def main(cmdLineArgs,stream=None):
numpy.seterr(divide='ignore', invalid='ignore', over='ignore') # To avoid warnings
if not os.path.exists(cmdLineArgs.gridspec): raise ValueError('Specified gridspec directory/tar file does not exist.')
if os.path.isdir(cmdLineArgs.gridspec):
x = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['x'][::2,::2]
xcenter = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['x'][1::2,1::2]
y = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['y'][::2,::2]
ycenter = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['y'][1::2,1::2]
msk = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_mask.nc').variables['mask'][:]
area = msk*netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['area'][:,:].reshape([msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_topog.nc').variables['depth'][:]
elif os.path.isfile(cmdLineArgs.gridspec):
x = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','x')[::2,::2]
xcenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','x')[1::2,1::2]
y = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','y')[::2,::2]
ycenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','y')[1::2,1::2]
msk = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_mask.nc','mask')[:]
area = msk*m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','area')[:,:].reshape([msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_topog.nc','depth')[:]
else:
raise ValueError('Unable to extract grid information from gridspec directory/tar file.')
Sobs = netCDF4.Dataset( cmdLineArgs.woa ).variables['salt']
if len(Sobs.shape)==3: Sobs = Sobs[0]
else: Sobs = Sobs[:,0].mean(axis=0)
rootGroup = netCDF4.MFDataset( cmdLineArgs.annual_file )
if 'salt' in rootGroup.variables: varName = 'salt'
elif 'so' in rootGroup.variables: varName = 'so'
else: raise Exception('Could not find "salt" or "so" in file "%s"'%(cmdLineArgs.annual_file))
if rootGroup.variables[varName].shape[0]>1: Smod = rootGroup.variables[varName][:,0].mean(axis=0)
else: Smod = rootGroup.variables[varName][0,0]
if cmdLineArgs.suptitle != '': suptitle = cmdLineArgs.suptitle + ' ' + cmdLineArgs.label
else: suptitle = rootGroup.title + ' ' + cmdLineArgs.label
ci=m6plot.pmCI(0.125,2.25,.25)
if stream is None: stream = cmdLineArgs.outdir+'/SSS_bias_WOA05.png'
m6plot.xyplot( Smod - Sobs , x, y, area=area,
suptitle=suptitle, title='SSS bias (w.r.t. WOA\'05) [ppt]',
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=stream)
m6plot.xycompare( Smod, Sobs , x, y, area=area,
suptitle=suptitle,
title1='SSS [ppt]',
title2='WOA\'05 SSS [ppt]',
clim=m6plot.linCI(20,30,10, 31,39,.5), colormap='dunneRainbow', extend='both',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/SSS_bias_WOA05.3_panel.png')
def main(cmdLineArgs, stream=False):
numpy.seterr(divide='ignore', invalid='ignore',
over='ignore') # To avoid warnings
if not os.path.exists(cmdLineArgs.gridspec):
raise ValueError(
'Specified gridspec directory/tar file does not exist.')
if os.path.isdir(cmdLineArgs.gridspec):
x = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['x'][::2, ::2]
xcenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['x'][1::2, 1::2]
y = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][::2, ::2]
ycenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][1::2, 1::2]
msk = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_mask.nc').variables['mask'][:]
area = msk * netCDF4.Dataset(
cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['area'][:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_topog.nc').variables['depth'][:]
elif os.path.isfile(cmdLineArgs.gridspec):
x = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'x')[::2, ::2]
xcenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'x')[1::2, 1::2]
y = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'y')[::2, ::2]
ycenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'y')[1::2, 1::2]
msk = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_mask.nc',
'mask')[:]
area = msk * m6toolbox.readNCFromTar(
cmdLineArgs.gridspec, 'ocean_hgrid.nc', 'area')[:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_topog.nc',
'depth')[:]
else:
raise ValueError(
'Unable to extract grid information from gridspec directory/tar file.'
)
Tobs = netCDF4.Dataset(cmdLineArgs.woa)
if 'temp' in Tobs.variables: Tobs = Tobs.variables['temp']
elif 'ptemp' in Tobs.variables: Tobs = Tobs.variables['ptemp']
else:
raise Exception('Could not find "temp" or "ptemp" in file "%s"' %
(cmdLineArgs.woa))
if len(Tobs.shape) == 3: Tobs = Tobs[0]
else: Tobs = Tobs[:, 0].mean(axis=0)
rootGroup = netCDF4.MFDataset(cmdLineArgs.infile)
if 'temp' in rootGroup.variables: varName = 'temp'
elif 'ptemp' in rootGroup.variables: varName = 'ptemp'
elif 'thetao' in rootGroup.variables: varName = 'thetao'
else:
raise Exception(
'Could not find "temp", "ptemp" or "thetao" in file "%s"' %
(cmdLineArgs.infile))
if rootGroup.variables[varName].shape[0] > 1:
Tmod = rootGroup.variables[varName][:, 0].mean(axis=0)
else:
Tmod = rootGroup.variables[varName][0, 0]
if cmdLineArgs.suptitle != '':
suptitle = cmdLineArgs.suptitle + ' ' + cmdLineArgs.label
else:
suptitle = rootGroup.title + ' ' + cmdLineArgs.label
imgbufs = []
ci = m6plot.pmCI(0.25, 4.5, .5)
if stream is True: img = io.BytesIO()
else: img = cmdLineArgs.outdir + '/SST_bias_WOA05.png'
m6plot.xyplot(Tmod - Tobs,
x,
y,
area=area,
suptitle=suptitle,
title='SST bias (w.r.t. WOA\'05) [$\degree$C]',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=img)
if stream is True: imgbufs.append(img)
m6plot.xycompare(Tmod,
Tobs,
x,
y,
area=area,
suptitle=suptitle,
title1='SST [$\degree$C]',
title2='WOA\'05 SST [$\degree$C]',
clim=m6plot.linCI(-2, 29, .5),
colormap='dunneRainbow',
extend='max',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir + '/SST_bias_WOA05.3_panel.png')
if stream is True:
return imgbufs
Zref[k+1] = numpy.maximum( Zref[k] - abs(zw[k+1] - zw[k]), D)
rootGroup = netCDF4.Dataset( cmdLineArgs.annual_file )
if 'salt' in rootGroup.variables: varName = 'salt'
elif 'so' in rootGroup.variables: varName = 'so'
else: raise Exception('Could not find "salt" or "so" in file "%s"'%(cmdLineArgs.annual_file))
if len(rootGroup.variables[varName].shape)==4: Smod = rootGroup.variables[varName][:].mean(axis=0)
else: Smod = rootGroup.variables[varName][:]
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e'][0]
else: Zmod = Zref
def zonalAverage(T, eta, area, mask=1.):
vols = ( mask * area ) * ( eta[:-1] - eta[1:] ) # mask * area * level thicknesses
return numpy.sum( vols * T, axis=-1 ) / numpy.sum( vols, axis=-1 ), (mask*eta).min(axis=-1)
ci=m6plot.pmCI(0.0125,.225,.025)
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
# Global
sPlot, z = zonalAverage(Smod, Zmod, area)
sRefPlot, _ = zonalAverage(Sref, Zref, area)
m6plot.yzplot( sPlot - sRefPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=title1+' - '+title2, title='''Global zonal-average salinity response [ppt] '''+cmdLineArgs.label,
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=cmdLineArgs.outdir+'/S_global_xave_response.png')
m6plot.yzcompare( sPlot, sRefPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle='Global zonal-average salinity [ppt] '+cmdLineArgs.label,
MLDref = variable[:].reshape(shape[0]/12,12,shape[1],shape[2]).mean(axis=0)
rootGroup = netCDF4.Dataset( cmdLineArgs.monthly_file )
if 'MLD_003' not in rootGroup.variables: raise Exception('Could not find "MLD_003" in file "%s"'%(cmdLineArgs.monthly_file))
variable = rootGroup.variables['MLD_003']
shape = variable.shape
MLDmod = variable[:].reshape(shape[0]/12,12,shape[1],shape[2]).mean(axis=0)
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
ciMin = m6plot.linCI(0,95,5)
ciMax = m6plot.linCI(0,680,20)
diMin = m6plot.pmCI(2.5,32.5,5)
diMax = m6plot.pmCI(0,20,5, 40,100,20)
diMax = numpy.array([-250,-200,-150,-100,-50,-20,-10,-3,3,10,20,50,100,150,200,250])
m6plot.xyplot( MLDmod.min(axis=0) - MLDref.min(axis=0), x, y, area=area,
suptitle=title1+' - '+title2,
title='MLD difference [m] '+cmdLineArgs.label,
clim=diMin, colormap='dunnePM', centerlabels=True, extend='both',
save=cmdLineArgs.outdir+'/MLD_minimum_difference.png')
m6plot.xycompare( MLDmod.min(axis=0), MLDref.min(axis=0), x, y, area=area,
suptitle='MLD difference [m] '+cmdLineArgs.label,
title1=title1, title2=title2,
clim=ciMin, colormap='dunneRainbow', extend='max',
dlim=diMin, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/MLD_minimum_difference.3_panel.png')
if 'salt' in rootGroup.variables: varName = 'salt'
elif 'so' in rootGroup.variables: varName = 'so'
else:
raise Exception('Could not find "salt" or "so" in file "%s"' %
(cmdLineArgs.annual_file))
if len(rootGroup.variables[varName].shape) == 4:
Smod = rootGroup.variables[varName][:, 0].mean(axis=0)
else:
Smod = rootGroup.variables[varName][0]
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
ci = m6plot.pmCI(0.025, 0.525, .05)
m6plot.xyplot(Smod - Sref,
x,
y,
area=area,
suptitle=title1 + ' - ' + title2,
title='SSS difference [ppt] ' + cmdLineArgs.label,
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=cmdLineArgs.outdir + '/SSS_difference.png')
m6plot.xycompare(Smod,
Sref,
x,
msk = numpy.ma.array(msk, mask=(msk==0))
Tobs = netCDF4.Dataset( cmdLineArgs.woa )
if 'temp' in Tobs.variables: Tobs = Tobs.variables['temp']
elif 'ptemp' in Tobs.variables: Tobs = Tobs.variables['ptemp']
else: raise Exception('Could not find "temp" or "ptemp" in file "%s"'%(cmdLineArgs.woa))
if len(Tobs.shape)==3: Tobs = Tobs[0]
else: Tobs = Tobs[:,0].mean(axis=0)
rootGroup = netCDF4.Dataset( cmdLineArgs.annual_file )
if 'temp' in rootGroup.variables: varName = 'temp'
elif 'ptemp' in rootGroup.variables: varName = 'ptemp'
elif 'thetao' in rootGroup.variables: varName = 'thetao'
else: raise Exception('Could not find "temp", "ptemp" or "thetao" in file "%s"'%(cmdLineArgs.annual_file))
if rootGroup.variables[varName].shape[0]>1: salt = rootGroup.variables[varName][:,0].mean(axis=0)
else: Tmod = rootGroup.variables[varName][0,0]
ci=m6plot.pmCI(0.25,4.5,.5)
m6plot.xyplot( Tmod - Tobs , x, y, area=area,
suptitle=rootGroup.title+' '+cmdLineArgs.label, title='SST bias (w.r.t. WOA\'05) [$\degree$C]',
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=cmdLineArgs.outdir+'/SST_bias_WOA05.png')
m6plot.xycompare( Tmod, Tobs , x, y, area=area,
suptitle=rootGroup.title+' '+cmdLineArgs.label,
title1='SST [$\degree$C]',
title2='WOA\'05 SST [$\degree$C]',
clim=m6plot.linCI(-2,29,.5), colormap='dunneRainbow', extend='max',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/SST_bias_WOA05.3_panel.png')
obsRootGroup = netCDF4.Dataset( cmdLineArgs.woa )
if 'temp' in obsRootGroup.variables: OTvar = 'temp'
else: OTvar = 'ptemp'
Zobs = netCDF4.Dataset( cmdLineArgs.woa ).variables['eta']
rootGroup = netCDF4.Dataset( cmdLineArgs.annual_file )
if 'temp' in rootGroup.variables: varName = 'temp'
elif 'ptemp' in rootGroup.variables: varName = 'ptemp'
elif 'thetao' in rootGroup.variables: varName = 'thetao'
else: raise Exception('Could not find "temp", "ptemp" or "thetao" in file "%s"'%(cmdLineArgs.annual_file))
if len(rootGroup.variables[varName].shape)==4: need_time_average = True
else: need_time_average = False
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e']
else: Zmod = Zobs # Using model z-output
ci=m6plot.pmCI(0.1,0.5,.2, 1.0,4.5,1.)
# Pacific equator
j = numpy.abs( y[:,0] - 0. ).argmin()
i0 = numpy.abs( numpy.mod( x[j,:] - 117. + 360., 360. ) ).argmin()
i1 = numpy.abs( numpy.mod( x[j,:] + 78. + 360., 360. ) ).argmin()
tPlot = rootGroup.variables[varName][0,:,j,i0:i1]
tObsPlot = obsRootGroup.variables[OTvar][0,:,j,i0:i1]
zi = Zobs[:,j,i0:i1]; z = 0.5 * ( zi[:-1] + zi[1:] )
m6plot.yzplot( tPlot - tObsPlot , x[j,i0:i1][:], zi,
ylabel='Longitude',
suptitle=rootGroup.title+' '+cmdLineArgs.label, title=r'''Pacific equator $\theta$ bias (w.r.t. WOA'05) [$\degree$C]''',
clim=ci, colormap='dunnePM', centerlabels=True, extend='both')
cs1 = plt.contour( x[j,i0:i1][:] + 0*z, z, tObsPlot, levels=numpy.arange(0.,45,2.), colors='k' ); plt.clabel(cs1,fmt='%.0f')
cs2 = plt.contour( x[j,i0:i1][:] + 0*z, z, tPlot, levels=numpy.arange(0.,45,2.), colors='g' ); plt.clabel(cs2,fmt='%.0f')
plt.ylim(-1200,0)
def main(cmdLineArgs,stream=None):
if not os.path.exists(cmdLineArgs.gridspec): raise ValueError('Specified gridspec directory/tar file does not exist.')
if os.path.isdir(cmdLineArgs.gridspec):
xcenter = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['x'][1::2,1::2]
y = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['y'][1::2,1::2].max(axis=-1)
ycenter = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['y'][1::2,1::2]
msk = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_mask.nc').variables['mask'][:]
area = msk*netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['area'][:,:].reshape([msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_topog.nc').variables['depth'][:]
try: basin = netCDF4.Dataset(cmdLineArgs.gridspec+'/basin_codes.nc').variables['basin'][:]
except: basin = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
elif os.path.isfile(cmdLineArgs.gridspec):
xcenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','x')[1::2,1::2]
y = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','y')[1::2,1::2].max(axis=-1)
ycenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','y')[1::2,1::2]
msk = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_mask.nc','mask')[:]
area = msk*m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','area')[:,:].reshape([msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_topog.nc','depth')[:]
try: basin = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'basin_codes.nc','basin')[:]
except: basin = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
else:
raise ValueError('Unable to extract grid information from gridspec directory/tar file.')
if stream != None:
if len(stream) != 4:
raise ValueError('If specifying output streams, exactly four (4) streams are needed for this analysis')
Sobs = netCDF4.Dataset( cmdLineArgs.woa ).variables['salt']
if len(Sobs.shape)==3: Sobs = Sobs[:]
else: Sobs = Sobs[:].mean(axis=0)
Zobs = netCDF4.Dataset( cmdLineArgs.woa ).variables['eta'][:]
rootGroup = netCDF4.MFDataset( cmdLineArgs.annual_file )
if 'salt' in rootGroup.variables: varName = 'salt'
elif 'so' in rootGroup.variables: varName = 'so'
else:raise Exception('Could not find "salt" or "so" in file "%s"'%(cmdLineArgs.annual_file))
if len(rootGroup.variables[varName].shape)==4: Smod = rootGroup.variables[varName][:].mean(axis=0)
else: Smod = rootGroup.variables[varName][:]
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e'][0]
else: Zmod = Zobs # Using model z-output
def zonalAverage(S, eta, area, mask=1.):
vols = ( mask * area ) * ( eta[:-1] - eta[1:] ) # mask * area * level thicknesses
return numpy.sum( vols * S, axis=-1 ) / numpy.sum( vols, axis=-1 ), (mask*eta).min(axis=-1)
ci=m6plot.pmCI(0.125,2.25,.25)
if cmdLineArgs.suptitle != '': suptitle = cmdLineArgs.suptitle + ' ' + cmdLineArgs.label
else: suptitle = rootGroup.title + ' ' + cmdLineArgs.label
# Global
sPlot, z = zonalAverage(Smod, Zmod, area)
sObsPlot, _ = zonalAverage(Sobs, Zobs, area)
if stream != None: objOut = stream[0]
else: objOut = cmdLineArgs.outdir+'/S_global_xave_bias_WOA05.png'
m6plot.yzplot( sPlot - sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=suptitle, title='''Global zonal-average salinity bias (w.r.t. WOA'05) [ppt]''',
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=objOut)
if stream is None:
m6plot.yzcompare( sPlot, sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1='Global zonal-average salinity [ppt]',
title2='''WOA'05 salinity [ppt]''',
clim=m6plot.linCI(20,30,10, 31,39,.5), colormap='dunneRainbow', extend='both',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/S_global_xave_bias_WOA05.3_panel.png')
# Atlantic + Arctic
newMask = 1.*msk; newMask[ (basin!=2) & (basin!=4) ] = 0.
sPlot, z = zonalAverage(Smod, Zmod, area, mask=newMask)
sObsPlot, _ = zonalAverage(Sobs, Zobs, area, mask=newMask)
if stream != None: objOut = stream[1]
else: objOut = cmdLineArgs.outdir+'/S_Atlantic_xave_bias_WOA05.png'
m6plot.yzplot( sPlot - sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=suptitle, title='''Atlantic zonal-average salinity bias (w.r.t. WOA'05) [ppt]''',
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=objOut)
if stream is None:
m6plot.yzcompare( sPlot, sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1='Atlantic zonal-average salinity [ppt]',
title2='''WOA'05 salinity [ppt]''',
clim=m6plot.linCI(20,30,10, 31,39,.5), colormap='dunneRainbow', extend='both',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/S_Atlantic_xave_bias_WOA05.3_panel.png')
# Pacific
newMask = 1.*msk; newMask[ (basin!=3) ] = 0.
sPlot, z = zonalAverage(Smod, Zmod, area, mask=newMask)
sObsPlot, _ = zonalAverage(Sobs, Zobs, area, mask=newMask)
if stream != None: objOut = stream[2]
else: objOut = cmdLineArgs.outdir+'/S_Pacific_xave_bias_WOA05.png'
m6plot.yzplot( sPlot - sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=suptitle, title='''Pacific zonal-average salinity bias (w.r.t. WOA'05) [ppt]''',
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=objOut)
if stream is None:
m6plot.yzcompare( sPlot, sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1='Pacific zonal-average salinity [ppt]',
title2='''WOA'05 salinity [ppt]''',
clim=m6plot.linCI(20,30,10, 31,39,.5), colormap='dunneRainbow', extend='both',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/S_Pacific_xave_bias_WOA05.3_panel.png')
# Indian
newMask = 1.*msk; newMask[ (basin!=5) ] = 0.
sPlot, z = zonalAverage(Smod, Zmod, area, mask=newMask)
sObsPlot, _ = zonalAverage(Sobs, Zobs, area, mask=newMask)
if stream != None: objOut = stream[3]
else: objOut = cmdLineArgs.outdir+'/S_Indian_xave_bias_WOA05.png'
m6plot.yzplot( sPlot - sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=suptitle, title='''Indian zonal-average salinity bias (w.r.t. WOA'05) [ppt]''',
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=objOut)
if stream is None:
m6plot.yzcompare( sPlot, sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1='Indian zonal-average salinity [ppt]',
title2='''WOA'05 salinity [ppt]''',
clim=m6plot.linCI(20,30,10, 31,39,.5), colormap='dunneRainbow', extend='both',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/S_Indian_xave_bias_WOA05.3_panel.png')
def main(cmdLineArgs, stream=False):
if not os.path.exists(cmdLineArgs.gridspec):
raise ValueError(
'Specified gridspec directory/tar file does not exist.')
if os.path.isdir(cmdLineArgs.gridspec):
x = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['x'][::2, ::2]
xcenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['x'][1::2, 1::2]
y = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][::2, ::2]
ycenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][1::2, 1::2]
msk = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_mask.nc').variables['mask'][:]
area = msk * netCDF4.Dataset(
cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['area'][:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_topog.nc').variables['depth'][:]
try:
basin_code = netCDF4.Dataset(
cmdLineArgs.gridspec + '/basin_codes.nc').variables['basin'][:]
except:
basin_code = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
elif os.path.isfile(cmdLineArgs.gridspec):
x = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'x')[::2, ::2]
xcenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'x')[1::2, 1::2]
y = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'y')[::2, ::2]
ycenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'y')[1::2, 1::2]
msk = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_mask.nc',
'mask')[:]
area = msk * m6toolbox.readNCFromTar(
cmdLineArgs.gridspec, 'ocean_hgrid.nc', 'area')[:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_topog.nc',
'depth')[:]
try:
basin_code = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'basin_codes.nc', 'basin')[:]
except:
basin_code = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
else:
raise ValueError(
'Unable to extract grid information from gridspec directory/tar file.'
)
rootGroup = netCDF4.MFDataset(cmdLineArgs.infile)
if 'vmo' in rootGroup.variables:
varName = 'vmo'
conversion_factor = 1.e-9
elif 'vh' in rootGroup.variables:
varName = 'vh'
conversion_factor = 1.e-6
if 'zw' in rootGroup.variables:
conversion_factor = 1.e-9 # Backwards compatible for when we had wrong units for 'vh'
else:
raise Exception('Could not find "vh" or "vmo" in file "%s"' %
(cmdLineArgs.infile))
if len(rootGroup.variables[varName].shape) == 4:
VHmod = rootGroup.variables[varName][:].mean(axis=0)
else:
VHmod = rootGroup.variables[varName][:]
try:
VHmod = VHmod.filled(0.)
except:
pass
Zmod = m6toolbox.get_z(rootGroup, depth, varName)
def MOCpsi(vh, vmsk=None):
"""Sums 'vh' zonally and cumulatively in the vertical to yield an overturning stream function, psi(y,z)."""
shape = list(vh.shape)
shape[-3] += 1
psi = numpy.zeros(shape[:-1])
if len(shape) == 3:
for k in range(shape[-3] - 1, 0, -1):
if vmsk is None:
psi[k - 1, :] = psi[k, :] - vh[k - 1].sum(axis=-1)
else:
psi[k - 1, :] = psi[k, :] - (vmsk * vh[k - 1]).sum(axis=-1)
else:
for n in range(shape[0]):
for k in range(shape[-3] - 1, 0, -1):
if vmsk is None:
psi[n, k -
1, :] = psi[n, k, :] - vh[n, k - 1].sum(axis=-1)
else:
psi[n, k -
1, :] = psi[n, k, :] - (vmsk *
vh[n, k - 1]).sum(axis=-1)
return psi
def plotPsi(y, z, psi, ci, title):
cmap = plt.get_cmap('dunnePM')
plt.contourf(y, z, psi, levels=ci, cmap=cmap, extend='both')
cbar = plt.colorbar()
plt.contour(y, z, psi, levels=ci, colors='k', hold='on')
plt.gca().set_yscale('splitscale', zval=[0., -2000., -6500.])
plt.title(title)
cbar.set_label('[Sv]')
plt.ylabel('Elevation [m]')
def findExtrema(y,
z,
psi,
min_lat=-90.,
max_lat=90.,
min_depth=0.,
mult=1.):
psiMax = mult * numpy.amax(
mult * numpy.ma.array(psi)[(y >= min_lat) & (y <= max_lat) &
(z < -min_depth)])
idx = numpy.argmin(numpy.abs(psi - psiMax))
(j, i) = numpy.unravel_index(idx, psi.shape)
plt.plot(y[j, i], z[j, i], 'kx', hold=True)
plt.text(y[j, i], z[j, i], '%.1f' % (psi[j, i]))
if cmdLineArgs.suptitle != '':
suptitle = cmdLineArgs.suptitle + ' ' + cmdLineArgs.label
else:
suptitle = rootGroup.title + ' ' + cmdLineArgs.label
imgbufs = []
# Global MOC
m6plot.setFigureSize([16, 9], 576, debug=False)
axis = plt.gca()
cmap = plt.get_cmap('dunnePM')
z = Zmod.min(axis=-1)
psiPlot = MOCpsi(VHmod) * conversion_factor
yy = y[1:, :].max(axis=-1) + 0 * z
ci = m6plot.pmCI(0., 40., 5.)
plotPsi(yy, z, psiPlot, ci, 'Global MOC [Sv]')
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(suptitle)
findExtrema(yy, z, psiPlot, max_lat=-30.)
findExtrema(yy, z, psiPlot, min_lat=25.)
findExtrema(yy, z, psiPlot, min_depth=2000., mult=-1.)
if stream is True: objOut = io.BytesIO()
else: objOut = cmdLineArgs.outdir + '/MOC_global.png'
plt.savefig(objOut)
if stream is True: imgbufs.append(objOut)
# Atlantic MOC
m6plot.setFigureSize([16, 9], 576, debug=False)
cmap = plt.get_cmap('dunnePM')
m = 0 * basin_code
m[(basin_code == 2) | (basin_code == 4) | (basin_code == 6) |
(basin_code == 7) | (basin_code == 8)] = 1
ci = m6plot.pmCI(0., 22., 2.)
z = (m * Zmod).min(axis=-1)
psiPlot = MOCpsi(VHmod,
vmsk=m * numpy.roll(m, -1, axis=-2)) * conversion_factor
yy = y[1:, :].max(axis=-1) + 0 * z
plotPsi(yy, z, psiPlot, ci, 'Atlantic MOC [Sv]')
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(suptitle)
findExtrema(yy, z, psiPlot, min_lat=26.5, max_lat=27.) # RAPID
findExtrema(yy, z, psiPlot, max_lat=-33.)
findExtrema(yy, z, psiPlot)
findExtrema(yy, z, psiPlot, min_lat=5.)
if stream is True: objOut = io.BytesIO()
else: objOut = cmdLineArgs.outdir + '/MOC_Atlantic.png'
plt.savefig(objOut, format='png')
if stream is True: imgbufs.append(objOut)
if stream is True:
return imgbufs
(j,i) = numpy.unravel_index(idx, psi.shape)
plt.plot(y[j,i],z[j,i],'kx',hold=True)
plt.text(y[j,i],z[j,i],'%.1f'%(psi[j,i]))
m6plot.setFigureSize(npanels=3)
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
# Global MOC
z = Zmod.min(axis=-1)
yy = y[1:,:].max(axis=-1)+0*z
psiPlotM = MOCpsi(VHmod)/1e6
psiPlotR = MOCpsi(VHref)/1e6
ci=m6plot.pmCI(0.,40.,5.)
di=m6plot.pmCI(0.,5.,0.5)
plt.subplot(311)
plotPsi(yy, z, psiPlotM, ci, title1)
findExtrema(yy, z, psiPlotM, max_lat=-30.)
findExtrema(yy, z, psiPlotM, min_lat=25.)
findExtrema(yy, z, psiPlotM, min_depth=2000., mult=-1.)
plt.subplot(312)
plotPsi(yy, z, psiPlotR, ci, title2)
findExtrema(yy, z, psiPlotR, max_lat=-30.)
findExtrema(yy, z, psiPlotR, min_lat=25.)
findExtrema(yy, z, psiPlotR, min_depth=2000., mult=-1.)
plt.subplot(313)
plotPsi(yy, z, psiPlotM-psiPlotR, di, title1 + ' - ' + title2)
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle('Global MOC')
def main(cmdLineArgs, stream=False):
if not os.path.exists(cmdLineArgs.gridspec):
raise ValueError(
'Specified gridspec directory/tar file does not exist.')
if os.path.isdir(cmdLineArgs.gridspec):
xcenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['x'][1::2, 1::2]
y = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][1::2, 1::2].max(
axis=-1)
ycenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][1::2, 1::2]
msk = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_mask.nc').variables['mask'][:]
area = msk * netCDF4.Dataset(
cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['area'][:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_topog.nc').variables['depth'][:]
try:
basin = netCDF4.Dataset(cmdLineArgs.gridspec +
'/basin_codes.nc').variables['basin'][:]
except:
basin = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
elif os.path.isfile(cmdLineArgs.gridspec):
xcenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'x')[1::2, 1::2]
y = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'y')[1::2, 1::2].max(axis=-1)
ycenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'y')[1::2, 1::2]
msk = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_mask.nc',
'mask')[:]
area = msk * m6toolbox.readNCFromTar(
cmdLineArgs.gridspec, 'ocean_hgrid.nc', 'area')[:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_topog.nc',
'depth')[:]
try:
basin = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'basin_codes.nc', 'basin')[:]
except:
basin = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
else:
raise ValueError(
'Unable to extract grid information from gridspec directory/tar file.'
)
Sobs = netCDF4.Dataset(cmdLineArgs.woa).variables['salt']
if len(Sobs.shape) == 3: Sobs = Sobs[:]
else: Sobs = Sobs[:].mean(axis=0)
Zobs = netCDF4.Dataset(cmdLineArgs.woa).variables['eta'][:]
rootGroup = netCDF4.MFDataset(cmdLineArgs.infile)
if 'salt' in rootGroup.variables: varName = 'salt'
elif 'so' in rootGroup.variables: varName = 'so'
else:
raise Exception('Could not find "salt" or "so" in file "%s"' %
(cmdLineArgs.infile))
if len(rootGroup.variables[varName].shape) == 4:
Smod = rootGroup.variables[varName][:].mean(axis=0)
else:
Smod = rootGroup.variables[varName][:]
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e'][0]
else: Zmod = Zobs # Using model z-output
def zonalAverage(S, eta, area, mask=1.):
vols = (mask * area) * (eta[:-1] - eta[1:]
) # mask * area * level thicknesses
return numpy.sum(vols * S, axis=-1) / numpy.sum(vols, axis=-1), (
mask * eta).min(axis=-1)
ci = m6plot.pmCI(0.125, 2.25, .25)
if cmdLineArgs.suptitle != '':
suptitle = cmdLineArgs.suptitle + ' ' + cmdLineArgs.label
else:
suptitle = rootGroup.title + ' ' + cmdLineArgs.label
imgbufs = []
# Global
sPlot, z = zonalAverage(Smod, Zmod, area)
sObsPlot, _ = zonalAverage(Sobs, Zobs, area)
if stream is True: objOut = io.BytesIO()
else: objOut = cmdLineArgs.outdir + '/S_global_xave_bias_WOA05.png'
m6plot.yzplot(
sPlot - sObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title='''Global zonal-average salinity bias (w.r.t. WOA'05) [ppt]''',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=objOut)
if stream is True: imgbufs.append(objOut)
if stream is None:
m6plot.yzcompare(sPlot,
sObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1='Global zonal-average salinity [ppt]',
title2='''WOA'05 salinity [ppt]''',
clim=m6plot.linCI(20, 30, 10, 31, 39, .5),
colormap='dunneRainbow',
extend='both',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir +
'/S_global_xave_bias_WOA05.3_panel.png')
# Atlantic + Arctic
newMask = 1. * msk
newMask[(basin != 2) & (basin != 4)] = 0.
sPlot, z = zonalAverage(Smod, Zmod, area, mask=newMask)
sObsPlot, _ = zonalAverage(Sobs, Zobs, area, mask=newMask)
if stream is True: objOut = io.BytesIO()
else: objOut = cmdLineArgs.outdir + '/S_Atlantic_xave_bias_WOA05.png'
m6plot.yzplot(
sPlot - sObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title='''Atlantic zonal-average salinity bias (w.r.t. WOA'05) [ppt]''',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=objOut)
if stream is True: imgbufs.append(objOut)
if stream is None:
m6plot.yzcompare(sPlot,
sObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1='Atlantic zonal-average salinity [ppt]',
title2='''WOA'05 salinity [ppt]''',
clim=m6plot.linCI(20, 30, 10, 31, 39, .5),
colormap='dunneRainbow',
extend='both',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir +
'/S_Atlantic_xave_bias_WOA05.3_panel.png')
# Pacific
newMask = 1. * msk
newMask[(basin != 3)] = 0.
sPlot, z = zonalAverage(Smod, Zmod, area, mask=newMask)
sObsPlot, _ = zonalAverage(Sobs, Zobs, area, mask=newMask)
if stream is True: objOut = io.BytesIO()
else: objOut = cmdLineArgs.outdir + '/S_Pacific_xave_bias_WOA05.png'
m6plot.yzplot(
sPlot - sObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title='''Pacific zonal-average salinity bias (w.r.t. WOA'05) [ppt]''',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=objOut)
if stream is True: imgbufs.append(objOut)
if stream is None:
m6plot.yzcompare(sPlot,
sObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1='Pacific zonal-average salinity [ppt]',
title2='''WOA'05 salinity [ppt]''',
clim=m6plot.linCI(20, 30, 10, 31, 39, .5),
colormap='dunneRainbow',
extend='both',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir +
'/S_Pacific_xave_bias_WOA05.3_panel.png')
# Indian
newMask = 1. * msk
newMask[(basin != 5)] = 0.
sPlot, z = zonalAverage(Smod, Zmod, area, mask=newMask)
sObsPlot, _ = zonalAverage(Sobs, Zobs, area, mask=newMask)
if stream is True: objOut = io.BytesIO()
else: objOut = cmdLineArgs.outdir + '/S_Indian_xave_bias_WOA05.png'
m6plot.yzplot(
sPlot - sObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title='''Indian zonal-average salinity bias (w.r.t. WOA'05) [ppt]''',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=objOut)
if stream is True: imgbufs.append(objOut)
if stream is None:
m6plot.yzcompare(sPlot,
sObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1='Indian zonal-average salinity [ppt]',
title2='''WOA'05 salinity [ppt]''',
clim=m6plot.linCI(20, 30, 10, 31, 39, .5),
colormap='dunneRainbow',
extend='both',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir +
'/S_Indian_xave_bias_WOA05.3_panel.png')
if stream is True:
return imgbufs
if len(rootGroup.variables[varName].shape) == 4:
Tmod = rootGroup.variables[varName][:].mean(axis=0)
else:
Tmod = rootGroup.variables[varName][:]
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e'][0]
else: Zmod = Zref
def zonalAverage(T, eta, area, mask=1.):
vols = (mask * area) * (eta[:-1] - eta[1:]
) # mask * area * level thicknesses
return numpy.sum(vols * T, axis=-1) / numpy.sum(vols, axis=-1), (
mask * eta).min(axis=-1)
ci = m6plot.pmCI(0.025, .45, .05)
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
# Global
tPlot, z = zonalAverage(Tmod, Zmod, area)
tRefPlot, _ = zonalAverage(Tref, Zref, area)
m6plot.yzplot(
tPlot - tRefPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=title1 + ' - ' + title2,
title=r'''Global zonal-average $\theta$ response [$\degree$C] ''' +
rootGroup = netCDF4.Dataset( cmdLineArgs.annual_file )
if 'temp' in rootGroup.variables: varName = 'temp'
elif 'ptemp' in rootGroup.variables: varName = 'ptemp'
elif 'thetao' in rootGroup.variables: varName = 'thetao'
else: raise Exception('Could not find "temp", "ptemp" or "thetao" in file "%s"'%(cmdLineArgs.annual_file))
if len(rootGroup.variables[varName].shape)==4: Tmod = rootGroup.variables[varName][:].mean(axis=0)
else: Tmod = rootGroup.variables[varName][:]
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e'][0]
else: Zmod = Zref
def zonalAverage(T, eta, area, mask=1.):
vols = ( mask * area ) * ( eta[:-1] - eta[1:] ) # mask * area * level thicknesses
return numpy.sum( vols * T, axis=-1 ) / numpy.sum( vols, axis=-1 ), (mask*eta).min(axis=-1)
ci=m6plot.pmCI(0.025,.45,.05)
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
# Global
tPlot, z = zonalAverage(Tmod, Zmod, area)
tRefPlot, _ = zonalAverage(Tref, Zref, area)
m6plot.yzplot( tPlot - tRefPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=title1+' - '+title2, title=r'''Global zonal-average $\theta$ response [$\degree$C] '''+cmdLineArgs.label,
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=cmdLineArgs.outdir+'/T_global_xave_response.png')
m6plot.yzcompare( tPlot, tRefPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=r'Global zonal-average $\theta$ [$\degree$C] '+cmdLineArgs.label,
HT = numpy.zeros(T.shape[1:])
for k in range(T.shape[0]):
zTop = numpy.minimum( z[k], -upperDepth )
zBot = numpy.maximum( z[k+1], -D )
dh = numpy.maximum( zTop -zBot, 0. )
#dh = numpy.minimum( z[k]-z[k+1], D-H)
H = H + dh
HT = HT + dh*T[k]
return HT/(H+1.e-20)
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
ci=m6plot.pmCI(0.05,1.,.1)
tPlot = depthAverageT(Tmod, Zmod, depth, lDepth, uDepth)
tRefPlot = depthAverageT(Tref, Zref, depth, lDepth, uDepth)
m6plot.xyplot( tPlot - tRefPlot , x, y, area=area,
suptitle=rootGroup.title+' '+cmdLineArgs.label,
title=r'%i-%im depth-average $\theta$ difference [$\degree$C]'%(uDepth,lDepth),
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=cmdLineArgs.outdir+'/T_%i-%im_zave_difference.png'%(uDepth,lDepth))
m6plot.xycompare( tPlot, tRefPlot , x, y, area=area,
suptitle=r'%i-%im depth-average $\theta$ difference [$\degree$C]'%(uDepth,lDepth)+cmdLineArgs.label,
title1=title1, title2=title2,
clim=m6plot.linCI(-2,29,.5), colormap='dunneRainbow', extend='max',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/T_%i-%im_zave_difference.3_panel.png'%(uDepth,lDepth))
if len(rootGroup.variables[varName].shape) == 4:
Smod = rootGroup.variables[varName][:].mean(axis=0)
else:
Smod = rootGroup.variables[varName][:]
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e'][0]
else: Zmod = Zref
def zonalAverage(T, eta, area, mask=1.):
vols = (mask * area) * (eta[:-1] - eta[1:]
) # mask * area * level thicknesses
return numpy.sum(vols * T, axis=-1) / numpy.sum(vols, axis=-1), (
mask * eta).min(axis=-1)
ci = m6plot.pmCI(0.0125, .225, .025)
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
# Global
sPlot, z = zonalAverage(Smod, Zmod, area)
sRefPlot, _ = zonalAverage(Sref, Zref, area)
m6plot.yzplot(sPlot - sRefPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=title1 + ' - ' + title2,
title='''Global zonal-average salinity response [ppt] ''' +
cmdLineArgs.label,
def main(cmdLineArgs, stream=False):
numpy.seterr(divide='ignore', invalid='ignore',
over='ignore') # To avoid warnings
if not os.path.exists(cmdLineArgs.gridspec):
raise ValueError(
'Specified gridspec directory/tar file does not exist.')
if os.path.isdir(cmdLineArgs.gridspec):
x = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['x'][::2, ::2]
xcenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['x'][1::2, 1::2]
y = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][::2, ::2]
ycenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][1::2, 1::2]
msk = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_mask.nc').variables['mask'][:]
area = msk * netCDF4.Dataset(
cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['area'][:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_topog.nc').variables['depth'][:]
elif os.path.isfile(cmdLineArgs.gridspec):
x = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'x')[::2, ::2]
xcenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'x')[1::2, 1::2]
y = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'y')[::2, ::2]
ycenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'y')[1::2, 1::2]
msk = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_mask.nc',
'mask')[:]
area = msk * m6toolbox.readNCFromTar(
cmdLineArgs.gridspec, 'ocean_hgrid.nc', 'area')[:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_topog.nc',
'depth')[:]
else:
raise ValueError(
'Unable to extract grid information from gridspec directory/tar file.'
)
# open dataset
rootGroup = netCDF4.MFDataset(cmdLineArgs.infile)
# gather months from input dataset
tvar = rootGroup.variables['time']
times = [
netCDF4.num2date(i, tvar.units, calendar=tvar.calendar.lower())
for i in tvar[:]
]
idx = list(set([i.month - 1 for i in times]))
month_label = [i.strftime('%b') for i in times]
month_label = month_label[:len(idx)]
month_label = str.join(', ', month_label)
# read sst from model
if 'sst' in rootGroup.variables: varName = 'sst'
elif 'tos' in rootGroup.variables: varName = 'tos'
else:
raise Exception('Could not find "sst", "ptemp" or "tos" in file "%s"' %
(cmdLineArgs.infile))
if rootGroup.variables[varName].shape[0] > 1:
Tmod = rootGroup.variables[varName][:].mean(axis=0)
else:
Tmod = rootGroup.variables[varName][0]
# read sst from obs
Tobs = netCDF4.Dataset(cmdLineArgs.woa_monthly)
if 'temp' in Tobs.variables: Tobs = Tobs.variables['temp']
elif 'ptemp' in Tobs.variables: Tobs = Tobs.variables['ptemp']
else:
raise Exception('Could not find "temp" or "ptemp" in file "%s"' %
(cmdLineArgs.woa_monthly))
if len(Tobs.shape) == 3: Tobs = Tobs[0]
else: Tobs = Tobs[idx, 0].mean(axis=0)
# create title for plot
if cmdLineArgs.suptitle != '':
suptitle = cmdLineArgs.suptitle + ' ' + cmdLineArgs.label
else:
suptitle = rootGroup.title + ' ' + cmdLineArgs.label
# invoke m6plot
imgbufs = []
ci = m6plot.pmCI(0.25, 4.5, .5)
if stream is True: objOut = io.BytesIO()
else: objOut = cmdLineArgs.outdir + '/SST_bias_WOA05.png'
m6plot.xyplot(Tmod - Tobs,
x,
y,
area=area,
suptitle=suptitle,
title=month_label +
' SST bias (w.r.t. WOA\'05) [$\degree$C]',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=objOut)
if stream is True: imgbufs.append(objOut)
m6plot.xycompare(Tmod,
Tobs,
x,
y,
area=area,
suptitle=suptitle,
title1=month_label + ' SST [$\degree$C]',
title2='WOA\'05 ' + month_label + ' SST [$\degree$C]',
clim=m6plot.linCI(-2, 29, .5),
colormap='dunneRainbow',
extend='max',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir + '/SST_bias_WOA05.3_panel.png')
if stream is True:
return imgbufs
x = netCDF4.Dataset(cmdLineArgs.gridspecdir+'/ocean_hgrid.nc').variables['x'][::2,::2]
y = netCDF4.Dataset(cmdLineArgs.gridspecdir+'/ocean_hgrid.nc').variables['y'][::2,::2]
msk = netCDF4.Dataset(cmdLineArgs.gridspecdir+'/ocean_mask.nc').variables['mask'][:]
area = msk*netCDF4.Dataset(cmdLineArgs.gridspecdir+'/ocean_hgrid.nc').variables['area'][:,:].reshape([msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
msk = numpy.ma.array(msk, mask=(msk==0))
Sobs = netCDF4.Dataset( cmdLineArgs.woa ).variables['salt']
if len(Sobs.shape)==3: Sobs = Sobs[0]
else: Sobs = Sobs[:,0].mean(axis=0)
rootGroup = netCDF4.Dataset( cmdLineArgs.annual_file )
if 'salt' in rootGroup.variables: varName = 'salt'
elif 'so' in rootGroup.variables: varName = 'so'
else: raise Exception('Could not find "salt" or "so" in file "%s"'%(cmdLineArgs.annual_file))
if rootGroup.variables[varName].shape[0]>1: salt = rootGroup.variables[varName][:,0].mean(axis=0)
else: Smod = rootGroup.variables[varName][0,0]
ci=m6plot.pmCI(0.125,2.25,.25)
m6plot.xyplot( Smod - Sobs , x, y, area=area,
suptitle=rootGroup.title+' '+cmdLineArgs.label, title='SSS bias (w.r.t. WOA\'05) [ppt]',
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=cmdLineArgs.outdir+'/SSS_bias_WOA05.png')
m6plot.xycompare( Smod, Sobs , x, y, area=area,
suptitle=rootGroup.title+' '+cmdLineArgs.label,
title1='SSS [ppt]',
title2='WOA\'05 SSS [ppt]',
clim=m6plot.linCI(20,30,10, 31,39,.5), colormap='dunneRainbow', extend='both',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/SSS_bias_WOA05.3_panel.png')
for k in range(T.shape[0]):
zTop = numpy.minimum(z[k], -upperDepth)
zBot = numpy.maximum(z[k + 1], -D)
dh = numpy.maximum(zTop - zBot, 0.)
#dh = numpy.minimum( z[k]-z[k+1], D-H)
H = H + dh
HT = HT + dh * T[k]
return HT / (H + 1.e-20)
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
ci = m6plot.pmCI(0.05, 1., .1)
tPlot = depthAverageT(Tmod, Zmod, depth, lDepth, uDepth)
tRefPlot = depthAverageT(Tref, Zref, depth, lDepth, uDepth)
m6plot.xyplot(tPlot - tRefPlot,
x,
y,
area=area,
suptitle=title1 + ' - ' + title2,
title=r'%i-%im depth-average $\theta$ difference [$\degree$C]' %
(uDepth, lDepth) + cmdLineArgs.label,
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=cmdLineArgs.outdir + '/T_%i-%im_zave_difference.png' %
Zobs = netCDF4.Dataset( cmdLineArgs.woa ).variables['eta'][:]
rootGroup = netCDF4.Dataset( cmdLineArgs.annual_file )
if 'salt' in rootGroup.variables: varName = 'salt'
elif 'so' in rootGroup.variables: varName = 'so'
else:raise Exception('Could not find "salt" or "so" in file "%s"'%(cmdLineArgs.annual_file))
if len(rootGroup.variables[varName].shape)==4: Smod = rootGroup.variables[varName][:].mean(axis=0)
else: Smod = rootGroup.variables[varName][:]
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e'][0]
else: Zmod = Zobs # Using model z-output
def zonalAverage(S, eta, area, mask=1.):
vols = ( mask * area ) * ( eta[:-1] - eta[1:] ) # mask * area * level thicknesses
return numpy.sum( vols * S, axis=-1 ) / numpy.sum( vols, axis=-1 ), (mask*eta).min(axis=-1)
ci=m6plot.pmCI(0.125,2.25,.25)
# Global
sPlot, z = zonalAverage(Smod, Zmod, area)
sObsPlot, _ = zonalAverage(Sobs, Zobs, area)
m6plot.yzplot( sPlot - sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=rootGroup.title+' '+cmdLineArgs.label, title='''Global zonal-average salinity bias (w.r.t. WOA'05) [ppt]''',
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=cmdLineArgs.outdir+'/S_global_xave_bias_WOA05.png')
m6plot.yzcompare( sPlot, sObsPlot , y, z, splitscale=[0., -1000., -6500.],
suptitle=rootGroup.title+' '+cmdLineArgs.label,
title1='Global zonal-average salinity [ppt]',
title2='''WOA'05 salinity [ppt]''',
clim=m6plot.linCI(20,30,10, 31,39,.5), colormap='dunneRainbow', extend='both',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
def main(cmdLineArgs,stream=None):
if not os.path.exists(cmdLineArgs.gridspec): raise ValueError('Specified gridspec directory/tar file does not exist.')
if os.path.isdir(cmdLineArgs.gridspec):
x = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['x'][::2,::2]
xcenter = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['x'][1::2,1::2]
y = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['y'][::2,::2]
ycenter = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['y'][1::2,1::2]
msk = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_mask.nc').variables['mask'][:]
area = msk*netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_hgrid.nc').variables['area'][:,:].reshape([msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = netCDF4.Dataset(cmdLineArgs.gridspec+'/ocean_topog.nc').variables['depth'][:]
try: basin_code = netCDF4.Dataset(cmdLineArgs.gridspec+'/basin_codes.nc').variables['basin'][:]
except: basin_code = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
elif os.path.isfile(cmdLineArgs.gridspec):
x = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','x')[::2,::2]
xcenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','x')[1::2,1::2]
y = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','y')[::2,::2]
ycenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','y')[1::2,1::2]
msk = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_mask.nc','mask')[:]
area = msk*m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_hgrid.nc','area')[:,:].reshape([msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'ocean_topog.nc','depth')[:]
try: basin_code = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,'basin_codes.nc','basin')[:]
except: basin_code = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
else:
raise ValueError('Unable to extract grid information from gridspec directory/tar file.')
if stream != None:
if len(stream) != 2:
raise ValueError('If specifying output streams, exactly two streams are needed for this analysis')
rootGroup = netCDF4.MFDataset( cmdLineArgs.annual_file )
if 'vh' in rootGroup.variables:
varName = 'vh'; conversion_factor = 1.e-9
elif 'vmo' in rootGroup.variables:
varName = 'vmo'; conversion_factor = 1.e-9
else: raise Exception('Could not find "vh" or "vmo" in file "%s"'%(cmdLineArgs.annual_file))
if len(rootGroup.variables[varName].shape)==4: VHmod = rootGroup.variables[varName][:].mean(axis=0)
else: VHmod = rootGroup.variables[varName][:]
try: VHmod = VHmod.filled(0.)
except: pass
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e'][0]
elif 'zw' in rootGroup.variables:
zw = rootGroup.variables['zw'][:]
Zmod = numpy.zeros((zw.shape[0], depth.shape[0], depth.shape[1] ))
for k in range(zw.shape[0]):
Zmod[k] = -numpy.minimum( depth, abs(zw[k]) )
else: raise Exception('Neither a model-space output file or a z-space diagnostic file?')
def MOCpsi(vh, vmsk=None):
"""Sums 'vh' zonally and cumulatively in the vertical to yield an overturning stream function, psi(y,z)."""
shape = list(vh.shape); shape[-3] += 1
psi = numpy.zeros(shape[:-1])
if len(shape)==3:
for k in range(shape[-3]-1,0,-1):
if vmsk is None: psi[k-1,:] = psi[k,:] - vh[k-1].sum(axis=-1)
else: psi[k-1,:] = psi[k,:] - (vmsk*vh[k-1]).sum(axis=-1)
else:
for n in range(shape[0]):
for k in range(shape[-3]-1,0,-1):
if vmsk is None: psi[n,k-1,:] = psi[n,k,:] - vh[n,k-1].sum(axis=-1)
else: psi[n,k-1,:] = psi[n,k,:] - (vmsk*vh[n,k-1]).sum(axis=-1)
return psi
def plotPsi(y, z, psi, ci, title):
cmap = plt.get_cmap('dunnePM')
plt.contourf(y, z, psi, levels=ci, cmap=cmap, extend='both')
cbar = plt.colorbar()
plt.contour(y, z, psi, levels=ci, colors='k', hold='on')
plt.gca().set_yscale('splitscale',zval=[0,-2000,-6500])
plt.title(title)
cbar.set_label('[Sv]'); plt.ylabel('Elevation [m]')
def findExtrema(y, z, psi, min_lat=-90., max_lat=90., min_depth=0., mult=1.):
psiMax = mult*numpy.amax( mult * numpy.ma.array(psi)[(y>=min_lat) & (y<=max_lat) & (z<-min_depth)] )
idx = numpy.argmin(numpy.abs(psi-psiMax))
(j,i) = numpy.unravel_index(idx, psi.shape)
plt.plot(y[j,i],z[j,i],'kx',hold=True)
plt.text(y[j,i],z[j,i],'%.1f'%(psi[j,i]))
m6plot.setFigureSize(npanels=1)
cmap = plt.get_cmap('dunnePM')
if cmdLineArgs.suptitle != '': suptitle = cmdLineArgs.suptitle + ' ' + cmdLineArgs.label
else: suptitle = rootGroup.title + ' ' + cmdLineArgs.label
# Global MOC
z = Zmod.min(axis=-1); psiPlot = MOCpsi(VHmod)*conversion_factor
yy = y[1:,:].max(axis=-1)+0*z
ci=m6plot.pmCI(0.,40.,5.)
plotPsi(yy, z, psiPlot, ci, 'Global MOC [Sv]')
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(suptitle)
findExtrema(yy, z, psiPlot, max_lat=-30.)
findExtrema(yy, z, psiPlot, min_lat=25.)
findExtrema(yy, z, psiPlot, min_depth=2000., mult=-1.)
if stream != None:
plt.savefig(stream[0])
else:
plt.savefig(cmdLineArgs.outdir+'/MOC_global.png')
# Atlantic MOC
plt.clf()
m = 0*basin_code; m[(basin_code==2) | (basin_code==4) | (basin_code==6) | (basin_code==7) | (basin_code==8)]=1
ci=m6plot.pmCI(0.,22.,2.)
z = (m*Zmod).min(axis=-1); psiPlot = MOCpsi(VHmod, vmsk=m*numpy.roll(m,-1,axis=-2))*conversion_factor
yy = y[1:,:].max(axis=-1)+0*z
plotPsi(yy, z, psiPlot, ci, 'Atlantic MOC [Sv]')
plt.xlabel(r'Latitude [$\degree$N]')
plt.suptitle(suptitle)
findExtrema(yy, z, psiPlot, min_lat=26.5, max_lat=27.) # RAPID
findExtrema(yy, z, psiPlot, max_lat=-33.)
findExtrema(yy, z, psiPlot)
findExtrema(yy, z, psiPlot, min_lat=5.)
if stream != None:
plt.savefig(stream[1])
else:
plt.savefig(cmdLineArgs.outdir+'/MOC_Atlantic.png')
def depthAverageT(T, z, d, lowerDepth, upperDepth):
D = numpy.minimum(d, lowerDepth)
H = numpy.zeros(T.shape[1:])
HT = numpy.zeros(T.shape[1:])
for k in range(T.shape[0]):
zTop = numpy.minimum(z[k], -upperDepth)
zBot = numpy.maximum(z[k + 1], -D)
dh = numpy.maximum(zTop - zBot, 0.)
#dh = numpy.minimum( z[k]-z[k+1], D-H)
H = H + dh
HT = HT + dh * T[k]
return HT / (H + 1.e-20)
ci = m6plot.pmCI(0.25, 4.5, .5)
tPlot = depthAverageT(Tmod, Zmod, depth, lDepth, uDepth)
tObsPlot = depthAverageT(Tobs, Zobs, depth, lDepth, uDepth)
m6plot.xyplot(
tPlot - tObsPlot,
x,
y,
area=area,
suptitle=rootGroup.title + ' ' + cmdLineArgs.label,
title=r'''%i-%im depth-average $\theta$ bias (w.r.t. WOA'05) [$\degree$C]'''
% (uDepth, lDepth),
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
elif 'so' in rootGroupRef.variables: varName='so'
else: raise Exception('Could not find "salt" or "so" in file "%s"'%(cmdLineArgs.ref))
if rootGroupRef.variables[varName].shape[0]>1: Sref = rootGroupRef.variables[varName][:,0].mean(axis=0)
else: Sref = rootGroupRef.variables[varName][0,0]
rootGroup = netCDF4.Dataset( cmdLineArgs.annual_file )
if 'salt' in rootGroup.variables: varName='salt'
elif 'so' in rootGroup.variables: varName='so'
else: raise Exception('Could not find "salt" or "so" in file "%s"'%(cmdLineArgs.annual_file))
if len(rootGroup.variables[varName].shape)==4: Smod = rootGroup.variables[varName][:,0].mean(axis=0)
else: Smod = rootGroup.variables[varName][0]
if len(cmdLineArgs.label1): title1 = cmdLineArgs.label1
else: title1 = rootGroup.title
if len(cmdLineArgs.label2): title2 = cmdLineArgs.label2
else: title2 = rootGroupRef.title
ci=m6plot.pmCI(0.025,0.525,.05)
m6plot.xyplot( Smod - Sref , x, y, area=area,
suptitle=title1+' - '+title2,
title='SSS difference [ppt] '+cmdLineArgs.label,
clim=ci, colormap='dunnePM', centerlabels=True, extend='both',
save=cmdLineArgs.outdir+'/SSS_difference.png')
m6plot.xycompare( Smod, Sref , x, y, area=area,
suptitle='SSS difference [ppt] '+cmdLineArgs.label,
title1=title1, title2=title2,
clim=m6plot.linCI(20,30,10, 31,39,.5), colormap='dunneRainbow', extend='max',
dlim=ci, dcolormap='dunnePM', dextend='both', centerdlabels=True,
save=cmdLineArgs.outdir+'/SSS_difference.3_panel.png')
def main(cmdLineArgs, stream=None):
if not os.path.exists(cmdLineArgs.gridspec):
raise ValueError(
'Specified gridspec directory/tar file does not exist.')
if os.path.isdir(cmdLineArgs.gridspec):
xcenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['x'][1::2, 1::2]
y = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][1::2, 1::2].max(
axis=-1)
ycenter = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][1::2, 1::2]
msk = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_mask.nc').variables['mask'][:]
area = msk * netCDF4.Dataset(
cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['area'][:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_topog.nc').variables['depth'][:]
try:
basin = netCDF4.Dataset(cmdLineArgs.gridspec +
'/basin_codes.nc').variables['basin'][:]
except:
basin = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
elif os.path.isfile(cmdLineArgs.gridspec):
xcenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'x')[1::2, 1::2]
y = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'y')[1::2, 1::2].max(axis=-1)
ycenter = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'ocean_hgrid.nc', 'y')[1::2, 1::2]
msk = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_mask.nc',
'mask')[:]
area = msk * m6toolbox.readNCFromTar(
cmdLineArgs.gridspec, 'ocean_hgrid.nc', 'area')[:, :].reshape(
[msk.shape[0], 2, msk.shape[1], 2]).sum(axis=-3).sum(axis=-1)
depth = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_topog.nc',
'depth')[:]
try:
basin = m6toolbox.readNCFromTar(cmdLineArgs.gridspec,
'basin_codes.nc', 'basin')[:]
except:
basin = m6toolbox.genBasinMasks(xcenter, ycenter, depth)
else:
raise ValueError(
'Unable to extract grid information from gridspec directory/tar file.'
)
if stream != None:
if len(stream) != 4:
raise ValueError(
'If specifying output streams, exactly four (4) streams are needed for this analysis'
)
Tobs = netCDF4.Dataset(cmdLineArgs.woa)
if 'temp' in Tobs.variables: Tobs = Tobs.variables['temp']
else: Tobs = Tobs.variables['ptemp']
if len(Tobs.shape) == 3: Tobs = Tobs[:]
else: Tobs = Tobs[:].mean(axis=0)
Zobs = netCDF4.Dataset(cmdLineArgs.woa).variables['eta'][:]
rootGroup = netCDF4.MFDataset(cmdLineArgs.annual_file)
if 'temp' in rootGroup.variables: varName = 'temp'
elif 'ptemp' in rootGroup.variables: varName = 'ptemp'
elif 'thetao' in rootGroup.variables: varName = 'thetao'
else:
raise Exception(
'Could not find "temp", "ptemp" or "thetao" in file "%s"' %
(cmdLineArgs.annual_file))
if len(rootGroup.variables[varName].shape) == 4:
Tmod = rootGroup.variables[varName][:].mean(axis=0)
else:
Tmod = rootGroup.variables[varName][:]
if 'e' in rootGroup.variables: Zmod = rootGroup.variables['e'][0]
else: Zmod = Zobs # Using model z-ou:put
def zonalAverage(T, eta, area, mask=1.):
vols = (mask * area) * (eta[:-1] - eta[1:]
) # mask * area * level thicknesses
return numpy.sum(vols * T, axis=-1) / numpy.sum(vols, axis=-1), (
mask * eta).min(axis=-1)
ci = m6plot.pmCI(0.25, 4.5, .5)
if cmdLineArgs.suptitle != '':
suptitle = cmdLineArgs.suptitle + ' ' + cmdLineArgs.label
else:
suptitle = rootGroup.title + ' ' + cmdLineArgs.label
# Global
tPlot, z = zonalAverage(Tmod, Zmod, area)
tObsPlot, _ = zonalAverage(Tobs, Zobs, area)
if stream != None: objOut = stream[0]
else: objOut = cmdLineArgs.outdir + '/T_global_xave_bias_WOA05.png'
m6plot.yzplot(
tPlot - tObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title=
r'''Global zonal-average $\theta$ bias (w.r.t. WOA'05) [$\degree$C]''',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=objOut)
if stream is None:
m6plot.yzcompare(tPlot,
tObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1=r'Global zonal-average $\theta$ [$\degree$C]',
title2=r'''WOA'05 $\theta$ [$\degree$C]''',
clim=m6plot.linCI(-2, 29, .5),
colormap='dunneRainbow',
extend='max',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir +
'/T_global_xave_bias_WOA05.3_panel.png')
# Atlantic + Arctic
newMask = 1. * msk
newMask[(basin != 2) & (basin != 4)] = 0.
tPlot, z = zonalAverage(Tmod, Zmod, area, mask=newMask)
tObsPlot, _ = zonalAverage(Tobs, Zobs, area, mask=newMask)
if stream != None: objOut = stream[1]
else: objOut = cmdLineArgs.outdir + '/T_Atlantic_xave_bias_WOA05.png'
m6plot.yzplot(
tPlot - tObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title=
r'''Atlantic zonal-average $\theta$ bias (w.r.t. WOA'05) [$\degree$C]''',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=objOut)
if stream is None:
m6plot.yzcompare(
tPlot,
tObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1=r'Atlantic zonal-average $\theta$ [$\degree$C]',
title2=r'''WOA'05 $\theta$ [$\degree$C]''',
clim=m6plot.linCI(-2, 29, .5),
colormap='dunneRainbow',
extend='max',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir +
'/T_Atlantic_xave_bias_WOA05.3_panel.png')
# Pacific
newMask = 1. * msk
newMask[(basin != 3)] = 0.
tPlot, z = zonalAverage(Tmod, Zmod, area, mask=newMask)
tObsPlot, _ = zonalAverage(Tobs, Zobs, area, mask=newMask)
if stream != None: objOut = stream[2]
else: objOut = cmdLineArgs.outdir + '/T_Pacific_xave_bias_WOA05.png'
m6plot.yzplot(
tPlot - tObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title=
r'''Pacific zonal-average $\theta$ bias (w.r.t. WOA'05) [$\degree$C]''',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=objOut)
if stream is None:
m6plot.yzcompare(tPlot,
tObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1=r'Pacific zonal-average $\theta$ [$\degree$C]',
title2=r'''WOA'05 $\theta$ [$\degree$C]''',
clim=m6plot.linCI(-2, 29, .5),
colormap='dunneRainbow',
extend='max',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir +
'/T_Pacific_xave_bias_WOA05.3_panel.png')
# Indian
newMask = 1. * msk
newMask[(basin != 5)] = 0.
tPlot, z = zonalAverage(Tmod, Zmod, area, mask=newMask)
tObsPlot, _ = zonalAverage(Tobs, Zobs, area, mask=newMask)
if stream != None: objOut = stream[3]
else: objOut = cmdLineArgs.outdir + '/T_Indian_xave_bias_WOA05.png'
m6plot.yzplot(
tPlot - tObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title=
r'''Indian zonal-average $\theta$ bias (w.r.t. WOA'05) [$\degree$C]''',
clim=ci,
colormap='dunnePM',
centerlabels=True,
extend='both',
save=objOut)
if stream is None:
m6plot.yzcompare(tPlot,
tObsPlot,
y,
z,
splitscale=[0., -1000., -6500.],
suptitle=suptitle,
title1=r'Indian zonal-average $\theta$ [$\degree$C]',
title2=r'''WOA'05 $\theta$ [$\degree$C]''',
clim=m6plot.linCI(-2, 29, .5),
colormap='dunneRainbow',
extend='max',
dlim=ci,
dcolormap='dunnePM',
dextend='both',
centerdlabels=True,
save=cmdLineArgs.outdir +
'/T_Indian_xave_bias_WOA05.3_panel.png')