Additional SciPy functionality includes several waveforms that can be used when you're designing a signal processing algorithm or testing it. These include sawtooth(), square(), gausspulse(), and chirp():
from pylab import * from scipy import signal cycles = 10
for i, waveform in enumerate(waveforms): subplot(2, 2, i+l)
axis([0, 2*pi*cycles, -1.1, l.l]) Figure 8-11 shows the resulting waveforms.
The difference between waveforms and the triangular window used earlier is that they're repetitive, whereas triang() generates a single window.
The functions gausspulse() and chirp() are a bit more specialized; refer to the interactive help for information on using them.
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