()
csdic = contexts.csdict(len(cmaker.imts), 1, 1, 0, R)
for g_, rlz_ids in enumerate(rlzs_by_g):
for r in rlz_ids:
csdic[r] += tot[g_]
df = csdic_to_dframe(csdic, cmaker.imts, 0, 0)
# check the results
expected = os.path.join(CWD, 'expected', 'spectra6.csv')
if OVERWRITE_EXPECTED:
df.to_csv(expected,
index=False,
line_terminator='\r\n',
float_format='%.6f')
expdf = pandas.read_csv(expected)
pandas.testing.assert_frame_equal(df, expdf, atol=1E-6)
def test(self):
inp = read_input(JOB)
[[trt, cmaker]] = inp.cmakerdict.items()
[[area]] = inp.groups # there is a single AreaSource
srcs = list(area) # split in 3+3 PointSources
cmaker.set_weight(srcs, inp.sitecol)
weights = [src.weight for src in srcs] # 3 within, 3 outside
numpy.testing.assert_allclose(weights, [3.04, 3.04, 3.04, 1, 1, 1])
def test_1_rlz(self):
# test with one GMPE, 1 TRT, checking additivity
inp = read_input(
PARAM, gsim_logic_tree_file=os.path.join(CWD, 'data', 'lt01.xml'))
[cmaker] = inp.cmakerdict.values()
[src_group] = inp.groups
ctxs = cmaker.from_srcs(src_group, inp.sitecol)
assert len(ctxs) == 100
ctxs1 = ctxs[:50]
ctxs2 = ctxs[50:]
c1, s1 = cmaker.get_cs_contrib(ctxs1, imti, imls)
c2, s2 = cmaker.get_cs_contrib(ctxs2, imti, imls)
c, s = cmaker.get_cs_contrib(ctxs, imti, imls)
aac((c1 + c2) / (s1 + s2), c / s)
def main(params):
# example with 2x2=4 realizations with weights .36, .24, .24, .16
inp = read_input(params)
print(inp)
print(inp.gsim_lt.get_rlzs_by_gsim_trt())
acc = AccumDict(accum=[])
ebrs = sample_ebruptures(inp.groups, inp.cmakerdict)
ne = sum(ebr.n_occ for ebr in ebrs)
print('There are %d ruptures and %d events' % (len(ebrs), ne))
df = get_ebr_df(ebrs, inp.cmakerdict)
print(df.groupby('rlz').count()) # there are 8, 9, 11, 8 events per rlz
for ebr in ebrs:
cmaker = inp.cmakerdict[ebr.rupture.tectonic_region_type]
gmf_dict, _ = GmfComputer(ebr, inp.sitecol, cmaker).compute_all()
acc += gmf_dict
print(pandas.DataFrame(acc))
def test_1_rlz(self):
# test with one GMPE, 1 TRT, checking additivity
inp = read_input(PARAM,
gsim_logic_tree_file=os.path.join(
CWD, 'data', 'lt01.xml'))
[cmaker] = inp.cmakerdict.values()
[src_group] = inp.groups
ctxs = cmaker.from_srcs(src_group, inp.sitecol)
assert len(ctxs) == 100
ctxs1 = ctxs[:50]
ctxs2 = ctxs[50:]
dic1 = cmaker.get_cs_contrib(ctxs1, imti, imls)[0]
dic2 = cmaker.get_cs_contrib(ctxs2, imti, imls)[0]
dic = cmaker.get_cs_contrib(ctxs, imti, imls)[0]
aac((dic1['_c'] + dic2['_c']) / (dic1['_s'] + dic2['_s']),
dic['_c'] / dic['_s'])
def test_2_rlzs(self):
# test with two GMPEs, 1 TRT
inp = read_input(PARAM)
[cmaker] = inp.cmakerdict.values()
[src_group] = inp.groups
ctxs = cmaker.from_srcs(src_group, inp.sitecol)
csdic = cmaker.get_cs_contrib(ctxs, imti, imls)
df = csdic_to_dframe(csdic, cmaker.imts, 0, 0)
# check the result
expected = os.path.join(CWD, 'expected', 'spectra2.csv')
if OVERWRITE_EXPECTED:
df.to_csv(expected,
index=False,
line_terminator='\r\n',
float_format='%.6f')
expdf = pandas.read_csv(expected)
pandas.testing.assert_frame_equal(df, expdf, atol=1E-6)
def test1rup(self):
param = dict(rupture_model_file=RUP_XML,
number_of_ground_motion_fields=10,
gsim='AtkinsonBoore2006',
ses_seed=42,
sites=[(0, 1), (0, 0)],
reference_vs30_value="760",
maximum_distance=IntegrationDistance.new('200'),
imtls={'PGA': [0]})
inp = read_input(param)
[grp] = inp.groups
[ebr] = grp
cmaker = inp.cmakerdict[grp.trt]
gc = gmf.GmfComputer(ebr, inp.sitecol, cmaker)
gmfdata = gc.compute_all()
self.assertIn('sid', gmfdata)
self.assertIn('eid', gmfdata)
self.assertIn('rlz', gmfdata)
self.assertIn('gmv_0', gmfdata)
def test_point(self):
# point source with 3 ruptures, checking additivity
inp = read_input(
PARAM, source_model_file=os.path.join(CWD, 'data', 'point.xml'),
gsim_logic_tree_file=os.path.join(CWD, 'data', 'lt01.xml'))
[cmaker] = inp.cmakerdict.values()
[src_group] = inp.groups
ctxs = cmaker.from_srcs(src_group, inp.sitecol)
aac([ctx.occurrence_rate for ctx in ctxs], [0.00018, 0.00018, 0.00054])
ctxs1 = ctxs[:2]
ctxs2 = ctxs[2:]
# check that the total spectra is a weighted mean of the two
# rupture spectra; the weight is the same for all IMTs
c1, s1 = cmaker.get_cs_contrib(ctxs1, imti, imls)
c2, s2 = cmaker.get_cs_contrib(ctxs2, imti, imls)
comp_spectra = (c1 + c2) / (s1 + s2)
c, s = cmaker.get_cs_contrib(ctxs, imti, imls)
aac(comp_spectra, c / s)
def test_2_rlzs(self):
# test with two GMPEs, 1 TRT
inp = read_input(PARAM)
[cmaker] = inp.cmakerdict.values()
[src_group] = inp.groups
ctxs = cmaker.from_srcs(src_group, inp.sitecol)
[c_], [s_] = cmaker.get_cs_contrib(ctxs, imti, imls)
spectra = [c / s for c, s in zip(c_, s_)]
# check the result
expected = os.path.join(CWD, 'expected', 'spectra2.csv')
if OVERWRITE_EXPECTED:
rlzs = list(inp.gsim_lt)
spectra_to_df(spectra, cmaker.imts, rlzs).to_csv(
expected, index=False, line_terminator='\r\n',
float_format='%.6f')
df = pandas.read_csv(expected)
for g, gsim in enumerate(cmaker.gsims):
dfg = df[df.rlz_id == g]
aac(dfg.cs_exp, np.exp(spectra[g][0]), atol=1e-6)
aac(dfg.cs_std, np.sqrt(spectra[g][1]), atol=1e-6)
def test_6_rlzs(self):
# test with 2x3 realizations and TRTA, TRTB
# rlzs_by_g = 012, 345, 03, 14, 25
inp = read_input(
PARAM, source_model_file=os.path.join(CWD, 'data', 'sm02.xml'))
rlzs = list(inp.gsim_lt)
R = len(rlzs)
# compute the contributions by trt
all_cs = []
for src_group in inp.groups:
cmaker = inp.cmakerdict[src_group.trt]
ctxs = cmaker.from_srcs(src_group, inp.sitecol)
[c], [s] = cmaker.get_cs_contrib(ctxs, imti, imls)
for cs in zip(c, s):
all_cs.append(cs)
# compose the contributions by rlz, 0+2, 0+3, 0+4, 1+2, 1+3, 1+4
rlzs_by_g = inp.gsim_lt.get_rlzs_by_g()
nums = np.zeros((R, 2, len(cmaker.imts)))
denums = np.zeros(R)
for g, rlz_ids in enumerate(rlzs_by_g):
c, s = all_cs[g]
for r in rlz_ids:
nums[r] += c
denums[r] += s
spectra = [nums[r] / denums[r] for r in range(R)]
# check the results
expected = os.path.join(CWD, 'expected', 'spectra6.csv')
if OVERWRITE_EXPECTED:
spectra_to_df(spectra, cmaker.imts, rlzs).to_csv(
expected, index=False, line_terminator='\r\n',
float_format='%.6f')
df = pandas.read_csv(expected)
for rlz in rlzs:
r = rlz.ordinal
df_rlz = df[df.rlz_id == r]
aac(df_rlz.cs_exp, np.exp(spectra[r][0]), atol=1e-6)
aac(df_rlz.cs_std, np.sqrt(spectra[r][1]), atol=1e-6)
