from IPython.core.display import HTML
HTML('<iframe src=http://scitools.org.uk/iris/ width=800 height=350></iframe>')

import numpy
import matplotlib.pyplot as plt
import datetime as dt

import iris
import iris.plot as iplt
import cartopy.crs as ccrs

def time_near(cube,start):
    timevar=cube.coord('time')
    itime = timevar.nearest_neighbour_index(timevar.units.date2num(start))
    return timevar.points[itime]

def myplot(slice,model=None):
    # make the plot
    figure(figsize=(12,8))
    lat=slice.coord(axis='Y').points
    lon=slice.coord(axis='X').points
    time=slice.coord('time')[0]
    subplot(111,aspect=(1.0/cos(mean(lat)*pi/180.0)))
    pcolormesh(lon,lat,masked_invalid(slice.data));
    colorbar()
    grid()
    date_str=time.units.num2date(time.points)
    plt.title('%s: %s: %s' % (model,slice.long_name,date_str));

# DAP URL: 30 year East Coast wave hindcast (Wave Watch 3 driven by CFSR Winds) 
#cubes = iris.load('http://geoport.whoi.edu/thredds/dodsC/fmrc/NCEP/ww3/cfsr/4m/best'); # 4 arc minute resolution
cubes = iris.load('http://geoport.whoi.edu/thredds/dodsC/fmrc/NCEP/ww3/cfsr/10m/best'); # 10 arc minute resolution

print cubes

hsig=cubes[0]

# use contraints to select geographic subset and nearest time
mytime=dt.datetime(1991,10,31,12)  #specified time...   Nov 1, 1991 was the "Perfect Storm"
#mytime=dt.datetime.utcnow()      # .... or now
slice=hsig.extract(iris.Constraint(time=time_near(hsig,mytime),
    longitude=lambda cell: -71.5 < cell < -64.0,
    latitude=lambda cell: 40.0 < cell < 46.0))


print slice

print[coord.name() for coord in slice.coords()]

figure(figsize=(12,8))

# set the projection
ax1 = plt.axes(projection=ccrs.Mercator())

# color filled contour plot
h = iplt.contourf(slice,64)

# add coastlines, colorbar and title
plt.gca().coastlines(resolution='10m')
colorbar(h,orientation='vertical');
title('WaveWatch 3 significant wave height driven by CFSR winds');

# first add a Geographic Coord system  (required by GRIB2 writer)

# here we add a spherical earth with specified radius
slice.coord(axis='X').coord_system=iris.coord_systems.GeogCS(654321)
slice.coord(axis='Y').coord_system=iris.coord_systems.GeogCS(654321)

# add a forecast_period (required by GRIB2 writer)
slice.add_aux_coord(iris.coords.DimCoord(0, standard_name='forecast_period', units='hours'))

# add a vertical coordinate (required by GRIB2 writer)
slice.add_aux_coord(iris.coords.DimCoord(0, "height", units="m"))

# GRIB2 stores data in long values.  Do we have huge values that would create problems?
slice.data.max()

# ah, we have NaNs.  This causes problems for GRIB2 writer
# So convert to masked-array instead.
slice.data=ma.masked_invalid(slice.data)

# Finally... save slice as grib2
iris.save(slice,'hsig.grib2')