HvsrTraditional

class HvsrTraditional(frequency, amplitude, meta=None)

Class for creating and manipulating HvsrTraditional objects.

Variables:

amplitude (ndarray) – Array of HVSR amplitudes. Each row represents an individual curve (e.g., from a time window or earthquake recording) and each column a frequency.
frequency (ndarray) – Vector of frequencies, one per amplitude column.
n_curves (int) – Number of HVSR curves in HvsrTraditional object; one HVSR curve per time window or earthquake recording.
valid_curve_boolean_mask (ndarray) – Boolean array indicating whether each HVSR curve is valid (True) or invalid (False).
valid_peak_boolean_mask (ndarray) – Boolean array indicating whether the peak of each HVSR curve is valid (True) or invalid (False).

__init__(frequency, amplitude, meta=None)

Create HvsrTraditional from amplitude and frequency.

Parameters:

frequency (ndarray) – Vector of frequencies, corresponding to each column of amplitude.
amplitude (ndarray) – Array of HVSR amplitudes. Each row represents an individual curve (e.g., from a time window or earthquake record) and each column a frequency.
meta (dict, optional) – Meta information about the object, default is None.

Returns:

HvsrTraditional – Initialized with amplitude and frequency.

cov_fn(distribution='lognormal')

Covariance of HVSR resonance across all valid HVSR curves.

Parameters:: distribution ({“normal”, “lognormal”}, optional) – Assumed distribution of resonance, default is "lognormal".
Returns:: ndarray – Tensor of shape (2,2) that represents the covariance matrix of frequency and amplitude of HVSR resonance across all valid time windows.
Raises:: NotImplementedError – If distribution does not match the available options.

classmethod from_hvsr_curves(hvsr_curves, meta=None)

Instantiate HvsrTraditional from iterable of HvsrCurve.

Parameters:

amplitude (iterable of HvsrCurve) – Iterable of HvsrCurve objects one curve for each time window or earthquake record and common frequency sampling.
meta (dict, optional) – Meta information about the object, default is None.

Returns:

HvsrTraditional – Instantiated from HvsrCurve data.

is_similar(other): Determine if other is similar to self.

mean_curve(distribution='lognormal')

Mean HVSR curve.

Parameters:: distribution ({“normal”, “lognormal”}, optional) – Assumed distribution of mean curve, default is “lognormal”.
Returns:: ndarray – Mean HVSR curve according to the distribution specified.
Raises:: NotImplementedError – If distribution does not match the available options.

mean_curve_peak(distribution='lognormal')

Frequency and amplitude of the peak of the mean HVSR curve.

Parameters:: distribution ({“normal”, “lognormal”}, optional) – Assumed distribution of HVSR curve, default is "lognormal".
Returns:: tuple – Frequency and amplitude associated with the peak of the mean HVSR curve of the form (mean_curve_peak_frequency, mean_curve_peak_amplitude).

mean_fn_amplitude(distribution='lognormal')

Mean amplitude of peaks associated with fn from valid HVSR curves.

Parameters:: distribution ({“normal”, “lognormal”}) – Assumed distribution of fn, default is "lognormal".
Returns:: float – Mean amplitude of fn according to the distribution specified.
Raises:: NotImplementedError – If distribution does not match the available options.

mean_fn_frequency(distribution='lognormal')

Mean frequency of peaks associated with fn from valid HVSR curves.

Parameters:: distribution ({“normal”, “lognormal”}) – Assumed distribution of fn, default is "lognormal".
Returns:: float – Mean value of fn according to the distribution specified.
Raises:: NotImplementedError – If distribution does not match the available options.

nth_std_curve(n, distribution='lognormal')

nth standard deviation curve.

Parameters:

n (float) – Number of standard deviations away from the mean curve.
distribution ({“lognormal”, “normal”}, optional) – Assumed distribution of mean curve, default is "lognormal".

Returns:

ndarray – nth standard deviation curve.

nth_std_fn_amplitude(n, distribution='lognormal')

Value n standard deviations from mean fn amplitude.

Parameters:

n (float) – Number of standard deviations away from the mean amplitude of fn computed from valid HVSR curves.
distribution ({“lognormal”, “normal”}, optional) – Assumed distribution of fn, the default is "lognormal".

Returns:

float – Value n standard deviations from mean fn amplitude.

nth_std_fn_frequency(n, distribution='lognormal')

Value n standard deviations from mean fn frequency.

Parameters:

n (float) – Number of standard deviations away from the mean frequency of fn computed from valid HVSR curves.
distribution ({“lognormal”, “normal”}, optional) – Assumed distribution of fn, the default is "lognormal".

Returns:

float – Value n standard deviations from mean fn frequency.

property peak_amplitudes: Valid peak amplitude vector, one per window or earthquake recording.

property peak_frequencies: Valid peak frequency vector, one per window or earthquake recording.

std_curve(distribution='lognormal')

Sample standard deviation of the HVSR curves.

Parameters:

distribution ({“normal”, “lognormal”}, optional) – Assumed distribution of HVSR curve, default is "lognormal".

Returns:

ndarray – Sample standard deviation of HVSR curve according to the distribution specified.

Raises:

ValueError – If only single HVSR curve is defined.
NotImplementedError – If distribution does not match the available options.

std_fn_amplitude(distribution='lognormal')

Sample standard deviation of amplitude of peaks associated with fn from valid HVSR curves.

Parameters:: distribution ({“normal”, “lognormal”}, optional) – Assumed distribution of fn, default is "lognormal".
Returns:: float – Sample standard deviation of the amplitude of fn considering only the valid HVSR curves.
Raises:: NotImplementedError – If distribution does not match the available options.

std_fn_frequency(distribution='lognormal')

Sample standard deviation of frequency of peaks associated with fn from valid HVSR curves.

Parameters:: distribution ({“normal”, “lognormal”}, optional) – Assumed distribution of fn, default is "lognormal".
Returns:: float – Sample standard deviation of the frequency of fn considering only the valid HVSR curves.
Raises:: NotImplementedError – If distribution does not match the available options.

update_peaks_bounded(search_range_in_hz=(None, None), find_peaks_kwargs=None)

Update peak associated with each HVSR curve, can be over bounded range.

Parameters:

search_range_in_hz (tuple, optional) – Frequency range to be searched for peaks. Half open ranges can be specified with None, default is (None, None) indicating the full frequency range will be searched.
find_peaks_kwargs (dict) – Keyword arguments for the scipy function find_peaks see scipy documentation for details.

Returns:

None – Updates internal peak-related attributes.