class_docstr: A class for storing scores for trials.  The modelset and segset
  fields are lists of model and test segment names respectively.
  The element i,j of scoremat and scoremask corresponds to the
  trial involving model i and test segment j.
  
  Arguments
  ---------
  modelset : list
      List of unique models in a ndarray.
  segset : list
      List of unique test segments in a ndarray.
  scoremask : 2D ndarray of bool
      Indicates the trials of interest, i.e.,
      the entry i,j in scoremat should be ignored if scoremask[i,j] is False.
  scoremat : 2D ndarray
      Scores matrix.
docstr: Initialize a Scores object by loading information from a file HDF5 format.
  
  Arguments
  ---------
  scores_file_name : str
      Name of the file to load.
docstr_tok: ["Initialize","a","Scores","object","by","loading","information","from","a","file","HDF5","format",".","Arguments","--","--","--","--","-","scores_file_name",":","str","Name","of","the","file","to","load","."]
code: def __init__(self, scores_file_name=""):
      """Initialize a Scores object by loading information from a file HDF5 format.
  
      Arguments
      ---------
      scores_file_name : str
          Name of the file to load.
      """
      self.modelset = numpy.empty(0, dtype="|O")
      self.segset = numpy.empty(0, dtype="|O")
      self.scoremask = numpy.array([], dtype="bool")
      self.scoremat = numpy.array([])
  
      if scores_file_name == "":
          pass
      else:
          tmp = Scores.read(scores_file_name)
          self.modelset = tmp.modelset
          self.segset = tmp.segset
          self.scoremask = tmp.scoremask
          self.scoremat = tmp.scoremat
  
imports: import numpy
  import copy
  import pickle
  from scipy import linalg
label_desc: Use image node_id 1 to create a new Scores object  with example: obj = Scores(scores_file_name)