diff --git a/docs/pyerrors/linalg.html b/docs/pyerrors/linalg.html
index 35e47717..ed9c92e1 100644
--- a/docs/pyerrors/linalg.html
+++ b/docs/pyerrors/linalg.html
@@ -290,49 +290,49 @@
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
@@ -970,49 +970,49 @@ Obs valued.
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