def _convert_pes_to_pos(hazard_curve, imls):
"""
For each IML (Intensity Measure Level) given compute the
PoOs (Probability of Occurence) from the PoEs
(Probability of Exceendance) defined in the given hazard curve.
"""
return collect(loop(_compute_pes_from_imls(hazard_curve, imls),
lambda x, y: subtract(array(x), array(y))))
def _generate_curve(losses, probs_of_exceedance):
"""Generate a loss ratio (or loss) curve, given a set of losses
and corresponding PoEs (Probabilities of Exceedance).
This function is intended to be used internally.
"""
mean_losses = collect(loop(losses, lambda x, y: mean([x, y])))
return shapes.Curve(zip(mean_losses, probs_of_exceedance))
def _convert_pes_to_pos(hazard_curve, imls):
"""
For each IML (Intensity Measure Level) given compute the
PoOs (Probability of Occurence) from the PoEs
(Probability of Exceendance) defined in the given hazard curve.
"""
return collect(
loop(_compute_pes_from_imls(hazard_curve, imls),
lambda x, y: subtract(array(x), array(y))))
def _compute_imls(vuln_function):
"""
Computes Intensity Measure Levels considering
the highest/lowest values a special case
"""
imls = vuln_function.imls
# "special" cases for lowest part and highest part of the curve
lowest_curve_value = imls[0] - ((imls[1] - imls[0]) / 2)
highest_curve_value = imls[-1] + ((imls[-1] - imls[-2]) / 2)
between_curve_values = collect(loop(imls, lambda x, y: mean([x, y])))
return [lowest_curve_value] + between_curve_values + [highest_curve_value]
def _split_loss_ratios(loss_ratios, steps=None):
"""Split the loss ratios, producing a new set of loss ratios.
Keyword arguments:
steps -- the number of steps we make to go from one loss
ratio to the next. For example, if we have [1.0, 2.0]:
steps = 1 produces [1.0, 2.0]
steps = 2 produces [1.0, 1.5, 2.0]
steps = 3 produces [1.0, 1.33, 1.66, 2.0]
"""
if steps is None:
steps = STEPS_PER_INTERVAL
splitted_ratios = set()
for interval in loop(array(loss_ratios), linspace, steps + 1):
splitted_ratios.update(interval)
return array(sorted(splitted_ratios))
def _compute_imls(vuln_function):
"""
Compute the mean IMLs (Intensity Measure Level)
for the given vulnerability function.
"""
imls = vuln_function.imls
# "special" cases for lowest part and highest part of the curve
lowest_curve_value = imls[0] - ((imls[1] - imls[0]) / 2)
# if the calculated lowest_curve_value goes < 0 we have to force the 0
# IMLs have to be >= 0
if lowest_curve_value < 0:
lowest_curve_value = 0
highest_curve_value = imls[-1] + ((imls[-1] - imls[-2]) / 2)
between_curve_values = collect(loop(imls, lambda x, y: mean([x, y])))
return [lowest_curve_value] + between_curve_values + [highest_curve_value]
def _compute_imls(vuln_function):
"""
Compute the mean IMLs (Intensity Measure Level)
for the given vulnerability function.
"""
imls = vuln_function.imls
# "special" cases for lowest part and highest part of the curve
lowest_curve_value = imls[0] - ((imls[1] - imls[0]) / 2)
# if the calculated lowest_curve_value goes < 0 we have to force the 0
# IMLs have to be >= 0
if lowest_curve_value < 0:
lowest_curve_value = 0
highest_curve_value = imls[-1] + ((imls[-1] - imls[-2]) / 2)
between_curve_values = collect(loop(imls, lambda x, y: mean([x, y])))
return [lowest_curve_value] + between_curve_values + [highest_curve_value]
def _split_loss_ratios(loss_ratios, steps=None):
"""Split the loss ratios, producing a new set of loss ratios.
Keyword arguments:
steps -- the number of steps we make to go from one loss
ratio to the next. For example, if we have [1.0, 2.0]:
steps = 1 produces [1.0, 2.0]
steps = 2 produces [1.0, 1.5, 2.0]
steps = 3 produces [1.0, 1.33, 1.66, 2.0]
"""
if steps is None:
steps = STEPS_PER_INTERVAL
splitted_ratios = set()
for interval in loop(array(loss_ratios), linspace, steps + 1):
splitted_ratios.update(interval)
return array(sorted(splitted_ratios))
def _convert_pes_to_pos(hazard_curve, imls):
"""
Computes the probability occurences from the probability exceedances
"""
return collect(loop(_compute_pes_from_imls(hazard_curve, imls),
lambda x, y: subtract(array(x), array(y))))
