4.3.3. 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.

See also

MDAnalysis.analysis.contacts

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:

A (AtomGroup) – AtomGroup with the same number of atoms
B (AtomGroup) – 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[0] is added to resids_A and offset[1] 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.

Returns:

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.distance_array(reference: ndarray[Any, dtype[_ScalarType_co]] | AtomGroup, configuration: ndarray[Any, dtype[_ScalarType_co]] | AtomGroup, box: ndarray[Any, dtype[_ScalarType_co]] | None = None, result: ndarray[Any, dtype[_ScalarType_co]] | None = None, backend: str = 'serial') → ndarray[Any, dtype[_ScalarType_co]][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 or AtomGroup) – Reference coordinate array of shape (3,) or (n, 3) (dtype is arbitrary, will be converted to numpy.float32 internally). Also accepts an AtomGroup.
configuration (numpy.ndarray or AtomGroup) – Configuration coordinate array of shape (3,) or (m, 3) (dtype is arbitrary, will be converted to numpy.float32 internally). Also accepts an AtomGroup.
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.timestep.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.

Returns:

d – Array containing the distances d[i,j] between reference coordinates i and configuration coordinates j.

Return type:

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.

Changed in version 2.3.0: Can now accept an AtomGroup as an argument in any position and checks inputs using type hinting.

MDAnalysis.analysis.distances.self_distance_array(reference: ndarray[Any, dtype[_ScalarType_co]] | AtomGroup, box: ndarray[Any, dtype[_ScalarType_co]] | None = None, result: ndarray[Any, dtype[_ScalarType_co]] | None = None, backend: str = 'serial') → ndarray[Any, dtype[_ScalarType_co]][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 or AtomGroup) – Reference coordinate array of shape (3,) or (n, 3) (dtype is arbitrary, will be converted to numpy.float32 internally). Also accepts an AtomGroup.
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.timestep.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.

Returns:

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]

Return type:

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.

Changed in version 2.3.0: Can now accept an AtomGroup as an argument in any position and checks inputs using type hinting.

4.3.3. Distance analysis — MDAnalysis.analysis.distances

4.3.3. Distance analysis — `MDAnalysis.analysis.distances`