Ensembles

We perform Markov-chain Monte Carlo and estimate expectation values stochastically.

Ensembls are made up of configurations.

class supervillain.configurations.Configurations(dictionary)

Bases: Extendable, ReadWriteable

A group of configurations has fields (which you can access by doing cfgs.field) and other auxiliary information (one per configuration).

However, you can also use cfgs[step] to get a dictionary with keys that correspond to the names of the fields (and the auxiliary information) and associated values.

If you like you can think of a set of Configurations as a very lightweight barely-featured pandas DataFrame.

Parameters

dictionary (dict) – A dictionary with {key: value} pairs, where each value is an array whose first dimension is one per configuration.

__getitem__(index)

Parameters

index (fancy indexing) – A subset of numpy fancy indexing is supported; this selection is used for selecting configurations based on their location in the dataset, rather than their index. Some valid choices are 7, [1,2,3], slice(1,4), slice(1,10,2).

Returns

If index is an integer, returns a dictionary with key/value pairs for the requested configuration. If the index is fancier, return another set of Configurations.

Return type

one or many configurations

__setitem__(index, new)

Parameters

• index (fancy indexing) – Index or indices to overwrite.

• new (dictionary or Configurations) – Data to write.

items()

Like a dictionary’s .items(), iterates over the fields and auxiliary information.

extend_h5(group, _top=True)

copy()

class supervillain.ensemble.Ensemble(action)

Bases: Extendable

An ensemble of configurations importance-sampled according to the action.

Parameters

Action (an action) – An action which describes the path integral of interest.

Action

The action for the ensemble.

from_configurations(configurations)

Parameters

configurations – A set of pre-computed configurations.

Return type

The ensemble itself, so that one can do ensemble = Ensemble(action).from_configurations(cfgs).

generate(steps, generator, start='cold', progress=<function _no_op>, starting_index=0, index_stride=1)

Parameters

• steps (int) – Number of configurations to generate.

• generator – Something which produces a new configuration if called as generator.step(previous_configuration).

• start (‘cold’, or a configuration as a dictionary) – A cold start beins with the all-zero configuration. If a dictionary is passed it is used as the zeroeth configuration.

• progress (something which wraps an iterator and provides a progress bar.) – In a script you might use tqdm.tqdm, and in a notebook tqdm.notebook. Defaults to no progress reporting. Must accept a desc keyword argument.

• starting_index (int) – An ensemble has a .index which is an array of regularly-spaced integers labeling the configurations; this sets the lower value.

• index_stride (int) – The increment of the .index for each call of the generator.

Return type

the ensemble itself, so that one can do ensemble = GrandCanonical(action).generate(...).

classmethod continue_from(ensemble, steps, progress=<function _no_op>)

Use the last configuration and generator of ensemble to produce a new ensemble of steps configurations.

Parameters

• ensemble (supervillain.Ensemble or an h5py.Group that encodes such an ensemble) – The ensemble to continue. Raises a ValueError if it is not a supervillain.Ensemble or an h5py.Group with an action, generator, and at least one configuration.

• steps (int) – Number of configurations to generate.

• progress – As in generate().

Returns

An ensemble with steps new configurataions generted in the same way as ensemble.

Return type

supervillain.Ensemble

Todo

The starting weight should automatically be read in; currently not.

measure(observables=None)

If observables is None, measure every known primary observable on this ensemble. Otherwise measure only those observables named. If an observable is already computed, no new computation occurs.

Parameters

observables (None or iterable of strings naming observables.) – Observables to compute on this ensemble.

Returns

Keys are observable names, values are the measurements.

Return type

dict

property measured

A set of strings naming measured observables.

autocorrelation_time(observables=None, every=False)

Compute the autocorrelation time for the ensemble’s measurements. However, the autocorrelation time for any observable is only computed if that observable’s autocorrelation() is true for this ensemble.

However, if no measurements have been made so that measured is empty, try every observable with a true autocorrelation(). This may trigger measurement, and is usually what you want; after generation you want to thermalize or decorrelate.

Note

The measurement of some observables, particularly those for which autocorrelation() is false for this ensemble, is not triggered automatically, unless it is a prerequisite for an observable for autocorrelation() is true.

Parameters

• observables (None or iterable of strings naming observables.) – Which observables to consider. If None, consider all previously-measured observables.

• every (boolean) – If True returns a dictionary with keys given by observable names and values the computed autocorrelation times.

cut(start)

Good for thermalization.

thermalized = ensemble.cut(start)

Parameters

start (int) – How many configurations to drop from the beginning of the ensemble.

Returns

An ensemble with fewer configurations.

Return type

Ensemble

every(stride)

Good for decorrelation.

The generator is wrapped in KeepEvery so that continue_from() produces a strided follow-on ensemble.

decorrelated = thermalized.every(stride)

Parameters

stride (int) – How many configurations to skip.

Returns

An ensemble with fewer configurations.

Return type

Ensemble

plot_history(axes, observable, label=None, histogram_label=None, bins=31, density=True, alpha=0.5, color=None, history_kwargs={'label': None})

See also

Blocking.plot_history.