# ==========================================
# 気象GPV 局地分析ツール ビューワー (App B)
# VERSION INFO: 134.0_EXE (ANALスライダー・メタグラム拡張版)
# ==========================================
import sys, os, glob, re, gc, time, logging
# --- EXE化対策: ライブラリの読み込み順序を強制 ---
import pyproj
import shapely
import cartopy
if getattr(sys, 'frozen', False):
exe_dir = os.path.dirname(sys.executable)
os.environ["CARTOPY_DATA_DIR"] = os.path.join(exe_dir, "cartopy_data")
import numpy as np
import textwrap
from datetime import datetime, timedelta
import scipy.ndimage as ndimage
from PyQt6.QtWidgets import (QApplication, QMainWindow, QWidget, QVBoxLayout,
QHBoxLayout, QSlider, QPushButton, QLabel,
QTabWidget, QListWidget, QTextEdit, QComboBox, QFileDialog, QDialog,
QMessageBox, QLineEdit, QTableWidget, QTableWidgetItem, QHeaderView)
from PyQt6.QtCore import Qt, QTimer, QSettings
from PyQt6.QtGui import QColor, QFont, QCloseEvent
os.environ['QT_API'] = 'pyqt6'
import matplotlib
matplotlib.use('qtagg')
import matplotlib.colors as mcolors
import matplotlib as mpl
import matplotlib.path as mpath
import matplotlib.pyplot as plt
mpl.rcParams['font.family'] = 'MS Gothic'
from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader
import matplotlib.patheffects as pe
STATIONS = [
("札幌", 43.060, 141.328), ("稚内", 45.415, 141.677), ("旭川", 43.762, 142.358),
("網走", 44.018, 144.279), ("釧路", 42.984, 144.381), ("帯広", 42.923, 143.219),
("函館", 41.817, 140.753), ("青森", 40.824, 140.740), ("秋田", 39.718, 140.102),
("盛岡", 39.698, 141.155), ("仙台", 38.261, 140.896), ("山形", 38.255, 140.339),
("福島", 37.750, 140.467), ("新潟", 37.940, 139.043), ("富山", 36.710, 137.202),
("金沢", 36.588, 136.625), ("福井", 36.055, 136.224), ("宇都宮", 36.555, 139.870),
("前橋", 36.396, 139.060), ("熊谷", 36.147, 139.388), ("水戸", 36.381, 140.452),
("東京", 35.691, 139.756), ("銚子", 35.738, 140.828), ("横浜", 35.438, 139.654),
("長野", 36.662, 138.196), ("甲府", 35.670, 138.568), ("静岡", 34.978, 138.403),
("名古屋", 35.166, 136.965), ("岐阜", 35.400, 136.758), ("津", 34.733, 136.510),
("大津", 35.004, 135.859), ("京都", 35.011, 135.733), ("大阪", 34.682, 135.522),
("神戸", 34.696, 135.211), ("奈良", 34.692, 135.827), ("和歌山", 34.226, 135.167),
("鳥取", 35.485, 134.237), ("松江", 35.467, 133.064), ("岡山", 34.661, 133.919),
("広島", 34.396, 132.459), ("山口", 34.155, 131.472), ("徳島", 34.066, 134.554),
("高松", 34.316, 134.053), ("松山", 33.843, 132.766), ("高知", 33.566, 133.546),
("福岡", 33.581, 130.375), ("佐賀", 33.262, 130.301), ("長崎", 32.733, 129.873),
("熊本", 32.813, 130.704), ("大分", 33.237, 131.612), ("宮崎", 31.942, 131.411),
("鹿児島", 31.554, 130.549), ("名瀬", 28.378, 129.497), ("那覇", 26.206, 127.686),
("南大東", 25.845, 131.229), ("宮古島", 24.783, 125.276), ("石垣島", 24.336, 124.164)
]
MEPS_STAT_MODELS = ["MEPS_GUID_MAX", "MEPS_GUID_MIN", "MEPS_GUID_PROB1", "MEPS_GUID_PROB10", "MEPS_GUID_PROB20", "MEPS_GUID_PROB30"]
MEPS_STAT_TAB_TITLES = {
"MEPS_GUID_MAX": "MEPS_GUID(上位90%)",
"MEPS_GUID_MIN": "MEPS_GUID(下位10%)",
"MEPS_GUID_PROB1": "MEPS_GUID(確率≥1)",
"MEPS_GUID_PROB10": "MEPS_GUID(確率≥10)",
"MEPS_GUID_PROB20": "MEPS_GUID(確率≥20)",
"MEPS_GUID_PROB30": "MEPS_GUID(確率≥30)",
}
CACHED_SHAPES = {'other_cities': None, 'sapporo_districts': None, 'sapporo_city': None, 'kamikawa': None}
SHAPEFILE_FOUND = False
def search_shapefile():
base_dir = os.path.dirname(sys.executable) if getattr(sys, 'frozen', False) else os.getcwd()
paths = glob.glob(os.path.join(base_dir, '**', 'N03*.shp'), recursive=True)
return paths[0] if paths else None
def get_simplified_geometries():
global CACHED_SHAPES, SHAPEFILE_FOUND
if CACHED_SHAPES['other_cities'] is not None: return CACHED_SHAPES['other_cities'], CACHED_SHAPES['sapporo_districts'], CACHED_SHAPES['sapporo_city'], CACHED_SHAPES['kamikawa']
shp_path = search_shapefile()
if not shp_path or not os.path.exists(shp_path): return [], [], [], []
SHAPEFILE_FOUND = True
other_cities_g = []; sapporo_districts_g = {}; sapporo_city_g = []; kamikawa_g = []
sapporo_dist_names = ['中央区', '北区', '東区', '白石区', '厚別区', '豊平区', '清田区', '南区', '西区', '手稲区']
for d_name in sapporo_dist_names: sapporo_districts_g[d_name] = []
try:
reader = shpreader.Reader(shp_path, encoding='cp932')
for r in reader.records():
geom = r.geometry
try: geom = geom.simplify(0.002, preserve_topology=False)
except: pass
pref_name = str(r.attributes.get('N03_001', '')); subpref_name = str(r.attributes.get('N03_002', ''))
city_name = str(r.attributes.get('N03_004', '')); cit_name_spec = str(r.attributes.get('N03_003', ''))
is_sap_dist = False
if '札幌市' in cit_name_spec:
for d_name in sapporo_dist_names:
if d_name == city_name: is_sap_dist = True; sapporo_districts_g[d_name].append(geom); sapporo_city_g.append(geom); break
if '上川' in subpref_name: kamikawa_g.append(geom)
if not is_sap_dist and '北海道' in pref_name:
try:
if geom.area > 0.0005: other_cities_g.append(geom)
except: pass
CACHED_SHAPES['other_cities'] = other_cities_g; CACHED_SHAPES['sapporo_districts'] = sapporo_districts_g;
CACHED_SHAPES['sapporo_city'] = sapporo_city_g; CACHED_SHAPES['kamikawa'] = kamikawa_g
return other_cities_g, sapporo_districts_g, sapporo_city_g, kamikawa_g
except Exception as e:
logging.error(f"Shapefile processing error: {e}")
return [], [], [], []
def calc_t_td(t, rh):
rh_s = np.clip(rh, 1.0, 100.0)
e = 6.112 * np.exp((17.67 * t) / (t + 243.5)) * (rh_s / 100.0)
e = np.clip(e, 0.001, 1000)
td = (243.5 * np.log(e / 6.112)) / (17.67 - np.log(e / 6.112))
return t - td
def rh_from_t_td(t, t_td):
td = t - t_td
e = 6.112 * np.exp((17.67 * td) / (td + 243.5))
es = 6.112 * np.exp((17.67 * t) / (t + 243.5))
rh = (e / es) * 100.0
return np.clip(rh, 0.0, 100.0)
def calc_ept(t, rh, p):
t_k = t + 273.15
rh_s = np.clip(rh, 1.0, 100.0)
e = 6.112 * np.exp((17.67 * t) / (t + 243.5)) * (rh_s / 100.0)
e = np.clip(e, 0.001, p - 0.1)
q = 0.622 * e / (p - e)
tlcl = 2840.0 / (3.5 * np.log(t_k) - np.log(e) - 4.805) + 55.0
theta = t_k * (1000.0 / p) ** 0.2854
ept = theta * np.exp(((3376.0 / tlcl) - 2.54) * q * (1.0 + 0.81 * q))
return ept
def get_t_from_ept(target_ept, p):
t_min, t_max = -80.0, 50.0
for _ in range(15):
t_mid = (t_min + t_max) / 2.0
ept_mid = calc_ept(t_mid, 100.0, p)
if ept_mid < target_ept: t_min = t_mid
else: t_max = t_mid
return (t_min + t_max) / 2.0
class SingleImageWindow(QDialog):
def __init__(self, parent, title, model_name, stat_name, init_z_str, canvas):
super().__init__(parent)
self.model_name = model_name; self.stat_name = stat_name; self.init_z_str = init_z_str
self.setWindowTitle(title)
self.resize(1100, 950)
self.setStyleSheet("QDialog { background-color: #0A192F; color: #E0E0E0; }")
layout = QVBoxLayout(self)
layout.setContentsMargins(10, 10, 10, 10)
layout.addWidget(canvas, stretch=1)
btn_layout = QHBoxLayout()
btn_layout.setContentsMargins(10, 10, 10, 10)
save_btn = QPushButton("📷 画像として保存")
save_btn.setStyleSheet("background-color: #27AE60; color: white; font-size: 14pt; font-weight: bold; padding: 12px; border-radius: 5px;")
save_btn.clicked.connect(self.save_image)
close_btn = QPushButton("閉じる")
close_btn.setStyleSheet("background-color: #C0392B; color: white; font-size: 14pt; font-weight: bold; padding: 12px; border-radius: 5px;")
close_btn.clicked.connect(self.accept)
btn_layout.addStretch()
btn_layout.addWidget(save_btn)
btn_layout.addWidget(close_btn)
btn_layout.addStretch()
layout.addLayout(btn_layout)
def save_image(self):
desktop = os.path.expanduser("~/Desktop")
filename = f"{self.model_name}_{self.stat_name}_{self.init_z_str}.png"
path, _ = QFileDialog.getSaveFileName(self, "画像を保存", os.path.join(desktop, filename), "Images (*.png)")
if path:
canvas_widget = self.layout().itemAt(0).widget()
pixmap = canvas_widget.grab() if hasattr(canvas_widget, 'grab') else None
if pixmap: pixmap.save(path)
else: self.layout().itemAt(0).widget().figure.savefig(path, dpi=150, bbox_inches='tight', facecolor='white')
QMessageBox.information(self, "保存完了", f"保存しました:\n{os.path.basename(path)}")
class SplashWindow(QWidget):
def __init__(self):
super().__init__()
self.setWindowFlags(Qt.WindowType.FramelessWindowHint | Qt.WindowType.WindowStaysOnTopHint)
self.setFixedSize(480, 250)
self.setStyleSheet("background-color: #0A192F; border: 3px solid #64FFDA; border-radius: 12px;")
layout = QVBoxLayout(self)
title_lbl = QLabel("気象GPV 局地分析ツール\n(Ver 134.0 統合版)")
title_lbl.setAlignment(Qt.AlignmentFlag.AlignCenter)
title_lbl.setStyleSheet("font-size: 18pt; font-weight: bold; color: #E0E0E0; border: none; margin-top: 15px;")
layout.addWidget(title_lbl)
msg_lbl = QLabel("最新の解析データをロードしています...\n(約5秒で自動起動します)")
msg_lbl.setAlignment(Qt.AlignmentFlag.AlignCenter)
msg_lbl.setStyleSheet("font-size: 14pt; color: #64FFDA; border: none;")
layout.addWidget(msg_lbl)
class WeatherApp(QMainWindow):
def __init__(self):
super().__init__()
self.settings = QSettings('WeatherApp', 'Viewer')
self.cache_dir = self.settings.value('cache_dir', os.path.join(os.getcwd(), "gpv_cache_npz"))
self.setup_logging()
self.setWindowTitle("気象GPV 局地分析ツール (Ver 134.0 - ANALスライダー対応・メタグラム拡張版)")
self.resize(1850, 1050)
self.model_init_times = {"MSM": None, "GSM_JP": None, "MSM_GUID": None, "GSM_GUID": None, "ANAL": None}
for _m in MEPS_STAT_MODELS: self.model_init_times[_m] = None
self.tab_ui = {}
self.monitor_timer = QTimer(self); self.monitor_timer.timeout.connect(self.scan_cache_dir)
self.anim_timer = QTimer(self); self.anim_timer.timeout.connect(self.anim_step)
self.current_anim_model = None
self.setStyleSheet("""
* { font-size: 13pt; font-family: "MS Gothic", sans-serif; color: #E0E0E0; }
QMainWindow, QDialog, QMessageBox { background-color: #0A192F; color: #E0E0E0; }
QMessageBox QLabel { color: #E0E0E0; }
QMessageBox QPushButton { background-color: #1D3557; color: white; padding: 5px 15px; border-radius: 3px; font-weight: bold; }
QMessageBox QPushButton:hover { background-color: #457B9D; }
QTabWidget::pane { border: 1px solid #444; background: #112240; }
QTabBar::tab { background: #1B2B4A; color: #8892B0; padding: 6px 12px; font-size: 11pt; border: 1px solid #333; border-bottom: none; margin-right: 2px; }
QTabBar::tab:selected { background: #112240; color: #64FFDA; font-weight: bold; font-size: 11pt; border-top: 3px solid #64FFDA; }
QSlider::groove:vertical { border: 1px solid #444; background: #0A192F; width: 14px; border-radius: 7px; }
QSlider::handle:vertical { background: #64FFDA; border: 2px solid #000; height: 32px; margin: 0 -10px; border-radius: 16px; }
QPushButton { background-color: #1D3557; border: 1px solid #457B9D; padding: 8px; border-radius: 4px; color: #F1FAEE; }
QPushButton:hover { background-color: #457B9D; }
QPushButton::checked { background-color: #E63946; color: white; font-weight: bold; }
QListWidget { background: #112240; border: 1px solid #444; color: #E0E0E0; }
QListWidget::item { padding: 3px 5px; margin: 1px 5px; background: #1D3557; border: 1px solid #333; border-radius: 6px; }
QListWidget::item:selected { background: #E63946; color: white; font-weight: bold; }
QListWidget::item:hover:!selected { background: #457B9D; }
QComboBox { background: #112240; border: 1px solid #8892B0; color: white; padding: 5px; }
QComboBox QAbstractItemView { background: #112240; color: white; }
QLabel { background: transparent; }
QTextEdit { background-color: #112240; color: #64FFDA; border: 1px solid #444; }
.NavButton { background-color: #2980B9; color: white; font-weight: bold; padding: 5px; border-radius: 5px; }
.PlayButton { background-color: #27AE60; color: white; font-weight: bold; padding: 5px 15px; border-radius: 5px; }
.StopButton { background-color: #C0392B; color: white; font-weight: bold; padding: 5px 15px; border-radius: 5px; }
.SyncButton { background-color: #F39C12; color: white; font-weight: bold; border: 2px solid #D68910; border-radius: 5px; padding: 5px 15px;}
.SaveButton { background-color: #8E44AD; color: white; font-weight: bold; border-radius: 5px; padding: 4px 10px; font-size: 10pt;}
.MetaButton { background-color: #16A085; color: white; font-weight: bold; border: 2px solid #0E6655; border-radius: 5px; padding: 5px 15px; }
.MetaButton:checked { background-color: #0E6655; border: 2px solid #1ABC9C; }
.AdminButton { background-color: #34495E; color: white; font-weight: bold; border-radius: 5px; padding: 4px 10px; font-size: 10pt;}
""")
main_widget = QWidget(); self.setCentralWidget(main_widget); main_layout = QVBoxLayout(main_widget)
self.tabs = QTabWidget()
corner_widget = QWidget()
corner_layout = QHBoxLayout(corner_widget)
corner_layout.setContentsMargins(0, 0, 5, 0)
self.status_banner = QLabel("待機中...")
self.status_banner.setStyleSheet("color: white; font-weight: bold; background-color: #E63946; padding: 4px 10px; border-radius: 4px; font-size: 10pt;")
self.save_img_btn = QPushButton("📷 画面保存")
self.save_img_btn.setProperty("class", "SaveButton")
self.save_img_btn.clicked.connect(self.save_current_image)
self.change_dir_btn = QPushButton("📁 読込先変更")
self.change_dir_btn.setProperty("class", "AdminButton")
self.change_dir_btn.clicked.connect(self.change_dir)
self.sync_btn = QPushButton("🔄 画面同期")
self.sync_btn.setProperty("class", "SyncButton")
self.sync_btn.clicked.connect(lambda: self.scan_cache_dir(force_draw=True))
self.exit_app_btn = QPushButton("🛑 完全停止")
self.exit_app_btn.setProperty("class", "StopButton")
self.exit_app_btn.clicked.connect(self.close)
corner_layout.addWidget(self.status_banner)
corner_layout.addWidget(self.save_img_btn)
corner_layout.addWidget(self.change_dir_btn)
corner_layout.addWidget(self.sync_btn)
corner_layout.addWidget(self.exit_app_btn)
self.tabs.setCornerWidget(corner_widget, Qt.Corner.TopRightCorner)
self.setup_tab("MSM", "MSM GPV", is_guidance=False, is_anal=False)
self.setup_tab("GSM_JP", "GSM_JP_GPV", is_guidance=False, is_anal=False)
self.setup_tab("ANAL", "毎時大気解析", is_guidance=False, is_anal=True)
self.setup_tab("MSM_GUID", "MSM_GUID", is_guidance=True, is_anal=False)
self.setup_tab("GSM_GUID", "GSM_GUID", is_guidance=True, is_anal=False)
for _m in MEPS_STAT_MODELS:
self.setup_tab(_m, MEPS_STAT_TAB_TITLES[_m], is_guidance=True, is_anal=False)
self.setup_history_tab()
main_layout.addWidget(self.tabs)
get_simplified_geometries()
self.splash = SplashWindow()
self.splash.show()
QTimer.singleShot(5000, self.finish_startup_loading)
def closeEvent(self, event: QCloseEvent):
reply = QMessageBox.question(self, '終了の確認', 'システムを完全に停止してよろしいですか？',
QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No, QMessageBox.StandardButton.No)
if reply == QMessageBox.StandardButton.Yes:
event.accept()
else:
event.ignore()
def setup_logging(self):
self.log_file = os.path.join(self.cache_dir, "system_log.txt")
os.makedirs(self.cache_dir, exist_ok=True)
logging.basicConfig(filename=self.log_file, level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s', encoding='utf-8')
def change_dir(self):
directory = QFileDialog.getExistingDirectory(self, "GPVデータ(npz)が保存されるフォルダを選択", self.cache_dir)
if directory:
self.cache_dir = directory
self.settings.setValue('cache_dir', directory)
self.setup_logging()
QMessageBox.information(self, "設定完了", f"読込先フォルダを更新しました:\n{directory}")
self.scan_cache_dir(force_draw=True)
def finish_startup_loading(self):
self.splash.close()
self.scan_cache_dir(quiet=True)
self.monitor_timer.start(5000)
for i in range(self.tabs.count()):
if "MSM" in self.tabs.tabText(i) and "GUID" not in self.tabs.tabText(i):
self.tabs.setCurrentIndex(i); break
if "MSM" in self.tab_ui:
items = self.tab_ui["MSM"]['list'].findItems("地上気圧", Qt.MatchFlag.MatchExactly)
if items: self.tab_ui["MSM"]['list'].setCurrentItem(items[0])
self.show()
def save_current_image(self):
current_tab = self.tabs.tabText(self.tabs.currentIndex())
_meps_hit = next((m for m in MEPS_STAT_MODELS if MEPS_STAT_TAB_TITLES[m] == current_tab), None)
if _meps_hit: model_name = _meps_hit
elif "MSM_GUID" in current_tab: model_name = "MSM_GUID"
elif "GSM_GUID" in current_tab: model_name = "GSM_GUID"
elif "毎時大気" in current_tab: model_name = "ANAL"
elif "MSM" in current_tab: model_name = "MSM"
elif "GSM_JP" in current_tab: model_name = "GSM_JP"
else: return
if model_name not in self.tab_ui or self.tab_ui[model_name]['ax'] is None: return
ui = self.tab_ui[model_name]
val = ui['slider'].value(); elem = ui['list'].currentItem().text()
it_str = self.model_init_times.get(model_name)
if model_name == "ANAL":
val_idx = int(val)
combo = ui['init_combo']
if val_idx < combo.count():
it_str = combo.itemData(val_idx)
if not it_str: return
dt_utc = datetime.strptime(it_str, "%Y%m%d%H%M%S")
init_z = dt_utc.strftime("%m%d%H") + "Z"
filename = f"{model_name}_{elem}_{init_z}_FT{val:02d}.png"
desktop = os.path.expanduser("~/Desktop")
path, _ = QFileDialog.getSaveFileName(self, "画像を保存", os.path.join(desktop, filename), "Images (*.png *.jpg)")
if path:
ui['fig'].savefig(path, dpi=150, bbox_inches='tight', facecolor='white')
self.status_banner.setText(f"保存完了: {os.path.basename(path)}")
def init_map(self, fig, area_idx, model):
fig.clear()
if area_idx == 4:
gs = fig.add_gridspec(1, 2)
ax1 = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())
ax2 = fig.add_subplot(gs[0, 1], projection=ccrs.PlateCarree())
ax1.set_extent([141.20, 141.70, 43.00, 43.65], crs=ccrs.PlateCarree())
ax2.set_extent([141.30, 141.80, 42.50, 43.15], crs=ccrs.PlateCarree())
self.draw_base_map(ax1, 3)
self.draw_base_map(ax2, 3)
# --- 追加: マップを上部に詰めて余白を消す ---
ax1.set_anchor('N')
ax2.set_anchor('N')
fig.subplots_adjust(left=0.03, right=0.92, top=0.93, bottom=0.05, wspace=0.1)
return [ax1, ax2]
else:
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax.set_extent(self.get_extent(area_idx, model), crs=ccrs.PlateCarree())
self.draw_base_map(ax, area_idx)
# --- 追加: マップを上部に詰めて余白を消す ---
ax.set_anchor('N')
fig.subplots_adjust(left=0.05, right=0.9, top=0.92, bottom=0.05)
return ax
def get_extent(self, area_index, model_name):
if area_index == 0: return [120, 150, 27.5, 48] if "MSM" in model_name or "ANAL" in model_name else [120, 150, 20, 50]
elif area_index == 1: return [138.5, 146.5, 40.5, 46.5]
elif area_index == 2: return [141.6, 143.6, 42.8, 45.0]
elif area_index == 3: return [141.05, 141.65, 42.85, 43.27]
elif area_index == 4: return [141.35, 141.80, 42.50, 43.25]
elif area_index == 5: return [141.10, 141.45, 43.15, 43.70]
else: return [141.05, 141.65, 42.85, 43.27]
def crop_data(self, lon, lat, data, extent):
if lon is None or lat is None or data is None or data.size == 0:
return np.array([]), np.array([]), np.array([])
buffer = 1.0; lon_min, lon_max = extent[0] - buffer, extent[1] + buffer; lat_min, lat_max = extent[2] - buffer, extent[3] + buffer
if lon.ndim == 1:
m_lon = (lon >= lon_min) & (lon <= lon_max); m_lat = (lat >= lat_min) & (lat <= lat_max)
if not np.any(m_lon) or not np.any(m_lat): return np.array([]), np.array([]), np.array([])
return lon[m_lon], lat[m_lat], data[np.ix_(m_lat, m_lon)]
else:
mask = (lon >= lon_min) & (lon <= lon_max) & (lat >= lat_min) & (lat <= lat_max)
j, i = np.where(mask)
if len(j) == 0: return np.array([]), np.array([]), np.array([])
return lon[j.min():j.max()+1, i.min():i.max()+1], lat[j.min():j.max()+1, i.min():i.max()+1], data[j.min():j.max()+1, i.min():i.max()+1]
def draw_base_map(self, ax, area_index):
SEA_COLOR = '#A4C2E4'; LAND_COLOR = '#F5F5F0'; COASTLINE_COLOR = '#333333'
ax.set_facecolor(SEA_COLOR); ax.add_feature(cfeature.LAND.with_scale('10m'), facecolor=LAND_COLOR, zorder=0)
ax.add_feature(cfeature.OCEAN.with_scale('10m'), facecolor=SEA_COLOR, zorder=0)
ax.add_feature(cfeature.LAKES.with_scale('10m'), edgecolor='#4682B4', facecolor=SEA_COLOR, zorder=1)
ax.add_feature(cfeature.RIVERS.with_scale('10m'), linewidth=0.8, edgecolor='#4682B4', zorder=1)
try:
ax.add_feature(cfeature.COASTLINE.with_scale('10m'), linewidth=0.6, edgecolor=COASTLINE_COLOR, zorder=2)
except Exception as e:
logging.error(f"Cartopy COASTLINE error: {e}")
other_cities_g, sapporo_districts_g, sapporo_city_g, kamikawa_g = get_simplified_geometries()
if area_index >= 1 and other_cities_g:
ax.add_geometries(other_cities_g, ccrs.PlateCarree(), edgecolor='black', facecolor='none', linewidth=0.8, linestyle='-', zorder=3)
if area_index == 3 and sapporo_districts_g:
for d_name, geoms in sapporo_districts_g.items():
if geoms: ax.add_geometries(geoms, ccrs.PlateCarree(), edgecolor='#FF0000', facecolor='none', linewidth=1.5, zorder=10)
if sapporo_city_g and area_index == 3: ax.add_geometries(sapporo_city_g, ccrs.PlateCarree(), edgecolor='#FF0000', facecolor='#FF8C00', alpha=0.3, linewidth=2.0, linestyle='-', zorder=4)
elif sapporo_city_g and area_index >= 1: ax.add_geometries(sapporo_city_g, ccrs.PlateCarree(), edgecolor='#FF0000', facecolor='#FF8C00', alpha=0.2, linewidth=1.0, linestyle='-', zorder=4)
if area_index >= 1 and kamikawa_g: ax.add_geometries(kamikawa_g, ccrs.PlateCarree(), edgecolor='#FF0000', facecolor='#FF8C00', alpha=0.25 if area_index == 2 else 0.15, linewidth=1.5, linestyle='-', zorder=5)
try:
pref_lines = cfeature.NaturalEarthFeature('cultural', 'admin_1_states_provinces_lines', '10m', edgecolor='#444444', facecolor='none')
ax.add_feature(pref_lines, linewidth=0.8, linestyle='-.', zorder=7)
except: pass
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color='gray', alpha=0.5, linestyle='--', zorder=6)
gl.top_labels = False; gl.right_labels = False
gl.xlabel_style = {'size': 11, 'color': 'black', 'fontname': 'MS Gothic'}; gl.ylabel_style = {'size': 11, 'color': 'black', 'fontname': 'MS Gothic'}
if area_index == 4 or area_index == 5: gl.xlocator = mpl.ticker.MultipleLocator(0.1); gl.ylocator = mpl.ticker.MultipleLocator(0.1)
elif area_index == 3: gl.xlocator = mpl.ticker.MultipleLocator(0.05); gl.ylocator = mpl.ticker.MultipleLocator(0.05)
elif area_index == 2: gl.xlocator = mpl.ticker.MultipleLocator(0.5); gl.ylocator = mpl.ticker.MultipleLocator(0.5)
elif area_index == 1: gl.xlocator = mpl.ticker.MultipleLocator(1.0); gl.ylocator = mpl.ticker.MultipleLocator(1.0)
else: gl.xlocator = mpl.ticker.MultipleLocator(5.0) if area_index == 0 else mpl.ticker.MultipleLocator(1.0); gl.ylocator = mpl.ticker.MultipleLocator(5.0) if area_index == 0 else mpl.ticker.MultipleLocator(1.0)
def get_local_info(self, elem_name, lon, lat, data, u=None, v=None, area_index=0, model_name=""):
if data is None or lon is None or lat is None or data.size == 0: return ""
target_lon, target_lat, loc_name = (142.368, 43.760, "旭川") if area_index == 2 else (141.35, 43.06, "札幌")
if lon.ndim == 1: lon_2d, lat_2d = np.meshgrid(lon, lat)
else: lon_2d, lat_2d = lon, lat
dist = (lon_2d - target_lon)**2 + (lat_2d - target_lat)**2
sy, sx = np.unravel_index(np.argmin(dist), dist.shape)
if sy >= data.shape[0] or sx >= data.shape[1]: return ""
s_val = data[sy, sx]
if np.isnan(s_val): return ""
is_prob = "PROB" in model_name
thresh = model_name.replace("MEPS_GUID_PROB", "") if is_prob else ""
unit = "mm" if "降水" in elem_name else "cm"
if is_prob and ("降水" in elem_name or "降雪" in elem_name):
info = f"[{loc_name}現況]\n{elem_name}(≥{thresh}{unit})確率: {s_val:.0f} %"
elif "降水量" in elem_name or "降水" in elem_name: info = f"[{loc_name}現況]\n降水量: {s_val:.1f} mm"
elif "降雪量" in elem_name or "降雪" in elem_name: info = f"[{loc_name}現況]\n降雪量: {s_val:.1f} cm"
else: info = f"[{loc_name}現況]\n"
if "降水" not in elem_name and "降雪" not in elem_name:
if "気圧" in elem_name: info += f"気圧: {s_val:.1f} hPa"
elif "確率" in elem_name and "発雷" not in elem_name: info += f"{elem_name}: {s_val:.0f} %"
elif "発雷確率" in elem_name: info += f"発雷確率: {s_val:.0f} %"
elif "天気" in elem_name:
wx_map = {1: "晴", 2: "曇", 3: "雨", 4: "雪"}
info += f"卓越天気: {wx_map.get(int(s_val), '不明')}"
elif "雲量" in elem_name: info += f"{elem_name}: {s_val:.1f} %"
elif "高度" in elem_name: info += f"{elem_name[:6]}高度: {s_val:.0f} m"
elif "渦度" in elem_name: info += f"正渦度(10^-5/s): {s_val:.1f}"
elif "気温" in elem_name: info += f"{elem_name[:6]}気温: {s_val:.1f} ℃"
elif "鉛直流" in elem_name: info += f"鉛直流: {s_val:.1f} hPa/h"
elif "湿数" in elem_name: info += f"{elem_name[:6]}湿数: {s_val:.1f} ℃"
elif "相当温位" in elem_name: info += f"{elem_name[:6]}相当温位: {s_val:.1f} K"
else: info += f"値: {s_val:.1f}"
if u is not None and v is not None and sy < u.shape[0] and sx < u.shape[1]: info += f"\n風速: {np.hypot(u[sy, sx], v[sy, sx]):.1f} m/s"
return info
def on_area_btn_clicked(self, model_name, idx):
ui = self.tab_ui[model_name]
for i, btn in enumerate(ui['area_btns']): btn.setChecked(i == idx)
is_meta_btn = ("メタグラム" in ui['area_btns'][-1].text() and idx == len(ui['area_btns'])-1)
if is_meta_btn:
ui['canvas'].hide()
if 'info_panel' in ui: ui['info_panel'].hide()
if 'meta_ctrl' in ui: ui['meta_ctrl'].show()
else:
if 'meta_ctrl' in ui: ui['meta_ctrl'].hide()
ui['canvas'].show()
self.draw_frame(model_name, ui['slider'].value())
def on_init_changed(self, model_name, idx):
ui = self.tab_ui[model_name]
if idx < 0: return
selected_time_str = ui['init_combo'].itemData(idx)
if selected_time_str:
self.model_init_times[model_name] = selected_time_str
self.update_slider_max(model_name, selected_time_str)
if len(ui['area_btns']) > 4 and ui['area_btns'][-1].isChecked(): return
elem_name = ui['list'].currentItem().text()
val = ui['slider'].value()
if "降水" in elem_name and val == 0 and "確率" not in elem_name and "時間降水" not in elem_name and model_name != "ANAL":
val = ui['step']
ui['slider'].blockSignals(True)
ui['slider'].setValue(val)
ui['slider'].blockSignals(False)
self.draw_frame(model_name, val)
def on_element_changed(self, model_name):
ui = self.tab_ui[model_name]
elem_name = ui['list'].currentItem().text()
if "GUID" in model_name:
min_ft = 0
if "6時間" in elem_name: min_ft = 6
elif "12時間" in elem_name: min_ft = 12
elif "24時間" in elem_name: min_ft = 24
elif "3時間" in elem_name: min_ft = 3
ui['slider'].setMinimum(min_ft)
if ui['slider'].value() < min_ft:
ui['slider'].blockSignals(True)
ui['slider'].setValue(min_ft)
ui['slider'].blockSignals(False)
else:
ui['slider'].setMinimum(0)
self.on_slider_changed(model_name, ui['slider'].value())
def on_slider_changed(self, model_name, val):
ui = self.tab_ui[model_name]
elem_name = ui['list'].currentItem().text()
if model_name == "GSM_JP":
ground_cloud_elems = ["地上気圧", "地上風向風速", "地上降水", "全雲量", "上層雲量", "中層雲量", "下層雲量", "地上３種重ね"]
threshold = 132 if elem_name in ground_cloud_elems else 84
if val > threshold and val % 6 != 0:
last_val = ui.get('last_val', 0)
direction = 1 if val >= last_val else -1
if direction == 1: new_val = val + (6 - (val % 6))
else: new_val = val - (val % 6)
if new_val > ui['slider'].maximum(): new_val = ui['slider'].maximum()
ui['slider'].blockSignals(True)
ui['slider'].setValue(new_val)
ui['slider'].blockSignals(False)
val = new_val
ui['last_val'] = val
self.draw_frame(model_name, val)
def update_slider_max(self, ui_key, init_time_str):
ui = self.tab_ui.get(ui_key)
if not ui: return
if ui_key == "ANAL":
count = ui['init_combo'].count()
final_max = max(0, count - 1)
step = 1; tick_step = 1
else:
files = glob.glob(os.path.join(self.cache_dir, f"{ui_key}_{init_time_str}_FT*.npz"))
max_ft_found = max([0] + [int(re.search(r'_FT(\d+)\.npz', f).group(1)) for f in files if re.search(r'_FT(\d+)\.npz', f)])
hh = init_time_str[8:10] if len(init_time_str) >= 10 else '00'
if ui_key == "MSM": default_max = 78 if hh in ['00', '12'] else 39; step = 1; tick_step = 6
elif ui_key == "GSM_JP": default_max = 264 if hh in ['00', '12'] else 132; step = 3; tick_step = 12
elif ui_key == "MSM_GUID": default_max = 84; step = 3; tick_step = 6
elif ui_key == "GSM_GUID": default_max = 84; step = 3; tick_step = 12
elif ui_key in MEPS_STAT_MODELS: default_max = 39; step = 3; tick_step = 12
else: default_max = 132; step = 6; tick_step = 24
final_max = max(default_max, max_ft_found); final_max = (final_max // step) * step
ui['slider'].setMaximum(final_max)
ui['slider'].setTickInterval(step); ui['slider'].setSingleStep(step); ui['slider'].setPageStep(step)
ticks_layout = ui['ticks_layout']
for i in reversed(range(ticks_layout.count())):
w = ticks_layout.itemAt(i).widget()
if w: w.setParent(None)
if ui_key == "ANAL":
for t in range(0, final_max + 1, tick_step):
time_offset = t * 30
lbl = QLabel(f"-{time_offset}分"); lbl.setStyleSheet("font-size: 8pt; color: #8892B0;"); lbl.setAlignment(Qt.AlignmentFlag.AlignLeft | Qt.AlignmentFlag.AlignVCenter); ticks_layout.addWidget(lbl)
else:
for t in range(0, final_max + 1, tick_step):
lbl = QLabel(f"{t}h"); lbl.setStyleSheet("font-size: 8pt; color: #8892B0;"); lbl.setAlignment(Qt.AlignmentFlag.AlignLeft | Qt.AlignmentFlag.AlignVCenter); ticks_layout.addWidget(lbl)
def create_compressed_left_panel(self, model_name):
left_panel = QVBoxLayout(); left_panel.setContentsMargins(5, 5, 5, 5)
step = 1 if model_name in ["MSM", "ANAL"] else 3
left_panel.addWidget(QLabel(f"📡 {model_name} 初期時刻(UTC):", styleSheet="font-weight: bold; font-size: 11pt; color: #E0E0E0;"))
init_combo = QComboBox(); init_combo.addItem("(データ待機中...)", None); left_panel.addWidget(init_combo)
interval_label = QLabel(f"【{step}時間間隔表示】", styleSheet="color: #64FFDA; font-weight: bold; font-size: 11pt;"); left_panel.addWidget(interval_label)
mid_hbox = QHBoxLayout(); elem_vbox = QVBoxLayout(); elem_vbox.addWidget(QLabel("■ 要素選択"))
element_list = QListWidget(); element_list.setFixedWidth(200)
if model_name in MEPS_STAT_MODELS:
elems = ["3時間降水量", "6時間降水量", "12時間降水量", "1時間最大降水量", "3時間最大降水量", "24時間最大降水量", "3時間降雪量", "6時間降雪量", "12時間降雪量", "24時間降雪量", "発雷確率"]
elif "GUID" in model_name:
elems = ["卓越天気", "3時間降水量", "6時間降水量", "12時間降水量", "24時間降水量", "3時間降雪量", "6時間降雪量", "12時間降雪量", "24時間降雪量", "発雷確率"]
elif model_name == "ANAL":
elems = ["地上気温", "地上風向風速", "300hPa風向・風速", "500hPa風向・風速", "500hPa気温", "700hPa気温・風", "850hPa気温・風", "925hPa気温・風", "975hPa気温・風"]
else:
elems = ["地上３種重ね", "地上気圧","地上風向風速", "地上降水", "全雲量", "上層雲量", "中層雲量", "下層雲量", "300hPa高度", "300hPa風向・風速", "500hPa高度", "500hPa風向・風速", "500hPa渦度", "500hPa気温", "700hPa湿数・風", "700hPa鉛直流", "850hPa気温・風", "850hPa相当温位", "850hPa湿数・風", "925hPa相当温位", "925hPa湿数・風", "975hPa相当温位", "975hPa湿数・風"]
element_list.addItems(elems); element_list.setCurrentRow(0); elem_vbox.addWidget(element_list); mid_hbox.addLayout(elem_vbox)
slider_vbox = QVBoxLayout()
time_label = QLabel("FT=0h\n(--/--)"); time_label.setStyleSheet("font-weight: bold; color: #64FFDA; font-size: 11pt; background: #1D3557; padding: 5px; border: 2px solid #457B9D; border-radius: 5px;"); time_label.setAlignment(Qt.AlignmentFlag.AlignCenter); slider_vbox.addWidget(time_label)
slider_control_hbox = QHBoxLayout()
btn_prev = QPushButton("▲"); btn_prev.setProperty("class", "NavButton"); btn_prev.setFixedWidth(30)
btn_next = QPushButton("▼"); btn_next.setProperty("class", "NavButton"); btn_next.setFixedWidth(30)
slider = QSlider(Qt.Orientation.Vertical); slider.setRange(0, 39); slider.setTickPosition(QSlider.TickPosition.TicksLeft); slider.setTickInterval(step); slider.setSingleStep(step); slider.setPageStep(step); slider.setInvertedAppearance(True)
btn_vbox = QVBoxLayout(); btn_vbox.addWidget(slider, stretch=1); btn_vbox.setAlignment(Qt.AlignmentFlag.AlignHCenter)
nav_hbox = QHBoxLayout(); nav_hbox.addWidget(btn_prev); nav_hbox.addWidget(btn_next)
inner_slider_vbox = QVBoxLayout(); inner_slider_vbox.addLayout(nav_hbox); inner_slider_vbox.addLayout(btn_vbox); slider_control_hbox.addLayout(inner_slider_vbox)
ticks_vbox = QVBoxLayout(); ticks_vbox.setContentsMargins(0, 30, 0, 10)
slider_control_hbox.addLayout(ticks_vbox); slider_vbox.addLayout(slider_control_hbox, stretch=1)
play_hbox = QHBoxLayout()
btn_play = QPushButton("▶"); btn_play.setProperty("class", "PlayButton"); btn_play.setFixedWidth(40)
btn_stop = QPushButton("■"); btn_stop.setProperty("class", "StopButton"); btn_stop.setFixedWidth(40)
play_hbox.addWidget(btn_play); play_hbox.addWidget(btn_stop); slider_vbox.addLayout(play_hbox)
combo_speed = QComboBox(); combo_speed.addItems(["速1", "速2", "速3", "速4", "速5"]); combo_speed.setCurrentIndex(2); slider_vbox.addWidget(combo_speed)
mid_hbox.addLayout(slider_vbox); left_panel.addLayout(mid_hbox, stretch=1)
return left_panel, init_combo, interval_label, element_list, time_label, btn_prev, btn_next, slider, btn_play, btn_stop, combo_speed, ticks_vbox
def setup_tab(self, model_name, tab_title, is_guidance=False, is_anal=False):
tab = QWidget(); main_hbox = QHBoxLayout(tab)
left_panel, init_combo, interval_label, element_list, time_label, btn_prev, btn_next, slider, btn_play, btn_stop, combo_speed, ticks_layout = self.create_compressed_left_panel(model_name)
main_hbox.addLayout(left_panel, stretch=2)
right_panel = QVBoxLayout()
area_layout = QHBoxLayout(); area_layout.addWidget(QLabel("表示領域:"))
area_btns = [QPushButton("日本周辺"), QPushButton("北海道全域"), QPushButton("上川地方"), QPushButton("札幌近郊"), QPushButton("恵庭・石狩"), QPushButton("📊 メタグラム")]
for i, btn in enumerate(area_btns):
btn.setCheckable(True); area_layout.addWidget(btn); btn.clicked.connect(lambda checked, idx=i, m=model_name: self.on_area_btn_clicked(m, idx))
area_btns[0].setChecked(True)
btn_prev.clicked.connect(lambda: step_slider(-1)); btn_next.clicked.connect(lambda: step_slider(1))
slider.valueChanged.connect(lambda val, m=model_name: self.on_slider_changed(m, val))
if not is_anal:
btn_play.clicked.connect(lambda: self.start_animation(model_name)); btn_stop.clicked.connect(self.stop_animation)
element_list.itemSelectionChanged.connect(lambda m=model_name: self.on_element_changed(m))
init_combo.currentIndexChanged.connect(lambda idx, m=model_name: self.on_init_changed(m, idx))
self.tabs.addTab(tab, tab_title)
area_layout.addStretch()
right_panel.addLayout(area_layout)
# ノートPC等の画面向けにベースサイズをややコンパクトにし、レスポンシブ性を向上
fig = Figure(figsize=(12, 10)); fig.patch.set_facecolor('white'); canvas = FigureCanvas(fig)
meta_ctrl_widget = QWidget(); meta_ctrl_layout = QVBoxLayout(meta_ctrl_widget)
h_ctrl = QHBoxLayout()
h_ctrl.addWidget(QLabel("地点選択:")); station_combo = QComboBox()
for name, lat, lon in STATIONS: station_combo.addItem(name, (lat, lon))
h_ctrl.addWidget(station_combo)
if is_anal:
btn_generate_meta = QPushButton("表示 (解析要素時系列グラフ生成)")
btn_generate_meta.setProperty("class", "SyncButton")
btn_generate_meta.clicked.connect(lambda: self.draw_anal_visual_metagram(station_combo))
h_ctrl.addWidget(btn_generate_meta)
else:
btn_text = "表示 (A4横サイズ テーブル画像生成)" if is_guidance else "表示 (図解メタグラム生成)"
btn_generate_meta = QPushButton(btn_text)
btn_generate_meta.setProperty("class", "SyncButton")
if is_guidance or model_name in MEPS_STAT_MODELS: btn_generate_meta.clicked.connect(lambda: self.draw_guidance_metagram(station_combo, model_name))
elif model_name == "GSM_JP": btn_generate_meta.clicked.connect(lambda: self.draw_gsm_visual_metagram(station_combo))
elif model_name == "MSM": btn_generate_meta.clicked.connect(lambda: self.draw_msm_visual_metagram(station_combo))
h_ctrl.addWidget(btn_generate_meta)
h_ctrl.addStretch()
meta_ctrl_layout.addLayout(h_ctrl); meta_ctrl_layout.addStretch(); meta_ctrl_widget.hide()
right_panel.addWidget(canvas, stretch=10)
right_panel.addWidget(meta_ctrl_widget, stretch=10)
main_hbox.addLayout(right_panel, stretch=8)
info_panel = QLabel(canvas)
info_panel.setStyleSheet("background-color: rgba(17, 34, 64, 0.85); border: 2px solid #64FFDA; border-radius: 5px; padding: 5px; font-weight: bold; font-size: 12pt; color: #E0E0E0;"); info_panel.hide()
step = 1 if model_name in ["MSM", "ANAL"] else 3
self.tab_ui[model_name] = {
'list': element_list, 'area_btns': area_btns, 'slider': slider, 'time_label': time_label, 'init_combo': init_combo,
'interval_label': interval_label, 'fig': fig, 'canvas': canvas, 'meta_ctrl': meta_ctrl_widget, 'ax': None, 'current_area_index': -1,
'dynamic_artists': [], 'colorbars': [], 'info_panel': info_panel, 'combo_speed': combo_speed, 'ticks_layout': ticks_layout,
'step': step,'last_val':0
}
if is_anal:
btn_play.hide(); btn_stop.hide(); combo_speed.hide()
interval_label.setText("【過去履歴閲覧 (30分間隔)】")
def step_slider(val_change, m=model_name):
ui = self.tab_ui[m]
current_val = ui['slider'].value()
elem_name = ui['list'].currentItem().text()
s_step = ui['step']
if m == "GSM_JP":
ground_cloud_elems = ["地上気圧", "地上風向風速", "地上降水", "全雲量", "上層雲量", "中層雲量", "下層雲量", "地上３種重ね"]
threshold = 132 if elem_name in ground_cloud_elems else 84
if val_change > 0: s_step = 6 if current_val >= threshold else 3
else: s_step = 6 if current_val > threshold else 3
elif m == "ANAL":
s_step = 1
new_val = current_val + (s_step * val_change)
if new_val > ui['slider'].maximum(): new_val = ui['slider'].minimum()
if new_val < ui['slider'].minimum(): new_val = ui['slider'].maximum()
ui['slider'].setValue(new_val)
def get_exact_key(self, data_obj, exact_candidates):
for c in exact_candidates:
if c in data_obj.files: return c
for k in data_obj.files:
if k.lower() in [c.lower() for c in exact_candidates]: return k
return None
def get_precip_key(self, data):
return self.get_exact_key(data, ['precip', 'pr', 'tp', 'apcp', 'prate', 'rain', 'rn', 'pop', 'var_0_1_8', 'var_0_1_52'])
def get_w_key(self, data):
return self.get_exact_key(data, ['w', 'w700', 'vvel', 'vvel700', 'dzdt', 'dzdt700'])
def get_coords_for_data(self, data_obj, data_array):
if data_array is None: return None, None
ny = data_array.shape[0]; nx = data_array.shape[1] if data_array.ndim > 1 else len(data_array)
for k in data_obj.files:
if 'lon' in k.lower():
if (data_obj[k].ndim == 1 and data_obj[k].shape[0] == nx) or (data_obj[k].ndim == 2 and data_obj[k].shape == data_array.shape):
lat_k = k.replace('lon', 'lat').replace('Lon', 'Lat')
if lat_k in data_obj.files: return data_obj[k], data_obj[lat_k]
for lk in data_obj.files:
if 'lat' in lk.lower() and ((data_obj[lk].ndim == 1 and data_obj[lk].shape[0] == ny) or data_obj[lk].shape == data_array.shape):
return data_obj[k], data_obj[lk]
return None, None
def extract_val_with_key(self, data, key, stat_lon, stat_lat):
if key not in data.files: return np.nan
arr = data[key]
lon_arr, lat_arr = self.get_coords_for_data(data, arr)
if lon_arr is None or lat_arr is None:
lon_k = next((k for k in data.files if 'lon' in k.lower()), None)
lat_k = next((k for k in data.files if 'lat' in k.lower()), None)
if not lon_k or not lat_k: return np.nan
lon_arr, lat_arr = data[lon_k], data[lat_k]
if lon_arr.ndim == 1 and arr.shape == (len(lon_arr), len(lat_arr)): arr = arr.T
if lon_arr.ndim == 1 and arr.shape != (len(lat_arr), len(lon_arr)):
if arr.shape[1] > arr.shape[0] and len(lat_arr) > len(lon_arr): arr = arr.T
lon_arr = np.linspace(lon_arr.min(), lon_arr.max(), arr.shape[1])
lat_arr = np.linspace(lat_arr.min(), lat_arr.max(), arr.shape[0])
if lon_arr.ndim == 1: lon_2d, lat_2d = np.meshgrid(lon_arr, lat_arr)
else: lon_2d, lat_2d = lon_arr, lat_arr
dist = (lon_2d - stat_lon)**2 + (lat_2d - stat_lat)**2
sy, sx = np.unravel_index(np.argmin(dist), dist.shape)
if sy < arr.shape[0] and sx < arr.shape[1]:
v = arr[sy, sx]
if np.isnan(v) or v > 200000 or v < -10000: return np.nan
return float(v)
return np.nan
def extract_val_robust(self, data, keys, stat_lon, stat_lat):
k = self.get_exact_key(data, keys)
if not k:
for dk in data.files:
if any(x in dk.lower() for x in keys): k = dk; break
if not k: return np.nan
return self.extract_val_with_key(data, k, stat_lon, stat_lat)
def draw_msm_visual_metagram(self, station_combo):
it_str = self.model_init_times.get("MSM")
if not it_str:
QMessageBox.warning(self, "エラー", "データが存在しません。"); return
stat_name = station_combo.currentText(); stat_lat, stat_lon = station_combo.currentData()
dt_utc = datetime.strptime(it_str, "%Y%m%d%H%M%S")
dt_jst = dt_utc + timedelta(hours=9)
init_z_str = f"{dt_utc.strftime('%m%d%H')}Z"
title_time_str = f"{dt_utc.strftime('%m月%d日%H')}Z / {dt_jst.strftime('%H')}JST"
max_ft = self.tab_ui['MSM']['slider'].maximum()
fts_surf = list(range(0, max_ft + 1))
fts_prof = [ft for ft in fts_surf if ft % 3 == 0]
plevs = [1000, 975, 950, 925, 850, 700, 600, 500, 300]; y_coords = [0.0, 0.05, 0.1, 0.15, 0.25, 0.5, 0.625, 0.75, 1.0]
T_prof = np.full((len(plevs), len(fts_prof)), np.nan); RH_prof = np.full((len(plevs), len(fts_prof)), np.nan)
U_prof = np.full((len(plevs), len(fts_prof)), np.nan); V_prof = np.full((len(plevs), len(fts_prof)), np.nan)
SSI_850_series = np.full(len(fts_prof), np.nan)
SSI_700_series = np.full(len(fts_prof), np.nan)
SSI_925_series = np.full(len(fts_prof), np.nan)
P_surf_series = np.full(len(fts_surf), np.nan); T_surf_series = np.full(len(fts_surf), np.nan); Pr_surf_series = np.full(len(fts_surf), np.nan)
Vort_500_series = np.full(len(fts_prof), np.nan)
W_700_series = np.full(len(fts_prof), np.nan)
for c_idx, ft in enumerate(fts_surf):
gpv_file = os.path.join(self.cache_dir, f"MSM_{it_str}_FT{ft:02d}.npz")
if not os.path.exists(gpv_file): continue
try:
d = np.load(gpv_file)
P_surf_series[c_idx] = self.extract_val_robust(d, ['prmsl', 'mslet', 'msl', 'slp'], stat_lon, stat_lat)
if not np.isnan(P_surf_series[c_idx]) and P_surf_series[c_idx] > 2000: P_surf_series[c_idx] /= 100.0
T_surf_series[c_idx] = self.extract_val_robust(d, ['t2m', 'tmp2m', 'temp2m'], stat_lon, stat_lat)
if not np.isnan(T_surf_series[c_idx]) and T_surf_series[c_idx] > 100: T_surf_series[c_idx] -= 273.15
p_key = self.get_precip_key(d)
if p_key:
pr = self.extract_val_with_key(d, p_key, stat_lon, stat_lat)
Pr_surf_series[c_idx] = pr if not np.isnan(pr) else 0.0
else: Pr_surf_series[c_idx] = 0.0
d.close()
except Exception as e: logging.error(f"Error reading {gpv_file}: {e}")
for c_idx, ft in enumerate(fts_prof):
gpv_file = os.path.join(self.cache_dir, f"MSM_{it_str}_FT{ft:02d}.npz")
if not os.path.exists(gpv_file): continue
try:
d = np.load(gpv_file)
t2m = self.extract_val_robust(d, ['t2m', 'tmp2m', 'temp2m'], stat_lon, stat_lat)
if not np.isnan(t2m) and t2m > 100: t2m -= 273.15
T_prof[0, c_idx] = t2m
RH_prof[0, c_idx] = self.extract_val_robust(d, ['rh2m', 'r2m'], stat_lon, stat_lat)
U_prof[0, c_idx] = self.extract_val_robust(d, ['u10', '10u', 'u_10m'], stat_lon, stat_lat)
V_prof[0, c_idx] = self.extract_val_robust(d, ['v10', '10v', 'v_10m'], stat_lon, stat_lat)
h_map = {975:1, 950:2, 925:3, 850:4, 700:5, 600:6, 500:7, 300:8}
for p_lvl, idx in h_map.items():
t_val = self.extract_val_robust(d, [f't{p_lvl}', f'tmp{p_lvl}', f't_{p_lvl}', f'tmp_{p_lvl}'], stat_lon, stat_lat)
if not np.isnan(t_val) and t_val > 100: t_val -= 273.15
rh_val = self.extract_val_robust(d, [f'rh{p_lvl}', f'r{p_lvl}', f'rh_{p_lvl}', f'r_{p_lvl}'], stat_lon, stat_lat)
if np.isnan(rh_val) and not np.isnan(t_val):
t_td = self.extract_val_robust(d, [f't_td{p_lvl}', f'dew{p_lvl}'], stat_lon, stat_lat)
if not np.isnan(t_td): rh_val = rh_from_t_td(t_val, t_td)
T_prof[idx, c_idx] = t_val
RH_prof[idx, c_idx] = rh_val if not np.isnan(rh_val) else 0.0
U_prof[idx, c_idx] = self.extract_val_robust(d, [f'u{p_lvl}', f'u_{p_lvl}'], stat_lon, stat_lat)
V_prof[idx, c_idx] = self.extract_val_robust(d, [f'v{p_lvl}', f'v_{p_lvl}'], stat_lon, stat_lat)
t925 = T_prof[3, c_idx]; rh925 = RH_prof[3, c_idx]
t850 = T_prof[4, c_idx]; rh850 = RH_prof[4, c_idx]
t700 = T_prof[5, c_idx]; rh700 = RH_prof[5, c_idx]
t500 = T_prof[7, c_idx]
vort = self.extract_val_robust(d, ['vort500', 'vort', 'abs_vort'], stat_lon, stat_lat)
if not np.isnan(vort):
Vort_500_series[c_idx] = vort * 100000.0 if abs(vort) < 1.0 else vort
w_val = self.extract_val_robust(d, ['w', 'w700', 'vvel', 'vvel700', 'dzdt', 'dzdt700'], stat_lon, stat_lat)
if not np.isnan(w_val):
W_700_series[c_idx] = w_val * 36.0 if abs(w_val) < 15.0 else w_val
if not np.isnan(t850) and not np.isnan(rh850):
if not np.isnan(t500):
SSI_850_series[c_idx] = t500 - get_t_from_ept(calc_ept(t850, rh850, 850), 500)
if not np.isnan(t700):
SSI_700_series[c_idx] = t700 - get_t_from_ept(calc_ept(t850, rh850, 850), 700)
if not np.isnan(t700) and not np.isnan(t925) and not np.isnan(rh925):
SSI_925_series[c_idx] = t700 - get_t_from_ept(calc_ept(t925, rh925, 925), 700)
d.close()
except: pass
fig = Figure(figsize=(16, 10.6)); fig.patch.set_facecolor('white')
fig.subplots_adjust(left=0.06, right=0.96, top=0.88, bottom=0.08, hspace=0.65)
fig.suptitle(f"MSM 図解メタグラム - {stat_name} (初期時: {title_time_str})", fontsize=15, weight='bold', y=0.96)
gs = fig.add_gridspec(14, 1)
ax_cross = fig.add_subplot(gs[0:5, 0])
ax_t2m = fig.add_subplot(gs[5:7, 0], sharex=ax_cross)
ax_slp = ax_t2m.twinx()
ax_dyn = fig.add_subplot(gs[7:9, 0], sharex=ax_cross)
ax_pr = fig.add_subplot(gs[9:11, 0], sharex=ax_cross)
ax_ssi = fig.add_subplot(gs[11:14, 0], sharex=ax_cross)
X_prof, Y_prof = np.meshgrid(fts_prof, y_coords)
cmap = mcolors.ListedColormap(['#E8F8F5', '#A3E4D7', '#48C9B0', '#117A65'])
bounds = [70, 80, 90, 95, 100]; norm = mcolors.BoundaryNorm(bounds, cmap.N)
cf = ax_cross.contourf(X_prof, Y_prof, RH_prof, levels=bounds, cmap=cmap, norm=norm, extend='max', alpha=0.6)
cs = ax_cross.contour(X_prof, Y_prof, T_prof, levels=np.arange(-60, 40, 6), colors='red', linewidths=1.0, alpha=0.7)
ax_cross.clabel(cs, inline=True, fontsize=9, fmt='%1.0f')
cs_zero = ax_cross.contour(X_prof, Y_prof, T_prof, levels=[0], colors='blue', linewidths=2.0)
ax_cross.clabel(cs_zero, inline=True, fontsize=10, fmt='0℃')
ax_cross.barbs(X_prof[:, ::1], Y_prof[:, ::1], U_prof[:, ::1], V_prof[:, ::1], length=4.5, color='black', linewidth=0.6)
ax_cross.set_yticks(y_coords); ax_cross.set_yticklabels([str(p) for p in plevs])
ax_cross.set_ylabel("気\n圧\n\n[hPa]", weight='bold', rotation=0, labelpad=10, va='center')
ax_cross.set_title("湿域・気温・風 断面図 (3時間毎)", loc='left', weight='bold')
ax_t2m.plot(fts_surf, T_surf_series, color='#E74C3C', marker='o', markersize=3, linestyle='-', linewidth=2, label="地上気温")
ax_t2m.set_ylabel("地\n上\n気\n温\n\n[℃]", color='#E74C3C', weight='bold', rotation=0, labelpad=10, va='center')
ax_slp.plot(fts_surf, P_surf_series, color='#2980B9', marker='s', markersize=3, linestyle='-', linewidth=2, label="海面気圧")
ax_slp.set_ylabel("海\n面\n気\n圧\n\n[hPa]", color='#2980B9', weight='bold', rotation=0, labelpad=15, va='center')
ax_t2m.legend(loc='upper left')
ax_slp.legend(loc='upper right')
ax_top_jst = ax_cross.twiny()
ax_top_jst.set_xlim(ax_cross.get_xlim())
ax_top_jst.set_xticks(range(0, fts_surf[-1]+1, 6))
jsts = [(dt_jst + timedelta(hours=x)).strftime("%d日%H時") for x in range(0, fts_surf[-1]+1, 6)]
ax_top_jst.set_xticklabels(jsts, fontsize=9, color='#333333', weight='bold')
color_vort = '#8E44AD'
ax_dyn.plot(fts_prof, Vort_500_series, color=color_vort, marker='^', markersize=4, linestyle='-', linewidth=2, label="500hPa 正渦度")
ax_dyn.set_ylabel("渦\n度\n\n[10⁻⁵/s]", color=color_vort, weight='bold', rotation=0, labelpad=15, va='center')
ax_dyn.tick_params(axis='y', labelcolor=color_vort)
ax_dyn.axhline(0, color='gray', linestyle='--', linewidth=1)
ax_updraft = ax_dyn.twinx()
color_updraft = '#D35400'
ax_updraft.plot(fts_prof, W_700_series, color=color_updraft, marker='v', markersize=4, linestyle='-', linewidth=2, label="700hPa 鉛直速度")
ax_updraft.set_ylabel("鉛\n直\n速\n度\n\n[hPa/h]", color=color_updraft, weight='bold', rotation=0, labelpad=25, va='center')
ax_updraft.tick_params(axis='y', labelcolor=color_updraft)
ax_updraft.invert_yaxis()
ax_dyn.legend(loc='upper left')
ax_updraft.legend(loc='upper right')
ax_pr.bar(fts_surf, Pr_surf_series, width=0.6, color='#3498DB', alpha=0.9, label="期間降水量 (1時間積算)")
ax_pr.set_ylabel("降\n水\n量\n\n[mm]", color='#3498DB', weight='bold', rotation=0, labelpad=10, va='center')
ax_pr.legend(loc='upper left')
ax_pr.axhline(0, color='gray', linewidth=1.0)
ax_ssi.plot(fts_prof, SSI_850_series, color='#C0392B', marker='o', markersize=4, linestyle='-', linewidth=2, label="SSI (850-500)")
ax_ssi.plot(fts_prof, SSI_700_series, color='#E67E22', marker='^', markersize=4, linestyle='-', linewidth=2, label="SSI (850-700)")
ax_ssi.plot(fts_prof, SSI_925_series, color='#8E44AD', marker='s', markersize=4, linestyle='-', linewidth=2, label="SSI (925-700)")
ax_ssi.axhline(0, color='gray', linestyle=':', linewidth=1)
ax_ssi.set_ylabel("Ｓ\nＳ\nＩ\n\n[℃]", weight='bold', rotation=0, labelpad=10, va='center')
ax_ssi.set_xlabel("予報時間 (FT)", weight='bold', fontsize=12)
ax_ssi.legend(loc='lower left')
for ax in [ax_cross, ax_t2m, ax_dyn, ax_pr, ax_ssi]:
ax.set_xlim(fts_surf[0]-1, fts_surf[-1]+1)
ax.set_xticks(range(0, fts_surf[-1]+1, 6))
ax.grid(True, axis='x', color='gray', linestyle='--', alpha=0.5)
ax.grid(True, axis='y', color='gray', linestyle='--', alpha=0.3)
if ax != ax_ssi: plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax_ssi.get_xticklabels(), rotation=45, ha='right', fontsize=11)
fig.align_ylabels([ax_cross, ax_t2m, ax_dyn, ax_pr, ax_ssi])
canvas = FigureCanvas(fig)
win = SingleImageWindow(self, f"MSM 図解メタグラム - {stat_name}", "MSM", stat_name, init_z_str, canvas)
win.exec()
def draw_gsm_visual_metagram(self, station_combo):
it_str = self.model_init_times.get("GSM_JP")
if not it_str:
QMessageBox.warning(self, "エラー", "データが存在しません。"); return
stat_name = station_combo.currentText(); stat_lat, stat_lon = station_combo.currentData()
dt_utc = datetime.strptime(it_str, "%Y%m%d%H%M%S")
dt_jst = dt_utc + timedelta(hours=9)
init_z_str = f"{dt_utc.strftime('%m%d%H')}Z"
title_time_str = f"{dt_utc.strftime('%m月%d日%H')}Z / {dt_jst.strftime('%H')}JST"
max_ft = self.tab_ui['GSM_JP']['slider'].maximum()
fts = list(range(0, max_ft + 1, 3))
plevs = [1000, 975, 950, 925, 850, 700, 600, 500, 300]; y_coords = [0.0, 0.05, 0.1, 0.15, 0.25, 0.5, 0.625, 0.75, 1.0]
T_prof = np.full((len(plevs), len(fts)), np.nan); RH_prof = np.full((len(plevs), len(fts)), np.nan)
U_prof = np.full((len(plevs), len(fts)), np.nan); V_prof = np.full((len(plevs), len(fts)), np.nan)
SSI_850_series = np.full(len(fts), np.nan)
SSI_700_series = np.full(len(fts), np.nan)
SSI_925_series = np.full(len(fts), np.nan)
P_surf_series = np.full(len(fts), np.nan); T_surf_series = np.full(len(fts), np.nan); Pr_surf_series = np.full(len(fts), np.nan)
Vort_500_series = np.full(len(fts), np.nan)
W_700_series = np.full(len(fts), np.nan)
for c_idx, ft in enumerate(fts):
disp_ft = ft if ft <= 132 or ft % 6 == 0 else ft - (ft % 6)
gpv_file = os.path.join(self.cache_dir, f"GSM_JP_{it_str}_FT{disp_ft:02d}.npz")
if os.path.exists(gpv_file):
try:
d = np.load(gpv_file)
P_surf_series[c_idx] = self.extract_val_robust(d, ['prmsl', 'mslet', 'msl', 'slp'], stat_lon, stat_lat)
if not np.isnan(P_surf_series[c_idx]) and P_surf_series[c_idx] > 2000: P_surf_series[c_idx] /= 100.0
T_surf_series[c_idx] = self.extract_val_robust(d, ['t2m', 'tmp2m', 'temp2m'], stat_lon, stat_lat)
if not np.isnan(T_surf_series[c_idx]) and T_surf_series[c_idx] > 100: T_surf_series[c_idx] -= 273.15
p_key = self.get_precip_key(d)
if p_key:
pr = self.extract_val_with_key(d, p_key, stat_lon, stat_lat)
if disp_ft >= 3:
offset = 6 if disp_ft > 132 else 3
prev_file = os.path.join(self.cache_dir, f"GSM_JP_{it_str}_FT{disp_ft-offset:02d}.npz")
if os.path.exists(prev_file):
try:
with np.load(prev_file) as d_prev:
prev_p_key = self.get_precip_key(d_prev)
if prev_p_key:
prev_pr = self.extract_val_with_key(d_prev, prev_p_key, stat_lon, stat_lat)
if not np.isnan(prev_pr): pr = max(0, pr - prev_pr)
except: pass
Pr_surf_series[c_idx] = pr if not np.isnan(pr) else 0.0
else: Pr_surf_series[c_idx] = 0.0
T_prof[0, c_idx] = T_surf_series[c_idx] if not np.isnan(T_surf_series[c_idx]) else self.extract_val_robust(d, ['t2m', 'tmp2m', 'temp2m'], stat_lon, stat_lat)
RH_prof[0, c_idx] = self.extract_val_robust(d, ['rh2m', 'r2m'], stat_lon, stat_lat)
U_prof[0, c_idx] = self.extract_val_robust(d, ['u10', '10u', 'u_10m'], stat_lon, stat_lat)
V_prof[0, c_idx] = self.extract_val_robust(d, ['v10', '10v', 'v_10m'], stat_lon, stat_lat)
h_map = {975:1, 950:2, 925:3, 850:4, 700:5, 600:6, 500:7, 300:8}
for p_lvl, idx in h_map.items():
t_val = self.extract_val_robust(d, [f't{p_lvl}', f'tmp{p_lvl}', f't_{p_lvl}'], stat_lon, stat_lat)
if not np.isnan(t_val) and t_val > 100: t_val -= 273.15
rh_val = self.extract_val_robust(d, [f'rh{p_lvl}', f'r{p_lvl}', f'rh_{p_lvl}'], stat_lon, stat_lat)
if np.isnan(rh_val) and not np.isnan(t_val):
t_td = self.extract_val_robust(d, [f't_td{p_lvl}', f'dew{p_lvl}'], stat_lon, stat_lat)
if not np.isnan(t_td): rh_val = rh_from_t_td(t_val, t_td)
T_prof[idx, c_idx] = t_val
RH_prof[idx, c_idx] = rh_val if not np.isnan(rh_val) else 0.0
U_prof[idx, c_idx] = self.extract_val_robust(d, [f'u{p_lvl}', f'u_{p_lvl}'], stat_lon, stat_lat)
V_prof[idx, c_idx] = self.extract_val_robust(d, [f'v{p_lvl}', f'v_{p_lvl}'], stat_lon, stat_lat)
t925 = T_prof[3, c_idx]; rh925 = RH_prof[3, c_idx]
t850 = T_prof[4, c_idx]; rh850 = RH_prof[4, c_idx]
t700 = T_prof[5, c_idx]; rh700 = RH_prof[5, c_idx]
t500 = T_prof[7, c_idx]
vort = self.extract_val_robust(d, ['vort500', 'vort', 'abs_vort'], stat_lon, stat_lat)
if not np.isnan(vort):
Vort_500_series[c_idx] = vort * 100000.0 if abs(vort) < 1.0 else vort
w_val = self.extract_val_robust(d, ['w', 'w700', 'vvel', 'vvel700', 'dzdt', 'dzdt700'], stat_lon, stat_lat)
if not np.isnan(w_val):
W_700_series[c_idx] = w_val * 36.0 if abs(w_val) < 15.0 else w_val
if not np.isnan(t850) and not np.isnan(rh850):
if not np.isnan(t500):
SSI_850_series[c_idx] = t500 - get_t_from_ept(calc_ept(t850, rh850, 850), 500)
if not np.isnan(t700):
SSI_700_series[c_idx] = t700 - get_t_from_ept(calc_ept(t850, rh850, 850), 700)
if not np.isnan(t700) and not np.isnan(t925) and not np.isnan(rh925):
SSI_925_series[c_idx] = t700 - get_t_from_ept(calc_ept(t925, rh925, 925), 700)
d.close()
except: pass
fig = Figure(figsize=(16, 10.6)); fig.patch.set_facecolor('white')
fig.subplots_adjust(left=0.06, right=0.96, top=0.88, bottom=0.08, hspace=0.65)
fig.suptitle(f"GSM_JP 図解メタグラム - {stat_name} (初期時: {title_time_str})", fontsize=15, weight='bold', y=0.96)
gs = fig.add_gridspec(14, 1)
ax_cross = fig.add_subplot(gs[0:5, 0])
ax_t2m = fig.add_subplot(gs[5:7, 0], sharex=ax_cross)
ax_slp = ax_t2m.twinx()
ax_dyn = fig.add_subplot(gs[7:9, 0], sharex=ax_cross)
ax_pr = fig.add_subplot(gs[9:11, 0], sharex=ax_cross)
ax_ssi = fig.add_subplot(gs[11:14, 0], sharex=ax_cross)
X, Y = np.meshgrid(fts, y_coords)
cmap = mcolors.ListedColormap(['#E8F8F5', '#A3E4D7', '#48C9B0', '#117A65'])
bounds = [70, 80, 90, 95, 100]; norm = mcolors.BoundaryNorm(bounds, cmap.N)
cf = ax_cross.contourf(X, Y, RH_prof, levels=bounds, cmap=cmap, norm=norm, extend='max', alpha=0.6)
cs = ax_cross.contour(X, Y, T_prof, levels=np.arange(-60, 40, 6), colors='red', linewidths=1.0, alpha=0.7)
ax_cross.clabel(cs, inline=True, fontsize=9, fmt='%1.0f')
cs_zero = ax_cross.contour(X, Y, T_prof, levels=[0], colors='blue', linewidths=2.0)
ax_cross.clabel(cs_zero, inline=True, fontsize=10, fmt='0℃')
ax_cross.barbs(X[:, ::1], Y[:, ::1], U_prof[:, ::1], V_prof[:, ::1], length=4.5, color='black', linewidth=0.6)
ax_cross.set_yticks(y_coords); ax_cross.set_yticklabels([str(p) for p in plevs])
ax_cross.set_ylabel("気\n圧\n\n[hPa]", weight='bold', rotation=0, labelpad=10, va='center')
ax_cross.set_title("湿域・気温・風 断面図 (※132h以降は全要素6時間間隔で補間展開)", loc='left', weight='bold')
ax_t2m.plot(fts, T_surf_series, color='#E74C3C', marker='o', markersize=4, linestyle='-', linewidth=2, label="地上気温")
ax_t2m.set_ylabel("地\n上\n気\n温\n\n[℃]", color='#E74C3C', weight='bold', rotation=0, labelpad=10, va='center')
ax_slp.plot(fts, P_surf_series, color='#2980B9', marker='s', markersize=4, linestyle='-', linewidth=2, label="海面気圧")
ax_slp.set_ylabel("海\n面\n気\n圧\n\n[hPa]", color='#2980B9', weight='bold', rotation=0, labelpad=15, va='center')
ax_t2m.legend(loc='upper left')
ax_slp.legend(loc='upper right')
ax_top_jst = ax_cross.twiny()
ax_top_jst.set_xlim(ax_cross.get_xlim())
ax_top_jst.set_xticks(range(0, fts[-1]+1, 12))
jsts = [(dt_jst + timedelta(hours=x)).strftime("%d日%H時") for x in range(0, fts[-1]+1, 12)]
ax_top_jst.set_xticklabels(jsts, fontsize=9, color='#333333', weight='bold')
color_vort = '#8E44AD'
ax_dyn.plot(fts, Vort_500_series, color=color_vort, marker='^', markersize=4, linestyle='-', linewidth=2, label="500hPa 正渦度")
ax_dyn.set_ylabel("渦\n度\n\n[10⁻⁵/s]", color=color_vort, weight='bold', rotation=0, labelpad=15, va='center')
ax_dyn.tick_params(axis='y', labelcolor=color_vort)
ax_dyn.axhline(0, color='gray', linestyle='--', linewidth=1)
ax_updraft = ax_dyn.twinx()
color_updraft = '#D35400'
ax_updraft.plot(fts, W_700_series, color=color_updraft, marker='v', markersize=4, linestyle='-', linewidth=2, label="700hPa 鉛直速度")
ax_updraft.set_ylabel("鉛\n直\n速\n度\n\n[hPa/h]", color=color_updraft, weight='bold', rotation=0, labelpad=25, va='center')
ax_updraft.tick_params(axis='y', labelcolor=color_updraft)
ax_updraft.invert_yaxis()
ax_dyn.legend(loc='upper left')
ax_updraft.legend(loc='upper right')
ax_pr.bar(fts, Pr_surf_series, width=1.8, color='#3498DB', alpha=0.9, label="期間降水量 (差分)")
ax_pr.set_ylabel("降\n水\n量\n\n[mm]", color='#3498DB', weight='bold', rotation=0, labelpad=10, va='center')
ax_pr.legend(loc='upper left')
ax_pr.axhline(0, color='gray', linewidth=1.0)
ax_ssi.plot(fts, SSI_850_series, color='#C0392B', marker='o', markersize=4, linestyle='-', linewidth=2, label="SSI (850-500)")
ax_ssi.plot(fts, SSI_700_series, color='#E67E22', marker='^', markersize=4, linestyle='-', linewidth=2, label="SSI (850-700)")
ax_ssi.plot(fts, SSI_925_series, color='#8E44AD', marker='s', markersize=4, linestyle='-', linewidth=2, label="SSI (925-700)")
ax_ssi.axhline(0, color='gray', linestyle=':', linewidth=1)
ax_ssi.set_ylabel("Ｓ\nＳ\nＩ\n\n[℃]", weight='bold', rotation=0, labelpad=10, va='center')
ax_ssi.set_xlabel("予報時間 (FT)", weight='bold', fontsize=12)
ax_ssi.legend(loc='lower left')
for ax in [ax_cross, ax_t2m, ax_dyn, ax_pr, ax_ssi]:
ax.set_xlim(fts[0]-1, fts[-1]+1)
ax.set_xticks(range(0, fts[-1]+1, 12))
ax.grid(True, axis='x', color='gray', linestyle='--', alpha=0.5)
ax.grid(True, axis='y', color='gray', linestyle='--', alpha=0.3)
if ax != ax_ssi: plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax_ssi.get_xticklabels(), rotation=45, ha='right', fontsize=11)
fig.align_ylabels([ax_cross, ax_t2m, ax_dyn, ax_pr, ax_ssi])
canvas = FigureCanvas(fig)
win = SingleImageWindow(self, f"GSM_JP 図解メタグラム - {stat_name}", "GSM_JP", stat_name, init_z_str, canvas)
win.exec()
def draw_anal_visual_metagram(self, station_combo):
model_name = "ANAL"
ui = self.tab_ui[model_name]
elem_name = ui['list'].currentItem().text()
stat_name = station_combo.currentText()
stat_lat, stat_lon = station_combo.currentData()
combo = ui['init_combo']
count = min(6, combo.count())
if count == 0:
QMessageBox.warning(self, "エラー", "データが存在しません。")
return
times = []
vals = []
u_vals = []
v_vals = []
for i in range(count - 1, -1, -1):
it_str = combo.itemData(i)
dt_utc = datetime.strptime(it_str, "%Y%m%d%H%M%S")
dt_jst = dt_utc + timedelta(hours=9)
times.append(dt_jst)
target_file = os.path.join(self.cache_dir, f"ANAL_{it_str}_FT00.npz")
v, u, v_w = np.nan, np.nan, np.nan
if os.path.exists(target_file):
try:
with np.load(target_file) as d:
if elem_name == "地上気温":
v = self.extract_val_robust(d, ['t2m', 'tmp2m', 'temp2m'], stat_lon, stat_lat)
if not np.isnan(v) and v > 100: v -= 273.15
elif elem_name == "地上風向風速":
u = self.extract_val_robust(d, ['u10', '10u', 'u_10m'], stat_lon, stat_lat)
v_w = self.extract_val_robust(d, ['v10', '10v', 'v_10m'], stat_lon, stat_lat)
if not np.isnan(u) and not np.isnan(v_w): v = np.hypot(u, v_w)
elif "気温" in elem_name or "風" in elem_name:
lvl_match = re.search(r'(\d+)hpa', elem_name.lower())
if lvl_match:
lvl = lvl_match.group(1)
if "気温" in elem_name:
v = self.extract_val_robust(d, [f't{lvl}', f'tmp{lvl}', f't_{lvl}'], stat_lon, stat_lat)
if not np.isnan(v) and v > 100: v -= 273.15
if "風" in elem_name:
u = self.extract_val_robust(d, [f'u{lvl}', f'u_{lvl}'], stat_lon, stat_lat)
v_w = self.extract_val_robust(d, [f'v{lvl}', f'v_{lvl}'], stat_lon, stat_lat)
if "風向・風速" in elem_name and not np.isnan(u) and not np.isnan(v_w):
v = np.hypot(u, v_w)
except Exception as e:
logging.error(f"ANAL meta error: {e}")
vals.append(v)
u_vals.append(u)
v_vals.append(v_w)
fig = Figure(figsize=(10, 6)); fig.patch.set_facecolor('white')
fig.subplots_adjust(left=0.1, right=0.9, top=0.85, bottom=0.15)
ax = fig.add_subplot(111)
x_pos = np.arange(len(times))
x_labels = [t.strftime("%H:%M") for t in times]
valid_vals = [val for val in vals if not np.isnan(val)]
if valid_vals:
ax.plot(x_pos, vals, marker='o', linestyle='-', color='#C0392B', linewidth=2)
ylabel = "値"
if "気温" in elem_name: ylabel = "気温 [℃]"
elif "風" in elem_name: ylabel = "風速 [m/s]"
ax.set_ylabel(ylabel, weight='bold', fontsize=12)
if any(not np.isnan(u) for u in u_vals):
for i in range(len(x_pos)):
if not np.isnan(u_vals[i]) and not np.isnan(v_vals[i]) and not np.isnan(vals[i]):
ax.barbs(x_pos[i], vals[i], u_vals[i], v_vals[i], length=6, color='blue')
else:
ax.text(0.5, 0.5, "有効なデータがありません", ha='center', va='center', transform=ax.transAxes)
ax.set_xticks(x_pos)
ax.set_xticklabels(x_labels)
ax.grid(True, linestyle='--', alpha=0.7)
title_str = f"毎時大気解析({elem_name}) メタグラム - {stat_name}\n"
title_str += f"(最新: {times[-1].strftime('%m/%d %H:%M')} JST)" if times else ""
ax.set_title(title_str, weight='bold', fontsize=14)
canvas = FigureCanvas(fig)
win = SingleImageWindow(self, f"ANALメタグラム - {stat_name}", "ANAL", stat_name, times[-1].strftime("%m%d%H") if times else "none", canvas)
win.exec()
def draw_guidance_metagram(self, station_combo, model_name):
it_str = self.model_init_times.get(model_name)
if not it_str:
QMessageBox.warning(self, "エラー", "データが存在しません。"); return
stat_name = station_combo.currentText(); stat_lat, stat_lon = station_combo.currentData()
dt_utc = datetime.strptime(it_str, "%Y%m%d%H%M%S")
dt_jst = dt_utc + timedelta(hours=9)
is_meps = model_name in MEPS_STAT_MODELS
if is_meps:
elements = [
("3時間降水量", "precip", 1),
("6時間降水量", "precip6", 2),
("12時間降水量", "precip12", 4),
("1時間最大\n降水量", "precip1max", 1),
("3時間最大\n降水量", "precip3max", 1),
("3時間降雪量", "snow3", 1),
("6時間降雪量", "snow6", 2),
("12時間降雪量", "snow12", 4),
("24時間降雪量", "snow24", 8),
("発雷確率", "thund", 1)
]
stats = [
("上位90%", "MEPS_GUID_MAX"),
("確率≥1", "MEPS_GUID_PROB1"),
("確率≥10", "MEPS_GUID_PROB10"),
("確率≥20", "MEPS_GUID_PROB20"),
("確率≥30", "MEPS_GUID_PROB30"),
("下位10%", "MEPS_GUID_MIN")
]
max_ft = 39
fts = list(range(3, max_ft + 1, 3))
data_dict = {elem[1]: {s[0]: {} for s in stats} for elem in elements}
for s_name, s_model in stats:
s_it = self.model_init_times.get(s_model, it_str)
for ft in fts:
guid_file = os.path.join(self.cache_dir, f"{s_model}_{s_it}_FT{ft:02d}.npz")
if os.path.exists(guid_file):
try:
with np.load(guid_file) as d:
for e_name, e_key, span in elements:
search_keys = [e_key]
span_h = span * 3 if span < 8 else 24
if "降水量" in e_name:
if "最大" in e_name:
span_h = 1 if "1時間" in e_name else (3 if "3時間" in e_name else 24)
search_keys.extend([k for k in d.files if k.startswith("tp_") and k.endswith(f"_{max(0, ft-span_h)}_{ft}")])
else:
search_keys.extend(['tp', 'precip'] + [k for k in d.files if k.startswith("tp_") and k.endswith(f"_{max(0, ft-3)}_{ft}")])
elif "降雪" in e_name:
search_keys.extend(['asnow'] + [k for k in d.files if k.startswith("asnow_") and k.endswith(f"_{max(0, ft-span_h)}_{ft}")])
elif "発雷" in e_name:
search_keys.extend(['tstm', 'thund'] + [k for k in d.files if k.startswith("tstm_") and k.endswith(f"_{max(0, ft-3)}_{ft}")])
val = self.extract_val_robust(d, search_keys, stat_lon, stat_lat)
data_dict[e_key][s_name][ft] = val
except: pass
fig = Figure(figsize=(16, 12)); fig.patch.set_facecolor('white')
fig.subplots_adjust(left=0.01, right=0.99, top=0.94, bottom=0.01)
ax = fig.add_subplot(111); ax.axis('off')
n_rows = len(elements) * len(stats) + 3
n_cols = 2 + len(fts)
ax.set_xlim(0, n_cols)
ax.set_ylim(0, n_rows)
# fontsize引数を追加してフォントサイズを動的調整可能に
def draw_cell(x, y, w, h, text, bg_color='white', font_weight='normal', text_color='black', fontsize=10):
rect = plt.Rectangle((x, y), w, h, facecolor=bg_color, edgecolor='black', linewidth=0.5)
ax.add_patch(rect)
ax.text(x + w/2.0, y + h/2.0, text, color=text_color, ha='center', va='center', weight=font_weight, fontsize=fontsize)
current_y = n_rows - 1
# ヘッダーマスを 2x3 サイズに変更
draw_cell(0, current_y - 2, 2, 3, "要素 / 日時(JST)", bg_color='#1D3557', font_weight='bold', text_color='white', fontsize=11)
for i, ft in enumerate(fts):
target_jst = dt_jst + timedelta(hours=ft)
# 日付（高さ1）と、時間・FT（高さ2）に分割
draw_cell(2 + i, current_y, 1, 1, target_jst.strftime("%m/%d"), bg_color='#1D3557', font_weight='bold', text_color='white', fontsize=9)
draw_cell(2 + i, current_y - 2, 1, 2, f"{target_jst.strftime('%H')}時\n(FT{ft:02d})", bg_color='#457B9D', font_weight='bold', text_color='white', fontsize=9)
current_y -= 3
for e_name, e_key, span in elements:
draw_cell(0, current_y - len(stats) + 1, 1, len(stats), e_name, bg_color='#F1FAEE', font_weight='bold')
for s_idx, (s_name, _) in enumerate(stats):
row_y = current_y - s_idx
draw_cell(1, row_y, 1, 1, s_name, bg_color='#E0E0E0' if s_idx%2==0 else 'white')
col_idx = 0
while col_idx < len(fts):
ft = fts[col_idx]
target_ft = ft + (span - 1) * 3
# --- 修正: 単純な累積降水量の合算処理 ---
if e_key in ["precip6", "precip12"]:
val = 0
v_count = 0
for step in range(span):
v = data_dict[e_key][s_name].get(ft + step * 3, np.nan)
if not np.isnan(v):
val += v
v_count += 1
if v_count == 0: val = np.nan
else:
val = data_dict[e_key][s_name].get(target_ft, np.nan)
is_prob_stat = "確率" in s_name
val_str = f"{val:.1f}" if not np.isnan(val) else "-"
if ("確率" in e_name or is_prob_stat) and not np.isnan(val):
val_str = f"{val:.0f}"
if val_str == "0.0" or val_str == "0":
val_str = "0"
if is_prob_stat and val_str != "-" and val_str != "0":
val_str += "%"
w = min(span, len(fts) - col_idx)
bg_c = 'white' if s_idx%2!=0 else '#F8F9FA'
if not np.isnan(val) and val > 0:
if is_prob_stat or "確率" in e_name:
if val >= 50: bg_c = '#FFCDD2'
elif val >= 20: bg_c = '#FFF9C4'
elif "降水" in e_name or "降雪" in e_name:
if val >= 20: bg_c = '#FFCDD2'
elif val >= 10: bg_c = '#FFF9C4'
elif val >= 1: bg_c = '#E3F2FD'
draw_cell(2 + col_idx, row_y, w, 1, val_str, bg_color=bg_c)
col_idx += span
current_y -= len(stats)
fig.suptitle(f"{stat_name} MEPS 統合メタグラム (初期時: {dt_utc.strftime('%m/%d %H:00Z')})", fontsize=20, weight='bold', y=0.99)
canvas = FigureCanvas(fig)
win = SingleImageWindow(self, f"GUID_{stat_name}", model_name, stat_name, dt_utc.strftime("%m%d%H") + "Z", canvas)
win.exec()
else:
fig = Figure(figsize=(11.69, 8.27)); fig.patch.set_facecolor('white')
ax = fig.add_subplot(111); ax.axis('off')
table_data = []
headers = ["FT", "日時(JST)", "卓越天気", "降水量", "降雪量", "発雷確率", "気温(℃)", "風向", "風速(m/s)"]
max_ft = self.tab_ui[model_name]['slider'].maximum()
fts = list(range(0, max_ft + 1, 3))
gpv_model = "GSM_JP" if model_name == "GSM_GUID" else "MSM"
gpv_it_str = self.model_init_times.get(gpv_model)
for ft in fts:
target_jst = dt_utc + timedelta(hours=9+ft)
row = [f"FT={ft:02d}", target_jst.strftime("%m/%d %H:00"), "-", "-", "-", "-", "-", "-", "-"]
guid_file = os.path.join(self.cache_dir, f"{model_name}_{it_str}_FT{ft:02d}.npz")
if os.path.exists(guid_file):
try:
with np.load(guid_file) as d:
pop = self.extract_val_robust(d, ['precip', 'tp', 'apcp', 'pop', 'var_0_1_8', 'var_0_1_52'], stat_lon, stat_lat)
pos = self.extract_val_robust(d, ['snow', 'weasd', 'snod', 'var_0_1_11', 'var_0_1_13', 'var_0_1_29'], stat_lon, stat_lat)
pol = self.extract_val_robust(d, ['thund', 'pol', 'lig', 'thunder', 'tstm', 'var_0_19_193'], stat_lon, stat_lat)
wx = self.extract_val_robust(d, ['wea', 'weather', 'wx', 'var_-1_-1_-1', 'var_0_19_192'], stat_lon, stat_lat)
if not np.isnan(wx): row[2] = {1:"晴", 2:"曇", 3:"雨", 4:"雪"}.get(int(wx), "不明")
if not np.isnan(pop): row[3] = f"{pop:.1f} mm"
if not np.isnan(pos): row[4] = f"{pos:.1f} cm"
if not np.isnan(pol): row[5] = f"{pol:.0f} %"
except: pass
gpv_ft = ft
if gpv_model == "GSM_JP" and ft > 132: gpv_ft = ft - (ft % 6) if ft % 6 != 0 else ft
gpv_file = os.path.join(self.cache_dir, f"{gpv_model}_{gpv_it_str}_FT{gpv_ft:02d}.npz")
if not os.path.exists(gpv_file): gpv_file = os.path.join(self.cache_dir, f"{gpv_model}_{it_str}_FT{gpv_ft:02d}.npz")
if not os.path.exists(gpv_file):
p_files = glob.glob(os.path.join(self.cache_dir, f"{gpv_model}_*_FT{gpv_ft:02d}.npz"))
if p_files: gpv_file = sorted(p_files, key=lambda x: abs(int(re.search(r'_(\d{14})_', x).group(1)) - int(it_str)))[0]
if os.path.exists(gpv_file):
try:
with np.load(gpv_file) as d_gpv:
t = self.extract_val_robust(d_gpv, ['t2m', 'tmp2m', 'temp2m'], stat_lon, stat_lat)
if not np.isnan(t): row[6] = f"{(t - 273.15 if t > 100 else t):.1f}"
u = self.extract_val_robust(d_gpv, ['u10', '10u', 'u_10m'], stat_lon, stat_lat)
v = self.extract_val_robust(d_gpv, ['v10', '10v', 'v_10m'], stat_lon, stat_lat)
if not np.isnan(u) and not np.isnan(v):
row[8] = f"{np.hypot(u, v):.1f}"
deg = (np.degrees(np.arctan2(u, v)) + 180) % 360
dirs = ["北", "北北東", "北東", "東北東", "東", "東南東", "南東", "南南東", "南", "南南西", "南西", "西南西", "西", "西北西", "北西", "北北西", "北"]
row[7] = dirs[int(round(deg / 22.5)) % 16]
except: pass
table_data.append(row)
table = ax.table(cellText=table_data, colLabels=headers, loc='center', cellLoc='center')
table.auto_set_font_size(False); table.set_fontsize(10); table.scale(1, 1.4)
for j in range(len(headers)):
cell = table[0, j]; cell.set_facecolor('#1D3557'); cell.get_text().set_color('white'); cell.get_text().set_weight('bold')
fig.suptitle(f"{stat_name} 統合ガイダンス時系列表 (初期時: {dt_utc.strftime('%m/%d %H:00Z')})", fontsize=15, weight='bold', y=0.96)
canvas = FigureCanvas(fig)
win = SingleImageWindow(self, f"GUID_{stat_name}", model_name, stat_name, dt_utc.strftime("%m%d%H") + "Z", canvas)
win.exec()
def draw_guidance(self, ax, ui, data, elem_name, val, extent, area_index, model_name, it_str):
drawn_flag = False; info_text = ""
is_meps = model_name in MEPS_STAT_MODELS
is_prob = "PROB" in model_name
def _cl(arr, minv, maxv, fill=np.nan):
if arr is None: return None
a = np.array(arr, dtype=float)
if not np.isnan(fill): a = np.nan_to_num(a, nan=fill, posinf=fill, neginf=fill)
else: a[np.isinf(a)] = np.nan
return np.clip(a, minv, maxv)
def add_vertical_colorbar(mappable, label_text):
cax = ui['fig'].add_axes([0.93, 0.15, 0.018, 0.7])
cb = ui['fig'].colorbar(mappable, cax=cax, orientation='vertical')
cb.set_label(label_text, fontsize=12, weight='bold'); ui['colorbars'].append(cb)
if "降水量" in elem_name or "降雪量" in elem_name:
accum_val = None; valid_data = False; lon_c = None; lat_c = None
if "1時間" in elem_name: accum_hours = 1
elif "3時間" in elem_name: accum_hours = 3
elif "6時間" in elem_name: accum_hours = 6
elif "12時間" in elem_name: accum_hours = 12
elif "24時間" in elem_name: accum_hours = 24
else: accum_hours = 3
is_meps = model_name in MEPS_STAT_MODELS
# npzに事前計算されている要素（最大降水量、降雪量、通常の3時間降水）か判定
is_meps_precalculated = False
if is_meps:
if "最大" in elem_name or "降雪" in elem_name or accum_hours == 3:
is_meps_precalculated = True
if is_meps_precalculated:
fixed_key = None
if "降水量" in elem_name:
if "1時間最大" in elem_name: fixed_key = "precip1max"
elif "3時間最大" in elem_name: fixed_key = "precip3max"
elif "24時間最大" in elem_name: fixed_key = "precip24max"
else: fixed_key = "precip"
else:
fixed_key = f"snow{accum_hours}"
meps_key = None
if fixed_key and fixed_key in data.files:
meps_key = fixed_key
else:
vc = "tp_" if "降水量" in elem_name else "asnow_"
tr = f"_{max(0, val-accum_hours)}_{val}"
for k in data.files:
if k.startswith(vc) and k.endswith(tr) and not ("lon" in k or "lat" in k):
meps_key = k; break
if not meps_key:
for k in data.files:
if k.startswith(vc) and k.endswith(f"_{val}") and not ("lon" in k or "lat" in k):
meps_key = k; break
if meps_key and meps_key in data.files:
accum_val = _cl(data[meps_key], 0, 1000, 0.0)
lon_c, lat_c = self.get_coords_for_data(data, accum_val)
valid_data = True
else:
# 事前計算されていない要素（MEPSの6・12・24時間降水量、非MEPSモデル）は遡って足し算する
for hr_offset in range(0, accum_hours, 3):
target_ft = val - hr_offset
if target_ft <= 0: continue
d_curr = None
if target_ft == val: d_curr = data
else:
target_file = os.path.join(self.cache_dir, f"{model_name}_{it_str}_FT{target_ft:02d}.npz")
if os.path.exists(target_file):
try: d_curr = np.load(target_file)
except: pass
if d_curr is not None:
if "降水量" in elem_name: key = self.get_exact_key(d_curr, ['precip', 'tp', 'apcp', 'pop', 'var_0_1_8', 'var_0_1_52'])
else: key = self.get_exact_key(d_curr, ['snow', 'weasd', 'snod', 'var_0_1_11', 'var_0_1_13', 'var_0_1_29', 'asnow'])
if key:
v = _cl(d_curr[key], 0, 1000, 0.0)
if accum_val is None:
accum_val = v.copy()
lon_c, lat_c = self.get_coords_for_data(d_curr, v)
valid_data = True
else:
if accum_val.shape == v.shape:
accum_val += v
if target_ft != val: d_curr.close()
if valid_data and accum_val is not None:
accum_val_masked = np.ma.masked_less(accum_val, 1.0 if is_prob else 0.1)
if is_prob:
thresh = model_name.replace("MEPS_GUID_PROB", "")
unit = "mm" if "降水" in elem_name else "cm"
label_text = f'{elem_name} (≥{thresh}{unit}) 超過確率 [%]'
levels = [1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
cmap = mcolors.ListedColormap(['#F7DC6F', '#F4D03F', '#F5B041', '#EB984E', '#DC7633', '#E74C3C', '#C0392B', '#9B59B6', '#8E44AD', '#1F618D'])
cmap.set_under('none'); cmap.set_over('#0B5345')
else:
levels = [0.1, 1, 5, 10, 20, 30, 50, 80]
if "降水量" in elem_name:
cmap = mcolors.ListedColormap(['#A0E8A0', '#D4F05A', '#FFFF00', '#FFA500', '#FF5500', '#FF0000', '#FF00FF'])
cmap.set_under('none'); cmap.set_over('#800080')
label_text = f'降水量 ({accum_hours}時間{"最大" if is_meps and "最大" in elem_name else "積算"}) [mm]'
else:
cmap = mcolors.ListedColormap(['#29B6F6', '#0288D1', '#01579B', '#1A237E', '#311B92', '#4A148C', '#880E4F'])
cmap.set_under('none'); cmap.set_over('#4A0024')
label_text = f'降雪量 ({accum_hours}時間積算) [cm等]'
norm = mcolors.BoundaryNorm(levels, cmap.N)
is_snow = "降雪" in elem_name
pm_alpha = 0.55 if is_prob else (0.8 if is_snow else 0.5)
pm = self.plot_blocky(ax, lon_c, lat_c, accum_val_masked, ui, cmap, extent, norm=norm, alpha=pm_alpha)
if pm:
add_vertical_colorbar(pm, label_text)
drawn_flag = True
lc, lac, dc = self.crop_data(lon_c, lat_c, accum_val, extent)
if lc.size > 0:
l2, la2 = np.meshgrid(lc, lac) if lc.ndim == 1 else (lc, lac)
min_lon, max_lon, min_lat, max_lat = extent
if area_index in [3, 4]:
try:
zoom_factor = 8 if "GSM" in model_name else 2
dc_z = ndimage.zoom(dc, zoom_factor, order=1)
if lc.ndim == 1:
lc_z = ndimage.zoom(lc, zoom_factor, order=1)
lac_z = ndimage.zoom(lac, zoom_factor, order=1)
l2_z, la2_z = np.meshgrid(lc_z, lac_z)
else:
l2_z = ndimage.zoom(l2, zoom_factor, order=1)
la2_z = ndimage.zoom(la2, zoom_factor, order=1)
for r in range(dc_z.shape[0]):
for c in range(dc_z.shape[1]):
if r % zoom_factor == 0 and c % zoom_factor == 0: continue
lon_v, lat_v, val_p = l2_z[r, c], la2_z[r, c], dc_z[r, c]
limit = 1.0 if is_prob else 0.1
if val_p >= limit and min_lon <= lon_v <= max_lon and min_lat <= lat_v <= max_lat:
txt_fmt = f"{val_p:.0f}" if is_prob else f"{val_p:.1f}"
txt = ax.text(lon_v, lat_v, txt_fmt, color='#1f77b4', fontsize=10, ha='center', va='center', transform=ccrs.PlateCarree(), zorder=12, weight='bold', clip_on=True, path_effects=[pe.withStroke(linewidth=2, foreground='white')])
ui['dynamic_artists'].append(txt)
for r in range(dc.shape[0]):
for c in range(dc.shape[1]):
lon_v, lat_v, val_p = l2[r, c], la2[r, c], dc[r, c]
limit = 1.0 if is_prob else 0.1
if val_p >= limit and min_lon <= lon_v <= max_lon and min_lat <= lat_v <= max_lat:
txt_fmt = f"{val_p:.0f}" if is_prob else f"{val_p:.1f}"
txt = ax.text(lon_v, lat_v, txt_fmt, color='black', fontsize=11, ha='center', va='center', transform=ccrs.PlateCarree(), zorder=13, weight='bold', bbox=dict(facecolor='white', edgecolor='black', boxstyle='square,pad=0.2', alpha=0.9), clip_on=True)
ui['dynamic_artists'].append(txt)
except Exception as e:
print(f"札幌近郊 内挿・格子点描画エラー: {e}")
elif area_index == 2:
try:
zoom_factor = 4 if "GSM" in model_name else 1
dc_z = ndimage.zoom(dc, zoom_factor, order=1)
l2_z = ndimage.zoom(l2, zoom_factor, order=1)
la2_z = ndimage.zoom(la2, zoom_factor, order=1)
for r in range(dc_z.shape[0]):
for c in range(dc_z.shape[1]):
if r % zoom_factor == 0 and c % zoom_factor == 0:
if (r // max(1, zoom_factor) + c // max(1, zoom_factor)) % 3 == 0:
continue
lon_v, lat_v, val_p = l2_z[r, c], la2_z[r, c], dc_z[r, c]
limit = 1.0 if is_prob else 0.1
if val_p >= limit and min_lon <= lon_v <= max_lon and min_lat <= lat_v <= max_lat:
txt_fmt = f"{val_p:.0f}" if is_prob else f"{val_p:.1f}"
txt_color = '#0288D1' if "降雪" in elem_name else '#1f77b4'
if is_prob: txt_color = '#D35400'
txt = ax.text(lon_v, lat_v, txt_fmt, color=txt_color, fontsize=6, ha='center', va='center', transform=ccrs.PlateCarree(), zorder=13, weight='bold', path_effects=[pe.withStroke(linewidth=1.5, foreground='white')], clip_on=True)
ui['dynamic_artists'].append(txt)
except Exception as e:
print(f"上川地方 透過文字描画エラー: {e}")
elif "発雷" in elem_name:
key = None
if is_meps:
if "thund" in data.files:
key = "thund"
else:
tr = f"_{max(0, val-3)}_{val}"
for k in data.files:
if k.startswith("tstm_") and k.endswith(tr) and not ("lon" in k or "lat" in k):
key = k; break
if not key:
for k in data.files:
if k.startswith("tstm_") and k.endswith(f"_{val}") and not ("lon" in k or "lat" in k):
key = k; break
else:
for k in data.files:
if any(x in k.lower() for x in ['thund', 'pol', 'lig', 'ts', 'tstm', 'var_0_19_193']) and not ("lon" in k or "lat" in k):
key = k; break
if key:
try:
val_data = _cl(data[key], 0, 100, np.nan)
lon_c, lat_c = self.get_coords_for_data(data, val_data)
if lon_c is None or lat_c is None:
lon_k = next((k for k in data.files if 'lon' in k.lower()), None)
lat_k = next((k for k in data.files if 'lat' in k.lower()), None)
if lon_k and lat_k: lon_c, lat_c = data[lon_k], data[lat_k]
if lon_c is not None and lat_c is not None:
if lon_c.ndim == 1 and val_data.shape == (len(lon_c), len(lat_c)): val_data = val_data.T
if lon_c.ndim == 1 and val_data.shape != (len(lat_c), len(lon_c)):
if val_data.shape[1] > val_data.shape[0] and len(lat_c) > len(lon_c): val_data = val_data.T
lon_c = np.linspace(lon_c.min(), lon_c.max(), val_data.shape[1])
lat_c = np.linspace(lat_c.min(), lat_c.max(), val_data.shape[0])
val_masked = np.ma.masked_less(val_data, 1.0)
levels = [1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
cmap = mcolors.ListedColormap(['#F7DC6F', '#F4D03F', '#F5B041', '#EB984E', '#DC7633', '#E74C3C', '#C0392B', '#9B59B6', '#8E44AD', '#1F618D'])
cmap.set_under('none'); cmap.set_bad('none')
norm = mcolors.BoundaryNorm(levels, cmap.N)
pm = self.plot_blocky(ax, lon_c, lat_c, val_masked, ui, cmap, extent, norm=norm, alpha=0.6)
if pm:
add_vertical_colorbar(pm, '発雷確率凡例 (1%～100%) [%]')
info_text = self.get_local_info(elem_name, lon_c, lat_c, val_data, area_index=area_index, model_name=model_name)
drawn_flag = True
lc, lac, dc = self.crop_data(lon_c, lat_c, val_data, extent)
if lc.size > 0 and area_index != 0:
l2, la2 = np.meshgrid(lc, lac) if lc.ndim == 1 else (lc, lac)
min_lon, max_lon, min_lat, max_lat = extent
for r in range(dc.shape[0]):
for c in range(dc.shape[1]):
lon_v, lat_v, val_p = l2[r, c], la2[r, c], dc[r, c]
if val_p >= 1.0 and min_lon <= lon_v <= max_lon and min_lat <= lat_v <= max_lat:
txt = ax.text(lon_v, lat_v, f"{val_p:.0f}", color='black', fontsize=9, ha='center', va='center', transform=ccrs.PlateCarree(), zorder=13, weight='bold')
ui['dynamic_artists'].append(txt)
except Exception as e: print(f"Lightning graphic render exception: {e}")
elif "天気" in elem_name:
key = None
for k in data.files:
if any(x in k.lower() for x in ['wea', 'wx', 'var_-1_-1_-1', 'var_0_19_192']): key = k; break
if key:
val_data = _cl(data[key], 0, 10, np.nan)
lon_c, lat_c = self.get_coords_for_data(data, val_data)
if lon_c is None or lat_c is None:
lon_k = next((k for k in data.files if 'lon' in k.lower()), None)
lat_k = next((k for k in data.files if 'lat' in k.lower()), None)
if lon_k and lat_k: lon_c, lat_c = data[lon_k], data[lat_k]
if lon_c is not None and lat_c is not None:
if lon_c.ndim == 1 and val_data.shape == (len(lon_c), len(lat_c)):
val_data = val_data.T
val_masked = np.ma.masked_outside(val_data, 0.5, 4.5)
levels = [0.5, 1.5, 2.5, 3.5, 4.5]
colors = ['#FF9800', '#B0C4DE', '#1E90FF', '#FFFFFF']
cmap_wx = mcolors.ListedColormap(colors)
cmap_wx.set_under('none')
cmap_wx.set_over('none')
cmap_wx.set_bad('none')
norm_wx = mcolors.BoundaryNorm(levels, cmap_wx.N)
pm = self.plot_blocky(ax, lon_c, lat_c, val_masked, ui, cmap_wx, extent, norm=norm_wx, alpha=0.6)
if pm:
add_vertical_colorbar(pm, '卓越天気 (1:晴 2:曇 3:雨 4:雪)')
info_text = self.get_local_info(elem_name, lon_c, lat_c, val_data, area_index=area_index, model_name=model_name)
drawn_flag = True
return drawn_flag, info_text
def plot_contour(self, ax, lon, lat, data, ui, levels, color, extent, linewidths=1.5):
lon_c, lat_c, data_c = self.crop_data(lon, lat, data, extent)
if lon_c.size == 0 or lat_c.size == 0 or data_c.size == 0: return None
cs = ax.contour(lon_c, lat_c, data_c, levels=levels, colors=color, linewidths=linewidths, transform=ccrs.PlateCarree(), zorder=7)
ui['dynamic_artists'].append(cs); ax.clabel(cs, inline=True, fontsize=11, fmt='%1.0f'); return cs
def plot_contourf(self, ax, lon, lat, data, ui, levels, cmap_or_colors, extent, extend='both', alpha=0.6, norm=None):
lon_c, lat_c, data_c = self.crop_data(lon, lat, data, extent)
if lon_c.size == 0 or lat_c.size == 0 or data_c.size == 0: return None
kwargs = {'levels': levels, 'extend': extend, 'transform': ccrs.PlateCarree(), 'alpha': alpha, 'zorder': 6}
if isinstance(cmap_or_colors, str) or isinstance(cmap_or_colors, mcolors.Colormap): kwargs['cmap'] = cmap_or_colors
else: kwargs['colors'] = cmap_or_colors
if norm is not None: kwargs['norm'] = norm
cf = ax.contourf(lon_c, lat_c, data_c, **kwargs)
ui['dynamic_artists'].append(cf); return cf
def plot_blocky(self, ax, lon, lat, data, ui, cmap_or_colors, extent, norm=None, alpha=0.85):
lon_c, lat_c, data_c = self.crop_data(lon, lat, data, extent)
if lon_c.size == 0 or lat_c.size == 0 or data_c.size == 0: return None
if lon_c.ndim == 1: lon_c, lat_c = np.meshgrid(lon_c, lat_c)
kwargs = {'transform': ccrs.PlateCarree(), 'alpha': alpha, 'zorder': 6, 'shading': 'nearest'}
if isinstance(cmap_or_colors, str) or isinstance(cmap_or_colors, mcolors.Colormap): kwargs['cmap'] = cmap_or_colors
else: kwargs['cmap'] = mcolors.ListedColormap(cmap_or_colors)
if norm is not None: kwargs['norm'] = norm
pm = ax.pcolormesh(lon_c, lat_c, data_c, **kwargs)
ui['dynamic_artists'].append(pm); return pm
def plot_barbs(self, ax, lon, lat, u, v, ui, area_index, extent, model_name=""):
lon_c, lat_c, u_c = self.crop_data(lon, lat, u, extent)
_, _, v_c = self.crop_data(lon, lat, v, extent)
if lon_c.size == 0 or lat_c.size == 0 or u_c.size == 0 or v_c.size == 0: return None
u_c = np.nan_to_num(u_c, nan=0.0); v_c = np.nan_to_num(v_c, nan=0.0)
if lon_c.ndim == 1: lon_2d, lat_2d = np.meshgrid(lon_c, lat_c)
else: lon_2d, lat_2d = lon_c, lat_c
if lon_2d.shape != u_c.shape: return None
# 間引き率の最適化（1/4, 1/2）
if area_index == 0: skip = 40
elif area_index == 1:
if "MSM" in model_name or "ANAL" in model_name: skip = 8
else: skip = 6
elif area_index == 2: skip = 2
else: skip = 1
q = ax.barbs(lon_2d[::skip,::skip], lat_2d[::skip,::skip], u_c[::skip,::skip], v_c[::skip,::skip], length=5.5, color='black', transform=ccrs.PlateCarree(), zorder=10)
ui['dynamic_artists'].append(q)
def show_error_msg(self, ax, ui, msg):
wrapped_msg = "\n".join(textwrap.wrap(msg, width=50))
txt = ax.text(0.5, 0.5, wrapped_msg, transform=ax.transAxes, color='red', fontsize=12, ha='center', va='center', weight='bold', bbox=dict(facecolor='white', alpha=0.9, edgecolor='red'))
ui['dynamic_artists'].append(txt)
def extract_2d_array(self, data, exact_keys, target_level=None, fuzzy_keys=None):
for k in exact_keys:
if k in data.files:
arr = np.squeeze(data[k])
if arr.ndim == 2: return arr
if fuzzy_keys:
for k in data.files:
kl = k.lower()
if any(fk in kl for fk in fuzzy_keys) and not any(ex in kl for ex in ['lon', 'lat', 'time', 'valid']):
arr = np.squeeze(data[k])
if arr.ndim == 2: return arr
if arr.ndim == 3 and target_level is not None:
levels_16 = [1000, 925, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10]
levels_msm = [1000, 975, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300]
axis = 0 if arr.shape[0] < arr.shape[1] else 2
sz = arr.shape[axis]
if sz == len(levels_16) and target_level in levels_16:
return arr[levels_16.index(target_level),:,:] if axis == 0 else arr[:,:,levels_16.index(target_level)]
elif sz == len(levels_msm) and target_level in levels_msm:
return arr[levels_msm.index(target_level),:,:] if axis == 0 else arr[:,:,levels_msm.index(target_level)]
return None
def draw_frame(self, model_name, val):
ui = self.tab_ui[model_name]; it_str = self.model_init_times.get(model_name)
area_index = next(i for i, btn in enumerate(ui['area_btns']) if btn.isChecked())
if ui['ax'] is None or ui['current_area_index'] != area_index:
ui['ax'] = self.init_map(ui['fig'], area_index, model_name)
ui['current_area_index'] = area_index
axes_list = ui['ax'] if isinstance(ui['ax'], list) else [ui['ax']]
elem_name = ui['list'].currentItem().text()
display_val = val; fallback_msg = ""
if model_name == "ANAL":
val_idx = int(val)
combo = ui['init_combo']
if val_idx < combo.count():
it_str = combo.itemData(val_idx)
else:
it_str = None
display_val = 0
if it_str:
dt_utc = datetime.strptime(it_str, "%Y%m%d%H%M%S")
dt_jst = dt_utc + timedelta(hours=9)
target_time = dt_jst
else:
target_time = datetime.now()
ui['time_label'].setText(f"過去 {val*30}分\n({target_time.strftime('%m/%d %H:%M')})")
else:
if model_name == "GSM_JP":
ground_cloud_elems = ["地上気圧", "地上風向風速", "地上降水", "全雲量", "上層雲量", "中層雲量", "下層雲量", "地上３種重ね"]
threshold = 132 if elem_name in ground_cloud_elems else 84
if val > threshold and val % 6 != 0:
display_val = val - (val % 6); fallback_msg = f" (※FT={display_val}h 代替表示)"
elif model_name == "MSM" and "hpa" in elem_name.lower() and val % 3 != 0:
display_val = (val // 3) * 3; fallback_msg = f" (※FT={display_val}h 代替表示)"
if "GUID" in model_name and "気温" not in elem_name and "風" not in elem_name and val % 3 != 0:
display_val = (val // 3) * 3; fallback_msg += f" (※FT={display_val}h 代替表示)"
if it_str:
dt_utc = datetime.strptime(it_str, "%Y%m%d%H%M%S")
dt_jst = dt_utc + timedelta(hours=9)
target_time = dt_jst + timedelta(hours=val)
ui['time_label'].setText(f"FT={val:02d}h\n({target_time.strftime('%m/%d %H:%M')})")
if not it_str:
self.show_error_msg(axes_list[0], ui, f"{model_name} のデータがありません。")
ui['canvas'].draw_idle(); return
while ui['dynamic_artists']:
art = ui['dynamic_artists'].pop(0)
try:
if hasattr(art, 'collections'):
for c in art.collections:
try: c.remove()
except: pass
else: art.remove()
except: pass
for cb in ui['colorbars']:
try:
cb.remove()
# 追加: カラーバー用の透明な軸枠(Axes)が残らないよう完全に削除（メモリリーク対策）
if hasattr(cb, 'ax') and cb.ax in ui['fig'].axes:
cb.ax.remove()
except: pass
ui['colorbars'].clear(); ui['info_panel'].hide()
target_file = os.path.join(self.cache_dir, f"{model_name}_{it_str}_FT{display_val:02d}.npz")
for ax_idx, ax in enumerate(axes_list):
if model_name == "ANAL":
ax.title.set_text(f"[{model_name}] {elem_name} / 基準初期:{dt_utc.strftime('%m/%d %H:00Z')}\n過去{val*30}分 ({target_time.strftime('%m/%d %H:%M')} JST)")
else:
ax.title.set_text(f"[{model_name}] {elem_name} / 初期:{dt_utc.strftime('%m/%d %H:00Z')}\nFT={val}h ({target_time.strftime('%m/%d %H:%M')} JST){fallback_msg}")
if not os.path.exists(target_file):
if ax_idx == 0: self.show_error_msg(ax, ui, f"データ未収録 (FT={display_val}h)")
continue
try:
data = np.load(target_file)
lon_key = self.get_exact_key(data, ['lon_surf', 'lon', 'lon_p', 'lon_pall'])
lat_key = self.get_exact_key(data, ['lat_surf', 'lat', 'lat_p', 'lat_pall'])
if not lon_key or not lat_key:
if ax_idx == 0: self.show_error_msg(ax, ui, "座標軸欠損エラー")
data.close(); continue
extent = ax.get_extent(crs=ccrs.PlateCarree())
drawn_flag = False; info_text = ""
def add_vertical_colorbar(mappable, label_text):
cax = ui['fig'].add_axes([0.92, 0.15, 0.015, 0.7])
cb = ui['fig'].colorbar(mappable, cax=cax, orientation='vertical')
cb.set_label(label_text, fontsize=11, weight='bold'); ui['colorbars'].append(cb)
def _cl(arr, minv, maxv, fill=0.0):
if arr is None: return None
a = np.array(arr, dtype=float)
return np.clip(np.nan_to_num(a, nan=fill), minv, maxv)
if elem_name == "地上３種重ね":
p_key = self.get_precip_key(data)
if p_key:
pval = _cl(data[p_key], 0, 1000, 0.0)
if model_name == "GSM_JP" and display_val >= 3:
offset = 6 if display_val > 132 else 3
prev_file = os.path.join(self.cache_dir, f"{model_name}_{it_str}_FT{display_val-offset:02d}.npz")
if os.path.exists(prev_file):
try:
with np.load(prev_file) as d_prev:
prev_p_key = self.get_precip_key(d_prev)
if prev_p_key:
prev_pval = _cl(d_prev[prev_p_key], 0, 1000, 0.0)
if prev_pval.shape == pval.shape: pval = np.clip(pval - prev_pval, 0, None)
except: pass
lon_c, lat_c = self.get_coords_for_data(data, pval)
pval_smooth = ndimage.gaussian_filter(pval, sigma=0.2)
pval_smooth = np.ma.masked_less(pval_smooth, 0.1)
levels = [0.1, 1, 5, 10, 20, 30, 50, 80]
cmap_precip = mcolors.ListedColormap(['#A0E8A0', '#D4F05A', '#FFFF00', '#FFA500', '#FF5500', '#FF0000', '#FF00FF'])
cmap_precip.set_under('none'); cmap_precip.set_over('#800080')
norm_precip = mcolors.BoundaryNorm(levels, cmap_precip.N)
cf = self.plot_contourf(ax, lon_c, lat_c, pval_smooth, ui, levels, cmap_precip, extent, extend='max', alpha=0.55, norm=norm_precip)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '降水量重ね表示 [mm]')
slp = self.extract_2d_array(data, ['prmsl', 'mslet', 'msl', 'slp'], fuzzy_keys=['msl', 'prmsl'])
if slp is not None:
if np.nanmax(slp) > 2000: slp = slp / 100.0
slp_hpa = _cl(slp, 800, 1150, 1013.0)
lon_c, lat_c = self.get_coords_for_data(data, slp_hpa)
slp_smooth = ndimage.gaussian_filter(slp_hpa, sigma=1.0)
self.plot_contour(ax, lon_c, lat_c, slp_smooth, ui, np.arange(920, 1080, 4), '#1F618D', extent, linewidths=1.2)
u_val = self.extract_2d_array(data, ['u10', '10u', 'u_10m'], fuzzy_keys=['u10', 'u_wind'])
v_val = self.extract_2d_array(data, ['v10', '10v', 'v_10m'], fuzzy_keys=['v10', 'v_wind'])
if u_val is not None and v_val is not None:
u_val = _cl(u_val, -200, 200, 0.0); v_val = _cl(v_val, -200, 200, 0.0)
lon_c, lat_c = self.get_coords_for_data(data, u_val)
self.plot_barbs(ax, lon_c, lat_c, u_val, v_val, ui, area_index, extent, model_name)
drawn_flag = True
elif "GUID" in model_name:
drawn_flag, info_text = self.draw_guidance(ax, ui, data, elem_name, val, extent, area_index, model_name, it_str)
elif elem_name == "地上気温":
t_val = self.extract_2d_array(data, ['t2m', 'tmp2m', 'temp2m'], fuzzy_keys=['tmp', 'temp', 't'])
if t_val is not None:
if np.nanmax(t_val) > 100: t_val = t_val - 273.15
t_val = _cl(t_val, -50, 40, 0.0); lon_c, lat_c = self.get_coords_for_data(data, t_val)
cf = self.plot_contourf(ax, lon_c, lat_c, t_val, ui, np.arange(-30, 40, 3), 'jet', extent, extend='both', alpha=0.4)
self.plot_contour(ax, lon_c, lat_c, t_val, ui, np.arange(-30, 40, 3), 'black', extent, linewidths=0.5)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '地上気温 [℃]')
info_text = self.get_local_info(elem_name, lon_c, lat_c, t_val, area_index=area_index, model_name=model_name); drawn_flag = True
elif elem_name == "地上気圧":
slp = self.extract_2d_array(data, ['prmsl', 'mslet', 'msl', 'slp'], fuzzy_keys=['msl', 'prmsl'])
if slp is not None:
if np.nanmax(slp) > 2000: slp = slp / 100.0
slp_hpa = _cl(slp, 800, 1150, 1013.0); lon_c, lat_c = self.get_coords_for_data(data, slp_hpa)
cs = self.plot_contour(ax, lon_c, lat_c, ndimage.gaussian_filter(slp_hpa, sigma=1.0), ui, np.arange(940, 1060, 4), '#2980B9', extent)
if cs: info_text += self.get_local_info("気圧", lon_c, lat_c, slp, area_index=area_index, model_name=model_name); drawn_flag = True
elif elem_name == "地上風向風速":
u_val = self.extract_2d_array(data, ['u10', '10u', 'u_10m'], fuzzy_keys=['u10', 'u_wind'])
v_val = self.extract_2d_array(data, ['v10', '10v', 'v_10m'], fuzzy_keys=['v10', 'v_wind'])
if u_val is not None and v_val is not None:
u_val = _cl(u_val, -200, 200, 0.0); v_val = _cl(v_val, -200, 200, 0.0)
spd = np.hypot(u_val, v_val); lon_c, lat_c = self.get_coords_for_data(data, spd)
cf = self.plot_contourf(ax, lon_c, lat_c, spd, ui, [2, 4, 6, 8, 10, 15, 20, 25], ['#E0F7FA', '#B2EBF2', '#4DD0E1', '#00BCD4', '#00838F', '#9C27B0', '#4A148C'], extent, extend='max', alpha=0.5)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '地上風速 [m/s]')
self.plot_barbs(ax, lon_c, lat_c, u_val, v_val, ui, area_index, extent, model_name)
info_text = self.get_local_info("風向風速", lon_c, lat_c, spd, u_val, v_val, area_index=area_index, model_name=model_name); drawn_flag = True
elif "地上降水" in elem_name:
p_key = self.get_precip_key(data)
if p_key:
pval = _cl(data[p_key], 0, 1000, 0.0)
if model_name == "GSM_JP" and display_val >= 3:
offset = 6 if display_val > 132 else 3
prev_file = os.path.join(self.cache_dir, f"{model_name}_{it_str}_FT{display_val-offset:02d}.npz")
if os.path.exists(prev_file):
try:
with np.load(prev_file) as d_prev:
prev_p_key = self.get_precip_key(d_prev)
if prev_p_key:
prev_pval = _cl(d_prev[prev_p_key], 0, 1000, 0.0)
if prev_pval.shape == pval.shape: pval = np.clip(pval - prev_pval, 0, None)
except: pass
lon_c, lat_c = self.get_coords_for_data(data, pval)
if np.nanmax(pval) < 0.1:
txt = ax.text(0.5, 0.5, "降水域なし", transform=ax.transAxes, color='blue', fontsize=16, ha='center', va='center', weight='bold', bbox=dict(facecolor='white', alpha=0.8, edgecolor='blue'))
ui['dynamic_artists'].append(txt)
info_text = self.get_local_info("降水", lon_c, lat_c, pval, area_index=area_index, model_name=model_name)
drawn_flag = True
else:
pval_smooth = np.ma.masked_less(ndimage.gaussian_filter(pval, sigma=0.2), 0.1)
levels = [0.1, 1, 5, 10, 20, 30, 50, 80]
cmap_precip = mcolors.ListedColormap(['#A0E8A0', '#D4F05A', '#FFFF00', '#FFA500', '#FF5500', '#FF0000', '#FF00FF'])
cmap_precip.set_under('none'); cmap_precip.set_over('#800080')
norm_precip = mcolors.BoundaryNorm(levels, cmap_precip.N)
cf = self.plot_contourf(ax, lon_c, lat_c, pval_smooth, ui, levels, cmap_precip, extent, extend='max', alpha=0.85, norm=norm_precip)
if cf:
self.plot_contour(ax, lon_c, lat_c, pval_smooth, ui, levels, '#444444', extent, linewidths=0.5)
if ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, f'降水量 [mm]')
info_text = self.get_local_info("降水", lon_c, lat_c, pval, area_index=area_index, model_name=model_name)
drawn_flag = True
elif "雲量" in elem_name:
c_val = None
if elem_name == "全雲量":
c_val = self.extract_2d_array(data, ['tcc', 'tcdc', 'cloud', 'tcdc_surf', 'var_0_6_1', 'var_0_6_192'], fuzzy_keys=['tcc', 'tcdc', 'cloud', 'cld'])
if c_val is None:
hcc = self.extract_2d_array(data, ['hcc', 'hcdc', 'high', 'var_0_6_3'], fuzzy_keys=['hcc', 'high'])
mcc = self.extract_2d_array(data, ['mcc', 'mcdc', 'mid', 'var_0_6_4'], fuzzy_keys=['mcc', 'mid'])
lcc = self.extract_2d_array(data, ['lcc', 'lcdc', 'low', 'var_0_6_5'], fuzzy_keys=['lcc', 'low'])
if hcc is not None or mcc is not None or lcc is not None:
hcc_v = hcc if hcc is not None else 0
mcc_v = mcc if mcc is not None else 0
lcc_v = lcc if lcc is not None else 0
c_val = np.maximum(np.maximum(hcc_v, mcc_v), lcc_v)
elif elem_name == "上層雲量":
c_val = self.extract_2d_array(data, ['hcc', 'hcdc', 'high', 'var_0_6_3'], fuzzy_keys=['hcc', 'high'])
elif elem_name == "中層雲量":
c_val = self.extract_2d_array(data, ['mcc', 'mcdc', 'mid', 'var_0_6_4'], fuzzy_keys=['mcc', 'mid'])
elif elem_name == "下層雲量":
c_val = self.extract_2d_array(data, ['lcc', 'lcdc', 'low', 'var_0_6_5'], fuzzy_keys=['lcc', 'low'])
if c_val is not None:
c_val = _cl(c_val, 0, 150, 0.0)
lon_c, lat_c = self.get_coords_for_data(data, c_val)
if lon_c is None or lat_c is None:
lon_k = self.get_exact_key(data, ['lon_surf', 'lon', 'lon_p', 'lon_pall'])
lat_k = self.get_exact_key(data, ['lat_surf', 'lat', 'lat_p', 'lat_pall'])
if lon_k and lat_k: lon_c, lat_c = data[lon_k], data[lat_k]
if lon_c is not None and lat_c is not None:
if np.nanmax(c_val) <= 1.0 and np.nanmax(c_val) > 0: c_val = c_val * 100.0
if np.nanmax(c_val) < 0.1:
txt = ax.text(0.5, 0.5, "雲域なし", transform=ax.transAxes, color='gray', fontsize=14, ha='center', va='center', weight='bold', bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
ui['dynamic_artists'].append(txt)
info_text = self.get_local_info(elem_name, lon_c, lat_c, c_val, area_index=area_index, model_name=model_name)
drawn_flag = True
else:
levels = [20, 30, 40, 50, 60, 70, 80, 90, 100]
cmap_cloud = mcolors.ListedColormap(['#D9D9D9', '#BDBDBD', '#969696', '#737373', '#525252', '#252525', '#0F0F0F', '#000000'])
cmap_cloud.set_under('none')
cmap_cloud.set_over('#000000')
norm_cloud = mcolors.BoundaryNorm(levels, cmap_cloud.N)
cf = self.plot_contourf(ax, lon_c, lat_c, c_val, ui, levels, cmap_cloud, extent, extend='max', alpha=0.7, norm=norm_cloud)
if cf:
if len(ui['colorbars']) == 0:
add_vertical_colorbar(cf, f'{elem_name} [%]')
info_text = self.get_local_info(elem_name, lon_c, lat_c, c_val, area_index=area_index, model_name=model_name)
drawn_flag = True
elif "300hpa高度" in elem_name.lower():
h_val = self.extract_2d_array(data, ['gh300', 'hgt300', 'hgt_300'], 300, ['hgt', 'gh', 'z'])
if h_val is not None:
h_val = _cl(h_val, -500, 20000, 0.0); lon_p, lat_p = self.get_coords_for_data(data, h_val)
cf = self.plot_contourf(ax, lon_p, lat_p, h_val, ui, np.arange(8400, 10800, 60), 'PuBu', extent, alpha=0.6)
self.plot_contour(ax, lon_p, lat_p, h_val, ui, np.arange(8400, 10800, 60), 'darkblue', extent)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '300hPa 高度 [m]'); info_text = self.get_local_info(elem_name, lon_p, lat_p, h_val, area_index=area_index, model_name=model_name); drawn_flag = True
elif "300hpa風向・風速" in elem_name.lower():
u_val = self.extract_2d_array(data, ['u300', 'u_300'], 300, ['u', 'ugrd'])
v_val = self.extract_2d_array(data, ['v300', 'v_300'], 300, ['v', 'vgrd'])
if u_val is not None and v_val is not None:
u_val = _cl(u_val, -300, 300, 0.0); v_val = _cl(v_val, -300, 300, 0.0)
spd = np.hypot(u_val, v_val); lon_p, lat_p = self.get_coords_for_data(data, spd)
cf = self.plot_contourf(ax, lon_p, lat_p, spd, ui, [20, 30, 40, 50, 60, 80, 100], 'YlOrRd', extent, extend='max', alpha=0.35)
self.plot_barbs(ax, lon_p, lat_p, u_val, v_val, ui, area_index, extent, model_name)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '300hPa 風速 [m/s]'); info_text = self.get_local_info(elem_name, lon_p, lat_p, spd, u_val, v_val, area_index=area_index, model_name=model_name); drawn_flag = True
elif "500hpa高度" in elem_name.lower():
h_val = self.extract_2d_array(data, ['gh500', 'hgt500', 'hgt_500'], 500, ['hgt', 'gh', 'z'])
if h_val is not None:
h_val = _cl(h_val, -500, 20000, 0.0); lon_p, lat_p = self.get_coords_for_data(data, h_val)
cf = self.plot_contourf(ax, lon_p, lat_p, h_val, ui, np.arange(4800, 6000, 60), 'Greens', extent, alpha=0.6)
self.plot_contour(ax, lon_p, lat_p, h_val, ui, np.arange(4800, 6000, 60), 'darkgreen', extent)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '500hPa 高度 [m]'); info_text = self.get_local_info(elem_name, lon_p, lat_p, h_val, area_index=area_index, model_name=model_name); drawn_flag = True
elif "500hpa風向・風速" in elem_name.lower():
u_val = self.extract_2d_array(data, ['u500', 'u_500'], 500, ['u', 'ugrd'])
v_val = self.extract_2d_array(data, ['v500', 'v_500'], 500, ['v', 'vgrd'])
if u_val is not None and v_val is not None:
u_val = _cl(u_val, -300, 300, 0.0); v_val = _cl(v_val, -300, 300, 0.0)
spd = np.hypot(u_val, v_val); lon_p, lat_p = self.get_coords_for_data(data, spd)
cf = self.plot_contourf(ax, lon_p, lat_p, spd, ui, [10, 20, 30, 40, 50, 60, 80], 'YlOrRd', extent, extend='max', alpha=0.35)
self.plot_barbs(ax, lon_p, lat_p, u_val, v_val, ui, area_index, extent, model_name)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '500hPa 風速 [m/s]'); info_text = self.get_local_info(elem_name, lon_p, lat_p, spd, u_val, v_val, area_index=area_index, model_name=model_name); drawn_flag = True
elif "500hpa渦度" in elem_name.lower():
vort = self.extract_2d_array(data, ['vort500', 'vort', 'abs_vort'], 500, ['vort', 'abs_vort'])
if vort is not None:
if np.nanmax(np.abs(vort)) < 1.0: vort = vort * 100000.0
vort = _cl(vort, -1000, 1000, 0.0); lon_p, lat_p = self.get_coords_for_data(data, vort)
# --- 修正: 滑らかにして正渦度のみ色付け、グラデーションカラーマップを適用 ---
vort_smooth = ndimage.gaussian_filter(vort, sigma=1.0)
vort_masked = np.ma.masked_less(vort_smooth, 10)
levels_v = [10, 20, 30, 40, 60, 80, 100, 120, 150, 200]
cmap_v = mcolors.ListedColormap(['#FFF5E6', '#FDEBD0', '#FAD7A1', '#F8C471', '#F39C12', '#D35400', '#C0392B', '#922B21', '#641E16'])
cmap_v.set_under('none')
cmap_v.set_over('#4A235A')
norm_v = mcolors.BoundaryNorm(levels_v, cmap_v.N)
cf = self.plot_contourf(ax, lon_p, lat_p, vort_masked, ui, levels_v, cmap_v, extent, extend='max', alpha=0.6, norm=norm_v)
# 等値線は見やすさのため正負で色を分けて描画
self.plot_contour(ax, lon_p, lat_p, vort_smooth, ui, np.arange(20, 300, 20), '#4A235A', extent, linewidths=0.8)
self.plot_contour(ax, lon_p, lat_p, vort_smooth, ui, np.arange(-200, 0, 20), '#154360', extent, linewidths=0.5)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '500hPa 正渦度 [10^-5/s]'); info_text = self.get_local_info(elem_name, lon_p, lat_p, vort, area_index=area_index, model_name=model_name); drawn_flag = True
elif "hpa気温" in elem_name.lower() or "hpa気温・風" in elem_name.lower():
w = re.search(r'(\d+)hpa', elem_name.lower())
if w:
lvl = int(w.group(1))
t_val = self.extract_2d_array(data, [f't{lvl}', f'tmp{lvl}', f't_{lvl}', f'tmp_{lvl}'], lvl, ['tmp', 'temp', 't'])
if t_val is not None:
if np.nanmax(t_val) > 100: t_val = t_val - 273.15
t_val = _cl(t_val, -100, 60, 0.0); lon_p, lat_p = self.get_coords_for_data(data, t_val)
cf = self.plot_contourf(ax, lon_p, lat_p, t_val, ui, np.arange(-54, 40, 3), 'jet', extent, extend='both', alpha=0.3)
self.plot_contour(ax, lon_p, lat_p, t_val, ui, np.arange(-54, 40, 3), 'blue', extent, linewidths=0.5)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, f'{lvl}hPa 気温 [℃]')
lon_c, lat_c, t_c = self.crop_data(lon_p, lat_p, t_val, extent)
if lon_c.size > 0:
cs = ax.contour(lon_c, lat_c, t_c, levels=[-36, -6, 0, 15, 30], colors='red', linewidths=2.0, transform=ccrs.PlateCarree(), zorder=8)
ui['dynamic_artists'].append(cs); ax.clabel(cs, inline=True, fontsize=12, fmt='%1.0f')
u_val = self.extract_2d_array(data, [f'u{lvl}', f'u_{lvl}'], lvl, ['u', 'ugrd'])
v_val = self.extract_2d_array(data, [f'v{lvl}', f'v_{lvl}'], lvl, ['v', 'vgrd'])
if u_val is not None: u_val = _cl(u_val, -200, 200, 0.0)
if v_val is not None: v_val = _cl(v_val, -200, 200, 0.0)
info_text = self.get_local_info(elem_name, lon_p, lat_p, t_val, u_val, v_val, area_index=area_index, model_name=model_name)
drawn_flag = True
if u_val is not None and v_val is not None: self.plot_barbs(ax, lon_p, lat_p, u_val, v_val, ui, area_index, extent, model_name)
elif "700hpa湿数" in elem_name.lower():
t = self.extract_2d_array(data, ['t700', 'tmp700', 't_700'], 700, ['tmp', 'temp', 't'])
rh_raw = self.extract_2d_array(data, ['rh700', 'r700', 'rh_700'], 700, ['rh', 'hum', 'r'])
if t is not None and rh_raw is not None:
if np.nanmax(t) > 100: t = t - 273.15
rh = _cl(rh_raw, 0, 150, 0.0)
if np.nanmax(rh) <= 1.0 and np.nanmax(rh) > 0: rh = rh * 100.0
lon_p, lat_p = self.get_coords_for_data(data, t); t_td = calc_t_td(t, rh)
cf = self.plot_contourf(ax, lon_p, lat_p, t_td, ui, [0, 3, 6, 9, 12, 15], ['#27AE60', '#82E0AA', '#D5F5E3', '#F9E79F', '#F5CBA7', '#E74C3C'], extent, extend='max', alpha=0.35)
u_val = self.extract_2d_array(data, ['u700', 'u_700'], 700, ['u', 'ugrd'])
v_val = self.extract_2d_array(data, ['v700', 'v_700'], 700, ['v', 'vgrd'])
if u_val is not None: u_val = _cl(u_val, -200, 200, 0.0)
if v_val is not None: v_val = _cl(v_val, -200, 200, 0.0)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '700hPa 湿数 [℃]')
info_text = self.get_local_info("湿数", lon_p, lat_p, t_td, u_val, v_val, area_index=area_index, model_name=model_name); drawn_flag = True
if u_val is not None and v_val is not None: self.plot_barbs(ax, lon_p, lat_p, u_val, v_val, ui, area_index, extent, model_name)
elif "700hpa鉛直流" in elem_name.lower():
w_val = self.extract_2d_array(data, ['w', 'w700', 'vvel', 'vvel700', 'dzdt', 'dzdt700'], 700, ['w', 'vvel', 'dzdt'])
if w_val is not None:
# --- 修正: 最大値ではなく、全体の平均値から確実な単位判定を行う ---
if np.nanmean(np.abs(w_val)) < 3.0:
w_val = w_val * 36.0
w_val = _cl(w_val, -200, 200, 0.0); lon_p, lat_p = self.get_coords_for_data(data, w_val)
# --- 修正: 弱いノイズをカットし、透過度(alpha)を 0.85 -> 0.5 に下げて下絵を見せる ---
w_val_masked = np.ma.masked_inside(ndimage.gaussian_filter(w_val, sigma=0.8), -3.0, 3.0)
levels_w = [-80, -50, -30, -10, -3, 3, 10, 30, 50, 80]
cf = self.plot_contourf(ax, lon_p, lat_p, w_val_masked, ui, levels_w, 'PiYG', extent, extend='both', alpha=0.5)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '700hPa 鉛直流 [hPa/h]'); info_text = self.get_local_info(elem_name, lon_p, lat_p, w_val, area_index=area_index, model_name=model_name); drawn_flag = True
elif "850hpa相当温位" in elem_name.lower():
t = self.extract_2d_array(data, ['t850', 'tmp850', 't_850'], 850, ['tmp', 'temp', 't'])
rh_raw = self.extract_2d_array(data, ['rh850', 'r850', 'rh_850'], 850, ['rh', 'hum', 'r'])
if t is not None and rh_raw is not None:
if np.nanmax(t) > 100: t = t - 273.15
rh = _cl(rh_raw, 0, 150, 0.0)
if np.nanmax(rh) <= 1.0 and np.nanmax(rh) > 0: rh = rh * 100.0
lon_p, lat_p = self.get_coords_for_data(data, t); ept = calc_ept(t, rh, 850); levels = np.arange(270, 360, 6)
cf = self.plot_contourf(ax, lon_p, lat_p, ept, ui, levels, 'rainbow', extent, extend='both', alpha=0.6)
self.plot_contour(ax, lon_p, lat_p, ept, ui, levels, 'black', extent)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '850hPa 相当温位 [K]'); info_text = self.get_local_info(elem_name, lon_p, lat_p, ept, area_index=area_index, model_name=model_name); drawn_flag = True
elif "850hpa湿数" in elem_name.lower():
t = self.extract_2d_array(data, ['t850', 'tmp850', 't_850'], 850, ['tmp', 'temp', 't'])
rh_raw = self.extract_2d_array(data, ['rh850', 'r850', 'rh_850'], 850, ['rh', 'hum', 'r'])
if t is not None and rh_raw is not None:
if np.nanmax(t) > 100: t = t - 273.15
rh = _cl(rh_raw, 0, 150, 0.0)
if np.nanmax(rh) <= 1.0 and np.nanmax(rh) > 0: rh = rh * 100.0
lon_p, lat_p = self.get_coords_for_data(data, t); t_td = calc_t_td(t, rh)
cf = self.plot_contourf(ax, lon_p, lat_p, t_td, ui, [0, 3, 6, 9, 12, 15], ['#27AE60', '#82E0AA', '#D5F5E3', '#F9E79F', '#F5CBA7', '#E74C3C'], extent, extend='max', alpha=0.35)
u_val = self.extract_2d_array(data, ['u850', 'u_850'], 850, ['u', 'ugrd'])
v_val = self.extract_2d_array(data, ['v850', 'v_850'], 850, ['v', 'vgrd'])
if u_val is not None: u_val = _cl(u_val, -200, 200, 0.0)
if v_val is not None: v_val = _cl(v_val, -200, 200, 0.0)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, '850hPa 湿数 [℃]')
info_text = self.get_local_info("湿数", lon_p, lat_p, t_td, u_val, v_val, area_index=area_index, model_name=model_name); drawn_flag = True
if u_val is not None and v_val is not None: self.plot_barbs(ax, lon_p, lat_p, u_val, v_val, ui, area_index, extent, model_name)
elif "925hpa" in elem_name.lower() or "975hpa" in elem_name.lower():
lvl = 925 if "925" in elem_name else 975
t = self.extract_2d_array(data, [f't{lvl}', f'tmp{lvl}', f't_{lvl}'], lvl, ['tmp', 'temp', 't'])
rh_raw = self.extract_2d_array(data, [f'rh{lvl}', f'r{lvl}', f'rh_{lvl}'], lvl, ['rh', 'hum', 'r'])
if t is not None and rh_raw is not None:
if np.nanmax(t) > 100: t = t - 273.15
rh = _cl(rh_raw, 0, 150, 0.0)
if np.nanmax(rh) <= 1.0 and np.nanmax(rh) > 0: rh = rh * 100.0
lon_p, lat_p = self.get_coords_for_data(data, t)
if "相当温位" in elem_name:
ept = calc_ept(t, rh, lvl); levels = np.arange(270, 360, 6)
cf = self.plot_contourf(ax, lon_p, lat_p, ept, ui, levels, 'rainbow', extent, extend='both', alpha=0.6)
self.plot_contour(ax, lon_p, lat_p, ept, ui, levels, 'black', extent)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, f'{lvl}hPa 相当温位 [K]'); info_text = self.get_local_info(elem_name, lon_p, lat_p, ept, area_index=area_index, model_name=model_name); drawn_flag = True
else:
t_td = calc_t_td(t, rh); levels = [0, 3, 6, 9, 12, 15]
cf = self.plot_contourf(ax, lon_p, lat_p, t_td, ui, levels, ['#27AE60', '#82E0AA', '#D5F5E3', '#F9E79F', '#F5CBA7', '#E74C3C'], extent, extend='max', alpha=0.35)
u_val = self.extract_2d_array(data, [f'u{lvl}', f'u_{lvl}'], lvl, ['u', 'ugrd'])
v_val = self.extract_2d_array(data, [f'v{lvl}', f'v_{lvl}'], lvl, ['v', 'vgrd'])
if u_val is not None: u_val = _cl(u_val, -200, 200, 0.0)
if v_val is not None: v_val = _cl(v_val, -200, 200, 0.0)
if cf and ax_idx == len(axes_list)-1: add_vertical_colorbar(cf, f'{lvl}hPa 湿数 [℃]')
info_text = self.get_local_info("湿数", lon_p, lat_p, t_td, u_val, v_val, area_index=area_index, model_name=model_name); drawn_flag = True
if u_val is not None and v_val is not None: self.plot_barbs(ax, lon_p, lat_p, u_val, v_val, ui, area_index, extent, model_name)
data.close(); del data
if info_text and ax_idx == 0:
ui['info_panel'].setText(info_text); ui['info_panel'].adjustSize(); ui['info_panel'].move(10, 10); ui['info_panel'].show()
if not drawn_flag and ax_idx == 0:
self.show_error_msg(ax, ui, f"[!] {elem_name} 描画エラー")
except Exception as e: logging.error(f"Draw Error for {elem_name} at FT={display_val}: {e}")
ui['canvas'].draw_idle(); gc.collect()
def setup_history_tab(self):
tab = QWidget(); layout = QVBoxLayout(tab); history_text = QTextEdit(); history_text.setReadOnly(True)
history_text.setStyleSheet("background-color: #112240; color: #E0E0E0; font-family: 'MS Gothic'; font-size: 12pt; padding: 10px; border: none;")
html_content = """
<h2 style='color: #64FFDA;'>気象GPV 局地分析ツール 更新履歴</h2><hr style='border-color: #457B9D;'><ul>
<li><b>Ver 134.0 (ANALスライダー・メタグラム拡張版)</b><br>
1. <b>大気解析(ANAL)履歴ブラウザ化</b>: 過去のANALデータをスライダーで遡って閲覧できるように改善。<br>
2. <b>風矢羽根の最適化表示</b>: 日本周辺・北海道全域で1/4、上川地方で1/2に間引き、視認性を大幅に向上。<br>
3. <b>大気解析専用メタグラム搭載</b>: ANALの過去6枠分（3時間分）の解析要素時系列グラフを描画する機能を追加。<br>
4. <b>MEPSガイダンスメタグラム改修</b>: テーブル内の文字サイズを縮小し、一覧表示を最適化。<br>
</ul>"""
history_text.setHtml(html_content); layout.addWidget(history_text); self.tabs.addTab(tab, "更新履歴")
def scan_cache_dir(self, quiet=False, force_draw=False):
if not os.path.exists(self.cache_dir): return
files = glob.glob(os.path.join(self.cache_dir, "*.npz"))
updated = False
for m in ["MSM", "GSM_JP", "ANAL", "MSM_GUID", "GSM_GUID"] + MEPS_STAT_MODELS:
m_files = [f for f in files if os.path.basename(f).startswith(f"{m}_")]
times = sorted(list(set(re.search(r'_(\d{14})_', f).group(1) for f in m_files if re.search(r'_(\d{14})_', f))), reverse=True)
if times:
combo = self.tab_ui.get(m, {}).get('init_combo')
if combo:
current_items = [combo.itemData(i) for i in range(combo.count())]
if current_items != times:
combo.blockSignals(True); combo.clear()
for t in times:
dt_utc = datetime.strptime(t, "%Y%m%d%H%M%S")
combo.addItem(dt_utc.strftime('%m/%d %H:%M Z') + (" (最新)", "")[t != times[0]], t)
combo.blockSignals(False)
if self.model_init_times.get(m) not in times:
self.model_init_times[m] = times[0];
self.update_slider_max(m, times[0])
self.tab_ui[m]['slider'].setValue(0); combo.setCurrentIndex(0); updated = True
current_it = self.model_init_times.get(m)
if current_it:
self.update_slider_max(m, current_it)
if (updated or force_draw) and not quiet:
self.status_banner.setText("最新データを画面に同期しました")
current_tab_name = self.tabs.tabText(self.tabs.currentIndex())
_meps_hit = next((m for m in MEPS_STAT_MODELS if MEPS_STAT_TAB_TITLES[m] == current_tab_name), None)
if _meps_hit: current_m = _meps_hit
elif "MSM_GUID" in current_tab_name: current_m = "MSM_GUID"
elif "GSM_GUID" in current_tab_name: current_m = "GSM_GUID"
elif "毎時大気" in current_tab_name: current_m = "ANAL"
elif "MSM" in current_tab_name: current_m = "MSM"
elif "GSM_JP" in current_tab_name: current_m = "GSM_JP"
else: current_m = None
if current_m in self.tab_ui:
if len(self.tab_ui[current_m]['area_btns']) > 5 and self.tab_ui[current_m]['area_btns'][-1].isChecked(): pass
else:
self.draw_frame(current_m, self.tab_ui[current_m]['slider'].value())
def start_animation(self, model_name):
self.current_anim_model = model_name
speeds = [2000, 1000, 500, 200, 50]
self.anim_timer.start(speeds[self.tab_ui[model_name]['combo_speed'].currentIndex()])
def remove_vertical_colorbar(self):
pass
def stop_animation(self):
self.anim_timer.stop()
def anim_step(self):
if not self.current_anim_model: return
ui = self.tab_ui[self.current_anim_model]
current_val = ui['slider'].value()
elem_name = ui['list'].currentItem().text()
step_val = ui['step']
if self.current_anim_model == "GSM_JP":
ground_cloud_elems = ["地上気圧", "地上風向風速", "地上降水", "全雲量", "上層雲量", "中層雲量", "下層雲量", "地上３種重ね"]
threshold = 132 if elem_name in ground_cloud_elems else 84
if current_val >= threshold: step_val = 6
new_val = current_val + step_val
ui['slider'].setValue(new_val if new_val <= ui['slider'].maximum() else ui['slider'].minimum())
if __name__ == '__main__':
import multiprocessing
multiprocessing.freeze_support()
import traceback
try:
app = QApplication(sys.argv)
window = WeatherApp()
sys.exit(app.exec())
except Exception as e:
print("起動時にエラーが発生しました:")
traceback.print_exc()
input("Enterキーを押すと終了します...")
未分類
