Coverage for /builds/ase/ase/ase/ga/particle_crossovers.py: 73.91%
115 statements
« prev ^ index » next coverage.py v7.5.3, created at 2025-08-02 00:12 +0000
« prev ^ index » next coverage.py v7.5.3, created at 2025-08-02 00:12 +0000
1# fmt: off
3from itertools import chain
5import numpy as np
7from ase import Atoms
8from ase.ga.offspring_creator import OffspringCreator
11class Crossover(OffspringCreator):
12 """Base class for all particle crossovers.
14 Originally intended for medium sized particles
16 Do not call this class directly."""
18 def __init__(self, rng=np.random):
19 OffspringCreator.__init__(self, rng=rng)
20 self.descriptor = 'Crossover'
21 self.min_inputs = 2
24class CutSpliceCrossover(Crossover):
25 """Crossover that cuts two particles through a plane in space and
26 merges two halfes from different particles together.
28 Implementation of the method presented in:
29 D. M. Deaven and K. M. Ho, Phys. Rev. Lett., 75, 2, 288-291 (1995)
31 It keeps the correct composition by randomly assigning elements in
32 the new particle. If some of the atoms in the two particle halves
33 are too close, the halves are moved away from each other perpendicular
34 to the cutting plane.
36 Parameters:
38 blmin: dictionary of minimum distance between atomic numbers.
39 e.g. {(28,29): 1.5}
41 keep_composition: boolean that signifies if the composition should
42 be the same as in the parents.
44 rng: Random number generator
45 By default numpy.random.
46 """
48 def __init__(self, blmin, keep_composition=True, rng=np.random):
49 Crossover.__init__(self, rng=rng)
50 self.blmin = blmin
51 self.keep_composition = keep_composition
52 self.descriptor = 'CutSpliceCrossover'
54 def get_new_individual(self, parents):
55 f, m = parents
57 indi = self.initialize_individual(f)
58 indi.info['data']['parents'] = [i.info['confid'] for i in parents]
60 theta = self.rng.random() * 2 * np.pi # 0,2pi
61 phi = self.rng.random() * np.pi # 0,pi
62 e = np.array((np.sin(phi) * np.cos(theta),
63 np.sin(theta) * np.sin(phi),
64 np.cos(phi)))
65 eps = 0.0001
67 f.translate(-f.get_center_of_mass())
68 m.translate(-m.get_center_of_mass())
70 # Get the signed distance to the cutting plane
71 # We want one side from f and the other side from m
72 fmap = [np.dot(x, e) for x in f.get_positions()]
73 mmap = [-np.dot(x, e) for x in m.get_positions()]
74 ain = sorted([i for i in chain(fmap, mmap) if i > 0],
75 reverse=True)
76 aout = sorted([i for i in chain(fmap, mmap) if i < 0],
77 reverse=True)
79 off = len(ain) - len(f)
81 # Translating f and m to get the correct number of atoms
82 # in the offspring
83 if off < 0:
84 # too few
85 # move f and m away from the plane
86 dist = (abs(aout[abs(off) - 1]) + abs(aout[abs(off)])) * .5
87 f.translate(e * dist)
88 m.translate(-e * dist)
89 elif off > 0:
90 # too many
91 # move f and m towards the plane
92 dist = (abs(ain[-off - 1]) + abs(ain[-off])) * .5
93 f.translate(-e * dist)
94 m.translate(e * dist)
95 if off != 0 and dist == 0:
96 # Exactly same position => we continue with the wrong number
97 # of atoms. What should be done? Fail or return None or
98 # remove one of the two atoms with exactly the same position.
99 pass
101 # Determine the contributing parts from f and m
102 tmpf, tmpm = Atoms(), Atoms()
103 for atom in f:
104 if np.dot(atom.position, e) > 0:
105 atom.tag = 1
106 tmpf.append(atom)
107 for atom in m:
108 if np.dot(atom.position, e) < 0:
109 atom.tag = 2
110 tmpm.append(atom)
112 # Check that the correct composition is employed
113 if self.keep_composition:
114 opt_sm = sorted(f.numbers)
115 tmpf_numbers = list(tmpf.numbers)
116 tmpm_numbers = list(tmpm.numbers)
117 cur_sm = sorted(tmpf_numbers + tmpm_numbers)
118 # correct_by: dictionary that specifies how many
119 # of the atom_numbers should be removed (a negative number)
120 # or added (a positive number)
121 correct_by = {j: opt_sm.count(j) for j in set(opt_sm)}
122 for n in cur_sm:
123 correct_by[n] -= 1
124 correct_in = tmpf if self.rng.choice([0, 1]) else tmpm
125 to_add, to_rem = [], []
126 for num, amount in correct_by.items():
127 if amount > 0:
128 to_add.extend([num] * amount)
129 elif amount < 0:
130 to_rem.extend([num] * abs(amount))
131 for add, rem in zip(to_add, to_rem):
132 tbc = [a.index for a in correct_in if a.number == rem]
133 if len(tbc) == 0:
134 pass
135 ai = self.rng.choice(tbc)
136 correct_in[ai].number = add
138 # Move the contributing apart if any distance is below blmin
139 maxl = 0.
140 for sv, min_dist in self.get_vectors_below_min_dist(tmpf + tmpm):
141 lsv = np.linalg.norm(sv) # length of shortest vector
142 d = [-np.dot(e, sv)] * 2
143 d[0] += np.sqrt(np.dot(e, sv)**2 - lsv**2 + min_dist**2)
144 d[1] -= np.sqrt(np.dot(e, sv)**2 - lsv**2 + min_dist**2)
145 L = sorted([abs(i) for i in d])[0] / 2. + eps
146 if L > maxl:
147 maxl = L
148 tmpf.translate(e * maxl)
149 tmpm.translate(-e * maxl)
151 # Put the two parts together
152 for atom in chain(tmpf, tmpm):
153 indi.append(atom)
155 parent_message = ':Parents {} {}'.format(f.info['confid'],
156 m.info['confid'])
157 return (self.finalize_individual(indi),
158 self.descriptor + parent_message)
160 def get_numbers(self, atoms):
161 """Returns the atomic numbers of the atoms object using only
162 the elements defined in self.elements"""
163 ac = atoms.copy()
164 if self.elements is not None:
165 del ac[[a.index for a in ac
166 if a.symbol in self.elements]]
167 return ac.numbers
169 def get_vectors_below_min_dist(self, atoms):
170 """Generator function that returns each vector (between atoms)
171 that is shorter than the minimum distance for those atom types
172 (set during the initialization in blmin)."""
173 norm = np.linalg.norm
174 ap = atoms.get_positions()
175 an = atoms.numbers
176 for i in range(len(atoms)):
177 pos = atoms[i].position
178 for j, d in enumerate(norm(k - pos) for k in ap[i:]):
179 if d == 0:
180 continue
181 min_dist = self.blmin[tuple(sorted((an[i], an[j + i])))]
182 if d < min_dist:
183 yield atoms[i].position - atoms[j + i].position, min_dist
185 def get_shortest_dist_vector(self, atoms):
186 norm = np.linalg.norm
187 mind = 10000.
188 ap = atoms.get_positions()
189 for i in range(len(atoms)):
190 pos = atoms[i].position
191 for j, d in enumerate(norm(k - pos) for k in ap[i:]):
192 if d == 0:
193 continue
194 if d < mind:
195 mind = d
196 lowpair = (i, j + i)
197 return atoms[lowpair[0]].position - atoms[lowpair[1]].position