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''' import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.ticker as mticker
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader
from adjustText import adjust_text
from ash_animator.interpolation import interpolate_grid
from ash_animator.basemaps import draw_etopo_basemap
class Plot_Horizontal_Data:
def __init__(self, animator, output_dir="plots", cmap="rainbow", fps=2,
include_metadata=True, threshold=0.1,
zoom_width_deg=6.0, zoom_height_deg=6.0, zoom_level=7, static_frame_export=False):
self.animator = animator
self.output_dir = os.path.abspath(os.path.join(os.getcwd(), output_dir))
os.makedirs(self.output_dir, exist_ok=True)
self.cmap = cmap
self.fps = fps
self.include_metadata = include_metadata
self.threshold = threshold
self.zoom_width = zoom_width_deg
self.zoom_height = zoom_height_deg
shp = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries')
self.country_geoms = list(shpreader.Reader(shp).records())
self.interpolate_grid= interpolate_grid
self._draw_etopo_basemap=draw_etopo_basemap
self.zoom_level=zoom_level
self.static_frame_export=static_frame_export
def _make_dirs(self, path):
os.makedirs(os.path.abspath(os.path.join(os.getcwd(), os.path.dirname(path))), exist_ok=True)
def _get_max_concentration_location(self, field):
max_val = -np.inf
lat = lon = None
for ds in self.animator.datasets:
data = ds[field].values
if np.max(data) > max_val:
max_val = np.max(data)
idx = np.unravel_index(np.argmax(data), data.shape)
lat = self.animator.lat_grid[idx]
lon = self.animator.lon_grid[idx]
return lat, lon
def _get_zoom_indices(self, center_lat, center_lon):
lon_min = center_lon - self.zoom_width / 2
lon_max = center_lon + self.zoom_width / 2
lat_min = center_lat - self.zoom_height / 2
lat_max = center_lat + self.zoom_height / 2
lat_idx = np.where((self.animator.lats >= lat_min) & (self.animator.lats <= lat_max))[0]
lon_idx = np.where((self.animator.lons >= lon_min) & (self.animator.lons <= lon_max))[0]
return lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max
def _add_country_labels(self, ax, extent):
proj = ccrs.PlateCarree()
texts = []
for country in self.country_geoms:
name = country.attributes['NAME_LONG']
geom = country.geometry
try:
lon, lat = geom.centroid.x, geom.centroid.y
if extent[0] <= lon <= extent[1] and extent[2] <= lat <= extent[3]:
text = ax.text(lon, lat, name, fontsize=6, transform=proj,
ha='center', va='center', color='white',
bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
texts.append(text)
except:
continue
adjust_text(texts, ax=ax, only_move={'points': 'y', 'text': 'y'},
arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
def _draw_metadata_sidebar(self, fig, meta_dict):
lines = [
f"Run name: {meta_dict.get('run_name', 'N/A')}",
f"Run time: {meta_dict.get('run_time', 'N/A')}",
f"Met data: {meta_dict.get('met_data', 'N/A')}",
f"Start release: {meta_dict.get('start_of_release', 'N/A')}",
f"End release: {meta_dict.get('end_of_release', 'N/A')}",
f"Source strength: {meta_dict.get('source_strength', 'N/A')} g/s",
f"Release loc: {meta_dict.get('release_location', 'N/A')}",
f"Release height: {meta_dict.get('release_height', 'N/A')} m asl",
f"Run duration: {meta_dict.get('run_duration', 'N/A')}"
]
# Split into two columns
mid = len(lines) // 2 + len(lines) % 2
left_lines = lines[:mid]
right_lines = lines[mid:]
left_text = "\n".join(left_lines)
right_text = "\n".join(right_lines)
# right column
fig.text(0.05, 0.05, left_text, va='bottom', ha='left',
fontsize=9, family='monospace', color='black',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
# left column
fig.text(0.3, 0.05, right_text, va='bottom', ha='left',
fontsize=9, family='monospace', color='black',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
def _plot_frame(self, ax, data, lons, lats, title, levels, scale_label, proj):
self._draw_etopo_basemap(ax, mode='basemap', zoom=self.zoom_level)
c = ax.contourf(lons, lats, data, levels=levels, cmap=self.cmap, alpha=0.6, transform=proj)
ax.set_title(title)
ax.set_extent([lons.min(), lons.max(), lats.min(), lats.max()])
ax.coastlines()
ax.add_feature(cfeature.BORDERS, linestyle=':')
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
return c
def get_available_2d_fields(self):
ds = self.animator.datasets[0]
return [v for v in ds.data_vars if ds[v].ndim == 2]
def plot_single_field_over_time(self, field, filename="field.gif"):
output_path = os.path.join(self.output_dir, "2d_fields", field, filename)
meta = self.animator.datasets[0].attrs
center_lat, center_lon = self._get_max_concentration_location(field)
lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
lat_zoom = self.animator.lats[lat_idx]
lon_zoom = self.animator.lons[lon_idx]
valid_frames = []
for t in range(len(self.animator.datasets)):
data = self.animator.datasets[t][field].values
interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
if np.isfinite(interp).sum() > 0:
valid_frames.append(t)
if not valid_frames:
print(f"No valid frames to plot for field '{field}'.")
return
fig = plt.figure(figsize=(16, 8))
proj = ccrs.PlateCarree()
ax1 = fig.add_subplot(1, 2, 1, projection=proj)
ax2 = fig.add_subplot(1, 2, 2, projection=proj)
def update(t):
ax1.clear()
ax2.clear()
data = self.animator.datasets[t][field].values
interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
zoom = interp[np.ix_(lat_idx, lon_idx)]
valid = interp[np.isfinite(interp)]
if valid.size == 0:
return []
min_val, max_val = np.nanmin(valid), np.nanmax(valid)
log_cutoff = 1e-3
use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
plot_data = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
scale_label = "Log" if use_log else "Linear"
c = self._plot_frame(ax1, plot_data, self.animator.lons, self.animator.lats,
f"T{t+1} | {field} (Full - {scale_label})", levels, scale_label, proj)
self._plot_frame(ax2, zoom, lon_zoom, lat_zoom,
f"T{t+1} | {field} (Zoom - {scale_label})", levels, scale_label, proj)
self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
self.animator.lats.min(), self.animator.lats.max()])
self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
# Inside update() function:
if not hasattr(update, "colorbar"):
unit_label = f"{field}:({self.animator.datasets[0][field].attrs.get("units", field)})" #self.animator.datasets[0][field].attrs.get("units", field)
update.colorbar = fig.colorbar(c, ax=[ax1, ax2], orientation='vertical', label=unit_label)
formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
update.colorbar.ax.yaxis.set_major_formatter(formatter)
if np.nanmax(valid) > self.threshold:
ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
colors='red', linewidths=2, transform=proj)
ax2.contour(lon_zoom, lat_zoom, zoom, levels=[self.threshold],
colors='red', linewidths=2, transform=proj)
ax2.text(0.99, 0.01, f"⚠ Max Thresold Exceed: {np.nanmax(valid):.2f} > {self.threshold}",
transform=ax2.transAxes, ha='right', va='bottom',
fontsize=9, color='red',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
if self.static_frame_export:
frame_folder = os.path.join(self.output_dir, "frames", field)
os.makedirs(frame_folder, exist_ok=True)
frame_path = os.path.join(frame_folder, f"frame_{t+1:04d}.jpg")
plt.savefig(frame_path, dpi=300, bbox_inches='tight')
print(f"🖼️ Saved static frame: {frame_path}")
return []
if self.include_metadata:
self._draw_metadata_sidebar(fig, meta)
self._make_dirs(output_path)
fig.tight_layout()
ani = animation.FuncAnimation(fig, update, frames=valid_frames, blit=False, cache_frame_data =False)
ani.save(output_path, writer='pillow', fps=self.fps)
plt.close()
print(f"✅ Saved enhanced 2D animation for {field} to {output_path}")
# def export_frames_as_jpgs(self, fields=None, include_metadata=True):
# all_fields = self.get_available_2d_fields()
# if fields:
# fields = [f for f in fields if f in all_fields]
# else:
# fields = all_fields
# meta = self.animator.datasets[0].attrs
# for field in fields:
# print(f"📤 Exporting frames for field: {field}")
# output_folder = os.path.join(self.output_dir, "frames", field)
# os.makedirs(output_folder, exist_ok=True)
# center_lat, center_lon = self._get_max_concentration_location(field)
# lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
# lat_zoom = self.animator.lats[lat_idx]
# lon_zoom = self.animator.lons[lon_idx]
# for t, ds in enumerate(self.animator.datasets):
# data = ds[field].values
# interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
# if not np.isfinite(interp).any():
# continue
# fig = plt.figure(figsize=(16, 8))
# proj = ccrs.PlateCarree()
# ax1 = fig.add_subplot(1, 2, 1, projection=proj)
# ax2 = fig.add_subplot(1, 2, 2, projection=proj)
# zoom = interp[np.ix_(lat_idx, lon_idx)]
# valid = interp[np.isfinite(interp)]
# min_val, max_val = np.nanmin(valid), np.nanmax(valid)
# log_cutoff = 1e-3
# use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
# levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
# plot_data = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
# scale_label = "Log" if use_log else "Linear"
# c = self._plot_frame(ax1, plot_data, self.animator.lons, self.animator.lats,
# f"T{t+1} | {field} (Full - {scale_label})", levels, scale_label, proj)
# self._plot_frame(ax2, zoom, lon_zoom, lat_zoom,
# f"T{t+1} | {field} (Zoom - {scale_label})", levels, scale_label, proj)
# self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
# self.animator.lats.min(), self.animator.lats.max()])
# self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
# if include_metadata:
# self._draw_metadata_sidebar(fig, meta)
# cbar = fig.colorbar(c, ax=[ax1, ax2], orientation='vertical', shrink=0.75, pad=0.03)
# unit_label = f"{field}:({self.animator.datasets[0][field].attrs.get('units', field)})"
# cbar.set_label(unit_label)
# formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
# cbar.ax.yaxis.set_major_formatter(formatter)
# if np.nanmax(valid) > self.threshold:
# ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
# colors='red', linewidths=2, transform=proj)
# ax2.contour(lon_zoom, lat_zoom, zoom, levels=[self.threshold],
# colors='red', linewidths=2, transform=proj)
# ax2.text(0.99, 0.01, f"⚠ Max: {np.nanmax(valid):.2f} > {self.threshold}",
# transform=ax2.transAxes, ha='right', va='bottom',
# fontsize=9, color='red',
# bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
# frame_path = os.path.join(output_folder, f"frame_{t+1:04d}.jpg")
# plt.savefig(frame_path, dpi=150, bbox_inches='tight')
# plt.close(fig)
# print(f"📸 Saved {frame_path}")
'''
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.ticker as mticker
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader
from adjustText import adjust_text
from ash_animator.interpolation import interpolate_grid
from ash_animator.basemaps import draw_etopo_basemap
class Plot_Horizontal_Data:
def __init__(self, animator, output_dir="plots", cmap="rainbow", fps=2,
include_metadata=True, threshold=0.1,
zoom_width_deg=6.0, zoom_height_deg=6.0, zoom_level=7, static_frame_export=False):
self.animator = animator
self.output_dir = os.path.abspath(os.path.join(os.getcwd(), output_dir))
os.makedirs(self.output_dir, exist_ok=True)
self.cmap = cmap
self.fps = fps
self.include_metadata = include_metadata
self.threshold = threshold
self.zoom_width = zoom_width_deg
self.zoom_height = zoom_height_deg
shp = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries')
self.country_geoms = list(shpreader.Reader(shp).records())
self.interpolate_grid= interpolate_grid
self._draw_etopo_basemap=draw_etopo_basemap
self.zoom_level=zoom_level
self.static_frame_export=static_frame_export
def _make_dirs(self, path):
os.makedirs(os.path.abspath(os.path.join(os.getcwd(), os.path.dirname(path))), exist_ok=True)
def _get_max_concentration_location(self, field):
max_val = -np.inf
lat = lon = None
for ds in self.animator.datasets:
data = ds[field].values
if np.max(data) > max_val:
max_val = np.max(data)
idx = np.unravel_index(np.argmax(data), data.shape)
lat = self.animator.lat_grid[idx]
lon = self.animator.lon_grid[idx]
return lat, lon
def _get_zoom_indices(self, center_lat, center_lon):
lon_min = center_lon - self.zoom_width / 2
lon_max = center_lon + self.zoom_width / 2
lat_min = center_lat - self.zoom_height / 2
lat_max = center_lat + self.zoom_height / 2
lat_idx = np.where((self.animator.lats >= lat_min) & (self.animator.lats <= lat_max))[0]
lon_idx = np.where((self.animator.lons >= lon_min) & (self.animator.lons <= lon_max))[0]
return lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max
def _add_country_labels(self, ax, extent):
proj = ccrs.PlateCarree()
texts = []
for country in self.country_geoms:
name = country.attributes['NAME_LONG']
geom = country.geometry
try:
lon, lat = geom.centroid.x, geom.centroid.y
if extent[0] <= lon <= extent[1] and extent[2] <= lat <= extent[3]:
text = ax.text(lon, lat, name, fontsize=6, transform=proj,
ha='center', va='center', color='white',
bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
texts.append(text)
except:
continue
adjust_text(texts, ax=ax, only_move={'points': 'y', 'text': 'y'},
arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
def _draw_metadata_sidebar(self, fig, meta_dict):
lines = [
f"Run name: {meta_dict.get('run_name', 'N/A')}",
f"Run time: {meta_dict.get('run_time', 'N/A')}",
f"Met data: {meta_dict.get('met_data', 'N/A')}",
f"Start release: {meta_dict.get('start_of_release', 'N/A')}",
f"End release: {meta_dict.get('end_of_release', 'N/A')}",
f"Source strength: {meta_dict.get('source_strength', 'N/A')} g/s",
f"Release loc: {meta_dict.get('release_location', 'N/A')}",
f"Release height: {meta_dict.get('release_height', 'N/A')} m asl",
f"Run duration: {meta_dict.get('run_duration', 'N/A')}"
]
# Split into two columns
mid = len(lines) // 2 + len(lines) % 2
left_lines = lines[:mid]
right_lines = lines[mid:]
left_text = "\n".join(left_lines)
right_text = "\n".join(right_lines)
# right column
fig.text(0.05, 0.05, left_text, va='bottom', ha='left',
fontsize=9, family='monospace', color='black',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
# left column
fig.text(0.3, 0.05, right_text, va='bottom', ha='left',
fontsize=9, family='monospace', color='black',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
def _plot_frame(self, ax, data, lons, lats, title, levels, scale_label, proj):
self._draw_etopo_basemap(ax, mode='basemap', zoom=self.zoom_level)
c = ax.contourf(lons, lats, data, levels=levels, cmap=self.cmap, alpha=0.6, transform=proj)
ax.set_title(title)
ax.set_extent([lons.min(), lons.max(), lats.min(), lats.max()])
ax.coastlines()
ax.add_feature(cfeature.BORDERS, linestyle=':')
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
return c
def get_available_2d_fields(self):
ds = self.animator.datasets[0]
return [v for v in ds.data_vars if ds[v].ndim == 2]
def plot_single_field_over_time(self, field, filename="field.gif"):
output_path = os.path.join(self.output_dir, "2d_fields", field, filename)
meta = self.animator.datasets[0].attrs
center_lat, center_lon = self._get_max_concentration_location(field)
lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
lat_zoom = self.animator.lats[lat_idx]
lon_zoom = self.animator.lons[lon_idx]
valid_frames = []
for t in range(len(self.animator.datasets)):
data = self.animator.datasets[t][field].values
interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
if np.isfinite(interp).sum() > 0:
valid_frames.append(t)
if not valid_frames:
print(f"No valid frames to plot for field '{field}'.")
return
fig = plt.figure(figsize=(16, 8))
proj = ccrs.PlateCarree()
ax1 = fig.add_subplot(1, 2, 1, projection=proj)
ax2 = fig.add_subplot(1, 2, 2, projection=proj)
def update(t):
ax1.clear()
ax2.clear()
data = self.animator.datasets[t][field].values
interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
zoom = interp[np.ix_(lat_idx, lon_idx)]
valid = interp[np.isfinite(interp)]
if valid.size == 0:
return []
min_val, max_val = np.nanmin(valid), np.nanmax(valid)
log_cutoff = 1e-3
use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
plot_data = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
scale_label = "Log" if use_log else "Linear"
c = self._plot_frame(ax1, plot_data, self.animator.lons, self.animator.lats,
f"T{t+1} | {field} (Full - {scale_label})", levels, scale_label, proj)
self._plot_frame(ax2, zoom, lon_zoom, lat_zoom,
f"T{t+1} | {field} (Zoom - {scale_label})", levels, scale_label, proj)
self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
self.animator.lats.min(), self.animator.lats.max()])
self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
# Inside update() function:
if not hasattr(update, "colorbar"):
unit_label = f"{field}:({self.animator.datasets[0][field].attrs.get('units', field)})" #self.animator.datasets[0][field].attrs.get("units", field)
update.colorbar = fig.colorbar(c, ax=[ax1, ax2], orientation='vertical', label=unit_label)
formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
update.colorbar.ax.yaxis.set_major_formatter(formatter)
if np.nanmax(valid) > self.threshold:
ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
colors='red', linewidths=2, transform=proj)
ax2.contour(lon_zoom, lat_zoom, zoom, levels=[self.threshold],
colors='red', linewidths=2, transform=proj)
ax2.text(0.99, 0.01, f"⚠ Max Thresold Exceed: {np.nanmax(valid):.2f} > {self.threshold}",
transform=ax2.transAxes, ha='right', va='bottom',
fontsize=9, color='red',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
if self.static_frame_export:
frame_folder = os.path.join(self.output_dir, "frames", field)
os.makedirs(frame_folder, exist_ok=True)
frame_path = os.path.join(frame_folder, f"frame_{t+1:04d}.jpg")
plt.savefig(frame_path, dpi=300, bbox_inches='tight')
print(f"🖼️ Saved static frame: {frame_path}")
return []
if self.include_metadata:
self._draw_metadata_sidebar(fig, meta)
self._make_dirs(output_path)
fig.tight_layout()
ani = animation.FuncAnimation(fig, update, frames=valid_frames, blit=False, cache_frame_data =False)
ani.save(output_path, writer='pillow', fps=self.fps)
plt.close()
print(f"✅ Saved enhanced 2D animation for {field} to {output_path}")
# def export_frames_as_jpgs(self, fields=None, include_metadata=True):
# all_fields = self.get_available_2d_fields()
# if fields:
# fields = [f for f in fields if f in all_fields]
# else:
# fields = all_fields
# meta = self.animator.datasets[0].attrs
# for field in fields:
# print(f"📤 Exporting frames for field: {field}")
# output_folder = os.path.join(self.output_dir, "frames", field)
# os.makedirs(output_folder, exist_ok=True)
# center_lat, center_lon = self._get_max_concentration_location(field)
# lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
# lat_zoom = self.animator.lats[lat_idx]
# lon_zoom = self.animator.lons[lon_idx]
# for t, ds in enumerate(self.animator.datasets):
# data = ds[field].values
# interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
# if not np.isfinite(interp).any():
# continue
# fig = plt.figure(figsize=(16, 8))
# proj = ccrs.PlateCarree()
# ax1 = fig.add_subplot(1, 2, 1, projection=proj)
# ax2 = fig.add_subplot(1, 2, 2, projection=proj)
# zoom = interp[np.ix_(lat_idx, lon_idx)]
# valid = interp[np.isfinite(interp)]
# min_val, max_val = np.nanmin(valid), np.nanmax(valid)
# log_cutoff = 1e-3
# use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
# levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
# plot_data = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
# scale_label = "Log" if use_log else "Linear"
# c = self._plot_frame(ax1, plot_data, self.animator.lons, self.animator.lats,
# f"T{t+1} | {field} (Full - {scale_label})", levels, scale_label, proj)
# self._plot_frame(ax2, zoom, lon_zoom, lat_zoom,
# f"T{t+1} | {field} (Zoom - {scale_label})", levels, scale_label, proj)
# self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
# self.animator.lats.min(), self.animator.lats.max()])
# self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
# if include_metadata:
# self._draw_metadata_sidebar(fig, meta)
# cbar = fig.colorbar(c, ax=[ax1, ax2], orientation='vertical', shrink=0.75, pad=0.03)
# unit_label = f"{field}:({self.animator.datasets[0][field].attrs.get('units', field)})"
# cbar.set_label(unit_label)
# formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
# cbar.ax.yaxis.set_major_formatter(formatter)
# if np.nanmax(valid) > self.threshold:
# ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
# colors='red', linewidths=2, transform=proj)
# ax2.contour(lon_zoom, lat_zoom, zoom, levels=[self.threshold],
# colors='red', linewidths=2, transform=proj)
# ax2.text(0.99, 0.01, f"⚠ Max: {np.nanmax(valid):.2f} > {self.threshold}",
# transform=ax2.transAxes, ha='right', va='bottom',
# fontsize=9, color='red',
# bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
# frame_path = os.path.join(output_folder, f"frame_{t+1:04d}.jpg")
# plt.savefig(frame_path, dpi=150, bbox_inches='tight')
# plt.close(fig)
# print(f"📸 Saved {frame_path}")
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