htmd.projections.tica module#
- class htmd.projections.tica.TICA(data: MetricData | Metric, lag: float, units: str = 'frames', dimensions: list | range | ndarray | None = None, njobs: int | None = None)#
Bases:
objectClass for calculating the TICA projections of a MetricData object.
Time-based Independent Component Analysis projects your data on the slowest coordinates identified for a given lagtime.
- Parameters:
data (
MetricDataobject orMetricobject) – The MetricData object whose data to project, or a Metric object for memory-efficient streaming TICA (projects trajectories on the fly).lag (float) – The correlation lagtime to use for TICA. Units are controlled by
units.units (str, optional) – The units of
lag. Can be'frames'or any time unit given as a string.dimensions (list, optional) – A list of dimensions of the original data on which to apply TICA. All other dimensions will stay unaltered. If None, TICA is applied on all dimensions.
njobs (int, optional) – Number of jobs to spawn for parallel computation of TICA components. If None it will use the default from htmd.config.
Examples
>>> from htmd.projections.tica import TICA >>> metr = Metric(sims) >>> metr.set(MetricSelfDistance('protein and name CA')) >>> data = metr.project() >>> tica = TICA(data, 20) >>> datatica = tica.project(3)
References
Perez-Hernandez, G. and Paul, F. and Giorgino, T. and de Fabritiis, G. and Noe, F. (2013) Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys., 139 . 015102.
- project(ndim: int | None = None, var_cutoff: float = 0.95) MetricData#
Project the data object given to the constructor onto the top TICA dimensions.
- Parameters:
- Returns:
dataTica – A new MetricData object containing the TICA projected data.
- Return type:
MetricDataobject
Examples
>>> from htmd.projections.tica import TICA >>> tica = TICA(data, 20) >>> dataTica = tica.project(5)