%matplotlib inline

import matplotlib.pyplot as plt

import arrow
import datetime
import pandas as pd
from salishsea_tools import tidetools
from salishsea_tools import stormtools
import numpy as np

yr=2014

stations = {'PointAtkinson': 7795, 'Victoria': 7120, 'PatriciaBay': 7277, 'CampbellRiver': 8074}

s = '30-Nov-' + str(2013) #start date 
e= '02-Dec-' + str(2014) #end date

tidetools.get_dfo_wlev(stations['CampbellRiver'],s,e)

tfile='Point Atkinson_t_tide_compare8_31-Dec-2013_02-Jan-2015.csv'
ttide1,msl= stormtools.load_tidal_predictions(tfile)


tfile='Point Atkinson_t_tide_compare8_31-Dec-2013_02-Dec-2015.csv'
ttide2,msl= stormtools.load_tidal_predictions(tfile)

%matplotlib inline


fig,ax=plt.subplots(1,1,figsize=(10,6))

ax.plot(ttide2.time,ttide2.pred_all)
ax.plot(ttide1.time,ttide1.pred_all)
ax.set_xlim([datetime.datetime(2014,8,14),datetime.datetime(2014,8,16)])

subtide=np.array(ttide2.pred_all)
subtime=np.array(ttide2.time)
time1=np.array(ttide1.time)
tide1=np.array(ttide1.pred_all)

indices=np.where(np.logical_and(subtime<=time1[-1],subtime>=time1[0]))
subtide=subtide[indices]
subtime=subtime[indices]




diff= subtide-tide1;
print np.max(diff), subtime[np.argmax(diff)]
print np.mean(diff)
print np.min(diff), subtime[np.argmin(diff)]

print subtime[0],time1[0]
print subtime[-1],time1[-1]

fig,ax=plt.subplots(1,1,figsize=(10,6))
ax.plot(subtime, diff)
ax.set_xlim([datetime.datetime(2014,8,14),datetime.datetime(2014,8,16)])