anomaly-detection-material-parameters-calibration

commit f140e8583ec54ec391135da43230455f0b7dcf3b
parent 6a8c8c9c5760ca6cffb5c0a3480dfc3415dedb7e
Author: Egor Achkasov <35174690+egor-achkasov@users.noreply.github.com>
Date:   Tue, 21 Oct 2025 17:05:53 +0200

Update README.md
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diff --git a/README.md b/README.md
@@ -52,6 +52,15 @@ pip install -r req.txt
 python src/v.py ./
 ```
 
+3. Use the results in your code by loading the saved `omega` into the scene. Load the scene with `sionna.rt.load_scene`, read the `omegas.npy` file with `np.load`, take the last `omega`, get the material parameters with the `calc_material_parameters(omega[1], omega[0])` and use the same code as in `set_omega`:
+```python
+for idx, mat in enumerate(g_trainable_materials):
+        g_scene._radio_materials[mat.name].conductivity = cond[idx]
+        g_scene._radio_materials[mat.name].relative_permittivity = perm[idx]
+        g_scene._radio_materials[mat.name].scattering_coefficient = scat[idx]
+        g_scene._radio_materials[mat.name].xpd_coefficient = xpd[idx]
+```
+
 # Notes
 
 - Main bottleneck of the numpy version is the gradient computation. Other method or parallelization should be considered. Otherwise, allocation for more than 1 CPU is questionable.