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Convolution with numpy - Tina - Mar-06-2017
Hello everyone,
I am trying to convolute 2 signals in the time-domain:
the first when is a gaussien function and the second one is a zero array but has an impulse at x1 and an increasing ramp between x2 and x3. Both peaks of the impulse and the increasing ramp have maximum 1.
when I try to convolute both with
np.convolve(gaussian, signal, 'same')I only get a non-zero signal for the increasing ramp. Python seams to ignore the convolution with the impulse. but when I set the ramp to zero and redo the convolution python convolves with the impulse and I get the result.
So separately, means : Convolution with impulse --> works
Convolution with increasing ramp till 1 --> works
Convolution with a signal containing both : Impulse AND ramp --> does not work, shows just result of convolution with ramp.
Can anyone help?
Thank you in advance
RE: Convolution with numpy - sparkz_alot - Mar-06-2017
I am in no way an expert about any of this, but what type of output do you get if you use mode "full" instead of "same"?
RE: Convolution with numpy - zivoni - Mar-06-2017
Neither I am an expert about signal processing, but out of curiosity I tried it and np.convolve convoluted happily.
import numpy as np
from scipy.signal import gaussian
import matplotlib.pyplot as plt
def convoluplot(signal, kernel):
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True)
ax1.plot(signal)
ax1.set_title('Signal')
ax2.plot(kernel)
ax2.set_title('Filter')
filtered = np.convolve(signal, kernel, "same") / sum(kernel)
ax3.plot(filtered)
ax3.set_title("Convolution")
fig.show()
signal = np.zeros(500)
signal[100:150] = 1
signal[250:400] = np.linspace(0,1,150)
kernel = gaussian(100, 10)
convoluplot(signal, kernel)Spoiled image of output:When kernel was "padded" with zeros, it "zeroed" part of input signal. Perhaps try shorter filtering window or sparkz's advice to use full mode to avoid cutting?
RE: Convolution with numpy - Tina - Mar-07-2017
(Mar-06-2017, 03:38 PM)zivoni Wrote: Neither I am an expert about signal processing, but out of curiosity I tried it and np.convolve convoluted happily.Thank you very much ! I will!
import numpy as np from scipy.signal import gaussian import matplotlib.pyplot as plt def convoluplot(signal, kernel): fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True) ax1.plot(signal) ax1.set_title('Signal') ax2.plot(kernel) ax2.set_title('Filter') filtered = np.convolve(signal, kernel, "same") / sum(kernel) ax3.plot(filtered) ax3.set_title("Convolution") fig.show() signal = np.zeros(500) signal[100:150] = 1 signal[250:400] = np.linspace(0,1,150) kernel = gaussian(100, 10) convoluplot(signal, kernel)Spoiled image of output:
When kernel was "padded" with zeros, it "zeroed" part of input signal. Perhaps try shorter filtering window or sparkz's advice to use full mode to avoid cutting?
(Mar-06-2017, 02:25 PM)sparkz_alot Wrote: I am in no way an expert about any of this, but what type of output do you get if you use mode "full" instead of "same"?
unfortunately I get the same thing. But thank you very much for your reply !