MPOModel¶

full name: tenpy.models.model.MPOModel
parent module: tenpy.models.model
type: class

Inheritance Diagram

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`()
`MPOModel.update_time_parameter`(new_time)	Reconstruct Hamiltonian for time-dependent models, potentially (!) in-place.

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

first (int) – See extract_segment().
last (int) – See extract_segment().
enlarge (int) – See extract_segment().

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.

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