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 ----
+# ---------- 可選依賴偵測（沒裝也能跑） ----------
 try:
     import tabulate as _tabulate  # noqa: F401
     HAS_TABULATE = True
@@ -42,7 +42,7 @@ def set_time_range(hours=None, days=None):
     return _fmt(t_from), _fmt(now)
 
 # -----------------------------
-# 取 API 資料
+# 呼叫 CWA API
 # -----------------------------
 API_URL = "https://opendata.cwa.gov.tw/api/v1/rest/datastore/E-A0015-001"
 
@@ -56,7 +56,7 @@ def fetch_reports(time_from, time_to):
     return r.json()
 
 # -----------------------------
-# 解析 JSON → DataFrame（彈性容錯）
+# JSON 解析（修正為讀 EarthquakeInfo）
 # -----------------------------
 def _safe_get(d, *keys, default=None):
     cur = d
@@ -77,45 +77,44 @@ def _to_float(x):
 
 def parse_ea0015(obj):
     """
-    支援 records/Records、earthquake/Earthquake 等大小寫差異。
-    輸出欄位：OriginTime, Lat, Lon, Depth_km, Magnitude, Location, ReportURL
+    CWA E-A0015-001 結構：
+    records.earthquake[] 裡主要欄位在 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 records.get("data") or []
+    quakes = records.get("earthquake") or records.get("Earthquake") or []
     if not isinstance(quakes, list):
         quakes = []
 
     rows = []
     for q in quakes:
+        ei = q.get("EarthquakeInfo") or q.get("earthquakeInfo") or {}
+        epic = ei.get("Epicenter") or ei.get("epicenter") or {}
+        magobj = ei.get("Magnitude") or ei.get("magnitude") or {}
+
         origin = (
-            _safe_get(q, "originTime")
-            or _safe_get(q, "OriginTime")
-            or _safe_get(q, "earthquakeInfo", "originTime")
-            or _safe_get(q, "EarthquakeInfo", "OriginTime")
+            ei.get("OriginTime") or ei.get("originTime")
+            or q.get("OriginTime") or q.get("originTime")
         )
-        lat = _safe_get(q, "epicenter", "epicenterLat") or _safe_get(q, "Epicenter", "EpicenterLat")
-        lon = _safe_get(q, "epicenter", "epicenterLon") or _safe_get(q, "Epicenter", "EpicenterLon")
-        depth = _safe_get(q, "depth") or _safe_get(q, "Depth")
+        lat = epic.get("EpicenterLat") or epic.get("epicenterLat")
+        lon = epic.get("EpicenterLon") or epic.get("epicenterLon")
+        depth = ei.get("Depth") or ei.get("depth")
         mag = (
-            _safe_get(q, "magnitude", "magnitudeValue")
-            or _safe_get(q, "Magnitude", "MagnitudeValue")
-            or _safe_get(q, "magnitude", "magnitude")
-            or _safe_get(q, "Magnitude", "Magnitude")
-        )
-        loc = _safe_get(q, "epicenter", "location") or _safe_get(q, "Epicenter", "Location")
-        url = _safe_get(q, "reportURL") or _safe_get(q, "ReportURL")
-
-        rows.append(
-            {
-                "OriginTime": origin,
-                "Lat": _to_float(lat),
-                "Lon": _to_float(lon),
-                "Depth_km": _to_float(depth),
-                "Magnitude": _to_float(mag),
-                "Location": loc,
-                "ReportURL": url,
-            }
+            magobj.get("MagnitudeValue") or magobj.get("magnitudeValue")
+            or magobj.get("Magnitude") or magobj.get("magnitude")
         )
+        loc = epic.get("Location") or epic.get("location")
+        url = q.get("Web") or q.get("ReportURL") or q.get("reportURL")
+
+        rows.append({
+            "OriginTime": origin,
+            "Lat": _to_float(lat),
+            "Lon": _to_float(lon),
+            "Depth_km": _to_float(depth),
+            "Magnitude": _to_float(mag),
+            "Location": loc,
+            "ReportURL": url,
+        })
 
     df = pd.DataFrame(rows)
     if not df.empty:
@@ -145,63 +144,58 @@ def plot_trend_path(df):
 
 def plot_map_path(df):
     """
-    優先使用 PyGMT 畫台灣區域地圖；若 PyGMT 不可用，退回 Matplotlib 版。
+    優先使用 PyGMT 畫台灣地圖；若不可用則退回 matplotlib。
     範圍：119/123, 21/26；顏色=深度(km)，大小=規模
     """
     lon_min, lon_max, lat_min, lat_max = 119, 123, 21, 26
 
-    # ---- PyGMT 版本 ----
+    # --- PyGMT 版 ---
     if HAS_PYGMT and not df.empty:
         d = df.dropna(subset=["Lon", "Lat"]).copy()
-        if d.empty:
-            return None
-
-        mag = d["Magnitude"].fillna(0).clip(lower=0)
-        size_cm = 0.06 * (mag + 1.5)             # 各點大小（cm）
-        depth = d["Depth_km"].fillna(0)
-
-        vmin = float(max(0, np.nanmin(depth)))
-        vmax = float(max(100, np.nanmax(depth)))
-        cmap = "roma"  # 或試 "turbo", "viridis"
-
-        fig = pygmt.Figure()
-        region = [lon_min, lon_max, lat_min, lat_max]
-
-        fig.coast(
-            region=region, projection="M12c",
-            land="lightgray", water="white",
-            shorelines="0.5p,black", borders="1/0.6p,black",
-            frame=["WSen", "xaf", "yaf"]
-        )
-
-        # 畫震央
-        # 這裡用 per-point size：把每個大小轉字串傳入 style
-        fig.plot(
-            x=d["Lon"].to_list(), y=d["Lat"].to_list(),
-            style=["c{}c".format(s) for s in size_cm.to_list()],
-            color=depth.to_list(), cmap=cmap, pen="0.25p,black"
-        )
-
-        fig.colorbar(frame=["x+lDepth (km)"], cmap=True, position="JMR+w7c/0.4c+o0.6c/0c")
-        fig.basemap(map_scale="jBL+w50k+o0.6c/0.6c+f+lkm")
-
-        outpath = tempfile.NamedTemporaryFile(delete=False, suffix=".png").name
-        fig.savefig(outpath, dpi=220)
-        return outpath
-
-    # ---- 備援：Matplotlib 版本 ----
+        if not d.empty:
+            mag = d["Magnitude"].fillna(0).clip(lower=0)
+            size_cm = 0.06 * (mag + 1.5)          # 每點大小（cm）
+            depth = d["Depth_km"].fillna(0)
+
+            vmin = float(max(0, float(depth.min()) if len(depth) else 0))
+            vmax = float(max(100, float(depth.max()) if len(depth) else 100))
+            cmap = "roma"
+
+            fig = pygmt.Figure()
+            region = [lon_min, lon_max, lat_min, lat_max]
+            fig.coast(
+                region=region, projection="M12c",
+                land="lightgray", water="white",
+                shorelines="0.5p,black", borders="1/0.6p,black",
+                frame=["WSen", "xaf", "yaf"]
+            )
+            # per-point size
+            fig.plot(
+                x=d["Lon"].to_list(), y=d["Lat"].to_list(),
+                style=["c{}c".format(s) for s in size_cm.to_list()],
+                color=depth.to_list(), cmap=cmap, pen="0.25p,black"
+            )
+            fig.colorbar(frame=["x+lDepth (km)"], cmap=True, position="JMR+w7c/0.4c+o0.6c/0c")
+            fig.basemap(map_scale="jBL+w50k+o0.6c/0.6c+f+lkm")
+
+            outpath = tempfile.NamedTemporaryFile(delete=False, suffix=".png").name
+            fig.savefig(outpath, dpi=220)
+            return outpath
+
+    # --- Matplotlib 備援 ---
     if df.empty:
         return None
     fig, ax = plt.subplots(figsize=(6, 6))
     ax.set_xlim(lon_min, lon_max)
     ax.set_ylim(lat_min, lat_max)
-    mags = df["Magnitude"].fillna(0)
-    sizes = (mags.clip(lower=0) + 2) ** 3
-    sc = ax.scatter(df["Lon"], df["Lat"], s=sizes, c=df["Depth_km"], alpha=0.8, edgecolor="black")
+    sc = ax.scatter(
+        df["Lon"], df["Lat"],
+        s=(df["Magnitude"].fillna(0).clip(lower=0) + 2) ** 3,
+        c=df["Depth_km"], edgecolor="black", alpha=0.85
+    )
     cb = plt.colorbar(sc, ax=ax, fraction=0.046, pad=0.04)
     cb.set_label("Depth (km)")
-    ax.set_xlabel("Longitude (°E)")
-    ax.set_ylabel("Latitude (°N)")
+    ax.set_xlabel("Longitude (°E)"); ax.set_ylabel("Latitude (°N)")
     ax.set_title("Epicenters in Taiwan Region (119–123E, 21–26N)")
     ax.grid(True, linestyle="--", alpha=0.3)
     return _save_fig_to_tmp(fig)
@@ -302,7 +296,7 @@ with gr.Blocks(fill_height=True) as demo:
         table_out = gr.Markdown("（尚未查詢）")
         trend_out = gr.Image(label="趨勢圖", type="filepath")
         map_out = gr.Image(label="台灣範圍圖（PyGMT）", type="filepath")
-        dl_btn = gr.DownloadButton(label="下載 CSV")  # 傳路徑，不用 file_name 參數
+        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])