def hazard_maps(self, curves):
"""
Compute the hazard maps associated to the curves
"""
n, p = len(self.sitecol), len(self.oqparam.poes)
maps = zero_maps((n, p), self.oqparam.imtls)
for imt in curves.dtype.fields:
maps[imt] = calc.compute_hazard_maps(curves[imt],
self.oqparam.imtls[imt],
self.oqparam.poes)
return maps
def hazard_maps(self, curves):
"""
Compute the hazard maps associated to the curves
"""
maps = zero_maps(
len(self.sitecol), self.oqparam.imtls, self.oqparam.poes)
for imt in curves.dtype.fields:
# build a matrix of size (N, P)
data = calc.compute_hazard_maps(
curves[imt], self.oqparam.imtls[imt], self.oqparam.poes)
for poe, hmap in zip(self.oqparam.poes, data.T):
maps['%s~%s' % (imt, poe)] = hmap
return maps
def hazard_maps(self, curves):
"""
Compute the hazard maps associated to the curves
"""
maps = zero_maps(
len(self.sitecol), self.oqparam.imtls, self.oqparam.poes)
for imt in curves.dtype.fields:
# build a matrix of size (N, P)
data = calc.compute_hazard_maps(
curves[imt], self.oqparam.imtls[imt], self.oqparam.poes)
for poe, hmap in zip(self.oqparam.poes, data.T):
maps['%s~%s' % (imt, poe)] = hmap
return maps
def test_compute_hazard_map(self):
curves = [
[0.8, 0.5, 0.1],
[0.98, 0.15, 0.05],
[0.6, 0.5, 0.4],
[0.1, 0.01, 0.001],
[0.8, 0.2, 0.1],
]
imls = [0.005, 0.007, 0.0098]
poe = 0.2
expected = [[0.00847798, 0.00664814, 0.0098, 0, 0.007]]
actual = calc.compute_hazard_maps(curves, imls, poe)
aaae(expected, actual.T)
def test_compute_hazard_map(self):
curves = [
[0.8, 0.5, 0.1],
[0.98, 0.15, 0.05],
[0.6, 0.5, 0.4],
[0.1, 0.01, 0.001],
[0.8, 0.2, 0.1],
]
imls = [0.005, 0.007, 0.0098]
poe = 0.2
expected = [[0.00847798, 0.00664814, 0.0098, 0, 0.007]]
actual = calc.compute_hazard_maps(curves, imls, poe)
aaae(expected, actual.T)
def hazard_curves_to_hazard_map(hazard_curves, poes, monitor):
"""
Function to process a set of hazard curves into 1 hazard map for each PoE
in ``poes``.
Hazard map results are written directly to the database.
:param hazard_curves:
a list of
:class:`hazard curves <openquake.engine.db.models.HazardCurve>`
:param list poes:
list of PoEs for which we want to iterpolate hazard maps
:param monitor:
monitor of the currently running job
"""
job = models.OqJob.objects.get(id=monitor.job_id)
for hc in hazard_curves:
hcd = list(models.HazardCurveData.objects.all_curves_simple(
filter_args=dict(hazard_curve=hc.id), order_by='location'
))
imt = hc.imt
if imt == 'SA':
# if it's SA, include the period using the standard notation
imt = 'SA(%s)' % hc.sa_period
# Gather all of the curves and compute the maps, for all PoEs
curves = [_poes for _, _, _poes in hcd]
hazard_maps = calc.compute_hazard_maps(curves, hc.imls, poes).T
# Prepare the maps to be saved to the DB
for i, poe in enumerate(poes):
map_values = hazard_maps[i]
lons = numpy.empty(map_values.shape)
lats = numpy.empty(map_values.shape)
for loc_idx, _ in enumerate(map_values):
lons[loc_idx] = hcd[loc_idx][0]
lats[loc_idx] = hcd[loc_idx][1]
# Create 'Output' records for the map for this PoE
if hc.statistics == 'mean':
disp_name = _HAZ_MAP_DISP_NAME_MEAN_FMT % dict(
poe=poe, imt=imt)
elif hc.statistics == 'quantile':
disp_name = _HAZ_MAP_DISP_NAME_QUANTILE_FMT % dict(
poe=poe, imt=imt, quantile=hc.quantile)
else:
disp_name = _HAZ_MAP_DISP_NAME_FMT % dict(
poe=poe, imt=imt, rlz=hc.lt_realization.id)
output = job.get_or_create_output(disp_name, 'hazard_map')
# Save the complete hazard map
models.HazardMap.objects.create(
output=output,
lt_realization=hc.lt_realization,
investigation_time=hc.investigation_time,
imt=hc.imt,
statistics=hc.statistics,
quantile=hc.quantile,
sa_period=hc.sa_period,
sa_damping=hc.sa_damping,
poe=poe,
lons=lons.tolist(),
lats=lats.tolist(),
imls=map_values.tolist(),
)
