GroundStateSearch¶

full name: tenpy.simulations.ground_state_search.GroundStateSearch
parent module: tenpy.simulations.ground_state_search
type: class

Inheritance Diagram

Methods

`GroundStateSearch.__init__`(options, *[, ...])
`GroundStateSearch.final_measurements`()	Perform a last set of measurements.
`GroundStateSearch.fix_output_filenames`()	Determine the output filenames.
`GroundStateSearch.from_saved_checkpoint`([...])	Re-initialize a given simulation class from checkpoint results.
`GroundStateSearch.get_backup_filename`(...)	Extract the name used for backups of output_filename.
`GroundStateSearch.get_measurement_psi_model`(...)	Get psi for measurements.
`GroundStateSearch.get_output_filename`()	Read out the output_filename from the options.
`GroundStateSearch.get_version_info`()	Try to save version info which is necessary to allow reproducability.
`GroundStateSearch.group_sites_for_algorithm`()	Coarse-grain the model and state for the algorithm.
`GroundStateSearch.group_split`()	Split sites of psi that were grouped in `group_sites_for_algorithm()`.
`GroundStateSearch.init_algorithm`(**kwargs)	Initialize the algorithm.
`GroundStateSearch.init_cache`()	Initialize the `cache` from the options.
`GroundStateSearch.init_measurements`()	Initialize and prepare measurements.
`GroundStateSearch.init_model`()	Initialize a `model` from the model parameters.
`GroundStateSearch.init_state`()	Initialize a tensor network `psi`.
`GroundStateSearch.make_measurements`()	Perform measurements and merge the results into `self.results['measurements']`.
`GroundStateSearch.perform_measurements`()	Emits the `measurement_event` to call measurement functions and collect results.
`GroundStateSearch.prepare_results_for_save`()	Bring the results into a state suitable for saving.
`GroundStateSearch.resume_run`()	Resume a simulation that was initialized from a checkpoint.
`GroundStateSearch.resume_run_algorithm`()	Run the algorithm.
`GroundStateSearch.run`()	Run the whole simulation.
`GroundStateSearch.run_algorithm`()	Run the algorithm.
`GroundStateSearch.save_at_checkpoint`(alg_engine)	Save the intermediate results at the checkpoint of an algorithm.
`GroundStateSearch.save_results`([results])	Save the `results` to an output file.
`GroundStateSearch.walltime`()	Wall time evolved since initialization of the simulation class.

Class Attributes and Properties

`GroundStateSearch.default_algorithm`	name of the default algorithm engine class
`GroundStateSearch.default_measurements`	tuples as for `Simulation.connect_measurements` that get added if the `Simulation.use_default_measurements` is True.
`GroundStateSearch.logger`	class attribute.
`GroundStateSearch.verbose`

class tenpy.simulations.ground_state_search.GroundStateSearch(options, *, setup_logging=True, resume_data=None)[source]¶

Bases: Simulation

Simutions for variational ground state searches.

Parameters: options (dict-like) – The simulation parameters. Ideally, these options should be enough to fully specify all parameters of a simulation to ensure reproducibility.

Options

config GroundStateSearch¶

option	summary
algorithm_class (from Simulation) in Simulation.init_algorithm	Class or name of a subclass of :class:`~tenpy.algorithms.algorithm.Algorith [...]
algorithm_params (from Simulation) in Simulation.init_algorithm	Dictionary with parameters for the algortihm; see the decoumentation of the [...]
cache_params (from Simulation) in GroundStateSearch.init_cache	Dictionary with parameters for the cache, see [...]
cache_threshold_chi (from Simulation) in GroundStateSearch.init_cache	If the `algorithm_params.trunc_params.chi_max` in :attr:`options` is smalle [...]
canonicalize_before_measurement (from Simulation) in GroundStateSearch.init_measurements	If True, call `psi.canonical_form()` on the state used for measurement.
connect_algorithm_checkpoint (from Simulation) in Simulation.init_algorithm	Functions to connect to the :attr:`~tenpy.algorithms.Algorith.checkpoint` e [...]
connect_measurements (from Simulation) in GroundStateSearch.init_measurements	Functions to connect to the :attr:`measurement_event`. [...]
directory (from Simulation) in Simulation	If not None (default), switch to that directory at the beginning of the sim [...]
group_sites (from Simulation) in GroundStateSearch.group_sites_for_algorithm	How many sites to group. 1 means no grouping.
group_to_NearestNeighborModel (from Simulation) in GroundStateSearch.group_sites_for_algorithm	If True, convert the grouped model to a [...]
initial_state_builder_class (from Simulation) in GroundStateSearch.init_state	Class or name of a subclass of :class:`~tenpy.networks.mps.InitialStateBuil [...]
initial_state_params (from Simulation) in GroundStateSearch.init_state	Dictionary with parameters for building `psi`; see the decoumentation of th [...]
log_params (from Simulation) in Simulation	Log parameters; see :cfg:config:`log`.
measure_at_algorithm_checkpoints (from Simulation) in GroundStateSearch.init_measurements	Defaults to False. If True, make measurements at each algorithm checkpoint. [...]
measure_initial (from Simulation) in GroundStateSearch.init_measurements	Whether to perform a measurement on the initial state, i.e., before startin [...]
model_class (from Simulation) in GroundStateSearch.init_model	Mandatory. Class or name of a subclass of :class:`~tenpy.models.model.Model`.
model_params (from Simulation) in GroundStateSearch.init_model	Dictionary with parameters for the model; see the documentation of the [...]
output_filename (from Simulation) in GroundStateSearch.get_output_filename	If ``None`` (default), no output is written to files. [...]
output_filename_params (from Simulation) in GroundStateSearch.get_output_filename	Instead of specifying the `output_filename` directly, this dictionary descr [...]
overwrite_output (from Simulation) in GroundStateSearch.fix_output_filenames	Only makes a difference if `skip_if_output_exists` is False and the file ex [...]
random_seed (from Simulation) in Simulation	If not ``None``, initialize the (legacy) numpy random generator with the gi [...]
safe_write (from Simulation) in GroundStateSearch.fix_output_filenames	If True (default), perform a "safe" overwrite of `output_filename` as descr [...]
save_every_x_seconds (from Simulation) in GroundStateSearch.save_at_checkpoint	By default (``None``), this feature is disabled. [...]
save_psi (from Simulation) in GroundStateSearch.init_state	Whether the final :attr:`psi` should be included into the output :attr:`res [...]
save_stats in GroundStateSearch.init_algorithm	Whether to include the `sweep_stats` and `update_stats` of the engine into [...]
sequential (from Simulation) in Simulation	Parameters for running simulations sequentially, see :cfg:config:`sequentia [...]
skip_if_output_exists (from Simulation) in GroundStateSearch.fix_output_filenames	If True, raise :class:`Skip` if the output file already exists.
use_default_measurements (from Simulation) in GroundStateSearch.init_measurements	Each Simulation class defines a list of :attr:`default_measurements` in the [...]

default_algorithm = 'TwoSiteDMRGEngine'¶: name of the default algorithm engine class

default_measurements = [('tenpy.simulations.measurement', 'm_measurement_index', {}, 1), ('tenpy.simulations.measurement', 'm_bond_dimension'), ('tenpy.simulations.measurement', 'm_energy_MPO'), ('tenpy.simulations.measurement', 'm_entropy')]¶: tuples as for Simulation.connect_measurements that get added if the Simulation.use_default_measurements is True.

init_algorithm(**kwargs)[source]¶

Initialize the algorithm.

Options

option GroundStateSearch.save_stats: bool¶: Whether to include the sweep_stats and update_stats of the engine into the output.

run_algorithm()[source]¶

Run the algorithm.

Calls self.engine.run().

resume_run_algorithm()[source]¶

Run the algorithm.

Calls self.engine.run().

final_measurements()[source]¶: Perform a last set of measurements.

fix_output_filenames()[source]¶

Determine the output filenames.

This function determines the output_filename and writes a one-line text into that file to indicate that we’re running a simulation generating it. Further, _backup_filename is determined.

Options

option Simulation.skip_if_output_exists: bool¶: If True, raise Skip if the output file already exists.

option Simulation.overwrite_output: bool¶: Only makes a difference if skip_if_output_exists is False and the file exists. In that case, with overwrite_output, just save everything under that name again, or with overwrite_output`=False, replace ``filename.ext` with filename_01.ext (and further increasing numbers) until we get a filename that doesn’t exist yet.

option Simulation.safe_write: bool¶: If True (default), perform a “safe” overwrite of output_filename as described in save_results().

classmethod from_saved_checkpoint(filename=None, checkpoint_results=None, **kwargs)[source]¶

Re-initialize a given simulation class from checkpoint results.

You should probably call resume_run() after sucessfull initialization.

Instead of calling this directly, consider using resume_from_checkpoint().

Parameters

filename (None | str) – The filename of the checkpoint to be loaded. You can either specify the filename or the checkpoint_results.
checkpoint_results (None | dict) – Alternatively to filename the results of the simulation so far, i.e. directly the data dicitonary saved at a simulation checkpoint.
**kwargs – Further keyword arguments given to the Simulation.__init__.

get_backup_filename(output_filename)[source]¶

Extract the name used for backups of output_filename.

Parameters: output_filename (pathlib.Path) – The filename where data is saved.
Returns: backup_filename – The filename where to keep a backup while writing files to avoid.
Return type: pathlib.Path

get_measurement_psi_model(psi, model)[source]¶

Get psi for measurements.

Sometimes, the psi we want to use for measurements is different from the one the algorithm actually acts on. Here, we split sites, if they were grouped in group_sites_for_algorithm().

Parameters

psi – Tensor network; initially just self.psi. The method should make a copy before modification.
model – Model matching psi (in terms of indexing, MPS order, grouped sites, …) Initially just self.model.

Returns

psi – The psi suitable as argument for generic measurement functions.
model – Model matching psi (in terms of indexing, MPS order, grouped sites, …)

get_output_filename()[source]¶

Read out the output_filename from the options.

You can easily overwrite this method in subclasses to customize the outputfilename depending on the options passed to the simulations.

Options

option Simulation.output_filename: path_like | None¶: If None (default), no output is written to files. If a string, this filename is used for output (up to modifications by fix_output_filenames() to avoid overwriting previous results).

option Simulation.output_filename_params: dict¶: Instead of specifying the output_filename directly, this dictionary describes the parameters that should be included into it. Entries of the dictionary are keyword arguments to output_filename_from_dict() with the simulation parameters (Simulation, or equivalently options) as options.

Returns: output_filename – Filename for output; None disables any writing to files. Relative to Simulation.directory, if specified. The file ending determines the output format.
Return type: str | None

get_version_info()[source]¶: Try to save version info which is necessary to allow reproducability.

group_sites_for_algorithm()[source]¶

Coarse-grain the model and state for the algorithm.

Options

option Simulation.group_sites: int¶: How many sites to group. 1 means no grouping.

option Simulation.group_to_NearestNeighborModel: bool¶: If True, convert the grouped model to a NearestNeighborModel. Use this if you want to run TEBD with a model that was originally next-nearest neighbor.

group_split()[source]¶: Split sites of psi that were grouped in group_sites_for_algorithm().

init_cache()[source]¶

Initialize the cache from the options.

This method is only called automatically when the simulation is used in a with ... statement. This is the case if you use run_simulation(), etc.

Options

option Simulation.cache_threshold_chi: int¶: If the algorithm_params.trunc_params.chi_max in options is smaller than this threshold, do not initialize a (non-trivial) cache.

option Simulation.cache_params: dict¶: Dictionary with parameters for the cache, see open().

init_measurements()[source]¶

Initialize and prepare measurements.

Options

option Simulation.connect_measurements: list of tuple¶: Functions to connect to the measurement_event. Each tuple can be of length 2 to 4, with entries (module, function, kwargs, priority), the last two optionally. The mandatory module and function specify a callback measurement function. kwargs can specify extra keyword-arguments for the function, priority allows to tune the order in which the measurement functions get called. See connect_by_name() for more details.

option Simulation.use_default_measurements: bool¶: Each Simulation class defines a list of default_measurements in the same format as Simulation.connect_measurements. This flag allows to explicitly disable them.

option Simulation.measure_initial: bool¶: Whether to perform a measurement on the initial state, i.e., before starting the algorithm run.

option Simulation.measure_at_algorithm_checkpoints: bool¶: Defaults to False. If True, make measurements at each algorithm checkpoint. This can be useful to study e.g. the DMRG convergence with the number of sweeps. Note that (depending on the algorithm) psi might not be in canonical form during the algorithm run. In that case, you might need to also enable the canonicalize_before_measurement option to get correct e.g. correct long-range correlation functions. (On the other hand, local onsite expectation values are likely fine without the explicit canonical_form() call.)

option Simulation.canonicalize_before_measurement: bool¶: If True, call psi.canonical_form() on the state used for measurement.

init_model()[source]¶

Initialize a model from the model parameters.

Skips initialization if model is already set.

Options

option Simulation.model_class: str | class¶: Mandatory. Class or name of a subclass of Model.

option Simulation.model_params: dict¶: Dictionary with parameters for the model; see the documentation of the corresponding model_class.

init_state()[source]¶

Initialize a tensor network psi.

Skips initialization if psi is already set.

Options

option Simulation.initial_state_builder_class: str | class¶: Class or name of a subclass of InitialStateBuilder. Used to initialize psi according to the initial_state_params.

option Simulation.initial_state_params: dict¶: Dictionary with parameters for building psi; see the decoumentation of the initial_state_builder_class, e.g. InitialStateBuilder.

option Simulation.save_psi: bool¶: Whether the final psi should be included into the output results.

logger = <Logger tenpy.simulations.simulation.Simulation (WARNING)>¶

class attribute.

Type: logger
Type: An instance of a logger; see Logging and terminal output. NB

make_measurements()[source]¶: Perform measurements and merge the results into self.results['measurements'].

perform_measurements()[source]¶

Emits the measurement_event to call measurement functions and collect results.

Returns: results – The results from calling the measurement functions.
Return type: dict

prepare_results_for_save()[source]¶

Bring the results into a state suitable for saving.

For example, this can be used to convert lists to numpy arrays, to add more meta-data, or to clean up unnecessarily large entries.

Options

Cfg:configoptions :

Simulation

save_resume_databool: If True, include data from get_resume_data() into the output as resume_data.

Returns

results – A copy of results containing everything to be saved. Measurement results are converted into a numpy array (if possible).

Return type

dict

resume_run()[source]¶

Resume a simulation that was initialized from a checkpoint.

Returns: results – The results as returned by prepare_results_for_save().
Return type: dict

run()[source]¶

Run the whole simulation.

Returns: results – The results as returned by prepare_results_for_save().
Return type: dict

save_at_checkpoint(alg_engine)[source]¶

Save the intermediate results at the checkpoint of an algorithm.

Parameters: alg_engine (Algorithm) – The engine of the algorithm. Not used in this function, mostly there for compatibility with the tenpy.algorithms.Algorithm.checkpoint event.

Options

option Simulation.save_every_x_seconds: float | None¶: By default (None), this feature is disabled. If given, save the results obtained so far at each tenpy.algorithm.Algorithm.checkpoint when at least save_every_x_seconds seconds evolved since the last save (or since starting the algorithm). To avoid unnecessary, slow disk input/output, the value will be increased if saving takes longer than 10% of save_every_x_seconds. Use 0. to force saving at each checkpoint.

save_results(results=None)[source]¶

Save the results to an output file.

Performs a “safe” overwrite of output_filename by first moving the old file to _backup_filename, then writing the new file, and finally removing the backup.

Parameters: results (dict | None) – The results to be safed. If not specified, call prepare_results_for_save() to allow last-minute adjustments to the saved results.

walltime()[source]¶

Wall time evolved since initialization of the simulation class.

Utility measurement method. To measure it, add the following entry to the Simulation.connect_measurements option:

- - simulation_method
  - wrap walltime

Returns: seconds – Elapsed (wall clock) time in seconds since the initialization of the simulation.
Return type: float