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.')
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.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 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
imgbufs = []
ci=m6plot.pmCI(0.125,2.25,.25)
if stream is True: img = io.BytesIO()
else: img = 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=img)
if stream is True: imgbufs.append(img)
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')
if stream is True:
return imgbufs
def subplot( path, axis, title ): #x = netCDF4.Dataset(path+'/Initial_state.nc').variables['lonh'][:] #e = netCDF4.Dataset(path+'/Initial_state.nc').variables['eta'][:,0,:] #S = netCDF4.Dataset(path+'/Initial_state.nc').variables['Salt'][0,:,0,:] x = netCDF4.Dataset(path+'/prog.nc').variables['xh'][:] e = netCDF4.Dataset(path+'/prog.nc').variables['e'][-1,:,0,:] S = netCDF4.Dataset(path+'/prog.nc').variables['salt'][-1,:,0,:] m6plot.yzplot(S, x, e, axis=axis, title=title, clim=m6plot.linCI(34,35,.05)) plt.plot(x, e.T, hold=True, color='k')
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')
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')
if 'MLD_003' not in rootGroup.variables: raise Exception('Could not find "MLD_003" in file "%s"'%(cmdLineArgs.monthly_file))
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)
variable = rootGroup.variables['MLD_003']
shape = variable.shape
MLD = variable[:].reshape(shape[0]/12,12,shape[1],shape[2]).mean(axis=0)
MLD_obs = netCDF4.Dataset(cmdLineArgs.obsdata).variables['MLD'][:]
x_obs = netCDF4.Dataset(cmdLineArgs.obsdata).variables['LONGITUDE'][:]
y_obs = netCDF4.Dataset(cmdLineArgs.obsdata).variables['LATITUDE'][:]
ciMin = m6plot.linCI(0,95,5)
ciMax = m6plot.linCI(0,680,20)
# Plot of shallowest model MLD (summer)
m6plot.xyplot( MLD.min(axis=0), x, y, area=area,
suptitle=rootGroup.title+' '+cmdLineArgs.label, title='Annual-minimum MLD$_{0.03}$ [m]',
clim=ciMin, extend='max', colormap='dunneRainbow',
save=cmdLineArgs.outdir+'/MLD_003_minimum.png')
# 2-panel plot of shallowest model MLD + obs (summer)
m6plot.setFigureSize(aspect=[3,3], verticalresolution=976, npanels=0)
ax1 = plt.subplot(2,1,1)
m6plot.xyplot( numpy.roll(MLD_obs.min(axis=0),300,axis=-1), x_obs-300, y_obs,
suptitle=rootGroup.title+' '+cmdLineArgs.label, title='Hosoda et al., 2010, annual-minimum MLD$_{0.03}$ [m]',
clim=ciMin, extend='max', colormap='dunneRainbow',
axis=ax1)
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')
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' %
(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))
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):
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
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
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)
else:
raise ValueError(
'Unable to extract grid information from gridspec directory/tar file.')
variable = rootGroup.variables['MLD_003']
shape = variable.shape
MLD = variable[:].reshape(shape[0] / 12, 12, shape[1], shape[2]).mean(axis=0)
MLD_obs = netCDF4.Dataset(cmdLineArgs.obsdata).variables['MLD'][:]
x_obs = netCDF4.Dataset(cmdLineArgs.obsdata).variables['LONGITUDE'][:]
y_obs = netCDF4.Dataset(cmdLineArgs.obsdata).variables['LATITUDE'][:]
ciMin = m6plot.linCI(0, 95, 5)
ciMax = m6plot.linCI(0, 680, 20)
# Plot of shallowest model MLD (summer)
m6plot.xyplot(MLD.min(axis=0),
x,
y,
area=area,
suptitle=rootGroup.title + ' ' + cmdLineArgs.label,
title='Annual-minimum MLD$_{0.03}$ [m]',
clim=ciMin,
extend='max',
colormap='dunneRainbow',
save=cmdLineArgs.outdir + '/MLD_003_minimum.png')
# 2-panel plot of shallowest model MLD + obs (summer)
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.'
)
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.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:
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
ci = m6plot.pmCI(0.25, 4.5, .5)
if stream is None: stream = 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=stream)
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')
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')
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)
plt.savefig(cmdLineArgs.outdir+'/T_Pacific_equator_bias_WOA05.png')
m6plot.yzcompare( tPlot, tObsPlot , x[j,i0:i1], zi, splitscale=[0., -1000., -6500.],
suptitle=rootGroup.title+' '+cmdLineArgs.label,
ylabel='Longitude',
title1=r'Pacific equator $\theta$ [$\degree$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_equator_bias_WOA05.3_panel.png')
# Atlantic equator
j = numpy.abs( y[:,0] - 0. ).argmin()
i0 = numpy.abs( numpy.mod( x[j,:] + 52. + 360., 360. ) ).argmin()
i1 = numpy.abs( numpy.mod( x[j,:] - 10.5 + 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'''Atlantic 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')
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')
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))
def main(cmdLineArgs, stream=False):
rootGroup = netCDF4.MFDataset(cmdLineArgs.infile)
if 'MLD_003' not in rootGroup.variables:
raise Exception('Could not find "MLD_003" in file "%s"' %
(cmdLineArgs.infile))
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]
y = netCDF4.Dataset(cmdLineArgs.gridspec +
'/ocean_hgrid.nc').variables['y'][::2, ::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)
elif os.path.isfile(cmdLineArgs.gridspec):
x = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'x')[::2, ::2]
y = m6toolbox.readNCFromTar(cmdLineArgs.gridspec, 'ocean_hgrid.nc',
'y')[::2, ::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)
else:
raise ValueError(
'Unable to extract grid information from gridspec directory/tar file.'
)
variable = rootGroup.variables['MLD_003']
shape = variable.shape
MLD = variable[:].reshape(shape[0] / 12, 12, shape[1],
shape[2]).mean(axis=0)
if not hasattr(cmdLineArgs, 'obsdata') or cmdLineArgs.obsdata == '':
cmdLineArgs.obsdata = '/archive/gold/datasets/obs/Hosada2010_MLD_climatology.v20140515.nc'
MLD_obs = netCDF4.Dataset(cmdLineArgs.obsdata).variables['MLD'][:]
x_obs = netCDF4.Dataset(cmdLineArgs.obsdata).variables['LONGITUDE'][:]
y_obs = netCDF4.Dataset(cmdLineArgs.obsdata).variables['LATITUDE'][:]
ciMin = m6plot.linCI(0, 95, 5)
ciMax = m6plot.linCI(0, 680, 20)
imgbufs = []
# Plot of shallowest model MLD (summer)
m6plot.xyplot(MLD.min(axis=0),
x,
y,
area=area,
suptitle=rootGroup.title + ' ' + cmdLineArgs.label,
title='Annual-minimum MLD$_{0.03}$ [m]',
clim=ciMin,
extend='max',
colormap='dunneRainbow',
save=cmdLineArgs.outdir + '/MLD_003_minimum.png')
# 2-panel plot of shallowest model MLD + obs (summer)
if stream is True: img = io.BytesIO()
else: img = cmdLineArgs.outdir + '/MLD_003_minimum.2_panel.png'
m6plot.setFigureSize(aspect=[3, 3], verticalresolution=976, npanels=0)
ax1 = plt.subplot(2, 1, 1)
m6plot.xyplot(numpy.roll(MLD_obs.min(axis=0), 300, axis=-1),
x_obs - 300,
y_obs,
suptitle=rootGroup.title + ' ' + cmdLineArgs.label,
title='Hosoda et al., 2010, annual-minimum MLD$_{0.03}$ [m]',
clim=ciMin,
extend='max',
colormap='dunneRainbow',
axis=ax1)
ax2 = plt.subplot(2, 1, 2)
m6plot.xyplot(MLD.min(axis=0),
x,
y,
area=area,
suptitle=rootGroup.title + ' ' + cmdLineArgs.label,
title='Annual-minimum MLD$_{0.03}$ [m]',
clim=ciMin,
extend='max',
colormap='dunneRainbow',
axis=ax2,
save=img)
if stream is True: imgbufs.append(img)
# Plot of deepest model MLD (winter)
m6plot.xyplot(MLD.max(axis=0),
x,
y,
area=area,
suptitle=rootGroup.title + ' ' + cmdLineArgs.label,
title='Annual-maximum MLD$_{0.03}$ [m]',
clim=ciMax,
extend='max',
colormap='dunneRainbow',
save=cmdLineArgs.outdir + '/MLD_003_maximum.png')
# 2-panel plot of deepest model MLD + obs (winter)
if stream is True: img = io.BytesIO()
else: img = cmdLineArgs.outdir + '/MLD_003_maximum.2_panel.png'
m6plot.setFigureSize(aspect=[3, 3], verticalresolution=976, npanels=0)
ax1 = plt.subplot(2, 1, 1)
m6plot.xyplot(numpy.roll(MLD_obs.max(axis=0), 300, axis=-1),
x_obs - 300,
y_obs,
suptitle=rootGroup.title + ' ' + cmdLineArgs.label,
title='Hosoda et al., 2010, annual-maximum MLD$_{0.03}$ [m]',
clim=ciMax,
extend='max',
colormap='dunneRainbow',
axis=ax1)
ax2 = plt.subplot(2, 1, 2)
m6plot.xyplot(MLD.max(axis=0),
x,
y,
area=area,
suptitle=rootGroup.title + ' ' + cmdLineArgs.label,
title='Annual-maximum MLD$_{0.03}$ [m]',
clim=ciMax,
extend='max',
colormap='dunneRainbow',
axis=ax2,
save=img)
if stream is True: imgbufs.append(img)
if stream is True:
return imgbufs
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')