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Rename backend.issparse as backend.is_sparse #185

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Aug 2, 2024
Merged

Rename backend.issparse as backend.is_sparse #185

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f8a42f7
rename method
renatomello Jul 15, 2024
085f147
updating qibo version
scarrazza Aug 2, 2024
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10 changes: 5 additions & 5 deletions src/qibojit/backends/gpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,7 +140,7 @@ def to_numpy(self, x):
return x.toarray()
return np.array(x, copy=False)

def issparse(self, x):
def is_sparse(self, x):
return self.sparse.issparse(x) or self.npsparse.issparse(x)

def zero_state(self, nqubits):
Expand Down Expand Up @@ -474,7 +474,7 @@ def calculate_expectation_density_matrix(self, matrix, state, normalize):
return ev

def calculate_eigenvalues(self, matrix, k=6):
if self.issparse(matrix):
if self.is_sparse(matrix):
log.warning(
"Calculating sparse matrix eigenvectors because "
"sparse modules do not provide ``eigvals`` method."
Expand All @@ -483,7 +483,7 @@ def calculate_eigenvalues(self, matrix, k=6):
return self.cp.linalg.eigvalsh(matrix)

def calculate_eigenvectors(self, matrix, k=6):
if self.issparse(matrix):
if self.is_sparse(matrix):
if k < matrix.shape[0]:
# Fallback to numpy because cupy's ``sparse.eigh`` does not support 'SA'
from scipy.sparse.linalg import eigsh # pylint: disable=import-error
Expand All @@ -499,8 +499,8 @@ def calculate_eigenvectors(self, matrix, k=6):
return self.cp.linalg.eigh(matrix)

def calculate_matrix_exp(self, a, matrix, eigenvectors=None, eigenvalues=None):
if eigenvectors is None or self.issparse(matrix):
if self.issparse(matrix):
if eigenvectors is None or self.is_sparse(matrix):
if self.is_sparse(matrix):
from scipy.sparse.linalg import expm

return self.cast(expm(-1j * a * matrix.get()))
Expand Down
4 changes: 2 additions & 2 deletions src/qibojit/tests/test_backends.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,13 +35,13 @@ def test_sparse_cast(backend, array_type, format):
from scipy import sparse

sptarget = sparse.rand(512, 512, dtype=array_type, format=format)
assert backend.issparse(sptarget)
assert backend.is_sparse(sptarget)
final = backend.to_numpy(backend.cast(sptarget))
target = sptarget.toarray()
backend.assert_allclose(final, target)
if backend.platform != "numba": # pragma: no cover
sptarget = getattr(backend.sparse, sptarget.__class__.__name__)(sptarget)
assert backend.issparse(sptarget)
assert backend.is_sparse(sptarget)
final = backend.to_numpy(backend.cast(sptarget))
backend.assert_allclose(final, target)

Expand Down
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