pyemma.thermo.MBAR¶
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class
pyemma.thermo.
MBAR
(maxiter=10000, maxerr=1e-15, save_convergence_info=0, dt_traj='1 step', direct_space=False)¶ Multi-state Bennet Acceptance Ratio Method.
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__init__
(maxiter=10000, maxerr=1e-15, save_convergence_info=0, dt_traj='1 step', direct_space=False)¶ Multi-state Bennet Acceptance Ratio Method
- Parameters
maxiter (int, optional, default=10000) – The maximum number of self-consistent iterations before the estimator exits unsuccessfully.
maxerr (float, optional, default=1.0E-15) – Convergence criterion based on the maximal free energy change in a self-consistent iteration step.
save_convergence_info (int, optional, default=0) – Every save_convergence_info iteration steps, store the actual increment and the actual loglikelihood; 0 means no storage.
dt_traj (str, optional, default='1 step') –
Description of the physical time corresponding to the lag. May be used by analysis algorithms such as plotting tools to pretty-print the axes. By default ‘1 step’, i.e. there is no physical time unit. Specify by a number, whitespace and unit. Permitted units are (* is an arbitrary string):
’fs’, ‘femtosecond*’’ps’, ‘picosecond*’’ns’, ‘nanosecond*’’us’, ‘microsecond*’’ms’, ‘millisecond*’’s’, ‘second*’stride (int, optional, default=1) – not used
Example
References
Methods
__init__
([maxiter, maxerr, …])Multi-state Bennet Acceptance Ratio Method
estimate
(X)- param X
Simulation trajectories. ttrajs contain the indices of the thermodynamic state, dtrajs
expectation
(a)Equilibrium expectation value of a given observable.
fit
(X[, y])Estimates parameters - for compatibility with sklearn.
get_model_params
([deep])Get parameters for this model.
get_params
([deep])Get parameters for this estimator.
load
(file_name[, model_name])Loads a previously saved PyEMMA object from disk.
meval
(f, *args, **kw)Evaluates the given function call for all models Returns the results of the calls in a list
pointwise_free_energies
([therm_state])save
(file_name[, model_name, overwrite, …])saves the current state of this object to given file and name.
set_model_params
([models, f_therm, pi, f, label])Call to set all basic model parameters.
set_params
(**params)Set the parameters of this estimator.
update_model_params
(**params)Update given model parameter if they are set to specific values
Attributes
The active set of states on which all computations and estimations will be done.
dt_traj
The free energies (in units of kT) on the configuration states.
f_full_state
The individual force matrices labelled accordingly to ttrajs.
The free energies (in units of kT) on the configuration states.
free_energies_full_state
Human-readable description for the thermodynamic state of this model.
The logger for this class instance
The model estimated by this Estimator
The name of this instance
Number of active states on which all computations and estimations are done.
Size of the full set of states.
The stationary distribution on the configuration states.
pi_full_state
The stationary distribution on the configuration states.
stationary_distribution_full_state
The individual temperatures labelled accordingly to ttrajs.
The individual umbrella centers labelled accordingly to ttrajs.
Index of the unbiased thermodynamic state.
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active_set
¶ The active set of states on which all computations and estimations will be done.
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estimate
(X)¶ - Parameters
X (tuple of (ttrajs, dtrajs, btrajs)) –
Simulation trajectories. ttrajs contain the indices of the thermodynamic state, dtrajs contains the indices of the configurational states and btrajs contain the biases.
- ttrajslist of numpy.ndarray(X_i, dtype=int)
Every elements is a trajectory (time series). ttrajs[i][t] is the index of the thermodynamic state visited in trajectory i at time step t.
- dtrajslist of numpy.ndarray(X_i, dtype=int)
dtrajs[i][t] is the index of the configurational state (Markov state) visited in trajectory i at time step t.
- btrajslist of numpy.ndarray((X_i, T), dtype=numpy.float64)
For every simulation frame seen in trajectory i and time step t, btrajs[i][t,k] is the bias energy of that frame evaluated in the k’th thermodynamic state (i.e. at the k’th Umbrella/Hamiltonian/temperature).
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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 \(T\).
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f
¶ The free energies (in units of kT) on the configuration states.
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fit
(X, y=None)¶ Estimates parameters - for compatibility with sklearn.
- Parameters
X (object) – A reference to the data from which the model will be estimated
- Returns
estimator – The estimator (self) with estimated model.
- Return type
object
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force_constants
¶ The individual force matrices labelled accordingly to ttrajs. (only set, when estimated from umbrella data).
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free_energies
¶ The free energies (in units of kT) on the configuration states.
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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
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get_params
(deep=True)¶ Get parameters for this estimator.
- 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
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label
¶ Human-readable description for the thermodynamic state of this model.
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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
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logger
¶ The logger for this class instance
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meval
(f, *args, **kw)¶ Evaluates the given function call for all models Returns the results of the calls in a list
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model
¶ The model estimated by this Estimator
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name
¶ The name of this instance
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nstates
¶ Number of active states on which all computations and estimations are done.
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nstates_full
¶ Size of the full set of states.
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pi
¶ The stationary distribution on the configuration states.
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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
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set_model_params
(models=None, f_therm=None, pi=None, f=None, label='ground state')¶ 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’.
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set_params
(**params)¶ Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object. :returns: :rtype: self
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stationary_distribution
¶ The stationary distribution on the configuration states.
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temperatures
¶ The individual temperatures labelled accordingly to ttrajs. (only set, when estimated from multi-temperature data).
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umbrella_centers
¶ The individual umbrella centers labelled accordingly to ttrajs. (only set, when estimated from umbrella data).
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unbiased_state
¶ Index of the unbiased thermodynamic state.
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update_model_params
(**params)¶ Update given model parameter if they are set to specific values
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