improve antenna order detection

where_fi/cli/__init__.py

@@ -52,7 +52,7 @@ def heatmap() -> None:
     def callback(antenna_data: npt.NDArray[np.complex64]) -> None:
         logger.info(f"Got final CSI data with shape {antenna_data.shape}")
         visualise_data(antenna_data, visualise.DataType.RAW_CSI)
-        processed = preprocessor.preprocess(antenna_data)
+        processed = preprocessor.preprocess(antenna_data, visualiser=visualise_data)
         visualise_data(processed, visualise.DataType.PROCESSED_CSI)
         logger.info(f"Processed CSI data with shape {processed.shape}")
         processed_tensor = torch.tensor(processed, device=device)

where_fi/processing/preprocess.py

@@ -1,11 +1,13 @@
 import logging
 from queue import Queue
+from typing import Any, Callable
 
 import numpy as np
 import numpy.typing as npt
 from scipy.signal import butter, correlate, sosfilt, sosfilt_zi
 
 from ..config import config
+from ..visualise import server as visualise
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
@@ -26,9 +28,14 @@ class Preprocessor:
             output="sos",
         )
 
-    def preprocess(self, h: npt.NDArray[np.complex64]) -> npt.NDArray[np.complex64]:
+    def preprocess(
+        self,
+        h: npt.NDArray[np.complex64],
+        visualiser: None
+        | Callable[[npt.NDArray[Any], visualise.DataType], None] = None,
+    ) -> npt.NDArray[np.complex64]:
         # CSI data is not available for pilot subcarriers.
-        h_hat = np.where(
+        h_hat: npt.NDArray[np.complex64] = np.where(
             np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)),
             correlate(h, [[[1 / 2]], [[0]], [[1 / 2]]], mode="same"),
             h,
@@ -38,12 +45,33 @@ class Preprocessor:
         h_hat = h_hat[:: config.preprocessing.subcarrier_step, :, :]
         # logger.info(f"CSI shape: {h_hat.shape}")
 
-        h_hat = np.multiply(h_hat, h_hat.conj() / abs(h_hat.conj()))
+        # h_hat = np.multiply(h_hat, h_hat.conj() / abs(h_hat.conj()))
         h_hat = np.nan_to_num(h_hat)
 
         # h_hat = correlate(h_hat, np.ones((3, 1, 1)) / 3)
-        h_hat = correlate(h_hat, [[[1 / 4]], [[1 / 2]], [[1 / 4]]], mode="valid")
+        # h_hat = correlate(h_hat, [[[1 / 4]], [[1 / 2]], [[1 / 4]]], mode="valid")
 
+        # Unwrap phase and remove linear fit
+        print(h_hat.shape)
+        unwrapped = np.unwrap(np.angle(h_hat[:, :, 0]), axis=0).reshape(
+            h_hat.shape[0], h_hat.shape[1], 1
+        )
+        if visualiser:
+            visualiser(unwrapped, visualise.DataType.UNWRAPPED_PHASE)
+        for antenna in range(h_hat.shape[1]):
+            tau, rho = np.linalg.lstsq(
+                np.vstack([np.arange(h_hat.shape[0]), np.ones(h_hat.shape[0])]).T,
+                unwrapped[:, antenna, 0],
+            )[0]
+            h_hat[:, antenna, 0] = np.abs(h_hat[:, antenna, 0]) * np.exp(
+                1j
+                * (
+                    np.angle(h_hat[:, antenna, 0])
+                    - (tau * np.arange(h_hat.shape[0]) + rho)
+                )
+            )
+
+        return h_hat
         # Assume that all csi matrices will have the same shape
         if self.long_term_avg.shape != h_hat.shape:
             self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex64)

where_fi/utils/antenna_order.py

@@ -11,13 +11,13 @@ AntennaIdentifier = tuple[ingest.Host, int]
 order: list[AntennaIdentifier] = []
 prev_unplugged: set[AntennaIdentifier] = set()
 long_antenna_average: dict[AntennaIdentifier, deque[int]] = {
-    (host, 0): deque(maxlen=10 * config.sample_rate) for host in config.receive_hosts
+    (host, 0): deque(maxlen=15 * config.sample_rate) for host in config.receive_hosts
-} | {(host, 1): deque(maxlen=10 * config.sample_rate) for host in config.receive_hosts}
+} | {(host, 1): deque(maxlen=15 * config.sample_rate) for host in config.receive_hosts}
 short_antenna_average: dict[AntennaIdentifier, deque[int]] = {
-    (host, 0): deque(maxlen=config.sample_rate) for host in config.receive_hosts
+    (host, 0): deque(maxlen=2 * config.sample_rate) for host in config.receive_hosts
-} | {(host, 1): deque(maxlen=config.sample_rate) for host in config.receive_hosts}
+} | {(host, 1): deque(maxlen=2 * config.sample_rate) for host in config.receive_hosts}
 
-RSSI_THRESHOLD = 8
+RSSI_THRESHOLD = 10
 
 
 def average(data: Collection[Any]) -> float:

where_fi/visualise/Makefile

@@ -6,4 +6,5 @@ server/generated: protos/*.proto
 	find protos/ -type f -name "*.proto" | xargs uv run protol --create-package --in-place --python-out server/generated/  protoc --proto-path=protos/
 
 frontend/src/grpc: protos/*.proto
-	find protos/ -name "*.proto" | xargs npm exec --prefix frontend/ protoc --ts_out frontend/src/grpc -I protos/
+	mkdir -p frontend/src/grpc
+	cd frontend && find ../protos/ -name "*.proto" | xargs npx protoc --ts_out=src/grpc -I../protos/

where_fi/visualise/frontend/src/components/Figure.vue

@@ -11,6 +11,18 @@ import { computed } from 'vue'
 const { figure, paused = false } = defineProps<{ figure: Figure; paused?: boolean }>()
 const cancel = ref<boolean>(false)
 
+function reshape(data: number[], width: number) {
+  if (data.length % width != 0) {
+    throw new Error('Data length is not divisible by width')
+  }
+  const height = data.length / width
+  const result: number[][] = new Array(height)
+  for (let i = 0; i < height; i++) {
+    result[i] = data.slice(i * width, (i + 1) * width)
+  }
+  return result
+}
+
 const data = ref<FigureData | null>(null)
 const plotData = computed(() => {
   if (data.value) {
@@ -24,16 +36,12 @@ const plotData = computed(() => {
           color: line.color ? line.color : undefined,
         }))
       case 'heatmap':
-        return []
+        return [
-      /* data.value.figure.heatmap.lines.map((line) => {
+          {
-          return {
+            z: reshape(data.value.figure.heatmap.data, data.value.figure.heatmap.width),
-            x: line.x,
+            type: 'heatmap' as const,
-            y: line.y,
+          },
-            type: 'scatter',
+        ]
-            name: line.label,
-            color: line.color,
-          }
-        })*/
     }
   }
   return []
@@ -43,6 +51,7 @@ const layout = {
   title: { text: figure.title },
   xaxis: { title: { text: figure.xLabel } },
   yaxis: { title: { text: figure.yLabel } },
+  height: 700,
 }
 
 function updateGraph() {

where_fi/visualise/protos/figure_type/heatmap.proto

@@ -2,9 +2,8 @@ syntax = "proto3";
 
 message HeatmapData {
     string uuid = 1;
-    repeated float x = 2;
+    repeated float data = 2;
-    repeated float y = 3;
+    uint32 width = 3;
-    uint32 width = 4;
+    uint32 height = 4;
-    uint32 height = 5;
+    string cmap = 5;
-    string cmap = 6;
 }

where_fi/visualise/server/__init__.py

@@ -23,8 +23,9 @@ clients_lock = threading.Lock()
 
 class DataType(Enum):
     RAW_CSI = 1
-    PROCESSED_CSI = 2
+    UNWRAPPED_PHASE = 2
-    HEATMAP = 3
+    PROCESSED_CSI = 3
+    HEATMAP = 4
 
 
 @dataclass
@@ -40,6 +41,12 @@ figures = {
         x_label="Subcarrier",
         y_label="Phase",
     ),
+    "unwrapped_phase": figure_pb2.Figure(
+        uuid=str(uuid.uuid4()),
+        title="Unwrapped CSI Phase",
+        x_label="Subcarrier",
+        y_label="Unwrapped phase",
+    ),
     "processed_phase": figure_pb2.Figure(
         uuid=str(uuid.uuid4()),
         title="Preprocessed CSI Phase",
@@ -48,9 +55,9 @@ figures = {
     ),
     "aoa_heatmap": figure_pb2.Figure(
         uuid=str(uuid.uuid4()),
-        title="Preprocessed CSI Phase",
+        title="AoA Heatmap",
-        x_label="Subcarrier",
+        x_label="ToF",
-        y_label="Phase",
+        y_label="AoA",
     ),
 }
 
@@ -104,7 +111,25 @@ def add_data(dtype: DataType, new_data: npt.NDArray[np.complex128]) -> None:
             for q in clients.get(uuid, []):
                 q.put(figure_pb2.FigureData(uuid=uuid, line=linechart))
         case DataType.HEATMAP:
+            uuid = figures["aoa_heatmap"].uuid
+            heatmap = heatmap_pb2.HeatmapData(
+                uuid=uuid,
+                data=new_data.flatten(),
+                width=new_data.shape[1],
+                height=new_data.shape[0],
+            )
+            for q in clients.get(uuid, []):
+                q.put(figure_pb2.FigureData(uuid=uuid, heatmap=heatmap))
             pass
+        case DataType.UNWRAPPED_PHASE:
+            uuid = figures["unwrapped_phase"].uuid
+            lines = [
+                line_pb2.LineChartData.Line(y=new_data[:, i, 0], label=f"Antenna {i}")
+                for i in range(new_data.shape[1])
+            ]
+            linechart = line_pb2.LineChartData(lines=lines)
+            for q in clients.get(uuid, []):
+                q.put(figure_pb2.FigureData(uuid=uuid, line=linechart))
 
 
 def listen_for_data(data_queue: "mp.Queue[VisualiserData]") -> None: