2D charge density contour plot (how to get arbitrary 'Z' values in the plot using python)

Question

I need to generate a contour plot from a file (named abc.gnu) which has X, Y and Z columns of data. It has 1000000 rows of data. I have got the contour plot with a python code (shown at the end). What I need is to know the 'Z' values in any arbitrary place of choice (or to see 'Z' values in different regions of the contour plot). If I zoom any particular region, still the color bar is showing the same min and max values, so I can't get any information of 'Z' values in the zoomed region as well. Do you have any idea to solve this for my used file. I have given my used python script at the end. Any other idea to get more effective 2D contour plot is also welcome.

Used Python Code for the contour plot

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data=pd.read_csv('abc.gnu',delimiter='      ',header=None,engine='python')
data_M=np.array(data)

X=data_M[:,0]
Y=data_M[:,1]
Z=data_M[:,2]
Xs=X.reshape((1000,1000))
Ys=Y.reshape((1000,1000))
Zs=Z.reshape((1000,1000))

fig = plt.figure(figsize=(10,8))
left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax = fig.add_axes([left, bottom, width, height])

cp = plt.contourf(Xs, Ys, Zs)
plt.colorbar(cp)

ax.set_title('Contour Plot')
ax.set_xlabel('X')
ax.set_ylabel('Y')

x = np.linspace(0, 5, 150)
y = np.linspace(0, 5, 150)

X, Y = np.meshgrid(x, y)

plt.xlim(0, 25)
plt.ylim(0, 4)

plt.show()

Answer 1

This can be achieved with a 3d interactive contour plot.
Here I have higlighted the current cursor position with a red dot.
It gives you all the x, y, z values at that point. The below plot is generated with a small sample of your data from the screenshot of the x,y,z values. I am plotting on a google colab notebook.

Install and initialize backend

!pip install ipympl

%matplotlib widget

from google.colab import output
output.enable_custom_widget_manager()

Main code:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits import mplot3d

data= [ [0,0,0.254], [0, 0.14,0.27], [0,0.29, 0.30], [0.1, 0.43, 0.31]] # Using a subset of your data
data_M = np.array(data)

X=data_M[:,0]
Y=data_M[:,1]
Z=data_M[:,2]

# change to 1000 for your original script
Xs=X.reshape((2,2))
Ys=Y.reshape((2,2))
Zs=Z.reshape((2,2))

fig = plt.figure()
ax = plt.axes(projection='3d')
ax.contour3D(Xs, Ys, Zs)

ax.set_title('Contour Plot')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

plt.show()