问题描述
我试图找到一种方法来可视化 MJO 的各个区域/阶段。我相信这样做的一种方法是绘制分隔每个相位区域的经度线（大约在 60E、80E、100E、120E、140E、160E、180），但我不确定是否可以添加到我现有的地块。
我正在使用来自 NCEI 的 GRID-Sat B1 数据。这是我当前的代码：
import matplotlib.pyplot as plt
from metpy.plots import declarative,colortables
import cartopy.crs as ccrs
import xarray as xr
file = "GRIDSAT-B1.2003.11.23.00.v02r01.nc"
dataset = xr.open_dataset(file)
vtime = dataset.time.values.astype('datetime64[s]').astype('O')
date_long = vtime[0]
date = date_long.strftime("%d-%b-%Y-%HZ")
# Create water vapor image
img = declarative.ImagePlot()
img.data = dataset
img.field = 'irwvp'
img.colormap = 'WVCimsS_r'
img.image_range = (180,280)
panel = declarative.MapPanel()
panel.layers = ['coastline','borders']
panel.title = f'GridSat-B1 (Water Vapor Imagery): {date}'
panel.projection = (ccrs.Mollweide(central_longitude=-240))
panel.area = ([-370,-140,-30,30])
panel.layout = (2,1,2)
panel.plots = [img]
# Create the IR image
img2 = declarative.ImagePlot()
img2.data = dataset
img2.field = 'irwin_cdr'
img2.colormap = 'turbo_r' #maybe use cubehelix instead?
img2.image_range = (180,300)
panel2 = declarative.MapPanel()
panel2.layers = ['coastline','borders']
panel2.title = f'GridSat-B1 (Infrared Imagery): {date}'
panel2.projection = (ccrs.Mollweide(central_longitude=-240))
panel2.area = ([-370,30])
panel2.layout = (2,1)
panel2.plots = [img2]
# Plot both panels in one figure
pc = declarative.PanelContainer()
pc.size = (20,14)
pc.panels = [panel,panel2]
pc.show()
感谢任何帮助/建议 - 提前致谢！
解决方法
MetPy 的声明式接口没有内置任何内容，但幸运的是 MapPanel 对象公开了一个 .ax 属性，该属性为您提供了一个 Matplotlib Axes 对象及其所有绘图方法：
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import metpy.plots as mpplots
import numpy as np
import xarray as xr
file = "/Users/rmay/Downloads/GRIDSAT-B1.2003.11.23.00.v02r01.nc"
dataset = xr.open_dataset(file)
vtime = dataset.time.values.astype('datetime64[s]').astype('O')
date_long = vtime[0]
date = date_long.strftime("%d-%b-%Y-%HZ")
# Create water vapor image
img = mpplots.ImagePlot()
img.data = dataset
img.field = 'irwvp'
img.colormap = 'WVCIMSS_r'
img.image_range = (180,280)
panel = mpplots.MapPanel()
panel.layers = ['coastline','borders']
panel.title = f'GridSat-B1 (Water Vapor Imagery): {date}'
panel.projection = ccrs.Mollweide(central_longitude=-240)
panel.area = (-370,-140,-30,30)
panel.layout = (2,1,2)
panel.plots = [img]
# Create the IR image
img2 = mpplots.ImagePlot()
img2.data = dataset
img2.field = 'irwin_cdr'
img2.colormap = 'turbo_r' #maybe use cubehelix instead?
img2.image_range = (180,300)
panel2 = mpplots.MapPanel()
panel2.layers = ['coastline','borders']
panel2.title = f'GridSat-B1 (Infrared Imagery): {date}'
panel2.projection = ccrs.Mollweide(central_longitude=-240)
panel2.area = (-370,30)
panel2.layout = (2,1)
panel2.plots = [img2]
# Plot both panels in one figure
pc = mpplots.PanelContainer()
pc.size = (20,14)
pc.panels = [panel,panel2]
lons = np.array([60,80,100,120,140,160,180]).reshape(1,-1)
lats = np.linspace(-90,90).reshape(-1,1)
# Match up the arrays into 2xN arrays fit to plot in call
lons,lats = np.broadcast_arrays(lons,lats)
# Needs to be *after* the panels are assigned to a PanelContainer
# Using Geodetic gives lines interpolated on the curved globe
panel.ax.plot(lons,lats,transform=ccrs.Geodetic(),color='black',linewidth=3)
panel2.ax.plot(lons,linewidth=3)
pc.show()
（注意：不建议直接从 metpy 的声明性模块导入，因为这是一个可能会更改的实现细节——只需从 metpy.plots 获取内容）。所以这是使用 Matplotlib 对 plot 的标准调用来绘制线条。另一种选择是使用 CartoPy 的 Gridliner。