pypet/parameter.py
def _load(self, load_dict):
        """Loads data from `load_dict`

        Reconstruction of sparse matrices similar to the :class:`~pypet.parameter.SparseParameter`.

        """
        for key in list(load_dict.keys()):
            # We delete keys over time:
            if key in load_dict:
                if SparseResult.IDENTIFIER in key:
                    new_key = key.split(SparseResult.IDENTIFIER)[0]

                    is_dia = load_dict.pop(new_key + SparseResult.IDENTIFIER + 'is_dia')

                    name_list = SparseParameter._get_name_list(is_dia)
                    rename_list = ['%s%s%s' % (new_key, SparseResult.IDENTIFIER, name)
                                   for name in name_list]

                    data_list = [load_dict.pop(name) for name in rename_list]
                    matrix = SparseParameter._reconstruct_matrix(data_list)
                    self._data[new_key] = matrix
                else:
                    self._data[key] = load_dict[key]
def _load(self, load_dict):
        for key in list(load_dict.keys()):
            # We delete keys over time:
            if key in load_dict:
                if SparseResult.IDENTIFIER in key:
                    new_key = key.split(SparseResult.IDENTIFIER)[0]

                    is_dia = load_dict.pop(new_key + SparseResult.IDENTIFIER + 'is_dia')

                    name_list = SparseParameter._get_name_list(is_dia)
                    rename_list = ['%s%s%s' % (new_key, SparseResult.IDENTIFIER, name)
                                   for name in name_list]

                    data_list = [load_dict.pop(name) for name in rename_list]
                    matrix = SparseParameter._reconstruct_matrix(data_list)
                    self._data[new_key] = matrix
                else:
                    self._data[key] = load_dict[key]
def _load ( self , load_dict ) : for key in list ( load_dict . keys ( ) ) : # We delete keys over time: if key in load_dict : if SparseResult . IDENTIFIER in key : new_key = key . split ( SparseResult . IDENTIFIER ) [ 0 ] is_dia = load_dict . pop ( new_key + SparseResult . IDENTIFIER + 'is_dia' ) name_list = SparseParameter . _get_name_list ( is_dia ) rename_list = [ '%s%s%s' % ( new_key , SparseResult . IDENTIFIER , name ) for name in name_list ] data_list = [ load_dict . pop ( name ) for name in rename_list ] matrix = SparseParameter . _reconstruct_matrix ( data_list ) self . _data [ new_key ] = matrix else : self . _data [ key ] = load_dict [ key ]
Loads data from `load_dict`

        Reconstruction of sparse matrices similar to the :class:`~pypet.parameter.SparseParameter`.
Loads data from load_dict