绘制训练过程热力图时使用传感器数据
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@ -3,6 +3,7 @@ import csv
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import json
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import os
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from typing import Dict, Iterable, List, Optional, Sequence, Tuple
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import xml.etree.ElementTree as ET
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import matplotlib
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import numpy as np
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@ -80,32 +81,173 @@ def _write_csv(path: str, rows: Iterable[Dict], fieldnames: Sequence[str]):
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writer.writerows(rows)
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def _plot_episode_heatmap(path: str, edge_rows: Sequence[Dict], edge_ids: Sequence[str], title_prefix: str):
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if not edge_rows or not edge_ids:
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def _lane_to_edge_id(lane_id: str) -> str:
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if "_" not in lane_id:
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return lane_id
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return lane_id.rsplit("_", 1)[0]
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def _parse_detector_layout(additional_path: str) -> List[Dict]:
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if not os.path.isfile(additional_path):
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return []
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try:
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root = ET.parse(additional_path).getroot()
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except ET.ParseError:
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return []
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cells: List[Dict] = []
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current_key = None
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current_cell: Optional[Dict] = None
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edge_pos_counts: Dict[str, int] = {}
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for elem in root.findall("inductionLoop"):
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det_id = elem.get("id")
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lane_id = elem.get("lane")
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pos_raw = elem.get("pos")
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if not det_id or not lane_id or pos_raw is None:
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continue
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edge_id = _lane_to_edge_id(lane_id)
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position_m = _safe_float(pos_raw)
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key = (edge_id, round(position_m, 3))
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if key != current_key:
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pos_index = edge_pos_counts.get(edge_id, 0)
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edge_pos_counts[edge_id] = pos_index + 1
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current_cell = {
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"cell_id": f"{edge_id}@{pos_index}",
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"edge_id": edge_id,
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"pos_index": pos_index,
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"position_m": position_m,
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"detector_ids": [],
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}
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cells.append(current_cell)
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current_key = key
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current_cell["detector_ids"].append(det_id)
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return cells
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def _build_detector_rows_from_xml(log_dir: str, episode: int) -> List[Dict]:
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metrics_dir = os.path.join(log_dir, "sumo_metrics")
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suffix = f"ep{episode:04d}"
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additional_path = os.path.join(metrics_dir, f"runtime_metrics_il_{suffix}.add.xml")
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metrics_path = os.path.join(metrics_dir, f"metrics_il_output_{suffix}.xml")
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cells = _parse_detector_layout(additional_path)
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if not cells or not os.path.isfile(metrics_path):
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return []
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try:
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root = ET.parse(metrics_path).getroot()
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except ET.ParseError:
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return []
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interval_map: Dict[Tuple[float, float], Dict[str, Dict[str, float]]] = {}
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for elem in root.findall("interval"):
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det_id = elem.get("id")
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if not det_id:
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continue
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begin = _safe_float(elem.get("begin"))
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end = _safe_float(elem.get("end"))
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if not np.isfinite(begin) or not np.isfinite(end):
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continue
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interval_key = (begin, end)
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interval_values = interval_map.setdefault(interval_key, {})
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interval_values[det_id] = {
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"speed_ms": _safe_float(elem.get("speed")),
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"flow_vehph": _safe_float(elem.get("flow"), default=0.0),
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"n_veh_contrib": _safe_float(elem.get("nVehContrib"), default=0.0),
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"occupancy": _safe_float(elem.get("occupancy"), default=0.0),
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}
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detector_rows: List[Dict] = []
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sorted_intervals = sorted(interval_map.keys())
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for step_idx, interval_key in enumerate(sorted_intervals, start=1):
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interval_values = interval_map[interval_key]
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for cell_order, cell in enumerate(cells):
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speed_weight_sum = 0.0
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speed_weight_total = 0.0
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density_sum = 0.0
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for det_id in cell["detector_ids"]:
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lane_metrics = interval_values.get(det_id)
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if not lane_metrics:
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continue
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speed_ms = lane_metrics["speed_ms"]
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flow_vehph = max(lane_metrics["flow_vehph"], 0.0)
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n_veh_contrib = max(lane_metrics["n_veh_contrib"], 0.0)
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if np.isfinite(speed_ms) and speed_ms > 0.0:
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speed_weight = n_veh_contrib if n_veh_contrib > 0.0 else flow_vehph
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if speed_weight <= 0.0:
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speed_weight = 1.0
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speed_weight_sum += speed_ms * 3.6 * speed_weight
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speed_weight_total += speed_weight
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density_sum += flow_vehph / max(speed_ms * 3.6, 1e-6)
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measured_speed_kmh = (
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speed_weight_sum / speed_weight_total
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if speed_weight_total > 0.0
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else np.nan
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)
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detector_rows.append(
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{
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"episode": episode,
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"step": step_idx,
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"cell_order": cell_order,
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"cell_id": cell["cell_id"],
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"edge_id": cell["edge_id"],
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"pos_index": int(cell["pos_index"]),
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"position_m": float(cell["position_m"]),
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"measured_speed_kmh": float(measured_speed_kmh) if np.isfinite(measured_speed_kmh) else np.nan,
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"density_vehpkm": float(density_sum),
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}
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)
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return detector_rows
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def _plot_episode_heatmap(path: str, detector_rows: Sequence[Dict], title_prefix: str):
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if not detector_rows:
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return
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step_values = sorted({int(row["step"]) for row in edge_rows})
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num_edges = len(edge_ids)
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step_values = sorted({int(row["step"]) for row in detector_rows})
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ordered_cells = []
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seen_cells = set()
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for row in sorted(
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detector_rows,
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key=lambda item: (int(item["cell_order"]), str(item["cell_id"])),
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):
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cell_id = str(row["cell_id"])
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if cell_id in seen_cells:
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continue
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seen_cells.add(cell_id)
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ordered_cells.append(cell_id)
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num_cells = len(ordered_cells)
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num_steps = len(step_values)
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step_to_col = {step: idx for idx, step in enumerate(step_values)}
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edge_to_row = {edge_id: idx for idx, edge_id in enumerate(edge_ids)}
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cell_to_row = {cell_id: idx for idx, cell_id in enumerate(ordered_cells)}
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action_grid = np.full((num_edges, num_steps), np.nan, dtype=np.float32)
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speed_grid = np.full((num_edges, num_steps), np.nan, dtype=np.float32)
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occupancy_grid = np.full((num_edges, num_steps), np.nan, dtype=np.float32)
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speed_grid = np.full((num_cells, num_steps), np.nan, dtype=np.float32)
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density_grid = np.full((num_cells, num_steps), np.nan, dtype=np.float32)
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for row in edge_rows:
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row_idx = edge_to_row[row["edge_id"]]
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for row in detector_rows:
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row_idx = cell_to_row[str(row["cell_id"])]
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col_idx = step_to_col[int(row["step"])]
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action_grid[row_idx, col_idx] = _safe_float(row["action_speed_kmh"])
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speed_grid[row_idx, col_idx] = _safe_float(row["measured_speed_kmh"])
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occupancy_grid[row_idx, col_idx] = _safe_float(row["occupancy"])
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density_grid[row_idx, col_idx] = _safe_float(row["density_vehpkm"])
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fig, axes = plt.subplots(1, 3, figsize=(18, 7), sharex=True, sharey=True)
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fig, axes = plt.subplots(1, 2, figsize=(18, 7), sharex=True, sharey=True)
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plots = [
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(action_grid, "viridis", "Applied VSL (km/h)"),
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(speed_grid, "RdYlGn", "Measured Speed (km/h)"),
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(occupancy_grid, "magma", "Occupancy (%)"),
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(density_grid, "YlOrRd", "Density (veh/km)"),
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]
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for ax, (grid, cmap, title) in zip(axes, plots):
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@ -119,9 +261,11 @@ def _plot_episode_heatmap(path: str, edge_rows: Sequence[Dict], edge_ids: Sequen
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)
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ax.set_title(f"{title_prefix} {title}")
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ax.set_xlabel("Decision Step")
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ax.set_ylabel("Controlled Edge")
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ax.set_yticks(np.arange(num_edges))
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ax.set_yticklabels(edge_ids)
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ax.set_ylabel("Detector Cell")
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tick_step = max(num_cells // 12, 1)
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tick_idx = np.arange(0, num_cells, tick_step)
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ax.set_yticks(tick_idx)
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ax.set_yticklabels([ordered_cells[idx] for idx in tick_idx], fontsize=8)
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plt.colorbar(image, ax=ax, fraction=0.046, pad=0.04)
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plt.tight_layout()
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@ -137,15 +281,29 @@ def _write_summary(path: str, summary: Optional[Dict]):
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json.dump(summary, f, ensure_ascii=False, indent=2)
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def _save_episode_bundle(bundle_dir: str, episode: int, step_rows: Sequence[Dict], edge_rows: Sequence[Dict], edge_ids: Sequence[str], summary: Optional[Dict]):
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def _save_episode_bundle(
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bundle_dir: str,
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episode: int,
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step_rows: Sequence[Dict],
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edge_rows: Sequence[Dict],
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edge_ids: Sequence[str],
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detector_rows: Sequence[Dict],
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summary: Optional[Dict],
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):
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step_csv_path = os.path.join(bundle_dir, "step_metrics.csv")
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edge_csv_path = os.path.join(bundle_dir, "edge_metrics.csv")
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detector_csv_path = os.path.join(bundle_dir, "detector_metrics.csv")
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heatmap_path = os.path.join(bundle_dir, "episode_heatmap.png")
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summary_path = os.path.join(bundle_dir, "summary.json")
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_write_csv(step_csv_path, step_rows, fieldnames=list(step_rows[0].keys()) if step_rows else ["episode", "step"])
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_write_csv(edge_csv_path, edge_rows, fieldnames=list(edge_rows[0].keys()) if edge_rows else ["episode", "step", "edge_index"])
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_plot_episode_heatmap(heatmap_path, edge_rows, edge_ids, title_prefix=f"Episode {episode}")
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_write_csv(
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detector_csv_path,
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detector_rows,
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fieldnames=list(detector_rows[0].keys()) if detector_rows else ["episode", "step", "cell_id"],
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)
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_plot_episode_heatmap(heatmap_path, detector_rows, title_prefix=f"Episode {episode}")
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_write_summary(summary_path, summary)
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@ -161,6 +319,7 @@ def save_training_episode_artifacts(
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return
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step_rows, edge_rows, edge_ids = _normalize_step_rows(episode, episode_metrics)
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detector_rows = _build_detector_rows_from_xml(log_dir, episode)
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if control_edges and len(control_edges) >= len(edge_ids):
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edge_ids = list(control_edges[: len(edge_ids)])
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for row in edge_rows:
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@ -168,8 +327,8 @@ def save_training_episode_artifacts(
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artifacts_root = os.path.join(log_dir, "episode_artifacts")
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latest_dir = os.path.join(artifacts_root, "latest")
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_save_episode_bundle(latest_dir, episode, step_rows, edge_rows, edge_ids, summary)
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_save_episode_bundle(latest_dir, episode, step_rows, edge_rows, edge_ids, detector_rows, summary)
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if snapshot_interval > 0 and episode % snapshot_interval == 0:
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snapshot_dir = os.path.join(artifacts_root, "snapshots", f"episode_{episode:04d}")
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_save_episode_bundle(snapshot_dir, episode, step_rows, edge_rows, edge_ids, summary)
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_save_episode_bundle(snapshot_dir, episode, step_rows, edge_rows, edge_ids, detector_rows, summary)
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