def db_magnitude_distance_by_trt(db1,
dist_type,
figure_size=(7, 5),
filename=None,
filetype="png",
dpi=300):
"""
Plot magnitude-distance comparison by tectonic region
"""
trts = []
for i in db1.records:
trts.append(i.event.tectonic_region)
trt_types = list(set(trts))
selector = SMRecordSelector(db1)
plt.figure(figsize=figure_size)
for trt in trt_types:
subdb = selector.select_trt_type(trt, as_db=True)
mag, dists = get_magnitude_distances(subdb, dist_type)
plt.semilogx(dists, mag, "o", mec='k', mew=0.5, label=trt)
plt.xlabel(DISTANCE_LABEL[dist_type], fontsize=14)
plt.ylabel("Magnitude", fontsize=14)
plt.title("Magnitude vs Distance by Tectonic Region", fontsize=18)
plt.legend(loc='lower right', numpoints=1)
plt.grid()
_save_image(filename, plt.gcf(), filetype, dpi)
plt.show()
def get_residuals(self, database, nodal_plane_index=1,
component="Geometric", normalise=True):
"""
Calculate the residuals for a set of ground motion records
"""
# Contexts is a list of dictionaries
contexts = database.get_contexts(nodal_plane_index)
self.database = SMRecordSelector(database)
self.contexts = []
for context in contexts:
# Get the observed strong ground motions
context = self.get_observations(context, component)
# Get the expected ground motions
context = self.get_expected_motions(context)
context = self.calculate_residuals(context, normalise)
for gmpe in self.residuals.keys():
for imtx in self.residuals[gmpe].keys():
if not context["Residual"][gmpe][imtx]:
continue
for res_type in self.residuals[gmpe][imtx].keys():
if res_type == "Inter event":
inter_ev = \
context["Residual"][gmpe][imtx][res_type]
if np.all(
np.fabs(inter_ev - inter_ev[0]) < 1.0E-12):
# Single inter-event residual
self.residuals[gmpe][imtx][res_type].append(
inter_ev[0])
# Append indices
self.unique_indices[gmpe][imtx].append(
np.array([0]))
else:
# Inter event residuals per-site e.g. Chiou
# & Youngs (2008; 2014) case
self.residuals[gmpe][imtx][res_type].extend(
inter_ev.tolist())
self.unique_indices[gmpe][imtx].append(
np.arange(len(inter_ev)))
else:
self.residuals[gmpe][imtx][res_type].extend(
context["Residual"][gmpe][imtx][res_type].tolist())
self.modelled[gmpe][imtx][res_type].extend(
context["Expected"][gmpe][imtx][res_type].tolist())
self.modelled[gmpe][imtx]["Mean"].extend(
context["Expected"][gmpe][imtx]["Mean"].tolist())
self.contexts.append(context)
for gmpe in self.residuals.keys():
for imtx in self.residuals[gmpe].keys():
if not self.residuals[gmpe][imtx]:
continue
for res_type in self.residuals[gmpe][imtx].keys():
self.residuals[gmpe][imtx][res_type] = np.array(
self.residuals[gmpe][imtx][res_type])
self.modelled[gmpe][imtx][res_type] = np.array(
self.modelled[gmpe][imtx][res_type])
self.modelled[gmpe][imtx]["Mean"] = np.array(
self.modelled[gmpe][imtx]["Mean"])
def db_magnitude_distance_by_site(db1, dist_type, classification="NEHRP",
figure_size=(7, 5), filename=None, filetype="png", dpi=300):
"""
Plot magnitude-distance comparison by site NEHRP or Eurocode 8 Site class
"""
if classification == "NEHRP":
site_bounds = NEHRP_BOUNDS
elif classification == "EC8":
site_bounds = EC8_BOUNDS
else:
raise ValueError("Unrecognised Site Classifier!")
selector = SMRecordSelector(db1)
plt.figure(figsize=figure_size)
total_idx = []
for site_class in site_bounds.keys():
site_idx = _site_selection(db1, site_class, classification)
if site_idx:
site_db = selector.select_records(site_idx, as_db=True)
mags, dists = get_magnitude_distances(site_db, dist_type)
plt.plot(np.array(dists), np.array(mags), "o", mec='k',
mew=0.5, label="Site Class %s" % site_class)
total_idx.extend(site_idx)
unc_idx = set(range(db1.number_records())).difference(set(total_idx))
unc_db = selector.select_records(unc_idx, as_db=True)
mag, dists = get_magnitude_distances(site_db, dist_type)
plt.semilogx(np.array(dists), np.array(mags), "o", mfc="None", mec='k',
mew=0.5, label="Unclassified", zorder=0)
plt.xlabel(DISTANCE_LABEL[dist_type], fontsize=14)
plt.ylabel("Magnitude", fontsize=14)
plt.grid()
plt.legend(ncol=2,loc="lower right", numpoints=1)
plt.title("Magnitude vs Distance (by %s Site Class)" % classification,
fontsize=18)
_save_image(filename, filetype, dpi)
plt.show()
def get_residuals(self,
database,
nodal_plane_index=1,
component="Geometric",
normalise=True):
"""
Calculate the residuals for a set of ground motion records
"""
# Contexts is a list of dictionaries
contexts = database.get_contexts(nodal_plane_index)
self.database = SMRecordSelector(database)
self.contexts = []
for context in contexts:
# Get the observed strong ground motions
context = self.get_observations(context, component)
# Get the expected ground motions
context = self.get_expected_motions(context)
context = self.calculate_residuals(context, normalise)
for gmpe in self.residuals.keys():
for imtx in self.residuals[gmpe].keys():
if not context["Residual"][gmpe][imtx]:
continue
for res_type in self.residuals[gmpe][imtx].keys():
if res_type == "Inter event":
inter_ev = \
context["Residual"][gmpe][imtx][res_type]
inter_ev, inter_idx = np.unique(inter_ev,
return_index=True)
self.residuals[gmpe][imtx][res_type].extend(
inter_ev.tolist())
#inter_mags = (context["Rupture"].mag *
# np.ones(len(inter_ev))).tolist()
self.unique_indices[gmpe][imtx].append(inter_idx)
else:
self.residuals[gmpe][imtx][res_type].extend(
context["Residual"][gmpe][imtx]
[res_type].tolist())
self.modelled[gmpe][imtx][res_type].extend(
context["Expected"][gmpe][imtx][res_type].tolist())
self.modelled[gmpe][imtx]["Mean"].extend(
context["Expected"][gmpe][imtx]["Mean"].tolist())
self.contexts.append(context)
for gmpe in self.residuals.keys():
for imtx in self.residuals[gmpe].keys():
if not self.residuals[gmpe][imtx]:
continue
for res_type in self.residuals[gmpe][imtx].keys():
self.residuals[gmpe][imtx][res_type] = np.array(
self.residuals[gmpe][imtx][res_type])
self.modelled[gmpe][imtx][res_type] = np.array(
self.modelled[gmpe][imtx][res_type])
self.modelled[gmpe][imtx]["Mean"] = np.array(
self.modelled[gmpe][imtx]["Mean"])
def get_site_residuals(self, database, component="Geometric"):
"""
Calculates the total, inter-event and within-event residuals for
each site
"""
imt_dict = dict([(imtx, {}) for imtx in self.imts])
for site_id in self.site_ids:
print site_id
selector = SMRecordSelector(database)
site_db = selector.select_from_site_id(site_id, as_db=True)
resid = Residuals(self.input_gmpe_list, self.imts)
resid.get_residuals(site_db, normalise=False, component=component)
setattr(resid, "site_analysis", self._set_empty_dict())
setattr(resid, "site_expected", self._set_empty_dict())
self.site_residuals.append(resid)
def get_site_residuals(self, database):
"""
Calculates the total, inter-event and within-event residuals for
each site
"""
imt_dict = dict([(imtx, {}) for imtx in self.imts])
for site_id in self.site_ids:
print site_id
selector = SMRecordSelector(database)
site_db = selector.select_from_site_id(site_id, as_db=True)
resid = Residuals(self.gmpe_list, self.imts)
resid.get_residuals(site_db, normalise=False)
setattr(
resid,
"site_analysis",
OrderedDict([(gmpe, imt_dict) for gmpe in self.gmpe_list]))
setattr(
resid,
"site_expected",
OrderedDict([(gmpe, imt_dict) for gmpe in self.gmpe_list]))
self.site_residuals.append(resid)
def get_residuals(self, database, nodal_plane_index=1,
component="Geometric", normalise=True):
"""
Calculate the residuals for a set of ground motion records
"""
# Contexts is a list of dictionaries
contexts = database.get_contexts(nodal_plane_index)
self.database = SMRecordSelector(database)
self.contexts = []
for context in contexts:
#print context
# Get the observed strong ground motions
context = self.get_observations(context, component)
# Get the expected ground motions
context = self.get_expected_motions(context)
context = self.calculate_residuals(context, normalise)
for gmpe in self.residuals.keys():
for imtx in self.residuals[gmpe].keys():
for res_type in self.residuals[gmpe][imtx].keys():
self.residuals[gmpe][imtx][res_type].extend(
context["Residual"][gmpe][imtx][res_type].tolist())
self.modelled[gmpe][imtx][res_type].extend(
context["Expected"][gmpe][imtx][res_type].tolist())
self.modelled[gmpe][imtx]["Mean"].extend(
context["Expected"][gmpe][imtx]["Mean"].tolist())
self.contexts.append(context)
for gmpe in self.residuals.keys():
for imtx in self.residuals[gmpe].keys():
for res_type in self.residuals[gmpe][imtx].keys():
self.residuals[gmpe][imtx][res_type] = np.array(
self.residuals[gmpe][imtx][res_type])
self.modelled[gmpe][imtx][res_type] = np.array(
self.modelled[gmpe][imtx][res_type])
self.modelled[gmpe][imtx]["Mean"] = np.array(
self.modelled[gmpe][imtx]["Mean"])
