%matplotlib inline
from matplotlib import pylab
import matplotlib.pyplot as plt
import netCDF4 as NC
import numpy as np
import datetime
import requests

from salishsea_tools import nc_tools
from salishsea_tools import tidetools
import os
import csv

yr=2013

statID = '7795'# station ID number
start = '31-Dec-' + str(yr-1) #start date 
end = '02-Jan-' + str(yr+1) #end date

tidetools.get_dfo_wlev(statID,start,end)
# now there is a csv file in this directory with the sear surface height information. Let's take a look at the plots.
filename = 'wlev_' +str(statID) + '_' + start +'_' +end +'.csv'

yr=2014

start = '31-Dec-' + str(yr-1) #start date 
end = '02-Jan-' + str(yr+1) #end date

filename = 'Point Atkinson_t_tide_compare8_' + start+'_'+end+'.csv'


import datetime
import pandas as pd
import pytz

def dateParserMeasured2(s):
    #convert the string to a datetime object
    unaware = datetime.datetime.strptime(s, "%d-%b-%Y %H:%M:%S ")
    #add in the local time zone (Canada/Pacific)
    aware = unaware.replace(tzinfo=pytz.timezone('Canada/Pacific'))
    #convert to UTC
    return aware.astimezone(pytz.timezone('UTC'))
    
ttide = pd.read_csv(filename,skiprows=3,parse_dates=[0],date_parser=dateParserMeasured2,delimiter='\t')
ttide = ttide.rename(columns={'Time_Local ': 'time', ' pred_8 ': 'pred_8', ' pred_all ': 'pred_all'})