#imports

import os

import sys

import ctypes

flags = sys.getdlopenflags()

sys.setdlopenflags(flags|ctypes.RTLD_GLOBAL)

import numpy as np

from pyproj import Proj

from datetime import datetime, timedelta

from dateutil import tz

import pandas

import matplotlib

#matplotlib.use('Agg')

import matplotlib.pyplot as plt

import pyselfe

sys.setdlopenflags(flags)

tcoon_zone = tz.gettz('UTC')

utc_6 = tz.gettz('UTC-6')

start_date = datetime(2000,1,1,0,0,0,0, utc_6)

end_date = datetime(2001,1,1,0,0,0,0, utc_6)

#start_date.replace(tzinfo=utc_6)

def mk_tcoon_date(text):

utc = datetime.strptime(text.strip('"'), '%m-%d-%Y %H%M')

utc = utc.replace(tzinfo=tcoon_zone)

return utc.astimezone(utc_6)

def skill(xm,x0):

return (1 - ((xm - x0)**2).sum() / ((np.abs(xm - x0.mean()) + np.abs(x0 - x0.mean()))**2).sum())

def rmse(xm,x0):

return np.sqrt(((xm-x0)**2).mean())

def reindex(obs, mod):

return r_obs, r_mod

def calc_metrics(obs, mod):

return [rmse(xm,x0), skill(xm,x0)]

mon_sites_file = '../field_data/corpus_station_list.csv'

selfe_data_dir = '/home/snegusse/modeling/corpus_christi_bay/laquinta_current_modeling/depth_sensitivty/47ft/windon/outputs/'

mon_sites = np.genfromtxt(mon_sites_file, dtype=None, names=True, delimiter=',', skip_header=1)

#convert from latlon to utm14

p = Proj(proj='utm',zone=14,ellps='WGS84')

xy = np.array(p(mon_sites['DDLon'], mon_sites['DDLat'])).transpose()

#initialize model data readers

selfe = pyselfe.Dataset(selfe_data_dir + '1_salt.63', nfiles=7)

for site, xy in zip(mon_sites, xy):

print 'processing site: ', site['Name']

pd = []

if 'tc0' in site['Name']:

data = np.genfromtxt('../field_data/' + site['Name']+'.csv', delimiter=',',

names='datetime,water_level,water_temperature',

dtype=[datetime, np.float, np.float],

missing_values='NA',

converters={'datetime':mk_tcoon_date})

d = {'water_level': data['water_level'],

'water_temperature': data['water_temperature']}

pd = pandas.DataFrame(d, index=data['datetime'])

elif 'tcSALT' in site['Name']:

data = np.genfromtxt('../field_data/' + site['Name']+'.csv', delimiter=',',

names='datetime,water_temperature,salinity',

dtype=[datetime, np.float, np.float],

missing_values='NA',

converters={'datetime':mk_tcoon_date},

usecols=(0,2,3))

d = {'water_temperature': data['water_temperature'],

'salinity': data['salinity']}

pd = pandas.DataFrame(d, index=data['datetime'])

elif 'twdb' in site['Name']:

data = np.genfromtxt('../field_data/' + site['Name'] + '.txt', delimiter='',

names='Y,m,d,hh,mm,water_temperature,salinity,water_level',

dtype=[np.float, np.float, np.float,

np.float,np.float, np.float,

np.float, np.float],

usecols=(0,1,2,3,4,5,8,10),

comments='#', missing_values=-9.99)

field_dates_raw = np.array([datetime(int(Y), int(m), int(d), int(hh),

int(mm), 0, 0, utc_6) for Y,m,d,hh,mm in

zip(data['Y'],data['m'],data['d'],data['hh'],data['mm'])])

field_dates, duplicate_index = np.unique(field_dates_raw, return_index=True)

print (field_dates_raw.size - duplicate_index.size).__str__() + "records removed."

d = {'water_level': data['water_level'][duplicate_index],

'water_temperature': data['water_temperature'][duplicate_index],

'salinity': data['salinity'][duplicate_index]}

pd = pandas.DataFrame(d, index=field_dates)

elif 'model' in site['Name']:

pass

else:

continue

params = site['Type'].split()

# params = ['salinity', 'water_level']

#salinity mid level

#sal_series=[]

if 'salinity' in params:

print 'reading selfe salinity data'

sal = selfe.read_time_series_xy('salt.63', xy[0], xy[1])

dates = [start_date + timedelta(seconds=int(ts)) for ts in sal[:-1,0]]

selfe_sal_ts = pandas.Series(sal[:-1,1], index=dates).dropna()

if not 'model' in site['Name']:

obs_sal_ts = pd['salinity'].dropna()

idx = (obs_sal_ts.index > start_date) * (obs_sal_ts.index < end_date)

obs_sal_ts = obs_sal_ts[idx]

fig = plt.figure()

ax = fig.add_subplot(111)

plt.title('Salinity at' + ' ' + site['Name'])

selfe_sal_plt = ax.plot_date(selfe_sal_ts.index, selfe_sal_ts.values, 'b-')

if not 'model' in site['Name']:

obs_sal_plt = ax.plot_date(obs_sal_ts.index, obs_sal_ts.values, 'r.', markersize=4)

ax.set_xlim(start_date, end_date)

ax.set_ylim(0, 50)

ax.set_ylabel('Salinity, psu')

# plt.legend(["selfe", "fvcom", "utbest", "observed"])

ax.grid(True)

fig.autofmt_xdate()

# plt.savefig(site['Name'] + '_salinity.png')

if 'water_level' in params:

print 'reading selfe water level data'

sal, eta = selfe.read_time_series_xy('salt.63', xy[0], xy[1], return_eta=True)

dates = [start_date + timedelta(seconds=int(ts)) for ts in eta[:-1,0]]

selfe_eta_ts = pandas.Series(eta[:-1,1], index=dates).dropna()

etaplotstart = datetime(2000,12,6,0,0,0)

etaplotend = datetime(2000,12,21,0,0,0)

fig = plt.figure()

ax = fig.add_subplot(111)

plt.title('Water Surface Elevation at' + ' ' + site['Name'])

selfe_eta_plt = ax.plot_date(selfe_eta_ts.index, selfe_eta_ts.values, 'b-')

if not 'model' in site['Name']:

obs_eta_plt = ax.plot_date(obs_eta_ts.index, obs_eta_ts.values, 'r.', markersize=4)

ax.set_ylim(-1, 1)

ax.set_xlim(start_date, end_date)

ax.set_ylabel('water surface elevation above MSL, m')

ax.grid(True)

# ax.legend(["selfe", "fvcom", "utbest", "observed"])

fig.autofmt_xdate()

# plt.savefig(site['Name'] + '_2000_eta.png')

plt.show()