分析气象数据，向Python Cartopy地图中添加循环点

import xarray as xr 
import cartopy.crs as ccrs 
import matplotlib.pyplot as plt 
import numpy as np

# 读取形状为（16, 1801, 3600）的数据集
# 选择第5个时间索引，以获得2D数组
# 将开尔文转换为摄氏度
ds = xr.open_dataset("data/era5_temperature.grib", engine="cfgrib")
ds = ds.isel(time=5) - 273.15

# 定义绘图函数
def plot_data(ds):
    # 指定投影
    proj = ccrs.PlateCarree()

    # 用投影对象创建一个图和轴
    fig, ax = plt.subplots(figsize=(16,9), subplot_kw={'projection': proj})

    # 为等高线图创建层次
    temperature_levels = np.arange(-40,41,2)

    # 增加更多的参数
    additional_kwargs = {
        "antialised": True,
        "transform": proj,
        "levels": temperature_levels
    }

    # 绘制数据
    ds["t2m"].plot.contourf(ax=ax, cmap='nipy_spectral', **additional_kwargs)

    # 设置范围
    ax.set_extent([-10, 20, 40, 60], crs=proj)

    plt.show()

plot_data(ds)

from cartopy.util import add_cyclic_point
import xarray as xr 
import cartopy.crs as ccrs 
import matplotlib.pyplot as plt 
import numpy as np

def add_cyclic_point_to_dataset(ds):
    # 加载数据
    data = ds["t2m"]

    # 用循环点生成数据，用循环点生成经度
    cyclic_data, cyclic_longitude = add_cyclic_point(data.values, coord=data['longitude'])

    # 创建新的坐标，将用于创建新的数据集
    # 复制现有数据集的坐标，用循环经度替换经度
    coords = {dim: data.coords[dim] for dim in data.dims}
    coords["longitude"] = cyclic_longitude

    new_ds = xr.Dataset(
        data_vars={
            "t2m": xr.DataArray(cyclic_data, dims=data.dims, coords=coords)
        })
    return new_ds 

new_ds = add_cyclic_point_to_dataset(ds)
new_ds["t2m"].shape
# (1801, 3601)

difference = ds["t2m"].sel(lnotallow=0, method="nearest") - new_ds["t2m"].sel(lnotallow=360, method="nearest")
difference = difference.fillna(0)  # fill nan values with 0
any(difference != 0)
# False