Can we plot a straight heatmap on a 3D axis? The heatmap is as follows: I am able to get a 3D elevation map, but I am not looking for that. I just want this straight lying on a 3D axis.
Code:
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import interpolate
excel_data_df = pd.read_excel('test.xlsx')
X= excel_data_df['x'].tolist()
Y= excel_data_df['y'].tolist()
Z= excel_data_df['z'].tolist()
X = np.array(X)
Y = np.array(Y)
Z = np.array(Z)
# Flatten trial dataset to meet your requirement:
x = X.ravel()
y = Y.ravel()
z = Z.ravel()
# Resampling on as square grid with given resolution:
resolution = 8
xlin = np.linspace(min(x), max(x), resolution)
ylin = np.linspace(min(y), max(y), resolution)
Xlin, Ylin = np.meshgrid(xlin, ylin)
# Linear multi-dimensional interpolation:
interpolant = interpolate.NearestNDInterpolator([r for r in zip(x, y)], z)
Zhat = interpolant(Xlin.ravel(), Ylin.ravel()).reshape(Xlin.shape)
cmap = 'jet'
# Render and interpolate again if necessary:
fig, axe = plt.subplots()
axe.imshow(Zhat, origin="lower", cmap=cmap, interpolation='bicubic',extent=[min(x),max(x),min(y),max(y)])
plt.xticks(np.arange(min(x), max(x)+1, 1.0))
plt.yticks(np.arange(min(y), max(y)+1, 1.0))
axe.grid(True, linewidth=0.3, color='w')
norm = matplotlib.colors.Normalize(vmin = min(z), vmax = max(z), clip = False)
plt.colorbar(plt.cm.ScalarMappable(cmap = cmap, norm=norm))
plt.show()
