def do_collapse_logic_tree(data, event_num, weights,
eqrm_flags, use_C=True):
"""
Collapse data, such as when several events are used to repressent
one event.
Data is the array to be collapsed (eg ground_motion or loss)
"""
if len(data.shape) >= 4:
# Assume the extra dimension is the ground motion model
if eqrm_flags.atten_collapse_Sa_of_atten_models is True:
new_data = _collapse_att_model_dimension(data,
weights)
else:
new_data = data
else:
# if there is only one attenuation model.
no_attn_collapse = (
(len(weights) == 1) or
eqrm_flags.atten_collapse_Sa_of_atten_models is False)
if no_attn_collapse:
new_data = data
else:
weights = asarray(weights)
num_of_att_models = int(len(event_num)/(max(event_num) + 1))
new_data = _collapse_att_model_results(data,
weights,
num_of_att_models)
return new_data, None, None
def do_collapse_logic_tree(data, event_num, weights, eqrm_flags, use_C=True):
"""
Collapse data, such as when several events are used to repressent
one event.
Data is the array to be collapsed (eg ground_motion or loss)
"""
if len(data.shape) >= 4:
# Assume the extra dimension is the ground motion model
if eqrm_flags.atten_collapse_Sa_of_atten_models is True:
new_data = _collapse_att_model_dimension(data, weights)
else:
new_data = data
else:
# if there is only one attenuation model.
no_attn_collapse = (
(len(weights) == 1)
or eqrm_flags.atten_collapse_Sa_of_atten_models is False)
if no_attn_collapse:
new_data = data
else:
weights = asarray(weights)
num_of_att_models = int(len(event_num) / (max(event_num) + 1))
new_data = _collapse_att_model_results(data, weights,
num_of_att_models)
return new_data, None, None
def test_collapse_att_model_dimension3(self):
gmm = 2
site = 1
rec_model = 1
events = 2
periods = 1
size = gmm * rec_model * site * events * periods
data = array([[1., 0], [0, 1.]])
data = reshape(data, (gmm, rec_model, site, events, periods))
weights = [2., 0]
sum = _collapse_att_model_dimension(data, weights)
actual = array([[2., 0]])
actual = reshape(actual, (1, site, events, periods))
self.assert_ (allclose(sum, actual))
weights = [2.]
sum = _collapse_att_model_dimension(data, weights)
actual = array([[2., 0]])
actual = reshape(actual, (1, site, events, periods))
self.assert_ (allclose(sum, actual))
def test_collapse_att_model_dimension3(self):
gmm = 2
site = 1
rec_model = 1
events = 2
periods = 1
size = gmm * rec_model * site * events * periods
data = array([[1., 0], [0, 1.]])
data = reshape(data, (gmm, rec_model, site, events, periods))
weights = [2., 0]
sum = _collapse_att_model_dimension(data, weights)
actual = array([[2., 0]])
actual = reshape(actual, (1, site, events, periods))
self.assert_(allclose(sum, actual))
weights = [2.]
sum = _collapse_att_model_dimension(data, weights)
actual = array([[2., 0]])
actual = reshape(actual, (1, site, events, periods))
self.assert_(allclose(sum, actual))
def test_collapse_att_model_dimension2(self):
gmm = 3
site = 1
rec_model = 1
events = 2
periods = 4
size = gmm * site * events * periods
data = arange(0, size, 1)
data = reshape(data, (gmm, rec_model, site, events, periods))
weights = [0.2, 0.3]
sum = _collapse_att_model_dimension(data, weights)
actual = zeros((gmm, site, events, periods))
for i in [0,1]:
actual[i,:] = data[i,...] * weights[i]
sum_act = scipy.sum(actual, 0)
self.assert_ (allclose(sum, sum_act))
self.assert_ (sum[0, 0, 1, 3] == 7*0.2 + 15*0.3) # FIXME FP precision
def test_collapse_att_model_dimension(self):
gmm = 3
rec_model = 1
site = 1
events = 2
periods = 4
size = gmm * rec_model * site * events * periods
data = arange(0, size, 1)
data = reshape(data, (gmm, rec_model, site, events, periods))
weights = [0.2, 0.3, 0.5]
sum = _collapse_att_model_dimension(data, weights)
actual = zeros((gmm, site, events, periods))
for i in [0, 1, 2]:
actual[i, :] = data[i, :, :, :] * weights[i]
sum_act = scipy.sum(actual, 0)
self.assert_(allclose(sum, sum_act))
self.assert_(sum[0, 0, 1, 3] == 7 * 0.2 + 15 * 0.3 + 23 * 0.5)
