plots.md

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

from filterbank.filterbank import Filterbank
import matplotlib.pyplot as plt
import numpy as np
from plot import psd
from filterbank.header import read_header

# Instatiate the filterbank reader and point to the filterbank file
fb = Filterbank(filename='examples/pspm32.fil')

# read the data in the filterbank file
_, samples = fb.select_data()

# Convert 2D Array to 1D Array with complex numbers
samples = samples[0] + (samples[1] * 1j)

# Read the header of the filterbank file
header = read_header('examples/pspm32.fil')

# Calculate the center frequency with the data in the header
center_freq = header[b'fch1'] + float(header[b'nchans']) * header[b'foff'] / 2.0

print(center_freq)
# Get the powerlevels and the frequencies
PXX, freqs, _ = psd(samples, nfft=1024, sample_rate=80, sides='twosided')

# Calculate the powerlevel dB's
power_levels =  10 * np.log10(PXX/(80))

# Add the center frequency to the frequencies so they match the actual frequencies
freqs = freqs + center_freq

# Plot the PSD
plt.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

import matplotlib.animation as animation
from filterbank.header import read_header
from filterbank.filterbank import Filterbank
from plot import waterfall
import pylab as pyl
from plot.plot import next_power_of_2

fb = 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

import matplotlib.animation as animation
from filterbank.header import read_header
from filterbank.filterbank import Filterbank
from plot import waterfall
import pylab as pyl
from plot.plot import next_power_of_2


fb = 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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