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Module « scipy.signal »

Fonction find_peaks_cwt - module scipy.signal

Signature de la fonction find_peaks_cwt

def find_peaks_cwt(vector, widths, wavelet=None, max_distances=None, gap_thresh=None, min_length=None, min_snr=1, noise_perc=10, window_size=None) 

Description

help(scipy.signal.find_peaks_cwt)

Find peaks in a 1-D array with wavelet transformation.

The general approach is to smooth `vector` by convolving it with
`wavelet(width)` for each width in `widths`. Relative maxima which
appear at enough length scales, and with sufficiently high SNR, are
accepted.

Parameters
----------
vector : ndarray
    1-D array in which to find the peaks.
widths : float or sequence
    Single width or 1-D array-like of widths to use for calculating
    the CWT matrix. In general,
    this range should cover the expected width of peaks of interest.
wavelet : callable, optional
    Should take two parameters and return a 1-D array to convolve
    with `vector`. The first parameter determines the number of points
    of the returned wavelet array, the second parameter is the scale
    (`width`) of the wavelet. Should be normalized and symmetric.
    Default is the ricker wavelet.
max_distances : ndarray, optional
    At each row, a ridge line is only connected if the relative max at
    row[n] is within ``max_distances[n]`` from the relative max at
    ``row[n+1]``.  Default value is ``widths/4``.
gap_thresh : float, optional
    If a relative maximum is not found within `max_distances`,
    there will be a gap. A ridge line is discontinued if there are more
    than `gap_thresh` points without connecting a new relative maximum.
    Default is the first value of the widths array i.e. widths[0].
min_length : int, optional
    Minimum length a ridge line needs to be acceptable.
    Default is ``cwt.shape[0] / 4``, ie 1/4-th the number of widths.
min_snr : float, optional
    Minimum SNR ratio. Default 1. The signal is the maximum CWT coefficient
    on the largest ridge line. The noise is `noise_perc` th percentile of
    datapoints contained within the same ridge line.
noise_perc : float, optional
    When calculating the noise floor, percentile of data points
    examined below which to consider noise. Calculated using
    `stats.scoreatpercentile`.  Default is 10.
window_size : int, optional
    Size of window to use to calculate noise floor.
    Default is ``cwt.shape[1] / 20``.

Returns
-------
peaks_indices : ndarray
    Indices of the locations in the `vector` where peaks were found.
    The list is sorted.

See Also
--------
find_peaks
    Find peaks inside a signal based on peak properties.

Notes
-----
This approach was designed for finding sharp peaks among noisy data,
however with proper parameter selection it should function well for
different peak shapes.

The algorithm is as follows:
 1. Perform a continuous wavelet transform on `vector`, for the supplied
    `widths`. This is a convolution of `vector` with `wavelet(width)` for
    each width in `widths`. See `cwt`.
 2. Identify "ridge lines" in the cwt matrix. These are relative maxima
    at each row, connected across adjacent rows. See identify_ridge_lines
 3. Filter the ridge_lines using filter_ridge_lines.

.. versionadded:: 0.11.0

References
----------
.. [1] Bioinformatics (2006) 22 (17): 2059-2065.
   :doi:`10.1093/bioinformatics/btl355`

Examples
--------
>>> import numpy as np
>>> from scipy import signal
>>> xs = np.arange(0, np.pi, 0.05)
>>> data = np.sin(xs)
>>> peakind = signal.find_peaks_cwt(data, np.arange(1,10))
>>> peakind, xs[peakind], data[peakind]
([32], array([ 1.6]), array([ 0.9995736]))

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