I have a table that has 2 features (x,y) - and a vector with the same length that contains their corresponding values (z).
I'm trying to use matplotlib to print this as a 2D plot but I am get an error:
TypeError: Input z must be at least a (2, 2) shaped array, but has shape (5797, 1)
Is there any way to solve this? (since I am trying to use 1d arrays instead of 2d arrays)
The relevant code:
x, y = train_features[:,0], train_features[:,1]
z = train_predictions.detach()
print(x.size())
print(y.size())
print(z.size())
plt.figure()
CS = plt.contour(x, y, z)
CS = plt.contourf(x, y, z)
plt.clabel(CS, fontsize=8, colors='black')
cbar = plt.colorbar(CS)
The prints that result from the prints commands:
torch.Size([5797])
torch.Size([5797])
torch.Size([5797, 1])
EDIT:
I tried to implement this with a second method:
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
npts = 200
ngridx = 100
ngridy = 200
x = train_features[:,0]
y = train_features[:,1]
z = train_predictions.detach().squeeze()
fig, ax1 = plt.subplots()
# -----------------------
# Interpolation on a grid
# -----------------------
# A contour plot of irregularly spaced data coordinates
# via interpolation on a grid.
# Create grid values first.
xi = np.linspace(1, 10, ngridx)
yi = np.linspace(1, 10, ngridy)
# Perform linear interpolation of the data (x,y)
# on a grid defined by (xi,yi)
triang = tri.Triangulation(x, y)
interpolator = tri.LinearTriInterpolator(triang, z)
Xi, Yi = np.meshgrid(xi, yi)
zi = interpolator(Xi, Yi)
ax1.contour(xi, yi, zi, levels=100, linewidths=0.5, colors='k')
cntr1 = ax1.contourf(xi, yi, zi, levels=14, cmap="RdBu_r")
fig.colorbar(cntr1, ax=ax1)
ax1.plot(x, y, 'ko', ms=3)
ax1.set_title('grid and contour (%d points, %d grid points)' %
(npts, ngridx * ngridy))
But the resulting image was the following:
even though z's values are:
tensor([-0.2434, -0.2155, -0.1900, ..., 64.7516, 65.2064, 65.6612])
