Сохранение нескольких графиков/png прогнозируемых часов из модели GFS в виде файла .gif. ⇐ Python

[Anonymous]

Мне удалось построить несколько прогнозных часов модели GFS, но теперь я хочу сохранить все эти графики в виде файла .gif, чтобы можно было анимировать их прямо из Python, но я не знаю, как это сделать. Вот мой код:

Код: Выделить всё

from datetime import datetime
from datetime import timedelta
import pandas as pd
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import xarray as xr
import numpy as np
import metpy.calc as mpcalc
from metpy.plots import USCOUNTIES
import netCDF4
from netCDF4 import Dataset
from netCDF4 import num2date
from metpy.units import units
from scipy.ndimage import gaussian_filter
import scipy.ndimage as ndimage
from siphon.catalog import TDSCatalog

start_time = datetime(2025, 1, 14, 12)

hours_intervals = 40

for k in range(0,hours_intervals):
dt = start_time + timedelta(hours= 6*(k+1))

#dt = datetime(2025,1,4,12)
best_gfs = TDSCatalog('https://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p25deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p25deg/Best')
best_ds = best_gfs.datasets[0]
ncss = best_ds.subset()
query = ncss.query()
query.accept('netcdf')
query.lonlat_box(north=75, south=15, east=320, west=185)
query.time(dt)
query.variables('Geopotential_height_isobaric', 'Pressure_reduced_to_MSL_msl', 'Precipitation_rate_surface', 'Snow_depth_surface', 'Categorical_Snow_surface','Categorical_Freezing_Rain_surface', 'Categorical_Ice_Pellets_surface')

data = ncss.get_data(query)
print(list(data.variables))

plev = list(data.variables['isobaric'][:])

lat = data.variables['latitude'][:].squeeze()
lon = data.variables['longitude'][:].squeeze()
time1 = data['time']
vtime = num2date(time1[:].squeeze(), units=time1.units)
emsl_var = data.variables['Pressure_reduced_to_MSL_msl']
preciprate = data.variables['Precipitation_rate_surface'][:].squeeze()
snowdepth = data.variables['Snow_depth_surface'][:].squeeze()
catsnow = data.variables['Categorical_Snow_surface'][:].squeeze()
catice = data.variables['Categorical_Freezing_Rain_surface'][:].squeeze()
catsleet = data.variables['Categorical_Ice_Pellets_surface'][:].squeeze()
EMSL = units.Quantity(emsl_var[:], emsl_var.units).to('hPa')
mslp = gaussian_filter(EMSL[0], sigma=3.0)
hght_1000 = data.variables['Geopotential_height_isobaric'][0, plev.index(100000)]
hght_500 = data.variables['Geopotential_height_isobaric'][0, plev.index(50000)]
thickness_1000_500 = gaussian_filter((hght_500 - hght_1000)/10, sigma=3.0)
lon_2d, lat_2d = np.meshgrid(lon, lat)

precip_inch_hour = preciprate * 141.73228346457
precip2 = mpcalc.smooth_n_point(precip_inch_hour, 5, 1)

precip_colors = [
"#bde9bf",  # 0.01 - 0.02 inches 1
"#adddb0",  # 0.02 - 0.03 inches 2
"#9ed0a0",  # 0.03 - 0.04 inches 3
"#8ec491",  # 0.04 - 0.05 inches 4
"#7fb882",  # 0.05 - 0.06 inches 5
"#70ac74",  # 0.06 - 0.07 inches 6
"#60a065",  # 0.07 - 0.08 inches 7
"#519457",  # 0.08 - 0.09 inches 8
"#418849",  # 0.09 - 0.10 inches 9
"#307c3c",  # 0.10 - 0.12 inches 10
"#1c712e",  # 0.12 - 0.14 inches 11
"#f7f370",  # 0.14 - 0.16 inches 12
"#fbdf65",  # 0.16 - 0.18 inches 13
"#fecb5a",  # 0.18 - 0.2 inches 14
"#ffb650",  # 0.2 - 0.3 inches 15
"#ffa146",  # 0.3 - 0.4 inches 16
"#ff8b3c",   # 0.4 - 0.5 inches 17
"#f94609",   # 0.5 - 0.6 inches 18
]

precip_colormap = mcolors.ListedColormap(precip_colors)

clev_precip =  np.concatenate((np.arange(0.01, 0.1, .01), np.arange(.1, .2, .02), np.arange(.2, .61, .1)))
norm = mcolors.BoundaryNorm(clev_precip, 18)

datacrs = ccrs.PlateCarree()
plotcrs = ccrs.LambertConformal(central_latitude=35, central_longitude=-100,standard_parallels=(30, 60))
bounds = ([-105, -90, 30, 40])
fig = plt.figure(figsize=(14,12))
ax = fig.add_subplot(1,1,1, projection=plotcrs)
ax.set_extent(bounds, crs=ccrs.PlateCarree())
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth = 0.75)
ax.add_feature(cfeature.STATES,  linewidth = 1)
ax.add_feature(USCOUNTIES, edgecolor='grey', linewidth = .5)
clevs = (np.arange(0, 540, 6),
np.array([540]),
np.arange(546, 700, 6))
colors = ('tab:blue', 'b', 'tab:red')
kw_clabels = {'fontsize': 11, 'inline': True, 'inline_spacing': 5, 'fmt': '%i',
'rightside_up': True, 'use_clabeltext': True}

# Plot MSLP
clevmslp = np.arange(800., 1120., 2)
cs2 = ax.contour(lon_2d, lat_2d, mslp, clevmslp, colors='k', linewidths=1.25,
linestyles='solid', transform=ccrs.PlateCarree())

cf = ax.contourf(lon_2d, lat_2d, precip2, clev_precip, cmap=precip_colormap, norm=norm, extend='max', transform=ccrs.PlateCarree())

ax.set_title('GFS Precip Type, Rate(in/hr), MSLP (hPa), & 1000-500mb Thickness (dam)', loc='left', fontsize=10, weight = 'bold')
ax.set_title('Valid Time: {}z'.format(vtime), loc = 'right', fontsize=8)
plt.show()

Что мне делать дальше после отображения всех графиков и сохранения этих графиков прогноза GFS в виде файла .gif? Спасибо.

Подробнее здесь: https://stackoverflow.com/questions/793 ... a-gif-file