def compute_probability(self, total_fatalities):
"""Pager method compute probaility of fatality in each magnitude bin.
[0, 10), [10,10^2), [10^2,10^3), [10^3, 10^4), [10^4, 10^5),
[10^5,)
:param total_fatalities: List of total fatalities in each MMI class.
:type total_fatalities: int, float
:returns: Probability of fatality magnitude bin from
lognorm.cdf(bin, shape=Zeta, scale=total_fatalities)
:rtype: list(float)
"""
zeta = self.hardcoded_parameters['Zeta']
magnitude_bin = self.hardcoded_parameters['magnitude_bin']
cprob = numpy.ones_like(magnitude_bin, dtype=float)
if total_fatalities > 1:
cprob = log_normal_cdf(magnitude_bin,
median=total_fatalities,
sigma=zeta)
cprob = numpy.append(cprob, 1.0)
prob = numpy.hstack((cprob[0], numpy.diff(cprob))) * 100.0
return self.round_to_sum(prob)
def compute_fatality_rate(self):
"""Pager method to compute fatality rate.
:returns: Fatality rate calculated as:
lognorm.cdf(mmi, shape=Beta, scale=Theta)
:rtype: dic
"""
mmi_range = self.hardcoded_parameters['mmi_range']
theta = self.hardcoded_parameters['Theta']
beta = self.hardcoded_parameters['Beta']
fatality_rate = {mmi: 0 if mmi < 4 else log_normal_cdf(
mmi, median=theta, sigma=beta) for mmi in mmi_range}
return fatality_rate
def compute_fatality_rate(self):
"""Pager method to compute fatality rate.
:returns: Fatality rate calculated as:
lognorm.cdf(mmi, shape=Beta, scale=Theta)
:rtype: dic
"""
mmi_range = self.hardcoded_parameters['mmi_range']
theta = self.hardcoded_parameters['Theta']
beta = self.hardcoded_parameters['Beta']
fatality_rate = {mmi: 0 if mmi < 4 else log_normal_cdf(
mmi, median=theta, sigma=beta) for mmi in mmi_range}
return fatality_rate
def compute_probability(self, total_fatalities):
"""Pager method compute probaility of fatality in each magnitude bin.
[0, 10), [10,10^2), [10^2,10^3), [10^3, 10^4), [10^4, 10^5),
[10^5,)
:param total_fatalities: List of total fatalities in each MMI class.
:type total_fatalities: int, float
:returns: Probability of fatality magnitude bin from
lognorm.cdf(bin, shape=Zeta, scale=total_fatalities)
:rtype: list(float)
"""
zeta = self.hardcoded_parameters['Zeta']
magnitude_bin = self.hardcoded_parameters['magnitude_bin']
cprob = numpy.ones_like(magnitude_bin, dtype=float)
if total_fatalities > 1:
cprob = log_normal_cdf(
magnitude_bin, median=total_fatalities, sigma=zeta)
cprob = numpy.append(cprob, 1.0)
prob = numpy.hstack((cprob[0], numpy.diff(cprob))) * 100.0
return self.round_to_sum(prob)