-
Notifications
You must be signed in to change notification settings - Fork 172
/
detect_peaks.py
173 lines (152 loc) · 6.46 KB
/
detect_peaks.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
"""Detect peaks in data based on their amplitude and other features."""
from __future__ import division, print_function
import numpy as np
__author__ = "Marcos Duarte, https://github.com/demotu/BMC"
__version__ = "1.0.4"
__license__ = "MIT"
def detect_peaks(x, mph=None, mpd=1, threshold=0, edge='rising',
kpsh=False, valley=False, show=False, ax=None):
"""Detect peaks in data based on their amplitude and other features.
Parameters
----------
x : 1D array_like
data.
mph : {None, number}, optional (default = None)
detect peaks that are greater than minimum peak height.
mpd : positive integer, optional (default = 1)
detect peaks that are at least separated by minimum peak distance (in
number of data).
threshold : positive number, optional (default = 0)
detect peaks (valleys) that are greater (smaller) than `threshold`
in relation to their immediate neighbors.
edge : {None, 'rising', 'falling', 'both'}, optional (default = 'rising')
for a flat peak, keep only the rising edge ('rising'), only the
falling edge ('falling'), both edges ('both'), or don't detect a
flat peak (None).
kpsh : bool, optional (default = False)
keep peaks with same height even if they are closer than `mpd`.
valley : bool, optional (default = False)
if True (1), detect valleys (local minima) instead of peaks.
show : bool, optional (default = False)
if True (1), plot data in matplotlib figure.
ax : a matplotlib.axes.Axes instance, optional (default = None).
Returns
-------
ind : 1D array_like
indeces of the peaks in `x`.
Notes
-----
The detection of valleys instead of peaks is performed internally by simply
negating the data: `ind_valleys = detect_peaks(-x)`
The function can handle NaN's
See this IPython Notebook [1]_.
References
----------
.. [1] http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb
Examples
--------
>>> from detect_peaks import detect_peaks
>>> x = np.random.randn(100)
>>> x[60:81] = np.nan
>>> # detect all peaks and plot data
>>> ind = detect_peaks(x, show=True)
>>> print(ind)
>>> x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
>>> # set minimum peak height = 0 and minimum peak distance = 20
>>> detect_peaks(x, mph=0, mpd=20, show=True)
>>> x = [0, 1, 0, 2, 0, 3, 0, 2, 0, 1, 0]
>>> # set minimum peak distance = 2
>>> detect_peaks(x, mpd=2, show=True)
>>> x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
>>> # detection of valleys instead of peaks
>>> detect_peaks(x, mph=0, mpd=20, valley=True, show=True)
>>> x = [0, 1, 1, 0, 1, 1, 0]
>>> # detect both edges
>>> detect_peaks(x, edge='both', show=True)
>>> x = [-2, 1, -2, 2, 1, 1, 3, 0]
>>> # set threshold = 2
>>> detect_peaks(x, threshold = 2, show=True)
"""
x = np.atleast_1d(x).astype('float64')
if x.size < 3:
return np.array([], dtype=int)
if valley:
x = -x
# find indexes of all peaks
dx = x[1:] - x[:-1]
# handle NaN's
indnan = np.where(np.isnan(x))[0]
if indnan.size:
x[indnan] = np.inf
dx[np.where(np.isnan(dx))[0]] = np.inf
ine, ire, ife = np.array([[], [], []], dtype=int)
if not edge:
ine = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) > 0))[0]
else:
if edge.lower() in ['rising', 'both']:
ire = np.where((np.hstack((dx, 0)) <= 0) & (np.hstack((0, dx)) > 0))[0]
if edge.lower() in ['falling', 'both']:
ife = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) >= 0))[0]
ind = np.unique(np.hstack((ine, ire, ife)))
# handle NaN's
if ind.size and indnan.size:
# NaN's and values close to NaN's cannot be peaks
ind = ind[np.in1d(ind, np.unique(np.hstack((indnan, indnan-1, indnan+1))), invert=True)]
# first and last values of x cannot be peaks
if ind.size and ind[0] == 0:
ind = ind[1:]
if ind.size and ind[-1] == x.size-1:
ind = ind[:-1]
# remove peaks < minimum peak height
if ind.size and mph is not None:
ind = ind[x[ind] >= mph]
# remove peaks - neighbors < threshold
if ind.size and threshold > 0:
dx = np.min(np.vstack([x[ind]-x[ind-1], x[ind]-x[ind+1]]), axis=0)
ind = np.delete(ind, np.where(dx < threshold)[0])
# detect small peaks closer than minimum peak distance
if ind.size and mpd > 1:
ind = ind[np.argsort(x[ind])][::-1] # sort ind by peak height
idel = np.zeros(ind.size, dtype=bool)
for i in range(ind.size):
if not idel[i]:
# keep peaks with the same height if kpsh is True
idel = idel | (ind >= ind[i] - mpd) & (ind <= ind[i] + mpd) \
& (x[ind[i]] > x[ind] if kpsh else True)
idel[i] = 0 # Keep current peak
# remove the small peaks and sort back the indexes by their occurrence
ind = np.sort(ind[~idel])
if show:
if indnan.size:
x[indnan] = np.nan
if valley:
x = -x
_plot(x, mph, mpd, threshold, edge, valley, ax, ind)
return ind
def _plot(x, mph, mpd, threshold, edge, valley, ax, ind):
"""Plot results of the detect_peaks function, see its help."""
try:
import matplotlib.pyplot as plt
except ImportError:
print('matplotlib is not available.')
else:
if ax is None:
_, ax = plt.subplots(1, 1, figsize=(8, 4))
ax.plot(x, 'b', lw=1)
if ind.size:
label = 'valley' if valley else 'peak'
label = label + 's' if ind.size > 1 else label
ax.plot(ind, x[ind], '+', mfc=None, mec='r', mew=2, ms=8,
label='%d %s' % (ind.size, label))
ax.legend(loc='best', framealpha=.5, numpoints=1)
ax.set_xlim(-.02*x.size, x.size*1.02-1)
ymin, ymax = x[np.isfinite(x)].min(), x[np.isfinite(x)].max()
yrange = ymax - ymin if ymax > ymin else 1
ax.set_ylim(ymin - 0.1*yrange, ymax + 0.1*yrange)
ax.set_xlabel('Data #', fontsize=14)
ax.set_ylabel('Amplitude', fontsize=14)
mode = 'Valley detection' if valley else 'Peak detection'
ax.set_title("%s (mph=%s, mpd=%d, threshold=%s, edge='%s')"
% (mode, str(mph), mpd, str(threshold), edge))
# plt.grid()
plt.show()