Order Module¶
Overview
Compute the cubatic order parameter [HajiAkbari2015] for a system of particles using simulated annealing instead of NewtonRaphson root finding. 

Compute the nematic order parameter for a system of particles. 

Calculates the \(k\)atic order parameter for each particle in the system. 

Compute the translational order parameter for each particle. 

Compute the local Steinhardt [Steinhardt1983] rotationally invariant \(Q_l\) order parameter for a set of points. 

A variant of the 

Compute the local Steinhardt [Steinhardt1983] rotationally invariant \(W_l\) order parameter for a set of points. 

A variant of the 

Uses dot products of \(Q_{lm}\) between particles for clustering. 

A variant of the 

Calculates a measure of total rotational autocorrelation based on hyperspherical harmonics as laid out in “Design rules for engineering colloidal plastic crystals of hard polyhedra  phase behavior and directional entropic forces” by Karas et al. 
Details
The freud.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.
Cubatic Order Parameter¶

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 NewtonRaphson root finding.
Module author: Eric Harper <harperic@umich.edu>
 Parameters
 Variables
t_initial (float) – The value of the initial temperature.
t_final (float) – The value of the final temperature.
scale (float) – The scale
cubatic_order_parameter (float) – The cubatic order parameter.
orientation (\(\left(4 \right)\)
numpy.ndarray
) – The quaternion of global orientation.particle_order_parameter (
numpy.ndarray
) – Cubatic order parameter.particle_tensor (\(\left(N_{particles}, 3, 3, 3, 3 \right)\)
numpy.ndarray
) – Rank 5 tensor corresponding to each individual particle orientation.global_tensor (\(\left(3, 3, 3, 3 \right)\)
numpy.ndarray
) – Rank 4 tensor corresponding to global orientation.cubatic_tensor (\(\left(3, 3, 3, 3 \right)\)
numpy.ndarray
) – Rank 4 cubatic tensor.gen_r4_tensor (\(\left(3, 3, 3, 3 \right)\)
numpy.ndarray
) – Rank 4 tensor corresponding to each individual particle orientation.

compute
¶ Calculates the perparticle and global order parameter.
 Parameters
orientations ((\(N_{particles}\), 4)
numpy.ndarray
) – Orientations as angles to use in computation.
Nematic Order Parameter¶

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). Variables
nematic_order_parameter (float) – Nematic order parameter.
director (\(\left(3 \right)\)
numpy.ndarray
) – The average nematic director.particle_tensor (\(\left(N_{particles}, 3, 3 \right)\)
numpy.ndarray
) – One 3x3 matrix perparticle corresponding to each individual particle orientation.nematic_tensor (\(\left(3, 3 \right)\)
numpy.ndarray
) – 3x3 matrix corresponding to the average particle orientation.

compute
¶ Calculates the perparticle and global order parameter.
 Parameters
orientations (\(\left(N_{particles}, 4 \right)\)
numpy.ndarray
) – Orientations to calculate the order parameter.
Hexatic Order Parameter¶

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.order.HexOrderParameter
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
¶ 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.
Translational Order Parameter¶

class
freud.order.
TransOrderParameter
(rmax, k, n)¶ Compute the translational order parameter for each particle.
Module author: Wenbo Shen <shenwb@umich.edu>
 Parameters
 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.
K (float) – Normalization value (d_r is divided by K).

compute
¶ 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.
Steinhardt \(Q_l\) Order Parameter¶

class
freud.order.
LocalQl
(box, rmax, l, rmin)¶ Compute the local Steinhardt [Steinhardt1983] rotationally invariant \(Q_l\) 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: \(\overline{Q}_{lm}(i) = \frac{1}{N_b} \displaystyle\sum_{j=1}^{N_b} Y_{lm}(\theta(\vec{r}_{ij}), \phi(\vec{r}_{ij}))\). The particles included in the sum are determined by the rmax argument to the constructor.
This is then combined in a rotationally invariant fashion to remove local orientational order as follows: \(Q_l(i)=\sqrt{\frac{4\pi}{2l+1} \displaystyle\sum_{m=l}^{l} \overline{Q}_{lm}^2 }\).
The
computeAve()
method provides access to a variant of this parameter that performs a average over the first and second shell combined [Lechner2008]. To compute this parameter, we perform a second averaging over the first neighbor shell of the particle to implicitly include information about the second neighbor shell. This averaging is performed by replacing the value \(\overline{Q}_{lm}(i)\) in the original definition by the average value of \(\overline{Q}_{lm}(k)\) over all the \(k\) neighbors of particle \(i\) as well as itself.The
computeNorm()
andcomputeAveNorm()
methods provide normalized versions ofcompute()
andcomputeAve()
, where the normalization is performed by dividing by the average \(Q_{lm}\) values over all particles.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 integer.
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 (Default value = 0).
 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
¶ Compute the 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
¶ Compute the order parameter over two nearest neighbor shells.
 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
¶ Compute the order parameter over two nearest neighbor shells normalized by the average spherical harmonic value over all the particles.
 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
¶ Compute the order parameter normalized by the average spherical harmonic value over all the particles.
 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).

plot
¶ Plot Ql.
 Parameters
ax (
matplotlib.axes.Axes
) – Axis to plot on. IfNone
, make a new figure and axis. (Default value =None
)mode (str) – Plotting mode. Must be one of “Ql”, “ave_Ql”, “norm_Ql” and “ave_norm_Ql”. Plots the given attribute. If
None
, plot the most recent computed attribute. (Default value =None
)
 Returns
Axis with the plot.
 Return type

setBox
¶ Reset the simulation box.
 Parameters
box (
freud.box.Box
) – Simulation box.

class
freud.order.
LocalQlNear
(box, rmax, l, kn)¶ A variant of the
LocalQl
class that performs its average over nearest neighbor particles as determined by an instance offreud.locality.NeighborList
. The number of included neighbors is determined by the kn parameter to the constructor.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).

compute
¶ Compute the 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
¶ Compute the order parameter over two nearest neighbor shells.
 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
¶ Compute the order parameter over two nearest neighbor shells normalized by the average spherical harmonic value over all the particles.
 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
¶ Compute the order parameter normalized by the average spherical harmonic value over all the particles.
 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).

plot
¶ Plot Ql.
 Parameters
ax (
matplotlib.axes.Axes
) – Axis to plot on. IfNone
, make a new figure and axis. (Default value =None
)mode (str) – Plotting mode. Must be one of “Ql”, “ave_Ql”, “norm_Ql” and “ave_norm_Ql”. Plots the given attribute. If
None
, plot the most recent computed attribute. (Default value =None
)
 Returns
Axis with the plot.
 Return type

setBox
¶ Reset the simulation box.
 Parameters
box (
freud.box.Box
) – Simulation box.
Steinhardt \(W_l\) Order Parameter¶

class
freud.order.
LocalWl
(box, rmax, l)¶ Compute the local Steinhardt [Steinhardt1983] rotationally invariant \(W_l\) order parameter for a set of points.
Implements the local rotationally invariant \(W_l\) order parameter described by Steinhardt. For a particle i, we calculate the average \(W_l\) by summing the spherical harmonics between particle \(i\) and its neighbors \(j\) in a local region: \(\overline{Q}_{lm}(i) = \frac{1}{N_b} \displaystyle\sum_{j=1}^{N_b} Y_{lm}(\theta(\vec{r}_{ij}), \phi(\vec{r}_{ij}))\). The particles included in the sum are determined by the rmax argument to the constructor.
The \(W_l\) is then defined as a weighted average over the \(\overline{Q}_{lm}(i)\) values using Wigner 3j symbols (ClebschGordan coefficients). The resulting combination is rotationally (i.e. frame) invariant.
The
computeAve()
method provides access to a variant of this parameter that performs a average over the first and second shell combined [Lechner2008]. To compute this parameter, we perform a second averaging over the first neighbor shell of the particle to implicitly include information about the second neighbor shell. This averaging is performed by replacing the value \(\overline{Q}_{lm}(i)\) in the original definition by the average value of \(\overline{Q}_{lm}(k)\) over all the \(k\) neighbors of particle \(i\) as well as itself.The
computeNorm()
andcomputeAveNorm()
methods provide normalized versions ofcompute()
andcomputeAve()
, where the normalization is performed by dividing by the average \(Q_{lm}\) values over all particles.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 integer.
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 (Default value = 0).
 Variables
box (
freud.box.Box
) – Box used in the calculation.num_particles (unsigned int) – Number of particles.
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).

compute
¶ Compute the 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
¶ Compute the order parameter over two nearest neighbor shells.
 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
¶ Compute the order parameter over two nearest neighbor shells normalized by the average spherical harmonic value over all the particles.
 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
¶ Compute the order parameter normalized by the average spherical harmonic value over all the particles.
 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).

plot
¶ Plot Wl.
 Parameters
ax (
matplotlib.axes.Axes
) – Axis to plot on. IfNone
, make a new figure and axis. (Default value =None
)mode (str) – Plotting mode. Must be one of “Wl”, “ave_Wl”, “norm_Wl” and “ave_norm_Wl”. Plots the given attribute. If
None
, plot the most recent computed attribute. (Default value =None
)
 Returns
Axis with the plot.
 Return type

setBox
¶ Reset the simulation box.
 Parameters
box (
freud.box.Box
) – Simulation box.

class
freud.order.
LocalWlNear
(box, rmax, l, kn)¶ A variant of the
LocalWl
class that performs its average over nearest neighbor particles as determined by an instance offreud.locality.NeighborList
. The number of included neighbors is determined by the kn parameter to the constructor.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.
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).

compute
¶ Compute the 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
¶ Compute the order parameter over two nearest neighbor shells.
 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
¶ Compute the order parameter over two nearest neighbor shells normalized by the average spherical harmonic value over all the particles.
 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
¶ Compute the order parameter normalized by the average spherical harmonic value over all the particles.
 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).

plot
¶ Plot Wl.
 Parameters
ax (
matplotlib.axes.Axes
) – Axis to plot on. IfNone
, make a new figure and axis. (Default value =None
)mode (str) – Plotting mode. Must be one of “Wl”, “ave_Wl”, “norm_Wl” and “ave_norm_Wl”. Plots the given attribute. If
None
, plot the most recent computed attribute. (Default value =None
)
 Returns
Axis with the plot.
 Return type

setBox
¶ Reset the simulation box.
 Parameters
box (
freud.box.Box
) – Simulation box.
SolidLiquid Order Parameter¶

class
freud.order.
SolLiq
(box, rmax, Qthreshold, Sthreshold, l)¶ Uses dot products of \(Q_{lm}\) between particles 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 solidlike bond. (For \(l=6\), 0.7 generally good for FCC or BCC structures).
Sthreshold (unsigned int) – Minimum required number of adjacent solidlink bonds for a particle to be considered solidlike for clustering. (For \(l=6\), 68 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 solidlike 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
¶ Compute the solidliquid 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
¶ Compute the solidliquid order parameter without normalizing the dot product.
 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
¶ Compute a variant of the solidliquid order parameter.
This variant method places a minimum threshold on the number of solidlike bonds a particle must have to be considered solidlike for clustering purposes.
 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.
SolLiqNear
(box, rmax, Qthreshold, Sthreshold, l, kn)¶ A variant of the
SolLiq
class that performs its average over nearest neighbor particles as determined by an instance offreud.locality.NeighborList
. The number of included neighbors is determined by the kn parameter to the constructor.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 solidlike bond. (For \(l=6\), 0.7 generally good for FCC or BCC structures).
Sthreshold (unsigned int) – Minimum required number of adjacent solidlink bonds for a particle to be considered solidlike for clustering. (For \(l=6\), 68 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 solidlike 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
¶ 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
¶ 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
¶ 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.
Rotational Autocorrelation¶

class
freud.order.
RotationalAutocorrelation
(l)¶ Calculates a measure of total rotational autocorrelation based on hyperspherical harmonics as laid out in “Design rules for engineering colloidal plastic crystals of hard polyhedra  phase behavior and directional entropic forces” by Karas et al. (currently in preparation). The output is not a correlation function, but rather a scalar value that measures total system orientational correlation with an initial state. As such, the output can be treated as an order parameter measuring degrees of rotational (de)correlation. For analysis of a trajectory, the compute call needs to be done at each trajectory frame.
Module author: Andrew Karas <askaras@umich.edu>
Module author: Vyas Ramasubramani <vramasub@umich.edu>
New in version 1.0.
 Parameters
l (int) – Order of the hyperspherical harmonic. Must be a positive, even integer.
 Variables
num_orientations (unsigned int) – The number of orientations used in computing the last set.
azimuthal (int) – The azimuthal quantum number, which defines the order of the hyperspherical harmonic. Must be a positive, even integer.
ra_array ((\(N_{orientations}\))
numpy.ndarray
) – The perorientation array of rotational autocorrelation values calculated by the last call to compute.autocorrelation (float) – The autocorrelation computed in the last call to compute.

compute
¶ Calculates the rotational autocorrelation function for a single frame.
 Parameters
ref_ors ((\(N_{orientations}\), 4)
numpy.ndarray
) – Reference orientations for the initial frame.ors ((\(N_{orientations}\), 4)
numpy.ndarray
) – Orientations for the frame of interest.