pyUTSAlgorithms 0.2.1

pyUTSAlgorithms (Unevenly Spaced Time Series Algorithms) Python package computes rolling statistics for uneven time series data.

pyUTSAlgorithms

Unevenly Spaced Time Series Algorithms

Unevenly Spaced Time Series (UTS) algorithms for moving averages, rolling operators, and peak detection. This repository provides a comprehensive set of tools to handle time series where observations are not recorded at regular intervals.

This library started as a conversion of the C++ code from andreas50/utsAlgorithms. It has since been expanded with additional algorithms specifically designed for unevenly spaced data.

Features

• Exponential Moving Averages (EMA): Supports last, next, and linear interpolation schemes for numerical stability in UTS.
• Simple Moving Averages (SMA): Efficient rolling window averages for uneven grids using last, next, and linear interpolation.
• Gradient Computation: Central first and second-order derivatives, and arbitrary order derivatives via Fornberg's algorithm.
• Peak and Valley Detection:
  • Basic peak/valley identification.
  • Significant peaks based on Z-score, Kneedle algorithm, or peak prominence.
• Rolling Operators: Generic rolling statistics (mean, variance, max, min, product, etc.) on unevenly spaced time intervals.
• Thresholding & Z-Score: Specialized utilities (like ISODATA) for data analysis and noise reduction in UTS.

Quick Start

import numpy as np
import uts.ema as ema

# Sample unevenly spaced data: (time, value)
data = np.array([
    [0.0, 10.0],
    [1.5, 12.0],
    [2.1, 11.0],
    [4.5, 15.0],
    [5.0, 14.0]
])

# Compute EMA with a tau (time constant) of 1.0 using linear interpolation
results = ema.linear(data, tau=1.0)

print("Original Data:\n", data)
print("EMA Results:\n", results)

Running Unit Tests

Several unit tests were written to validate corner cases and ensure accuracy. The unit tests use the standard unittest framework.

python -m unittest

Documentation

The library is documented using Google-style docstrings. Detailed API documentation is available here.

To generate the documentation locally:

pip install pdoc
pdoc --math -d google -o docs src/uts

Installation

From PyPI

Install the latest version directly from PyPI:

pip install pyUTSAlgorithms

From Source

To install the library locally:

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install .

Authors

• Mário Antunes - mariolpantunes

License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2020 Mário Antunes

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.