pyedautils 0.0.22

Python Energy Data Analysis Utilities

pyedautils

Python Energy Data Analysis Utilities

A pip-installable library of compact utility functions for analyzing and visualizing energy and comfort time-series data.

Features

• Plotting — Plotly-based daily profile visualizations with confidence bands and decomposed weekly patterns
• Data quality — Gap, stuck-value and range-outlier detection, interval inference, ok/warning/critical flags
• Thermal comfort — SIA 180:2014 adaptive comfort curves, overheating hours/KPIs, comfort donuts, overheating bar
• Gradients — Heating/cooling gradients (K/h) by direction and season, with grouped boxplots
• Solar influence — Detect direct solar influence on a sensor, with a dual-axis plot
• Data I/O — Save/load DataFrames in CSV, pickle, compressed pickle, and JSON formats
• Geocoding — Address geocoding, WGS84/LV95 conversion, altitude lookup, Swiss postal codes, Haversine distance
• Season detection — Astronomical or meteorological season classification for any date
• Solar position — Sun elevation and azimuth for a location and time (single timestamp or vectorized over a pandas Series/DatetimeIndex)
• MeteoSwiss — Find nearest weather station by sensor type and altitude

Installation

pip install pyedautils

Quick start

from pyedautils.plots import plot_daily_profiles_overview
from pyedautils.data_io import load_data

df = load_data("my_data.csv")
fig = plot_daily_profiles_overview(df)
fig.show()

Documentation

Full API reference, examples with interactive plots, and usage guides:

retomarek.github.io/pyedautils

License

Disclaimer — The author declines any liability or responsibility in connection with the published code and documentation.