pytides-py3 0.8.0

조석 분석 및 예측을 위한 Python 라이브러리 (Tidal analysis and prediction library)

pytides-py3

Tidal analysis and prediction library for Python 3.11+. An improved fork of the original pytides, updated for NumPy 2.x and modern SciPy.

Installation

pip install pytides-py3

Or with UV:

uv add pytides-py3

Quick Start

from datetime import datetime, timedelta
from pytidespy3 import Tide, constituent

# Build a model from known constituents
model = Tide(
    constituents=[constituent._M2, constituent._S2, constituent._K1],
    amplitudes=[1.0, 0.5, 0.3],
    phases=[0.0, 90.0, 180.0],
)

# Predict tidal heights
times = [datetime(2023, 1, 1) + timedelta(hours=i) for i in range(24)]
heights = model.at(times)

# Get high/low water times
highs = list(model.highs(datetime(2023, 1, 1), datetime(2023, 1, 8)))
lows = list(model.lows(datetime(2023, 1, 1), datetime(2023, 1, 8)))

# Decompose observed data into harmonic constituents
fitted = Tide.decompose(heights=observed_heights, t=observed_times)

Main Features

• Tidal analysis and prediction via Schureman's harmonic constituent method
• 37 NOAA harmonic constituents (M2, S2, K1, O1, N2, etc.)
• Robust fitting: NaN/inf auto-removal, weighted least squares, robust loss functions (huber, soft_l1, cauchy, arctan)
• High/low water prediction with highs(), lows(), extrema()
• Tide classification: form number-based (semidiurnal, mixed, diurnal)

Requirements

• Python >= 3.11, < 3.14
• NumPy >= 2.3.1
• SciPy >= 1.16.0

Development

uv sync
uv run python -m unittest discover -s tests -v
uv build

References

• Original pytides: https://github.com/sam-cox/pytides
• Schureman, P. (1958). Manual of Harmonic Analysis and Prediction of Tides. U.S. Coast and Geodetic Survey, Special Publication No. 98.
  • Link1
• Meeus, J. (1991). Astronomical Algorithms. Willmann-Bell.

License

MIT