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1 | 1 | import numpy as np |
2 | 2 | import pylab as pl |
3 | | -import timeit |
4 | | -import dbrprop |
5 | 3 |
|
6 | 4 |
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7 | 5 | class DBRMirror: |
8 | | - def __init__(self, data, incidentwavelength): |
9 | | - if isinstance(data, str): |
10 | | - data = np.loadtxt(data) |
11 | | - self.n = data[:, 0] |
12 | | - self.d = np.array(np.concatenate(([0], data[1:-1, 1], [0]))) |
13 | | - self.r = self.reflection_coeff(incidentwavelength) |
14 | | - self.R = np.abs(self.r)**2 |
15 | | - self.GD = (self.r.imag * self.diff_omega(self.r.real, incidentwavelength) - self.r.real * self.diff_omega(self.r.imag, incidentwavelength))/self.R |
16 | | - self.GDD = self.diff_omega(self.GD, incidentwavelength) |
| 6 | + def __init__(self, data, incidentwavelength): |
| 7 | + if isinstance(data, str): |
| 8 | + data = np.loadtxt(data) |
| 9 | + if data.ndim == 1: |
| 10 | + self.n = np.loadtxt("dbr.txt")[:, 0] |
| 11 | + self.d = data |
| 12 | + else: |
| 13 | + self.n = data[:, 0] |
| 14 | + self.d = np.array(np.concatenate(([0], data[1:-1, 1], [0]))) |
| 15 | + self.r = self.reflection_coeff(incidentwavelength) |
| 16 | + self.R = np.abs(self.r)**2 |
| 17 | + self.GD = (self.r.imag * self.diff_omega(self.r.real, incidentwavelength) - self.r.real * self.diff_omega(self.r.imag, incidentwavelength))/self.R |
| 18 | + self.GDD = self.diff_omega(self.GD, incidentwavelength) |
| 19 | + |
| 20 | + def reflection_coeff(self, incidentwavelength): |
| 21 | + nm = (self.n[:-1] / self.n[1:]) |
| 22 | + general_transition_matrix = np.broadcast_to(np.eye(2, dtype= complex), (incidentwavelength.shape[0], 2, 2)) |
| 23 | + |
| 24 | + for i in range(0, self.n.shape[0]-1): |
| 25 | + phi = 2j * np.pi * self.n[i+1] * self.d[i+1] / incidentwavelength |
| 26 | + transition_matrix = np.array([[0.5 * (1 + nm[i]) * np.exp(-phi), 0.5 * (1 - nm[i]) * np.exp(phi)], |
| 27 | + [0.5 * (1 - nm[i]) * np.exp(-phi), 0.5 * (1 + nm[i]) * np.exp(phi)]]) |
| 28 | + |
| 29 | + transition_matrix = np.swapaxes(transition_matrix, 2, 0) |
| 30 | + transition_matrix = np.swapaxes(transition_matrix, 2, 1) |
| 31 | + general_transition_matrix = np.array(list(map(lambda x, y: np.dot(x, y), general_transition_matrix, transition_matrix))) |
| 32 | + |
| 33 | + r = - general_transition_matrix[:,1,0] / general_transition_matrix[:,1,1] |
| 34 | + return r |
| 35 | + |
| 36 | + def diff_omega(self, y, incidentwavelength): |
| 37 | + c = 299.792458 # in nm/fs |
| 38 | + return (-incidentwavelength ** 2 / (2 * np.pi * c)) * np.gradient(y, incidentwavelength) |
17 | 39 |
|
18 | | - def reflection_coeff(self, incidentwavelength): |
19 | | - nm = (self.n[:-1] / self.n[1:]) |
20 | | - r = np.empty(incidentwavelength. size) + 0j |
21 | | - general_transition_matrix = np.broadcast_to(np.eye(2, dtype= complex), (incidentwavelength.shape[0], 2, 2)) |
22 | | - |
23 | | - for i in range(0, self.n.shape[0]-1): |
24 | | - phi = 2j * np.pi * self.n[i+1] * self.d[i+1] / incidentwavelength |
25 | | - transition_matrix = np.array([[0.5 * (1 + nm[i]) * np.exp(-phi), 0.5 * (1 - nm[i]) * np.exp(phi)], |
26 | | - [0.5 * (1 - nm[i]) * np.exp(-phi), 0.5 * (1 + nm[i]) * np.exp(phi)]]) |
27 | | - |
28 | | - transition_matrix = np.swapaxes(transition_matrix, 2, 0) |
29 | | - transition_matrix = np.swapaxes(transition_matrix, 2, 1) |
30 | | - general_transition_matrix = np.array(list(map(lambda x, y: np.dot(x, y), general_transition_matrix, transition_matrix))) |
31 | | - |
32 | | - r = - general_transition_matrix[:,1,0] / general_transition_matrix[:,1,1] |
33 | | - return r |
34 | | - |
35 | | - def diff_omega(self, y, incidentwavelength): |
36 | | - c = 299.792458 # in nm/fs |
37 | | - return (-incidentwavelength ** 2 / (2 * np.pi * c)) * np.gradient(y, incidentwavelength) |
38 | | - |
39 | | - |
40 | | -start = timeit.default_timer() |
41 | | -wavelength = np.linspace(800, 1200, 1000) |
42 | | -NewMirror = DBRMirror("dbr.txt", wavelength) |
43 | | - |
44 | | -stop = timeit.default_timer() |
45 | | -print(stop - start) |
46 | | -sR = np.loadtxt("s245-R.txt", unpack=True) |
47 | | -sGD = np.loadtxt("s245-GD.txt", unpack=True) |
48 | | -sGDD = np.loadtxt("s245-GDD.txt", unpack=True) |
49 | | - |
50 | | - |
51 | | -f = pl.figure() |
52 | | -pl.plot(wavelength, NewMirror.R) |
53 | | -pl.plot(*sR) |
54 | | -pl.xlabel("$\lambda$") |
55 | | -pl.ylabel("R") |
56 | | -pl.xlim(950, 1150) |
57 | | -pl.show(block=False) |
58 | | -f.canvas.set_window_title("R") |
59 | | - |
60 | | -f = pl.figure() |
61 | | -pl.plot(wavelength, NewMirror.GD) |
62 | | -pl.plot(*sGD) |
63 | | -pl.xlabel("$\lambda$") |
64 | | -pl.ylabel("GD [fs]") |
65 | | -pl.xlim(950, 1150) |
66 | | -f.canvas.set_window_title("GD") |
67 | | - |
68 | | -f = pl.figure() |
69 | | -pl.plot(wavelength, NewMirror.GDD) |
70 | | -pl.plot(*sGDD) |
71 | | -pl.xlabel("$\lambda$") |
72 | | -pl.ylabel("GDD [fs$^2$]") |
73 | | -pl.xlim(1000, 1080) |
74 | | -pl.ylim(-1000, 0) |
75 | | -f.canvas.set_window_title("GDD") |
76 | | - |
77 | | -pl.show() |
78 | 40 |
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79 | 41 |
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