为（x，y，f（x，y））形式的轮廓图设置任意轴值？

Question

So I have a data set that is in the matrix form:

x1, Y1, VALUE1
x2, Y1, VALUE2
x3, Y1, VALUE3

x1, Y2, VALUE4
x2, Y2, VALUE5
x3, Y2, VALUE6

and so on. I get my contours properly except my x and y axes go from say 1, 2, 3...N. This is fine because it is representing pixels so isn't incorrect, but I would like to change the axes values from pixels to the actual units. I can't seem to find a way to instruct contour to allow me to add this.

bsquare=np.reshape(value,(x length,y length))
blue=contour(bsquare,colors='b')
plt.show()

where xlength and ylength are the number of points in either axis.

Answer 1

plt.contour can be given arrays X, Y, Z then it takes the Z as the contour values and the X and Y are used on their respective axes. Here is a script that first makes some data to play with, then gets into an array of the form you describe:

import matplotlib.pyplot as plt
import numpy as np

# Make some test data 
nx = 2
ny = 3
x = np.linspace(0, 3, nx)
y = np.linspace(50, 55, ny)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) + Y

# Now get it into the form you describe
data = [[[x[i], y[j], Z[j, i]] for i in range(nx)] for j in range(ny)]
data = np.array(data)
print data

>>> 
[[[  0.          50.          50.        ]
  [  3.          50.          50.14112001]]

 [[  0.          52.5         52.5       ]
  [  3.          52.5         52.64112001]]

 [[  0.          55.          55.        ]
  [  3.          55.          55.14112001]]]

Note I am using a numpy.array not just a normal list this is important in the next step. Lets split up that data as I presume you have done into the x and y values and the values themselves:

# Now extract the data 
x_values = data[:, :, 0]
y_values = data[:, :, 1]
values = data[:, :, 2]

Now all of these things are nx, ny arrays, that is they have the same shape as your bsquare . You can check this by printing values.shape and changing the integers nx, ny . Now I will plot three things:

1. Firstly as you have done simply contour plot the values, this automatically adds the axes values

2. Secondly I plot using the arrays to give the correct scalings and

3. Finally I will plot the origin data set to show it properly recovers the data.

You will need to compare the axis values with where the fake data was created:

fig, axes = plt.subplots(ncols=3, figsize=(10, 2))
axes[0].contour(values)
axes[1].contour(x_values, y_values, values)
axes[2].contour(X, Y, Z)

How you implement this will largely depend on how you have imported your data. If you can simply turn it into a numpy.array() then I think this will solve your issue.

Hopefully I understood your problem correctly.