#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
import scipy.signal
print('Detect peaks without any filters.')
indexes = scipy.signal.find_peaks_cwt(
vector,
np.arange(1, 4),
max_distances=np.arange(1, 4)*2
)
indexes = np.array(indexes) - 1
print('Peaks are: %s' % (indexes))
plot_peaks(
np.array(vector),
np.array(indexes),
algorithm='scipy.signal.find_peaks_cwt'
)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
To install the mlpy, you may read (and run) /tests/install_mlpy.sh
"""
import numpy as np
from vector import vector, plot_peaks
import mlpy
print('Detect peaks without any filters.')
indexes = mlpy.findpeaks_dist(vector)
print('Peaks are: {}'.format(indexes))
plot_peaks(np.array(vector), indexes, algorithm='mlpy.findpeaks_dist')
print('Detect peaks with minimum distance filter.')
mdp = 2.1
indexes = mlpy.findpeaks_dist(vector, mindist=mdp)
print('Peaks are: {}'.format(indexes))
plot_peaks(np.array(vector), indexes, mpd=mdp, algorithm='mlpy.findpeaks_dist')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
from oct2py import octave
# Load the Octage-Forge signal package.
octave.eval("pkg load signal")
print("Detect peaks without any filters.")
(_, indexes) = octave.findpeaks(
np.array(vector), "DoubleSided", "MinPeakHeight", 0, "MinPeakDistance", 0, "MinPeakWidth", 0
)
# The results are in a 2D array and in floats: get back to 1D array and convert
# peak indexes to integer. Also this is MatLab-style indexation (one-based),
# so we must substract one to get back to Python indexation (zero-based).
indexes = indexes[0].astype(int) - 1
print("Peaks are: %s" % (indexes))
plot_peaks(np.array(vector), indexes, algorithm="Octave-Forge findpeaks")
print("Detect peaks with minimum height and distance filters.")
(pks, indexes) = octave.findpeaks(
np.array(vector), "DoubleSided", "MinPeakHeight", 6, "MinPeakDistance", 2, "MinPeakWidth", 0
)
# The results are in a 2D array and in floats: get back to 1D array and convert
# peak indexes to integer. Also this is MatLab-style indexation (one-based),
# so we must substract one to get back to Python indexation (zero-based).
indexes = indexes[0].astype(int) - 1
print("Peaks are: %s" % (indexes))
plot_peaks(np.array(vector), indexes, mph=6, mpd=2, algorithm="Octave-Forge findpeaks")
import numpy as np
from vector import vector, plot_peaks
from libs import peakdetect
print('Detect peaks without any filters.')
peaks = peakdetect.peakdetect(np.array(vector), lookahead=2)
# peakdetect returns two lists, respectively positive and negative peaks,
# with for each peak a tuple of (indexes, values).
indexes = []
for posOrNegPeaks in peaks:
for peak in posOrNegPeaks:
indexes.append(peak[0])
print('Peaks are: %s' % (indexes))
plot_peaks(
np.array(vector),
np.array(indexes),
algorithm='peakdetect from sixtenbe'
)
print('Detect peaks with distance filters.')
peaks = peakdetect.peakdetect(np.array(vector), lookahead=2, delta=2)
# peakdetect returns two lists, respectively positive and negative peaks,
# with for each peak a tuple of (indexes, values).
indexes = []
for posOrNegPeaks in peaks:
for peak in posOrNegPeaks:
indexes.append(peak[0])
print('Peaks are: %s' % (indexes))
plot_peaks(
np.array(vector),
np.array(indexes),
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
import scipy.signal
print('Detect peaks without any filters.')
indexes, _ = scipy.signal.find_peaks(np.array(vector))
print('Peaks are: {}'.format(indexes))
plot_peaks(np.array(vector), indexes, algorithm='scipy.signal.find_peaks')
print('Detect peaks with minimum height and distance filters.')
indexes, _ = scipy.signal.find_peaks(np.array(vector), height=7, distance=2.1)
print('Peaks are: {}'.format(indexes))
plot_peaks(np.array(vector),
indexes,
mph=7,
mpd=2.1,
algorithm='scipy.signal.find_peaks')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
To install the mlpy, you may read (and run) /tests/install_mlpy.sh
"""
import numpy as np
from vector import vector, plot_peaks
import mlpy
print('Detect peaks without any filters.')
indexes = mlpy.findpeaks_dist(vector)
print('Peaks are: {}'.format(indexes))
plot_peaks(
np.array(vector),
indexes,
algorithm='mlpy.findpeaks_dist'
)
print('Detect peaks with minimum distance filter.')
mdp = 2.1
indexes = mlpy.findpeaks_dist(vector, mindist=mdp)
print('Peaks are: {}'.format(indexes))
plot_peaks(
np.array(vector),
indexes,
mpd=mdp, algorithm='mlpy.findpeaks_dist'
)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
import scipy.signal
print('Detect peaks without any filters (maxima).')
indexes = scipy.signal.argrelextrema(
np.array(vector),
comparator=np.greater
)
print('Peaks are: %s' % (indexes[0]))
plot_peaks(
np.array(vector),
indexes[0],
algorithm='scipy.signal.argrelmax'
)
print('Detect peaks without any filters (minima).')
indexes = scipy.signal.argrelextrema(
np.array(vector),
comparator=np.less
)
print('Peaks are: %s' % (indexes[0]))
plot_peaks(
np.array(vector),
indexes[0],
algorithm='scipy.signal.argrelmax'
)
print('Detect peaks with order (distance) filter.')
# only dependencies required import numpy as np from math import sqrt # threshold used to discard peaks too small def detect_peaks(signal, threshold=0.5): # compute root mean square root_mean_square = sqrt(np.sum(np.square(signal) / len(signal))) # compute peak to average ratios ratios = np.array([pow(x / root_mean_square, 2) for x in signal]) # apply first order logic peaks = (ratios > np.roll(ratios, 1)) & (ratios > np.roll(ratios, -1)) & (ratios > threshold) # optional: return peak indices peak_indexes = [] for i in range(0, len(peaks)): if peaks[i]: peak_indexes.append(i) return peak_indexes # example from vector import vector, plot_peaks indexes = detect_peaks(vector, 1.5) print indexes plot_peaks(np.array(vector), np.array(indexes), mph=1.5, algorithm='lightweight standalone peaks')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
import peakutils.peak
print('Detect peaks without any filters.')
indexes = peakutils.peak.indexes(np.array(vector), thres=0, min_dist=0)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector), indexes,
algorithm='peakutils.peak.indexes')
print('Detect peaks with minimum height and distance filters.')
indexes = peakutils.peak.indexes(np.array(vector),
thres=7.0/max(vector), min_dist=2)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector), indexes, mph=7, mpd=2,
algorithm='peakutils.peak.indexes')
# Load the Octage-Forge signal package.
octave.eval("pkg load signal")
print('Detect peaks without any filters.')
(_, indexes) = octave.findpeaks(
np.array(vector),
'DoubleSided', 'MinPeakHeight', 0, 'MinPeakDistance', 0, 'MinPeakWidth', 0
)
# The results are in a 2D array and in floats: get back to 1D array and convert
# peak indexes to integer. Also this is MatLab-style indexation (one-based),
# so we must substract one to get back to Python indexation (zero-based).
indexes = indexes[0].astype(int) - 1
print('Peaks are: %s' % (indexes))
plot_peaks(
np.array(vector),
indexes,
algorithm='Octave-Forge findpeaks'
)
print('Detect peaks with minimum height and distance filters.')
(pks, indexes) = octave.findpeaks(
np.array(vector),
'DoubleSided', 'MinPeakHeight', 6, 'MinPeakDistance', 2, 'MinPeakWidth', 0
)
# The results are in a 2D array and in floats: get back to 1D array and convert
# peak indexes to integer. Also this is MatLab-style indexation (one-based),
# so we must substract one to get back to Python indexation (zero-based).
indexes = indexes[0].astype(int) - 1
print('Peaks are: %s' % (indexes))
plot_peaks(
np.array(vector),
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
from libs.findpeaks import findpeaks
print('Detect peaks without any filters.')
indexes = findpeaks(np.array(vector), spacing=1)
print('Peaks are: %s' % (indexes))
plot_peaks(
np.array(vector),
np.array(indexes),
algorithm='findpeaks from Janko Slavic'
)
print('Detect peaks with distance and height filters.')
indexes = findpeaks(np.array(vector), spacing=2, limit=7)
print('Peaks are: %s' % (indexes))
plot_peaks(
np.array(vector),
np.array(indexes), mph=7, mpd=2,
algorithm='findpeaks from Janko Slavic'
)
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
from libs import peakdetect
print('Detect peaks without any filters.')
peaks = peakdetect.peakdetect(np.array(vector), lookahead=2)
# peakdetect returns two lists, respectively positive and negative peaks,
# with for each peak a tuple of (indexes, values).
indexes = []
for posOrNegPeaks in peaks:
for peak in posOrNegPeaks:
indexes.append(peak[0])
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector),
np.array(indexes),
algorithm='peakdetect from sixtenbe')
print('Detect peaks with distance filters.')
peaks = peakdetect.peakdetect(np.array(vector), lookahead=2, delta=2)
# peakdetect returns two lists, respectively positive and negative peaks,
# with for each peak a tuple of (indexes, values).
indexes = []
for posOrNegPeaks in peaks:
for peak in posOrNegPeaks:
indexes.append(peak[0])
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector),
np.array(indexes),
mph=None,
mpd=2,
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
from libs.findpeaks import findpeaks
print('Detect peaks without any filters.')
indexes = findpeaks(np.array(vector), spacing=1)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector),
np.array(indexes),
algorithm='findpeaks from Janko Slavic')
print('Detect peaks with distance and height filters.')
indexes = findpeaks(np.array(vector), spacing=2, limit=7)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector),
np.array(indexes),
mph=7,
mpd=2,
algorithm='findpeaks from Janko Slavic')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
from libs import detect_peaks
print('Detect peaks without any filters.')
indexes = detect_peaks.detect_peaks(vector)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector), indexes,
algorithm='detect_peaks from Marcos Duarte')
print('Detect peaks with minimum height and distance filters.')
indexes = detect_peaks.detect_peaks(vector, mph=7, mpd=2)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector), indexes, mph=7, mpd=2,
algorithm='detect_peaks from Marcos Duarte')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
from libs.tony_beltramelli_detect_peaks import detect_peaks
print('Detect peaks without any filters.')
indexes = detect_peaks(vector)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector), np.array(indexes),
algorithm='detect_peaks from Tony Beltramelli')
print('Detect peaks with height threshold.')
indexes = detect_peaks(vector, 1.5)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector), np.array(indexes), mph=1.5,
algorithm='detect_peaks from Tony Beltramelli')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
import scipy.signal
print('Detect peaks without any filters (maxima).')
indexes = scipy.signal.argrelextrema(np.array(vector), comparator=np.greater)
print('Peaks are: %s' % (indexes[0]))
# To get number of peaks:
# print("{} peaks".format(len(indexes[0])))
plot_peaks(np.array(vector), indexes[0], algorithm='scipy.signal.argrelmax')
print('Detect peaks without any filters (minima).')
indexes = scipy.signal.argrelextrema(np.array(vector), comparator=np.less)
print('Peaks are: %s' % (indexes[0]))
plot_peaks(np.array(vector), indexes[0], algorithm='scipy.signal.argrelmax')
print('Detect peaks with order (distance) filter.')
indexes = scipy.signal.argrelextrema(np.array(vector),
comparator=np.greater,
order=2)
print('Peaks are: %s' % (indexes[0]))
plot_peaks(np.array(vector),
indexes[0],
mpd=2,
algorithm='scipy.signal.argrelmax')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
from libs.tony_beltramelli_detect_peaks import detect_peaks
print('Detect peaks without any filters.')
indexes = detect_peaks(vector)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector),
np.array(indexes),
algorithm='detect_peaks from Tony Beltramelli')
print('Detect peaks with height threshold.')
indexes = detect_peaks(vector, 1.5)
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector),
np.array(indexes),
mph=1.5,
algorithm='detect_peaks from Tony Beltramelli')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from vector import vector, plot_peaks
import scipy.signal
print('Detect peaks without any filters.')
indexes = scipy.signal.find_peaks_cwt(vector,
np.arange(1, 4),
max_distances=np.arange(1, 4) * 2)
indexes = np.array(indexes) - 1
print('Peaks are: %s' % (indexes))
plot_peaks(np.array(vector),
np.array(indexes),
algorithm='scipy.signal.find_peaks_cwt')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
To install the mlpy, you may read (and run) /tests/install_mlpy.sh
"""
import numpy as np
from vector import vector, plot_peaks
import mlpy
span = 3
print('Detect peaks with a sliding window of {} (minimum possible).'.format(
span))
indexes = mlpy.findpeaks_win(vector, span=span)
print('Peaks are: {}'.format(indexes))
plot_peaks(np.array(vector), indexes, mpd=span, algorithm='mlpy.findpeaks_win')
span = 5
print('Detect peaks with a sliding window of {}.'.format(span))
indexes = mlpy.findpeaks_win(vector, span=span)
print('Peaks are: {}'.format(indexes))
plot_peaks(np.array(vector), indexes, mpd=span, algorithm='mlpy.findpeaks_win')