python numpy scipy griddata 是 nan 或所有相同的值

Question

我正在尝试使用 numpy、matplotlib plyplot 和 scipy 在 python 中绘制具有不均匀间隔数据的等高线。

给定以下代码片段，为什么 zi 为空或所有值相同？

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata

lon_min = 1.8783669
lon_max = 1.8792678
lat_min = 57.45827
lat_max = 57.459293

x = [ 520.99012099,652.23665224,800.,0.,520.99012099
  652.23665224,800.,0.,520.99012099,652.23665224 ...]

y = [   0.,379.47214076,437.53665689,600.,0.
  379.47214076,437.53665689,600.,0.,379.47214076 ...]

z = [ 56.6,56.6,56.6,56.6,45.3,45.3,45.3,45.3,57.8,57.8 ...]

xi = np.linspace(lon_min,lon_max,10)
yi = np.linspace(lat_min,lat_max,10)
zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='nearest')

plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k') # this is blank or all the same colour because zi is either nan or all the same number depending on the method I use.

应用一些调试，如果我使用 method=cubic/linear，则 zi 看起来是 NAN，如果我使用 method=nearest，则看起来是所有相同的数字

print xi
print yi
print zi    

给出: 习 = [ 1.8783669 1.878376 1.8783851 1.8783942 1.8784033 1.8784124 1.8784215 1.8784306 1.8784397 1.8784488 1.8784579 1.878467 1.8784761 1.8784852 1.8784943 1.8785034 1.8785125 ....]

yi = [57.45827     57.45828033  57.45829067  57.458301    57.45831133
  57.45832167  57.458332    57.45834233  57.45835267  57.458363
  57.45837333  57.45838367  57.458394    57.45840433  57.45841467
  57.458425    57.45843533  57.45844567  57.458456    57.45846633 .... ]

zi = [[ nan  nan  nan ...,  nan  nan  nan]
 [ nan  nan  nan ...,  nan  nan  nan]
 [ nan  nan  nan ...,  nan  nan  nan]
 ...,
 [ nan  nan  nan ...,  nan  nan  nan]
 [ nan  nan  nan ...,  nan  nan  nan]
 [ nan  nan  nan ...,  nan  nan  nan]]

zi = [[ 46.7  46.7  46.7 ...,  46.7  46.7  46.7]
 [ 46.7  46.7  46.7 ...,  46.7  46.7  46.7]
 [ 46.7  46.7  46.7 ...,  46.7  46.7  46.7]
 ...,
 [ 46.7  46.7  46.7 ...,  46.7  46.7  46.7]
 [ 46.7  46.7  46.7 ...,  46.7  46.7  46.7]
 [ 46.7  46.7  46.7 ...,  46.7  46.7  46.7]]

Answer 1

您是否尝试使用 tricontour 直接绘制数据轮廓？

http://matplotlib.org/api/pyplot_api.html?highlight=tricontour#matplotlib.pyplot.tricontour

plt.tricontour(x, y, z)

或者如果您需要查看底层网格:

import matplotlib.tri as mtri
triang = mtri.Triangulation(x, y)
plt.tricontour(triang, z)
plt.triplot(triang)

在您的情况下，三角测量实际上减少为 3 个三角形，因为您有重复的点，因此最多必须为相同位置选择一个唯一的 z 值。对于填充轮廓，使用 tricontourf 可以更好地看到发生了什么。重复点还解释了为什么插值例程可能对该数据集有问题...

现在，如果您为 4 个数据点中的每一个随机选择 1 个任意 z 值

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as mtri

x = np.array([520.99012099, 652.23665224, 800., 0.])
y = np.array([0., 379.47214076, 437.53665689, 600.])
z = np.array([45.3, 57.8, 57.8, 57.8])

triang = mtri.Triangulation(x, y)
refiner = mtri.UniformTriRefiner(triang)
refi_triang, refi_z = refiner.refine_field(z, subdiv=4)

levels = np.linspace(45, 61, 33)

CS_colors = plt.tricontourf(refi_triang, refi_z, levels=levels)
plt.triplot(triang, color="white")
plt.colorbar()

CS_lines = plt.tricontour(refi_triang, refi_z, levels=levels, colors=['black'])
plt.clabel(CS_lines, CS_lines.levels, inline=True, fontsize=10)

plt.show()