pygmm.hermkes_kuehn_riggelsen_2014.HermkesKuehnRiggelsen2014¶

class pygmm.hermkes_kuehn_riggelsen_2014.HermkesKuehnRiggelsen2014(scenario)[source]¶

Hermkes, Kuehn, Riggelsen (2014, [Hermkes et al., 2014]) model.

Only the GPSELinCorr model is implemented. This model must be imported directly by:

from pygmm.hermkes_kuehn_riggelsen_2014 import
HermkesKuehnRiggelsen2014

This is to due to the large file size of the model data, which takes time to load.

Note that this model was developed using a Bayesian non-parametric method, which means it is should only be used over the data range used to develop the model. See the paper for more details.

Parameters:: scenario (pygmm.model.Scenario) – earthquake scenario

NAME = 'Hermkes, Kuehn, Riggelsen (2014)'¶: Long name of the model

ABBREV = 'HKR14'¶: Short name of the model

V_REF = None¶

PERIODS = array([-1. , 0.01, 0.1 , 0.5 , 1. , 4. ])¶: Indices of the periods

INDICES_PSA = array([1, 2, 3, 4, 5])¶: Indices for the spectral accelerations

INDEX_PGA = 1¶: Index of the peak ground acceleration

INDEX_PGV = 0¶: Index of the peak ground velocity

PARAMS = [<pygmm.model.NumericParameter object>, <pygmm.model.NumericParameter object>, <pygmm.model.NumericParameter object>, <pygmm.model.NumericParameter object>, <pygmm.model.CategoricalParameter object>]¶: Model parameters

__init__(scenario)[source]¶

Initialize the model.

Parameters:: scenario (Scenario)

INDEX_PGD = None¶: Index of the peak ground displacement

LIMITS = {}¶: Limits of model applicability

PGD_SCALE = 1.0¶: Scale factor to apply to get PGD in cm

PGV_SCALE = 1.0¶: Scale factor to apply to get PGV in cm/sec

interp_ln_spec_accels(periods, kind='linear')¶

Interpolate the spectral acceleration.

Interpolation of the spectral acceleration is done in natural log space.

Parameters:

periods (array_like) – spectral periods to interpolate the response.
kind (str, optional) – see scipy.interpolate.interp1d() for description of kind. Options include: ‘linear’ (default), ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, and ‘cubic’

Returns:

ln_spec_accels – interpolated spectral accelerations

Return type:

np.ndarray

interp_ln_stds(periods, kind='linear')¶

Interpolate the logarithmic standard deviation.

Interpolate the logarithmic standard deviation (\(\sigma_{\ln}\)) of spectral acceleration at the provided damping at specified periods.

Parameters:

periods (array_like) – spectral periods to interpolate the response.
kind (str, optional) – see scipy.interpolate.interp1d() for description of kind. Options include: ‘linear’ (default), ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, and ‘cubic’

Returns:

ln_stds – interpolated logarithmic standard deviations

Return type:

np.ndarray

interp_spec_accels(periods, kind='linear')¶

Interpolate the spectral acceleration.

Interpolation of the spectral acceleration is done in natural log space.

Parameters:

periods (array_like) – spectral periods to interpolate the response.
kind (str, optional) – see scipy.interpolate.interp1d() for description of kind. Options include: ‘linear’ (default), ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, and ‘cubic’

Returns:

spec_accels – interpolated spectral accelerations

Return type:

np.ndarray

property ln_std_pga: float¶: Peak ground accelaration log-standard deviation.

property ln_std_pgd: float¶: Peak ground displacement log-standard deviation.

property ln_std_pgv: float¶: Peak ground velocity log-standard deviation.

property ln_stds: ndarray¶: Pseudo-spectral accelerations log-standard deviation.

property periods: ndarray¶: Periods specified by the model.

property pga: float¶: Peak ground acceleration (PGA) computed by the model (g).

property pgd: float¶: Peak ground displacement (PGD) computed by the model (cm).

property pgv: float¶: Peak ground velocity (PGV) computed by the model (cm/sec).

property scenario¶

property spec_accels: ndarray¶: Pseudo-spectral accelerations computed by the model (g).