pygmm.kishida_2017.calc_cond_mean_spectrum_vector¶
- pygmm.kishida_2017.calc_cond_mean_spectrum_vector(periods: ~typing.Union[~typing.List[float], ~numpy.ndarray], ln_psas: ~typing.Union[~typing.List[float], ~numpy.ndarray], ln_stds: ~typing.Union[~typing.List[float], ~numpy.ndarray], ln_psas_cond: ~typing.Union[~typing.List[float], ~numpy.ndarray]) -> (<class 'numpy.ndarray'>, <class 'numpy.ndarray'>)[source]¶
Kishida (2017, []) conditional spectrum.
Conditional mean spectrum vector (CMSV) by Kishida (2017, []) is specifying the target spectral acceleration at multiple periods, rather than the single conditioning period by Cornell and Baker (2008). If this approach is used for a single period, then the resulting spectrum is the same as computed by Cornell and Baker (2008) – implemented by
calc_cond_mean_spectrum()
.- Parameters
periods (array_like) – Spectral periods of the response spectrum [sec]. This array must be increasing.
ln_psas (array_like) – Natural logarithm of the spectral acceleration. Same length as periods.
ln_stds (array_like) – Logarithmic standard deviation of the spectral acceleration. Same length as periods.
ln_psas_cond (
np.ma.masked_array
) – The vector of conditioning spectral accelerations. This is a masked array with the same length as periods. Masked values are not used for defining the CMSV.
- Returns
ln_psas_cmsv (
np.ndarray
) – Natural logarithm of the conditional mean spectral accelerations.ln_stds_cmsv (
np.ndarray
) – Logarithmic standard deviation of the conditional mean spectral acceleration.
- Raises
ValueError – If periods are monotonically increasing.