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=200))   # plot a chirp
>>> plt.title("Chirp signal")
>>> plt.subplot(222); plt.ylim([-2,2])
>>> plt.plot(t,gausspulse(t,fc=10,bw=0.5))      # Gauss pulse
>>> plt.title("Gauss pulse")
>>> plt.subplot(223); plt.ylim([-2,2])
>>> t*=3*numpy.pi
>>> plt.plot(t,sawtooth(t))                     # sawtooth
>>> plt.xlabel("Sawtooth signal")
>>> plt.subplot(224); plt.ylim([-2,2])
>>> plt.plot(t,square(t))                       # Square wave
>>> plt.xlabel("Square signal")
>>> plt.show()

Generated by this code, the following diagram shows waveforms for chirp, gausspulse, sawtooth, and square:

The usual method of creating signals is to import them from a file. This is possible by using purely NumPy routines; for example, fromfile:

fromfile(file, dtype=float, count=-1, sep='')

The file argument may point to either a file or a string, the count argument is used to determine the number of items to read, and sep indicates what constitutes a separator in the original file/string. For images, we have the versatile routine, imread in in either the scipy.ndimage or scipy.misc module:

imread(fname, flatten=False)

The fname argument is a string containing the location of an image. The routine infers the type of file, and reads the data into array accordingly. In case if the flatten argument is turned to True, the image is converted to gray scale. Note that, in order for fromfile and imread to work, the Python Imaging Library (PIL) needs to be installed.

It is also possible to load .wav files for analysis, with the read and write routines from the wavfile submodule in the scipy.io module. For instance, the following line of code reads an audio file, say audio.wav, using the read routine:

>>> rate,data = scipy.io.wavfile.read("audio.wav")

The command assigns an integer value to the rate variable, indicating the sample rate of the file (in samples per second), and a NumPy ndarray to the data variable, containing the numerical values assigned to the different notes. If we wish to write some one-dimensional ndarray data into an audio file of this kind, with the sample rate given by the rate variable, we may do so by issuing the following command:

>>> scipy.io.wavfile.write("filename.wav",rate,data)