adaptivemd.analysis.pyemma package

Submodules

adaptivemd.analysis.pyemma.emma module

class adaptivemd.analysis.pyemma.emma.PyEMMAAnalysis(engine, outtype='master', features=None)[source]

Bases: adaptivemd.analysis.analysis.Analysis

Common computation of correlations between features using PyEmma

Variables:
  • engine (Engine) – reference to an engine that knows about the topology
  • outtype (str) – name of the output description to pick the frames from
  • features (dict or list or None) –

    a feature descriptor in the format. A dict has exactly one entry: functionname: [attr1, attr2, ...]. attributes can be results of function calls. All function calls are to the featurizer object! If a list is given each element is considered to be a feature descriptor. If None (default) all coordinates will be added as features .add_all()

    Examples

    ::code

    # feat.add_backbone_torsions() {‘add_backbone_torsions’: None}

    # feat.add_distances([[0,10], [2,20]]) {‘add_distances’: [ [[0,10], [2,20]] ]}

    # feat.add_inverse_distances(select_backbone()) {‘add_inverse_distances’: {‘select_backbone’: None}}
classmethod from_dict(dct)[source]
to_dict()[source]
static then_func(project, task, data, inputs)[source]
execute(trajectories, tica_lag=2, tica_dim=2, msm_states=5, msm_lag=2, stride=1)[source]

Create a task that computes an msm using a given set of trajectories

Parameters:
  • trajectories (list of Trajectory) – the list of trajectory references to be used in the computation
  • tica_lag (int) – the lag-time used for tCIA
  • tica_dim (int) – number of dimensions using in tICA. This refers to the number of tIC used
  • msm_states (int) – number of micro-states used for the MSM
  • msm_lag (int) – lagtime used for the MSM construction
  • stride (int) – a stride to be used on the data. Can speed up computation at reduced accuracy
Returns:

a task object describing a simple python RPC call using pyemma
Return type:

Task

adaptivemd.analysis.pyemma.msmanalyze module

Module contents