Toolboxes
Signal Processing Toolbox → scipy.signal
Auto-convertedFilter design, spectral analysis, and signal transforms. The most commonly migrated MATLAB toolbox.
Install
pip install scipy| MATLAB | Python | Note |
|---|---|---|
| butter(n, Wn) | signal.butter(n, Wn) | Butterworth filter design |
| cheby1(n, Rp, Wn) | signal.cheby1(n, Rp, Wn) | Chebyshev Type I |
| cheby2(n, Rs, Wn) | signal.cheby2(n, Rs, Wn) | Chebyshev Type II |
| ellip(n, Rp, Rs, Wn) | signal.ellip(n, Rp, Rs, Wn) | Elliptic filter |
| filtfilt(b, a, x) | signal.filtfilt(b, a, x) | Zero-phase filtering |
| filter(b, a, x) | signal.lfilter(b, a, x) | Note: lfilter, not filter |
| freqz(b, a) | signal.freqz(b, a) | Frequency response |
| spectrogram(x) | signal.spectrogram(x) | |
| pwelch(x) | signal.welch(x) | Note: welch, not pwelch |
| periodogram(x) | signal.periodogram(x) | |
| resample(x, p, q) | signal.resample_poly(x, p, q) | |
| decimate(x, r) | signal.decimate(x, r) | |
| hilbert(x) | signal.hilbert(x) | |
| conv(a, b) | np.convolve(a, b) | Uses NumPy, not SciPy |
| conv2(A, B) | signal.convolve2d(A, B) | |
| xcorr(a, b) | np.correlate(a, b, 'full') |
The converter automatically detects Signal Processing functions and adds the correct imports.
Try the converter