pygmm.idriss_2014.Idriss2014

class pygmm.idriss_2014.Idriss2014(scenario)

Idriss (2014, [Idriss, 2014]) model.

This model was developed for active tectonic regions as part of the NGA-West2 effort.

Parameters:

scenario (pygmm.model.Scenario) – earthquake scenario

NAME = 'Idriss (2014)'

Long name of the model

ABBREV = 'I14'

Short name of the model

V_REF = 1200.0

COEFF = {'large': rec.array([( 0.01 ,  9.0138, -0.0794,  0.0589, 2.9935, -0.2287, -0.854, -0.0027, 0.08),            ( 0.02 ,  9.0408, -0.0794,  0.0589, 2.9935, -0.2287, -0.854, -0.0027, 0.08),            ( 0.03 ,  9.1338, -0.0794,  0.0589, 2.9935, -0.2287, -0.854, -0.0027, 0.08),            ( 0.04 ,  9.2538, -0.0794,  0.0589, 2.9935, -0.2287, -0.854, -0.0027, 0.08),            ( 0.05 ,  7.9837, -0.1923,  0.0417, 2.7995, -0.2319, -0.631, -0.0061, 0.08),            ( 0.075,  7.756 , -0.1614,  0.0527, 2.8143, -0.2326, -0.591, -0.0056, 0.08),            ( 0.1  ,  9.4252, -0.1887,  0.0442, 2.8131, -0.2211, -0.757, -0.0042, 0.08),            ( 0.15 ,  9.6242, -0.0665,  0.0329, 2.4091, -0.1676, -0.911, -0.0046, 0.08),            ( 0.2  , 11.13  , -0.1698,  0.0188, 2.4938, -0.1685, -0.998, -0.003 , 0.08),            ( 0.25 , 11.3629, -0.1766,  0.0095, 2.3773, -0.1531, -1.042, -0.0028, 0.08),            ( 0.3  , 11.7818, -0.2798, -0.0039, 2.3772, -0.1595, -1.03 , -0.0029, 0.08),            ( 0.4  , 11.6097, -0.3048, -0.0133, 2.3413, -0.1594, -1.019, -0.0028, 0.08),            ( 0.5  , 11.4484, -0.2911, -0.0224, 2.3477, -0.1584, -1.023, -0.0021, 0.08),            ( 0.75 , 10.9065, -0.3097, -0.0267, 2.2042, -0.1577, -1.056, -0.0029, 0.08),            ( 1.   ,  9.8565, -0.2565, -0.0198, 2.1493, -0.1532, -1.009, -0.0032, 0.06),            ( 1.5  ,  8.3363, -0.232 , -0.0367, 2.0408, -0.147 , -0.898, -0.0033, 0.04),            ( 2.   ,  6.8656, -0.1226, -0.0291, 2.0013, -0.1439, -0.851, -0.0032, 0.02),            ( 3.   ,  4.1178,  0.1724, -0.0214, 1.9408, -0.1278, -0.761, -0.0031, 0.02),            ( 4.   ,  1.8102,  0.3001, -0.024 , 1.7763, -0.1326, -0.675, -0.0051, 0.  ),            ( 5.   ,  0.0977,  0.4609, -0.0202, 1.703 , -0.1291, -0.629, -0.0059, 0.  ),            ( 7.5  , -3.0563,  0.6948, -0.0219, 1.5212, -0.122 , -0.531, -0.0057, 0.  ),            (10.   , -4.4387,  0.8393, -0.0035, 1.4195, -0.1145, -0.586, -0.0061, 0.  )],           dtype=[('period', '<f8'), ('alpha_1', '<f8'), ('alpha_2', '<f8'), ('alpha_3', '<f8'), ('beta_1', '<f8'), ('beta_2', '<f8'), ('epsilon', '<f8'), ('gamma', '<f8'), ('phi', '<f8')]), 'small': rec.array([( 0.01 ,  7.0887, 2.0580e-01,  0.0589, 2.9935, -0.2287, -0.854, -0.0027, 0.08),            ( 0.02 ,  7.1157, 2.0580e-01,  0.0589, 2.9935, -0.2287, -0.854, -0.0027, 0.08),            ( 0.03 ,  7.2087, 2.0580e-01,  0.0589, 2.9935, -0.2287, -0.854, -0.0027, 0.08),            ( 0.04 ,  7.3287, 2.0580e-01,  0.0589, 2.9935, -0.2287, -0.854, -0.0027, 0.08),            ( 0.05 ,  6.2638, 6.2500e-02,  0.0417, 2.8664, -0.2418, -0.631, -0.0061, 0.08),            ( 0.075,  5.9051, 1.1280e-01,  0.0527, 2.9406, -0.2513, -0.591, -0.0056, 0.08),            ( 0.1  ,  7.5791, 8.4800e-02,  0.0442, 3.019 , -0.2516, -0.757, -0.0042, 0.08),            ( 0.15 ,  8.019 , 1.7130e-01,  0.0329, 2.7871, -0.2236, -0.911, -0.0046, 0.08),            ( 0.2  ,  9.2812, 1.0410e-01,  0.0188, 2.8611, -0.2229, -0.998, -0.003 , 0.08),            ( 0.25 ,  9.5804, 8.7500e-02,  0.0095, 2.8289, -0.22  , -1.042, -0.0028, 0.08),            ( 0.3  ,  9.8912, 3.0000e-04, -0.0039, 2.8423, -0.2284, -1.03 , -0.0029, 0.08),            ( 0.4  ,  9.5342, 2.7000e-03, -0.0133, 2.83  , -0.2318, -1.019, -0.0028, 0.08),            ( 0.5  ,  9.2142, 3.9900e-02, -0.0224, 2.856 , -0.2337, -1.023, -0.0021, 0.08),            ( 0.75 ,  8.3517, 6.8900e-02, -0.0267, 2.7544, -0.2392, -1.056, -0.0029, 0.08),            ( 1.   ,  7.0453, 1.6000e-01, -0.0198, 2.7339, -0.2398, -1.009, -0.0032, 0.06),            ( 1.5  ,  5.1307, 2.4290e-01, -0.0367, 2.68  , -0.2417, -0.898, -0.0033, 0.04),            ( 2.   ,  3.361 , 3.9660e-01, -0.0291, 2.6837, -0.245 , -0.851, -0.0032, 0.02),            ( 3.   ,  0.1784, 7.5600e-01, -0.0214, 2.6907, -0.2389, -0.761, -0.0031, 0.02),            ( 4.   , -2.4301, 9.2830e-01, -0.024 , 2.5782, -0.2514, -0.675, -0.0051, 0.  ),            ( 5.   , -4.357 , 1.1209e+00, -0.0202, 2.5468, -0.2541, -0.629, -0.0059, 0.  ),            ( 7.5  , -7.8275, 1.4016e+00, -0.0219, 2.4478, -0.2593, -0.531, -0.0057, 0.  ),            (10.   , -9.2857, 1.5574e+00, -0.0035, 2.3922, -0.2586, -0.586, -0.0061, 0.  )],           dtype=[('period', '<f8'), ('alpha_1', '<f8'), ('alpha_2', '<f8'), ('alpha_3', '<f8'), ('beta_1', '<f8'), ('beta_2', '<f8'), ('epsilon', '<f8'), ('gamma', '<f8'), ('phi', '<f8')])}

PERIODS = array([ 0.01 ,  0.02 ,  0.03 ,  0.04 ,  0.05 ,  0.075,  0.1  ,  0.15 ,         0.2  ,  0.25 ,  0.3  ,  0.4  ,  0.5  ,  0.75 ,  1.   ,  1.5  ,         2.   ,  3.   ,  4.   ,  5.   ,  7.5  , 10.   ])

Indices of the periods

INDEX_PGA = 0

Index of the peak ground acceleration

INDICES_PSA = array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,        17, 18, 19, 20, 21])

Indices for the spectral accelerations

PARAMS = [<pygmm.model.NumericParameter object>, <pygmm.model.NumericParameter object>, <pygmm.model.NumericParameter object>, <pygmm.model.CategoricalParameter object>]

Model parameters

__init__(scenario)

Initialize the model.

Parameters:

scenario (Scenario)

INDEX_PGD = None

Index of the peak ground displacement

INDEX_PGV = None

Index of the peak ground velocity

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).