Order Module
Overview
Compute the cubatic order parameter [HAG15] for a system of particles using simulated annealing instead of Newton-Raphson root finding. |
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Compute the nematic order parameter for a system of particles. |
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Calculates the \(k\)-atic order parameter for 2D systems. |
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Compute the translational order parameter for each particle. |
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Compute one or more of the rotationally invariant Steinhardt order parameter \(q_l\) or \(w_l\) for a set of points [Ste83]. |
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Identifies solid-like clusters using dot products of \(q_{lm}\). |
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Calculates a measure of total rotational autocorrelation. |
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.
- class freud.order.Cubatic
Bases:
freud.util._Compute
Compute the cubatic order parameter [HAG15] for a system of particles using simulated annealing instead of Newton-Raphson root finding.
- Parameters
t_initial (float) – Starting temperature.
t_final (float) – Final temperature.
scale (float) – Scaling factor to reduce temperature.
n_replicates (unsigned int, optional) – Number of replicate simulated annealing runs. (Default value =
1
).seed (unsigned int, optional) – Random seed to use in calculations. If
None
, system time is used. (Default value =None
).
- 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.
- property cubatic_tensor
Rank 4 homogeneous tensor representing the optimal system-wide coordinates.
- Type
\(\left(3, 3, 3, 3 \right)\)
numpy.ndarray
- property global_tensor
Rank 4 tensor corresponding to the global orientation. Computed from all orientations.
- Type
\(\left(3, 3, 3, 3 \right)\)
numpy.ndarray
- n_replicates
Number of replicate simulated annealing runs.
- Type
unsigned int
- property orientation
The quaternion of global orientation.
- Type
\(\left(4 \right)\)
numpy.ndarray
- property particle_order
Order parameter.
- Type
\(\left(N_{particles} \right)\)
numpy.ndarray
- seed
Random seed to use in calculations.
- Type
unsigned int
- class freud.order.Hexatic
Bases:
freud.locality._PairCompute
Calculates the \(k\)-atic order parameter for 2D systems.
The \(k\)-atic order parameter (called the hexatic order parameter for \(k = 6\)) is analogous to Steinhardt order parameters, and is used to measure order in the bonds of 2D systems.
The \(k\)-atic order parameter for a particle \(i\) and its \(N_b\) neighbors \(j\) is given by:
\(\psi_k \left( i \right) = \frac{1}{N_b} \sum \limits_{j=1}^{N_b} e^{i k \phi_{ij}}\)
The parameter \(k\) governs the symmetry of the order parameter and typically matches the number of neighbors to be found for each particle. The quantity \(\phi_{ij}\) is the angle between the vector \(r_{ij}\) and \(\left(1, 0\right)\).
If the weighted mode is enabled, contributions of each neighbor are weighted. Neighbor weights \(w_{ij}\) default to 1 but are defined for a
freud.locality.NeighborList
fromfreud.locality.Voronoi
or one with user-provided weights. The formula is modified as follows:\(\psi'_k \left( i \right) = \frac{1}{\sum_{j=1}^{N_b} w_{ij}} \sum \limits_{j=1}^{N_b} w_{ij} e^{i k \phi_{ij}}\)
The hexatic order parameter as written above is complex-valued. The magnitude of the complex value,
np.abs(hex_order.particle_order)
, is frequently what is desired when determining the \(k\)-atic order for each particle. The complex phase anglenp.angle(hex_order.particle_order)
indicates the orientation of the bonds as an angle measured counterclockwise from the vector \(\left(1, 0\right)\). The complex valued order parameter is not rotationally invariant because of this phase angle, but the magnitude is rotationally invariant.Note
2D:
freud.order.Hexatic
is only defined for 2D systems. The points must be passed in as[x, y, 0]
.- Parameters
k (unsigned int, optional) – Symmetry of order parameter (Default value =
6
).weighted (bool, optional) – Determines whether to use neighbor weights in the computation of spherical harmonics over neighbors. If enabled and used with a Voronoi neighbor list, this results in the 2D Minkowski Structure Metrics \(\psi'_k\) [MKSTM13] (Default value =
False
).
- compute(self, system, neighbors=None)
Calculates the hexatic order parameter.
Example:
>>> box, points = freud.data.make_random_system( ... box_size=10, num_points=100, is2D=True, seed=0) >>> # Compute the hexatic (6-fold) order for the 2D system >>> hex_order = freud.order.Hexatic(k=6) >>> hex_order.compute(system=(box, points)) freud.order.Hexatic(...) >>> print(hex_order.particle_order) [...]
- Parameters
system – Any object that is a valid argument to
freud.locality.NeighborQuery.from_system
.neighbors (
freud.locality.NeighborList
or dict, optional) – Either aNeighborList
of neighbor pairs to use in the calculation, or a dictionary of query arguments (Default value: None).
- default_query_args
The default query arguments are
{'mode': 'nearest', 'num_neighbors': self.k}
.
- k
Symmetry of the order parameter.
- Type
unsigned int
- property particle_order
Order parameter.
- Type
\(\left(N_{particles} \right)\)
numpy.ndarray
- plot(self, ax=None)
Plot order parameter distribution.
- Parameters
ax (
matplotlib.axes.Axes
, optional) – Axis to plot on. IfNone
, make a new figure and axis (Default value =None
).- Returns
Axis with the plot.
- Return type
- class freud.order.Nematic
Bases:
freud.util._Compute
Compute the nematic order parameter for a system of particles.
- 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.
Example:
>>> orientations = np.array([[1, 0, 0, 0]] * 100) >>> director = np.array([1, 1, 0]) >>> nematic = freud.order.Nematic(director) >>> nematic.compute(orientations) freud.order.Nematic(u=[...])
- Parameters
orientations (\(\left(N_{particles}, 4 \right)\)
numpy.ndarray
) – Orientations to calculate the order parameter.
- property director
The average nematic director.
- Type
\(\left(3 \right)\)
numpy.ndarray
- property nematic_tensor
3x3 matrix corresponding to the average particle orientation.
- Type
\(\left(3, 3 \right)\)
numpy.ndarray
- property particle_tensor
\(\left(N_{particles}, 3, 3 \right)\)
numpy.ndarray
: One 3x3 matrix per-particle corresponding to each individual particle orientation.
- u
The normalized reference director (the normalized vector provided on construction).
- Type
\(\left(3 \right)\)
numpy.ndarray
- class freud.order.RotationalAutocorrelation
Bases:
freud.util._Compute
Calculates a measure of total rotational autocorrelation.
For any calculation of rotational correlations of extended (i.e. non-point) particles, encoding the symmetries of these particles is crucial to appropriately determining correlations. For systems of anisotropic particles in three dimensions, representing such equivalence can be quite mathematically challenging. This calculation is based on the hyperspherical harmonics as laid out in [KDvAG19]. Generalizations of spherical harmonics to four dimensions, hyperspherical harmonics provide a natural basis for periodic functions in 4 dimensions just as harmonic functions (sines and cosines) or spherical harmonics do in lower dimensions. The idea behind this calculation is to embed orientation quaternions into a 4-dimensional space and then use hyperspherical harmonics to find correlations in a symmetry-aware fashion.
The choice of the hyperspherical harmonic parameter \(l\) determines the symmetry of the functions. 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.
- Parameters
l (int) – Order of the hyperspherical harmonic. Must be a positive, even integer.
- compute(self, ref_orientations, orientations)
Calculates the rotational autocorrelation function for a single frame.
- Parameters
ref_orientations ((\(N_{orientations}\), 4)
numpy.ndarray
) – Orientations for the initial frame.orientations ((\(N_{orientations}\), 4)
numpy.ndarray
) – Orientations for the frame of interest.
- property particle_order
Rotational autocorrelation values calculated for each orientation.
- Type
(\(N_{orientations}\))
numpy.ndarray
- class freud.order.SolidLiquid
Bases:
freud.locality._PairCompute
Identifies solid-like clusters using dot products of \(q_{lm}\).
The solid-liquid order parameter [FHND10, tW95] uses a Steinhardt-like approach to identify solid-like particles. First, a bond parameter \(q_l(i, j)\) is computed for each neighbor bond.
If
normalize_q
is true (default), the bond parameter is given by \(q_l(i, j) = \frac{\sum \limits_{m=-l}^{l} \text{Re}~q_{lm}(i) q_{lm}^*(j)} {\sqrt{\sum \limits_{m=-l}^{l} \lvert q_{lm}(i) \rvert^2} \sqrt{\sum \limits_{m=-l}^{l} \lvert q_{lm}(j) \rvert^2}}\)If
normalize_q
is false, then the denominator of the above expression is left out.Next, the bonds are filtered to keep only “solid-like” bonds with \(q_l(i, j)\) above a cutoff value \(q_{threshold}\).
If a particle has more than \(S_{threshold}\) solid-like bonds, then the particle is considered solid-like. Finally, solid-like particles are clustered.
- Parameters
l (unsigned int) – Spherical harmonic quantum number l.
q_threshold (float) – Value of dot product threshold when evaluating \(q_l(i, j)\) to determine if a bond is solid-like. For \(l=6\), 0.7 is generally good for FCC or BCC structures [FHND10].
solid_threshold (unsigned int) – Minimum required number of adjacent solid-like bonds for a particle to be considered solid-like for clustering. For \(l=6\), 6-8 is generally good for FCC or BCC structures.
normalize_q (bool) – Whether to normalize the dot product (Default value =
True
).
- property cluster_idx
\(\left(N_{particles}\right)\)
numpy.ndarray
: Solid-like cluster indices for each particle.
- property cluster_sizes
The sizes of all clusters.
- Type
\((N_{clusters}, )\)
np.ndarray
- compute(self, system, neighbors=None)
Compute the order parameter.
- Parameters
system – Any object that is a valid argument to
freud.locality.NeighborQuery.from_system
.neighbors (
freud.locality.NeighborList
or dict, optional) –Either a
NeighborList
of neighbor pairs to use in the calculation, or a dictionary of query arguments (Default value: None).
- default_query_args
No default query arguments.
- l
Spherical harmonic quantum number l.
- Type
unsigned int
- property largest_cluster_size
The largest cluster size.
- Type
unsigned int
- property nlist
Neighbor list of solid-like bonds.
- property num_connections
The number of solid-like bonds for each particle.
- Type
\(\left(N_{particles}\right)\)
numpy.ndarray
- property particle_harmonics
\(\left(N_{particles}, 2*l+1\right)\)
numpy.ndarray
: The raw array of overline{q}_{lm}(i). The array is provided in the order given by fsph: \(m = 0, 1, ..., l, -1, ..., -l\).
- plot(self, ax=None)
Plot solid-like cluster distribution.
- Parameters
ax (
matplotlib.axes.Axes
, optional) – Axis to plot on. IfNone
, make a new figure and axis (Default value =None
).- Returns
Axis with the plot.
- Return type
- property ql_ij
Bond dot products \(q_l(i, j)\). Indexed by the elements of
self.nlist
.- Type
\(\left(N_{bonds}\right)\)
numpy.ndarray
- class freud.order.Steinhardt
Bases:
freud.locality._PairCompute
Compute one or more of the rotationally invariant Steinhardt order parameter \(q_l\) or \(w_l\) for a set of points [Ste83].
Implements the local rotationally invariant \(q_l\) or \(w_l\) order parameter described by Steinhardt.
First, we describe the computation of \(q_l(i)\). For a particle \(i\), we calculate the quantity \(q_{lm}\) by summing the spherical harmonics between particle \(i\) and its neighbors \(j\) in a local region:
\[q_{lm}(i) = \frac{1}{N_b} \sum \limits_{j=1}^{N_b} Y_{lm}(\theta(\vec{r}_{ij}), \phi(\vec{r}_{ij}))\]Then the \(q_l\) order parameter is computed by combining the \(q_{lm}\) in a rotationally invariant fashion to remove local orientational order:
\[q_l(i) = \sqrt{\frac{4\pi}{2l+1} \sum \limits_{m=-l}^{l} |q_{lm}(i)|^2 }\]If the
wl
parameter isTrue
, this class computes the quantity \(w_l\), defined as a weighted average over the \(q_{lm}(i)\) values using Wigner 3-j symbols (related to Clebsch-Gordan coefficients). The resulting combination is rotationally invariant:\[\begin{split}w_l(i) = \sum \limits_{m_1 + m_2 + m_3 = 0} \begin{pmatrix} l & l & l \\ m_1 & m_2 & m_3 \end{pmatrix} q_{lm_1}(i) q_{lm_2}(i) q_{lm_3}(i)\end{split}\]If
wl
isTrue
, then setting thewl_normalize
parameter toTrue
will normalize the \(w_l\) order parameter as follows (ifwl=False
,wl_normalize
has no effect):\[\begin{split}w_l(i) = \frac{ \sum \limits_{m_1 + m_2 + m_3 = 0} \begin{pmatrix} l & l & l \\ m_1 & m_2 & m_3 \end{pmatrix} q_{lm_1}(i) q_{lm_2}(i) q_{lm_3}(i)} {\left(\sum \limits_{m=-l}^{l} |q_{lm}(i)|^2 \right)^{3/2}}\end{split}\]If
average
isTrue
, the class computes a variant of this order parameter that performs an average over the first and second shell combined [LD08]. 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 \(q_{lm}(i)\) in the original definition by \(\overline{q}_{lm}(i)\), the average value of \(q_{lm}(k)\) over all the \(N_b\) neighbors \(k\) of particle \(i\), including particle \(i\) itself:\[\overline{q}_{lm}(i) = \frac{1}{N_b} \sum \limits_{k=0}^{N_b} q_{lm}(k)\]If
weighted
is True, the contributions of each neighbor are weighted. Neighbor weights \(w_{ij}\) are defined for afreud.locality.NeighborList
obtained fromfreud.locality.Voronoi
or one with user-provided weights, and default to 1 if not otherwise provided. The formulas are modified as follows, replacing \(q_{lm}(i)\) with the weighted value \(q'_{lm}(i)\):\[q'_{lm}(i) = \frac{1}{\sum_{j=1}^{N_b} w_{ij}} \sum \limits_{j=1}^{N_b} w_{ij} Y_{lm}(\theta(\vec{r}_{ij}), \phi(\vec{r}_{ij}))\]Note
The value of per-particle order parameter will be set to NaN for particles with no neighbors. We choose this value rather than setting the order parameter to 0 because in more complex order parameter calculations (such as when computing the \(w_l\)), it is possible to observe a value of 0 for the per-particle order parameter even with a finite number of neighbors. If you would like to ignore this distinction, you can mask the output order parameter values using NumPy:
numpy.nan_to_num(particle_order)
.- Parameters
l (unsigned int or sequence of unsigned int) – One or more spherical harmonic quantum number l’s used to compute the Steinhardt order parameter.
average (bool, optional) – Determines whether to calculate the averaged Steinhardt order parameter (Default value =
False
).wl (bool, optional) – Determines whether to use the \(w_l\) version of the Steinhardt order parameter (Default value =
False
).weighted (bool, optional) – Determines whether to use neighbor weights in the computation of spherical harmonics over neighbors. If enabled and used with a Voronoi neighbor list, this results in the 3D Minkowski Structure Metrics \(q'_l\) [MKSTM13] (Default value =
False
).wl_normalize (bool, optional) – Determines whether to normalize the \(w_l\) version of the Steinhardt order parameter (Default value =
False
).
- compute(self, system, neighbors=None)
Compute the order parameter.
Example:
>>> box, points = freud.data.make_random_system(10, 100, seed=0) >>> ql = freud.order.Steinhardt(l=6) >>> ql.compute((box, points), {'r_max':3}) freud.order.Steinhardt(l=6, ...)
- Parameters
system – Any object that is a valid argument to
freud.locality.NeighborQuery.from_system
.neighbors (
freud.locality.NeighborList
or dict, optional) –Either a
NeighborList
of neighbor pairs to use in the calculation, or a dictionary of query arguments (Default value: None).
- default_query_args
No default query arguments.
- l
Spherical harmonic quantum number l.
- Type
unsigned int
- property order
The system wide normalization of the order parameter, computed by averaging the \(q_{lm}\) values (or \(\overline{q}_{lm}\) values if
average
is enabled) over all particles before computing the rotationally-invariant order parameter.- Type
- property particle_harmonics
\(\left(N_{particles}, 2l+1\right)\)
numpy.ndarray
: The raw array of \(q_{lm}(i)\). The array is provided in the order given by fsph: \(m = 0, 1, ..., l, -1, ..., -l\).
- property particle_order
\(\left(N_{particles}, N_l \right)\)
numpy.ndarray
: Variant of the Steinhardt order parameter for each particle (filled withnan
for particles with no neighbors).
- plot(self, ax=None)
Plot order parameter distribution.
- Parameters
ax (
matplotlib.axes.Axes
, optional) – Axis to plot on. IfNone
, make a new figure and axis (Default value =None
).- Returns
Axis with the plot.
- Return type
- property ql
\(\left(N_{particles}, N_l\right)\)
numpy.ndarray
: \(q_l\) Steinhardt order parameter for each particle (filled withnan
for particles with no neighbors). This is always available, no matter which other options are selected. It obeys theweighted
argument but otherwise returns the “plain” \(q_l\) regardless ofaverage
,wl
,wl_normalize
.
- class freud.order.Translational
Bases:
freud.locality._PairCompute
Compute the translational order parameter for each particle.
The translational order parameter is used to measure order in the bonds of 2D systems. The translational order parameter for a particle \(i\) and its \(N_b\) neighbors \(j\) is given by a sum over the neighbors, treating the 2D vectors between each pair of particles as a complex number with real part corresponding to the x-component of the vector and imaginary part corresponding to the y-component of the vector, divided by a normalization constant \(k\):
\(\psi\left( i \right) = \frac{1}{k} \sum \limits_{j=1}^{N_b} x_{ij} + y_{ij} i\)
The translational order parameter as written above is complex-valued.
Note
2D:
freud.order.Translational
is only defined for 2D systems. The points must be passed in as[x, y, 0]
.Note
This class is slated for deprecation and will be removed in freud 3.0.
- Parameters
k (float, optional) – Normalization of order parameter (Default value =
6.0
).
- compute(self, system, neighbors=None)
Calculates the local descriptors.
- Parameters
system – Any object that is a valid argument to
freud.locality.NeighborQuery.from_system
.neighbors (
freud.locality.NeighborList
or dict, optional) –Either a
NeighborList
of neighbor pairs to use in the calculation, or a dictionary of query arguments (Default value: None).
- default_query_args
The default query arguments are
{'mode': 'nearest', 'num_neighbors': int(self.k)}
.
- property particle_order
Order parameter.
- Type
\(\left(N_{particles} \right)\)
numpy.ndarray