adaptivemd.analysis.pyemma package¶
Submodules¶
adaptivemd.analysis.pyemma.emma module¶
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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}}
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classmethod
from_dict
(dct)[source]¶ Reconstruct an object from a dictionary representation
Parameters: dct (dict) – the dictionary containing a state representation of the class. Returns: the reconstructed storable object Return type: StorableMixin
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to_dict
()[source]¶ Convert object into a dictionary representation
Used to convert the dictionary into JSON string for serialization
Returns: the dictionary representing the (immutable) state of the object Return type: dict
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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