Model

Inheritance Diagram

Inheritance diagram of tenpy.models.model.Model

Methods

Model.__init__(lattice)

Model.copy()

Shallow copy of self.

Model.enlarge_mps_unit_cell([factor])

Repeat the unit cell for infinite MPS boundary conditions; in place.

Model.estimate_RAM_saving_factor()

Returns the expected saving factor for RAM based on charge conservation.

Model.extract_segment([first, last, enlarge])

Return a (shallow) copy with extracted segment of MPS.

Model.from_hdf5(hdf5_loader, h5gr, subpath)

Load instance from a HDF5 file.

Model.group_sites([n, grouped_sites])

Modify self in place to group sites.

Model.save_hdf5(hdf5_saver, h5gr, subpath)

Export self into a HDF5 file.

Model.update_time_parameter(new_time)

Reconstruct Hamiltonian for time-dependent models, potentially (!) in-place.

Class Attributes and Properties

Model.logger

class attribute.

Model.rng

Reproducible numpy pseudo random number generator.

class tenpy.models.model.Model(lattice)[source]

Bases: Hdf5Exportable

Base class for all models.

The common base to all models is the underlying Hilbert space and geometry, specified by a Lattice.

Parameters:

lattice (Lattice) – The lattice defining the geometry and the local Hilbert space(s).

lat

The lattice defining the geometry and the local Hilbert space(s).

Type:

Lattice

dtype

The data type of the Hamiltonian

Type:

dtype

logger = <Logger tenpy.models.model.Model (WARNING)>

class attribute.

Type:

logging.Logger

Type:

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

property rng

Reproducible numpy pseudo random number generator.

If you want to add randomness/disorder to your model, it is recommended use this random number generator for reproducibility of the model:

self.rng.random(size=[3, 5])

Especially for models with time-dependence, you can/will otherwise end up generating a new disordered at each time-step!

Options

random_seed :: None | int

Defaults to 123456789. Seed for numpy pseudo random number generator which can be used as e.g. self.rng.random(...).

copy()[source]

Shallow copy of self.

save_hdf5(hdf5_saver, h5gr, subpath)[source]

Export self into a HDF5 file.

Same as save_hdf5(), but handle rng.

classmethod from_hdf5(hdf5_loader, h5gr, subpath)[source]

Load instance from a HDF5 file.

Same as from_hdf5(), but handle rng.

extract_segment(first=0, last=None, enlarge=None)[source]

Return a (shallow) copy with extracted segment of MPS.

Parameters:
Returns:

cp – A shallow copy of self with MPO and lattice extracted for the segment.

Return type:

Model

enlarge_mps_unit_cell(factor=2)[source]

Repeat the unit cell for infinite MPS boundary conditions; in place.

This has to be done after finishing initialization and can not be reverted.

Parameters:

factor (int) – The new number of sites in the MPS unit cell will be increased from N_sites to factor*N_sites_per_ring. Since MPS unit cells are repeated in the x-direction in our convention, the lattice shape goes from (Lx, Ly, ..., Lu) to (Lx*factor, Ly, ..., Lu).

group_sites(n=2, grouped_sites=None)[source]

Modify self in place to group sites.

Group each n sites together using the GroupedSite. This might allow to do TEBD with a Trotter decomposition, or help the convergence of DMRG (in case of too long range interactions).

This has to be done after finishing initialization and can not be reverted.

Parameters:
  • n (int) – Number of sites to be grouped together.

  • grouped_sites (None | list of GroupedSite) – The sites grouped together.

Returns:

grouped_sites – The sites grouped together.

Return type:

list of GroupedSite

update_time_parameter(new_time)[source]

Reconstruct Hamiltonian for time-dependent models, potentially (!) in-place.

For TimeDependentHAlgorithm, we assume that the model reads out the parameter self.options['time'], and reinitialize/update the model calling this method.

Parameters:

new_time (float) – Time at which the (time-dependent) Hamiltonian should be constructed.

Returns:

updated_model – Model of the same class as self with Hamiltonian at time new_time. Note that it can just be a reference to self if modified in place, or an entirely new constructed model.

Return type:

model

estimate_RAM_saving_factor()[source]

Returns the expected saving factor for RAM based on charge conservation.

Returns:

factor – saving factor, due to conservation

Return type:

int

Options

mem_saving_factor :: None | float

Quantizes the RAM saving, due to conservation laws, to be used by estimate_simulation_RAM(). By default it is 1/mod, or 1/4 in case of mod=1. However, for some classes this factor might be overwritten, if a better approximation is known. In the best case, the user can adjust this model parameter to enhance the estimate.