dynsight.trajectory.Trj¶

class dynsight.trajectory.Trj(universe, trajslice=None)[source]¶

Contains a trajectory.

Parameters:

universe (Universe)
trajslice (slice | None)

universe¶

a MDAnalysis.Universe containing the trajectory.

Type:: MDAnalysis.core.universe.Universe

Warning

This class is under development. The name and type of the “universe” attribute may change in the future.

Methods

`dump_colored_trj`	Save an .xyz file with the labels for each atom.
`dump_xyz_with_insight`	Save an .xyz file with the insights values for each atom.
`get_coord_number`	Compute coordination number on the trajectory.
`get_coordinates`	Returns the coordinates as an array.
`get_lens`	Compute LENS on the trajectory.
`get_orientational_op`	Compute the magnitude of the orientational order parameter.
`get_rdf`	Compute the radial distribution function g(r).
`get_slice`	Returns a Trj with a subset of frames.
`get_soap`	Compute SOAP on the trajectory.
`get_timesoap`	Compute SOAP and then timeSOAP on the trajectory.
`get_velocity_alignment`	Compute the average velocity alignment.
`init_from_universe`	Initialize Trj object from MDAnalysis.Universe.
`init_from_xtc`	Initialize Trj object from .gro and .xtc files.
`init_from_xyz`	Initialize Trj object from .xyz file.
`with_slice`	Returns a Trj with a different frames' slice.

Attributes

`trajslice`
`universe`
`n_atoms`
`n_frames`

dump_colored_trj(labels, file_path)[source]¶

Save an .xyz file with the labels for each atom.

The output file has columns: atom_type, x, y, z, label.

Parameters:

labels (NDArray[np.int64])
file_path (Path)

Return type:

None

dump_xyz_with_insight(insight_list, file_path)[source]¶

Save an .xyz file with the insights values for each atom.

The output file has columns: atom_type, x, y, z, ins_1, ins_2, …

Parameters:

insight_list (list[Insight])
file_path (Path)

Return type:

None

get_coord_number(r_cut, centers='all', selection='all', respect_pbc=True, neigcounts=None, n_jobs=1)[source]¶

Compute coordination number on the trajectory.

Returns:

neighcounts: a list[list[AtomGroup]], it can be used to
speed up subsequent descriptors’ computations.
An Insight containing the number of neighbors. It has the
following meta: name, r_cut, centers, selection.

Return type:

tuple

Parameters:

r_cut (float)
centers (str)
selection (str)
respect_pbc (bool)
neigcounts (list[list[AtomGroup]] | None)
n_jobs (int)

get_coordinates(selection)[source]¶

Returns the coordinates as an array.

The array has shape (n_frames, n_atoms, n_coordinates).

Parameters:: selection (str)
Return type:: NDArray[np.float64]

get_lens(r_cut, delay=1, centers='all', selection='all', respect_pbc=True, n_jobs=1)[source]¶

Compute LENS on the trajectory.

Returns:

Insight: An Insight containing LENS. It has the following meta: name, r_cut, delay, centers, selection.

Parameters:

r_cut (float)
delay (int)
centers (str)
selection (str)
respect_pbc (bool)
n_jobs (int)

Return type:

Insight

get_orientational_op(r_cut, order=6, centers='all', selection='all', respect_pbc=True, neigcounts=None, n_jobs=1)[source]¶

Compute the magnitude of the orientational order parameter.

Returns:

neighcounts: a list[list[AtomGroup]], it can be used to
speed up subsequent descriptors’ computations.
An Insight containing the orientational order parameter.
It has the following meta: name, r_cut, order, centers, selection.

Return type:

tuple

Parameters:

r_cut (float)
order (int)
centers (str)
selection (str)
respect_pbc (bool)
neigcounts (list[list[AtomGroup]] | None)
n_jobs (int)

get_rdf(distances_range, s1='all', s2='all', exclusion_block=None, nbins=200, norm='rdf')[source]¶

Compute the radial distribution function g(r).

See https://docs.mdanalysis.org/1.1.1/documentation_pages/analysis/rdf.html.

Returns:

A list of values of the interparticle distance r
The corresponding list of values of g(r)

Return type:

tuple

Parameters:

distances_range (list[float])
s1 (str)
s2 (str)
exclusion_block (list[int] | None)
nbins (int)
norm (Literal['rdf', 'density', 'none'])

get_slice(start, stop, step)[source]¶

Returns a Trj with a subset of frames.

Warning

This function could fill up the memory in case of large trajectories and it’s deprecated. Use Trj.with_slice() instead.

Parameters:

start (int)
stop (int)
step (int)

Return type:

Trj

get_soap(r_cut, n_max, l_max, selection='all', centers='all', respect_pbc=True, n_jobs=1)[source]¶

Compute SOAP on the trajectory.

The returned Insight contains the following meta: name, r_cut, n_max, l_max, respect_pbc, selection, centers.

Parameters:

r_cut (float)
n_max (int)
l_max (int)
selection (str)
centers (str)
respect_pbc (bool)
n_jobs (int)

Return type:

Insight

get_timesoap(r_cut=None, n_max=None, l_max=None, soap_insight=None, selection='all', centers='all', respect_pbc=True, n_jobs=1, delay=1)[source]¶

Compute SOAP and then timeSOAP on the trajectory.

The returned Insights (soap and timesoap) contain the following meta: name, r_cut, n_max, l_max, respect_pbc, selection, centers. Regarding the timeSOAP Insight, the delay used is also included.

Parameters:

r_cut (float | None)
n_max (int | None)
l_max (int | None)
soap_insight (Insight | None)
selection (str)
centers (str)
respect_pbc (bool)
n_jobs (int)
delay (int)

Return type:

tuple[Insight, Insight]

get_velocity_alignment(r_cut, centers='all', selection='all', respect_pbc=True, neigcounts=None, n_jobs=1)[source]¶

Compute the average velocity alignment.

Returns:

neighcounts: a list[list[AtomGroup]], it can be used to
speed up subsequent descriptors’ computations.
An Insight containing the average velocities alignment.
It has the following meta: name, r_cut, centers, selection.

Return type: