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(y)¶

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.