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Cadences

Radio SETI observations are often taken as part of a cadence, with repeated observations in a target direction. The order of observations is often described using a sequence of letters, like "ABACAD" or "ABABAB", to indicate which pointings are unique.

setigen provides the Cadence object to organize multiple frames. This allows you to inject signals into multiple frames in one step, for example.

You can either create an empty Cadence and add frames to it later or initialize the object with a list of frames:

c = stg.Cadence()
c.append(stg.Frame(...))
c.append(stg.Frame(...))

c = stg.Cadence(frame_list=[stg.Frame(...), stg.Frame(...)])

The Cadence object doesn't have an imposed order; it serves as a bare-bones organizational structure for frames. If you would like to impose an order, use the OrderedCadence:

c = stg.OrderedCadence(frame_list, 
                       order="ABACAD")

The OrderedCadence adds an order_label field to each frame's metadata, tracking the unique pointings.

Cadence objects supports Python list operations, such as append and slicing. In addition, OrderedCadences can be filtered by order labels with ~setigen.cadence.OrderedCadence.by_label, which returns the appropriate frames as a new Cadence object.

You can execute functions on each frame in a cadence using ~setigen.cadence.Cadence.apply. This can be used, for example, to add noise to each frame:

c.apply(lambda fr: fr.add_noise(4e6))

For convenience, cadences also have ~setigen.cadence.Cadence.add_signal, which passes all arguments through to ~setigen.frame.Frame.add_signal, but adjusts the times used in the signal calculation appropriately.

As an example, if you wanted to add a drifting signal only to "A" observations:

c.by_label("A").add_signal(stg.constant_path(f_start=c[0].get_frequency(index=128),
                               drift_rate=0.2*u.Hz/u.s),
                           stg.constant_t_profile(level=c[0].get_intensity(snr=30)),
                           stg.sinc2_f_profile(width=2*c[0].df*u.Hz),
                           stg.constant_bp_profile(level=1),
                           doppler_smearing=True)
fig = plt.figure(figsize=(10, 10))
c.plot()

image

Cadence plotting has similar arguments to frame plotting, but you can also have the subplots spaced out vertically in proportion to the slew time in between observations by setting slew_times=True.

fig = plt.figure(figsize=(10, 10))
c.plot(slew_times=True)

image

These examples are using fully synthetic frames, but the cadence frames can just as easily use observational data, as long as the resolutions and frequency extent match.