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Last modified by rangeadm on 2025/04/23 16:13
From version 4.1
edited by sndueste
on 2020/02/05 15:18
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To version 9.1
edited by sndueste
on 2020/07/07 16:55
Change comment: There is no comment for this version

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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.
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.
2 2  
3 -The only input parameters are the spectral bandwidth and the pulse duration.
3 +The only input parameters are the center wavelength, spectral bandwidth and the pulse duration.
4 4  
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"]]
5 +Here you can find a small python script (by (% class="twikiNewLink" %)MartinB(%%)) implementing the partial coherence method as described in:
6 6  
7 -The pulse shapes in time AND corresponding spectral dstribution can be easily created with the Jupyter Notebook**[[attach:SASEPulseGenV2.ipynb]]**
7 +* **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"]]
8 8  
9 +\\
9 9  
10 -Some examples of results:
11 +The pulse shapes in time AND corresponding spectral distribution can be easily created with:
11 11  
13 +* (((
14 +a python script
15 +
16 +{{expand title="Click here to expand the script ..."}}
17 +import numpy as np
18 +import matplotlib.pyplot as plt
19 +
20 +def GetSASE(CentralEnergy, dE_FWHM, dt_FWHM, samples=0, Axis=True):
21 +h=4.135667662 #in eV*fs
22 +dE=dE_FWHM/2.355 #in eV, converts to sigma
23 +dt=dt_FWHM/2.355 #in fs, converts to sigma
24 +if samples == 0:
25 +samples=int(400.*dt*CentralEnergy/h)
26 +else:
27 +if (samples < 400.*dt*CentralEnergy/h):
28 +print("Number of samples is a little small, proceeding anyway. Got", samples, "prefer more than",400.*dt*CentralEnergy/h)
29 +
30 +EnAxis=np.linspace(0.,20.*CentralEnergy,num=samples)
31 +EnInput=np.zeros(samples, dtype=np.complex64)
32 +EnInput=np.exp(-(EnAxis-CentralEnergy)~*~*2/2./dE~*~*2+2*np.pi*1j*np.random.random(size=samples))
33 +En_FFT=np.fft.fft(EnInput)
34 +TAxis=np.fft.fftfreq(samples,d=(20.*CentralEnergy)/samples)*h
35 +TOutput=np.exp(-TAxis~*~*2/2./dt~*~*2)*En_FFT
36 +EnOutput=np.fft.ifft(TOutput)
37 +if (Axis):
38 +return EnAxis, EnOutput, TAxis, TOutput
39 +else:
40 +return EnOutput, TOutput
41 +
12 12  \\
13 13  
14 -[[image:attach:partia__coherence2.png]] or: [[image:attach:image2020-2-5_15-14-4.png||width="480"]]
44 +# set the main parameters here:
45 +CentralEnergy=80. # in eV
46 +bandwidth=0.5 # bandwidth in %
47 +dt_FWHM=30. # FWHM of the temporal duration on average
15 15  
49 +dE_FWHM=CentralEnergy/100 *bandwidth # calculate bandwidth of the spectrum in eV
50 +
51 +# calculate 3 SASE pulses
52 +EnAxis, EnOutput, TAxis, TOutput = GetSASE(CentralEnergy=CentralEnergy, dE_FWHM=dE_FWHM, dt_FWHM=dt_FWHM)
53 +EnAxis2, EnOutput2, TAxis2, TOutput2 = GetSASE(CentralEnergy=CentralEnergy, dE_FWHM=dE_FWHM, dt_FWHM=dt_FWHM)
54 +EnAxis3, EnOutput3, TAxis3, TOutput3 = GetSASE(CentralEnergy=CentralEnergy, dE_FWHM=dE_FWHM, dt_FWHM=dt_FWHM)
55 +
56 +
57 +# plot spectrum
58 +ax1 = plt.subplot(1, 2, 1)
59 +plt.plot(EnAxis,np.absolute(EnOutput),EnAxis2,np.absolute(EnOutput2),EnAxis3,np.absolute(EnOutput3) )
60 +plt.xlim(CentralEnergy-2.*dE_FWHM,CentralEnergy+2.*dE_FWHM)
61 +plt.title('Average pulse duration: %.1f fs' % dt_FWHM )
62 +ax1.set_xlabel('Photon energy in eV')
63 +ax1.set_ylabel('spectral intensity')
64 +
65 +# plot time structure
66 +ax1 =plt.subplot(1, 2, 2)
67 +plt.plot(TAxis,np.absolute(TOutput),TAxis2,np.absolute(TOutput2), TAxis3,np.absolute(TOutput3))
68 +plt.xlim(-2.*dt_FWHM,+2.*dt_FWHM)
69 +ax1.set_xlabel('time in fs')
70 +ax1.set_ylabel('pulse amplitude')
71 +
72 +plt.show()
73 +{{/expand}}
74 +)))
75 +* or the same as a Jupyter Notebook** [[attach:GenerateSASE.ipynb]] **
76 +
77 +==
78 +Some examples: ==
79 +
80 +//CentralEnergy=80 # in eV//
81 +
82 +//bandwidth=0.5 # bandwidth in %//
83 +
84 +//dt_FWHM=10, 30., 70  # FWHM of the temporal duration on average//
85 +
86 +
87 +
88 +
89 + [[image:attach:2020-07-07 16_51_14-Window.png||height="250"]]
90 +
16 16  \\
17 17  
93 +[[image:attach:2020-07-07 16_53_22-Window.png||height="250"]]
94 +
95 +[[image:attach:2020-07-07 16_52_27-Window.png||height="250"]]
96 +
18 18  \\
19 19  
20 20  \\
... ... @@ -21,10 +21,8 @@
21 21  
22 22  \\
23 23  
24 -Here are the screenshots of the script:
103 +\\
25 25  
26 -[[image:attach:partia__coherence1.png]]
27 -
28 28  \\
29 29  
30 30  \\