# TermList¶

Inheritance Diagram

Methods

 TermList.__init__(terms[, strength]) Initialize self. TermList.from_hdf5(hdf5_loader, h5gr, subpath) Load instance from a HDF5 file. TermList.from_lattice_locations(lattice, terms) Initialize from a list of terms given in lattice indices instead of MPS indices. Order and combine operators in each term. TermList.save_hdf5(hdf5_saver, h5gr, subpath) Export self into a HDF5 file. Convert to OnsiteTerms and CouplingTerms
class tenpy.networks.terms.TermList(terms, strength=1.0)[source]

A list of terms (=operator names and sites they act on) and associated strengths.

A representation of terms, similar as OnsiteTerms, CouplingTerms and MultiCouplingTerms.

This class does not store operator strings between the sites. Jordan-Wigner strings of fermions are added during conversion to (Multi)CouplingTerms.

Parameters
• terms (list of list of (str, int)) – List of terms where each term is a list of tuples (opname, i) of an operator name and a site i it acts on. For Fermions, the order is the order in the mathematic sense, i.e., the right-most/last operator in the list acts last.

• strengths ((list of) float/complex) – For each term in terms an associated prefactor or strength. A single number holds for all terms equally.

terms

List of terms where each term is a tuple (opname, i) of an operator name and a site i it acts on.

Type

list of list of (str, int)

strengths

For each term in terms an associated prefactor or strength.

Type

1D ndarray

Examples

For fermions, the term $$0.5(c^\dagger_0 c_2 + h.c.) + 1.3 * n_1$$ can be represented by:

>>> t = TermList([[('Cd', 0), ('C', 2)], [('Cd', 2), ('C', 0)], [('N', 1)]],
...              [0.5,                   0.5,                   1.3])
>>> print(t)
0.50000 * Cd_0 C_2 +
0.50000 * Cd_2 C_0 +
1.30000 * N_1


If you have a Lattice, you might also want to specify the location of the operators by lattice indices insted of MPS indices. For example, you can obtain the nearest-neighbor density terms without double counting each pair) on a TriangularLattice:

>>> lat = tenpy.models.lattice.Triangular(6, 6, None, bc_MPS='infinite', bc='periodic')
>>> t2_terms = [[('N', [0, 0, u1]), ('N', [dx[0], dx[1], u2])]
...             for (u1, u2, dx) in lat.pairs['nearest_neighbors']]
>>> t2 = TermList.from_lattice_locations(lat, t2_terms)
>>> print(t2)
1.00000 * N_0 N_6 +
1.00000 * N_0 N_-5 +
1.00000 * N_0 N_5


The negative index -5 here indicates a tensor left of the current MPS unit cell.

classmethod from_lattice_locations(lattice, terms, strength=1.0, shift=None)[source]

Initialize from a list of terms given in lattice indices instead of MPS indices.

Parameters
• lattice (Lattice) – The underlying lattice to be used for conversion, e.g. M.lat from a Model.

• terms (list of list of (str, tuple)) – List of terms, where each term is a tuple (opname, lat_idx) with lat_idx itself being a tuple (x, y, u) (for a 2D lattice) of the lattice corrdinates.

• strengths ((list of) float/complex) – For each term in terms an associated prefactor or strength. A single number holds for all terms equally.

• shift (None | tuple of int) – Overall shift added to all lattice coordinates lat_idx in terms before conversion. None defaults to no shift.

Returns

term_list – Representation of the terms.

Return type

TermList

to_OnsiteTerms_CouplingTerms(sites)[source]

Convert to OnsiteTerms and CouplingTerms

Performs Jordan-Wigner transformation for fermionic operators.

Parameters

sites (list of Site) – Defines the local Hilbert space for each site. Used to check whether the operators need Jordan-Wigner strings. The length is used as L for the onsite_terms and coupling_terms.

Returns

order_combine(sites)[source]

Order and combine operators in each term.

Parameters

sites (list of Site) – Defines the local Hilbert space for each site. Used to check whether the operators anticommute (= whether they need Jordan-Wigner strings) and for multiplication rules.

order_and_combine_term()

does it for a single term.

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

Load instance from a HDF5 file.

This method reconstructs a class instance from the data saved with save_hdf5().

Parameters
• hdf5_loader (Hdf5Loader) – Instance of the loading engine.

• h5gr (Group) – HDF5 group which is represent the object to be constructed.

• subpath (str) – The name of h5gr with a '/' in the end.

Returns

obj – Newly generated class instance containing the required data.

Return type

cls

save_hdf5(hdf5_saver, h5gr, subpath)[source]

Export self into a HDF5 file.

This method saves all the data it needs to reconstruct self with from_hdf5().

This implementation saves the content of __dict__ with save_dict_content(), storing the format under the attribute 'format'.

Parameters
• hdf5_saver (Hdf5Saver) – Instance of the saving engine.

• h5gr (:classGroup) – HDF5 group which is supposed to represent self.

• subpath (str) – The name of h5gr with a '/' in the end.