def subdivide_observed_eqs(bin_gdf: GeoDataFrame, subcat_n_years: int):
# collate earthquakes from bins
obs_eqs = get_total_obs_eqs(bin_gdf, prospective=False)
# divide earthquakes into groups, starting with the first observed year.
# this could be changed to account for years with no events bounding the
# catalog, but that would mean refactoring of the input yaml.
obs_eqs.sort(key=lambda x: x.time)
first_year = obs_eqs[0].time.year
interval_start = first_year * 1
last_year = obs_eqs[-1].time.year
n_eqs = []
while (interval_start + subcat_n_years) <= last_year:
interval_end = interval_start + subcat_n_years
n_eqs.append(
len([
eq for eq in obs_eqs
if (interval_start <= eq.time.year <= interval_end)
]))
interval_start += subcat_n_years + 1
return n_eqs
def s_test_function(
bin_gdf: GeoDataFrame,
t_yrs: float,
n_iters: int,
likelihood_fn: str,
prospective: bool = False,
critical_pct: float = 0.25,
not_modeled_likelihood: float = 0.0,
append_results: bool = False,
):
N_obs = len(get_total_obs_eqs(bin_gdf, prospective=prospective))
N_pred = get_model_annual_eq_rate(bin_gdf) * t_yrs
N_norm = N_obs / N_pred
bin_like_cfg = {
"investigation_time": t_yrs,
"likelihood_fn": likelihood_fn,
"not_modeled_likelihood": not_modeled_likelihood,
"n_iters": n_iters,
}
bin_likes = s_test_gdf_series(bin_gdf, bin_like_cfg, N_norm)
obs_likes = np.array([bl[0] for bl in bin_likes])
stoch_likes = np.vstack([bl[1] for bl in bin_likes]).T
bad_bins = list(unique(list(chain(*[bl[2] for bl in bin_likes]))))
obs_like_total = sum(obs_likes)
stoch_like_totals = np.sum(stoch_likes, axis=1)
if append_results:
bin_pcts = []
for i, obs_like in enumerate(obs_likes):
stoch_like = stoch_likes[:, i]
bin_pct = len(stoch_like[stoch_like <= obs_like]) / n_iters
bin_pcts.append(bin_pct)
bin_gdf["S_bin_pct"] = bin_pcts
bin_gdf["N_model"] = bin_gdf.SpacemagBin.apply(
lambda x: get_n_eqs_from_mfd(x.get_rupture_mfd()) * t_yrs)
bin_gdf["N_obs"] = bin_gdf.SpacemagBin.apply(
lambda x: get_n_eqs_from_mfd(x.observed_earthquakes))
pctile = (len(stoch_like_totals[stoch_like_totals <= obs_like_total]) /
n_iters)
test_pass = True if pctile >= critical_pct else False
test_res = "Pass" if test_pass else "Fail"
test_result = {
"critical_pct": critical_pct,
"percentile": pctile,
"test_pass": bool(test_pass),
"test_res": test_res,
"bad_bins": bad_bins,
}
return test_result
def test_get_total_obs_eqs(self):
obs_eqs = get_total_obs_eqs(self.bin_gdf)
# just test some aspects instead of instantiating Earthquakes
for eq in obs_eqs:
assert eq.magnitude in [7.4, 7.54, 6.07, 5.95, 6.26, 6.44]
assert len(obs_eqs) == 6