add phase analysis visualisation

where_fi/cli/__init__.py

@@ -1,6 +1,7 @@
 import logging
 import multiprocessing as mp
-from typing import Any
+from typing import Any, cast
+from queue import Queue
 
 import numpy as np
 import numpy.typing as npt
@@ -63,4 +64,50 @@ def heatmap() -> None:
     logger.info("Finished processing CSI data")
 
 
+@app.command()
+def phase_analysis(
+    subcarrier: int = 0, rx_antenna: int = 0, tx_antenna: int = 0
+) -> None:
+    """
+    Visualise the phase information in the CSI data received from the antennas.
+
+    The data goes through the same preprocessing steps as the heatmap command, but
+    instead of going through the AoA estimation, we simply analyse the phase of the
+    selected subcarrier and antenna.
+    """
+
+    preprocessor = Preprocessor()
+
+    # Start webapp in background process
+    webapp_queue: "mp.Queue[visualise.VisualiserData]" = mp.Queue(config.sample_rate)
+    webapp = mp.Process(target=visualise.start, args=(webapp_queue,))
+    webapp.start()
+
+    subcarrier_phase: Queue[float] = Queue(config.sample_rate)
+
+    def visualise_data(data: npt.NDArray[Any], dtype: visualise.figures.Figure) -> None:
+        if not webapp_queue.full():
+            webapp_queue.put(visualise.VisualiserData(data, dtype))
+
+    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.figures.Figure.RAW_CSI)
+
+        processed = preprocessor.preprocess(antenna_data, visualiser=visualise_data)
+        visualise_data(processed, visualise.figures.Figure.PROCESSED_CSI)
+
+        phase = cast(float, np.angle(processed[subcarrier, rx_antenna, tx_antenna]))
+
+        if subcarrier_phase.full():
+            subcarrier_phase.get()
+        subcarrier_phase.put(phase)
+
+        visualise_data(
+            np.array(subcarrier_phase), visualise.figures.Figure.PHASE_ANALYSIS
+        )
+
+    globals.csi_producer(csi_callback=callback)
+    logger.info("Finished processing CSI data")
+
+
 app.add_typer(file.app, name="file", help="Commands for working with CSI files")

where_fi/visualise/server/__init__.py

@@ -30,7 +30,7 @@ class FigureServer(figure_pb2_grpc.FigureServiceServicer):
         self, request: figure_pb2.FigureRequest, context: grpc.ServicerContext
     ) -> Generator[figure_pb2.Figure, None, None]:
         for figure_group in figures.Figure:
-            for figure in figures.all_figures[figure_group].figures:
+            for figure in figure_group.figure_class().figures:
                 yield figure
 
     def GetFigureUpdate(

where_fi/visualise/server/figures.py

@@ -58,10 +58,17 @@ class SimpleLineChart:
         )
 
     def update(
-        self, new_data: npt.NDArray[np.complex128], labels: Sequence[str]
+        self,
+        new_data: npt.NDArray[np.complex128],
+        labels: Sequence[str],
+        x_values: None | npt.NDArray[np.float64] = None,
     ) -> FigureUpdate:
         lines = [
-            line_pb2.LineChartData.Line(y=new_data[i], label=labels[i])
+            line_pb2.LineChartData.Line(
+                y=new_data[i],
+                label=labels[i],
+                x=x_values[i] if x_values is not None else None,
+            )
             for i in range(new_data.shape[0])
         ]
         return {
@@ -156,12 +163,28 @@ class HeatmapFigure(SpecificFigure):
         return {self.figure.uuid: figure_pb2.FigureData(heatmap=heatmap)}
 
 
+class EmpiricalCDF(SpecificFigure):
+    """Helper class to create a graph of the empirical cumulative distribution function
+    of a set of observations.
+    """
+
+    def __init__(self, title: str, x_label: str) -> None:
+        self.chart = SimpleLineChart(title, x_label, "Cumulative Probability")
+        self.figures = [self.chart.figure]
+
+    def update(self, new_data: npt.NDArray[np.complex128]) -> FigureUpdate:
+        new_data = np.expand_dims(np.sort(new_data), 0)
+        y_values = np.expand_dims(np.linspace(0, 1, new_data.size), 0)
+        return self.chart.update(y_values, labels=["Frequency"], x_values=new_data)
+
+
 class Figure(Enum):
     RAW_CSI = 0
     UNWRAPPED_PHASE = 1
     PROCESSED_CSI = 2
     MUSIC_EIGENVALUES = 3
     AOA_HEATMAP = 4
+    PHASE_ANALYSIS = 5
 
     def figure_class(self) -> SpecificFigure:
         return all_figures[self]
@@ -187,4 +210,5 @@ all_figures = {
     ),
     Figure.MUSIC_EIGENVALUES: MusicEigenvalueHistogram(),
     Figure.AOA_HEATMAP: HeatmapFigure(),
+    Figure.PHASE_ANALYSIS: EmpiricalCDF("Phase Analysis", "Phase"),
 }