Skip to main content

Tide prediction library

Project description

tide-prediction

Tidal prediction library for French Atlantic and English Channel ports. Computes high/low water times, heights, tidal coefficients, and full height curves — all from a fast Rust core exposed via Python bindings.

Disclaimer

This library is provided AS IS, without warranty of any kind. It is not intended for navigation, maritime safety, or any use where inaccurate predictions could cause harm to persons, property, or the environment. Predictions are produced from a simplified 13-constituent harmonic model and do not account for meteorological effects (storm surge, wind, barometric pressure) or exceptional astronomical events. Typical residual RMSE is 0.10–0.25 m; local error may be larger.

For official tidal predictions in France, refer to the SHOM. The author disclaims all liability arising from the use of this software. By using this library you do so at your own risk and sole responsibility.

Installation

pip install tide-prediction

The package is imported as import tide (not tide_prediction).

Quick start

import tide

# Predict a full day
pred = tide.predict_day("FR-BREST", "2024-06-15")

from datetime import datetime, timezone

for e in pred.extremes:
    kind = "HW" if e.is_high_water else "LW"
    coef = f"  coef={e.coefficient}" if e.coefficient else ""
    dt = datetime.fromtimestamp(e.time, tz=timezone.utc).strftime("%H:%M")
    print(f"{kind}  {dt}  {e.height:.2f} m{coef}")

# HW  00:26  4.83 m  coef=39
# LW  06:41  2.40 m
# HW  13:07  4.80 m  coef=42
# LW  19:07  2.61 m

API

tide.predict_day(port_id, date) → DayPrediction

Returns high/low water extremes and a 10-minute height curve for a single day.

  • port_id — port identifier (e.g. "FR-BREST")
  • date — date string "YYYY-MM-DD" (UTC)

tide.predict_range(port_id, from_date, to_date) → list[DayPrediction]

Same as predict_day over a date range (inclusive).

tide.height_at(port_id, timestamp) → float

Instantaneous water height in metres at a given Unix UTC timestamp.

from datetime import datetime, timezone

ts = int(datetime(2024, 6, 15, 12, 0, tzinfo=timezone.utc).timestamp())
h = tide.height_at("FR-BREST", ts)
print(f"{h:.3f} m")  # 4.607 m

Port discovery

# List all available ports
ports = tide.list_ports()

# Search by name
results = tide.search_ports("saint")

# Get a specific port
port = tide.get_port("FR-BREST")
print(port.name, port.latitude, port.longitude)

Data model

DayPrediction
├── port_id        str
├── date           str          "YYYY-MM-DD"
├── extremes       list[TidalExtreme]
│   ├── time           int      Unix timestamp UTC
│   ├── height         float    metres
│   ├── is_high_water  bool
│   └── coefficient    int|None 20–120, Atlantic/Channel HW only
└── heights        list[HeightPoint]
    ├── timestamp   int          every 10 minutes
    └── height      float        metres

Available ports

ID Port Calibration
FR-BREST Brest (coefficient reference) REFMAR
FR-PORT-TUDY Port Tudy (Île de Groix) REFMAR
FR-CONCARNEAU Concarneau REFMAR
FR-SAINT-NAZAIRE Saint-Nazaire REFMAR
FR-LA-ROCHELLE La Rochelle — La Pallice REFMAR
FR-ROSCOFF Roscoff REFMAR
FR-SAINT-MALO Saint-Malo REFMAR
FR-CHERBOURG Cherbourg REFMAR
FR-LE-HAVRE Le Havre REFMAR
FR-DIEPPE Dieppe REFMAR
FR-DUNKERQUE Dunkerque REFMAR
FR-BAYONNE Boucau-Bayonne REFMAR
FR-ARCACHON Arcachon REFMAR
FR-PORT-NAVALO Port-Navalo (Golfe du Morbihan) SHOM SPM
FR-ARRADON Arradon (Golfe du Morbihan) SHOM SPM
FR-AURAY Auray — Saint-Goustan SHOM SPM
FR-ETEL Entrée rivière d'Étel SHOM SPM

Tidal coefficients

Coefficients (20–120) are computed for Atlantic and English Channel high waters relative to the Brest reference tidal range, following the French SHOM convention. They are only available for high water (is_high_water = True) at ports on the Atlantic/Channel coast.

Requirements

  • Python ≥ 3.8
  • Currently tested on Linux x86-64 (manylinux). macOS and Windows wheels may be added in future releases.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

tide_prediction-1.0.2-py3-none-musllinux_1_2_aarch64.whl (312.7 kB view details)

Uploaded Python 3musllinux: musl 1.2+ ARM64

tide_prediction-1.0.2-py3-none-manylinux_2_34_x86_64.whl (355.0 kB view details)

Uploaded Python 3manylinux: glibc 2.34+ x86-64

tide_prediction-1.0.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (314.6 kB view details)

Uploaded Python 3manylinux: glibc 2.17+ ARM64

File details

Details for the file tide_prediction-1.0.2-py3-none-musllinux_1_2_aarch64.whl.

File metadata

File hashes

Hashes for tide_prediction-1.0.2-py3-none-musllinux_1_2_aarch64.whl
Algorithm Hash digest
SHA256 e36fcb515bfd12280c80b42181f82ed7e3e3ac834ef44cdcd47c6e2e396a2801
MD5 0de9f147171c51d8b8c1a2b3c9e2837e
BLAKE2b-256 459c94e81a9eb213f538efd9c65d034fcc6b617884692f3c188847eb4aaf0c36

See more details on using hashes here.

File details

Details for the file tide_prediction-1.0.2-py3-none-manylinux_2_34_x86_64.whl.

File metadata

File hashes

Hashes for tide_prediction-1.0.2-py3-none-manylinux_2_34_x86_64.whl
Algorithm Hash digest
SHA256 a888c2b443a7e049f67d6648ce3c7a0808ae574a86993e3ed2f6169653187f2d
MD5 d2657a95750f34143d366623871bf1f9
BLAKE2b-256 3e067dade59c79b0f04a6faca005f1b33bdbbd053e70cc094fd327fc334232fa

See more details on using hashes here.

File details

Details for the file tide_prediction-1.0.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for tide_prediction-1.0.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8db7ee985f0c3bbccceca8e9c4018f9b9e4db87523d8ece00afc28fab558e878
MD5 be8699e89c8d987a74351959718b285d
BLAKE2b-256 b28fd6f8a0108adf7f2595b86d8a4bba17696f816b25684180fa2f9c5551ad43

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page