# 3.5.10. horton.grid.visual – Grids suitable for visualization¶

class horton.grid.visual.LineGrid(p1, p2, size, extend=0)

Arguments:

p1, p2
Two points defining the line segment
size
The number of grid points

Optional arguments:

extend
Used to control how far the grid points extrapolate from the line segment.
eval_decomposition(*args, **kwargs)

Evaluate a spherical decomposition

Arguments:

cubic_splines
A list cubic splines, where each item is a radial function that is associated with a corresponding real spherical harmonic.
center
The center of the spherically symmetric function
output
The output array

Optional arguments:

cell
A unit cell when periodic boundary conditions are used.
eval_spline(*args, **kwargs)

Evaluate a spherically symmetric function

Arguments:

cubic_spline
A cubic spline with the radial dependence
center
The center of the spherically symmetric function
output
The output array

Optional arguments:

cell
A unit cell when periodic boundary conditions are used.
integrate(*args, **kwargs)

Integrate the product of all arguments

Arguments:

data1, data2, ...
All arguments must be arrays with the same size as the number of grid points. The arrays contain the functions, evaluated at the grid points, that must be multiplied and integrated.

Optional arguments:

center=None
When given, multipole moments are computed with respect to this center instead of a plain integral.
lmax=0
The maximum angular momentum to consider when computing multipole moments
mtype=1
The type of multipole moments: 1=cartesian, 2=pure, 3=radial, 4=surface.
segments=None
This argument can be used to divide the grid in segments. When given, it must be an array with the number of grid points in each consecutive segment. The integration is then carried out over each segment separately and an array of results is returned. The sum over all elements gives back the total integral.
zeros()
p1

The first point of the line segment

p2

The second point of the line segment

points

The grid points.

shape

The shape of the grid.

size

The size of the grid.

subgrids

A list of grid objects used to construct this grid.

weights

The grid weights.

x

The 1D axis for the grid points, useful for plotting

$$p_1$$ and $$p_2$$ correspond to $$x_1=-|p_2-p_1|/2$$ and $$x_2=+|p_2-p_1|/2$$, respectively.

class horton.grid.visual.RectangleGrid(origin, axis0, axis1, l0, h0, l1, h1)

Arguments:

origin
The origin for the definition of the rectangle.
axis0, axis1
The basis vectors that define the plane of the rectangle and the 2D space in this plane.
l0, h0
The lowest and highest position along axis0. (These must be integers.) Hence, along the first axis, there are (h0-l0+1) grid points.
l1, h1
The lowest and highest position along axis1.
eval_decomposition(*args, **kwargs)

Evaluate a spherical decomposition

Arguments:

cubic_splines
A list cubic splines, where each item is a radial function that is associated with a corresponding real spherical harmonic.
center
The center of the spherically symmetric function
output
The output array

Optional arguments:

cell
A unit cell when periodic boundary conditions are used.
eval_spline(*args, **kwargs)

Evaluate a spherically symmetric function

Arguments:

cubic_spline
A cubic spline with the radial dependence
center
The center of the spherically symmetric function
output
The output array

Optional arguments:

cell
A unit cell when periodic boundary conditions are used.
integrate(*args, **kwargs)

Integrate the product of all arguments

Arguments:

data1, data2, ...
All arguments must be arrays with the same size as the number of grid points. The arrays contain the functions, evaluated at the grid points, that must be multiplied and integrated.

Optional arguments:

center=None
When given, multipole moments are computed with respect to this center instead of a plain integral.
lmax=0
The maximum angular momentum to consider when computing multipole moments
mtype=1
The type of multipole moments: 1=cartesian, 2=pure, 3=radial, 4=surface.
segments=None
This argument can be used to divide the grid in segments. When given, it must be an array with the number of grid points in each consecutive segment. The integration is then carried out over each segment separately and an array of results is returned. The sum over all elements gives back the total integral.
prepare_contour(data)

Returns arguments suitable for matplotlib.pyplot.contour

Arguments:

data
A vector with the correct number of elements (self.size)

Returns: suitable arguments for matplotlib.pyplot.contour

x, y
A numpy vector with the x and y axes (measured in units defined by the lengths of axis0 and axis1, respectively.
z
the same as data, transformed in a suitable 2D numpy array
zeros()
axis0

The first basis vector

axis1

The second basis vector

h0

The highest index along axis0

h1

The highest index along axis1

l0

The lowest index along axis0

l1

The lowest index along axis1

origin

The origin for the definition of the rectangle

points

The grid points.

shape

The shape of the grid.

size

The size of the grid.

subgrids

A list of grid objects used to construct this grid.

weights

The grid weights.