Update app.py

app.py

@@ -8,29 +8,13 @@ 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
 except Exception:
     HAS_TABULATE = False
 
-try:
-    import numpy as np  # noqa: F401
-    import pygmt
-    HAS_PYGMT = True
-except Exception:
-    HAS_PYGMT = False
-
-# ---- 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)            # 海岸線
-        pygmt.which("@dcw", download=True)              # 國界
-        pygmt.which("@earth_relief_04m", download=True) # 地形備援
-    except Exception:
-        pass
-
 # -----------------------------
 # 台北時區 (UTC+8)
 # -----------------------------
@@ -40,6 +24,7 @@ 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)
@@ -64,10 +49,15 @@ def fetch_reports(time_from, time_to):
     return r.json()
 
 # -----------------------------
-# 解析 JSON
+# JSON 解析（讀 EarthquakeInfo）＋ 強化數字解析
 # -----------------------------
 def _to_float(x):
-    """將字串(含單位)抽出第一個數字成 float；失敗回 None。"""
+    """
+    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
+    """
     if x is None:
         return None
     if isinstance(x, (int, float)):
@@ -79,6 +69,11 @@ 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):
@@ -88,6 +83,8 @@ 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")
@@ -99,6 +96,7 @@ 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")
@@ -110,12 +108,14 @@ 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")
@@ -143,7 +143,7 @@ def parse_ea0015(obj):
     return df
 
 # -----------------------------
-# 視覺化
+# 視覺化（回傳檔案路徑；相容舊/新 Gradio）
 # -----------------------------
 def _save_fig_to_tmp(fig, suffix=".png", dpi=180):
     outpath = tempfile.NamedTemporaryFile(delete=False, suffix=suffix).name
@@ -163,199 +163,21 @@ def plot_trend_path(df):
     fig.autofmt_xdate()
     return _save_fig_to_tmp(fig)
 
-def _auto_region_from_df(d, pad=0.5):
-    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())
-    lat_max = float(pd.to_numeric(d["Lat"], errors="coerce").max())
-    return [lon_min - pad, lon_max + pad, lat_min - pad, lat_max + pad]
-
 def plot_map_path(df):
-    """PyGMT（含海岸線三段式備援）→ 失敗退回 Matplotlib。"""
+    """
+    最簡版地圖：純 Matplotlib 散點圖
+    - 自動依據資料決定範圍（加一點 padding）
+    - 點大小 = 規模函數；點顏色 = 深度(km)
+    """
     if df.empty:
         return None
+
     d = df.dropna(subset=["Lon", "Lat"]).copy()
     if d.empty:
         return None
 
+    # 數值化
     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 ---
-    if HAS_PYGMT:
-        d["Size"] = 0.06 * (d["Magnitude"] + 1.5)  # 圓半徑(cm)
-        region = _auto_region_from_df(d, pad=0.5)
-
-        fig = pygmt.Figure()
-        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",
-            color="Depth_km", cmap="roma", 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（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)
-    cb.set_label("Depth (km)")
-    ax.set_xlabel("Longitude (°E)")
-    ax.set_ylabel("Latitude (°N)")
-    ax.set_title("Epicenters (auto region)")
-    ax.grid(True, linestyle="--", alpha=0.3)
-    return _save_fig_to_tmp(fig)
-
-# -----------------------------
-# 表格輸出
-# -----------------------------
-def _format_taipei(series):
-    try:
-        if series.dt.tz is None:
-            s = series.dt.tz_localize(TAIPEI_TZ)
-        else:
-            s = series.dt.tz_convert(TAIPEI_TZ)
-        return s.dt.strftime("%Y-%m-%d %H:%M:%S %Z")
-    except Exception:
-        return series.astype(str)
-
-def _to_simple_md_table(df: pd.DataFrame) -> str:
-    cols = list(df.columns)
-    header = "|" + "|".join(cols) + "|\n"
-    sep = "|" + "|".join(["---"] * len(cols)) + "|\n"
-    rows = []
-    for _, r in df.iterrows():
-        cells = []
-        for c in cols:
-            v = r.get(c, "")
-            cells.append("" if pd.isna(v) else str(v))
-        rows.append("|" + "|".join(cells) + "|")
-    return header + sep + "\n".join(rows)
-
-def df_to_markdown(df, top_n=100):
-    if df.empty:
-        return "（查無資料）"
-    cols = ["OriginTime", "Magnitude", "Depth_km", "Lat", "Lon", "Location", "ReportURL"]
-    cols = [c for c in cols if c in df.columns]
-    slim = df[cols].head(top_n).copy()
-    if "OriginTime" in slim.columns:
-        slim["OriginTime"] = _format_taipei(slim["OriginTime"])
-    header = f"共 {len(df)} 筆，顯示前 {min(len(slim), top_n)} 筆\n\n"
-    if HAS_TABULATE:
-        table = slim.to_markdown(index=False)
-    else:
-        table = _to_simple_md_table(slim.reset_index(drop=True))
-    return header + table
-
-# -----------------------------
-# 主流程
-# -----------------------------
-def query_and_render(time_from, time_to, sort_order):
-    try:
-        raw = fetch_reports(time_from, time_to)
-        df = parse_ea0015(raw)
-        if df.empty:
-            return "（查無資料）", None, None, None
-
-        if sort_order == "OriginTime (舊→新)":
-            df = df.sort_values("OriginTime", ascending=True, na_position="last").reset_index(drop=True)
-
-        md = df_to_markdown(df)
-        trend_path = plot_trend_path(df)
-        map_path = plot_map_path(df)
-
-        csv_bytes = df.to_csv(index=False).encode("utf-8-sig")
-        csv_path = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", prefix="CWA_E-A0015-001_").name
-        with open(csv_path, "wb") as f:
-            f.write(csv_bytes)
-
-        return md, trend_path, map_path, csv_path
-    except Exception as e:
-        return f"錯誤：{e}", None, None, None
-
-# -----------------------------
-# 介面
-# -----------------------------
-default_from, default_to = set_time_range(days=3)
-
-with gr.Blocks(fill_height=True) as demo:
-    gr.Markdown("## CWA 顯著有感地震報告 (E-A0015-001)\n預設查詢最近 3 天（台北時間）")
-
-    with gr.Column():
-        time_from = gr.Textbox(label="timeFrom yyyy-MM-ddTHH:mm:ss", value=default_from)
-        time_to = gr.Textbox(label="timeTo yyyy-MM-ddTHH:mm:ss", value=default_to)
-
-        with gr.Row():
-            btn_12h = gr.Button("最近 12 小時")
-            btn_24h = gr.Button("最近 24 小時")
-            btn_3d = gr.Button("最近 3 天")
-            btn_5d = gr.Button("最近 5 天")
-
-        sort_dd = gr.Dropdown(
-            choices=["OriginTime (新→舊)", "OriginTime (舊→新)"],
-            value="OriginTime (新→舊)",
-            label="排序",
-        )
-
-        run_btn = gr.Button("查詢", variant="primary")
-
-        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")  # 回傳路徑即可
-
-    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])
-
-    run_btn.click(
-        query_and_render,
-        inputs=[time_from, time_to, sort_dd],
-        outputs=[table_out, trend_out, map_out, dl_btn],
-    )
+    d["Depth_km"]  = pd.to_numeric(d["Depth_km"], errors="coerce")
 
-if __name__ == "__main__":
-    demo.launch()
+    # 自動