utide
Python distribution of the MatLab package UTide
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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.
- Low friction installation with only numpy and scipy as dependencies.
- Repository is active with recent commits and a permissive MIT license.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both academic and commercial projects.
last release 2025-04-28 (473 days) · last repo commit 2026-08-13 · 166 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 118,468 downloads/mo, #12,117 on PyPI
Alternatives
Verify before relying
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,
)- 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)
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 on this page. Verify before relying on it.
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
utide on PyPI
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.
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.
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,
)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 the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpyscipy |
| Maintenance | Actively maintained 473 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 118,468 / month, #12,117 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: utide-0.3.1-py3-none-any.whl
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