不久前，我也遇到了同样的问题，我发现这个函数运行得很好。这是一个Matlab等价物，试试看，告诉我们它是否对你有用。密码不是我的。在# %load ./../functions/detect_peaks.py

"""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 indices 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 indices 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()