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328 lines
10 KiB
Python
328 lines
10 KiB
Python
import os
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import h5py
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import numpy as np
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from ..obs import Obs, CObs
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from ..correlators import Corr
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def _get_files(path, filestem, idl):
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ls = os.listdir(path)
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# Clean up file list
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files = list(filter(lambda x: x.startswith(filestem), ls))
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if not files:
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raise Exception('No files starting with', filestem, 'in folder', path)
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def get_cnfg_number(n):
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return int(n[len(filestem) + 1:-3])
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# Sort according to configuration number
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files.sort(key=get_cnfg_number)
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cnfg_numbers = []
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filtered_files = []
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for line in files:
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no = get_cnfg_number(line)
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if idl:
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if no in list(idl):
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filtered_files.append(line)
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cnfg_numbers.append(no)
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else:
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filtered_files.append(line)
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cnfg_numbers.append(no)
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# Check that configurations are evenly spaced
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dc = np.unique(np.diff(cnfg_numbers))
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if np.any(dc < 0):
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raise Exception("Unsorted files")
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if len(dc) == 1:
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idx = range(cnfg_numbers[0], cnfg_numbers[-1] + dc[0], dc[0])
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else:
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raise Exception('Configurations are not evenly spaced.')
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return filtered_files, idx
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def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson', idl=None):
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"""Read hadrons meson hdf5 file and extract the meson labeled 'meson'
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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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meson : str
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label of the meson to be extracted, standard value meson_0 which
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corresponds to the pseudoscalar pseudoscalar two-point function.
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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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tree = meson.rsplit('_')[0]
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corr_data = []
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infos = []
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for hd5_file in files:
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file = h5py.File(path + '/' + hd5_file, "r")
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raw_data = list(file[tree + '/' + meson + '/corr'])
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real_data = [o[0] for o in raw_data]
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corr_data.append(real_data)
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if not infos:
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for k, i in file[tree + '/' + meson].attrs.items():
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infos.append(k + ': ' + i[0].decode())
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file.close()
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corr_data = np.array(corr_data)
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l_obs = []
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for c in corr_data.T:
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l_obs.append(Obs([c], [ens_id], idl=[idx]))
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corr = Corr(l_obs)
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corr.tag = r", ".join(infos)
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return corr
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class Npr_matrix(np.ndarray):
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def __new__(cls, input_array, mom_in=None, mom_out=None):
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obj = np.asarray(input_array).view(cls)
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obj.mom_in = mom_in
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obj.mom_out = mom_out
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return obj
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@property
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def g5H(self):
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"""Gamma_5 hermitean conjugate
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Uses the fact that the propagator is gamma5 hermitean, so just the
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in and out momenta of the propagator are exchanged.
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"""
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return Npr_matrix(self,
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mom_in=self.mom_out,
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mom_out=self.mom_in)
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def _propagate_mom(self, other, name):
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s_mom = getattr(self, name, None)
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o_mom = getattr(other, name, None)
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if s_mom is not None and o_mom is not None:
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if not np.allclose(s_mom, o_mom):
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raise Exception(name + ' does not match.')
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return o_mom if o_mom is not None else s_mom
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def __matmul__(self, other):
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return self.__new__(Npr_matrix,
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super().__matmul__(other),
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self._propagate_mom(other, 'mom_in'),
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self._propagate_mom(other, 'mom_out'))
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def __array_finalize__(self, obj):
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if obj is None:
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return
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self.mom_in = getattr(obj, 'mom_in', None)
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self.mom_out = getattr(obj, 'mom_out', None)
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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 : 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 = None
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corr_data = []
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for hd5_file in files:
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file = h5py.File(path + '/' + hd5_file, "r")
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raw_data = file['ExternalLeg/corr'][0][0].view('complex')
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corr_data.append(raw_data)
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if mom is None:
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mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=float)
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file.close()
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corr_data = np.array(corr_data)
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rolled_array = np.rollaxis(corr_data, 0, 5)
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matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
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for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
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real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
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imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
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matrix[si, sj, ci, cj] = CObs(real, imag)
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return Npr_matrix(matrix, mom_in=mom)
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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 : 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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for hd5_file in files:
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file = h5py.File(path + '/' + hd5_file, "r")
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for i in range(16):
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name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][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['Bilinear/Bilinear_' + 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['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=float)
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if mom_out is None:
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mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(' '), dtype=float)
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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.rollaxis(local_data, 0, 5)
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matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
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for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
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real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
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imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
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matrix[si, sj, ci, cj] = 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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return result_dict
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def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
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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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vertices : list
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Vertex functions to be extracted.
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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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vertex_names = []
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for vertex in vertices:
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vertex_names += _get_lorentz_names(vertex)
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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(32):
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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 in vertex_names:
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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=float)
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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=float)
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file.close()
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intermediate_dict = {}
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for vertex in vertices:
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lorentz_names = _get_lorentz_names(vertex)
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for v_name in lorentz_names:
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if vertex not in intermediate_dict:
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intermediate_dict[vertex] = np.array(corr_data[v_name])
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else:
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intermediate_dict[vertex] += np.array(corr_data[v_name])
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result_dict = {}
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for key, data in intermediate_dict.items():
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rolled_array = np.moveaxis(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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return result_dict
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def _get_lorentz_names(name):
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assert len(name) == 2
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res = []
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if not set(name) <= set(['S', 'P', 'V', 'A', 'T']):
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raise Exception("Name can only contain 'S', 'P', 'V', 'A' or 'T'")
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if 'S' in name or 'P' in name:
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if not set(name) <= set(['S', 'P']):
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raise Exception("'" + name + "' is not a Lorentz scalar")
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g_names = {'S': 'Identity',
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'P': 'Gamma5'}
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res.append((g_names[name[0]], g_names[name[1]]))
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elif 'T' in name:
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if not set(name) <= set(['T']):
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raise Exception("'" + name + "' is not a Lorentz scalar")
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raise Exception("Tensor operators not yet implemented.")
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else:
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if not set(name) <= set(['V', 'A']):
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raise Exception("'" + name + "' is not a Lorentz scalar")
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lorentz_index = ['X', 'Y', 'Z', 'T']
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for ind in lorentz_index:
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res.append(('Gamma' + ind + (name[0] == 'A') * 'Gamma5',
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'Gamma' + ind + (name[1] == 'A') * 'Gamma5'))
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return res
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