Plotting streamlines with Matplotlib - Python

Question

I'm trying to plot some streamlines with Matplotlib.

I have this code so far, as an example to plot a 10 x 10 vector field:

def plot_streamlines(file_path, vector_field_x, vector_field_y):
    plt.figure()
    y, x = numpy.mgrid[-2:2:10j, -2:2:10j]
    plt.streamplot(x, y, vector_field_x, vector_field_y, color='y', cmap=plt.cm.autumn)
    plt.savefig(file_path + '.png')
    plt.close()

This works properly, but if I just change this line:

y, x = numpy.mgrid[-2:2:10j, -2:2:10j]

To that one:

x, y = numpy.mgrid[-2:2:10j, -2:2:10j]

I get some errors:

Traceback (most recent call last):
  File "Library/Python/2.7/lib/python/site-packages/matplotlib/pyplot.py", line 3224, in streamplot minlength=minlength, transform=transform)
  File "/Library/Python/2.7/lib/python/site-packages/matplotlib/axes.py", line 6861, in streamplot transform=transform)
  File "Library/Python/2.7/lib/python/site-packages/matplotlib/streamplot.py", line 67, in streamplot grid = Grid(x, y)
  File "Library/Python/2.7/lib/python/site-packages/matplotlib/streamplot.py", line 256, in __init__
    assert np.allclose(x_row, x)
AssertionError

I didn't understand how I can use the "standard" order x / y, since my mesh grid is squared. Moreover, I don't know how I get these erros if my x and y dimensions are the same.

Any help would be appreciated.

Thank you.

Answer 1

The pylab examples use Y,X then plot X,Y for streamplots using numpy.mgrid.

import numpy as np
import matplotlib.pyplot as plt

Y, X = np.mgrid[-3:3:100j, -3:3:100j]
U = -1 - X**2 + Y
V = 1 + X - Y**2
speed = np.sqrt(U*U + V*V)

plt.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)
plt.colorbar()

f, (ax1, ax2) = plt.subplots(ncols=2)
ax1.streamplot(X, Y, U, V, density=[0.5, 1]    
lw = 5*speed/speed.max()
ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)

plt.show()

Taken from here.

y,x = numpy.mgrid[-2:2:4j,-2:2:4j]
x = [[-2.         -0.66666667  0.66666667  2.        ]
    [-2.         -0.66666667  0.66666667  2.        ]
    [-2.         -0.66666667  0.66666667  2.        ]
    [-2.         -0.66666667  0.66666667  2.        ]]


y = [[-2.         -2.         -2.         -2.        ]
     [-0.66666667 -0.66666667 -0.66666667 -0.66666667]
     [ 0.66666667  0.66666667  0.66666667  0.66666667]
     [ 2.          2.          2.          2.        ]]

There seems a direct relation with how the data looks in regard to x and y axis, the -2,2 etc.. resembling an x-axis and y values resembling a y-axis.