mdof.transform module

mdof.transform.power_transfer(inputs, outputs, step, **options)

Power spectrum transfer function from input and output data.

Parameters:

• inputs (1D array) – input time history.

• outputs (1D array) – output response history.

• step (float) – timestep.

• period_band (tuple) – (optional) minimum and maximum period of interest, in seconds.

Returns:

(periods, amplitudes)

Return type:

tuple of arrays

mdof.transform.fourier_transfer(inputs, outputs, step, **options)

Fourier spectrum transfer function from input and output data.

Parameters:

• inputs (1D array) – input time history.

• outputs (1D array) – output response history.

• step (float) – timestep.

• period_band (tuple) – (optional) minimum and maximum period of interest, in seconds.

Returns:

(periods, amplitudes)

Return type:

tuple of arrays

mdof.transform.response_transfer(inputs, outputs, step, **options)

Response spectrum transfer function from input and output data.

Parameters:

• inputs (1D array) – input time history.

• outputs (1D array) – output response history.

• step (float) – timestep.

• pseudo (bool) – (optional) if True, uses pseudo accelerations. default: False

• period_band (tuple) – (optional) minimum and maximum period of interest, in seconds.

Returns:

(periods, amplitudes)

Return type:

tuple of arrays

mdof.transform.fdd_spectrum(outputs, step, **options)

Frequency Domain Decomposition spectrum [1] from output data.

Parameters:

• outputs (ND array.) – output response history. dimensions: \((p,nt)\), where \(p\) = number of outputs, and \(nt\) = number of timesteps

• step (float) – timestep.

• period_band (tuple) – (optional) minimum and maximum period of interest, in seconds.

Returns:

(periods, amplitudes)

Return type:

tuple of arrays

References

[1]

Brincker, R., Zhang, L., & Andersen, P. (2001). Modal identification of output-only systems using frequency domain decomposition. Smart materials and structures, 10(3), 441. (https://doi.org/10.1088/0964-1726/10/3/303

mdof.transform.fdd(outputs, step)

Frequency Domain Decomposition [2] from output data.

Parameters:

• outputs (ND array.) – output response history. dimensions: \((p,nt)\), where \(p\) = number of outputs, and \(nt\) = number of timesteps

• step (float) – timestep.

Returns:

(frequencies, `U`, `S`)

Return type:

tuple of arrays

References

[2]

Brincker, R., Zhang, L., & Andersen, P. (2001). Modal identification of output-only systems using frequency domain decomposition. Smart materials and structures, 10(3), 441. (https://doi.org/10.1088/0964-1726/10/3/303

mdof.transform.power_spectrum(series, step, period_band=None, **options)

Power spectrum of a signal, as a function of period (i.e., periodogram).

Parameters:

• series (1D array) – time series.

• step (float) – timestep.

• period_band (tuple) – (optional) minimum and maximum period of interest, in seconds.

Returns:

(periods, amplitudes)

Return type:

tuple of arrays.

mdof.transform.fourier_spectrum(series, step, period_band=None, **options)

Fourier amplitude spectrum of a signal, as a function of period.

Parameters:

• series (1D array) – time series.

• step (float) – timestep.

• period_band (tuple) – (optional) minimum and maximum period of interest, in seconds.

Returns:

(periods, amplitudes)

Return type:

tuple of arrays.