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feat: linalg.jack_matmul now also works with irregular monte carlo

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chains
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Fabian Joswig 2021-11-18 11:17:20 +00:00
parent 0954ebee6e
commit 28bf0f1701
2 changed files with 15 additions and 9 deletions
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20
pyerrors/linalg.py
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@ -187,40 +187,46 @@ def jack_matmul(*operands):
"""
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]
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()
return base_matrix
def _imp_from_jack(matrix, name):
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),
import_jackknife(entry.imag, name))
base_matrix[n, m] = 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:]:
r = r @ _exp_to_jack(op)
return _imp_from_jack(r, op.ravel()[0].real.names[0])
return _imp_from_jack(r)
else:
name = operands[0][0, 0].names[0]
idl = operands[0][0, 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()
return base_matrix
def _imp_from_jack(matrix, name):
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)
base_matrix[n, m] = import_jackknife(entry, name, [idl])
return base_matrix
r = _exp_to_jack(operands[0])
for op in operands[1:]:
r = r @ _exp_to_jack(op)
return _imp_from_jack(r, op.ravel()[0].names[0])
return _imp_from_jack(r)
def inv(x):