pytutorial/numpy/mesh_grid

Meshgrid

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The goal

Questions to David Rotermund

numpy.meshgrid

numpy.meshgrid(*xi, copy=True, sparse=False, indexing='xy')

Return a list of coordinate matrices from coordinate vectors.

Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn.

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 1, 100)
y = np.linspace(0, 1, 100)

xv, yv = np.meshgrid(x, y)

plt.imshow(xv, cmap="hot")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.title("xv")
plt.show()

plt.imshow(yv, cmap="hot")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.title("yv")
plt.show()

An example:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 1, 100)
y = np.linspace(0, 1, 100)

xv, yv = np.meshgrid(x, y)

a = np.sin(xv * 2 * np.pi) * np.sin(yv * 8 * np.pi)

plt.imshow(a, cmap="hot")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.show()

The question is if you really need a mesh or if just using broadcasting can do the job too. I guess this depends on your need.

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 1, 100)[np.newaxis, :]
y = np.linspace(0, 1, 100)[:, np.newaxis]

a = np.sin(x * 2 * np.pi) * np.sin(y * 8 * np.pi)

plt.imshow(a, cmap="hot")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.show()