George Hotz
5 years ago
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#!/usr/bin/env python3 |
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# Copyright (C) 2016 Sixten Bergman |
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# License WTFPL |
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# |
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# This program is free software. It comes without any warranty, to the extent |
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# permitted by applicable law. |
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# You can redistribute it and/or modify it under the terms of the Do What The |
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# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See |
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# http://www.wtfpl.net/ for more details. |
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# |
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# note that the function peakdetect is derived from code which was released to |
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# public domain see: http://billauer.co.il/peakdet.html |
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# |
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from math import log |
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import numpy as np |
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__all__ = ["peakdetect"] |
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def _datacheck_peakdetect(x_axis, y_axis): |
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if x_axis is None: |
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x_axis = range(len(y_axis)) |
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if len(y_axis) != len(x_axis): |
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raise ValueError("Input vectors y_axis and x_axis must have same length") |
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#needs to be a numpy array |
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y_axis = np.array(y_axis) |
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x_axis = np.array(x_axis) |
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return x_axis, y_axis |
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def _pad(fft_data, pad_len): |
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""" |
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Pads fft data to interpolate in time domain |
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keyword arguments: |
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fft_data -- the fft |
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pad_len -- By how many times the time resolution should be increased by |
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return: padded list |
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""" |
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length = len(fft_data) |
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n = _n(length * pad_len) |
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fft_data = list(fft_data) |
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return fft_data[:length // 2] + [0] * (2**n-length) + fft_data[length // 2:] |
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def _n(x): |
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""" |
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Find the smallest value for n, which fulfils 2**n >= x |
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keyword arguments: |
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x -- the value, which 2**n must surpass |
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return: the integer n |
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""" |
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return int(log(x)/log(2)) + 1 |
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def peakdetect(y_axis, x_axis=None, lookahead=200, delta=0): |
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""" |
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Converted from/based on a MATLAB script at: |
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http://billauer.co.il/peakdet.html |
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function for detecting local maxima and minima in a signal. |
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Discovers peaks by searching for values which are surrounded by lower |
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or larger values for maxima and minima respectively |
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keyword arguments: |
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y_axis -- A list containing the signal over which to find peaks |
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x_axis -- A x-axis whose values correspond to the y_axis list and is used |
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in the return to specify the position of the peaks. If omitted an |
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index of the y_axis is used. |
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(default: None) |
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lookahead -- distance to look ahead from a peak candidate to determine if |
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it is the actual peak |
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(default: 200) |
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'(samples / period) / f' where '4 >= f >= 1.25' might be a good value |
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delta -- this specifies a minimum difference between a peak and |
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the following points, before a peak may be considered a peak. Useful |
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to hinder the function from picking up false peaks towards to end of |
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the signal. To work well delta should be set to delta >= RMSnoise * 5. |
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(default: 0) |
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When omitted delta function causes a 20% decrease in speed. |
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When used Correctly it can double the speed of the function |
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return: two lists [max_peaks, min_peaks] containing the positive and |
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negative peaks respectively. Each cell of the lists contains a tuple |
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of: (position, peak_value) |
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to get the average peak value do: np.mean(max_peaks, 0)[1] on the |
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results to unpack one of the lists into x, y coordinates do: |
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x, y = zip(*max_peaks) |
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""" |
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max_peaks = [] |
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min_peaks = [] |
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dump = [] # Used to pop the first hit which almost always is false |
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# check input data |
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x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis) |
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# store data length for later use |
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length = len(y_axis) |
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#perform some checks |
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if lookahead < 1: |
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raise ValueError("Lookahead must be '1' or above in value") |
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if not (np.isscalar(delta) and delta >= 0): |
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raise ValueError("delta must be a positive number") |
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#maxima and minima candidates are temporarily stored in |
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#mx and mn respectively |
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mn, mx = np.Inf, -np.Inf |
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#Only detect peak if there is 'lookahead' amount of points after it |
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for index, (x, y) in enumerate(zip(x_axis[:-lookahead], |
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y_axis[:-lookahead])): |
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if y > mx: |
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mx = y |
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mxpos = x |
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if y < mn: |
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mn = y |
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mnpos = x |
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####look for max#### |
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if y < mx-delta and mx != np.Inf: |
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#Maxima peak candidate found |
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#look ahead in signal to ensure that this is a peak and not jitter |
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if y_axis[index:index+lookahead].max() < mx: |
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max_peaks.append([mxpos, mx]) |
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dump.append(True) |
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#set algorithm to only find minima now |
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mx = np.Inf |
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mn = np.Inf |
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if index+lookahead >= length: |
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#end is within lookahead no more peaks can be found |
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break |
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continue |
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#else: #slows shit down this does |
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# mx = ahead |
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# mxpos = x_axis[np.where(y_axis[index:index+lookahead]==mx)] |
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####look for min#### |
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if y > mn+delta and mn != -np.Inf: |
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#Minima peak candidate found |
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#look ahead in signal to ensure that this is a peak and not jitter |
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if y_axis[index:index+lookahead].min() > mn: |
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min_peaks.append([mnpos, mn]) |
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dump.append(False) |
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#set algorithm to only find maxima now |
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mn = -np.Inf |
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mx = -np.Inf |
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if index+lookahead >= length: |
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#end is within lookahead no more peaks can be found |
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break |
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#else: #slows shit down this does |
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# mn = ahead |
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# mnpos = x_axis[np.where(y_axis[index:index+lookahead]==mn)] |
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#Remove the false hit on the first value of the y_axis |
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try: |
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if dump[0]: |
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max_peaks.pop(0) |
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else: |
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min_peaks.pop(0) |
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del dump |
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except IndexError: |
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#no peaks were found, should the function return empty lists? |
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pass |
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return [max_peaks, min_peaks] |
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