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Merge branch 'develop' into feature/covobs
This commit is contained in:
Simon Kuberski
commit
0e75cf48bc
2 changed files with 92 additions and 27 deletions
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@ -63,7 +63,7 @@ def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson', idl=No
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for outputs of the Meson module. Can be altered to read input
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from other modules with similar structures.
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idl : range
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If specified only conifgurations in the given range are read in.
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If specified only configurations in the given range are read in.
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"""
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files, idx = _get_files(path, filestem, idl)
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@ -134,12 +134,15 @@ def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
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"""Read hadrons ExternalLeg hdf5 file and output an array of CObs
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Parameters
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-----------------
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path -- path to the files to read
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filestem -- namestem of the files to read
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ens_id -- name of the ensemble, required for internal bookkeeping
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----------
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path : str
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path to the files to read
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filestem : str
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namestem of the files to read
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ens_id : str
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name of the ensemble, required for internal bookkeeping
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idl : range
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If specified only conifgurations in the given range are read in.
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If specified only configurations in the given range are read in.
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"""
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files, idx = _get_files(path, filestem, idl)
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@ -171,12 +174,15 @@ def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
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"""Read hadrons Bilinear hdf5 file and output an array of CObs
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Parameters
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-----------------
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path -- path to the files to read
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filestem -- namestem of the files to read
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ens_id -- name of the ensemble, required for internal bookkeeping
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----------
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path : str
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path to the files to read
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filestem : str
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namestem of the files to read
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ens_id : str
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name of the ensemble, required for internal bookkeeping
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idl : range
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If specified only conifgurations in the given range are read in.
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If specified only configurations in the given range are read in.
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"""
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files, idx = _get_files(path, filestem, idl)
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@ -216,3 +222,62 @@ def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
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result_dict[key] = Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order='F'), mom_in=mom_in, mom_out=mom_out)
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return result_dict
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def read_Fourquark_hd5(path, filestem, ens_id, idl=None):
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"""Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
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Parameters
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----------
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path : str
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path to the files to read
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filestem : str
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namestem of the files to read
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ens_id : str
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name of the ensemble, required for internal bookkeeping
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idl : range
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If specified only configurations in the given range are read in.
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"""
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files, idx = _get_files(path, filestem, idl)
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mom_in = None
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mom_out = None
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corr_data = {}
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tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
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for hd5_file in files:
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file = h5py.File(path + '/' + hd5_file, "r")
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for i in range(1):
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name = file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8') + '_' + file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8')
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if name not in corr_data:
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corr_data[name] = []
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raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
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corr_data[name].append(raw_data)
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if mom_in is None:
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mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=int)
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if mom_out is None:
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mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(' '), dtype=int)
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file.close()
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result_dict = {}
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for key, data in corr_data.items():
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local_data = np.array(data)
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rolled_array = np.moveaxis(local_data, 0, 8)
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matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
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for index in np.ndindex(rolled_array.shape[:-1]):
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real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
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imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
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matrix[index] = CObs(real, imag)
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result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
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# result_dict[key] = Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order='F'), mom_in=mom_in, mom_out=mom_out)
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return result_dict
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@ -186,49 +186,49 @@ def jack_matmul(*operands):
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For large matrices this is considerably faster compared to matmul.
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"""
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if any(isinstance(o[0, 0], CObs) for o in operands):
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name = operands[0][0, 0].real.names[0]
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idl = operands[0][0, 0].real.idl[name]
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if any(isinstance(o.flat[0], CObs) for o in operands):
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name = operands[0].flat[0].real.names[0]
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idl = operands[0].flat[0].real.idl[name]
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def _exp_to_jack(matrix):
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base_matrix = np.empty_like(matrix)
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for (n, m), entry in np.ndenumerate(matrix):
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base_matrix[n, m] = entry.real.export_jackknife() + 1j * entry.imag.export_jackknife()
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for index, entry in np.ndenumerate(matrix):
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base_matrix[index] = entry.real.export_jackknife() + 1j * entry.imag.export_jackknife()
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return base_matrix
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def _imp_from_jack(matrix):
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base_matrix = np.empty_like(matrix)
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for (n, m), entry in np.ndenumerate(matrix):
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base_matrix[n, m] = CObs(import_jackknife(entry.real, name, [idl]),
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import_jackknife(entry.imag, name, [idl]))
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for index, entry in np.ndenumerate(matrix):
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base_matrix[index] = CObs(import_jackknife(entry.real, name, [idl]),
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import_jackknife(entry.imag, name, [idl]))
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return base_matrix
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r = _exp_to_jack(operands[0])
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for op in operands[1:]:
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if isinstance(op[0, 0], CObs):
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if isinstance(op.flat[0], CObs):
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r = r @ _exp_to_jack(op)
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else:
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r = r @ op
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return _imp_from_jack(r)
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else:
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name = operands[0][0, 0].names[0]
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idl = operands[0][0, 0].idl[name]
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name = operands[0].flat[0].names[0]
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idl = operands[0].flat[0].idl[name]
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def _exp_to_jack(matrix):
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base_matrix = np.empty_like(matrix)
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for (n, m), entry in np.ndenumerate(matrix):
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base_matrix[n, m] = entry.export_jackknife()
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for index, entry in np.ndenumerate(matrix):
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base_matrix[index] = entry.export_jackknife()
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return base_matrix
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def _imp_from_jack(matrix):
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base_matrix = np.empty_like(matrix)
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for (n, m), entry in np.ndenumerate(matrix):
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base_matrix[n, m] = import_jackknife(entry, name, [idl])
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for index, entry in np.ndenumerate(matrix):
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base_matrix[index] = import_jackknife(entry, name, [idl])
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return base_matrix
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r = _exp_to_jack(operands[0])
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for op in operands[1:]:
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if isinstance(op[0, 0], Obs):
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if isinstance(op.flat[0], Obs):
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r = r @ _exp_to_jack(op)
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else:
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r = r @ op
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