<jupyter_start><jupyter_text>Let's simulate a general timeseries, that's been stacked together using the a Central 8/10 strategy:<jupyter_code># let's create a stacking strategy object
strategy = Central(10)

# let's create a general timeseries
timeseries = Timeseries(model=Transit(a_over_rs=5, period=1.6), 
                                   tmin=-5, tmax=5,
                                   cadence=1800.0, subcadence=2.0, 
                                   subcadenceuncertainty=0.01,
                                   cosmickw=dict(probability=0.001, height=1))<jupyter_output><empty_output><jupyter_text>Now, let's process that timeseries using the stacking strategy.<jupyter_code># stack the subcadences together into the binned cadence
timeseries.stack(strategy)

# print out the noise associated with these
for k in timeseries.rms.keys():
    print('{:>20} noise is {:.3}'.format(k, timeseries.rms[k]))<jupyter_output>unmitigated noise is 0.000468
            achieved noise is 0.000363
            expected noise is 0.000333<jupyter_text>Let's see what this looks like as a static plot.<jupyter_code>timeseries.plot(xlim=[-.5, 0.5])<jupyter_output><empty_output><jupyter_text>It'd also be good to show *all* the data points. Let's see what they look like by sliding through the whole light curve as an animation.<jupyter_code>timeseries.movie(filename='generaltransit.mp4')<jupyter_output>100%|██████████| 301/301 [00:13<00:00, 22.74it/s]
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