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from __future__ import division,print_function

import matplotlib as mpl

import scipy as sp

from folderpath import *

from fvcomtools import *

from datatools import *

from gridtools import *

from plottools import *

from projtools import *

import matplotlib.tri as mplt

import matplotlib.pyplot as plt

#from mpl_toolkits.basemap import Basemap

import os as os

import sys

np.set_printoptions(precision=8,suppress=True,threshold=sys.maxsize)

import collections

import pandas as pd

name='template'

grid='passbay_v3'

dry=True

if dry:

data=load_nei2fvcom('/media/moflaher/data/grids/100m_pass_2/add_stuff_cleanup_3/makerun_1/passbay_v3/input/passbay_v3.nei')

else:

data=loadnc('/home/suh001/scratch/{}/runs/{}/output/'.format(grid,name),'{}_0001.nc'.format(grid))

st=0

et=-1

data['x'],data['y'],data['proj']=lcc(data['lon'],data['lat'])

#data['xc']=data['x'][data['nv']].mean(axis=1)

#data['yc']=data['y'][data['nv']].mean(axis=1)

#data['lonc']=data['lon'][data['nv']].mean(axis=1)

#data['latc']=data['lat'][data['nv']].mean(axis=1)

station=collections.OrderedDict()

obsloc=pd.read_csv('data/NEMO-FVCOM_SaintJohn_BOF_Observations_oct2017_phase1.csv',delimiter=',')

for i,iid in enumerate(obsloc.Num):

name=iid

station[str(name)]=[obsloc.Lon[i],obsloc.Lat[i]]

station2=collections.OrderedDict()

for key in station:

print(key)

xloc,yloc = data['proj'](station[key][0],station[key][1])

tidx=np.argmin((data['x']-xloc)**2+(data['y']-yloc)**2)

station2[key]=station[key]+[(tidx+1),data['h'][tidx]]

if not dry:

dist=np.sqrt((data['x']-xloc)**2+(data['y']-yloc)**2)

asort=np.argsort(dist)

close=0

while np.sum(data['wet_nodes'][st:et,asort[close]])<len(data['time'][st:et]):

close+=1

node=asort[close]

if node!=tidx:

station2[key+'W']=station[key]+[(cell+1),data['h'][node]]

if dry:

drystr='without_wet'

else:

drystr='with_wet'

save_stationfile(station2,'{}_sjh_obs_{}_{}.dat'.format(grid,drystr,time.strftime('%y%m%d')))