# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
#
# MDAnalysis --- https://www.mdanalysis.org
# Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors
# (see the file AUTHORS for the full list of names)
#
# Released under the GNU Public Licence, v2 or any higher version
#
# Please cite your use of MDAnalysis in published work:
#
# R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler,
# D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein.
# MDAnalysis: A Python package for the rapid analysis of molecular dynamics
# simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th
# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.
#
# N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein.
# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
#
#
"""Fast distance array computation --- :mod:`MDAnalysis.lib.distances`
===================================================================
Fast C-routines to calculate distance arrays from coordinate
arrays. Many of the functions also exist in parallel versions, that
typically provide higher performance than the serial code.
The boolean attribute MDAnalysis.lib.distances.USED_OPENMP can be
checked to see if OpenMP was used in the compilation of MDAnalysis.
Selection of acceleration ("backend")
-------------------------------------
All functions take the optional keyword *backend*, which determines
the type of acceleration. Currently, the following choices are
implemented (*backend* is case-insensitive):
.. Table:: Available *backends* for accelerated distance functions.
========== ========================= ======================================
*backend* module description
========== ========================= ======================================
"serial" :mod:`c_distances` serial implementation in C/Cython
"OpenMP" :mod:`c_distances_openmp` parallel implementation in C/Cython
with OpenMP
========== ========================= ======================================
.. versionadded:: 0.13.0
Functions
---------
.. autofunction:: distance_array(reference, configuration [, box [, result [, backend]]])
.. autofunction:: self_distance_array(reference [, box [,result [, backend]]])
.. autofunction:: calc_bonds(atom1, atom2 [, box, [, result [, backend]]])
.. autofunction:: calc_angles(atom1, atom2, atom3 [,box [, result [, backend]]])
.. autofunction:: calc_dihedrals(atom1, atom2, atom3, atom4 [,box [, result [, backend]]])
.. autofunction:: apply_PBC(coordinates, box [, backend])
.. autofunction:: capped_distance(reference, configuration, max_cutoff [, min_cutoff [, box [, method]]])
.. autofunction:: self_capped_distance(reference, max_cutoff, [, min_cutoff [, box [, method]]])
.. autofunction:: transform_RtoS(coordinates, box [, backend])
.. autofunction:: transform_StoR(coordinates, box [,backend])
.. autofunction:: MDAnalysis.lib._augment.augment_coordinates(coordinates, box, radius)
.. autofunction:: MDAnalysis.lib._augment.undo_augment(indices, translation, nreal)
"""
from __future__ import division, absolute_import
from six.moves import range
import numpy as np
from numpy.lib.utils import deprecate
from .mdamath import triclinic_vectors, triclinic_box
from ._augment import augment_coordinates, undo_augment
# hack to select backend with backend=<backend> kwarg. Note that
# the cython parallel code (prange) in parallel.distances is
# independent from the OpenMP code
import importlib
_distances = {}
_distances['serial'] = importlib.import_module(".c_distances",
package="MDAnalysis.lib")
try:
_distances['openmp'] = importlib.import_module(".c_distances_openmp",
package="MDAnalysis.lib")
except ImportError:
pass
del importlib
def _run(funcname, args=None, kwargs=None, backend="serial"):
"""Helper function to select a backend function *funcname*."""
args = args if args is not None else tuple()
kwargs = kwargs if kwargs is not None else dict()
backend = backend.lower()
try:
func = getattr(_distances[backend], funcname)
except KeyError:
raise ValueError("Function {0} not available with backend {1}; try one of: {2}".format(
funcname, backend, ", ".join(_distances.keys())))
return func(*args, **kwargs)
# serial versions are always available (and are typically used within
# the core and topology modules)
from .c_distances import (calc_distance_array,
calc_distance_array_ortho,
calc_distance_array_triclinic,
calc_self_distance_array,
calc_self_distance_array_ortho,
calc_self_distance_array_triclinic,
coord_transform,
calc_bond_distance,
calc_bond_distance_ortho,
calc_bond_distance_triclinic,
calc_angle,
calc_angle_ortho,
calc_angle_triclinic,
calc_dihedral,
calc_dihedral_ortho,
calc_dihedral_triclinic,
ortho_pbc,
triclinic_pbc)
from .c_distances_openmp import OPENMP_ENABLED as USED_OPENMP
def _box_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.
Parameters
----------
box : array
Box information of unknown format.
Returns
-------
boxtype : str
* ``ortho`` orthogonal box
* ``tri_vecs`` triclinic box vectors
* ``tri_box`` triclinic box lengths and angles
Raises
------
TypeError
If box is not float32.
ValueError
If box type not detected.
"""
if box.dtype != np.float32:
raise TypeError("Box must be of type float32")
boxtype = 'unknown'
if box.shape == (3,):
boxtype = 'ortho'
elif box.shape == (3, 3):
if np.all([box[0][1] == 0.0, # Checks that tri box is properly formatted
box[0][2] == 0.0,
box[1][2] == 0.0]):
boxtype = 'tri_vecs'
else:
boxtype = 'tri_vecs_bad'
elif box.shape == (6,):
if np.all(box[3:] == 90.):
boxtype = 'ortho'
else:
boxtype = 'tri_box'
if boxtype == 'unknown':
raise ValueError("box input not recognised"
", must be an array of box dimensions")
return boxtype
def _check_array(coords, desc):
"""Check an array is a valid array of coordinates
Must be:
(n,3) in shape
float32 data
"""
if (coords.ndim != 2 or coords.shape[1] != 3):
raise ValueError("{0} must be a sequence of 3 dimensional coordinates"
"".format(desc))
_check_array_dtype(coords, desc)
def _check_array_dtype(coords, desc):
"""Check whether an array contains values of dtype: np.float32 or not"""
if coords.dtype != np.float32:
raise TypeError("{0} must be of type float32".format(desc))
def _check_results_array(results, size):
"""Check the results array is ok to use
Must be:
same shape as size
float64
"""
if results.shape != size:
raise ValueError("Result array has incorrect size,"
"should be {0}, got {1}".format(size, results.shape))
if results.dtype != np.float64:
raise TypeError("Results array must be of type float64")
def _check_lengths_match(*arrays):
"""Check all arrays are same shape"""
ref = arrays[0].shape
if not all( a.shape == ref for a in arrays):
raise ValueError("Input arrays must all be same shape"
"Got {0}".format([a.shape for a in arrays]))
[docs]def distance_array(reference, configuration, box=None, result=None, backend="serial"):
"""Calculate all distances between a reference set and another configuration.
If there are *i* positions in reference, and *j* positions in configuration,
will calculate a *i* x *j* array of distances
If an *box* is supplied then a minimum image convention is used when
calculating distances.
If a 2D numpy array of dtype ``numpy.float64`` with the shape ``(len(reference),
len(configuration))`` is provided in *result* then this preallocated array is
filled. This can speed up calculations.
Parameters
----------
reference : numpy.ndarray
Reference coordinate array of shape ``(n, 3)`` (``dtype`` is arbitrary,
will be converted to ``dtype=numpy.float32`` internally)
configuration : numpy.ndarray
Configuration coordinate array of shape ``(m, 3)`` (``dtype`` is
arbitrary, will be converted to ``dtype=numpy.float32`` internally)
box : numpy.ndarray or None
Dimensions of the cell; if provided, the minimum image convention is
applied. The dimensions must be provided in the same format as returned
by by :attr:`MDAnalysis.coordinates.base.Timestep.dimensions`: ``[lx,
ly, lz, alpha, beta, gamma]``.
result : numpy.ndarray(dtype=numpy.float64), optional
Preallocated result array which must have the
shape ``(len(ref), len(conf))`` and ``dtype=numpy.float64``.
Avoids creating the array which saves time when the function
is called repeatedly. [``None``]
backend : str
Select the type of acceleration; "serial" is always available. Other
possibilities are "OpenMP" (OpenMP).
Returns
-------
d : numpy.ndarray
``(len(reference),len(configuration))`` numpy array with the distances
``d[i,j]`` between reference coordinates `i` and configuration
coordinates `j`.
Note
----
This method is slower than it could be because internally we need to make
copies of the ref and conf arrays.
.. versionchanged:: 0.13.0
Added *backend* keyword.
.. versionchanged:: 0.19.0
Internal dtype conversion of input coordinates to ``numpy.float32``.
"""
ref = reference.astype(np.float32, order='C', copy=True)
conf = configuration.astype(np.float32, order='C', copy=True)
_check_array(conf, 'conf')
_check_array(ref, 'ref')
if box is not None:
boxtype = _box_check(box)
# Convert [A,B,C,alpha,beta,gamma] to [[A],[B],[C]]
if (boxtype == 'tri_box'):
box = triclinic_vectors(box)
if (boxtype == 'tri_vecs_bad'):
box = triclinic_vectors(triclinic_box(box[0], box[1], box[2]))
confnum = conf.shape[0]
refnum = ref.shape[0]
if result is not None:
_check_results_array(result, (refnum, confnum))
distances = np.asarray(result)
else:
distances = np.zeros((refnum, confnum), np.float64)
if box is not None:
if boxtype == 'ortho':
_run("calc_distance_array_ortho",
args=(ref, conf, box, distances),
backend=backend)
else:
_run("calc_distance_array_triclinic",
args=(ref, conf, box, distances),
backend=backend)
else:
_run("calc_distance_array",
args=(ref, conf, distances),
backend=backend)
return distances
[docs]def self_distance_array(reference, box=None, result=None, backend="serial"):
"""Calculate all distances within a configuration *reference*.
If a *box* is supplied then a minimum image convention is used before
calculating distances.
If a 1D numpy array of dtype ``numpy.float64`` with the shape
``(N*(N-1)/2)`` is provided in *result* then this preallocated array
is filled. This can speed up calculations.
Parameters
----------
reference : numpy.ndarray
Reference coordinate array with ``N=len(ref)`` coordinates (``dtype`` is
arbitrary, will be converted to ``dtype=numpy.float32`` internally)
box : numpy.ndarray or None
Dimensions of the cell; if provided, the minimum image convention is
applied. The dimensions must be provided in the same format as returned
by :attr:`MDAnalysis.coordinates.base.Timestep.dimensions`: ``[lx,
ly, lz, alpha, beta, gamma]``.
result : numpy.ndarray(dtype=numpy.float64), optional
Preallocated result array which must have the shape ``(N*(N-1)/2,)`` and
dtype ``numpy.float64``. Avoids creating the array which saves time when
the function is called repeatedly. [``None``]
backend : str
Select the type of acceleration; "serial" is always available. Other
possibilities are "OpenMP" (OpenMP).
Returns
-------
d : numpy.ndarray
``N*(N-1)/2`` numpy 1D array with the distances dist[i,j] between ref
coordinates i and j at position d[k]. Loop through d:
.. code-block:: python
for i in range(N):
for j in range(i+1, N):
k += 1
dist[i,j] = d[k]
Note
----
This method is slower than it could be because internally we need to make
copies of the coordinate array.
.. versionchanged:: 0.13.0
Added *backend* keyword.
.. versionchanged:: 0.19.0
Internal dtype conversion of input coordinates to ``numpy.float32``.
"""
ref = reference.astype(np.float32, order='C', copy=True)
_check_array(ref, 'ref')
with_PBC = (box is not None)
if box is not None:
boxtype = _box_check(box)
# Convert [A,B,C,alpha,beta,gamma] to [[A],[B],[C]]
if (boxtype == 'tri_box'):
box = triclinic_vectors(box)
if (boxtype == 'tri_vecs_bad'):
box = triclinic_vectors(triclinic_box(box[0], box[1], box[2]))
refnum = ref.shape[0]
distnum = refnum * (refnum - 1) // 2
if result is not None:
_check_results_array(result, (distnum,))
distances = np.asarray(result)
else:
distances = np.zeros((distnum,), np.float64)
if box is not None:
if boxtype == 'ortho':
_run("calc_self_distance_array_ortho",
args=(ref, box, distances),
backend=backend)
else:
_run("calc_self_distance_array_triclinic",
args=(ref, box, distances),
backend=backend)
else:
_run("calc_self_distance_array",
args=(ref, distances),
backend=backend)
return distances
[docs]def capped_distance(reference, configuration, max_cutoff, min_cutoff=None,
box=None, method=None):
"""Calculates the pairs and distances within a specified distance
If a *box* is supplied, then a minimum image convention is used
to evaluate the distances.
An automatic guessing of optimized method to calculate the distances is
included in the function. An optional keyword for the method is also
provided. Users can override the method with this functionality.
Currently pkdtree and bruteforce are implemented.
Parameters
-----------
reference : array
reference coordinates array with shape ``reference.shape = (3,)``
or ``reference.shape = (len(reference), 3)``
configuration : array
Configuration coordinate array with shape ``reference.shape = (3,)``
or ``reference.shape = (len(reference), 3)``
max_cutoff : float
Maximum cutoff distance between the reference and configuration
min_cutoff : (optional) float
Minimum cutoff distance between reference and configuration [None]
box : (optional) array or None
The dimensions, if provided, must be provided in the same
The unitcell dimesions for this system format as returned
by :attr:`MDAnalysis.coordinates.base.Timestep.dimensions`:
``[lx,ly, lz, alpha, beta, gamma]``. Minimum image convention
is applied if the box is provided [None]
method : (optional) 'bruteforce' or 'pkdtree' or 'None'
Keyword to override the automatic guessing of method built-in
in the function [None]
Returns
-------
pairs : array
Pair of indices, one from each reference and configuration such that
distance between them is within the ``max_cutoff`` and ``min_cutoff``
pairs[i,j] contains the indices i from reference coordinates, and
j from configuration
distances : array
Distances corresponding to each pair of indices.
d[k] corresponding to the pairs[i,j] gives the distance between
i-th and j-th coordinate in reference and configuration respectively
.. code-block:: python
pairs, distances = capped_distances(reference, coordinates, max_cutoff)
for indx, [a,b] in enumerate(pairs):
coord1 = reference[a]
coord2 = configuration[b]
distance = distances[indx]
Note
-----
Currently only supports brute force and Periodic KDtree
.. SeeAlso:: :func:'MDAnalysis.lib.distances.distance_array'
.. SeeAlso:: :func:'MDAnalysis.lib.pkdtree.PeriodicKDTree'
"""
if box is not None:
if box.shape[0] != 6:
raise ValueError('Box Argument is of incompatible type. The dimension'
'should be either None or '
'of the type [lx, ly, lz, alpha, beta, gamma]')
method = _determine_method(reference, configuration,
max_cutoff, min_cutoff=min_cutoff,
box=box, method=method)
pairs, dist = method(reference, configuration, max_cutoff,
min_cutoff=min_cutoff, box=box)
return np.asarray(pairs), np.asarray(dist)
def _determine_method(reference, configuration, max_cutoff, min_cutoff=None,
box=None, method=None):
"""
Switch between different methods based on the the optimized time.
All the rules to select the method based on the input can be
incorporated here.
Parameters
----------
reference : array
reference coordinates array with shape ``reference.shape = (3,)``
or ``reference.shape = (len(reference), 3)``
configuration : array
Configuration coordinate array with shape ``reference.shape = (3,)``
or ``reference.shape = (len(reference), 3)``
max_cutoff : float
Maximum cutoff distance between the reference and configuration
min_cutoff : (optional) float
Minimum cutoff distance between reference and configuration [None]
box : (optional) array or None
The dimensions, if provided, must be provided in the same
The unitcell dimesions for this system format as returned
by :attr:`MDAnalysis.coordinates.base.Timestep.dimensions`:
``[lx,ly, lz, alpha, beta, gamma]``. Minimum image convention
is applied if the box is provided [None]
method : (optional) 'bruteforce' or 'pkdtree' or 'None'
Keyword to override the automatic guessing of method built-in
in the function [None]
Returns
-------
Method : Function object
Returns function object based on the rules and specified method
Note
----
Currently implemented methods are present in the ``methods`` dictionary
bruteforce : returns ``_bruteforce_capped``
PKDtree : return ``_pkdtree_capped`
"""
methods = {'bruteforce': _bruteforce_capped,
'pkdtree': _pkdtree_capped}
if method is not None:
return methods[method]
if len(reference) > 5000 and len(configuration) > 5000:
if box is None:
min_dim = np.array([reference.min(axis=0),
configuration.min(axis=0)])
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):
size = box[:3]
else:
tribox = triclinic_vectors(box)
size = tribox.max(axis=0) - tribox.min(axis=0)
if ((np.any(size < 10.0*max_cutoff) and
len(reference) > 100000 and
len(configuration) > 100000)):
return methods['bruteforce']
else:
return methods['pkdtree']
return methods['bruteforce']
def _bruteforce_capped(reference, configuration, max_cutoff,
min_cutoff=None, box=None):
"""Internal method for bruteforce calculations
Uses naive distance calulations and returns a list
containing the indices with one from each
reference and configuration arrays, such that the distance between
them is less than the specified cutoff distance
Returns
-------
pairs : list
List of ``[(i, j)]`` pairs such that atom-index ``i`` is
from reference and ``j`` from configuration array
distance: list
Distance between ``reference[i]`` and ``configuration[j]``
atom coordinate
"""
pairs, distance = [], []
reference = np.asarray(reference, dtype=np.float32)
configuration = np.asarray(configuration, dtype=np.float32)
if reference.shape == (3, ):
reference = reference[None, :]
if configuration.shape == (3, ):
configuration = configuration[None, :]
_check_array(reference, 'reference')
_check_array(configuration, 'configuration')
for i, coords in enumerate(reference):
dist = distance_array(coords[None, :], configuration, box=box)[0]
if min_cutoff is not None:
idx = np.where((dist < max_cutoff) & (dist > min_cutoff))[0]
else:
idx = np.where((dist < max_cutoff))[0]
for j in idx:
pairs.append((i, j))
distance.append(dist[j])
return pairs, distance
def _pkdtree_capped(reference, configuration, max_cutoff,
min_cutoff=None, box=None):
""" Capped Distance evaluations using KDtree.
Uses minimum image convention if *box* is specified
Returns:
--------
pairs : list
List of atom indices which are within the specified cutoff distance.
pairs `(i, j)` corresponds to i-th particle in reference and
j-th particle in configuration
distance : list
Distance between two atoms corresponding to the (i, j) indices
in pairs.
"""
from .pkdtree import PeriodicKDTree
pairs, distances = [], []
reference = np.asarray(reference, dtype=np.float32)
configuration = np.asarray(configuration, dtype=np.float32)
if reference.shape == (3, ):
reference = reference[None, :]
if configuration.shape == (3, ):
configuration = configuration[None, :]
_check_array(reference, 'reference')
_check_array(configuration, 'configuration')
kdtree = PeriodicKDTree(box=box)
cut = max_cutoff if box is not None else None
kdtree.set_coords(configuration, cutoff=cut)
# Search for every query point
for idx, centers in enumerate(reference):
kdtree.search(centers, max_cutoff)
indices = kdtree.get_indices()
dist = distance_array(centers.reshape((1, 3)),
configuration[indices], box=box)[0]
if min_cutoff is not None:
mask = np.where(dist > min_cutoff)[0]
dist = dist[mask]
indices = [indices[mask[i]] for i in range(len(mask))]
if len(indices) != 0:
for num, j in enumerate(indices):
pairs.append((idx, j))
distances.append(dist[num])
return pairs, distances
[docs]def self_capped_distance(reference, max_cutoff, min_cutoff=None,
box=None, method=None):
"""Finds all the pairs and respective distances within a specified cutoff
for a configuration *reference*
If a *box* is supplied, then a minimum image convention is used
to evaluate the distances.
An automatic guessing of optimized method to calculate the distances is
included in the function. An optional keyword for the method is also
provided. Users can override the method with this functionality.
Currently pkdtree and bruteforce are implemented.
Parameters
-----------
reference : array
reference coordinates array with shape ``reference.shape = (3,)``
or ``reference.shape = (len(reference), 3)``
max_cutoff : float
Maximum cutoff distance to check the neighbors with itself
min_cutoff : (optional) float
Minimum cutoff distance [None]
box : (optional) array or None
The dimensions, if provided, must be provided in the same
The unitcell dimesions for this system format as returned
by :attr:`MDAnalysis.coordinates.base.Timestep.dimensions`:
``[lx,ly, lz, alpha, beta, gamma]``. Minimum image convention
is applied if the box is provided [None]
method : (optional) 'bruteforce' or 'pkdtree' or 'None'
Keyword to override the automatic guessing of method built-in
in the function [None]
Returns
-------
pairs : array
Pair of indices such that distance between them is
within the ``max_cutoff`` and ``min_cutoff``
distances : array
Distances corresponding to each pair of indices.
d[k] corresponding to the pairs[i,j] gives the distance between
i-th and j-th coordinate in reference
.. code-block:: python
pairs, distances = self_capped_distances(reference, max_cutoff)
for indx, [a,b] in enumerate(pairs):
coord1, coords2 = reference[a], reference[b]
distance = distances[indx]
Note
-----
Currently only supports brute force and Periodic KDtree
.. SeeAlso:: :func:'MDAnalysis.lib.distances.self_distance_array'
.. SeeAlso:: :func:'MDAnalysis.lib.pkdtree.PeriodicKDTree'
"""
if box is not None:
if box.shape[0] != 6:
raise ValueError('Box Argument is of incompatible type. The dimension'
'should be either None or '
'of the type [lx, ly, lz, alpha, beta, gamma]')
method = _determine_method_self(reference, max_cutoff,
min_cutoff=min_cutoff,
box=box, method=method)
pairs, dist = method(reference, max_cutoff,
min_cutoff=min_cutoff, box=box)
return np.asarray(pairs), np.asarray(dist)
def _determine_method_self(reference, max_cutoff, min_cutoff=None,
box=None, method=None):
"""
Switch between different methods based on the the optimized time.
All the rules to select the method based on the input can be
incorporated here.
Parameters
----------
reference : array
reference coordinates array with shape ``reference.shape = (3,)``
or ``reference.shape = (len(reference), 3)``
max_cutoff : float
Maximum cutoff distance
min_cutoff : (optional) float
Minimum cutoff distance [None]
box : (optional) array or None
The dimensions, if provided, must be provided in the same
The unitcell dimesions for this system format as returned
by :attr:`MDAnalysis.coordinates.base.Timestep.dimensions`:
``[lx,ly, lz, alpha, beta, gamma]``. Minimum image convention
is applied if the box is provided [None]
method : (optional) 'bruteforce' or 'pkdtree' or 'None'
Keyword to override the automatic guessing of method built-in
in the function [None]
Returns
-------
Method : Function object
Returns function object based on the rules and specified method
Note
----
Currently implemented methods are present in the ``methods`` dictionary
bruteforce : returns ``_bruteforce_capped_self``
PKDtree : return ``_pkdtree_capped_self``
"""
methods = {'bruteforce': _bruteforce_capped_self,
'pkdtree': _pkdtree_capped_self}
if method is not None:
return methods[method]
if len(reference) > 5000:
if box is 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):
size = box[:3]
else:
tribox = triclinic_vectors(box)
size = tribox.max(axis=0) - tribox.min(axis=0)
if ((np.any(size < 10.0*max_cutoff) and
(len(reference) > 100000))):
return methods['bruteforce']
else:
return methods['pkdtree']
return methods['bruteforce']
def _bruteforce_capped_self(reference, max_cutoff, min_cutoff=None,
box=None):
"""Finds all the pairs among the *reference* coordinates within
a fixed distance using brute force method
Internal method using brute force method to evaluate all the pairs
of atoms within a fixed distance.
Returns
-------
pairs : array
Arrray of ``[i, j]`` pairs such that atom-index ``i``
and ``j`` from reference array are within the fixed distance
distance: array
Distance between ``reference[i]`` and ``reference[j]``
atom coordinate
"""
pairs, distance = [], []
reference = np.asarray(reference, dtype=np.float32)
if reference.shape == (3, ):
reference = reference[None, :]
for i, coords in enumerate(reference):
# Each pair of atoms needs to be checked only once.
# Only calculate distance for atomA and atomB
# if atomidA < atomidB
dist = distance_array(coords[None, :], reference[i+1:],
box=box)[0]
if min_cutoff is not None:
idx = np.where((dist < max_cutoff) & (dist > min_cutoff))[0]
else:
idx = np.where((dist < max_cutoff))[0]
for other_idx in idx:
# Actual atomid for atomB
# can be direclty obtained in this way
j = other_idx + 1 + i
pairs.append((i, j))
distance.append(dist[other_idx])
return np.asarray(pairs), np.asarray(distance)
def _pkdtree_capped_self(reference, max_cutoff, min_cutoff=None,
box=None):
"""Finds all the pairs among the coordinates within a fixed distance
using PeriodicKDTree
Internal method using PeriodicKDTree method to evaluate all the pairs
of atoms within a fixed distance.
Returns
-------
pairs : array
Array of ``[(i, j)]`` pairs such that atom-index ``i``
and ``j`` from reference array are within the fixed distance
distance: array
Distance between ``reference[i]`` and ``reference[j]``
atom coordinate
"""
from .pkdtree import PeriodicKDTree
reference = np.asarray(reference, dtype=np.float32)
if reference.shape == (3, ):
reference = reference[None, :]
pairs, distance = [], []
kdtree = PeriodicKDTree(box=box)
cut = max_cutoff if box is not None else None
kdtree.set_coords(reference, cutoff=cut)
pairs = kdtree.search_pairs(max_cutoff)
if pairs.size > 0:
refA, refB = pairs[:, 0], pairs[:, 1]
distance = calc_bonds(reference[refA], reference[refB], box=box)
if min_cutoff is not None:
mask = np.where(distance > min_cutoff)[0]
pairs, distance = pairs[mask], distance[mask]
return np.asarray(pairs), np.asarray(distance)
[docs]def calc_bonds(coords1, coords2, box=None, result=None, backend="serial"):
"""
Calculate all distances between a pair of atoms. *atom1* and *atom2* are both
arrays of coordinates, where atom1[i] and atom2[i] represent a bond.
In comparison to distance_array and self_distance_array which calculate distances
between all combinations of coordinates, calc_bonds can be used to calculate distance
between pairs of objects, similar to::
numpy.linalg.norm(a - b) for a, b in zip(coords1, coords2)
The optional argument *box* applies minimum image convention if supplied.
*box* can be either orthogonal or triclinic
If a 1D numpy array of dtype ``numpy.float64`` with ``len(atom1)`` elements is
provided in *result* then this preallocated array is filled. This can speed
up calculations.
bondlengths = calc_bonds(coords1, coords2 [, box [,result=bondlengths]])
Parameters
----------
coords1 : array
An array of coordinates for one half of the bond (``dtype`` is
arbitrary, will be converted to ``dtype=numpy.float32`` internally)
coords2 : array
An array of coordinates for the other half of bond (``dtype`` is
arbitrary, will be converted to ``dtype=numpy.float32`` internally)
box : array
The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by :attr:`MDAnalysis.coordinates.base.Timestep.dimensions`: ``[lx,
ly, lz, alpha, beta, gamma]``.
result : array, optional
Preallocated result array which must be same length as coord
arrays and ``dtype=numpy.float64``. Avoids creating the
array which saves time when the function is called repeatedly. [None]
backend : str
Select the type of acceleration; "serial" is always available. Other
possibilities are "OpenMP" (OpenMP).
Returns
-------
bondlengths : array
The length between each pair in coords1 and coords2
.. versionadded:: 0.8
.. versionchanged:: 0.13.0
Added *backend* keyword.
.. versionchanged:: 0.19.0
Internal dtype conversion of input coordinates to ``numpy.float32``.
"""
atom1 = coords1.astype(np.float32, order='C', copy=True)
atom2 = coords2.astype(np.float32, order='C', copy=True)
_check_array(atom1, 'atom1')
_check_array(atom2, 'atom2')
_check_lengths_match(atom1, atom2)
if box is not None:
boxtype = _box_check(box)
# Convert [A,B,C,alpha,beta,gamma] to [[A],[B],[C]]
if (boxtype == 'tri_box'):
box = triclinic_vectors(box)
if (boxtype == 'tri_vecs_bad'):
box = triclinic_vectors(triclinic_box(box[0], box[1], box[2]))
numatom = atom1.shape[0]
if result is not None:
_check_results_array(result, (numatom,))
distances = np.asarray(result)
else:
distances = np.zeros((numatom,), np.float64)
if box is not None:
if boxtype == 'ortho':
_run("calc_bond_distance_ortho",
args=(atom1, atom2, box, distances),
backend=backend)
else:
_run("calc_bond_distance_triclinic",
args=(atom1, atom2, box, distances),
backend=backend)
else:
_run("calc_bond_distance",
args=(atom1, atom2, distances),
backend=backend)
return distances
[docs]def calc_angles(coords1, coords2, coords3, box=None, result=None, backend="serial"):
"""
Calculates the angle formed between three atoms, over a list of coordinates.
All *atom* inputs are lists of coordinates of equal length, with *atom2*
representing the apex of the angle.
If a 1D numpy array of dtype ``numpy.float64`` with ``len(atom1)`` elements is
provided in *result* then this preallocated array is filled. This can speed
up calculations.
The optional argument ``box`` ensures that periodic boundaries are taken into account when
constructing the connecting vectors between atoms, ie that the vector between atoms 1 & 2
goes between coordinates in the same image.
angles = calc_angles(coords1, coords2, coords3, [[box=None],result=angles])
Parameters
----------
coords1 : numpy.ndarray
Coordinate array of one side of angles (``dtype`` is arbitrary, will be
converted to ``dtype=numpy.float32`` internally)
coords2 : numpy.ndarray
Coordinate array of apex of angles (``dtype`` is arbitrary, will be
converted to ``dtype=numpy.float32`` internally)
coords3 : numpy.ndarray
Coordinate array of other side of angles (``dtype`` is arbitrary, will be
converted to ``dtype=numpy.float32`` internally)
box : numpy.ndarray, optional
The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by :attr:`MDAnalysis.coordinates.base.Timestep.dimensions`: ``[lx,
ly, lz, alpha, beta, gamma]``.
result : numpy.ndarray, optional
Preallocated result array which must be same length as coord
arrays and ``dtype=numpy.float64``. Avoids creating the
array which saves time when the function is called repeatedly. [None]
backend : str
Select the type of acceleration; "serial" is always available. Other
possibilities are "OpenMP" (OpenMP).
Returns
-------
angles : numpy.ndarray
An array of angles in radians.
.. versionadded:: 0.8
.. versionchanged:: 0.9.0
Added optional box argument to account for periodic boundaries in calculation
.. versionchanged:: 0.13.0
Added *backend* keyword.
.. versionchanged:: 0.19.0
Internal dtype conversion of input coordinates to ``numpy.float32``.
"""
atom1 = coords1.astype(np.float32, order='C', copy=True)
atom2 = coords2.astype(np.float32, order='C', copy=True)
atom3 = coords3.astype(np.float32, order='C', copy=True)
numatom = atom1.shape[0]
_check_array(atom1, 'coords1')
_check_array(atom2, 'coords2')
_check_array(atom3, 'coords3')
_check_lengths_match(atom1, atom2, atom3)
if box is not None:
boxtype = _box_check(box)
# Convert [A,B,C,alpha,beta,gamma] to [[A],[B],[C]]
if (boxtype == 'tri_box'):
box = triclinic_vectors(box)
if (boxtype == 'tri_vecs_bad'):
box = triclinic_vectors(triclinic_box(box[0], box[1], box[2]))
if result is not None:
_check_results_array(result, (numatom,))
angles = np.asarray(result)
else:
angles = np.zeros((numatom,), np.float64)
if box is not None:
if boxtype == 'ortho':
_run("calc_angle_ortho",
args=(atom1, atom2, atom3, box, angles),
backend=backend)
else:
_run("calc_angle_triclinic",
args=(atom1, atom2, atom3, box, angles),
backend=backend)
else:
_run("calc_angle",
args=(atom1, atom2, atom3, angles),
backend=backend)
return angles
[docs]def calc_dihedrals(coords1, coords2, coords3, coords4, box=None, result=None,
backend="serial"):
"""
Calculate the dihedral angle formed by four atoms, over a list of coordinates.
Dihedral angle around axis connecting atoms 1 and 2 (i.e. the angle
between the planes spanned by atoms (0,1,2) and (1,2,3))::
3
|
1-----2
/
0
If a 1D numpy array of dtype ``numpy.float64`` with ``len(atom1)`` elements
is provided in *result* then this preallocated array is filled. This can
speed up calculations.
The optional argument ``box`` ensures that periodic boundaries are taken
into account when constructing the connecting vectors between atoms, ie
that the vector between atoms 1 & 2 goes between coordinates in the same
image::
angles = calc_dihedrals(coords1, coords2, coords3, coords4 [,box=box, result=angles])
Parameters
----------
coords1 : array
Coordinate array of 1st atom in dihedrals (``dtype`` is arbitrary, will
be converted to ``dtype=numpy.float32`` internally)
coords2 : array
Coordinate array of 2nd atom in dihedrals (``dtype`` is arbitrary, will
be converted to ``dtype=numpy.float32`` internally)
coords3 : array
Coordinate array of 3rd atom in dihedrals (``dtype`` is arbitrary, will
be converted to ``dtype=numpy.float32`` internally)
coords4 : array
Coordinate array of 4th atom in dihedrals (``dtype`` is arbitrary, will
be converted to ``dtype=numpy.float32`` internally)
box : array
The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by :attr:`MDAnalysis.coordinates.base.Timestep.dimensions`: ``[lx,
ly, lz, alpha, beta, gamma]``.
result : array, optional
Preallocated result array which must be same length as coord
arrays and ``dtype=numpy.float64``. Avoids creating the
array which saves time when the function is called repeatedly. [None]
backend : str
Select the type of acceleration; "serial" is always available. Other
possibilities are "OpenMP" (OpenMP).
Returns
-------
angles : array
A numpy.array of angles in radians.
.. versionadded:: 0.8
.. versionchanged:: 0.9.0
Added optional box argument to account for periodic boundaries in calculation
.. versionchanged:: 0.11.0
Renamed from calc_torsions to calc_dihedrals
.. versionchanged:: 0.13.0
Added *backend* keyword.
.. versionchanged:: 0.19.0
Internal dtype conversion of input coordinates to ``numpy.float32``.
"""
atom1 = coords1.astype(np.float32, order='C', copy=True)
atom2 = coords2.astype(np.float32, order='C', copy=True)
atom3 = coords3.astype(np.float32, order='C', copy=True)
atom4 = coords4.astype(np.float32, order='C', copy=True)
_check_array(atom1, 'atom1')
_check_array(atom2, 'atom2')
_check_array(atom3, 'atom3')
_check_array(atom4, 'atom4')
_check_lengths_match(atom1, atom2, atom3, atom4)
numatom = atom1.shape[0]
if box is not None:
boxtype = _box_check(box)
# Convert [A,B,C,alpha,beta,gamma] to [[A],[B],[C]]
if (boxtype == 'tri_box'):
box = triclinic_vectors(box)
if (boxtype == 'tri_vecs_bad'):
box = triclinic_vectors(triclinic_box(box[0], box[1], box[2]))
if result is not None:
_check_results_array(result, (numatom,))
angles = np.asarray(result)
else:
angles = np.zeros((numatom,), np.float64)
if box is not None:
if boxtype == 'ortho':
_run("calc_dihedral_ortho",
args=(atom1, atom2, atom3, atom4, box, angles),
backend=backend)
else:
_run("calc_dihedral_triclinic",
args=(atom1, atom2, atom3, atom4, box, angles),
backend=backend)
else:
_run("calc_dihedral",
args=(atom1, atom2, atom3, atom4, angles),
backend=backend)
return angles
[docs]def apply_PBC(incoords, box, backend="serial"):
"""Moves a set of coordinates to all be within the primary unit cell
newcoords = apply_PBC(coords, box)
Parameters
----------
incoords : numpy.ndarray
Coordinate array of shape ``(n, 3)`` (``dtype`` is arbitrary, will be
converted to ``dtype=numpy.float32`` internally)
box : array
The unitcell dimesions for this system; can be either orthogonal or
triclinic information. The dimensions must be provided in the same
format as returned by
:attr:`MDAnalysis.coordinates.base.Timestep.dimensions`: ``[lx, ly, lz,
alpha, beta, gamma]``.
backend : str
Select the type of acceleration; ``"serial"`` is always available. Other
possibilities are ``"OpenMP"`` (OpenMP).
Returns
-------
newcoords : numpy.ndarray(dtype=numpy.float32)
Coordinates that are now all within the primary unit cell, as defined
by box.
.. versionadded:: 0.8
.. versionchanged:: 0.13.0
Added *backend* keyword.
.. versionchanged:: 0.19.0
Internal dtype conversion of input coordinates to ``numpy.float32``.
"""
coords = incoords.astype(np.float32, order='C', copy=True)
_check_array(coords, 'coords')
coordnum = coords.shape[0]
# determine boxtype
boxtype = _box_check(box)
# Convert [A,B,C,alpha,beta,gamma] to [[A],[B],[C]]
if boxtype == 'tri_box':
box = triclinic_vectors(box)
if boxtype == 'tri_vecs_bad':
box = triclinic_vectors(triclinic_box(box[0], box[1], box[2]))
box_inv = np.zeros((3), dtype=np.float32)
if boxtype == 'ortho':
box_inv[0] = 1.0 / box[0]
box_inv[1] = 1.0 / box[1]
box_inv[2] = 1.0 / box[2]
_run("ortho_pbc",
args=(coords, box, box_inv),
backend=backend)
else:
box_inv[0] = 1.0 / box[0][0]
box_inv[1] = 1.0 / box[1][1]
box_inv[2] = 1.0 / box[2][2]
_run("triclinic_pbc",
args=(coords, box, box_inv),
backend=backend)
return coords
[docs]def calc_distance(a, b, box=None):
"""Distance between a and b
Parameters
----------
a, b : numpy.ndarray
single coordinate vectors
box : numpy.ndarray, optional
simulation box, if given periodic boundary conditions will be applied
.. versionadded:: 0.18.1
"""
return calc_bonds(a[None, :], b[None, :], box=box)[0]
[docs]def calc_angle(a, b, c, box=None):
"""Angle (in degrees) between a, b and c, where b is apex of angle
Parameters
----------
a, b, c : numpy.ndarray
single coordinate vectors
box : numpy.ndarray
simulation box if given periodic boundary conditions will be applied to
the vectors between atoms
.. versionadded:: 0.18.1
"""
return np.rad2deg(calc_angles(a[None, :], b[None, :], c[None, :], box=box)[0])
[docs]def calc_dihedral(a, b, c, d, box=None):
"""Dihedral angle (in degrees) between planes (a, b, c) and (b, c, d)
Parameters
----------
a, b, c, d : numpy.ndarray
single coordinate vectors
box : numpy.ndarray, optional
simulation box, if given periodic boundary conditions will be applied
.. versionadded:: 0.18.1
"""
return np.rad2deg(
calc_dihedrals(a[None, :], b[None, :], c[None, :], d[None, :], box)[0])