from sklearn.preprocessing import StandardScaler
obj = StandardScaler()
data = obj.fit_transform(data)
gridx = np.linspace(min(data[:, 0]), max(data[:, 0]), 50)
gridy = np.linspace(min(data[:, 0]), max(data[:, 0]), 50)
obj = OrdinaryKriging(data[:, 0],
data[:, 1],
data[:, 2],
variogram_model='power',
verbose=False,
enable_plotting=True)
Z, SS = obj.execute('grid', gridx, gridy)
X, Y = np.meshgrid(gridx, gridy)
# Writes the kriged grid to an ASCII grid file.
# kt.write_asc_grid(gridx, gridy, Z, filename="output.asc")
# fig = plt.figure(figsize=(12,6))
# ax = fig.add_subplot(111, projection='3d')
# ax.plot_wireframe(X, Y, Z, rstride=1)
# ax.set_title('Meshgrid wireframe')
# ax.set_xlabel('X')
# ax.set_ylabel('Y')
# ax.set_zlabel('M')
plt.imshow(Z, origin="lower")
plt.show()
