# Differentiation and Evaluation#

## Visualization of Potentials#

sumpy.visualization.make_field_plotter_from_bbox(bbox, h, extend_factor=0)[source]#
Parameters:
• bbox – a tuple (low, high) of points represented as 1D numpy arrays indicating the low and high ends of the extent of a bounding box.

• h – Either a number or a sequence of numbers indicating the desired (approximate) grid spacing in all or each of the dimensions. If a sequence, the length must match the number of dimensions.

• extend_factor – A floating point number indicating by what percentage the plot area should be grown compared to bbox.

class sumpy.visualization.FieldPlotter(center, extent=1, npoints=1000)[source]#
set_matplotlib_limits()[source]#
show_scalar_in_matplotlib(fld, max_val=None, func_name='imshow', **kwargs)[source]#
show_scalar_in_mayavi(fld, max_val=None, **kwargs)[source]#
write_vtk_file(file_name, data, real_only=False, overwrite=False)[source]#

## Differentiation of Potentials#

class sumpy.point_calculus.CalculusPatch(center, h=0.1, order=4, nodes='chebyshev')[source]#

Sets up a grid of points on which derivatives can be calculated. Useful to verify that an evaluated potential actually solves a PDE.

dim#
points#

shape: `(dim, npoints_total)`

weights()[source]#

” :returns: a vector of high-order quadrature weights on the `points`

basis()[source]#
Returns:

a `list` containing functions that realize a high-order interpolation basis on the `points`.

diff(axis, f_values, nderivs=1)[source]#

Return the derivative along axis of f_values.

Parameters:

f_values – an array of shape `(npoints_total,)`

Returns:

an array of shape `(npoints_total,)`

dx(f_values)[source]#
dy(f_values)[source]#
dz(f_values)[source]#
laplace(f_values)[source]#

Return the Laplacian of f_values.

Parameters:

f_values – an array of shape `(npoints_total,)`

Returns:

an array of shape `(npoints_total,)`

div(arg)[source]#
Parameters:

arg – an object array containing `numpy.ndarray`s with shape `(npoints_total,)`.

curl(arg)[source]#

Take the curl of the vector quantity arg.

Parameters:

arg – an object array of shape `(3,)` containing `numpy.ndarray`s with shape `(npoints_total,)`.

eval_at_center(f_values)[source]#

Interpolate f_values to the center point.

Parameters:

f_values – an array of shape `(npoints_total,)`

Returns:

a scalar.

x#
y#
z#
norm(arg, p)[source]#
plot_nodes()[source]#
plot(f)[source]#
sumpy.point_calculus.frequency_domain_maxwell(cpatch, e, h, k)[source]#

## Support for Numerical Experiments with Expansions#

This module provides a convenient interface for numerical experiments with local and multipole expansions.

class sumpy.toys.ToyContext(cl_context, kernel, mpole_expn_class=None, local_expn_class=None, expansion_factory=None, extra_source_kwargs=None, extra_kernel_kwargs=None)[source]#

This class functions as a container for generated code and ‘behind-the-scenes’ information.

__init__(cl_context, kernel, mpole_expn_class=None, local_expn_class=None, expansion_factory=None, extra_source_kwargs=None, extra_kernel_kwargs=None)[source]#
class sumpy.toys.PotentialSource(toy_ctx)[source]#

A base class for all classes representing potentials that can be evaluated anywhere in space.

eval(targets)[source]#

Supports (lazy) arithmetic:

__neg__()[source]#
__sub__(other)[source]#
__rsub__(other)[source]#
__mul__(other)[source]#
__rmul__(other)[source]#
class sumpy.toys.ConstantPotential(toy_ctx, value)[source]#
__init__(toy_ctx, value)[source]#
class sumpy.toys.PointSources(toy_ctx, points, weights, center=None)[source]#
points#

`[ndim, npoints]`

__init__(toy_ctx, points, weights, center=None)[source]#

These functions manipulate these potentials:

sumpy.toys.multipole_expand(psource, center, order=None, rscale=1, **expn_kwargs)[source]#
sumpy.toys.local_expand(psource, center, order=None, rscale=1, **expn_kwargs)[source]#
sumpy.toys.logplot(fp, psource, **kwargs)[source]#
sumpy.toys.combine_halfspace(psource_pos, psource_neg, axis, center=None)[source]#
sumpy.toys.combine_halfspace_and_outer(psource_pos, psource_neg, psource_outer, axis, radius=None, center=None)[source]#

These functions help with plotting:

sumpy.toys.draw_box(el, eh, **kwargs)[source]#
sumpy.toys.draw_annotation(to_pt, from_pt, label, arrowprops=None, **kwargs)[source]#
Parameters:
• to_pt – Head of arrow

• from_pt – Tail of arrow

• label – Annotation label

• arrowprops – Passed to arrowprops

• kwargs – Passed to annotate

sumpy.toys.draw_schematic(psource, **kwargs)[source]#

These are created behind the scenes and are not typically directly instantiated by users:

class sumpy.toys.HalfspaceOnePotential(toy_ctx, center, axis, side=1)[source]#
__init__(toy_ctx, center, axis, side=1)[source]#
class sumpy.toys.ExpansionPotentialSource(toy_ctx, center, rscale, order, coeffs, derived_from, radius=None, expn_style=None, text_kwargs=None)[source]#
Passed to `matplotlib.pyplot.annotate()`. Used for customizing the expansion label. Changing the label text is supported by passing the kwarg s. Just for visualization, purely advisory.