Contourf在有限数据上绘制空白.过去的答案不正确 [英] contourf plots whitespace over finite data. Past answers do not correct

问题描述

代码中的注释包括使用np.linespace限制的建议修复程序.尽管下面给出了问题contourf() 在有限数据上绘制空白"的答案，但这并不能解决问题:如果您想确保包含所有数据，您可以定义自己的级别以使用

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))提供的解决方案被认为可以正常工作，然后在较旧的帖子中却发现无效.声明是因为问题没有被看到或不可重复，所以从未给出答案.此处的代码在pi和win7平台上均重现了该问题，并且是可重复的.具有不需要的空白区域的区域似乎与在图像中不循环的平行轮廓线相关联.注意代码中的注释，可以修改 xc 来改变数据的形状.

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()

推荐答案

问题是网格过于粗糙.图中会有不能进行等高线绘制的部分.

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

解决方案是将数据插值到更精细的网格上.

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()

这里我用黑点标记原始网格，用红点标记精制网格.插值仍然是线性的.但是对于更平滑的曲线，您可以使用 method ='cubic'.

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'.

这篇关于Contourf在有限数据上绘制空白.过去的答案不正确的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！