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 5 yr
FR-PORT-TUDY Port Tudy (Île de Groix) REFMAR 5 yr
FR-CONCARNEAU Concarneau REFMAR 5 yr
FR-SAINT-NAZAIRE Saint-Nazaire REFMAR 5 yr
FR-LA-ROCHELLE La Rochelle — La Pallice REFMAR 5 yr
FR-ROSCOFF Roscoff REFMAR 5 yr
FR-SAINT-MALO Saint-Malo REFMAR 5 yr
FR-CHERBOURG Cherbourg REFMAR 5 yr
FR-LE-HAVRE Le Havre REFMAR 5 yr
FR-DIEPPE Dieppe REFMAR 5 yr
FR-DUNKERQUE Dunkerque REFMAR 5 yr
FR-BAYONNE Boucau-Bayonne REFMAR 5 yr
FR-ARCACHON Arcachon REFMAR 5 yr
FR-PORT-NAVALO Port-Navalo (Golfe du Morbihan) SHOM SPM 3 yr
FR-ARRADON Arradon (Golfe du Morbihan) SHOM SPM 3 yr
FR-AURAY Auray — Saint-Goustan SHOM SPM 3 yr
FR-ETEL Entrée rivière d'Étel SHOM SPM 3 yr

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-1.1.0-py3-none-musllinux_1_2_aarch64.whl (347.3 kB view details)

Uploaded Python 3musllinux: musl 1.2+ ARM64

tide_prediction-1.1.0-py3-none-manylinux_2_34_x86_64.whl (394.6 kB view details)

Uploaded Python 3manylinux: glibc 2.34+ x86-64

tide_prediction-1.1.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (348.7 kB view details)

Uploaded Python 3manylinux: glibc 2.17+ ARM64

File details

Details for the file tide_prediction-1.1.0-py3-none-musllinux_1_2_aarch64.whl.

File metadata

File hashes

Hashes for tide_prediction-1.1.0-py3-none-musllinux_1_2_aarch64.whl
Algorithm Hash digest
SHA256 0c79f5af8bd466022b56ed0d9bba6a4a4b46260713a0ccba25f810dbf673a0bc
MD5 48900c59ab20fd3dc973c8390b2ef0e2
BLAKE2b-256 951bef3f046c70d83a194cf514ebd79789b1675d3489af091036b456b64164b0

See more details on using hashes here.

File details

Details for the file tide_prediction-1.1.0-py3-none-manylinux_2_34_x86_64.whl.

File metadata

File hashes

Hashes for tide_prediction-1.1.0-py3-none-manylinux_2_34_x86_64.whl
Algorithm Hash digest
SHA256 15056a6da39ccd4dfa2f89cb5de4de1d8df196beb65bd35581f0ca2d352c2c73
MD5 3f4e422ab6968d5fc380d96c51f97864
BLAKE2b-256 77237968a43c72e05d74bce6ce417dc887103f986974bd9a593c00c68615b3dc

See more details on using hashes here.

File details

Details for the file tide_prediction-1.1.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for tide_prediction-1.1.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 17873ce044aa5bd2f9a7580315b2eaaacb8c3f1bbc5365e1bdd559ead867827f
MD5 c46e4d388fc682a7ae7a887da9833dc9
BLAKE2b-256 42205bbe01d0e59130796deaef5cd5bb025aa016631f59d51422848f612edb6d

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