To aid the construction of signals with predetermined properties, the scipy.signal module has a nice collection of the most frequent one-dimensional waveforms in the literature – chirp and sweep_poly (for the frequency-swept cosine generator), gausspulse (a Gaussian modulated sinusoid), sawtooth and square (for the waveforms with those names). They all take as their main parameter a one-dimensional ndarray representing the times at which the signal is to be evaluated. Other parameters control the design of the signal according to frequency or time constraints. Let's take a look into the following code snippet which illustrates the use of these one dimensional waveforms that we just discussed:

>>> import numpy
>>> from scipy.signal import chirp, sawtooth, square, gausspulse
>>> import matplotlib.pyplot as plt
>>> t=numpy.linspace(-1,1,1000)
>>> plt.subplot(221); plt.ylim([-2,2])
>>> plt.plot(t,chirp(t,f0=100,t1=0.5,f1...