Python绘制相关系数空间分布图
本帖最后由 第八星系人气爱 于 2024-2-29 17:48 编辑作者：第八星系—GuGu邮箱：gu2001yi@163.com
1.导入库
import numpy as np
import xarray as xr
import cmaps
import pandas as pd
from eofs.standard import Eof
import matplotlib.pyplot as plt
from scipy import signal
from sklearn import preprocessing
from scipy.stats import pearsonr
#from scipy.stats import linregress
import datetime as dt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.mpl.ticker as cticker
from cartopy.util import add_cyclic_point
import matplotlib.patches as patches2.读取数据
path = 'D:\\NCEP\\NCEPdata\\air.mon.mean.nc'
f_t=xr.open_dataset(path)
#挑选时间·位势高度·经纬度
yy= f_t['air'].loc)].loc['1954-01-01':'2003-09-01'].loc[:,500:200,90:0, 0:360]
#作夏季平均
yy1=np.array(yy).reshape(50,3,5,37,144).mean(1)
#纬向平均
xx2=np.array(yy1).mean(3)
xx3=xx2[:,:,:,np.newaxis]
#计算扰动温度
tt=yy1-xx3
apo1=np.array(yy1).mean(1)
#计算指数
apo0,apo= np.zeros(50),np.zeros(50)
for i in range(0,50):
apo0 = np.mean(apo1) - np.mean(apo1)
path = "D:\\SSTdata\\sst.mnmean.nc"
f_t = xr.open_dataset(path)
tt1=np.array(f_t.sst.loc)].loc['1954-05-01':'2003-09-01']).reshape(50,3,89,180)
tt1=0
tt1=np.array(tt1).mean(1)#目标区域海温
lat = f_t.lat
lon = f_t.lon
r,p = np.zeros((89,180)),np.zeros((89,180))
for i in range(len(lat)):
for j in range(len(lon)):
r, p, = pearsonr(apo0,tt1[:,i,j])3.绘图
fig = plt.figure(figsize=(10,10))
proj = ccrs.PlateCarree(central_longitude=180)
leftlon, rightlon, lowerlat, upperlat = (120,240,20,60)
img_extent =
ax = fig.add_axes(,projection = proj)
ax.set_extent(img_extent, crs=ccrs.PlateCarree())
ax.add_feature(cfeature.COASTLINE)
#ax.add_feature(cfeature.LAND,color='whitesmoke')
#ax.add_feature(cfeature.OCEAN.with_scale('50m'))
#ax.add_feature(cfeature.LAKES.with_scale('50m'))
ax.set_xticks(np.arange(leftlon,rightlon+20,20), crs=ccrs.PlateCarree())
ax.set_yticks(np.arange(lowerlat,upperlat+10,10), crs=ccrs.PlateCarree())
lon_formatter = cticker.LongitudeFormatter()
lat_formatter = cticker.LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.set_title('corr:APOI-sst',loc='left',fontsize =15)
c1 = ax.contourf(lon,lat, r, zorder=0,levels =np.arange(-1,1.1,0.1) , extend = 'both', transform=ccrs.PlateCarree(), cmap=plt.cm.bwr)
c1b = ax.contourf(lon,lat, p,levels=, zorder=1,hatches=['..', None],colors="none", transform=ccrs.PlateCarree())#显著性检验打点
position=fig.add_axes()
fig.colorbar(c1,cax=position,orientation='horizontal',format='%.1f',)
plt.show()
示意图
4.完整代码
import numpy as np
import xarray as xr
import cmaps
import pandas as pd
from eofs.standard import Eof
import matplotlib.pyplot as plt
from scipy import signal
from sklearn import preprocessing
from scipy.stats import pearsonr
#from scipy.stats import linregress
import datetime as dt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.mpl.ticker as cticker
from cartopy.util import add_cyclic_point
import matplotlib.patches as patches
path = 'D:\\NCEP\\NCEPdata\\air.mon.mean.nc'
f_t=xr.open_dataset(path)
#挑选时间·位势高度·经纬度
yy= f_t['air'].loc)].loc['1954-01-01':'2003-09-01'].loc[:,500:200,90:0, 0:360]
#作夏季平均
yy1=np.array(yy).reshape(50,3,5,37,144).mean(1)
#纬向平均
xx2=np.array(yy1).mean(3)
xx3=xx2[:,:,:,np.newaxis]
#计算扰动温度
tt=yy1-xx3
apo1=np.array(yy1).mean(1)
#计算指数
apo0,apo= np.zeros(50),np.zeros(50)
for i in range(0,50):
apo0 = np.mean(apo1) - np.mean(apo1)
path = "D:\\SSTdata\\sst.mnmean.nc"
f_t = xr.open_dataset(path)
tt1=np.array(f_t.sst.loc)].loc['1954-05-01':'2003-09-01']).reshape(50,3,89,180)
tt1=0
tt1=np.array(tt1).mean(1)#目标区域海温
lat = f_t.lat
lon = f_t.lon
r,p = np.zeros((89,180)),np.zeros((89,180))
for i in range(len(lat)):
for j in range(len(lon)):
r, p, = pearsonr(apo0,tt1[:,i,j])
#绘图
fig = plt.figure(figsize=(10,10))
proj = ccrs.PlateCarree(central_longitude=180)
leftlon, rightlon, lowerlat, upperlat = (120,240,20,60)
img_extent =
ax = fig.add_axes(,projection = proj)
ax.set_extent(img_extent, crs=ccrs.PlateCarree())
ax.add_feature(cfeature.COASTLINE)
#ax.add_feature(cfeature.LAND,color='whitesmoke')
#ax.add_feature(cfeature.OCEAN.with_scale('50m'))
#ax.add_feature(cfeature.LAKES.with_scale('50m'))
ax.set_xticks(np.arange(leftlon,rightlon+20,20), crs=ccrs.PlateCarree())
ax.set_yticks(np.arange(lowerlat,upperlat+10,10), crs=ccrs.PlateCarree())
lon_formatter = cticker.LongitudeFormatter()
lat_formatter = cticker.LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.set_title('corr:APOI-sst',loc='left',fontsize =15)
c1 = ax.contourf(lon,lat, r, zorder=0,levels =np.arange(-1,1.1,0.1) , extend = 'both', transform=ccrs.PlateCarree(), cmap=plt.cm.bwr)
c1b = ax.contourf(lon,lat, p,levels=, zorder=1,hatches=['..', None],colors="none", transform=ccrs.PlateCarree())#显著性检验打点
position=fig.add_axes()
fig.colorbar(c1,cax=position,orientation='horizontal',format='%.1f',)
plt.show()
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