Conditional Entropy¶

The conditional entropy period finder [G2013] phase-folds the data at each trial frequencies and estimates the conditional entropy $$H(m|\phi)$$ of the data. The idea is that the data with the least entropy (intuitively: the greatest “structure” or “non-randomness”), should correspond to the correct frequency of a stationary signal.

Here,

$H(m|\phi) = H(m, \phi) - H(\phi) = \sum_{m,\phi}p(m, \phi)\log\left(\frac{p(\phi)}{p(m, \phi)}\right)$

where $$p(m, \phi)$$ is the density of points that fall within the bin located at phase $$\phi$$ and magnitude $$m$$ and $$p(\phi) = \sum_m p(m, \phi)$$ is the density of points that fall within the phi range.

An example with cuvarbase¶

import cuvarbase.ce as ce
import numpy as np

# make some fake data
t = np.sort(np.random.rand(100))
y = np.cos(2 * np.pi * 10 * t)
y += np.random.randn(len(t))
dy = np.ones_like(t)

# start a conditional entropy process
proc = ConditionalEntropyAsyncProcess(phase_bins=10, mag_bins=5)

# format your data as a list of lightcurves (t, y, dy)
data = [(t, y, dy)]

# run the CE process with your data
results = proc.run(data)

# finish the process (probably not necessary but ensures
# all data has been transferred)
proc.finish()

# Results is a list of [(freqs, CE), ...] for each lightcurve
# in data.
freqs, ce_spectrum = results[0]


If you want to run CE on large datasets, you can do

proc.large_run(data, max_memory=1e9)


instead of run, which will ensure that the memory limit (1 GB in this case) is not exceeded on the GPU (unless of course you have other processes running).