# Option 1. if FES2014 global tide model is setup

from SDS_slope import compute_tide
from SDS_slope import compute_tide_dates
from SDS_slope import get_min_max
import pyfes
import os
import pytz
from datetime import datetime, timedelta
import pickle
import numpy as np
import matplotlib.pyplot as plt

# load the sitename_output.pkl generated by CoastSat
#sitename = 'NARRA'
with open('Pekalongan_longer_time_output.pkl','rb') as f:
    output = pickle.load(f) 

# From earlier part of notebook
days_in_year = 365.2425
seconds_in_day = 24*3600
settings_slope = {'slope_min':        0.035,                  # minimum slope to trial
                  'slope_max':        0.2,                    # maximum slope to trial
                  'delta_slope':      0.005,                  # slope increment
                  'date_range':       [1999,2020],            # range of dates over which to perform the analysis
                  'n_days':           8,                      # sampling period [days]
                  'n0':               50,                     # parameter for Nyquist criterium in Lomb-Scargle transforms
                  'freqs_cutoff':     1./(seconds_in_day*30), # 1 month frequency
                  'delta_f':          100*1e-10,              # deltaf for identifying peak tidal frequency band
                  'prc_conf':         0.05,                   # percentage above minimum to define confidence bands in energy curve
                  }
settings_slope['date_range'] = [pytz.utc.localize(datetime(settings_slope['date_range'][0],5,1)),
                                pytz.utc.localize(datetime(settings_slope['date_range'][1],1,1))]

# clip the dates between 1999 and 2020 as we need at least 2 Landsat satellites 
idx_dates = [np.logical_and(_>settings_slope['date_range'][0],_<settings_slope['date_range'][1]) for _ in output['dates']]
dates_sat = [output['dates'][_] for _ in np.where(idx_dates)[0]]


# point to the folder where you downloaded the .nc files
filepath = r'C:\Users\slomb\OneDrive\Desktop\tide_alt_env\fes-2.9.1-Source\data\fes2014'
config_ocean = os.path.join(filepath, 'ocean_tide.ini') # change to ocean_tide.ini
config_load =  os.path.join(filepath, 'load_tide.ini')  # change to load_tide.ini
ocean_tide = pyfes.Handler("ocean", "io", config_ocean)
load_tide = pyfes.Handler("radial", "io", config_load)

# coordinates of the location (always select a point 1-2km offshore from the beach)
# if the model returns NaNs, change the location of your point further offshore.
coords = [109.66644, -6.829584]
# get tide time-series with 15 minutes intervals
time_step = 15*60

#....dates_fes, tide_fes = SDS_slope.compute_tide(coords,settings_slope['date_range'],time_step,ocean_tide,load_tide)
dates_fes, tide_fes = compute_tide(coords,settings_slope['date_range'],time_step,ocean_tide,load_tide)
# get tide level at time of image acquisition

#....tide_sat = SDS_slope.compute_tide_dates(coords, dates_sat, ocean_tide, load_tide)
tide_sat = compute_tide_dates(coords, dates_sat, ocean_tide, load_tide)

# plot tide time-series
plt.figure
fig, ax = plt.subplots(1,1,figsize=(12,3), tight_layout=True)
ax.set_title('Sub-sampled tide levels')
ax.grid(which='major', linestyle=':', color='0.5')
ax.plot(dates_fes, tide_fes, '-', color='0.6')
ax.plot(dates_sat, tide_sat, '-o', color='k', ms=4, mfc='w',lw=1)
ax.set_ylabel('tide level [m]')
ax.set_ylim(get_min_max(tide_fes));
#plt.show()

print(type(dates_sat))
print(type(dates_fes))

#Make 4 janky CSVs to combine in Excel
np.savetxt("tide_fes.csv", tide_fes, delimiter=",")
np.savetxt("tide_sat.csv", tide_sat, delimiter=",")
np.savetxt("date_sat.csv", 
           dates_sat,
           delimiter =", ", 
           fmt ='% s')
np.savetxt("date_fes.csv", 
           dates_fes,
           delimiter =", ", 
           fmt ='% s')