Peak search function

This module provides a peak search function and tools related to peak analysis.

silx.math.fit.peaks.peak_search(y, fwhm, sensitivity=3.5, begin_index=None, end_index=None, debug=False, relevance_info=False)

Find peaks in a curve.

Parameters:

• y (numpy.ndarray) – Data array
• fwhm – Estimated full width at half maximum of the typical peaks we are interested in (expressed in number of samples)
• sensitivity – Threshold factor used for peak detection. Only peaks with amplitudes higher than σ * sensitivity - where σ is the standard deviation of the noise - qualify as peaks.
• begin_index – Index of the first sample of the region of interest in the y array. If None, start from the first sample.
• end_index – Index of the last sample of the region of interest in the y array. If None, process until the last sample.
• debug – If True, print debug messages. Default: False
• relevance_info – If True, add a second dimension with relevance information to the output array. Default: False

Returns:

1D sequence with indices of peaks in the data if relevance_info is False. Else, sequence of (peak_index, peak_relevance) tuples (one tuple per peak).

Raise:

IndexError if the number of peaks is too large to fit in the output array.

silx.math.fit.peaks.guess_fwhm()

Return the full-width at half maximum for the largest peak in the data array.

The algorithm removes the background, then finds a global maximum and its corresponding FWHM.

This value can be used as an initial fit parameter, used as input for an iterative fit function.

Parameters:y – Data to be used for guessing the fwhm. Returns:Estimation of full-width at half maximum, based on fwhm of the global maximum.