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Working gain pattern class
This commit is contained in:
bareissvincent
parent
89c327acd2
commit
bf58484dfa
15
.vscode/launch.json
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15
.vscode/launch.json
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{
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// Use IntelliSense to learn about possible attributes.
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// Hover to view descriptions of existing attributes.
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// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
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"version": "0.2.0",
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"configurations": [
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{
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"name": "Python Debugger: Current File",
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"type": "debugpy",
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"request": "launch",
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"program": "testing.py",
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"console": "integratedTerminal"
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}
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]
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}
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365
data/gain_pattern/farfield_2_45GHz_exite_all_Phi_0.txt
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data/gain_pattern/farfield_2_45GHz_exite_all_Phi_0.txt
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#Parameters = {hull_eps=7}
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farfield (f=2.45)
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Theta [deg.] Phi [deg.] Abs(Dir.)[dBi ] Abs(Theta)[dBi ] Phase(Theta)[deg.] Abs(Phi )[dBi ] Phase(Phi )[deg.] Ax.Ratio[dB ]
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------------------------------------------------------------------------------------------------------------------------------------------------------
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0.000 0.000 -6.266e-02 -1.493e+01 0.070 -2.066e-01 333.311 2.189e+01
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|
1.000 0.000 -8.928e-02 -1.548e+01 0.977 -2.166e-01 333.744 2.226e+01
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|
2.000 0.000 -1.713e-01 -1.604e+01 1.722 -2.852e-01 334.097 2.261e+01
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||||||
|
3.000 0.000 -3.105e-01 -1.661e+01 2.259 -4.136e-01 334.372 2.296e+01
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|
4.000 0.000 -5.095e-01 -1.717e+01 2.533 -6.041e-01 334.571 2.330e+01
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|
5.000 0.000 -7.718e-01 -1.774e+01 2.481 -8.599e-01 334.692 2.365e+01
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|
6.000 0.000 -1.102e+00 -1.830e+01 2.033 -1.186e+00 334.729 2.402e+01
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||||||
|
7.000 0.000 -1.506e+00 -1.884e+01 1.115 -1.587e+00 334.674 2.441e+01
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||||||
|
8.000 0.000 -1.992e+00 -1.936e+01 359.653 -2.073e+00 334.511 2.485e+01
|
||||||
|
9.000 0.000 -2.571e+00 -1.982e+01 357.586 -2.654e+00 334.219 2.534e+01
|
||||||
|
10.000 0.000 -3.256e+00 -2.023e+01 354.880 -3.344e+00 333.762 2.590e+01
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||||||
|
11.000 0.000 -4.066e+00 -2.054e+01 351.542 -4.165e+00 333.088 2.654e+01
|
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|
12.000 0.000 -5.025e+00 -2.073e+01 347.646 -5.144e+00 332.110 2.725e+01
|
||||||
|
13.000 0.000 -6.169e+00 -2.079e+01 343.336 -6.322e+00 330.684 2.795e+01
|
||||||
|
14.000 0.000 -7.545e+00 -2.069e+01 338.816 -7.760e+00 328.547 2.833e+01
|
||||||
|
15.000 0.000 -9.215e+00 -2.045e+01 334.318 -9.555e+00 325.179 2.754e+01
|
||||||
|
16.000 0.000 -1.125e+01 -2.007e+01 330.057 -1.186e+01 319.405 2.406e+01
|
||||||
|
17.000 0.000 -1.361e+01 -1.958e+01 326.195 -1.487e+01 308.139 1.725e+01
|
||||||
|
18.000 0.000 -1.567e+01 -1.900e+01 322.824 -1.839e+01 282.621 8.759e+00
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||||||
|
19.000 0.000 -1.569e+01 -1.836e+01 319.975 -1.907e+01 236.712 1.247e+00
|
||||||
|
20.000 0.000 -1.354e+01 -1.769e+01 317.632 -1.564e+01 204.529 4.172e+00
|
||||||
|
21.000 0.000 -1.098e+01 -1.701e+01 315.752 -1.222e+01 190.425 7.674e+00
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||||||
|
22.000 0.000 -8.724e+00 -1.632e+01 314.278 -9.554e+00 183.502 1.001e+01
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||||||
|
23.000 0.000 -6.836e+00 -1.564e+01 313.154 -7.449e+00 179.566 1.164e+01
|
||||||
|
24.000 0.000 -5.246e+00 -1.498e+01 312.324 -5.735e+00 177.091 1.282e+01
|
||||||
|
25.000 0.000 -3.890e+00 -1.433e+01 311.740 -4.301e+00 175.428 1.370e+01
|
||||||
|
26.000 0.000 -2.719e+00 -1.371e+01 311.358 -3.079e+00 174.260 1.438e+01
|
||||||
|
27.000 0.000 -1.696e+00 -1.311e+01 311.145 -2.021e+00 173.414 1.491e+01
|
||||||
|
28.000 0.000 -7.952e-01 -1.254e+01 311.069 -1.096e+00 172.792 1.533e+01
|
||||||
|
29.000 0.000 2.955e-03 -1.200e+01 311.107 -2.802e-01 172.330 1.567e+01
|
||||||
|
30.000 0.000 7.133e-01 -1.148e+01 311.238 4.427e-01 171.987 1.595e+01
|
||||||
|
31.000 0.000 1.347e+00 -1.098e+01 311.445 1.086e+00 171.736 1.617e+01
|
||||||
|
32.000 0.000 1.914e+00 -1.051e+01 311.716 1.658e+00 171.558 1.635e+01
|
||||||
|
33.000 0.000 2.421e+00 -1.007e+01 312.039 2.168e+00 171.438 1.650e+01
|
||||||
|
34.000 0.000 2.872e+00 -9.646e+00 312.406 2.622e+00 171.368 1.661e+01
|
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|
35.000 0.000 3.275e+00 -9.249e+00 312.808 3.025e+00 171.339 1.670e+01
|
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|
36.000 0.000 3.631e+00 -8.875e+00 313.240 3.380e+00 171.347 1.677e+01
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|
37.000 0.000 3.944e+00 -8.524e+00 313.697 3.691e+00 171.387 1.682e+01
|
||||||
|
38.000 0.000 4.217e+00 -8.194e+00 314.175 3.960e+00 171.456 1.684e+01
|
||||||
|
39.000 0.000 4.452e+00 -7.886e+00 314.672 4.190e+00 171.552 1.686e+01
|
||||||
|
40.000 0.000 4.650e+00 -7.599e+00 315.183 4.383e+00 171.674 1.685e+01
|
||||||
|
41.000 0.000 4.813e+00 -7.332e+00 315.708 4.540e+00 171.821 1.683e+01
|
||||||
|
42.000 0.000 4.943e+00 -7.086e+00 316.245 4.662e+00 171.993 1.680e+01
|
||||||
|
43.000 0.000 5.039e+00 -6.859e+00 316.792 4.749e+00 172.189 1.675e+01
|
||||||
|
44.000 0.000 5.104e+00 -6.651e+00 317.349 4.804e+00 172.410 1.669e+01
|
||||||
|
45.000 0.000 5.136e+00 -6.463e+00 317.914 4.825e+00 172.657 1.661e+01
|
||||||
|
46.000 0.000 5.138e+00 -6.293e+00 318.488 4.813e+00 172.932 1.651e+01
|
||||||
|
47.000 0.000 5.107e+00 -6.141e+00 319.070 4.769e+00 173.236 1.640e+01
|
||||||
|
48.000 0.000 5.046e+00 -6.008e+00 319.660 4.691e+00 173.572 1.628e+01
|
||||||
|
49.000 0.000 4.953e+00 -5.893e+00 320.258 4.580e+00 173.942 1.613e+01
|
||||||
|
50.000 0.000 4.829e+00 -5.795e+00 320.864 4.435e+00 174.351 1.597e+01
|
||||||
|
51.000 0.000 4.672e+00 -5.715e+00 321.479 4.255e+00 174.803 1.578e+01
|
||||||
|
52.000 0.000 4.481e+00 -5.652e+00 322.101 4.039e+00 175.303 1.557e+01
|
||||||
|
53.000 0.000 4.257e+00 -5.607e+00 322.733 3.785e+00 175.859 1.534e+01
|
||||||
|
54.000 0.000 3.998e+00 -5.579e+00 323.374 3.491e+00 176.478 1.507e+01
|
||||||
|
55.000 0.000 3.703e+00 -5.568e+00 324.025 3.156e+00 177.171 1.478e+01
|
||||||
|
56.000 0.000 3.370e+00 -5.574e+00 324.687 2.778e+00 177.952 1.445e+01
|
||||||
|
57.000 0.000 2.998e+00 -5.597e+00 325.360 2.352e+00 178.836 1.408e+01
|
||||||
|
58.000 0.000 2.585e+00 -5.638e+00 326.045 1.876e+00 179.844 1.366e+01
|
||||||
|
59.000 0.000 2.129e+00 -5.695e+00 326.743 1.345e+00 181.005 1.319e+01
|
||||||
|
60.000 0.000 1.628e+00 -5.769e+00 327.454 7.549e-01 182.352 1.266e+01
|
||||||
|
61.000 0.000 1.080e+00 -5.861e+00 328.179 9.872e-02 183.935 1.207e+01
|
||||||
|
62.000 0.000 4.841e-01 -5.970e+00 328.921 -6.300e-01 185.817 1.139e+01
|
||||||
|
63.000 0.000 -1.608e-01 -6.096e+00 329.678 -1.439e+00 188.087 1.063e+01
|
||||||
|
64.000 0.000 -8.534e-01 -6.240e+00 330.454 -2.337e+00 190.871 9.761e+00
|
||||||
|
65.000 0.000 -1.590e+00 -6.401e+00 331.248 -3.331e+00 194.346 8.781e+00
|
||||||
|
66.000 0.000 -2.360e+00 -6.580e+00 332.062 -4.426e+00 198.772 7.674e+00
|
||||||
|
67.000 0.000 -3.148e+00 -6.777e+00 332.897 -5.616e+00 204.522 6.432e+00
|
||||||
|
68.000 0.000 -3.921e+00 -6.992e+00 333.755 -6.871e+00 212.111 5.063e+00
|
||||||
|
69.000 0.000 -4.634e+00 -7.226e+00 334.636 -8.107e+00 222.157 3.614e+00
|
||||||
|
70.000 0.000 -5.222e+00 -7.477e+00 335.543 -9.148e+00 235.106 2.314e+00
|
||||||
|
71.000 0.000 -5.618e+00 -7.748e+00 336.477 -9.733e+00 250.545 2.081e+00
|
||||||
|
72.000 0.000 -5.768e+00 -8.038e+00 337.439 -9.671e+00 266.664 3.405e+00
|
||||||
|
73.000 0.000 -5.661e+00 -8.347e+00 338.430 -9.022e+00 281.158 5.304e+00
|
||||||
|
74.000 0.000 -5.333e+00 -8.675e+00 339.453 -8.035e+00 292.820 7.345e+00
|
||||||
|
75.000 0.000 -4.850e+00 -9.023e+00 340.508 -6.943e+00 301.711 9.380e+00
|
||||||
|
76.000 0.000 -4.279e+00 -9.391e+00 341.596 -5.879e+00 308.410 1.133e+01
|
||||||
|
77.000 0.000 -3.678e+00 -9.780e+00 342.719 -4.900e+00 313.506 1.314e+01
|
||||||
|
78.000 0.000 -3.083e+00 -1.019e+01 343.876 -4.024e+00 317.451 1.479e+01
|
||||||
|
79.000 0.000 -2.519e+00 -1.062e+01 345.068 -3.250e+00 320.564 1.627e+01
|
||||||
|
80.000 0.000 -1.999e+00 -1.107e+01 346.295 -2.573e+00 323.063 1.757e+01
|
||||||
|
81.000 0.000 -1.529e+00 -1.154e+01 347.556 -1.986e+00 325.102 1.871e+01
|
||||||
|
82.000 0.000 -1.114e+00 -1.203e+01 348.847 -1.481e+00 326.788 1.970e+01
|
||||||
|
83.000 0.000 -7.527e-01 -1.254e+01 350.165 -1.050e+00 328.196 2.055e+01
|
||||||
|
84.000 0.000 -4.458e-01 -1.308e+01 351.504 -6.895e-01 329.383 2.129e+01
|
||||||
|
85.000 0.000 -1.924e-01 -1.363e+01 352.856 -3.937e-01 330.389 2.194e+01
|
||||||
|
86.000 0.000 8.526e-03 -1.421e+01 354.209 -1.592e-01 331.245 2.251e+01
|
||||||
|
87.000 0.000 1.576e-01 -1.480e+01 355.550 1.681e-02 331.973 2.302e+01
|
||||||
|
88.000 0.000 2.554e-01 -1.542e+01 356.857 1.363e-01 332.590 2.348e+01
|
||||||
|
89.000 0.000 3.021e-01 -1.606e+01 358.104 2.004e-01 333.109 2.391e+01
|
||||||
|
90.000 0.000 2.973e-01 -1.671e+01 359.257 2.099e-01 333.540 2.431e+01
|
||||||
|
91.000 0.000 2.404e-01 -1.738e+01 0.274 1.646e-01 333.887 2.471e+01
|
||||||
|
92.000 0.000 1.299e-01 -1.807e+01 1.100 6.370e-02 334.154 2.511e+01
|
||||||
|
93.000 0.000 -3.597e-02 -1.877e+01 1.668 -9.448e-02 334.342 2.553e+01
|
||||||
|
94.000 0.000 -2.601e-01 -1.948e+01 1.893 -3.123e-01 334.450 2.598e+01
|
||||||
|
95.000 0.000 -5.460e-01 -2.020e+01 1.676 -5.933e-01 334.471 2.648e+01
|
||||||
|
96.000 0.000 -8.986e-01 -2.091e+01 0.900 -9.421e-01 334.398 2.704e+01
|
||||||
|
97.000 0.000 -1.324e+00 -2.160e+01 359.438 -1.365e+00 334.217 2.771e+01
|
||||||
|
98.000 0.000 -1.830e+00 -2.224e+01 357.164 -1.870e+00 333.907 2.851e+01
|
||||||
|
99.000 0.000 -2.427e+00 -2.281e+01 353.980 -2.467e+00 333.439 2.951e+01
|
||||||
|
100.000 0.000 -3.130e+00 -2.326e+01 349.861 -3.172e+00 332.767 3.080e+01
|
||||||
|
101.000 0.000 -3.955e+00 -2.355e+01 344.904 -4.003e+00 331.824 3.254e+01
|
||||||
|
102.000 0.000 -4.928e+00 -2.363e+01 339.361 -4.987e+00 330.499 3.500e+01
|
||||||
|
103.000 0.000 -6.082e+00 -2.348e+01 333.615 -6.162e+00 328.613 3.866e+01
|
||||||
|
104.000 0.000 -7.460e+00 -2.311e+01 328.087 -7.580e+00 325.841 4.000e+01
|
||||||
|
105.000 0.000 -9.118e+00 -2.255e+01 323.118 -9.319e+00 321.561 4.000e+01
|
||||||
|
106.000 0.000 -1.110e+01 -2.186e+01 318.904 -1.148e+01 314.454 3.334e+01
|
||||||
|
107.000 0.000 -1.332e+01 -2.109e+01 315.499 -1.412e+01 301.463 2.079e+01
|
||||||
|
108.000 0.000 -1.511e+01 -2.026e+01 312.857 -1.670e+01 276.378 1.051e+01
|
||||||
|
109.000 0.000 -1.498e+01 -1.942e+01 310.884 -1.692e+01 239.544 3.846e+00
|
||||||
|
110.000 0.000 -1.298e+01 -1.859e+01 309.470 -1.438e+01 211.779 4.381e+00
|
||||||
|
111.000 0.000 -1.059e+01 -1.777e+01 308.511 -1.152e+01 197.210 7.200e+00
|
||||||
|
112.000 0.000 -8.451e+00 -1.697e+01 307.914 -9.109e+00 189.352 9.354e+00
|
||||||
|
113.000 0.000 -6.633e+00 -1.621e+01 307.606 -7.141e+00 184.670 1.093e+01
|
||||||
|
114.000 0.000 -5.091e+00 -1.548e+01 307.523 -5.508e+00 181.644 1.211e+01
|
||||||
|
115.000 0.000 -3.769e+00 -1.478e+01 307.616 -4.127e+00 179.572 1.302e+01
|
||||||
|
116.000 0.000 -2.623e+00 -1.411e+01 307.848 -2.943e+00 178.092 1.373e+01
|
||||||
|
117.000 0.000 -1.620e+00 -1.347e+01 308.187 -1.913e+00 177.005 1.430e+01
|
||||||
|
118.000 0.000 -7.349e-01 -1.287e+01 308.609 -1.009e+00 176.192 1.476e+01
|
||||||
|
119.000 0.000 5.002e-02 -1.229e+01 309.095 -2.110e-01 175.576 1.513e+01
|
||||||
|
120.000 0.000 7.493e-01 -1.175e+01 309.631 4.977e-01 175.108 1.544e+01
|
||||||
|
121.000 0.000 1.374e+00 -1.123e+01 310.205 1.129e+00 174.755 1.570e+01
|
||||||
|
122.000 0.000 1.933e+00 -1.074e+01 310.808 1.692e+00 174.491 1.591e+01
|
||||||
|
123.000 0.000 2.433e+00 -1.028e+01 311.432 2.194e+00 174.302 1.608e+01
|
||||||
|
124.000 0.000 2.879e+00 -9.842e+00 312.072 2.640e+00 174.172 1.623e+01
|
||||||
|
125.000 0.000 3.276e+00 -9.430e+00 312.724 3.037e+00 174.095 1.634e+01
|
||||||
|
126.000 0.000 3.629e+00 -9.043e+00 313.384 3.387e+00 174.061 1.644e+01
|
||||||
|
127.000 0.000 3.939e+00 -8.678e+00 314.050 3.694e+00 174.067 1.650e+01
|
||||||
|
128.000 0.000 4.210e+00 -8.336e+00 314.719 3.961e+00 174.108 1.655e+01
|
||||||
|
129.000 0.000 4.443e+00 -8.016e+00 315.391 4.189e+00 174.182 1.659e+01
|
||||||
|
130.000 0.000 4.640e+00 -7.718e+00 316.064 4.380e+00 174.286 1.660e+01
|
||||||
|
131.000 0.000 4.804e+00 -7.440e+00 316.737 4.536e+00 174.418 1.660e+01
|
||||||
|
132.000 0.000 4.934e+00 -7.183e+00 317.410 4.658e+00 174.580 1.658e+01
|
||||||
|
133.000 0.000 5.032e+00 -6.945e+00 318.082 4.747e+00 174.769 1.655e+01
|
||||||
|
134.000 0.000 5.098e+00 -6.727e+00 318.754 4.803e+00 174.986 1.650e+01
|
||||||
|
135.000 0.000 5.134e+00 -6.528e+00 319.425 4.827e+00 175.232 1.643e+01
|
||||||
|
136.000 0.000 5.139e+00 -6.348e+00 320.096 4.819e+00 175.509 1.635e+01
|
||||||
|
137.000 0.000 5.114e+00 -6.185e+00 320.766 4.779e+00 175.817 1.625e+01
|
||||||
|
138.000 0.000 5.058e+00 -6.041e+00 321.436 4.707e+00 176.160 1.613e+01
|
||||||
|
139.000 0.000 4.972e+00 -5.915e+00 322.106 4.602e+00 176.540 1.599e+01
|
||||||
|
140.000 0.000 4.855e+00 -5.806e+00 322.777 4.465e+00 176.961 1.583e+01
|
||||||
|
141.000 0.000 4.706e+00 -5.714e+00 323.448 4.293e+00 177.427 1.565e+01
|
||||||
|
142.000 0.000 4.526e+00 -5.639e+00 324.121 4.087e+00 177.944 1.545e+01
|
||||||
|
143.000 0.000 4.314e+00 -5.582e+00 324.795 3.845e+00 178.518 1.522e+01
|
||||||
|
144.000 0.000 4.068e+00 -5.541e+00 325.472 3.565e+00 179.158 1.496e+01
|
||||||
|
145.000 0.000 3.787e+00 -5.517e+00 326.152 3.245e+00 179.875 1.467e+01
|
||||||
|
146.000 0.000 3.471e+00 -5.509e+00 326.834 2.884e+00 180.680 1.434e+01
|
||||||
|
147.000 0.000 3.118e+00 -5.518e+00 327.521 2.479e+00 181.590 1.397e+01
|
||||||
|
148.000 0.000 2.727e+00 -5.544e+00 328.212 2.026e+00 182.625 1.355e+01
|
||||||
|
149.000 0.000 2.295e+00 -5.586e+00 328.907 1.523e+00 183.812 1.309e+01
|
||||||
|
150.000 0.000 1.822e+00 -5.645e+00 329.608 9.641e-01 185.184 1.256e+01
|
||||||
|
151.000 0.000 1.306e+00 -5.720e+00 330.314 3.455e-01 186.784 1.197e+01
|
||||||
|
152.000 0.000 7.456e-01 -5.812e+00 331.027 -3.387e-01 188.673 1.130e+01
|
||||||
|
153.000 0.000 1.414e-01 -5.920e+00 331.747 -1.094e+00 190.931 1.055e+01
|
||||||
|
154.000 0.000 -5.055e-01 -6.045e+00 332.475 -1.928e+00 193.665 9.707e+00
|
||||||
|
155.000 0.000 -1.191e+00 -6.187e+00 333.210 -2.844e+00 197.030 8.756e+00
|
||||||
|
156.000 0.000 -1.907e+00 -6.346e+00 333.953 -3.844e+00 201.239 7.687e+00
|
||||||
|
157.000 0.000 -2.639e+00 -6.523e+00 334.706 -4.923e+00 206.585 6.494e+00
|
||||||
|
158.000 0.000 -3.363e+00 -6.716e+00 335.468 -6.055e+00 213.463 5.173e+00
|
||||||
|
159.000 0.000 -4.040e+00 -6.927e+00 336.239 -7.177e+00 222.331 3.743e+00
|
||||||
|
160.000 0.000 -4.621e+00 -7.155e+00 337.020 -8.167e+00 233.554 2.299e+00
|
||||||
|
161.000 0.000 -5.049e+00 -7.401e+00 337.811 -8.836e+00 246.997 1.440e+00
|
||||||
|
162.000 0.000 -5.275e+00 -7.666e+00 338.613 -9.009e+00 261.589 2.403e+00
|
||||||
|
163.000 0.000 -5.276e+00 -7.948e+00 339.424 -8.653e+00 275.581 4.175e+00
|
||||||
|
164.000 0.000 -5.067e+00 -8.249e+00 340.244 -7.912e+00 287.593 6.122e+00
|
||||||
|
165.000 0.000 -4.693e+00 -8.569e+00 341.074 -6.981e+00 297.209 8.100e+00
|
||||||
|
166.000 0.000 -4.210e+00 -8.908e+00 341.911 -6.008e+00 304.681 1.004e+01
|
||||||
|
167.000 0.000 -3.673e+00 -9.266e+00 342.756 -5.075e+00 310.466 1.190e+01
|
||||||
|
168.000 0.000 -3.122e+00 -9.644e+00 343.604 -4.217e+00 314.986 1.364e+01
|
||||||
|
169.000 0.000 -2.585e+00 -1.004e+01 344.455 -3.445e+00 318.567 1.526e+01
|
||||||
|
170.000 0.000 -2.079e+00 -1.046e+01 345.304 -2.761e+00 321.449 1.674e+01
|
||||||
|
171.000 0.000 -1.615e+00 -1.089e+01 346.148 -2.160e+00 323.800 1.809e+01
|
||||||
|
172.000 0.000 -1.197e+00 -1.135e+01 346.980 -1.638e+00 325.743 1.932e+01
|
||||||
|
173.000 0.000 -8.289e-01 -1.183e+01 347.793 -1.188e+00 327.365 2.042e+01
|
||||||
|
174.000 0.000 -5.108e-01 -1.233e+01 348.578 -8.064e-01 328.732 2.143e+01
|
||||||
|
175.000 0.000 -2.429e-01 -1.285e+01 349.323 -4.882e-01 329.890 2.236e+01
|
||||||
|
176.000 0.000 -2.485e-02 -1.338e+01 350.014 -2.301e-01 330.876 2.321e+01
|
||||||
|
177.000 0.000 1.437e-01 -1.394e+01 350.633 -2.931e-02 331.716 2.401e+01
|
||||||
|
178.000 0.000 2.632e-01 -1.451e+01 351.157 1.161e-01 332.431 2.476e+01
|
||||||
|
179.000 0.000 3.336e-01 -1.511e+01 351.559 2.077e-01 333.037 2.550e+01
|
||||||
|
180.000 0.000 3.549e-01 -1.571e+01 351.805 2.461e-01 333.544 2.624e+01
|
||||||
|
179.000 180.000 3.263e-01 -1.633e+01 171.858 2.316e-01 153.961 2.698e+01
|
||||||
|
178.000 180.000 2.469e-01 -1.696e+01 171.669 1.635e-01 154.293 2.777e+01
|
||||||
|
177.000 180.000 1.150e-01 -1.759e+01 171.184 4.077e-02 154.543 2.863e+01
|
||||||
|
176.000 180.000 -7.152e-02 -1.822e+01 170.343 -1.386e-01 154.711 2.959e+01
|
||||||
|
175.000 180.000 -3.158e-01 -1.883e+01 169.084 -3.774e-01 154.793 3.072e+01
|
||||||
|
174.000 180.000 -6.216e-01 -1.941e+01 167.348 -6.794e-01 154.785 3.209e+01
|
||||||
|
173.000 180.000 -9.939e-01 -1.994e+01 165.089 -1.050e+00 154.675 3.386e+01
|
||||||
|
172.000 180.000 -1.439e+00 -2.040e+01 162.289 -1.495e+00 154.449 3.632e+01
|
||||||
|
171.000 180.000 -1.966e+00 -2.077e+01 158.977 -2.024e+00 154.083 4.000e+01
|
||||||
|
170.000 180.000 -2.585e+00 -2.101e+01 155.242 -2.648e+00 153.544 4.000e+01
|
||||||
|
169.000 180.000 -3.309e+00 -2.112e+01 151.231 -3.382e+00 152.781 4.000e+01
|
||||||
|
168.000 180.000 -4.158e+00 -2.107e+01 147.143 -4.247e+00 151.715 3.897e+01
|
||||||
|
167.000 180.000 -5.154e+00 -2.088e+01 143.183 -5.272e+00 150.221 3.408e+01
|
||||||
|
166.000 180.000 -6.330e+00 -2.056e+01 139.535 -6.497e+00 148.086 3.094e+01
|
||||||
|
165.000 180.000 -7.721e+00 -2.012e+01 136.327 -7.978e+00 144.927 2.915e+01
|
||||||
|
164.000 180.000 -9.365e+00 -1.961e+01 133.625 -9.797e+00 139.996 2.976e+01
|
||||||
|
163.000 180.000 -1.125e+01 -1.903e+01 131.439 -1.205e+01 131.675 4.000e+01
|
||||||
|
162.000 180.000 -1.316e+01 -1.841e+01 129.742 -1.470e+01 116.290 1.936e+01
|
||||||
|
161.000 180.000 -1.429e+01 -1.778e+01 128.486 -1.687e+01 87.803 8.681e+00
|
||||||
|
160.000 180.000 -1.366e+01 -1.714e+01 127.612 -1.625e+01 52.171 2.404e+00
|
||||||
|
159.000 180.000 -1.176e+01 -1.649e+01 127.061 -1.354e+01 28.436 3.238e+00
|
||||||
|
158.000 180.000 -9.642e+00 -1.586e+01 126.778 -1.083e+01 15.925 6.077e+00
|
||||||
|
157.000 180.000 -7.716e+00 -1.524e+01 126.714 -8.561e+00 8.938 8.213e+00
|
||||||
|
156.000 180.000 -6.048e+00 -1.464e+01 126.828 -6.695e+00 4.653 9.824e+00
|
||||||
|
155.000 180.000 -4.611e+00 -1.405e+01 127.084 -5.135e+00 1.825 1.107e+01
|
||||||
|
154.000 180.000 -3.366e+00 -1.349e+01 127.454 -3.810e+00 359.854 1.207e+01
|
||||||
|
153.000 180.000 -2.277e+00 -1.294e+01 127.913 -2.666e+00 358.427 1.287e+01
|
||||||
|
152.000 180.000 -1.318e+00 -1.242e+01 128.441 -1.669e+00 357.366 1.353e+01
|
||||||
|
151.000 180.000 -4.683e-01 -1.192e+01 129.024 -7.907e-01 356.562 1.409e+01
|
||||||
|
150.000 180.000 2.884e-01 -1.144e+01 129.648 -1.328e-02 355.946 1.455e+01
|
||||||
|
149.000 180.000 9.645e-01 -1.099e+01 130.302 6.782e-01 355.471 1.495e+01
|
||||||
|
148.000 180.000 1.570e+00 -1.055e+01 130.980 1.295e+00 355.105 1.529e+01
|
||||||
|
147.000 180.000 2.112e+00 -1.014e+01 131.674 1.846e+00 354.827 1.558e+01
|
||||||
|
146.000 180.000 2.598e+00 -9.747e+00 132.380 2.337e+00 354.620 1.583e+01
|
||||||
|
145.000 180.000 3.032e+00 -9.375e+00 133.092 2.775e+00 354.472 1.605e+01
|
||||||
|
144.000 180.000 3.419e+00 -9.024e+00 133.810 3.164e+00 354.374 1.623e+01
|
||||||
|
143.000 180.000 3.762e+00 -8.693e+00 134.529 3.508e+00 354.319 1.639e+01
|
||||||
|
142.000 180.000 4.064e+00 -8.382e+00 135.248 3.809e+00 354.302 1.652e+01
|
||||||
|
141.000 180.000 4.327e+00 -8.091e+00 135.966 4.070e+00 354.320 1.663e+01
|
||||||
|
140.000 180.000 4.553e+00 -7.819e+00 136.682 4.294e+00 354.370 1.672e+01
|
||||||
|
139.000 180.000 4.744e+00 -7.565e+00 137.395 4.481e+00 354.449 1.679e+01
|
||||||
|
138.000 180.000 4.901e+00 -7.331e+00 138.106 4.633e+00 354.557 1.684e+01
|
||||||
|
137.000 180.000 5.025e+00 -7.115e+00 138.814 4.751e+00 354.692 1.688e+01
|
||||||
|
136.000 180.000 5.117e+00 -6.917e+00 139.519 4.837e+00 354.854 1.690e+01
|
||||||
|
135.000 180.000 5.178e+00 -6.737e+00 140.222 4.889e+00 355.044 1.690e+01
|
||||||
|
134.000 180.000 5.208e+00 -6.575e+00 140.922 4.910e+00 355.263 1.688e+01
|
||||||
|
133.000 180.000 5.207e+00 -6.431e+00 141.621 4.899e+00 355.511 1.684e+01
|
||||||
|
132.000 180.000 5.176e+00 -6.304e+00 142.319 4.855e+00 355.791 1.679e+01
|
||||||
|
131.000 180.000 5.114e+00 -6.194e+00 143.016 4.780e+00 356.104 1.672e+01
|
||||||
|
130.000 180.000 5.020e+00 -6.102e+00 143.713 4.671e+00 356.453 1.662e+01
|
||||||
|
129.000 180.000 4.896e+00 -6.026e+00 144.410 4.530e+00 356.843 1.651e+01
|
||||||
|
128.000 180.000 4.739e+00 -5.968e+00 145.110 4.354e+00 357.277 1.637e+01
|
||||||
|
127.000 180.000 4.550e+00 -5.926e+00 145.812 4.143e+00 357.760 1.621e+01
|
||||||
|
126.000 180.000 4.328e+00 -5.901e+00 146.517 3.895e+00 358.301 1.602e+01
|
||||||
|
125.000 180.000 4.071e+00 -5.893e+00 147.226 3.609e+00 358.907 1.579e+01
|
||||||
|
124.000 180.000 3.777e+00 -5.902e+00 147.941 3.283e+00 359.588 1.554e+01
|
||||||
|
123.000 180.000 3.447e+00 -5.928e+00 148.662 2.914e+00 0.357 1.524e+01
|
||||||
|
122.000 180.000 3.077e+00 -5.971e+00 149.389 2.499e+00 1.231 1.490e+01
|
||||||
|
121.000 180.000 2.666e+00 -6.030e+00 150.125 2.036e+00 2.230 1.451e+01
|
||||||
|
120.000 180.000 2.212e+00 -6.107e+00 150.871 1.520e+00 3.380 1.407e+01
|
||||||
|
119.000 180.000 1.713e+00 -6.200e+00 151.627 9.467e-01 4.717 1.356e+01
|
||||||
|
118.000 180.000 1.166e+00 -6.311e+00 152.394 3.105e-01 6.286 1.298e+01
|
||||||
|
117.000 180.000 5.697e-01 -6.439e+00 153.175 -3.946e-01 8.150 1.231e+01
|
||||||
|
116.000 180.000 -7.715e-02 -6.585e+00 153.970 -1.176e+00 10.391 1.155e+01
|
||||||
|
115.000 180.000 -7.746e-01 -6.748e+00 154.781 -2.040e+00 13.128 1.067e+01
|
||||||
|
114.000 180.000 -1.520e+00 -6.929e+00 155.610 -2.994e+00 16.526 9.669e+00
|
||||||
|
113.000 180.000 -2.305e+00 -7.128e+00 156.457 -4.040e+00 20.820 8.525e+00
|
||||||
|
112.000 180.000 -3.115e+00 -7.345e+00 157.325 -5.174e+00 26.343 7.224e+00
|
||||||
|
111.000 180.000 -3.921e+00 -7.581e+00 158.215 -6.367e+00 33.547 5.757e+00
|
||||||
|
110.000 180.000 -4.678e+00 -7.836e+00 159.129 -7.546e+00 42.968 4.122e+00
|
||||||
|
109.000 180.000 -5.321e+00 -8.110e+00 160.069 -8.565e+00 55.017 2.354e+00
|
||||||
|
108.000 180.000 -5.773e+00 -8.403e+00 161.036 -9.201e+00 69.445 8.332e-01
|
||||||
|
107.000 180.000 -5.971e+00 -8.716e+00 162.033 -9.264e+00 84.843 2.035e+00
|
||||||
|
106.000 180.000 -5.892e+00 -9.048e+00 163.061 -8.761e+00 99.160 4.112e+00
|
||||||
|
105.000 180.000 -5.570e+00 -9.401e+00 164.122 -7.890e+00 111.051 6.247e+00
|
||||||
|
104.000 180.000 -5.073e+00 -9.775e+00 165.218 -6.870e+00 120.323 8.335e+00
|
||||||
|
103.000 180.000 -4.478e+00 -1.017e+01 166.350 -5.843e+00 127.404 1.032e+01
|
||||||
|
102.000 180.000 -3.846e+00 -1.058e+01 167.521 -4.881e+00 132.829 1.215e+01
|
||||||
|
101.000 180.000 -3.221e+00 -1.102e+01 168.729 -4.009e+00 137.043 1.383e+01
|
||||||
|
100.000 180.000 -2.627e+00 -1.148e+01 169.977 -3.233e+00 140.372 1.533e+01
|
||||||
|
99.000 180.000 -2.079e+00 -1.196e+01 171.264 -2.550e+00 143.046 1.667e+01
|
||||||
|
98.000 180.000 -1.584e+00 -1.246e+01 172.588 -1.954e+00 145.228 1.786e+01
|
||||||
|
97.000 180.000 -1.144e+00 -1.298e+01 173.947 -1.439e+00 147.032 1.891e+01
|
||||||
|
96.000 180.000 -7.616e-01 -1.353e+01 175.335 -9.976e-01 148.541 1.983e+01
|
||||||
|
95.000 180.000 -4.347e-01 -1.410e+01 176.747 -6.257e-01 149.814 2.066e+01
|
||||||
|
94.000 180.000 -1.625e-01 -1.468e+01 178.172 -3.185e-01 150.896 2.139e+01
|
||||||
|
93.000 180.000 5.613e-02 -1.530e+01 179.596 -7.241e-02 151.820 2.206e+01
|
||||||
|
92.000 180.000 2.221e-01 -1.593e+01 181.001 1.154e-01 152.612 2.267e+01
|
||||||
|
91.000 180.000 3.362e-01 -1.658e+01 182.359 2.469e-01 153.291 2.323e+01
|
||||||
|
90.000 180.000 3.987e-01 -1.725e+01 183.639 3.235e-01 153.871 2.377e+01
|
||||||
|
89.000 180.000 4.095e-01 -1.795e+01 184.796 3.456e-01 154.363 2.430e+01
|
||||||
|
88.000 180.000 3.680e-01 -1.866e+01 185.775 3.133e-01 154.775 2.482e+01
|
||||||
|
87.000 180.000 2.731e-01 -1.939e+01 186.504 2.258e-01 155.112 2.535e+01
|
||||||
|
86.000 180.000 1.229e-01 -2.013e+01 186.892 8.170e-02 155.377 2.590e+01
|
||||||
|
85.000 180.000 -8.513e-02 -2.088e+01 186.831 -1.214e-01 155.571 2.651e+01
|
||||||
|
84.000 180.000 -3.544e-01 -2.163e+01 186.188 -3.869e-01 155.693 2.718e+01
|
||||||
|
83.000 180.000 -6.895e-01 -2.237e+01 184.810 -7.191e-01 155.737 2.796e+01
|
||||||
|
82.000 180.000 -1.096e+00 -2.307e+01 182.539 -1.124e+00 155.695 2.889e+01
|
||||||
|
81.000 180.000 -1.583e+00 -2.370e+01 179.236 -1.610e+00 155.554 3.006e+01
|
||||||
|
80.000 180.000 -2.159e+00 -2.421e+01 174.834 -2.186e+00 155.294 3.158e+01
|
||||||
|
79.000 180.000 -2.838e+00 -2.454e+01 169.415 -2.868e+00 154.886 3.374e+01
|
||||||
|
78.000 180.000 -3.639e+00 -2.464e+01 163.262 -3.673e+00 154.281 3.716e+01
|
||||||
|
77.000 180.000 -4.585e+00 -2.447e+01 156.843 -4.629e+00 153.407 4.000e+01
|
||||||
|
76.000 180.000 -5.710e+00 -2.405e+01 150.682 -5.774e+00 152.139 4.000e+01
|
||||||
|
75.000 180.000 -7.062e+00 -2.342e+01 145.200 -7.163e+00 150.257 3.756e+01
|
||||||
|
74.000 180.000 -8.707e+00 -2.265e+01 140.619 -8.886e+00 147.331 3.276e+01
|
||||||
|
73.000 180.000 -1.073e+01 -2.179e+01 136.979 -1.108e+01 142.422 3.187e+01
|
||||||
|
72.000 180.000 -1.317e+01 -2.088e+01 134.202 -1.397e+01 133.108 4.000e+01
|
||||||
|
71.000 180.000 -1.563e+01 -1.997e+01 132.158 -1.761e+01 112.200 1.543e+01
|
||||||
|
70.000 180.000 -1.630e+01 -1.907e+01 130.713 -1.956e+01 67.932 4.321e+00
|
||||||
|
69.000 180.000 -1.426e+01 -1.820e+01 129.743 -1.650e+01 29.287 2.338e+00
|
||||||
|
68.000 180.000 -1.152e+01 -1.736e+01 129.145 -1.283e+01 12.424 6.300e+00
|
||||||
|
67.000 180.000 -9.114e+00 -1.655e+01 128.836 -9.978e+00 4.570 8.904e+00
|
||||||
|
66.000 180.000 -7.118e+00 -1.578e+01 128.751 -7.753e+00 0.255 1.072e+01
|
||||||
|
65.000 180.000 -5.454e+00 -1.504e+01 128.838 -5.960e+00 357.598 1.205e+01
|
||||||
|
64.000 180.000 -4.046e+00 -1.435e+01 129.058 -4.471e+00 355.836 1.305e+01
|
||||||
|
63.000 180.000 -2.836e+00 -1.368e+01 129.379 -3.209e+00 354.606 1.384e+01
|
||||||
|
62.000 180.000 -1.783e+00 -1.305e+01 129.778 -2.120e+00 353.720 1.448e+01
|
||||||
|
61.000 180.000 -8.599e-01 -1.246e+01 130.236 -1.171e+00 353.066 1.499e+01
|
||||||
|
60.000 180.000 -4.351e-02 -1.189e+01 130.739 -3.370e-01 352.579 1.541e+01
|
||||||
|
59.000 180.000 6.818e-01 -1.136e+01 131.277 4.013e-01 352.215 1.576e+01
|
||||||
|
58.000 180.000 1.328e+00 -1.085e+01 131.840 1.057e+00 351.944 1.606e+01
|
||||||
|
57.000 180.000 1.905e+00 -1.037e+01 132.421 1.640e+00 351.747 1.630e+01
|
||||||
|
56.000 180.000 2.421e+00 -9.922e+00 133.016 2.160e+00 351.610 1.651e+01
|
||||||
|
55.000 180.000 2.881e+00 -9.497e+00 133.622 2.622e+00 351.522 1.668e+01
|
||||||
|
54.000 180.000 3.290e+00 -9.097e+00 134.234 3.031e+00 351.475 1.683e+01
|
||||||
|
53.000 180.000 3.652e+00 -8.720e+00 134.850 3.393e+00 351.464 1.695e+01
|
||||||
|
52.000 180.000 3.971e+00 -8.368e+00 135.470 3.710e+00 351.485 1.704e+01
|
||||||
|
51.000 180.000 4.249e+00 -8.038e+00 136.092 3.984e+00 351.535 1.712e+01
|
||||||
|
50.000 180.000 4.488e+00 -7.729e+00 136.716 4.219e+00 351.611 1.717e+01
|
||||||
|
49.000 180.000 4.691e+00 -7.443e+00 137.340 4.417e+00 351.712 1.721e+01
|
||||||
|
48.000 180.000 4.858e+00 -7.177e+00 137.964 4.578e+00 351.837 1.723e+01
|
||||||
|
47.000 180.000 4.992e+00 -6.931e+00 138.589 4.703e+00 351.986 1.723e+01
|
||||||
|
46.000 180.000 5.092e+00 -6.705e+00 139.215 4.795e+00 352.158 1.722e+01
|
||||||
|
45.000 180.000 5.160e+00 -6.499e+00 139.841 4.853e+00 352.354 1.719e+01
|
||||||
|
44.000 180.000 5.196e+00 -6.311e+00 140.469 4.878e+00 352.575 1.715e+01
|
||||||
|
43.000 180.000 5.201e+00 -6.142e+00 141.098 4.870e+00 352.822 1.709e+01
|
||||||
|
42.000 180.000 5.174e+00 -5.992e+00 141.730 4.829e+00 353.097 1.701e+01
|
||||||
|
41.000 180.000 5.117e+00 -5.859e+00 142.364 4.755e+00 353.402 1.692e+01
|
||||||
|
40.000 180.000 5.027e+00 -5.744e+00 143.001 4.648e+00 353.739 1.680e+01
|
||||||
|
39.000 180.000 4.906e+00 -5.647e+00 143.643 4.506e+00 354.113 1.667e+01
|
||||||
|
38.000 180.000 4.753e+00 -5.567e+00 144.289 4.329e+00 354.527 1.651e+01
|
||||||
|
37.000 180.000 4.566e+00 -5.504e+00 144.941 4.117e+00 354.986 1.633e+01
|
||||||
|
36.000 180.000 4.346e+00 -5.459e+00 145.599 3.867e+00 355.497 1.612e+01
|
||||||
|
35.000 180.000 4.091e+00 -5.430e+00 146.264 3.577e+00 356.067 1.589e+01
|
||||||
|
34.000 180.000 3.801e+00 -5.417e+00 146.936 3.247e+00 356.706 1.562e+01
|
||||||
|
33.000 180.000 3.472e+00 -5.422e+00 147.618 2.872e+00 357.426 1.531e+01
|
||||||
|
32.000 180.000 3.105e+00 -5.443e+00 148.309 2.452e+00 358.243 1.497e+01
|
||||||
|
31.000 180.000 2.697e+00 -5.480e+00 149.012 1.980e+00 359.175 1.457e+01
|
||||||
|
30.000 180.000 2.247e+00 -5.534e+00 149.725 1.455e+00 0.247 1.413e+01
|
||||||
|
29.000 180.000 1.752e+00 -5.605e+00 150.452 8.697e-01 1.493 1.362e+01
|
||||||
|
28.000 180.000 1.210e+00 -5.692e+00 151.192 2.193e-01 2.955 1.305e+01
|
||||||
|
27.000 180.000 6.213e-01 -5.796e+00 151.947 -5.037e-01 4.692 1.239e+01
|
||||||
|
26.000 180.000 -1.658e-02 -5.916e+00 152.717 -1.307e+00 6.787 1.165e+01
|
||||||
|
25.000 180.000 -7.024e-01 -6.052e+00 153.505 -2.201e+00 9.353 1.080e+01
|
||||||
|
24.000 180.000 -1.433e+00 -6.206e+00 154.312 -3.193e+00 12.555 9.842e+00
|
||||||
|
23.000 180.000 -2.200e+00 -6.376e+00 155.138 -4.292e+00 16.635 8.758e+00
|
||||||
|
22.000 180.000 -2.988e+00 -6.563e+00 155.984 -5.498e+00 21.945 7.544e+00
|
||||||
|
21.000 180.000 -3.769e+00 -6.766e+00 156.853 -6.792e+00 28.993 6.209e+00
|
||||||
|
20.000 180.000 -4.501e+00 -6.987e+00 157.745 -8.108e+00 38.446 4.795e+00
|
||||||
|
19.000 180.000 -5.125e+00 -7.225e+00 158.661 -9.288e+00 50.931 3.444e+00
|
||||||
|
18.000 180.000 -5.571e+00 -7.480e+00 159.603 -1.006e+01 66.384 2.630e+00
|
||||||
|
17.000 180.000 -5.784e+00 -7.752e+00 160.572 -1.017e+01 83.194 3.108e+00
|
||||||
|
16.000 180.000 -5.741e+00 -8.041e+00 161.568 -9.602e+00 98.740 4.580e+00
|
||||||
|
15.000 180.000 -5.470e+00 -8.347e+00 162.593 -8.617e+00 111.345 6.389e+00
|
||||||
|
14.000 180.000 -5.030e+00 -8.671e+00 163.647 -7.490e+00 120.896 8.256e+00
|
||||||
|
13.000 180.000 -4.490e+00 -9.012e+00 164.731 -6.381e+00 128.011 1.006e+01
|
||||||
|
12.000 180.000 -3.909e+00 -9.371e+00 165.844 -5.362e+00 133.358 1.175e+01
|
||||||
|
11.000 180.000 -3.327e+00 -9.746e+00 166.985 -4.452e+00 137.453 1.329e+01
|
||||||
|
10.000 180.000 -2.773e+00 -1.014e+01 168.155 -3.653e+00 140.655 1.467e+01
|
||||||
|
9.000 180.000 -2.260e+00 -1.055e+01 169.349 -2.957e+00 143.206 1.588e+01
|
||||||
|
8.000 180.000 -1.797e+00 -1.097e+01 170.565 -2.357e+00 145.275 1.695e+01
|
||||||
|
7.000 180.000 -1.389e+00 -1.142e+01 171.799 -1.843e+00 146.977 1.788e+01
|
||||||
|
6.000 180.000 -1.035e+00 -1.188e+01 173.044 -1.409e+00 148.393 1.869e+01
|
||||||
|
5.000 180.000 -7.376e-01 -1.235e+01 174.292 -1.048e+00 149.583 1.940e+01
|
||||||
|
4.000 180.000 -4.948e-01 -1.284e+01 175.531 -7.556e-01 150.590 2.002e+01
|
||||||
|
3.000 180.000 -3.062e-01 -1.334e+01 176.749 -5.277e-01 151.445 2.056e+01
|
||||||
|
2.000 180.000 -1.715e-01 -1.386e+01 177.927 -3.614e-01 152.172 2.105e+01
|
||||||
|
1.000 180.000 -9.026e-02 -1.439e+01 179.043 -2.547e-01 152.790 2.149e+01
|
||||||
|
|
||||||
2666
data/gain_pattern/farfield_all.txt
Normal file
2666
data/gain_pattern/farfield_all.txt
Normal file
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -2,9 +2,18 @@
|
|||||||
"cells": [
|
"cells": [
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 1,
|
"execution_count": 3,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"The autoreload extension is already loaded. To reload it, use:\n",
|
||||||
|
" %reload_ext autoreload\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"%load_ext autoreload\n",
|
"%load_ext autoreload\n",
|
||||||
"%autoreload 2\n",
|
"%autoreload 2\n",
|
||||||
@ -33,9 +42,24 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 2,
|
"execution_count": 4,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [
|
||||||
|
{
|
||||||
|
"ename": "ModuleNotFoundError",
|
||||||
|
"evalue": "No module named 'numpy.typing'",
|
||||||
|
"output_type": "error",
|
||||||
|
"traceback": [
|
||||||
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||||
|
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||||
|
"Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspatz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimulation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Simulation, UniformTimeSteps\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Construct a time model.\u001b[39;00m\n\u001b[0;32m 4\u001b[0m timesteps \u001b[38;5;241m=\u001b[39m UniformTimeSteps(\u001b[38;5;241m0.1\u001b[39m, mu\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, sigma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, delay_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
|
||||||
|
"File \u001b[1;32mc:\\Users\\Vincent\\Documents\\Projekte\\STA\\STAHR\\SPATZ\\spatz\\__init__.py:7\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mspatz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mutils\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspatz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mspatz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtransforms\u001b[39;00m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspatz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspatz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdataset\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n",
|
||||||
|
"File \u001b[1;32mc:\\Users\\Vincent\\Documents\\Projekte\\STA\\STAHR\\SPATZ\\spatz\\transforms\\__init__.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspatz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransform\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Transform\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspatz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnoise\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GaussianNoise\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspatz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfailures\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Downtime\n",
|
||||||
|
"File \u001b[1;32mc:\\Users\\Vincent\\Documents\\Projekte\\STA\\STAHR\\SPATZ\\spatz\\transforms\\transform.py:3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ArrayLike\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Tuple\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mTransform\u001b[39;00m:\n",
|
||||||
|
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'numpy.typing'"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"from spatz.simulation import Simulation, UniformTimeSteps\n",
|
"from spatz.simulation import Simulation, UniformTimeSteps\n",
|
||||||
"\n",
|
"\n",
|
||||||
@ -436,7 +460,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.12.1"
|
"version": "3.8.1"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
@ -2,62 +2,113 @@ from numpy.typing import ArrayLike
|
|||||||
from typing import List, AnyStr
|
from typing import List, AnyStr
|
||||||
from numpy import matrix
|
from numpy import matrix
|
||||||
from typing import List
|
from typing import List
|
||||||
|
import re
|
||||||
|
from io import StringIO
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
import math
|
||||||
|
|
||||||
from spatz.sensors import Sensor
|
# from spatz.sensors import Sensor
|
||||||
from spatz.transforms import Transform
|
# from spatz.transforms import Transform
|
||||||
from spatz.dataset import Dataset
|
# from spatz.dataset import Dataset
|
||||||
from spatz.logger import Logger
|
# from spatz.logger import Logger
|
||||||
|
|
||||||
'''
|
'''
|
||||||
Sensor to simulate TX antenna gain in direction of ground station
|
Sensor to simulate TX antenna gain in direction of ground station
|
||||||
You will need to supply a gain pattern in the form of a R^3 matrix in the following form:
|
|
||||||
|
|
||||||
Returns the gain in dBi per timestep.
|
Returns the gain in dBi per timestep.
|
||||||
|
|
||||||
|
|
||||||
gain_pattern: matrix, groundstation_offset_vector
|
gain_pattern: matrix, groundstation_offset_vector
|
||||||
|
|
||||||
'''
|
'''
|
||||||
|
|
||||||
class GainPattern():
|
class GainPattern():
|
||||||
|
|
||||||
def __init__(self, pattern_file):
|
def __init__(self, filepath: str):
|
||||||
self._df = pd.read_csv(pattern_file,delimiter='\t')
|
# This is a cursed parser. If it breaks, though luck.
|
||||||
print(self._df)
|
with open(filepath,"r") as file:
|
||||||
|
# Read Header
|
||||||
|
header = file.readline()
|
||||||
|
header = re.sub(r'\[(.*?)\]',",",header).replace(" ","").replace(",\n",'\n')
|
||||||
|
|
||||||
def get_gain(self, phi, theta):
|
# Discard ---- line
|
||||||
phi_left = round(phi,-1)
|
file.readline()
|
||||||
phi_right = round(phi ,-1)
|
|
||||||
|
|
||||||
theta_left = theta
|
# Parse to DF
|
||||||
theta_right = theta
|
lines = file.readlines()
|
||||||
|
clean_csv = header
|
||||||
class AntennaTxGain(Sensor):
|
for line in lines:
|
||||||
|
cleaned = re.sub(r'\s+',',',line).removeprefix(',').removesuffix(',').strip()
|
||||||
|
clean_csv = clean_csv + cleaned + '\n'
|
||||||
|
filelike = StringIO(clean_csv)
|
||||||
|
self._df = pd.read_csv(filelike)
|
||||||
|
print(self._df.head())
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def __init__(self, dataset: Dataset, logger: Logger, transforms: List[Transform] = []):
|
|
||||||
super().__init__(dataset, logger, transforms)
|
|
||||||
|
|
||||||
|
|
||||||
def _get_data(self) -> ArrayLike | float:
|
|
||||||
# Get current position of rocket
|
|
||||||
[x,y,z] = self._dataset.fetch_values(['x', 'y', 'z'])
|
|
||||||
|
|
||||||
# Get current rotation of rocket
|
def __get_gain_internal(self,phi5:float,theta5:float):
|
||||||
[pitch,roll,yaw] = self._dataset.fetch_values(['pitch','roll','yaw'])
|
assert phi5%5 ==0
|
||||||
|
assert theta5%5==0
|
||||||
|
|
||||||
# Calculate angle between the vectors
|
row = self._df.loc[(self._df["Theta"] == theta5) & (self._df["Phi"] == phi5)].iloc[0]
|
||||||
|
return row["Abs(Dir.)"]
|
||||||
|
|
||||||
# Fetch gain in this direction
|
def get_gain(self, phi, theta) -> float:
|
||||||
|
assert 0 <= phi <= 180
|
||||||
|
assert 0 <= theta <= 360
|
||||||
|
|
||||||
|
#Interpolate using binlinear interpolation https://en.wikipedia.org/wiki/Bilinear_interpolation
|
||||||
|
phi_lower = math.floor(phi/5)*5
|
||||||
|
phi_upper = phi_lower + 5
|
||||||
|
theta_lower = math.floor(theta/5)*5
|
||||||
|
theta_upper = theta_lower + 5
|
||||||
|
|
||||||
return 0
|
G11 = self.__get_gain_internal(phi_lower,theta_lower)
|
||||||
|
G12 = self.__get_gain_internal(phi_lower,theta_upper)
|
||||||
|
G21 = self.__get_gain_internal(phi_upper,theta_lower)
|
||||||
|
G22 = self.__get_gain_internal(phi_upper,theta_upper)
|
||||||
|
|
||||||
def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike:
|
v1 = np.array([phi_upper-phi,phi-phi_lower])
|
||||||
return x
|
v2 = np.array([[theta_upper-theta],[theta-theta_lower]])
|
||||||
|
A = np.array([[G11,G12],[G21,G22]])
|
||||||
|
|
||||||
|
interpolated = 1/25 * v1 @ A @ v2
|
||||||
|
|
||||||
|
return interpolated[0]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# class AntennaTxGain(Sensor):
|
||||||
|
|
||||||
|
# def __init__(self, dataset: Dataset, logger: Logger, transforms: List[Transform] = []):
|
||||||
|
# super().__init__(dataset, logger, transforms)
|
||||||
|
|
||||||
|
|
||||||
|
# def _get_data(self) -> ArrayLike | float:
|
||||||
|
# # Get current position of rocket
|
||||||
|
# [x,y,z] = self._dataset.fetch_values(['x', 'y', 'z'])
|
||||||
|
|
||||||
|
# # Get current rotation of rocket
|
||||||
|
# [pitch,roll,yaw] = self._dataset.fetch_values(['pitch','roll','yaw'])
|
||||||
|
|
||||||
|
# # Calculate angle between the vectors
|
||||||
|
|
||||||
|
# # Fetch gain in this direction
|
||||||
|
|
||||||
|
# return 0
|
||||||
|
|
||||||
|
# def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike:
|
||||||
|
# return x
|
||||||
|
|
||||||
|
|
||||||
def _get_name(self) -> AnyStr:
|
# def _get_name(self) -> AnyStr:
|
||||||
return 'Generic Antenna TX'
|
# return 'Generic Antenna TX'
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
pattern = GainPattern("data/gain_pattern/farfield_all.txt")
|
||||||
|
print(pattern.get_gain(0,12))
|
||||||
|
print(pattern.get_gain(0,16))
|
||||||
|
print(pattern.get_gain(6,12))
|
||||||
|
print(pattern.get_gain(0,10))
|
||||||
10
testing.py
Normal file
10
testing.py
Normal file
@ -0,0 +1,10 @@
|
|||||||
|
from spatz.sensors.antenna.tx_gain import GainPattern
|
||||||
|
import math
|
||||||
|
|
||||||
|
pattern = GainPattern("data/gain_pattern/farfield_all.txt")
|
||||||
|
|
||||||
|
# pattern.get_gain(41,66)
|
||||||
|
# pattern.get_gain(40,100)
|
||||||
|
# pattern.get_gain(10,180)
|
||||||
|
# pattern.get_gain(0,95)
|
||||||
|
# pattern.get_gain(21,100)
|
||||||
Loading…
x
Reference in New Issue
Block a user