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.estimate_RAM_saving_factor()	Returns the expected saving factor for RAM based on charge conservation.
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.get_extra_default_measurements()	Get list of model-dependent extra default measurements.
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)

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_conjugate(H_MPO).

Type:

tenpy.networks.mpo.MPO

copy()

Shallow copy of self.

extract_segment(*args, **kwargs)

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)

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)

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)

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.:

get_extra_default_measurements()

Get list of model-dependent extra default measurements.

Extra measurements for a Simulation, which depend on the model itself - subclasses should override this method). E.g., a MPOModel should measure the energy w.r.t. the MPO (See m_energy_MPO()). However, a NearestNeighborModel should use the function m_bond_energies(). The extra measurements are added to the default measurements in _connect_measurements().

Returns:

m_extra_default_list

Return type:

list

estimate_RAM_saving_factor()

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.

classmethod from_hdf5(hdf5_loader, h5gr, subpath)

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)

Export self into a HDF5 file.

Same as save_hdf5(), but handle rng.

update_time_parameter(new_time)

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