求助！！【已经解决了，麻烦了】

胡萝卜糊了

代码：import xarray as xrimport numpy as np
import datetime as dt
import pandas as pd
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import scipy.stats
import matplotlib
from scipy.stats import pearsonr
from netCDF4 import Dataset
from scipy.stats import linregress
matplotlib.rcParams['axes.unicode_minus']=False#负号
matplotlib.rcParams['font.family'] = 'SimHei'#中文

f = xr.open_dataset('rainfall_and_sst/HadISST_sst.nc')
df = pd.read_csv('rainfall_and_sst/多年平均值.csv',encoding='gbk')

years = np.arange(1960, 2023)

sst_year_data = f['sst'].loc[f.time.dt.year.isin(years) & f.time.dt.month.isin([6,7,8])]
rainfall_data = np.array(df['多年平均值'])

sst_year_data_filtered = sst_year_data.where(sst_year_data != -1000, drop=True)
annual_mean_data = sst_year_data_filtered.groupby('time.year').mean('time', skipna=True)

# 获取经度和纬度坐标
lon_values = annual_mean_data.coords['longitude'].values
lat_values = annual_mean_data.coords['latitude'].values

correlation_data = []

time_steps = annual_mean_data.shape[0]

for lat_idx in range(annual_mean_data.shape[1]):  # 纬度
    for lon_idx in range(annual_mean_data.shape[2]):  # 经度
        lon = lon_values[lon_idx]
        lat = lat_values[lat_idx]

        sst_data_station = annual_mean_data[:, lat_idx, lon_idx]

        slope, intercept, r_value, p_value, std_err = linregress( sst_data_station,rainfall_data )

        correlation_data.append((slope, intercept, r_value, p_value, lon, lat))

correlation_coefficients = np.array([data[2] for data in correlation_data])
correlation_coefficients_2d = correlation_coefficients.reshape(annual_mean_data.shape[1], annual_mean_data.shape[2])

lon_grid,lat_grid = np.meshgrid( lon_values,lat_values)

# 绘制相关系数填色图
plt.figure(figsize=(20, 12), dpi=600)
ax = plt.axes(projection=ccrs.PlateCarree())
plt.contourf( lon_grid,lat_grid, correlation_coefficients_2d, levels=np.arange(-0.5,0.6,0.1), cmap='coolwarm')
ax.coastlines()
colorbar = plt.colorbar(orientation='horizontal', ticks=np.arange(-0.5, 0.6, 0.1), shrink=0.7, extend='both', pad=0.05)
colorbar.ax.tick_params(labelsize=15)
colorbar.set_label('相关系数(打点表示通过90%置信水平检验)', fontsize=15)

# 设置经度和纬度刻度
ax.set_xticks([-180,-150, -120,-90, -60,-30, 0,30, 60,90, 120,150, 180], crs=ccrs.PlateCarree())
ax.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())
ax.set_xticklabels(['180°W','150°W', '120°W','90°W', '60°W','30°W', '0°', '30°E','60°E','90°E', '120°E','150°E', '180°E'], fontsize=12)
ax.set_yticklabels(['90°S', '60°S', '30°S', '0°', '30°N', '60°N', '90°N'], fontsize=12)

# 显著性填充
p_value = np.asarray([data[3] for data in correlation_data]).reshape((annual_mean_data.shape[1], annual_mean_data.shape[2]))
c1b = ax.contourf(lon_grid,lat_grid, p_value, [np.nanmin(p_value), 0.1, np.nanmax(p_value)], hatches=['.', None], colors="none", transform=ccrs.PlateCarree())

plt.show()

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