This notebook covers the basics of creating TransferFunction object, obtaining time and energy resolved responses, plotting them and using IO methods available. Finally, artificial responses are introduced which provide a way for quick testing.
Set up some useful libraries.
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import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
Import relevant stingray libraries.
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from stingray.simulator.transfer import TransferFunction
from stingray.simulator.transfer import simple_ir, relativistic_ir
A transfer function can be initialized by passing a 2-d array containing time across the first dimension and energy across the second. For example, if the 2-d array is defined by arr, then arr[1][5] defines a time of 5 units and energy of 1 unit.
For the purpose of this tutorial, we have stored a 2-d array in a text file named intensity.txt. The script to generate this file is explained in Data Preparation notebook.
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response = np.loadtxt('intensity.txt')
Initialize transfer function by passing the array defined above.
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transfer = TransferFunction(response)
transfer.data.shape
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By default, time and energy spacing across both axes are set to 1. However, they can be changed by supplying additional parameters dt and de.
The 2-d transfer function can be converted into a time-resolved/energy-averaged response.
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transfer.time_response()
This sets time parameter which can be accessed by transfer.time
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transfer.time[1:10]
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Additionally, energy interval over which to average, can be specified by specifying e0 and e1 parameters.
Energy-resolved/time-averaged response can be also be formed from 2-d transfer function.
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transfer.energy_response()
This sets energy parameter which can be accessed by transfer.energy
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transfer.energy[1:10]
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TransferFunction() creates plots of time-resolved, energy-resolved and 2-d responses. These plots can be saved by setting save parameter.
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transfer.plot(response='2d')
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transfer.plot(response='time')
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transfer.plot(response='energy')
By enabling save=True parameter, the plots can be also saved.
TransferFunction can be saved in pickle format and retrieved later.
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transfer.write('transfer.pickle')
Saved files can be read using static read() method.
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transfer_new = TransferFunction.read('transfer.pickle')
transfer_new.time[1:10]
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For quick testing, two helper impulse response models are provided.
simple_ir() allows to define an impulse response of constant height. It takes in time resolution starting time, width and intensity as arguments.
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s_ir = simple_ir(dt=0.125, start=10, width=5, intensity=0.1)
plt.plot(s_ir)
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A more realistic impulse response mimicking black hole dynamics can be created using relativistic_ir(). Its arguments are: time_resolution, primary peak time, secondary peak time, end time, primary peak value, secondary peak value, rise slope and decay slope. These paramaters are set to appropriate values by default.
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r_ir = relativistic_ir(dt=0.125)
plt.plot(r_ir)
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