mutis.flares.bayblocks

Complexity

Total Complexity

50

Size/Duplication

Total Lines	233
Duplicated Lines	0 %

Importance

Changes

0

4 Methods

Rating	Name	Duplication	Size	Complexity
F	BayesianBlocks.plot()	0	52	21
A	BayesianBlocks.get_flare_list()	0	24	3
A	BayesianBlocks.__init__()	0	17	3
F	BayesianBlocks.get_flares()	0	92	23

""" Flare analysis """

import logging

import numpy as np

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import scipy as sp

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import pandas as pd

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import matplotlib as mplt

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import matplotlib.pyplot as plt

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import sys

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import os

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import glob

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import re

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from datetime import datetime

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from astropy.stats import bayesian_blocks

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import warnings 

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import mutis.flares.flare as flare

log = logging.getLogger(__name__)

class BayesianBlocks:
    """ Return a Bayesian Block representation of the signal
    

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    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.
    """

    def __init__(self, signal, p=0.1):
        edges = bayesian_blocks(signal.times, signal.values, signal.dvalues, fitness='measures', p0=p)

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        values = list()
        for i in range( len(edges)-1 ):

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            msk = (edges[i] < signal.times) & (signal.times < edges[i+1])
            if np.sum(msk) > 0:
                value = np.average( signal.values[msk], weights=1/signal.dvalues[msk] )

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                values.append( value )

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            else:
                values.append( 0 )

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        self.edges = np.array(edges)
        self.values = np.array(values)
        self.signal = signal
        

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        self.inflare = None
    

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

Argument name "ax" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        """ Plot the bayesian block representation
        

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

        """
        

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        ax = plt.gca() if ax is None else ax
        

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        #self.signal.plot()
        

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        x = self.edges

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        y = self.values[np.r_[0:len(self.values),-1]]

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        if self.inflare is None or style == 'none':
            

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            ax.step(x, y, 'k', where='post', **kwargs)
            

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        else:
            

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            if style == 'area':
                

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                ax.step(x, y, 'k', where='post')
                for i in range(len(self.inflare)):
                    if self.inflare[i]:
                        ax.axvspan(x[i], x[i+1], facecolor='r', edgecolor=None, alpha=0.2, **kwargs)
                        

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            elif style == 'color.simple':
                

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                for i in range(len(self.edges)-2):
                    ax.step(self.edges[[i,i+1]], self.values[[i,i+1]], 'r' if self.inflare[i] else 'k', where='post', **kwargs)

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                ax.step(self.edges[[-2,-1]], self.values[[-1,-1]], 'r' if self.inflare[-1] else 'k', where='post', **kwargs)

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            elif style == 'color.loose':
                

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                for i in range(len(self.edges)-2):
                    ax.step(self.edges[[i,i+1]], self.values[[i,i]], 'r' if self.inflare[i] else 'k', where='post', **kwargs)

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                    ax.step(self.edges[[i+1,i+1]], self.values[[i,i+1]], 'r' if self.inflare[i] or self.inflare[i+1] else 'k', where='post', **kwargs)

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                ax.step(self.edges[[-2,-1]], self.values[[-1,-1]], 'r' if self.inflare[-1] else 'k', where='post', **kwargs)

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            elif style == 'color.strict':
                

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                for i in range(len(self.edges)-2):
                    ax.step(self.edges[[i,i+1]], self.values[[i,i]], 'r' if self.inflare[i] else 'k', where='post', **kwargs)

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                    ax.step(self.edges[[i+1,i+1]], self.values[[i,i+1]], 'r' if self.inflare[i] and self.inflare[i+1] else 'k', where='post', **kwargs)

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                ax.step(self.edges[[-2,-1]], self.values[[-1,-1]], 'r' if self.inflare[-1] else 'k', where='post', **kwargs)

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            else:
                raise Exception("Unknown style. Available styles are 'none', 'area', 'color.simple', 'color.strict', 'color.loose'.")

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    def get_flares(self, thresh=1):
        """ Get a list of flares following the algorithm proposed in 

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[Meyer, Scargle, Blandford (2019)]
(https://iopscience.iop.org/article/10.3847/1538-4357/ab1651/pdf):
        

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```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.```
        """
        di_max = 5
        

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        # get list of local maxima
        # bad beheaviour at 0 and -1:
        #imaxL = sp.signal.argrelextrema(self.values, np.greater)[0]
        imaxL = list()

Variable name "imaxL" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        for i in range(0,len(self.values)):

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            if i == 0 and thresh*self.values[0] > self.values[1]:
                imaxL.append(i)
            elif i == len(self.values)-1 and self.values[i-1] < thresh*self.values[i]:
                imaxL.append(i)
            elif self.values[i-1] < thresh*self.values[i] > self.values[i+1]:
                imaxL.append(i)
        imaxL = np.array(imaxL)

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        inflareL = np.full(self.values.shape, False)

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        # get list of local maxima over a threshold (flare peaks)
        for imax in imaxL:
            inflare = True
            

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            if imax == 0:
                pass
            elif not (self.values[imax-1] < thresh*self.values[imax]):

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                inflare = False
            

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            if imax == len(self.values)-1:
                pass
            elif  not (thresh*self.values[imax] > self.values[imax+1]):

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                inflare = False
                

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            inflareL[imax] = inflare
           

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        # extend the flare to adyacent blocks
        for imax in np.argwhere(inflareL):
            

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            if imax == 0:
                pass
            else:
                di = 1

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                stop = False
                while not stop and di < di_max:
                    if imax-di-1 < 0:
                        break
                    

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                    if self.values[imax-di-1] < thresh*self.values[imax-di]:
                        inflareL[imax-di] = True
                    else:
                        stop = True
                    

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                    di = di + 1

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            if imax == len(self.values)-1:
                pass
            else:
                di = 1

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                stop = False
                while not stop and di < di_max:
                    if imax+di+1 > len(self.values)-1:
                        break
                    

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                    if thresh*self.values[imax+di] > self.values[imax+di+1]:
                        inflareL[imax+di] = True
                    else:
                        stop = True
                    

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                    di = di + 1

Variable name "di" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

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        self.inflare = np.array(inflareL)
        

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    def get_flare_list(self):
        """ Join all flares into a list of mutis.flares.Flare() """

        groups = np.split(np.arange(len(self.inflare)), 

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                          np.where((np.abs(np.diff(np.asarray(self.inflare, dtype=int))) == 1))[0]+1)

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        # (groups contains the groups of indices of self.inflare/self.values 

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        # that correspond to flares or no flares)
        

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        #print(self.inflare)
        #print([list(grp) for grp in groups])
        

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        flareL = list()

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        for i, grp in enumerate(groups):

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            if np.all(self.inflare[grp]):
                #print(grp)
                tstart = self.edges[grp[0]]
                tstop = self.edges[grp[-1]+1] # flare ends when no flare begins
                

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                flareL.append(flare.Flare(tstart,tstop))

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        #print(flareL)
        

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        return flareL

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