Class RichTextLines

tensorflow/python/debug/cli/debugger_cli_common.py:154–378

Rich multi-line text. Line-by-line text output, with font attributes (e.g., color) and annotations (e.g., indices in a multi-dimensional tensor). Used as the text output of CLI commands. Can be rendered on terminal environments such as curses. This is not to be confused with Rich Text Form

class RichTextLines(object):
  """Rich multi-line text.

  Line-by-line text output, with font attributes (e.g., color) and annotations
  (e.g., indices in a multi-dimensional tensor). Used as the text output of CLI
  commands. Can be rendered on terminal environments such as curses.

  This is not to be confused with Rich Text Format (RTF). This class is for text
  lines only.
  """

  def __init__(self, lines, font_attr_segs=None, annotations=None):
    """Constructor of RichTextLines.

    Args:
      lines: A list of str or a single str, representing text output to
        screen. The latter case is for convenience when the text output is
        single-line.
      font_attr_segs: A map from 0-based row index to a list of 3-tuples.
        It lists segments in each row that have special font attributes, such
        as colors, that are not the default attribute. For example:
        {1: [(0, 3, "red"), (4, 7, "green")], 2: [(10, 20, "yellow")]}

        In each tuple, the 1st element is the start index of the segment. The
        2nd element is the end index, in an "open interval" fashion. The 3rd
        element is an object or a list of objects that represents the font
        attribute. Colors are represented as strings as in the examples above.
      annotations: A map from 0-based row index to any object for annotating
        the row. A typical use example is annotating rows of the output as
        indices in a multi-dimensional tensor. For example, consider the
        following text representation of a 3x2x2 tensor:
          [[[0, 0], [0, 0]],
           [[0, 0], [0, 0]],
           [[0, 0], [0, 0]]]
        The annotation can indicate the indices of the first element shown in
        each row, i.e.,
          {0: [0, 0, 0], 1: [1, 0, 0], 2: [2, 0, 0]}
        This information can make display of tensors on screen clearer and can
        help the user navigate (scroll) to the desired location in a large
        tensor.

    Raises:
      ValueError: If lines is of invalid type.
    """
    if isinstance(lines, list):
      self._lines = lines
    elif isinstance(lines, six.string_types):
      self._lines = [lines]
    else:
      raise ValueError("Unexpected type in lines: %s" % type(lines))

    self._font_attr_segs = font_attr_segs
    if not self._font_attr_segs:
      self._font_attr_segs = {}
      # TODO(cais): Refactor to collections.defaultdict(list) to simplify code.

    self._annotations = annotations
    if not self._annotations: