python--绘制WRF模式近地面风场以及辐射
使用python自动化绘制WRF模式输出的风场以及辐射
本脚本主要用来自动化处理WRF模式数据，可以根据自己指定的时间范围以及时间步长绘制相应的数据
1 导入库
import cmaps
import numpy as np
import glob
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from wrf import (to_np, getvar, smooth2d, get_cartopy, cartopy_xlim,
cartopy_ylim, latlon_coords,extract_global_attrs)
import proplot as pplt
import pandas as pd
import datetime
import os
2 选择数据范围
需要自己手动选择：初始时间、结束时间、时间步长
输入格式为：年-月-日-时、年-月-日-时、时
2022071000
2022071012
03
date = input()
frst = input()
step = input()
path = r'G:/'+ date #只能是已经存在的文件目录且有数据才可以进行读取
start = datetime.datetime.strptime(date,'%Y%m%d%H').strftime("%Y-%m-%d_%H_%M_%S")
end = (datetime.datetime.strptime(date,'%Y%m%d%H')+datetime.timedelta(hours=int(frst))).strftime("%Y-%m-%d_%H_%M_%S")
intp = (datetime.datetime.strptime(date,'%Y%m%d%H')+datetime.timedelta(hours=int(step))).strftime("%Y-%m-%d_%H_%M_%S")
fstart = path+'/*'+start
fintp = path+'/*'+intp
fend = path+'/*'+end
file = path+'/*'
filestart = glob.glob(fstart)
fileintp = glob.glob(fintp)
fileend = glob.glob(fend)
filelist = glob.glob(file)
filelist.sort()
rstart = np.array(np.where(np.array(filelist)==filestart))[0][0]
rintp = np.array(np.where(np.array(filelist)==fileintp))[0][0]
rend = np.array(np.where(np.array(filelist)==fileend))[0][0]
3定义读取数据函数以及绘图函数
定义 函数读取风速资料U、V，以及辐射（辐射包括三部分）
- 将读取的资料进行绘图，并将绘图后的结果自动 保存到指定路径下，方面后续绘制动图
def plot(i):
ncfile = Dataset(i)
#get u10 v10
u10 = getvar(ncfile,'U10')
v10 = getvar(ncfile,'V10')
w10 = np.sqrt(u10*u10+v10*v10)
#get net_rad
swdown = getvar(ncfile,'SWDOWN')
glw = getvar(ncfile,'GLW')
olr = getvar(ncfile,'OLR')
rad = swdown+glw+olr
WE = extract_global_attrs(ncfile,'WEST-EAST_GRID_DIMENSION')['WEST-EAST_GRID_DIMENSION']
SN = extract_global_attrs(ncfile,'SOUTH-NORTH_GRID_DIMENSION')['SOUTH-NORTH_GRID_DIMENSION']
lev= extract_global_attrs(ncfile,'BOTTOM-TOP_GRID_DIMENSION')['BOTTOM-TOP_GRID_DIMENSION']
t =u10.Time
# time = pd.to_datetime((t)).strftime('%Y-%m-%d-%H')
# Get the latitude and longitude points
lats, lons = latlon_coords(u10)
# Create a figure
f = pplt.figure(figsize=(5,5))
ax = f.subplot(111, proj='lcyl')
m=ax.contourf(to_np(lons), to_np(lats), to_np(w10),
cmap=cmaps.MPL_RdYlBu_r,
levels=np.arange(4,10.5,0.5),
extend='both'
)
ax.contour(to_np(lons), to_np(lats), to_np(rad),levels=8,
linewidth=0.9,
color='grey',
# cmap=cmaps.MPL_RdYlBu_r,
)
step=4
ax.quiver(to_np(lons)[::step,::step],to_np(lats)[::step,::step],
to_np(u10)[::step,::step],to_np(v10)[::step,::step],
)
ax.format(
ltitle='Sea surface WindSpeed (m/s)',
coast=True,
labels=True,
lonlim=(120, 130),
latlines=2,
lonlines=2,
latlim=(25, 35),
)
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=1)
textstr = '\n'.join((
r'$ Init:$'+ ncfile.START_DATE,
r'$Valid:$' + i[-19:],
# r'$\sigma=%.2f$' % (2, ))
))
ax.text(0.0, 1.2, textstr, transform=ax.transAxes, fontsize=9,
verticalalignment='top', )
ax.text(0.65, 1.2, 'U at 10 M(m s$^{-1}$)'+'\n'+'net_radiation(W m$^{-2}$)',
transform=ax.transAxes, fontsize=9,
verticalalignment='top', )
ax.text(0.00, -0.07,
ncfile.TITLE+'\n'+
r'WE='+str(WE)+';SN='+str(SN)+';Lev='+str(lev)+';dx='+str(int(ncfile.DX))+'km;'+'Phys opt='+str(ncfile.MP_PHYSICS)
+';PBL Opt='+str(ncfile.BL_PBL_PHYSICS)+';Cu Opt='+str(ncfile.CU_PHYSICS),
fontsize=8,
horizontalalignment='left',
verticalalignment='top',
transform=ax.transAxes,)
f.colorbar(m, loc='r',label='',length=0.75,width=0.15)
plt.show()
savepath = r'J:/'+ date
if not os.path.exists(savepath):
os.makedirs(savepath)
f.savefig(savepath+'\\uv10'+i[-19:]+'.jpg')
return
4 定义代码运行函数。
def plot_uv(fn):
for i in fn:
plot(i)
print('finish'+i)
return
if __name__ == "__main__":
plot_uv(fn)
print('uvplot is finish')
5 个例测试
- 方面数据测试，取削封装函数运行。
path = r'G:\2022071000/*'
file = glob.glob(path)
file.sort()
ncfile = Dataset(file[0])
#get u10 v10
u10 = getvar(ncfile,'U10')
v10 = getvar(ncfile,'V10')
w10 = np.sqrt(u10*u10+v10*v10)
#get net_rad
swdown = getvar(ncfile,'SWDOWN')
glw = getvar(ncfile,'GLW')
olr = getvar(ncfile,'OLR')
rad = swdown+glw+olr
WE = extract_global_attrs(ncfile,'WEST-EAST_GRID_DIMENSION')['WEST-EAST_GRID_DIMENSION']
SN = extract_global_attrs(ncfile,'SOUTH-NORTH_GRID_DIMENSION')['SOUTH-NORTH_GRID_DIMENSION']
lev= extract_global_attrs(ncfile,'BOTTOM-TOP_GRID_DIMENSION')['BOTTOM-TOP_GRID_DIMENSION']
t =datetime.datetime.strptime(u10.Time.values,'%Y%m%d%H').strftime("%Y-%m-%d_%H_%M_%S")
# time = pd.to_datetime((t)).strftime('%Y-%m-%d-%H')
# Get the latitude and longitude points
lats, lons = latlon_coords(u10)
# Create a figure
f = pplt.figure(figsize=(5,5))
ax = f.subplot(111, proj='lcyl')
m=ax.contourf(to_np(lons), to_np(lats), to_np(w10),
cmap=cmaps.MPL_RdYlBu_r,
)
ax.contour(to_np(lons), to_np(lats), to_np(rad),levels=8,
linewidth=0.9,
color='grey',
# cmap=cmaps.MPL_RdYlBu_r,
)
step=4
ax.quiver(to_np(lons)[::step,::step],to_np(lats)[::step,::step],
to_np(u10)[::step,::step],to_np(v10)[::step,::step],
)
ax.format(
ltitle='Sea surface WindSpeed (m/s)',
coast=True,
labels=True,
lonlim=(120, 130),
latlines=2,
lonlines=2,
latlim=(25, 35),
)
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=1)
textstr = '\n'.join((
r'$ Init:$'+ ncfile.START_DATE,
r'$Valid:$' + file[],
# r'$\sigma=%.2f$' % (2, ))
))
ax.text(0.0, 1.2, textstr, transform=ax.transAxes, fontsize=9,
verticalalignment='top', )
ax.text(0.65, 1.2, 'U at 10 M(m s$^{-1}$)'+'\n'+'net_radiation(W m$^{-2}$)',
transform=ax.transAxes, fontsize=9,
verticalalignment='top', )
ax.text(0.00, -0.07,
ncfile.TITLE+'\n'+
r'WE='+str(WE)+';SN='+str(SN)+';Lev='+str(lev)+';dx='+str(int(ncfile.DX))+'km;'+'Phys opt='+str(ncfile.MP_PHYSICS)
+';PBL Opt='+str(ncfile.BL_PBL_PHYSICS)+';Cu Opt='+str(ncfile.CU_PHYSICS),
fontsize=8,
horizontalalignment='left',
verticalalignment='top',
transform=ax.transAxes,)
f.colorbar(m, loc='r',label='',length=0.75,width=0.15)
plt.show()
# f.savefig('J:\\20220710\\'+i[35:]+'.png')
# for i in file:
# plot_p(i)
# print('finish'+i)
# print(i)
# -*- coding: utf-8 -*-
"""
Created on %(date)s
@author: %(jixianpu)s
Email : [email protected]
introduction : keep learning althongh walk slowly
"""
"""
introduction :
绘制近地面的风场和辐射
"""
import cmaps
import numpy as np
import glob
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from wrf import (to_np, getvar, smooth2d, get_cartopy, cartopy_xlim,
cartopy_ylim, latlon_coords,extract_global_attrs)
import proplot as pplt
import pandas as pd
import datetime
import os
date = input()
frst = input()
step = input()
path = r'G:/'+ date #只能是已经存在的文件目录且有数据才可以进行读取
start = datetime.datetime.strptime(date,'%Y%m%d%H').strftime("%Y-%m-%d_%H_%M_%S")
end = (datetime.datetime.strptime(date,'%Y%m%d%H')+datetime.timedelta(hours=int(frst))).strftime("%Y-%m-%d_%H_%M_%S")
intp = (datetime.datetime.strptime(date,'%Y%m%d%H')+datetime.timedelta(hours=int(step))).strftime("%Y-%m-%d_%H_%M_%S")
fstart = path+'/*'+start
fintp = path+'/*'+intp
fend = path+'/*'+end
file = path+'/*'
filestart = glob.glob(fstart)
fileintp = glob.glob(fintp)
fileend = glob.glob(fend)
filelist = glob.glob(file)
filelist.sort()
rstart = np.array(np.where(np.array(filelist)==filestart))[0][0]
rintp = np.array(np.where(np.array(filelist)==fileintp))[0][0]
rend = np.array(np.where(np.array(filelist)==fileend))[0][0]
fn = filelist[rstart:rend:rintp]
# fn = filelist[rstart:rend:rintp]
def plot(i):
ncfile = Dataset(i)
#get u10 v10
u10 = getvar(ncfile,'U10')
v10 = getvar(ncfile,'V10')
w10 = np.sqrt(u10*u10+v10*v10)
#get net_rad
swdown = getvar(ncfile,'SWDOWN')
glw = getvar(ncfile,'GLW')
olr = getvar(ncfile,'OLR')
rad = swdown+glw+olr
WE = extract_global_attrs(ncfile,'WEST-EAST_GRID_DIMENSION')['WEST-EAST_GRID_DIMENSION']
SN = extract_global_attrs(ncfile,'SOUTH-NORTH_GRID_DIMENSION')['SOUTH-NORTH_GRID_DIMENSION']
lev= extract_global_attrs(ncfile,'BOTTOM-TOP_GRID_DIMENSION')['BOTTOM-TOP_GRID_DIMENSION']
t =u10.Time
# time = pd.to_datetime((t)).strftime('%Y-%m-%d-%H')
# Get the latitude and longitude points
lats, lons = latlon_coords(u10)
# Create a figure
f = pplt.figure(figsize=(5,5))
ax = f.subplot(111, proj='lcyl')
m=ax.contourf(to_np(lons), to_np(lats), to_np(w10),
cmap=cmaps.MPL_RdYlBu_r,
levels=np.arange(4,10.5,0.5),
extend='both'
)
ax.contour(to_np(lons), to_np(lats), to_np(rad),levels=8,
linewidth=0.9,
color='grey',
# cmap=cmaps.MPL_RdYlBu_r,
)
step=4
ax.quiver(to_np(lons)[::step,::step],to_np(lats)[::step,::step],
to_np(u10)[::step,::step],to_np(v10)[::step,::step],
)
ax.format(
ltitle='Sea surface WindSpeed (m/s)',
coast=True,
labels=True,
lonlim=(120, 130),
latlines=2,
lonlines=2,
latlim=(25, 35),
)
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=1)
textstr = '\n'.join((
r'$ Init:$'+ ncfile.START_DATE,
r'$Valid:$' + i[-19:],
# r'$\sigma=%.2f$' % (2, ))
))
ax.text(0.0, 1.2, textstr, transform=ax.transAxes, fontsize=9,
verticalalignment='top', )
ax.text(0.65, 1.2, 'U at 10 M(m s$^{-1}$)'+'\n'+'net_radiation(W m$^{-2}$)',
transform=ax.transAxes, fontsize=9,
verticalalignment='top', )
ax.text(0.00, -0.07,
ncfile.TITLE+'\n'+
r'WE='+str(WE)+';SN='+str(SN)+';Lev='+str(lev)+';dx='+str(int(ncfile.DX))+'km;'+'Phys opt='+str(ncfile.MP_PHYSICS)
+';PBL Opt='+str(ncfile.BL_PBL_PHYSICS)+';Cu Opt='+str(ncfile.CU_PHYSICS),
fontsize=8,
horizontalalignment='left',
verticalalignment='top',
transform=ax.transAxes,)
f.colorbar(m, loc='r',label='',length=0.75,width=0.15)
plt.show()
savepath = r'J:/'+ date
if not os.path.exists(savepath):
os.makedirs(savepath)
f.savefig(savepath+'\\uv10'+i[-19:]+'.jpg')
return
def plot_uv(fn):
for i in fn:
plot(i)
print('finish'+i)
return
if __name__ == "__main__":
plot_uv(fn)
print('uvplot is finish')
绘制结果如下图所示：
