vnc

To analyze the returned data, the package also contains functions based on the work of Gries and Hilpert (2012) for Variability-Based Neighbor Clustering.

The idea is to use hierarchical clustering to aid “bottom up” periodization of language change. The python functions are built on their original R code.

Distances, therefore, are calculated in sums of standard deviations and coefficients of variation, according to their stated method.

Dendrograms are plotted using matplotlib, with custom implementations for hierarchical clustering that maintain the plotting order of the leaves according to the requirements of the method.

The package also has a custom implementation of dendrogram truncation that consolidates leaves under a specified number of time periods (or clusters) while also maintaining the leaf order to facilitate the reading and interpretation of large dendrograms.

Lightweight Implementation

Starting with version 0.2.0, google_ngrams uses lightweight, custom implementations for statistical computations instead of heavy dependencies like scipy and statsmodels. This design choice reduces installation overhead while maintaining full functionality for the core VNC methodology and smoothing operations.

Installation

You can install the released version of google_ngrams from PyPI:

pip install google-ngrams

Usage

To use the google_ngrams package, import google_ngram to fetch data and TimeSeries for analysis.

from google_ngrams import google_ngram, TimeSeries

xray_year = google_ngram(word_forms = ["x-ray"], variety = "us", by = "year")

xray_decade = google_ngram(word_forms = ["x-ray", "x-rays"], variety = "gb", by = "decade")

import polars as pl

xray_filtered = xray_decade.filter(pl.col("Decade") >= 1900)

xray_ts = TimeSeries(xray_filtered, time_col="Decade", values_col="RF")

xray_ts.timeviz_vnc()

License

Code licensed under MIT License. See LICENSE file.