diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 18 |
1 files changed, 13 insertions, 5 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index a0c94114a..1a840669c 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -4956,16 +4956,25 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'): >>> yv array([[0., 0., 0.], [1., 1., 1.]]) - >>> xv, yv = np.meshgrid(x, y, sparse=True) # make sparse output arrays + + The result of `meshgrid` is a coordinate grid: + + >>> import matplotlib.pyplot as plt + >>> plt.plot(xv, yv, marker='o', color='k', linestyle='none') + >>> plt.show() + + You can create sparse output arrays to save memory and computation time. + + >>> xv, yv = np.meshgrid(x, y, sparse=True) >>> xv array([[0. , 0.5, 1. ]]) >>> yv array([[0.], [1.]]) - `meshgrid` is very useful to evaluate functions on a grid. If the - function depends on all coordinates, you can use the parameter - ``sparse=True`` to save memory and computation time. + `meshgrid` is very useful to evaluate functions on a grid. If the + function depends on all coordinates, both dense and sparse outputs can be + used. >>> x = np.linspace(-5, 5, 101) >>> y = np.linspace(-5, 5, 101) @@ -4982,7 +4991,6 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'): >>> np.array_equal(zz, zs) True - >>> import matplotlib.pyplot as plt >>> h = plt.contourf(x, y, zs) >>> plt.axis('scaled') >>> plt.colorbar() |