# Reference¶

spack: a package for spherical packing analysis

class spack.Packing(rs, diameters, shear=0.0, L=1.0)[source]

A class representing a packing of spheres in a periodic box.

class Backbone(indices, adjacency)
__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.

__getstate__()

Exclude the OrderedDict from pickling

__repr__()

Return a nicely formatted representation string

adjacency

Alias for field number 1

indices

Alias for field number 0

class Packing.Contacts(Nc, stable, floaters)
Nc

Alias for field number 0

__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.

__getstate__()

Exclude the OrderedDict from pickling

__repr__()

Return a nicely formatted representation string

floaters

Alias for field number 2

stable

Alias for field number 1

Packing.DM(masses=None)[source]

Dynamical matrix for array rs, size ds. Assumes epsilon is the same for all.

Parameters: masses (an array of length N of the masses of the particles.) –
Packing.DM_freqs(masses=None)[source]

Find the frequencies corresponding to the eigenvalues of the dynamical matrix.

This is just a short wrapper around DM().

class Packing.Neighbors(adjacency, diffs)
__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.

__getstate__()

Exclude the OrderedDict from pickling

__repr__()

Return a nicely formatted representation string

adjacency

Alias for field number 0

diffs

Alias for field number 1

Packing.backbone(tol=1e-08)[source]

Returns (backbone indices, neighbor matrix)

Packing.cages(M=10000, R=None, Rfactor=1.2, padding=0.1, Mfactor=0.1)[source]

Find all cages in the current “packing”.

The algorithm uses Monte Carlo: it finds M random points within a sphere of radius R from each particle, and sees if that particle could sit there without conflicting with other particles. Then (number of accepted points) / (number of test points) * (volume of sphere) is the volume of the cage.

The algorithm is adaptive: if not enough test points are accepted (n < M * Mfactor), it tries more test points. If any test points are within padding of the edge, R is (temporarily) expanded.

Parameters: M (Number of points in the sphere to test) – R (Size of sphere to test (will be expanded if necessary)) – Rfactor (How much to increase R by when the cage doesn't fit) – padding (How much larger the sphere should be than the cage (if it isn't, the sphere is) – expanded) Mfactor (Mfactor * M is the minimum number of points to find per cage. If they aren't) – found, more points are tested. points (a list of (A x 3) lists, A indeterminate (but larger than M * Mfactor), with each) – list corresponding to the points within one cage. Vs (The approximate volumes of each cage.)
Packing.contacts(tol=1e-08)[source]

Returns (number of backbone contacts, stable number, number of floaters)

Packing.dist(other, tol=1e-08, maxt=1000000)[source]

Returns the distance between two packings, with the particles paired up in the most efficient way, and also with center-of-mass accounted for.

Packing.dist_tree(other, tol=1e-08)[source]

Find the distance between two packings.

Requires pyparm.

Packing.forces()[source]

Find Fij on each particle, assuming a harmonic potential, U = 1/2 (1 - r/σ)^2

Returns a dxNxN matrix.

Packing.neighbors(tol=1e-08)[source]

For a set of particles at xs,ys with diameters diameters, finds the distance vector matrix (d x N x N) and the adjacency matrix.

Assumes box size 1, returns (adjacency matrix, diffs)

Packing.paired_dists(other, match_com=True)[source]

Returns the distance between two packings, assuming particle 1 of packing 1 goes with particle 2 of packing 2.

Distance is calculated as :math:d = sqrt{sum_i left(

ec r_i ominus_ ec{L} ec s_i

ight)^2, where
:math:
ec r_i are the particles from one packing,
:math:
ec s_i are the particles from the other packing,
and :math:ominus_
ec{L} means “shortest distance given periodic
boundary conditions in a box of shape :math:

ec{L}”.

other : Another Packing. match_com : Subtract off center-of-mass motion as well.

For match_com = True, this yields

:math:d = sqrt{sum_i left(

ec{r}_{i} ominus_ ec{L} ec{s}_{i} -

ec{delta} ight)^2}

Minimized over 

ec delta. It does this by actually evaluating

:math:d = sqrt{

rac{1}{N}sum_{leftlangle i,j ight angle

}left(

ec{r}_{ij} ominus_ ec{L} ec{s}_{ij} ight)^2},

which turns out to be equivalent.
Packing.phi

Volume of the spheres in the box.

Packing.plot_contacts(ax=None, tol=0, reshape=True, **kw)[source]

Designed for use with plot_disks, this will plot a line between neighboring particles.

Packing.plot_disks(ax=None, color=None, alpha=0.4, reshape=True)[source]

Plot the packing as a set of disks.

Color can be None (uses the standard sets), ‘diameter’ (colors by diameter), or a list of colors.

‘reshape’ means set axis scaled, etc.

Packing.scene(pack, cmap=None, rot=0, camera_height=0.7, camera_dist=1.5, angle=None, lightstrength=1.1, orthographic=False, pad=None, floater_color=(0.6, 0.6, 0.6), bgcolor=(1, 1, 1), box_color=(0.5, 0.5, 0.5), group_indexes=None, clip=False)[source]

Render a 3D scene.

Requires vapory package, which requires the povray binary.

Parameters: cmap (a colormap) – box_color (Color to draw the box. 'None' => don't draw box.) – floater_color (Color for floaters. 'None' => same color as non-floaters (use cmap).) – group_indexes (a list of indexes for each "group" that should remain) – together on the same side of the box. clip (clip the spheres at the edge of the box.) – scene vapory.Scene, which can be rendered using its .render() method.
Packing.size_indices(tol=1e-08)[source]

Returns [idx of sigma1, idx of sigma2, ...]

Packing.tess()[source]

Get a tess.Container instance of this.

Requires tess.