I have 3 lots of data. These are x and y values as well as a temperature value for each xy point. I would like to plot each point and interpolate the area between points to get a continuous surface. The issue I have is specifying the temperature values. I can't get it to work with an equal number of x,y and z (temperature) values and all the examples I can find online use a function of x and y to create z or have z values for every point on an xy grid. Is there a simple way to do this?
import numpy as np import matplotlib.pyplot as plt fig, axs = plt.subplots() x = np.linspace(0, 1, 100) y = np.linspace(0,1,100) X, Y = np.meshgrid(x, y) #Z = np.sin(X)*np.sin(Y) # want to specify not an equation Z = np.linspace(1,2,100) levels = np.linspace(-1, 1, 40) cs = axs.contourf(X, Y, Z, levels=levels) fig.colorbar(cs, ax=axs, format="%.2f") plt.show() Update:
Here is what I have so far. I still need to work out a good method to fill in the area between points. Does anyone have any ideas?
import numpy as np import matplotlib.pyplot as plt fig, axs = plt.subplots() # create a grid in the correct shape / size x = np.linspace(0, 1, 3) y = np.linspace(0,1,3) X, Y = np.meshgrid(x, y) # specify and change the relevent areas y = [1,2,0] # location of point in x direction x =[2,1,1] #location of point in y direction z = [40,30,20] #temperature Z = np.arange(1,10).reshape((3,3)) Z[y,x] = z levels = np.linspace(0, 40, 40) cs = axs.contourf(X, Y, Z, levels=levels) fig.colorbar(cs, ax=axs, format="%.2f") plt.show() 1 Answer
The reason people use a function of x and y is because your Z value has to be a function of x and y. In your test code Z is 1D but it needs to be 2D to plot the contours.
If you have Z (temperature) values that have the same shape as your x and y coordinates then it should work.
x = np.linspace(0, 1, 100) y = np.linspace(0,1,100) X, Y = np.meshgrid(x, y) #Z = np.sin(X)*np.sin(Y) # want to specify not an equation Z = np.linspace(1,2,100) print X.shape print Z.shape (100L,100L)
(100L)
