2. Read TESEO’s forcing files

2.1. Install pyTESEO

• If you are running this notebook from Google colab uncomment and run !pip install pyteseo && pyteseo-tests in the cell below. Doing this you will install pyteseo from pypi repository and run pyteseo-tests to check the installation (all tests have to succeed).

• If you are using this notebook in your own local machine you need to install the library from the local repo pip install . or pypi pip install pyteseo.

# !pip install pyteseo && pyteseo-tests

2.2. Read TESEO’s forcings files (currents and winds) and plot them.

import warnings

warnings.simplefilter(action="ignore")

1. Define Paths

from pathlib import Path
import pyteseo.tests as tests_

data_path = Path(tests_.__file__).parent / "data"
lstcurr_path = data_path / "lstcurr.pre"
lstwinds_path = data_path / "lstwinds.pre"

2. Read bathymetry and coastline from TESEO’s format files

from pyteseo.io.forcings import read_forcing

currents_df = read_forcing(lstcurr_path, forcing_type="currents")
winds_df = read_forcing(lstwinds_path, forcing_type="winds")
winds_df.head(5)

	time	lon	lat	u	v
0	0.0	9.618706	43.98785	-3.765736	0.509148
1	0.0	9.618706	44.11285	-3.765736	0.509148
2	0.0	9.618706	44.23785	-3.765736	0.509148
3	0.0	9.743706	43.98785	-3.765736	0.509148
4	0.0	9.743706	44.11285	-3.765736	0.509148

3. Plot velocity fields

from matplotlib import pyplot as plt
import numpy as np


currents_df["mod"] = np.sqrt(currents_df.u**2 + currents_df.v**2)
winds_df["mod"] = np.sqrt(winds_df.u**2 + winds_df.v**2)

currents_time = 1
currents = currents_df.loc[currents_df["time"] == currents_time]

winds_time = 3
winds = winds_df.loc[winds_df["time"] == winds_time]

fig = plt.figure()
ax1 = fig.add_subplot(211)
plt.title(f"currents @ time = {currents_time}")
q1 = ax1.quiver(
    currents["lon"],
    currents["lat"],
    currents["u"],
    currents["v"],
    currents["mod"],
    cmap="jet",
)

time = 3
ax2 = fig.add_subplot(212)
plt.title(f"winds @ time = {winds_time}")
q2 = ax2.quiver(
    winds["lon"],
    winds["lat"],
    winds["u"],
    winds["v"],
    winds["mod"],
    cmap="jet",
)

plt.subplots_adjust(hspace=0.5)