Note
Go to the end to download the full example code. or to run this example in your browser via Binder
Prediction example#
In this example, we will use the model to predict the tidal elevation on a specific location, like a tide gauge.
Warning
The model employed is an older FES tidal-atlas model due to its significantly smaller size compared to newer models. Do not use it for real applications. You can download the model from the AVISO website.
First, we import the required modules.
from __future__ import annotations
import os
import pathlib
import numpy
import pyfes
First we create an environment variable to store the path to the model file.
os.environ['DATASET_DIR'] = str(pathlib.Path().absolute().parent / 'tests' /
'python' / 'dataset')
Now we need to create the instances of the model used to calculate the ocean tide and the radial tide. To do this, we need to create a YAML file that describes the models and their parameters. The configuration file is fully documented in the documentation.
handlers = pyfes.load_config(pathlib.Path().absolute() / 'fes_slev.yml')
handlers
is a dictionary that contains the handlers to the ocean and
radial tide models.
print(handlers)
{'radial': <pyfes.core.tidal_model.CartesianComplex64 object at 0x7f523066c130>, 'tide': <pyfes.core.tidal_model.CartesianComplex64 object at 0x7f523064a1f0>}
Setup the longitude and latitude of the location where we want to calculate the tide.
lon = -7.688
lat = 59.195
date = numpy.datetime64('1983-01-01T00:00:00')
Generate the coordinates where we want to calculate the tide.
dates = numpy.arange(date, date + numpy.timedelta64(1, 'D'),
numpy.timedelta64(1, 'h'))
lons = numpy.full(dates.shape, lon)
lats = numpy.full(dates.shape, lat)
We can now calculate the ocean tide and the radial tide.
tide, lp, _ = pyfes.evaluate_tide(handlers['tide'],
dates,
lons,
lats,
num_threads=1)
load, load_lp, _ = pyfes.evaluate_tide(handlers['radial'],
dates,
lons,
lats,
num_threads=1)
Print the results
cnes_julian_days = (dates - numpy.datetime64('1950-01-01T00:00:00')
).astype('M8[s]').astype(float) / 86400
hours = cnes_julian_days % 1 * 24
print(f"{'JulDay':>6s} {'Hour':>5s} {'Latitude':>10s} {'Longitude':>10s} "
f"{'Short_tide':>10s} {'LP_tide':>10s} {'Pure_Tide':>10s} "
f"{'Geo_Tide':>10s} {'Rad_Tide':>10s}")
print('=' * 89)
for ix, jd in enumerate(cnes_julian_days):
print(f'{jd:>6.0f} {hours[ix]:>5.0f} {lats[ix]:>10.3f} {lons[ix]:>10.3f} '
f'{tide[ix]:>10.3f} {lp[ix]:>10.3f} {tide[ix] + lp[ix]:>10.3f} '
f'{tide[ix] + lp[ix] + load[ix]:>10.3f} {load[ix]:>10.3f}')
JulDay Hour Latitude Longitude Short_tide LP_tide Pure_Tide Geo_Tide Rad_Tide
=========================================================================================
12053 0 59.195 -7.688 -100.991 0.917 -100.075 -96.194 3.881
12053 1 59.195 -7.688 -137.105 0.890 -136.215 -131.887 4.328
12053 2 59.195 -7.688 -138.483 0.863 -137.620 -133.910 3.711
12053 3 59.195 -7.688 -104.346 0.835 -103.511 -101.377 2.134
12053 4 59.195 -7.688 -42.516 0.806 -41.710 -41.762 -0.052
12053 5 59.195 -7.688 32.374 0.777 33.151 30.810 -2.341
12053 6 59.195 -7.688 102.167 0.747 102.914 98.720 -4.194
12053 7 59.195 -7.688 149.469 0.717 150.186 145.014 -5.172
12053 8 59.195 -7.688 162.102 0.686 162.789 157.743 -5.046
12053 9 59.195 -7.688 136.505 0.655 137.161 133.308 -3.852
12053 10 59.195 -7.688 78.896 0.624 79.519 77.634 -1.885
12053 11 59.195 -7.688 3.645 0.592 4.236 4.618 0.382
12054 12 59.195 -7.688 -70.659 0.559 -70.100 -67.689 2.411
12054 13 59.195 -7.688 -126.152 0.526 -125.626 -121.892 3.734
12054 14 59.195 -7.688 -150.115 0.493 -149.622 -145.551 4.071
12054 15 59.195 -7.688 -137.779 0.459 -137.320 -133.927 3.393
12054 16 59.195 -7.688 -93.132 0.425 -92.707 -90.779 1.928
12054 17 59.195 -7.688 -27.820 0.391 -27.429 -27.331 0.097
12054 18 59.195 -7.688 41.541 0.356 41.898 40.305 -1.593
12054 19 59.195 -7.688 97.250 0.322 97.571 94.888 -2.684
12054 20 59.195 -7.688 124.939 0.286 125.226 122.344 -2.882
12054 21 59.195 -7.688 117.465 0.251 117.716 115.584 -2.132
12054 22 59.195 -7.688 77.026 0.215 77.242 76.607 -0.635
12054 23 59.195 -7.688 14.661 0.180 14.841 16.051 1.210
Total running time of the script: (0 minutes 0.041 seconds)