def setUp(self):
super(HazardCurveDataManagerTestCase, self).setUp()
self.manager = models.HazardCurveData.objects
# Setup some data
# Requires a working version of Django models
output = models.Output.objects.create_output(
self.job, "fake output", "hazard_curve")
curves_per_location = (
self.job.hazard_calculation.individual_curves_per_location())
self.a_location = (
helpers.random_location_generator(max_x=50, max_y=50))
self.a_bigger_location = helpers.random_location_generator(
min_x=50,
min_y=50)
for curve_nr in range(0, curves_per_location):
realization = models.LtRealization.objects.all()[curve_nr]
curve = models.HazardCurve.objects.create(
output=output,
lt_realization=realization,
investigation_time=10,
imt="PGA", imls=[1, 2, 3])
models.HazardCurveData.objects.create(
hazard_curve=curve,
location=self.a_location.wkt,
poes=[random.random()])
models.HazardCurveData.objects.create(
hazard_curve=curve,
location=self.a_bigger_location.wkt,
poes=[random.random()])
def setUp(self):
"""
Setup a curve database with presets data
"""
self.location_nr = 2
self.curves_per_location = 3
self.level_nr = 3
self.location_db = [random_location_generator().wkb,
random_location_generator().wkb]
self.curve_db = [
dict(wkb=self.location_db[0],
weight=0.5,
poes=numpy.array([9.9996e-01, 9.9962e-01, 9.9674e-01])),
dict(wkb=self.location_db[0],
weight=0.3,
poes=numpy.array([6.9909e-01, 6.0859e-01, 5.0328e-01])),
dict(wkb=self.location_db[0],
weight=0.2,
poes=numpy.array([1.0000e+00, 9.9996e-01, 9.9947e-01])),
dict(wkb=self.location_db[1],
weight=0.5,
poes=numpy.array([9.1873e-01, 8.6697e-01, 7.8992e-01])),
dict(wkb=self.location_db[1],
weight=0.3,
poes=numpy.array([8.9556e-01, 8.3045e-01, 7.3646e-01])),
dict(wkb=self.location_db[1],
weight=0.2,
poes=numpy.array([9.2439e-01, 8.6700e-01, 7.7785e-01]))]
self.curve_writer = SimpleCurveWriter()
def setUp(self):
super(HazardCurveDataManagerTestCase, self).setUp()
self.manager = models.HazardCurveData.objects
self.manager.current_job = self.job
# Setup some data
# Requires a working version of Django models
output = models.Output.objects.create_output(
self.job, "fake output", "hazard_curve")
realization = models.LtRealization.objects.all()[0]
curve = models.HazardCurve.objects.create(
output=output,
lt_realization=realization,
investigation_time=10,
imt="PGA", imls=[1, 2, 3])
self.a_location = helpers.random_location_generator(max_x=50, max_y=50)
models.HazardCurveData.objects.create(
hazard_curve=curve,
location=self.a_location.wkt,
poes=[random.random()])
self.a_bigger_location = helpers.random_location_generator(
min_x=50,
min_y=50)
models.HazardCurveData.objects.create(
hazard_curve=curve,
location=self.a_bigger_location.wkt,
poes=[random.random()])
def _curve_db(location_nr, level_nr, curves_per_location, sigma):
"""
Create a random db of curves stored in a list of dictionaries
"""
curve_db = []
location_db = []
weights = [1.0 for _ in range(0, curves_per_location)]
weights = [w / sum(weights) for w in weights]
for i in range(0, location_nr):
location = random_location_generator()
# individual curve poes set with a gauss distribution with
# mean set to [1 / (1 + i + j) for j in level_indexes].
# So we can easily calculate mean and 0.5 quantile
location_db.append(location.wkb)
for j in range(0, curves_per_location):
poes = []
for k in range(0, level_nr):
poe = random.gauss(1.0 / (1 + i + k), sigma)
poes.append(min(1, poe))
curve_db.append(
dict(wkb=location.wkb,
weight=weights[j],
poes=numpy.array(poes)))
return curve_db, location_db