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from IPython.core.display import HTML
HTML('<iframe src=http://scitools.org.uk/iris/ width=800 height=350></iframe>')
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import numpy
import matplotlib.pyplot as plt
import datetime as dt
import iris
import iris.plot as iplt
import cartopy.crs as ccrs
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def time_near(cube,start):
timevar=cube.coord('time')
itime = timevar.nearest_neighbour_index(timevar.units.date2num(start))
return timevar.points[itime]
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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));
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# 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
cubes = iris.load('/usgs/data2/notebook/data/colpex.pp')
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print cubes
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temp=cubes[0]
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# 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))
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def var_lev_date(url=None,var=None,mytime=None,lev=0,subsample=1):
time0=time.time()
cubes = iris.load(url)
cube = cubes.extract(iris.Constraint(name=var))[0]
try:
cube.coord(axis='T').rename('time')
except:
pass
slice = cube.extract(iris.Constraint(time=time_near(cube,mytime)))
slice = slice[lev,::subsample,::subsample]
print 'slice retrieved in %f seconds' % (time.time()-time0)
return slice
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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,ma.masked_invalid(slice.data));
colorbar()
grid()
date=time.units.num2date(time.points)
date_str=date[0].strftime('%Y-%m-%d %H:%M:%S %Z')
plt.title('%s: %s: %s' % (model,slice.long_name,date_str));
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print temp
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print[coord.name() for coord in slice.coords()]
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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');
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# 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)
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# add a forecast_period (required by GRIB2 writer)
slice.add_aux_coord(iris.coords.DimCoord(0, standard_name='forecast_period', units='hours'))
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# add a vertical coordinate (required by GRIB2 writer)
slice.add_aux_coord(iris.coords.DimCoord(0, "height", units="m"))
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# GRIB2 stores data in long values. Do we have huge values that would create problems?
slice.data.max()
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# ah, we have NaNs. This causes problems for GRIB2 writer
# So convert to masked-array instead.
slice.data=ma.masked_invalid(slice.data)
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# Finally... save slice as grib2
iris.save(slice,'hsig.grib2')