Slices across Contourf plots at different angles to get 2D line plots

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I am trying to generate 2D line plots at different angles or slices of a matplotlib contourf plot.

As an example from the matplotlib contourf demo example below

import numpy as np
import matplotlib.pyplot as plt

origin = 'lower'

delta = 0.025

x = y = np.arange(-3.0, 3.01, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

nr, nc = Z.shape


fig1, ax2 = plt.subplots(constrained_layout=True)
CS = ax2.contourf(X, Y, Z, 10, cmap=plt.cm.viridis, origin=origin,extend='both')



ax2.set_title('Random Plot')
ax2.set_xlabel('X Axis')
ax2.set_ylabel('Y Axis')
cbar = fig1.colorbar(CS)

Ideally, I want to generate lines at different angles (30,45,60 degrees) across the map (starting at any arbitrary point till the end of existing array) and then plot the Z variations across that line.

I think a simpler problem in principle would be, lines from (X2,Y2) to (X1,Y1) and plot the Z variation for the given contour (which is already interpolated data).

As an example, original problem would be line from (-3,-3) at angle 45 deg across. Analogous problem would be lets say a line from (-3,-3) to (3,3) and plot the Z variation at different locations on that line.

The source contour plot generated is : Contourf Plot

contourf matplotlib numpy python
1 Answers

Here is a rather inefficient approach, but it does the job. It recalculates the function on a new grid of which it only needs the diagonal.

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import RectBivariateSpline

delta = 0.025

x = y = np.arange(-3.0, 3.01, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X ** 2 - Y ** 2)
Z2 = np.exp(-(X - 1) ** 2 - (Y - 1) ** 2)
Z = (Z1 - Z2) * 2

nr, nc = Z.shape

x1, y1 = -3, -2
x2, y2 = 3, 2

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15, 5))
CS = ax1.contourf(X, Y, Z, 10, cmap=plt.cm.viridis, origin='lower', extend='both')
ax1.plot([x1, x2], [y1, y2], color='k', ls='--', lw=3, alpha=0.6)
ax1.set_xlabel('X Axis')
ax1.set_ylabel('Y Axis')
cbar = fig.colorbar(CS, ax=ax1)

spline_func = RectBivariateSpline(x, y, Z)
xline = np.linspace(x1, x2, 200)
yline = np.linspace(y1, y2, 200)
zline = spline_func(xline, yline)
ax2.plot(xline, zline.diagonal())
ax2.set_xlabel('X Axis')
ax2.set_ylabel('Z Axis')

plt.show()

example plot

user_120464090
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