Literature fingerprinting

anyts.visualizers.fingerprinting()

Description

Visualization of literature fingerprinting (Keim and Oelke 2007).

Every text is cut into segments of segment_len words with a sliding step of a tenth of a segment, and a measure of lexical diversity is computed for every segment. A text is a block of squares in the order of its segments, row by row, 8 rows high, or a single column when it has at most 8 segments; a block wider than a row of the drawing area wraps into rows of that width. The blocks are laid out left to right and wrap between the rows of an area 2 · x_size wide, which grows downwards from 2 · y_size when they need more height. The colour of a square is the value of the measure on the scale of the colorbar, from its smallest to its greatest finite value over all the texts. Segments where the measure is undefined (nan on segments too short for it) and the empty cells of a block are light grey, apart from every value, zero included. Every segment is segment_len words long, so the values are comparable: when the step leaves words after the last segment, one more segment ends with the text, and a text not longer than a segment is one segment. A list without texts or a text without words raises SourceError.

Parameters

Parameter	Type	Default	Description
texts	list[list[str]]	-	List of lists of words
segment_len	int	10	Size of a segment
metric	Callable	None	Function of a measure of lexical diversity; calc_ttr by default
x_size	int	800	Half the width of the drawing area, greater than 25 (the margin between the blocks)
y_size	int	600	Half the height of the drawing area, which grows when the blocks need more
cmap	str	'viridis'	Colour map
ax	Axes	None	Axes of matplotlib for the plot; if not given, a 15×10 figure is created
labels	dict[str, str]	None	Labels over the defaults: title

The function returns the Axes with the visualization, set to an equal aspect so that the squares stay square; the figure is ax.figure.

Usage example

Example

from anyts.diversity_stats import calc_mattr
from anyts.visualizers import fingerprinting

texts = [
    ("the cat sat on the mat " * 20).split(),
    " ".join(f"word{index % 37}" for index in range(150)).split(),
]
fingerprinting(texts, segment_len=20, metric=calc_mattr)