BayesianBlocks

class mutis.flares.BayesianBlocks(signal, p=0.1)

Bases: object

Return a Bayesian Block representation of the signal

Attributes

edges: list

list of edges defining the blocks.

values: list

list of values defining the height of the blocks.

signal: mutis.Signal

the mutis.Signal() instance that this BayesianBlocks() represents.

inflare: list

list of boolean representing whether a block is in flare or not.

Methods Summary

get_flare_list()	Join all flares into a list of mutis.flares.Flare()
get_flares([thresh])	Get a list of flares following the algorithm proposed in [Meyer, Scargle, Blandford (2019)] (https://iopscience.iop.org/article/10.3847/1538-4357/ab1651/pdf):
plot([style, ax])	Plot the bayesian block representation

Methods Documentation

get_flare_list()

Join all flares into a list of mutis.flares.Flare()

get_flares(thresh=1)

Get a list of flares following the algorithm proposed in [Meyer, Scargle, Blandford (2019)] (https://iopscience.iop.org/article/10.3847/1538-4357/ab1651/pdf):

`There is no generally accepted consensus on the best way to determine which data points belong to a flaring state and which characterize the quiescent level. Nalewajko (2013)suggested the simple definition that a flare is a continuous time interval associated with a flux peak in which the flux is larger than half the peak flux value. This definition is intuitive, however, and it is unclear how to treat overlapping flares and identify flux peaks in an objective way. Here we use a simple two-step procedure tailored to the block representation: (1)identify a block that is higher than both the previous and subsequent blocks as a peak, and (2)proceed downward from the peak in both directions as long as the blocks are successively lower.`

plot(style='color.strict', ax=None, **kwargs)

Plot the bayesian block representation

E.g: >>> signal = mutis.Signal(data[‘jyear’], data[‘Flux’], data[‘Flux_err’]) >>> BayesianBlocks(signal).plot(color=’k’)