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Working gain pattern class
This commit is contained in:
bareissvincent
parent
89c327acd2
commit
bf58484dfa
15
.vscode/launch.json
vendored
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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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365
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
|
||||
2.000 0.000 -1.713e-01 -1.604e+01 1.722 -2.852e-01 334.097 2.261e+01
|
||||
3.000 0.000 -3.105e-01 -1.661e+01 2.259 -4.136e-01 334.372 2.296e+01
|
||||
4.000 0.000 -5.095e-01 -1.717e+01 2.533 -6.041e-01 334.571 2.330e+01
|
||||
5.000 0.000 -7.718e-01 -1.774e+01 2.481 -8.599e-01 334.692 2.365e+01
|
||||
6.000 0.000 -1.102e+00 -1.830e+01 2.033 -1.186e+00 334.729 2.402e+01
|
||||
7.000 0.000 -1.506e+00 -1.884e+01 1.115 -1.587e+00 334.674 2.441e+01
|
||||
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
|
||||
11.000 0.000 -4.066e+00 -2.054e+01 351.542 -4.165e+00 333.088 2.654e+01
|
||||
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
|
||||
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
|
||||
22.000 0.000 -8.724e+00 -1.632e+01 314.278 -9.554e+00 183.502 1.001e+01
|
||||
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
|
||||
35.000 0.000 3.275e+00 -9.249e+00 312.808 3.025e+00 171.339 1.670e+01
|
||||
36.000 0.000 3.631e+00 -8.875e+00 313.240 3.380e+00 171.347 1.677e+01
|
||||
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
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67.000 0.000 -3.148e+00 -6.777e+00 332.897 -5.616e+00 204.522 6.432e+00
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68.000 0.000 -3.921e+00 -6.992e+00 333.755 -6.871e+00 212.111 5.063e+00
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||||
69.000 0.000 -4.634e+00 -7.226e+00 334.636 -8.107e+00 222.157 3.614e+00
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||||
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
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||||
94.000 0.000 -2.601e-01 -1.948e+01 1.893 -3.123e-01 334.450 2.598e+01
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||||
95.000 0.000 -5.460e-01 -2.020e+01 1.676 -5.933e-01 334.471 2.648e+01
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||||
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||||
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98.000 0.000 -1.830e+00 -2.224e+01 357.164 -1.870e+00 333.907 2.851e+01
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||||
100.000 0.000 -3.130e+00 -2.326e+01 349.861 -3.172e+00 332.767 3.080e+01
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101.000 0.000 -3.955e+00 -2.355e+01 344.904 -4.003e+00 331.824 3.254e+01
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||||
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||||
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||||
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||||
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||||
108.000 0.000 -1.511e+01 -2.026e+01 312.857 -1.670e+01 276.378 1.051e+01
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
159.000 0.000 -4.040e+00 -6.927e+00 336.239 -7.177e+00 222.331 3.743e+00
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||||
160.000 0.000 -4.621e+00 -7.155e+00 337.020 -8.167e+00 233.554 2.299e+00
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||||
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
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165.000 0.000 -4.693e+00 -8.569e+00 341.074 -6.981e+00 297.209 8.100e+00
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166.000 0.000 -4.210e+00 -8.908e+00 341.911 -6.008e+00 304.681 1.004e+01
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167.000 0.000 -3.673e+00 -9.266e+00 342.756 -5.075e+00 310.466 1.190e+01
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168.000 0.000 -3.122e+00 -9.644e+00 343.604 -4.217e+00 314.986 1.364e+01
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||||
169.000 0.000 -2.585e+00 -1.004e+01 344.455 -3.445e+00 318.567 1.526e+01
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||||
170.000 0.000 -2.079e+00 -1.046e+01 345.304 -2.761e+00 321.449 1.674e+01
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171.000 0.000 -1.615e+00 -1.089e+01 346.148 -2.160e+00 323.800 1.809e+01
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||||
172.000 0.000 -1.197e+00 -1.135e+01 346.980 -1.638e+00 325.743 1.932e+01
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173.000 0.000 -8.289e-01 -1.183e+01 347.793 -1.188e+00 327.365 2.042e+01
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174.000 0.000 -5.108e-01 -1.233e+01 348.578 -8.064e-01 328.732 2.143e+01
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175.000 0.000 -2.429e-01 -1.285e+01 349.323 -4.882e-01 329.890 2.236e+01
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176.000 0.000 -2.485e-02 -1.338e+01 350.014 -2.301e-01 330.876 2.321e+01
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177.000 0.000 1.437e-01 -1.394e+01 350.633 -2.931e-02 331.716 2.401e+01
|
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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": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 3,
|
||||
"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": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
@ -33,9 +42,24 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 4,
|
||||
"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": [
|
||||
"from spatz.simulation import Simulation, UniformTimeSteps\n",
|
||||
"\n",
|
||||
@ -436,7 +460,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.1"
|
||||
"version": "3.8.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -2,62 +2,113 @@ from numpy.typing import ArrayLike
|
||||
from typing import List, AnyStr
|
||||
from numpy import matrix
|
||||
from typing import List
|
||||
import re
|
||||
from io import StringIO
|
||||
import numpy as np
|
||||
|
||||
import pandas as pd
|
||||
import math
|
||||
|
||||
from spatz.sensors import Sensor
|
||||
from spatz.transforms import Transform
|
||||
from spatz.dataset import Dataset
|
||||
from spatz.logger import Logger
|
||||
# from spatz.sensors import Sensor
|
||||
# from spatz.transforms import Transform
|
||||
# from spatz.dataset import Dataset
|
||||
# from spatz.logger import Logger
|
||||
|
||||
'''
|
||||
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.
|
||||
|
||||
|
||||
gain_pattern: matrix, groundstation_offset_vector
|
||||
|
||||
'''
|
||||
|
||||
class GainPattern():
|
||||
|
||||
def __init__(self, pattern_file):
|
||||
self._df = pd.read_csv(pattern_file,delimiter='\t')
|
||||
print(self._df)
|
||||
def __init__(self, filepath: str):
|
||||
# This is a cursed parser. If it breaks, though luck.
|
||||
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):
|
||||
phi_left = round(phi,-1)
|
||||
phi_right = round(phi ,-1)
|
||||
# Discard ---- line
|
||||
file.readline()
|
||||
|
||||
theta_left = theta
|
||||
theta_right = theta
|
||||
|
||||
class AntennaTxGain(Sensor):
|
||||
# Parse to DF
|
||||
lines = file.readlines()
|
||||
clean_csv = header
|
||||
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
|
||||
[pitch,roll,yaw] = self._dataset.fetch_values(['pitch','roll','yaw'])
|
||||
def __get_gain_internal(self,phi5:float,theta5:float):
|
||||
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:
|
||||
return x
|
||||
v1 = np.array([phi_upper-phi,phi-phi_lower])
|
||||
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:
|
||||
return 'Generic Antenna TX'
|
||||
# def _get_name(self) -> AnyStr:
|
||||
# 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