pyemma.thermo.StationaryModel¶

class pyemma.thermo.StationaryModel(pi=None, f=None, normalize_energy=True, label='ground state')¶

StationaryModel combines a stationary vector with discrete-state free energies.

__init__(pi=None, f=None, normalize_energy=True, label='ground state')¶

StationaryModel combines a stationary vector with discrete-state free energies.

Parameters

pi (ndarray(n)) – Stationary distribution. If not already normalized, pi will be scaled to fulfill \(\sum_i \pi_i = 1\). The free energies f will be computed from pi via \(f_i = - \log(\pi_i)\). Only if normalize_f is True, a constant will be added to ensure consistency with \(\sum_i \pi_i = 1\).
f (ndarray(n)) – Discrete-state free energies. If normalized_f = True, a constant will be added to normalize the stationary distribution. Otherwise f is left as given. If both (pi and f) are given, f takes precedence.
normalize_energy (bool, default=True) – If parametrized by free energy f, normalize them such that \(\sum_i \pi_i = 1\), which is achieved by \(\log \sum_i \exp(-f_i) = 0\).
label (str, default='ground state') – Human-readable description for the thermodynamic state of this model. May contain a temperature description, such as ‘300 K’ or a description of bias energy such as ‘unbiased’ or ‘Umbrella 1’

Methods

`__init__`([pi, f, normalize_energy, label])	StationaryModel combines a stationary vector with discrete-state free energies.
`expectation`(a)	Equilibrium expectation value of a given observable.
`get_model_params`([deep])	Get parameters for this model.
`load`(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
`save`(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
`set_model_params`([pi, f, normalize_f, label])	Call to set all basic model parameters.
`update_model_params`(**params)	Update given model parameter if they are set to specific values

Attributes

`active_set`	The active set of states on which all computations and estimations will be done.
`f`	The free energies (in units of kT) on the configuration states.
`f_full_state`
`free_energies`	The free energies (in units of kT) on the configuration states.
`free_energies_full_state`
`label`	Human-readable description for the thermodynamic state of this model.
`nstates`	Number of active states on which all computations and estimations are done.
`nstates_full`	Size of the full set of states.
`pi`	The stationary distribution on the configuration states.
`pi_full_state`
`stationary_distribution`	The stationary distribution on the configuration states.
`stationary_distribution_full_state`

active_set¶: The active set of states on which all computations and estimations will be done.

expectation(a)¶

Equilibrium expectation value of a given observable.

Parameters: a ((M,) ndarray) – Observable vector
Returns: val – Equilibrium expectation value of the given observable
Return type: float

Notes

The equilibrium expectation value of an observable a is defined as follows

\[\mathbb{E}_{\mu}[a] = \sum_i \mu_i a_i\]

\(\mu=(\mu_i)\) is the stationary vector of the transition matrix \(T\).

f¶: The free energies (in units of kT) on the configuration states.

free_energies¶: The free energies (in units of kT) on the configuration states.

get_model_params(deep=True)¶

Get parameters for this model.

Parameters: deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: mapping of string to any

label¶: Human-readable description for the thermodynamic state of this model.

classmethod load(file_name, model_name='default')¶

Loads a previously saved PyEMMA object from disk.

Parameters

file_name (str or file like object (has to provide read method)) – The file like object tried to be read for a serialized object.
model_name (str, default='default') – if multiple models are contained in the file, these can be accessed by their name. Use pyemma.list_models() to get a representation of all stored models.

Returns

obj

Return type

the de-serialized object

nstates¶: Number of active states on which all computations and estimations are done.

nstates_full¶: Size of the full set of states.

pi¶: The stationary distribution on the configuration states.

save(file_name, model_name='default', overwrite=False, save_streaming_chain=False)¶

saves the current state of this object to given file and name.

Parameters

file_name (str) – path to desired output file
model_name (str, default='default') – creates a group named ‘model_name’ in the given file, which will contain all of the data. If the name already exists, and overwrite is False (default) will raise a RuntimeError.
overwrite (bool, default=False) – Should overwrite existing model names?
save_streaming_chain (boolean, default=False) – if True, the data_producer(s) of this object will also be saved in the given file.

Examples

>>> import pyemma, numpy as np
>>> from pyemma.util.contexts import named_temporary_file
>>> m = pyemma.msm.MSM(P=np.array([[0.1, 0.9], [0.9, 0.1]]))

>>> with named_temporary_file() as file: # doctest: +SKIP
...    m.save(file, 'simple') # doctest: +SKIP
...    inst_restored = pyemma.load(file, 'simple') # doctest: +SKIP
>>> np.testing.assert_equal(m.P, inst_restored.P) # doctest: +SKIP

set_model_params(pi=None, f=None, normalize_f=None, label=None)¶

Call to set all basic model parameters.

Parameters

pi (ndarray(n)) – Stationary distribution. If not already normalized, pi will be scaled to fulfill \(\sum_i \pi_i = 1\). The free energies f will then be computed from pi via \(f_i = - \log(\pi_i)\).
f (ndarray(n)) – Discrete-state free energies. If normalized_f = True, a constant will be added to normalize the stationary distribution. Otherwise f is left as given. Then, pi will be computed from f via \(\pi_i = \exp(-f_i)\) and, if necessary, scaled to fulfill \(\sum_i \pi_i = 1\). If both (pi and f) are given, f takes precedence over pi.
normalize_energy (bool, default=True) – If parametrized by free energy f, normalize them such that \(\sum_i \pi_i = 1\), which is achieved by \(\log \sum_i \exp(-f_i) = 0\).
label (str, default=None) – Human-readable description for the thermodynamic state of this model. May contain a temperature description, such as ‘300 K’ or a description of bias energy such as ‘unbiased’ or ‘Umbrella 1’.

stationary_distribution¶: The stationary distribution on the configuration states.

update_model_params(**params)¶: Update given model parameter if they are set to specific values