amd._nearest_neighbours._distkey() - Code Metrics - Inspection of "scrutinizer" - dwiddo/average-minimum-distance - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Branch — master (3a171e)

by Daniel

created 2022-05-17 01:34 UTC

amd._nearest_neighbours._distkey() A

↳ Parent: amd._nearest_neighbours

Complexity

Conditions

Size

Total Lines	6
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	6
dl	0
loc	6
rs	10
c	0
b	0
f	0
cc	2
nop	1

"""Implements core function nearest_neighbours used for AMD and PDD calculations."""

from typing import Iterable, Optional
import itertools
import collections

import numba
import numpy as np
import scipy.spatial


@numba.njit()
def _dist(xy, z):
    s = z ** 2
    for val in xy:
        s += val ** 2
    return s

@numba.njit()
def _distkey(pt):
    s = 0
    for val in pt:
        s += val ** 2
    return s


def generate_integer_lattice(dims: int) -> Iterable[np.ndarray]:
    """Generates batches of integer lattice points.

    Each yield gives all points (that have not already been yielded)
    inside a sphere centered at the origin with radius d. d starts at 0
    and increments by 1 on each loop.

    Parameters
    ----------
    dims : int
        The dimension of Euclidean space the lattice is in.

    Yields
    -------
    ndarray
        Yields arrays of integer points in dims dimensional Euclidean space.
    """

    ymax = collections.defaultdict(int)
    d = 0

    if dims == 1:
        yield np.array([[0]])
        while True:
            d += 1
            yield np.array([[-d], [d]])

    while True:

        # get integer lattice points in +ve directions
        positive_int_lattice = []
        while True:
            batch = []
            for xy in itertools.product(range(d+1), repeat=dims-1):
                if _dist(xy, ymax[xy]) <= d**2:
                    batch.append((*xy, ymax[xy]))
                    ymax[xy] += 1
            if not batch:
                break
            positive_int_lattice += batch
        positive_int_lattice.sort(key=_distkey)

        # expand +ve integer lattice to full lattice with reflections
        int_lattice = []
        for p in positive_int_lattice:
            int_lattice.append(p)
            for n_reflections in range(1, dims+1):
                for indexes in itertools.combinations(range(dims), n_reflections):
                    if all((p[i] for i in indexes)):

                        p_ = list(p)
                        for i in indexes:
                            p_[i] *= -1
                        int_lattice.append(p_)

        yield np.array(int_lattice)
        d += 1


def generate_concentric_cloud(
        motif: np.ndarray,
        cell: np.ndarray
) -> Iterable[np.ndarray]:
    """
    Generates batches of points from a periodic set given by (motif, cell)
    which get successively further away from the origin.

    Each yield gives all points (that have not already been yielded) which
    lie in a unit cell whose corner lattice point was generated by
    _generate_integer_lattice(motif.shape[1]).

    Parameters
    ----------
    motif : ndarray
        Cartesian representation of the motif, shape (no points, dims).
    cell : ndarray
        Cartesian representation of the unit cell, shape (dims, dims).

    Yields
    -------
    ndarray
        Yields arrays of points from the periodic set.
    """

    int_lattice_generator = generate_integer_lattice(cell.shape[0])

    while True:
        int_lattice = next(int_lattice_generator) @ cell

        yield np.concatenate([motif + translation for translation in int_lattice])


def nearest_neighbours(
        motif: np.ndarray,
        cell: np.ndarray,
        k: int,
        asymmetric_unit: Optional[np.ndarray] = None):
    """
    Given a periodic set represented by (motif, cell) and an integer k, find
    the k nearest neighbours of the motif points in the periodic set.

    Note that cloud and inds are not used yet but may be in the future.

    Parameters
    ----------
    motif : ndarray
        Cartesian coords of the full motif, shape (no points, dims).
    cell : ndarray
        Cartesian coords of the unit cell, shape (dims, dims).
    k : int
        Number of nearest neighbours to find for each motif point.
    asymmetric_unit : ndarray, optional
        Indices pointing to an asymmetric unit in motif.

    Returns
    -------
    pdd : ndarray
        An array shape (motif.shape[0], k) of distances from each motif
        point to its k nearest neighbours in order. Points do not count
        as their own nearest neighbour. E.g. the distance to the n-th
        nearest neighbour of the m-th motif point is pdd[m][n].
    cloud : ndarray
        The collection of points in the periodic set that were generated
        during the nearest neighbour search.
    inds : ndarray
        An array shape (motif.shape[0], k) containing the indices of
        nearest neighbours in cloud. E.g. the n-th nearest neighbour to
        the m-th motif point is cloud[inds[m][n]].
    """

    if asymmetric_unit is not None:
        asym_unit = motif[asymmetric_unit]
    else:
        asym_unit = motif

    cloud_generator = generate_concentric_cloud(motif, cell)
    n_points = 0
    cloud = []
    while n_points <= k:
        l = next(cloud_generator)
        n_points += l.shape[0]
        cloud.append(l)
    cloud.append(next(cloud_generator))
    cloud = np.concatenate(cloud)

    tree = scipy.spatial.KDTree(cloud,
                                compact_nodes=False,
                                balanced_tree=False)
    pdd_, inds = tree.query(asym_unit, k=k+1, workers=-1)
    pdd = np.zeros_like(pdd_)

    while not np.allclose(pdd, pdd_, atol=1e-12, rtol=0):
        pdd = pdd_
        cloud = np.vstack((cloud,
                           next(cloud_generator),
                           next(cloud_generator)))
        tree = scipy.spatial.KDTree(cloud,
                                    compact_nodes=False,
                                    balanced_tree=False)
        pdd_, inds = tree.query(asym_unit, k=k+1, workers=-1)

    return pdd_[:, 1:], cloud, inds[:, 1:]


def nearest_neighbours_minval(motif, cell, min_val):
    """PDD large enough to be reconstructed from
    (such that last col values all > 2 * diam(cell))."""

    cloud_generator = generate_concentric_cloud(motif, cell)

    cloud = []
    for _ in range(3):
        cloud.append(next(cloud_generator))

    cloud = np.concatenate(cloud)
    tree = scipy.spatial.KDTree(cloud,
                                compact_nodes=False,
                                balanced_tree=False)
    pdd_, _ = tree.query(motif, k=cloud.shape[0], workers=-1)
    pdd = np.zeros_like(pdd_)

    while True:
        if np.all(pdd[:, -1] >= min_val):
            col_where = np.argwhere(np.all(pdd >= min_val, axis=0))[0][0]
            if np.array_equal(pdd[:, :col_where+1], pdd_[:, :col_where+1]):
                break

        pdd = pdd_
        cloud = np.vstack((cloud,
                           next(cloud_generator),
                           next(cloud_generator)))
        tree = scipy.spatial.KDTree(cloud,
                                    compact_nodes=False,
                                    balanced_tree=False)
        pdd_, _ = tree.query(motif, k=cloud.shape[0], workers=-1)

    k = np.argwhere(np.all(pdd >= min_val, axis=0))[0][0]

    return pdd[:, 1:k+1]


1			"""Implements core function nearest_neighbours used for AMD and PDD calculations."""
2
3			from typing import Iterable, Optional
4			import itertools
5			import collections
6
7			import numba
8			import numpy as np
9			import scipy.spatial
10
11
12			@numba.njit()
13			def _dist(xy, z):
14			s = z ** 2
15			for val in xy:
16			s += val ** 2
17			return s
18
19			@numba.njit()
20			def _distkey(pt):
21			s = 0
22			for val in pt:
23			s += val ** 2
24			return s
25
26
27			def generate_integer_lattice(dims: int) -> Iterable[np.ndarray]:
28			"""Generates batches of integer lattice points.
29
30			Each yield gives all points (that have not already been yielded)
31			inside a sphere centered at the origin with radius d. d starts at 0
32			and increments by 1 on each loop.
33
34			Parameters
35			----------
36			dims : int
37			The dimension of Euclidean space the lattice is in.
38
39			Yields
40			-------
41			ndarray
42			Yields arrays of integer points in dims dimensional Euclidean space.
43			"""
44
45			ymax = collections.defaultdict(int)
46			d = 0
47
48			if dims == 1:
49			yield np.array([[0]])
50			while True:
51			d += 1
52			yield np.array([[-d], [d]])
53
54			while True:
			0 ignored issues – show unused-code introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report Too many nested blocks (6/5) Loading history...
55			# get integer lattice points in +ve directions
56			positive_int_lattice = []
57			while True:
58			batch = []
59			for xy in itertools.product(range(d+1), repeat=dims-1):
60			if _dist(xy, ymax[xy]) <= d**2:
61			batch.append((*xy, ymax[xy]))
62			ymax[xy] += 1
63			if not batch:
64			break
65			positive_int_lattice += batch
66			positive_int_lattice.sort(key=_distkey)
67
68			# expand +ve integer lattice to full lattice with reflections
69			int_lattice = []
70			for p in positive_int_lattice:
71			int_lattice.append(p)
72			for n_reflections in range(1, dims+1):
73			for indexes in itertools.combinations(range(dims), n_reflections):
74			if all((p[i] for i in indexes)):
			0 ignored issues – show introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report The variable `i` does not seem to be defined for all execution paths. Loading history...
75			p_ = list(p)
76			for i in indexes:
77			p_[i] *= -1
78			int_lattice.append(p_)
79
80			yield np.array(int_lattice)
81			d += 1
82
83
84			def generate_concentric_cloud(
85			motif: np.ndarray,
86			cell: np.ndarray
87			) -> Iterable[np.ndarray]:
88			"""
89			Generates batches of points from a periodic set given by (motif, cell)
90			which get successively further away from the origin.
91
92			Each yield gives all points (that have not already been yielded) which
93			lie in a unit cell whose corner lattice point was generated by
94			_generate_integer_lattice(motif.shape[1]).
95
96			Parameters
97			----------
98			motif : ndarray
99			Cartesian representation of the motif, shape (no points, dims).
100			cell : ndarray
101			Cartesian representation of the unit cell, shape (dims, dims).
102
103			Yields
104			-------
105			ndarray
106			Yields arrays of points from the periodic set.
107			"""
108
109			int_lattice_generator = generate_integer_lattice(cell.shape[0])
110
111			while True:
112			int_lattice = next(int_lattice_generator) @ cell
			0 ignored issues – show introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report Do not raise StopIteration in generator, use return statement instead Loading history...
113			yield np.concatenate([motif + translation for translation in int_lattice])
114
115
116			def nearest_neighbours(
117			motif: np.ndarray,
118			cell: np.ndarray,
119			k: int,
120			asymmetric_unit: Optional[np.ndarray] = None):
121			"""
122			Given a periodic set represented by (motif, cell) and an integer k, find
123			the k nearest neighbours of the motif points in the periodic set.
124
125			Note that cloud and inds are not used yet but may be in the future.
126
127			Parameters
128			----------
129			motif : ndarray
130			Cartesian coords of the full motif, shape (no points, dims).
131			cell : ndarray
132			Cartesian coords of the unit cell, shape (dims, dims).
133			k : int
134			Number of nearest neighbours to find for each motif point.
135			asymmetric_unit : ndarray, optional
136			Indices pointing to an asymmetric unit in motif.
137
138			Returns
139			-------
140			pdd : ndarray
141			An array shape (motif.shape[0], k) of distances from each motif
142			point to its k nearest neighbours in order. Points do not count
143			as their own nearest neighbour. E.g. the distance to the n-th
144			nearest neighbour of the m-th motif point is pdd[m][n].
145			cloud : ndarray
146			The collection of points in the periodic set that were generated
147			during the nearest neighbour search.
148			inds : ndarray
149			An array shape (motif.shape[0], k) containing the indices of
150			nearest neighbours in cloud. E.g. the n-th nearest neighbour to
151			the m-th motif point is cloud[inds[m][n]].
152			"""
153
154			if asymmetric_unit is not None:
155			asym_unit = motif[asymmetric_unit]
156			else:
157			asym_unit = motif
158
159			cloud_generator = generate_concentric_cloud(motif, cell)
160			n_points = 0
161			cloud = []
162			while n_points <= k:
163			l = next(cloud_generator)
164			n_points += l.shape[0]
165			cloud.append(l)
166			cloud.append(next(cloud_generator))
167			cloud = np.concatenate(cloud)
168
169			tree = scipy.spatial.KDTree(cloud,
170			compact_nodes=False,
171			balanced_tree=False)
172			pdd_, inds = tree.query(asym_unit, k=k+1, workers=-1)
173			pdd = np.zeros_like(pdd_)
174
175			while not np.allclose(pdd, pdd_, atol=1e-12, rtol=0):
176			pdd = pdd_
177			cloud = np.vstack((cloud,
178			next(cloud_generator),
179			next(cloud_generator)))
180			tree = scipy.spatial.KDTree(cloud,
181			compact_nodes=False,
182			balanced_tree=False)
183			pdd_, inds = tree.query(asym_unit, k=k+1, workers=-1)
184
185			return pdd_[:, 1:], cloud, inds[:, 1:]
186
187
188			def nearest_neighbours_minval(motif, cell, min_val):
189			"""PDD large enough to be reconstructed from
190			(such that last col values all > 2 * diam(cell))."""
191
192			cloud_generator = generate_concentric_cloud(motif, cell)
193
194			cloud = []
195			for _ in range(3):
196			cloud.append(next(cloud_generator))
197
198			cloud = np.concatenate(cloud)
199			tree = scipy.spatial.KDTree(cloud,
200			compact_nodes=False,
201			balanced_tree=False)
202			pdd_, _ = tree.query(motif, k=cloud.shape[0], workers=-1)
203			pdd = np.zeros_like(pdd_)
204
205			while True:
206			if np.all(pdd[:, -1] >= min_val):
207			col_where = np.argwhere(np.all(pdd >= min_val, axis=0))[0][0]
208			if np.array_equal(pdd[:, :col_where+1], pdd_[:, :col_where+1]):
209			break
210
211			pdd = pdd_
212			cloud = np.vstack((cloud,
213			next(cloud_generator),
214			next(cloud_generator)))
215			tree = scipy.spatial.KDTree(cloud,
216			compact_nodes=False,
217			balanced_tree=False)
218			pdd_, _ = tree.query(motif, k=cloud.shape[0], workers=-1)
219
220			k = np.argwhere(np.all(pdd >= min_val, axis=0))[0][0]
221
222			return pdd[:, 1:k+1]
223

dwiddo / average-minimum-distance

Branch — master (3a171e)

amd._nearest_neighbours._distkey() A

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