Flare analysis

%load_ext autoreload
%autoreload 2

import numpy as np
import scipy as sp
import pandas as pd

import matplotlib as mplt
import matplotlib.pyplot as plt

import astropy
import astropy.units as u

import mutis
from mutis.flares import BayesianBlocks

Load data

data = pd.read_csv('data/gamma-I.dat', comment='!')
data

	jyear	CFlux	CFluxErr
0	2008.605065	1.894027e-07	3.326300e-08
1	2008.624230	5.572932e-07	4.754800e-08
2	2008.643395	5.439230e-07	5.684800e-08
3	2008.662560	6.763391e-07	6.980500e-08
4	2008.681725	9.020173e-07	7.435100e-08
...	...	...	...
631	2020.763861	1.119233e-07	5.747500e-08
632	2020.783026	9.138770e-08	3.188800e-08
633	2020.802191	6.064478e-08	1.657600e-08
634	2020.821356	4.770228e-08	2.429000e-08
635	2020.840520	1.733534e-07	3.886500e-08

636 rows × 3 columns

Build signal and plot its Bayesian Block representation

sig = mutis.Signal(data['jyear'], 1e6*data['CFlux'], 1e6*data['CFluxErr'])

fig, ax = plt.subplots(figsize=(10,6))
bayblocks = BayesianBlocks(sig, p=1e-8)
bayblocks.get_flares()
bayblocks.signal.plot()
bayblocks.plot()

Get flares from BayesianBlocks, and plot them

fig, ax = plt.subplots(figsize=(10,6))
bayblocks.signal.plot()

bayblocks.get_flares()

bayblocks.plot('color.strict')

plt.axhline(y=np.median(sig.values), color='r', linestyle='--', alpha=1, label='1 MAD')
plt.axhline(y=np.median(sig.values)+np.sqrt(np.median((sig.values-np.median(sig.values))**2)), color='m', linestyle='--', alpha=1, label='Mad')

for flare in bayblocks.get_flare_list():
    flare.plot()

plt.legend()

plt.show()