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

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by Daniel

created 2023-04-01 16:02 UTC

nearest_neighbours_minval() B

↳ Parent: amd._nearest_neighbours

Complexity

Conditions

Size

Total Lines	48
Code Lines	31

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	31
dl	0
loc	48
rs	8.2026
c	0
b	0
f	0
cc	6
nop	3

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

from typing import Tuple, Iterable
from itertools import product

import numba
import numpy as np
import numpy.typing as npt
from scipy.spatial import KDTree
from scipy.spatial.distance import cdist


def nearest_neighbours(
        motif: npt.NDArray,
        cell: npt.NDArray,
        x: npt.NDArray,
        k: int
) -> Tuple[npt.NDArray[np.float64], ...]:
    """Find the ``k`` nearest neighbours in a periodic set for points in
    ``x``.

    Given a periodic set described by ``motif`` and ``cell``, a query
    set of points ``x`` and an integer ``k``, find the ``k`` nearest
    neighbours in the periodic set for all points in ``x``. Return
    distances to neighbours in order, the point cloud generated during
    the search and the indices of which points in the cloud are the
    neighbours of points in ``x``.

    Parameters
    ----------
    motif : :class:`numpy.ndarray`
        Cartesian coordinates of the motif, shape (no points, dims).
    cell : :class:`numpy.ndarray`
        The unit cell as a square array, shape (dims, dims).
    x : :class:`numpy.ndarray`
        Array of points to query for neighbours. For AMD/PDD invariants
        this is the motif, or more commonly an asymmetric unit of it.
    k : int
        Number of nearest neighbours to find for each point in ``x``.

    Returns
    -------
    dists : numpy.ndarray
        Array shape ``(x.shape[0], k)`` of distances from points in
        ``x`` to their ``k`` nearest neighbours in the periodic set, in
        order. E.g. ``dists[m][n]`` is the distance from ``x[m]`` to its
        n-th nearest neighbour in the periodic set.
    cloud : numpy.ndarray
        Collection of points in the periodic set that was generated
        during the nearest neighbour search.
    inds : numpy.ndarray
        Array shape ``(x.shape[0], k)`` containing the indices of
        nearest neighbours in ``cloud``. E.g. the n-th nearest neighbour
        to ``x[m]`` is ``cloud[inds[m][n]]``.
    """

    # Generate a cloud of lattice points such that lattice + motif has k points
    if cell.shape[0] == 3:
        int_lat_generator = _generate_integer_lattice_3D()
    else:
        int_lat_generator = _generate_integer_lattice(cell.shape[0])

    n_points = 0
    int_lat_cloud = []
    while n_points <= k:
        layer = next(int_lat_generator)
        n_points += layer.shape[0] * len(motif)
        int_lat_cloud.append(layer)

    # Add one layer to the lattice, on average this is faster
    int_lat_cloud.append(next(int_lat_generator))
    cloud = _int_lattice_to_cloud(motif, cell, np.concatenate(int_lat_cloud))

    # Find k neighbours in the point cloud for points in x
    dists_, inds = KDTree(
        cloud, leafsize=30, compact_nodes=False, balanced_tree=False
    ).query(x, k=k)

    # Generate layers of lattice points until they are too far away to give
    # nearer neighbours than have already been found. For a lattice point l,
    # points in l + motif are further away from x than |l| - max|p-p'| (where
    # p in x, p' in motif), used to check if l is too far away.
    motif_diameter = np.amax(cdist(x, motif))
    lattice_layers = []
    while True:
        lattice = _close_lattice_points(
            next(int_lat_generator), cell, dists_[:, -1], motif_diameter
        )
        if lattice.size == 0:
            break
        lattice_layers.append(lattice)

    if lattice_layers:
        lattice_layers = np.concatenate(lattice_layers)
        cloud = np.vstack((
            cloud[np.unique(inds)], _lattice_to_cloud(motif, lattice_layers)
        ))
        dists_, inds = KDTree(
            cloud, leafsize=30, compact_nodes=False, balanced_tree=False
        ).query(x, k=k)

    return dists_, cloud, inds


def _generate_integer_lattice(dims: int) -> Iterable[npt.NDArray[np.float64]]:
    """Generate 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
    -------
    :class:`numpy.ndarray`
        Yields arrays of integer points in `dims`-dimensional Euclidean
        space.
    """

    d = 0

    if dims == 1:
        yield np.array([[0]], dtype=np.float64)
        while True:
            d += 1
            yield np.array([[-d], [d]], dtype=np.float64)

    ymax = {}
    while True:
        positive_int_lattice = []
        while True:
            batch = False
            for xy in product(range(d + 1), repeat=dims-1):
                if xy not in ymax:
                    ymax[xy] = 0
                if _in_sphere(xy, ymax[xy], d):
                    positive_int_lattice.append((*xy, ymax[xy]))
                    batch = True
                    ymax[xy] += 1
            if not batch:
                break
        yield _reflect_positive_integer_lattice(np.array(positive_int_lattice))
        d += 1


@numba.njit(cache=True)
def _generate_integer_lattice_3D() -> Iterable[npt.NDArray[np.float64]]:
    """3D specific version of _generate_integer_lattice() which is
    accelerated by numba. 3D is the most common case and the function is
    difficult to generalise to any dimension with numba.

    Yields
    -------
    :class:`numpy.ndarray`
        Yields arrays of integer points in 3-dimensional Euclidean
        space.
    """

    d = 0
    ymax = {}
    while True:
        positive_int_lattice = []
        while True:
            batch = False
            for x in range(d + 1):
                for y in range(d + 1):
                    xy = (x, y)
                    if xy not in ymax:
                        ymax[xy] = 0
                    if x ** 2 + y ** 2 + ymax[xy] ** 2 <= d ** 2:
                        positive_int_lattice.append((x, y, ymax[xy]))
                        batch = True
                        ymax[xy] += 1
            if not batch:
                break
        yield _reflect_positive_integer_lattice(np.array(positive_int_lattice))
        d += 1


@numba.njit(cache=True)
def _reflect_positive_integer_lattice(
        positive_int_lattice: npt.NDArray
) -> npt.NDArray[np.float64]:
    """Reflect points in the positive quadrant across all combinations
    of axes, without duplicating points that are invariant under
    reflections.
    """

    dims = positive_int_lattice.shape[-1]
    batches = []
    batches.extend(positive_int_lattice)

    for n_reflections in range(1, dims + 1):

        axes = np.arange(n_reflections)
        batches.extend(_reflect_in_axes(positive_int_lattice, axes))

        while True:
            i = n_reflections - 1
            for _ in range(n_reflections):
                if axes[i] != i + dims - n_reflections:
                    break
                i -= 1
            else:
                break
            axes[i] += 1
            for j in range(i + 1, n_reflections):
                axes[j] = axes[j-1] + 1
            batches.extend(_reflect_in_axes(positive_int_lattice, axes))

    int_lattice = np.empty(shape=(len(batches), dims), dtype=np.float64)
    for i in range(len(batches)):
        int_lattice[i] = batches[i]

    return int_lattice


@numba.njit(cache=True)
def _reflect_in_axes(
        positive_int_lattice: npt.NDArray,
        axes: npt.NDArray
) -> npt.NDArray:
    """Reflect points in `positive_int_lattice` in the axes described by
    `axes`, without duplicating invariant points.
    """

    not_on_axes = (positive_int_lattice[:, axes] == 0).sum(axis=-1) == 0
    int_lattice = positive_int_lattice[not_on_axes]
    int_lattice[:, axes] *= -1
    return int_lattice


@numba.njit(cache=True)
def _close_lattice_points(
        int_lattice: npt.NDArray,
        cell: npt.NDArray,
        max_nn_dists: npt.NDArray,
        max_cdist: float
) -> npt.NDArray[np.float64]:
    """Given integer lattice points, a unit cell, ``max_cdist`` (max of
    cdist(x, motif)) and ``max_nn_dist`` (max of the dists to k-th
    nearest neighbours found so far), return lattice points which are
    close enough such that the corresponding motif copy could contain
    nearest neighbours.
    """

    lattice = int_lattice @ cell
    bound = np.amax(max_nn_dists) + max_cdist
    return lattice[np.sqrt(np.sum(lattice ** 2, axis=-1)) < bound]


@numba.njit(cache=True)
def _lattice_to_cloud(
        motif: npt.NDArray,
        lattice: npt.NDArray
) -> npt.NDArray[np.float64]:
    """Transform a batch of non-integer lattice points (generated by
    _generate_integer_lattice then mutliplied by the cell) into a cloud
    of points from a periodic set with the motif and cell.
    """

    m = len(motif)
    layer = np.empty((m * len(lattice), motif.shape[-1]), dtype=np.float64)
    i1 = 0
    for translation in lattice:
        i2 = i1 + m
        layer[i1:i2] = motif + translation
        i1 = i2
    return layer


@numba.njit(cache=True)
def _int_lattice_to_cloud(
        motif: npt.NDArray,
        cell: npt.NDArray,
        int_lattice: npt.NDArray
) -> npt.NDArray[np.float64]:
    """Transform a batch of integer lattice points (generated by
    _generate_integer_lattice) into a cloud of points from a periodic
    set with the motif and cell.
    """
    return _lattice_to_cloud(motif, int_lattice @ cell)


@numba.njit(cache=True)
def _in_sphere(xy: Tuple[float, float], z: float, d: float) -> bool:
    """True if sum(i^2 for i in xy) + z^2 <= d^2."""

    s = z ** 2
    for val in xy:
        s += val ** 2
    return s <= d ** 2


def nearest_neighbours_minval(
        motif: npt.NDArray,
        cell: npt.NDArray,
        min_val: float
) -> npt.NDArray[np.float64]:
    """Return the same ``dists``/PDD matrix as ``nearest_neighbours``,
    but with enough columns such that all values in the last column are
    at least ``min_val``. Unlike ``nearest_neighbours``, does not take a
    query array ``x`` but only finds neighbours to motif points, and
    does not return the point cloud or indices of the nearest
    neighbours. Used in ``PDD_reconstructable``.
    """
    
    
    # Generate initial cloud of points from the periodic set
    int_lat_generator = _generate_integer_lattice(cell.shape[0])
    cloud = []
    for _ in range(3):
        cloud.append(_lattice_to_cloud(motif, next(int_lat_generator) @ cell))
    cloud = np.concatenate(cloud)

    # Find k neighbours in the point cloud for points in motif
    dists_, inds = KDTree(
        cloud, leafsize=30, compact_nodes=False, balanced_tree=False
    ).query(motif, k=cloud.shape[0])
    dists = np.zeros_like(dists_, dtype=np.float64)

    # Add layers & find k nearest neighbours until all distances smaller than
    # min_val don't change
    max_cdist = np.amax(cdist(motif, motif))
    while True:
        if np.all(dists_[:, -1] >= min_val):
            col = np.argwhere(np.all(dists_ >= min_val, axis=0))[0][0] + 1
            if np.array_equal(dists[:, :col], dists_[:, :col]):
                break
        dists = dists_
        lattice = next(int_lat_generator) @ cell
        closest_dist_bound = np.linalg.norm(lattice, axis=-1) - max_cdist
        is_close = closest_dist_bound <= np.amax(dists_[:, -1])
        if not np.any(is_close):
            break
        cloud = np.vstack((cloud, _lattice_to_cloud(motif, lattice[is_close])))
        dists_, inds = KDTree(
            cloud, leafsize=30, compact_nodes=False, balanced_tree=False
        ).query(motif, k=cloud.shape[0])

    k = np.argwhere(np.all(dists >= min_val, axis=0))[0][0]
    return dists_[:, 1:k+1], cloud, inds


def generate_concentric_cloud(motif, cell):
    """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 : :class:`numpy.ndarray`
        Cartesian representation of the motif, shape (no points, dims).
    cell : :class:`numpy.ndarray`
        Cartesian representation of the unit cell, shape (dims, dims).

    Yields
    -------
    :class:`numpy.ndarray`
        Yields arrays of points from the periodic set.
    """

    int_lat_generator = _generate_integer_lattice(cell.shape[0])
    for layer in int_lat_generator:
        yield _lattice_to_cloud(motif, layer @ cell)


1			"""Implements core function nearest_neighbours used for AMD and PDD
2			calculations.
3			"""
4
5			from typing import Tuple, Iterable
6			from itertools import product
7
8			import numba
9			import numpy as np
10			import numpy.typing as npt
11			from scipy.spatial import KDTree
12			from scipy.spatial.distance import cdist
13
14
15			def nearest_neighbours(
16			motif: npt.NDArray,
17			cell: npt.NDArray,
18			x: npt.NDArray,
19			k: int
20			) -> Tuple[npt.NDArray[np.float64], ...]:
21			"""Find the ``k`` nearest neighbours in a periodic set for points in
22			``x``.
23
24			Given a periodic set described by ``motif`` and ``cell``, a query
25			set of points ``x`` and an integer ``k``, find the ``k`` nearest
26			neighbours in the periodic set for all points in ``x``. Return
27			distances to neighbours in order, the point cloud generated during
28			the search and the indices of which points in the cloud are the
29			neighbours of points in ``x``.
30
31			Parameters
32			----------
33			motif : :class:`numpy.ndarray`
34			Cartesian coordinates of the motif, shape (no points, dims).
35			cell : :class:`numpy.ndarray`
36			The unit cell as a square array, shape (dims, dims).
37			x : :class:`numpy.ndarray`
38			Array of points to query for neighbours. For AMD/PDD invariants
39			this is the motif, or more commonly an asymmetric unit of it.
40			k : int
41			Number of nearest neighbours to find for each point in ``x``.
42
43			Returns
44			-------
45			dists : numpy.ndarray
46			Array shape ``(x.shape[0], k)`` of distances from points in
47			``x`` to their ``k`` nearest neighbours in the periodic set, in
48			order. E.g. ``dists[m][n]`` is the distance from ``x[m]`` to its
49			n-th nearest neighbour in the periodic set.
50			cloud : numpy.ndarray
51			Collection of points in the periodic set that was generated
52			during the nearest neighbour search.
53			inds : numpy.ndarray
54			Array shape ``(x.shape[0], k)`` containing the indices of
55			nearest neighbours in ``cloud``. E.g. the n-th nearest neighbour
56			to ``x[m]`` is ``cloud[inds[m][n]]``.
57			"""
58
59			# Generate a cloud of lattice points such that lattice + motif has k points
60			if cell.shape[0] == 3:
61			int_lat_generator = _generate_integer_lattice_3D()
62			else:
63			int_lat_generator = _generate_integer_lattice(cell.shape[0])
64
65			n_points = 0
66			int_lat_cloud = []
67			while n_points <= k:
68			layer = next(int_lat_generator)
69			n_points += layer.shape[0] * len(motif)
70			int_lat_cloud.append(layer)
71
72			# Add one layer to the lattice, on average this is faster
73			int_lat_cloud.append(next(int_lat_generator))
74			cloud = _int_lattice_to_cloud(motif, cell, np.concatenate(int_lat_cloud))
75
76			# Find k neighbours in the point cloud for points in x
77			dists_, inds = KDTree(
78			cloud, leafsize=30, compact_nodes=False, balanced_tree=False
79			).query(x, k=k)
80
81			# Generate layers of lattice points until they are too far away to give
82			# nearer neighbours than have already been found. For a lattice point l,
83			# points in l + motif are further away from x than \|l\| - max\|p-p'\| (where
84			# p in x, p' in motif), used to check if l is too far away.
85			motif_diameter = np.amax(cdist(x, motif))
86			lattice_layers = []
87			while True:
88			lattice = _close_lattice_points(
89			next(int_lat_generator), cell, dists_[:, -1], motif_diameter
90			)
91			if lattice.size == 0:
92			break
93			lattice_layers.append(lattice)
94
95			if lattice_layers:
96			lattice_layers = np.concatenate(lattice_layers)
97			cloud = np.vstack((
98			cloud[np.unique(inds)], _lattice_to_cloud(motif, lattice_layers)
99			))
100			dists_, inds = KDTree(
101			cloud, leafsize=30, compact_nodes=False, balanced_tree=False
102			).query(x, k=k)
103
104			return dists_, cloud, inds
105
106
107			def _generate_integer_lattice(dims: int) -> Iterable[npt.NDArray[np.float64]]:
108			"""Generate batches of integer lattice points. Each yield gives all
109			points (that have not already been yielded) inside a sphere centered
110			at the origin with radius d; d starts at 0 and increments by 1 on
111			each loop.
112
113			Parameters
114			----------
115			dims : int
116			The dimension of Euclidean space the lattice is in.
117
118			Yields
119			-------
120			:class:`numpy.ndarray`
121			Yields arrays of integer points in `dims`-dimensional Euclidean
122			space.
123			"""
124
125			d = 0
126
127			if dims == 1:
128			yield np.array([[0]], dtype=np.float64)
129			while True:
130			d += 1
131			yield np.array([[-d], [d]], dtype=np.float64)
132
133			ymax = {}
134			while True:
135			positive_int_lattice = []
136			while True:
137			batch = False
138			for xy in product(range(d + 1), repeat=dims-1):
139			if xy not in ymax:
140			ymax[xy] = 0
141			if _in_sphere(xy, ymax[xy], d):
142			positive_int_lattice.append((*xy, ymax[xy]))
143			batch = True
144			ymax[xy] += 1
145			if not batch:
146			break
147			yield _reflect_positive_integer_lattice(np.array(positive_int_lattice))
148			d += 1
149
150
151			@numba.njit(cache=True)
152			def _generate_integer_lattice_3D() -> Iterable[npt.NDArray[np.float64]]:
153			"""3D specific version of _generate_integer_lattice() which is
154			accelerated by numba. 3D is the most common case and the function is
155			difficult to generalise to any dimension with numba.
156
157			Yields
158			-------
159			:class:`numpy.ndarray`
160			Yields arrays of integer points in 3-dimensional Euclidean
161			space.
162			"""
163
164			d = 0
165			ymax = {}
166			while True:
167			positive_int_lattice = []
168			while True:
169			batch = False
170			for x in range(d + 1):
171			for y in range(d + 1):
172			xy = (x, y)
173			if xy not in ymax:
174			ymax[xy] = 0
175			if x 2 + y 2 + ymax[xy] 2 <= d 2:
176			positive_int_lattice.append((x, y, ymax[xy]))
177			batch = True
178			ymax[xy] += 1
179			if not batch:
180			break
181			yield _reflect_positive_integer_lattice(np.array(positive_int_lattice))
182			d += 1
183
184
185			@numba.njit(cache=True)
186			def _reflect_positive_integer_lattice(
187			positive_int_lattice: npt.NDArray
188			) -> npt.NDArray[np.float64]:
189			"""Reflect points in the positive quadrant across all combinations
190			of axes, without duplicating points that are invariant under
191			reflections.
192			"""
193
194			dims = positive_int_lattice.shape[-1]
195			batches = []
196			batches.extend(positive_int_lattice)
197
198			for n_reflections in range(1, dims + 1):
199
200			axes = np.arange(n_reflections)
201			batches.extend(_reflect_in_axes(positive_int_lattice, axes))
202
203			while True:
204			i = n_reflections - 1
205			for _ in range(n_reflections):
206			if axes[i] != i + dims - n_reflections:
207			break
208			i -= 1
209			else:
210			break
211			axes[i] += 1
212			for j in range(i + 1, n_reflections):
213			axes[j] = axes[j-1] + 1
214			batches.extend(_reflect_in_axes(positive_int_lattice, axes))
215
216			int_lattice = np.empty(shape=(len(batches), dims), dtype=np.float64)
217			for i in range(len(batches)):
218			int_lattice[i] = batches[i]
219
220			return int_lattice
221
222
223			@numba.njit(cache=True)
224			def _reflect_in_axes(
225			positive_int_lattice: npt.NDArray,
226			axes: npt.NDArray
227			) -> npt.NDArray:
228			"""Reflect points in `positive_int_lattice` in the axes described by
229			`axes`, without duplicating invariant points.
230			"""
231
232			not_on_axes = (positive_int_lattice[:, axes] == 0).sum(axis=-1) == 0
233			int_lattice = positive_int_lattice[not_on_axes]
234			int_lattice[:, axes] *= -1
235			return int_lattice
236
237
238			@numba.njit(cache=True)
239			def _close_lattice_points(
240			int_lattice: npt.NDArray,
241			cell: npt.NDArray,
242			max_nn_dists: npt.NDArray,
243			max_cdist: float
244			) -> npt.NDArray[np.float64]:
245			"""Given integer lattice points, a unit cell, ``max_cdist`` (max of
246			cdist(x, motif)) and ``max_nn_dist`` (max of the dists to k-th
247			nearest neighbours found so far), return lattice points which are
248			close enough such that the corresponding motif copy could contain
249			nearest neighbours.
250			"""
251
252			lattice = int_lattice @ cell
253			bound = np.amax(max_nn_dists) + max_cdist
254			return lattice[np.sqrt(np.sum(lattice ** 2, axis=-1)) < bound]
255
256
257			@numba.njit(cache=True)
258			def _lattice_to_cloud(
259			motif: npt.NDArray,
260			lattice: npt.NDArray
261			) -> npt.NDArray[np.float64]:
262			"""Transform a batch of non-integer lattice points (generated by
263			_generate_integer_lattice then mutliplied by the cell) into a cloud
264			of points from a periodic set with the motif and cell.
265			"""
266
267			m = len(motif)
268			layer = np.empty((m * len(lattice), motif.shape[-1]), dtype=np.float64)
269			i1 = 0
270			for translation in lattice:
271			i2 = i1 + m
272			layer[i1:i2] = motif + translation
273			i1 = i2
274			return layer
275
276
277			@numba.njit(cache=True)
278			def _int_lattice_to_cloud(
279			motif: npt.NDArray,
280			cell: npt.NDArray,
281			int_lattice: npt.NDArray
282			) -> npt.NDArray[np.float64]:
283			"""Transform a batch of integer lattice points (generated by
284			_generate_integer_lattice) into a cloud of points from a periodic
285			set with the motif and cell.
286			"""
287			return _lattice_to_cloud(motif, int_lattice @ cell)
288
289
290			@numba.njit(cache=True)
291			def _in_sphere(xy: Tuple[float, float], z: float, d: float) -> bool:
292			"""True if sum(i^2 for i in xy) + z^2 <= d^2."""
293
294			s = z ** 2
295			for val in xy:
296			s += val ** 2
297			return s <= d ** 2
298
299
300			def nearest_neighbours_minval(
301			motif: npt.NDArray,
302			cell: npt.NDArray,
303			min_val: float
304			) -> npt.NDArray[np.float64]:
305			"""Return the same ``dists``/PDD matrix as ``nearest_neighbours``,
306			but with enough columns such that all values in the last column are
307			at least ``min_val``. Unlike ``nearest_neighbours``, does not take a
308			query array ``x`` but only finds neighbours to motif points, and
309			does not return the point cloud or indices of the nearest
310			neighbours. Used in ``PDD_reconstructable``.
311			"""
312
313
314			# Generate initial cloud of points from the periodic set
315			int_lat_generator = _generate_integer_lattice(cell.shape[0])
316			cloud = []
317			for _ in range(3):
318			cloud.append(_lattice_to_cloud(motif, next(int_lat_generator) @ cell))
319			cloud = np.concatenate(cloud)
320
321			# Find k neighbours in the point cloud for points in motif
322			dists_, inds = KDTree(
323			cloud, leafsize=30, compact_nodes=False, balanced_tree=False
324			).query(motif, k=cloud.shape[0])
325			dists = np.zeros_like(dists_, dtype=np.float64)
326
327			# Add layers & find k nearest neighbours until all distances smaller than
328			# min_val don't change
329			max_cdist = np.amax(cdist(motif, motif))
330			while True:
331			if np.all(dists_[:, -1] >= min_val):
332			col = np.argwhere(np.all(dists_ >= min_val, axis=0))[0][0] + 1
333			if np.array_equal(dists[:, :col], dists_[:, :col]):
334			break
335			dists = dists_
336			lattice = next(int_lat_generator) @ cell
337			closest_dist_bound = np.linalg.norm(lattice, axis=-1) - max_cdist
338			is_close = closest_dist_bound <= np.amax(dists_[:, -1])
339			if not np.any(is_close):
340			break
341			cloud = np.vstack((cloud, _lattice_to_cloud(motif, lattice[is_close])))
342			dists_, inds = KDTree(
343			cloud, leafsize=30, compact_nodes=False, balanced_tree=False
344			).query(motif, k=cloud.shape[0])
345
346			k = np.argwhere(np.all(dists >= min_val, axis=0))[0][0]
347			return dists_[:, 1:k+1], cloud, inds
348
349
350			def generate_concentric_cloud(motif, cell):
351			"""Generates batches of points from a periodic set given by (motif,
352			cell) which get successively further away from the origin.
353
354			Each yield gives all points (that have not already been yielded)
355			which lie in a unit cell whose corner lattice point was generated by
356			``generate_integer_lattice(motif.shape[1])``.
357
358			Parameters
359			----------
360			motif : :class:`numpy.ndarray`
361			Cartesian representation of the motif, shape (no points, dims).
362			cell : :class:`numpy.ndarray`
363			Cartesian representation of the unit cell, shape (dims, dims).
364
365			Yields
366			-------
367			:class:`numpy.ndarray`
368			Yields arrays of points from the periodic set.
369			"""
370
371			int_lat_generator = _generate_integer_lattice(cell.shape[0])
372			for layer in int_lat_generator:
373			yield _lattice_to_cloud(motif, layer @ cell)
374

dwiddo / average-minimum-distance

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nearest_neighbours_minval() B

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