Stingray Simulator (stingray.simulator
)¶
Introduction¶
stingray.simulator
provides a framework to simulate light curves with given variability distributions. In time series experiments, understanding the certainty is crucial to interpret the derived results in context of physical models. The simulator module provides tools to assess this uncertainty by simulating time series and spectral data.
Stingray simulator supports multiple methods to carry out these simulation. Light curves can be simulated through powerlaw spectrum, through a userdefined or predefined model, or through impulse responses. The module is designed in a way such that all these methods can be accessed using similar set of commands.
Note
stingray.simulator
is currently a workinprogress, and thus it is likely
there will still be API changes in later versions of Stingray. Backwards
compatibility support between versions will still be maintained as much as
possible, but new features and enhancements are coming in future versions.
Getting started¶
The examples here assume that the following libraries and modules have been imported:
>>> import numpy as np
>>> from stingray import Lightcurve, sampledata
>>> from stingray.simulator import simulator, models
Creating a Simulator Object¶
Stingray has a simulator class which can be used to instantiate a simulator object and subsequently, perform simulations. We can pass on arguments to this class class to set the properties of the desired light curve.
The simulator object can be instantiated as:
>>> sim = simulator.Simulator(N=1024, mean=0.5, dt=0.125)
Here, N
specifies the bins count of the simulated light curve, mean
specifies
the mean value, and dt
is the time resolution. Additional arguments can be
provided to specify the rms
of the simulated light curve, or to account for the
effect of red noise leakage.
Simulate Method¶
Stingray provides multiple ways to simulate a light curve. However, all these methods follow a common recipe:
>>> sim = simulator.Simulator(N=1024, mean=0.5, dt=0.125)
>>> lc = sim.simulate(2)
Using PowerLaw Spectrum¶
When only an integer argument (beta) is provided to the simulate
method, that integer defines the shape of the power law spectrum. Passing beta
as 1 gives a flickernoise distribution, while a beta of 2 generates a randomwalk distribution.
from matplotlib import rcParams
rcParams['font.family'] = 'sansserif'
rcParams['font.sansserif'] = ['Tahoma']
import matplotlib.pyplot as plt
from stingray.simulator import simulator
# Instantiate simulator object
sim = simulator.Simulator(N=1024, mean=0.5, dt=0.125)
# Specify beta value
lc = sim.simulate(2)
plt.plot(lc.counts, 'g')
plt.title('Randomwalk Distribution Simulation', fontsize='16')
plt.xlabel('Counts', fontsize='14', )
plt.ylabel('Flux', fontsize='14')
plt.show()
(Source code, png, hires.png, pdf)
Using Userdefined Model¶
Light curve can also be simulated using a userdefined spectrum, which can be passed on as a numpy array.
from matplotlib import rcParams
rcParams['font.family'] = 'sansserif'
rcParams['font.sansserif'] = ['Tahoma']
import matplotlib.pyplot as plt
from stingray.simulator import simulator
# Instantiate simulator object
sim = simulator.Simulator(N=1024, mean=0.5, dt=0.125)
# Define a spectrum
w = np.fft.rfftfreq(sim.N, d=sim.dt)[1:]
spectrum = np.power((1/w),2/2)
# Simulate
lc = sim.simulate(spectrum)
plt.plot(lc.counts, 'g')
plt.title('Userdefined Model Simulation', fontsize='16')
plt.xlabel('Counts', fontsize='14')
plt.ylabel('Flux', fontsize='14')
plt.show()
(Source code, png, hires.png, pdf)
Using Predefined Models¶
One of the predefined spectrum models can be used to simulate a light curve. In this case, model name and model parameters (as list iterable) need to be passed on as function arguments.
Using Impulse Response¶
In order to simulate a light curve using impulse response, we need the original light curve and impulse response. Stingray provides TransferFunction
class which can be used to obtain time and energy averaged impulse response by passing in a 2D intensity profile as the input. A detailed tutorial on obtaining impulse response is provided here.
Here, for the sake of simplicity, we use a simulated impulse response.
from matplotlib import rcParams
rcParams['font.family'] = 'sansserif'
rcParams['font.sansserif'] = ['Tahoma']
import matplotlib.pyplot as plt
from stingray import sampledata
from stingray.simulator import simulator
# Obtain a sample light curve
lc = sampledata.sample_data().counts
# Instantiate simulator object
sim = simulator.Simulator(N=1024, mean=0.5, dt=0.125)
# Obtain an artificial impulse response
ir = sim.relativistic_ir()
# Simulate
lc_new = sim.simulate(lc, ir)
plt.plot(lc_new.counts, 'g')
plt.title('Impulse Response based Simulation', fontsize='16')
plt.xlabel('Counts', fontsize='14')
plt.ylabel('Flux', fontsize='14')
plt.show()
(Source code, png, hires.png, pdf)
Since, the new light curve is produced by the convolution of original light curveand impulse response, its length is truncated by default for ease of analysis. This can be changed, however, by supplying an additional parameter full
. However, at times, we do not need to include lag delay portion in the output light curve. This can be done by changing the final function parameter to filtered
. For a more detailed analysis on lagfrequency spectrum, follow the notebook here.
Channel Simulation¶
The simulator
class provides the functionality to simulate light curves independently for each channel. This is useful, for example, when dealing with energy dependent impulse responses where we can create a di↵erent simulation channel for each energy range. The module provides options to count, retrieve and delete channels.:
>>> sim = simulator.Simulator(N=1024, mean=0.5, dt=0.125)
>>> sim.simulate_channel('3.5  4.5', 2)
>>> sim.count_channels()
1
>>> lc = sim.get_channel('3.5  4.5')
>>> sim.delete_channel('3.5  4.5')
Alternatively, assume that we have light curves in the simulated energy channels 3.5  4.5
, 4.5  5.5
and 5.5  6.5
. These channels can be retreived or deleted in single commands.
>>> sim.count_channels()
0
>>> sim.simulate_channel('3.5  4.5', 2)
>>> sim.simulate_channel('4.5  5.5', 2)
>>> sim.simulate_channel('5.5  6.5', 2)
>>> chans = sim.get_channels(['3.5  4.5','4.5  5.5','5.5  6.5'])
>>> sim.delete_channels(['3.5  4.5','4.5  5.5','5.5  6.5'])
Reference/API¶

class
stingray.simulator.simulator.
Simulator
(dt=1, N=1024, mean=0, rms=1, red_noise=1, random_state=None)[source]¶ Methods to simulate and visualize light curves.
Parameters: dt : int, default 1
time resolution of simulated light curve
N : int, default 1024
bins count of simulated light curve
mean : float, default 0
mean value of the simulated light curve
rms : float, default 1
fractional rms of the simulated light curve, actual rms is calculated by mean*rms
red_noise : int, default 1
multiple of real length of light curve, by which to simulate, to avoid red noise leakage
seed : int, default None
seed value for random processes

powerspectrum
(lc, seg_size=None)[source]¶ Make a powerspectrum of the simulated light curve.
Parameters: lc : lightcurve.Lightcurve object OR
iterable of lightcurve.Lightcurve objects The light curve data to be Fouriertransformed.
Returns: power : numpy.ndarray
The array of normalized squared absolute values of Fourier amplitudes

static
read
(filename, format_='pickle')[source]¶ Imports Simulator object.
Parameters: filename : str
Name of the Simulator object to be read.
format_ : str
Available option is ‘pickle.’
Returns: object :
Simulator
object

relativistic_ir
(t1=3, t2=4, t3=10, p1=1, p2=1.4, rise=0.6, decay=0.1)[source]¶ Construct a realistic impulse response considering the relativistic effects.
Parameters: t1 : int
primary peak time
t2 : int
secondary peak time
t3 : int
end time
p1 : float
value of primary peak
p2 : float
value of secondary peak
rise : float
slope of rising exponential from primary peak to secondary peak
decay : float
slope of decaying exponential from secondary peak to end time
Returns: h : numpy.ndarray
Constructed impulse response

simple_ir
(start=0, width=1000, intensity=1)[source]¶ Construct a simple impulse response using start time, width and scaling intensity. To create a delta impulse response, set width to 1.
Parameters: start : int
start time of impulse response
width : int
width of impulse response
intensity : float
scaling parameter to set the intensity of delayed emission corresponding to direct emission.
Returns: h : numpy.ndarray
Constructed impulse response

simulate
(*args)[source]¶ Simulate light curve generation using power spectrum or impulse response.
Parameters: args
See examples below.
Returns: lightCurve :
LightCurve
objectExamples
 x = simulate(beta)
 For generating a light curve using power law spectrum.
