Find local maxima using scipy.signal
I have a 1D array (A) of 64 integer values. How can I find the 2 local maxima corresponding to the values 56 and 50 (indices 10 and 45, respectively) using the scipy.signal module?
At first I tried importing
from scipy.signal import find_peaks
A
array([ 0., 1., 3., 8., 6., 16., 29., 29., 47., 42., 56.,
44., 49., 40., 34., 34., 26., 24., 25., 21., 22., 24.,
34., 17., 17., 29., 24., 26., 13., 25., 16., 19., 19.,
26., 24., 26., 41., 34., 24., 37., 37., 39., 34., 40.,
45., 50., 28., 45., 43., 46., 47., 41., 30., 23., 19.,
15., 9., 10., 3., 6., 4., 1., 1., 0.])
but before I got any further I got the error message that
"AttributeError: 'module' object has no attribute 'find_peaks'",
so then instead I tried importing
from scipy import signal
peakind = signal.find_peaks_cwt(A, widths=32)
but then I get the error message that
"TypeError: 'int' object is not subscriptable".
I still get this error message even if I first do A.astype(np.int64).
Can't I use scipy.signal_find_peaks on an array of integer values?
python python-3.x scipy
edited Nov 23 '18 at 9:30
Cleb
10.8k125482
asked Nov 19 '18 at 15:37
kirerikkirerik
668
add a comment |
I have a 1D array (A) of 64 integer values. How can I find the 2 local maxima corresponding to the values 56 and 50 (indices 10 and 45, respectively) using the scipy.signal module?
At first I tried importing
from scipy.signal import find_peaks
A
array([ 0., 1., 3., 8., 6., 16., 29., 29., 47., 42., 56.,
44., 49., 40., 34., 34., 26., 24., 25., 21., 22., 24.,
34., 17., 17., 29., 24., 26., 13., 25., 16., 19., 19.,
26., 24., 26., 41., 34., 24., 37., 37., 39., 34., 40.,
45., 50., 28., 45., 43., 46., 47., 41., 30., 23., 19.,
15., 9., 10., 3., 6., 4., 1., 1., 0.])
but before I got any further I got the error message that
"AttributeError: 'module' object has no attribute 'find_peaks'",
so then instead I tried importing
from scipy import signal
peakind = signal.find_peaks_cwt(A, widths=32)
but then I get the error message that
"TypeError: 'int' object is not subscriptable".
I still get this error message even if I first do A.astype(np.int64).
Can't I use scipy.signal_find_peaks on an array of integer values?
python python-3.x scipy
edited Nov 23 '18 at 9:30
Cleb
10.8k125482
asked Nov 19 '18 at 15:37
kirerikkirerik
668
@Cleb Thank you for your response! Not quite I'm afraid. I found the potential solution to use argrelextrema, but it would return all local maxima and I am only interested in a function/module who finds the two values I want (56 & 49). Actually, the documentation I found about scipy.signal.find_peaks seemed very promising so it was disappointing that it could not be loaded.
– kirerik
Nov 19 '18 at 16:11
2
As the docs state,find_peaksis new in version 1.1.0. Updating scipy should work
– Brenlla
Nov 19 '18 at 16:14
add a comment |
I have a 1D array (A) of 64 integer values. How can I find the 2 local maxima corresponding to the values 56 and 50 (indices 10 and 45, respectively) using the scipy.signal module?
At first I tried importing
from scipy.signal import find_peaks
A
array([ 0., 1., 3., 8., 6., 16., 29., 29., 47., 42., 56.,
44., 49., 40., 34., 34., 26., 24., 25., 21., 22., 24.,
34., 17., 17., 29., 24., 26., 13., 25., 16., 19., 19.,
26., 24., 26., 41., 34., 24., 37., 37., 39., 34., 40.,
45., 50., 28., 45., 43., 46., 47., 41., 30., 23., 19.,
15., 9., 10., 3., 6., 4., 1., 1., 0.])
but before I got any further I got the error message that
"AttributeError: 'module' object has no attribute 'find_peaks'",
so then instead I tried importing
from scipy import signal
peakind = signal.find_peaks_cwt(A, widths=32)
but then I get the error message that
"TypeError: 'int' object is not subscriptable".
I still get this error message even if I first do A.astype(np.int64).
Can't I use scipy.signal_find_peaks on an array of integer values?
python python-3.x scipy
edited Nov 23 '18 at 9:30
Cleb
10.8k125482
asked Nov 19 '18 at 15:37
kirerikkirerik
668
I have a 1D array (A) of 64 integer values. How can I find the 2 local maxima corresponding to the values 56 and 50 (indices 10 and 45, respectively) using the scipy.signal module?
At first I tried importing
from scipy.signal import find_peaks
A
array([ 0., 1., 3., 8., 6., 16., 29., 29., 47., 42., 56.,
44., 49., 40., 34., 34., 26., 24., 25., 21., 22., 24.,
34., 17., 17., 29., 24., 26., 13., 25., 16., 19., 19.,
26., 24., 26., 41., 34., 24., 37., 37., 39., 34., 40.,
45., 50., 28., 45., 43., 46., 47., 41., 30., 23., 19.,
15., 9., 10., 3., 6., 4., 1., 1., 0.])
but before I got any further I got the error message that
"AttributeError: 'module' object has no attribute 'find_peaks'",
so then instead I tried importing
from scipy import signal
peakind = signal.find_peaks_cwt(A, widths=32)
but then I get the error message that
"TypeError: 'int' object is not subscriptable".
I still get this error message even if I first do A.astype(np.int64).
Can't I use scipy.signal_find_peaks on an array of integer values?
python python-3.x scipy
python python-3.x scipy
edited Nov 23 '18 at 9:30
Cleb
10.8k125482
asked Nov 19 '18 at 15:37
kirerikkirerik
668
edited Nov 23 '18 at 9:30
Cleb
10.8k125482
asked Nov 19 '18 at 15:37
kirerikkirerik
668
edited Nov 23 '18 at 9:30
Cleb
10.8k125482
edited Nov 23 '18 at 9:30
Cleb
10.8k125482
edited Nov 23 '18 at 9:30
Cleb
10.8k125482
10.8k125482
asked Nov 19 '18 at 15:37
kirerikkirerik
668
asked Nov 19 '18 at 15:37
kirerikkirerik
668
asked Nov 19 '18 at 15:37
kirerikkirerik
668
668
@Cleb Thank you for your response! Not quite I'm afraid. I found the potential solution to use argrelextrema, but it would return all local maxima and I am only interested in a function/module who finds the two values I want (56 & 49). Actually, the documentation I found about scipy.signal.find_peaks seemed very promising so it was disappointing that it could not be loaded.
– kirerik
Nov 19 '18 at 16:11
2
As the docs state,find_peaksis new in version 1.1.0. Updating scipy should work
– Brenlla
Nov 19 '18 at 16:14
add a comment |
@Cleb Thank you for your response! Not quite I'm afraid. I found the potential solution to use argrelextrema, but it would return all local maxima and I am only interested in a function/module who finds the two values I want (56 & 49). Actually, the documentation I found about scipy.signal.find_peaks seemed very promising so it was disappointing that it could not be loaded.
– kirerik
Nov 19 '18 at 16:11
2
As the docs state,find_peaksis new in version 1.1.0. Updating scipy should work
– Brenlla
Nov 19 '18 at 16:14
@Cleb Thank you for your response! Not quite I'm afraid. I found the potential solution to use argrelextrema, but it would return all local maxima and I am only interested in a function/module who finds the two values I want (56 & 49). Actually, the documentation I found about scipy.signal.find_peaks seemed very promising so it was disappointing that it could not be loaded.
– kirerik
Nov 19 '18 at 16:11
@Cleb Thank you for your response! Not quite I'm afraid. I found the potential solution to use argrelextrema, but it would return all local maxima and I am only interested in a function/module who finds the two values I want (56 & 49). Actually, the documentation I found about scipy.signal.find_peaks seemed very promising so it was disappointing that it could not be loaded.
– kirerik
Nov 19 '18 at 16:11
2
2
As the docs state,
find_peaks is new in version 1.1.0. Updating scipy should work– Brenlla
Nov 19 '18 at 16:14
As the docs state,
find_peaks is new in version 1.1.0. Updating scipy should work– Brenlla
Nov 19 '18 at 16:14
add a comment |
1 Answer
1
active
oldest
votes
You can use find_peaks as follows (make sure that you work with SciPy >= 1.1):
from scipy.signal import find_peaks
import numpy as np
import matplotlib.pyplot as plt
A = np.array([0., 1., 3., 8., 6., 16., 29., 29., 47., 42., 56.,
44., 49., 40., 34., 34., 26., 24., 25., 21., 22., 24.,
34., 17., 17., 29., 24., 26., 13., 25., 16., 19., 19.,
26., 24., 26., 41., 34., 24., 37., 37., 39., 34., 40.,
45., 50., 28., 45., 43., 46., 47., 41., 30., 23., 19.,
15., 9., 10., 3., 6., 4., 1., 1., 0.])
peaks, _ = find_peaks(A, distance=32)
print(peaks)
# This prints the desired indices [10 45]
plt.plot(A)
plt.plot(peaks, A[peaks], "x")
plt.show()
So, instead of using widths, you use distance which defines the minimum distance between two peaks.
This will plot:
As you can see, only the two desired peaks are marked by a x and all remaining local maxima are ignored.
answered Nov 23 '18 at 8:27
ClebCleb
10.8k125482
add a comment |
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1 Answer
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oldest
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oldest
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oldest
votes
You can use find_peaks as follows (make sure that you work with SciPy >= 1.1):
from scipy.signal import find_peaks
import numpy as np
import matplotlib.pyplot as plt
A = np.array([0., 1., 3., 8., 6., 16., 29., 29., 47., 42., 56.,
44., 49., 40., 34., 34., 26., 24., 25., 21., 22., 24.,
34., 17., 17., 29., 24., 26., 13., 25., 16., 19., 19.,
26., 24., 26., 41., 34., 24., 37., 37., 39., 34., 40.,
45., 50., 28., 45., 43., 46., 47., 41., 30., 23., 19.,
15., 9., 10., 3., 6., 4., 1., 1., 0.])
peaks, _ = find_peaks(A, distance=32)
print(peaks)
# This prints the desired indices [10 45]
plt.plot(A)
plt.plot(peaks, A[peaks], "x")
plt.show()
So, instead of using widths, you use distance which defines the minimum distance between two peaks.
This will plot:
As you can see, only the two desired peaks are marked by a x and all remaining local maxima are ignored.
answered Nov 23 '18 at 8:27
ClebCleb
10.8k125482
add a comment |
You can use find_peaks as follows (make sure that you work with SciPy >= 1.1):
from scipy.signal import find_peaks
import numpy as np
import matplotlib.pyplot as plt
A = np.array([0., 1., 3., 8., 6., 16., 29., 29., 47., 42., 56.,
44., 49., 40., 34., 34., 26., 24., 25., 21., 22., 24.,
34., 17., 17., 29., 24., 26., 13., 25., 16., 19., 19.,
26., 24., 26., 41., 34., 24., 37., 37., 39., 34., 40.,
45., 50., 28., 45., 43., 46., 47., 41., 30., 23., 19.,
15., 9., 10., 3., 6., 4., 1., 1., 0.])
peaks, _ = find_peaks(A, distance=32)
print(peaks)
# This prints the desired indices [10 45]
plt.plot(A)
plt.plot(peaks, A[peaks], "x")
plt.show()
So, instead of using widths, you use distance which defines the minimum distance between two peaks.
This will plot:
As you can see, only the two desired peaks are marked by a x and all remaining local maxima are ignored.
answered Nov 23 '18 at 8:27
ClebCleb
10.8k125482
add a comment |
You can use find_peaks as follows (make sure that you work with SciPy >= 1.1):
from scipy.signal import find_peaks
import numpy as np
import matplotlib.pyplot as plt
A = np.array([0., 1., 3., 8., 6., 16., 29., 29., 47., 42., 56.,
44., 49., 40., 34., 34., 26., 24., 25., 21., 22., 24.,
34., 17., 17., 29., 24., 26., 13., 25., 16., 19., 19.,
26., 24., 26., 41., 34., 24., 37., 37., 39., 34., 40.,
45., 50., 28., 45., 43., 46., 47., 41., 30., 23., 19.,
15., 9., 10., 3., 6., 4., 1., 1., 0.])
peaks, _ = find_peaks(A, distance=32)
print(peaks)
# This prints the desired indices [10 45]
plt.plot(A)
plt.plot(peaks, A[peaks], "x")
plt.show()
So, instead of using widths, you use distance which defines the minimum distance between two peaks.
This will plot:
As you can see, only the two desired peaks are marked by a x and all remaining local maxima are ignored.
answered Nov 23 '18 at 8:27
ClebCleb
10.8k125482
You can use find_peaks as follows (make sure that you work with SciPy >= 1.1):
from scipy.signal import find_peaks
import numpy as np
import matplotlib.pyplot as plt
A = np.array([0., 1., 3., 8., 6., 16., 29., 29., 47., 42., 56.,
44., 49., 40., 34., 34., 26., 24., 25., 21., 22., 24.,
34., 17., 17., 29., 24., 26., 13., 25., 16., 19., 19.,
26., 24., 26., 41., 34., 24., 37., 37., 39., 34., 40.,
45., 50., 28., 45., 43., 46., 47., 41., 30., 23., 19.,
15., 9., 10., 3., 6., 4., 1., 1., 0.])
peaks, _ = find_peaks(A, distance=32)
print(peaks)
# This prints the desired indices [10 45]
plt.plot(A)
plt.plot(peaks, A[peaks], "x")
plt.show()
So, instead of using widths, you use distance which defines the minimum distance between two peaks.
This will plot:
As you can see, only the two desired peaks are marked by a x and all remaining local maxima are ignored.
answered Nov 23 '18 at 8:27
ClebCleb
10.8k125482
edited Nov 27 '18 at 15:58
answered Nov 23 '18 at 8:27
ClebCleb
10.8k125482
answered Nov 23 '18 at 8:27
ClebCleb
10.8k125482
answered Nov 23 '18 at 8:27
ClebCleb
10.8k125482
10.8k125482
add a comment |
add a comment |
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@Cleb Thank you for your response! Not quite I'm afraid. I found the potential solution to use argrelextrema, but it would return all local maxima and I am only interested in a function/module who finds the two values I want (56 & 49). Actually, the documentation I found about scipy.signal.find_peaks seemed very promising so it was disappointing that it could not be loaded.
– kirerik
Nov 19 '18 at 16:11
2
As the docs state,
find_peaksis new in version 1.1.0. Updating scipy should work– Brenlla
Nov 19 '18 at 16:14