from datetime import datetime, timezone

from roguewave import get_spotter_data, load

from roguewave.wavephysics.windestimate import estimate_u10_from_spectrum

import matplotlib.pyplot as plt

import pandas as pd

spotter_id = "SPOT-010340"

s2022_params = load('s3://sofar-wx-data-dev-os1/proxy_wind/calibration/final_calibration_peak_weighted=True.zip')

methods_to_plot = {

}

wave_spectra = load('./data/SPOT-010340_spectra_1d')

wind_estimate = estimate_u10_from_spectrum(spectrum=wave_spectra,

method=methods_to_plot['peak']['method'],

**methods_to_plot['peak']['params']) # spectra needs to be 1D

fig, axs = plt.subplots(nrows=1,

ncols=1,

figsize=(14, 10))

fig.suptitle(f'{spotter_id} wind estimates from S2022: '

f'{pd.to_datetime(wind_estimate.time.values[0]).month}/{pd.to_datetime(wind_estimate.time.values[0]).day}/'

f'{pd.to_datetime(wind_estimate.time.values[0]).year} - '

f'{pd.to_datetime(wind_estimate.time.values[-1]).month}/{pd.to_datetime(wind_estimate.time.values[-1]).day}/'

f'{pd.to_datetime(wind_estimate.time.values[-1]).year}',

fontsize=20)

fig.tight_layout()

axs.plot(wind_estimate['time'], wind_estimate['u10'], color='#020966')

axs.grid()

axs.set_xlabel('Time (UTC)', fontsize=16)

axs.set_ylabel('$U_{10}$ [m/s]', fontsize=16)

axs.set_xticks(axs.get_xticks(), axs.get_xticklabels(), rotation=45)

fig.subplots_adjust(top=0.92, bottom=0.12, left=0.08, right=0.92)

fig.savefig('./figures/S2022_wind_estimates.png', dpi=300)

plt.show()