---
id: utide
version: "0.3.1"
license: MIT
license_treatment: permissive
maintenance: active
---

# utide — Python distribution of the MatLab package UTide

License: permissive · Maintenance: active · Downloads: 118.5K/mo

## What it is and what it does

UTide is a Python port of the Matlab UTide package for unified tidal analysis and prediction. It takes time series observations of water velocity or elevation and decomposes them into tidal constituents using harmonic analysis, then allows reconstruction of predicted tidal signals from those coefficients. The package depends on numpy and scipy for numerical computation and is actively maintained, supporting Python 3.9 through 3.13.

The package is designed for oceanographic and coastal engineering applications where understanding tidal components is essential. It implements the methods described in Codiga (2011) and provides a Python-native interface, though the user interface differs from the original Matlab implementation—developers must consult docstrings rather than relying on direct API parity. The project is still in active development, so the API and available functionality may change.

Use it for:

- Extract tidal constituents from observed water velocity or elevation time series at a specific latitude.
- Generate tidal predictions for future time periods based on harmonic coefficients from historical observations.
- Analyze coastal water dynamics by decomposing observed signals into individual tidal harmonics.
- Support oceanographic research requiring harmonic tidal analysis as part of a larger data processing pipeline.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

UTide performs tidal analysis and prediction on time series data, decomposing water velocity or elevation into tidal constituents and reconstructing predictions from those coefficients.

Yes, if you need tidal harmonic analysis in Python. The package has low install friction, active maintenance, no known vulnerabilities, and a permissive license. However, verify that the specific functionality you need is implemented—the documentation notes that some Matlab features are not yet available, and the API differs from the original, requiring careful reference to docstrings.

## Install

pip install utide
uv add utide
poetry add utide

## Installing utide

Before you install: Low friction installation with only numpy and scipy as dependencies. Repository is active with recent commits and a permissive MIT license. Supports modern Python versions from 3.9 onward.

License in practice: MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both academic and commercial projects.

Quickstart:

pip install utide

from utide import solve, reconstruct

coef = solve(
    time,
    time_series_u,
    time_series_v,
    lat=30,
    nodal=False,
    trend=False,
    method="ols",
    conf_int="linear",
    Rayleigh_min=0.95,
)

Requires time series data as numpy arrays and knowledge of the observation latitude; the user interface differs from the Matlab version, so refer to function docstrings for parameter specification.

Verify before relying:

- Whether all Matlab UTide functionality is available in this Python port, or what specific features remain unimplemented
- Performance characteristics compared to the original Matlab implementation for large datasets
- Whether the package handles edge cases in tidal analysis (e.g., polar regions, shallow water)

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 118.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

tidal analysis python, tidal harmonic decomposition, tidal prediction, utide matlab python, ocean tide analysis, tidal constituent extraction, harmonic tidal modeling, oceanography, harmonic-analysis, time-series

[View on SkillFed](https://skillfed.io/packages/utide) · [View on PyPI](https://pypi.org/project/utide/)