Skip to Content
Last modified by rangeadm on 2025/04/23 16:13
From version 13.1
edited by cpassow
on 2020/09/22 14:05
Change comment: There is no comment for this version
To version 7.1
edited by sndueste
on 2020/07/03 11:25
Change comment: There is no comment for this version

Summary

Details

Page properties
Author
... ... @@ -1,1 +1,1 @@
1 -XWiki.cpassow
1 +XWiki.sndueste
Content
... ... @@ -1,47 +1,92 @@
1 -In order to simulate the temporal and spectral distribution of SASE pulses there is an easy way based random fluctuations filtered spectrally and temporally.
1 +In order to simulate the temporal and spectral distribution of SASE pulses there is an easy way based random fluctuations filtered spectraly and temporally.
2 2  
3 -The only input parameters are the center wavelength, spectral bandwidth and the pulse duration.
3 +The only input parameters are the spectral bandwidth and the pulse duration.
4 4  
5 -Below you can find a python implementation (by (% class="twikiNewLink" %)MartinB(%%)) of the partial coherence method as described in:
5 +Here you can find a small python script (by (% class="twikiNewLink" %)MartinB(%%)) implementing the partial coherence methode as described in Thomas Pfeifer et al. //Partial-coherence method to model experimental free-electron laser pulse statistics,// Opt. Lett. 35, 3441-3443 (2010); [[link to the paper>>url:http://dx.doi.org/10.1364/OL.35.003441||shape="rect"]]
6 6  
7 -(% style="margin-left: 60.0px;" %)
8 -**Thomas Pfeifer et al. //Partial-coherence method to model experimental free-electron laser pulse statistics,// Opt. Lett. 35, 3441-3443 (2010);** [[link to the paper>>url:http://dx.doi.org/10.1364/OL.35.003441||shape="rect"]]
7 +The pulse shapes in time AND corresponding spectral dstribution can be easily created with:
9 9  
10 -==
11 -Examples: ==
9 +* (((
10 +a python script
12 12  
13 -{{markdown LinkifyHeaders="false"}}
14 -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fgitlab.desy.de%2Fchristopher.passow%2Fsase-pulses/master?filepath=simulating_SASE_pulses.ipynb)
12 +{{expand title="Click here to expand the script ..."}}
13 +import numpy as np
14 +import matplotlib.pyplot as plt
15 15  
16 +def GetSASE(CentralEnergy, dE_FWHM, dt_FWHM, samples=0, Axis=True):
17 +h=4.135667662 #in eV*fs
18 +dE=dE_FWHM/2.355 #in eV, converts to sigma
19 +dt=dt_FWHM/2.355 #in fs, converts to sigma
20 +if samples == 0:
21 +samples=int(400.*dt*CentralEnergy/h)
22 +else:
23 +if (samples < 400.*dt*CentralEnergy/h):
24 +print("Number of samples is a little small, proceeding anyway. Got", samples, "prefer more than",400.*dt*CentralEnergy/h)
16 16  
17 -{{/markdown}}
26 +EnAxis=np.linspace(0.,20.*CentralEnergy,num=samples)
27 +EnInput=np.zeros(samples, dtype=np.complex64)
28 +#for i in range(samples):
29 +EnInput=np.exp(-(EnAxis-CentralEnergy)~*~*2/2./dE~*~*2+2*np.pi*1j*np.random.random(size=samples))
30 +En_FFT=np.fft.fft(EnInput)
31 +TAxis=np.fft.fftfreq(samples,d=(20.*CentralEnergy)/samples)*h
32 +TOutput=np.exp(-TAxis~*~*2/2./dt~*~*2)*En_FFT
33 +EnOutput=np.fft.ifft(TOutput)
34 +if (Axis):
35 +return EnAxis, EnOutput, TAxis, TOutput
36 +else:
37 +return EnOutput, TOutput
18 18  
19 19  \\
20 20  
21 -//CentralEnergy=80 # in eV//
41 +# set the main parameters here:
42 +CentralEnergy=80. # in eV
43 +bandwidth=0.5 # bandwidth in %
44 +dt_FWHM=30. # FWHM of the temporal duration on average
22 22  
23 -//bandwidth=0.5 # bandwidth in %//
46 +dE_FWHM=CentralEnergy/100 *bandwidth # calculate bandwidth of the spectrum in eV
24 24  
25 -//dt_FWHM=10, 30., 70  # FWHM of the temporal duration on average//
48 +# calculate 3 SASE pulses
49 +EnAxis, EnOutput, TAxis, TOutput = GetSASE(CentralEnergy=CentralEnergy, dE_FWHM=dE_FWHM, dt_FWHM=dt_FWHM)
50 +EnAxis2, EnOutput2, TAxis2, TOutput2 = GetSASE(CentralEnergy=CentralEnergy, dE_FWHM=dE_FWHM, dt_FWHM=dt_FWHM)
51 +EnAxis3, EnOutput3, TAxis3, TOutput3 = GetSASE(CentralEnergy=CentralEnergy, dE_FWHM=dE_FWHM, dt_FWHM=dt_FWHM)
26 26  
27 27  
28 -
54 +# plot spectrum
55 +ax1 = plt.subplot(1, 2, 1)
56 +plt.plot(EnAxis,np.absolute(EnOutput),EnAxis2,np.absolute(EnOutput2),EnAxis3,np.absolute(EnOutput3) )
57 +plt.xlim(CentralEnergy-2.*dE_FWHM,CentralEnergy+2.*dE_FWHM)
58 +plt.title('Average pulse duration: %.1f fs' % dt_FWHM )
59 +ax1.set_xlabel('Photon energy in eV')
60 +ax1.set_ylabel('spectral intensity')
29 29  
30 - [[image:attach:2020-07-07 16_51_14-Window.png||height="250"]]
62 +# plot time structure
63 +ax1 =plt.subplot(1, 2, 2)
64 +plt.plot(TAxis,np.absolute(TOutput),TAxis2,np.absolute(TOutput2), TAxis3,np.absolute(TOutput3))
65 +plt.xlim(-2.*dt_FWHM,+2.*dt_FWHM)
66 +ax1.set_xlabel('time in fs')
67 +ax1.set_ylabel('pulse amplitude')
31 31  
69 +plt.show()
70 +{{/expand}}
71 +)))
72 +* a Jupyter Notebook** [[attach:GenerateSASE.ipynb]] **
73 +
74 +
75 +Some examples of results:
76 +
32 32  \\
33 33  
34 -[[image:attach:2020-07-07 16_53_22-Window.png||height="250"]]
79 +[[image:attach:partia__coherence2.png]] or: [[image:attach:image2020-2-5_15-14-4.png||width="480"]]
35 35  
36 -[[image:attach:2020-07-07 16_52_27-Window.png||height="250"]]
81 +\\
37 37  
38 38  \\
39 39  
40 40  \\
41 41  
42 -[[attach:GenerateSASE.ipynb]]
87 +\\
43 43  
44 -[[attach:GenerateSASE.py]]
89 +\\
45 45  
46 46  \\
47 47