Source code for netket.hilbert.homogeneous

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from typing import Optional, List, Callable

from numbers import Real

import numpy as np
from numba import jit

from .discrete_hilbert import DiscreteHilbert
from .hilbert_index import HilbertIndex


@jit(nopython=True)
def _gen_to_bare_numbers(conditions):
    return np.nonzero(conditions)[0]


@jit(nopython=True)
def _to_constrained_numbers_kernel(bare_numbers, numbers):
    found = np.searchsorted(bare_numbers, numbers)
    if np.max(found) >= bare_numbers.shape[0]:
        raise RuntimeError("The required state does not satisfy the given constraints.")
    return found


class HomogeneousHilbert(DiscreteHilbert):
    r"""The Abstract base class for homogeneous hilbert spaces.

    This class should only be subclassed and should not be instantiated directly.
    """

[docs] def __init__( self, local_states: Optional[List[Real]], N: int = 1, constraint_fn: Optional[Callable] = None, ): r""" Constructs a new ``HomogeneousHilbert`` given a list of eigenvalues of the states and a number of sites, or modes, within this hilbert space. This method should only be called from the subclasses `__init__` method. Args: local_states (list or None): Eigenvalues of the states. If the allowed states are an infinite number, None should be passed as an argument. N: Number of modes in this hilbert space (default 1). constraint_fn: A function specifying constraints on the quantum numbers. Given a batch of quantum numbers it should return a vector of bools specifying whether those states are valid or not. """ assert isinstance(N, int) self._size = N self._is_finite = local_states is not None if self._is_finite: self._local_states = np.asarray(local_states) assert self._local_states.ndim == 1 self._local_size = self._local_states.shape[0] self._local_states = self._local_states.tolist() self._local_states_frozen = frozenset(self._local_states) else: self._local_states = None self._local_states_frozen = None self._local_size = np.iinfo(np.intp).max self._has_constraint = constraint_fn is not None self._constraint_fn = constraint_fn self._hilbert_index = None shape = tuple(self._local_size for _ in range(self.size)) super().__init__(shape=shape)
@property def size(self) -> int: r"""The total number number of degrees of freedom.""" return self._size @property def local_size(self) -> int: r"""Size of the local degrees of freedom that make the total hilbert space.""" return self._local_size
[docs] def size_at_index(self, i: int) -> int: return self.local_size
@property def local_states(self) -> Optional[List[float]]: r"""A list of discreet local quantum numbers. If the local states are infinitely many, None is returned.""" return self._local_states
[docs] def states_at_index(self, i: int): return self.local_states
@property def n_states(self) -> int: r"""The total dimension of the many-body Hilbert space. Throws an exception iff the space is not indexable.""" hind = self._get_hilbert_index() if not self._has_constraint: return hind.n_states else: return self._bare_numbers.shape[0] @property def is_finite(self) -> bool: r"""Whether the local hilbert space is finite.""" return self._is_finite @property def constrained(self) -> bool: r"""Returns True if the hilbert space is constrained.""" return self._has_constraint def _numbers_to_states(self, numbers: np.ndarray, out: np.ndarray) -> np.ndarray: hind = self._get_hilbert_index() return hind.numbers_to_states(self._to_bare_numbers(numbers), out) def _states_to_numbers(self, states, out): hind = self._get_hilbert_index() hind.states_to_numbers(states, out) if self._has_constraint: out[:] = _to_constrained_numbers_kernel( self._bare_numbers, out, ) return out def _get_hilbert_index(self): if self._hilbert_index is None: if not self.is_indexable: raise RuntimeError("The hilbert space is too large to be indexed.") self._hilbert_index = HilbertIndex( np.asarray(self.local_states, dtype=np.float64), self.size ) if self._has_constraint: self._bare_numbers = _gen_to_bare_numbers( self._constraint_fn(self._hilbert_index.all_states()) ) else: self._bare_numbers = np.empty(0, dtype=np.intp) return self._hilbert_index def _to_bare_numbers(self, numbers): if self._constraint_fn is None: return numbers else: return self._bare_numbers[numbers] def __repr__(self): constr = ( ", has_constraint={}".format(self._has_constraint) if self._has_constraint else "" ) clsname = type(self).__name__ return f"{clsname}(local_size={self._local_size}, N={self.size}{constr})" @property def _attrs(self): return ( self.size, self.local_size, self._local_states_frozen, self._has_constraint, self._constraint_fn, )