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refactor: jackknife helper functions in linalg module refactored

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Fabian Joswig 2021-12-01 09:22:16 +00:00
parent bb91c37ac4
commit 6bc8102f87
1 changed files with 33 additions and 29 deletions
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62
pyerrors/linalg.py
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@ -174,6 +174,35 @@ def matmul(*operands):
return derived_array(multi_dot, operands) return derived_array(multi_dot, operands)
def _exp_to_jack(matrix):
base_matrix = np.empty_like(matrix)
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = entry.export_jackknife()
return base_matrix
def _imp_from_jack(matrix, name, idl):
base_matrix = np.empty_like(matrix)
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = import_jackknife(entry, name, [idl])
return base_matrix
def _exp_to_jack_c(matrix):
base_matrix = np.empty_like(matrix)
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_c(matrix, name, idl):
base_matrix = np.empty_like(matrix)
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
def jack_matmul(*operands): def jack_matmul(*operands):
"""Matrix multiply both operands making use of the jackknife approximation. """Matrix multiply both operands making use of the jackknife approximation.
@ -190,49 +219,24 @@ def jack_matmul(*operands):
name = operands[0].flat[0].real.names[0] name = operands[0].flat[0].real.names[0]
idl = operands[0].flat[0].real.idl[name] idl = operands[0].flat[0].real.idl[name]
def _exp_to_jack(matrix): r = _exp_to_jack_c(operands[0])
base_matrix = np.empty_like(matrix)
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 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:]: for op in operands[1:]:
if isinstance(op.flat[0], CObs): if isinstance(op.flat[0], CObs):
r = r @ _exp_to_jack(op) r = r @ _exp_to_jack_c(op)
else: else:
r = r @ op r = r @ op
return _imp_from_jack(r) return _imp_from_jack_c(r, name, idl)
else: else:
name = operands[0].flat[0].names[0] name = operands[0].flat[0].names[0]
idl = operands[0].flat[0].idl[name] idl = operands[0].flat[0].idl[name]
def _exp_to_jack(matrix):
base_matrix = np.empty_like(matrix)
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 index, entry in np.ndenumerate(matrix):
base_matrix[index] = import_jackknife(entry, name, [idl])
return base_matrix
r = _exp_to_jack(operands[0]) r = _exp_to_jack(operands[0])
for op in operands[1:]: for op in operands[1:]:
if isinstance(op.flat[0], Obs): if isinstance(op.flat[0], Obs):
r = r @ _exp_to_jack(op) r = r @ _exp_to_jack(op)
else: else:
r = r @ op r = r @ op
return _imp_from_jack(r) return _imp_from_jack(r, name, idl)
def inv(x): def inv(x):