init commit

python_app/gui/plotting/__init__.py

@@ -0,0 +1,19 @@
+"""Plotting helpers for trace and B-scan visualization."""
+
+from python_app.gui.plotting.bscan_history import (
+    build_bscan_signature,
+    pick_bscan_display_key,
+    rebuild_bscan_history_from_results,
+)
+from python_app.gui.plotting.bscan_math import (
+    bscan_levels,
+    bscan_lookup_table,
+)
+
+__all__ = [
+    "bscan_levels",
+    "bscan_lookup_table",
+    "build_bscan_signature",
+    "pick_bscan_display_key",
+    "rebuild_bscan_history_from_results",
+]

python_app/gui/plotting/bscan_history.py

@@ -0,0 +1,114 @@
+"""Helpers for B-scan history signatures and cache rebuilding."""
+
+from __future__ import annotations
+
+from collections import deque
+
+import numpy as np
+
+from python_app.models.dataset_model import ResultCollection
+from python_app.orchestration.live_processing_config import ProcessingLiveConfig
+
+
+def _result_tail(
+    *,
+    result_history: list[ResultCollection],
+    history_limit: int,
+    floor_collection_id: int,
+) -> list[ResultCollection]:
+    """Return filtered and de-duplicated result-history tail for B-scan usage."""
+    filtered = [
+        collection
+        for collection in result_history[-history_limit:]
+        if int(collection.collection_id) > int(floor_collection_id)
+    ]
+    unique_tail: list[ResultCollection] = []
+    seen_keys: set[tuple[int, int]] = set()
+    for collection in filtered:
+        key = (int(collection.collection_id), int(collection.monotonic_ns))
+        if key in seen_keys:
+            continue
+        seen_keys.add(key)
+        unique_tail.append(collection)
+    return unique_tail
+
+
+def build_bscan_signature(
+    live_config: ProcessingLiveConfig,
+    result_history: list[ResultCollection],
+    history_limit: int,
+    floor_collection_id: int,
+) -> tuple[object, ...]:
+    """Build deterministic signature used to detect B-scan cache invalidation."""
+    result_tail = _result_tail(
+        result_history=result_history,
+        history_limit=history_limit,
+        floor_collection_id=floor_collection_id,
+    )
+    return (
+        str(live_config.bscan_axis),
+        float(live_config.bscan_cut_m),
+        float(live_config.bscan_max_depth_m),
+        float(live_config.bscan_gain),
+        float(live_config.bscan_start_freq_mhz),
+        float(live_config.bscan_stop_freq_mhz),
+        int(floor_collection_id),
+        tuple((int(collection.collection_id), int(collection.monotonic_ns), len(collection.blocks)) for collection in result_tail),
+    )
+
+
+def rebuild_bscan_history_from_results(
+    result_history: list[ResultCollection],
+    history_limit: int,
+    floor_collection_id: int,
+) -> tuple[dict[tuple[int, int], deque[np.ndarray]], dict[tuple[int, int], np.ndarray]]:
+    """Rebuild B-scan history and depth axes from processed result payloads."""
+    history_by_combo: dict[tuple[int, int], deque[np.ndarray]] = {}
+    depth_axis_by_combo: dict[tuple[int, int], np.ndarray] = {}
+
+    result_tail = _result_tail(
+        result_history=result_history,
+        history_limit=history_limit,
+        floor_collection_id=floor_collection_id,
+    )
+
+    for collection in result_tail:
+        for block in collection.blocks:
+            key = (block.combo.input_pos, block.combo.output_pos)
+            for payload in block.payloads:
+                if payload.kind != 1 or payload.processing_name != "bscan":
+                    continue
+                if payload.frequency_hz.size == 0 or payload.trace.size == 0:
+                    continue
+                if payload.frequency_hz.size != payload.trace.size:
+                    continue
+
+                depth_axis = np.asarray(payload.frequency_hz, dtype=np.float32)
+                amplitudes = np.asarray(np.real(payload.trace), dtype=np.float32)
+                if depth_axis.size == 0 or amplitudes.size == 0:
+                    continue
+
+                history = history_by_combo.get(key)
+                stored_axis = depth_axis_by_combo.get(key)
+                if (
+                    history is None
+                    or stored_axis is None
+                    or stored_axis.shape != depth_axis.shape
+                    or not np.allclose(stored_axis, depth_axis, rtol=1e-4, atol=1e-6)
+                ):
+                    history = deque(maxlen=history_limit)
+                    history_by_combo[key] = history
+                    depth_axis_by_combo[key] = depth_axis.copy()
+
+                history.append(amplitudes.copy())
+
+    return history_by_combo, depth_axis_by_combo
+
+
+def pick_bscan_display_key(
+    history_by_combo: dict[tuple[int, int], deque[np.ndarray]],
+) -> tuple[int, int] | None:
+    """Choose combo key to display when multiple histories are present."""
+    if not history_by_combo:
+        return None
+    return next(iter(history_by_combo.keys()))

python_app/gui/plotting/bscan_math.py

@@ -0,0 +1,37 @@
+"""Color-scaling helpers for B-scan visualization."""
+
+from __future__ import annotations
+
+import numpy as np
+import pyqtgraph as pg
+
+def bscan_lookup_table(axis_mode: str) -> np.ndarray:
+    """Build B-scan colormap table for selected axis mode."""
+    if axis_mode == "abs":
+        return build_lut(["#440154", "#31688e", "#35b779", "#fde725"])
+    return build_lut(["#2166ac", "#67a9cf", "#f7f7f7", "#ef8a62", "#b2182b"])
+
+
+def build_lut(stops: list[str], *, size: int = 256) -> np.ndarray:
+    """Interpolate hex color stops into 8-bit RGB LUT array."""
+    stop_positions = np.linspace(0.0, 1.0, num=len(stops), dtype=np.float32)
+    sample_positions = np.linspace(0.0, 1.0, num=size, dtype=np.float32)
+    stop_colors = np.asarray([pg.mkColor(value).getRgb()[:3] for value in stops], dtype=np.float32)
+
+    lut = np.empty((size, 3), dtype=np.uint8)
+    for channel in range(3):
+        lut[:, channel] = np.interp(sample_positions, stop_positions, stop_colors[:, channel]).astype(np.uint8)
+    return lut
+
+
+def bscan_levels(sweeps: np.ndarray, axis_mode: str) -> tuple[float, float]:
+    """Compute image levels for B-scan data based on axis mode."""
+    min_value = float(np.min(sweeps))
+    max_value = float(np.max(sweeps))
+    if axis_mode == "abs":
+        if max_value <= min_value:
+            return min_value, min_value + 1e-6
+        return min_value, max_value
+
+    max_abs = max(abs(min_value), abs(max_value), 1e-6)
+    return -max_abs, max_abs