neuroprob/utils/plot.py
def compute_mesh(grid_n, grid_size, func):
    """
    Create a uniformly spaced mesh over which to evaluate func.
    
    :param list colors: colors to be included in the colormap
    :param string name: name the colormap
    :returns: figure and axis
    :rtype: tuple
    """
    mesh = ()
    for n, gn in enumerate(grid_n):
        mesh += (np.linspace(grid_size[n][0], grid_size[n][1], gn),)
        
    cc = np.meshgrid(*mesh)
    z = np.stack(list(cc))
    field = func(z)
    return cc, field

    Create a uniformly spaced mesh over which to evaluate func.
    
    :param list colors: colors to be included in the colormap
    :param string name: name the colormap
    :returns: figure and axis
    :rtype: tuple
    
Create a uniformly spaced mesh over which to evaluate func.
Create a uniformly spaced mesh over which to evaluate func .
def compute_mesh(grid_n, grid_size, func):
    mesh = ()
    for n, gn in enumerate(grid_n):
        mesh += (np.linspace(grid_size[n][0], grid_size[n][1], gn),)
    cc = np.meshgrid(*mesh)
    z = np.stack(list(cc))
    field = func(z)
    return cc, field
def compute_mesh ( grid_n , grid_size , func ) : mesh = ( ) for n , gn in enumerate ( grid_n ) : mesh += ( np . linspace ( grid_size [ n ] [ 0 ] , grid_size [ n ] [ 1 ] , gn ) , ) cc = np . meshgrid ( * mesh ) z = np . stack ( list ( cc ) ) field = func ( z ) return cc , field
Create a uniformly spaced mesh over which to evaluate func.
Create a uniformly spaced mesh over which to evaluate func .
"""
    Create a uniformly spaced mesh over which to evaluate func.
    
    :param list colors: colors to be included in the colormap
    :param string name: name the colormap
    :returns: figure and axis
    :rtype: tuple
    """
figure and axis
figure and axis
tuple
grid_n
grid_size
func
colors
colors to be included in the colormap
colors to be included in the colormap
name
name the colormap
name the colormap