def calculateProximalLimitEstimate(isopachs,coefficient,exponent):
"""
Returns the estimate for the proximal limit of integration
suggested by Bonadonna and Houghton 2005
"""
expResults = exponentialModelAnalysis(isopachs,2)
return ((expResults["segmentCoefficients"][0]/coefficient)**(-(1/exponent)))/np.sqrt(np.pi)
def calculateProximalLimitEstimate(isopachs, coefficient, exponent):
"""
Returns the estimate for the proximal limit of integration
suggested by Bonadonna and Houghton 2005
"""
expResults = exponentialModelAnalysis(isopachs, 2)
return ((expResults["segmentCoefficients"][0] / coefficient)
**(-(1 / exponent))) / np.sqrt(np.pi)
def fit_isopachs(isopachs, model_settings):
"""
([list of Isopach], AshCalcModelSettings) -> dictionary of results.
Runs the model to fit the isopachs and return the results.
"""
params = model_settings.get_params()
if model_settings.model == 'exponential':
results = exponential.exponentialModelAnalysis(isopachs, *params)
elif model_settings.model == 'power_law':
results = power_law.powerLawModelAnalysis(isopachs, *params)
elif model_settings.model == 'weibull':
results = weibull.weibullModelAnalysis(isopachs, *params)
return results
def fit_isopachs(isopachs, model_settings):
"""
([list of Isopach], AshCalcModelSettings) -> dictionary of results.
Runs the model to fit the isopachs and return the results.
"""
params = model_settings.get_params()
if model_settings.model == 'exponential':
results = exponential.exponentialModelAnalysis(isopachs, *params)
elif model_settings.model == 'power_law':
results = power_law.powerLawModelAnalysis(isopachs, *params)
elif model_settings.model == 'weibull':
results = weibull.weibullModelAnalysis(isopachs, *params)
return results
