wrtds-py

A Python implementation of WRTDS (Weighted Regressions on Time, Discharge, and Season), the USGS method for estimating long-term trends in river water quality.

Documentation: https://mullenkamp.github.io/wrtds-py/

Source Code: https://github.com/mullenkamp/wrtds-py

Overview

This package is a Python transcription of the USGS R package EGRET. It uses pandas DataFrames as the base data structure with scipy for optimization and interpolation and matplotlib for plotting.

Key features:

• Weighted censored regression — locally weighted MLE with tricube kernels on time, discharge, and season
• Flow normalization — isolate water-quality trends from discharge variability
• WRTDS-K — AR(1) residual interpolation for improved daily estimates
• Trend analysis — pairwise, group, and time-series decomposition (CQTC/QTC)
• Bootstrap confidence intervals — block resampling with bias correction
• Plotting — data overview, annual histories, contour surfaces, and diagnostics

Installation

pip install wrtds

Requires Python >= 3.10.

Quick Example

import pandas as pd
from wrtds import WRTDS

daily = pd.read_csv('daily.csv', parse_dates=['Date'])
sample = pd.read_csv('sample.csv', parse_dates=['Date'])

w = WRTDS(daily, sample, info={'station_name': 'Choptank River'})
w.fit()
w.kalman()

print(w.table_results())
print(w.run_pairs(year1=1985, year2=2010))
w.plot_conc_hist()

See the Quickstart for a full walkthrough.

Development

We use uv to manage the development environment.

uv sync            # install dependencies
uv run pytest      # run tests

License

This project is licensed under the terms of the Apache Software License 2.0.