bayesian_blocks¶

astropy.stats.
bayesian_blocks
(t, x=None, sigma=None, fitness='events', **kwargs)[source] [edit on github]¶ Compute optimal segmentation of data with Scargle’s Bayesian Blocks
This is a flexible implementation of the Bayesian Blocks algorithm described in Scargle 2012 [1].
Parameters:  t : array_like
data times (one dimensional, length N)
 x : array_like (optional)
data values
 sigma : array_like or float (optional)
data errors
 fitness : str or object
the fitness function to use for the model. If a string, the following options are supported:
 ‘events’ : binned or unbinned event data. Arguments are
gamma
, which gives the slope of the prior on the number of bins, orncp_prior
, which is \(\ln({\tt gamma})\).  ‘regular_events’ : nonoverlapping events measured at multiples of a
fundamental tick rate,
dt
, which must be specified as an additional argument. Extra arguments arep0
, which gives the false alarm probability to compute the prior, orgamma
, which gives the slope of the prior on the number of bins, orncp_prior
, which is \(\ln({\tt gamma})\).  ‘measures’ : fitness for a measured sequence with Gaussian errors.
Extra arguments are
p0
, which gives the false alarm probability to compute the prior, orgamma
, which gives the slope of the prior on the number of bins, orncp_prior
, which is \(\ln({\tt gamma})\).
In all three cases, if more than one of
p0
,gamma
, andncp_prior
is chosen,ncp_prior
takes precedence overgamma
which takes precedence overp0
.Alternatively, the fitness parameter can be an instance of
FitnessFunc
or a subclass thereof. ‘events’ : binned or unbinned event data. Arguments are
 **kwargs :
any additional keyword arguments will be passed to the specified
FitnessFunc
derived class.
Returns:  edges : ndarray
array containing the (N+1) edges defining the N bins
See also
astropy.stats.histogram
 compute a histogram using bayesian blocks
References
[1] (1, 2) Scargle, J et al. (2012) http://adsabs.harvard.edu/abs/2012arXiv1207.5578S Examples
Event data:
>>> t = np.random.normal(size=100) >>> edges = bayesian_blocks(t, fitness='events', p0=0.01)
Event data with repeats:
>>> t = np.random.normal(size=100) >>> t[80:] = t[:20] >>> edges = bayesian_blocks(t, fitness='events', p0=0.01)
Regular event data:
>>> dt = 0.05 >>> t = dt * np.arange(1000) >>> x = np.zeros(len(t)) >>> x[np.random.randint(0, len(t), len(t) // 10)] = 1 >>> edges = bayesian_blocks(t, x, fitness='regular_events', dt=dt)
Measured point data with errors:
>>> t = 100 * np.random.random(100) >>> x = np.exp(0.5 * (t  50) ** 2) >>> sigma = 0.1 >>> x_obs = np.random.normal(x, sigma) >>> edges = bayesian_blocks(t, x_obs, sigma, fitness='measures')