Python numpy.block方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def get_k_ix(self, item, like):
        """
        Retrieves block indexes: row and column.
        Args:
            item (str): a string of 'mknj' letters;
            like (tuple): a 2-tuple with sample pair of k-points;

        Returns:
            Row and column indexes of a sub-block with conserving momentum.
        """
        item_i = numpy.argsort(mknj2i(item))
        item_code = ''.join("++--"[i] for i in item_i)
        if item_code[0] == item_code[1]:
            kc = self.kconserv  # ++-- --++
        elif item_code[0] == item_code[2]:
            kc = self.kconserv.swapaxes(1, 2)  # +-+- -+-+
        elif item_code[1] == item_code[2]:
            kc = self.kconserv.transpose(2, 0, 1)  # +--+ -++-
        else:
            raise RuntimeError("Unknown case: {}".format(item_code))

        y = kc[like]
        x = kc[0, y[0]]

        return x, y 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def eri_mknj(self, item, pair_row, pair_column):
        """
        Retrieves the merged ERI block using 'mknj' notation with pairs of k-indexes (k1, k1, k2, k2).
        Args:
            item (str): a 4-character string of 'mknj' letters;
            pair_row (Iterable): a k-point pair `k2 = pair_row[k1]` for each k1 (row indexes in the final matrix);
            pair_column (Iterable): a k-point pair `k4 = pair_row[k3]` for each k3 (column indexes in the final matrix);

        Returns:
            The corresponding block of ERI (phys notation).
        """
        return super(PhysERI, self).eri_mknj(
            item,
            pairs_row=enumerate(pair_row),
            pairs_column=enumerate(pair_column),
        ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def tdhf_primary_form(self, k):
        """
        A primary form of TDHF matrixes (full).
        Args:
            k (tuple, int): momentum transfer: either a pair of k-point indexes specifying the momentum transfer
            vector or a single integer with the second index assuming the first index being zero;

        Returns:
            Output type: "full", and the corresponding matrix.
        """
        r1, r2, c1, c2 = get_block_k_ix(self, k)
        d1 = self.tdhf_diag(r1)
        d2 = self.tdhf_diag(r2)
        a = d1 + 2 * self["knmj", r1, c1] - self["knjm", r1, c1]
        b = 2 * self["kjmn", r1, c2] - self["kjnm", r1, c2]
        a_ = d2 + 2 * self["mjkn", r2, c2] - self["mjnk", r2, c2]
        b_ = 2 * self["mnkj", r2, c1] - self["mnjk", r2, c1]
        return "full", numpy.block([[a, b], [-b_, -a_]]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def eri_mknj(self, item, pairs_row=None, pairs_column=None):
        """
        Retrieves the merged ERI block using 'mknj' notation with all k-indexes.
        Args:
            item (str): a 4-character string of 'mknj' letters;
            pairs_row (Iterable): iterator for pairs of row k-points (first index in the output matrix);
            pairs_column (Iterable): iterator for pairs of column k-points (second index in the output matrix);

        Returns:
            The corresponding block of ERI (phys notation).
        """
        if pairs_row is None:
            pairs_row = product(range(len(self.model.kpts)), range(len(self.model.kpts)))
        if pairs_column is None:
            pairs_column = product(range(len(self.model.kpts)), range(len(self.model.kpts)))
        # Second index has to support re-iterations
        pairs_column = tuple(pairs_column)
        result = []
        for k1, k2 in pairs_row:
            result.append([])
            for k3, k4 in pairs_column:
                result[-1].append(self.eri_mknj_k(item, (k1, k2, k3, k4)))

        r = numpy.block(result)
        return r / len(self.model.kpts) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def eri_mknj(self, item, *args):
        """
        Retrieves ERI block using 'mknj' notation.
        Args:
            item (str): a 4-character string of 'mknj' letters;
            *args: other arguments passed to `get_block_ov_notation`;

        Returns:
            The corresponding block of ERI (matrix with paired dimensions).
        """
        if len(item) != 4 or not isinstance(item, str) or set(item) != set('mknj'):
            raise ValueError("Unknown item: {}".format(repr(item)))

        item = mknj2i(item)
        n_ov = ''.join('o' if i % 2 == 0 else 'v' for i in item)
        args = tuple(
            tuple(arg[i] for i in item)
            for arg in args
        )
        result = self.eri_ov(n_ov, *args).transpose(*numpy.argsort(item))
        i, j, k, l = result.shape
        result = result.reshape((i * j, k * l))
        return result 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def cascade(T: np.ndarray, U: np.ndarray) -> np.ndarray:
    n = int(T.shape[1] / 2)
    J = np.linalg.inv(np.eye(n) - np.matmul(U[0:n, 0:n], T[n : 2 * n, n : 2 * n]))
    K = np.linalg.inv(np.eye(n) - np.matmul(T[n : 2 * n, n : 2 * n], U[0:n, 0:n]))
    S = np.block(
        [
            [
                T[0:n, 0:n]
                + np.matmul(
                    np.matmul(np.matmul(T[0:n, n : 2 * n], J), U[0:n, 0:n]),
                    T[n : 2 * n, 0:n],
                ),
                np.matmul(np.matmul(T[0:n, n : 2 * n], J), U[0:n, n : 2 * n]),
            ],
            [
                np.matmul(np.matmul(U[n : 2 * n, 0:n], K), T[n : 2 * n, 0:n]),
                U[n : 2 * n, n : 2 * n]
                + np.matmul(
                    np.matmul(np.matmul(U[n : 2 * n, 0:n], K), T[n : 2 * n, n : 2 * n]),
                    U[0:n, n : 2 * n],
                ),
            ],
        ]
    )
    return S  # type: ignore 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def creneau(k0: float, a0: float, pol: float, e1: float, e2: float, a: float, n: int, x0: float) -> tp.Tuple[np.ndarray, np.ndarray]:
    nmod = int(n / 2)
    alpha = np.diag(a0 + 2 * np.pi * np.arange(-nmod, nmod + 1))
    if pol == 0:
        M = alpha * alpha - k0 * k0 * marche(e1, e2, a, n, x0)
        L, E = np.linalg.eig(M)
        L = np.sqrt(-L + 0j)
        L = (1 - 2 * (np.imag(L) < -1e-15)) * L
        P = np.block([[E], [np.matmul(E, np.diag(L))]])
    else:
        U = marche(1 / e1, 1 / e2, a, n, x0)
        T = np.linalg.inv(U)
        M = (
            np.matmul(
                np.matmul(np.matmul(T, alpha), np.linalg.inv(marche(e1, e2, a, n, x0))),
                alpha,
            )
            - k0 * k0 * T
        )
        L, E = np.linalg.eig(M)
        L = np.sqrt(-L + 0j)
        L = (1 - 2 * (np.imag(L) < -1e-15)) * L
        P = np.block([[E], [np.matmul(np.matmul(U, E), np.diag(L))]])
    return P, L 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def test_block_complicated(self):
        # a bit more complicated
        one_2d = np.array([[1, 1, 1]])
        two_2d = np.array([[2, 2, 2]])
        three_2d = np.array([[3, 3, 3, 3, 3, 3]])
        four_1d = np.array([4, 4, 4, 4, 4, 4])
        five_0d = np.array(5)
        six_1d = np.array([6, 6, 6, 6, 6])
        zero_2d = np.zeros((2, 6))

        expected = np.array([[1, 1, 1, 2, 2, 2],
                             [3, 3, 3, 3, 3, 3],
                             [4, 4, 4, 4, 4, 4],
                             [5, 6, 6, 6, 6, 6],
                             [0, 0, 0, 0, 0, 0],
                             [0, 0, 0, 0, 0, 0]])

        result = block([[one_2d, two_2d],
                        [three_2d],
                        [four_1d],
                        [five_0d, six_1d],
                        [zero_2d]])
        assert_equal(result, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def concatenate_state_matrices(G):
    """
    Takes a State() model as input and returns the A, B, C, D matrices
    combined into a full matrix. For static gain models, the feedthrough
    matrix D is returned.

    Parameters
    ----------
    G : State

    Returns
    -------
    M : ndarray
    """
    if not isinstance(G, State):
        raise ValueError('concatenate_state_matrices() works on state '
                         'representations, but I found \"{0}\" object '
                         'instead.'.format(type(G).__name__))
    if G._isgain:
        return G.d

    return np.block([[G.a, G.b], [G.c, G.d]]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def test_bogoliubov_transform_quadratic_hamiltonian_inverse_is_dagger(
        n_qubits, real, particle_conserving, atol):
    quad_ham = random_quadratic_hamiltonian(
            n_qubits,
            real=real,
            conserves_particle_number=particle_conserving,
            seed=46533)
    _, transformation_matrix, _ = quad_ham.diagonalizing_bogoliubov_transform()

    qubits = cirq.LineQubit.range(n_qubits)

    if transformation_matrix.shape == (n_qubits, n_qubits):
        daggered_transformation_matrix = transformation_matrix.T.conj()
    else:
        left_block = transformation_matrix[:, :n_qubits]
        right_block = transformation_matrix[:, n_qubits:]
        daggered_transformation_matrix = numpy.block(
            [left_block.T.conj(), right_block.T])

    circuit1 = cirq.Circuit(
            cirq.inverse(bogoliubov_transform(qubits, transformation_matrix)))

    circuit2 = cirq.Circuit(
            bogoliubov_transform(qubits, daggered_transformation_matrix))

    cirq.testing.assert_allclose_up_to_global_phase(
            circuit1.unitary(),
            circuit2.unitary(),
            atol=atol) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def _gaussian_basis_change(qubits: Sequence[cirq.Qid],
                           transformation_matrix: numpy.ndarray,
                           initially_occupied_orbitals: Optional[Sequence[int]]
                           ) -> cirq.OP_TREE:
    n_qubits = len(qubits)

    # Rearrange the transformation matrix because the OpenFermion routine
    # expects it to describe annihilation operators rather than creation
    # operators
    left_block = transformation_matrix[:, :n_qubits]
    right_block = transformation_matrix[:, n_qubits:]
    transformation_matrix = numpy.block(
            [numpy.conjugate(right_block), numpy.conjugate(left_block)])

    decomposition, left_decomposition, _, left_diagonal = (
        fermionic_gaussian_decomposition(transformation_matrix))

    if (initially_occupied_orbitals is not None and
            len(initially_occupied_orbitals) == 0):
        # Starting with the vacuum state yields additional symmetry
        circuit_description = list(reversed(decomposition))
    else:
        if initially_occupied_orbitals is None:
            # The initial state is not a computational basis state so the
            # phases left on the diagonal in the Givens decomposition matter
            yield (cirq.rz(rads=
                       numpy.angle(left_diagonal[j])).on(qubits[j])
                   for j in range(n_qubits))
        circuit_description = list(reversed(decomposition + left_decomposition))

    yield _ops_from_givens_rotations_circuit_description(
            qubits, circuit_description) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def eri_ov(self, item):
        result = []
        k = numpy.arange(self.nk)
        for k1 in k:
            result.append([])
            for k2 in k:
                result[-1].append([])
                for k3 in k:
                    result[-1][-1].append([])
                    for k4 in k:
                        result[-1][-1][-1].append(self.eri.eri_ov(item, (k1, k2, k3, k4)))

        r = numpy.block(result)
        return r / len(k) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def tdhf_diag(self, block):
        """
        Retrieves the merged diagonal block only with specific pairs of k-indexes (k, block[k]).
        Args:
            block (Iterable): a k-point pair `k2 = pair[k1]` for each k1;

        Returns:
            The diagonal block.
        """
        return super(PhysERI, self).tdhf_diag(pairs=enumerate(block)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def tdhf_primary_form(self, k):
        """
        A primary form of TD matrices (full).
        Args:
            k (tuple, int): momentum transfer: either a pair of k-point indexes specifying the momentum transfer
            vector or a single integer with the second index assuming the first index being zero;

        Returns:
            Output type: "full", and the corresponding matrix.
        """
        r1, r2, c1, c2 = krhf_slow.get_block_k_ix(self, k)
        (a, _), (_, b), (_, b_star), (a_star, _) = self.proxy_response_ov_batch((r1, r1, r2, r2), (c1, c2, c1, c2))
        return "full", numpy.block([[a, b], [-b_star.conj(), -a_star.conj()]]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import block [as 別名]
def tdhf_diag_k(self, k1, k2):
        """
        Retrieves the diagonal block.
        Args:
            k1 (int): first k-index (row);
            k2 (int): second k-index (column);

        Returns:
            The diagonal block.
        """
        # Everything is already implemented in molecular code
        return super(PhysERI, self).tdhf_diag(k1, k2)