try to fix music

examples/music.py

@@ -0,0 +1,68 @@
+import logging
+
+import numpy as np
+import torch
+
+from where_fi.application import CSIApplication
+from where_fi.collection import CSIMatrix
+from where_fi.collection.ingest import RealtimeCSIProducer
+from where_fi.config import config
+from where_fi.processing import aoa
+from where_fi.visualise import server as visualise
+
+producer = RealtimeCSIProducer()
+app = CSIApplication(producer, visualise_raw=True)
+visualise.figures.all_figures["median"] = visualise.figures.RandomVariable(
+    "Median Phase"
+)
+visualise.figures.all_figures["magn"] = visualise.figures.RandomVariable(
+    "Median Magnitude"
+)
+
+logging.basicConfig(level=logging.INFO)
+
+T = 500
+
+N_sub1 = config.antennas.count // 2
+N_sub2 = config.subcarriers // 2
+L2 = config.subcarriers - N_sub2 + 1
+L1 = config.antennas.count - N_sub1 + 1
+N_sensors = config.subcarriers * config.antennas.count
+historical = torch.zeros(T, N_sensors, N_sensors, dtype=torch.complex64)
+
+cnt = 0
+
+
+@app.on_sample
+def _(sample: CSIMatrix) -> None:
+    global cnt
+    sample = sample.T.reshape(-1, 1)
+    sample_tensor = torch.tensor(sample)
+    historical[cnt] = sample_tensor @ torch.conj(sample_tensor).T
+    cnt = (cnt + 1) % T
+
+
+aoa = aoa.AoA()
+
+
+@app.on_process
+def _(_: CSIMatrix) -> None:
+    R: torch.Tensor = torch.mean(historical, axis=0)
+    Rss = torch.zeros(N_sub1 * N_sub2, N_sub1 * N_sub2, dtype=torch.complex64)
+    for i in range(L1):
+        for j in range(L2):
+            Rss += R[i : i + N_sub1 * N_sub2, j : j + N_sub1 * N_sub2]
+    Rss /= L1 * L2
+
+    aoa.historical_autocorr = torch.unsqueeze(Rss, 0)
+    aoa.heatmap(app.visualise_data)
+    # eigvals, eigvecs = torch.linalg.eig(Rss)
+    # app.visualise_data(eigvals.numpy(), visualise.figures.Figure.MUSIC_EIGENVALUES)
+    # E_n = eigvecs[:, torch.abs(eigvals) < config.music.eigval_threshold]
+
+    # print(E_n)
+    # c: torch.Tensor = 1 / (steering_h @ E_n @ E_n_H @ steering)
+    # return torch.abs(c)[:, 0, 0]
+
+
+app.start()

where_fi/config/models.py

@@ -23,7 +23,7 @@ class Preprocessing(BaseModel):
 
 
 class MUSIC(BaseModel):
-    eigval_threshold: int
+    eigval_threshold: float
     window_size: int
 
     class Heatmap(BaseModel):

where_fi/processing/aoa.py

@@ -18,8 +18,8 @@ torch.set_default_device(device)
 class AoA:
     def __init__(self) -> None:
         self.historical_autocorr = torch.tensor([], dtype=torch.complex64)
-        self.N_subcarriers = -1
+        self.N_subcarriers = config.subcarriers
-        self.N_rx = -1
+        self.N_rx = config.antennas.count
         self.timestamp = datetime.now()
         pass
 
@@ -84,7 +84,7 @@ class AoA:
             * np.pi
             * config.central_freq_hz
             * config.antennas.spacing
-            * (1 - torch.cos(theta))
+            * (torch.sin(theta))
             / 299_792_458
         )
         assert omega_t.shape == phi_theta.shape == (N,)
@@ -133,7 +133,7 @@ class AoA:
         logger.debug(f"Eigenvalues: {eigvals}")
         E_n = eigvecs[:, torch.abs(eigvals) < config.music.eigval_threshold]
 
-        logger.debug(f"Signal subspace: {E_n.shape}")
+        logger.info(f"Signal subspace: {E_n.shape}")
         steering = torch.unsqueeze(self.steering_vector(theta, tof), dim=-1)
         steering_h = torch.conj(steering).permute(0, 2, 1)