Matplotlib - Using 1-D arrays in streamplot()

Question

So I have found two previous similar questions asked here:

How to use streamplot function when 1D data of x-coordinate, y-coordinate, x-velocity and y-velocity are avail

Answer 1

Use griddata (see also scipy.interpolate.griddata) to interpolate 1D data to a 2D grid.

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

# lowercase variables are 1D arrays
x = np.linspace(0, 2 * np.pi, 10)
y = np.sin(x)
u = np.cos(x)
v = np.sin(x)

# capitalized variables are 2D arrays
xi = np.linspace(x.min(), x.max(), 100)
yi = np.linspace(y.min(), y.max(), 100)
X, Y = np.meshgrid(xi, yi)
U = interpolate.griddata((x, y), u, (X, Y), method='cubic')
V = interpolate.griddata((x, y), v, (X, Y), method='cubic')

plt.figure()
plt.quiver(x, y, u, v, scale_units='xy', angles='xy', scale=1.5)
plt.streamplot(X, Y, U, V, color=U**2+V**2, linewidth=2, cmap=plt.cm.autumn)
plt.show()

---

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

# lowercase variables are 1D arrays
x = np.array([1,2,3,4,5])
y = np.array([3,1,5,1,3])
u = np.array([1,1,0,-1,-1])
v = np.array([-0.5,1,-1,1,-0.5])

# capitalized variables are 2D arrays
xi = np.linspace(x.min(), x.max(), 100)
yi = np.linspace(y.min(), y.max(), 100)
X, Y = np.meshgrid(xi, yi)
U = interpolate.griddata((x, y), u, (X, Y), method='nearest')
V = interpolate.griddata((x, y), v, (X, Y), method='nearest')

plt.figure()
plt.quiver(x, y, u, v, scale_units='xy', angles='xy', scale=1.5)
plt.streamplot(X, Y, U, V, color=U**2+V**2, linewidth=2, cmap=plt.cm.autumn)
plt.show()

yields