"""
WRF后处理程序 slp
20230116 byp
"""
import netCDF4
import wrf
import os
import glob
import re
import matplotlib.pyplot


# 读取数据###############################################################开始
var = 'UV'  # 变量：海平面气压SLP
diagnostic = 'UV'  # wrf-python提供的诊断变量：slp\rh
title = '风速 2米$\mathbf{(K)}$'  # 图片标题 '海平面气压$\mathbf{(hPa)}$'/'相对湿度 2米$\mathbf{(\%)}$'
# 变量的数值范围及间隔
data_range_min = 0
data_range_max = 400
data_interval = 5

dir_name = "./data/WRF/"
dir_output = './output/image/'
list_of_files = glob.glob(dir_name+'*')
latest_file = max(list_of_files, key=os.path.getctime)  # 最新文件
print(latest_file)
mat = re.search(r"(\d{4}-\d{1,2}-\d{1,2}_\d{1,2})", latest_file)  # 正则匹配时间
dir_time_file = 'wrfout_d01_'+latest_file[mat.span()[0]:]
dir_time = latest_file[mat.span()[0]:mat.span()[1]].replace('-', '').replace('_', '')
data_nc = netCDF4.Dataset(dir_name+dir_time_file)
# print(data_nc)
# print(data_nc.variables['U10'].shape)
# print(data_nc.variables['U10'][1][1][1])
# print(data_nc.variables['V10'].shape)
# print(data_nc.variables['V10'][1][1][1])

u = wrf.getvar(data_nc, 'U10', timeidx=1)
u = u.values[1][10]
v = wrf.getvar(data_nc, 'V10', timeidx=1)
v = v.values[1][10]

fig = matplotlib.pyplot.figure()
ax = matplotlib.pyplot.axes()
ax.barbs(u, v)

matplotlib.pyplot.show()