#!/usr/bin/env python

# -*- coding: utf-8 -*-

from __future__ import print_function

import netCDF4

from Crocus.crocus_forcing_nc import CrocusForcing

from nc_index_by_coordinate import tunnel_fast

"""

Test to use the tunnel_fast function to extract point data from a netcdf grid.

Author: kmunve

TODO: add info as MD

"""

filename = "http://thredds.met.no/thredds/dodsC/arome25/arome_metcoop_test2_5km_latest.nc"

ncfile = netCDF4.Dataset(filename, 'r')

latvar = ncfile.variables['latitude']

lonvar = ncfile.variables['longitude']

points = {'Hemsedal': [60.86, 8.6],

'Nordøyan fyr': [64.8, 10.55]

}

indicies = [tunnel_fast(latvar, lonvar, coord[0], coord[1]) for coord in points.values()]

#[print(coord[0], coord[1]) for coord in points.values()]

print(indicies, type(indicies[0]), len(points))

# iy,ix = tunnel_fast(latvar, lonvar, 60.86, 8.6) # Hemsedal

# iy,ix = tunnel_fast(latvar, lonvar, 64.8, 10.55) # Nordøyan fyr

#print('Closest lat, lon: {0}, {1}; corresponding to indicies: {2}, {3}'.format(latvar[iy,ix], lonvar[iy,ix], iy, ix))

cnc = CrocusForcing(no_points=len(points))

for point in xrange(len(points)):

print(point, indicies[point][0], indicies[point][1])

cnc.tair_v[:, point] = ncfile.variables['air_temperature_2m'][:, 0, indicies[point][0], indicies[point][1]]

time_v = ncfile.variables['time']

cnc.time_v[:] = time_v[:]

cnc.time_v.units = time_v.units

cnc.close()

ncfile.close()

#temperature_plot(ta2m, times)