Contourf在有限数据上绘制空白。 过去的答案不正确

Question

Comments in code include proposed fix using np.linespace limits. This does not correct the problem in spite of the following given answer from the question "contourf() plots white space over finite data":If you want to make sure they all data is included you may define you own levels to use

plt.contourf(x, y, Z, np.linspace(Z.min(), Z.max(), 100)) The provided solution was thought to work then found not to in the older post. The statement was made the problem was not seen or not repeatable so the answer was never given. The code here reproduces the problem on both pi and win7 platforms and is repeatable. The areas with unwanted white space seem to associate with parallel contour lines which do not loop in the image. Note comments in the code where xc can be modified to change the shape of the data.

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

fig = plt.figure(figsize=(16,12))   #fill the screen
fig.canvas.set_window_title('<Test>')
ax = fig.gca()               # to work in 2d contour

x=[  274.0, 3174.6, 6075.2, 8975.8, 11876.4, 14777.0, 14777.0 , 11876.4, 8975.8,
  6075.2, 3174.6,  274.0, 274.0, 3174.6, 6075.2, 8975.8, 11876.4, 14777.0,
 14777.0, 11876.4, 8975.8, 6075.2, 3174.6, 274.0,   274.0,  3174.6, 6075.2,
  8975.8, 11876.4, 14777.0, 14777.0, 11876.4, 8975.8, 6075.2, 3174.6,  274.0 ]

y=[ 6737.2,  6737.2, 6737.2, 6737.2, 6737.2, 6737.2, 9907.4, 9907.4, 9907.4,
  9907.4, 9907.4, 9907.4, 13077.6, 13077.6,13077.6, 13077.6, 13077.6, 13077.6,
 16247.6, 16247.6, 16247.6, 16247.6, 16247.6, 16247.6, 19418.0, 19418.0, 19418.0,
 19418.0, 19418.0, 19418.0, 22588.2, 22588.2, 22588.2, 22588.2, 22588.2, 22588.2]

z=[154.11000061, 142.88999939, 137.19000244, 137.5, 143.42999268,
   155.47000122, 140.53999329, 126.16000366, 118.51999664, 118.43000031,
 125.22000122, 138.96000671, 131.03999329, 116.23999786, 108.23999786,
 108.90000153, 116.66999817, 132.6000061, 132.75999451, 117.56999969,
 111.65000153, 109.80000305, 117.29000092, 132.11000061, 141.44000244,
 127.08000183, 120.48000336, 120.58999634, 127.70999908, 141.05999756,
 156.22999573, 145.16000366, 139.33999634, 139.27999878, 145.63000488,
 157.00999451]

print(z)
xmax=(np.amax(x))
xmin=(np.amin(x))
ymax=(np.amax(y))
ymin=(np.amin(y))
zmax=(np.amax(z))
zmin=(np.amin(z))
xc=1 #change this from -40 to 1 to 40
yc=xc
Zheight=zmin
if xc==0:
    xc=.001
if yc==0:
    yc=.001
xcurv=int(1000000/xc)
ycurv=int(1000000/yc)  
z_surf = ((((x-(xmax+xmin)/2)/10)*((x-(xmax+xmin)/2)/10))/-xcurv + (((y-(ymax+ymin)/2)/10*(y-(ymax+ymin)/2)/10))/-ycurv ) +Zheight
zcorr=z-z_surf
zcorrmin=(np.amin(zcorr))
zcorrmax=(np.amax(zcorr))
X,Y= np.meshgrid(x,y)
Z = griddata((x, y), zcorr, (X, Y),method='nearest')
print("Zmin=",zmin,"Zmax=",zmax)
print("Zcorrmin=",zcorrmin,"Zcorrmax=",zcorrmax)

#im=ax.contourf(X, Y, Z, 15, alpha=.75, cmap = 'rainbow')   #white areas in contour map
im=ax.contourf(X, Y, Z,  np.linspace(Z.min(), Z.max(), 15), alpha=.75, cmap = 'rainbow') #supposed to fix white space but doesn't (3d surface and wireframe work fine with this data)
C = plt.contour(X, Y, Z, 15, colors='black')
plt.clabel(C, inline=1, fontsize=10)

v = np.linspace(zcorrmin, zcorrmax, 15, endpoint=True)
fig.colorbar(im,ax=ax,ticks=v)

plt.xticks(())
plt.yticks(())
plt.show()

Answer 1

The problem is that the grid is too coarse. There will be parts in the figure where no contouring can happen.

The solution is to interpolate the data on a finer grid.

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

fig = plt.figure(figsize=(16,12))   #fill the screen
fig.canvas.set_window_title('<Test>')
ax = fig.gca()               # to work in 2d contour

x=[  274.0, 3174.6, 6075.2, 8975.8, 11876.4, 14777.0, 14777.0 , 11876.4, 8975.8,
  6075.2, 3174.6,  274.0, 274.0, 3174.6, 6075.2, 8975.8, 11876.4, 14777.0,
 14777.0, 11876.4, 8975.8, 6075.2, 3174.6, 274.0,   274.0,  3174.6, 6075.2,
  8975.8, 11876.4, 14777.0, 14777.0, 11876.4, 8975.8, 6075.2, 3174.6,  274.0 ]

y=[ 6737.2,  6737.2, 6737.2, 6737.2, 6737.2, 6737.2, 9907.4, 9907.4, 9907.4,
  9907.4, 9907.4, 9907.4, 13077.6, 13077.6,13077.6, 13077.6, 13077.6, 13077.6,
 16247.6, 16247.6, 16247.6, 16247.6, 16247.6, 16247.6, 19418.0, 19418.0, 19418.0,
 19418.0, 19418.0, 19418.0, 22588.2, 22588.2, 22588.2, 22588.2, 22588.2, 22588.2]

z=[154.11000061, 142.88999939, 137.19000244, 137.5, 143.42999268,
   155.47000122, 140.53999329, 126.16000366, 118.51999664, 118.43000031,
 125.22000122, 138.96000671, 131.03999329, 116.23999786, 108.23999786,
 108.90000153, 116.66999817, 132.6000061, 132.75999451, 117.56999969,
 111.65000153, 109.80000305, 117.29000092, 132.11000061, 141.44000244,
 127.08000183, 120.48000336, 120.58999634, 127.70999908, 141.05999756,
 156.22999573, 145.16000366, 139.33999634, 139.27999878, 145.63000488,
 157.00999451]


xmax=(np.amax(x))
xmin=(np.amin(x))
ymax=(np.amax(y))
ymin=(np.amin(y))
zmax=(np.amax(z))
zmin=(np.amin(z))

z_surf = ((((x-(xmax+xmin)/2)/10)*((x-(xmax+xmin)/2)/10))/-1e6 + \
          (((y-(ymax+ymin)/2)/10*(y-(ymax+ymin)/2)/10))/-1e6 ) + zmin
zcorr=z-z_surf

X,Y= np.meshgrid(np.linspace(xmin, xmax, 51),np.linspace(ymin, ymax, 51))
Z = griddata((x, y), zcorr, (X, Y),method='linear')

im=ax.contourf(X, Y, Z,  15, alpha=.75, cmap = 'rainbow')
C = ax.contour(X, Y, Z, 15, colors='black')

ax.clabel(C, inline=1, fontsize=10)

fig.colorbar(im,ax=ax)

ax.scatter(x,y,c="k", label="original grid")
ax.scatter(X,Y,c="crimson", s=4, label="fine grid")
ax.legend(framealpha=1)
plt.xticks(())
plt.yticks(())
plt.show()

Here I marked the original grid with black dots, and the refined grid with red dots. The interpolation is still linear. But for smoother curves you may use method='cubic' .