MPOModel

Inheritance Diagram

Inheritance diagram of tenpy.models.model.MPOModel

Methods

MPOModel.__init__(lattice, H_MPO)

MPOModel.calc_H_bond_from_MPO([tol_zero])

Calculate the bond Hamiltonian from the MPO Hamiltonian.

MPOModel.copy()

Shallow copy of self.

MPOModel.enlarge_mps_unit_cell([factor])

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

MPOModel.extract_segment(*args, **kwargs)

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

MPOModel.from_hdf5(hdf5_loader, h5gr, subpath)

Load instance from a HDF5 file.

MPOModel.group_sites([n, grouped_sites])

Modify self in place to group sites.

MPOModel.save_hdf5(hdf5_saver, h5gr, subpath)

Export self into a HDF5 file.

MPOModel.test_sanity()

Class Attributes and Properties

MPOModel.logger

class attribute.

MPOModel.rng

Reproducible numpy pseudo random number generator.

class tenpy.models.model.MPOModel(lattice, H_MPO)[source]

Bases: Model

Base class for a model with an MPO representation of the Hamiltonian.

In this class, the Hamiltonian gets represented by an MPO. Thus, instances of this class are suitable for MPO-based algorithms like DMRG dmrg and MPO time evolution.

Parameters

H_MPO (MPO) – The Hamiltonian rewritten as an MPO.

H_MPO

MPO representation of the Hamiltonian. If the explicit_plus_hc flag of the MPO is True, the represented Hamiltonian is H_MPO + hermitian_cojugate(H_MPO).

Type

tenpy.networks.mpo.MPO

copy()[source]

Shallow copy of self.

extract_segment(*args, **kwargs)[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 convetion, 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

calc_H_bond_from_MPO(tol_zero=1e-15)[source]

Calculate the bond Hamiltonian from the MPO Hamiltonian.

Parameters

tol_zero (float) – Arrays with norm < tol_zero are considered to be zero.

Returns

H_bond – Bond terms as required by the constructor of NearestNeighborModel. Legs are ['p0', 'p0*', 'p1', 'p1*']

Return type

list of Array

:raises ValueError : if the Hamiltonian contains longer-range terms.:

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

Load instance from a HDF5 file.

Same as from_hdf5(), but handle rng.

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(...).

save_hdf5(hdf5_saver, h5gr, subpath)[source]

Export self into a HDF5 file.

Same as save_hdf5(), but handle rng.