freud documentation¶

“Neurosis is the inability to tolerate ambiguity” - Sigmund Freud

Welcome to the documentation for freud, a Python package for analyzing particle simulation trajectories of periodic systems. The library contains a diverse array of analysis routines designed for molecular dynamics and Monte Carlo simulation trajectories. Since any scientific investigation is likely to benefit from a range of analyses, freud is designed to work as part of a larger analysis pipeline. In order to maximize its interoperability with other systems, freud works with and returns NumPy arrays.

Installing freud¶

The recommended method of installing freud is using conda through the conda-forge channel. First download and install miniconda following conda’s instructions. Then, install freud from conda-forge:

$ conda install -c conda-forge freud

Alternatively, freud can be installed directly from source.

$ mkdir build
$ cd build
$ cmake ../
$ make install

Examples¶

Examples are provided as Jupyter notebooks in a separate freud-examples repository. These can be run locally with the jupyter notebook command. These examples will also be provided as static notebooks on NBViewer and interactive notebooks on MyBinder.

Visualization of data is done via Bokeh [Bokeh].

Installation¶

Installing freud¶

You can either install freud via conda or compile it from source.

Install via conda¶

The code below will install freud from conda-forge.

conda install -c conda-forge freud

Compile from source¶

The following are required for installing freud:

The following are optional for installing freud:

Cython: The freud repository contains a Cython-generated _freud.cpp file that can be used directly. However, Cython is necessary if you wish to recompile this file.

The code that follows creates a build directory inside the freud source directory and builds freud:

mkdir build
cd build
cmake ../
# Use `cmake ../ -DENABLE_CYTHON=ON` to rebuild _freud.cpp
make install

By default, freud installs to the USER_SITE directory, which is in ~/.local on Linux and in ~/Library on macOS. USER_SITE is on the Python search path by default, so there is no need to modify PYTHONPATH.

Note

freud makes use of submodules. CMake has been configured to automatically initialize and update submodules. However, if this does not work, or you would like to do this yourself, please execute:

git submodule update --init

Unit Tests¶

The unit tests for freud are included in the repository and are configured to be run using the Python unittest library:

# Run tests from the tests directory
cd tests
python -m unittest discover .

Note that because freud is designed to require installation to run (i.e. it cannot be run directly out of the build directory), importing freud from the root of the repository will fail because it will try and import the package folder. As a result, unit tests must be run from outside the root directory.

Documentation¶

The documentation for freud is hosted online at ReadTheDocs, but you may also build the documentation yourself:

Building the documentation¶

The following are required for building freud documentation:

Sphinx

You can install sphinx using conda

conda install sphinx

or from PyPi

pip install sphinx

To build the documentation, run the following commands in the source directory:

cd doc
make html
# Then open build/html/index.html

To build a PDF of the documentation (requires LaTeX and/or PDFLaTeX):

cd doc
make latexpdf
# Then open build/latex/freud.pdf

Modules¶

Below is a list of modules in freud. To add your own module, read the development guide.

Bond Module¶

Overview

`freud.bond.BondingAnalysis`	Analyze the bond lifetimes and flux present in the system.
`freud.bond.BondingR12`	Compute bonds in a 2D system using a ($r$, $\theta_1$, $\theta_2$) coordinate system.
`freud.bond.BondingXY2D`	Compute bonds in a 2D system using a ($x$, $y$) coordinate system.
`freud.bond.BondingXYT`	Compute bonds in a 2D system using a ($x$, $y$, $\theta$) coordinate system.
`freud.bond.BondingXYZ`	Compute bonds in a 3D system using a ($x$, $y$, $z$) coordinate system.

Details

The bond module allows for the computation of bonds as defined by a map. Depending on the coordinate system desired, either a two or three dimensional array is supplied, with each element containing the bond index mapped to the pair geometry of that element. The user provides a list of indices to track, so that not all bond indices contained in the bond map need to be tracked in computation.

The bond module is designed to take in arrays using the same coordinate systems as the PMFT Module in freud.

Note

The coordinate system in which the calculation is performed is not the same as the coordinate system in which particle positions and orientations should be supplied. Only certain coordinate systems are available for certain particle positions and orientations:

2D particle coordinates (position: [$x$, $y$, $0$], orientation: $\theta$):
- $r$, $\theta_1$, $\theta_2$.
- $x$, $y$.
- $x$, $y$, $\theta$.
3D particle coordinates:
- $x$, $y$, $z$.

class freud.bond.BondingAnalysis(num_particles, num_bonds)¶

Analyze the bond lifetimes and flux present in the system.

Module author: Eric Harper <harperic@umich.edu>

Parameters:

num_particles (unsigned int) – Number of particles over which to calculate bonds.
num_bonds (unsigned int) – Number of bonds to track.

Variables:

bond_lifetimes (($N_{particles}$, varying) numpy.ndarray) – Bond lifetimes.
overall_lifetimes (($N_{particles}$, varying) numpy.ndarray) – Overall bond lifetimes.
transition_matrix (numpy.ndarray) – Transition matrix.
num_frames (unsigned int) – Number of frames calculated.
num_particles (unsigned int) – Number of tracked particles.
num_bonds (unsigned int) – Number of tracked bonds.

compute(self, frame_0, frame_1)¶

Calculates the changes in bonding states from one frame to the next.

Parameters:	frame_0 (($N_{particles}$, $N_{bonds}$) `numpy.ndarray`) – Current/previous bonding frame (as output from `BondingR12` modules). frame_1 (($N_{particles}$, $N_{bonds}$) `numpy.ndarray`) – Next/current bonding frame (as output from `BondingR12` modules).

getBondLifetimes(self)¶

Return the bond lifetimes.

Returns:	Lifetime of bonds.
Return type:	($N_{particles}$, varying) `numpy.ndarray`

getNumBonds(self)¶

Get number of bonds tracked.

Returns:	Number of bonds.
Return type:	unsigned int

getNumFrames(self)¶

Get number of frames calculated.

Returns:	Number of frames.
Return type:	unsigned int

getNumParticles(self)¶

Get number of particles being tracked.

Returns:	Number of particles.
Return type:	unsigned int

getOverallLifetimes(self)¶

Return the overall lifetimes.

Returns:	Lifetime of bonds.
Return type:	($N_{particles}$, varying) `numpy.ndarray`

getTransitionMatrix(self)¶

Return the transition matrix.

Returns:	Transition matrix.
Return type:	`numpy.ndarray`

initialize(self, frame_0)¶

Calculates the changes in bonding states from one frame to the next.

Parameters:	frame_0 (($N_{particles}$, $N_{bonds}$) `numpy.ndarray`) – First bonding frame (as output from `BondingR12` modules).

class freud.bond.BondingR12(r_max, bond_map, bond_list)¶

Compute bonds in a 2D system using a ($r$, $\theta_1$, $\theta_2$) coordinate system.

Module author: Eric Harper <harperic@umich.edu>

Parameters:	r_max (float) – Distance to search for bonds. bond_map (`numpy.ndarray`) – 3D array containing the bond index for each r, $\theta_2$, $\theta_1$ coordinate. bond_list (`numpy.ndarray`) – List containing the bond indices to be tracked, `bond_list[i] = bond_index`.
Variables:	bonds (`numpy.ndarray`) – Particle bonds. box (`freud.box.Box`) – Box used in the calculation. list_map (dict) – The dict used to map bond index to list index. rev_list_map (dict) – The dict used to map list idx to bond idx.

compute(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the bonding.
ref_orientations (($N_{particles}$, 4) – Orientations as angles to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the bonding.
orientations (($N_{particles}$, 4) numpy.ndarray) – Orientations as angles to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBonds(self)¶

Return the particle bonds.

Returns:	Particle bonds.
Return type:	`numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getListMap(self)¶

Get the dict used to map bond idx to list idx.

Returns:	The mapping from bond to particle index.
Return type:	dict

getRevListMap(self)¶

Get the dict used to map list idx to bond idx.

Returns:	The mapping from particle to bond index.
Return type:	dict

class freud.bond.BondingXY2D(x_max, y_max, bond_map, bond_list)¶

Compute bonds in a 2D system using a ($x$, $y$) coordinate system.

Module author: Eric Harper <harperic@umich.edu>

Parameters:	x_max (float) – Maximum $x$ distance at which to search for bonds. y_max (float) – Maximum $y$ distance at which to search for bonds. bond_map (`numpy.ndarray`) – 3D array containing the bond index for each $x$, $y$ coordinate. bond_list (`numpy.ndarray`) – List containing the bond indices to be tracked, `bond_list[i] = bond_index`.
Variables:	bonds (`numpy.ndarray`) – Particle bonds. box (`freud.box.Box`) – Box used in the calculation. list_map (dict) – The dict used to map bond index to list index. rev_list_map (dict) – The dict used to map list idx to bond idx.

compute(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the bonding.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Orientations as angles to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the bonding.
orientations (($N_{particles}$, 4) numpy.ndarray) – Orientations as angles to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBonds(self)¶

Return the particle bonds.

Returns:	Particle bonds.
Return type:	`numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getListMap(self)¶

Get the dict used to map list idx to bond idx.

Returns:	The mapping from bond to particle index.
Return type:	dict

getRevListMap(self)¶

Get the dict used to map list idx to bond idx.

Returns:	The mapping from particle to bond index.
Return type:	dict

class freud.bond.BondingXYT(x_max, y_max, bond_map, bond_list)¶

Compute bonds in a 2D system using a ($x$, $y$, $\theta$) coordinate system.

For each particle in the system determine which other particles are in which bonding sites.

Module author: Eric Harper <harperic@umich.edu>

Parameters:	x_max (float) – Maximum $x$ distance at which to search for bonds. y_max (float) – Maximum $y$ distance at which to search for bonds. bond_map (`numpy.ndarray`) – 3D array containing the bond index for each $x$, $y$ coordinate. bond_list (`numpy.ndarray`) – List containing the bond indices to be tracked, `bond_list[i] = bond_index`.
Variables:	bonds (`numpy.ndarray`) – Particle bonds. box (`freud.box.Box`) – Box used in the calculation. list_map (dict) – The dict used to map bond index to list index. rev_list_map (dict) – The dict used to map list idx to bond idx.

compute(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the bonding.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Orientations as angles to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the bonding.
orientations (($N_{particles}$, 4) numpy.ndarray) – Orientations as angles to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBonds(self)¶

Return the particle bonds.

Returns:	Particle bonds.
Return type:	`numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getListMap(self)¶

Get the dict used to map list idx to bond idx.

Returns:	The mapping from bond to particle index.
Return type:	dict

getRevListMap(self)¶

Get the dict used to map list idx to bond idx.

Returns:	The mapping from particle to bond index.
Return type:	dict

class freud.bond.BondingXYZ(x_max, y_max, z_max, bond_map, bond_list)¶

Compute bonds in a 3D system using a ($x$, $y$, $z$) coordinate system.

For each particle in the system determine which other particles are in which bonding sites.

Module author: Eric Harper <harperic@umich.edu>

Parameters:

x_max (float) – Maximum $x$ distance at which to search for bonds.
y_max (float) – Maximum $y$ distance at which to search for bonds.
z_max (float) – Maximum $z$ distance at which to search for bonds.
bond_map (numpy.ndarray) – 3D array containing the bond index for each $x$, $y$, $z$ coordinate.
bond_list (numpy.ndarray) – List containing the bond indices to be tracked, bond_list[i] = bond_index.

Variables:

bonds (numpy.ndarray) – Particle bonds.
box (freud.box.Box) – Box used in the calculation.
list_map (dict) – The dict used to map bond index to list index.
rev_list_map (dict) – The dict used to map list idx to bond idx.

compute(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the bonding.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Orientations as angles to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the bonding.
orientations (($N_{particles}$, 4) numpy.ndarray) – Orientations as angles to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBonds(self)¶

Return the particle bonds.

Returns:	Particle bonds.
Return type:	`numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getListMap(self)¶

Get the dict used to map list idx to bond idx.

Returns:	The mapping from bond to particle index.
Return type:	dict

getRevListMap(self)¶

Get the dict used to map list idx to bond idx.

Returns:	The mapping from particle to bond index.
Return type:	dict

Box Module¶

Overview

freud.box.Box The freud Box class for simulation boxes.

Details

The box module provides the Box class, which defines the geometry of the simulation box. The module natively supports periodicity by providing the fundamental features for wrapping vectors outside the box back into it.

class freud.box.Box(Lx, Ly, Lz, xy, xz, yz, is2D=None)¶

The freud Box class for simulation boxes.

Module author: Richmond Newman <newmanrs@umich.edu>

Module author: Carl Simon Adorf <csadorf@umich.edu>

Module author: Bradley Dice <bdice@bradleydice.com>

Changed in version 0.7.0: Added box periodicity interface

The Box class is defined according to the conventions of the HOOMD-blue simulation software. For more information, please see:

http://hoomd-blue.readthedocs.io/en/stable/box.html

Parameters:

Lx (float) – Length of side x.
Ly (float) – Length of side y.
Lz (float) – Length of side z.
xy (float) – Tilt of xy plane.
xz (float) – Tilt of xz plane.
yz (float) – Tilt of yz plane.
is2D (bool) – Specify that this box is 2-dimensional, default is 3-dimensional.

Variables:

xy (float) – The xy tilt factor.
xz (float) – The xz tilt factor.
yz (float) – The yz tilt factor.
L (tuple, settable) – The box lengths
Lx (tuple, settable) – The x-dimension length.
Ly (tuple, settable) – The y-dimension length.
Lz (tuple, settable) – The z-dimension length.
Linv (tuple) – The inverse box lengths.
volume (float) – The box volume (area in 2D).
dimensions (int, settable) – The number of dimensions (2 or 3).
periodic (list, settable) – Whether or not the box is periodic.

cube(type cls, L=None)¶

Construct a cubic box with equal lengths.

Parameters:	L (float) – The edge length

from_box(type cls, box, dimensions=None)¶

Initialize a box instance from a box-like object.

Parameters:	box – A box-like object dimensions (int) – Dimensionality of the box (Default value = None)

Note

Objects that can be converted to freud boxes include lists like [Lx, Ly, Lz, xy, xz, yz], dictionaries with keys 'Lx', 'Ly', 'Lz', 'xy', 'xz', 'yz', 'dimensions', namedtuples with properties Lx, Ly, Lz, xy, xz, yz, dimensions, 3x3 matrices (see from_matrix()), or existing freud.box.Box objects.

If any of Lz, xy, xz, yz are not provided, they will be set to 0.

If all values are provided, a triclinic box will be constructed. If only Lx, Ly, Lz are provided, an orthorhombic box will be constructed. If only Lx, Ly are provided, a rectangular (2D) box will be constructed.

If the optional dimensions argument is given, this will be used as the box dimensionality. Otherwise, the box dimensionality will be detected from the dimensions of the provided box. If no dimensions can be detected, the box will be 2D if Lz == 0, and 3D otherwise.

Returns:	The resulting box object.
Return type:	`freud.box:Box`

from_matrix(type cls, boxMatrix, dimensions=None)¶

Initialize a box instance from a box matrix.

For more information and the source for this code, see: http://hoomd-blue.readthedocs.io/en/stable/box.html

Parameters:	boxMatrix (array-like) – A 3x3 matrix or list of lists dimensions (int) – Number of dimensions (Default value = None)

getCoordinates(self, f)¶

Alias for makeCoordinates()

Deprecated since version 0.8: Use makeCoordinates() instead.

Parameters:	f ($\left(3\right)$ `numpy.ndarray`) – Fractional coordinates $\left(x, y, z\right)$ between 0 and 1 within parallelepipedal box.

getImage(self, vec)¶

Returns the image corresponding to a wrapped vector.

New in version 0.8.

Parameters:	vec ($\left(3\right)$ `numpy.ndarray`) – Coordinates of unwrapped vector.
Returns:	Image index vector.
Return type:	$\left(3\right)$ `numpy.ndarray`

getL(self)¶

Return the lengths of the box as a tuple (x, y, z).

Returns:	Dimensions of the box as (x, y, z).
Return type:	(float, float, float)

getLatticeVector(self, i)¶

Get the lattice vector with index $i$.

Parameters:	i (unsigned int) – Index ($0 \leq i < d$) of the lattice vector, where $d$ is the box dimension (2 or 3).
Returns:	Lattice vector with index $i$.
Return type:	list[float, float, float]

getLinv(self)¶

Return the inverse lengths of the box (1/Lx, 1/Ly, 1/Lz).

Returns:	dimensions of the box as (1/Lx, 1/Ly, 1/Lz).
Return type:	(float, float, float)

getLx(self)¶

Length of the x-dimension of the box.

Returns:	This box’s x-dimension length.
Return type:	float

getLy(self)¶

Length of the y-dimension of the box.

Returns:	This box’s y-dimension length.
Return type:	float

getLz(self)¶

Length of the z-dimension of the box.

Returns:	This box’s z-dimension length.
Return type:	float

getPeriodic(self)¶

Get the box’s periodicity in each dimension.

Returns:	Periodic attributes in x, y, z.
Return type:	list[bool, bool, bool]

getPeriodicX(self)¶

Get the box periodicity in the x direction.

Returns:	True if periodic, False if not.
Return type:	bool

getPeriodicY(self)¶

Get the box periodicity in the y direction..

Returns:	True if periodic, False if not.
Return type:	bool

getPeriodicZ(self)¶

Get the box periodicity in the z direction.

Returns:	True if periodic, False if not.
Return type:	bool

getTiltFactorXY(self)¶

Return the tilt factor xy.

Returns:	This box’s xy tilt factor.
Return type:	float

getTiltFactorXZ(self)¶

Return the tilt factor xz.

Returns:	This box’s xz tilt factor.
Return type:	float

getTiltFactorYZ(self)¶

Return the tilt factor yz.

Returns:	This box’s yz tilt factor.
Return type:	float

getVolume(self)¶

Return the box volume (area in 2D).

Returns:	Box volume.
Return type:	float

is2D(self)¶

Return if box is 2D (True) or 3D (False).

Returns:	True if 2D, False if 3D.
Return type:	bool

makeCoordinates(self, f)¶

Convert fractional coordinates into real coordinates.

Parameters:	f ($\left(3\right)$ `numpy.ndarray`) – Fractional coordinates $\left(x, y, z\right)$ between 0 and 1 within parallelepipedal box.
Returns:	Vector of real coordinates $\left(x, y, z\right)$.
Return type:	list[float, float, float]

makeFraction(self, vec)¶

Convert real coordinates into fractional coordinates.

Parameters:	vec ($\left(3\right)$ `numpy.ndarray`) – Real coordinates within parallelepipedal box.
Returns:	A fractional coordinate vector.
Return type:	list[float, float, float]

set2D(self, val)¶

Set the dimensionality to 2D (True) or 3D (False).

Parameters:	val (bool) – 2D=True, 3D=False.

setL(self, L)¶

Set all side lengths of box to L.

Parameters:	L (float) – Side length of box.

setPeriodic(self, x, y, z)¶

Set the box’s periodicity in each dimension.

Parameters:	x (bool) – True if periodic in x, False if not. y (bool) – True if periodic in y, False if not. z (bool) – True if periodic in z, False if not.

setPeriodicX(self, val)¶

Set the box periodicity in the x direction.

Parameters:	val (bool) – True if periodic, False if not.

setPeriodicY(self, val)¶

Set the box periodicity in the y direction.

Parameters:	val (bool) – True if periodic, False if not.

setPeriodicZ(self, val)¶

Set the box periodicity in the z direction.

Parameters:	val (bool) – True if periodic, False if not.

square(type cls, L=None)¶

Construct a 2-dimensional (square) box with equal lengths.

Parameters:	L (float) – The edge length

to_dict(self)¶

Return box as dictionary.

Returns:	Box parameters
Return type:	dict

to_matrix(self)¶

Returns the box matrix (3x3).

Returns:	box matrix
Return type:	list of lists, shape 3x3

to_tuple(self)¶

Returns the box as named tuple.

Returns:	Box parameters
Return type:	namedtuple

unwrap(self, vecs, imgs)¶

Unwrap a given array of vectors inside the box back into real space, using an array of image indices that determine how many times to unwrap in each dimension.

Parameters:	vecs ($\left(3\right)$ or $\left(N, 3\right)$ `numpy.ndarray`) – Single vector or array of $N$ vectors. The vectors are modified in place. imgs ($\left(3\right)$ or $\left(N, 3\right)$ `numpy.ndarray`) – Single image index or array of $N$ image indices.
Returns:	Vectors unwrapped by the image indices provided.
Return type:	$\left(3\right)$ or $\left(N, 3\right)$ `numpy.ndarray`

wrap(self, vecs)¶

Wrap a given array of vectors from real space into the box, using the periodic boundaries.

Note

Since the origin of the box is in the center, wrapping is equivalent to applying the minimum image convention to the input vectors.

Parameters:	vecs ($\left(3\right)$ or $\left(N, 3\right)$ `numpy.ndarray`) – Single vector or array of $N$ vectors. The vectors are altered in place and returned.
Returns:	Vectors wrapped into the box.
Return type:	$\left(3\right)$ or $\left(N, 3\right)$ `numpy.ndarray`

Cluster Module¶

Overview

`freud.cluster.Cluster`	Finds clusters in a set of points.
`freud.cluster.ClusterProperties`	Routines for computing properties of point clusters.

Details

The cluster module aids in finding and computing the properties of clusters of points in a system.

class freud.cluster.Cluster(box, rcut)¶

Finds clusters in a set of points.

Given a set of coordinates and a cutoff, freud.cluster.Cluster will determine all of the clusters of points that are made up of points that are closer than the cutoff. Clusters are 0-indexed. The class contains an index array, the cluster_idx attribute, which can be used to identify which cluster a particle is associated with: cluster_obj.cluster_idx[i] is the cluster index in which particle i is found. By the definition of a cluster, points that are not within the cutoff of another point end up in their own 1-particle cluster.

Identifying micelles is one primary use-case for finding clusters. This operation is somewhat different, though. In a cluster of points, each and every point belongs to one and only one cluster. However, because a string of points belongs to a polymer, that single polymer may be present in more than one cluster. To handle this situation, an optional layer is presented on top of the cluster_idx array. Given a key value per particle (i.e. the polymer id), the computeClusterMembership function will process cluster_idx with the key values in mind and provide a list of keys that are present in each cluster.

Module author: Joshua Anderson <joaander@umich.edu>

Parameters:	box (`freud.box.Box`) – The simulation box. rcut (float) – Particle distance cutoff.

Note

2D: freud.cluster.Cluster properly handles 2D boxes. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Variables:	box (`freud.box.Box`) – Box used in the calculation. num_clusters (int) – The number of clusters. num_particles (int) – The number of particles. cluster_idx (($N_{particles}$) `numpy.ndarray`) – The cluster index for each particle. cluster_keys (list(list)) – A list of lists of the keys contained in each cluster.

computeClusterMembership(self, keys)¶

Compute the clusters with key membership. Loops over all particles and adds them to a list of sets. Each set contains all the keys that are part of that cluster. Get the computed list with getClusterKeys().

Parameters:	keys (($N_{particles}$) `numpy.ndarray`) – Membership keys, one for each particle.

computeClusters(self, points, nlist=None, box=None)¶

Compute the clusters for the given set of points.

Parameters:	points (($N_{particles}$, 3) `np.ndarray`) – Particle coordinates. nlist (`freud.locality.NeighborList`, optional) – Object to use to find bonds (Default value = None). box (`freud.box.Box`, optional) – Simulation box (Default value = None).

getBox(self)¶

Return the stored freud Box.

Returns:	freud Box.
Return type:	`freud.box.Box`

getClusterIdx(self)¶

Returns 1D array of Cluster idx for each particle

Returns:	1D array of cluster idx.
Return type:	($N_{particles}$) `numpy.ndarray`

getClusterKeys(self)¶

Returns the keys contained in each cluster.

Returns:	List of lists of each key contained in clusters.
Return type:	list

getNumClusters(self)¶

Returns the number of clusters.

Returns:	Number of clusters.
Return type:	int

getNumParticles(self)¶

Returns the number of particles.

Returns:	Number of particles.
Return type:	int

class freud.cluster.ClusterProperties(box)¶

Routines for computing properties of point clusters.

Given a set of points and cluster ids (from Cluster, or another source), ClusterProperties determines the following properties for each cluster:

Center of mass

Gyration tensor

The computed center of mass for each cluster (properly handling periodic boundary conditions) can be accessed with getClusterCOM(). This returns a $\left(N_{clusters}, 3 \right)$ numpy.ndarray.

The $3 \times 3$ gyration tensor $G$ can be accessed with getClusterG(). This returns a numpy.ndarray, shape= $\left(N_{clusters} \times 3 \times 3\right)$. The tensor is symmetric for each cluster.

Module author: Joshua Anderson <joaander@umich.edu>

Parameters:

box (freud.box.Box) – Simulation box.

Variables:

box (freud.box.Box) – Box used in the calculation.
num_clusters (int) – The number of clusters.
cluster_COM (($N_{clusters}$, 3) numpy.ndarray) – The center of mass of the last computed cluster.
cluster_G (($N_{clusters}$, 3, 3) numpy.ndarray) – The cluster $G$ tensors computed by the last call to computeProperties().
cluster_sizes (($N_{clusters}$) numpy.ndarray) – The cluster sizes computed by the last call to computeProperties().

computeProperties(self, points, cluster_idx, box=None)¶

Compute properties of the point clusters. Loops over all points in the given array and determines the center of mass of the cluster as well as the $G$ tensor. These can be accessed after the call to computeProperties() with getClusterCOM() and getClusterG().

Parameters:	points (($N_{particles}$, 3) `np.ndarray`) – Positions of the particles making up the clusters. cluster_idx (`np.ndarray`) – List of cluster indexes for each particle. box (`freud.box.Box`, optional) – Simulation box (Default value = None).

getBox(self)¶

Return the stored freud.box.Box object.

Returns:	freud Box
Return type:	`freud.box.Box`

getClusterCOM(self)¶

Returns the center of mass of the last computed cluster.

Returns:	Cluster center of mass coordinates $\left(x, y, z\right)$.
Return type:	($N_{clusters}$, 3) `numpy.ndarray`

getClusterG(self)¶

Returns the cluster $G$ tensors computed by the last call to computeProperties().

Returns:	List of gyration tensors for each cluster.
Return type:	($N_{clusters}$, 3, 3) `numpy.ndarray`

getClusterSizes(self)¶

Returns the cluster sizes computed by the last call to computeProperties().

Returns:	Sizes of each cluster.
Return type:	($N_{clusters}$) `numpy.ndarray`

getNumClusters(self)¶

Count the number of clusters found in the last call to computeProperties().

Returns:	Number of clusters.
Return type:	int

Density Module¶

Overview

`freud.density.FloatCF`	Computes the pairwise correlation function $\left< p*q \right> \left( r \right)$ between two sets of points with associated values $p$ and $q$.
`freud.density.ComplexCF`	Computes the pairwise correlation function $\left< p*q \right> \left( r \right)$ between two sets of points with associated values $p$ and $q$.
`freud.density.GaussianDensity`	Computes the density of a system on a grid.
`freud.density.LocalDensity`	Computes the local density around a particle.
`freud.density.RDF`	Computes RDF for supplied data.

Details

The density module contains various classes relating to the density of the system. These functions allow evaluation of particle distributions with respect to other particles.

Correlation Functions¶

class freud.density.FloatCF(rmax, dr)¶

Computes the pairwise correlation function $\left< p*q \right> \left( r \right)$ between two sets of points with associated values $p$ and $q$.

Two sets of points and two sets of real values associated with those points are given. Computing the correlation function results in an array of the expected (average) product of all values at a given radial distance.

The values of $r$ where the correlation function is computed are controlled by the rmax and dr parameters to the constructor. rmax determines the maximum distance at which to compute the correlation function and dr is the step size for each bin.

Note

2D: freud.density.FloatCF properly handles 2D boxes. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Self-correlation: It is often the case that we wish to compute the correlation function of a set of points with itself. If given the same arrays for both points and ref_points, we omit accumulating the self-correlation value in the first bin.

Module author: Matthew Spellings <mspells@umich.edu>

Parameters:	rmax (float) – Distance over which to calculate. dr (float) – Bin size.
Variables:	RDF (($N_{bins}$) `numpy.ndarray`) – Expected (average) product of all values at a given radial distance. box (`freud.box.Box`) – Box used in the calculation. counts (($N_{bins}$) `numpy.ndarray`) – The counts of each histogram bin. R (($N_{bins}$) `numpy.ndarray`) – The values of bin centers.

accumulate(self, box, ref_points, refValues, points, values, nlist=None)¶

Calculates the correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
refValues (($N_{particles}$) numpy.ndarray) – Values to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the bonding.
values (($N_{particles}$) – Values to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

compute(self, box, ref_points, refValues, points, values, nlist=None)¶

Calculates the correlation function for the given points. Will overwrite the current histogram.

Parameters:

box (freud.box.Box) – Simulation box
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
refValues (($N_{particles}$) numpy.ndarray) – Values to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the local density.
values (($N_{particles}$) – Values to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getCounts(self)¶

Get counts of each histogram bin.

Returns:	Counts of each histogram bin.
Return type:	($N_{bins}$) `numpy.ndarray`

getR(self)¶

Get bin centers.

Returns:	Values of bin centers.
Return type:	($N_{bins}$) `numpy.ndarray`

getRDF(self)¶

Returns the radial distribution function.

Returns:	Expected (average) product of all values at a given radial distance.
Return type:	($N_{bins}$) `numpy.ndarray`

reduceCorrelationFunction(self)¶: Reduces the histogram in the values over N processors to a single histogram. This is called automatically by freud.density.FloatCF.getRDF(), freud.density.FloatCF.getCounts().

resetCorrelationFunction(self)¶: Resets the values of the correlation function histogram in memory.

class freud.density.ComplexCF(rmax, dr)¶

Computes the pairwise correlation function $\left< p*q \right> \left( r \right)$ between two sets of points with associated values $p$ and $q$.

Two sets of points and two sets of complex values associated with those points are given. Computing the correlation function results in an array of the expected (average) product of all values at a given radial distance.

The values of $r$ where the correlation function is computed are controlled by the rmax and dr parameters to the constructor. rmax determines the maximum distance at which to compute the correlation function and dr is the step size for each bin.

Note

2D: freud.density.ComplexCF properly handles 2D boxes. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Self-correlation: It is often the case that we wish to compute the correlation function of a set of points with itself. If given the same arrays for both points and ref_points, we omit accumulating the self-correlation value in the first bin.

Module author: Matthew Spellings <mspells@umich.edu>

Parameters:	rmax (float) – Distance over which to calculate. dr (float) – Bin size.
Variables:	RDF (($N_{bins}$) `numpy.ndarray`) – Expected (average) product of all values at a given radial distance. box (`freud.box.Box`) – Box used in the calculation. counts (($N_{bins}$) `numpy.ndarray`) – The counts of each histogram bin. R (($N_{bins}$) `numpy.ndarray`) – The values of bin centers.

accumulate(self, box, ref_points, refValues, points, values, nlist=None)¶

Calculates the correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
refValues (($N_{particles}$) numpy.ndarray) – Values to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the bonding.
values (($N_{particles}$) – Values to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

compute(self, box, ref_points, refValues, points, values, nlist=None)¶

Calculates the correlation function for the given points. Will overwrite the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
refValues (($N_{particles}$) numpy.ndarray) – Values to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the bonding.
values (($N_{particles}$) – Values to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None)

getBox(self)¶

Get the box used in the calculations.

Returns:	freud Box.
Return type:	`freud.box.Box`

getCounts(self)¶

Get the counts of each histogram bin.

Returns:	Counts of each histogram bin.
Return type:	($N_{bins}$) `numpy.ndarray`

getR(self)¶

Get The value of bin centers.

Returns:	Values of bin centers.
Return type:	($N_{bins}$) `numpy.ndarray`

getRDF(self)¶

Get the RDF.

Returns:	Expected (average) product of all values at a given radial distance.
Return type:	($N_{bins}$) `numpy.ndarray`

reduceCorrelationFunction(self)¶: Reduces the histogram in the values over N processors to a single histogram. This is called automatically by freud.density.ComplexCF.getRDF(), freud.density.ComplexCF.getCounts().

resetCorrelationFunction(self)¶: Resets the values of the correlation function histogram in memory.

Gaussian Density¶

class freud.density.GaussianDensity(*args)¶

Computes the density of a system on a grid.

Replaces particle positions with a Gaussian blur and calculates the contribution from the grid based upon the distance of the grid cell from the center of the Gaussian. The dimensions of the image (grid) are set in the constructor, and can either be set equally for all dimensions or for each dimension independently.

Constructor Calls:

Initialize with all dimensions identical:
freud.density.GaussianDensity(width, r_cut, dr)
Initialize with each dimension specified:
freud.density.GaussianDensity(width_x, width_y, width_z, r_cut, dr)

Module author: Joshua Anderson <joaander@umich.edu>

Parameters:

width (unsigned int) – Number of pixels to make the image.
width_x (unsigned int) – Number of pixels to make the image in x.
width_y (unsigned int) – Number of pixels to make the image in y.
width_z (unsigned int) – Number of pixels to make the image in z.
r_cut (float) – Distance over which to blur.
sigma (float) – Sigma parameter for Gaussian.

Variables:

box (freud.box.Box) – Box used in the calculation.
gaussian_density (($w_x$, $w_y$, $w_z$) numpy.ndarray) – The image grid with the Gaussian density.
counts (($N_{bins}$) numpy.ndarray) – The counts of each histogram bin.
R (($N_{bins}$) numpy.ndarray) – The values of bin centers.

compute(self, box, points)¶

Calculates the Gaussian blur for the specified points. Does not accumulate (will overwrite current image).

Parameters:	box (`freud.box.Box`) – Simulation box. points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the local density.

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getGaussianDensity(self)¶

Get the image grid with the Gaussian density.

Returns:	Image (grid) with values of Gaussian.
Return type:	($w_x$, $w_y$, $w_z$) `numpy.ndarray`

resetDensity(self)¶: Resets the values of GaussianDensity in memory.

Local Density¶

class freud.density.LocalDensity(r_cut, volume, diameter)¶

Computes the local density around a particle.

The density of the local environment is computed and averaged for a given set of reference points in a sea of data points. Providing the same points calculates them against themselves. Computing the local density results in an array listing the value of the local density around each reference point. Also available is the number of neighbors for each reference point, giving the user the ability to count the number of particles in that region.

The values to compute the local density are set in the constructor. r_cut sets the maximum distance at which to calculate the local density. volume is the volume of a single particle. diameter is the diameter of the circumsphere of an individual particle.

Note

2D: freud.density.LocalDensity properly handles 2D boxes. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Module author: Joshua Anderson <joaander@umich.edu>

Parameters:	r_cut (float) – Maximum distance over which to calculate the density. volume (float) – Volume of a single particle. diameter (float) – Diameter of particle circumsphere.
Variables:	box (`freud.box.Box`) – Box used in the calculation. density (($N_{particles}$) `numpy.ndarray`) – Density per particle. num_neighbors (($N_{particles}$) `numpy.ndarray`) – Number of neighbors for each particle..

compute(self, box, ref_points, points=None, nlist=None)¶

Calculates the local density for the specified points. Does not accumulate (will overwrite current data).

Parameters:	box (`freud.box.Box`) – Simulation box. ref_points (($N_{particles}$, 3) `numpy.ndarray`) – Reference points to calculate the local density. points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the local density. nlist (`freud.locality.NeighborList`, optional) – NeighborList to use to find bonds (Default value = None).

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getDensity(self)¶

Get the density array for each particle.

Returns:	Density array for each particle.
Return type:	($N_{particles}$) `numpy.ndarray`

getNumNeighbors(self)¶

Return the number of neighbors for each particle.

Returns:	Number of neighbors for each particle.
Return type:	($N_{particles}$) `numpy.ndarray`

Radial Distribution Function¶

class freud.density.RDF(rmax, dr, rmin=0)¶

Computes RDF for supplied data.

The RDF ($g \left( r \right)$) is computed and averaged for a given set of reference points in a sea of data points. Providing the same points calculates them against themselves. Computing the RDF results in an RDF array listing the value of the RDF at each given $r$, listed in the r array.

The values of $r$ to compute the RDF are set by the values of rmin, rmax, dr in the constructor. rmax sets the maximum distance at which to calculate the $g \left( r \right)$, rmin sets the minimum distance at which to calculate the $g \left( r \right)$, and dr determines the step size for each bin.

Module author: Eric Harper <harperic@umich.edu>

Note

2D: freud.density.RDF properly handles 2D boxes. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Parameters:	rmax (float) – Maximum distance to calculate. dr (float) – Distance between histogram bins. rmin (float) – Minimum distance to calculate, default 0.
Variables:	box (`freud.box.Box`) – Box used in the calculation. RDF (($N_{bins}$) `numpy.ndarray`) – Histogram of RDF values. R (($N_{bins}$, 3) `numpy.ndarray`) – The values of bin centers. n_r (($N_{bins}$, 3) `numpy.ndarray`) – Histogram of cumulative RDF values.

Changed in version 0.7.0: Added optional rmin argument.

accumulate(self, box, ref_points, points, nlist=None)¶

Calculates the RDF and adds to the current RDF histogram.

Parameters:	box (`freud.box.Box`) – Simulation box. ref_points (($N_{particles}$, 3) `numpy.ndarray`) – Reference points to calculate the local density. points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the bonding. nlist (`freud.locality.NeighborList`, optional) – NeighborList to use to find bonds (Default value = None).

compute(self, box, ref_points, points, nlist=None)¶

Calculates the RDF for the specified points. Will overwrite the current histogram.

Parameters:	box (`freud.box.Box`) – Simulation box. ref_points (($N_{particles}$, 3) `numpy.ndarray`) – Reference points to calculate the local density. points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the bonding. nlist (`freud.locality.NeighborList`) – NeighborList to use to find bonds (Default value = None)

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getNr(self)¶

Get the histogram of cumulative RDF values.

Returns:	Histogram of cumulative RDF values.
Return type:	($N_{bins}$, 3) `numpy.ndarray`

getR(self)¶

Get values of the histogram bin centers.

Returns:	Values of the histogram bin centers.
Return type:	($N_{bins}$, 3) `numpy.ndarray`

getRDF(self)¶

Histogram of RDF values.

Returns:	Histogram of RDF values.
Return type:	($N_{bins}$, 3) `numpy.ndarray`

reduceRDF(self)¶: Reduces the histogram in the values over N processors to a single histogram. This is called automatically by freud.density.RDF.getRDF(), freud.density.RDF.getNr().

resetRDF(self)¶: Resets the values of RDF in memory.

Environment Module¶

Overview

`freud.environment.BondOrder`	Compute the bond order diagram for the system of particles.
`freud.environment.LocalDescriptors`	Compute a set of descriptors (a numerical “fingerprint”) of a particle’s local environment.
`freud.environment.MatchEnv`	Clusters particles according to whether their local environments match or not, according to various shape matching metrics.
`freud.environment.Pairing2D`	Compute pairs for the system of particles.
`freud.environment.AngularSeparation`	Calculates the minimum angles of separation between particles and references.

Details

The environment module contains functions which characterize the local environments of particles in the system. These methods use the positions and orientations of particles in the local neighborhood of a given particle to characterize the particle environment.

class freud.environment.BondOrder(rmax, k, n, nBinsT, nBinsP)¶

Compute the bond order diagram for the system of particles.

Available modes of calculation:

If mode='bod' (Bond Order Diagram, default): Create the 2D histogram containing the number of bonds formed through the surface of a unit sphere based on the azimuthal $\left( \theta \right)$ and polar $\left( \phi \right)$ angles.
If mode='lbod' (Local Bond Order Diagram): Create the 2D histogram containing the number of bonds formed, rotated into the local orientation of the central particle, through the surface of a unit sphere based on the azimuthal $\left( \theta \right)$ and polar $\left( \phi \right)$ angles.
If mode='obcd' (Orientation Bond Correlation Diagram): Create the 2D histogram containing the number of bonds formed, rotated by the rotation that takes the orientation of neighboring particle j to the orientation of each particle i, through the surface of a unit sphere based on the azimuthal $\left( \theta \right)$ and polar $\left( \phi \right)$ angles.
If mode='oocd' (Orientation Orientation Correlation Diagram): Create the 2D histogram containing the directors of neighboring particles ($\hat{z}$ rotated by their quaternion), rotated into the local orientation of the central particle, through the surface of a unit sphere based on the azimuthal $\left( \theta \right)$ and polar $\left( \phi \right)$ angles.

Module author: Erin Teich <erteich@umich.edu>

Parameters:

r_max (float) – Distance over which to calculate.
k (unsigned int) – Order parameter i. To be removed.
n (unsigned int) – Number of neighbors to find.
n_bins_t (unsigned int) – Number of $\theta$ bins.
n_bins_p (unsigned int) – Number of $\phi$ bins.

Variables:

bond_order ($\left(N_{\phi}, N_{\theta} \right)$ numpy.ndarray) – Bond order.
box (freud.box.Box) – Box used in the calculation.
theta ($\left(N_{\theta} \right)$ numpy.ndarray) – The values of bin centers for $\theta$.
phi ($\left(N_{\phi} \right)$ numpy.ndarray) – The values of bin centers for $\phi$.
n_bins_theta (unsigned int) – The number of bins in the $\theta$ dimension.
n_bins_phi (unsigned int) – The number of bins in the $\phi$ dimension.

accumulate(self, box, ref_points, ref_orientations, points, orientations, str mode='bod', nlist=None)¶

Calculates the correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate bonds.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Reference orientations to calculate bonds.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the bonding.
orientations (($N_{particles}$, 3) numpy.ndarray) – Orientations to calculate the bonding.
mode (str, optional) – Mode to calculate bond order. Options are 'bod', 'lbod', 'obcd', or 'oocd' (Default value = 'bod').
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

compute(self, box, ref_points, ref_orientations, points, orientations, mode='bod', nlist=None)¶

Calculates the bond order histogram. Will overwrite the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate bonds.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Reference orientations to calculate bonds.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the bonding.
orientations (($N_{particles}$, 3) numpy.ndarray) – Orientations to calculate the bonding.
mode (str, optional) – Mode to calculate bond order. Options are 'bod', 'lbod', 'obcd', or 'oocd' (Default value = 'bod').
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBondOrder(self)¶

Get the bond order.

Returns:	Bond order.
Return type:	$\left(N_{\phi}, N_{\theta} \right)$ `numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getNBinsPhi(self)¶

Get the number of bins in the $\phi$-dimension of histogram.

Returns:	$N_{\phi}$
Return type:	unsigned int

getNBinsTheta(self)¶

Get the number of bins in the $\theta$-dimension of histogram.

Returns:	$N_{\theta}$.
Return type:	unsigned int

getPhi(self)¶

Get $\phi$.

Returns:	Values of bin centers for $\phi$.
Return type:	$\left(N_{\phi} \right)$ `numpy.ndarray`

getTheta(self)¶

Get $\theta$.

Returns:	Values of bin centers for $\theta$.
Return type:	$\left(N_{\theta} \right)$ `numpy.ndarray`

reduceBondOrder(self)¶: Reduces the histogram in the values over N processors to a single histogram. This is called automatically by freud.environment.BondOrder.getBondOrder().

resetBondOrder(self)¶: Resets the values of the bond order in memory.

class freud.environment.LocalDescriptors(box, nNeigh, lmax, rmax)¶

Compute a set of descriptors (a numerical “fingerprint”) of a particle’s local environment.

The resulting spherical harmonic array will be a complex-valued array of shape (num_bonds, num_sphs). Spherical harmonic calculation can be restricted to some number of nearest neighbors through the num_neighbors argument; if a particle has more bonds than this number, the last one or more rows of bond spherical harmonics for each particle will not be set.

Module author: Matthew Spellings <mspells@umich.edu>

Parameters:	num_neighbors (unsigned int) – Maximum number of neighbors to compute descriptors for. lmax (unsigned int) – Maximum spherical harmonic $l$ to consider. rmax (float) – Initial guess of the maximum radius to looks for neighbors. negative_m (bool) – True if we should also calculate $Y_{lm}$ for negative $m$.
Variables:	sph ($\left(N_{bonds}, \text{SphWidth} \right)$ `numpy.ndarray`) – A reference to the last computed spherical harmonic array. num_particles (unsigned int) – The number of particles. num_neighbors (unsigned int) – The number of neighbors. l_max (unsigned int) – The maximum spherical harmonic $l$ to calculate for. r_max (float) – The cutoff radius.

compute(self, box, unsigned int num_neighbors, points_ref, points=None, orientations=None, mode='neighborhood', nlist=None)¶

Calculates the local descriptors of bonds from a set of source points to a set of destination points.

Parameters:

box (freud.box.Box) – Simulation box.
num_neighbors (unsigned int) – Number of neighbors to compute with or to limit to, if the neighbor list is precomputed.
points_ref (($N_{particles}$, 3) numpy.ndarray) – Source points to calculate the order parameter.
points (($N_{particles}$, 3) numpy.ndarray, optional) – Destination points to calculate the order parameter (Default value = None).
orientations (($N_{particles}$, 4) numpy.ndarray, optional) – Orientation of each reference point (Default value = None).
mode (str, optional) – Orientation mode to use for environments, either 'neighborhood' to use the orientation of the local neighborhood, 'particle_local' to use the given particle orientations, or 'global' to not rotate environments (Default value = 'neighborhood').
nlist (freud.locality.NeighborList, optional) – Neighborlist to use to find bonds or 'precomputed' if using computeNList() (Default value = None).

computeNList(self, box, points_ref, points=None)¶

Compute the neighbor list for bonds from a set of source points to a set of destination points.

Parameters:	box (`freud.box.Box`) – Simulation box. points_ref (($N_{particles}$, 3) `numpy.ndarray`) – Source points to calculate the order parameter. points (($N_{particles}$, 3) `numpy.ndarray`, optional) – Destination points to calculate the order parameter (Default value = None).

getLMax(self)¶

Get the maximum spherical harmonic $l$ to calculate for.

Returns:	$l$.
Return type:	unsigned int

getNP(self)¶

Get the number of particles.

Returns:	$N_{particles}$.
Return type:	unsigned int

getNSphs(self)¶

Get the number of neighbors.

Returns:	$N_{neighbors}$.
Return type:	unsigned int

getRMax(self)¶

Get the cutoff radius.

Returns:	$r$.
Return type:	float

getSph(self)¶

Get a reference to the last computed spherical harmonic array.

Returns:	Order parameter.
Return type:	$\left(N_{bonds}, \text{SphWidth} \right)$ `numpy.ndarray`

class freud.environment.MatchEnv(box, rmax, k)¶

Clusters particles according to whether their local environments match or not, according to various shape matching metrics.

Module author: Erin Teich <erteich@umich.edu>

Parameters:	box (`freud.box.Box`) – Simulation box. rmax (float) – Cutoff radius for cell list and clustering algorithm. Values near the first minimum of the RDF are recommended. k (unsigned int) – Number of nearest neighbors taken to define the local environment of any given particle.
Variables:	tot_environment ($\left(N_{particles}, N_{neighbors}, 3\right)$ `numpy.ndarray`) – All environments for all particles. num_particles (unsigned int) – The number of particles. num_clusters (unsigned int) – The number of clusters.

cluster(self, points, threshold, hard_r=False, registration=False, global_search=False, env_nlist=None, nlist=None)¶

Determine clusters of particles with matching environments.

Parameters:

points (($N_{particles}$, 3) numpy.ndarray) – Destination points to calculate the order parameter.
threshold (float) – Maximum magnitude of the vector difference between two vectors, below which they are “matching.”
hard_r (bool) – If True, add all particles that fall within the threshold of m_rmaxsq to the environment.
registration (bool) – If True, first use brute force registration to orient one set of environment vectors with respect to the other set such that it minimizes the RMSD between the two sets.
global_search (bool) – If True, do an exhaustive search wherein the environments of every single pair of particles in the simulation are compared. If False, only compare the environments of neighboring particles.
env_nlist (freud.locality.NeighborList, optional) – Neighborlist to use to find the environment of every particle (Default value = None).
nlist (freud.locality.NeighborList, optional) – Neighborlist to use to find neighbors of every particle, to compare environments (Default value = None).

getClusters(self)¶

Get a reference to the particles, indexed into clusters according to their matching local environments.

Returns:	Clusters.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getEnvironment(self, i)¶

Returns the set of vectors defining the environment indexed by i.

Parameters:	i (unsigned int) – Environment index.
Returns:	The array of vectors.
Return type:	$\left(N_{neighbors}, 3\right)$ `numpy.ndarray`

getNP(self)¶

Get the number of particles.

Returns:	$N_{particles}$.
Return type:	unsigned int

getNumClusters(self)¶

Get the number of clusters.

Returns:	$N_{clusters}$.
Return type:	unsigned int

getTotEnvironment(self)¶

Returns the matrix of all environments for all particles.

Returns:	The array of vectors.
Return type:	$\left(N_{particles}, N_{neighbors}, 3\right)$ `numpy.ndarray`

isSimilar(self, refPoints1, refPoints2, threshold, registration=False)¶

Test if the motif provided by refPoints1 is similar to the motif provided by refPoints2.

Parameters:	refPoints1 (($N_{particles}$, 3) `numpy.ndarray`) – Vectors that make up motif 1. refPoints2 (($N_{particles}$, 3) `numpy.ndarray`) – Vectors that make up motif 2. threshold (float) – Maximum magnitude of the vector difference between two vectors, below which they are considered “matching.” registration (bool, optional) – If True, first use brute force registration to orient one set of environment vectors with respect to the other set such that it minimizes the RMSD between the two sets (Default value = False).
Returns:	A doublet that gives the rotated (or not) set of `refPoints2`, and the mapping between the vectors of `refPoints1` and `refPoints2` that will make them correspond to each other. Empty if they do not correspond to each other.
Return type:	tuple (($\left(N_{particles}, 3\right)$ `numpy.ndarray`), map[int, int])

matchMotif(self, points, refPoints, threshold, registration=False, nlist=None)¶

Determine clusters of particles that match the motif provided by refPoints.

Parameters:

points (($N_{particles}$, 3) numpy.ndarray) – Particle positions.
refPoints (($N_{particles}$, 3) numpy.ndarray) – Vectors that make up the motif against which we are matching.
threshold (float) – Maximum magnitude of the vector difference between two vectors, below which they are considered “matching.”
registration (bool, optional) – If True, first use brute force registration to orient one set of environment vectors with respect to the other set such that it minimizes the RMSD between the two sets (Default value = False).
nlist (freud.locality.NeighborList, optional) – Neighborlist to use to find bonds (Default value = None).

minRMSDMotif(self, points, refPoints, registration=False, nlist=None)¶

Rotate (if registration=True) and permute the environments of all particles to minimize their RMSD with respect to the motif provided by refPoints.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Particle positions. refPoints (($N_{particles}$, 3) `numpy.ndarray`) – Vectors that make up the motif against which we are matching. registration (bool, optional) – If True, first use brute force registration to orient one set of environment vectors with respect to the other set such that it minimizes the RMSD between the two sets (Default value = False). nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).
Returns:	Vector of minimal RMSD values, one value per particle.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

minimizeRMSD(self, refPoints1, refPoints2, registration=False)¶

Get the somewhat-optimal RMSD between the set of vectors refPoints1 and the set of vectors refPoints2.

Parameters:	refPoints1 (($N_{particles}$, 3) `numpy.ndarray`) – Vectors that make up motif 1. refPoints2 (($N_{particles}$, 3) `numpy.ndarray`) – Vectors that make up motif 2. registration (bool, optional) – If true, first use brute force registration to orient one set of environment vectors with respect to the other set such that it minimizes the RMSD between the two sets (Default value = False).
Returns:	A triplet that gives the associated min_rmsd, rotated (or not) set of refPoints2, and the mapping between the vectors of refPoints1 and refPoints2 that somewhat minimizes the RMSD.
Return type:	tuple (float, ($\left(N_{particles}, 3\right)$ `numpy.ndarray`), map[int, int])

setBox(self, box)¶

Reset the simulation box.

Parameters:	box (`freud.box.Box`) – Simulation box.

Note

Pairing2D is deprecated and is replaced with Bond Module.

class freud.environment.Pairing2D(rmax, k, compDotTol)¶

Compute pairs for the system of particles.

Module author: Eric Harper <harperic@umich.edu>

Deprecated since version 0.8.2: Use freud.bond instead.

Parameters:	rmax (float) – Distance over which to calculate. k (unsigned int) – Number of neighbors to search. compDotTol (float) – Value of the dot product below which a pair is determined.
Variables:	match ($\left(N_{particles}\right)$ `numpy.ndarray`) – The match. pair ($\left(N_{particles}\right)$ `numpy.ndarray`) – The pair. box (`freud.box.Box`) – Box used in the calculation.

compute(self, box, points, orientations, compOrientations, nlist=None)¶

Calculates the correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
orientations (($N_{particles}$, 4) numpy.ndarray) – Orientations to use in computation.
compOrientations (($N_{particles}$, 4) numpy.ndarray) – Possible orientations to check for bonds.
nlist (freud.locality.NeighborList, optional) – Neighborlist to use to find bonds (Default value = None).

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getMatch(self)¶

Get the match.

Returns:	The match.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getPair(self)¶

Get the pair.

Returns:	The pair.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

class freud.environment.AngularSeparation(box, rmax, n)¶

Calculates the minimum angles of separation between particles and references.

Module author: Erin Teich

Module author: Andrew Karas

Parameters:	rmax (float) – Cutoff radius for cell list and clustering algorithm. Values near the first minimum of the RDF are recommended. n (int) – The number of neighbors.
Variables:	nlist (`freud.locality.NeighborList`) – The neighbor list. n_p (unsigned int) – The number of particles used in computing the last set. n_ref (unsigned int) – The number of reference particles used in computing the neighbor angles. n_global (unsigned int) – The number of global orientations to check against.

computeGlobal(self, global_ors, ors, equiv_quats)¶

Calculates the minimum angles of separation between global_ors and ors, checking for underlying symmetry as encoded in equiv_quats.

Parameters:

ors (($N_{particles}$, 3) numpy.ndarray) – Orientations to calculate the order parameter.
global_ors (($N_{particles}$, 4) numpy.ndarray) – Reference orientations to calculate the order parameter.
equiv_quats (($N_{particles}$, 4) numpy.ndarray) – The set of all equivalent quaternions that takes the particle as it is defined to some global reference orientation. Important: equiv_quats must include both $q$ and $-q$, for all included quaternions.

computeNeighbor(self, box, ref_ors, ors, ref_points, points, equiv_quats, nlist=None)¶

Calculates the minimum angles of separation between ref_ors and ors, checking for underlying symmetry as encoded in equiv_quats.

Parameters:

box (freud.box.Box) – Simulation box.
orientations (($N_{particles}$, 3) numpy.ndarray) – Orientations to calculate the order parameter.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Reference orientations to calculate the order parameter.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the order parameter.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the order parameter.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).
equiv_quats (($N_{particles}$, 4) numpy.ndarray, optional) – The set of all equivalent quaternions that takes the particle as it is defined to some global reference orientation. Important: equiv_quats must include both $q$ and $-q$, for all included quaternions.
nlist – Neighborlist to use to find bonds (Default value = None).

getGlobalAngles(self)¶

The global angles in radians

Returns:	Angles in radians.
Return type:	$\left(N_{particles}, N_{global} \right)$ `numpy.ndarray`

getNGlobal(self)¶

Get the number of global orientations to check against.

Returns:	$N_{global orientations}$.
Return type:	unsigned int

getNP(self)¶

Get the number of particles used in computing the last set.

Returns:	$N_{particles}$.
Return type:	unsigned int

getNReference(self)¶

Get the number of reference particles used in computing the neighbor angles.

Returns:	$N_{particles}$.
Return type:	unsigned int

getNeighborAngles(self)¶

The neighbor angles in radians.

Returns:	Angles in radians.
Return type:	$\left(N_{reference}, N_{neighbors} \right)$ `numpy.ndarray`

Index Module¶

Overview

`freud.index.Index2D`	freud-style indexer for flat arrays.
`freud.index.Index3D`	freud-style indexer for flat arrays.

Details

The index module exposes the $1$-dimensional indexer utilized in freud at the C++ level. At the C++ level, freud utilizes flat arrays to represent multidimensional arrays. $N$-dimensional arrays with $n_i$ elements in each dimension $i$ are represented as $1$-dimensional arrays with $\prod_{i=1}^N n_i$ elements.

class freud.index.Index2D(*args)¶

freud-style indexer for flat arrays.

Once constructed, the object provides direct access to the flat index equivalent:

Constructor Calls:
Initialize with all dimensions identical:
freud.index.Index2D(w)
Initialize with each dimension specified:
freud.index.Index2D(w, h)

Note

freud indexes column-first i.e. Index2D(i, j) will return the $1$-dimensional index of the $i^{th}$ column and the $j^{th}$ row. This is the opposite of what occurs in a numpy array, in which array[i, j] returns the element in the $i^{th}$ row and the $j^{th}$ column.

Module author: Joshua Anderson <joaander@umich.edu>

Parameters:	w (unsigned int) – Width of 2D array (number of columns). h (unsigned int) – Height of 2D array (number of rows).
Variables:	num_elements (unsigned int) – Number of elements in the array.

Example:

index = Index2D(10)
i = index(3, 5)

__call__(self, i, j)¶

Parameters:	i (unsigned int) – Column index. j (unsigned int) – Row index.
Returns:	Index in flat (e.g. $1$-dimensional) array.
Return type:	unsigned int

getNumElements(self)¶

Get the number of elements in the array.

Returns:	Number of elements in the array.
Return type:	unsigned int

class freud.index.Index3D(*args)¶

freud-style indexer for flat arrays.

Once constructed, the object provides direct access to the flat index equivalent:

Constructor Calls:
Initialize with all dimensions identical:
freud.index.Index3D(w)
Initialize with each dimension specified:
freud.index.Index3D(w, h, d)

Note

freud indexes column-first i.e. Index3D(i, j, k) will return the $1$-dimensional index of the $i^{th}$ column, $j^{th}$ row, and the $k^{th}$ frame. This is the opposite of what occurs in a numpy array, in which array[i, j, k] returns the element in the $i^{th}$ frame, $j^{th}$ row, and the $k^{th}$ column.

Module author: Joshua Anderson <joaander@umich.edu>

Parameters:	w (unsigned int) – Width of 2D array (number of columns). h (unsigned int) – Height of 2D array (number of rows). d (unsigned int) – Depth of 2D array (number of frames).
Variables:	num_elements (unsigned int) – Number of elements in the array.

Example:

index = Index3D(10)
i = index(3, 5, 4)

__call__(self, i, j, k)¶

Parameters:	i (unsigned int) – Column index. j (unsigned int) – Row index. k (unsigned int) – Frame index.
Returns:	Index in flat (e.g. $1$-dimensional) array.
Return type:	unsigned int

getNumElements(self)¶

Get the number of elements in the array.

Returns:	Number of elements in the array.
Return type:	unsigned int

Interface Module¶

Overview

freud.interface.InterfaceMeasure Measures the interface between two sets of points.

Details

The interface module contains functions to measure the interface between sets of points.

class freud.interface.InterfaceMeasure(box, r_cut)¶

Measures the interface between two sets of points.

Module author: Matthew Spellings <mspells@umich.edu>

Parameters:	box (`freud.box.Box`) – Simulation box. r_cut (float) – Distance to search for particle neighbors.

compute(self, ref_points, points, nlist=None)¶

Compute and return the number of particles at the interface between the two given sets of points.

Parameters:	ref_points (($N_{particles}$, 3) `numpy.ndarray`) – One set of particle positions. points (($N_{particles}$, 3) `numpy.ndarray`) – Other set of particle positions. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

KSpace Module¶

Overview

`freud.kspace.meshgrid2`	Computes an n-dimensional meshgrid.
`freud.kspace.SFactor3DPoints`	Compute the full 3D structure factor of a given set of points.
`freud.kspace.AnalyzeSFactor3D`	Analyze the peaks in a 3D structure factor.
`freud.kspace.SingleCell3D`	SingleCell3D objects manage data structures necessary to call the Fourier Transform functions that evaluate FTs for given form factors at a list of $K$ points.
`freud.kspace.FTfactory`	Factory to return an FT object of the requested type.
`freud.kspace.FTbase`	Base class for FT calculation classes.
`freud.kspace.FTdelta`	Fourier transform a list of delta functions.
`freud.kspace.FTsphere`	Fourier transform for sphere.
`freud.kspace.FTpolyhedron`	Fourier Transform for polyhedra.
`freud.kspace.FTconvexPolyhedron`	Fourier Transform for convex polyhedra.
`freud.kspace.rotate`	Axis-angle rotation.
`freud.kspace.quatrot`	Apply a rotation quaternion.
`freud.kspace.Constraint`	Constraint base class.
`freud.kspace.AlignedBoxConstraint`	Axis-aligned Box constraint.
`freud.kspace.constrainedLatticePoints`	Generate a list of points satisfying a constraint.
`freud.kspace.reciprocalLattice3D`	Calculate reciprocal lattice vectors.
`freud.kspace.DeltaSpot`	Base class for drawing diffraction spots on a 2D grid.
`freud.kspace.GaussianSpot`	Draw diffraction spot as a Gaussian blur.

Details

Modules for calculating quantities in reciprocal space, including Fourier transforms of shapes and diffraction pattern generation.

Structure Factor¶

class freud.kspace.SFactor3DPoints(box, g)[source]¶

Compute the full 3D structure factor of a given set of points.

Given a set of points $\vec{r}_i$, SFactor3DPoints computes the static structure factor $S \left( \vec{q} \right) = C_0 \left| {\sum_{m=1}^{N} e^{\mathit{i}\vec{q}\cdot\vec{r_i}}} \right|^2$.

In this expression, $C_0$ is a scaling constant chosen so that $S\left(0\right) = 1$, and $N$ is the number of particles.

$S$ is evaluated on a grid of $q$-values $\vec{q} = h \frac{2\pi}{L_x} \hat{i} + k \frac{2\pi}{L_y} \hat{j} + l \frac{2\pi}{L_z} \hat{k}$ for integer $h,k,l: \left[-g,g\right]$ and $L_x, L_y, L_z$ are the box lengths in each direction.

After calling compute(), access the $q$ values with getQ(), the static structure factor values with getS(), and (if needed) the un-squared complex version of $S$ with getSComplex(). All values are stored in 3D numpy.ndarray structures. They are indexed by $a, b, c$ where $a=h+g, b=k+g, c=l+g$.

Note

Due to the way that numpy arrays are indexed, access the returned S array as S[c,b,a] to get the value at $q = \left(qx\left[a\right], qy\left[b\right], qz\left[c\right]\right)$.

Parameters:	box (`freud.box.Box`) – The simulation box. g (int) – The number of grid points for $q$ in each direction is 2*g+1.

compute(points)[source]¶

Compute the static structure factor of a given set of points.

After calling compute(), you can access the results with getS(), getSComplex(), and the grid with getQ().

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points used to compute the static structure factor.

getQ()[source]¶

Get the $q$ values at each point.

The structure factor S[c,b,a] is evaluated at the vector $q = \left(qx\left[a\right], qy\left[b\right], qz\left[c\right]\right)$.

Returns:	(qx, qy, qz).
Return type:	tuple

getS()[source]¶

Get the computed static structure factor.

Returns:	The computed static structure factor as a copy.
Return type:	(X, Y) `numpy.ndarray`

getSComplex()[source]¶

Get the computed complex structure factor (if you need the phase information).

Returns:	The computed static structure factor, as a copy, without taking the magnitude squared.
Return type:	(X, Y) `numpy.ndarray`

class freud.kspace.AnalyzeSFactor3D(S)[source]¶

Analyze the peaks in a 3D structure factor.

Given a structure factor $S\left(q\right)$ computed by classes such as SFactor3DPoints, AnalyzeSFactor3D performs a variety of analysis tasks.

Identifies peaks.

Provides a list of peaks and the vector $\vec{q}$ positions at which they occur.

Provides a list of peaks grouped by $q^2$

Provides a full list of $S\left(\left|q\right|\right)$ values vs $q^2$ suitable for plotting the 1D analog of the structure factor.

Scans through the full 3D peaks and reconstructs the Bravais lattice.

Note

All of these operations work in an indexed integer $q$-space $h,k,l$. Any peak position values returned must be multiplied by $2\pi/L$ to get to real $q$ values in simulation units.

Parameters:	S (`numpy.ndarray`) – Static structure factor to analyze.

getPeakDegeneracy(cut)[source]¶

Get a dictionary of peaks indexed by $q^2$.

Parameters:	cut (`numpy.ndarray`) – All $S\left(q\right)$ values greater than cut will be counted as peaks.
Returns:	A dictionary with keys $q^2$ and a list of peaks for the corresponding values.
Return type:	dict

getPeakList(cut)[source]¶

Get a list of peaks in the structure factor.

Parameters:	cut (float) – All $S\left(q\right)$ values greater than cut will be counted as peaks.
Returns:	peaks, $q$ as lists.
Return type:	list

getSvsQ()[source]¶

Get a list of all $S\left(\left|q\right|\right)$ values vs $q^2$.

Returns:	S, qsquared.
Return type:	`numpy.ndarray`

class freud.kspace.SingleCell3D(k, ndiv, dK, boxMatrix)[source]¶

SingleCell3D objects manage data structures necessary to call the Fourier Transform functions that evaluate FTs for given form factors at a list of $K$ points. SingleCell3D provides an interface to helper functions to calculate $K$ points for a desired grid from the reciprocal lattice vectors calculated from an input boxMatrix. State is maintained as set_ and update_ functions invalidate internal data structures and as fresh data is restored with update_ function calls. This should facilitate management with a higher-level UI such as a GUI with an event queue.

I’m not sure what sort of error checking would be most useful, so I’m mostly allowing ValueErrors and such exceptions to just occur and then propagate up through the calling functions to be dealt with by the user.

Parameters:

ndiv (int) – The resolution of the diffraction image grid.
k (float) – The angular wave number of the plane wave probe (Currently unused).
dK (float) – The k-space unit associated with the diffraction image grid spacing.
boxMatrix (($N_{particles}$, 3) numpy.ndarray) – The unit cell lattice vectors as columns in a 3x3 matrix.
scale (float) – nm per unit length (default 1.0).

Note

The set_ functions take a single parameeter and cause other internal data structures to become invalid.
The update_ and calculate functions restore the validity of these structures using internal data.
The functions are separate to make it easier to avoid unnecessary computation such as when changing multiple parameters before seeking output or when wrapping the code with an interface with an event queue.

add_ptype(name)[source]¶

Create internal data structures for new particle type by name.

Particle type is inactive when added because parameters must be set before FT can be performed.

Parameters:	name (str) – particle name

calculate(*args, **kwargs)[source]¶

Calculate FT. The details and arguments will vary depending on the form factor chosen for the particles.

For any particle type-dependent parameters passed as keyword arguments, the parameter must be passed as a list of length max(p_type)+1 with indices corresponding to the particle types defined. In other words, type-dependent parameters are optional (depending on the set of form factors being calculated), but if included must be defined for all particle types.

get_form_factors()[source]¶

Get form factor names and indices.

Returns:	List of factor names and indices.
Return type:	list

get_ptypes()[source]¶

Get ordered list of particle names.

Returns:	List of particle names.
Return type:	list

remove_ptype(name)[source]¶

Remove internal data structures associated with ptype name.

Parameters:	name (str) – Particle type to remove.

Note

This shouldn’t usually be necessary, since particle types may be set inactive or have any of their properties updated through set_ methods.

set_active(name)[source]¶

Set particle type active.

Parameters:	name (str) – Particle name.

set_box(boxMatrix)[source]¶

Set box matrix.

Parameters:	boxMatrix ((3, 3) `numpy.ndarray`) – Unit cell box matrix.

set_dK(dK)[source]¶

Set grid spacing in diffraction image.

Parameters:	dK (float) – Difference in $K$ vector between two adjacent diffraction image grid points.

set_form_factor(name, ff)[source]¶

Set scattering form factor.

Parameters:	name (str) – Particle type name. ff (str) – Scattering form factor named in `get_form_factors()`.

set_inactive(name)[source]¶

Set particle type inactive.

Parameters:	name (str) – Particle name.

set_k(k)[source]¶

Set angular wave number of plane wave probe.

Parameters:	k (float) – $\left\|k_0\right\|$.

set_ndiv(ndiv)[source]¶

Set number of grid divisions in diffraction image.

Parameters:	ndiv (int) – Define diffraction image as ndiv x ndiv grid.

set_param(particle, param, value)[source]¶

Set named parameter for named particle.

Parameters:	particle (str) – Particle name. param (str) – Parameter name. value (float) – Parameter value.

set_rq(name, position, orientation)[source]¶

Set positions and orientations for a particle type.

To best maintain valid state in the event of changing numbers of particles, position and orientation are updated in a single method.

Parameters:	name (str) – Particle type name. position ((N,3) `numpy.ndarray`) – Array of particle positions. orientation ((N,4) `numpy.ndarray`) – Array of particle quaternions.

set_scale(scale)[source]¶

Set scale factor. Store global value and set for each particle type.

Parameters:	scale (float) – nm per unit for input file coordinates.

update_K_constraint()[source]¶

Recalculate constraint used to select $K$ values.

The constraint used is a slab of epsilon thickness in a plane perpendicular to the $k_0$ propagation, intended to provide easy emulation of TEM or relatively high-energy scattering.

update_Kpoints()[source]¶: Update $K$ points at which to evaluate FT.

Note

If the diffraction image dimensions change relative to the reciprocal lattice, the $K$ points need to be recalculated.

update_bases()[source]¶: Update the direct and reciprocal space lattice vectors.

Note

If scale or boxMatrix is updated, the lattice vectors in direct and reciprocal space need to be recalculated.

class freud.kspace.FTfactory[source]¶

Factory to return an FT object of the requested type.

addFT(name, constructor, args=None)[source]¶

Add an FT class to the factory.

Parameters:	name (str) – Identifying string to be returned by getFTlist(). constructor (str) – Class / function name to be used to create new FT objects. args (list) – Set default argument object to be used to construct FT objects.

getFTlist()[source]¶

Get an ordered list of named FT types.

Returns:	List of FT names.
Return type:	list

getFTobject(i, args=None)[source]¶

Get a new instance of an FT type from list returned by getFTlist().

Parameters:	i (int) – Index into list returned by `getFTlist()`. args – Argument object used to initialize FT, overriding default set at `addFT()`.

class freud.kspace.FTbase[source]¶

Base class for FT calculation classes.

getFT()[source]¶

Return Fourier Transform.

Returns:	Fourier Transform.
Return type:	`numpy.ndarray`

get_density(density)[source]¶

Get density.

Returns:	Density.
Return type:	`numpy.complex64`

get_parambyname(name)[source]¶

Get named parameter for object.

Parameters:	name (str) – Parameter name. Must exist in list returned by `get_params()`.
Returns:	Parameter value.
Return type:	float

get_params()[source]¶

Get the parameter names accessible with set_parambyname().

Returns:	Parameter names.
Return type:	list

get_scale()[source]¶

Get scale.

Returns:	Scale.
Return type:	float

set_K(K)[source]¶

Set $K$ points to be evaluated.

Parameters:	K (`numpy.ndarray`) – List of $K$ vectors at which to evaluate FT.

set_density(density)[source]¶

Set density.

Parameters:	density (`numpy.complex64`) – Density.

set_parambyname(name, value)[source]¶

Set named parameter for object.

Parameters:	name (str) – Parameter name. Must exist in list returned by `get_params()`. value (float) – Parameter value to set.

set_rq(r, q)[source]¶

Set $r$, $q$ values.

Parameters:	r (float) – $r$. q (float) – $q$.

set_scale(scale)[source]¶

Set scale.

Parameters:	scale (float) – Scale.

class freud.kspace.FTdelta[source]¶

Fourier transform a list of delta functions.

compute(*args, **kwargs)[source]¶

Compute FT.

Calculate $S = \sum_{\alpha} e^{-i \mathbf{K} \cdot \mathbf{r}_{\alpha}}$.

set_K(K)[source]¶

Set $K$ points to be evaluated.

Parameters:	K (`numpy.ndarray`) – List of $K$ vectors at which to evaluate FT.

set_density(density)[source]¶

Set density.

Parameters:	density (`numpy.complex64`) – density

set_rq(r, q)[source]¶

Set $r$, $q$ values.

Parameters:	r (float) – $r$. q (float) – $q$.

set_scale(scale)[source]¶

Set scale.

Parameters:	scale (float) – Scale.

Note

For a scale factor, $\lambda$, affecting the scattering density $\rho\left(r\right)$, $S_{\lambda}\left (k\right) == \lambda^3 * S\left(\lambda * k\right)$

class freud.kspace.FTsphere[source]¶

Fourier transform for sphere.

Calculate $S = \sum_{\alpha} e^{-i \mathbf{K} \cdot \mathbf{r}_{\alpha}}$.

get_radius()[source]¶

Get radius parameter.

If appropriate, return value should be scaled by get_parambyname('scale') for interpretation.

Returns:	Unscaled radius.
Return type:	float

set_radius(radius)[source]¶

Set radius parameter.

Parameters:	radius (float) – Sphere radius will be stored as given, but scaled by scale parameter when used by methods.

class freud.kspace.FTpolyhedron[source]¶

Fourier Transform for polyhedra.

compute(*args, **kwargs)[source]¶

Compute FT.

Calculate $S = \sum_{\alpha} e^{-i \mathbf{K} \cdot \mathbf{r}_{\alpha}}$.

get_radius()[source]¶

Get radius parameter.

If appropriate, return value should be scaled by get_parambyname('scale') for interpretation.

Returns:	Unscaled radius.
Return type:	float

set_K(K)[source]¶

Set $K$ points to be evaluated.

Parameters:	K (`numpy.ndarray`) – List of $K$ vectors at which to evaluate FT.

set_density(density)[source]¶

Set density.

Parameters:	density (`numpy.complex64`) – Density.

set_params(verts, facets, norms, d, areas, volume)[source]¶

Construct list of facet offsets.

Parameters:	verts (($N_{particles}$, 3) `numpy.ndarray`) – Vertex coordinates. facets (($N_{facets}$, 3) `numpy.ndarray`) – Facet vertex indices. norms (($N_{facets}$, 3) `numpy.ndarray`) – Facet normals. d (($N_{facets}-1$) `numpy.ndarray`) – Facet distances. area (($N_{facets}-1$) `numpy.ndarray`) – Facet areas. volume (float) – Polyhedron volume.

set_radius(radius)[source]¶

Set radius of in-sphere.

Parameters:	radius (float) – Radius of inscribed sphere without scale applied.

set_rq(r, q)[source]¶

Set $r$, $q$ values.

Parameters:	r (float) – $r$. q (float) – $q$.

class freud.kspace.FTconvexPolyhedron[source]¶

Fourier Transform for convex polyhedra.

Parameters:	hull (($N_{verts}$, 3) `numpy.ndarray`) – Convex hull object.

Spoly2D(i, k)[source]¶

Calculate Fourier transform of polygon.

Parameters:	i (float) – Face index into self.hull simplex list. k (`numpy.ndarray`) – Angular wave vector at which to calculate $S\left(i\right)$.

Spoly3D(k)[source]¶

Calculate Fourier transform of polyhedron.

Parameters:	k (int) – Angular wave vector at which to calculate $S\left(i\right)$.

compute_py(*args, **kwargs)[source]¶

Compute FT.

Calculate $P = F * S$:

$S = \sum_{\alpha} e^{-i \mathbf{K} \cdot \mathbf{r}_{\alpha}}$.
F is the analytical form factor for a polyhedron, computed with Spoly3D().

get_radius()[source]¶

Get radius parameter.

If appropriate, return value should be scaled by get_parambyname(‘scale’) for interpretation.

Returns:	Unscaled radius.
Return type:	float

set_radius(radius)[source]¶

Set radius of in-sphere.

Parameters:	radius (float) – Radius of inscribed sphere without scale applied.

Diffraction Patterns¶

class freud.kspace.DeltaSpot[source]¶

Base class for drawing diffraction spots on a 2D grid.

Based on the dimensions of a grid, determines which grid points need to be modified to represent a diffraction spot and generates the values in that subgrid. Spot is a single pixel at the closest grid point.

Parameters:	shape – Number of grid points in each dimension. extent – Range of x,y values associated with grid points.

get_gridPoints()[source]¶

Get indices of sub-grid.

Based on the type of spot and its center, return the grid mask of points containing the spot.

makeSpot(cval)[source]¶

Generate intensity value(s) at sub-grid points.

Parameters:	cval (`numpy.complex64`) – Complex valued amplitude used to generate spot intensity.

set_xy(x, y)[source]¶

Set $x$, $y$ values of spot center.

Parameters:	x (float) – x value of spot center. y (float) – y value of spot center.

class freud.kspace.GaussianSpot[source]¶

Draw diffraction spot as a Gaussian blur.

Grid points filled according to Gaussian at spot center.

Parameters:	shape – Number of grid points in each dimension. extent – Range of x, y values associated with grid points.

makeSpot(cval)[source]¶

Generate intensity value(s) at sub-grid points.

Parameters:	cval (`numpy.complex64`) – Complex valued amplitude used to generate spot intensity.

set_sigma(sigma)[source]¶

Define Gaussian.

Parameters:	sigma (float) – Width of the Gaussian spot.

set_xy(x, y)[source]¶

Set $x$, $y$ values of spot center.

Parameters:	x (float) – x value of spot center. y (float) – y value of spot center.

Utilities¶

class freud.kspace.Constraint[source]¶

Constraint base class.

Base class for constraints on vectors to define the API. All constraints should have a ‘radius’ defining a bounding sphere and a ‘satisfies’ method to determine whether an input vector satisfies the constraint.

satisfies(v)[source]¶

Constraint test.

Parameters:	v – Vector to test against constraint.

class freud.kspace.AlignedBoxConstraint[source]¶

Axis-aligned Box constraint.

Tetragonal box aligned with the coordinate system. Consider using a small z dimension to serve as a plane plus or minus some epsilon. Set R < L for a cylinder.

satisfies(v)[source]¶

Constraint test.

Parameters:	v – Vector to test against constraint.

freud.kspace.constrainedLatticePoints()[source]¶

Generate a list of points satisfying a constraint.

Parameters:	v1 (`numpy.ndarray`) – Lattice vector 1 along which to test points. v2 (`numpy.ndarray`) – Lattice vector 2 along which to test points. v3 (`numpy.ndarray`) – Lattice vector 3 along which to test points. constraint (`Constraint`) – Constraint object to test lattice points against.

freud.kspace.reciprocalLattice3D()[source]¶

Calculate reciprocal lattice vectors.

3D reciprocal lattice vectors with magnitude equal to angular wave number.

Parameters:	a1 (`numpy.ndarray`) – Real space lattice vector 1. a2 (`numpy.ndarray`) – Real space lattice vector 2. a3 (`numpy.ndarray`) – real space lattice vector 3.
Returns:	Reciprocal space vectors.
Return type:	list

Note

For unit test, dot(g[i], a[j]) = 2 * pi * diracDelta(i, j):: list of reciprocal lattice vectors

freud.kspace.meshgrid2(*arrs)[source]¶

Computes an n-dimensional meshgrid.

source: http://stackoverflow.com/questions/1827489/numpy-meshgrid-in-3d

Parameters:	arrs (list) – Arrays to meshgrid.
Returns:	Tuple of arrays.
Return type:	`tuple`

Locality Module¶

Overview

`freud.locality.NeighborList`	Class representing a certain number of “bonds” between particles.
`freud.locality.IteratorLinkCell`	Iterates over the particles in a cell.
`freud.locality.LinkCell`	Supports efficiently finding all points in a set within a certain distance from a given point.
`freud.locality.NearestNeighbors`	Supports efficiently finding the $N$ nearest neighbors of each point in a set for some fixed integer $N$.

Details

The locality module contains data structures to efficiently locate points based on their proximity to other points.

class freud.locality.NeighborList¶

Class representing a certain number of “bonds” between particles. Computation methods will iterate over these bonds when searching for neighboring particles.

NeighborList objects are constructed for two sets of position arrays A (alternatively reference points; of length $n_A$) and B (alternatively target points; of length $n_B$) and hold a set of $\left(i, j\right): i < n_A, j < n_B$ index pairs corresponding to near-neighbor points in A and B, respectively.

For efficiency, all bonds for a particular reference particle $i$ are contiguous and bonds are stored in order based on reference particle index $i$. The first bond index corresponding to a given particle can be found in $\log(n_{bonds})$ time using find_first_index().

Module author: Matthew Spellings <mspells@umich.edu>

New in version 0.6.4.

Note

Typically, there is no need to instantiate this class directly. In most cases, users should manipulate freud.locality.NeighborList objects received from a neighbor search algorithm, such as freud.locality.LinkCell, freud.locality.NearestNeighbors, or freud.voronoi.Voronoi.

Variables:

index_i (np.ndarray) – The reference point indices from the last set of points this object was evaluated with. This array is read-only to prevent breakage of find_first_index().
index_j (np.ndarray) – The reference point indices from the last set of points this object was evaluated with. This array is read-only to prevent breakage of find_first_index().
weights (($N_{bonds}$) np.ndarray) – The per-bond weights from the last set of points this object was evaluated with.
segments (($N_{ref\_points}$) np.ndarray) – A segment array, which is an array of length $N_{ref}$ indicating the first bond index for each reference particle from the last set of points this object was evaluated with.
neighbor_counts (($N_{ref\_points}$) np.ndarray) – A neighbor count array, which is an array of length $N_{ref}$ indicating the number of neighbors for each reference particle from the last set of points this object was evaluated with.

Example:

# Assume we have position as Nx3 array
lc = LinkCell(box, 1.5).compute(box, positions)
nlist = lc.nlist

# Get all vectors from central particles to their neighbors
rijs = positions[nlist.index_j] - positions[nlist.index_i]
box.wrap(rijs)

copy(self, other=None)¶

Create a copy. If other is given, copy its contents into this object. Otherwise, return a copy of this object.

Parameters:	other (`freud.locality.NeighborList`, optional) – A Neighborlist to copy into this object (Default value = None).

filter(self, filt)¶

Removes bonds that satisfy a boolean criterion.

Parameters:	filt (`np.ndarray`) – Boolean-like array of bonds to keep (True means the bond will not be removed).

Note

This method modifies this object in-place.

Example:

# Keep only the bonds between particles of type A and type B
nlist.filter(types[nlist.index_i] != types[nlist.index_j])

filter_r(self, box, ref_points, points, float rmax, float rmin=0)¶

Removes bonds that are outside of a given radius range.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to use for filtering.
points (($N_{particles}$, 3) numpy.ndarray) – Target points to use for filtering.
rmax (float) – Maximum bond distance in the resulting neighbor list.
rmin (float, optional) – Minimum bond distance in the resulting neighbor list (Default value = 0).

find_first_index(self, unsigned int i)¶

Returns the lowest bond index corresponding to a reference particle with an index $\geq i$.

Parameters:	i (unsigned int) – The particle index.

from_arrays(type cls, Nref, Ntarget, index_i, index_j, weights=None)¶

Create a NeighborList from a set of bond information arrays.

Parameters:

Nref (int) – Number of reference points (corresponding to index_i).
Ntarget (int) – Number of target points (corresponding to index_j).
index_i (np.array) – Array of integers corresponding to indices in the set of reference points.
index_j (np.array) – Array of integers corresponding to indices in the set of target points.
weights (np.array, optional) – Array of per-bond weights (if None is given, use a value of 1 for each weight) (Default value = None).

class freud.locality.IteratorLinkCell¶

Iterates over the particles in a cell.

Module author: Joshua Anderson <joaander@umich.edu>

Example:

# Grab particles in cell 0
for j in linkcell.itercell(0):
    print(positions[j])

next(self)¶: Implements iterator interface

class freud.locality.LinkCell(box, cell_width)¶

Supports efficiently finding all points in a set within a certain distance from a given point.

Module author: Joshua Anderson <joaander@umich.edu>

Parameters:

box (freud.box.Box) – Simulation box.
cell_width (float) – Maximum distance to find particles within.

Variables:

box (freud.box.Box) – Simulation box.
num_cells (unsigned int) – The number of cells in the box.
nlist (freud.locality.NeighborList) – The neighbor list stored by this object, generated by compute().
index_i (np.ndarray) – The reference point indices from the last set of points this object was evaluated with. This array is read-only to prevent breakage of find_first_index().
index_j (np.ndarray) – The reference point indices from the last set of points this object was evaluated with. This array is read-only to prevent breakage of find_first_index().
weights (($N_{bonds}$) np.ndarray) – The per-bond weights from the last set of points this object was evaluated with.

Note

2D: freud.locality.LinkCell properly handles 2D boxes. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Example:

# Assume positions are an Nx3 array
lc = LinkCell(box, 1.5)
lc.computeCellList(box, positions)
for i in range(positions.shape[0]):
    # Cell containing particle i
    cell = lc.getCell(positions[0])
    # List of cell's neighboring cells
    cellNeighbors = lc.getCellNeighbors(cell)
    # Iterate over neighboring cells (including our own)
    for neighborCell in cellNeighbors:
        # Iterate over particles in each neighboring cell
        for neighbor in lc.itercell(neighborCell):
            pass # Do something with neighbor index

# Using NeighborList API
dens = density.LocalDensity(1.5, 1, 1)
dens.compute(box, positions, nlist=lc.nlist)

compute(self, box, ref_points, points=None, exclude_ii=None)¶

Update the data structure for the given set of points and compute a NeighborList.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference point coordinates.
points (($N_{particles}$, 3) numpy.ndarray, optional) – Point coordinates (Default value = None).
exclude_ii (bool, optional) – True if pairs of points with identical indices should be excluded; if None, is set to True if points is None or the same object as ref_points (Default value = None).

computeCellList(self, box, ref_points, points=None, exclude_ii=None)¶

Update the data structure for the given set of points and compute a NeighborList.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference point coordinates.
points (($N_{particles}$, 3) numpy.ndarray, optional) – Point coordinates (Default value = None).
exclude_ii (bool, optional) – True if pairs of points with identical indices should be excluded; if None, is set to True if points is None or the same object as ref_points (Default value = None).

getBox(self)¶

Get the freud Box.

Returns:	freud Box.
Return type:	`freud.box.Box`

getCell(self, point)¶

Returns the index of the cell containing the given point.

Parameters:	point ($\left(3\right)$ `numpy.ndarray`) – Point coordinates $\left(x,y,z\right)$.
Returns:	Cell index.
Return type:	unsigned int

getCellNeighbors(self, cell)¶

Returns the neighboring cell indices of the given cell.

Parameters:	cell (unsigned int) – Cell index.
Returns:	Array of cell neighbors.
Return type:	$\left(N_{neighbors}\right)$ `numpy.ndarray`

getNumCells(self)¶

Get the number of cells in this box.

Returns:	The number of cells in this box.
Return type:	unsigned int

itercell(self, unsigned int cell)¶

Return an iterator over all particles in the given cell.

Parameters:	cell (unsigned int) – Cell index.
Returns:	Iterator to particle indices in specified cell.
Return type:	iter

class freud.locality.NearestNeighbors(rmax, n_neigh, scale=1.1, strict_cut=False)¶

Supports efficiently finding the $N$ nearest neighbors of each point in a set for some fixed integer $N$.

strict_cut == True: rmax will be strictly obeyed, and any particle which has fewer than $N$ neighbors will have values of UINT_MAX assigned.
strict_cut == False (default): rmax will be expanded to find the requested number of neighbors. If rmax increases to the point that a cell list cannot be constructed, a warning will be raised and the neighbors already found will be returned.

Module author: Eric Harper <harperic@umich.edu>

Parameters:

rmax (float) – Initial guess of a distance to search within to find N neighbors.
n_neigh (unsigned int) – Number of neighbors to find for each point.
scale (float) – Multiplier by which to automatically increase rmax value if the requested number of neighbors is not found. Only utilized if strict_cut is False. Scale must be greater than 1.
strict_cut (bool) – Whether to use a strict rmax or allow for automatic expansion, default is False.

Variables:

UINTMAX (unsigned int) – Value of C++ UINTMAX used to pad the arrays.
box (freud.box.Box) – Simulation box.
num_neighbors (unsigned int) – The number of neighbors this object will find.
n_ref (unsigned int) – The number of particles this object found neighbors of.
r_max (float) – Current nearest neighbors search radius guess.
wrapped_vectors ($\left(N_{particles}\right)$ numpy.ndarray) – The wrapped vectors padded with -1 for empty neighbors.
r_sq_list ($\left(N_{particles}, N_{neighbors}\right)$ numpy.ndarray) – The Rsq values list.
nlist (freud.locality.NeighborList) – The neighbor list stored by this object, generated by compute().

Example:

nn = NearestNeighbors(2, 6)
nn.compute(box, positions, positions)
hexatic = order.HexOrderParameter(2)
hexatic.compute(box, positions, nlist=nn.nlist)

compute(self, box, ref_points, points=None, exclude_ii=None)¶

Update the data structure for the given set of points.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference point coordinates.
points (($N_{particles}$, 3) numpy.ndarray, optional) – Point coordinates (Default value = None).
exclude_ii (bool, optional) – True if pairs of points with identical indices should be excluded; if None, is set to True if points is None or the same object as ref_points (Default value = None).

getBox(self)¶

Get the freud Box.

Returns:	freud Box.
Return type:	`freud.box.Box`

getNRef(self)¶

Get the number of particles this object found neighbors of.

Returns:	The number of particles this object found neighbors of.
Return type:	unsigned int

getNeighborList(self)¶

Return the entire neighbor list.

Returns:	Neighbor List.
Return type:	$\left(N_{particles}, N_{neighbors}\right)$ `numpy.ndarray`

getNeighbors(self, unsigned int i)¶

Return the $N$ nearest neighbors of the reference point with index $i$.

Parameters:	i (unsigned int) – Index of the reference point whose neighbors will be returned.

getNumNeighbors(self)¶

The number of neighbors this object will find.

Returns:	The number of neighbors this object will find.
Return type:	unsigned int

getRMax(self)¶

Return the current neighbor search distance guess.

Returns:	Nearest neighbors search radius.
Return type:	float

getRsq(self, unsigned int i)¶

Return the squared distances to the $N$ nearest neighbors of the reference point with index $i$.

Parameters:	i (unsigned int) – Index of the reference point of which to fetch the neighboring point distances.
Returns:	Squared distances to the $N$ nearest neighbors.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getRsqList(self)¶

Return the entire Rsq values list.

Returns:	Rsq list.
Return type:	$\left(N_{particles}, N_{neighbors}\right)$ `numpy.ndarray`

getUINTMAX(self)¶

Returns:	Value of C++ UINTMAX used to pad the arrays.
Return type:	unsigned int

getWrappedVectors(self)¶

Return the wrapped vectors for computed neighbors. Array padded with -1 for empty neighbors.

Returns:	Wrapped vectors.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

setCutMode(self, strict_cut)¶

Set mode to handle rmax by Nearest Neighbors.

strict_cut == True: rmax will be strictly obeyed, and any particle which has fewer than $N$ neighbors will have values of UINT_MAX assigned.
strict_cut == False: rmax will be expanded to find the requested number of neighbors. If rmax increases to the point that a cell list cannot be constructed, a warning will be raised and the neighbors already found will be returned.

Parameters:	strict_cut (bool) – Whether to use a strict `rmax` or allow for automatic expansion.

setRMax(self, float rmax)¶

Update the neighbor search distance guess.

Parameters:	rmax (float) – Nearest neighbors search radius.

Order Module¶

Overview

`freud.order.CubaticOrderParameter`	Compute the cubatic order parameter [HajiAkbari2015] for a system of particles using simulated annealing instead of Newton-Raphson root finding.
`freud.order.NematicOrderParameter`	Compute the nematic order parameter for a system of particles.
`freud.order.HexOrderParameter`	Calculates the $k$-atic order parameter for each particle in the system.
`freud.order.TransOrderParameter`	Compute the translational order parameter for each particle.
`freud.order.LocalQl`	Compute the local Steinhardt [Steinhardt1983] rotationally invariant $Q_l$ [Lechner2008] order parameter for a set of points.
`freud.order.LocalQlNear`	Compute the local Steinhardt [Steinhardt1983] rotationally invariant $Q_l$ order parameter [Lechner2008] for a set of points.
`freud.order.LocalWl`	Compute the local Steinhardt [Steinhardt1983] rotationally invariant $W_l$ order parameter [Lechner2008] for a set of points.
`freud.order.LocalWlNear`	Compute the local Steinhardt [Steinhardt1983] rotationally invariant $W_l$ order parameter [Lechner2008] for a set of points.
`freud.order.SolLiq`	Computes dot products of $Q_{lm}$ between particles and uses these for clustering.
`freud.order.SolLiqNear`	Computes dot products of $Q_{lm}$ between particles and uses these for clustering.
`freud.order.BondOrder`
`freud.order.LocalDescriptors`
`freud.order.MatchEnv`
`freud.order.Pairing2D`
`freud.order.AngularSeparation`

Details

The order module contains functions which compute order parameters for the whole system or individual particles. Order parameters take bond order data and interpret it in some way to quantify the degree of order in a system using a scalar value. This is often done through computing spherical harmonics of the bond order diagram, which are the spherical analogue of Fourier Transforms.

class freud.order.CubaticOrderParameter(t_initial, t_final, scale, n_replicates, seed)¶

Compute the cubatic order parameter [HajiAkbari2015] for a system of particles using simulated annealing instead of Newton-Raphson root finding.

Module author: Eric Harper <harperic@umich.edu>

Parameters:	t_initial (float) – Starting temperature. t_final (float) – Final temperature. scale (float) – Scaling factor to reduce temperature. n_replicates (unsigned int) – Number of replicate simulated annealing runs. seed (unsigned int) – Random seed to use in calculations. If None, system time is used.

compute(self, orientations)¶

Calculates the per-particle and global order parameter.

Parameters:	orientations (($N_{particles}$, 4) `numpy.ndarray`) – Orientations as angles to use in computation.

get_cubatic_order_parameter(self)¶

Get cubatic order parameter.

Returns:	Cubatic order parameter.
Return type:	float

get_cubatic_tensor(self)¶

Get cubatic tensor.

Returns:	Rank 4 tensor corresponding to cubatic tensor.
Return type:	$\left(3, 3, 3, 3 \right)$ `numpy.ndarray`

get_gen_r4_tensor(self)¶

Get R4 Tensor.

Returns:	Rank 4 tensor corresponding to each individual particle orientation.
Return type:	$\left(3, 3, 3, 3 \right)$ `numpy.ndarray`

get_global_tensor(self)¶

Get global tensor.

Returns:	Rank 4 tensor corresponding to global orientation.
Return type:	$\left(3, 3, 3, 3 \right)$ `numpy.ndarray`

get_orientation(self)¶

Get orientations.

Returns:	Orientation of global orientation.
Return type:	$\left(4 \right)$ `numpy.ndarray`

get_particle_op(self)¶

Get per-particle order parameter.

Returns:	Cubatic order parameter.
Return type:	`np.ndarray`

get_particle_tensor(self)¶

Get per-particle cubatic tensor.

Returns:	Rank 5 tensor corresponding to each individual particle orientation.
Return type:	$\left(N_{particles}, 3, 3, 3, 3 \right)$ `numpy.ndarray`

get_scale(self)¶

Get scale.

Returns:	Value of scale.
Return type:	float

get_t_final(self)¶

Get final temperature.

Returns:	Value of final temperature.
Return type:	float

get_t_initial(self)¶

Get initial temperature.

Returns:	Value of initial temperature.
Return type:	float

class freud.order.NematicOrderParameter(u)¶

Compute the nematic order parameter for a system of particles.

Module author: Jens Glaser <jsglaser@umich.edu>

New in version 0.7.0.

Parameters:	u ($\left(3 \right)$ `numpy.ndarray`) – The nematic director of a single particle in the reference state (without any rotation applied).

compute(self, orientations)¶

Calculates the per-particle and global order parameter.

Parameters:	orientations ($\left(N_{particles}, 4 \right)$ `numpy.ndarray`) – Orientations to calculate the order parameter.

get_director(self)¶

The director (eigenvector corresponding to the order parameter).

Returns:	The average nematic director.
Return type:	$\left(3 \right)$ `numpy.ndarray`

get_nematic_order_parameter(self)¶

The nematic order parameter.

Returns:	Nematic order parameter.
Return type:	float

get_nematic_tensor(self)¶

The nematic Q tensor.

Returns:	3x3 matrix corresponding to the average particle orientation.
Return type:	$\left(3, 3 \right)$ `numpy.ndarray`

get_particle_tensor(self)¶

The full per-particle tensor of orientation information.

Returns:	3x3 matrix corresponding to each individual particle orientation.
Return type:	$\left(N_{particles}, 3, 3 \right)$ `numpy.ndarray`

class freud.order.HexOrderParameter(rmax, k, n)¶

Calculates the $k$-atic order parameter for each particle in the system.

The $k$-atic order parameter for a particle $i$ and its $n$ neighbors $j$ is given by:

$\psi_k \left( i \right) = \frac{1}{n} \sum_j^n e^{k i \phi_{ij}}$

The parameter $k$ governs the symmetry of the order parameter while the parameter $n$ governs the number of neighbors of particle $i$ to average over. $\phi_{ij}$ is the angle between the vector $r_{ij}$ and $\left( 1,0 \right)$.

Note

2D: freud.cluster.Cluster properly handles 2D boxes. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Module author: Eric Harper <harperic@umich.edu>

Parameters:	rmax (float) – +/- r distance to search for neighbors. k (unsigned int) – Symmetry of order parameter ($k=6$ is hexatic). n (unsigned int) – Number of neighbors ($n=k$ if $n$ not specified).
Variables:	psi ($\left(N_{particles} \right)$ `numpy.ndarray`) – Order parameter. box (`freud.box.Box`) – Box used in the calculation. num_particles (unsigned int) – Number of particles. k (unsigned int) – Symmetry of the order parameter.

compute(self, box, points, nlist=None)¶

Calculates the correlation function and adds to the current histogram.

Parameters:	box (`freud.box.Box`) – Simulation box. points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`) – Neighborlist to use to find bonds.

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getK(self)¶

Get the symmetry of the order parameter.

Returns:	$k$.
Return type:	unsigned int

getNP(self)¶

Get the number of particles.

Returns:	$N_{particles}$.
Return type:	unsigned int

getPsi(self)¶

Get the order parameter.

Returns:	Order parameter.
Return type:	$\left(N_{particles} \right)$ `numpy.ndarray`

class freud.order.TransOrderParameter(rmax, k, n)¶

Compute the translational order parameter for each particle.

Module author: Michael Engel <engelmm@umich.edu>

Parameters:	rmax (float) – +/- r distance to search for neighbors. k (float) – Symmetry of order parameter ($k=6$ is hexatic). n (unsigned int) – Number of neighbors ($n=k$ if $n$ not specified).
Variables:	d_r ($\left(N_{particles}\right)$ `numpy.ndarray`) – Reference to the last computed translational order array. box (`freud.box.Box`) – Box used in the calculation. num_particles (unsigned int) – Number of particles.

compute(self, box, points, nlist=None)¶

Calculates the local descriptors.

Parameters:	box (`freud.box.Box`) – Simulation box. points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`) – Neighborlist to use to find bonds.

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getDr(self)¶

Get a reference to the last computed spherical harmonic array.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getNP(self)¶

Get the number of particles.

Returns:	$N_{particles}$.
Return type:	unsigned int

class freud.order.LocalQl(box, rmax, l, rmin)¶

Compute the local Steinhardt [Steinhardt1983] rotationally invariant $Q_l$ [Lechner2008] order parameter for a set of points.

Implements the local rotationally invariant $Q_l$ order parameter described by Steinhardt. For a particle i, we calculate the average $Q_l$ by summing the spherical harmonics between particle $i$ and its neighbors $j$ in a local region: $\overline{Q}_{lm}(i) = \frac{1}{N_b} \displaystyle\sum_{j=1}^{N_b} Y_{lm}(\theta(\vec{r}_{ij}), \phi(\vec{r}_{ij}))$.

This is then combined in a rotationally invariant fashion to remove local orientational order as follows: $Q_l(i)=\sqrt{\frac{4\pi}{2l+1} \displaystyle\sum_{m=-l}^{l} |\overline{Q}_{lm}|^2 }$.

Added first/second shell combined average $Q_l$ order parameter for a set of points:

Variation of the Steinhardt $Q_l$ order parameter
For a particle i, we calculate the average $Q_l$ by summing the spherical harmonics between particle i and its neighbors j and the neighbors k of neighbor j in a local region.

Module author: Xiyu Du <xiyudu@umich.edu>

Module author: Vyas Ramasubramani <vramasub@umich.edu>

Parameters:

box (freud.box.Box) – Simulation box.
rmax (float) – Cutoff radius for the local order parameter. Values near the first minimum of the RDF are recommended.
l (unsigned int) – Spherical harmonic quantum number l. Must be a positive number.
rmin (float) – Can look at only the second shell or some arbitrary RDF region.

Variables:

box (freud.box.Box) – Box used in the calculation.
num_particles (unsigned int) – Number of particles.
Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_l$ for each particle (filled with NaN for particles with no neighbors).
ave_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{Q_l}$ for each particle (filled with NaN for particles with no neighbors).
norm_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_l$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).
ave_norm_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{Q_l}$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).

compute(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

computeAve(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

computeAveNorm(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

computeNorm(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

getAveQl(self)¶

Get a reference to the last computed $Q_l$ for each particle. Returns NaN instead of $Q_l$ for particles with no neighbors.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getNP(self)¶

Get the number of particles.

Returns:	$N_{particles}$.
Return type:	unsigned int

getQl(self)¶

Get a reference to the last computed $Q_l$ for each particle. Returns NaN instead of $Q_l$ for particles with no neighbors.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getQlAveNorm(self)¶

Get a reference to the last computed $Q_l$ for each particle. Returns NaN instead of $Q_l$ for particles with no neighbors.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getQlNorm(self)¶

Get a reference to the last computed $Q_l$ for each particle. Returns NaN instead of $Q_l$ for particles with no neighbors.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

setBox(self, box)¶

Reset the simulation box.

Parameters:	box (`freud.box.Box`) – Simulation box.

class freud.order.LocalQlNear(box, rmax, l, kn)¶

Compute the local Steinhardt [Steinhardt1983] rotationally invariant $Q_l$ order parameter [Lechner2008] for a set of points.

Implements the local rotationally invariant $Q_l$ order parameter described by Steinhardt. For a particle i, we calculate the average $Q_l$ by summing the spherical harmonics between particle $i$ and its neighbors $j$ in a local region: $\overline{Q}_{lm}(i) = \frac{1}{N_b} \displaystyle\sum_{j=1}^{N_b} Y_{lm}(\theta(\vec{r}_{ij}), \phi(\vec{r}_{ij}))$

This is then combined in a rotationally invariant fashion to remove local orientational order as follows: $Q_l(i)=\sqrt{\frac{4\pi}{2l+1} \displaystyle\sum_{m=-l}^{l} |\overline{Q}_{lm}|^2 }$

Added first/second shell combined average $Q_l$ order parameter for a set of points:

Variation of the Steinhardt $Q_l$ Order parameter.
For a particle i, we calculate the average $Q_l$ by summing the spherical harmonics between particle i and its neighbors j and the neighbors k of neighbor j in a local region.

Module author: Xiyu Du <xiyudu@umich.edu>

Module author: Vyas Ramasubramani <vramasub@umich.edu>

Parameters:

box (freud.box.Box) – Simulation box.
rmax (float) – Cutoff radius for the local order parameter. Values near the first minimum of the RDF are recommended.
l (unsigned int) – Spherical harmonic quantum number l. Must be a positive number.
kn (unsigned int) – Number of nearest neighbors. must be a positive integer.

Variables:

box (freud.box.Box) – Box used in the calculation.
num_particles (unsigned int) – Number of particles.
Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_l$ for each particle (filled with NaN for particles with no neighbors).
ave_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{Q_l}$ for each particle (filled with NaN for particles with no neighbors).
norm_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_l$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).
ave_norm_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{Q_l}$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).

computeAve(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

computeAveNorm(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

computeNorm(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

class freud.order.LocalWl(box, rmax, l)¶

Compute the local Steinhardt [Steinhardt1983] rotationally invariant $W_l$ order parameter [Lechner2008] for a set of points.

Implements the local rotationally invariant $W_l$ order parameter described by Steinhardt that can aid in distinguishing between FCC, HCP, and BCC.

Added first/second shell combined average $W_l$ order parameter for a set of points:

Variation of the Steinhardt $W_l$ order parameter.
For a particle i, we calculate the average $W_l$ by summing the spherical harmonics between particle i and its neighbors j and the neighbors k of neighbor j in a local region.

Module author: Xiyu Du <xiyudu@umich.edu>

Module author: Vyas Ramasubramani <vramasub@umich.edu>

Parameters:

box (freud.box.Box) – Simulation box.
rmax (float) – Cutoff radius for the local order parameter. Values near the first minimum of the RDF are recommended.
l (unsigned int) – Spherical harmonic quantum number l. Must be a positive number
rmin (float) – Lower bound for computing the local order parameter. Allows looking at, for instance, only the second shell, or some other arbitrary RDF region.

Variables:

box (freud.box.Box) – Box used in the calculation.
num_particles (unsigned int) – Number of particles.
Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_l$ for each particle (filled with NaN for particles with no neighbors).
ave_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{Q_l}$ for each particle (filled with NaN for particles with no neighbors).
norm_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_l$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).
ave_norm_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{Q_l}$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).
Wl ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $W_l$ for each particle (filled with NaN for particles with no neighbors).
ave_Wl ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{W}_l$ for each particle (filled with NaN for particles with no neighbors).
norm_Wl ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $W_l$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).
ave_norm_Wl ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{W}_l$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).

getAveWl(self)¶

Get a reference to the last computed $W_l$ for each particle. Returns NaN instead of $W_l$ for particles with no neighbors.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getWl(self)¶

Get a reference to the last computed $W_l$ for each particle. Returns NaN instead of $W_l$ for particles with no neighbors.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getWlAveNorm(self)¶

Get a reference to the last computed $W_l$ for each particle. Returns NaN instead of $W_l$ for particles with no neighbors.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getWlNorm(self)¶

Get a reference to the last computed $W_l$ for each particle. Returns NaN instead of $W_l$ for particles with no neighbors.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

class freud.order.LocalWlNear(box, rmax, l, kn)¶

Compute the local Steinhardt [Steinhardt1983] rotationally invariant $W_l$ order parameter [Lechner2008] for a set of points.

Implements the local rotationally invariant $W_l$ order parameter described by Steinhardt that can aid in distinguishing between FCC, HCP, and BCC.

Added first/second shell combined average $W_l$ order parameter for a set of points:

Variation of the Steinhardt $W_l$ order parameter.
For a particle i, we calculate the average $W_l$ by summing the spherical harmonics between particle i and its neighbors j and the neighbors k of neighbor j in a local region.

Module author: Xiyu Du <xiyudu@umich.edu>

Module author: Vyas Ramasubramani <vramasub@umich.edu>

Parameters:

box (freud.box.Box) – Simulation box.
rmax (float) – Cutoff radius for the local order parameter. Values near the first minimum of the RDF are recommended.
l (unsigned int) – Spherical harmonic quantum number l. Must be a positive number
kn (unsigned int) – Number of nearest neighbors. Must be a positive number.

Variables:

box (freud.box.Box) – Box used in the calculation.
num_particles (unsigned int) – Number of particles.
Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_l$ for each particle (filled with NaN for particles with no neighbors).
ave_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{Q_l}$ for each particle (filled with NaN for particles with no neighbors).
norm_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_l$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).
ave_norm_Ql ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{Q_l}$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).
Wl ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $W_l$ for each particle (filled with NaN for particles with no neighbors).
ave_Wl ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{W}_l$ for each particle (filled with NaN for particles with no neighbors).
norm_Wl ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $W_l$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).
ave_norm_Wl ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $\bar{W}_l$ for each particle normalized by the value over all particles (filled with NaN for particles with no neighbors).

computeAve(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

computeAveNorm(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

computeNorm(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

class freud.order.SolLiq(box, rmax, Qthreshold, Sthreshold, l)¶

SolLiq(box, rmax, Qthreshold, Sthreshold, l)

Computes dot products of $Q_{lm}$ between particles and uses these for clustering.

Module author: Richmond Newman <newmanrs@umich.edu>

Parameters:

box (freud.box.Box) – Simulation box.
rmax (float) – Cutoff radius for the local order parameter. Values near first minimum of the RDF are recommended.
Qthreshold (float) – Value of dot product threshold when evaluating $Q_{lm}^*(i) Q_{lm}(j)$ to determine if a neighbor pair is a solid-like bond. (For $l=6$, 0.7 generally good for FCC or BCC structures).
Sthreshold (unsigned int) – Minimum required number of adjacent solid-link bonds for a particle to be considered solid-like for clustering. (For $l=6$, 6-8 is generally good for FCC or BCC structures).
l (unsigned int) – Choose spherical harmonic $Q_l$. Must be positive and even.

Variables:

box (freud.box.Box) – Box used in the calculation.
largest_cluster_size (unsigned int) – The largest cluster size. Must call a compute method first.
cluster_sizes (unsigned int) – The sizes of all clusters.
largest_cluster_size – The largest cluster size. Must call a compute method first.
Ql_mi ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_{lmi}$ for each particle.
clusters ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed set of solid-like cluster indices for each particle.
num_connections ($\left(N_{particles}\right)$ numpy.ndarray) – The number of connections per particle.
Ql_dot_ij ($\left(N_{particles}\right)$ numpy.ndarray) – Reference to the qldot_ij values.
num_particles (unsigned int) – Number of particles.

compute(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

computeSolLiqNoNorm(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

computeSolLiqVariant(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`, optional) – Neighborlist to use to find bonds (Default value = None).

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getClusterSizes(self)¶

Return the sizes of all clusters.

Returns:	The cluster sizes.
Return type:	$\left(N_{clusters}\right)$ `numpy.ndarray`

getClusters(self)¶

Get a reference to the last computed set of solid-like cluster indices for each particle.

Returns:	Clusters.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getLargestClusterSize(self)¶

Returns the largest cluster size. Must call a compute method first.

Returns:	Largest cluster size.
Return type:	unsigned int

getNP(self)¶

Get the number of particles.

Returns:	$N_p$.
Return type:	unsigned int

getNumberOfConnections(self)¶

Get a reference to the number of connections per particle.

Returns:	Clusters.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

getQldot_ij(self)¶

Get a reference to the qldot_ij values.

Returns:	The qldot values.
Return type:	$\left(N_{clusters}\right)$ `numpy.ndarray`

getQlmi(self)¶

Get a reference to the last computed $Q_{lmi}$ for each particle.

Returns:	Order parameter.
Return type:	$\left(N_{particles}\right)$ `numpy.ndarray`

setBox(self, box)¶

Reset the simulation box.

Parameters:	box (`freud.box.Box`) – Simulation box.

setClusteringRadius(self, rcutCluster)¶

Reset the clustering radius.

Parameters:	rcutCluster (float) – Radius for the cluster finding.

class freud.order.SolLiqNear(box, rmax, Qthreshold, Sthreshold, l)¶

SolLiqNear(box, rmax, Qthreshold, Sthreshold, l, kn=12)

Computes dot products of $Q_{lm}$ between particles and uses these for clustering.

Module author: Richmond Newman <newmanrs@umich.edu>

Parameters:

box (freud.box.Box) – Simulation box.
rmax (float) – Cutoff radius for the local order parameter. Values near the first minimum of the RDF are recommended.
Qthreshold (float) – Value of dot product threshold when evaluating $Q_{lm}^*(i) Q_{lm}(j)$ to determine if a neighbor pair is a solid-like bond. (For $l=6$, 0.7 generally good for FCC or BCC structures).
Sthreshold (unsigned int) – Minimum required number of adjacent solid-link bonds for a particle to be considered solid-like for clustering. (For $l=6$, 6-8 is generally good for FCC or BCC structures).
l (unsigned int) – Choose spherical harmonic $Q_l$. Must be positive and even.
kn (unsigned int) – Number of nearest neighbors. Must be a positive number.

Variables:

box (freud.box.Box) – Box used in the calculation.
largest_cluster_size (unsigned int) – The largest cluster size. Must call a compute method first.
cluster_sizes (unsigned int) – The sizes of all clusters.
largest_cluster_size – The largest cluster size. Must call a compute method first.
Ql_mi ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed $Q_{lmi}$ for each particle.
clusters ($\left(N_{particles}\right)$ numpy.ndarray) – The last computed set of solid-like cluster indices for each particle.
num_connections ($\left(N_{particles}\right)$ numpy.ndarray) – The number of connections per particle.
Ql_dot_ij ($\left(N_{particles}\right)$ numpy.ndarray) – Reference to the qldot_ij values.
num_particles (unsigned int) – Number of particles.

compute(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`) – Neighborlist to use to find bonds.

computeSolLiqNoNorm(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`) – Neighborlist to use to find bonds.

computeSolLiqVariant(self, points, nlist=None)¶

Compute the local rotationally invariant $Q_l$ order parameter.

Parameters:	points (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate the order parameter. nlist (`freud.locality.NeighborList`) – Neighborlist to use to find bonds.

Deprecated Classes¶

The below functions have all either been deprecated or moved to the Environment Module module

Bond Order¶

class freud.order.BondOrder(rmax, k, n, nBinsT, nBinsP)[source]¶: Note

This class is only retained for backwards compatibility. Please use freud.environment.BondOrder instead.

Deprecated since version 0.8.2: Use freud.environment.BondOrder instead.

Local Descriptors¶

class freud.order.LocalDescriptors(box, nNeigh, lmax, rmax)[source]¶: Note

This class is only retained for backwards compatibility. Please use freud.environment.LocalDescriptors instead.

Deprecated since version 0.8.2: Use freud.environment.LocalDescriptors instead.

Environment Matching¶

class freud.order.MatchEnv(box, rmax, k)[source]¶: Note

This class is only retained for backwards compatibility. Please use freud.environment.MatchEnv instead.

Deprecated since version 0.8.2: Use freud.environment.MatchEnv instead.

Pairing¶

Note

Pairing2D is deprecated and is replaced with Bond Module.

class freud.order.Pairing2D(rmax, k, compDotTol)[source]¶: Note

This class is only retained for backwards compatibility. Please use freud.bond instead.

Deprecated since version 0.8.2: Use freud.bond instead.

Angular Separation¶

class freud.order.AngularSeparation(box, rmax, n)[source]¶: Note

This class is only retained for backwards compatibility. Please use freud.environment.AngularSeparation instead.

Deprecated since version 0.8.2: Use freud.environment.AngularSeparation instead.

Parallel Module¶

Overview

`freud.parallel.NumThreads`	Context manager for managing the number of threads to use.
`freud.parallel.setNumThreads`	Set the number of threads for parallel computation.

Details

The freud.parallel module controls the parallelization behavior of freud, determining how many threads the TBB-enabled parts of freud will use. By default, freud tries to use all available threads for parallelization unless directed otherwise, with one exception.

parallel.setNumThreads(nthreads=None)¶

Set the number of threads for parallel computation.

Module author: Joshua Anderson <joaander@umich.edu>

Parameters:	nthreads (int, optional) – Number of threads to use. If None (default), use all threads available.

class freud.parallel.NumThreads(N=None)[source]¶

Context manager for managing the number of threads to use.

Module author: Joshua Anderson <joaander@umich.edu>

Parameters:	N (int) – Number of threads to use in this context. Defaults to None, which will use all available threads.

PMFT Module¶

Overview

`freud.pmft.PMFTR12`	Computes the PMFT [vanAndersKlotsa2014] [vanAndersAhmed2014] in a 2D system described by $r$, $\theta_1$, $\theta_2$.
`freud.pmft.PMFTXYT`	Computes the PMFT [vanAndersKlotsa2014] [vanAndersAhmed2014] for systems described by coordinates $x$, $y$, $\theta$ listed in the x, y, and t arrays.
`freud.pmft.PMFTXY2D`	Computes the PMFT [vanAndersKlotsa2014] [vanAndersAhmed2014] in coordinates $x$, $y$ listed in the x and y arrays.
`freud.pmft.PMFTXYZ`	Computes the PMFT [vanAndersKlotsa2014] [vanAndersAhmed2014] in coordinates $x$, $y$, $z$, listed in the x, y, and z arrays.

Details

The PMFT Module allows for the calculation of the Potential of Mean Force and Torque (PMFT) [vanAndersKlotsa2014] [vanAndersAhmed2014] in a number of different coordinate systems. The PMFT is defined as the negative algorithm of positional correlation function (PCF). A given set of reference points is given around which the PCF is computed and averaged in a sea of data points. The resulting values are accumulated in a PCF array listing the value of the PCF at a discrete set of points. The specific points are determined by the particular coordinate system used to represent the system.

Note

The coordinate system in which the calculation is performed is not the same as the coordinate system in which particle positions and orientations should be supplied. Only certain coordinate systems are available for certain particle positions and orientations:

2D particle coordinates (position: [$x$, $y$, $0$], orientation: $\theta$):
- $r$, $\theta_1$, $\theta_2$.
- $x$, $y$.
- $x$, $y$, $\theta$.
3D particle coordinates:
- $x$, $y$, $z$.

class freud.pmft.PMFTR12(r_max, n_r, n_t1, n_t2)¶

Computes the PMFT [vanAndersKlotsa2014] [vanAndersAhmed2014] in a 2D system described by $r$, $\theta_1$, $\theta_2$.

Note

2D: freud.pmft.PMFTR12 is only defined for 2D systems. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Module author: Eric Harper <harperic@umich.edu>

Module author: Vyas Ramasubramani <vramasub@umich.edu>

Parameters:

r_max (float) – Maximum distance at which to compute the PMFT.
n_r (unsigned int) – Number of bins in r.
n_t1 (unsigned int) – Number of bins in t1.
n_t2 (unsigned int) – Number of bins in t2.

Variables:

box (freud.box.Box) – Box used in the calculation.
bin_counts ($\left(N_{r}, N_{\theta2}, N_{\theta1}\right)$) – Bin counts.
PCF ($\left(N_{r}, N_{\theta2}, N_{\theta1}\right)$) – The positional correlation function.
PMFT ($\left(N_{r}, N_{\theta2}, N_{\theta1}\right)$) – The potential of mean force and torque.
r_cut (float) – The cutoff used in the cell list.
R ($\left(N_{r}\right)$ numpy.ndarray) – The array of r-values for the PCF histogram.
T1 ($\left(N_{\theta1}\right)$ numpy.ndarray) – The array of T1-values for the PCF histogram.
T2 ($\left(N_{\theta2}\right)$ numpy.ndarray) – The array of T2-values for the PCF histogram.
inverse_jacobian ($\left(N_{r}, N_{\theta2}, N_{\theta1}\right)$) – The inverse Jacobian used in the PMFT.
n_bins_r (unsigned int) – The number of bins in the r-dimension of histogram.
n_bins_T1 (unsigned int) – The number of bins in the T1-dimension of histogram.
n_bins_T2 (unsigned int) – The number of bins in the T2-dimension of histogram.

accumulate(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the positional correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of reference points to use in the calculation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the local density.
orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of particles to use in the calculation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

compute(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the positional correlation function for the given points. Will overwrite the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Reference orientations as angles to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the local density.
orientations (($N_{particles}$, 4) numpy.ndarray) – Orientations as angles to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBinCounts(self)¶

Get the raw bin counts.

Returns:	Bin Counts.
Return type:	$\left(N_{r}, N_{\theta2}, N_{\theta1}\right)$ `numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getInverseJacobian(self)¶

Get the inverse Jacobian used in the PMFT.

Returns:	Inverse Jacobian.
Return type:	$\left(N_{r}, N_{\theta2}, N_{\theta1}\right)$ `numpy.ndarray`

getNBinsR(self)¶

Get the number of bins in the r-dimension of histogram.

Returns:	$N_r$.
Return type:	unsigned int

getNBinsT1(self)¶

Get the number of bins in the T1-dimension of histogram.

Returns:	$N_{\theta_1}$.
Return type:	unsigned int

getNBinsT2(self)¶

Get the number of bins in the T2-dimension of histogram.

Returns:	$N_{\theta_2}$.
Return type:	unsigned int

getPCF(self)¶

Get the positional correlation function.

Returns:	PCF.
Return type:	$\left(N_{r}, N_{\theta2}, N_{\theta1}\right)$ `numpy.ndarray`

getPMFT(self)¶

Get the potential of mean force and torque.

Returns:	PMFT.
Return type:	(matches PCF) `numpy.ndarray`

getR(self)¶

Get the array of r-values for the PCF histogram.

Returns:	Bin centers of r-dimension of histogram.
Return type:	$\left(N_{r}\right)$ `numpy.ndarray`

getRCut(self)¶

Get the r_cut value used in the cell list.

Returns:	r_cut.
Return type:	float

getT1(self)¶

Get the array of T1-values for the PCF histogram.

Returns:	Bin centers of T1-dimension of histogram.
Return type:	$\left(N_{\theta_1}\right)$ `numpy.ndarray`

getT2(self)¶

Get the array of T2-values for the PCF histogram.

Returns:	Bin centers of T2-dimension of histogram.
Return type:	$\left(N_{\theta_2}\right)$ `numpy.ndarray`

reducePCF(self)¶: Reduces the histogram in the values over N processors to a single histogram. This is called automatically by freud.pmft.PMFT.PCF().

resetPCF(self)¶: Resets the values of the PCF histograms in memory.

class freud.pmft.PMFTXYT(x_max, y_max, n_x, n_y, n_t)¶

Computes the PMFT [vanAndersKlotsa2014] [vanAndersAhmed2014] for systems described by coordinates $x$, $y$, $\theta$ listed in the x, y, and t arrays.

The values of x, y, t to compute the PCF at are controlled by x_max, y_max and n_bins_x, n_bins_y, n_bins_t parameters to the constructor. The x_max and y_max parameters determine the minimum/maximum x, y values ($\min \left(\theta \right) = 0$, ($\max \left( \theta \right) = 2\pi$) at which to compute the PCF and n_bins_x, n_bins_y, n_bins_t is the number of bins in x, y, t.

Note

2D: freud.pmft.PMFTXYT is only defined for 2D systems. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Module author: Eric Harper <harperic@umich.edu>

Module author: Vyas Ramasubramani <vramasub@umich.edu>

Parameters:

x_max (float) – Maximum x distance at which to compute the PMFT.
y_max (float) – Maximum y distance at which to compute the PMFT.
n_x (unsigned int) – Number of bins in x.
n_y (unsigned int) – Number of bins in y.
n_t (unsigned int) – Number of bins in t.

Variables:

box (freud.box.Box) – Box used in the calculation.
bin_counts ($\left(N_{\theta}, N_{y}, N_{x}\right)$ numpy.ndarray) – Bin counts.
PCF ($\left(N_{\theta}, N_{y}, N_{x}\right)$ numpy.ndarray) – The positional correlation function.
PMFT ($\left(N_{\theta}, N_{y}, N_{x}\right)$ numpy.ndarray) – The potential of mean force and torque.
r_cut (float) – The cutoff used in the cell list.
X ($\left(N_{x}\right)$ numpy.ndarray) – The array of x-values for the PCF histogram.
Y ($\left(N_{y}\right)$ numpy.ndarray) – The array of y-values for the PCF histogram.
T ($\left(N_{\theta}\right)$ numpy.ndarray) – The array of T-values for the PCF histogram.
jacobian (float) – The Jacobian used in the PMFT.
n_bins_x (unsigned int) – The number of bins in the x-dimension of histogram.
n_bins_y (unsigned int) – The number of bins in the y-dimension of histogram.
n_bins_T (unsigned int) – The number of bins in the T-dimension of histogram.

accumulate(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the positional correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Reference orientations as angles to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the local density.
orientations (($N_{particles}$, 4) numpy.ndarray) – orientations as angles to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

compute(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the positional correlation function for the given points. Will overwrite the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Reference orientations as angles to use in computation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the local density.
orientations (($N_{particles}$, 4) numpy.ndarray) – orientations as angles to use in computation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBinCounts(self)¶

Get the raw bin counts.

Returns:	Bin Counts.
Return type:	$\left(N_{\theta}, N_{y}, N_{x}\right)$ `numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getJacobian(self)¶

Get the Jacobian used in the PMFT.

Returns:	Jacobian.
Return type:	float

getNBinsT(self)¶

Get the number of bins in the t-dimension of histogram.

Returns:	$N_{\theta}$.
Return type:	unsigned int

getNBinsX(self)¶

Get the number of bins in the x-dimension of histogram.

Returns:	$N_x$.
Return type:	unsigned int

getNBinsY(self)¶

Get the number of bins in the y-dimension of histogram.

Returns:	$N_y$.
Return type:	unsigned int

getPCF(self)¶

Get the positional correlation function.

Returns:	PCF.
Return type:	$\left(N_{\theta}, N_{y}, N_{x}\right)$ `numpy.ndarray`

getPMFT(self)¶

Get the potential of mean force and torque.

Returns:	PMFT.
Return type:	(matches PCF) `numpy.ndarray`

getRCut(self)¶

Get the r_cut value used in the cell list.

Returns:	r_cut.
Return type:	float

getT(self)¶

Get the array of t-values for the PCF histogram.

Returns:	Bin centers of t-dimension of histogram.
Return type:	$\left(N_{\theta}\right)$ `numpy.ndarray`

getX(self)¶

Get the array of x-values for the PCF histogram.

Returns:	Bin centers of x-dimension of histogram.
Return type:	$\left(N_{x}\right)$ `numpy.ndarray`

getY(self)¶

Get the array of y-values for the PCF histogram.

Returns:	Bin centers of y-dimension of histogram.
Return type:	$\left(N_{y}\right)$ `numpy.ndarray`

reducePCF(self)¶: Reduces the histogram in the values over N processors to a single histogram. This is called automatically by freud.pmft.PMFT.PCF().

resetPCF(self)¶: Resets the values of the PCF histograms in memory.

class freud.pmft.PMFTXY2D(x_max, y_max, n_x, n_y)¶

Computes the PMFT [vanAndersKlotsa2014] [vanAndersAhmed2014] in coordinates $x$, $y$ listed in the x and y arrays.

The values of x and y to compute the PCF at are controlled by x_max, y_max, n_x, and n_y parameters to the constructor. The x_max and y_max parameters determine the minimum/maximum distance at which to compute the PCF and n_x and n_y are the number of bins in x and y.

Note

2D: freud.pmft.PMFTXY2D is only defined for 2D systems. The points must be passed in as [x, y, 0]. Failing to set z=0 will lead to undefined behavior.

Module author: Eric Harper <harperic@umich.edu>

Module author: Vyas Ramasubramani <vramasub@umich.edu>

Parameters:

x_max (float) – Maximum x distance at which to compute the PMFT.
y_max (float) – Maximum y distance at which to compute the PMFT.
n_x (unsigned int) – Number of bins in x.
n_y (unsigned int) – Number of bins in y.

Variables:

box (freud.box.Box) – Box used in the calculation.
bin_counts ($\left(N_{y}, N_{x}\right)$ numpy.ndarray) – Bin counts.
PCF ($\left(N_{y}, N_{x}\right)$ numpy.ndarray) – The positional correlation function.
PMFT ($\left(N_{y}, N_{x}\right)$ numpy.ndarray) – The potential of mean force and torque.
r_cut (float) – The cutoff used in the cell list.
X ($\left(N_{x}\right)$ numpy.ndarray) – The array of x-values for the PCF histogram.
Y ($\left(N_{y}\right)$ numpy.ndarray) – The array of y-values for the PCF histogram.
jacobian (float) – The Jacobian used in the PMFT.
n_bins_x (unsigned int) – The number of bins in the x-dimension of histogram.
n_bins_y (unsigned int) – The number of bins in the y-dimension of histogram.

accumulate(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the positional correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of reference points to use in the calculation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the local density.
orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of particles to use in the calculation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

compute(self, box, ref_points, ref_orientations, points, orientations, nlist=None)¶

Calculates the positional correlation function for the given points. Will overwrite the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of reference points to use in the calculation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the local density.
orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of particles to use in the calculation.
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBinCounts(self)¶

Get the raw bin counts (non-normalized).

Returns:	Bin Counts.
Return type:	$\left(N_{y}, N_{x}\right)$ `numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getJacobian(self)¶

Get the Jacobian.

Returns:	Jacobian.
Return type:	float

getNBinsX(self)¶

Get the number of bins in the x-dimension of histogram.

Returns:	$N_x$.
Return type:	unsigned int

getNBinsY(self)¶

Get the number of bins in the y-dimension of histogram.

Returns:	$N_y$.
Return type:	unsigned int

getPCF(self)¶

Get the positional correlation function.

Returns:	PCF.
Return type:	$\left(N_{y}, N_{x}\right)$ `numpy.ndarray`

getPMFT(self)¶

Get the potential of mean force and torque.

Returns:	PMFT.
Return type:	(matches PCF) `numpy.ndarray`

getRCut(self)¶

Get the r_cut value used in the cell list.

Returns:	r_cut.
Return type:	float

getX(self)¶

Get the array of x-values for the PCF histogram.

Returns:	Bin centers of x-dimension of histogram.
Return type:	$\left(N_{x}\right)$ `numpy.ndarray`

getY(self)¶

Get the array of y-values for the PCF histogram.

Returns:	Bin centers of y-dimension of histogram.
Return type:	$\left(N_{y}\right)$ `numpy.ndarray`

reducePCF(self)¶: Reduces the histogram in the values over N processors to a single histogram. This is called automatically by freud.pmft.PMFT.PCF().

resetPCF(self)¶: Resets the values of the PCF histograms in memory.

class freud.pmft.PMFTXYZ(x_max, y_max, z_max, n_x, n_y, n_z)¶

Computes the PMFT [vanAndersKlotsa2014] [vanAndersAhmed2014] in coordinates $x$, $y$, $z$, listed in the x, y, and z arrays.

The values of x, y, z to compute the PCF at are controlled by x_max, y_max, z_max, n_x, n_y, and n_z parameters to the constructor. The x_max, y_max, and z_max parameters determine the minimum/maximum distance at which to compute the PCF and n_x, n_y, and n_z are the number of bins in x, y, z.

Note

3D: freud.pmft.PMFTXYZ is only defined for 3D systems. The points must be passed in as [x, y, z].

Module author: Eric Harper <harperic@umich.edu>

Module author: Vyas Ramasubramani <vramasub@umich.edu>

Parameters:

x_max (float) – Maximum x distance at which to compute the PMFT.
y_max (float) – Maximum y distance at which to compute the PMFT.
z_max (float) – Maximum z distance at which to compute the PMFT.
n_x (unsigned int) – Number of bins in x.
n_y (unsigned int) – Number of bins in y.
n_z (unsigned int) – Number of bins in z.
shiftvec (list) – Vector pointing from [0,0,0] to the center of the PMFT.

Variables:

box (freud.box.Box) – Box used in the calculation.
bin_counts ($\left(N_{z}, N_{y}, N_{x}\right)$ numpy.ndarray) – Bin counts.
PCF ($\left(N_{z}, N_{y}, N_{x}\right)$ numpy.ndarray) – The positional correlation function.
PMFT ($\left(N_{z}, N_{y}, N_{x}\right)$ numpy.ndarray) – The potential of mean force and torque.
r_cut (float) – The cutoff used in the cell list.
X ($\left(N_{x}\right)$ numpy.ndarray) – The array of x-values for the PCF histogram.
Y ($\left(N_{y}\right)$ numpy.ndarray) – The array of y-values for the PCF histogram.
Z ($\left(N_{z}\right)$ numpy.ndarray) – The array of z-values for the PCF histogram.
jacobian (float) – The Jacobian used in the PMFT.
n_bins_x (unsigned int) – The number of bins in the x-dimension of histogram.
n_bins_y (unsigned int) – The number of bins in the y-dimension of histogram.
n_bins_z (unsigned int) – The number of bins in the z-dimension of histogram.

accumulate(self, box, ref_points, ref_orientations, points, orientations, face_orientations=None, nlist=None)¶

Calculates the positional correlation function and adds to the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of reference points to use in the calculation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the local density.
orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of particles to use in the calculation.
face_orientations (($N_{particles}$, 4) numpy.ndarray, optional) – Orientations of particle faces to account for particle symmetry. If not supplied by user, unit quaternions will be supplied. If a 2D array of shape ($N_f$, $4$) or a 3D array of shape (1, $N_f$, $4$) is supplied, the supplied quaternions will be broadcast for all particles. (Default value = None).
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

compute(self, box, ref_points, ref_orientations, points, orientations, face_orientations=None, nlist=None)¶

Calculates the positional correlation function for the given points. Will overwrite the current histogram.

Parameters:

box (freud.box.Box) – Simulation box.
ref_points (($N_{particles}$, 3) numpy.ndarray) – Reference points to calculate the local density.
ref_orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of reference points to use in the calculation.
points (($N_{particles}$, 3) numpy.ndarray) – Points to calculate the local density.
orientations (($N_{particles}$, 4) numpy.ndarray) – Angles of particles to use in the calculation.
face_orientations (($N_{particles}$, 4) numpy.ndarray, optional) – Orientations of particle faces to account for particle symmetry. If not supplied by user, unit quaternions will be supplied. If a 2D array of shape ($N_f$, $4$) or a 3D array of shape (1, $N_f$, $4$) is supplied, the supplied quaternions will be broadcast for all particles. (Default value = None).
nlist (freud.locality.NeighborList, optional) – NeighborList to use to find bonds (Default value = None).

getBinCounts(self)¶

Get the raw bin counts.

Returns:	Bin Counts.
Return type:	$\left(N_{z}, N_{y}, N_{x}\right)$ `numpy.ndarray`

getBox(self)¶

Get the box used in the calculation.

Returns:	freud Box.
Return type:	`freud.box.Box`

getJacobian(self)¶

Get the Jacobian.

Returns:	Jacobian.
Return type:	float

getNBinsX(self)¶

Get the number of bins in the x-dimension of histogram.

Returns:	$N_x$.
Return type:	unsigned int

getNBinsY(self)¶

Get the number of bins in the y-dimension of histogram.

Returns:	$N_y$.
Return type:	unsigned int

getNBinsZ(self)¶

Get the number of bins in the z-dimension of histogram.

Returns:	$N_z$.
Return type:	unsigned int

getPCF(self)¶

Get the positional correlation function.

Returns:	PCF.
Return type:	$\left(N_{z}, N_{y}, N_{x}\right)$ `numpy.ndarray`

getPMFT(self)¶

Get the potential of mean force and torque.

Returns:	PMFT.
Return type:	$\left(N_{z}, N_{y}, N_{x}\right)$ `numpy.ndarray`

getRCut(self)¶

Get the r_cut value used in the cell list.

Returns:	r_cut.
Return type:	float

getX(self)¶

Get the array of x-values for the PCF histogram.

Returns:	Bin centers of x-dimension of histogram.
Return type:	$\left(N_{x}\right)$ `numpy.ndarray`

getY(self)¶

Get the array of y-values for the PCF histogram.

Returns:	Bin centers of y-dimension of histogram.
Return type:	$\left(N_{y}\right)$ `numpy.ndarray`

getZ(self)¶

Get the array of z-values for the PCF histogram.

Returns:	Bin centers of z-dimension of histogram.
Return type:	$\left(N_{z}\right)$ `numpy.ndarray`

reducePCF(self)¶: Reduces the histogram in the values over N processors to a single histogram. This is called automatically by freud.pmft.PMFTXYZ.PCF().

resetPCF(self)¶: Resets the values of the PCF histograms in memory.

Voronoi Module¶

Overview

freud.voronoi.Voronoi Compute the Voronoi tessellation of a 2D or 3D system using qhull.

Details

The voronoi module contains tools to characterize Voronoi cells of a system.

class freud.voronoi.Voronoi(box, buff)¶

Compute the Voronoi tessellation of a 2D or 3D system using qhull. This uses scipy.spatial.Voronoi, accounting for periodic boundary conditions.

Module author: Benjamin Schultz <baschult@umich.edu>

Module author: Yina Geng <yinageng@umich.edu>

Module author: Mayank Agrawal <amayank@umich.edu>

Module author: Bradley Dice <bdice@bradleydice.com>

Since qhull does not support periodic boundary conditions natively, we expand the box to include a portion of the particles’ periodic images. The buffer width is given by the parameter buff. The computation of Voronoi tessellations and neighbors is only guaranteed to be correct if buff >= L/2 where L is the longest side of the simulation box. For dense systems with particles filling the entire simulation volume, a smaller value for buff is acceptable.

Parameters:	box (`freud.box.Box`) – Simulation box. buff (float) – Buffer width.

compute¶

Compute Voronoi diagram.

Parameters:	positions (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate Voronoi diagram for. box (`freud.box.Box`) – Simulation box (Default value = None). buff (float) – Buffer distance within which to look for images (Default value = None).

computeNeighbors¶

Compute the neighbors of each particle based on the Voronoi tessellation. One can include neighbors from multiple Voronoi shells by specifying numShells in getNeighbors(). An example of computing neighbors from the first two Voronoi shells for a 2D mesh is shown below.

Retrieve the results with getNeighbors().

Example:

from freud import box, voronoi
import numpy as np
vor = voronoi.Voronoi(box.Box(5, 5, is2D=True))
pos = np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0],
                [1, 0, 0], [1, 1, 0], [1, 2, 0],
                [2, 0, 0], [2, 1, 0], [2, 2, 0]], dtype=np.float32)
first_shell = vor.computeNeighbors(pos).getNeighbors(1)
second_shell = vor.computeNeighbors(pos).getNeighbors(2)
print('First shell:', first_shell)
print('Second shell:', second_shell)

Note

Input positions must be a 3D array. For 2D, set the z value to 0.

Parameters:	positions (($N_{particles}$, 3) `numpy.ndarray`) – Points to calculate Voronoi diagram for. box (`freud.box.Box`) – Simulation box (Default value = None). buff (float) – Buffer distance within which to look for images (Default value = None). exclude_ii (bool, optional) – True if pairs of points with identical indices should be excluded (Default value = True).

computeVolumes¶

Computes volumes (areas in 2D) of Voronoi cells.

New in version 0.8.

Must call freud.voronoi.Voronoi.compute() before this method. Retrieve the results with freud.voronoi.Voronoi.getVolumes().

getBuffer¶

Returns the buffer width.

Returns:	Buffer width.
Return type:	float

getNeighborList¶

Returns a neighbor list object.

In the neighbor list, each neighbor pair has a weight value.

In 2D systems, the bond weight is the “ridge length” of the Voronoi boundary line between the neighboring particles.

In 3D systems, the bond weight is the “ridge area” of the Voronoi boundary polygon between the neighboring particles.

Returns:	Neighbor list.
Return type:	`NeighborList`

getNeighbors¶

Get numShells of neighbors for each particle

Must call computeNeighbors() before this method.

Parameters:	numShells (int) – Number of neighbor shells.

getVolumes¶

Returns an array of volumes (areas in 2D) corresponding to Voronoi cells.

New in version 0.8.

Must call freud.voronoi.Voronoi.computeVolumes() before this method.

If the buffer width is too small, then some polytopes may not be closed (they may have a boundary at infinity), and these polytopes’ volumes/areas are excluded from the list.

The length of the list returned by this method should be the same as the array of positions used in the freud.voronoi.Voronoi.compute() method, if all the polytopes are closed. Otherwise try using a larger buffer width.

Returns:	Voronoi polytope volumes/areas.
Return type:	($\left(N_{cells} \right)$) `numpy.ndarray`

getVoronoiPolytopes¶

Returns a list of polytope vertices corresponding to Voronoi cells.

If the buffer width is too small, then some polytopes may not be closed (they may have a boundary at infinity), and these polytopes’ vertices are excluded from the list.

The length of the list returned by this method should be the same as the array of positions used in the freud.voronoi.Voronoi.compute() method, if all the polytopes are closed. Otherwise try using a larger buffer width.

Returns:	List of `numpy.ndarray` containing Voronoi polytope vertices.
Return type:	list

setBox¶

Reset the simulation box.

Parameters:	box (`freud.box.Box`) – Simulation box.

setBufferWidth¶

Reset the buffer width.

Parameters:	buff (float) – Buffer width.

Development Guide¶

Contributions to freud are highly encouraged. The pages below offer information about freud’s design goals and how to contribute new modules.

Design Principles¶

Vision¶

The freud library is designed to be a powerful and flexible library for the analysis of simulation output. To support a variety of analysis routines, freud places few restrictions on its components. The primary requirement for an analysis routine in freud is that it should be substantially computationally intensive so as to require coding up in C++: all freud code should be composed of fast C++ routines operating on systems of particles in periodic boxes. To remain easy-to-use, all C++ modules should be wrapped in Python code so they can be easily accessed from Python scripts or through a Python interpreter.

In order to achieve this goal, freud takes the following viewpoints:

In order to remain as agnostic to inputs as possible, freud makes no attempt to interface directly with simulation software. Instead, freud works directly with NumPy <http://www.numpy.org/>_ arrays to retain maximum flexibility.
For ease of maintenance, freud uses Git for version control; Bitbucket for code hosting and issue tracking; and the PEP 8 standard for code, stressing explicitly written code which is easy to read.
To ensure correctness, freud employs unit testing using the Python unittest framework. In addition, freud utilizes CircleCI for continuous integration to ensure that all of its code works correctly and that any changes or new features do not break existing functionality.

Language choices¶

The freud library is written in two languages: Python and C++. C++ allows for powerful, fast code execution while Python allows for easy, flexible use. Intel Threading Building Blocks parallelism provides further power to C++ code. The C++ code is wrapped with Cython, allowing for user interaction in Python. NumPy provides the basic data structures in freud, which are commonly used in other Python plotting libraries and packages.

Unit Tests¶

All modules should include a set of unit tests which test the correct behavior of the module. These tests should be simple and short, testing a single function each, and completing as quickly as possible (ideally < 10 sec, but times up to a minute are acceptable if justified).

Make Execution Explicit¶

While it is tempting to make your code do things “automatically”, such as have a calculate method find all _calc methods in a class, call them, and add their returns to a dictionary to return to the user, it is preferred in freud to execute code explicitly. This helps avoid issues with debugging and undocumented behavior:

# this is bad
class SomeFreudClass(object):
    def __init__(self, **kwargs):
        for key in kwargs.keys:
            setattr(self, key, kwargs[key])

# this is good
class SomeOtherFreudClass(object):
    def __init__(self, x=None, y=None):
        self.x = x
        self.y = y

Code Duplication¶

When possible, code should not be duplicated. However, being explicit is more important. In freud this translates to many of the inner loops of functions being very similar:

// somewhere deep in function_a
for (int i = 0; i < n; i++)
    {
    vec3[float] pos_i = position[i];
    for (int j = 0; j < n; j++)
        {
        pos_j = = position[j];
        // more calls here
        }
    }

// somewhere deep in function_b
for (int i = 0; i < n; i++)
    {
    vec3[float] pos_i = position[i];
    for (int j = 0; j < n; j++)
        {
        pos_j = = position[j];
        // more calls here
        }
    }

While it might be possible to figure out a way to create a base C++ class all such classes inherit from, run through positions, call a calculation, and return, this would be rather complicated. Additionally, any changes to the internals of the code, and may result in performance penalties, difficulty in debugging, etc. As before, being explicit is better.

However, if you have a class which has a number of methods, each of which requires the calling of a function, this function should be written as its own method (instead of being copy-pasted into each method) as is typical in object-oriented programming.

Python vs. Cython vs. C++¶

The freud library is meant to leverage the power of C++ code imbued with parallel processing power from TBB with the ease of writing Python code. The bulk of your calculations should take place in C++, as shown in the snippet below:

# this is bad
def badHeavyLiftingInPython(positions):
    # check that positions are fine
    for i, pos_i in enumerate(positions):
        for j, pos_j in enumerate(positions):
            if i != j:
                r_ij = pos_j - pos_i
                # ...
                computed_array[i] += some_val
    return computed_array

# this is good
def goodHeavyLiftingInCPlusPlus(positions):
    # check that positions are fine
    cplusplus_heavy_function(computed_array, positions, len(pos))
    return computed_array

In the C++ code, implement the heavy lifting function called above from Python:

void cplusplus_heavy_function(float* computed_array,
                              float* positions,
                              int n)
    {
    for (int i = 0; i < n; i++)
        {
        for (int j = 0; j < n; j++)
            {
            if (i != j)
                {
                r_ij = pos_j - pos_i;
                // ...
                computed_array[i] += some_val;
                }
            }
        }
    }

Some functions may be necessary to write at the Python level due to a Python library not having an equivalent C++ library, complexity of coding, etc. In this case, the code should be written in Cython and a reasonable attempt to optimize the code should be made.

Source Code Conventions¶

The guidelines below should be followed for any new code added to freud. This guide is separated into three sections, one for guidelines common to Python and C++, one for Python alone, and one for C++.

Both¶

Naming Conventions¶

The following conventions should apply to Python, Cython, and C++ code.

Variable names use lower_case_with_underscores
Function and method names use lowerCaseWithNoUnderscores
Class names use CapWords

Python example:

class FreudClass(object):
    def __init__(self):
        pass
    def calcSomething(self, position_i, orientation_i, position_j, orientation_j):
        r_ij = position_j - position_i
        theta_ij = calcOrientationThing(orientation_i, orientation_j)
    def calcOrientationThing(self, orientation_i, orientation_j):
        ...

C++ example:

class FreudCPPClass
    {
    FreudCPPClass()
        {
        }
    computeSomeValue(int variable_a, float variable_b)
        {
        // do some things in here
        }
    };

Indentation¶

Spaces, not tabs, must be used for indentation
4 spaces are required per level of indentation and continuation lines
There should be no whitespace at the end of lines in the file.
Documentation comments and items broken over multiple lines should be aligned with spaces

class SomeClass
    {
    private:
        int m_some_member;        //!< Documentation for some_member
        int m_some_other_member;  //!< Documentation for some_other_member
    };

template<class BlahBlah> void some_long_func(BlahBlah with_a_really_long_argument_list,
                                             int b,
                                             int c);

Formatting Long Lines¶

All code lines should be hand-wrapped so that they are no more than 79 characters long. Simply break any excessively long line of code at any natural breaking point to continue on the next line.

cout << "This is a really long message, with "
     << message.length()
     << "Characters in it:"
     << message << endl;

Try to maintain some element of symmetry in the way the line is broken. For example, the above long message is preferred over the below:

cout << "This is a really long message, with " << message.length() << "Characters in it:"
   << message << endl;

There are special rules for function definitions and/or calls:

If the function definition (or call) cleanly fits within the character limit, leave it all on one line

int some_function(int arg1, int arg2)

(Option 1) If the function definition (or call) goes over the limit, you may be able to fix it by simply putting the template definition on the previous line:

// go from
template<class Foo, class Bar> int some_really_long_function_name(int with_really_long, Foo argument, Bar lists)
// to
template<class Foo, class Bar>
int some_really_long_function_name(int with_really_long, Foo argument, Bar lists)

(Option 2) If the function doesn’t have a template specifier, or splitting at that point isn’t enough, split out each argument onto a separate line and align them.

// Instead of this...
int someReallyLongFunctionName(int with_really_long_arguments, int or, int maybe, float there, char are, int just, float a, int lot, char of, int them)

// ...use this.
int someReallyLongFunctionName(int with_really_long_arguments,
                               int or,
                               int maybe,
                               float there,
                               char are,
                               int just,
                               float a,
                               int lot,
                               char of,
                               int them)

Python¶

Code in freud should follow PEP 8, as well as the following guidelines. Anything listed here takes precedence over PEP 8, but try to deviate as little as possible from PEP 8. When in doubt, follow these guidelines over PEP 8.

During continuous integration (CI), all Python and Cython code in freud is tested with flake8 to ensure PEP 8 compliance. It is strongly recommended to set up a pre-commit hook to ensure code is compliant before pushing to the repository:

flake8 --install-hook git
git config --bool flake8.strict true

Source¶

All code should be contained in Cython files
Python .py files are reserved for module level docstrings and minor miscellaneous tasks for, e.g, backwards compatibility.
Semicolons should not be used to mark the end of lines in Python.

Documentation Comments¶

Documentation is generated using sphinx.
The documentation should be written according to the Google Python Style Guide.
A few specific notes:
- The shapes of NumPy arrays should be documented as part of the type in the following manner: points ((N, 4) (:py:class:np.ndarray)): The points....
- Constructors should be documented at the class level.
- Class attributes (including properties) should be documented as class attributes within the class-level docstring.
- Optional arguments should be documented as such within the type after the actual type, and the default value should be included within the description e.g., r_max (float, optional): ... If None (the default), number is inferred....
- Properties that are settable should be documented the same way as optional arguments: Lx (float, settable): Length in x.
All docstrings should be contained within the Cython files except module docstrings, which belong in the Python code.
If you copy an existing file as a template, make sure to modify the comments to reflect the new file.
Good documentation comments are best demonstrated with an in-code example. Liberal addition of examples is encouraged.

CPP¶

Indentation¶

C++ code should follow Whitesmith’s style. An extended set of examples follows:

class SomeClass
    {
    public:
        SomeClass();
        int SomeMethod(int a);
    private:
        int m_some_member;
    };

// indent function bodies
int SomeClass::SomeMethod(int a)
    {
    // indent loop bodies
    while (condition)
        {
        b = a + 1;
        c = b - 2;
        }

    // indent switch bodies and the statements inside each case
    switch (b)
        {
        case 0:
            c = 1;
            break;
        case 1:
            c = 2;
            break;
        default:
            c = 3;
            break;
        }

    // indent the bodies of if statements
    if (something)
        {
        c = 5;
        b = 10;
        }
     else if (something_else)
        {
        c = 10;
        b = 5;
        }
     else
        {
        c = 20;
        b = 6;
        }

    // omitting the braces is fine if there is only one statement in a body (for loops, if, etc.)
    for (int i = 0; i < 10; i++)
        c = c + 1;

    return c;
    // the nice thing about this style is that every brace lines up perfectly with its mate
    }

TBB sections should use lambdas, not templates

void someC++Function(float some_var,
                     float other_var)
    {
    // code before parallel section
    parallel_for(blocked_range<size_t>(0,n),
        [=] (const blocked_range<size_t>& r)
            {
            // do stuff
            });

Documentation Comments¶

Documentation should be written in doxygen.

How to Add New Code¶

This document details the process of adding new code into freud.

Does my code belong in freud?¶

The freud library is not meant to simply wrap or augment external Python libraries. A good rule of thumb is if the code I plan to write does not require C++, it does not belong in freud. There are, of course, exceptions.

Create a new branch¶

You should branch your code from master into a new branch. Do not add new code directly into the master branch.

Add a New Module¶

If the code you are adding is in a new module, not an existing module, you must do the following:

Edit cpp/CMakeLists.txt
- Add ${CMAKE_CURRENT_SOURCE_DIR}/moduleName to include_directories.
- Add moduleName/SubModule.cc and moduleName/SubModule.h to the FREUD_SOURCES in set.
Create cpp/moduleName folder
Edit freud/__init__.py
- Add from . import moduleName so that your module is imported by default.
Edit freud/_freud.pyx
- Add include "moduleName.pxi". This must be done to have freud include your Python-level code.
Create freud/moduleName.pxi file
- This will house the python-level code.
- If you have a .pxd file exposing C++ classes, make sure to import that:

cimport freud._moduleName as moduleName

Create freud/moduleName.py file
- Make sure there is an import for each C++ class in your module:

from ._freud import MyC++Class

Create freud/_moduleName.pxd
- This file will expose the C++ classes in your module to python.
Add line to doc/source/modules.rst
- Make sure your new module is referenced in the documentation.
Create doc/source/moduleName.rst

Add to an Existing Module¶

To add a new class to an existing module, do the following:

Create cpp/moduleName/SubModule.h and cpp/moduleName/SubModule.cc
- New classes should be grouped into paired .h, .cc files. There may be a few instances where new classes could be added to an existing .h, .cc pairing.
Edit freud/moduleName.py file
- Add a line for each C++ class in your module:

from ._freud import MyC++Class

Expose C++ class in freud/_moduleName.pxd
Create Python interface in freud/moduleName.pxi

You must include sphinx-style documentation and unit tests.

Add extra documentation to doc/source/moduleName.rst
Add unit tests to freud/tests

References and Citations¶

[Bokeh]

Bokeh Development Team (2014). Bokeh: Python library for interactive visualization URL http://www.bokeh.pydata.org.

[HajiAkbari2015]

Haji-Akbari, A., & Glotzer, S. C. (2015). Strong orientational coordinates and orientational order parameters for symmetric objects. Journal of Physics A: Mathematical and Theoretical, 48. https://doi.org/10.1088/1751-8113/48/48/485201

[vanAndersKlotsa2014]

van Anders, G., Klotsa, D., Ahmed, N. K., Engel, M., & Glotzer, S. C. (2014). Understanding shape entropy through local dense packing. Proceedings of the National Academy of Sciences, 111(45), E4812–E4821. https://doi.org/10.1073/pnas.1418159111

[vanAndersAhmed2014]

van Anders, G., Ahmed, N. K., Smith, R., Engel, M., & Glotzer, S. C. (2014). Entropically patchy particles: Engineering valence through shape entropy. ACS Nano, 8(1), 931–940. https://doi.org/10.1021/nn4057353

[Lechner2008]

Lechner, W., & Dellago, C. (2008). Accurate determination of crystal structures based on averaged local bond order parameters. Journal of Chemical Physics, 129(11). https://doi.org/10.1063/1.2977970

[Steinhardt1983]

P.J. Steinhardt, D.R. Nelson, and M. Ronchetti (1983). Bond-orientational order in liquids and glasses. Phys. Rev. B 28(784). https://doi.org/10.1103/PhysRevFluids.2.093301

License¶

freud Open Source Software License Copyright 2010-2018 The Regents of
the University of Michigan All rights reserved.

freud may contain modifications ("Contributions") provided, and to which
copyright is held, by various Contributors who have granted The Regents of the
University of Michigan the right to modify and/or distribute such Contributions.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice,
   this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors
   may be used to endorse or promote products derived from this software without
   specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Credits¶

freud Developers¶

The following people contributed to the development of freud.

Eric Harper, University of Michigan - Former lead developer

TBB parallelism
PMFT module
NearestNeighbors
RDF
Bonding module
Cubatic order parameter
Hexatic order parameter
Pairing2D

Joshua A. Anderson, University of Michigan - Creator

Initial design and implementation
IteratorLinkCell
LinkCell
Various density modules
freud.parallel
Indexing modules
cluster.pxi

Matthew Spellings - Former lead developer

Added generic neighbor list
Enabled neighbor list usage across freud modules
Correlation functions
LocalDescriptors class
interface.pxi

Erin Teich

Wrote environment matching module
BondOrder (with Julia Dshemuchadse)
Angular separation (with Andrew Karas)
Contributed to LocalQl development

Eric Irrgang

Authored kspace CPP code

Chrisy Du

Authored all Steinhardt order parameters

Antonio Osorio

Vyas Ramasubramani - Lead developer

Ensured pep8 compliance
Added CircleCI continuous integration support
Rewrote docs
Fixed nematic order parameter
Add properties for accessing class members
Various minor bug fixes
Refactored PMFT code
Refactored Steinhardt order parameter code

Bradley Dice - Lead developer

Cleaned up various docstrings
HexOrderParameter bug fixes
Cleaned up testing code
Bumpversion support
Reduced all compile warnings
Added Python interface for box periodicity
Added Voronoi support for neighbor lists across periodic boundaries
Added Voronoi weights for 3D
Added Voronoi cell volume computation

Richmond Newman

Developed the freud box
Solid liquid order parameter

Carl Simon Adorf

Developed the python box module

Jens Glaser

Wrote kspace.pxi front-end
Nematic order parameter

Benjamin Schultz

Wrote Voronoi module

Bryan VanSaders

Ryan Marson

Tom Grubb

Yina Geng

Co-wrote Voronoi neighbor list module
Add properties for accessing class members

Carolyn Phillips

Initial design and implementation
Package name

Ben Swerdlow

James Antonaglia

Mayank Agrawal

Co-wrote Voronoi neighbor list module

William Zygmunt

Greg van Anders

James Proctor

Rose Cersonsky

Wenbo Shen

Andrew Karas

Angular separation

Paul Dodd

Tim Moore

Added optional rmin argument to density.RDF

Michael Engel

Translational order parameter

Alex Dutton

BiMap class for MatchEnv

Source code¶

Eigen (http://eigen.tuxfamily.org/) is included as a git submodule in freud. Eigen is made available under the Mozilla Public License v.2.0 (http://mozilla.org/MPL/2.0/). Its linear algebra routines are used for various tasks including the computation of eigenvalues and eigenvectors.

fsph (https://bitbucket.org/glotzer/fsph) is included as a git submodule in freud. fsph is made available under the MIT license. It is used for the calculation of spherical harmonics, which are then used in the calculation of various order parameters, under the following license:

Copyright (c) 2016 The Regents of the University of Michigan

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Support and Contribution¶

Please visit our repository on Bitbucket for the library source code. Any issues or bugs may be reported at our issue tracker, while questions and discussion can be directed to our forum. All contributions to freud are welcomed via pull requests! Please see the development guide for more information on requirements for new code.

freud documentation¶

Installing freud¶

Examples¶

Installation¶

Installing freud¶

Install via conda¶

Compile from source¶

Unit Tests¶

Documentation¶

Building the documentation¶

Modules¶

Bond Module¶

Box Module¶

Cluster Module¶

Density Module¶

Correlation Functions¶

Gaussian Density¶

Local Density¶

Radial Distribution Function¶

Environment Module¶

Index Module¶

Interface Module¶

KSpace Module¶

Structure Factor¶

Diffraction Patterns¶

Utilities¶

Locality Module¶

Order Module¶

Deprecated Classes¶

Bond Order¶

Local Descriptors¶

Environment Matching¶

Pairing¶

Angular Separation¶

Parallel Module¶

PMFT Module¶

Voronoi Module¶

Development Guide¶

Design Principles¶

Vision¶

Language choices¶

Unit Tests¶

Make Execution Explicit¶

Code Duplication¶

Python vs. Cython vs. C++¶

Source Code Conventions¶

Both¶

Naming Conventions¶

Indentation¶

Formatting Long Lines¶

Python¶

Source¶

Documentation Comments¶

CPP¶

Indentation¶

Documentation Comments¶

How to Add New Code¶

Does my code belong in freud?¶

Create a new branch¶

Add a New Module¶

Add to an Existing Module¶

References and Citations¶

License¶

Credits¶

freud Developers¶

Source code¶

Support and Contribution¶

Indices and tables¶