/
core.py
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/
core.py
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"""
"""
import pyscal.traj_process as ptp
import pyscal.routines as routines
import os
import numpy as np
import warnings
import pyscal.csystem as pc
from pyscal.catom import Atom
import itertools
from ase.io import write
import uuid
import gzip
import io
import pyscal.visualization as pv
#------------------------------------------------------------------------------------------------------------
"""
System class definitions
"""
#------------------------------------------------------------------------------------------------------------
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
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()
>>> sys.read_inputfile('atoms.dat')
"""
def __init__(self):
self.initialized = True
self.neighbors_found = False
self.neighbor_method = None
self.ghosts_created = False
self.actual_box = None
pc.System.__init__(self)
@property
def box(self):
"""
Wrap for inbuilt box
"""
if self.actual_box is not None:
return self.actual_box
else:
return self._box
@box.setter
def box(self, userbox):
"""
Box setter
"""
#we should automatically check for triclinic cells here
summ = 0
for i in range(3):
box1 = np.array(userbox[i-1])
box2 = np.array(userbox[i])
summ += np.dot(box1, box2)/(np.linalg.norm(box1)*np.linalg.norm(box2))
#check if the summ is zero
if np.abs(summ) > 0:
#this is a triclinic box
rot = np.array(userbox).T
rotinv = np.linalg.inv(rot)
self.assign_triclinic_params(rot, rotinv)
self._box = userbox
@property
def atoms(self):
"""
Atom access
"""
return self.get_atoms()
@atoms.setter
def atoms(self, atoms):
"""
Set atoms
"""
if(len(atoms) < 200):
#we need to estimate a rough idea
needed_atoms = 200 - len(atoms)
#get a rough cell
needed_cells = np.ceil(needed_atoms/len(atoms))
nx = int(needed_cells**(1/3))
nx = int(np.ceil(nx/2))
if np.sum(self.box) == 0:
raise ValueError("Simulation box should be initialized before atoms")
atoms = self.repeat((nx, nx, nx), atoms=atoms, ghost=True, scale_box=True)
self.set_atoms(atoms)
def read_inputfile(self, filename, format="lammps-dump", compressed = False, customkeys=None):
"""
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.
customkeys : list
A list containing names of headers of extra data that needs to be read in from the
input file.
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.
Triclinic simulation boxes can also be read in.
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']`
"""
atoms, box = ptp.read_file(filename, format=format,
compressed=compressed, customkeys=customkeys,
)
self.box = box
self.atoms = atoms
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
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
lead to loss of information.
"""
self.cset_atom(atom)
def calculate_rdf(self, histobins=100, histomin=0.0, histomax=None):
"""
Calculate the radial distribution function.
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
Radial distribution function
r : array of floats
radius in distance units
"""
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.box
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
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
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)
def find_neighbors(self, method='cutoff', cutoff=None, threshold=2, filter=None,
voroexp=1, padding=1.2, nlimit=6, cells=False,
nmax=12, assign_neighbor=True):
"""
Find neighbors of all atoms in the :class:`~pyscal.core.System`.
Parameters
----------
method : {'cutoff', 'voronoi', 'number'}
`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`
`number` method finds a specified number of closest neighbors to the given atom. Number only populates
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.
padding : double, optional
only used if ``cutoff=adaptive`` or ``cutoff=number``. 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.
nmax : int, optional
only used if ``cutoff=number``. The number of closest neighbors to be found for each atom. Default 12
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.
If method os `number`, instead of using a cutoff value for finding neighbors, a specified number of closest atoms are
found. This number can be set through the argument `nmax`.
.. warning::
Adaptive and number 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.")
elif cutoff=='adaptive' or cutoff==0:
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_adaptive(threshold, nlimit, padding)
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 == 'number':
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_bynumber(threshold, nmax, assign_neighbor)
if not finished:
raise RuntimeError("Could not find enough neighbors - try increasing threshold")
elif method == 'voronoi':
self.voroexp = int(voroexp)
#copy the simulation cell
backupbox = self._box.copy()
if self.triclinic:
self.embed_in_cubic_box()
#self.embed_in_cubic_box()
self.get_all_neighbors_voronoi()
#replace box
self.box = backupbox
self.neighbors_found = True
def find_diamond_neighbors(self):
"""
Find underlying fcc lattice in diamond
Parameters
----------
None
Returns
-------
None
Notes
-----
This method finds in the underlying fcc/hcp lattice in diamond. It works
by the method described in `this publication <http://dx.doi.org/10.1016/j.cpc.2016.04.001>`_ .
For each atom, 4 atoms closest to it are identified. The neighbors of the its neighbors
are further identified and the common neighbors shared with the host atom are selected.
These atom will fall in the underlying fcc lattice for cubic diamond or hcp lattice
for hexagonal lattice.
If neighbors are previously calculated, they are reset when this method is used.
"""
self.reset_neighbors()
self.find_neighbors(method="number", nmax=4, assign_neighbor=False)
self.get_diamond_neighbors()
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()
self.neighbors_found = False
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
def calculate_q(self, q, averaged = False, only_averaged=False, condition=None, clear_condition=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
only_averaged : bool, optional
If True, only calculate the averaged part. default False
condition : callable or atom property
Either function which should take an :class:`~Atom` object, and give a True/False output
or an attribute of atom class which has value or 1 or 0.
clear_condition: bool, optional
clear the `condition` variable for all atoms
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. If only the averaged
versions need to be calculated, `only_averaged` keyword can be set to False.
The neighbors over which the q values are calculated can also be filtered. This is done
through the argument `condition` which is passed as a parameter.
`condition` can be of two types. The first type is a function which takes an
:class:`~Atom` object and should give a True/False value. `condition` can also be an
:class:`~Atom` attribute or a value from `custom` values stored in an atom. See
:func:`~pyscal.core.System.cluster_atoms` for more details. If the
`condition` is equal for both host atom and the neighbor, the neighbor is considered for
calculation of q parameters. This is slightly different from :func:`~pyscal.core.System.cluster_atoms`
where the condition has to be True for both atoms. `condition` is only cleared when neighbors are
recalculated. Additionally, the keyword `clear_condition` can also be used to clear the condition
and reset it to 0. By default, `condition` is applied to both unaveraged and averaged q parameter
calculation. If `condition` is needed for only averaged q parameters, this function can be called
twice, initially without `condition` and `averaged=False`, and then with a condition specified
and `averaged=True`. This way, the `condition` will only be applied to the averaged q calculation.
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")
#test the condition
if condition is not None:
testatom = self.atoms[0]
isatomattr = False
try:
out = condition(testatom)
if out not in [True, False, 0, 1]:
raise RuntimeError("The output of condition should be either True or False. Received %s"%str(out))
except:
try:
out = self.get_custom(testatom, [condition])[0]
if out not in [True, False, 0, 1]:
raise RuntimeError("The output of condition should be either True or False. Received %s"%str(out))
isatomattr = True
except:
raise RuntimeError("condition did not work")
#now loop
atoms = self.atoms
if isatomattr:
for atom in atoms:
atom.condition = self.get_custom(atom, [condition])[0]
else:
for atom in atoms:
cval = condition(atom)
atom.condition = cval
self.atoms = atoms
if clear_condition:
atoms = self.atoms
for atom in atoms:
atom.condition = 0
self.atoms = atoms
if not only_averaged:
self.ccalculate_q(qq)
if averaged or only_averaged:
self.ccalculate_aq(qq)
def find_solids(self, bonds=0.5, threshold=0.5, avgthreshold=0.6,
cluster=True, q=6, cutoff=0, right=True):
"""
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.
cutoff : double, optional
Separate value used for cluster classification. If not specified, cutoff used
for finding neighbors is used.
right: bool, optional
If true, greater than comparison is to be used for finding solid particles.
default True.
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. In the above formulations, `>` comparison for `threshold` and `avgthreshold`
can be changed to `<` by setting the keyword `right` to False.
If `cluster` is True, a clustering is done for all solid particles. See :func:`~pyscal.csystem.find_clusters`
for more details.
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:
lc = self.cluster_atoms("solid", largest=True, cutoff=cutoff)
return lc
def set_atom_cutoff(self, factor=1.00):
"""
Set cutoff for each atom
Parameters
----------
factor : float, optional
factor for multiplication of cutoff value.
default 1
Returns
-------
None
Notes
-----
Assign cutoffs for each atom based on the nearest
neighbor distance. The cutoff assigned is the average nearest
neighbor distance multiplied by `factor`.
"""
self.cset_atom_cutoff(factor)
def cluster_atoms(self, condition, largest = True, cutoff=0):
"""
Cluster atoms based on a property
Parameters
----------
condition : callable or atom property
Either function which should take an :class:`~Atom` object, and give a True/False output
or an attribute of atom class which has value or 1 or 0.
largest : bool, optional
If True returns the size of the largest cluster. Default False.
cutoff : float, optional
If specified, use this cutoff for calculation of clusters. By default uses the cutoff
used for neighbor calculation.
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 argument `condition` which is passed as a parameter.
`condition` can be of two types. The first type is a function which takes an
:class:`~Atom` object and should give a True/False value. `condition` can also be an
:class:`~Atom` attribute or a value from `custom` values stored in an atom.
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)
The same can be done by passing `"solid"` as the condition argument instead of the above
function. Passing a function allows to evaluate complex conditions, but is slower than
passing an attribute.
"""
testatom = self.get_atom(0)
#test the condition
isatomattr = False
try:
out = condition(testatom)
if out not in [True, False, 0, 1]:
raise RuntimeError("The output of condition should be either True or False. Received %s"%str(out))
except: