In spectral analysis, it is common practice to multiply the input data by a tapering window.
Many windows are implemented and available in the ~spectrum.window module as well as utilities to plot the window in time and frequency domains. Some windows that have been implemented are:
spectrum.window.window_bartlett spectrum.window.window_blackman spectrum.window.window_gaussian spectrum.window.window_hamming spectrum.window.window_hann spectrum.window.window_kaiser spectrum.window.window_lanczos spectrum.window.window_nuttall spectrum.window.window_tukey
See ~spectrum.window module for a full list of windows. Note also that the ~spectrum.waveform provides additional waveforms/windows.
There is a class ~spectrum.window.Window that ease the manipulation of the tapering windows. It works as follows:
from spectrum.window import Window N = 64 w = Window(N, 'hamming') w.plot_time_freq()
where N is the length of the desired window, and "hamming" is the name. There are a lot of different windows, some of them require arguments. For example, the blackman window require an alpha argument:
w = Window(64, 'blackman', alpha=1)From the object, you can easily access to the window data (w.data) and frequency (w.frequencies), as well as quantities such as the equivalent noise band width:
>>> from spectrum.window import Window >>> N = 64 >>> w = Window(N, 'rectangular') >>> w.enbw 1.0
To have a list of valid names, omit the name. It should raise an error with the list of valid names. Alternatively, type:
window_names.keys()Finally, when a window require arguments, you need to know their names (e.g., in the blackman example above, the alpha parameter is required).
The only way to get this information is to look at the function window_<name> (e.g. window_blackman) and type:
window_blackman?You can explore the module to get the window function. For instance, if you look for the Hamming window, you should find a function called ~spectrum.window.window_hamming. You can look at it as follows:
from spectrum.window import window_hamming from pylab import plot
N = 64 w = window_hamming(N) plot(w)
If you want to have a quick look at the window shape and its frequency behaviour, you can use the ~spectrum.window.window_visu:
from spectrum.window import window_visu N = 64 window_visu(N, 'hamming')
If you do not want the object approach, you may want to use the Factory function called ~spectrum.window.create_window (the Window class relies on this function). The previous Hamming window can be called using:
from spectrum.window import create_window from pylab import plot
N = 64 w = create_window(N, 'hamming') plot(w)