Cartopy画地图第八天（冷空气南下，NCL色标使用）

相信很多朋友都看过这张漂亮的图吧

这是中国气象爱好者公众号画的一张反映冷空气的图片，当时就被惊艳到了，本帖就来复刻一下这张图。
本次的知识点大概总结一下就是：
1.画风的箭头流场
2.单画某一根等值线，比如588，0℃
3.引入NCL色标
4.制作gif图片
先上个效果图

一、分析图片

中国气象爱好者的图片拿到手以后开始分析
1.500的位势高度画等值线
2.850的温度填色
3.0℃等值线
4.588画等值线
5.850风场的箭头流场
6.注意投影方式
其实也不难，一个一个画就好了。

二、函数准备

（一）、使用NLC色标的函数

import re
import matplotlib.cm
from matplotlib import colors
import matplotlib.pyplot as plt
import numpy as np
class Colormap(colors.ListedColormap):def __init__(self, c, name='from_list', n=None):self.colors = cself.name = nameself.N = nsuper(Colormap, self).__init__(self.colors, name=self.name, N=self.N)def __getitem__(self, item):return Colormap(self.colors[item], name='sliced_' + self.name)def show(self):a = np.outer(np.ones(10), np.arange(0, 1, 0.001))plt.figure(figsize=(2.5, 0.5))plt.subplots_adjust(top=0.95, bottom=0.05, left=0.01, right=0.99)plt.subplot(111)plt.axis('off')plt.imshow(a, aspect='auto', cmap=self, origin='lower')plt.text(0.5, 0.5, self._name,verticalalignment='center', horizontalalignment='center',fontsize=12, transform=plt.gca().transAxes)plt.show()
def coltbl(cmap_file):pattern = re.compile(r'(\d\.?\d*)\s+(\d\.?\d*)\s+(\d\.?\d*).*')with open(cmap_file) as cmap:cmap_buff = cmap.read()cmap_buff = re.compile('ncolors.*\n').sub('', cmap_buff)if re.search(r'\s*\d\.\d*', cmap_buff):return np.asarray(pattern.findall(cmap_buff), 'f4')else:return np.asarray(pattern.findall(cmap_buff), 'u1') / 255.
def get_my_cmaps(cname):try:if cname in matplotlib.cm._cmap_registry:return matplotlib.cm.get_cmap(cname)except:passcmap_file = os.path.join( cname+ ".rgb")cmap = Colormap(coltbl(cmap_file), name=cname)matplotlib.cm.register_cmap(name=cname, cmap=cmap)return cmap

一个类，俩函数，大家可以研究一下怎么实现引入NCL色标的，这里因为篇幅的关系，就不介绍了，有兴趣的朋友可以留言，下次单独出一期来介绍引入NCL色标的原理。

（二）、数据读取的函数

本次数据使用的是Micaps导出的二进制欧细资料，读取函数如下

from read_mdfs import MDFS_Grid
from scipy.interpolate import interpolate
def get_lat_lon(filepath):#获取经纬度数据a = MDFS_Grid(filepath)lon = a.data['Lon']lat = a.data['Lat'][::-1]#翻了纬度lon_scipy = np.arange(lon.min(), lon.max(), 0.05)#插值lat_scipy = np.arange(lat.min(), lat.max(), 0.05)return lon,lat,lon_scipy,lat_scipy
def read_data_hgt_or_tmp(filepath,lon,lat,lon_scipy,lat_scipy):#读位势高度和温度a = MDFS_Grid(filepath)data = a.data['Grid'][::8,::8]#为了数据更平滑，降低了精度spline = interpolate.RectBivariateSpline(lat[::8], lon[::8],data,)data = spline(lat_scipy,lon_scipy)#插值return data[::-1,:]#翻了纬度
def read_data(filepath):#读取一般数据，这里是风a = MDFS_Grid(filepath)data = a.data['Grid']return data[::-1,:]#翻了纬度

（三）、地图数据读取

后面画图为了做gif图片，所以要画好几次地图，但是读取地图数据可以一次完成，想办法减少运行时间是个好习惯哦。

with open('CN-border-La.dat') as src:context = src.read()blocks = [cnt for cnt in context.split('>') if len(cnt) > 0]borders = [np.fromstring(block, dtype=float, sep=' ') for block in blocks]

（四）、其他准备

fig = plt.figure(figsize=(47, 30), dpi=30)#画布的设置，也是一次就够了，画完一张图清空一次就好了，减少运行内存。
frames = []#用来存放图片的列表，为的是制作gif
TIMES = ['000','003','006','009','012']#做循环用的时次

三、循环画图

（一）、读取数据

for TIME in TIMES:#读取经纬度、500位势高度file_path = r"ECMWF_HR\ECMWF_HR_HGT_500_21122420." + TIMElon ,lat,lon_scipy,lat_scipy = get_lat_lon(file_path)hgt_500 = read_data_hgt_or_tmp(file_path,lon ,lat,lon_scipy,lat_scipy)#读取850温度file_path = r"ECMWF_HR\ECMWF_HR_TMP_850_21122420." + TIMEtem_850 = read_data_hgt_or_tmp(file_path,lon ,lat,lon_scipy,lat_scipy)#读取850U风file_path = r"ECMWF_HR\ECMWF_HR_UGRD_850_21122420." + TIMEu_850 = read_data(file_path)#读取850V风file_path = r"ECMWF_HR\ECMWF_HR_VGRD_850_21122420." + TIMEv_850 = read_data(file_path)

（二）、画图和地图设置

#加子图，做一些地图设置ax = fig.add_axes([0.02, 0.05, 0.9, 0.9], projection=ccrs.LambertConformal(central_latitude=90, central_longitude=105))extent = [80,130,20,55]ax.set_extent(extent,crs=ccrs.Geodetic())gl = ax.gridlines( draw_labels=True, linewidth=2, color='k', alpha=0.5, linestyle='--')gl.xformatter = LONGITUDE_FORMATTER  ##坐标刻度转换为经纬度样式gl.yformatter = LATITUDE_FORMATTERgl.xlabel_style = {'size': 30}gl.ylabel_style = {'size': 30}resolution_map = '50m'ax.add_feature(cfeature.OCEAN.with_scale(resolution_map))ax.add_feature(cfeature.LAND.with_scale(resolution_map))ax.add_feature(cfeature.RIVERS.with_scale(resolution_map))ax.add_feature(cfeature.LAKES.with_scale(resolution_map))for line in borders:ax.plot(line[0::2], line[1::2], '-', color='k',transform=ccrs.Geodetic())

这些也不介绍了，在之前的帖子有介绍，可以往前学习。

（三）、500位势高度和588、0℃线绘制

#画500位势高度ct = ax.contour(lon_scipy, lat_scipy, hgt_500, 20, colors='k',linewidths=3, transform=ccrs.PlateCarree())ax.clabel(ct, inline=True, fontsize=30, fmt='%d')#单画一遍588，红色clev = [588]ct = ax.contour(lon_scipy, lat_scipy, hgt_500, clev, colors='r',linewidths=3, transform=ccrs.PlateCarree())ax.clabel(ct, inline=True, fontsize=30, fmt='%d')#画0℃线，白色clev = [0.0]ct = ax.contour(lon_scipy, lat_scipy, tem_850, clev, colors='w',linewidths=5, transform=ccrs.PlateCarree())ax.clabel(ct, inline=True, fontsize=30, fmt='%d')

（四）、850温度绘制

#画850温度clevs = range(-45,46,3)#引入NCL色标my_cmap = get_my_cmaps("MPL_hsv")#翻转色标my_cmap = my_cmap.reversed()#填色cf = ax.contourf(lon_scipy, lat_scipy, tem_850, clevs, transform=ccrs.PlateCarree(), cmap=my_cmap)#设置色标position = fig.add_axes([0.92, 0.05, 0.02, 0.9])cb = fig.colorbar(cf, cax=position, orientation='vertical',ticks=range(-45,46,3))cb.set_label('Temperature ℃', fontdict={'size': 30})cb.ax.tick_params(which='major', direction='in', length=6, labelsize=30)

这里使用了 get_my_cmaps(）函数，这是自己写的，在文章开头有说明，要使用他得到NCL的色标MPL_hsv，同时需要在文件夹中放入MPL_hsv.rgb文件，有需要的朋友可以留下邮箱，作者给你们发。

（五）、850风场箭头绘制

#画风流场step = 5#设置步长，防止箭头过密ax.quiver(lon[::step],lat[::step],u_850[::step,::step],v_850[::step,::step],color='w',scale=600, width=0.002, pivot='mid', transform=ccrs.PlateCarree())

（六）、其他设置，制作GIF

#显示预报时次ax.text(0.85, -0.03, '21122420+' + TIME,transform=ax.transAxes, fontsize=50)#设置图片标题ax.set_title('ECMWF 850mb Temperature(shaded),Wind(vector)&500mb Geopotential Height(contour)',color='k',fontsize= 60)#保存单张图片plt.savefig(TIME+'.png', dpi=30)#清理画布plt.clf()#将绘制gif需要的静态图片名放入列表frames.append(imageio.imread(TIME+'.png'))
#跳出循环，制作GIF并保存
imageio.mimsave('aa.gif', frames, 'GIF', duration=1.0)

三、完整代码

from read_mdfs import MDFS_Grid
import re
import matplotlib.cm
from matplotlib import colors
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interpolateimport cartopy.crs as ccrs
from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
import cartopy.feature as cfeature
import os
import imageioclass Colormap(colors.ListedColormap):def __init__(self, c, name='from_list', n=None):self.colors = cself.name = nameself.N = nsuper(Colormap, self).__init__(self.colors, name=self.name, N=self.N)def __getitem__(self, item):return Colormap(self.colors[item], name='sliced_' + self.name)def show(self):a = np.outer(np.ones(10), np.arange(0, 1, 0.001))plt.figure(figsize=(2.5, 0.5))plt.subplots_adjust(top=0.95, bottom=0.05, left=0.01, right=0.99)plt.subplot(111)plt.axis('off')plt.imshow(a, aspect='auto', cmap=self, origin='lower')plt.text(0.5, 0.5, self._name,verticalalignment='center', horizontalalignment='center',fontsize=12, transform=plt.gca().transAxes)plt.show()
def coltbl(cmap_file):pattern = re.compile(r'(\d\.?\d*)\s+(\d\.?\d*)\s+(\d\.?\d*).*')with open(cmap_file) as cmap:cmap_buff = cmap.read()cmap_buff = re.compile('ncolors.*\n').sub('', cmap_buff)if re.search(r'\s*\d\.\d*', cmap_buff):return np.asarray(pattern.findall(cmap_buff), 'f4')else:return np.asarray(pattern.findall(cmap_buff), 'u1') / 255.
def get_my_cmaps(cname):try:if cname in matplotlib.cm._cmap_registry:return matplotlib.cm.get_cmap(cname)except:passcmap_file = os.path.join( cname+ ".rgb")cmap = Colormap(coltbl(cmap_file), name=cname)matplotlib.cm.register_cmap(name=cname, cmap=cmap)return cmapdef get_lat_lon(filepath):#获取经纬度数据a = MDFS_Grid(filepath)lon = a.data['Lon']lat = a.data['Lat'][::-1]#翻了纬度lon_scipy = np.arange(lon.min(), lon.max(), 0.05)#插值lat_scipy = np.arange(lat.min(), lat.max(), 0.05)return lon,lat,lon_scipy,lat_scipy
def read_data_hgt_or_tmp(filepath,lon,lat,lon_scipy,lat_scipy):#读位势高度和温度a = MDFS_Grid(filepath)data = a.data['Grid'][::8,::8]#为了数据更平滑，降低了精度spline = interpolate.RectBivariateSpline(lat[::8], lon[::8],data,)data = spline(lat_scipy,lon_scipy)#插值return data[::-1,:]#翻了纬度
def read_data(filepath):#读取一般数据，这里是风a = MDFS_Grid(filepath)data = a.data['Grid']return data[::-1,:]#翻了纬度with open('CN-border-La.dat') as src:context = src.read()blocks = [cnt for cnt in context.split('>') if len(cnt) > 0]borders = [np.fromstring(block, dtype=float, sep=' ') for block in blocks]
#画布的设置，也是一次就够了，画完一张图清空一次就好了，减少运行内存。
fig = plt.figure(figsize=(47, 30), dpi=30)
#用来存放图片的列表，为的是制作gif
frames = []
#做循环用的时次
TIMES = ['000','003','006','009','012']
#循环开始
for TIME in TIMES:#读取经纬度、500位势高度file_path = r"ECMWF_HR\ECMWF_HR_HGT_500_21122420." + TIMElon ,lat,lon_scipy,lat_scipy = get_lat_lon(file_path)hgt_500 = read_data_hgt_or_tmp(file_path,lon ,lat,lon_scipy,lat_scipy)#读取850温度file_path = r"ECMWF_HR\ECMWF_HR_TMP_850_21122420." + TIMEtem_850 = read_data_hgt_or_tmp(file_path,lon ,lat,lon_scipy,lat_scipy)#读取850U风file_path = r"ECMWF_HR\ECMWF_HR_UGRD_850_21122420." + TIMEu_850 = read_data(file_path)#读取850V风file_path = r"ECMWF_HR\ECMWF_HR_VGRD_850_21122420." + TIMEv_850 = read_data(file_path)#加子图，做一些地图设置ax = fig.add_axes([0.02, 0.05, 0.9, 0.9], projection=ccrs.LambertConformal(central_latitude=90, central_longitude=105))extent = [80,130,20,55]ax.set_extent(extent,crs=ccrs.Geodetic())gl = ax.gridlines( draw_labels=True, linewidth=2, color='k', alpha=0.5, linestyle='--')gl.xformatter = LONGITUDE_FORMATTER  ##坐标刻度转换为经纬度样式gl.yformatter = LATITUDE_FORMATTERgl.xlabel_style = {'size': 30}gl.ylabel_style = {'size': 30}resolution_map = '50m'ax.add_feature(cfeature.OCEAN.with_scale(resolution_map))ax.add_feature(cfeature.LAND.with_scale(resolution_map))ax.add_feature(cfeature.RIVERS.with_scale(resolution_map))ax.add_feature(cfeature.LAKES.with_scale(resolution_map))for line in borders:ax.plot(line[0::2], line[1::2], '-', color='k',transform=ccrs.Geodetic())#画500位势高度ct = ax.contour(lon_scipy, lat_scipy, hgt_500, 20, colors='k',linewidths=3, transform=ccrs.PlateCarree())ax.clabel(ct, inline=True, fontsize=30, fmt='%d')#单画一遍588，红色clev = [588]ct = ax.contour(lon_scipy, lat_scipy, hgt_500, clev, colors='r',linewidths=3, transform=ccrs.PlateCarree())ax.clabel(ct, inline=True, fontsize=30, fmt='%d')#画0℃线，白色clev = [0.0]ct = ax.contour(lon_scipy, lat_scipy, tem_850, clev, colors='w',linewidths=5, transform=ccrs.PlateCarree())ax.clabel(ct, inline=True, fontsize=30, fmt='%d')#画850温度clevs = range(-45,46,3)#引入NCL色标my_cmap = get_my_cmaps("MPL_hsv")#翻转色标my_cmap = my_cmap.reversed()#填色cf = ax.contourf(lon_scipy, lat_scipy, tem_850, clevs, transform=ccrs.PlateCarree(), cmap=my_cmap)#设置色标position = fig.add_axes([0.92, 0.05, 0.02, 0.9])cb = fig.colorbar(cf, cax=position, orientation='vertical',ticks=range(-45,46,3))cb.set_label('Temperature ℃', fontdict={'size': 30})cb.ax.tick_params(which='major', direction='in', length=6, labelsize=30)#画风流场step = 5#设置步长，防止箭头过密ax.quiver(lon[::step],lat[::step],u_850[::step,::step],v_850[::step,::step],color='w',scale=600, width=0.002, pivot='mid', transform=ccrs.PlateCarree())#显示预报时次ax.text(0.85, -0.03, '21122420+' + TIME,transform=ax.transAxes, fontsize=50)#设置图片标题ax.set_title('ECMWF 850mb Temperature(shaded),Wind(vector)&500mb Geopotential Height(contour)',color='k',fontsize= 60)#保存单张图片plt.savefig(TIME+'.png', dpi=30)#清理画布plt.clf()#将绘制gif需要的静态图片名放入列表frames.append(imageio.imread(TIME+'.png'))
#跳出循环，制作GIF并保存
imageio.mimsave('aa.gif', frames, 'GIF', duration=1.0)

这样，图片就复刻成功了

需要数据文件和代码的请留言邮箱，作者给发