plots

4. Plotting diagrams

4.1 PSD

The plot.psd function can be used to generate the data for a Power Spectral Density plot.

4.1.1 Parameters

Parameter	Description
samples	1-D array or sequence. Array or sequence containing the data to be plotted.
nfft	Integer, optional. The number of bins to be used. Defaults to 256.
sample_rate	Integer, optional. The sample rate of the input data in samples. Defaults to 2.
window	Callable, optional. The window function to be used. Defaults to plot.window_hanning.
sides	{'default', 'onesided', 'twosided'}. Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided.

4.1.2 Returns

Variable	Description
Pxx	1-D array. The values for the power spectrum before scaling (real valued).
freqs	1-D array. The frequencies corresponding to the elements in Pxx.

4.1.3 Example Usage

fromfilterbank.filterbankimportFilterbankimportmatplotlib.pyplotaspltimportnumpyasnpfromplotimportpsdfromfilterbank.headerimportread_header# Instatiate the filterbank reader and point to the filterbank filefb=Filterbank(filename='examples/pspm32.fil')
# read the data in the filterbank file_, samples=fb.select_data()
# Convert 2D Array to 1D Array with complex numberssamples=samples[0] + (samples[1] *1j)
# Read the header of the filterbank fileheader=read_header('examples/pspm32.fil')
# Calculate the center frequency with the data in the headercenter_freq=header[b'fch1'] +float(header[b'nchans']) *header[b'foff'] /2.0print(center_freq)
# Get the powerlevels and the frequenciesPXX, freqs, _=psd(samples, nfft=1024, sample_rate=80, sides='twosided')
# Calculate the powerlevel dB'spower_levels=10*np.log10(PXX/(80))
# Add the center frequency to the frequencies so they match the actual frequenciesfreqs=freqs+center_freq# Plot the PSDplt.grid(True)
plt.xlabel('Frequency (MHz)')
plt.ylabel('Intensity (dB)')
plt.plot(freqs, power_levels)
plt.show()

4.2 Waterfall

The plot.waterfall.Waterfall class can be used to generate waterfall plots.

4.2.1 Construction

Parameter	Description
filter_bank	A filterbank object.
center_freq	The center frequency of the signal in the filterbank object
sample_freq	The sample frequency of the signal in the filterbank object
fig	An imaging object, like pyplot.figure()
mode	String {discrete, stream}. The mode to operate on. Use discrete for discrete datasets, and stream for stream data. Defaults to stream.

4.2.2 Methods

Method	Description
init_plot(self)	Initialize the plot
update_plot_labes(self)	Generate the plot labels
get_next(self)	Returns the next row of data in the filterbank object
get_image(self)	Returns the image data of the full dataset, if using a discrete dataset.
update(self, i)	Updates the image with the next row of data, when using a continuous datastream.
animated_plotter(self)	Returns the figure and update function for matplotlib animation
get_center_freq(self)	Returns the center frequency stored in the filterbank header

4.2.3 Example Usage

4.2.3.1 With discrete data

importmatplotlib.animationasanimationfromfilterbank.headerimportread_headerfromfilterbank.filterbankimportFilterbankfromplotimportwaterfallimportpylabaspylfromplot.plotimportnext_power_of_2fb=Filterbank(filename='./pspm32.fil', read_all=True)
wf=waterfall.Waterfall(filter_bank=fb, fig=pyl.figure(), mode="discrete")
img=wf.get_image()
pyl.show(img)

4.2.3.2 With stream data

importmatplotlib.animationasanimationfromfilterbank.headerimportread_headerfromfilterbank.filterbankimportFilterbankfromplotimportwaterfallimportpylabaspylfromplot.plotimportnext_power_of_2fb=Filterbank(filename='./pspm32.fil')
wf=waterfall.Waterfall(fb=fb, fig=pyl.figure(), mode="stream")
fig, update, frames, repeat=wf.animated_plotter()
ani=animation.FuncAnimation(fig, update, frames=frames,repeat=repeat)
pyl.show()

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