#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
import os
import pandas as pd
import arrow
import xarray as xr 


# In[26]:


basedir = '/results2/SalishSea/nowcast-green.202111/'

#For Rosario
# iX = 292
# iY = 267
iZ = 10
# #For VS files
# iY = 240
# iX = 240
#For BP files
iY = 344
iX = 276
##For Haro
# iY = 276
# iX = 344

start = dt.datetime(2017,1,1)
end = dt.datetime(2022,12,31)

timerange = arrow.Arrow.range('day',start,end)

for i,day in enumerate(timerange):
    dir1 = day.format('DDMMMYY').lower()
    ymd = day.format('YYYYMMDD')
    filename = 'SalishSea_1h_'+ ymd +'_' + ymd + '_grid_U.nc'
    fullfile = os.path.join(basedir,dir1,filename)
    u_vel = xr.open_dataset(fullfile)
    velocity = u_vel['vozocrtx'].isel(y=iY, x=iX).sel(depthu=iZ, method='nearest')
    u_vel.close()

    if i == 0:
        velocity_year = velocity.copy(deep=True)
        velocity.close()
    else:
        velocity_year = xr.concat([velocity_year, velocity], dim='time_counter')
        velocity.close()
    if i % 10 == 0:
        print (i)


# In[27]:


velocity_year.to_netcdf('velocity_2018-22_BP.nc')


# ### Low pass filter

# In[29]:


velocity_year.plot()


# In[30]:


velocity = xr.open_dataset('velocity_2018-22_BP.nc')
velocity.vozocrtx.plot()


# In[31]:


vel_squared = velocity*velocity

day_avg_tide_vel = vel_squared.resample(time_counter='1D').mean()
day_avg_tide_vel.vozocrtx.plot()


# In[32]:


day_avg_tide_pd = day_avg_tide_vel.to_dataframe()
day_avg_tide_pd = day_avg_tide_pd.drop('depthu', axis=1)
day_avg_tide_pd = day_avg_tide_pd.drop('nav_lat', axis=1)
day_avg_tide_pd = day_avg_tide_pd.drop('nav_lon', axis=1)
day_avg_tide_pd.to_csv('day_avg_tide_BP.csv')
day_avg_tide_pd.plot()


# In[33]:


low_pass_tide = day_avg_tide_pd.rolling(4, center=True).mean()
low_pass_tide.to_csv('low_pass_tide_2018-22_BP.csv')
low_pass_tide.plot()