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fix: linalg.jack_matmul now works with arrays of arbitrary shape

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Fabian Joswig 2021-11-30 11:52:53 +00:00
parent 443e4eb74e
commit 1fa3bc1291
1 changed files with 16 additions and 16 deletions
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32
pyerrors/linalg.py
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@ -186,49 +186,49 @@ def jack_matmul(*operands):
For large matrices this is considerably faster compared to matmul.
"""
if any(isinstance(o[0, 0], CObs) for o in operands):
name = operands[0][0, 0].real.names[0]
idl = operands[0][0, 0].real.idl[name]
if any(isinstance(o.flat[0], CObs) for o in operands):
name = operands[0].flat[0].real.names[0]
idl = operands[0].flat[0].real.idl[name]
def _exp_to_jack(matrix):
base_matrix = np.empty_like(matrix)
for (n, m), entry in np.ndenumerate(matrix):
base_matrix[n, m] = entry.real.export_jackknife() + 1j * entry.imag.export_jackknife()
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = entry.real.export_jackknife() + 1j * entry.imag.export_jackknife()
return base_matrix
def _imp_from_jack(matrix):
base_matrix = np.empty_like(matrix)
for (n, m), entry in np.ndenumerate(matrix):
base_matrix[n, m] = CObs(import_jackknife(entry.real, name, [idl]),
import_jackknife(entry.imag, name, [idl]))
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = CObs(import_jackknife(entry.real, name, [idl]),
import_jackknife(entry.imag, name, [idl]))
return base_matrix
r = _exp_to_jack(operands[0])
for op in operands[1:]:
if isinstance(op[0, 0], CObs):
if isinstance(op.flat[0], CObs):
r = r @ _exp_to_jack(op)
else:
r = r @ op
return _imp_from_jack(r)
else:
name = operands[0][0, 0].names[0]
idl = operands[0][0, 0].idl[name]
name = operands[0].flat[0].names[0]
idl = operands[0].flat[0].idl[name]
def _exp_to_jack(matrix):
base_matrix = np.empty_like(matrix)
for (n, m), entry in np.ndenumerate(matrix):
base_matrix[n, m] = entry.export_jackknife()
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = entry.export_jackknife()
return base_matrix
def _imp_from_jack(matrix):
base_matrix = np.empty_like(matrix)
for (n, m), entry in np.ndenumerate(matrix):
base_matrix[n, m] = import_jackknife(entry, name, [idl])
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = import_jackknife(entry, name, [idl])
return base_matrix
r = _exp_to_jack(operands[0])
for op in operands[1:]:
if isinstance(op[0, 0], Obs):
if isinstance(op.flat[0], Obs):
r = r @ _exp_to_jack(op)
else:
r = r @ op