#!/usr/bin/env bash
# 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]
#
# 默认:
#   数据集目录: ../../datasets/builtin
#   输出目录:   render_compare
#
# 管道: inject → render-column（自动检测 + 渲染）

set -uo pipefail

BUILTIN_DIR="${1:-../../datasets/builtin}"
OUTPUT="${2:-render_compare}"
URL="${TS_ANA_TD_URL:-ws://root:taosdata@localhost:6041}"

# 多列数据集映射: dataset_name → value_col
declare -A VALUE_COLS=(
    ["ecg_002"]="ECG1"
    ["sensor_007"]="ankle_horiz_fwd"
)

mkdir -p "$OUTPUT"

total=0
ok=0
skip=0

for dataset_dir in "$BUILTIN_DIR"/*/; do
    name=$(basename "$dataset_dir")
    csv="$dataset_dir/data.csv"
    [[ -f "$csv" ]] || { echo "SKIP $name: no data.csv"; ((skip++)); ((total++)); continue; }

    # 确定 value 列
    value_col="${VALUE_COLS[$name]:-value}"

    echo "=== $name (value_col=$value_col) ==="

    # 1. 注入
    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. 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"; }

    ((total++))
done

echo ""
echo "=== 完成: $ok/$total 成功, $skip 跳过 ==="
ls -la "$OUTPUT"/*_compare.png 2>/dev/null || echo "(无输出)"