Frame methods

Getting frame data

To just grab the underlying intensity data, you can do

data = frame.get_data(db=False)

As it implies, if you set the db flag to True, it will express the intensities in terms of decibels. This can help visualize data a little better, depending on the application.

Plotting frames

There are a variety of options for plotting frames, meant to produce publication-ready images. The main function is plot().

You may choose what style of axes ticks and labels to use with the parameter xtype. By default, xtype=fmid expresses the x-axis as the relative offset from the central frequency.

fr.plot()

xtype=fmid expresses the x-axis as the relative offset from the minimum frequency. In addition, we can disable the dB scaling of the colorbar and turn on minor ticks:

fr.plot(xtype="fmin", db=False, minor_ticks=True)

xtype=f expresses the x-axis as absolute frequencies in MHz. In addition, we can turn on a grid:

fr.plot(xtype="f", grid=True)

Finally, xtype=px expresses both axes in terms of pixels.

fr.plot(xtype="fmin", db=False)

Note that these plots are created using the corresponding units, so that you can actually plot over them in an intuitive way. For example, we can overplot a line following the path of our synthetic signals (frame attribute ts_ext is a time array of length tchans + 1 that includes the end of the frame):

fr.plot(xtype="fmid", db=True)
plt.plot(fr.ts_ext * drift_rate + fr.get_frequency(index=fr.fchans//2) - fr.fmid,
         fr.ts_ext,
         c='k')

The plotting function uses matplotlib.pyplot.imshow behind the scenes, which means you can still control plot parameters before and after these function calls, e.g.

fig = plt.figure(figsize=(10, 6))
frame.plot()
plt.title('My awesome title')
plt.savefig('frame.png')
plt.show()

Frame integration

To time integrate to get a spectrum, or to frequency integrate to get time series intensities, you can use integrate():

spectrum = frame.integrate() # stg.integrate(frame)
time_series = frame.integrate(axis='f') # or axis=1

This function is a wrapper for setigen.frame_utils.integrate(), with the same parameters. The axis parameter can be either ‘t’ or 0 to integrate along the time axis, or ‘f’ or 1 to integrate along the frequency axis. The mode parameter can be either ‘mean’ or ‘sum’ to determine the manner of integration.

Frame slicing

Given frequency boundary indices l and r, we can “slice” a frame by using get_slice(), a wrapper for setigen.frame_utils.get_slice():

s_fr = frame.get_slice(l, r) # stg.get_slice(frame, l, r)

Slicing is analogous to Numpy slicing, e.g. A[l:r], along the frequency axis. This method returns a new frame with only the sliced data. This is useful when chained together with boundary detection methods, or simply to isolate sections of a frame for analysis.

Doppler dedrifting

If you have a frame containing a Doppler drifting signal, you can “dedrift” the frame using dedrift(), specifying a target drift rate (Hz/s):

dd_fr = stg.dedrift(frame, drift_rate=2)

This returns a new frame with only the dedrifted data; this will be smaller in the frequency dimension depending on the drift rate and frame resolution.

Alternatively, if “drift_rate” is contained in the frame’s metadata (frame.metadata), the function will automatically dedrift the frame using that value.

drift_rate = 2
frame.metadata["drift_rate"] = drift_rate
dd_fr = stg.dedrift(frame)