Update app.py

app.py

@@ -8,7 +8,7 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import gradio as gr
 
-# ---------- 可選依賴偵測（沒裝也能跑） ----------
+# ---------- 可選依賴偵測 ----------
 try:
     import tabulate as _tabulate  # noqa: F401
     HAS_TABULATE = True
@@ -22,13 +22,13 @@ try:
 except Exception:
     HAS_PYGMT = False
 
-# ---- 自動抓取 PyGMT 所需的海岸線/國界資料（GSHHG/DCW） ----
+# ---- 自動抓取 PyGMT 所需資料（若可用） ----
 if 'HAS_PYGMT' in globals() and HAS_PYGMT:
     try:
-        pygmt.which("@gshhg", download=True)
-        pygmt.which("@dcw", download=True)
+        pygmt.which("@gshhg", download=True)            # 海岸線
+        pygmt.which("@dcw", download=True)              # 國界
+        pygmt.which("@earth_relief_04m", download=True) # 地形備援
     except Exception:
-        # 若下載失敗就靜默忽略，之後會走 matplotlib 備援
         pass
 
 # -----------------------------
@@ -40,7 +40,6 @@ def _fmt(dt: datetime) -> str:
     return dt.strftime("%Y-%m-%dT%H:%M:%S")
 
 def set_time_range(hours=None, days=None):
-    """依台北時間回傳 (timeFrom, timeTo) 字串"""
     now = datetime.now(TAIPEI_TZ)
     if hours is not None:
         t_from = now - timedelta(hours=hours)
@@ -65,15 +64,10 @@ def fetch_reports(time_from, time_to):
     return r.json()
 
 # -----------------------------
-# JSON 解析（讀 EarthquakeInfo）＋ 強化數字解析
+# 解析 JSON
 # -----------------------------
 def _to_float(x):
-    """
-    將各種數字表達轉成 float：
-    - 純數字：23.5
-    - 含單位/文字：'23.5°N'、'121.6 E'、'25.3 公里' -> 擷取第一個浮點數
-    - 其他不可解析 -> None
-    """
+    """將字串(含單位)抽出第一個數字成 float；失敗回 None。"""
     if x is None:
         return None
     if isinstance(x, (int, float)):
@@ -85,11 +79,6 @@ def _to_float(x):
     return float(m.group()) if m else None
 
 def parse_ea0015(obj):
-    """
-    解析 CWA E-A0015-001
-    主要欄位在 records.earthquake[].EarthquakeInfo.*
-    取出：OriginTime, Lat, Lon, Depth_km, Magnitude, Location, ReportURL
-    """
     records = obj.get("records") or obj.get("Records") or {}
     quakes = records.get("earthquake") or records.get("Earthquake") or []
     if not isinstance(quakes, list):
@@ -99,8 +88,6 @@ def parse_ea0015(obj):
     for q in quakes:
         ei   = q.get("EarthquakeInfo") or q.get("earthquakeInfo") or {}
         epic = ei.get("Epicenter")     or ei.get("epicenter")     or {}
-
-        # Magnitude 可能在 Magnitude 或 EarthquakeMagnitude
         mago = (
             ei.get("Magnitude") or ei.get("magnitude")
             or ei.get("EarthquakeMagnitude") or ei.get("earthquakeMagnitude")
@@ -112,7 +99,6 @@ def parse_ea0015(obj):
             or q.get("OriginTime") or q.get("originTime")
         )
 
-        # 經緯度多種鍵名
         lat_raw = (
             epic.get("EpicenterLat") or epic.get("epicenterLat")
             or epic.get("EpicenterLatitude") or epic.get("epicenterLatitude")
@@ -124,14 +110,12 @@ def parse_ea0015(obj):
             or epic.get("Lon") or epic.get("lon")
         )
 
-        # 深度：Depth / FocalDepth / FocalDepthKm / depth / focalDepth...
         depth_raw = (
             ei.get("Depth") or ei.get("depth")
             or ei.get("FocalDepth") or ei.get("focalDepth")
             or ei.get("FocalDepthKm") or ei.get("focalDepthKm")
         )
 
-        # 規模：MagnitudeValue / value / Magnitude / magnitude
         mag_raw = (
             mago.get("MagnitudeValue") or mago.get("magnitudeValue")
             or mago.get("Value") or mago.get("value")
@@ -159,7 +143,7 @@ def parse_ea0015(obj):
     return df
 
 # -----------------------------
-# 視覺化（回傳檔案路徑；相容舊/新 Gradio）
+# 視覺化
 # -----------------------------
 def _save_fig_to_tmp(fig, suffix=".png", dpi=180):
     outpath = tempfile.NamedTemporaryFile(delete=False, suffix=suffix).name
@@ -180,7 +164,6 @@ def plot_trend_path(df):
     return _save_fig_to_tmp(fig)
 
 def _auto_region_from_df(d, pad=0.5):
-    """由資料自動推算地圖範圍，並加上邊界緩衝（degrees）。"""
     lon_min = float(pd.to_numeric(d["Lon"], errors="coerce").min())
     lon_max = float(pd.to_numeric(d["Lon"], errors="coerce").max())
     lat_min = float(pd.to_numeric(d["Lat"], errors="coerce").min())
@@ -188,14 +171,9 @@ def _auto_region_from_df(d, pad=0.5):
     return [lon_min - pad, lon_max + pad, lat_min - pad, lat_max + pad]
 
 def plot_map_path(df):
-    """
-    優先使用 PyGMT 畫台灣地圖（含海岸線）；若不可用則退回 matplotlib。
-    - 自動依據資料決定地圖範圍（避免漏點）
-    - 顏色：深度(km)；大小：規模
-    """
+    """PyGMT（含海岸線三段式備援）→ 失敗退回 Matplotlib。"""
     if df.empty:
         return None
-
     d = df.dropna(subset=["Lon", "Lat"]).copy()
     if d.empty:
         return None
@@ -203,19 +181,44 @@ def plot_map_path(df):
     d["Magnitude"] = pd.to_numeric(d["Magnitude"], errors="coerce").fillna(0).clip(lower=0)
     d["Depth_km"]  = pd.to_numeric(d["Depth_km"], errors="coerce").fillna(0)
 
-    # --- PyGMT 版（DataFrame API + 海岸線 + 自動範圍） ---
+    # --- PyGMT 版 ---
     if HAS_PYGMT:
-        d["Size"] = 0.06 * (d["Magnitude"] + 1.5)  # cm
+        d["Size"] = 0.06 * (d["Magnitude"] + 1.5)  # 圓半徑(cm)
         region = _auto_region_from_df(d, pad=0.5)
 
         fig = pygmt.Figure()
-        fig.coast(
-            region=region, projection="M12c",
-            resolution="i",                 # 中解析度海岸線
-            land="lightgray", water="lightblue",
-            shorelines="0.8p,black", borders="1/0.6p,black",
-            frame=["WSen", "xaf", "yaf"]
-        )
+        drew_background = False
+        # 1) GSHHG 海岸線
+        try:
+            fig.coast(
+                region=region, projection="M12c",
+                resolution="i",
+                land="lightgray", water="lightblue",
+                shorelines="0.8p,black", borders="1/0.6p,black",
+                frame=["WSen", "xaf", "yaf"]
+            )
+            drew_background = True
+        except Exception:
+            pass
+        # 2) DCW 台灣填色
+        if not drew_background:
+            try:
+                fig.coast(region=region, projection="M12c",
+                          water="lightblue", frame=["WSen", "xaf", "yaf"])
+                fig.coast(region=region, projection="M12c", dcw="TW+glightgray")
+                drew_background = True
+            except Exception:
+                pass
+        # 3) 地形格網 +（可用則）海岸線
+        if not drew_background:
+            fig.grdimage("@earth_relief_04m", region=region, projection="M12c", cmap="gray")
+            try:
+                fig.coast(region=region, projection="M12c",
+                          shorelines="0.8p,black", frame=["WSen", "xaf", "yaf"])
+            except Exception:
+                fig.basemap(region=region, projection="M12c", frame=["WSen", "xaf", "yaf"])
+
+        # 畫震央
         fig.plot(
             data=d, x="Lon", y="Lat",
             style="c", size="Size",
@@ -228,14 +231,13 @@ def plot_map_path(df):
         fig.savefig(outpath, dpi=220)
         return outpath
 
-    # --- Matplotlib 備援（自動範圍，無海岸線） ---
+    # --- Matplotlib 備援（無海岸線，只畫散點） ---
     region = _auto_region_from_df(d, pad=0.5)
     lon_min, lon_max, lat_min, lat_max = region
 
     fig, ax = plt.subplots(figsize=(6, 6))
     ax.set_xlim(lon_min, lon_max)
     ax.set_ylim(lat_min, lat_max)
-
     s = (d["Magnitude"] + 2) ** 3
     sc = ax.scatter(d["Lon"], d["Lat"], s=s, c=d["Depth_km"], alpha=0.85, edgecolor="black")
     cb = plt.colorbar(sc, ax=ax, fraction=0.046, pad=0.04)
@@ -247,7 +249,7 @@ def plot_map_path(df):
     return _save_fig_to_tmp(fig)
 
 # -----------------------------
-# 表格輸出（tabulate 可選）
+# 表格輸出
 # -----------------------------
 def _format_taipei(series):
     try:
@@ -344,13 +346,11 @@ with gr.Blocks(fill_height=True) as demo:
         map_out = gr.Image(label="台灣範圍圖（PyGMT）", type="filepath")
         dl_btn = gr.DownloadButton(label="下載 CSV")  # 回傳路徑即可
 
-    # 快速鍵
     btn_12h.click(lambda: set_time_range(hours=12), outputs=[time_from, time_to])
     btn_24h.click(lambda: set_time_range(hours=24), outputs=[time_from, time_to])
     btn_3d.click(lambda: set_time_range(days=3), outputs=[time_from, time_to])
     btn_5d.click(lambda: set_time_range(days=5), outputs=[time_from, time_to])
 
-    # 查詢
     run_btn.click(
         query_and_render,
         inputs=[time_from, time_to, sort_dd],