pymodalib.implementations.python.wavelet.wavelet_transform module

Python implementation of the wavelet transform function.

class LognormWavelet(f0)

Bases: pymodalib.implementations.python.wavelet.wavelet_transform.WindowParams

fwt(xi)
module(xi)
name
class MorletWavelet(f0)

Bases: pymodalib.implementations.python.wavelet.wavelet_transform.WindowParams

assume_ompeak()
fwt(xi)
has_twf
module(xi)
name
twf(t)
class MorseWavelet(a, f0)

Bases: pymodalib.implementations.python.wavelet.wavelet_transform.WindowParams

fwt(xi)
module(xi)
name
exception WaveletWarning

Bases: RuntimeWarning

Warning which may be shown by the wavelet transform function.

class WindowParams

Bases: object

C = None
assume_ompeak()
f0 = None
fwtmax = None
has_twf
omg = None
ompeak = None
t1 = -inf
t1e = None
t1h = None
t2 = inf
t2e = None
t2h = None
tpeak = None
twf(t)
twfmax = None
xi1 = -inf
xi1e = None
xi1h = None
xi2 = inf
xi2e = None
xi2h = None
aminterp(X, Y, Z, XI, YI, method)

In the Matlab implementation, this function exists for plotting.

Note: This function is incomplete.

fcast(sig, fs, NP, fint, *args) → numpy.ndarray

Predictive padding function. Uses DFT and weighted least squares to find the main sinusoidal components present in the signal and uses them to predict the signal for NP consecutive time-steps.

The number of sinusoids is determined using Bayesian (Schwarz) information criterion, but it cannot exceed MaxOrder.

Parameters:
  • sig (ndarray) – The signal.
  • fs (float) – Sampling frequency of the signal.
  • NP (int) – Number of consecutive time-steps.
  • fint (Tuple[float, float]) – The allowable frequency range for sinusoids: tones with frequencies outside the range are not continued for prediction.
  • args (any) – If supplied, the first argument will be MaxOrder and the second will be the weighting, w, for the weighted least squares method.
Returns:
Return type:

The padding for the signal.
parcalc(racc, L, wp, fwt, twf, disp_mode, f0, fmax, wavelet='Lognorm', fs=-1)
rand(d0, d1, ..., dn)

Random values in a given shape.

Note

This is a convenience function for users porting code from Matlab, and wraps random_sample. That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like numpy.zeros and numpy.ones.

Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).

Parameters:d0, d1, …, dn (int, optional) – The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.
Returns:out – Random values.
Return type:ndarray, shape (d0, d1, ..., dn)

See also

random()

Examples

>>> np.random.rand(3,2)
array([[ 0.14022471,  0.96360618],  #random
       [ 0.37601032,  0.25528411],  #random
       [ 0.49313049,  0.94909878]]) #random
sqeps(vfun, xp, lim1, lim2, racc, MIC, nlims)
wavelet_transform(signal: numpy.ndarray, fs: float, wp: pymodalib.implementations.python.wavelet.wavelet_transform.WindowParams, fmin: float = None, fmax: float = None, padding: Union[int, str] = 'predictive', rel_tolerance: float = 0.01, preprocess: bool = False, disp_mode: bool = False, cut_edges: bool = False, nv: int = None, parallel: bool = None, return_opt: bool = False, *args, **kwargs)