def __init__(self,
func_id,
mean_loss_ratio,
coefficient_of_variation,
distribution,
var_method=2):
self.function_id = func_id
self.mean_loss_ratio = asarray(mean_loss_ratio)
self.coefficient_of_variation = asarray(coefficient_of_variation)
# Support lognormal and normal only initially
# (this could be covered in xsd validation)
if distribution == 'N':
self.distribution = Distribution_Normal(var_method=var_method)
elif distribution == 'LN':
self.distribution = Distribution_Log_Normal(var_method=var_method)
else:
raise NotImplementedError(
'%s: %s is not a supported probabilistic distribution' % (func_id,
distribution))
def __init__(self,
func_id,
mean_loss_ratio,
coefficient_of_variation,
distribution,
var_method=2):
self.function_id = func_id
self.mean_loss_ratio = asarray(mean_loss_ratio)
self.coefficient_of_variation = asarray(coefficient_of_variation)
# Support lognormal and normal only initially
# (this could be covered in xsd validation)
if distribution == 'N':
self.distribution = Distribution_Normal(var_method=var_method)
elif distribution == 'LN':
self.distribution = Distribution_Log_Normal(var_method=var_method)
else:
raise NotImplementedError(
'%s: %s is not a supported probabilistic distribution' %
(func_id, distribution))
class Vulnerability_Function(object):
"""
A vulnerability function defined by a specified set of points on a curve.
Methods:
- calc_mean - return mean loss and sigma based on the given set of points
- sample - return a sample based on the specified probabilistic distribution
Constructor input:
- func_id - identifier for this function
- mean_loss_ratio - array of ratio points
- coefficient_of_variation - array of uncertainty points (shape must match
shape of mean_loss_ratio)
- distribution - either normal ('N') or lognormal ('LN')
- var_method - variability method to be used in sampling (see sample doc)
"""
def __init__(self,
func_id,
mean_loss_ratio,
coefficient_of_variation,
distribution,
var_method=2):
self.function_id = func_id
self.mean_loss_ratio = asarray(mean_loss_ratio)
self.coefficient_of_variation = asarray(coefficient_of_variation)
# Support lognormal and normal only initially
# (this could be covered in xsd validation)
if distribution == 'N':
self.distribution = Distribution_Normal(var_method=var_method)
elif distribution == 'LN':
self.distribution = Distribution_Log_Normal(var_method=var_method)
else:
raise NotImplementedError(
'%s: %s is not a supported probabilistic distribution' % (func_id,
distribution))
def calc_mean(self, intensity, intensity_measure_level):
"""
Calculate mean loss ratio and sigma based on the specified points on
the curve:
|
| +
| +
Mean loss ratio | +
| +
| +
| +
| +
| +
| +
| +
| +
+-----------------------------------
Intensity measure level
For a given intensity, mean loss and sigma is determined by linearly
interpolating the points on the curve.
Note that sigma is calculated as cv * mean loss as cv = mean loss/sigma
"""
mean_loss = interp(intensity,
intensity_measure_level,
self.mean_loss_ratio)
cv = interp(intensity,
intensity_measure_level,
self.coefficient_of_variation)
# cv = sigma / mean
sigma = cv * mean_loss
return (mean_loss, sigma)
def sample(self, mean, sigma):
"""
Take a sample based on the given mean and sigma. The distribution used
is either normal or lognormal, and based on the specified variability
method:
None -> no sampling (mean loss is the deterministic figure)
2 -> random sampling
3 -> mean + 2 * sigma
4 -> mean + sigma
5 -> mean - sigma
6 -> mean - 2 * sigma
"""
# No period axis, so we want variability in the last axis
var_in_last_axis = True
return self.distribution.sample_for_eqrm(mean, sigma, var_in_last_axis)
class Vulnerability_Function(object):
"""
A vulnerability function defined by a specified set of points on a curve.
Methods:
- calc_mean - return mean loss and sigma based on the given set of points
- sample - return a sample based on the specified probabilistic distribution
Constructor input:
- func_id - identifier for this function
- mean_loss_ratio - array of ratio points
- coefficient_of_variation - array of uncertainty points (shape must match
shape of mean_loss_ratio)
- distribution - either normal ('N') or lognormal ('LN')
- var_method - variability method to be used in sampling (see sample doc)
"""
def __init__(self,
func_id,
mean_loss_ratio,
coefficient_of_variation,
distribution,
var_method=2):
self.function_id = func_id
self.mean_loss_ratio = asarray(mean_loss_ratio)
self.coefficient_of_variation = asarray(coefficient_of_variation)
# Support lognormal and normal only initially
# (this could be covered in xsd validation)
if distribution == 'N':
self.distribution = Distribution_Normal(var_method=var_method)
elif distribution == 'LN':
self.distribution = Distribution_Log_Normal(var_method=var_method)
else:
raise NotImplementedError(
'%s: %s is not a supported probabilistic distribution' %
(func_id, distribution))
def calc_mean(self, intensity, intensity_measure_level):
"""
Calculate mean loss ratio and sigma based on the specified points on
the curve:
|
| +
| +
Mean loss ratio | +
| +
| +
| +
| +
| +
| +
| +
| +
+-----------------------------------
Intensity measure level
For a given intensity, mean loss and sigma is determined by linearly
interpolating the points on the curve.
Note that sigma is calculated as cv * mean loss as cv = mean loss/sigma
"""
mean_loss = interp(intensity, intensity_measure_level,
self.mean_loss_ratio)
cv = interp(intensity, intensity_measure_level,
self.coefficient_of_variation)
# cv = sigma / mean
sigma = cv * mean_loss
return (mean_loss, sigma)
def sample(self, mean, sigma):
"""
Take a sample based on the given mean and sigma. The distribution used
is either normal or lognormal, and based on the specified variability
method:
None -> no sampling (mean loss is the deterministic figure)
2 -> random sampling
3 -> mean + 2 * sigma
4 -> mean + sigma
5 -> mean - sigma
6 -> mean - 2 * sigma
"""
# No period axis, so we want variability in the last axis
var_in_last_axis = True
return self.distribution.sample_for_eqrm(mean, sigma, var_in_last_axis)
