我试着把在一个由5个地点组成的城市地区观测到的温度数据进行插值。我正在使用cartopy来插值和绘制地图，但是，当我运行脚本时，温度插值并没有显示出来，我只能用调色板得到城市区域的图层。有人能帮我纠正这个错误吗？shapefile的链接是
https://www.dropbox.com/s/0u76k3yegvr09sx/LimiteAMG.shp?dl=0
https://www.dropbox.com/s/yxsmm3v2ey3ngsp/LimiteAMG.cpg?dl=0
https://www.dropbox.com/s/yx05n31dfkggbb6/LimiteAMG.dbf?dl=0
https://www.dropbox.com/s/a6nk0xczgjeen2d/LimiteAMG.prj?dl=0
https://www.dropbox.com/s/royw7s51n2f0a6x/LimiteAMG.qpj?dl=0
https://www.dropbox.com/s/7k44dcl1k5891qc/LimiteAMG.shx?dl=0
数据
Lat Lon tmax
0 20.8208 -103.4434 22.8
1 20.7019 -103.4728 17.7
2 20.6833 -103.3500 24.9
3 20.6280 -103.4261 NaN
4 20.7205 -103.3172 26.4
5 20.7355 -103.3782 25.7
6 20.6593 -103.4136 NaN
7 20.6740 -103.3842 25.8
8 20.7585 -103.3904 NaN
9 20.6230 -103.4265 NaN
10 20.6209 -103.5004 NaN
11 20.6758 -103.6439 24.5
12 20.7084 -103.3901 24.0
13 20.6353 -103.3994 23.0
14 20.5994 -103.4133 25.0
15 20.6302 -103.3422 NaN
16 20.7400 -103.3122 23.0
17 20.6061 -103.3475 NaN
18 20.6400 -103.2900 23.0
19 20.7248 -103.5305 24.0
20 20.6238 -103.2401 NaN
21 20.4753 -103.4451 NaN
代码：
import cartopy
import cartopy.crs as ccrs
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import cartopy.io.shapereader as shpreader
from metpy.calc import get_wind_components
from metpy.cbook import get_test_data
from metpy.gridding.gridding_functions import interpolate, remove_nan_observation
from metpy.plots import add_metpy_logo
from metpy.units import units
to_proj = ccrs.PlateCarree()
data=pd.read_csv('/home/borisvladimir/Documentos/Datos/EMAs/EstacionesZMG/RedZMG.csv',usecols=(1,2,3),names=['Lat','Lon','tmax'],na_values=-99999,header=0)
fname='/home/borisvladimir/Dropbox/Diversos/Shapes/LimiteAMG.shp'
adm1_shapes = list(shpreader.Reader(fname).geometries())
lon = data['Lon'].values
lat = data['Lat'].values
xp, yp, _ = to_proj.transform_points(ccrs.Geodetic(), lon, lat).T
x_masked, y_masked, t = remove_nan_observations(xp, yp, data['tmax'].values)
#Interpola temp usando Cressman
tempx, tempy, temp = interpolate(x_masked, y_masked, t, interp_type='cressman', minimum_neighbors=3, search_radius=400000, hres=35000)
temp = np.ma.masked_where(np.isnan(temp), temp)
levels = list(range(-20, 20, 1))
cmap = plt.get_cmap('viridis')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
fig = plt.figure(figsize=(15, 10))
view = fig.add_subplot(1, 1, 1, projection=to_proj)
view.add_geometries(adm1_shapes, ccrs.PlateCarree(),edgecolor='black', facecolor='white', alpha=0.5)
view.set_extent([-103.8, -103, 20.3, 21.099 ], ccrs.PlateCarree())
ZapLon,ZapLat=-103.50,20.80
GuadLon,GuadLat=-103.33,20.68
TonaLon,TonaLat=-103.21,20.62
TlaqLon,TlaqLat=-103.34,20.59
TlajoLon,TlajoLat=-103.44,20.47
plt.text(ZapLon,ZapLat,'Zapopan',transform=ccrs.Geodetic())
plt.text(GuadLon,GuadLat,'Guadalajara',transform=ccrs.Geodetic())
plt.text(TonaLon,TonaLat,'Tonala',transform=ccrs.Geodetic())
plt.text(TlaqLon,TlaqLat,'Tlaquepaque',transform=ccrs.Geodetic())
plt.text(TlajoLon,TlajoLat,'Tlajomulco',transform=ccrs.Geodetic())
mmb = view.pcolormesh(tempx, tempy, temp,transform=ccrs.PlateCarree(),cmap=cmap, norm=norm)
plt.colorbar(mmb, shrink=.4, pad=0.02, boundaries=levels)
plt.show()
最佳答案
问题在于调用MetPy的interpolate函数。当设置为hres=35000时，它将生成一个间距为35km的网格。但是，您的数据点的间距似乎比这要近得多；这样，生成的网格只有两个点，如下面的红色点所示（黑色点是具有非屏蔽数据的原始站点）：
结果是它只为网格创建两个点，这两个点都在数据点的边界之外；因此这些点最终被屏蔽。相反，如果我们将hres设置为更低的值，比如5km（即5000），则会得到一个更合理的结果：