tidepredictor: Predict tides with Python

Dependencies

• Utide

Predict the tides for a given location.

Contact: Ole Svenstrup Petersen

Usage:

$ tidepredictor [OPTIONS]

Options:

• --lon FLOAT: Longitude [required]
• --lat FLOAT: Latitude [required]
• -s, --start [%Y-%m-%d|%Y-%m-%dT%H:%M:%S|%Y-%m-%d %H:%M:%S]: Start date [required]
• -e, --end [%Y-%m-%d|%Y-%m-%dT%H:%M:%S|%Y-%m-%d %H:%M:%S]: End date [required]
• -i, --interval INTEGER: Interval in minutes [default: 30]
• -o, --output PATH: Output file, default is stdout
• --format [csv|json]: Output format [default: csv]
• --type [level|current]: Type of prediction, level or u,v [default: level]
• --install-completion: Install completion for the current shell.
• --show-completion: Show completion for the current shell, to copy it or customize the installation.
• --help: Show this message and exit.

Tidal constituents

Code

import polars as pl
from utide._ut_constants import ut_constants

used_consts = "Q1 MF P1 K1 MM O1 M2 S2 M4 MN4 MS4 N2 K2".split()

consts = (
    pl.DataFrame(ut_constants["const"])
    .select("name", pl.col("freq").alias("freq_cph"))
    .filter(pl.col("name").is_in(used_consts))
    .with_columns((1 / pl.col("freq_cph")).alias("period_h"))
    .sort("period_h", descending=True)
)

with pl.Config(set_float_precision=4):
    print(consts.head(10))

name	freq_cph	period_h
MM	0.001512	661.309268
MF	0.00305	327.858984
Q1	0.037219	26.868357
O1	0.038731	25.819342
P1	0.041553	24.06589
K1	0.041781	23.93447
N2	0.078999	12.658348
M2	0.080511	12.420601
S2	0.083333	12.0
K2	0.083561	11.967235