给定两个nc文件here：

data/CMIP6_UKESM1-0-LL_Lmon_piControl_r1i1p1f2_gpp_1960-3059.nc
data/CMIP6_UKESM1-0-LL_Amon_piControl_r1i1p1f2_tas_1960-3059.nc

读取第一个文件：

from netCDF4 import Dataset
import numpy as np

ds1 = Dataset('data/CMIP6_UKESM1-0-LL_Lmon_piControl_r1i1p1f2_gpp_1960-3059.nc')
print(ds1.variables.keys()) # get all variable names

输出：

odict_keys(['gpp', 'time', 'time_bnds', 'lat', 'lat_bnds', 'lon', 'lon_bnds', 'clim_season', 'season_year'])

读取第二个文件：

ds2 = Dataset('data/CMIP6_UKESM1-0-LL_Amon_piControl_r1i1p1f2_tas_1960-3059.nc')
print(ds2.variables.keys())

输出：

odict_keys(['tas', 'time', 'time_bnds', 'lat', 'lat_bnds', 'lon', 'lon_bnds', 'clim_season', 'height', 'season_year'])

检查gpp变量：

gpp = ds1.variables['gpp'] # gpp variable
print(gpp)

输出：

<class 'netCDF4._netCDF4.Variable'>
float32 gpp(time, lat, lon)
    _FillValue: 1e+20
    standard_name: gross_primary_productivity_of_biomass_expressed_as_carbon
    long_name: Carbon Mass Flux out of Atmosphere Due to Gross Primary Production on Land [kgC m-2 s-1]
    units: kg m-2 s-1
    cell_methods: area: mean where land time: mean
    coordinates: clim_season season_year
unlimited dimensions: 
current shape = (3300, 144, 192)
filling on

检查tas变量：

tas = ds2.variables['tas'] # tas variable
print(tas)

输出：

<class 'netCDF4._netCDF4.Variable'>
float32 tas(time, lat, lon)
    _FillValue: 1e+20
    standard_name: air_temperature
    long_name: Near-Surface Air Temperature
    units: K
    cell_methods: area: time: mean
    coordinates: clim_season height season_year
unlimited dimensions: 
current shape = (3300, 144, 192)
filling on

现在，我想计算gpp和tas之间的相关性，然后将它们的相关值绘制在Map上。
我怎么能这么做？谢谢。