Python 如何在等高线图上绘制具有条件的阵列？

我使用以下代码绘制了GPP的全球地图：

（“lon”和“lat”都是netCDF4属性，其形状分别为（144）和（90），而“gpp_avg”是形状为（90144）的numpy数组）

我有一个numpy数组“ci_95_gpp”，它的形状（90144）包含全局贴图每个网格单元的p值。我想在全球等高线图的顶部绘制p值大于2的点

---

我该怎么做呢？非常感谢。

我为Cartopy地图上的等高线图生成了一组数据。轮廓数据点分为两组，z值为负值和正值。Numpy

maskedarray

用于该操作。我希望这对普通读者有用，包括OP

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as mticker
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import pandas as pd

from numpy.random import uniform, seed
from matplotlib.mlab import griddata   
# TODO, use newer scipy.interpolate() instead of `griddata`
import numpy.ma as ma

# make up some data around long,lat: (90, 18)
seed(0)
npts = 200
x0, y0 = 90, 18   # center of map in (long, lat), degrees
x = x0+uniform(-2, 2, npts)
y = y0+uniform(-2, 2, npts)
#z = x*np.exp(-x**2 - y**2)
z = (x-x0)*np.exp(-(x-x0)**2 - (y-y0)**2)  # elevation in meters

# define grid, for points interpolation from the made-up data above
gridx, gridy = 50,50
xi = x0+np.linspace(-2.1, 2.1, gridx)
yi = y0+np.linspace(-2.1, 2.1, gridy)

# interpolate for gridded data of (gridx, gridy) 
zi = griddata(x, y, z, xi, yi, interp='linear')
# xi.shape, yi.shape, zi.shape  => ((50,), (50,), (50, 50))

xig,yig = np.meshgrid(xi, yi)

# projection
useproj = ccrs.PlateCarree()
fig = plt.figure(figsize = (9, 7))
rect = [0.05, 0.05, 0.95, 0.95]  # for map extent
ax = fig.add_axes( rect, projection=useproj )

# contour the gridded data, plotting dots at the nonuniform data points.
CS = ax.contour(xig, yig, zi, 15, linewidths=0.5, colors='k')
CS = ax.contourf(xig, yig, zi, 15,
                  vmax=abs(zi).max(), vmin=-abs(zi).max())
plt.colorbar(CS)  # draw colorbar

# prep points for scatterplot of the gridded points
# make 2 masked-arrays, based on `zi`
mag = ma.masked_greater(zi, 0)  # mask points with +ve zi values
mal = ma.masked_less(zi, 0)     # mask points with -ve zi values

# apply masking to xig,yig; borrowing mask from mag
xig_greater_masked = ma.MaskedArray(xig, mask=mag.mask)  # must have compatible values
yig_greater_masked = ma.MaskedArray(yig, mask=mag.mask)

# apply masking to xig,yig; borrowing mask from mal
xig_less_masked = ma.MaskedArray(xig, mask=mal.mask)
yig_less_masked = ma.MaskedArray(yig, mask=mal.mask)

# for points with -ve z values (result of .masked_greater)
plt.scatter(xig_greater_masked, yig_greater_masked, s=3, color="w", \
            alpha=1, zorder=15, label="masked_greater z")
# for points with +ve z values (result of .masked_less)
ax.scatter(xig_less_masked, yig_less_masked, s=3, color="r", alpha=1, \
           zorder=15, label="masked_less z") 

leg = ax.legend(title='Masked z', framealpha=1.0, facecolor="lightgray")
leg.set_zorder(20)

gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
                  linewidth=2, color='gray', alpha=0.5, linestyle='--')

gl.xlabels_top = False
gl.ylabels_right = False

gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 15, 'color': 'gray'}
#gl.xlabel_style = {'color': 'gray', 'weight': 'bold'}

plt.title('Masked data plot on contour')
plt.show()

结果图：

---

如何将数据输入这些变量：

lon

，

lat

，

gpp\u avg

。它们对函数

contourf（）

有效吗？我已经编辑了问题中的代码片段，以显示我如何加载

lon

lat

和

gpp\u avg

的数据。我已经设法解决了我的问题-非常感谢您的帮助！！

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as mticker
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import pandas as pd

from numpy.random import uniform, seed
from matplotlib.mlab import griddata   
# TODO, use newer scipy.interpolate() instead of `griddata`
import numpy.ma as ma

# make up some data around long,lat: (90, 18)
seed(0)
npts = 200
x0, y0 = 90, 18   # center of map in (long, lat), degrees
x = x0+uniform(-2, 2, npts)
y = y0+uniform(-2, 2, npts)
#z = x*np.exp(-x**2 - y**2)
z = (x-x0)*np.exp(-(x-x0)**2 - (y-y0)**2)  # elevation in meters

# define grid, for points interpolation from the made-up data above
gridx, gridy = 50,50
xi = x0+np.linspace(-2.1, 2.1, gridx)
yi = y0+np.linspace(-2.1, 2.1, gridy)

# interpolate for gridded data of (gridx, gridy) 
zi = griddata(x, y, z, xi, yi, interp='linear')
# xi.shape, yi.shape, zi.shape  => ((50,), (50,), (50, 50))

xig,yig = np.meshgrid(xi, yi)

# projection
useproj = ccrs.PlateCarree()
fig = plt.figure(figsize = (9, 7))
rect = [0.05, 0.05, 0.95, 0.95]  # for map extent
ax = fig.add_axes( rect, projection=useproj )

# contour the gridded data, plotting dots at the nonuniform data points.
CS = ax.contour(xig, yig, zi, 15, linewidths=0.5, colors='k')
CS = ax.contourf(xig, yig, zi, 15,
                  vmax=abs(zi).max(), vmin=-abs(zi).max())
plt.colorbar(CS)  # draw colorbar

# prep points for scatterplot of the gridded points
# make 2 masked-arrays, based on `zi`
mag = ma.masked_greater(zi, 0)  # mask points with +ve zi values
mal = ma.masked_less(zi, 0)     # mask points with -ve zi values

# apply masking to xig,yig; borrowing mask from mag
xig_greater_masked = ma.MaskedArray(xig, mask=mag.mask)  # must have compatible values
yig_greater_masked = ma.MaskedArray(yig, mask=mag.mask)

# apply masking to xig,yig; borrowing mask from mal
xig_less_masked = ma.MaskedArray(xig, mask=mal.mask)
yig_less_masked = ma.MaskedArray(yig, mask=mal.mask)

# for points with -ve z values (result of .masked_greater)
plt.scatter(xig_greater_masked, yig_greater_masked, s=3, color="w", \
            alpha=1, zorder=15, label="masked_greater z")
# for points with +ve z values (result of .masked_less)
ax.scatter(xig_less_masked, yig_less_masked, s=3, color="r", alpha=1, \
           zorder=15, label="masked_less z") 

leg = ax.legend(title='Masked z', framealpha=1.0, facecolor="lightgray")
leg.set_zorder(20)

gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
                  linewidth=2, color='gray', alpha=0.5, linestyle='--')

gl.xlabels_top = False
gl.ylabels_right = False

gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 15, 'color': 'gray'}
#gl.xlabel_style = {'color': 'gray', 'weight': 'bold'}

plt.title('Masked data plot on contour')
plt.show()