refactor: streamline event handling and improve input station visualization

app.py

@@ -91,11 +91,10 @@ except FileNotFoundError:
 except Exception as e:
     logger.error(f"{target_file} 載入失敗: {e}")
 
-
 # ============ 震央資訊管理 ============
 
 earthquake_metadata = {}
-event_json_path="waveform/event.json"
+event_json_path = "waveform/event.json"
 
 try:
     import json
@@ -135,7 +134,6 @@ except FileNotFoundError:
 except Exception as e:
     logger.error(f"讀取事件元資料時發生錯誤: {e}")
 
-
 # 載入模型
 model_path = hf_hub_download(
     repo_id="SeisBlue/TTSAM", filename="ttsam_trained_model_11.pt"
@@ -263,8 +261,8 @@ def select_nearest_stations(st, epicenter_lat, epicenter_lon, n_stations=25):
 
 
 def extract_waveforms_from_stream(event_name,
-    st, selected_stations, duration, vs30_input
-):
+                                  st, selected_stations, duration, vs30_input
+                                  ):
     """
     從 Stream 中提取選定測站的波形資料
 
@@ -403,170 +401,6 @@ def extract_waveforms_from_stream(event_name,
     return waveforms, station_info_list, valid_stations, missing_components_count
 
 
-def create_input_station_map(selected_stations, epicenter_lat, epicenter_lon):
-    """創建輸入測站分布地圖：顯示所有測站 + 突顯被選中的 25 個（使用 Plotly）"""
-
-    selected_station_codes = {s["station"] for s in selected_stations}
-
-    # 準備所有測站資料（未選中的測站）
-    all_stations_lat = []
-    all_stations_lon = []
-    all_stations_text = []
-
-    logger.info(f"繪製所有測站 ({len(site_info)} 個)...")
-    for idx, row in site_info.iterrows():
-        station_code = row["Station"]
-        if station_code not in selected_station_codes:
-            all_stations_lat.append(row["Latitude"])
-            all_stations_lon.append(row["Longitude"])
-            all_stations_text.append(station_code)
-
-    # 準備選中測站資料（按距離分組）
-    selected_group1_lat, selected_group1_lon, selected_group1_text = (
-        [],
-        [],
-        [],
-    )  # 前 5 近
-    selected_group2_lat, selected_group2_lon, selected_group2_text = (
-        [],
-        [],
-        [],
-    )  # 6-15 近
-    selected_group3_lat, selected_group3_lon, selected_group3_text = (
-        [],
-        [],
-        [],
-    )  # 16-25 近
-
-    for i, station_data in enumerate(selected_stations):
-        station_code = station_data["station"]
-        lat = station_data["latitude"]
-        lon = station_data["longitude"]
-        distance = station_data["distance"]
-
-        hover_text = f"{station_code}<br>✓ 已選中<br>第 {i+1} 近<br>距離: {distance:.2f}°<br>({lat:.3f}, {lon:.3f})"
-
-        if i < 5:
-            selected_group1_lat.append(lat)
-            selected_group1_lon.append(lon)
-            selected_group1_text.append(hover_text)
-        elif i < 15:
-            selected_group2_lat.append(lat)
-            selected_group2_lon.append(lon)
-            selected_group2_text.append(hover_text)
-        else:
-            selected_group3_lat.append(lat)
-            selected_group3_lon.append(lon)
-            selected_group3_text.append(hover_text)
-
-    # 創建 Plotly 地圖
-    fig = go.Figure()
-
-    # 添加所有測站（灰色小點）
-    fig.add_trace(
-        go.Scattermap(
-            lat=all_stations_lat,
-            lon=all_stations_lon,
-            mode="markers",
-            marker=dict(size=6, color="gray", opacity=0.6),
-            text=all_stations_text,
-            hovertemplate="%{text}<extra></extra>",
-            name=f"所有測站 ({len(all_stations_lat)} 個)",
-            showlegend=True,
-        )
-    )
-
-    # 添加選中測站 - 前 5 近（綠色）
-    if selected_group1_lat:
-        fig.add_trace(
-            go.Scattermap(
-                lat=selected_group1_lat,
-                lon=selected_group1_lon,
-                mode="markers",
-                marker=dict(size=12, color="green", opacity=0.8),
-                text=selected_group1_text,
-                hovertemplate="%{text}<extra></extra>",
-                name="前 5 近",
-                showlegend=True,
-            )
-        )
-
-    # 添加選中測站 - 6-15 近（藍色）
-    if selected_group2_lat:
-        fig.add_trace(
-            go.Scattermap(
-                lat=selected_group2_lat,
-                lon=selected_group2_lon,
-                mode="markers",
-                marker=dict(size=12, color="blue", opacity=0.8),
-                text=selected_group2_text,
-                hovertemplate="%{text}<extra></extra>",
-                name="6-15 近",
-                showlegend=True,
-            )
-        )
-
-    # 添加選中測站 - 16-25 近（橘色）
-    if selected_group3_lat:
-        fig.add_trace(
-            go.Scattermap(
-                lat=selected_group3_lat,
-                lon=selected_group3_lon,
-                mode="markers",
-                marker=dict(size=12, color="orange", opacity=0.8),
-                text=selected_group3_text,
-                hovertemplate="%{text}<extra></extra>",
-                name="16-25 近",
-                showlegend=True,
-            )
-        )
-
-    # 添加震央（紅色大點）
-    fig.add_trace(
-        go.Scattermap(
-            lat=[epicenter_lat],
-            lon=[epicenter_lon],
-            mode="markers",
-            marker=dict(size=25, color="red"),
-            text=[f"震央<br>({epicenter_lat:.3f}, {epicenter_lon:.3f})"],
-            hovertemplate="%{text}<extra></extra>",
-            name="震央",
-            showlegend=True,
-        )
-    )
-
-    fig.add_trace(
-        go.Scattermap(
-            lat=[epicenter_lat],
-            lon=[epicenter_lon],
-            mode="markers",
-            marker=dict(size=10, color="white"),
-            showlegend=False,
-        )
-    )
-
-    # 設置地圖佈局
-    fig.update_layout(
-        map=dict(
-            style="open-street-map",
-            center=dict(lat=epicenter_lat, lon=epicenter_lon),
-            zoom=7,
-        ),
-        height=500,  # 設置固定高度以適應 Gradio 容器
-        margin=dict(l=0, r=0, t=0, b=0),
-        showlegend=True,
-        legend=dict(
-            yanchor="top",
-            y=0.95,
-            xanchor="left",
-            x=0.01,
-            bgcolor="rgba(255, 255, 255, 0.8)",
-        ),
-    )
-
-    return fig
-
-
 def plot_waveform(st, selected_stations, first_pick, duration):
     """
     繪製選定測站的波形圖（距離-時間圖，可顯示全部 25 個測站）
@@ -580,7 +414,7 @@ def plot_waveform(st, selected_stations, first_pick, duration):
     # 計算結束時間
     end_time = first_pick + duration
 
-    fig, ax = plt.subplots(figsize=(14, 10))
+    fig, ax = plt.subplots(figsize=(14, 4))
 
     # 設定振幅縮放比例（避免波形重疊）
     amplitude_scale = 0.03  # 可調整此值來控制波形大小
@@ -624,7 +458,8 @@ def plot_waveform(st, selected_stations, first_pick, duration):
         except Exception as e:
             logger.warning(f"無法繪製測站 {station_code}: {e}")
 
-    ax.axvline(first_pick, color="blue", linestyle="--", linewidth=2, alpha=0.7, label="初動時間")
+    ax.axvline(first_pick, color="blue", linestyle="--", linewidth=2, alpha=0.7,
+               label="First Motion")
 
     # 標記選取時間範圍
     ax.axvline(
@@ -633,7 +468,7 @@ def plot_waveform(st, selected_stations, first_pick, duration):
         linestyle="--",
         linewidth=2,
         alpha=0.7,
-        label="選取範圍",
+        label="Input Waveform",
     )
     ax.axvline(end_time, color="red", linestyle="--", linewidth=2, alpha=0.7)
     ax.axvspan(0, end_time, alpha=0.15, color="blue")
@@ -685,9 +520,10 @@ def get_intensity_color(intensity):
 
 
 def create_intensity_map(
-    pga_list, target_names, epicenter_lat=None, epicenter_lon=None
+        pga_list, target_names, epicenter_lat=None, epicenter_lon=None,
+        selected_stations=None
 ):
-    """使用 Plotly 創建互動式震度分布地圖"""
+    """使用 Plotly 創建互動式震度分布地圖（合併輸入測站與預測震度）"""
 
     # 按震度等級分組資料
     intensity_groups = {
@@ -720,23 +556,45 @@ def create_intensity_map(
             intensity_groups[intensity]["lon"].append(lon)
             intensity_groups[intensity]["text"].append(hover_text)
 
-    # 決定地圖中心（優先使用 epicenter，否則計算平均值）
-    if epicenter_lat is not None and epicenter_lon is not None:
-        map_center_lat = epicenter_lat
-        map_center_lon = epicenter_lon
-    elif all_lats and all_lons:
-        map_center_lat = np.mean(all_lats)
-        map_center_lon = np.mean(all_lons)
-    else:
-        # 如果沒有任何測站資料，使用台灣的中心
-        map_center_lat = 23.5
-        map_center_lon = 121.0
-        logger.warning("無法決定地圖中心，使用台灣預設中心")
+    # 地圖中心固定為台灣中心
+    map_center_lat = 23.6
+    map_center_lon = 121.0
 
     # 創建 Plotly 地圖
     fig = go.Figure()
 
-    # 添加各震度等級的測站
+    # 【底層】添加輸入測站（灰色無填充圓圈，不搶視覺焦點）
+    if selected_stations:
+        input_station_lats = []
+        input_station_lons = []
+        input_station_texts = []
+
+        for station_data in selected_stations:
+            input_station_lats.append(station_data["latitude"])
+            input_station_lons.append(station_data["longitude"])
+            input_station_texts.append(
+                f"{station_data['station']}<br>"
+                f"輸入測站<br>"
+                f"位置: ({station_data['latitude']:.3f}, {station_data['longitude']:.3f})"
+            )
+
+        fig.add_trace(
+            go.Scattermap(
+                lat=input_station_lats,
+                lon=input_station_lons,
+                mode="markers",
+                marker=dict(
+                    size=8,
+                    color="rgba(128, 128, 128, 0.3)",  # 半透明灰色
+                ),
+                text=input_station_texts,
+                hovertemplate="%{text}<extra></extra>",
+                name="輸入測站",
+                showlegend=True,
+            )
+        )
+
+    # 【頂層】添加各震度等級的測站（預測結果）
     intensity_labels = ["0", "1", "2", "3", "4", "5-", "5+", "6-", "6+", "7"]
     for intensity_level in range(10):
         group = intensity_groups[intensity_level]
@@ -746,7 +604,7 @@ def create_intensity_map(
                     lat=group["lat"],
                     lon=group["lon"],
                     mode="markers",
-                    marker=dict(size=14, color=group["color"], opacity=0.8),
+                    marker=dict(size=14, color=group["color"], opacity=0.9),
                     text=group["text"],
                     hovertemplate="%{text}<extra></extra>",
                     name=f"震度 {intensity_labels[intensity_level]}",
@@ -760,21 +618,47 @@ def create_intensity_map(
                     lat=[None],
                     lon=[None],
                     mode="markers",
-                    marker=dict(size=14, color=group["color"], opacity=0.8),
+                    marker=dict(size=14, color=group["color"], opacity=0.9),
                     name=f"震度 {intensity_labels[intensity_level]}",
                     showlegend=True,
                     hoverinfo="skip",
                 )
             )
 
+    # 【中層】添加震央（紅色標記）
+    if epicenter_lat is not None and epicenter_lon is not None:
+        fig.add_trace(
+            go.Scattermap(
+                lat=[epicenter_lat],
+                lon=[epicenter_lon],
+                mode="markers",
+                marker=dict(size=25, color="red"),
+                text=[f"震央<br>({epicenter_lat:.3f}, {epicenter_lon:.3f})"],
+                hovertemplate="%{text}<extra></extra>",
+                name="震央",
+                showlegend=True,
+            )
+        )
+
+        fig.add_trace(
+            go.Scattermap(
+                lat=[epicenter_lat],
+                lon=[epicenter_lon],
+                mode="markers",
+                marker=dict(size=10, color="white"),
+                showlegend=False,
+                hoverinfo="skip",
+            )
+        )
+
     # 設置地圖佈局
     fig.update_layout(
         map=dict(
             style="open-street-map",
             center=dict(lat=map_center_lat, lon=map_center_lon),
-            zoom=7,
+            zoom=6.5,
         ),
-        height=800,  # 設置固定高度以適應 Gradio 容器
+        height=550,  # 設置固定高度以適應 Gradio 容器
         margin=dict(l=0, r=0, t=0, b=0),
         showlegend=True,
         legend=dict(
@@ -830,25 +714,21 @@ def step1_load_mseed_and_select_stations(event_name):
 
         if len(selected_stations) == 0:
             logger.error("找不到有效的測站資料")
-            return None, None, None, None, gr.update(interactive=False)
-
-        # 創建輸入測站地圖
-        station_map = create_input_station_map(
-            selected_stations, epicenter_lat, epicenter_lon
-        )
+            return None, None
 
         logger.info("[步驟 1] 完成 - mseed 已載入，測站已選擇")
-        return st, selected_stations, station_map, None
+        return st, selected_stations
 
     except Exception as e:
         logger.error(f"[步驟 1] 發生錯誤: {e}")
         import traceback
         traceback.print_exc()
-        return None, None, None, None
+        return None, None
 
 
 # ============ 步驟 2：提取波形（使用快取的 stream + stations）============
-def step2_extract_and_plot_waveforms(cached_stream, cached_stations, event_name, duration):
+def step2_extract_and_plot_waveforms(cached_stream, cached_stations, event_name,
+                                     duration):
     """
     步驟 2：根據時間範圍提取波形並繪圖
 
@@ -876,7 +756,8 @@ def step2_extract_and_plot_waveforms(cached_stream, cached_stations, event_name,
             return None, None, None, gr.update(interactive=False)
 
         # 繪製波形圖
-        waveform_plot = plot_waveform(cached_stream, cached_stations, first_pick, duration)
+        waveform_plot = plot_waveform(cached_stream, cached_stations, first_pick,
+                                      duration)
 
         logger.info(f"[步驟 2] 完成 - 已提取 {len(waveforms)} 個測站的波形")
         return waveforms, station_info_list, waveform_plot
@@ -889,7 +770,8 @@ def step2_extract_and_plot_waveforms(cached_stream, cached_stations, event_name,
 
 
 # ============ 步驟 3：執行模型推論（使用快取的波形）============
-def step3_predict_intensity(cached_waveforms, cached_station_info, event_name):
+def step3_predict_intensity(cached_waveforms, cached_station_info, cached_stations,
+                            event_name):
     """
     步驟 3：執行震度預測
 
@@ -897,11 +779,13 @@ def step3_predict_intensity(cached_waveforms, cached_station_info, event_name):
 
     spec #2：測站選擇上限 (25 站)、波形取樣率 (100 Hz)、時間窗長度 (30 秒)
     spec #3：推論流���、PGA → 震度轉換
+
+    注意：此函數只返回預測地圖，觀測圖片由 step1 單獨處理
     """
     try:
         if cached_waveforms is None or cached_station_info is None:
             logger.warning("[步驟 3] 快取資料不存在，請先載入並提取波形")
-            return None, None
+            return None
 
         epicenter_lat = earthquake_metadata[event_name]["epicenter_lat"]
         epicenter_lon = earthquake_metadata[event_name]["epicenter_lon"]
@@ -987,24 +871,21 @@ def step3_predict_intensity(cached_waveforms, cached_station_info, event_name):
         pga_list = all_pga_list
         target_names = all_target_names
 
-        # 繪製互動式地圖（固定高度 800）
+        # 繪製互動式地圖（固定高度 800）- 合併輸入測站與預測震度
         intensity_map = create_intensity_map(
-            pga_list, target_names, epicenter_lat, epicenter_lon
+            pga_list, target_names, epicenter_lat, epicenter_lon,
+            selected_stations=cached_stations
         )
 
-        # 載入實際觀測震度圖
-        observed_intensity_path = load_observed_intensity_image(event_name)
-
         logger.info("[步驟 3] 預測完成！")
-        return observed_intensity_path, intensity_map
+        return intensity_map
 
     except Exception as e:
         logger.error(f"[步驟 3] 發生錯誤: {e}")
         import traceback
 
         traceback.print_exc()
-        return None, None
-
+        return None
 
 
 # ============ Gradio 介面 ============
@@ -1033,85 +914,81 @@ with gr.Blocks(title="TTSAM 震度預測系統", fill_height=True) as demo:
             duration_slider = gr.Slider(
                 2, 15, value=15, step=1, label="P 波後時間 (秒)"
             )
-    # ========== 中層：輸入測站地圖與波形圖 ==========
-    with gr.Row():
-        # 中左：輸入測站地圖
-        with gr.Column(scale=1):
-            gr.Markdown("## 輸入測站分布")
-            input_station_map = gr.Plot(label="輸入測站地圖")
-
-        # 中右：輸入波形
-        with gr.Column(scale=1):
-            gr.Markdown("## 輸入波形")
             waveform_plot = gr.Plot(
                 label="地震波形（選定的 25 個測站）",
             )
 
-
-    # ========== 下層：實際觀測 vs 預測結果 ==========
+    # ========== 下層：合併地圖 vs 實際觀測 ==========
     with gr.Row():
-        # 左下：預測震度地圖
+        # 實際觀測震度圖
+
+        # 合併後的地圖（輸入測站 + 預測震度）
         with gr.Column(scale=1):
-            gr.Markdown("## 預測震度分布")
-            predicted_intensity_map = gr.Plot(label="互動式震度地圖")
+            predicted_intensity_map = gr.Plot(label="合併地圖")
 
-        # 右下：實際觀測震度圖
         with gr.Column(scale=1):
-            gr.Markdown("## 實際觀測震度分布")
             observed_intensity_image = gr.Image(
                 label="實際觀測震度",
                 type="filepath",
-                height=800,
-                value=load_observed_intensity_image(list(earthquake_metadata.keys())[0]),
+                value=load_observed_intensity_image(
+                    list(earthquake_metadata.keys())[0]),
             )
 
     # ========== 隱藏的 State 變數（用於快取中間結果）==========
-    cached_stream = gr.State(None)          # ObsPy Stream object
-    cached_stations = gr.State(None)        # 選中的 25 個測站列表
-    cached_waveforms = gr.State(None)       # 提取的波形資料
-    cached_station_info = gr.State(None)    # 測站資訊列表
+    cached_stream = gr.State(None)  # ObsPy Stream object
+    cached_stations = gr.State(None)  # 選中的 25 個測站列表
+    cached_waveforms = gr.State(None)  # 提取的波形資料
+    cached_station_info = gr.State(None)  # 測站資訊列表
 
     # ========== 事件綁定（使用鏈式觸發 + gr.State 快取）==========
 
-    # 【觸發點 1】事件切換：自動執行完整流程 步驟 1 → 步驟 2 → 步驟 3
+    # 【觸發點 1】事件切換：自動執行完整流程 步驟 1 → 步驟 2 → 步驟 3 + 載入觀測圖片
     event_dropdown.change(
         fn=step1_load_mseed_and_select_stations,
         inputs=[event_dropdown],
-        outputs=[cached_stream, cached_stations, input_station_map, waveform_plot]
+        outputs=[cached_stream, cached_stations]
+    ).then(  # 載入觀測圖片（只在事件切換時執行）
+        fn=load_observed_intensity_image,
+        inputs=[event_dropdown],
+        outputs=[observed_intensity_image]
     ).then(  # 鏈式觸發步驟 2
         fn=step2_extract_and_plot_waveforms,
         inputs=[cached_stream, cached_stations, event_dropdown, duration_slider],
         outputs=[cached_waveforms, cached_station_info, waveform_plot]
     ).then(  # 鏈式觸發步驟 3
         fn=step3_predict_intensity,
-        inputs=[cached_waveforms, cached_station_info, event_dropdown],
-        outputs=[observed_intensity_image, predicted_intensity_map]
+        inputs=[cached_waveforms, cached_station_info, cached_stations, event_dropdown],
+        outputs=[predicted_intensity_map]
     )
 
-    # 【觸發點 2】時間範圍調整：自動執行步驟 2 → 步驟 3（不重新讀檔）
+    # 【觸發點 2】時間範圍調整：自動執行步驟 2 → 步驟 3（不重新讀檔，不更新觀測圖片）
     duration_slider.change(
         fn=step2_extract_and_plot_waveforms,
         inputs=[cached_stream, cached_stations, event_dropdown, duration_slider],
         outputs=[cached_waveforms, cached_station_info, waveform_plot]
     ).then(  # 鏈式觸發步驟 3
         fn=step3_predict_intensity,
-        inputs=[cached_waveforms, cached_station_info, event_dropdown],
-        outputs=[observed_intensity_image, predicted_intensity_map]
+        inputs=[cached_waveforms, cached_station_info, cached_stations, event_dropdown],
+        outputs=[predicted_intensity_map]
     )
 
-    # 【冷啟動】應用載入時自動執行完整流程 步驟 1 → 步驟 2 → 步驟 3
+    # 【冷啟動】應用載入時自動執行完整流程 步驟 1 → 載入觀測圖片 → 步驟 2 → 步驟 3
     demo.load(
         fn=step1_load_mseed_and_select_stations,
         inputs=[event_dropdown],
-        outputs=[cached_stream, cached_stations, input_station_map, waveform_plot]
+        outputs=[cached_stream, cached_stations]
+    ).then(
+        fn=load_observed_intensity_image,
+        inputs=[event_dropdown],
+        outputs=[observed_intensity_image]
     ).then(
         fn=step2_extract_and_plot_waveforms,
         inputs=[cached_stream, cached_stations, event_dropdown, duration_slider],
         outputs=[cached_waveforms, cached_station_info, waveform_plot]
     ).then(
         fn=step3_predict_intensity,
-        inputs=[cached_waveforms, cached_station_info, event_dropdown],
-        outputs=[observed_intensity_image, predicted_intensity_map]
+        inputs=[cached_waveforms, cached_station_info, cached_stations, event_dropdown],
+        outputs=[predicted_intensity_map]
     )
 
 demo.launch()