feat: render-column (GT top + algos stacked), fix hour60 parsing, vivid anomaly markers

scripts/run_compare.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-# run_compare.sh — 对所有内置数据集 × 6 算法跑对比可视化
+# run_compare.sh — 对所有内置数据集跑纵向对比可视化（render-column）
 #
 # 用法:
 #   bash scripts/run_compare.sh [数据集目录] [输出目录]
@@ -8,23 +8,21 @@
 #   数据集目录: ../../datasets/builtin
 #   输出目录:   render_compare
 #
-# 管道: inject → detect-batch → render-algo → montage
+# 管道: inject → render-column（自动检测 + 渲染）
 
 set -uo pipefail
 
 BUILTIN_DIR="${1:-../../datasets/builtin}"
 OUTPUT="${2:-render_compare}"
-ALGOS="ksigma iqr grubbs shesd lof sample_ad_model"
 URL="${TS_ANA_TD_URL:-ws://root:taosdata@localhost:6041}"
 
 # 多列数据集映射: dataset_name → value_col
-# 未在此映射中的数据集默认使用 "value" 列
 declare -A VALUE_COLS=(
     ["ecg_002"]="ECG1"
     ["sensor_007"]="ankle_horiz_fwd"
 )
 
-mkdir -p "$OUTPUT/det" "$OUTPUT/algo"
+mkdir -p "$OUTPUT"
 
 total=0
 ok=0
@@ -41,48 +39,22 @@ for dataset_dir in "$BUILTIN_DIR"/*/; do
     echo "=== $name (value_col=$value_col) ==="
 
     # 1. 注入
-    echo "  [1/3] inject..."
+    echo "  [1/2] inject..."
     uv run python -m ts_anomaly_td inject \
         --csv "$csv" --stable "$name" --value-col "$value_col" --url "$URL" \
         || { echo "  inject FAILED, skipping $name"; ((total++)); continue; }
 
-    # 2. 逐算法检测 + 渲染
-    panels=()
-    for algo in $ALGOS; do
-        det_json="$OUTPUT/det/${name}_${algo}.json"
-        algo_png="$OUTPUT/algo/${name}_${algo}.png"
+    # 2. 纵向对比渲染（内部自动检测 6 算法）
+    echo "  [2/2] render-column..."
+    uv run python -m ts_anomaly_td render-column \
+        --gt-csv "$csv" --stable "$name" --value-col "$value_col" \
+        --output "$OUTPUT/${name}_compare.png" --url "$URL" \
+        && { echo "  → $OUTPUT/${name}_compare.png"; ((ok++)); } \
+        || { echo "  render-column FAILED"; }
 
-        echo "  [2/3] detect $algo..."
-        uv run python -m ts_anomaly_td detect-batch \
-            --stable "$name" --algo "$algo" --json --url "$URL" \
-            > "$det_json" 2>/dev/null || true
-
-        echo "  [2/3] render $algo..."
-        uv run python -m ts_anomaly_td render-algo \
-            --gt-csv "$csv" --det-json "$det_json" --algo "$algo" \
-            --output "$algo_png" --value-col "$value_col" \
-            --title "$name / $algo" \
-            || { echo "  render $algo FAILED"; continue; }
-
-        panels+=("$algo_png")
-    done
-
-    # 3. 拼图
-    if [[ ${#panels[@]} -gt 0 ]]; then
-        echo "  [3/3] montage (${#panels[@]} panels)..."
-        uv run python -m ts_anomaly_td montage \
-            --inputs "${panels[@]}" \
-            --output "$OUTPUT/${name}_compare.png" \
-            --cols 2 \
-            --title "$name — 算法对比"
-        echo "  → $OUTPUT/${name}_compare.png"
-        ((ok++))
-    else
-        echo "  no panels, skip montage for $name"
-    fi
     ((total++))
 done
 
 echo ""
 echo "=== 完成: $ok/$total 成功, $skip 跳过 ==="
-ls -la "$OUTPUT"/*_compare.png 2>/dev/null || echo "(无拼图输出)"
+ls -la "$OUTPUT"/*_compare.png 2>/dev/null || echo "(无输出)"

ts_anomaly_td/cli.py

@@ -225,6 +225,15 @@ def build_parser() -> argparse.ArgumentParser:
     p_mt.add_argument("--cols", type=int, default=2, help="列数 (default: 2)")
     p_mt.add_argument("--title", default="", help="总标题")
 
+    # render-column
+    p_rc = sub.add_parser("render-column", help="GT 顶部 + 各算法纵向对比")
+    p_rc.add_argument("--gt-csv", required=True, help="GT CSV 文件路径")
+    p_rc.add_argument("--stable", required=True, help="supertable 名称")
+    p_rc.add_argument("--db", default="ts_anomaly", help="数据库名")
+    p_rc.add_argument("--value-col", default="value", help="数值列名 (default: value)")
+    p_rc.add_argument("--output", required=True, help="PNG 输出路径")
+    p_rc.add_argument("--url", default="ws://root:taosdata@localhost:6041", help="TDengine WebSocket URL")
+
     return parser
 
 
@@ -258,6 +267,27 @@ def cmd_montage(args):
     logger.info("montage complete: %s", args.output)
 
 
+def cmd_render_column(args):
+    """render-column 子命令：GT 顶部 + 各算法纵向对比。"""
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+
+    from ts_anomaly_td.io_csv import read_csv
+    from ts_anomaly_td.visualize import render_column
+
+    ts, vals, labels = read_csv(args.gt_csv, value_col=args.value_col)
+
+    conn = TDConnection(args.url)
+    try:
+        db = args.db or "ts_anomaly"
+        conn.execute_no_result(f"USE {db}")
+        results = detect_all_algos(conn, args.stable)
+    finally:
+        conn.close()
+
+    title = f"{args.stable} — 算法对比"
+    render_column(ts, vals, labels, results, args.output, title=title)
+
+
 def main():
     parser = build_parser()
     args = parser.parse_args()
@@ -271,6 +301,7 @@ def main():
         "watch": cmd_watch,
         "render-algo": cmd_render_algo,
         "montage": cmd_montage,
+        "render-column": cmd_render_column,
     }
 
     fn = dispatch.get(args.command)

ts_anomaly_td/io_csv.py

@@ -26,7 +26,21 @@ def read_csv(path: str | Path, value_col: str = "value") -> tuple[list[int], lis
     if "timestamp" not in df.columns or value_col not in df.columns:
         raise ValueError(f"CSV 缺少 timestamp 或 {value_col} 列: {path}")
 
-    timestamps = pd.to_datetime(df["timestamp"])
+    timestamps = pd.to_datetime(df["timestamp"], format="mixed", errors="coerce")
+    if timestamps.isna().any():
+        # 对无法解析的行尝试修正（如 00:60:00 → 01:00:00）
+        import re
+        na_mask = timestamps.isna()
+        raw_strs = df.loc[na_mask, "timestamp"].astype(str)
+
+        def _fix_hour60(s: str) -> str:
+            def _repl(m):
+                h = (int(m.group(1)) + 1) % 24
+                return f"{h:02d}:00:00"
+            return re.sub(r"(\d{2}):60:00", _repl, s)
+
+        fixed = raw_strs.apply(_fix_hour60)
+        timestamps.loc[na_mask] = pd.to_datetime(fixed, errors="coerce")
     timestamps_ms = (timestamps.astype("int64") // 10**6).tolist()
 
     values = df[value_col].astype(float).tolist()

ts_anomaly_td/visualize.py

@@ -233,6 +233,144 @@ def render_algo(
     return output_path
 
 
+def render_column(
+    timestamps: list[int],
+    values: list[float],
+    gt_labels: list[int],
+    det_results: dict[str, dict],
+    output_path: str,
+    title: str = "",
+) -> str:
+    """单列纵向对比图：GT 顶部 + 各算法向下排布，共享 x 轴。
+
+    异常区域用鲜明颜色 + 竖线边框标记。
+
+    Args:
+        timestamps: 毫秒时间戳列表
+        values: 数值列表
+        gt_labels: GT 标签（0/1）
+        det_results: {algo: {"windows": [[start,end],...], "error": str|None}}
+        output_path: PNG 输出路径
+        title: 图表总标题
+
+    Returns:
+        output_path
+    """
+    font_name = _find_cjk_font()
+    plt.rcParams["font.family"] = font_name
+    plt.rcParams["font.size"] = 9
+
+    # 过滤有效算法
+    valid_algos = [(a, r) for a, r in det_results.items() if not r.get("error")]
+    n_panels = 1 + len(valid_algos)  # GT + 各算法
+
+    fig, axes = plt.subplots(n_panels, 1, sharex=True, figsize=(16, 3 * n_panels + 1))
+    if n_panels == 1:
+        axes = [axes]
+
+    t0 = timestamps[0]
+    hours = [(t - t0) / 3_600_000 for t in timestamps]
+
+    # ── 提取 GT windows ──
+    gt_windows_h = []
+    in_anom = False
+    anom_start = None
+    for i, label in enumerate(gt_labels):
+        if label == 1 and not in_anom:
+            in_anom = True
+            anom_start = hours[i]
+        elif label == 0 and in_anom:
+            in_anom = False
+            gt_windows_h.append((anom_start, hours[i]))
+    if in_anom:
+        gt_windows_h.append((anom_start, hours[-1]))
+
+    # ── Panel 0: Ground Truth ──
+    ax = axes[0]
+    has_gt = any(l == 1 for l in gt_labels)
+    ax.plot(hours, values, color="#333333", linewidth=0.8, alpha=0.9)
+    if has_gt:
+        for start_h, end_h in gt_windows_h:
+            ax.axvspan(start_h, end_h, alpha=0.35, color="#FF4444",
+                       edgecolor="#CC0000", linewidth=1.5)
+        # 异常点散点标记
+        anom_hours = [hours[i] for i, l in enumerate(gt_labels) if l == 1]
+        anom_vals = [values[i] for i, l in enumerate(gt_labels) if l == 1]
+        ax.scatter(anom_hours, anom_vals, color="red", s=8, zorder=5, label="GT 异常点")
+        ax.legend(loc="upper right", fontsize=7)
+    ax.set_ylabel("Value", fontsize=9)
+    gt_title = "Ground Truth"
+    if has_gt:
+        gt_title += f"（{len(gt_windows_h)} 个异常区间）"
+    else:
+        gt_title += "（无异常标注）"
+    ax.set_title(gt_title, fontsize=10, fontweight="bold")
+    ax.grid(True, alpha=0.3)
+
+    # ── Panels 1..N: 各算法 ──
+    for idx, (algo, result) in enumerate(valid_algos):
+        ax = axes[idx + 1]
+        color = ALGO_COLORS.get(algo, "#888888")
+
+        # 原始序列（浅色背景）
+        ax.plot(hours, values, color="#AAAAAA", linewidth=0.5, alpha=0.6)
+
+        # GT 红色竖线边框参考
+        for start_h, end_h in gt_windows_h:
+            ax.axvspan(start_h, end_h, alpha=0.12, color="#FF4444",
+                       linestyle="--", edgecolor="#FF6666", linewidth=0.8)
+
+        # 算法检测窗口 — 鲜明颜色 + 边框
+        windows = result.get("windows", [])
+        det_hours_list = []
+        for wstart, wend in windows:
+            ws_h = (wstart - t0) / 3_600_000
+            we_h = (wend - t0) / 3_600_000
+            ax.axvspan(ws_h, we_h, alpha=0.4, color=color,
+                       edgecolor=color, linewidth=1.5)
+            det_hours_list.append((ws_h, we_h))
+
+        # 在检测区间内的高亮数据点
+        det_points_h = set()
+        for ws_h, we_h in det_hours_list:
+            for i, h in enumerate(hours):
+                if ws_h <= h <= we_h:
+                    det_points_h.add(i)
+        if det_points_h:
+            dp_hours = [hours[i] for i in det_points_h]
+            dp_vals = [values[i] for i in det_points_h]
+            ax.scatter(dp_hours, dp_vals, color=color, s=6, zorder=5, alpha=0.7)
+
+        # IoU 计算
+        iou = _compute_iou(gt_windows_h, det_hours_list)
+        stats = f"检测窗口: {len(windows)}  |  IoU: {iou:.3f}"
+
+        # 图例方块
+        from matplotlib.patches import Patch
+        handles = [Patch(color=color, alpha=0.6, label=algo)]
+        if has_gt:
+            handles.insert(0, Patch(color="#FF4444", alpha=0.3, label="GT"))
+        ax.legend(handles=handles, loc="upper right", fontsize=7)
+
+        ax.set_title(f"{algo}  —  {stats}", fontsize=10)
+        ax.set_ylabel("Value", fontsize=9)
+        ax.grid(True, alpha=0.3)
+
+    # 底部 x 轴标签
+    axes[-1].set_xlabel("Time (hours)", fontsize=9)
+
+    # 总标题
+    if title:
+        fig.suptitle(title, fontsize=13, fontweight="bold", y=0.995)
+
+    plt.tight_layout(rect=[0, 0, 1, 0.98] if title else [0, 0, 1, 1])
+    Path(output_path).parent.mkdir(parents=True, exist_ok=True)
+    fig.savefig(output_path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("render_column saved: %s (%d panels)", output_path, n_panels)
+    return output_path
+
+
 def montage_pngs(
     image_paths: list[str],
     output_path: str,

uv.lock

@@ -607,6 +607,7 @@ dependencies = [
     { name = "matplotlib" },
     { name = "numpy" },
     { name = "pandas" },
+    { name = "pillow" },
     { name = "taos-ws-py" },
 ]
 
@@ -615,6 +616,7 @@ requires-dist = [
     { name = "matplotlib", specifier = ">=3.7" },
     { name = "numpy", specifier = ">=1.24" },
     { name = "pandas", specifier = ">=2.0" },
+    { name = "pillow", specifier = ">=10.0" },
     { name = "taos-ws-py", specifier = ">=0.2.0" },
 ]