clock-plot 0.1

This package provides a simple way to visualize patterns in timeseries data mapping 24 hours onto a polar plot

clock_plots

clock_plots provides a simple way to visualize timeseries data mapping 24 hours onto the 360 degrees of a polar plot. For usage, please see the examples.ipynb Jupyter notebook

Time features are automatically generated for your timeseries These features include: year (int): Calendar year e.g. 2022 month (str): Calendar month e.g. "January" year_month (int): Calendar year and month in the format YYYYMM e.g. 202201 day (int): Day of calendar year e.g. 25 date (str): Expressed in the format YYYY-MM-DD e.g. 2022-01-25 week (int): Week of the calendar year e.g. 5 dayofweek (str): Short version of day of week e.g. Tue weekend (str): Either "weekday" or "weekend" where weekends are where dayofweek is either "Sat" or "Sun" hour (int): Hour of the day in 24 clock e.g. 14 minute (int): Minute of the hour e.g. 42 degrees (int): Angle around 24 hour clock-face measured in degrees, calculated using hours and minutes season (str): Season of the year defined as: "Winter" where month is either "December", "January" or "February" "Spring" where month is either "March", "April" or "May" "Summer" where month is either "June", "July" or "August" "Autumn" where month is either "September", "October" or "November" Using these it is then simple to filter your data and format your plot. For example you could filter for a particular year, plot seasons with different colors and weekday vs weekend days with different line dashes. Examples of this are given in examples.ipynb

Installation

To install this package run: pip install /path/to/clock_plots

or to install in editable mode, use: pip install -editable /path/to/clock_plots