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read bilinear added to input/hadrons
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@ -109,3 +109,60 @@ def read_ExternalLeg_hd5(path, filestem, ens_id, order='F'):
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matrix[si, sj, ci, cj].gamma_method()
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return Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order=order), mom_in=mom)
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def read_Bilinear_hd5(path, filestem, ens_id, order='F'):
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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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order -- order in which the array is to be reshaped,
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'F' for the first index changing fastest (9 4x4 matrices) default.
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'C' for the last index changing fastest (16 3x3 matrices),
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"""
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files = _get_files(path, filestem)
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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 not None:
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assert np.allclose(mom_in, np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=int))
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else:
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mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=int)
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if mom_out is not None:
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assert np.allclose(mom_out, np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=int))
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else:
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mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[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.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])
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imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id])
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matrix[si, sj, ci, cj] = CObs(real, imag)
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matrix[si, sj, ci, cj].gamma_method()
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result_dict[key] = Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order=order), mom_in=mom_in, mom_out=mom_out)
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return result_dict
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