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