def get_residuals(params):
'''Core method to compute residuals plots data
:param params: dict with the request parameters
:return: json serializable dict to be passed into a Response object
'''
# params:
GMDB = 'gmdb' # pylint: disable=invalid-name
GSIM = 'gsim' # pylint: disable=invalid-name
IMT = 'imt' # pylint: disable=invalid-name
PLOTTYPE = 'plot_type' # pylint: disable=invalid-name
SEL = 'selexpr' # pylint: disable=invalid-name
func, kwargs = params[PLOTTYPE]
residuals = Residuals(params[GSIM], params[IMT])
# Compute residuals.
# params[GMDB] is the tuple (hdf file name, table name):
gmdb = GroundMotionTable(*params[GMDB], mode='r')
if params.get(SEL):
gmdb = gmdb.filter(params[SEL])
residuals.get_residuals(gmdb)
# statistics = residuals.get_residual_statistics()
ret = defaultdict(lambda: defaultdict(lambda: {}))
# extend keyword arguments:
kwargs = dict(kwargs, residuals=residuals, as_json=True)
# linestep = binwidth/10
for gsim in residuals.residuals:
for imt in residuals.residuals[gsim]:
kwargs['gmpe'] = gsim
kwargs['imt'] = imt
imt2 = _relabel_sa(imt)
res_plots = func(**kwargs)
for res_type, res_plot in res_plots.items():
for stat in RESIDUALS_STATS:
res_plot.setdefault(stat, None)
if imt2 != imt:
res_plot['xlabel'] = _relabel_sa(res_plot['xlabel'])
res_plot['ylabel'] = _relabel_sa(res_plot['ylabel'])
# make also x and y keys consistent with trellis response:
res_plot['xvalues'] = res_plot.pop('x')
res_plot['yvalues'] = res_plot.pop('y')
ret[imt2][res_type][gsim] = res_plot
return ret
def get_conditional_gmfs(database, rupture, sites, gsims, imts,
number_simulations, truncation_level,
correlation_model=DEFAULT_CORRELATION):
"""
Get a set of random fields conditioned on a set of observations
:param database:
Ground motion records for the event as instance of :class:
smtk.sm_database.GroundMotionDatabase
:param rupture:
Event rupture as instance of :class:
openquake.hazardlib.source.rupture.Rupture
:param sites:
Target sites as instance of :class:
openquake.hazardlib.site.SiteCollection
:param list gsims:
List of GMPEs required
:param list imts:
List of intensity measures required
:param int number_simulations:
Number of simulated fields required
:param float truncation_level:
Ground motion truncation level
"""
# Get known sites mesh
known_sites = database.get_site_collection()
# Get Observed Residuals
residuals = Residuals(gsims, imts)
residuals.get_residuals(database)
imt_dict = OrderedDict([
(imtx, np.zeros([len(sites.lons), number_simulations]))
for imtx in imts])
gmfs = OrderedDict([(gmpe, imt_dict) for gmpe in gsims])
gmpe_list = [GSIM_LIST[gmpe]() for gmpe in gsims]
cmaker = ContextMaker(gmpe_list)
sctx, rctx, dctx = cmaker.make_contexts(sites, rupture)
for gsim in gmpe_list:
gmpe = gsim.__class__.__name__
#gsim = GSIM_LIST[gmpe]()
#sctx, rctx, dctx = gsim.make_contexts(sites, rupture)
for imtx in imts:
if truncation_level == 0:
gmfs[gmpe][imtx], _ = gsim.get_mean_and_stddevs(sctx, rctx,
dctx, from_string(imtx), stddev_types=[])
continue
if "Intra event" in gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES:
epsilon = conditional_simulation(
known_sites,
residuals.residuals[gmpe][imtx]["Intra event"],
sites,
imtx,
number_simulations,
correlation_model)
tau = np.unique(residuals.residuals[gmpe][imtx]["Inter event"])
mean, [stddev_inter, stddev_intra] = gsim.get_mean_and_stddevs(
sctx,
rctx,
dctx,
from_string(imtx),
["Inter event", "Intra event"])
for iloc in range(0, number_simulations):
gmfs[gmpe][imtx][:, iloc] = np.exp(
mean +
(tau * stddev_inter) +
(epsilon[:, iloc].A1 * stddev_intra))
else:
epsilon = conditional_simulation(
known_sites,
residuals.residuals[gmpe][imtx]["Total"],
sites,
imtx,
number_simulations,
correlation_model)
tau = None
mean, [stddev_total] = gsim.get_mean_and_stddevs(
sctx,
rctx,
dctx,
from_string(imtx),
["Total"])
for iloc in range(0, number_simulations):
gmfs[gmpe][imtx][:, iloc] = np.exp(
mean +
(epsilon[:, iloc].A1 * stddev_total.flatten()))
return gmfs