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() >>> sys.read_inputfile('atoms.dat') """ 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 customread = (len(customkeys) > 0) 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: raise IOError("frame %d is not found in the trajectory"%frame) #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: raise IOError("input file %s not found"%filename) #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: raise IOError("input file %s not found"%filename) 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: raise IOError("input file %s not found"%filename) 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 lead to loss of information. """ self.cset_atom(atom)
[docs] 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.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, voroexp=1, padding=1.2, nlimit=6, cells=False): """ 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. padding : double, optional 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.") 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 == '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: raise RuntimeError("Neighbors not found, please find neighbors using the cutoff method") 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] #add atoms 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. See also -------- 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): psys = np.load(file, allow_pickle=True).flatten()[0] 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', customkeys=None, compressed=False, timestep=0): """ 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. customkeys : 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. timestep : int, optional timestep to be written to file. default 0 Returns ------- None Notes ----- """ if customkeys == None: customkeys = [] 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: raise AttributeError("custom key was not found") else: val = atom.custom[ckey] vals.append(val) return vals boxdims = self.box atoms = self.atoms if len(customkeys) > 0: cvals = [get_custom(atom, customkeys) for atom in atoms] #open files for writing if compressed: gz = gzip.open(outfile,'w') dump = io.BufferedReader(gz) else: gz = open(outfile,'w') dump = gz #now write dump.write("ITEM: TIMESTEP\n") dump.write("%d\n" % timestep) 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])) #now write header if len(customkeys) > 0: ckey = " ".join(customkeys) 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(customkeys) > 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()