George Hotz
6 years ago
parent
da5cb1842a
commit
c5b16df772
1 changed files with 0 additions and 163 deletions
@ -1,163 +0,0 @@ |
||||
#!/usr/bin/env python3 |
||||
|
||||
|
||||
# Copyright (C) 2016 Sixten Bergman |
||||
# License WTFPL |
||||
# |
||||
# This program is free software. It comes without any warranty, to the extent |
||||
# permitted by applicable law. |
||||
# You can redistribute it and/or modify it under the terms of the Do What The |
||||
# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See |
||||
# http://www.wtfpl.net/ for more details. |
||||
# |
||||
# note that the function peakdetect is derived from code which was released to |
||||
# public domain see: http://billauer.co.il/peakdet.html |
||||
# |
||||
|
||||
from math import log |
||||
import numpy as np |
||||
|
||||
__all__ = ["peakdetect"] |
||||
|
||||
|
||||
|
||||
def _datacheck_peakdetect(x_axis, y_axis): |
||||
if x_axis is None: |
||||
x_axis = range(len(y_axis)) |
||||
|
||||
if len(y_axis) != len(x_axis): |
||||
raise ValueError("Input vectors y_axis and x_axis must have same length") |
||||
|
||||
#needs to be a numpy array |
||||
y_axis = np.array(y_axis) |
||||
x_axis = np.array(x_axis) |
||||
return x_axis, y_axis |
||||
|
||||
|
||||
def _pad(fft_data, pad_len): |
||||
""" |
||||
Pads fft data to interpolate in time domain |
||||
|
||||
keyword arguments: |
||||
fft_data -- the fft |
||||
pad_len -- By how many times the time resolution should be increased by |
||||
|
||||
return: padded list |
||||
""" |
||||
length = len(fft_data) |
||||
n = _n(length * pad_len) |
||||
fft_data = list(fft_data) |
||||
|
||||
return fft_data[:length // 2] + [0] * (2**n-length) + fft_data[length // 2:] |
||||
|
||||
|
||||
def _n(x): |
||||
""" |
||||
Find the smallest value for n, which fulfils 2**n >= x |
||||
|
||||
keyword arguments: |
||||
x -- the value, which 2**n must surpass |
||||
return: the integer n |
||||
""" |
||||
return int(log(x)/log(2)) + 1 |
||||
|
||||
|
||||
def peakdetect(y_axis, x_axis=None, lookahead=200, delta=0): |
||||
""" |
||||
Converted from/based on a MATLAB script at: |
||||
http://billauer.co.il/peakdet.html |
||||
function for detecting local maxima and minima in a signal. |
||||
Discovers peaks by searching for values which are surrounded by lower |
||||
or larger values for maxima and minima respectively |
||||
keyword arguments: |
||||
y_axis -- A list containing the signal over which to find peaks |
||||
x_axis -- A x-axis whose values correspond to the y_axis list and is used |
||||
in the return to specify the position of the peaks. If omitted an |
||||
index of the y_axis is used. |
||||
(default: None) |
||||
lookahead -- distance to look ahead from a peak candidate to determine if |
||||
it is the actual peak |
||||
(default: 200) |
||||
'(samples / period) / f' where '4 >= f >= 1.25' might be a good value |
||||
delta -- this specifies a minimum difference between a peak and |
||||
the following points, before a peak may be considered a peak. Useful |
||||
to hinder the function from picking up false peaks towards to end of |
||||
the signal. To work well delta should be set to delta >= RMSnoise * 5. |
||||
(default: 0) |
||||
When omitted delta function causes a 20% decrease in speed. |
||||
When used Correctly it can double the speed of the function |
||||
return: two lists [max_peaks, min_peaks] containing the positive and |
||||
negative peaks respectively. Each cell of the lists contains a tuple |
||||
of: (position, peak_value) |
||||
to get the average peak value do: np.mean(max_peaks, 0)[1] on the |
||||
results to unpack one of the lists into x, y coordinates do: |
||||
x, y = zip(*max_peaks) |
||||
""" |
||||
max_peaks = [] |
||||
min_peaks = [] |
||||
dump = [] # Used to pop the first hit which almost always is false |
||||
# check input data |
||||
x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis) |
||||
# store data length for later use |
||||
length = len(y_axis) |
||||
#perform some checks |
||||
if lookahead < 1: |
||||
raise ValueError("Lookahead must be '1' or above in value") |
||||
if not (np.isscalar(delta) and delta >= 0): |
||||
raise ValueError("delta must be a positive number") |
||||
#maxima and minima candidates are temporarily stored in |
||||
#mx and mn respectively |
||||
mn, mx = np.Inf, -np.Inf |
||||
#Only detect peak if there is 'lookahead' amount of points after it |
||||
for index, (x, y) in enumerate(zip(x_axis[:-lookahead], |
||||
y_axis[:-lookahead])): |
||||
if y > mx: |
||||
mx = y |
||||
mxpos = x |
||||
if y < mn: |
||||
mn = y |
||||
mnpos = x |
||||
####look for max#### |
||||
if y < mx-delta and mx != np.Inf: |
||||
#Maxima peak candidate found |
||||
#look ahead in signal to ensure that this is a peak and not jitter |
||||
if y_axis[index:index+lookahead].max() < mx: |
||||
max_peaks.append([mxpos, mx]) |
||||
dump.append(True) |
||||
#set algorithm to only find minima now |
||||
mx = np.Inf |
||||
mn = np.Inf |
||||
if index+lookahead >= length: |
||||
#end is within lookahead no more peaks can be found |
||||
break |
||||
continue |
||||
#else: #slows shit down this does |
||||
# mx = ahead |
||||
# mxpos = x_axis[np.where(y_axis[index:index+lookahead]==mx)] |
||||
####look for min#### |
||||
if y > mn+delta and mn != -np.Inf: |
||||
#Minima peak candidate found |
||||
#look ahead in signal to ensure that this is a peak and not jitter |
||||
if y_axis[index:index+lookahead].min() > mn: |
||||
min_peaks.append([mnpos, mn]) |
||||
dump.append(False) |
||||
#set algorithm to only find maxima now |
||||
mn = -np.Inf |
||||
mx = -np.Inf |
||||
if index+lookahead >= length: |
||||
#end is within lookahead no more peaks can be found |
||||
break |
||||
#else: #slows shit down this does |
||||
# mn = ahead |
||||
# mnpos = x_axis[np.where(y_axis[index:index+lookahead]==mn)] |
||||
#Remove the false hit on the first value of the y_axis |
||||
try: |
||||
if dump[0]: |
||||
max_peaks.pop(0) |
||||
else: |
||||
min_peaks.pop(0) |
||||
del dump |
||||
except IndexError: |
||||
#no peaks were found, should the function return empty lists? |
||||
pass |
||||
return [max_peaks, min_peaks] |
||||
Loading…
Reference in new issue