11.2.1. Fast distance array computation — 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.
11.2.1.1. 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):
backend | module | description |
---|---|---|
“serial” | c_distances |
serial implementation in C/Cython |
“OpenMP” | c_distances_openmp |
parallel implementation in C/Cython with OpenMP |
New in version 0.13.0.
11.2.1.2. Functions¶
-
MDAnalysis.lib.distances.
distance_array
(reference, configuration[, box[, result[, backend]]])[source]¶ 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.array of numpy.float32) – Reference coordinate array.
- configuration (numpy.array of numpy.float32) – Configuration coordinate array.
- box (numpy.array 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
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - result (numpy.array of numpy.float64, optional) – Preallocated result array which must have the
shape
(len(ref), len(conf))
anddtype=numpy.float64
. Avoids creating the array which saves time when the function is called repeatedly. [None
] - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: d –
(len(reference),len(configuration))
numpy array with the distancesd[i,j]
between reference coordinates i and configuration coordinates j.Return type: Note
This method is slower than it could be because internally we need to make copies of the ref and conf arrays.
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
self_distance_array
(reference[, box[, result[, backend]]])[source]¶ 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 (array) – Reference coordinate array with
N=len(ref)
coordinates. - box (array 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
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - result (array, optional) – Preallocated result array which must have the shape
(N*(N-1)/2,)
and dtypenumpy.float64
. Avoids creating the array which saves time when the function is called repeatedly. [None
] - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: d –
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:for i in range(N): for j in range(i+1, N): k += 1 dist[i,j] = d[k]
Return type: Note
This method is slower than it could be because internally we need to make copies of the coordinate arrays.
Changed in version 0.13.0: Added backend keyword.
- reference (array) – Reference coordinate array with
-
MDAnalysis.lib.distances.
calc_bonds
(atom1, atom2[, box[, result[, backend]]])[source]¶ 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
withlen(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.
- coords2 (array) – An array of coordinates for the other half of bond
- box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
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 – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: bondlengths – The length between each pair in coords1 and coords2
Return type: New in version 0.8.
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
calc_angles
(atom1, atom2, atom3[, box[, result[, backend]]])[source]¶ 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
withlen(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 (array) – Coordinate array of one side of angles.
- coords2 (array) – Coordinate array of apex of angles.
- coords3 (array) – Coordinate array of other side of angles.
- box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
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 – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: angles – A numpy.array of angles in radians.
Return type: New in version 0.8.
Changed in version 0.9.0: Added optional box argument to account for periodic boundaries in calculation
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
calc_dihedrals
(atom1, atom2, atom3, atom4[, box[, result[, backend]]])[source]¶ 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
withlen(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.
- coords2 (array) – Coordinate array of 2nd atom in dihedrals.
- coords3 (array) – Coordinate array of 3rd atom in dihedrals.
- coords4 (array) – Coordinate array of 4th atom in dihedrals.
- box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
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 – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: angles – A numpy.array of angles in radians.
Return type: New in version 0.8.
Changed in version 0.9.0: Added optional box argument to account for periodic boundaries in calculation
Changed in version 0.11.0: Renamed from calc_torsions to calc_dihedrals
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
apply_PBC
(coordinates, box[, backend])[source]¶ Moves a set of coordinates to all be within the primary unit cell
newcoords = apply_PBC(coords, box)
Parameters: - coords (array) – Coordinate array (of type numpy.float32).
- 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
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: newcoords – Coordinates that are now all within the primary unit cell, as defined by box.
Return type: New in version 0.8.
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
transform_RtoS
(coordinates, box[, backend])[source]¶ Transform an array of coordinates from real space to S space (aka lambda space)
S space represents fractional space within the unit cell for this system
Reciprocal operation to
transform_StoR()
Parameters: - inputcoords (array) – A n x 3 array of coordinate data, of type
np.float32
. - box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: - outcoords (array) – A n x 3 array of fractional coordiantes.
- .. versionchanged:: 0.13.0 – Added backend keyword.
- inputcoords (array) – A n x 3 array of coordinate data, of type
-
MDAnalysis.lib.distances.
transform_StoR
(coordinates, box[, backend])[source]¶ Transform an array of coordinates from S space into real space.
S space represents fractional space within the unit cell for this system
Reciprocal operation to
transform_RtoS()
Parameters: - inputcoords (array) – A n x 3 array of coordinate data, of type np.float32
- box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: outcoords – A n x 3 array of fracional coordiantes.
Return type: Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
apply_PBC
(incoords, box, backend=’serial’)[source] Moves a set of coordinates to all be within the primary unit cell
newcoords = apply_PBC(coords, box)
Parameters: - coords (array) – Coordinate array (of type numpy.float32).
- 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
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: newcoords – Coordinates that are now all within the primary unit cell, as defined by box.
Return type: New in version 0.8.
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
calc_angles
(coords1, coords2, coords3, box=None, result=None, backend=’serial’)[source] 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
withlen(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 (array) – Coordinate array of one side of angles.
- coords2 (array) – Coordinate array of apex of angles.
- coords3 (array) – Coordinate array of other side of angles.
- box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
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 – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: angles – A numpy.array of angles in radians.
Return type: New in version 0.8.
Changed in version 0.9.0: Added optional box argument to account for periodic boundaries in calculation
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
calc_bonds
(coords1, coords2, box=None, result=None, backend=’serial’)[source] 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
withlen(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.
- coords2 (array) – An array of coordinates for the other half of bond
- box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
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 – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: bondlengths – The length between each pair in coords1 and coords2
Return type: New in version 0.8.
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
calc_dihedrals
(coords1, coords2, coords3, coords4, box=None, result=None, backend=’serial’)[source] 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
withlen(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.
- coords2 (array) – Coordinate array of 2nd atom in dihedrals.
- coords3 (array) – Coordinate array of 3rd atom in dihedrals.
- coords4 (array) – Coordinate array of 4th atom in dihedrals.
- box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
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 – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: angles – A numpy.array of angles in radians.
Return type: New in version 0.8.
Changed in version 0.9.0: Added optional box argument to account for periodic boundaries in calculation
Changed in version 0.11.0: Renamed from calc_torsions to calc_dihedrals
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
distance_array
(reference, configuration, box=None, result=None, backend=’serial’)[source] 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.array of numpy.float32) – Reference coordinate array.
- configuration (numpy.array of numpy.float32) – Configuration coordinate array.
- box (numpy.array 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
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - result (numpy.array of numpy.float64, optional) – Preallocated result array which must have the
shape
(len(ref), len(conf))
anddtype=numpy.float64
. Avoids creating the array which saves time when the function is called repeatedly. [None
] - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: d –
(len(reference),len(configuration))
numpy array with the distancesd[i,j]
between reference coordinates i and configuration coordinates j.Return type: Note
This method is slower than it could be because internally we need to make copies of the ref and conf arrays.
Changed in version 0.13.0: Added backend keyword.
-
MDAnalysis.lib.distances.
self_distance_array
(reference, box=None, result=None, backend=’serial’)[source] 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 (array) – Reference coordinate array with
N=len(ref)
coordinates. - box (array 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
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - result (array, optional) – Preallocated result array which must have the shape
(N*(N-1)/2,)
and dtypenumpy.float64
. Avoids creating the array which saves time when the function is called repeatedly. [None
] - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: d –
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:for i in range(N): for j in range(i+1, N): k += 1 dist[i,j] = d[k]
Return type: Note
This method is slower than it could be because internally we need to make copies of the coordinate arrays.
Changed in version 0.13.0: Added backend keyword.
- reference (array) – Reference coordinate array with
-
MDAnalysis.lib.distances.
transform_RtoS
(inputcoords, box, backend=’serial’)[source] Transform an array of coordinates from real space to S space (aka lambda space)
S space represents fractional space within the unit cell for this system
Reciprocal operation to
transform_StoR()
Parameters: - inputcoords (array) – A n x 3 array of coordinate data, of type
np.float32
. - box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: - outcoords (array) – A n x 3 array of fractional coordiantes.
- .. versionchanged:: 0.13.0 – Added backend keyword.
- inputcoords (array) – A n x 3 array of coordinate data, of type
-
MDAnalysis.lib.distances.
transform_StoR
(inputcoords, box, backend=’serial’)[source] Transform an array of coordinates from S space into real space.
S space represents fractional space within the unit cell for this system
Reciprocal operation to
transform_RtoS()
Parameters: - inputcoords (array) – A n x 3 array of coordinate data, of type np.float32
- box (array) – The unitcell dimesions for this system.
The dimensions must be provided in the same format as returned
by
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. - backend – Select the type of acceleration; “serial” is always available. Other possibilities are “OpenMP” (OpenMP).
Returns: outcoords – A n x 3 array of fracional coordiantes.
Return type: Changed in version 0.13.0: Added backend keyword.
11.2.2. Low-level modules for MDAnalysis.lib.distances
¶
MDAnalysis.lib._distances
contains low level access to the
serial MDAnalysis Cython functions in distances. These have
little to no error checking done on inputs so should be used with
caution. Similarly, MDAnalysis.lib._distances_openmp
contains
the OpenMP-enable functions.