4.2.5. Calculating path similarity — `MDAnalysis.analysis.psa`¶

Author:	Sean Seyler
Year:	2015
Copyright:	GNU Public License v3

New in version 0.10.0.

The module contains code to calculate the geometric similarity of trajectories using path metrics such as the Hausdorff or Fréchet distances [Seyler2015]. The path metrics are functions of two paths and return a nonnegative number, i.e., a distance. Two paths are identical if their distance is zero, and large distances indicate dissimilarity. Each path metric is a function of the individual points (e.g., coordinate snapshots) that comprise each path and, loosely speaking, identify the two points, one per path of a pair of paths, where the paths deviate the most. The distance between these points of maximal deviation is measured by the root mean square deviation (RMSD), i.e., to compute structural similarity.

One typically computes the pairwise similarity for an ensemble of paths to produce a symmetric distance matrix, which can be clustered to, at a glance, identify patterns in the trajectory data. To properly analyze a path ensemble, one must select a suitable reference structure to which all paths (each conformer in each path) will be universally aligned using the rotations determined by the best-fit rmsds. Distances between paths and their structures are then computed directly with no further alignment. This pre-processing step is necessary to preserve the metric properties of the Hausdorff and Fréchet metrics; using the best-fit rmsd on a pairwise basis does not generally preserve the triangle inequality.

Note

The PSAnalysisTutorial outlines a typical application of PSA to a set of trajectories, including doing proper alignment, performing distance comparisons, and generating heat map-dendrogram plots from hierarchical clustering.

References

[Seyler2015]

(1, 2, 3, 4) Seyler SL, Kumar A, Thorpe MF, Beckstein O (2015) Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways. PLoS Comput Biol 11(10): e1004568. doi: 10.1371/journal.pcbi.1004568

4.2.5.1. Helper functions and variables¶

The following convenience functions are used by other functions in this module.

MDAnalysis.analysis.psa.sqnorm(v, axis=None)[source]¶

Compute the sum of squares of elements along specified axes.

Parameters:	v (numpy.ndarray) – coordinates axes (None / int / tuple (optional)) – Axes or axes along which a sum is performed. The default (axes = `None`) performs a sum over all the dimensions of the input array. The value of axes may be negative, in which case it counts from the last axis to the zeroth axis.
Returns:	the sum of the squares of the elements of v along axes
Return type:	float

MDAnalysis.analysis.psa.get_msd_matrix(P, Q, axis=None)[source]¶

Generate the matrix of pairwise mean-squared deviations between paths.

The MSDs between all pairs of points in P and Q are calculated, each pair having a point from P and a point from Q.

P (Q) is a numpy.ndarray of \(N_p\) (\(N_q\)) time steps, \(N\) atoms, and \(3N\) coordinates (e.g., MDAnalysis.core.groups.AtomGroup.positions). The pairwise MSD matrix has dimensions \(N_p\) by \(N_q\).

Parameters:	P (numpy.ndarray) – the points in the first path Q (numpy.ndarray) – the points in the second path
Returns:	msd_matrix – matrix of pairwise MSDs between points in P and points in Q
Return type:	numpy.ndarray

Notes

We calculate the MSD matrix

\[M_{ij} = ||p_i - q_j||^2\]

where \(p_i \in P\) and \(q_j \in Q\).

MDAnalysis.analysis.psa.get_coord_axes(path)[source]¶

Return the number of atoms and the axes corresponding to atoms and coordinates for a given path.

The path is assumed to be a numpy.ndarray where the 0th axis corresponds to a frame (a snapshot of coordinates). The \(3N\) (Cartesian) coordinates are assumed to be either:

all in the 1st axis, starting with the x,y,z coordinates of the first atom, followed by the x,*y*,*z* coordinates of the 2nd, etc.
in the 1st and 2nd axis, where the 1st axis indexes the atom number and the 2nd axis contains the x,*y*,*z* coordinates of each atom.

Parameters:	path (numpy.ndarray) – representing a path
Returns:	the number of atoms and the axes containing coordinates
Return type:	(int, (int, ..))

4.2.5.2. Classes, methods, and functions¶

MDAnalysis.analysis.psa.get_path_metric_func(name)[source]¶

Selects a path metric function by name.

Parameters:	name (str) – name of path metric
Returns:	path_metric – The path metric function specified by name (if found).
Return type:	function

MDAnalysis.analysis.psa.hausdorff(P, Q)[source]¶

Calculate the symmetric Hausdorff distance between two paths.

The metric used is RMSD, as opposed to the more conventional L2 (Euclidean) norm, because this is convenient for i.e., comparing protein configurations.

P (Q) is a numpy.ndarray of \(N_p\) (\(N_q\)) time steps, \(N\) atoms, and \(3N\) coordinates (e.g., MDAnalysis.core.groups.AtomGroup.positions). P (Q) has either shape \(N_p \times N \times 3\) (\(N_q \times N \times 3\)), or \(N_p \times (3N)\) (\(N_q \times (3N)\)) in flattened form.

Note that reversing the path does not change the Hausdorff distance.

Parameters:	P (numpy.ndarray) – the points in the first path Q (numpy.ndarray) – the points in the second path
Returns:	the Hausdorff distance between paths P and Q
Return type:	float

Example

Calculate the Hausdorff distance between two halves of a trajectory:

>>> from MDAnalysis.tests.datafiles import PSF, DCD
>>> u = Universe(PSF,DCD)
>>> mid = len(u.trajectory)/2
>>> ca = u.select_atoms('name CA')
>>> P = numpy.array([
...                ca.positions for _ in u.trajectory[:mid:]
...              ]) # first half of trajectory
>>> Q = numpy.array([
...                ca.positions for _ in u.trajectory[mid::]
...              ]) # second half of trajectory
>>> hausdorff(P,Q)
4.7786639840135905
>>> hausdorff(P,Q[::-1]) # hausdorff distance w/ reversed 2nd trajectory
4.7786639840135905

Notes

scipy.spatial.distance.directed_hausdorff() is an optimized implementation of the early break algorithm of [Taha2015]; the latter code is used here to calculate the symmetric Hausdorff distance with an RMSD metric

References

[Taha2015]

A. A. Taha and A. Hanbury. An efficient algorithm for calculating the exact Hausdorff distance. IEEE Transactions On Pattern Analysis And Machine Intelligence, 37:2153-63, 2015.

MDAnalysis.analysis.psa.hausdorff_wavg(P, Q)[source]¶

Calculate the weighted average Hausdorff distance between two paths.

P (Q) is a numpy.ndarray of \(N_p\) (\(N_q\)) time steps, \(N\) atoms, and \(3N\) coordinates (e.g., MDAnalysis.core.groups.AtomGroup.positions). P (Q) has either shape \(N_p \times N \times 3\) (\(N_q \times N \times 3\)), or \(N_p \times (3N)\) (\(N_q \times (3N)\)) in flattened form. The nearest neighbor distances for P (to Q) and those of Q (to P) are averaged individually to get the average nearest neighbor distance for P and likewise for Q. These averages are then summed and divided by 2 to get a measure that gives equal weight to P and Q.

Parameters:	P (numpy.ndarray) – the points in the first path Q (numpy.ndarray) – the points in the second path
Returns:	the weighted average Hausdorff distance between paths P and Q
Return type:	float

Example

>>> from MDAnalysis import Universe
>>> from MDAnalysis.tests.datafiles import PSF, DCD
>>> u = Universe(PSF,DCD)
>>> mid = len(u.trajectory)/2
>>> ca = u.select_atoms('name CA')
>>> P = numpy.array([
...                ca.positions for _ in u.trajectory[:mid:]
...              ]) # first half of trajectory
>>> Q = numpy.array([
...                ca.positions for _ in u.trajectory[mid::]
...              ]) # second half of trajectory
>>> hausdorff_wavg(P,Q)
2.5669644353703447
>>> hausdorff_wavg(P,Q[::-1]) # weighted avg hausdorff dist w/ Q reversed
2.5669644353703447

Notes

The weighted average Hausdorff distance is not a true metric (it does not obey the triangle inequality); see [Seyler2015] for further details.

MDAnalysis.analysis.psa.hausdorff_avg(P, Q)[source]¶

Calculate the average Hausdorff distance between two paths.

P (Q) is a numpy.ndarray of \(N_p\) (\(N_q\)) time steps, \(N\) atoms, and \(3N\) coordinates (e.g., MDAnalysis.core.groups.AtomGroup.positions). P (Q) has either shape \(N_p \times N \times 3\) (\(N_q \times N \times 3\)), or \(N_p \times (3N)\) (\(N_q \times (3N)\)) in flattened form. The nearest neighbor distances for P (to Q) and those of Q (to P) are all averaged together to get a mean nearest neighbor distance. This measure biases the average toward the path that has more snapshots, whereas weighted average Hausdorff gives equal weight to both paths.

Parameters:	P (numpy.ndarray) – the points in the first path Q (numpy.ndarray) – the points in the second path
Returns:	the average Hausdorff distance between paths P and Q
Return type:	float

Example

>>> from MDAnalysis.tests.datafiles import PSF, DCD
>>> u = Universe(PSF,DCD)
>>> mid = len(u.trajectory)/2
>>> ca = u.select_atoms('name CA')
>>> P = numpy.array([
...                ca.positions for _ in u.trajectory[:mid:]
...              ]) # first half of trajectory
>>> Q = numpy.array([
...                ca.positions for _ in u.trajectory[mid::]
...              ]) # second half of trajectory
>>> hausdorff_avg(P,Q)
2.5669646575869005
>>> hausdorff_avg(P,Q[::-1]) # hausdorff distance w/ reversed 2nd trajectory
2.5669646575869005

Notes

The average Hausdorff distance is not a true metric (it does not obey the triangle inequality); see [Seyler2015] for further details.

MDAnalysis.analysis.psa.hausdorff_neighbors(P, Q)[source]¶

Find the Hausdorff neighbors of two paths.

P (Q) is a numpy.ndarray of \(N_p\) (\(N_q\)) time steps, \(N\) atoms, and \(3N\) coordinates (e.g., MDAnalysis.core.groups.AtomGroup.positions). P (Q) has either shape \(N_p \times N \times 3\) (\(N_q \times N \times 3\)), or \(N_p \times (3N)\) (\(N_q \times (3N)\)) in flattened form.

Parameters:	P (numpy.ndarray) – the points in the first path Q (numpy.ndarray) – the points in the second path
Returns:	dictionary of two pairs of numpy arrays, the first pair (key “frames”) containing the indices of (Hausdorff) nearest neighbors for P and Q, respectively, the second (key “distances”) containing (corresponding) nearest neighbor distances for P and Q, respectively
Return type:	dict

Notes

Hausdorff neighbors are those points on the two paths that are separated by the Hausdorff distance. They are the farthest nearest neighbors and are maximally different in the sense of the Hausdorff distance [Seyler2015].
scipy.spatial.distance.directed_hausdorff() can also provide the hausdorff neighbors.

MDAnalysis.analysis.psa.discrete_frechet(P, Q)[source]¶

Calculate the discrete Fréchet distance between two paths.

P (Q) is a numpy.ndarray of \(N_p\) (\(N_q\)) time steps, \(N\) atoms, and \(3N\) coordinates (e.g., MDAnalysis.core.groups.AtomGroup.positions). P (Q) has either shape \(N_p \times N \times 3\) (\(N_q \times N \times 3\)), or :\(N_p \times (3N)\) (\(N_q \times (3N)\)) in flattened form.

Parameters:	P (numpy.ndarray) – the points in the first path Q (numpy.ndarray) – the points in the second path
Returns:	the discrete Fréchet distance between paths P and Q
Return type:	float

Example

Calculate the discrete Fréchet distance between two halves of a trajectory.

>>> u = Universe(PSF,DCD)
>>> mid = len(u.trajectory)/2
>>> ca = u.select_atoms('name CA')
>>> P = np.array([
...                ca.positions for _ in u.trajectory[:mid:]
...              ]) # first half of trajectory
>>> Q = np.array([
...                ca.positions for _ in u.trajectory[mid::]
...              ]) # second half of trajectory
>>> discrete_frechet(P,Q)
4.7786639840135905
>>> discrete_frechet(P,Q[::-1]) # frechet distance w/ 2nd trj reversed 2nd
6.8429011177113832

Note that reversing the direction increased the Fréchet distance: it is sensitive to the direction of the path.

Notes

The discrete Fréchet metric is an approximation to the continuous Fréchet metric [Frechet1906] [Alt1995]. The calculation of the continuous Fréchet distance is implemented with the dynamic programming algorithm of [EiterMannila1994] [EiterMannila1997].

References

[Frechet1906]

M. Fréchet. Sur quelques points du calcul fonctionnel. Rend. Circ. Mat. Palermo, 22(1):1–72, Dec. 1906.

[Alt1995]

H. Alt and M. Godau. Computing the Fréchet distance between two polygonal curves. Int J Comput Geometry & Applications, 5(01n02):75–91, 1995. doi: 10.1142/S0218195995000064

[EiterMannila1994]

T. Eiter and H. Mannila. Computing discrete Fréchet distance. Technical Report CD-TR 94/64, Christian Doppler Laboratory for Expert Systems, Technische Universität Wien, Wien, 1994.

[EiterMannila1997]

T. Eiter and H. Mannila. Distance measures for point sets and their computation. Acta Informatica, 34:109–133, 1997. doi: 10.1007/s002360050075.

MDAnalysis.analysis.psa.dist_mat_to_vec(N, i, j)[source]¶

Convert distance matrix indices (in the upper triangle) to the index of the corresponding distance vector.

This is a convenience function to locate distance matrix elements (and the pair generating it) in the corresponding distance vector. The row index j should be greater than i+1, corresponding to the upper triangle of the distance matrix.

Parameters:	N (int) – size of the distance matrix (of shape N-by-N) i (int) – row index (starting at 0) of the distance matrix j (int) – column index (starting at 0) of the distance matrix
Returns:	index (of the matrix element) in the corresponding distance vector
Return type:	int

class MDAnalysis.analysis.psa.Path(universe, reference, select='name CA', path_select='all', ref_frame=0)[source]¶

Represent a path based on a Universe.

Pre-process a Universe object: (1) fit the trajectory to a reference structure, (2) convert fitted time series to a numpy.ndarray representation of Path.path.

The analysis is performed with PSAnalysis.run() and stores the result in the numpy.ndarray distance matrix PSAnalysis.D. PSAnalysis.run() also generates a fitted trajectory and path from alignment of the original trajectories to a reference structure.

New in version 0.9.1.

Setting up trajectory alignment and fitted path generation.

Parameters:

universe (Universe) – MDAnalysis.Universe object containing a trajectory
reference (Universe) – reference structure (uses ref_frame from the trajectory)
select (str or dict or tuple (optional)) –
The selection to operate on for rms fitting; can be one of:
1. any valid selection string for select_atoms() that produces identical selections in mobile and reference; or
2. a dictionary {'mobile':sel1, 'reference':sel2} (the MDAnalysis.analysis.align.fasta2select() function returns such a dictionary based on a ClustalW or STAMP sequence alignment); or
3. a tuple (sel1, sel2)
When using 2. or 3. with sel1 and sel2 then these selections can also each be a list of selection strings (to generate an AtomGroup with defined atom order as described under Ordered selections).
ref_frame (int) – frame index to select the coordinate frame from select.trajectory
path_select (selection_string) – atom selection composing coordinates of (fitted) path; if None then path_select is set to select [None]

u_original¶: MDAnalysis.Universe object with a trajectory

u_reference¶: MDAnalysis.Universe object containing a reference structure

select¶: string, selection for select_atoms() to select frame from Path.u_reference

path_select¶: string, selection for select_atoms() to select atoms to compose Path.path

ref_frame¶: int, frame index to select frame from Path.u_reference

u_fitted¶: MDAnalysis.Universe object with the fitted trajectory

path¶: numpy.ndarray object representation of the fitted trajectory

fit_to_reference(filename=None, prefix='', postfix='_fit', rmsdfile=None, targetdir='.', weights=None, tol_mass=0.1)[source]¶

Align each trajectory frame to the reference structure

Parameters:	filename (str (optional)) – file name for the RMS-fitted trajectory or pdb; defaults to the original trajectory filename (from `Path.u_original`) with prefix prepended prefix (str (optional)) – prefix for auto-generating the new output filename rmsdfile (str (optional)) – file name for writing the RMSD time series [`None`] weights ({“mass”, `None`} or array_like (optional)) – choose weights. With `"mass"` uses masses as weights; with `None` weigh each atom equally. If a float array of the same length as the selected AtomGroup is provided, use each element of the array_like as a weight for the corresponding atom in the AtomGroup. tol_mass (float (optional)) – Reject match if the atomic masses for matched atoms differ by more than tol_mass [0.1]
Returns:	`MDAnalysis.Universe` object containing a fitted trajectory
Return type:	Universe

Notes

Uses MDAnalysis.analysis.align.AlignTraj for the fitting.

Deprecated since version 0.16.1: Instead of mass_weighted=True use new weights='mass'; refactored to fit with AnalysisBase API

Changed in version 0.17.0: Deprecated keyword mass_weighted was removed.

get_num_atoms()[source]¶

Return the number of atoms used to construct the Path.

Must run Path.to_path() prior to calling this method.

Returns:	the number of atoms in the `Path`
Return type:	int

run(align=False, filename=None, postfix='_fit', rmsdfile=None, targetdir='.', weights=None, tol_mass=0.1, flat=False)[source]¶

Generate a path from a trajectory and reference structure.

As part of the path generation, the trajectory can be superimposed (“aligned”) to a reference structure if specified.

This is a convenience method to generate a fitted trajectory from an inputted universe (Path.u_original) and reference structure (Path.u_reference). Path.fit_to_reference() and Path.to_path() are used consecutively to generate a new universe (Path.u_fitted) containing the fitted trajectory along with the corresponding Path.path represented as an numpy.ndarray. The method returns a tuple of the topology name and new trajectory name, which can be fed directly into an MDAnalysis.Universe object after unpacking the tuple using the * operator, as in MDAnalysis.Universe(*(top_name, newtraj_name)).

Parameters:	align (bool (optional)) – Align trajectory to atom selection `Path.select` of `Path.u_reference`. If `True`, a universe containing an aligned trajectory is produced with `Path.fit_to_reference()` [`False`] filename (str (optional)) – filename for the RMS-fitted trajectory or pdb; defaults to the original trajectory filename (from `Path.u_original`) with prefix prepended postfix (str (optional)) – prefix for auto-generating the new output filename rmsdfile (str (optional)) – file name for writing the RMSD time series [`None`] weights ({“mass”, `None`} or array_like (optional)) – choose weights. With `"mass"` uses masses as weights; with `None` weigh each atom equally. If a float array of the same length as the selected AtomGroup is provided, use each element of the array_like as a weight for the corresponding atom in the AtomGroup. tol_mass (float (optional)) – Reject match if the atomic masses for matched atoms differ by more than tol_mass [0.1] flat (bool (optional)) – represent `Path.path` with 2D (\(N_p\times 3N\)) `numpy.ndarray`; if `False` then `Path.path` is a 3D (\(N_p\times N\times 3\)) `numpy.ndarray` [`False`]
Returns:	topology_trajectory – A tuple of the topology name and new trajectory name.
Return type:	tuple

Deprecated since version 0.16.1: Instead of mass_weighted=True use new weights='mass'; refactored to fit with AnalysisBase API

Changed in version 0.17.0: Deprecated keyword mass_weighted was removed.

to_path(fitted=False, select=None, flat=False)[source]¶

Generates a coordinate time series from the fitted universe trajectory.

Given a selection of N atoms from select, the atomic positions for each frame in the fitted universe (Path.u_fitted) trajectory (with \(N_p\) total frames) are appended sequentially to form a 3D or 2D (if flat is True) numpy.ndarray representation of the fitted trajectory (with dimensions \(N_p\times N\times 3\) or \(N_p\times 3N\), respectively).

Parameters:	fitted (bool (optional)) – construct a `Path.path` from the `Path.u_fitted` trajectory; if `False` then `Path.path` is generated with the trajectory from `Path.u_original` [`False`] select (str (optional)) – the selection for constructing the coordinates of each frame in `Path.path`; if `None` then `Path.path_select` is used, else it is overridden by select [`None`] flat (bool (optional)) – represent `Path.path` as a 2D (\(N_p\times 3N\)) `numpy.ndarray`; if `False` then `Path.path` is a 3D (\(N_p\times N\times 3\)) `numpy.ndarray` [`False`]
Returns:	representing a time series of atomic positions of an `MDAnalysis.core.groups.AtomGroup` selection from `Path.u_fitted.trajectory`
Return type:	numpy.ndarray

class MDAnalysis.analysis.psa.PSAPair(npaths, i, j)[source]¶

Generate nearest neighbor and Hausdorff pair information between a pair of paths from an all-pairs comparison generated by PSA.

The nearest neighbors for each path of a pair of paths is generated by PSAPair.compute_nearest_neighbors() and stores the result in a dictionary (nearest_neighbors): each path has a numpy.ndarray of the frames of its nearest neighbors, and a numpy.ndarray of its nearest neighbor distances PSAnalysis.D. For example, nearest_neighbors[‘frames’] is a pair of numpy.ndarray, the first being the frames of the nearest neighbors of the first path, i, the second being those of the second path, j.

The Hausdorff pair for the pair of paths is found by calling find_hausdorff_pair() (locates the nearest neighbor pair having the largest overall distance separating them), which stores the result in a dictionary (hausdorff_pair) containing the frames (indices) of the pair along with the corresponding (Hausdorff) distance. hausdorff_pair[‘frame’] contains a pair of frames in the first path, i, and the second path, j, respectively, that correspond to the Hausdorff distance between them.

New in version 0.11.

Set up a PSAPair for a pair of paths that are part of a PSA comparison of npaths total paths.

Each unique pair of paths compared using PSA is related by their nearest neighbors (and corresponding distances) and the Hausdorff pair and distance. PSAPair is a convenience class for calculating and encapsulating nearest neighbor and Hausdorff pair information for one pair of paths.

Given npaths, PSA performs and all-pairs comparison among all paths for a total of :math:` ext{npaths}*( ext{npaths}-1)/2` unique comparisons. If distances between paths are computed, the all-pairs comparison can be summarized in a symmetric distance matrix whose upper triangle can be mapped to a corresponding distance vector form in a one-to-one manner. A particular comparison of a pair of paths in a given instance of PSAPair is thus unique identified by the row and column indices in the distance matrix representation (whether or not distances are actually computed), or a single ID (index) in the corresponding distance vector.

Parameters:	npaths (int) – total number of paths in `PSA` used to generate this `PSAPair` i (int) – row index (starting at 0) of the distance matrix j (int) – column index (starting at 0) of the distance matrix

npaths¶: int, total number of paths in the comparison in which this PSAPair was generated

matrix_id¶: (int, int), (row, column) indices of the location of this PSAPair in the corresponding pairwise distance matrix

pair_id¶: int, ID of this PSAPair (the pair_id:math:^text{th} comparison) in the distance vector corresponding to the pairwise distance matrix

nearest_neighbors¶: dict, contains the nearest neighbors by frame index and the nearest neighbor distances for each path in this PSAPair

hausdorff_pair¶: dict, contains the frame indices of the Hausdorff pair for each path in this PSAPair and the corresponding (Hausdorff) distance

class MDAnalysis.analysis.psa.PSAnalysis(universes, reference=None, select='name CA', ref_frame=0, path_select=None, labels=None, targetdir='.')[source]¶

Perform Path Similarity Analysis (PSA) on a set of trajectories.

The analysis is performed with PSAnalysis.run() and stores the result in the numpy.ndarray distance matrix PSAnalysis.D. PSAnalysis.run() also generates a fitted trajectory and path from alignment of the original trajectories to a reference structure.

New in version 0.8.

Changed in version 1.0.0: save_result() method has been removed. You can use np.save() on PSAnalysis.D instead.

Setting up Path Similarity Analysis.

The mutual similarity between all unique pairs of trajectories are computed using a selected path metric.

Parameters:

universes (list) – a list of universes (MDAnalysis.Universe object), each containing a trajectory
reference (Universe) – reference coordinates; MDAnalysis.Universe object; if None the first time step of the first item in universes is used [None]
select (str or dict or tuple) –
The selection to operate on; can be one of:
1. any valid selection string for select_atoms() that produces identical selections in mobile and reference; or
2. a dictionary {'mobile':sel1, 'reference':sel2} (the MDAnalysis.analysis.align.fasta2select() function returns such a dictionary based on a ClustalW or STAMP sequence alignment); or
3. a tuple (sel1, sel2)
When using 2. or 3. with sel1 and sel2 then these selections can also each be a list of selection strings (to generate an AtomGroup with defined atom order as described under Ordered selections).
tol_mass (float) – Reject match if the atomic masses for matched atoms differ by more than tol_mass [0.1]
ref_frame (int) – frame index to select frame from reference [0]
path_select (str) – atom selection composing coordinates of (fitted) path; if None then path_select is set to select [None]
targetdir (str) – output files are saved there; if None then “./psadata” is created and used [.]
labels (list) – list of strings, names of trajectories to be analyzed (MDAnalysis.Universe); if None, defaults to trajectory names [None]

universes¶: list of MDAnalysis.Universe objects containing trajectories

u_reference¶: MDAnalysis.Universe object containing a reference structure

select¶: string, selection for select_atoms() to select frame from PSAnalysis.u_reference

path_select¶: string, selection for select_atoms() to select atoms to compose Path.path

ref_frame¶: int, frame index to select frame from Path.u_reference

paths¶: list of numpy.ndarray objects representing the set/ensemble of fitted trajectories

D¶: numpy.ndarray whichs store calculated distance matrix

cluster(dist_mat=None, method='ward', count_sort=False, distance_sort=False, no_plot=False, no_labels=True, color_threshold=4)[source]¶

Cluster trajectories and optionally plot the dendrogram.

This method is used by PSAnalysis.plot() to generate a heatmap- dendrogram combination plot. By default, the distance matrix, PSAnalysis.D, is assumed to exist, converted to distance-vector form, and inputted to cluster.hierarchy.linkage() to generate a clustering. For convenience in plotting arbitrary distance matrices, one can also be specify dist_mat, which will be checked for proper distance matrix form by spatial.distance.squareform()

Parameters:

dist_mat (numpy.ndarray) – user-specified distance matrix to be clustered [None]
method (str) – name of linkage criterion for clustering ['ward']
no_plot (bool) – if True, do not render the dendrogram [False]
no_labels (bool) – if True then do not label dendrogram [True]
color_threshold (float) – For brevity, let t be the color_threshold. Colors all the descendent links below a cluster node k the same color if k is the first node below the cut threshold t. All links connecting nodes with distances greater than or equal to the threshold are colored blue. If t is less than or equal to zero, all nodes are colored blue. If color_threshold is None or ‘default’, corresponding with MATLAB(TM) behavior, the threshold is set to 0.7*max(Z[:,2]). [4]]

Returns:

Z – output from scipy.cluster.hierarchy.linkage(); list of indices representing the row-wise order of the objects after clustering
dgram – output from scipy.cluster.hierarchy.dendrogram()

generate_paths(align=False, filename=None, infix='', weights=None, tol_mass=False, ref_frame=None, flat=False, save=True, store=False)[source]¶

Generate paths, aligning each to reference structure if necessary.

Parameters:

align (bool) – Align trajectories to atom selection PSAnalysis.select of PSAnalysis.u_reference [False]
filename (str) – strings representing base filename for fitted trajectories and paths [None]
infix (str) – additional tag string that is inserted into the output filename of the fitted trajectory files [‘’]
weights ({“mass”, None} or array_like (optional)) – choose weights. With "mass" uses masses as weights; with None weigh each atom equally. If a float array of the same length as the selected AtomGroup is provided, use each element of the array_like as a weight for the corresponding atom in the AtomGroup [None]
tol_mass (float) – Reject match if the atomic masses for matched atoms differ by more than tol_mass [False]
ref_frame (int) – frame index to select frame from reference [None]
flat (bool) – represent Path.path as a 2D (\(N_p\times 3N\)) numpy.ndarray; if False then Path.path is a 3D (\(N_p\times N\times 3\)) numpy.ndarray [False]
save (bool) – if True, pickle list of names for fitted trajectories [True]
store (bool) – if True then writes each path (numpy.ndarray) in PSAnalysis.paths to compressed npz (numpy) files [False]

The fitted trajectories are written to new files in the “/trj_fit” subdirectory in PSAnalysis.targetdir named “filename(trajectory)XXX*infix*_psa”, where “XXX” is a number between 000 and 999; the extension of each file is the same as its original. Optionally, the trajectories can also be saved in numpy compressed npz format in the “/paths” subdirectory in PSAnalysis.targetdir for persistence and can be accessed as the attribute PSAnalysis.paths.

Deprecated since version 0.16.1: Instead of mass_weighted=True use new weights='mass'; refactored to fit with AnalysisBase API

Changed in version 0.17.0: Deprecated keyword mass_weighted was removed.

Changed in version 1.0.0: Defaults for the store and filename keywords have been changed from True and fitted to False and None respectively. These now match the docstring documented defaults.

get_num_atoms()[source]¶

Return the number of atoms used to construct the Path instances in PSA.

Returns:	the number of atoms in any path
Return type:	int

Note

Must run PSAnalysis.generate_paths() prior to calling this method.

get_num_paths()[source]¶

Return the number of paths in PSA.

Note

Must run PSAnalysis.generate_paths() prior to calling this method.

Returns:	the number of paths in `PSA`
Return type:	int

get_pairwise_distances(vectorform=False, checks=False)[source]¶

Return the distance matrix (or vector) of pairwise path distances.

Note

Must run PSAnalysis.run() prior to calling this method.

Parameters:	vectorform (bool) – if `True`, return the distance vector instead [`False`] checks (bool) – if `True`, check that `PSAnalysis.D` is a proper distance matrix [`False`]
Returns:	representation of the distance matrix (or vector)
Return type:	numpy.ndarray

get_paths()[source]¶

Return the paths in PSA.

Note

Must run PSAnalysis.generate_paths() prior to calling this method.

Returns:	list of `numpy.ndarray` representations of paths in `PSA`
Return type:	list

hausdorff_pairs¶

The Hausdorff pair for each (unique) pairs of paths.

This attribute contains a list of Hausdorff pair information (in distance vector order), where each element is a dictionary containing the pair of frames and the (Hausdorff) distance between a pair of paths. See PSAnalysis.psa_pairs() and PSAPair.hausdorff_pair for more information about accessing Hausdorff pair data.

Note

Must run PSAnalysis.run_pairs_analysis() with hausdorff_pairs=True prior to calling this method.

load()[source]¶

Load fitted paths specified by ‘psa_path-names.pkl’ in PSAnalysis.targetdir.

All filenames are determined by PSAnalysis.

4.2.5. Calculating path similarity — MDAnalysis.analysis.psa¶

4.2.5.1. Helper functions and variables¶

4.2.5.2. Classes, methods, and functions¶

4.2.5. Calculating path similarity — `MDAnalysis.analysis.psa`¶