p3 improvements

docs/STATUS.md

@@ -1,10 +1,11 @@
 # Impakt -- Project Status
 
-**Date:** 2026-04-10
+**Date:** 2026-04-11
 **Version:** 0.1.0
-**Tests:** 181 passing
+**Tests:** 240 passing (69.7% coverage)
-**Source:** ~11,000 lines (py+js+css+html) | ~2,150 lines tests
+**Source:** ~10,300 lines (py) | ~2,700 lines tests
-**Tooling:** uv (Python 3.12.12), hatchling build backend
+**Quality:** 78.3/100 (Grade B) -- see `docs/QA-*.md`
+**Tooling:** uv (Python 3.12.12), hatchling build backend, ruff, mypy strict
 
 ---
 
@@ -13,7 +14,7 @@
 ```bash
 cd /Users/noise/Code/impakt
 uv sync --dev          # install all dependencies
-uv run pytest tests/   # run all 181 tests
+uv run pytest tests/   # run all 240 tests (with coverage)
 uv run impakt info tests/mme_data/3239         # show test metadata
 uv run impakt serve tests/mme_data/3239        # launch web UI on :8050
 ```
@@ -211,13 +212,59 @@ Every selected channel has a stable color index (position in selection order). T
 
 ---
 
-## Next Steps (Priority 3)
+## Roadmap
 
-1. **Annotations** -- text on plots, measurement lines, highlight regions
+Informed by competitive landscape survey (`research/landscape.md`). No open-source web-based tool covers this domain end-to-end. See `BRAINSTORM.md` for full feature ideas with priority tiers.
-2. **Comparison mode** -- side-by-side tests, delta plots, synced cursors
+
-3. **Report builder** -- drag-and-drop report composer, PDF preview
+### Priority 3 — Performance & Rendering
-4. **Keyboard shortcuts** -- Ctrl+O, Ctrl+S, 1-9 pane switch, F fullscreen, R reset zoom
+
-5. **Consider Python/WASM frontend** -- NiceGUI or Solara for pure-Python UI (no JS)
+Goal: handle large datasets (500+ channels, 100kHz sample rates) without lag.
+
+1. **plotly-resampler integration** -- Drop-in `FigureResampler` wrapper for Plotly figures. Handles 110M+ points via LTTB downsampling. Works natively with Dash. Repo cloned at `research/repos/plotly-resampler/`. This is the single highest-impact performance improvement.
+2. **Synchronized zoom/pan** -- When plotting multiple subplots, zoom/pan syncs across all panes sharing an X axis. Most-requested feature in crash test visualization. Implement via shared `xaxis` config or callback-based range sync.
+3. **Lazy channel loading** -- Load `.dat` files on first access, not at `Session.open()`. Load headers eagerly, data lazily. Keeps startup fast for tests with 500+ channels.
+4. **Channel sparklines** -- Tiny inline sparklines in the channel grid sidebar. Engineers visually scan 100+ channels before selecting. A 60px-wide sparkline column is transformative for signal browsing.
+
+### Priority 4 — Data Format Expansion
+
+Goal: read the world's crash test data, not just ISO MME.
+
+5. **UDS reader plugin** -- NHTSA's proprietary binary format. Required to access the largest public crash test database. The NHTSA-Tools Fortran source (`research/repos/NHTSA-Tools/`) documents the UDS spec.
+6. **ASAM MDF reader plugin** -- Standard for ECU/CAN bus measurement data. Many labs record vehicle bus data alongside crash instrumentation. asammdf (`research/repos/asammdf/`) is a mature library — add as optional dependency.
+7. **Flexible CSV reader** -- Column mapping, delimiter detection, header conventions. Engineers frequently receive data as CSV exports from other tools.
+
+### Priority 5 — Comparison & Reporting
+
+Goal: make multi-test comparison and deliverable generation effortless.
+
+8. **Quick comparison mode** -- Two tests side-by-side with synchronized cursors. One-click "compare" button. Color-by-test with channel differentiation via line dash.
+9. **Multi-page PDF reports** -- Combine plots + injury summary + protocol rating into a single PDF with table of contents. Currently each report type is standalone.
+10. **Excel export** -- Criteria results and cursor values to .xlsx. Engineers live in spreadsheets.
+11. **Static HTML export** -- Bundle data + Plotly.js into a self-contained HTML file. Opens in any browser without a Python server. Learned from FalCon's CustomerView distribution model.
+
+### Priority 6 — Video & Advanced Analysis
+
+Goal: close the biggest remaining gap vs. commercial tools.
+
+12. **Video synchronization** -- Link high-speed camera footage with channel data. Scrubbing video moves the time cursor; moving the cursor seeks the video. Every major commercial competitor (measX, DIAdem, Kistler, FalCon) has this. Foxglove and Rerun demonstrate web-native approaches.
+13. **Frequency spectrum viewer** -- FFT / PSD alongside time-domain plots. Diagnose noise, verify CFC filter behavior.
+14. **Integration / differentiation transforms** -- Acceleration -> velocity -> displacement with cumulative unit tracking.
+15. **Data quality dashboard** -- Automated polarity check, sensor sanity, missing channel detection, completeness scoring. No commercial competitor is strong here — opportunity to differentiate.
+
+### Priority 7 — Simulation Correlation & Ecosystem
+
+Goal: bridge the gap between physical test and CAE simulation.
+
+16. **LS-DYNA data import** via lasso-python (`research/repos/lasso-python/`). Enables test-vs-simulation overlay — a premium feature in Altair HyperGraph and Siemens Simcenter.
+17. **ISO/TS 18571 CORA correlation** -- Quantitative rating of test-vs-simulation agreement. Standard metric for model validation.
+18. **Additional injury criteria** -- BrIC, DAMAGE, TTI, pedestrian criteria, OLC. Required for broader Euro NCAP coverage.
+19. **Additional NCAP programs** -- J-NCAP, C-NCAP, K-NCAP, ANCAP, Latin NCAP as protocol plugins.
+20. **Jupyter integration** -- `_repr_html_` on Session, Channel, ProtocolResult for rich notebook output.
+
+### Validation (ongoing)
+
+- **Cross-validate CFC filter** against PyAvia's J211_2pole (`research/repos/pyavia/`) and NHTSA-Tools' BwFilt. PyAvia's author notes that scipy's generic `sosfiltfilt` may differ from the SAE J211 Appendix C digital Butterworth algorithm for CFC 60 and 180.
+- **Cross-validate injury criteria** against NHTSA-Tools Fortran reference implementations, pyisomme, and EPFL crash-tests-service-robots. Four independent codebases available in `research/repos/`.
 
 ---
 
@@ -238,7 +285,8 @@ Every selected channel has a stable color index (position in selection order). T
 
 Core: numpy, scipy, plotly, dash, dash-bootstrap-components, pandas, pyyaml, jinja2, weasyprint, pydantic
 Dev: pytest, pytest-cov, ruff, mypy
-Optional: nptdms (for future TDMS reader plugin)
+Optional: nptdms (TDMS reader plugin)
+Planned: plotly-resampler (P3), asammdf (P4), openpyxl (P5), lasso-python (P7)
 
 ---
 
@@ -249,4 +297,29 @@ Optional: nptdms (for future TDMS reader plugin)
 3. **DataTable deprecation warning** -- Dash recommends migrating to dash-ag-grid.
 4. **Cursor poll interval (80ms)** -- slight latency in cursor grid updates.
 5. **Chest deflection auto-detect** skips DS channels with peak > 150mm to avoid steering column displacement.
-6. **Dead files** -- `cursors.py` and `inspector.py` were replaced by `channel_values.py` and deleted; `inspector_callbacks.py` deleted. If old `.pyc` files cause issues, run `find src -name __pycache__ -type d | xargs rm -rf`.
+6. **CFC filter implementation** uses scipy `sosfiltfilt` which may diverge from SAE J211 Appendix C for CFC 60/180. Needs cross-validation against PyAvia and NHTSA-Tools reference implementations.
+
+---
+
+## Quality Assurance
+
+Automated QA scoring is configured:
+- **Scoring agent:** `.claude/agents/quality-scorer.md` -- collects metrics, applies rubrics, writes report
+- **Improvement agent:** `.claude/agents/qa-improver.md` -- reads QA report, auto-fixes mechanical issues
+- **Methodology:** `docs/QA-INSTRUCTIONS.md` -- reproducible 8-dimension rubric
+- **Reports:** `docs/QA-*.md` -- timestamped scorecards with deltas
+- **Scripts:** `scripts/qa-score.sh` (score only), `scripts/qa-improve.sh` (score -> fix -> re-score)
+
+---
+
+## Competitive Landscape
+
+Full survey at `research/landscape.md` with 11 cloned open-source repos in `research/repos/`.
+
+**Key finding:** No existing tool combines open-source + web-based + ISO-MME + CFC + injury criteria + protocol scoring + templates + reports. Commercial tools (measX X-Crash, NI DIAdem, Kistler) do this but are expensive and Windows-only. Impakt occupies a genuinely unserved niche.
+
+**Most actionable libraries:**
+- plotly-resampler (performance) -- `research/repos/plotly-resampler/`
+- asammdf (MDF format) -- `research/repos/asammdf/`
+- lasso-python (LS-DYNA) -- `research/repos/lasso-python/`
+- PyAvia, NHTSA-Tools (validation) -- `research/repos/pyavia/`, `research/repos/NHTSA-Tools/`

pyproject.toml

@@ -35,6 +35,8 @@ dependencies = [
     "jinja2>=3.1",
     "weasyprint>=60.0",
     "pydantic>=2.0",
+    "plotly-resampler>=0.11.0",
+    "pytz>=2026.1.post1",
 ]
 
 [project.optional-dependencies]

src/impakt/plot/engine.py

@@ -5,17 +5,24 @@ support for corridors, dual X-cursors, and export.
 
 The PlotEngine is the **single rendering path** — both the scripting
 API and the web UI construct PlotSpec objects and delegate here.
+
+When ``resample=True``, wraps the figure in a plotly-resampler
+``FigureResampler`` for dynamic LTTB downsampling on zoom/pan.
 """
 
 from __future__ import annotations
 
+import logging
 from typing import Any
 
 import plotly.graph_objects as go
+from plotly_resampler import FigureResampler
 
 from impakt.channel.model import Channel
 from impakt.plot.spec import Corridor, CursorValues, PlotSpec
 
+logger = logging.getLogger(__name__)
+
 # Default color palette (colorblind-friendly)
 DEFAULT_COLORS = [
     "#1f77b4",  # blue
@@ -39,11 +46,32 @@ class PlotEngine:
     - **Compact** (``spec.compact=True``): Web UI mode with no legend, no
       hover tooltips, tight margins, smaller axis labels. Cursor tracking
       is handled by external JS.
+
+    When ``resample=True``, the figure is wrapped in a
+    ``FigureResampler`` that dynamically downsamples traces via LTTB
+    aggregation on zoom/pan events.
     """
 
-    def render(self, spec: PlotSpec) -> go.Figure:
+    def render(
-        """Render a PlotSpec into an interactive Plotly figure."""
+        self, spec: PlotSpec, *, resample: bool = False
-        fig = go.Figure()
+    ) -> go.Figure | FigureResampler:
+        """Render a PlotSpec into an interactive Plotly figure.
+
+        Args:
+            spec: The plot specification.
+            resample: If True, wrap in FigureResampler for dynamic
+                downsampling. The caller must handle ``relayoutData``
+                callbacks to trigger resampling on zoom/pan.
+
+        Returns:
+            A ``go.Figure`` (default) or ``FigureResampler`` (when
+            ``resample=True``).
+        """
+        fig: go.Figure | FigureResampler
+        if resample:
+            fig = FigureResampler(go.Figure(), default_n_shown_samples=1500)
+        else:
+            fig = go.Figure()
 
         if not spec.channels:
             fig.update_layout(
@@ -67,7 +95,7 @@ class PlotEngine:
 
         # Add corridor fills first (behind data traces)
         for corridor in spec.corridors:
-            self._add_corridor(fig, corridor)
+            self._add_corridor(fig, corridor, resample=resample)
 
         # Add channel traces
         for i, ch_ref in enumerate(spec.channels):
@@ -84,8 +112,6 @@ class PlotEngine:
             label = style.label or ch_ref.label
 
             trace_kwargs: dict[str, Any] = {
-                "x": ch.time,
-                "y": ch.data,
                 "mode": "lines",
                 "name": label,
                 "line": dict(
@@ -101,7 +127,18 @@ class PlotEngine:
                     f"{label}<br>t=%{{x:.6f}}s<br>%{{y:.4f}} {ch.unit}<extra></extra>"
                 )
 
-            fig.add_trace(go.Scatter(**trace_kwargs))
+            if resample:
+                # FigureResampler: pass full-resolution data via hf_x/hf_y
+                # and use Scattergl for WebGL-accelerated rendering
+                fig.add_trace(
+                    go.Scattergl(**trace_kwargs),
+                    hf_x=ch.time,
+                    hf_y=ch.data,
+                )
+            else:
+                trace_kwargs["x"] = ch.time
+                trace_kwargs["y"] = ch.data
+                fig.add_trace(go.Scatter(**trace_kwargs))
 
         # Add cursor lines
         if spec.x_cursors:
@@ -181,33 +218,43 @@ class PlotEngine:
 
         return fig
 
-    def _add_corridor(self, fig: go.Figure, corridor: Corridor) -> None:
+    def _add_corridor(
+        self,
+        fig: go.Figure | FigureResampler,
+        corridor: Corridor,
+        *,
+        resample: bool = False,
+    ) -> None:
         """Add a corridor (tolerance band) to the figure."""
         style = corridor.style
 
-        fig.add_trace(
+        upper_trace = go.Scatter(
-            go.Scatter(
+            x=corridor.time,
-                x=corridor.time,
+            y=corridor.upper,
-                y=corridor.upper,
+            mode="lines",
-                mode="lines",
+            name=f"{corridor.name} (upper)",
-                name=f"{corridor.name} (upper)",
+            line=dict(color=style.line_color, width=style.line_width, dash=style.line_dash),
-                line=dict(color=style.line_color, width=style.line_width, dash=style.line_dash),
+            showlegend=False,
-                showlegend=False,
-            )
         )
-        fig.add_trace(
+        lower_trace = go.Scatter(
-            go.Scatter(
+            x=corridor.time,
-                x=corridor.time,
+            y=corridor.lower,
-                y=corridor.lower,
+            mode="lines",
-                mode="lines",
+            name=f"{corridor.name} (lower)",
-                name=f"{corridor.name} (lower)",
+            line=dict(color=style.line_color, width=style.line_width, dash=style.line_dash),
-                line=dict(color=style.line_color, width=style.line_width, dash=style.line_dash),
+            fill="tonexty",
-                fill="tonexty",
+            fillcolor=style.fill_color,
-                fillcolor=style.fill_color,
+            showlegend=True,
-                showlegend=True,
-            )
         )
 
+        if resample:
+            # Corridors are small — pass through without downsampling
+            fig.add_trace(upper_trace, limit_to_view=False)
+            fig.add_trace(lower_trace, limit_to_view=False)
+        else:
+            fig.add_trace(upper_trace)
+            fig.add_trace(lower_trace)
+
     def to_image(self, spec: PlotSpec, format: str = "png", scale: float = 2.0) -> bytes:
         """Render to a static image."""
         fig = self.render(spec)

src/impakt/web/callbacks/plot_callbacks.py

@@ -6,17 +6,23 @@ scripting API renders the web UI plots.
 
 Transform application uses TransformChain, making the pipeline
 serializable and reproducible.
+
+The plot figure is wrapped in a ``FigureResampler`` for dynamic LTTB
+downsampling on zoom/pan events. A separate ``relayoutData`` callback
+triggers resampling when the user interacts with the axes.
 """
 
 from __future__ import annotations
 
+import logging
 from collections import defaultdict
 from typing import Any
 
 import dash
 import numpy as np
 import plotly.graph_objects as go
-from dash import Input, Output, State
+from dash import Input, Output, State, no_update
+from plotly_resampler import FigureResampler
 
 from impakt.channel.model import Channel
 from impakt.plot.engine import DEFAULT_COLORS, PlotEngine
@@ -27,9 +33,15 @@ from impakt.transform.cfc import CFCFilter
 from impakt.transform.resultant import resultant_from_channels
 from impakt.web.state import AppState
 
+logger = logging.getLogger(__name__)
+
 # Module-level engine instance (stateless, safe to reuse)
 _engine = PlotEngine()
 
+# Server-side storage for the current FigureResampler instance.
+# Single-user desktop app — no multi-tenant isolation needed.
+_current_resampler: FigureResampler | None = None
+
 
 def _build_transform_chain(
     cfc_value: str,
@@ -201,6 +213,7 @@ def _build_plot_spec(
 
 def register_plot_callbacks(app: dash.Dash, app_state: AppState) -> None:
     """Register all plot-related callbacks."""
+    global _current_resampler
 
     @app.callback(
         Output({"type": "plot-graph", "index": 0}, "figure"),
@@ -231,6 +244,8 @@ def register_plot_callbacks(app: dash.Dash, app_state: AppState) -> None:
         corridors_data: list[dict[str, Any]] | None,
         x_align_value: float | None,
     ) -> go.Figure:
+        global _current_resampler
+
         if not selected_keys:
             selected_keys = []
 
@@ -245,4 +260,25 @@ def register_plot_callbacks(app: dash.Dash, app_state: AppState) -> None:
         )
 
         spec = _build_plot_spec(channels, cursor_x1, cursor_x2, corridors_data)
-        return _engine.render(spec)
+        fig = _engine.render(spec, resample=True)
+
+        # Store the resampler for relayoutData callbacks
+        if isinstance(fig, FigureResampler):
+            _current_resampler = fig
+
+        return fig
+
+    @app.callback(
+        Output({"type": "plot-graph", "index": 0}, "figure", allow_duplicate=True),
+        Input({"type": "plot-graph", "index": 0}, "relayoutData"),
+        prevent_initial_call=True,
+    )
+    def resample_on_zoom(relayout_data: dict[str, Any] | None) -> Any:
+        """Resample traces when the user zooms or pans.
+
+        FigureResampler intercepts relayoutData events and returns a
+        Patch that updates only the visible trace data — no full re-render.
+        """
+        if _current_resampler is None or relayout_data is None:
+            return no_update
+        return _current_resampler.construct_update_data_patch(relayout_data)

src/impakt/web/components/channel_grid.py

@@ -13,17 +13,59 @@ without reordering.
 
 from __future__ import annotations
 
+import base64
 import fnmatch
 from typing import Any
 
 import dash_bootstrap_components as dbc
+import numpy as np
 from dash import dash_table, dcc, html
 
 from impakt.channel.lookup import MAIN_LOCATIONS, MEASUREMENTS
+from impakt.channel.model import Channel
 from impakt.plot.engine import DEFAULT_COLORS
 from impakt.web.state import AppState
 
 
+def _sparkline_svg(ch: Channel, width: int = 60, height: int = 16) -> str:
+    """Generate a tiny inline SVG sparkline for a channel.
+
+    Returns a base64-encoded data URI suitable for use in a Dash
+    DataTable markdown cell: ``![](data:image/svg+xml;base64,...)``.
+    """
+    data = ch.data
+    n = len(data)
+    if n < 2:
+        return ""
+
+    # Downsample to ~60 points for the sparkline
+    if n > 60:
+        indices = np.linspace(0, n - 1, 60, dtype=int)
+        data = data[indices]
+        n = 60
+
+    # Normalize to SVG coordinate space
+    dmin = float(np.min(data))
+    dmax = float(np.max(data))
+    drange = dmax - dmin
+    if drange == 0:
+        drange = 1.0
+
+    xs = np.linspace(1, width - 1, n)
+    ys = height - 1 - ((data - dmin) / drange) * (height - 2)
+
+    # Build SVG polyline path
+    points = " ".join(f"{x:.1f},{y:.1f}" for x, y in zip(xs, ys))
+    svg = (
+        f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}">'
+        f'<polyline points="{points}" fill="none" stroke="#6c757d" stroke-width="1"/>'
+        f"</svg>"
+    )
+
+    encoded = base64.b64encode(svg.encode()).decode()
+    return f'<img src="data:image/svg+xml;base64,{encoded}" />'
+
+
 def _build_channel_rows(app_state: AppState) -> list[dict[str, Any]]:
     """Build flat row data for the channel grid.
 
@@ -63,6 +105,7 @@ def _build_channel_rows(app_state: AppState) -> list[dict[str, Any]]:
                     "test_id": test_id if multi_test else "",
                     "iso_code": ch.name,
                     "description": desc,
+                    "spark": _sparkline_svg(ch),
                     "unit": ch.unit,
                     "min": f"{float(ch.data.min()):.2f}",
                     "max": f"{float(ch.data.max()):.2f}",
@@ -115,6 +158,7 @@ def build_channel_grid(app_state: AppState) -> dbc.Card:
         {"name": "#", "id": "ch_num", "type": "numeric"},
         {"name": "ISO Code", "id": "iso_code"},
         {"name": "Description", "id": "description"},
+        {"name": "", "id": "spark", "presentation": "markdown"},
         {"name": "Unit", "id": "unit"},
         {"name": "Min", "id": "min", "type": "numeric"},
         {"name": "Max", "id": "max", "type": "numeric"},
@@ -207,6 +251,7 @@ def build_channel_grid(app_state: AppState) -> dbc.Card:
                             id="channel-grid",
                             columns=columns,
                             data=rows,
+                            markdown_options={"html": True},
                             row_selectable="multi",
                             selected_rows=[],
                             sort_action="native",
@@ -255,6 +300,12 @@ def build_channel_grid(app_state: AppState) -> dbc.Card:
                                     "width": "50px",
                                     "textAlign": "center",
                                 },
+                                {
+                                    "if": {"column_id": "spark"},
+                                    "width": "68px",
+                                    "textAlign": "center",
+                                    "padding": "2px 4px",
+                                },
                                 {"if": {"column_id": "min"}, "width": "65px", "textAlign": "right"},
                                 {"if": {"column_id": "max"}, "width": "65px", "textAlign": "right"},
                                 {"if": {"column_id": "test_id"}, "width": "60px"},

uv.lock

@@ -535,7 +535,9 @@ dependencies = [
     { name = "numpy" },
     { name = "pandas" },
     { name = "plotly" },
+    { name = "plotly-resampler" },
     { name = "pydantic" },
+    { name = "pytz" },
     { name = "pyyaml" },
     { name = "scipy" },
     { name = "weasyprint" },
@@ -563,7 +565,9 @@ requires-dist = [
     { name = "numpy", specifier = ">=1.24" },
     { name = "pandas", specifier = ">=2.0" },
     { name = "plotly", specifier = ">=5.18" },
+    { name = "plotly-resampler", specifier = ">=0.11.0" },
     { name = "pydantic", specifier = ">=2.0" },
+    { name = "pytz", specifier = ">=2026.1.post1" },
     { name = "pyyaml", specifier = ">=6.0" },
     { name = "scipy", specifier = ">=1.10" },
     { name = "weasyprint", specifier = ">=60.0" },
@@ -932,6 +936,74 @@ wheels = [
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 ]
 
+[[package]]
+name = "orjson"
+version = "3.11.8"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/1b/2024d06792d0779f9dbc51531b61c24f76c75b9f4ce05e6f3377a1814cea/orjson-3.11.8.tar.gz", hash = "sha256:96163d9cdc5a202703e9ad1b9ae757d5f0ca62f4fa0cc93d1f27b0e180cc404e", size = 5603832, upload-time = "2026-03-31T16:16:27.878Z" }
+wheels = [
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