from __future__ import division

import datetime
from glob import glob
import os

from IPython.core.display import HTML
import netCDF4 as nc

from salishsea_tools.nowcast import figures

%matplotlib inline

def results_dataset(period, grid, results_dir):
    """Return the results dataset for period (e.g. 1h or 1d)
    and grid (e.g. grid_T, grid_U) from results_dir.
    """
    filename_pattern = 'SalishSea_{period}_*_{grid}.nc'
    filepaths = glob(os.path.join(results_dir, filename_pattern.format(period=period, grid=grid)))
    return nc.Dataset(filepaths[0])

run_date = datetime.datetime(2014,12,7)

# Results dataset location
results_home = '/ocean/sallen/allen/research/MEOPAR/SalishSea/forecast/'
results_dir = os.path.join(results_home, run_date.strftime('%d%b%y').lower())
# model winds
model_path='/ocean/sallen/allen/research/MEOPAR/Operational/fcst/'

grid_T_hr = results_dataset('1h', 'grid_T', results_dir)

bathy = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')

HTML('<h2>{:%d%b%y} Forecast Figures</h2>'.format(run_date))

fig = figures.PA_tidal_predictions(grid_T_hr)

fig = figures.compare_tidalpredictions_maxSSH(grid_T_hr, bathy, model_path, name='Victoria')

fig = figures.compare_tidalpredictions_maxSSH(grid_T_hr, bathy, model_path)

fig = figures.compare_tidalpredictions_maxSSH(grid_T_hr, bathy, model_path, name='Campbell River')

fig = figures.compare_water_levels(grid_T_hr, bathy,PST=1)

fig = figures.plot_thresholds_all(grid_T_hr, bathy, model_path)

fig = figures.Sandheads_winds(grid_T_hr,bathy,model_path)

reload(figures)
fig = figures.average_winds_at_station(grid_T_hr, bathy, model_path, station = 'all')

reload(figures)
fig = figures.winds_at_max_ssh(grid_T_hr, bathy, model_path, station = 'all')