FindPeaksCWT — Peak finding with continuous wavelet transforms.¶
This module defines a wrapper class for the scipy.signal.find_peaks_cwt method.

class
admit.util.peakfinder.FindPeaksCWT.
FindPeaksCWT
(spec, x=None, **kwargs)[source]¶ Parameters: spec : List or numpy array
The spectrum to be analyzed.
x : List or numpy array, optional
The x coordinates for the spectrum. Default = None.
kwarg : Dict
Any additional arguments, see the Attributes list for a complete listing.
Attributes
spec (numpy array) The spectrum to be analyzed. x (numpy array) The x coordinates of the spectrum. widths (sequence) 1D array 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 a single variable and return a 1D array to convolve with vector. Should be normalized to unit area. Default: None (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: 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: 5. min_length (int, optional) Minimum length a ridge line needs to be acceptable. Default: cwt.shape[0] / 4, ie 1/4th the number of widths. min_snr (float, optional) Minimum SNR ratio. Default 1. The signal is the value of the cwt matrix at the shortest length scale (cwt[0, loc]), the noise is the noise_percth percentile of datapoints contained within a window of window_size around cwt[0, loc]. Default: 3. noise_perc (float, optional) When calculating the noise floor, percentile of data points examined below which to consider noise. Calculated using stats.scoreatpercentile. Default: 10. Methods
find
()Method to find any peaks in the spectrum. 
find
()[source]¶ Method to find any peaks in the spectrum. A baseline will be subtracted first if requested.
Parameters: None
Returns: numpy array of floats
containing the locations of the peaks

gap_thresh
= 5.0¶

max_distances
= None¶

min_length
= None¶

min_snr
= 3.0¶

noise_perc
= 10.0¶

wavelet
= None¶

widths
= array([ 5, 10, 15, 20, 25, 30])¶
