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Issue 2046 lib distances rework continued #2114

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Merged
zemanj merged 5 commits into from
Oct 25, 2018
Merged

Issue 2046 lib distances rework continued #2114

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5518d0b
moved check_box() to lib.util; changed cutoff criterion of all *cappe…
zemanj Oct 19, 2018
225dfc0
double precision distances in nsgrid; changed cutoff criterion to <= …
zemanj Oct 19, 2018
767c5b2
changed tests accordingly
zemanj Oct 19, 2018
145490d
nsgrid docs fixup
zemanj Oct 20, 2018
42b856a
minor doc fix-ups for lib.util
zemanj Oct 20, 2018
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85 changes: 15 additions & 70 deletions package/MDAnalysis/lib/distances.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,7 +71,7 @@
import numpy as np
from numpy.lib.utils import deprecate

from .util import check_coords
from .util import check_coords, check_box
from .mdamath import triclinic_vectors, triclinic_box
from ._augment import augment_coordinates, undo_augment
from .nsgrid import FastNS
Expand Down Expand Up @@ -126,61 +126,6 @@ def _run(funcname, args=None, kwargs=None, backend="serial"):
from .c_distances_openmp import OPENMP_ENABLED as USED_OPENMP


def _check_box(box):
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@zemanj zemanj Oct 22, 2018

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This function is not specific to distance computation and might be useful elsewhere --> moved to lib.util.check_box()

"""Take a box input and deduce what type of system it represents based on
the shape of the array and whether all angles are 90 degrees.

Parameters
----------
box : array_like
The unitcell dimensions of the system, which can be orthogonal or
triclinic and must be provided in the same format as returned by
:attr:`MDAnalysis.coordinates.base.Timestep.dimensions`:\n
``[lx, ly, lz, alpha, beta, gamma]``.

Returns
-------
boxtype : str
* ``'ortho'`` orthogonal box
* ``'tri_vecs'`` triclinic box vectors

checked_box : numpy.ndarray (``dtype=numpy.float32``)
Array containing box information:
* If `boxtype` is ``'ortho'``, `cecked_box` will have the shape ``(3,)``
containing the x-, y-, and z-dimensions of the orthogonal box.
* If `boxtype` is ``'tri_vecs'``, `cecked_box` will have the shape
``(3, 3)`` containing the triclinic box vectors in a lower triangular
matrix as returned by
:meth:`~MDAnalysis.lib.mdamath.triclinic_vectors`.

Raises
------
ValueError
If `box` is not of the form ``[lx, ly, lz, alpha, beta, gamma]``
or contains data that is not convertible to ``numpy.float32``.

See Also
--------
MDAnalysis.lib.mdamath.triclinic_vectors


.. versionchanged: 0.19.0
* Enforced correspondence of `box` with specified format.
* Added automatic conversion of input to :class:`numpy.ndarray` with
dtype ``numpy.float32``.
* Now also returns the box in the format expected by low-level functions
in :mod:`~MDAnalysis.lib.c_distances`.
* Removed obsolete box types ``tri_box`` and ``tri_vecs_bad``.
"""
box = np.asarray(box, dtype=np.float32, order='C')
if box.shape != (6,):
raise ValueError("Invalid box information. Must be of the form "
"[lx, ly, lz, alpha, beta, gamma].")
if np.all(box[3:] == 90.):
return 'ortho', box[:3]
return 'tri_vecs', triclinic_vectors(box)


def _check_result_array(result, shape):
"""Check if the result array is ok to use.

Expand Down Expand Up @@ -282,7 +227,7 @@ def distance_array(reference, configuration, box=None, result=None,
return distances

if box is not None:
boxtype, box = _check_box(box)
boxtype, box = check_box(box)
if boxtype == 'ortho':
_run("calc_distance_array_ortho",
args=(reference, configuration, box, distances),
Expand Down Expand Up @@ -355,7 +300,7 @@ def self_distance_array(reference, box=None, result=None, backend="serial"):
return distances

if box is not None:
boxtype, box = _check_box(box)
boxtype, box = check_box(box)
if boxtype == 'ortho':
_run("calc_self_distance_array_ortho",
args=(reference, box, distances),
Expand Down Expand Up @@ -509,7 +454,7 @@ def _determine_method(reference, configuration, max_cutoff, min_cutoff=None,
max_dim = np.array([reference.max(axis=0),
configuration.max(axis=0)])
size = max_dim.max(axis=0) - min_dim.min(axis=0)
elif np.allclose(box[3:], 90):
elif np.all(box[3:] == 90.0):
size = box[:3]
else:
tribox = triclinic_vectors(box)
Expand Down Expand Up @@ -585,7 +530,7 @@ def _bruteforce_capped(reference, configuration, max_cutoff, min_cutoff=None,
mask = np.where((_distances <= max_cutoff) & \
(_distances > min_cutoff))
else:
mask = np.where((_distances < max_cutoff))
mask = np.where((_distances <= max_cutoff))
if mask[0].size > 0:
pairs = np.c_[mask[0], mask[1]]
if return_distances:
Expand Down Expand Up @@ -899,7 +844,7 @@ def _determine_method_self(reference, max_cutoff, min_cutoff=None, box=None,
min_dim = np.array([reference.min(axis=0)])
max_dim = np.array([reference.max(axis=0)])
size = max_dim.max(axis=0) - min_dim.min(axis=0)
elif np.allclose(box[3:], 90):
elif np.all(box[3:] == 90.0):
size = box[:3]
else:
tribox = triclinic_vectors(box)
Expand Down Expand Up @@ -962,13 +907,13 @@ def _bruteforce_capped_self(reference, max_cutoff, min_cutoff=None, box=None):
# coordinates to find distances between them.
if N > 1:
distvec = self_distance_array(reference, box=box)
dist = np.full((N, N), max_cutoff, dtype=np.float64)
dist = np.full((N, N), np.finfo(np.float64).max, dtype=np.float64)
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@zemanj zemanj Oct 22, 2018

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Old fill value max_cutoff doesn't work with cutoff criterion distance <= max_cutoff.
np.finfo(np.float64).max is the largest number representable by np.float64, so this fill value is guaranteed to work for all possible values of max_cutoff

dist[np.triu_indices(N, 1)] = distvec

if min_cutoff is not None:
mask = np.where((dist < max_cutoff) & (dist > min_cutoff))
mask = np.where((dist <= max_cutoff) & (dist > min_cutoff))
else:
mask = np.where((dist < max_cutoff))
mask = np.where((dist <= max_cutoff))

if mask[0].size > 0:
pairs = np.c_[mask[0], mask[1]]
Expand Down Expand Up @@ -1162,7 +1107,7 @@ def transform_RtoS(coords, box, backend="serial"):
"""
if len(coords) == 0:
return coords
boxtype, box = _check_box(box)
boxtype, box = check_box(box)
if boxtype == 'ortho':
box = np.diag(box)

Expand Down Expand Up @@ -1210,7 +1155,7 @@ def transform_StoR(coords, box, backend="serial"):
"""
if len(coords) == 0:
return coords
boxtype, box = _check_box(box)
boxtype, box = check_box(box)
if boxtype == 'ortho':
box = np.diag(box)

Expand Down Expand Up @@ -1283,7 +1228,7 @@ def calc_bonds(coords1, coords2, box=None, result=None, backend="serial"):

if numatom > 0:
if box is not None:
boxtype, box = _check_box(box)
boxtype, box = check_box(box)
if boxtype == 'ortho':
_run("calc_bond_distance_ortho",
args=(coords1, coords2, box, bondlengths),
Expand Down Expand Up @@ -1375,7 +1320,7 @@ def calc_angles(coords1, coords2, coords3, box=None, result=None,

if numatom > 0:
if box is not None:
boxtype, box = _check_box(box)
boxtype, box = check_box(box)
if boxtype == 'ortho':
_run("calc_angle_ortho",
args=(coords1, coords2, coords3, box, angles),
Expand Down Expand Up @@ -1480,7 +1425,7 @@ def calc_dihedrals(coords1, coords2, coords3, coords4, box=None, result=None,

if numatom > 0:
if box is not None:
boxtype, box = _check_box(box)
boxtype, box = check_box(box)
if boxtype == 'ortho':
_run("calc_dihedral_ortho",
args=(coords1, coords2, coords3, coords4, box, dihedrals),
Expand Down Expand Up @@ -1530,7 +1475,7 @@ def apply_PBC(coords, box, backend="serial"):
"""
if len(coords) == 0:
return coords
boxtype, box = _check_box(box)
boxtype, box = check_box(box)
if boxtype == 'ortho':
box_inv = box ** (-1)
_run("ortho_pbc", args=(coords, box, box_inv), backend=backend)
Expand Down
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