Upload heatmap.py

quread/heatmap.py

@@ -3,13 +3,21 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass
-from typing import Dict, Optional, Tuple
+from typing import Dict, Optional, Tuple, Any
 
 import numpy as np
 import matplotlib.pyplot as plt
 
 from .metrics import compute_metrics_from_csv, MetricWeights, MetricThresholds
 
+_HAS_PLOTLY = False
+try:
+    import plotly.graph_objects as go
+
+    _HAS_PLOTLY = True
+except Exception:
+    _HAS_PLOTLY = False
+
 logger = logging.getLogger(__name__)
 
 
@@ -85,6 +93,39 @@ def _resolve_metric(metric: str) -> str:
     return _METRIC_ALIASES.get(m, "activity_count")
 
 
+def plotly_available() -> bool:
+    return bool(_HAS_PLOTLY)
+
+
+def _build_metric_grid(
+    csv_text: str,
+    n_qubits: int,
+    metric: str,
+    cfg: HeatmapConfig,
+    calibration_json: str,
+    state_vector: Optional[np.ndarray],
+    weights: Optional[MetricWeights],
+    thresholds: Optional[MetricThresholds],
+    qubit_coords: Optional[Dict[int, Tuple[int, int]]],
+) -> Tuple[str, np.ndarray, np.ndarray, Dict[str, Any], Dict[int, Tuple[int, int]]]:
+    metric_key = _resolve_metric(metric)
+    coords = qubit_coords or _default_qubit_coords(n_qubits, cfg.rows, cfg.cols)
+    grid = np.full((cfg.rows, cfg.cols), cfg.missing_value, dtype=float)
+    metrics, meta = compute_metrics_from_csv(
+        csv_text,
+        int(n_qubits),
+        calibration_json=calibration_json,
+        state_vector=state_vector,
+        weights=weights,
+        thresholds=thresholds,
+    )
+    values = metrics[metric_key]
+    for q, (rr, cc) in coords.items():
+        if 0 <= q < n_qubits and 0 <= rr < cfg.rows and 0 <= cc < cfg.cols:
+            grid[rr, cc] = values[q]
+    return metric_key, grid, values, meta, coords
+
+
 def make_metric_heatmap(
     csv_text: str,
     n_qubits: int,
@@ -101,24 +142,17 @@ def make_metric_heatmap(
     Builds a heatmap for a selected metric computed from circuit CSV and optional calibration JSON.
     """
     cfg = cfg or HeatmapConfig()
-    coords = qubit_coords or _default_qubit_coords(n_qubits, cfg.rows, cfg.cols)
-    metric_key = _resolve_metric(metric)
-
-    grid = np.full((cfg.rows, cfg.cols), cfg.missing_value, dtype=float)
-    metrics, meta = compute_metrics_from_csv(
+    metric_key, grid, values, meta, coords = _build_metric_grid(
         csv_text,
         int(n_qubits),
-        calibration_json=calibration_json,
-        state_vector=state_vector,
-        weights=weights,
-        thresholds=thresholds,
+        str(metric),
+        cfg,
+        calibration_json,
+        state_vector,
+        weights,
+        thresholds,
+        qubit_coords,
     )
-    values = metrics[metric_key]
-
-    # place into chip grid
-    for q, (rr, cc) in coords.items():
-        if 0 <= q < n_qubits and 0 <= rr < cfg.rows and 0 <= cc < cfg.cols:
-            grid[rr, cc] = values[q]
 
     # plot
     fig, ax = plt.subplots(figsize=(6, 5))
@@ -194,6 +228,121 @@ def make_metric_heatmap(
     return fig
 
 
+def make_metric_heatmap_plotly(
+    csv_text: str,
+    n_qubits: int,
+    metric: str = "activity_count",
+    cfg: Optional[HeatmapConfig] = None,
+    calibration_json: str = "",
+    state_vector: Optional[np.ndarray] = None,
+    weights: Optional[MetricWeights] = None,
+    thresholds: Optional[MetricThresholds] = None,
+    highlight_threshold: Optional[float] = None,
+    qubit_coords: Optional[Dict[int, Tuple[int, int]]] = None,
+) -> Any:
+    """
+    Builds an interactive Plotly heatmap for zoom/pan exploration.
+    """
+    if not _HAS_PLOTLY:
+        raise RuntimeError("Plotly is not available in this environment.")
+
+    cfg = cfg or HeatmapConfig()
+    metric_key, grid, values, meta, coords = _build_metric_grid(
+        csv_text,
+        int(n_qubits),
+        str(metric),
+        cfg,
+        calibration_json,
+        state_vector,
+        weights,
+        thresholds,
+        qubit_coords,
+    )
+
+    colorscale = _METRIC_CMAP[metric_key]
+    zmin = float(np.min(grid))
+    zmax = float(np.max(grid))
+    if zmax <= zmin:
+        zmax = zmin + 1e-9
+
+    fig = go.Figure(
+        data=go.Heatmap(
+            z=grid,
+            colorscale=colorscale,
+            zmin=zmin,
+            zmax=zmax,
+            colorbar={"title": metric_key},
+            hoverongaps=False,
+        )
+    )
+
+    for q, (rr, cc) in coords.items():
+        if 0 <= rr < cfg.rows and 0 <= cc < cfg.cols:
+            fig.add_annotation(
+                x=cc,
+                y=rr,
+                text=f"q{q}",
+                showarrow=False,
+                font={"size": 10, "color": "#0f172a"},
+            )
+
+    notes = []
+    skipped = int(meta.get("skipped_rows", 0))
+    if skipped:
+        logger.warning("Skipped %d malformed CSV rows while building heatmap.", skipped)
+        notes.append(f"Skipped malformed CSV rows: {skipped}")
+
+    calibration_note = str(meta.get("calibration_note", "") or "").strip()
+    if calibration_note:
+        notes.append(calibration_note)
+
+    if highlight_threshold is not None:
+        thr = float(np.clip(float(highlight_threshold), 0.0, 1e9))
+        highlighted = 0
+        for q, (rr, cc) in coords.items():
+            if 0 <= q < n_qubits and 0 <= rr < cfg.rows and 0 <= cc < cfg.cols:
+                if float(values[q]) >= thr:
+                    highlighted += 1
+                    fig.add_shape(
+                        type="rect",
+                        x0=cc - 0.5,
+                        x1=cc + 0.5,
+                        y0=rr - 0.5,
+                        y1=rr + 0.5,
+                        line={"color": "#f59e0b", "width": 2},
+                        fillcolor="rgba(0,0,0,0)",
+                    )
+        notes.append(f"Highlighted qubits (value >= {thr:.4g}): {highlighted}")
+
+    fig.update_layout(
+        title=_METRIC_TITLES[metric_key],
+        xaxis={"title": "Chip column", "tickmode": "array", "tickvals": list(range(cfg.cols))},
+        yaxis={
+            "title": "Chip row",
+            "tickmode": "array",
+            "tickvals": list(range(cfg.rows)),
+            "autorange": "reversed",
+            "scaleanchor": "x",
+            "scaleratio": 1,
+        },
+        margin={"l": 50, "r": 30, "t": 70, "b": 50},
+        dragmode="pan",
+    )
+
+    if notes:
+        fig.add_annotation(
+            text=" | ".join(notes),
+            xref="paper",
+            yref="paper",
+            x=0.5,
+            y=-0.17,
+            showarrow=False,
+            font={"size": 11, "color": "#92400e"},
+        )
+
+    return fig
+
+
 def make_activity_heatmap(
     csv_text: str,
     n_qubits: int,