tensornetwork.BaseCharge¶

class tensornetwork.BaseCharge(charges: Union[List[T], numpy.ndarray], charge_labels: Optional[numpy.ndarray] = None, charge_types: Optional[List[Type[BaseCharge]]] = None, charge_dtype: Optional[Type[numpy.number]] = <class 'numpy.int16'>)¶

Base class for charges of BlockSparseTensor. All user defined charges should be derived from this class. .. attribute:: * unique_charges

np.ndarray of shape (m,n) with m the number of charge types, and n the number of unique charges.

* charge_labels: np.ndarray of dtype np.int16. Used for identifying charges with integer labels. `unique_charges[:, charge_labels] is the np.ndarray of actual charges.

* charge_types: A list of type objects. Stored the different charge types, on for each row in unique_charges.

__init__(charges: Union[List[T], numpy.ndarray], charge_labels: Optional[numpy.ndarray] = None, charge_types: Optional[List[Type[BaseCharge]]] = None, charge_dtype: Optional[Type[numpy.number]] = <class 'numpy.int16'>) → None¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(charges, numpy.ndarray], …)	Initialize self.
`copy`()	Return a copy of `BaseCharge`.
`dual`(take_dual)	Return the charges of `BaseCharge`, possibly conjugated.
`dual_charges`(charges)
`fuse`(charge1, charge2)
`identity_charge`()
`intersect`(other[, assume_unique, return_indices])	Compute the intersection of `self` with `other`.
`BaseCharge.isin`
`random`(dimension, minval, maxval)
`reduce`(target_charges, numpy.ndarray], …)	Reduce the dimension of a charge to keep only the charge values that intersect target_charges :param target_charges: array of unique charges to keep.
`BaseCharge.sort_unique_charges`
`unique`(return_index, return_inverse, …)	Compute the unique charges in `BaseCharge`.

Attributes

`charges`
`BaseCharge.degeneracies`
`dim`
`dtype`
`identity_charges`(dim)	Returns the identity charge.
`BaseCharge.label_dtype`
`BaseCharge.num_symmetries`
`num_unique`	Return the number of different charges in `ChargeCollection`.

copy()¶: Return a copy of BaseCharge.

dual(take_dual: Optional[bool] = False) → tensornetwork.block_sparse.charge.BaseCharge¶

Return the charges of BaseCharge, possibly conjugated. :param take_dual: If True return the dual charges. If False return

regular charges.

Returns:	BaseCharge

identity_charges(dim: int = 1) → tensornetwork.block_sparse.charge.BaseCharge¶: Returns the identity charge. :returns: The identity charge. :rtype: BaseCharge

intersect(other, assume_unique=False, return_indices=False) → Any¶

Compute the intersection of self with other. See also np.intersect1d.

Parameters:

other – A BaseCharge object.
assume_unique – If True assume that elements are unique.
return_indices – If True, return index-labels.

Returns:

BaseCharge

np.ndarray: The indices of the first occurrences of the: common values in self.
np.ndarray: The indices of the first occurrences of the: common values in other.

If return_indices=False:

BaseCharge

Return type:

If return_indices=True

num_unique¶: Return the number of different charges in ChargeCollection.

reduce(target_charges: Union[int, numpy.ndarray], return_locations: bool = False, strides: Optional[int] = 1) → Any¶

Reduce the dimension of a charge to keep only the charge values that intersect target_charges :param target_charges: array of unique charges to keep. :param return_locations: If True, also return the locations of

target values within BaseCharge.

Parameters:	strides – An optional stride value.
Returns:	charge of reduced dimension. np.ndarray: If `return_locations = True`; the index locations of target values.
Return type:	BaseCharge

unique(return_index: bool = False, return_inverse: bool = False, return_counts: bool = False) → Any¶

Compute the unique charges in BaseCharge. See unique for a more detailed explanation. This function does the same but instead of a np.ndarray, it returns the unique elements (not neccessarily sorted in standard order) in a BaseCharge object.

Parameters:

return_index – If True, also return the indices of self.charges (along the specified axis, if provided, or in the flattened array) that result in the unique array.
return_inverse – If True, also return the indices of the unique array (for the specified axis, if provided) that can be used to reconstruct self.charges.
return_counts – If True, also return the number of times each unique item appears in self.charges.

Returns:

The sorted unique values. np.ndarray: The indices of the first occurrences of the unique values

in the original array. Only provided if return_index is True.

np.ndarray: The indices to reconstruct the original array from the: unique array. Only provided if return_inverse is True.
np.ndarray: The number of times each of the unique values comes up in the: original array. Only provided if return_counts is True.

Return type:

BaseCharge