amd.reconstruct._trilaterate() - Code Metrics - Inspection of "gemmi reader update" - dwiddo/average-minimum-distance - Measure and Improve Code Quality continuously with Scrutinizer

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by Daniel

created 2023-03-21 20:08 UTC

amd.reconstruct._trilaterate() A

↳ Parent: amd.reconstruct

Complexity

Conditions

Size

Total Lines	30
Code Lines	24

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	24
dl	0
loc	30
rs	9.304
c	0
b	0
f	0
cc	4
nop	8

How to fix Many Parameters

"""Functions for resconstructing a periodic set up to isometry from its
PDD. This is possible 'in a general position', see our papers for more.
"""

from itertools import combinations, permutations, product

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

from ._nearest_neighbours import generate_concentric_cloud
from .utils import diameter


def reconstruct(
        pdd: npt.NDArray,
        cell: npt.NDArray
) -> npt.NDArray:
    """Reconstruct a motif from a PDD and unit cell. This function will
    only work if ``pdd`` has enough columns, such that the last column
    has all values larger than 2 times the diameter of the unit cell. It
    also expects an uncollapsed PDD with no weights column. Do not use
    ``amd.PDD`` to compute the PDD for this function, instead use
    ``amd.PDD_reconstructable`` which returns a version of the PDD which
    is passable to this function. This function is quite slow and run
    time may vary a lot arbitrarily depending on input.

    Parameters
    ----------
    pdd : :class:`numpy.ndarray`
        The PDD of the periodic set to reconstruct. Needs `k` at least
        large enough so all values in the last column of pdd are greater
        than :code:`2 * diameter(cell)`, and needs to be uncollapsed
        without weights. Use amd.PDD_reconstructable to get a PDD which
        is acceptable for this argument.
    cell : :class:`numpy.ndarray`
        Unit cell of the periodic set to reconstruct.

    Returns
    -------
    :class:`numpy.ndarray`
        The reconstructed motif of the periodic set.
    """

    # TODO: get a more reduced neighbour set
    # TODO: find all shared distances in a big operation at the start
    # TODO: move PREC variable to its proper place
    PREC = 1e-10

    dims = cell.shape[0]
    if dims not in (2, 3):
        raise ValueError(
            'Reconstructing from PDD only implemented for 2 and 3 dimensions'
        )

    diam = diameter(cell)
    # set first point as origin wlog, return if 1 motif point
    motif = [np.zeros((dims, ))]

    if pdd.shape[0] == 1:
        motif = np.array(motif)
        return motif

    # finding lattice distances so we can ignore them
    cloud_generator = generate_concentric_cloud(np.array(motif), cell)
    cloud = []
    next(cloud_generator)
    layer = next(cloud_generator)
    while np.any(np.linalg.norm(layer, axis=-1) <= diam):
        cloud.append(layer)
        layer = next(cloud_generator)
    cloud = np.concatenate(cloud)

    # is (a superset of) lattice points close enough to Voronoi domain
    nn_set = _neighbour_set(cell, PREC)
    lattice_dists = np.linalg.norm(cloud, axis=-1)
    lattice_dists = lattice_dists[lattice_dists <= diam]
    lattice_dists = _unique_within_tol(lattice_dists, PREC)

    # remove lattice distances from first and second rows
    row1_reduced = _remove_vals(pdd[0], lattice_dists, PREC)
    row2_reduced = _remove_vals(pdd[1], lattice_dists, PREC)
    # get shared dists between first and second rows
    shared_dists = _shared_vals(row1_reduced, row2_reduced, PREC)
    shared_dists = _unique_within_tol(shared_dists, PREC)

    # all combinations of vecs in neighbour set forming a basis
    bases = []
    for vecs in combinations(nn_set, dims):
        vecs = np.asarray(vecs)
        if np.abs(np.linalg.det(vecs)) > PREC:
            bases.extend(basis for basis in permutations(vecs, dims))

    q = _find_second_point(shared_dists, bases, cloud, PREC)

    if q is None:
        raise RuntimeError('Second point of motif could not be found.')

    motif.append(q)

    if pdd.shape[0] == 2:
        motif = np.array(motif)
        return motif

    for row in pdd[2:, :]:
        row_reduced = _remove_vals(row, lattice_dists, PREC)
        shared_dists1 = _shared_vals(row1_reduced, row_reduced, PREC)
        shared_dists2 = _shared_vals(row2_reduced, row_reduced, PREC)
        shared_dists1 = _unique_within_tol(shared_dists1, PREC)
        shared_dists2 = _unique_within_tol(shared_dists2, PREC)
        q_ = _find_further_point(
            shared_dists1, shared_dists2, bases, cloud, q, PREC
        )

        if q_ is None:
            raise RuntimeError('Further point of motif could not be found.')

        motif.append(q_)

    motif = np.array(motif)
    motif = np.mod(motif @ np.linalg.inv(cell), 1) @ cell
    return motif


def _find_second_point(shared_dists, bases, cloud, prec):
    dims = cloud.shape[-1]
    abs_q = shared_dists[0]

    for distance_tup in combinations(shared_dists[1:], dims):
        for basis in bases:
            res = None
            if dims == 2:
                res = _bilaterate(*basis, *distance_tup, abs_q, prec)
            elif dims == 3:
                if not _four_sphere_pairwise_intersecion(
                    *basis, *distance_tup, abs_q, prec
                ):
                    continue
                res = _trilaterate(*basis, *distance_tup, abs_q, prec)

            if res is not None:
                cloud_res_dists = np.linalg.norm(cloud - res, axis=-1)
                if np.all(cloud_res_dists - abs_q + prec > 0):
                    return res


def _find_further_point(shared_dists1, shared_dists2, bases, cloud, q, prec):
    # distance from origin (first motif point) to further point
    dims = cloud.shape[-1]
    abs_q_ = shared_dists1[0]

    # try all ordered subsequences of distances shared between first and
    # further row, with all combinations of the vectors in the neighbour set
    # forming a basis
    for distance_tup in combinations(shared_dists1[1:], dims):
        for basis in bases:
            res = None
            if dims == 2:
                res = _bilaterate(*basis, *distance_tup, abs_q_, prec)
            elif dims == 3:
                if not _four_sphere_pairwise_intersecion(
                    *basis, *distance_tup, abs_q_, prec
                ):
                    continue
                res = _trilaterate(*basis, *distance_tup, abs_q_, prec)

            if res is not None:
                # check point is in the voronoi domain
                cloud_res_dists = np.linalg.norm(cloud - res, axis=-1)
                if np.all(cloud_res_dists - abs_q_ + prec > 0):
                    # check |p - point| is among the row's shared distances
                    dist_diff = np.abs(shared_dists2 - np.linalg.norm(q - res))
                    if np.any(dist_diff < prec):
                        return res


def _neighbour_set(cell, prec):
    """(A superset of) the neighbour set of origin for a lattice."""

    k_ = 5
    coeffs = np.array(list(product((-1, 0, 1), repeat=cell.shape[0])))
    coeffs = coeffs[coeffs.any(axis=-1)]    # remove (0,0,0)

    # half of all combinations of basis vectors
    vecs = []
    for c in coeffs:
        vecs.append(np.sum(cell * c[None, :].T, axis=0) / 2)
    vecs = np.array(vecs)

    origin = np.zeros((1, cell.shape[0]))
    cloud_generator = generate_concentric_cloud(origin, cell)
    cloud = np.concatenate((next(cloud_generator), next(cloud_generator)))
    tree = KDTree(cloud, compact_nodes=False, balanced_tree=False)
    dists, inds = tree.query(vecs, k=k_, workers=-1)
    dists_ = np.empty_like(dists)

    while not np.allclose(dists, dists_, atol=0, rtol=1e-12):
        dists = dists_
        cloud = np.vstack((cloud,
                           next(cloud_generator),
                           next(cloud_generator)))
        tree = KDTree(cloud, compact_nodes=False, balanced_tree=False)
        dists_, inds = tree.query(vecs, k=k_, workers=-1)

    tmp_inds = np.unique(inds[:, 1:].flatten())
    tmp_inds = tmp_inds[tmp_inds != 0]
    neighbour_set = cloud[tmp_inds]

    # reduce neighbour set
    # half the lattice points and find their nearest neighbours in the lattice
    neighbour_set_half = neighbour_set / 2
    # for each of these vectors, check if 0 is A nearest neighbour.
    # so, check if the dist to 0 is leq (within tol) than dist to all other
    # lattice points.
    nn_norms = np.linalg.norm(neighbour_set, axis=-1)
    halves_norms = nn_norms / 2
    halves_to_lattice_dists = cdist(neighbour_set_half, neighbour_set)

    # Do I need to + PREC?
    # inds of voronoi neighbours in cloud
    voronoi_neighbours = np.all(
        halves_to_lattice_dists - halves_norms + prec >= 0, axis=-1
    )
    neighbour_set = neighbour_set[voronoi_neighbours]
    return neighbour_set


def _four_sphere_pairwise_intersecion(p1, p2, p3, r1, r2, r3, abs_val, prec):
    """Return True if four spheres intersect at a point."""

    if np.linalg.norm(p1) > abs_val + r1 - prec:
        return False
    if np.linalg.norm(p2) > abs_val + r2 - prec:
        return False
    if np.linalg.norm(p3) > abs_val + r3 - prec:
        return False
    if np.linalg.norm(p1 - p2) > r1 + r2 - prec:
        return False
    if np.linalg.norm(p1 - p3) > r1 + r3 - prec:
        return False
    if np.linalg.norm(p2 - p3) > r2 + r3 - prec:
        return False
    return True


@numba.njit()
def _bilaterate(p1, p2, r1, r2, abs_val, prec):
    """Return True if three circles intersect at a point."""

    d = np.sqrt((p2[0] - p1[0]) ** 2 + (p2[1] - p1[1]) ** 2)
    v = (p2 - p1) / d

    if d > r1 + r2:
        return None
    if d < abs(r1 - r2):
        return None
    if d == 0 and r1 == r2:
        return None

    a = (r1 ** 2 - r2 ** 2 + d ** 2) / (2 * d)
    h = np.sqrt(r1 ** 2 - a ** 2)
    x2 = p1[0] + a * v[0]
    y2 = p1[1] + a * v[1]
    x3 = x2 + h * v[1]
    y3 = y2 - h * v[0]
    x4 = x2 - h * v[1]
    y4 = y2 + h * v[0]
    q1 = np.array((x3, y3))
    q2 = np.array((x4, y4))

    if np.abs(np.sqrt(x3 ** 2 + y3 ** 2) - abs_val) < prec:
        return q1
    if np.abs(np.sqrt(x4 ** 2 + y4 ** 2) - abs_val) < prec:
        return q2
    return None


@numba.njit()
def _trilaterate(p1, p2, p3, r1, r2, r3, abs_val, prec):
    """Return the intersection of four spheres."""

    temp1 = p2 - p1
    d = np.linalg.norm(temp1)
    e_x = temp1 / d
    temp2 = p3 - p1
    i = np.dot(e_x, temp2)
    temp3 = temp2 - i * e_x
    e_y = temp3 / np.linalg.norm(temp3)

    j = np.dot(e_y, temp2)
    x = (r1 * r1 - r2 * r2 + d * d) / (2 * d)
    y = (r1 * r1 - r3 * r3 - 2 * i * x + i * i + j * j) / (2 * j)
    temp4 = r1 * r1 - x * x - y * y

    if temp4 < 0:
        return None

    e_z = np.cross(e_x, e_y)
    z = np.sqrt(temp4)
    p_12_a = p1 + x * e_x + y * e_y + z * e_z
    p_12_b = p1 + x * e_x + y * e_y - z * e_z

    if np.abs(np.linalg.norm(p_12_a) - abs_val) < prec:
        return p_12_a
    if np.abs(np.linalg.norm(p_12_b) - abs_val) < prec:
        return p_12_b
    return None


def _unique_within_tol(arr, prec):
    """Return only unique values in a vector within ``prec``."""
    return arr[~np.any(np.triu(np.abs(arr[:, None] - arr) < prec, 1), axis=0)]


def _remove_vals(vec, vals_to_remove, prec):
    """Remove specified values in vec, within ``prec``."""
    return vec[~np.any(np.abs(vec[:, None] - vals_to_remove) < prec, axis=-1)]


def _shared_vals(v1, v2, prec):
    """Return values shared between v1, v2 within ``prec``."""
    return v1[np.argwhere(np.abs(v1[:, None] - v2) < prec)[:, 0]]


1			"""Functions for resconstructing a periodic set up to isometry from its
2			PDD. This is possible 'in a general position', see our papers for more.
3			"""
4
5			from itertools import combinations, permutations, product
6
7			import numpy as np
8			import numpy.typing as npt
9			import numba
10			from scipy.spatial.distance import cdist
11			from scipy.spatial import KDTree
12
13			from ._nearest_neighbours import generate_concentric_cloud
14			from .utils import diameter
15
16
17			def reconstruct(
18			pdd: npt.NDArray,
19			cell: npt.NDArray
20			) -> npt.NDArray:
21			"""Reconstruct a motif from a PDD and unit cell. This function will
22			only work if ``pdd`` has enough columns, such that the last column
23			has all values larger than 2 times the diameter of the unit cell. It
24			also expects an uncollapsed PDD with no weights column. Do not use
25			``amd.PDD`` to compute the PDD for this function, instead use
26			``amd.PDD_reconstructable`` which returns a version of the PDD which
27			is passable to this function. This function is quite slow and run
28			time may vary a lot arbitrarily depending on input.
29
30			Parameters
31			----------
32			pdd : :class:`numpy.ndarray`
33			The PDD of the periodic set to reconstruct. Needs `k` at least
34			large enough so all values in the last column of pdd are greater
35			than :code:`2 * diameter(cell)`, and needs to be uncollapsed
36			without weights. Use amd.PDD_reconstructable to get a PDD which
37			is acceptable for this argument.
38			cell : :class:`numpy.ndarray`
39			Unit cell of the periodic set to reconstruct.
40
41			Returns
42			-------
43			:class:`numpy.ndarray`
44			The reconstructed motif of the periodic set.
45			"""
46
47			# TODO: get a more reduced neighbour set
48			# TODO: find all shared distances in a big operation at the start
49			# TODO: move PREC variable to its proper place
50			PREC = 1e-10
51
52			dims = cell.shape[0]
53			if dims not in (2, 3):
54			raise ValueError(
55			'Reconstructing from PDD only implemented for 2 and 3 dimensions'
56			)
57
58			diam = diameter(cell)
59			# set first point as origin wlog, return if 1 motif point
60			motif = [np.zeros((dims, ))]
61
62			if pdd.shape[0] == 1:
63			motif = np.array(motif)
64			return motif
65
66			# finding lattice distances so we can ignore them
67			cloud_generator = generate_concentric_cloud(np.array(motif), cell)
68			cloud = []
69			next(cloud_generator)
70			layer = next(cloud_generator)
71			while np.any(np.linalg.norm(layer, axis=-1) <= diam):
72			cloud.append(layer)
73			layer = next(cloud_generator)
74			cloud = np.concatenate(cloud)
75
76			# is (a superset of) lattice points close enough to Voronoi domain
77			nn_set = _neighbour_set(cell, PREC)
78			lattice_dists = np.linalg.norm(cloud, axis=-1)
79			lattice_dists = lattice_dists[lattice_dists <= diam]
80			lattice_dists = _unique_within_tol(lattice_dists, PREC)
81
82			# remove lattice distances from first and second rows
83			row1_reduced = _remove_vals(pdd[0], lattice_dists, PREC)
84			row2_reduced = _remove_vals(pdd[1], lattice_dists, PREC)
85			# get shared dists between first and second rows
86			shared_dists = _shared_vals(row1_reduced, row2_reduced, PREC)
87			shared_dists = _unique_within_tol(shared_dists, PREC)
88
89			# all combinations of vecs in neighbour set forming a basis
90			bases = []
91			for vecs in combinations(nn_set, dims):
92			vecs = np.asarray(vecs)
93			if np.abs(np.linalg.det(vecs)) > PREC:
94			bases.extend(basis for basis in permutations(vecs, dims))
95
96			q = _find_second_point(shared_dists, bases, cloud, PREC)
97
98			if q is None:
99			raise RuntimeError('Second point of motif could not be found.')
100
101			motif.append(q)
102
103			if pdd.shape[0] == 2:
104			motif = np.array(motif)
105			return motif
106
107			for row in pdd[2:, :]:
108			row_reduced = _remove_vals(row, lattice_dists, PREC)
109			shared_dists1 = _shared_vals(row1_reduced, row_reduced, PREC)
110			shared_dists2 = _shared_vals(row2_reduced, row_reduced, PREC)
111			shared_dists1 = _unique_within_tol(shared_dists1, PREC)
112			shared_dists2 = _unique_within_tol(shared_dists2, PREC)
113			q_ = _find_further_point(
114			shared_dists1, shared_dists2, bases, cloud, q, PREC
115			)
116
117			if q_ is None:
118			raise RuntimeError('Further point of motif could not be found.')
119
120			motif.append(q_)
121
122			motif = np.array(motif)
123			motif = np.mod(motif @ np.linalg.inv(cell), 1) @ cell
124			return motif
125
126
127			def _find_second_point(shared_dists, bases, cloud, prec):
128			dims = cloud.shape[-1]
129			abs_q = shared_dists[0]
130
131			for distance_tup in combinations(shared_dists[1:], dims):
132			for basis in bases:
133			res = None
134			if dims == 2:
135			res = _bilaterate(basis, distance_tup, abs_q, prec)
136			elif dims == 3:
137			if not _four_sphere_pairwise_intersecion(
138			basis, distance_tup, abs_q, prec
139			):
140			continue
141			res = _trilaterate(basis, distance_tup, abs_q, prec)
142
143			if res is not None:
144			cloud_res_dists = np.linalg.norm(cloud - res, axis=-1)
145			if np.all(cloud_res_dists - abs_q + prec > 0):
146			return res
147
148
149			def _find_further_point(shared_dists1, shared_dists2, bases, cloud, q, prec):
150			# distance from origin (first motif point) to further point
151			dims = cloud.shape[-1]
152			abs_q_ = shared_dists1[0]
153
154			# try all ordered subsequences of distances shared between first and
155			# further row, with all combinations of the vectors in the neighbour set
156			# forming a basis
157			for distance_tup in combinations(shared_dists1[1:], dims):
158			for basis in bases:
159			res = None
160			if dims == 2:
161			res = _bilaterate(basis, distance_tup, abs_q_, prec)
162			elif dims == 3:
163			if not _four_sphere_pairwise_intersecion(
164			basis, distance_tup, abs_q_, prec
165			):
166			continue
167			res = _trilaterate(basis, distance_tup, abs_q_, prec)
168
169			if res is not None:
170			# check point is in the voronoi domain
171			cloud_res_dists = np.linalg.norm(cloud - res, axis=-1)
172			if np.all(cloud_res_dists - abs_q_ + prec > 0):
173			# check \|p - point\| is among the row's shared distances
174			dist_diff = np.abs(shared_dists2 - np.linalg.norm(q - res))
175			if np.any(dist_diff < prec):
176			return res
177
178
179			def _neighbour_set(cell, prec):
180			"""(A superset of) the neighbour set of origin for a lattice."""
181
182			k_ = 5
183			coeffs = np.array(list(product((-1, 0, 1), repeat=cell.shape[0])))
184			coeffs = coeffs[coeffs.any(axis=-1)] # remove (0,0,0)
185
186			# half of all combinations of basis vectors
187			vecs = []
188			for c in coeffs:
189			vecs.append(np.sum(cell * c[None, :].T, axis=0) / 2)
190			vecs = np.array(vecs)
191
192			origin = np.zeros((1, cell.shape[0]))
193			cloud_generator = generate_concentric_cloud(origin, cell)
194			cloud = np.concatenate((next(cloud_generator), next(cloud_generator)))
195			tree = KDTree(cloud, compact_nodes=False, balanced_tree=False)
196			dists, inds = tree.query(vecs, k=k_, workers=-1)
197			dists_ = np.empty_like(dists)
198
199			while not np.allclose(dists, dists_, atol=0, rtol=1e-12):
200			dists = dists_
201			cloud = np.vstack((cloud,
202			next(cloud_generator),
203			next(cloud_generator)))
204			tree = KDTree(cloud, compact_nodes=False, balanced_tree=False)
205			dists_, inds = tree.query(vecs, k=k_, workers=-1)
206
207			tmp_inds = np.unique(inds[:, 1:].flatten())
208			tmp_inds = tmp_inds[tmp_inds != 0]
209			neighbour_set = cloud[tmp_inds]
210
211			# reduce neighbour set
212			# half the lattice points and find their nearest neighbours in the lattice
213			neighbour_set_half = neighbour_set / 2
214			# for each of these vectors, check if 0 is A nearest neighbour.
215			# so, check if the dist to 0 is leq (within tol) than dist to all other
216			# lattice points.
217			nn_norms = np.linalg.norm(neighbour_set, axis=-1)
218			halves_norms = nn_norms / 2
219			halves_to_lattice_dists = cdist(neighbour_set_half, neighbour_set)
220
221			# Do I need to + PREC?
222			# inds of voronoi neighbours in cloud
223			voronoi_neighbours = np.all(
224			halves_to_lattice_dists - halves_norms + prec >= 0, axis=-1
225			)
226			neighbour_set = neighbour_set[voronoi_neighbours]
227			return neighbour_set
228
229
230			def _four_sphere_pairwise_intersecion(p1, p2, p3, r1, r2, r3, abs_val, prec):
231			"""Return True if four spheres intersect at a point."""
232
233			if np.linalg.norm(p1) > abs_val + r1 - prec:
234			return False
235			if np.linalg.norm(p2) > abs_val + r2 - prec:
236			return False
237			if np.linalg.norm(p3) > abs_val + r3 - prec:
238			return False
239			if np.linalg.norm(p1 - p2) > r1 + r2 - prec:
240			return False
241			if np.linalg.norm(p1 - p3) > r1 + r3 - prec:
242			return False
243			if np.linalg.norm(p2 - p3) > r2 + r3 - prec:
244			return False
245			return True
246
247
248			@numba.njit()
249			def _bilaterate(p1, p2, r1, r2, abs_val, prec):
250			"""Return True if three circles intersect at a point."""
251
252			d = np.sqrt((p2[0] - p1[0]) 2 + (p2[1] - p1[1]) 2)
253			v = (p2 - p1) / d
254
255			if d > r1 + r2:
256			return None
257			if d < abs(r1 - r2):
258			return None
259			if d == 0 and r1 == r2:
260			return None
261
262			a = (r1 2 - r2 2 + d ** 2) / (2 * d)
263			h = np.sqrt(r1 2 - a 2)
264			x2 = p1[0] + a * v[0]
265			y2 = p1[1] + a * v[1]
266			x3 = x2 + h * v[1]
267			y3 = y2 - h * v[0]
268			x4 = x2 - h * v[1]
269			y4 = y2 + h * v[0]
270			q1 = np.array((x3, y3))
271			q2 = np.array((x4, y4))
272
273			if np.abs(np.sqrt(x3 2 + y3 2) - abs_val) < prec:
274			return q1
275			if np.abs(np.sqrt(x4 2 + y4 2) - abs_val) < prec:
276			return q2
277			return None
278
279
280			@numba.njit()
281			def _trilaterate(p1, p2, p3, r1, r2, r3, abs_val, prec):
282			"""Return the intersection of four spheres."""
283
284			temp1 = p2 - p1
285			d = np.linalg.norm(temp1)
286			e_x = temp1 / d
287			temp2 = p3 - p1
288			i = np.dot(e_x, temp2)
289			temp3 = temp2 - i * e_x
290			e_y = temp3 / np.linalg.norm(temp3)
291
292			j = np.dot(e_y, temp2)
293			x = (r1 * r1 - r2 * r2 + d * d) / (2 * d)
294			y = (r1 * r1 - r3 * r3 - 2 * i * x + i * i + j * j) / (2 * j)
295			temp4 = r1 * r1 - x * x - y * y
296
297			if temp4 < 0:
298			return None
299
300			e_z = np.cross(e_x, e_y)
301			z = np.sqrt(temp4)
302			p_12_a = p1 + x * e_x + y * e_y + z * e_z
303			p_12_b = p1 + x * e_x + y * e_y - z * e_z
304
305			if np.abs(np.linalg.norm(p_12_a) - abs_val) < prec:
306			return p_12_a
307			if np.abs(np.linalg.norm(p_12_b) - abs_val) < prec:
308			return p_12_b
309			return None
310
311
312			def _unique_within_tol(arr, prec):
313			"""Return only unique values in a vector within ``prec``."""
314			return arr[~np.any(np.triu(np.abs(arr[:, None] - arr) < prec, 1), axis=0)]
315
316
317			def _remove_vals(vec, vals_to_remove, prec):
318			"""Remove specified values in vec, within ``prec``."""
319			return vec[~np.any(np.abs(vec[:, None] - vals_to_remove) < prec, axis=-1)]
320
321
322			def _shared_vals(v1, v2, prec):
323			"""Return values shared between v1, v2 within ``prec``."""
324			return v1[np.argwhere(np.abs(v1[:, None] - v2) < prec)[:, 0]]
325

dwiddo / average-minimum-distance

Push — master ( 37d7fb...c02a6e )

amd.reconstruct._trilaterate() A

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