API Documentation

Calculating path similarity — pathsimanalysis.psa

Added 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 [1]. 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.

Helper functions and variables

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

pathsimanalysis.psa.sqnorm(v, axis=None)

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

pathsimanalysis.psa.get_msd_matrix(P, Q, axis=None)

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 \(q_j \in Q\).

pathsimanalysis.psa.get_coord_axes(path)

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:

1. 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.

2. 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, …))

Classes, methods, and functions

pathsimanalysis.psa.get_path_metric_func(name)

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

pathsimanalysis.psa.hausdorff(P, Q)

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:

>>> import MDAnalysis as mda
>>> import numpy
>>> from MDAnalysis.tests.datafiles import PSF, DCD
>>> import pathsimanalysis as psa
>>> u = mda.Universe(PSF,DCD)
>>> mid = int(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
>>> psa.hausdorff(P,Q)
4.778663899862152
>>> psa.hausdorff(P,Q[::-1]) # hausdorff distance w/ reversed 2nd trajectory
4.778663899862152

Notes

scipy.spatial.distance.directed_hausdorff() is an optimized implementation of the early break algorithm of [2]; the latter code is used here to calculate the symmetric Hausdorff distance with an RMSD metric

pathsimanalysis.psa.hausdorff_wavg(P, Q)

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

>>> import MDAnalysis as mda
>>> from MDAnalysis import Universe
>>> from MDAnalysis.tests.datafiles import PSF, DCD
>>> import pathsimanalysis as psa
>>> u = mda.Universe(PSF,DCD)
>>> mid = int(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
>>> psa.hausdorff_wavg(P,Q)
2.5669644353703447
>>> psa.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 [1] for further details.

pathsimanalysis.psa.hausdorff_avg(P, Q)

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

>>> import MDAnalysis as mda
>>> from MDAnalysis.tests.datafiles import PSF, DCD
>>> import pathsimanalysis as psa
>>> u = mda.Universe(PSF,DCD)
>>> mid = int(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
>>> psa.hausdorff_avg(P,Q)
2.5669646575869005
>>> psa.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 [1] for further details.

pathsimanalysis.psa.hausdorff_neighbors(P, Q)

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 [1].

• scipy.spatial.distance.directed_hausdorff() can also provide the hausdorff neighbors.

pathsimanalysis.psa.discrete_frechet(P, Q)

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.

>>> import MDAnalysis as mda
>>> import numpy as np
>>> from MDAnalysis.tests.datafiles import PSF, DCD
>>> import pathsimanalysis as psa
>>> u = mda.Universe(PSF,DCD)
>>> mid = int(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
>>> psa.discrete_frechet(P,Q)
4.778663984013591
>>> psa.discrete_frechet(P,Q[::-1]) # frechet distance w/ 2nd trj reversed 2nd
6.842901117711383

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 [3][4]. The calculation of the continuous Fréchet distance is implemented with the dynamic programming algorithm of [5][6].

pathsimanalysis.psa.dist_mat_to_vec(N, i, j)

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 pathsimanalysis.psa.Path(universe, reference, select='name CA', path_select='all', ref_frame=0)

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.

Added in version 0.9.1.

u_original

Universe object with a trajectory

u_reference

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

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)

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()

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)

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)

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 pathsimanalysis.psa.PSAPair(npaths, i, j)

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.

Added in version 0.11.

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 pathsimanalysis.psa.PSAnalysis(universes, reference=None, select='name CA', ref_frame=0, path_select=None, labels=None, targetdir='.')

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.

Added 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.

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 which stores the calculated distance matrix

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

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)

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()

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()

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)

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()

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

property 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()

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

All filenames are determined by PSAnalysis.

See also

save_paths

property nearest_neighbors

The nearest neighbors for each (unique) pair of paths.

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

Note

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

plot(filename=None, linkage='ward', count_sort=False, distance_sort=False, figsize=4.5, labelsize=12)

Plot a clustered distance matrix.

Usese method linkage and plots the corresponding dendrogram. Rows (and columns) are identified using the list of strings specified by PSAnalysis.labels.

If filename is supplied then the figure is also written to file (the suffix determines the file type, e.g. pdf, png, eps, …). All other keyword arguments are passed on to matplotlib.pyplot.matshow().

Parameters:

• filename (str) – save figure to filename [None]

• linkage (str) – name of linkage criterion for clustering ['ward']

• count_sort (bool) – see scipy.cluster.hierarchy.dendrogram() [False]

• distance_sort (bool) – see scipy.cluster.hierarchy.dendrogram() [False]

• figsize (float) – set the vertical size of plot in inches [4.5]

• labelsize (float) – set the font size for colorbar labels; font size for path labels on dendrogram default to 3 points smaller [12]

Returns:

• Z – Z from cluster()

• dgram – dgram from cluster()

• dist_matrix_clus – clustered distance matrix (reordered)

• .. versionchanged:: 1.0.0 – tick1On, tick2On, label1On and label2On changed to tick1line, tick2line, label1 and label2 due to upstream deprecation (see #2493)

plot_annotated_heatmap(filename=None, linkage='ward', count_sort=False, distance_sort=False, figsize=8, annot_size=6.5)

Plot a clustered distance matrix.

Uses method linkage and plots annotated distances in the matrix. Rows (and columns) are identified using the list of strings specified by PSAnalysis.labels.

If filename is supplied then the figure is also written to file (the suffix determines the file type, e.g. pdf, png, eps, …). All other keyword arguments are passed on to matplotlib.pyplot.imshow().

Parameters:

• filename (str) – save figure to filename [None]

• linkage (str) – name of linkage criterion for clustering ['ward']

• count_sort (bool) – see scipy.cluster.hierarchy.dendrogram() [False]

• distance_sort (bool) – see scipy.cluster.hierarchy.dendrogram() [False]

• figsize (float) – set the vertical size of plot in inches [4.5]

• annot_size (float) – font size of annotation labels on heat map [6.5]

Returns:

• Z – Z from cluster()

• dgram – dgram from cluster()

• dist_matrix_clus – clustered distance matrix (reordered)

Note

This function requires the seaborn package, which can be installed with pip install seaborn or conda install seaborn.

Changed in version 1.0.0: tick1On, tick2On, label1On and label2On changed to tick1line, tick2line, label1 and label2 due to upstream deprecation (see #2493)

plot_nearest_neighbors(filename=None, idx=0, labels=('Path 1', 'Path 2'), figsize=4.5, multiplot=False, aspect_ratio=1.75, labelsize=12)

Plot nearest neighbor distances as a function of normalized frame number.

The frame number is mapped to the interval [0, 1].

If filename is supplied then the figure is also written to file (the suffix determines the file type, e.g. pdf, png, eps, …). All other keyword arguments are passed on to matplotlib.pyplot.imshow().

Parameters:

• filename (str) – save figure to filename [None]

• idx (int) – index of path (pair) comparison to plot [0]

• labels ((str, str)) – pair of names to label nearest neighbor distance curves [('Path 1', 'Path 2')]

• figsize (float) – set the vertical size of plot in inches [4.5]

• multiplot (bool) – set to True to enable plotting multiple nearest neighbor distances on the same figure [False]

• aspect_ratio (float) – set the ratio of width to height of the plot [1.75]

• labelsize (float) – set the font size for colorbar labels; font size for path labels on dendrogram default to 3 points smaller [12]

Returns:

ax

Return type:

axes

Note

This function requires the seaborn package, which can be installed with pip install seaborn or conda install seaborn.

property psa_pairs

The list of PSAPair instances for each pair of paths.

psa_pairs is a list of all PSAPair objects (in distance vector order). The elements of a PSAPair are pairs of paths that have been compared using PSAnalysis.run_pairs_analysis(). Each PSAPair contains nearest neighbor and Hausdorff pair information specific to a pair of paths. The nearest neighbor frames and distances for a PSAPair can be accessed in the nearest neighbor dictionary using the keys ‘frames’ and ‘distances’, respectively. E.g., PSAPair.nearest_neighbors['distances'] returns a pair of numpy.ndarray corresponding to the nearest neighbor distances for each path. Similarly, Hausdorff pair information can be accessed using PSAPair.hausdorff_pair with the keys ‘frames’ and ‘distance’.

Note

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

run(**kwargs)

Perform path similarity analysis on the trajectories to compute the distance matrix.

A number of parameters can be changed from the defaults. The result is stored as the array PSAnalysis.D.

Parameters:

• metric (str or callable) – selection string specifying the path metric to measure pairwise distances among PSAnalysis.paths or a callable with the same call signature as hausdorff() ['hausdorff']

• start (int) – start and stop frame index with step size: analyze trajectory[start:stop:step] [None]

• stop (int)

• step (int)

• .. versionchanged:: 1.0.0 – store and filename have been removed.

run_pairs_analysis(**kwargs)

Perform PSA Hausdorff (nearest neighbor) pairs analysis on all unique pairs of paths in PSAnalysis.paths.

Partial results can be stored in separate lists, where each list is indexed according to distance vector convention (i.e., element (i,j) in distance matrix representation corresponds to element \(s=N*i+j-(i+1)*(i+2)\) in distance vector representation, which is the \(s\)^th comparison). For each unique pair of paths, the nearest neighbors for that pair can be stored in NN and the Hausdorff pair in HP. PP stores the full information of Hausdorff pairs analysis that is available for each pair of path, including nearest neighbors lists and the Hausdorff pairs.

The pairwise distances are stored as the array PSAnalysis.D.

Parameters:

• start (int) – start and stop frame index with step size: analyze trajectory[start:stop:step] [None]

• stop (int)

• step (int)

• neighbors (bool) – if True, then stores dictionary of nearest neighbor frames/distances in PSAnalysis.NN [False]

• hausdorff_pairs (bool) – if True, then stores dictionary of Hausdorff pair frames/distances in PSAnalysis.HP [False]

save_paths(filename=None)

Save fitted PSAnalysis.paths to numpy compressed npz files.

The data are saved with numpy.savez_compressed() in the directory specified by PSAnalysis.targetdir.

Parameters:

filename (str) – specifies filename [None]

Returns:

filename

Return type:

str

See also

load

References

[1] (1,2,3,4)

Sean L. Seyler, Avishek Kumar, M. F. Thorpe, and Oliver Beckstein. Path similarity analysis: a method for quantifying macromolecular pathways. PLOS Computational Biology, 11(10):1–37, 10 2015. doi:10.1371/journal.pcbi.1004568.

[2]

Abdel Aziz Taha and Allan Hanbury. An efficient algorithm for calculating the exact hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(11):2153–2163, 2015. doi:10.1109/TPAMI.2015.2408351.

[3]

M. Maurice Frèchet. Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Matematico di Palermo (1884-1940), 22(1):1–72, 1906. doi:10.1007/BF03018603.

[4]

Helmut Alt and Michael Godau. Computing the frechet distance between two polygonal curves. International Journal of Computational Geometry & Applications, 05(01n02):75–91, 1995. doi:10.1142/S0218195995000064.

[5]

Thomas Eiter and Heikki Mannila. Computing discrete frechet distance. 05 1994.

[6]

Thomas Eiter and Heikki Mannila. Distance measures for point sets and their computation. Acta Informatica, 34(2):109–133, 1997. doi:10.1007/s002360050075.