# Source code for pyscal.core

"""

"""

import pyscal.traj_process as ptp
import pyscal.pickle_object as pp
import os
import numpy as np
import warnings
import pyscal.csystem as pc
from pyscal.catom import Atom
import itertools

#------------------------------------------------------------------------------------------------------------
"""
System class definitions
"""
#------------------------------------------------------------------------------------------------------------

[docs]def test():
"""
A simple function to test if the module works

Parameters
----------
None

Returns
-------
works : bool
True if the module works and could create a System and Atom object
False otherwise.

"""
try:
s = System()
a = Atom()
return True
except:
return False

[docs]class System(pc.System):
"""
A python/pybind11 hybrid class for holding the properties of a system.

Attributes
----------
box : list of list of floats
A list containing the dimensions of the simulation box in the format
[[x_low, x_high], [y_low, y_high], [z_low, z_high]]

atoms : list of :class:~pyscal.catom.Atom objects

Notes
-----
A System consists of two
major components - the simulation box and the atoms. All the associated variables
are then calculated using this class.

.. note::

atoms can be accessed or set as :attr:~pyscal.core.System.atoms. However, due to
technical reasons individual atoms should be accessed using the
:func:~pyscal.core.System.get_atom method. An atom can be assigned
to the atom using the :func:~pyscal.core.System.set_atom method.

Examples
--------
>>> sys = System()
"""
def __init__(self):

self.initialized = True
self.neighbors_found = False
pc.System.__init__(self)

[docs]    def read_inputfile(self, filename, format="lammps-dump", frame=-1, compressed = False, customkeys=None, is_triclinic = False):
"""

Read input file that contains the information of system configuration.

Parameters
----------
filename : string
name of the input file.

format : {'lammps-dump', 'poscar', 'ase', 'mdtraj'}
format of the input file, in case of ase the ASE Atoms object

compressed : bool, optional
If True, force to read a gz compressed format, default False.

frame : int
If the trajectory contains more than one time step, the slice can be specified
using the frame option.

.. note::

works only with lammps-dump format.

customkeys : list
A list containing names of headers of extra data that needs to be read in from the
input file.

is_triclinc : bool, optional
Only used in the case of format='ase'. If the read ase object is triclinic, this
options should be set to True.

Returns
-------
None

Notes
-----
format keyword specifies the format of the input file. Currently only
a lammps-dump and poscar files are supported.  Additionaly, the widely
use Atomic Simulation environment (https://wiki.fysik.dtu.dk/ase/ase/ase.html).
mdtraj objects (http://mdtraj.org/1.9.3/) are also supported by using the keyword
'mdtraj' for format. Please note that triclinic boxes are not yet supported for
mdtraj format.
Atoms object can also be used directly. This function uses the
:func:~pyscal.traj_process module to process a file which is then assigned to system.

compressed keyword is not required if a file ends with .gz extension, it is
automatically treated as a compressed file.

frame keyword allows to read in a particular slice from a long trajectory. If all slices
need to analysed, this is a very inefficient way. For handling multiple time slices,
the :func:~pyscal.traj_process module offers a better set of tools.

Triclinic simulation boxes can also be read in for lammps-dump. No special keyword is
necessary.

If custom_keys are provided, this extra information is read in from input files if
available. This information is not passed to the C++ instance of atom, and is stored
as a dictionary. It can be accessed directly as atom.custom['customval']

"""
if customkeys == None:
customkeys = []

if format == 'lammps-dump':
#check customkeys and assign a variable

if frame != -1:
#split the traj and returns set of filenames
filenames = ptp.split_traj_lammps_dump(filename, compressed=compressed)

#reassign filename
try:
filename = filenames[frame]
except:

#now if file exists
if os.path.exists(filename):
atoms, boxdims, box, triclinic = ptp.read_lammps_dump(filename, compressed=compressed, check_triclinic=True, box_vectors=True, customkeys=customkeys)
self.atoms = atoms
self.box = boxdims

if triclinic:
#we have to input rotation matrix and the inverse rotation matrix
rot = box.T
rotinv = np.linalg.inv(rot)
self.assign_triclinic_params(rot, rotinv)
else:

#now remove filenames
for file in filenames:
os.remove(file)

elif os.path.exists(filename):
atoms, boxdims, box, triclinic = ptp.read_lammps_dump(filename, compressed=compressed, check_triclinic=True, box_vectors=True, customkeys=customkeys)
self.atoms = atoms
self.box = boxdims

if triclinic:
rot = box.T
rotinv = np.linalg.inv(rot)
self.assign_triclinic_params(rot, rotinv)
else:

elif format == 'poscar':
if os.path.exists(filename):
atoms, boxdims, box = ptp.read_poscar(filename, compressed=compressed, box_vectors = True)
self.atoms = atoms
self.box = boxdims
if is_triclinic:
rot = box.T
rotinv = np.linalg.inv(rot)
self.assign_triclinic_params(rot, rotinv)
else:

elif format == 'ase':
atoms, boxdims, box = ptp.read_ase(filename, box_vectors = True)
self.atoms = atoms
self.box = boxdims
if is_triclinic:
rot = box.T
rotinv = np.linalg.inv(rot)
self.assign_triclinic_params(rot, rotinv)

elif format == 'mdtraj':
atoms, boxdims, box = ptp.read_mdtraj(filename, box_vectors = True)
self.atoms = atoms
self.box = boxdims
if is_triclinic:
rot = box.T
rotinv = np.linalg.inv(rot)
self.assign_triclinic_params(rot, rotinv)

else:
raise TypeError("format recieved an unknown option %s"%format)

[docs]    def get_atom(self, index):
"""

Get the :class:~pyscal.catom.Atom object at the queried position in the list of all atoms
in the :class:~pyscal.core.System.

Parameters
----------
index : int
index of required atom in the list of all atoms.

Returns
-------
atom : Atom object
atom object at the queried position.
"""
atom = self.cget_atom(index)
return atom

[docs]    def set_atom(self, atom):
"""
Return the atom to its original location after modification.

Parameters
----------
atom : Atom
atom to be replaced

Returns
-------
None

Notes
-----
For example, an :class:~pyscal.catom.Atom at location i in the list of all atoms in
:class:~pyscal.core.System can be queried by,
atom = System.get_atom(i), then any kind of modification, for example, the
position of the Atom can done by, atom.pos = [2.3, 4.5, 4.5]. After
modification, the Atom can be set back to its position in System by
:func:~pyscal.core.System.set_atom.

Although the complete list of atoms can be accessed or set using atoms = sys.atoms,
get_atom and set_atom functions should be used for accessing individual atoms.
If an atom already exists at that index in the list, it will be overwritten and will

"""
self.cset_atom(atom)

[docs]    def calculate_rdf(self, histobins=100, histomin=0.0, histomax=None):
"""

Parameters
----------
histobins : int
number of bins in the histogram
histomin : float, optional
minimum value of the distance histogram. Default 0.0.

histomax : float, optional
maximum value of the distance histogram. Default, the maximum value
in all pair distances is used.

Returns
-------
rdf : array of ints
r : array of floats

"""
distances = self.get_pairdistances()

if histomax == None:
histomax = max(distances)

hist, bin_edges = np.histogram(distances, bins=histobins, range=(histomin, histomax))
edgewidth = np.abs(bin_edges[1]-bin_edges[0])
hist = hist.astype(float)
r = bin_edges[:-1]

#get box density
boxvecs = self.get_boxvecs()
vol = np.dot(np.cross(boxvecs[0], boxvecs[1]), boxvecs[2])
natoms = self.nop
rho = natoms/vol

shell_vols = (4./3.)*np.pi*((r+edgewidth)**3 - r**3)
shell_rho = hist/shell_vols
#now divide to get final value
rdf = shell_rho/rho

return rdf, r

[docs]    def get_qvals(self, q, averaged = False):
"""
Get the required q_l (Steinhardt parameter) values of all atoms.

Parameters
----------
q_l : int or list of ints
required q_l value with l from 2-12

averaged : bool, optional
If True, return the averaged q values, default False

Returns
-------
qvals : list of floats
list of q_l of all atoms.

Notes
-----
The function returns a list of
q_l values in the same order as the list of the atoms in the system.

"""
if isinstance(q, int):
if q in range(2, 13):
if averaged:
rq = self.cget_aqvals(q)
else:
rq = self.cget_qvals(q)
return rq
else:
raise ValueError("the value of q should be between 2 and 12")

else:
for qq in q:
if not qq in range(2, 13):
raise ValueError("the value of q should be between 2 and 12")
if averaged:
rq = [ self.cget_aqvals(qq) for qq in q ]
else:
rq = [ self.cget_qvals(qq) for qq in q ]
return rq

[docs]    def get_distance(self, atom1, atom2, vector=False):
"""
Get the distance between two atoms.

Parameters
----------
atom1 : Atom object
first atom
atom2 : Atom object
second atom
vector : bool, optional
If True, the displacement vector connecting the atoms
is also returned. default false.

Returns
-------
distance : double
distance between the first and second atom.

Notes
-----
Periodic boundary conditions are assumed by default.
"""
if vector:
displacement_vector = self.get_absdistance_vector(atom1, atom2)
return self.get_absdistance(atom1, atom2), displacement_vector
else:
return self.get_absdistance(atom1, atom2)

[docs]    def find_neighbors(self, method='cutoff', cutoff=None, threshold=2, filter=None,
"""

Find neighbors of all atoms in the :class:~pyscal.core.System.

Parameters
----------
method : {'cutoff', 'voronoi'}
cutoff method finds neighbors of an atom within a specified or adaptive cutoff distance from the atom.
voronoi method finds atoms that share a Voronoi polyhedra face with the atom. Default, cutoff

cutoff : { float, 'sann', 'adaptive'}
the cutoff distance to be used for the cutoff based neighbor calculation method described above.
If the value is specified as 0 or adaptive, adaptive method is used.
If the value is specified as sann, sann algorithm is used.

threshold : float, optional
only used if cutoff=adaptive. A threshold which is used as safe limit for calculation of cutoff.

filter : {'None', 'type'}, optional
apply a filter to nearest neighbor calculation. If the filter keyword is set to
type, only atoms of the same type would be included in the neighbor calculations. Default None.

voroexp : int, optional
only used if method=voronoi. Power of the neighbor weight used to weight the contribution of each atom towards
Steinhardt parameter values. Default 1.

only used if cutoff=adaptive. A safe padding value used after an adaptive cutoff is found. Default 1.2.

nlimit : int, optional
only used if cutoff=adaptive. The number of particles to be considered for the calculation of adaptive cutoff.
Default 6.

Returns
-------
None

Raises
------
RuntimeWarning
raised when threshold value is too low. A low threshold value will lead to 'sann' algorithm not converging
when finding a neighbor. This function will try to automatically increase threshold and check again.

RuntimeError
raised when neighbor search was unsuccessful. This is due to a low threshold value.

Notes
-----
This function calculates the neighbors of each particle. There are several ways to do this. A complete description of
the methods can be found here <https://pyscal.readthedocs.io/en/latest/nearestneighbormethods.html>_.

Method cutoff and specifying a cutoff radius uses the traditional approach being the one in which the neighbors of an atom
are the ones that lie in the cutoff distance around it.

In order to reduce time during the distance sorting during the adaptive methods, pyscal sets an initial guess for a cutoff distance.
This is calculated as,

.. math:: r_{initial} = threshold * (simulation~box~volume/ number~of~particles)^{(1/3)}

threshold is a safe multiplier used for the guess value and can be set using the threshold keyword.

In Method cutoff, if cutoff='adaptive', an adaptive cutoff is found during runtime for each atom [1].
Setting the cutoff radius to 0 also uses this algorithm. The cutoff for an atom i is found using,

.. math:: r_c(i) = padding * ((1/nlimit) * \sum_{j=1}^{nlimit}(r_{ij}))

padding is a safe multiplier to the cutoff distance that can be set through the keyword padding. nlimit keyword sets the
limit for the top nlimit atoms to be taken into account to calculate the cutoff radius.

In Method cutoff, if cutoff='sann', sann algorithm is used [2]. There are no parameters to tune sann algorithm.

The second approach is using Voronoi polyhedra which also assigns a weight to each neighbor in the ratio of the face area between the two atoms.
Higher powers of this weight can also be used [3]. The keyword voroexp
can be used to set this weight.

.. warning::

Adaptive cutoff uses a padding over the intial guessed "neighbor distance". By default it is 2. In case
of a warning that threshold is inadequate, this parameter should be further increased. High/low value
of this parameter will correspond to the time taken for finding neighbors.

References
----------
.. [1] Stukowski, A, Model Simul Mater SC 20, 2012
.. [2] van Meel, JA, Filion, L, Valeriani, C, Frenkel, D, J Chem Phys 234107, 2012
.. [3] Haeberle, J, Sperl, M, Born, P, arxiv 2019

"""
#first reset all neighbors
self.reset_allneighbors()
self.filter = 0

if filter == 'type':
# type corresponds to 1
self.filter = 1

if method == 'cutoff':
if cutoff=='sann':
if threshold < 1:
raise ValueError("value of threshold should be at least 1.00")
self.usecells = (len(self.atoms) > 4000)
finished = self.get_all_neighbors_sann(threshold)
#if it finished without finding neighbors
if not finished:
finallydone = False
for i in range(1,10):
#threshold value is probably too low
#try increasing threshold
warnings.warn("Could not find sann cutoff. trying with a higher threshold", RuntimeWarning)
self.reset_allneighbors()
newfinished = self.get_all_neighbors_sann(threshold*i)
if newfinished:
finallydone = True
warnings.warn("found neighbors with higher threshold than default/user input")
break

if not finallydone:
raise RuntimeError("sann cutoff could not be converged. This is most likely, \
due to a low threshold value. Try increasing it.")
if threshold < 1:
raise ValueError("value of threshold should be at least 1.00")

self.usecells =  (len(self.atoms) > 4000)
if not finished:
raise RuntimeError("Could not find adaptive cutoff")
else:
#warnings.warn("THIS RAN")
self.set_neighbordistance(cutoff)
if len(self.atoms) > 2300:
#if cells:
self.get_all_neighbors_cells()
else:
self.get_all_neighbors_normal()

elif method == 'voronoi':
self.voroexp = int(voroexp)
self.get_all_neighbors_voronoi()

self.neighbors_found = True

[docs]    def reset_neighbors(self):
"""

Reset the neighbors of all atoms in the system.

Parameters
----------
None

Returns
-------
None

Notes
-----
It is used automatically when neighbors are recalculated.

"""
self.reset_allneighbors()

[docs]    def calculate_vorovector(self, edge_cutoff=0.05, area_cutoff=0.01, edge_length=False):
"""
get the voronoi structure identification vector.

Parameters
----------

edge_cutoff : float, optional
cutoff for edge length. Default 0.05.

area_cutoff : float, optional
cutoff for face area. Default 0.01.

edge_length : bool, optional
if True, a list of unrefined edge lengths are returned. Default false.

Returns
-------
vorovector : array like, int
array of the form (n3, n4, n5, n6)

Notes
-----
Returns a vector of the form (n3, n4, n5, n6), where n3 is the number
of faces with 3 vertices, n4 is the number of faces with 4
vertices and so on. This can be used to identify structures [1] [2].

The keywords edge_cutoff and area_cutoff can be used to tune the values to minimise
the effect of thermal distortions. Edges are only considered in the analysis if the
edge_length/sum(edge_lengths) is at least edge_cutoff. Similarly, faces are only
considered in the analysis if the  face_area/sum(face_areas) is at least face_cutoff.

References
----------
.. [1] Finney, JL, Proc. Royal Soc. Lond. A 319, 1970
.. [2] Tanemura, M, Hiwatari, Y, Matsuda, H,Ogawa, T, Ogita, N, Ueda, A. Prog. Theor. Phys. 58, 1977

"""
atoms = self.atoms
for atom in atoms:
#start looping over and eliminating short edges
st = 1
refined_edges = []
edge_lengths = []

for vno in atom.face_vertices:

vphase = atom.vertex_numbers[st:st+vno]
edgecount = 0
dummy_edge_lengths = []

#now calculate the length f each edge
for i in range(-1, len(vphase)-1):
#get pairs of indices
#verts are i, i+1
ipos = atom.vertex_vectors[vphase[i]*3:vphase[i]*3+3]
jpos = atom.vertex_vectors[vphase[i+1]*3:vphase[i+1]*3+3]

#now calculate edge length
edgeln = np.sqrt((ipos[0]-jpos[0])**2 + (ipos[1]-jpos[1])**2 + (ipos[2]-jpos[2])**2)
dummy_edge_lengths.append(edgeln)

edge_lengths.append(dummy_edge_lengths)
st += (vno+1)

#now all the edge lengths are saved
for c, ed in enumerate(edge_lengths):
#normalise the edge lengths
norm = (ed/np.sum(ed))
#apply face area cutoff
if (atom.neighbor_weights[c] > area_cutoff):
#check for edge length cutoff
edgecount = len([cc for cc,x in enumerate(norm) if x > edge_cutoff])
refined_edges.append(edgecount)

#now loop over refined edges and collect n3, n4, n5, n6
vorovector = [0, 0, 0, 0]

for ed in refined_edges:
if ed == 3:
vorovector[0] += 1
elif ed == 4:
vorovector[1] += 1
elif ed == 5:
vorovector[2] += 1
elif ed == 6:
vorovector[3] += 1

atom.edge_lengths = edge_lengths
atom.vorovector = vorovector
self.atoms = atoms

[docs]    def calculate_q(self, q, averaged = False):
"""
Find the Steinhardt parameter q_l for all atoms.

Parameters
----------
q_l : int or list of ints
A list of all Steinhardt parameters to be found from 2-12.

averaged : bool, optional
If True, return the averaged q values, default False

Returns
-------
None

Notes
-----
Enables calculation of the Steinhardt parameters [1] q from 2-12. The type of
q values depend on the method used to calculate neighbors. See the description
:func:~pyscal.core.System.find_neighbors for more details. If the keyword average is set to True,
the averaged versions of the bond order parameter [2] is returned.

References
----------
.. [1] Steinhardt, PJ, Nelson, DR, Ronchetti, M. Phys Rev B 28, 1983
.. [2] Lechner, W, Dellago, C, J Chem Phys, 2013

"""
if isinstance(q, int):
qq = [q]
else:
qq = q

for ql in qq:
if not ql in range(2,13):
raise ValueError("value of q should be between 2 and 13")

self.ccalculate_q(qq)

if averaged:
self.ccalculate_aq(qq)

[docs]    def find_solids(self, bonds=0.5, threshold=0.5, avgthreshold=0.6, cluster=True, q=6, new_algo=False, cutoff=0):
"""
Distinguish solid and liquid atoms in the system.

Parameters
----------
bonds : int or float, optional
Minimum number of solid bonds for an atom to be identified as
a solid if the value is an integer. Minimum fraction of neighbors
of an atom that should be solid for an atom to be solid if the
value is float between 0-1. Default 0.5.

threshold : double, optional
Solid bond cutoff value. Default 0.5.

avgthreshold : double, optional
Value required for Averaged solid bond cutoff for an atom to be identified
as solid. Default 0.6.

cluster : bool, optional
If True, cluster the solid atoms and return the number of atoms in the largest
cluster.

q : int, optional
The Steinhardt parameter value over which the bonds have to be calculated.
Default 6.

Returns
-------
solid : int
Size of the largest solid cluster. Returned only if cluster=True.

Notes
-----
The neighbors should be calculated before running this function.
Check :func:~pyscal.core.System.find_neighbors method.

bonds define the number of solid bonds of an atom to be identified as solid.
Two particles are said to be 'bonded' if [1],

.. math:: s_{ij} = \sum_{m=-6}^6 q_{6m}(i) q_{6m}^*(i) \geq threshold

where threshold values is also an optional parameter.

If the value of bonds is a fraction between 0 and 1, at least that much of an atom's neighbors
should be solid for the atom to be solid.

An additional parameter avgthreshold is an additional parameter to improve solid-liquid distinction.
In addition to having a the specified number of bonds,

.. math::  \langle s_{ij} \\rangle > avgthreshold

also needs to be satisfied. In case another q value has to be used for calculation of S_ij, it can be
set used the q attribute.

References
----------
.. [1] Auer, S, Frenkel, D. Adv Polym Sci 173, 2005

"""
#check if neighbors are found
if not self.neighbors_found:
raise RuntimeError("neighbors should be calculated before finding solid atoms. Run System.find_neighbors.")

if not isinstance(q, int):
raise TypeError("q should be interger value")
else:
if not ((q >= 2 ) and (q <= 12 )):
raise ValueError("Value of q should be between 2 and 12")

if not isinstance(threshold, (int, float)):
raise TypeError("threshold should be a float value")
else:
if not ((threshold >= 0 ) and (threshold <= 1 )):
raise ValueError("Value of threshold should be between 0 and 1")

if not isinstance(avgthreshold, (int, float)):
raise TypeError("avgthreshold should be a float value")
else:
if not ((avgthreshold >= 0 ) and (avgthreshold <= 1 )):
raise ValueError("Value of avgthreshold should be between 0 and 1")

#start identification routine
#check the value of bonds and set criteria depending on that
if isinstance(bonds, int):
self.criteria = 0
elif isinstance(bonds, float):
if ((bonds>=0) and (bonds<=1.0)):
self.criteria = 1
else:
raise TypeError("bonds if float should have value between 0-1")
else:
raise TypeError("bonds should be interger/float value")

#Set the vlaue of q
self.solidq = q
#first calculate q
self.ccalculate_q([q])
#self.calculate_q(6)
#calculate solid neighs
self.set_nucsize_parameters(bonds, threshold, avgthreshold)
self.calculate_frenkelnumbers()
#now find solids
self.find_solid_atoms()

if cluster:
def ccondition(atom):
return atom.solid

lc = self.cluster_atoms(ccondition, largest=True, new_algo=new_algo, cutoff=cutoff)
return lc

[docs]    def cluster_atoms(self, condition, largest = True, new_algo = False, cutoff=0):
"""
Cluster atoms based on a property

Parameters
----------
condition : callable
function which should take an :class:~Atom object, and give a True/False output

largest : bool, optional
If True returns the size of the largest cluster. Default False.

Returns
-------
lc : int
Size of the largest cluster. Returned only if largest is True.

Notes
-----
This function helps to cluster atoms based on a defined property. This property
is defined by the user through the function condition which is passed as a parameter.
For each atom in the system, the condition should give a True/False values.

When clustering, the code loops over each atom and its neighbors. If the
condition is true for both host atom and the neighbor, they are assigned to
the same cluster. For example, a condition to cluster solid atoms would be,

.. code:: python

def condition(atom):
#if both atom is solid
return (atom1.solid)

Check examples for more details.

"""
testatom = self.atoms[0]

#test the condition
try:
out = condition(testatom)
if out not in [True, False]:
raise RuntimeError("The output of condition should be either True or False. Received %s"%str(out))
except:
raise RuntimeError("condition did not work")

#now loop
atoms = self.atoms
for atom in atoms:
cval = condition(atom)
atom.condition = cval
self.atoms = atoms

self.cfind_clusters_recursive(cutoff)

#done!
lc = self.find_largest_cluster()
#pcs.System.get_largest_cluster_atoms(self)

if largest:
return lc

[docs]    def calculate_solidneighbors(self):
"""
Find Solid neighbors of all atoms in the system.

Parameters
----------
None

Returns
-------
None

Notes
-----
A solid bond is considered between two atoms if the connection <https://pyscal.readthedocs.io/en/latest/solidliquid.html>_
between them is greater than 0.6.

"""
self.calculate_frenkelnumbers()

[docs]    def find_clusters(self, recursive = True, largest = True):
"""
Find the clusters of all atoms in the system.

Parameters
----------
recursive : Bool, optional
If True, use a recursive clustering algorithm, otherwise use an id based clustering.
The difference in values between two methods can be upto 3 particles. Default True.

largest : Bool, optional
If True, return the number of particles in the largest cluster. Default True.

Returns
-------
cluster : int
The size of the largest cluster in the system. Only returned if largest is set to True.

Notes
-----
Go through all the atoms in the system and cluster them together based on the issolid parameter of the atom.
To cluster based on any user defined criteria, you can use set_solid method of Atom to explicitely
set the issolid value.

.. warning::

This function is deprecated and will be removed in a future release. Please use
:func:~System.cluster_atoms instead.

"""
warnings.simplefilter('always', DeprecationWarning)
warnings.warn("This function is deprecated - use cluster_atoms instead", DeprecationWarning)

if recursive:
self.cfind_clusters_recursive()
else:
self.cfind_clusters()

if largest:
cluster = self.find_largest_cluster()
return cluster

[docs]    def find_largestcluster(self):
"""
Find the largest solid cluster of atoms in the system from all the clusters.

Parameters
----------
None

Returns
-------
cluster : int
the size of the largest cluster

Notes
-----
:func:pyscal.core.System.find_clusters has to be used before using this function.

"""
return self.find_largest_cluster()

[docs]    def calculate_angularcriteria(self):
"""
Calculate the angular criteria for each atom
Parameters
----------
None

Returns
-------
None

Notes
-----
Calculates the angular criteria for each atom as defined in [1]_. Angular criteria is
useful for identification of diamond cubic structures. Angular criteria is defined by,

.. math::

A = \sum_{i=1}^6 (\cos(\\theta_i) + \\frac{1}{3})^2

where cos(theta) is the angle size suspended by each pair of neighbors of the central
atom. A will have a value close to 0 for structures if the angles are close to 109 degrees.
The calculated A parameter for each atom is stored in :attr:~pyscal.catom.Atom.angular.

References
----------
.. [1] Uttormark, MJ, Thompson, MO, Clancy, P, Phys. Rev. B 47, 1993
"""

atoms = self.atoms

for atom in atoms:
dists = []
distneighs = []
distvectors = []

neighs = atom.neighbors

for neigh in neighs:
dist, vectors = self.get_distance(atom, atoms[neigh], vector=True)
dists.append(dist)
distneighs.append(neigh)
distvectors.append(vectors)

args = np.argsort(dists)
#find top four
topfourargs = np.array(args)[:4]

combos = list(itertools.combinations(topfourargs, 2))
costhetasum = 0

for combo in combos:
vec1 = distvectors[combo[0]]
vec2 = distvectors[combo[1]]
modvec1 = np.sqrt(np.sum([x**2 for x in vec1]))
modvec2 = np.sqrt(np.sum([x**2 for x in vec2]))
costheta = np.dot(vec1, vec2)/(modvec1*modvec2)
costhetasum += (costheta +(1./3.))**2
atom.angular = costhetasum

self.atoms = atoms

[docs]    def calculate_chiparams(self, angles=False):
"""

Calculate the chi param vector for each atom

Parameters
----------
angles : bool, optional
If True, return the list of cosines of all neighbor pairs

Returns
-------
angles : array of floats
list of all cosine values, returned only if angles is True.

Notes
-----
This method tries to distinguish between crystal structures by finding the cosines of angles
formed by an atom with its neighbors. These cosines are then historgrammed with bins
[-1.0, -0.945, -0.915, -0.755, -0.705, -0.195, 0.195, 0.245, 0.795, 1.0] to find a vector for
each atom that is indicative of its local coordination. Compared to chi parameters from chi_0 to
chi_7 in the associated publication, the vector here is from chi_0 to chi_8. This is due to an additional
chi parameter which measures the number of neighbors between cosines -0.705 to -0.195.

Parameter nlimit specifies the number of nearest neighbors to be included in the analysis to find the cutoff.
If parameter angles is true, an array of all cosine values is returned. The publication further provides
combinations of chi parameters for structural identification which is not implemented here. The calculated
chi params can be accessed using :attr:~pyscal.catom.chiparams.

References
----------
.. [1] Ackland, Jones, Phys. Rev. B 73, 2006

"""

bins = [-1.0, -0.945, -0.915, -0.755, -0.705, -0.195, 0.195, 0.245, 0.795, 1.0]
atoms = self.atoms

for atom in atoms:
dists = []
distneighs = []
distvectors = []

neighs = atom.neighbors

for neigh in neighs:
dist, vectors = self.get_distance(atom, atoms[neigh], vector=True)
dists.append(dist)
distneighs.append(neigh)
distvectors.append(vectors)

args = np.argsort(dists)
topargs = np.array(args)

combos = list(itertools.combinations(topargs, 2))
costhetas = []
for combo in combos:
vec1 = distvectors[combo[0]]
vec2 = distvectors[combo[1]]
modvec1 = np.sqrt(np.sum([x**2 for x in vec1]))
modvec2 = np.sqrt(np.sum([x**2 for x in vec2]))
costheta = np.dot(vec1, vec2)/(modvec1*modvec2)
#found costheta
costhetas.append(costheta)

#now add according to classification in paper
chivector = np.histogram(costhetas, bins=bins)
atom.chiparams = chivector[0]
if angles:
atom.custom['cosines'] = costhetas

self.atoms = atoms

[docs]    def calculate_disorder(self, averaged=False, q=6):
"""
Calculate the disorder criteria for each atom

Parameters
----------
averaged : bool, optional
If True, calculate the averaged disorder. Default False.

q : int, optional
The Steinhardt parameter value over which the bonds have to be calculated.
Default 6.

Returns
-------
None

Notes
-----
Calculate the disorder criteria as introduced in [1]. The disorder criteria value for each atom is defined by,

.. math::

D_j = \\frac{1}{N_b^j} \sum_{i=1}^{N_b} [ S_{jj} + S_{kk} -2S_{jk}]

where .. math:: S_{ij} = \sum_{m=-6}^6 q_{6m}(i) q_{6m}^*(i)

The keyword averaged is True, the disorder value is averaged over the atom and its neighbors. The disorder value
can be accessed using :attr:~pyscal.catom.disorder and the averaged version can be accessed using
:attr:~pyscal.catom.avg_disorder. For ordered systems, the value of disorder would be zero which would increase
and reach one for disordered systems.

References
----------
.. [1] Kawasaki, T, Onuki, A, J. Chem. Phys. 135, 2011

"""
#now routine for calculation of disorder
if q in range(2,13):
self.solidq = q
else:
raise ValueError("q values should be between 2-12")

self.ccalculate_disorder()

if averaged:
self.ccalculate_avg_disorder()

[docs]    def calculate_sro(self, reference_type=1, average=True, shells=2):
"""
Calculate short range order

Parameters
----------
reference_type: int, optional
type of the atom to be used a reference. default 1

average: bool, optional
if True, average over all atoms of the reference type in the system.
default True.

Returns
-------
vec: list of float
The short range order averaged over the whole system for atom of
the reference type. Only returned if average is True. First value is SRO
of the first neighbor shell and the second value corresponds to the second
nearest neighbor shell.

Notes
-----
Calculates the short range order for an AB alloy using the approach by
Cowley [1]. Short range order is calculated as,

.. math::

\\alpha_i = 1 - \\frac{n_i}{m_A c_i}

where n_i is the number of atoms of the non reference type among the c_i atoms
in the ith shell. m_A is the concentration of the non reference atom. Please
note that the value is calculated for shells 1 and 2 by default. In order for
this to be possible, neighbors have to be found first using the :func:~pyscal.core.System.find_neighbors
method. The selected neighbor method should include the second shell as well. For this
purpose method=cutoff can be chosen with a cutoff long enough to include the second
shell. In order to estimate this cutoff, one can use the :func:~pyscal.core.System.calculate_rdf
method.

References
----------
.. [1] Cowley J. M., PR 77(5), 1950.

"""
if not self.neighbors_found:
if not reference_type in [1,2]:
raise ValueError("reference atom type should be either 1 or 2")

atoms = self.atoms

try:
type1 = len([1 for atom in atoms if atom.type == 1])
type2 = len([1 for atom in atoms if atom.type == 2])
concv = [type1, type2]
except:
raise RuntimeError("There should be two atom types")
if not ((type1>0) and (type2>0)):
raise RuntimeError("There should be two atom types")

mref = concv[reference_type-1]/float(np.sum(concv))
motr = 1 - mref

for atom in atoms:
if atom.type == reference_type:
neighs = atom.neighbors
#get all neighbor distances
distances = [self.get_distance(atom, atoms[n]) for n in neighs]
avgdistance = np.mean(distances)
distsplit = avgdistance/2.00
#find two shells of atoms
if shells == 2:
set1 = [neighs[n] for n, dist in enumerate(distances) if dist <= avgdistance]
set2 = [neighs[n] for n, dist in enumerate(distances) if dist > avgdistance]
#now evaluate types of atoms
set1ref = np.sum([1 for n in set1 if atoms[n].type == reference_type])
set1otr = len(set1) - set1ref
set2ref = np.sum([1 for n in set2 if atoms[n].type == reference_type])
set2otr = len(set2) - set2ref
#now calculate values
shell1 = 1 - (set1otr/(len(set1)*motr))
shell2 = 1 - (set2otr/(len(set2)*motr))
atom.sro = [shell1, shell2]
elif shells == 1:
set1ref = np.sum([1 for n in neighs if atoms[n].type == reference_type])
set1otr = len(neighs) - set1ref
shell1 = 1 - (set1otr/(len(neighs)*motr))
atom.sro = [shell1]

self.atoms = atoms
#now if avg is reqd, find it
if average:
vec = np.zeros(2)
count = 0
for atom in atoms:
if atom.type == reference_type:
vec += np.array(atom.sro)
count += 1
return vec/float(count)

[docs]    def prepare_pickle(self):
"""
Prepare the system for pickling and create a picklable system

Parameters
----------
None

Returns
-------
psys : picklable system object

Notes
-----
This function prepares the system object for pickling. From
a user perspective, the :func:~pyscal.core.System.to_pickle method should be used
directly.

--------
to_file()

"""

#get the basic system indicators
indicators = self.get_indicators()
#get box dims and triclinic params if triclinic
box = self.box
if indicators[6] == 1:
rot = self.get_triclinic_params()
else:
rot = 0

#now finally get atoms
atoms = self.atoms
#convert them to picklabale atoms
patoms = [pp.pickle_atom(atom) for atom in atoms]

#create System instance and assign things
psys = pp.pickleSystem()
psys.indicators = indicators
psys.atoms = patoms
psys.box = box
psys.rot = rot

return psys

[docs]    def to_pickle(self, file):
"""
Save a system to pickle file

Parameters
----------
file : string
name of output file

Returns
-------
None

Notes
-----
This function can be used to save a :class:~pyscal.core.System object directly to
file. This retains all the calculated quantities of the system,
including the atoms and their properties. This can be useful to
restart the calculation. The function uses numpy.save method
to save the information. Hence pickling between different versions
of python could lead to issues.

.. warning::

Pickling between different versions of numpy or python could be incompatible.
Pickling is not secure. You should only unpickle objects that you trust.

"""
psys = self.prepare_pickle()
np.save(file, psys, allow_pickle=True)

[docs]    def from_pickle(self, file):
"""
Read the contents of :class:~pyscal.core.System object from a
pickle file.

Parameters
----------
file : string
name of input file

Returns
-------
None

Notes
-----
This function can be used to set up a system
from a file. A :class:~pyscal.core.System object needs to be created first.

Examples
--------

>>> sys = System()
>>> sys.from_pickle(filename)

"""
if os.path.exists(file):
else:
raise IOError("file does not exist")
#set up indicators
self.set_indicators(psys.indicators)
#unpickle atoms
self.atoms = [pp.unpickle_atom(atom) for atom in psys.atoms]
self.box = psys.box

#if triclinic, get those
if psys.indicators[6] == 1:
rot = psys.rot
rotinv = np.linalg.inv(rot)
self.assign_triclinic_params(rot, rotinv)

[docs]    def to_file(self, outfile, format='lammps-dump', custom=None, compressed=False):
"""
Save the system instance to a trajectory file.

Parameters
----------
outfile : string
name of the output file

format : string, optional
format of the output file, default lammps-dump
Currently only lammps-dump format is supported.

custom : list of strings, optional
a list of extra atom wise values to be written in the output file.

compressed : bool, optional
If true, the output is written as a compressed file.

Returns
-------
None

Notes
-----

"""
if custom == None:
custom = []

def get_custom(atom, customkeys):
#first option - maybe it appears
vals = []
for ckey in customkeys:
#if the key is there - ignore it
if not ckey in atom.custom.keys():
#try to get from attribute
try:
val = getattr(atom, ckey)
vals.append(val)

except AttributeError:
#since attr failed, check if they are q or aq values
if ckey[0] == 'q':
qkey = ckey[1:]
#try to acess this value
val = atom.get_q(int(qkey))
#add this pair to dict - as string vals
vals.append(val)

elif ckey[:2] == 'aq':
qkey = ckey[2:]
val = atom.get_q(int(qkey), averaged=True)
vals.append(val)

else:
else:
val = atom.custom[ckey]
vals.append(val)
return vals

boxdims = self.box
atoms = self.atoms

if len(custom) > 0:
cvals = [get_custom(atom, custom) for atom in atoms]

#open files for writing
if compressed:
gz = gzip.open(outfile,'w')
else:
gz = open(outfile,'w')
dump = gz

#now write
dump.write("ITEM: TIMESTEP\n")
dump.write("0\n")
dump.write("ITEM: NUMBER OF ATOMS\n")
dump.write("%d\n" % len(atoms))
dump.write("ITEM: BOX BOUNDS\n")
dump.write("%f %f\n" % (boxdims[0][0], boxdims[0][1]))
dump.write("%f %f\n" % (boxdims[1][0], boxdims[1][1]))
dump.write("%f %f\n" % (boxdims[2][0], boxdims[2][1]))

if len(custom) > 0:
ckey = " ".join(custom)
title_str = "ITEM: ATOMS id type x y z %s\n"% ckey
else:
title_str = "ITEM: ATOMS id type x y z\n"

dump.write(title_str)

for cc, atom in enumerate(atoms):
pos = atom.pos
if len(custom) > 0:
cval_atom = " ".join(np.array(list(cvals[cc])).astype(str))
atomline = ("%d %d %f %f %f %s\n")%(atom.id, atom.type, pos[0], pos[1], pos[2], cval_atom)
else:
atomline = ("%d %d %f %f %f\n")%(atom.id, atom.type, pos[0], pos[1], pos[2])

dump.write(atomline)

dump.close()