# pyerrors.input.hadrons

View Source

      0import os
      1import warnings
      2from collections import Counter
      3import h5py
      4import numpy as np
      5from ..obs import Obs, CObs
      6from ..correlators import Corr
      7from ..dirac import epsilon_tensor_rank4
      8
      9
     10def _get_files(path, filestem, idl):
     11    ls = os.listdir(path)
     12
     13    # Clean up file list
     14    files = list(filter(lambda x: x.startswith(filestem), ls))
     15
     16    if not files:
     17        raise Exception('No files starting with', filestem, 'in folder', path)
     18
     19    def get_cnfg_number(n):
     20        return int(n.replace(".h5", "")[len(filestem) + 1:])  # From python 3.9 onward the safer 'removesuffix' method can be used.
     21
     22    # Sort according to configuration number
     23    files.sort(key=get_cnfg_number)
     24
     25    cnfg_numbers = []
     26    filtered_files = []
     27    for line in files:
     28        no = get_cnfg_number(line)
     29        if idl:
     30            if no in list(idl):
     31                filtered_files.append(line)
     32                cnfg_numbers.append(no)
     33        else:
     34            filtered_files.append(line)
     35            cnfg_numbers.append(no)
     36
     37    if idl:
     38        if Counter(list(idl)) != Counter(cnfg_numbers):
     39            raise Exception("Not all configurations specified in idl found, configurations " + str(list(Counter(list(idl)) - Counter(cnfg_numbers))) + " are missing.")
     40
     41    # Check that configurations are evenly spaced
     42    dc = np.unique(np.diff(cnfg_numbers))
     43    if np.any(dc < 0):
     44        raise Exception("Unsorted files")
     45    if len(dc) == 1:
     46        idx = range(cnfg_numbers[0], cnfg_numbers[-1] + dc[0], dc[0])
     47    elif idl:
     48        idx = idl
     49        warnings.warn("Configurations are not evenly spaced.", RuntimeWarning)
     50    else:
     51        raise Exception("Configurations are not evenly spaced.")
     52
     53    return filtered_files, idx
     54
     55
     56def read_meson_hd5(path, filestem, ens_id, meson='meson_0', idl=None):
     57    """Read hadrons meson hdf5 file and extract the meson labeled 'meson'
     58
     59    Parameters
     60    -----------------
     61    path : str
     62        path to the files to read
     63    filestem : str
     64        namestem of the files to read
     65    ens_id : str
     66        name of the ensemble, required for internal bookkeeping
     67    meson : str
     68        label of the meson to be extracted, standard value meson_0 which
     69        corresponds to the pseudoscalar pseudoscalar two-point function.
     70    idl : range
     71        If specified only configurations in the given range are read in.
     72    """
     73
     74    files, idx = _get_files(path, filestem, idl)
     75
     76    tree = meson.rsplit('_')[0]
     77    corr_data = []
     78    infos = []
     79    for hd5_file in files:
     80        h5file = h5py.File(path + '/' + hd5_file, "r")
     81        if not tree + '/' + meson in h5file:
     82            raise Exception("Entry '" + meson + "' not contained in the files.")
     83        raw_data = list(h5file[tree + '/' + meson + '/corr'])
     84        real_data = [o[0] for o in raw_data]
     85        corr_data.append(real_data)
     86        if not infos:
     87            for k, i in h5file[tree + '/' + meson].attrs.items():
     88                infos.append(k + ': ' + i[0].decode())
     89        h5file.close()
     90    corr_data = np.array(corr_data)
     91
     92    l_obs = []
     93    for c in corr_data.T:
     94        l_obs.append(Obs([c], [ens_id], idl=[idx]))
     95
     96    corr = Corr(l_obs)
     97    corr.tag = r", ".join(infos)
     98    return corr
     99
    100
    101class Npr_matrix(np.ndarray):
    102
    103    def __new__(cls, input_array, mom_in=None, mom_out=None):
    104        obj = np.asarray(input_array).view(cls)
    105        obj.mom_in = mom_in
    106        obj.mom_out = mom_out
    107        return obj
    108
    109    @property
    110    def g5H(self):
    111        """Gamma_5 hermitean conjugate
    112
    113        Uses the fact that the propagator is gamma5 hermitean, so just the
    114        in and out momenta of the propagator are exchanged.
    115        """
    116        return Npr_matrix(self,
    117                          mom_in=self.mom_out,
    118                          mom_out=self.mom_in)
    119
    120    def _propagate_mom(self, other, name):
    121        s_mom = getattr(self, name, None)
    122        o_mom = getattr(other, name, None)
    123        if s_mom is not None and o_mom is not None:
    124            if not np.allclose(s_mom, o_mom):
    125                raise Exception(name + ' does not match.')
    126        return o_mom if o_mom is not None else s_mom
    127
    128    def __matmul__(self, other):
    129        return self.__new__(Npr_matrix,
    130                            super().__matmul__(other),
    131                            self._propagate_mom(other, 'mom_in'),
    132                            self._propagate_mom(other, 'mom_out'))
    133
    134    def __array_finalize__(self, obj):
    135        if obj is None:
    136            return
    137        self.mom_in = getattr(obj, 'mom_in', None)
    138        self.mom_out = getattr(obj, 'mom_out', None)
    139
    140
    141def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
    142    """Read hadrons ExternalLeg hdf5 file and output an array of CObs
    143
    144    Parameters
    145    ----------
    146    path : str
    147        path to the files to read
    148    filestem : str
    149        namestem of the files to read
    150    ens_id : str
    151        name of the ensemble, required for internal bookkeeping
    152    idl : range
    153        If specified only configurations in the given range are read in.
    154    """
    155
    156    files, idx = _get_files(path, filestem, idl)
    157
    158    mom = None
    159
    160    corr_data = []
    161    for hd5_file in files:
    162        file = h5py.File(path + '/' + hd5_file, "r")
    163        raw_data = file['ExternalLeg/corr'][0][0].view('complex')
    164        corr_data.append(raw_data)
    165        if mom is None:
    166            mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
    167        file.close()
    168    corr_data = np.array(corr_data)
    169
    170    rolled_array = np.rollaxis(corr_data, 0, 5)
    171
    172    matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
    173    for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
    174        real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
    175        imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
    176        matrix[si, sj, ci, cj] = CObs(real, imag)
    177
    178    return Npr_matrix(matrix, mom_in=mom)
    179
    180
    181def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
    182    """Read hadrons Bilinear hdf5 file and output an array of CObs
    183
    184    Parameters
    185    ----------
    186    path : str
    187        path to the files to read
    188    filestem : str
    189        namestem of the files to read
    190    ens_id : str
    191        name of the ensemble, required for internal bookkeeping
    192    idl : range
    193        If specified only configurations in the given range are read in.
    194    """
    195
    196    files, idx = _get_files(path, filestem, idl)
    197
    198    mom_in = None
    199    mom_out = None
    200
    201    corr_data = {}
    202    for hd5_file in files:
    203        file = h5py.File(path + '/' + hd5_file, "r")
    204        for i in range(16):
    205            name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8')
    206            if name not in corr_data:
    207                corr_data[name] = []
    208            raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex')
    209            corr_data[name].append(raw_data)
    210            if mom_in is None:
    211                mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
    212            if mom_out is None:
    213                mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
    214
    215        file.close()
    216
    217    result_dict = {}
    218
    219    for key, data in corr_data.items():
    220        local_data = np.array(data)
    221
    222        rolled_array = np.rollaxis(local_data, 0, 5)
    223
    224        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
    225        for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
    226            real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
    227            imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
    228            matrix[si, sj, ci, cj] = CObs(real, imag)
    229
    230        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
    231
    232    return result_dict
    233
    234
    235def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
    236    """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
    237
    238    Parameters
    239    ----------
    240    path : str
    241        path to the files to read
    242    filestem : str
    243        namestem of the files to read
    244    ens_id : str
    245        name of the ensemble, required for internal bookkeeping
    246    idl : range
    247        If specified only configurations in the given range are read in.
    248    vertices : list
    249        Vertex functions to be extracted.
    250    """
    251
    252    files, idx = _get_files(path, filestem, idl)
    253
    254    mom_in = None
    255    mom_out = None
    256
    257    vertex_names = []
    258    for vertex in vertices:
    259        vertex_names += _get_lorentz_names(vertex)
    260
    261    corr_data = {}
    262
    263    tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
    264
    265    for hd5_file in files:
    266        file = h5py.File(path + '/' + hd5_file, "r")
    267
    268        for i in range(32):
    269            name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8'))
    270            if name in vertex_names:
    271                if name not in corr_data:
    272                    corr_data[name] = []
    273                raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
    274                corr_data[name].append(raw_data)
    275                if mom_in is None:
    276                    mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
    277                if mom_out is None:
    278                    mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
    279
    280        file.close()
    281
    282    intermediate_dict = {}
    283
    284    for vertex in vertices:
    285        lorentz_names = _get_lorentz_names(vertex)
    286        for v_name in lorentz_names:
    287            if v_name in [('SigmaXY', 'SigmaZT'),
    288                          ('SigmaXT', 'SigmaYZ'),
    289                          ('SigmaYZ', 'SigmaXT'),
    290                          ('SigmaZT', 'SigmaXY')]:
    291                sign = -1
    292            else:
    293                sign = 1
    294            if vertex not in intermediate_dict:
    295                intermediate_dict[vertex] = sign * np.array(corr_data[v_name])
    296            else:
    297                intermediate_dict[vertex] += sign * np.array(corr_data[v_name])
    298
    299    result_dict = {}
    300
    301    for key, data in intermediate_dict.items():
    302
    303        rolled_array = np.moveaxis(data, 0, 8)
    304
    305        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
    306        for index in np.ndindex(rolled_array.shape[:-1]):
    307            real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
    308            imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
    309            matrix[index] = CObs(real, imag)
    310
    311        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
    312
    313    return result_dict
    314
    315
    316def _get_lorentz_names(name):
    317    lorentz_index = ['X', 'Y', 'Z', 'T']
    318
    319    res = []
    320
    321    if name == "TT":
    322        for i in range(4):
    323            for j in range(i + 1, 4):
    324                res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[i] + lorentz_index[j]))
    325        return res
    326
    327    if name == "TTtilde":
    328        for i in range(4):
    329            for j in range(i + 1, 4):
    330                for k in range(4):
    331                    for o in range(k + 1, 4):
    332                        fac = epsilon_tensor_rank4(i, j, k, o)
    333                        if not np.isclose(fac, 0.0):
    334                            res.append(("Sigma" + lorentz_index[i] + lorentz_index[j], "Sigma" + lorentz_index[k] + lorentz_index[o]))
    335        return res
    336
    337    assert len(name) == 2
    338
    339    if 'S' in name or 'P' in name:
    340        if not set(name) <= set(['S', 'P']):
    341            raise Exception("'" + name + "' is not a Lorentz scalar")
    342
    343        g_names = {'S': 'Identity',
    344                   'P': 'Gamma5'}
    345
    346        res.append((g_names[name[0]], g_names[name[1]]))
    347
    348    else:
    349        if not set(name) <= set(['V', 'A']):
    350            raise Exception("'" + name + "' is not a Lorentz scalar")
    351
    352        for ind in lorentz_index:
    353            res.append(('Gamma' + ind + (name[0] == 'A') * 'Gamma5',
    354                        'Gamma' + ind + (name[1] == 'A') * 'Gamma5'))
    355
    356    return res

def read_meson_hd5(path, filestem, ens_id, meson='meson_0', idl=None):
View Source

    57def read_meson_hd5(path, filestem, ens_id, meson='meson_0', idl=None):
    58    """Read hadrons meson hdf5 file and extract the meson labeled 'meson'
    59
    60    Parameters
    61    -----------------
    62    path : str
    63        path to the files to read
    64    filestem : str
    65        namestem of the files to read
    66    ens_id : str
    67        name of the ensemble, required for internal bookkeeping
    68    meson : str
    69        label of the meson to be extracted, standard value meson_0 which
    70        corresponds to the pseudoscalar pseudoscalar two-point function.
    71    idl : range
    72        If specified only configurations in the given range are read in.
    73    """
    74
    75    files, idx = _get_files(path, filestem, idl)
    76
    77    tree = meson.rsplit('_')[0]
    78    corr_data = []
    79    infos = []
    80    for hd5_file in files:
    81        h5file = h5py.File(path + '/' + hd5_file, "r")
    82        if not tree + '/' + meson in h5file:
    83            raise Exception("Entry '" + meson + "' not contained in the files.")
    84        raw_data = list(h5file[tree + '/' + meson + '/corr'])
    85        real_data = [o[0] for o in raw_data]
    86        corr_data.append(real_data)
    87        if not infos:
    88            for k, i in h5file[tree + '/' + meson].attrs.items():
    89                infos.append(k + ': ' + i[0].decode())
    90        h5file.close()
    91    corr_data = np.array(corr_data)
    92
    93    l_obs = []
    94    for c in corr_data.T:
    95        l_obs.append(Obs([c], [ens_id], idl=[idx]))
    96
    97    corr = Corr(l_obs)
    98    corr.tag = r", ".join(infos)
    99    return corr

Read hadrons meson hdf5 file and extract the meson labeled 'meson'

###### Parameters

- path (str): path to the files to read
- filestem (str): namestem of the files to read
- ens_id (str): name of the ensemble, required for internal bookkeeping
- meson (str): label of the meson to be extracted, standard value meson_0 which corresponds to the pseudoscalar pseudoscalar two-point function.
- idl (range): If specified only configurations in the given range are read in.

class Npr_matrix(numpy.ndarray):
View Source

    102class Npr_matrix(np.ndarray):
    103
    104    def __new__(cls, input_array, mom_in=None, mom_out=None):
    105        obj = np.asarray(input_array).view(cls)
    106        obj.mom_in = mom_in
    107        obj.mom_out = mom_out
    108        return obj
    109
    110    @property
    111    def g5H(self):
    112        """Gamma_5 hermitean conjugate
    113
    114        Uses the fact that the propagator is gamma5 hermitean, so just the
    115        in and out momenta of the propagator are exchanged.
    116        """
    117        return Npr_matrix(self,
    118                          mom_in=self.mom_out,
    119                          mom_out=self.mom_in)
    120
    121    def _propagate_mom(self, other, name):
    122        s_mom = getattr(self, name, None)
    123        o_mom = getattr(other, name, None)
    124        if s_mom is not None and o_mom is not None:
    125            if not np.allclose(s_mom, o_mom):
    126                raise Exception(name + ' does not match.')
    127        return o_mom if o_mom is not None else s_mom
    128
    129    def __matmul__(self, other):
    130        return self.__new__(Npr_matrix,
    131                            super().__matmul__(other),
    132                            self._propagate_mom(other, 'mom_in'),
    133                            self._propagate_mom(other, 'mom_out'))
    134
    135    def __array_finalize__(self, obj):
    136        if obj is None:
    137            return
    138        self.mom_in = getattr(obj, 'mom_in', None)
    139        self.mom_out = getattr(obj, 'mom_out', None)

ndarray(shape, dtype=float, buffer=None, offset=0, strides=None, order=None)

An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)

Arrays should be constructed using `array`, `zeros` or `empty` (refer to the See Also section below). The parameters given here refer to a low-level method (`ndarray(...)`) for instantiating an array.

For more information, refer to the `numpy` module and examine the methods and attributes of an array.

###### Parameters

- (for the __new__ method; see Notes below)
- shape (tuple of ints): Shape of created array.
- dtype (data-type, optional): Any object that can be interpreted as a numpy data type.
- buffer (object exposing buffer interface, optional): Used to fill the array with data.
- offset (int, optional): Offset of array data in buffer.
- strides (tuple of ints, optional): Strides of data in memory.
- order ({'C', 'F'}, optional): Row-major (C-style) or column-major (Fortran-style) order.

###### Attributes

- T (ndarray): Transpose of the array.
- data (buffer): The array's elements, in memory.
- dtype (dtype object): Describes the format of the elements in the array.
- flags (dict): Dictionary containing information related to memory use, e.g., 'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
- flat (numpy.flatiter object): Flattened version of the array as an iterator. The iterator allows assignments, e.g., `x.flat = 3` (See `ndarray.flat` for assignment examples; TODO).
- imag (ndarray): Imaginary part of the array.
- real (ndarray): Real part of the array.
- size (int): Number of elements in the array.
- itemsize (int): The memory use of each array element in bytes.
- nbytes (int): The total number of bytes required to store the array data, i.e., `itemsize * size`.
- ndim (int): The array's number of dimensions.
- shape (tuple of ints): Shape of the array.
- strides (tuple of ints): The step-size required to move from one element to the next in memory. For example, a contiguous `(3, 4)` array of type `int16` in C-order has strides `(8, 2)`. This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 8 bytes at a time (`2 * 4`).
- ctypes (ctypes object): Class containing properties of the array needed for interaction with ctypes.
- base (ndarray): If the array is a view into another array, that array is its `base` (unless that array is also a view). The `base` array is where the array data is actually stored.

###### See Also

`array`: Construct an array.
`zeros`: Create an array, each element of which is zero.
`empty`: Create an array, but leave its allocated memory unchanged (i.e., it contains "garbage").
`dtype`: Create a data-type.
`numpy.typing.NDArray`: An ndarray alias :term:`generic <generic type>` w.r.t. its `dtype.type <numpy.dtype.type>`.

###### Notes

There are two modes of creating an array using `__new__`:

1. If `buffer` is None, then only `shape`, `dtype`, and `order` are used.
2. If `buffer` is an object exposing the buffer interface, then all keywords are interpreted.

No `__init__` method is needed because the array is fully initialized after the `__new__` method.

###### Examples

These examples illustrate the low-level `ndarray` constructor. Refer to the `See Also` section above for easier ways of constructing an ndarray.

First mode, `buffer` is None:

    >>> np.ndarray(shape=(2,2), dtype=float, order='F')
    array([[0.0e+000, 0.0e+000], # random
           [     nan, 2.5e-323]])

Second mode:

    >>> np.ndarray((2,), buffer=np.array([1,2,3]),
    ...            offset=np.int_().itemsize,
    ...            dtype=int) # offset = 1*itemsize, i.e. skip first element
    array([2, 3])

Npr_matrix()
g5H

Gamma_5 hermitean conjugate

Uses the fact that the propagator is gamma5 hermitean, so just the in and out momenta of the propagator are exchanged.

##### Inherited Members

numpy.ndarray
  dumps
  dump
  all
  any
  argmax
  argmin
  argpartition
  argsort
  astype
  byteswap
  choose
  clip
  compress
  conj
  conjugate
  copy
  cumprod
  cumsum
  diagonal
  dot
  fill
  flatten
  getfield
  item
  itemset
  max
  mean
  min
  newbyteorder
  nonzero
  partition
  prod
  ptp
  put
  ravel
  repeat
  reshape
  resize
  round
  searchsorted
  setfield
  setflags
  sort
  squeeze
  std
  sum
  swapaxes
  take
  tobytes
  tofile
  tolist
  tostring
  trace
  transpose
  var
  view
  ndim
  flags
  shape
  strides
  data
  itemsize
  size
  nbytes
  base
  dtype
  real
  imag
  flat
  ctypes
  T

def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
View Source

    142def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
    143    """Read hadrons ExternalLeg hdf5 file and output an array of CObs
    144
    145    Parameters
    146    ----------
    147    path : str
    148        path to the files to read
    149    filestem : str
    150        namestem of the files to read
    151    ens_id : str
    152        name of the ensemble, required for internal bookkeeping
    153    idl : range
    154        If specified only configurations in the given range are read in.
    155    """
    156
    157    files, idx = _get_files(path, filestem, idl)
    158
    159    mom = None
    160
    161    corr_data = []
    162    for hd5_file in files:
    163        file = h5py.File(path + '/' + hd5_file, "r")
    164        raw_data = file['ExternalLeg/corr'][0][0].view('complex')
    165        corr_data.append(raw_data)
    166        if mom is None:
    167            mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
    168        file.close()
    169    corr_data = np.array(corr_data)
    170
    171    rolled_array = np.rollaxis(corr_data, 0, 5)
    172
    173    matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
    174    for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
    175        real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
    176        imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
    177        matrix[si, sj, ci, cj] = CObs(real, imag)
    178
    179    return Npr_matrix(matrix, mom_in=mom)

Read hadrons ExternalLeg hdf5 file and output an array of CObs

###### Parameters

- path (str): path to the files to read
- filestem (str): namestem of the files to read
- ens_id (str): name of the ensemble, required for internal bookkeeping
- idl (range): If specified only configurations in the given range are read in.

def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
View Source

    182def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
    183    """Read hadrons Bilinear hdf5 file and output an array of CObs
    184
    185    Parameters
    186    ----------
    187    path : str
    188        path to the files to read
    189    filestem : str
    190        namestem of the files to read
    191    ens_id : str
    192        name of the ensemble, required for internal bookkeeping
    193    idl : range
    194        If specified only configurations in the given range are read in.
    195    """
    196
    197    files, idx = _get_files(path, filestem, idl)
    198
    199    mom_in = None
    200    mom_out = None
    201
    202    corr_data = {}
    203    for hd5_file in files:
    204        file = h5py.File(path + '/' + hd5_file, "r")
    205        for i in range(16):
    206            name = file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['gamma'][0].decode('UTF-8')
    207            if name not in corr_data:
    208                corr_data[name] = []
    209            raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex')
    210            corr_data[name].append(raw_data)
    211            if mom_in is None:
    212                mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
    213            if mom_out is None:
    214                mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
    215
    216        file.close()
    217
    218    result_dict = {}
    219
    220    for key, data in corr_data.items():
    221        local_data = np.array(data)
    222
    223        rolled_array = np.rollaxis(local_data, 0, 5)
    224
    225        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
    226        for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
    227            real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
    228            imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
    229            matrix[si, sj, ci, cj] = CObs(real, imag)
    230
    231        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
    232
    233    return result_dict

Read hadrons Bilinear hdf5 file and output an array of CObs

###### Parameters

- path (str): path to the files to read
- filestem (str): namestem of the files to read
- ens_id (str): name of the ensemble, required for internal bookkeeping
- idl (range): If specified only configurations in the given range are read in.

def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=['VA', 'AV']):
View Source

    236def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
    237    """Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
    238
    239    Parameters
    240    ----------
    241    path : str
    242        path to the files to read
    243    filestem : str
    244        namestem of the files to read
    245    ens_id : str
    246        name of the ensemble, required for internal bookkeeping
    247    idl : range
    248        If specified only configurations in the given range are read in.
    249    vertices : list
    250        Vertex functions to be extracted.
    251    """
    252
    253    files, idx = _get_files(path, filestem, idl)
    254
    255    mom_in = None
    256    mom_out = None
    257
    258    vertex_names = []
    259    for vertex in vertices:
    260        vertex_names += _get_lorentz_names(vertex)
    261
    262    corr_data = {}
    263
    264    tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
    265
    266    for hd5_file in files:
    267        file = h5py.File(path + '/' + hd5_file, "r")
    268
    269        for i in range(32):
    270            name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8'))
    271            if name in vertex_names:
    272                if name not in corr_data:
    273                    corr_data[name] = []
    274                raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
    275                corr_data[name].append(raw_data)
    276                if mom_in is None:
    277                    mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(), dtype=float)
    278                if mom_out is None:
    279                    mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(), dtype=float)
    280
    281        file.close()
    282
    283    intermediate_dict = {}
    284
    285    for vertex in vertices:
    286        lorentz_names = _get_lorentz_names(vertex)
    287        for v_name in lorentz_names:
    288            if v_name in [('SigmaXY', 'SigmaZT'),
    289                          ('SigmaXT', 'SigmaYZ'),
    290                          ('SigmaYZ', 'SigmaXT'),
    291                          ('SigmaZT', 'SigmaXY')]:
    292                sign = -1
    293            else:
    294                sign = 1
    295            if vertex not in intermediate_dict:
    296                intermediate_dict[vertex] = sign * np.array(corr_data[v_name])
    297            else:
    298                intermediate_dict[vertex] += sign * np.array(corr_data[v_name])
    299
    300    result_dict = {}
    301
    302    for key, data in intermediate_dict.items():
    303
    304        rolled_array = np.moveaxis(data, 0, 8)
    305
    306        matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
    307        for index in np.ndindex(rolled_array.shape[:-1]):
    308            real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
    309            imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
    310            matrix[index] = CObs(real, imag)
    311
    312        result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
    313
    314    return result_dict

Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs

###### Parameters

- path (str): path to the files to read
- filestem (str): namestem of the files to read
- ens_id (str): name of the ensemble, required for internal bookkeeping
- idl (range): If specified only configurations in the given range are read in.
- vertices (list): Vertex functions to be extracted.