4.2.3. Distance analysis — MDAnalysis.analysis.distances¶

This module provides functions to rapidly compute distances between atoms or groups of atoms.

dist() and between() can take atom groups that do not even have to be from the same Universe.

MDAnalysis.analysis.distances.distance_array(reference, configuration, box=None, result=None, backend='serial')[source]

Calculate all possible distances between a reference set and another configuration.

If there are n positions in reference and m positions in configuration, a distance array of shape (n, m) will be computed.

If the optional argument box is supplied, the minimum image convention is applied when calculating distances. Either orthogonal or triclinic boxes are supported.

If a 2D numpy array of dtype numpy.float64 with the shape (n, m) is provided in result, then this preallocated array is filled. This can speed up calculations.

Parameters: reference (numpy.ndarray) – Reference coordinate array of shape (3,) or (n, 3) (dtype is arbitrary, will be converted to numpy.float32 internally). configuration (numpy.ndarray) – Configuration coordinate array of shape (3,) or (m, 3) (dtype is arbitrary, will be converted to numpy.float32 internally). box (array_like, optional) – The unitcell dimensions of the system, which can be orthogonal or triclinic and must be provided in the same format as returned by MDAnalysis.coordinates.base.Timestep.dimensions: [lx, ly, lz, alpha, beta, gamma]. result (numpy.ndarray, optional) – Preallocated result array which must have the shape (n, m) and dtype numpy.float64. Avoids creating the array which saves time when the function is called repeatedly. backend ({'serial', 'OpenMP'}, optional) – Keyword selecting the type of acceleration. d – Array containing the distances d[i,j] between reference coordinates i and configuration coordinates j. numpy.ndarray (dtype=numpy.float64, shape=(n, m))

Changed in version 0.13.0: Added backend keyword.

Changed in version 0.19.0: Internal dtype conversion of input coordinates to numpy.float32. Now also accepts single coordinates as input.

MDAnalysis.analysis.distances.self_distance_array(reference, box=None, result=None, backend='serial')[source]

Calculate all possible distances within a configuration reference.

If the optional argument box is supplied, the minimum image convention is applied when calculating distances. Either orthogonal or triclinic boxes are supported.

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 of shape (3,) or (n, 3) (dtype is arbitrary, will be converted to numpy.float32 internally). box (array_like, optional) – The unitcell dimensions of the system, which can be orthogonal or triclinic and must be provided in the same format as returned by MDAnalysis.coordinates.base.Timestep.dimensions: [lx, ly, lz, alpha, beta, gamma]. result (numpy.ndarray, 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. backend ({'serial', 'OpenMP'}, optional) – Keyword selecting the type of acceleration. d – Array containing the distances dist[i,j] between reference 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]  numpy.ndarray (dtype=numpy.float64, shape=(n*(n-1)/2,))

Changed in version 0.13.0: Added backend keyword.

Changed in version 0.19.0: Internal dtype conversion of input coordinates to numpy.float32.

MDAnalysis.analysis.distances.contact_matrix(coord, cutoff=15.0, returntype='numpy', box=None)[source]

Calculates a matrix of contacts.

There is a fast, high-memory-usage version for small systems (returntype = ‘numpy’), and a slower, low-memory-usage version for larger systems (returntype = ‘sparse’).

If box dimensions are passed then periodic boundary conditions are applied.

Parameters: coord (array) – Array of coordinates of shape (N, 3) and dtype float32. cutoff (float, optional, default 15) – Particles within cutoff are considered to form a contact. returntype (string, optional, default "numpy") – Select how the contact matrix is returned. * "numpy": return as an (N. N) numpy.ndarray * "sparse": return as a scipy.sparse.lil_matrix box (array-like or None, optional, default None) – Simulation cell dimensions in the form of MDAnalysis.trajectory.base.Timestep.dimensions when periodic boundary conditions should be taken into account for the calculation of contacts. The contact matrix is returned in a format determined by the returntype keyword. array or sparse matrix

Changed in version 0.11.0: Keyword suppress_progmet and progress_meter_freq were removed.

MDAnalysis.analysis.distances.dist(A, B, offset=0, box=None)[source]

Return distance between atoms in two atom groups.

The distance is calculated atom-wise. The residue ids are also returned because a typical use case is to look at CA distances before and after an alignment. Using the offset keyword one can also add a constant offset to the resids which facilitates comparison with PDB numbering.

Parameters: B (A,) – AtomGroup with the same number of atoms offset (integer or tuple, optional, default 0) – An integer offset is added to resids_A and resids_B (see below) in order to produce PDB numbers. If offset is tuple then offset is added to resids_A and offset to resids_B. Note that one can actually supply numpy arrays of the same length as the atom group so that an individual offset is added to each resid. resids_A (array) – residue ids of the A group (possibly changed with offset) resids_B (array) – residue ids of the B group (possibly changed with offset) distances (array) – distances between the atoms
MDAnalysis.analysis.distances.between(group, A, B, distance)[source]

Return sub group of group that is within distance of both A and B

This function is not aware of periodic boundary conditions.

Can be used to find bridging waters or molecules in an interface.

Similar to “group and (AROUND A distance and AROUND B distance)”.

Parameters: group (AtomGroup) – Find members of group that are between A and B A (AtomGroup) – B (AtomGroup) – A and B are the groups of atoms between which atoms in group are searched for. The function works is more efficient if group is bigger than either A or B. distance (float) – maximum distance for an atom to be counted as in the vicinity of A or B AtomGroup of atoms that fulfill the criterion AtomGroup