def build_graph(path, term_depth=<NUM_LIT:1000>, skim_depth=<NUM_LIT:10>,<EOL>d_weights=False, **kwargs):
<EOL>click.echo('<STR_LIT>')<EOL>t = Text.from_file(path)<EOL>click.echo('<STR_LIT>' % len(t.tokens))<EOL>m = Matrix()<EOL>click.echo('<STR_LIT>')<EOL>m.index(t, t.most_frequent_terms(term_depth), **kwargs)<EOL>g = Skimmer()<EOL>click.echo('<STR_LIT>')<EOL>g.build(t, m, skim_depth, d_weights)<EOL>return g<EOL>
Tokenize a text, index a term matrix, and build out a graph.

Args:
    path (str): The file path.
    term_depth (int): Consider the N most frequent terms.
    skim_depth (int): Connect each word to the N closest siblings.
    d_weights (bool): If true, give "close" nodes low weights.

Returns:
    Skimmer: The indexed graph.