Add new functions for generating trend figures: single subplot and individual figures by tag. Update UI for display mode selection and enhance rendering logic.

app.py

@@ -5,6 +5,8 @@ import os
 import re
 from typing import Dict, Tuple, List, Optional
 import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import plotly.io as pio
 
 # ======================================
 # 設定（添付CSVの既定パス：必要に応じて変更可）
@@ -191,7 +193,7 @@ STATUS_COLOR = {
 LINE_COLOR = "#4a5568"  # 濃いグレー
 
 # ======================================
-# 図作成
+# 図作成（既存：グルーピングごとに個別のFigureを返す）
 # ======================================
 def make_trend_figs(
     df: pd.DataFrame,
@@ -317,6 +319,240 @@ def make_trend_figs(
 
     return figs
 
+# ======================================
+# 新規：サブプロット1枚でまとめる図
+# ======================================
+def make_trend_figure(
+    df: pd.DataFrame,
+    process_map: Dict[str, List[dict]],
+    process_name: str,
+    selected_items: List[str],
+    thr_df: Optional[pd.DataFrame],
+    thr_mode: str,  # "excel" or "auto"
+    date_min: Optional[str] = None,
+    date_max: Optional[str] = None,
+) -> Optional[go.Figure]:
+    if df is None or not process_name:
+        return None
+    recs = process_map.get(process_name, [])
+    if not recs:
+        return None
+    selected_items_set = set([normalize(x) for x in (selected_items or [])])
+    recs = [r for r in recs if normalize(r["item"]) in selected_items_set]
+    if not recs:
+        return None
+
+    dfw = df.copy()
+    if date_min:
+        dfw = dfw[dfw["timestamp"] >= pd.to_datetime(date_min)]
+    if date_max:
+        dfw = dfw[dfw["timestamp"] <= pd.to_datetime(date_max)]
+    if dfw.empty:
+        return None
+
+    thr_lookup = build_threshold_lookup(thr_df) if thr_mode == "excel" else {}
+
+    # 計測項目タグでグルーピング
+    groups: Dict[str, List[dict]] = {}
+    for r in recs:
+        tag = extract_measure_tag(r["item"])
+        groups.setdefault(tag, []).append(r)
+    tags = list(groups.keys())
+    if not tags:
+        return None
+
+    fig = make_subplots(
+        rows=len(tags), cols=1, shared_xaxes=True,
+        vertical_spacing=0.03,
+        subplot_titles=[f"{process_name} | 計測項目: {t}" for t in tags]
+    )
+
+    row_idx = 1
+    for tag in tags:
+        cols = groups[tag]
+        for r in cols:
+            col = r["col_tuple"]
+            col_str = r["col_str"]
+            if col in dfw.columns:
+                series = dfw[col]
+            elif col_str in dfw.columns:
+                series = dfw[col_str]
+            else:
+                continue
+
+            x = dfw["timestamp"]
+            y = pd.to_numeric(series, errors="coerce")
+
+            if thr_mode == "excel":
+                key = (normalize(r["id"]), normalize(r["item"]), normalize(r["process"]))
+                LL, L, H, HH = thr_lookup.get(key, (np.nan, np.nan, np.nan, np.nan))
+                if all(pd.isna(v) for v in [LL, L, H, HH]):
+                    LL, L, H, HH = auto_threshold(y)
+            else:
+                LL, L, H, HH = auto_threshold(y)
+
+            # ライン
+            fig.add_trace(
+                go.Scatter(
+                    x=x, y=y, mode="lines",
+                    name=f"{r['item']} ({r['id']})",
+                    line=dict(color=LINE_COLOR, width=1.5),
+                    hovertemplate="%{x}<br>%{y}<extra>"+f"{r['item']} ({r['id']})"+"</extra>"
+                ),
+                row=row_idx, col=1
+            )
+            # マーカー（色分け）
+            colors = []
+            for v in y:
+                if pd.isna(v):
+                    colors.append("rgba(0,0,0,0)")
+                else:
+                    st = judge_status(v, LL, L, H, HH)
+                    colors.append(STATUS_COLOR.get(st, STATUS_COLOR["OK"]))
+            fig.add_trace(
+                go.Scatter(
+                    x=x, y=y, mode="markers",
+                    name=f"{r['item']} markers",
+                    marker=dict(size=6, color=colors),
+                    showlegend=False,
+                    hovertemplate="%{x}<br>%{y}<extra></extra>"
+                ),
+                row=row_idx, col=1
+            )
+            # しきい値ガイド
+            for val, label in [(LL, "LL"), (L, "L"), (H, "H"), (HH, "HH")]:
+                if pd.notna(val):
+                    fig.add_hline(
+                        y=float(val), line=dict(width=1, dash="dot"),
+                        annotation_text=label, annotation_position="top left",
+                        row=row_idx, col=1
+                    )
+        row_idx += 1
+
+    fig.update_layout(
+        title=f"{process_name} ｜ 計測項目タグごとのトレンド",
+        xaxis_title="timestamp",
+        showlegend=True,
+        margin=dict(l=10, r=10, t=40, b=10),
+        hovermode="x unified",
+        height=max(400, 260 * len(tags)),
+    )
+    return fig
+
+# ======================================
+# 新規：計測項目タグごとに個別Figure
+# ======================================
+def make_trend_figs_by_tag(
+    df: pd.DataFrame,
+    process_map: Dict[str, List[dict]],
+    process_name: str,
+    selected_items: List[str],
+    thr_df: Optional[pd.DataFrame],
+    thr_mode: str,
+    date_min: Optional[str] = None,
+    date_max: Optional[str] = None,
+) -> Dict[str, go.Figure]:
+    if df is None or not process_name:
+        return {}
+    recs = process_map.get(process_name, [])
+    if not recs:
+        return {}
+    selected_items_set = set([normalize(x) for x in (selected_items or [])])
+    recs = [r for r in recs if normalize(r["item"]) in selected_items_set]
+    if not recs:
+        return {}
+
+    dfw = df.copy()
+    if date_min:
+        dfw = dfw[dfw["timestamp"] >= pd.to_datetime(date_min)]
+    if date_max:
+        dfw = dfw[dfw["timestamp"] <= pd.to_datetime(date_max)]
+    if dfw.empty:
+        return {}
+
+    thr_lookup = build_threshold_lookup(thr_df) if thr_mode == "excel" else {}
+
+    groups: Dict[str, List[dict]] = {}
+    for r in recs:
+        tag = extract_measure_tag(r["item"])
+        groups.setdefault(tag, []).append(r)
+
+    out: Dict[str, go.Figure] = {}
+    for tag, cols in groups.items():
+        fig = go.Figure()
+        for r in cols:
+            col = r["col_tuple"]
+            col_str = r["col_str"]
+            if col in dfw.columns:
+                series = dfw[col]
+            elif col_str in dfw.columns:
+                series = dfw[col_str]
+            else:
+                continue
+
+            x = dfw["timestamp"]
+            y = pd.to_numeric(series, errors="coerce")
+
+            if thr_mode == "excel":
+                key = (normalize(r["id"]), normalize(r["item"]), normalize(r["process"]))
+                LL, L, H, HH = thr_lookup.get(key, (np.nan, np.nan, np.nan, np.nan))
+                if all(pd.isna(v) for v in [LL, L, H, HH]):
+                    LL, L, H, HH = auto_threshold(y)
+            else:
+                LL, L, H, HH = auto_threshold(y)
+
+            fig.add_trace(go.Scatter(
+                x=x, y=y, mode="lines",
+                name=f"{r['item']} ({r['id']})",
+                line=dict(color=LINE_COLOR, width=1.5),
+                hovertemplate="%{x}<br>%{y}<extra>"+f"{r['item']} ({r['id']})"+"</extra>"
+            ))
+
+            colors = []
+            for v in y:
+                if pd.isna(v):
+                    colors.append("rgba(0,0,0,0)")
+                else:
+                    st = judge_status(v, LL, L, H, HH)
+                    colors.append(STATUS_COLOR.get(st, STATUS_COLOR["OK"]))
+
+            fig.add_trace(go.Scatter(
+                x=x, y=y, mode="markers",
+                name=f"{r['item']} markers",
+                marker=dict(size=6, color=colors),
+                showlegend=False,
+                hovertemplate="%{x}<br>%{y}<extra></extra>"
+            ))
+
+            for val, label in [(LL, "LL"), (L, "L"), (H, "H"), (HH, "HH")]:
+                if pd.notna(val):
+                    fig.add_hline(y=float(val), line=dict(width=1, dash="dot"),
+                                  annotation_text=label, annotation_position="top left")
+
+        fig.update_layout(
+            title=f"{process_name} ｜ 計測項目: {tag}",
+            xaxis_title="timestamp",
+            yaxis_title=tag,
+            legend_title="系列",
+            margin=dict(l=10, r=10, t=40, b=10),
+            hovermode="x unified",
+        )
+        out[tag] = fig
+    return out
+
+def figures_to_html(figs_by_tag: Dict[str, go.Figure]) -> str:
+    """
+    各 Figure を <div> で順番に並べた HTML を返す。
+    最初の図だけ PlotlyJS をCDNで同梱し、以降はスリムに。
+    """
+    parts = []
+    first = True
+    for tag, fig in figs_by_tag.items():
+        html = pio.to_html(fig, include_plotlyjs='cdn' if first else False, full_html=False)
+        parts.append(html)
+        first = False
+    return "<br>".join(parts) if parts else "<p>図がありません。</p>"
+
 # ======================================
 # グローバル状態（UI間共有）
 # ======================================
@@ -386,7 +622,7 @@ def update_items(process_name: str):
 
 def render_figs(process_name: str, items: List[str], thr_mode: str, date_min, date_max):
     """
-    図を生成して返す（複数図）。GradioではList[plotly.Figure]を直接返せる。
+    （旧）図を生成して返す（複数図）。今は未使用だが残置。
     """
     if G_DF is None:
         return "⚠ データ未読み込み", []
@@ -402,6 +638,31 @@ def render_figs(process_name: str, items: List[str], thr_mode: str, date_min, da
         return "⚠ 図を生成できませんでした（データ無し or 条件不一致）", []
     return f"✅ {process_name}: {len(figs)}枚のトレンド図を生成しました（計測項目タグごと）", figs
 
+def render_any(process_name: str, items: List[str], display_mode: str, thr_mode_label: str, date_min, date_max):
+    """
+    表示形式に応じて Plot（サブプロット1枚）または HTML（個別複数枚）を返す。
+    """
+    if G_DF is None:
+        return "⚠ データ未読み込み", gr.update(visible=False), gr.update(value="", visible=False)
+    if not process_name:
+        return "⚠ プロセスを選択してください", gr.update(visible=False), gr.update(value="", visible=False)
+    if not items:
+        return "⚠ 項目を選択してください", gr.update(visible=False), gr.update(value="", visible=False)
+
+    mode = "excel" if str(thr_mode_label).startswith("excel") else "auto"
+
+    if str(display_mode).startswith("サブプロット"):
+        fig = make_trend_figure(G_DF, G_PROCESS_MAP, process_name, items, G_THRESHOLDS_DF, mode, date_min, date_max)
+        if fig is None:
+            return "⚠ 図を生成できませんでした（データ無し or 条件不一致）", gr.update(visible=False), gr.update(value="", visible=False)
+        return "✅ トレンド図（1枚サブプロット）を生成しました", gr.update(value=fig, visible=True), gr.update(value="", visible=False)
+    else:
+        figs_by_tag = make_trend_figs_by_tag(G_DF, G_PROCESS_MAP, process_name, items, G_THRESHOLDS_DF, mode, date_min, date_max)
+        if not figs_by_tag:
+            return "⚠ 図を生成できませんでした（データ無し or 条件不一致）", gr.update(visible=False), gr.update(value="", visible=False)
+        html = figures_to_html(figs_by_tag)
+        return f"✅ 個別トレンド図 {len(figs_by_tag)} 枚を生成しました", gr.update(visible=False), gr.update(value=html, visible=True)
+
 # ======================================
 # UI
 # ======================================
@@ -412,7 +673,7 @@ with gr.Blocks(css="""
 
     with gr.Row():
         csv_uploader = gr.File(label="① 時系列CSV（3行ヘッダー）", file_count="single", file_types=[".csv"])
-        thr_uploader = gr.File(label="② 閾値Excel（任意: LL/L/H/HH）", file_count="single", file_types=[".xlsx", ".xls"])
+        thr_uploader = gr.File(label="② 閾値Excel（任意: LL/L/HH/HH）", file_count="single", file_types=[".xlsx", ".xls"])
 
     with gr.Row():
         thr_mode = gr.Radio(
@@ -423,6 +684,13 @@ with gr.Blocks(css="""
         date_min = gr.Textbox(label="抽出開始日時（任意）例: 2024-07-01 00:00")
         date_max = gr.Textbox(label="抽出終了日時（任意）例: 2024-07-31 23:59")
 
+    # 表示形式の切り替え
+    display_mode = gr.Radio(
+        ["サブプロット(1枚)", "個別(複数枚)"],
+        value="サブプロット(1枚)",
+        label="表示形式"
+    )
+
     status_csv = gr.Markdown()
     status_thr = gr.Markdown()
 
@@ -433,7 +701,10 @@ with gr.Blocks(css="""
         btn_render = gr.Button("トレンド図を生成", variant="primary")
 
     msg = gr.Markdown()
-    plots = gr.Plotly(label="トレンド図（計測項目タグごとに複数枚）", height=540, every=1, interactive=True, show_label=True, scale=100, container=True, visible=True, elem_id="plot_container", elem_classes=["w-full"], )
+    # サブプロット用（1枚）
+    plot = gr.Plot(label="トレンド図（タグ別サブプロット）", height=540, show_label=True, visible=True)
+    # 個別(複数枚)用
+    html_multi = gr.HTML(label="個別トレンド図（複数枚）", visible=False)
 
     # コールバック接続
     # 1) 既定CSVの自動ロード
@@ -464,15 +735,12 @@ with gr.Blocks(css="""
     )
 
     # 5) 図生成
-    def _thr_mode_key(s):
-        return "excel" if s.startswith("excel") else "auto"
-
     btn_render.click(
-        fn=lambda proc, items, mode, dmin, dmax: render_figs(proc, items, _thr_mode_key(mode), dmin, dmax),
-        inputs=[process_dd, items_cb, thr_mode, date_min, date_max],
-        outputs=[msg, plots],
+        fn=lambda proc, items, disp_mode, mode, dmin, dmax: render_any(proc, items, disp_mode, mode, dmin, dmax),
+        inputs=[process_dd, items_cb, display_mode, thr_mode, date_min, date_max],
+        outputs=[msg, plot, html_multi],
     )
 
 if __name__ == "__main__":
-    # gradio>=4 で Plotly がそのままレンダリング可能
+    # gradio>=5: gr.Plot で Plotly Figure を直接表示可
     demo.launch()