Bispectrum Tutorial¶
This tutorial is intended to demonstrate bispectrum Analysis on Lightcurve data.
Bispectrum is an example of a Higher Order Spectra(HOS) and contains more information that simple Powerspectrum or non-ploy spectra. For detailed information on Bispectra visit : https://arxiv.org/pdf/1308.3150.pdf
In Stingray, Bispectrum can be created from a Lightcurve(For more information on Lightcurve, visit Lightcurve Notebook).
First we import relevant classes.
[2]:
from stingray import lightcurve
import numpy as np
from stingray.bispectrum import Bispectrum
import matplotlib.pyplot as plt
%matplotlib inline
Lightcurve Object can be created from an array of time stamps and an array of counts. Creating a simple lightcurve to demonstrate Bispectrum.
[3]:
times = np.arange(1,11)
counts = np.array([2, 1, 3, 4, 2, 5, 1, 0, 2, 3])
lc = lightcurve.Lightcurve(times,counts)
lc.counts
[3]:
array([2, 1, 3, 4, 2, 5, 1, 0, 2, 3])
[4]:
lc.plot(labels=['times','counts'])
A Bispectrum Object takes 4 parameter.
lc: The light curve (lc).maxlag: Maximum lag on both positive and negative sides of 3rd order cumulant (Similar to lags in correlation).window: Specifies the type of window to apply as as stringscale: ‘biased’ or ‘unbiased’ for normalization
Arguments 2 and 3 are optional. If maxlag is not specified, it is set to no. of observations in lightcurve divided by 2. i.e lc.n/2 .
[5]:
bs = Bispectrum(lc)
Different attribute values can be observed by calling relevant properties. Most common are: 1. self.freq - Frequencies against which Bispectrum is calculated. 2. self.lags - Time lags in lightcurve against which 3rd order cumulant is calculated. 3. self.cum3 - 3rd Order cumulant function 4. self.bispec_mag - Magnitude of Bispectrum 5. self.bispecphase - Phase of Bispectrum
[6]:
bs.freq
[6]:
array([-0.5, -0.4, -0.3, -0.2, -0.1, 0. , 0.1, 0.2, 0.3, 0.4, 0.5])
[7]:
bs.lags
[7]:
array([-5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5.])
[8]:
bs.cum3
[8]:
array([[-0.3885, -0.0915, 0.1685, -0.5085, 0.8135, -0.0675, -0.2708,
0.0229, 0.1426, -0.0567, 0. ],
[-0.0915, 0.2328, -0.5162, -2.0652, 0.3058, 0.1968, 0.8135,
0.5492, 0.0209, -0.2484, 0.0063],
[ 0.1685, -0.5162, -0.3999, 0.9821, -0.4989, 0.5011, 0.3058,
-0.5085, -0.2348, 0.2379, 0.0426],
[-0.5085, -2.0652, 0.9821, -0.3096, 0.5704, 2.1084, -0.4989,
-2.0652, 0.1685, 0.8632, 0.0999],
[ 0.8135, 0.3058, -0.4989, 0.5704, -1.3613, -0.3823, 0.5704,
0.9821, -0.5162, -0.0915, 0.0872],
[-0.0675, 0.1968, 0.5011, 2.1084, -0.3823, 0.864 , -1.3613,
-0.3096, -0.3999, 0.2328, -0.3885],
[-0.2708, 0.8135, 0.3058, -0.4989, 0.5704, -1.3613, -0.3823,
0.5704, 0.9821, -0.5162, -0.0915],
[ 0.0229, 0.5492, -0.5085, -2.0652, 0.9821, -0.3096, 0.5704,
2.1084, -0.4989, -2.0652, 0.1685],
[ 0.1426, 0.0209, -0.2348, 0.1685, -0.5162, -0.3999, 0.9821,
-0.4989, 0.5011, 0.3058, -0.5085],
[-0.0567, -0.2484, 0.2379, 0.8632, -0.0915, 0.2328, -0.5162,
-2.0652, 0.3058, 0.1968, 0.8135],
[ 0. , 0.0063, 0.0426, 0.0999, 0.0872, -0.3885, -0.0915,
0.1685, -0.5085, 0.8135, -0.0675]])
[9]:
bs.bispec_mag
[9]:
array([[ 6.1870122 , 9.78649295, 6.29941723, 8.10990858,
3.90975859, 1.49707597, 10.53408125, 8.44275685,
7.73419771, 7.91909148, 3.40576093],
[ 9.78649295, 12.99063169, 11.9523207 , 12.31681 ,
7.34404789, 1.93438197, 5.05536311, 15.92827099,
6.61153784, 3.09535492, 7.91909148],
[ 6.29941723, 11.9523207 , 4.84009298, 8.98535468,
5.6746004 , 1.71227576, 9.35566037, 12.00797853,
1.60576409, 6.61153784, 7.73419771],
[ 8.10990858, 12.31681 , 8.98535468, 18.69373893,
9.83780286, 2.72630968, 7.87985137, 5.32007463,
12.00797853, 15.92827099, 8.44275685],
[ 3.90975859, 7.34404789, 5.6746004 , 9.83780286,
5.93123174, 1.60598497, 0.51743271, 7.87985137,
9.35566037, 5.05536311, 10.53408125],
[ 1.49707597, 1.93438197, 1.71227576, 2.72630968,
1.60598497, 1.262 , 1.60598497, 2.72630968,
1.71227576, 1.93438197, 1.49707597],
[ 10.53408125, 5.05536311, 9.35566037, 7.87985137,
0.51743271, 1.60598497, 5.93123174, 9.83780286,
5.6746004 , 7.34404789, 3.90975859],
[ 8.44275685, 15.92827099, 12.00797853, 5.32007463,
7.87985137, 2.72630968, 9.83780286, 18.69373893,
8.98535468, 12.31681 , 8.10990858],
[ 7.73419771, 6.61153784, 1.60576409, 12.00797853,
9.35566037, 1.71227576, 5.6746004 , 8.98535468,
4.84009298, 11.9523207 , 6.29941723],
[ 7.91909148, 3.09535492, 6.61153784, 15.92827099,
5.05536311, 1.93438197, 7.34404789, 12.31681 ,
11.9523207 , 12.99063169, 9.78649295],
[ 3.40576093, 7.91909148, 7.73419771, 8.44275685,
10.53408125, 1.49707597, 3.90975859, 8.10990858,
6.29941723, 9.78649295, 6.1870122 ]])
[10]:
bs.bispec_phase
[10]:
array([[ -7.65814471e-01, -8.39758950e-01, 7.49083269e-01,
-9.35797260e-01, -1.22623935e+00, -3.13514588e+00,
4.35308043e-01, 6.65460441e-01, 6.17269495e-01,
4.39881603e-01, -3.14159265e+00],
[ -8.39758950e-01, 1.84719564e+00, 1.70902436e+00,
-6.50042861e-01, -5.76818268e-01, -9.16177187e-02,
1.76512372e+00, 2.97853199e+00, 1.45401552e+00,
0.00000000e+00, -4.39881603e-01],
[ 7.49083269e-01, 1.70902436e+00, 1.64851065e+00,
-5.51373516e-01, -1.32816666e+00, 2.45429375e-01,
2.86246989e+00, 3.08272440e+00, -1.10623774e-15,
-1.45401552e+00, -6.17269495e-01],
[ -9.35797260e-01, -6.50042861e-01, -5.51373516e-01,
-2.97776986e+00, -2.96295975e+00, -4.83162811e-01,
1.34000660e+00, 0.00000000e+00, -3.08272440e+00,
-2.97853199e+00, -6.65460441e-01],
[ -1.22623935e+00, -5.76818268e-01, -1.32816666e+00,
-2.96295975e+00, -1.30996608e+00, -1.24358981e-01,
-3.14159265e+00, -1.34000660e+00, -2.86246989e+00,
-1.76512372e+00, -4.35308043e-01],
[ -3.13514588e+00, -9.16177187e-02, 2.45429375e-01,
-4.83162811e-01, -1.24358981e-01, 3.14159265e+00,
1.24358981e-01, 4.83162811e-01, -2.45429375e-01,
9.16177187e-02, 3.13514588e+00],
[ 4.35308043e-01, 1.76512372e+00, 2.86246989e+00,
1.34000660e+00, 3.14159265e+00, 1.24358981e-01,
1.30996608e+00, 2.96295975e+00, 1.32816666e+00,
5.76818268e-01, 1.22623935e+00],
[ 6.65460441e-01, 2.97853199e+00, 3.08272440e+00,
0.00000000e+00, -1.34000660e+00, 4.83162811e-01,
2.96295975e+00, 2.97776986e+00, 5.51373516e-01,
6.50042861e-01, 9.35797260e-01],
[ 6.17269495e-01, 1.45401552e+00, 1.10623774e-15,
-3.08272440e+00, -2.86246989e+00, -2.45429375e-01,
1.32816666e+00, 5.51373516e-01, -1.64851065e+00,
-1.70902436e+00, -7.49083269e-01],
[ 4.39881603e-01, 0.00000000e+00, -1.45401552e+00,
-2.97853199e+00, -1.76512372e+00, 9.16177187e-02,
5.76818268e-01, 6.50042861e-01, -1.70902436e+00,
-1.84719564e+00, 8.39758950e-01],
[ 3.14159265e+00, -4.39881603e-01, -6.17269495e-01,
-6.65460441e-01, -4.35308043e-01, 3.13514588e+00,
1.22623935e+00, 9.35797260e-01, -7.49083269e-01,
8.39758950e-01, 7.65814471e-01]])
Plots¶
Bispectrum in stingray also provides functionality for contour plots of:
3rd Order Cumulant function
Magnitude Bispectrum
Phase Bispectrum
[11]:
p = bs.plot_cum3()
p.show()
[12]:
p = bs.plot_mag()
p.show()
[13]:
p = bs.plot_phase()
p.show()
Another Example¶
Another example is demostrated here for a periodic lighturve with poisson noise.
[14]:
dt = 0.0001 # seconds
freq = 1 #Hz
exposure = 50. # seconds
times = np.arange(0, exposure, dt) # seconds
signal = 300 * np.sin(2.*np.pi*freq*times/0.5) + 1000 # counts/s
noisy = np.random.poisson(signal*dt) # counts
lc = lightcurve.Lightcurve(times,noisy)
[15]:
lc.n
[15]:
500000
[16]:
lc.plot()
In this example, ‘unbiased’ scaled Bispectrum is calculated.
[17]:
bs = Bispectrum(lc, maxlag=25, scale='unbiased')
[18]:
bs.freq[:5]
[18]:
array([-5000.00000001, -4800.00000001, -4600.00000001, -4400.00000001,
-4200.00000001])
[19]:
bs.lags[-5:]
[19]:
array([ 0.0021, 0.0022, 0.0023, 0.0024, 0.0025])
[20]:
bs.n
[20]:
500000
[21]:
bs.cum3[0]
[21]:
array([ 4.16469688e-04, -1.15175317e-06, -1.07527932e-05,
3.12465067e-05, -1.49891250e-05, -1.13491830e-05,
-3.01378025e-05, 8.84909091e-06, -9.76499980e-06,
-4.03093430e-05, -1.39169834e-05, -1.06733571e-05,
-3.56900080e-05, -4.36904080e-05, -1.64739272e-05,
-6.07642325e-06, -9.40724231e-05, 3.20972054e-05,
1.10825598e-06, 1.57445478e-05, 1.50738698e-04,
-1.53088049e-05, -1.06758132e-05, -8.50761732e-05,
-2.70732731e-05, 5.15575763e-04, -2.26276548e-06,
-5.46966498e-05, -3.49049233e-05, 6.93111630e-05,
-1.96629892e-05, -4.00897434e-05, -5.37940654e-07,
-1.25908665e-04, -4.04722751e-05, -1.95122973e-05,
7.48985545e-06, -1.59418559e-05, -3.40950546e-07,
-5.28946188e-05, -6.77547458e-05, -2.58282563e-06,
-2.16597857e-05, 2.08264564e-05, 1.62145798e-05,
6.20770115e-05, 5.74011370e-05, 3.04301082e-05,
5.42455829e-05, 6.16520488e-05, 5.25699675e-05])
[22]:
bs.bispec_mag[1]
[22]:
array([ 0.10270301, 0.09674684, 0.1026435 , 0.10278492, 0.09607422,
0.09961388, 0.10090391, 0.10316149, 0.09881147, 0.10027435,
0.09052907, 0.10086312, 0.09964639, 0.09224589, 0.10189853,
0.09783874, 0.1029246 , 0.10003251, 0.1003841 , 0.09654483,
0.10021589, 0.10265071, 0.09913028, 0.10406698, 0.10248613,
0.12079938, 0.10038381, 0.09376602, 0.09916139, 0.10218425,
0.09798569, 0.10296954, 0.10377357, 0.10144925, 0.09848511,
0.09731673, 0.10031293, 0.09733791, 0.10085873, 0.09769191,
0.10021328, 0.1000008 , 0.10362033, 0.10352851, 0.09763424,
0.10249754, 0.09752426, 0.09520164, 0.09959243, 0.12395456,
0.10188173])
[23]:
bs.bispec_phase[1]
[23]:
array([ -1.44942123e-02, 1.67988284e-02, -3.06544878e-03,
1.24304742e-02, -4.69267453e-04, 1.80410887e-02,
1.18875941e-03, -1.85154750e-03, 2.17338081e-02,
1.03821918e-02, -7.09489717e-03, 1.05358508e-02,
4.01625879e-03, -2.05403388e-02, 1.17686452e-03,
2.56746832e-02, 2.17353559e-02, -7.69020683e-03,
1.54447950e-02, -9.03814639e-04, 3.43660863e-03,
-5.37971533e-04, 9.42017522e-03, 1.42720920e-03,
1.17025084e-03, -5.00982277e-03, -1.53439701e-02,
-7.63874625e-04, -4.10637611e-02, 2.41131565e-02,
-1.95500843e-02, -2.98681684e-02, 1.23914953e-03,
-2.75100800e-02, -3.88428578e-03, -7.87537903e-03,
-1.53613857e-03, 1.47624077e-02, -4.86162981e-03,
-2.76731089e-03, 9.30828311e-03, -2.86531767e-02,
-1.16465064e-02, -2.30165990e-02, -7.71187242e-03,
2.00694116e-02, -5.16511843e-02, -1.98737477e-03,
-9.87738671e-03, -2.09922507e-17, 1.39146079e-02])
[24]:
p = bs.plot_cum3()
p.show()
[25]:
p = bs.plot_mag()
p.show()
[26]:
p = bs.plot_phase()
p.show()
Window Functions for Bispectrum¶
Bispectrum in Stingray now supports 2D windows to apply before calculating Bispectrum.
Windows currently available in Stingray include: 1. Uniform or Rectangular window 2. Parzen Window 3. Hamming Window 4. Hanning Window 5. Triangular Window 6. Blackmann’s Window 7. Welch Window 8. Flat-top Window
Windows are available in stingray.utils package and can be used by calling create_window function.
Now, we demonstrate Bispectrum with windows applied. By default, now window is applied.
[29]:
window = 'uniform'
bs = Bispectrum(lc,maxlag=25,window = window, scale ='unbiased')
[30]:
bs.window_name
[30]:
'uniform'
Plot Window¶
[32]:
cont = plt.contourf(bs.lags, bs.lags, bs.window, 100, cmap=plt.cm.Spectral_r)
plt.colorbar(cont)
plt.title('2D Uniform window')
[32]:
<matplotlib.text.Text at 0x1ac8b7e8e80>
[34]:
mag_plot = bs.plot_mag()
mag_plot.show()
[35]:
phase_plot = bs.plot_phase()
phase_plot.show()
Now, let us try some more window functions.
[36]:
bs = Bispectrum(lc, maxlag=25,window = 'hamming',scale='biased')
[37]:
bs.window_name
[37]:
'hamming'
[38]:
cont = plt.contourf(bs.lags, bs.lags, bs.window, 100, cmap=plt.cm.Spectral_r)
plt.colorbar(cont)
plt.title('2D Hamming window')
[38]:
<matplotlib.text.Text at 0x1ac8bbfe710>
[39]:
mag_plot = bs.plot_mag()
mag_plot.show()
[40]:
phase_plot = bs.plot_phase()
phase_plot.show()
Another Window demonstrated¶
[45]:
bs = Bispectrum(lc, maxlag = 25, window='triangular',scale='unbiased')
[46]:
bs.window_name
[46]:
'triangular'
[47]:
cont = plt.contourf(bs.lags, bs.lags, bs.window, 100, cmap=plt.cm.Spectral_r)
plt.colorbar(cont)
plt.title('2D Flat Top window')
[47]:
<matplotlib.text.Text at 0x1ac8bdc15f8>
[48]:
bs.plot_mag().show()
[52]:
bs.plot_phase().show()
