def compute_and_write_climatologies_keepvars( varkeys, reduced_variables, season, case='', variant='', path='' ):
"""Computes climatologies and writes them to a file.
Inputs: varkeys, names of variables whose climatologies are to be computed
reduced_variables, dict (key:rv) where key is a variable name and rv an instance
of the class reduced_variable
season: the season on which the climatologies will be computed
variant: a string to be inserted in the filename"""
# Compute the value of every variable we need.
varvals = {}
# First compute all the reduced variables
# Probably this loop consumes most of the running time. It's what has to read in all the data.
for key in varkeys:
if key in reduced_variables:
varvals[key] = reduced_variables[key].reduce()
for key in varkeys:
if key in reduced_variables:
var = reduced_variables[key]
if varvals[key] is not None:
if 'case' in var._file_attributes.keys():
case = var._file_attributes['case']+'_'
break
logger.info("writing climatology file for %s %s %s ",case,variant,season)
if variant!='':
variant = variant+'_'
logger.info('case: %s',case)
logger.info('variant: %s', variant)
logger.info('season: %s', season)
filename = case + variant + season + "_climo.nc"
# ...actually we want to write this to a full directory structure like
# root/institute/model/realm/run_name/season/
value=0
cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateLevelFlag(value) ## where value is a integer between 0 and 9 included
g = cdms2.open( os.path.join(path,filename), 'w' ) # later, choose a better name and a path!
store_provenance(g)
for key in varkeys:
if key in reduced_variables:
var = reduced_variables[key]
if varvals[key] is not None:
varvals[key].id = var.variableid
varvals[key].reduced_variable=varvals[key].id
if hasattr(var,'units'):
varvals[key].units = var.units+'*'+var.units
g.write(varvals[key])
for attr,val in var._file_attributes.items():
if not hasattr( g, attr ):
setattr( g, attr, val )
g.season = season
g.close()
return varvals,case
def create_precip_PDF_netcdf(mv, model_handle, path=''):
""" If a netcdf file containing the probability distribution function (PDF)
does not exist for the model data, this script takes the data and
creates a netcdf with the frequency and amount PDFs based on the
specified data. Name of the file will follow other climo files with the format:
filename = case + variable + "_PDF_climo.nc"
'path' defines the location on which to write the files
Currently, the bin edge values and the scale of the bin widths are specified in the script.
This feature can and may be changed in the future.
"""
case = model_handle.attributes['case']
version = model_handle.attributes['version']
variablename = mv.id
vid_m = ''.join([variablename, 'FREQPDF'])
vid2_m = ''.join([variablename, 'AMNTPDF'])
vid3_m = 'bincenter'
logger.info("calculating pdf for %s %s ", case, variablename)
#specify bin edges of PDF
#specify minimum precipitation threshold
threshold = 0.1 # in mm d-1
bin_increase = 1.07 # multiplicative factor
binedges_input = threshold * bin_increase**(numpy.arange(0, 130)
) #these are the bin edges
[bincenter, output_mapped_freqpdf, output_mapped_amntpdf
] = create_amount_freq_PDF(mv,
binedges_input,
binwidthtype='logarithmic',
bincentertype='arithmetic',
vid=vid_m,
vid2=vid2_m,
vid3=vid3_m)
logger.info("writing pdf file for %s %s %s ", case, version, variablename)
outputfilename = '_'.join([case, version, variablename, 'PDF_climo.nc'])
f_out = cdms2.open(os.path.join(path, outputfilename), 'w')
store_provenance(f_out)
att_keys = model_handle.attributes.keys()
att_dic = {}
for i in range(len(att_keys)):
att_dic[i] = att_keys[i], model_handle.attributes[att_keys[i]]
to_out = att_dic[i]
if not hasattr(f_out, to_out[0]):
setattr(f_out, to_out[0], to_out[1])
def create_precip_PDF_netcdf(mv, model_handle, path=''):
""" If a netcdf file containing the probability distribution function (PDF)
does not exist for the model data, this script takes the data and
creates a netcdf with the frequency and amount PDFs based on the
specified data. Name of the file will follow other climo files with the format:
filename = case + variable + "_PDF_climo.nc"
'path' defines the location on which to write the files
Currently, the bin edge values and the scale of the bin widths are specified in the script.
This feature can and may be changed in the future.
"""
case= model_handle.attributes['case']
version=model_handle.attributes['version']
variablename=mv.id
vid_m=''.join([variablename,'FREQPDF'])
vid2_m=''.join([variablename,'AMNTPDF'])
vid3_m='bincenter'
logger.info("calculating pdf for %s %s ",case,variablename)
#specify bin edges of PDF
#specify minimum precipitation threshold
threshold=0.1 # in mm d-1
bin_increase=1.07 # multiplicative factor
binedges_input=threshold * bin_increase ** (numpy.arange(0,130)) #these are the bin edges
[bincenter, output_mapped_freqpdf, output_mapped_amntpdf] = create_amount_freq_PDF(mv,binedges_input, binwidthtype='logarithmic', bincentertype='arithmetic', vid=vid_m, vid2=vid2_m,vid3=vid3_m)
logger.info("writing pdf file for %s %s %s ",case,version,variablename)
outputfilename='_'.join([case,version,variablename,'PDF_climo.nc'])
f_out = cdms2.open(os.path.join(path,outputfilename),'w')
store_provenance(f_out)
att_keys = model_handle.attributes.keys()
att_dic = {}
for i in range(len(att_keys)):
att_dic[i]=att_keys[i],model_handle.attributes[att_keys[i]]
to_out = att_dic[i]
if not hasattr(f_out,to_out[0]):
setattr(f_out,to_out[0],to_out[1])
def get_variables_for_atmospheric_heat_transport(infilename1, outfilename1,
compare):
infile1 = cdms2.open(infilename1)
value = 0
cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateLevelFlag(
value) ## where value is a integer between 0 and 9 included
outfile1 = cdms2.open(outfilename1, "w")
store_provenance(outfile1)
case1 = infile1.case # e.g. "b30.009"
if 'lat' in infile1.variables.keys() and infile1['lat'].isLatitude():
lat1 = infile1['lat']
else:
for an, ax in infile1.axes.iteritems():
if ax.isLatitude():
lat1 = ax
break
nlat1 = lat1.shape
# Gauss weights:
if 'gw' in f.variables.keys():
gw = infile1['gw']
elif 'wgt' in infile1.variables.keys():
gw = infile1['wgt']
else: # no Gauss weights in file
gw = cdms2.createVariable(latRegWgt(lat1), axes=[lat1])
oro = get_ORO_OCNFRAC(infile1, outfile1)
fsns = get_FSNS(infile1, outfile1)
flns = get_FLNS(infile1, outfile1)
shflx = get_SHFLX(infile1, outfile1)
lhflx = get_LHFLX(infile1, outfile1)
if 'FLUT' in infile1.variables.keys():
flut = get_FLUT(infile1, outfile1) # <<< this function doesn't exist
fsntoa = get_FSNTOA(infile1,
outfile1) # <<< this function doesn't exist
else:
flut = None
flnt = get_FLNT(infile1, outfile1)
fsnt = get_FSNT(infile1, outfile1) # <<< this function doesn't exist
def get_variables_for_atmospheric_heat_transport( infilename1, outfilename1, compare ):
infile1 = cdms2.open(infilename1)
value=0
cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateLevelFlag(value) ## where value is a integer between 0 and 9 included
outfile1 = cdms2.open(outfilename1,"w")
store_provenance(outfile1)
case1 = infile1.case # e.g. "b30.009"
if 'lat' in infile1.variables.keys() and infile1['lat'].isLatitude():
lat1 = infile1['lat']
else:
for an,ax in infile1.axes.iteritems():
if ax.isLatitude():
lat1 = ax
break
nlat1 = lat1.shape
# Gauss weights:
if 'gw' in f.variables.keys():
gw = infile1['gw']
elif 'wgt' in infile1.variables.keys():
gw = infile1['wgt']
else: # no Gauss weights in file
gw = cdms2.createVariable( latRegWgt(lat1), axes=[lat1] )
oro = get_ORO_OCNFRAC(infile1,outfile1)
fsns = get_FSNS(infile1,outfile1)
flns = get_FLNS(infile1,outfile1)
shflx = get_SHFLX(infile1,outfile1)
lhflx = get_LHFLX(infile1,outfile1)
if 'FLUT' in infile1.variables.keys():
flut = get_FLUT( infile1, outfile1 ) # <<< this function doesn't exist
fsntoa = get_FSNTOA( infile1,outfile1 ) # <<< this function doesn't exist
else:
flut = None
flnt = get_FLNT(infile1,outfile1)
fsnt = get_FSNT( infile1, outfile1 ) # <<< this function doesn't exist
print vars
new_dir = old_dir + suffix
print new_dir
os.makedirs(new_dir)
fns = os.listdir('./'+old_dir)
print fns
#pdb.set_trace()
for fn in fns:
f = cdms2.open(old_dir + '/' + fn)
g = cdms2.open(new_dir + '/' + fn, 'w')
store_provenance(g)
for key, value in f.attributes.iteritems():
setattr(g, key, value)
for varid in vars:
#print varid
var = f(varid)
#print varid, type(var)
if isinstance(var,numpy.float64):
var = MV2.array(var, id=varid)
elif hasattr(var, 'getLatitude'):
l = var.getLatitude()
if hasattr(l, '_bounds_'):
l._bounds_ = None
def test_driver(path1, path2=None, filt2=None):
""" Test driver for setting up data for plots"""
# First, find and index the data files.
datafiles1 = dirtree_datafiles(path1)
print "jfp datafiles1=", datafiles1
datafiles2 = dirtree_datafiles(path2, filt2)
print "jfp datafiles2=", datafiles2
filetable1 = basic_filetable(datafiles1)
filetable2 = basic_filetable(datafiles2)
# Next we'll compute reduced variables. They have generally been reduced by averaging in time,
# and often more axes as well. Reducing the data first is the fastest way to compute, important
# if we need to be interactive. And it is correct if whatever we plot is linear in the
# variables, as is almost always the case. But if we want to plot a highly nonlinear function
# of the data variables, the averaging will have to wait until later.
# The reduced_variables dict names and contains all the reduced variables which we have defined.
# They will be used in defining instances of plotspec.
reduced_variables = {
'hyam_1':
reduced_variable(variableid='hyam',
filetable=filetable1,
reduction_function=(lambda x, vid=None: x)),
'hybm_1':
reduced_variable(variableid='hybm',
filetable=filetable1,
reduction_function=(lambda x, vid=None: x)),
'PS_ANN_1':
reduced_variable(variableid='PS',
filetable=filetable1,
reduction_function=reduce2lat),
'T_CAM_ANN_1':
reduced_variable(variableid='T',
filetable=filetable1,
reduction_function=reduce2levlat),
'T_CAM_ANN_2':
reduced_variable(variableid='T',
filetable=filetable2,
reduction_function=reduce2levlat),
'TREFHT_ANN_latlon_Npole_1':
reduced_variable(variableid='TREFHT',
filetable=filetable1,
reduction_function=(lambda x, vid=None: restrict_lat(
reduce2latlon(x, vid=vid), 50, 90))),
'TREFHT_ANN_latlon_Npole_2':
reduced_variable(variableid='TREFHT',
filetable=filetable2,
reduction_function=(lambda x, vid=None: restrict_lat(
reduce2latlon(x, vid=vid), 50, 90))),
'TREFHT_ANN_lat_1':
reduced_variable(variableid='TREFHT',
filetable=filetable1,
reduction_function=reduce2lat),
'TREFHT_DJF_lat_1':
reduced_variable(
variableid='TREFHT',
filetable=filetable1,
reduction_function=(lambda x, vid=None: reduce2lat_seasonal(
x, seasonsDJF, vid=vid))),
'TREFHT_DJF_lat_2':
reduced_variable(
variableid='TREFHT',
filetable=filetable2,
reduction_function=(lambda x, vid=None: reduce2lat_seasonal(
x, seasonsDJF, vid=vid))),
'TREFHT_DJF_latlon_1':
reduced_variable(
variableid='TREFHT',
filetable=filetable1,
reduction_function=(lambda x, vid=None: reduce2latlon_seasonal(
x, seasonsDJF, vid=vid))),
'TREFHT_DJF_latlon_2':
reduced_variable(
variableid='TREFHT',
filetable=filetable2,
reduction_function=(lambda x, vid=None: reduce2latlon_seasonal(
x, seasonsDJF, vid=vid))),
'TREFHT_JJA':
reduced_variable(
variableid='TREFHT',
filetable=filetable1,
reduction_function=(lambda x, vid=None: reduce2lat_seasonal(
x, seasonsJJA, vid=vid))),
'PRECT_JJA_lat_1':
reduced_variable(
variableid='PRECT',
filetable=filetable1,
reduction_function=(lambda x, vid=None: reduce2lat_seasonal(
x, seasonsJJA, vid=vid))),
'PRECT_JJA_lat_2':
reduced_variable(
variableid='PRECT',
filetable=filetable2,
reduction_function=(lambda x, vid=None: reduce2lat_seasonal(
x, seasonsJJA, vid=vid))),
# CAM variables needed for heat transport:
# FSNS, FLNS, FLUT, FSNTOA, FLNT, FSNT, SHFLX, LHFLX,
'FSNS_1':
reduced_variable(variableid='FSNS',
filetable=filetable1,
reduction_function=(lambda x, vid: x)),
'FSNS_ANN_latlon_1':
reduced_variable(variableid='FSNS',
filetable=filetable1,
reduction_function=reduce2latlon),
'FLNS_1':
reduced_variable(variableid='FLNS',
filetable=filetable1,
reduction_function=(lambda x, vid: x)),
'FLNS_ANN_latlon_1':
reduced_variable(variableid='FLNS',
filetable=filetable1,
reduction_function=reduce2latlon),
'FLUT_ANN_latlon_1':
reduced_variable(variableid='FLUT',
filetable=filetable1,
reduction_function=reduce2latlon),
'FSNTOA_ANN_latlon_1':
reduced_variable(variableid='FSNTOA',
filetable=filetable1,
reduction_function=reduce2latlon),
'FLNT_1':
reduced_variable(variableid='FLNT',
filetable=filetable1,
reduction_function=(lambda x, vid: x)),
'FLNT_ANN_latlon_1':
reduced_variable(variableid='FLNT',
filetable=filetable1,
reduction_function=reduce2latlon),
'FSNT_1':
reduced_variable(variableid='FSNT',
filetable=filetable1,
reduction_function=(lambda x, vid: x)),
'FSNT_ANN_latlon_1':
reduced_variable(variableid='FSNT',
filetable=filetable1,
reduction_function=reduce2latlon),
'QFLX_1':
reduced_variable(variableid='QFLX',
filetable=filetable1,
reduction_function=(lambda x, vid: x)),
'SHFLX_1':
reduced_variable(variableid='SHFLX',
filetable=filetable1,
reduction_function=(lambda x, vid: x)),
'SHFLX_ANN_latlon_1':
reduced_variable(variableid='SHFLX',
filetable=filetable1,
reduction_function=reduce2latlon),
'LHFLX_ANN_latlon_1':
reduced_variable(variableid='LHFLX',
filetable=filetable1,
reduction_function=reduce2latlon),
'ORO_ANN_latlon_1':
reduced_variable(variableid='ORO',
filetable=filetable1,
reduction_function=reduce2latlon),
'OCNFRAC_ANN_latlon_1':
reduced_variable(variableid='OCNFRAC',
filetable=filetable1,
reduction_function=reduce2latlon),
'ts_lat_old':
reduced_variable(
variableid=
'surface_temperature', # normally a CF standard_name, even for non-CF data.
filetable=filetable1,
reduction_function=reduce2lat_old),
'ts_lat_new':
reduced_variable(
variableid=
'surface_temperature', # normally a CF standard_name, even for non-CF data.
filetable=filetable1,
reduction_function=reduce2lat
# The reduction function will take just one argument, a variable (MV). But it might
# be expressed here as a lambda wrapping a more general function.
# Often there will be ranges in time, space, etc. specified here. No range means
# everything.
),
'ts_scalar_tropical_o':
reduced_variable(
variableid='surface_temperature',
filetable=filetable1,
reduction_function=(lambda mv, vid=None: reduce2scalar_zonal_old(
mv, -20, 20, vid=vid))),
'ts_scalar_tropical_n':
reduced_variable(
variableid='surface_temperature',
filetable=filetable1,
reduction_function=(lambda mv, vid=None: reduce2scalar_zonal(
mv, -20, 20, vid=vid)))
}
# Derived variables have to be treated separately from reduced variables
# because derived variables generally depend on reduced variables.
# But N.B.: the dicts reduced_variables and derived_variables
# must never use the same key!
derived_variables = {
'CAM_HEAT_TRANSPORT_ALL_1':
derived_var(vid='CAM_HEAT_TRANSPORT_ALL_1',
inputs=[
'FSNS_ANN_latlon_1', 'FLNS_ANN_latlon_1',
'FLUT_ANN_latlon_1', 'FSNTOA_ANN_latlon_1',
'FLNT_ANN_latlon_1', 'FSNT_ANN_latlon_1',
'SHFLX_ANN_latlon_1', 'LHFLX_ANN_latlon_1',
'OCNFRAC_ANN_latlon_1'
],
outputs=[
'atlantic_heat_transport', 'pacific_heat_transport',
'indian_heat_transport', 'global_heat_transport'
],
func=oceanic_heat_transport),
'NCEP_OBS_HEAT_TRANSPORT_ALL_2':
derived_var(vid='NCEP_OBS_HEAT_TRANSPORT_ALL_2',
inputs=[],
outputs=('latitude', [
'atlantic_heat_transport', 'pacific_heat_transport',
'indian_heat_transport', 'global_heat_transport'
]),
func=(lambda: ncep_ocean_heat_transport(path2))),
'T_ANN_1':
derived_var(vid='T_ANN_1',
inputs=[
'T_CAM_ANN_1', 'hyam_1', 'hybm_1', 'PS_ANN_1',
'T_CAM_ANN_2'
],
outputs=('temperature'),
func=verticalize)
}
plotvars = dict(reduced_variables.items() + derived_variables.items())
# The plotvspecs dict names and contains all plotspec objects which we have defined.
# The plotspeckeys variable, below, names the ones for which we will generate output.
# A dict value can be a plotspec object, or a list of such objects. A list of
# plotspec instances specifies a page containing multiple plots in separate panes.
plotspecs = {
'TREFHT_ANN_Npole_ALL':
['TREFHT_ANN_Npole_1', 'TREFHT_ANN_Npole_2', 'TREFHT_ANN_Npole_diff'],
'TREFHT_ANN_Npole_1':
plotspec(vid='TREFHT_ANN_Npole_1',
xvars=['TREFHT_ANN_latlon_Npole_1'],
xfunc=lonvar,
yvars=['TREFHT_ANN_latlon_Npole_1'],
yfunc=latvar,
zvars=['TREFHT_ANN_latlon_Npole_1'],
zfunc=(lambda z: z),
plottype='polar contour plot'),
'TREFHT_ANN_Npole_2':
plotspec(vid='TREFHT_ANN_Npole_2',
xvars=['TREFHT_ANN_latlon_Npole_2'],
xfunc=lonvar,
yvars=['TREFHT_ANN_latlon_Npole_2'],
yfunc=latvar,
zvars=['TREFHT_ANN_latlon_Npole_2'],
zfunc=(lambda z: z),
plottype='polar contour plot'),
'TREFHT_ANN_Npole_diff':
plotspec(
vid='TREFHT_ANN_Npole_diff',
xvars=['TREFHT_ANN_latlon_Npole_1', 'TREFHT_ANN_latlon_Npole_2'],
xfunc=lonvar_min,
yvars=['TREFHT_ANN_latlon_Npole_1', 'TREFHT_ANN_latlon_Npole_2'],
yfunc=latvar_min,
zvars=['TREFHT_ANN_latlon_Npole_1', 'TREFHT_ANN_latlon_Npole_2'],
zfunc=aminusb_2ax,
plottype='polar contour plot'),
'TREFHT_DJF_laton_ALL': [
'TREFHT_DJF_latlon_1', 'TREFHT_DJF_latlon_2',
'TREFHT_DJF_latlon_diff'
],
'TREFHT_DJF_latlon_1':
plotspec(vid='TREFHT_DJF_latlon_1',
xvars=['TREFHT_DJF_latlon_1'],
xfunc=lonvar,
yvars=['TREFHT_DJF_latlon_1'],
yfunc=latvar,
zvars=['TREFHT_DJF_latlon_1'],
zfunc=(lambda z: z),
plottype='contour plot'),
'TREFHT_DJF_latlon_2':
plotspec(vid='TREFHT_DJF_latlon_2',
xvars=['TREFHT_DJF_latlon_2'],
xfunc=lonvar,
yvars=['TREFHT_DJF_latlon_2'],
yfunc=latvar,
zvars=['TREFHT_DJF_latlon_2'],
zfunc=(lambda z: z),
plottype='contour plot'),
'TREFHT_DJF_latlon_diff':
plotspec(vid='TREFHT_DJF_latlon_diff',
xvars=['TREFHT_DJF_latlon_1', 'TREFHT_DJF_latlon_2'],
xfunc=lonvar_min,
yvars=['TREFHT_DJF_latlon_1', 'TREFHT_DJF_latlon_2'],
yfunc=latvar_min,
zvars=['TREFHT_DJF_latlon_1', 'TREFHT_DJF_latlon_2'],
zfunc=aminusb_2ax,
plottype='contour_plot'),
'T_ANN_VERT_CAM_OBS_ALL':
['T_VERT_ANN_1', 'T_VERT_ANN_2', 'T_VERT_difference'],
'T_VERT_difference':
plotspec(vid='T_VERT_difference',
xvars=['T_ANN_1', 'T_CAM_ANN_2'],
xfunc=latvar_min,
yvars=['T_ANN_1', 'T_CAM_ANN_2'],
yfunc=levvar_min,
ya1vars=['T_ANN_1', 'T_CAM_ANN_2'],
ya1func=(lambda y1, y2: heightvar(levvar_min(y1, y2))),
zvars=['T_ANN_1', 'T_CAM_ANN_2'],
zfunc=aminusb_ax2,
plottype="contour plot"),
'T_VERT_ANN_2':
plotspec(vid='T_ANN_2',
xvars=['T_CAM_ANN_2'],
xfunc=latvar,
yvars=['T_CAM_ANN_2'],
yfunc=levvar,
ya1vars=['T_CAM_ANN_2'],
ya1func=heightvar,
zvars=['T_CAM_ANN_2'],
plottype='contour plot',
zrangevars=['T_ANN_1', 'T_CAM_ANN_2'],
zrangefunc=minmin_maxmax),
'T_VERT_ANN_1':
plotspec(vid='T_ANN_1',
xvars=['T_ANN_1'],
xfunc=latvar,
yvars=['T_ANN_1'],
yfunc=levvar,
ya1vars=['T_ANN_1'],
ya1func=heightvar,
zvars=['T_ANN_1'],
plottype='contour plot',
zrangevars=['T_ANN_1', 'T_CAM_ANN_2'],
zrangefunc=minmin_maxmax),
'NCEP_OBS_HEAT_TRANSPORT_GLOBAL_2':
plotspec(vid='NCEP_OBS_HEAT_TRANSPORT_GLOBAL_2',
xvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
xfunc=(lambda x: x[0]),
yvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
yfunc=(lambda y: y[1][3]),
plottype='line plot'),
'NCEP_OBS_HEAT_TRANSPORT_PACIFIC_2':
plotspec(vid='NCEP_OBS_HEAT_TRANSPORT_PACIFIC_2',
xvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
xfunc=(lambda x: x[0]),
yvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
yfunc=(lambda y: y[1][0]),
plottype='line plot'),
'NCEP_OBS_HEAT_TRANSPORT_ATLANTIC_2':
plotspec(vid='NCEP_OBS_HEAT_TRANSPORT_ATLANTIC_2',
xvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
xfunc=(lambda x: x[0]),
yvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
yfunc=(lambda y: y[1][1]),
plottype='line plot'),
'NCEP_OBS_HEAT_TRANSPORT_INDIAN_2':
plotspec(vid='NCEP_OBS_HEAT_TRANSPORT_INDIAN_2',
xvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
xfunc=(lambda x: x[0]),
yvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
yfunc=(lambda y: y[1][2]),
plottype='line plot'),
'CAM_HEAT_TRANSPORT_GLOBAL_1':
plotspec(vid='CAM_HEAT_TRANSPORT_GLOBAL_1',
xvars=['FSNS_ANN_latlon_1'],
xfunc=latvar,
yvars=['CAM_HEAT_TRANSPORT_ALL_1'],
yfunc=(lambda y: y[3]),
plottype='line plot'),
'CAM_HEAT_TRANSPORT_PACIFIC_1':
plotspec(vid='CAM_HEAT_TRANSPORT_PACIFIC_1',
xvars=['FSNS_ANN_latlon_1'],
xfunc=latvar,
yvars=['CAM_HEAT_TRANSPORT_ALL_1'],
yfunc=(lambda y: y[0]),
plottype='line plot'),
'CAM_HEAT_TRANSPORT_ATLANTIC_1':
plotspec(vid='CAM_HEAT_TRANSPORT_ATLANTIC_1',
xvars=['FSNS_ANN_latlon_1'],
xfunc=latvar,
yvars=['CAM_HEAT_TRANSPORT_ALL_1'],
yfunc=(lambda y: y[1]),
plottype='line plot'),
'CAM_HEAT_TRANSPORT_INDIAN_1':
plotspec(vid='CAM_HEAT_TRANSPORT_INDIAN_1',
xvars=['FSNS_ANN_latlon_1'],
xfunc=latvar,
yvars=['CAM_HEAT_TRANSPORT_ALL_1'],
yfunc=(lambda y: y[2]),
plottype='line plot'),
'CAM_HEAT_TRANSPORT_ALL_1': [
'CAM_HEAT_TRANSPORT_GLOBAL_1', 'CAM_HEAT_TRANSPORT_PACIFIC_1',
'CAM_HEAT_TRANSPORT_ATLANTIC_1', 'CAM_HEAT_TRANSPORT_INDIAN_1'
],
'CAM_NCEP_HEAT_TRANSPORT_GLOBAL':
plotspec(vid='CAM_NCEP_HEAT_TRANSPORT_GLOBAL',
x1vars=['FSNS_ANN_latlon_1'],
x1func=latvar,
y1vars=['CAM_HEAT_TRANSPORT_ALL_1'],
y1func=(lambda y: y[3]),
x2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
x2func=(lambda x: x[0]),
y2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
y2func=(lambda y: y[1][3]),
plottype='2 line plot'),
'CAM_NCEP_HEAT_TRANSPORT_PACIFIC':
plotspec(vid='CAM_NCEP_HEAT_TRANSPORT_PACIFIC',
x1vars=['FSNS_ANN_latlon_1'],
x1func=latvar,
y1vars=['CAM_HEAT_TRANSPORT_ALL_1'],
y1func=(lambda y: y[0]),
x2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
x2func=(lambda x: x[0]),
y2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
y2func=(lambda y: y[1][0]),
plottype='2 line plot'),
'CAM_NCEP_HEAT_TRANSPORT_ATLANTIC':
plotspec(vid='CAM_NCEP_HEAT_TRANSPORT_ATLANTIC',
x1vars=['FSNS_ANN_latlon_1'],
x1func=latvar,
y1vars=['CAM_HEAT_TRANSPORT_ALL_1'],
y1func=(lambda y: y[0]),
x2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
x2func=(lambda x: x[0]),
y2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
y2func=(lambda y: y[1][1]),
plottype='2 line plot'),
'CAM_NCEP_HEAT_TRANSPORT_INDIAN':
plotspec(vid='CAM_NCEP_HEAT_TRANSPORT_INDIAN',
x1vars=['FSNS_ANN_latlon_1'],
x1func=latvar,
y1vars=['CAM_HEAT_TRANSPORT_ALL_1'],
y1func=(lambda y: y[0]),
x2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
x2func=(lambda x: x[0]),
y2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'],
y2func=(lambda y: y[1][2]),
plottype='2 line plot'),
'CAM_NCEP_HEAT_TRANSPORT_ALL': [
'CAM_NCEP_HEAT_TRANSPORT_GLOBAL',
'CAM_NCEP_HEAT_TRANSPORT_PACIFIC',
'CAM_NCEP_HEAT_TRANSPORT_ATLANTIC',
'CAM_NCEP_HEAT_TRANSPORT_INDIAN'
],
'past_CAM_HEAT_TRANSPORT_GLOBAL_1':
plotspec(vid='CAM_HEAT_TRANSPORT_GLOBAL_1',
xvars=['FSNS_ANN_latlon_1'],
xfunc=latvar,
yvars=[
'FSNS_ANN_latlon_1', 'FLNS_ANN_latlon_1',
'FLUT_ANN_latlon_1', 'FSNTOA_ANN_latlon_1',
'FLNT_ANN_latlon_1', 'FSNT_ANN_latlon_1',
'SHFLX_ANN_latlon_1', 'LHFLX_ANN_latlon_1',
'OCNFRAC_ANN_latlon_1'
],
yfunc=oceanic_heat_transport,
plottype='line plot'),
'PRECT_JJA': ['PRECT_JJA_2line', 'PRECT_JJA_diff'],
'PRECT_JJA_2line':
plotspec(vid='PRECT_JJA_2line',
x1vars=['PRECT_JJA_lat_1'],
x1func=latvar,
x2vars=['PRECT_JJA_lat_2'],
x2func=latvar,
y1vars=['PRECT_JJA_lat_1'],
y1func=(lambda y: y),
y2vars=['PRECT_JJA_lat_2'],
y2func=(lambda y: y),
plottype='2-line plot'),
'PRECT_JJA_diff':
plotspec(
vid='PRECT_JJA_difference',
xvars=['PRECT_JJA_lat_1', 'PRECT_JJA_lat_2'],
xfunc=latvar_min,
yvars=['PRECT_JJA_lat_1', 'PRECT_JJA_lat_2'],
yfunc=
aminusb_1ax, # aminusb_1ax(y1,y2)=y1-y2; each y has 1 axis, use min axis
plottype='line plot'),
'TREFHT_ANN':
plotspec(vid='TREFHT_ANN',
xvars=['TREFHT_ANN_lat_1'],
xfunc=latvar,
yvars=['TREFHT_ANN_lat_1'],
yfunc=(lambda y: y),
plottype='line plot'),
'TREFHT_DJF': ['TREFHT_DJF_2line', 'TREFHT_DJF_diff'],
'TREFHT_DJF_2line':
plotspec(vid='TREFHT_DJF_2line',
x1vars=['TREFHT_DJF_lat_1'],
x1func=latvar,
x2vars=['TREFHT_DJF_lat_2'],
x2func=latvar,
y1vars=['TREFHT_DJF_lat_1'],
y1func=(lambda y: y),
y2vars=['TREFHT_DJF_lat_2'],
y2func=(lambda y: y),
plottype='2-line plot'),
'TREFHT_DJF_diff':
plotspec(
vid='TREFHT_DJF_diff',
xvars=['TREFHT_DJF_lat_1', 'TREFHT_DJF_lat_2'],
xfunc=latvar_min,
yvars=['TREFHT_DJF_lat_1', 'TREFHT_DJF_lat_2'],
yfunc=
aminusb_1ax, # aminusb_1ax(y1,y2)=y1-y2; each y has 1 axis, use min axis
plottype='line plot'),
'TREFHT_DJF_line':
plotspec(vid='TREFHT_DJF_line',
xvars=['TREFHT_DJF_lat_1'],
xfunc=latvar,
yvars=['TREFHT_DJF_lat_1'],
plottype='line plot'),
'TREFHT_DJF_contour':
plotspec(vid='TREFHT_DJF_contour',
xvars=['TREFHT_DJF_latlon_1'],
xfunc=(lambda x: x),
plottype='line plot'),
#plotspec( vid='TREFHT_JJA',xvars=['TREFHT_JJA'], xfiletable=filetable1, xfunc = latvar,
# yvars=['TREFHT_JJA'], yfunc=(lambda y: y), plottype='line plot'),
#plotspec(
# vid='ts_by_lat_old', # suitable for filenames
# xfiletable=filetable1,
# xfunc = latvar, # function to return x axis values
# xvars = ['ts_lat_old'], # names of variables or axes, args of xfunc
# yfiletable=filetable1, # can differ from xfiletable, e.g. comparing 2 runs
# yfunc = (lambda y: y), # function to return y axis values
# yvars = ['ts_lat_old'], # names of variables or axes, args of xfunc
# zfiletable=filetable1,
# zfunc = (lambda: None),
# zvars = [], # would be needed for countour or 3D plot
# # ... the ?vars variable will be converted (using the filetable and
# # plotvars) to actual variables which become the arguments for a call
# # of ?func, which returns the data we write out for plotting use.
# plottype='line plot' ),
'ts_by_lat':
plotspec(
vid='ts_by_lat', # suitable for filenames
xfunc=latvar, # function to return x axis values
xvars=['ts_lat_new'], # names of variables or axes, args of xfunc
yfunc=(lambda y: y), # function to return y axis values
yvars=['ts_lat_new'], # names of variables or axes, args of xfunc
zfunc=(lambda: None),
zvars=[], # would be needed for countour or 3D plot
# ... the ?vars variable will be converted (using the filetable and
# plotvars) to actual variables which become the arguments for a call
# of ?func, which returns the data we write out for plotting use.
plottype='line plot'),
#plotspec( vid="ts_global_old",xvars=['ts_scalar_tropical_o'], xfiletable=filetable1 ),
'ts_global':
plotspec(vid="ts_global", xvars=['ts_scalar_tropical_n']),
}
# Plotspeckeys specifies what plot data we will compute and write out.
# In the future we may add a command line option, or provide other ways to
# define plotspeckeys.
#plotspeckeys = [['TREFHT_DJF_2line','TREFHT_DJF_difference']]
#plotspeckeys = ['TREFHT_DJF_2line']
#plotspeckeys = ['NCEP_OBS_HEAT_TRANSPORT_GLOBAL_2','CAM_HEAT_TRANSPORT_ALL_1']
#plotspeckeys = ['CAM_NCEP_HEAT_TRANSPORT_GLOBAL']
#plotspeckeys = ['CAM_NCEP_HEAT_TRANSPORT_ALL']
#plotspeckeys = ['T_ANN_VERT_CAM_OBS_ALL']
#plotspeckeys = ['TREFHT_DJF_laton_ALL']
#plotspeckeys = ['TREFHT_ANN_Npole_ALL']
plotspeckeys = ['TREFHT_DJF']
#plotspeckeys = ['GLOBAL_AVERAGES']
# Find the variable names required by the plotspecs.
varkeys = []
for psk in plotspeckeys:
if type(psk) is str and type(plotspecs[psk]) is list:
psk = plotspecs[psk]
if type(psk) is str:
write_xml = False
psl = [plotspecs[psk]]
else:
write_xml = True
psl = [plotspecs[k] for k in psk]
xml_name = '_'.join([ps._strid for ps in psl]) + '.xml'
h = open(xml_name, 'w')
h.write("<plotdata>\n")
for ps in psl:
varkeys = varkeys + ps.xvars + ps.x1vars + ps.x2vars + ps.x3vars
varkeys = varkeys + ps.yvars + ps.y1vars + ps.y2vars + ps.y3vars
varkeys = varkeys + ps.zvars + ps.zrangevars
for key in varkeys:
if key in derived_variables.keys():
varkeys = varkeys + derived_variables[key]._inputs
varkeys = list(set(varkeys))
# Compute the value of every variable we need.
varvals = {}
# First compute all the reduced variables
for key in varkeys:
if key in reduced_variables.keys():
varvals[key] = reduced_variables[key].reduce()
# Then use the reduced variables to compute the derived variables
# Note that the derive() method is allowed to return a tuple. This way
# we can use one function to compute what's really several variables.
for key in varkeys:
if key in derived_variables.keys():
varvals[key] = derived_variables[key].derive(varvals)
# Now use the reduced and derived variables to compute the plot data.
for psk in plotspeckeys:
if type(psk) is str and type(plotspecs[psk]) is list:
psk = plotspecs[psk]
if type(psk) is str:
write_xml = False
psl = [plotspecs[psk]]
else:
write_xml = True
psl = [plotspecs[k] for k in psk]
xml_name = '_'.join([ps._strid for ps in psl]) + '.xml'
h = open(xml_name, 'w')
h.write("<plotdata>\n")
varkeys = []
for ps in psl:
print "jfp preparing data for", ps._strid
xrv = [varvals[k] for k in ps.xvars]
x1rv = [varvals[k] for k in ps.x1vars]
x2rv = [varvals[k] for k in ps.x2vars]
x3rv = [varvals[k] for k in ps.x3vars]
yrv = [varvals[k] for k in ps.yvars]
y1rv = [varvals[k] for k in ps.y1vars]
y2rv = [varvals[k] for k in ps.y2vars]
y3rv = [varvals[k] for k in ps.y3vars]
yarv = [varvals[k] for k in ps.yavars]
ya1rv = [varvals[k] for k in ps.ya1vars]
zrv = [varvals[k] for k in ps.zvars]
zrrv = [varvals[k] for k in ps.zrangevars]
xax = apply(ps.xfunc, xrv)
x1ax = apply(ps.x1func, x1rv)
x2ax = apply(ps.x2func, x2rv)
x3ax = apply(ps.x3func, x3rv)
yax = apply(ps.yfunc, yrv)
y1ax = apply(ps.y1func, y1rv)
y2ax = apply(ps.y2func, y2rv)
y3ax = apply(ps.y3func, y3rv)
yaax = apply(ps.yafunc, yarv)
ya1ax = apply(ps.ya1func, ya1rv)
zax = apply(ps.zfunc, zrv)
zr = apply(ps.zrangefunc, zrrv)
if (xax is None or len(xrv)==0) and (x1ax is None or len(x1rv)==0)\
and (x2ax is None or len(x2rv)==0) and (x3ax is None or len(x3rv)==0)\
and (yax is None or len(yrv)==0) and (y1ax is None or len(y1rv)==0)\
and (y2ax is None or len(y2rv)==0) and (y3ax is None or len(y3rv)==0)\
and (zax is None or len(zrv)==0):
continue
filename = ps._strid + "_test.nc"
value = 0
cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateLevelFlag(
value) ## where value is a integer between 0 and 9 included
g = cdms2.open(filename,
'w') # later, choose a better name and a path!
store_provenance(g)
# Much more belongs in g, e.g. axis and graph names.
if xax is not None and len(xrv) > 0:
xax.id = 'X'
g.write(xax)
if x1ax is not None and len(x1rv) > 0:
x1ax.id = 'X1'
g.write(x1ax)
if x2ax is not None and len(x2rv) > 0:
x2ax.id = 'X2'
g.write(x2ax)
if x3ax is not None and len(x3rv) > 0:
x3ax.id = 'X3'
g.write(x3ax)
if yax is not None and len(yrv) > 0:
yax.id = 'Y'
g.write(yax)
if y1ax is not None and len(y1rv) > 0:
y1ax.id = 'Y1'
g.write(y1ax)
if y2ax is not None and len(y2rv) > 0:
y2ax.id = 'Y2'
g.write(y2ax)
if y3ax is not None and len(y3rv) > 0:
y3ax.id = 'Y3'
g.write(y3ax)
if yaax is not None and len(yarv) > 0:
yaax.id = 'YA'
g.write(yaax)
if ya1ax is not None and len(ya1rv) > 0:
ya1ax.id = 'YA1'
g.write(ya1ax)
if zax is not None and len(zrv) > 0:
zax.id = 'Z'
g.write(zax)
if zr is not None:
zr.id = 'Zrange'
g.write(zr)
g.presentation = ps.plottype
# Note: For table output, it would be convenient to use a string-valued variable X
# to specify string parts of the table. Butcdms2 doesn't support them usefully.
# Instead we'll manage with a convention that a table row plotspec's id is the name of
# the row, thus available to be printed in, e.g., the first column.
if ps.plottype == "table row":
g.rowid = ps._strid
g.close()
if write_xml:
h.write("<ncfile>" + filename + "</ncfile>\n")
if write_xml:
h.write("</plotdata>\n")
h.close()
def test_driver( path1, path2=None, filt2=None ):
""" Test driver for setting up data for plots"""
# First, find and index the data files.
datafiles1 = dirtree_datafiles( path1 )
logger.info("jfp datafiles1=%s",datafiles1)
datafiles2 = dirtree_datafiles( path2, filt2 )
logger.info("jfp datafiles2=%s",datafiles2)
filetable1 = basic_filetable( datafiles1 )
filetable2 = basic_filetable( datafiles2 )
# Next we'll compute reduced variables. They have generally been reduced by averaging in time,
# and often more axes as well. Reducing the data first is the fastest way to compute, important
# if we need to be interactive. And it is correct if whatever we plot is linear in the
# variables, as is almost always the case. But if we want to plot a highly nonlinear function
# of the data variables, the averaging will have to wait until later.
# The reduced_variables dict names and contains all the reduced variables which we have defined.
# They will be used in defining instances of plotspec.
reduced_variables = {
'hyam_1': reduced_variable(
variableid='hyam', filetable=filetable1,
reduction_function=(lambda x,vid=None: x) ),
'hybm_1': reduced_variable(
variableid='hybm', filetable=filetable1,
reduction_function=(lambda x,vid=None: x) ),
'PS_ANN_1': reduced_variable(
variableid='PS', filetable=filetable1,
reduction_function=reduce2lat ),
'T_CAM_ANN_1': reduced_variable(
variableid='T', filetable=filetable1,
reduction_function=reduce2levlat ),
'T_CAM_ANN_2': reduced_variable(
variableid='T', filetable=filetable2,
reduction_function=reduce2levlat ),
'TREFHT_ANN_latlon_Npole_1': reduced_variable(
variableid='TREFHT', filetable=filetable1,
reduction_function=(lambda x,vid=None: restrict_lat(reduce2latlon(x,vid=vid),50,90)) ),
'TREFHT_ANN_latlon_Npole_2': reduced_variable(
variableid='TREFHT', filetable=filetable2,
reduction_function=(lambda x,vid=None: restrict_lat(reduce2latlon(x,vid=vid),50,90)) ),
'TREFHT_ANN_lat_1': reduced_variable(
variableid='TREFHT', filetable=filetable1,
reduction_function=reduce2lat ),
'TREFHT_DJF_lat_1': reduced_variable(
variableid='TREFHT',
filetable=filetable1,
reduction_function=(lambda x,vid=None: reduce2lat_seasonal(x,seasonsDJF,vid=vid)) ),
'TREFHT_DJF_lat_2': reduced_variable(
variableid='TREFHT',
filetable=filetable2,
reduction_function=(lambda x,vid=None: reduce2lat_seasonal(x,seasonsDJF,vid=vid)) ),
'TREFHT_DJF_latlon_1': reduced_variable(
variableid='TREFHT',
filetable=filetable1,
reduction_function=(lambda x,vid=None: reduce2latlon_seasonal(x,seasonsDJF,vid=vid)) ),
'TREFHT_DJF_latlon_2': reduced_variable(
variableid='TREFHT',
filetable=filetable2,
reduction_function=(lambda x,vid=None: reduce2latlon_seasonal(x,seasonsDJF,vid=vid)) ),
'TREFHT_JJA': reduced_variable(
variableid='TREFHT',
filetable=filetable1,
reduction_function=(lambda x,vid=None: reduce2lat_seasonal(x,seasonsJJA,vid=vid)) ),
'PRECT_JJA_lat_1': reduced_variable(
variableid='PRECT',
filetable=filetable1,
reduction_function=(lambda x,vid=None: reduce2lat_seasonal(x,seasonsJJA,vid=vid)) ),
'PRECT_JJA_lat_2': reduced_variable(
variableid='PRECT',
filetable=filetable2,
reduction_function=(lambda x,vid=None: reduce2lat_seasonal(x,seasonsJJA,vid=vid)) ),
# CAM variables needed for heat transport:
# FSNS, FLNS, FLUT, FSNTOA, FLNT, FSNT, SHFLX, LHFLX,
'FSNS_1': reduced_variable(
variableid='FSNS',filetable=filetable1,reduction_function=(lambda x,vid:x) ),
'FSNS_ANN_latlon_1': reduced_variable(
variableid='FSNS',
filetable=filetable1,
reduction_function=reduce2latlon ),
'FLNS_1': reduced_variable(
variableid='FLNS',filetable=filetable1,reduction_function=(lambda x,vid:x) ),
'FLNS_ANN_latlon_1': reduced_variable(
variableid='FLNS',
filetable=filetable1,
reduction_function=reduce2latlon ),
'FLUT_ANN_latlon_1': reduced_variable(
variableid='FLUT',
filetable=filetable1,
reduction_function=reduce2latlon ),
'FSNTOA_ANN_latlon_1': reduced_variable(
variableid='FSNTOA',
filetable=filetable1,
reduction_function=reduce2latlon ),
'FLNT_1': reduced_variable(
variableid='FLNT',filetable=filetable1,reduction_function=(lambda x,vid:x) ),
'FLNT_ANN_latlon_1': reduced_variable(
variableid='FLNT',
filetable=filetable1,
reduction_function=reduce2latlon ),
'FSNT_1': reduced_variable(
variableid='FSNT',filetable=filetable1,reduction_function=(lambda x,vid:x) ),
'FSNT_ANN_latlon_1': reduced_variable(
variableid='FSNT',
filetable=filetable1,
reduction_function=reduce2latlon ),
'QFLX_1': reduced_variable(
variableid='QFLX',filetable=filetable1,reduction_function=(lambda x,vid:x) ),
'SHFLX_1': reduced_variable(
variableid='SHFLX',filetable=filetable1,reduction_function=(lambda x,vid:x) ),
'SHFLX_ANN_latlon_1': reduced_variable(
variableid='SHFLX',
filetable=filetable1,
reduction_function=reduce2latlon ),
'LHFLX_ANN_latlon_1': reduced_variable(
variableid='LHFLX',
filetable=filetable1,
reduction_function=reduce2latlon ),
'ORO_ANN_latlon_1': reduced_variable(
variableid='ORO',
filetable=filetable1,
reduction_function=reduce2latlon ),
'OCNFRAC_ANN_latlon_1': reduced_variable(
variableid='OCNFRAC',
filetable=filetable1,
reduction_function=reduce2latlon ),
'ts_lat_old': reduced_variable(
variableid='surface_temperature', # normally a CF standard_name, even for non-CF data.
filetable=filetable1,
reduction_function=reduce2lat_old ),
'ts_lat_new': reduced_variable(
variableid='surface_temperature', # normally a CF standard_name, even for non-CF data.
filetable=filetable1,
reduction_function=reduce2lat
# The reduction function will take just one argument, a variable (MV). But it might
# be expressed here as a lambda wrapping a more general function.
# Often there will be ranges in time, space, etc. specified here. No range means
# everything.
),
'ts_scalar_tropical_o': reduced_variable(
variableid = 'surface_temperature',
filetable=filetable1,
reduction_function=(lambda mv,vid=None: reduce2scalar_zonal_old(mv,-20,20,vid=vid))
),
'ts_scalar_tropical_n': reduced_variable(
variableid = 'surface_temperature',
filetable=filetable1,
reduction_function=(lambda mv,vid=None: reduce2scalar_zonal(mv,-20,20,vid=vid))
)
}
# Derived variables have to be treated separately from reduced variables
# because derived variables generally depend on reduced variables.
# But N.B.: the dicts reduced_variables and derived_variables
# must never use the same key!
derived_variables = {
'CAM_HEAT_TRANSPORT_ALL_1': derived_var(
vid='CAM_HEAT_TRANSPORT_ALL_1',
inputs=['FSNS_ANN_latlon_1', 'FLNS_ANN_latlon_1', 'FLUT_ANN_latlon_1',
'FSNTOA_ANN_latlon_1', 'FLNT_ANN_latlon_1', 'FSNT_ANN_latlon_1',
'SHFLX_ANN_latlon_1', 'LHFLX_ANN_latlon_1', 'OCNFRAC_ANN_latlon_1' ],
outputs=['atlantic_heat_transport','pacific_heat_transport',
'indian_heat_transport', 'global_heat_transport' ],
func=oceanic_heat_transport ),
'NCEP_OBS_HEAT_TRANSPORT_ALL_2': derived_var(
vid='NCEP_OBS_HEAT_TRANSPORT_ALL_2',
inputs=[],
outputs=('latitude', ['atlantic_heat_transport','pacific_heat_transport',
'indian_heat_transport', 'global_heat_transport' ]),
func=(lambda: ncep_ocean_heat_transport(path2) ) ),
'T_ANN_1': derived_var(
vid='T_ANN_1', inputs=['T_CAM_ANN_1', 'hyam_1', 'hybm_1', 'PS_ANN_1', 'T_CAM_ANN_2'],
outputs=('temperature'),
func=verticalize )
}
plotvars = dict( reduced_variables.items() + derived_variables.items() )
# The plotvspecs dict names and contains all plotspec objects which we have defined.
# The plotspeckeys variable, below, names the ones for which we will generate output.
# A dict value can be a plotspec object, or a list of such objects. A list of
# plotspec instances specifies a page containing multiple plots in separate panes.
plotspecs = {
'TREFHT_ANN_Npole_ALL':
['TREFHT_ANN_Npole_1', 'TREFHT_ANN_Npole_2', 'TREFHT_ANN_Npole_diff'],
'TREFHT_ANN_Npole_1': plotspec(
vid='TREFHT_ANN_Npole_1',
xvars=['TREFHT_ANN_latlon_Npole_1'], xfunc = lonvar,
yvars=['TREFHT_ANN_latlon_Npole_1'], yfunc = latvar,
zvars=['TREFHT_ANN_latlon_Npole_1'], zfunc = (lambda z: z),
plottype='polar contour plot' ),
'TREFHT_ANN_Npole_2': plotspec(
vid='TREFHT_ANN_Npole_2',
xvars=['TREFHT_ANN_latlon_Npole_2'], xfunc = lonvar,
yvars=['TREFHT_ANN_latlon_Npole_2'], yfunc = latvar,
zvars=['TREFHT_ANN_latlon_Npole_2'], zfunc = (lambda z: z),
plottype='polar contour plot' ),
'TREFHT_ANN_Npole_diff': plotspec(
vid='TREFHT_ANN_Npole_diff',
xvars=['TREFHT_ANN_latlon_Npole_1','TREFHT_ANN_latlon_Npole_2'], xfunc = lonvar_min,
yvars=['TREFHT_ANN_latlon_Npole_1','TREFHT_ANN_latlon_Npole_2'], yfunc = latvar_min,
zvars=['TREFHT_ANN_latlon_Npole_1','TREFHT_ANN_latlon_Npole_2'], zfunc = aminusb_2ax,
plottype='polar contour plot' ),
'TREFHT_DJF_laton_ALL':
['TREFHT_DJF_latlon_1', 'TREFHT_DJF_latlon_2', 'TREFHT_DJF_latlon_diff'],
'TREFHT_DJF_latlon_1': plotspec(
vid='TREFHT_DJF_latlon_1',
xvars=['TREFHT_DJF_latlon_1'], xfunc = lonvar,
yvars=['TREFHT_DJF_latlon_1'], yfunc = latvar,
zvars=['TREFHT_DJF_latlon_1'], zfunc = (lambda z: z),
plottype='contour plot' ),
'TREFHT_DJF_latlon_2': plotspec(
vid='TREFHT_DJF_latlon_2',
xvars=['TREFHT_DJF_latlon_2'], xfunc = lonvar,
yvars=['TREFHT_DJF_latlon_2'], yfunc = latvar,
zvars=['TREFHT_DJF_latlon_2'], zfunc = (lambda z: z),
plottype='contour plot' ),
'TREFHT_DJF_latlon_diff': plotspec(
vid='TREFHT_DJF_latlon_diff',
xvars=['TREFHT_DJF_latlon_1','TREFHT_DJF_latlon_2'], xfunc=lonvar_min,
yvars=['TREFHT_DJF_latlon_1','TREFHT_DJF_latlon_2'], yfunc=latvar_min,
zvars=['TREFHT_DJF_latlon_1','TREFHT_DJF_latlon_2'], zfunc= aminusb_2ax,
plottype='contour_plot'
),
'T_ANN_VERT_CAM_OBS_ALL':
['T_VERT_ANN_1', 'T_VERT_ANN_2', 'T_VERT_difference' ],
'T_VERT_difference': plotspec(
vid='T_VERT_difference', xvars=['T_ANN_1','T_CAM_ANN_2'], xfunc = latvar_min,
yvars=['T_ANN_1','T_CAM_ANN_2'], yfunc = levvar_min,
ya1vars=['T_ANN_1','T_CAM_ANN_2'], ya1func = (lambda y1,y2: heightvar(levvar_min(y1,y2))),
zvars=['T_ANN_1','T_CAM_ANN_2'], zfunc=aminusb_ax2, plottype="contour plot" ),
'T_VERT_ANN_2': plotspec(
vid='T_ANN_2', xvars=['T_CAM_ANN_2'], xfunc=latvar,
yvars=['T_CAM_ANN_2'], yfunc=levvar, ya1vars=['T_CAM_ANN_2'], ya1func=heightvar,
zvars=['T_CAM_ANN_2'], plottype='contour plot',
zrangevars=['T_ANN_1','T_CAM_ANN_2'], zrangefunc=minmin_maxmax ),
'T_VERT_ANN_1': plotspec(
vid='T_ANN_1', xvars=['T_ANN_1'], xfunc=latvar,
yvars=['T_ANN_1'], yfunc=levvar, ya1vars=['T_ANN_1'], ya1func=heightvar,
zvars=['T_ANN_1'], plottype='contour plot',
zrangevars=['T_ANN_1','T_CAM_ANN_2'], zrangefunc=minmin_maxmax ),
'NCEP_OBS_HEAT_TRANSPORT_GLOBAL_2': plotspec(
vid='NCEP_OBS_HEAT_TRANSPORT_GLOBAL_2',
xvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'], xfunc=(lambda x: x[0]),
yvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2' ],
yfunc=(lambda y: y[1][3]), plottype='line plot'),
'NCEP_OBS_HEAT_TRANSPORT_PACIFIC_2': plotspec(
vid='NCEP_OBS_HEAT_TRANSPORT_PACIFIC_2',
xvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'], xfunc=(lambda x: x[0]),
yvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2' ],
yfunc=(lambda y: y[1][0]), plottype='line plot'),
'NCEP_OBS_HEAT_TRANSPORT_ATLANTIC_2': plotspec(
vid='NCEP_OBS_HEAT_TRANSPORT_ATLANTIC_2',
xvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'], xfunc=(lambda x: x[0]),
yvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2' ],
yfunc=(lambda y: y[1][1]), plottype='line plot'),
'NCEP_OBS_HEAT_TRANSPORT_INDIAN_2': plotspec(
vid='NCEP_OBS_HEAT_TRANSPORT_INDIAN_2',
xvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'], xfunc=(lambda x: x[0]),
yvars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2' ],
yfunc=(lambda y: y[1][2]), plottype='line plot'),
'CAM_HEAT_TRANSPORT_GLOBAL_1': plotspec(
vid='CAM_HEAT_TRANSPORT_GLOBAL_1',
xvars=['FSNS_ANN_latlon_1'], xfunc=latvar,
yvars=['CAM_HEAT_TRANSPORT_ALL_1' ],
yfunc=(lambda y: y[3]), plottype='line plot'),
'CAM_HEAT_TRANSPORT_PACIFIC_1': plotspec(
vid='CAM_HEAT_TRANSPORT_PACIFIC_1',
xvars=['FSNS_ANN_latlon_1'], xfunc=latvar,
yvars=['CAM_HEAT_TRANSPORT_ALL_1' ],
yfunc=(lambda y: y[0]), plottype='line plot'),
'CAM_HEAT_TRANSPORT_ATLANTIC_1': plotspec(
vid='CAM_HEAT_TRANSPORT_ATLANTIC_1',
xvars=['FSNS_ANN_latlon_1'], xfunc=latvar,
yvars=['CAM_HEAT_TRANSPORT_ALL_1' ],
yfunc=(lambda y: y[1]), plottype='line plot'),
'CAM_HEAT_TRANSPORT_INDIAN_1': plotspec(
vid='CAM_HEAT_TRANSPORT_INDIAN_1',
xvars=['FSNS_ANN_latlon_1'], xfunc=latvar,
yvars=['CAM_HEAT_TRANSPORT_ALL_1' ],
yfunc=(lambda y: y[2]), plottype='line plot'),
'CAM_HEAT_TRANSPORT_ALL_1':
['CAM_HEAT_TRANSPORT_GLOBAL_1','CAM_HEAT_TRANSPORT_PACIFIC_1',
'CAM_HEAT_TRANSPORT_ATLANTIC_1','CAM_HEAT_TRANSPORT_INDIAN_1'],
'CAM_NCEP_HEAT_TRANSPORT_GLOBAL': plotspec(
vid='CAM_NCEP_HEAT_TRANSPORT_GLOBAL',
x1vars=['FSNS_ANN_latlon_1'], x1func=latvar,
y1vars=['CAM_HEAT_TRANSPORT_ALL_1' ],
y1func=(lambda y: y[3]),
x2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'], x2func=(lambda x: x[0]),
y2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2' ],
y2func=(lambda y: y[1][3]),
plottype='2 line plot' ),
'CAM_NCEP_HEAT_TRANSPORT_PACIFIC': plotspec(
vid='CAM_NCEP_HEAT_TRANSPORT_PACIFIC',
x1vars=['FSNS_ANN_latlon_1'], x1func=latvar,
y1vars=['CAM_HEAT_TRANSPORT_ALL_1' ],
y1func=(lambda y: y[0]),
x2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'], x2func=(lambda x: x[0]),
y2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2' ],
y2func=(lambda y: y[1][0]),
plottype='2 line plot' ),
'CAM_NCEP_HEAT_TRANSPORT_ATLANTIC': plotspec(
vid='CAM_NCEP_HEAT_TRANSPORT_ATLANTIC',
x1vars=['FSNS_ANN_latlon_1'], x1func=latvar,
y1vars=['CAM_HEAT_TRANSPORT_ALL_1' ],
y1func=(lambda y: y[0]),
x2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'], x2func=(lambda x: x[0]),
y2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2' ],
y2func=(lambda y: y[1][1]),
plottype='2 line plot' ),
'CAM_NCEP_HEAT_TRANSPORT_INDIAN': plotspec(
vid='CAM_NCEP_HEAT_TRANSPORT_INDIAN',
x1vars=['FSNS_ANN_latlon_1'], x1func=latvar,
y1vars=['CAM_HEAT_TRANSPORT_ALL_1' ],
y1func=(lambda y: y[0]),
x2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2'], x2func=(lambda x: x[0]),
y2vars=['NCEP_OBS_HEAT_TRANSPORT_ALL_2' ],
y2func=(lambda y: y[1][2]),
plottype='2 line plot' ),
'CAM_NCEP_HEAT_TRANSPORT_ALL':
['CAM_NCEP_HEAT_TRANSPORT_GLOBAL','CAM_NCEP_HEAT_TRANSPORT_PACIFIC',
'CAM_NCEP_HEAT_TRANSPORT_ATLANTIC','CAM_NCEP_HEAT_TRANSPORT_INDIAN'],
'past_CAM_HEAT_TRANSPORT_GLOBAL_1': plotspec(
vid='CAM_HEAT_TRANSPORT_GLOBAL_1',
xvars=['FSNS_ANN_latlon_1'], xfunc=latvar,
yvars=['FSNS_ANN_latlon_1', 'FLNS_ANN_latlon_1', 'FLUT_ANN_latlon_1',
'FSNTOA_ANN_latlon_1', 'FLNT_ANN_latlon_1', 'FSNT_ANN_latlon_1',
'SHFLX_ANN_latlon_1', 'LHFLX_ANN_latlon_1', 'OCNFRAC_ANN_latlon_1' ],
yfunc=oceanic_heat_transport, plottype='line plot'),
'PRECT_JJA': ['PRECT_JJA_2line','PRECT_JJA_diff'],
'PRECT_JJA_2line': plotspec(
vid='PRECT_JJA_2line',
x1vars=['PRECT_JJA_lat_1'], x1func = latvar,
x2vars=['PRECT_JJA_lat_2'], x2func = latvar,
y1vars=['PRECT_JJA_lat_1'], y1func=(lambda y: y),
y2vars=['PRECT_JJA_lat_2'], y2func=(lambda y: y),
plottype='2-line plot'),
'PRECT_JJA_diff': plotspec(
vid='PRECT_JJA_difference',
xvars=['PRECT_JJA_lat_1','PRECT_JJA_lat_2'], xfunc = latvar_min,
yvars=['PRECT_JJA_lat_1','PRECT_JJA_lat_2'],
yfunc=aminusb_1ax, # aminusb_1ax(y1,y2)=y1-y2; each y has 1 axis, use min axis
plottype='line plot'),
'TREFHT_ANN': plotspec(
vid='TREFHT_ANN',xvars=['TREFHT_ANN_lat_1'], xfunc = latvar,
yvars=['TREFHT_ANN_lat_1'], yfunc=(lambda y: y), plottype='line plot'),
'TREFHT_DJF': ['TREFHT_DJF_2line','TREFHT_DJF_diff'],
'TREFHT_DJF_2line': plotspec(
vid='TREFHT_DJF_2line',
x1vars=['TREFHT_DJF_lat_1'], x1func = latvar,
x2vars=['TREFHT_DJF_lat_2'], x2func = latvar,
y1vars=['TREFHT_DJF_lat_1'], y1func=(lambda y: y),
y2vars=['TREFHT_DJF_lat_2'], y2func=(lambda y: y),
plottype='2-line plot'),
'TREFHT_DJF_diff': plotspec(
vid='TREFHT_DJF_diff',
xvars=['TREFHT_DJF_lat_1','TREFHT_DJF_lat_2'], xfunc = latvar_min,
yvars=['TREFHT_DJF_lat_1','TREFHT_DJF_lat_2'],
yfunc=aminusb_1ax, # aminusb_1ax(y1,y2)=y1-y2; each y has 1 axis, use min axis
plottype='line plot'),
'TREFHT_DJF_line': plotspec(
vid='TREFHT_DJF_line',
xvars=['TREFHT_DJF_lat_1'], xfunc = latvar,
yvars=['TREFHT_DJF_lat_1'], plottype='line plot'),
'TREFHT_DJF_contour': plotspec(
vid='TREFHT_DJF_contour',
xvars=['TREFHT_DJF_latlon_1'], xfunc = (lambda x: x),
plottype='line plot'),
#plotspec( vid='TREFHT_JJA',xvars=['TREFHT_JJA'], xfiletable=filetable1, xfunc = latvar,
# yvars=['TREFHT_JJA'], yfunc=(lambda y: y), plottype='line plot'),
#plotspec(
# vid='ts_by_lat_old', # suitable for filenames
# xfiletable=filetable1,
# xfunc = latvar, # function to return x axis values
# xvars = ['ts_lat_old'], # names of variables or axes, args of xfunc
# yfiletable=filetable1, # can differ from xfiletable, e.g. comparing 2 runs
# yfunc = (lambda y: y), # function to return y axis values
# yvars = ['ts_lat_old'], # names of variables or axes, args of xfunc
# zfiletable=filetable1,
# zfunc = (lambda: None),
# zvars = [], # would be needed for countour or 3D plot
# # ... the ?vars variable will be converted (using the filetable and
# # plotvars) to actual variables which become the arguments for a call
# # of ?func, which returns the data we write out for plotting use.
# plottype='line plot' ),
'ts_by_lat': plotspec(
vid='ts_by_lat', # suitable for filenames
xfunc = latvar, # function to return x axis values
xvars = ['ts_lat_new'], # names of variables or axes, args of xfunc
yfunc = (lambda y: y), # function to return y axis values
yvars = ['ts_lat_new'], # names of variables or axes, args of xfunc
zfunc = (lambda: None),
zvars = [], # would be needed for countour or 3D plot
# ... the ?vars variable will be converted (using the filetable and
# plotvars) to actual variables which become the arguments for a call
# of ?func, which returns the data we write out for plotting use.
plottype='line plot' ),
#plotspec( vid="ts_global_old",xvars=['ts_scalar_tropical_o'], xfiletable=filetable1 ),
'ts_global': plotspec( vid="ts_global",xvars=['ts_scalar_tropical_n'] ),
}
# Plotspeckeys specifies what plot data we will compute and write out.
# In the future we may add a command line option, or provide other ways to
# define plotspeckeys.
#plotspeckeys = [['TREFHT_DJF_2line','TREFHT_DJF_difference']]
#plotspeckeys = ['TREFHT_DJF_2line']
#plotspeckeys = ['NCEP_OBS_HEAT_TRANSPORT_GLOBAL_2','CAM_HEAT_TRANSPORT_ALL_1']
#plotspeckeys = ['CAM_NCEP_HEAT_TRANSPORT_GLOBAL']
#plotspeckeys = ['CAM_NCEP_HEAT_TRANSPORT_ALL']
#plotspeckeys = ['T_ANN_VERT_CAM_OBS_ALL']
#plotspeckeys = ['TREFHT_DJF_laton_ALL']
#plotspeckeys = ['TREFHT_ANN_Npole_ALL']
plotspeckeys = ['TREFHT_DJF']
#plotspeckeys = ['GLOBAL_AVERAGES']
# Find the variable names required by the plotspecs.
varkeys = []
for psk in plotspeckeys:
if type(psk) is str and type(plotspecs[psk]) is list:
psk = plotspecs[psk]
if type(psk) is str:
write_xml = False
psl = [ plotspecs[psk] ]
else:
write_xml = True
psl = [ plotspecs[k] for k in psk ]
xml_name = '_'.join( [ ps._strid for ps in psl ] ) +'.xml'
h = open( xml_name, 'w' )
h.write("<plotdata>\n")
for ps in psl:
varkeys = varkeys+ps.xvars+ps.x1vars+ps.x2vars+ps.x3vars
varkeys = varkeys+ps.yvars+ps.y1vars+ps.y2vars+ps.y3vars
varkeys = varkeys + ps.zvars + ps.zrangevars
for key in varkeys:
if key in derived_variables.keys():
varkeys = varkeys + derived_variables[key]._inputs
varkeys = list( set(varkeys) )
# Compute the value of every variable we need.
varvals = {}
# First compute all the reduced variables
for key in varkeys:
if key in reduced_variables.keys():
varvals[key] = reduced_variables[key].reduce()
# Then use the reduced variables to compute the derived variables
# Note that the derive() method is allowed to return a tuple. This way
# we can use one function to compute what's really several variables.
for key in varkeys:
if key in derived_variables.keys():
varvals[key] = derived_variables[key].derive(varvals)
# Now use the reduced and derived variables to compute the plot data.
for psk in plotspeckeys:
if type(psk) is str and type(plotspecs[psk]) is list:
psk = plotspecs[psk]
if type(psk) is str:
write_xml = False
psl = [ plotspecs[psk] ]
else:
write_xml = True
psl = [ plotspecs[k] for k in psk ]
xml_name = '_'.join( [ ps._strid for ps in psl ] ) +'.xml'
h = open( xml_name, 'w' )
h.write("<plotdata>\n")
varkeys = []
for ps in psl:
logger.info("jfp preparing data for %s", ps._strid)
xrv = [ varvals[k] for k in ps.xvars ]
x1rv = [ varvals[k] for k in ps.x1vars ]
x2rv = [ varvals[k] for k in ps.x2vars ]
x3rv = [ varvals[k] for k in ps.x3vars ]
yrv = [ varvals[k] for k in ps.yvars ]
y1rv = [ varvals[k] for k in ps.y1vars ]
y2rv = [ varvals[k] for k in ps.y2vars ]
y3rv = [ varvals[k] for k in ps.y3vars ]
yarv = [ varvals[k] for k in ps.yavars ]
ya1rv = [ varvals[k] for k in ps.ya1vars ]
zrv = [ varvals[k] for k in ps.zvars ]
zrrv = [ varvals[k] for k in ps.zrangevars ]
xax = apply( ps.xfunc, xrv )
x1ax = apply( ps.x1func, x1rv )
x2ax = apply( ps.x2func, x2rv )
x3ax = apply( ps.x3func, x3rv )
yax = apply( ps.yfunc, yrv )
y1ax = apply( ps.y1func, y1rv )
y2ax = apply( ps.y2func, y2rv )
y3ax = apply( ps.y3func, y3rv )
yaax = apply( ps.yafunc, yarv )
ya1ax = apply( ps.ya1func, ya1rv )
zax = apply( ps.zfunc, zrv )
zr = apply( ps.zrangefunc, zrrv )
if (xax is None or len(xrv)==0) and (x1ax is None or len(x1rv)==0)\
and (x2ax is None or len(x2rv)==0) and (x3ax is None or len(x3rv)==0)\
and (yax is None or len(yrv)==0) and (y1ax is None or len(y1rv)==0)\
and (y2ax is None or len(y2rv)==0) and (y3ax is None or len(y3rv)==0)\
and (zax is None or len(zrv)==0):
continue
filename = ps._strid+"_test.nc"
value=0
cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateLevelFlag(value) ## where value is a integer between 0 and 9 included
g = cdms2.open( filename, 'w' ) # later, choose a better name and a path!
store_provenance(g)
# Much more belongs in g, e.g. axis and graph names.
if xax is not None and len(xrv)>0:
xax.id = 'X'
g.write(xax)
if x1ax is not None and len(x1rv)>0:
x1ax.id = 'X1'
g.write(x1ax)
if x2ax is not None and len(x2rv)>0:
x2ax.id = 'X2'
g.write(x2ax)
if x3ax is not None and len(x3rv)>0:
x3ax.id = 'X3'
g.write(x3ax)
if yax is not None and len(yrv)>0:
yax.id = 'Y'
g.write(yax)
if y1ax is not None and len(y1rv)>0:
y1ax.id = 'Y1'
g.write(y1ax)
if y2ax is not None and len(y2rv)>0:
y2ax.id = 'Y2'
g.write(y2ax)
if y3ax is not None and len(y3rv)>0:
y3ax.id = 'Y3'
g.write(y3ax)
if yaax is not None and len(yarv)>0:
yaax.id = 'YA'
g.write(yaax)
if ya1ax is not None and len(ya1rv)>0:
ya1ax.id = 'YA1'
g.write(ya1ax)
if zax is not None and len(zrv)>0:
zax.id = 'Z'
g.write(zax)
if zr is not None:
zr.id = 'Zrange'
g.write(zr)
g.presentation = ps.plottype
# Note: For table output, it would be convenient to use a string-valued variable X
# to specify string parts of the table. Butcdms2 doesn't support them usefully.
# Instead we'll manage with a convention that a table row plotspec's id is the name of
# the row, thus available to be printed in, e.g., the first column.
if ps.plottype=="table row":
g.rowid = ps._strid
g.close()
if write_xml:
h.write( "<ncfile>"+filename+"</ncfile>\n" )
if write_xml:
h.write( "</plotdata>\n" )
h.close()
if vars == []:
vars = f.variables.keys()
if not "lat" in vars:
vars.append("lat")
if not "lon" in vars:
vars.append("lon")
## Now we will open the grid file
fg = cdms2.open(args.grid)
## Preparing output file
if args.out is None:
args.out = args.file[:-3] + "_with_grid_info.nc"
fo = cdms2.open(args.out, "w")
store_provenance(fo)
for v in vars:
print "Dealing with:", v
try:
V = f(v)
try:
axIds = V.getAxisIds()
except:
axIds = [] # num variable
if v == "lat":
if not hasattr(V, "bounds"):
V.bounds = "grid_corner_lat"
## Ok now we need to read in the vertices
b = fg(V.bounds)
b.setAxis(0, V.getAxis(-1))
default=False,
help="quiet mode (no output printed to screen)")
args = parser.parse_args(sys.argv[1:])
# Read the weights file
regdr = WeightFileRegridder(args.weights, True, fix_bounds=args.fix_bounds)
f = cdms2.open(args.file)
if args.out is None:
onm = ".".join(args.file.split(".")[:-1]) + "_regrid.nc"
else:
onm = args.out
fo = cdms2.open(onm, "w")
store_provenance(fo, script_file_name=__file__)
history = ""
# Ok now let's start by copying the attributes back onto the new file
for a in f.attributes:
if a != "history":
setattr(fo, a, getattr(f, a))
history = fo.history + "\n"
history += "%s: weights applied via acme_regrid (git commit: %s), " % (
str(datetime.datetime.utcnow()),
metrics.git.commit,
)
fo.history = history
dirnm = os.path.dirname(args.file)
basenm = os.path.basename(args.file)
if dirnm == '': # no dirname using current dir
def write_plot_data(self, format="", where=""):
"""Writes the plot's data in the specified file format and to the location given."""
if format == "" or format == "NetCDF" or format == "NetCDF file":
format = "NetCDF file"
elif format == "JSON string":
pass
elif format == "JSON file":
pass
else:
logger.warning("write_plot_data cannot recognize format name %s",
format)
logger.warning("will write a NetCDF file.")
format = "NetCDF file"
filename = self.outfile(format, where)
if format == "NetCDF file":
value = 0
cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateLevelFlag(
value) ## where value is a integer between 0 and 9 included
writer = cdms2.open(filename,
'w') # later, choose a better name and a path!
store_provenance(writer)
elif format == "JSON file":
logger.error("JSON file not implemented yet")
elif format == "JSON string":
return json.dumps(self, cls=DiagsEncoder)
writer.source = "UV-CDAT Diagnostics"
writer.presentation = self.ptype
plot_these = []
for zax in self.vars:
try:
if not hasattr(zax, 'filetableid'):
zax.filetableid = zax.filetable.id()
del zax.filetable # we'll write var soon, and can't write a filetable
if hasattr(zax, 'filetable2'):
zax.filetable2id = zax.filetable2.id()
del zax.filetable2 # we'll write var soon, and can't write a filetable
except:
pass
try:
zax._filetableid = zax.filetableid # and the named tuple ids aren't writeable as such
zax.filetableid = str(
zax.filetableid
) # and the named tuple ids aren't writeable as such
except:
pass
try:
zax._filetable2id = zax.filetable2id # and the named tuple ids aren't writeable as such
zax.filetable2id = str(
zax.filetable2id
) # and the named tuple ids aren't writeable as such
except:
pass
for ax in zax.getAxisList():
try:
del ax.filetable
except:
pass
writer.write(zax)
plot_these.append(str(seqgetattr(zax, 'id', '')))
writer.plot_these = ' '.join(plot_these)
# Once the finalized method guarantees that varmax,varmin are numbers...
#if self.finalized==True:
# writer.varmax = self.varmax
# writer.varmin = self.varmin
writer.close()
return [filename]
def write_plot_data( self, format="", where="" ):
"""Writes the plot's data in the specified file format and to the location given."""
if format=="" or format=="NetCDF" or format=="NetCDF file":
format = "NetCDF file"
elif format=="JSON string":
pass
elif format=="JSON file":
pass
else:
logger.warning("write_plot_data cannot recognize format name %s",format)
logger.warning("will write a NetCDF file.")
format = "NetCDF file"
filename = self.outfile( format, where )
if format=="NetCDF file":
value=0
cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateLevelFlag(value) ## where value is a integer between 0 and 9 included
writer = cdms2.open( filename, 'w' ) # later, choose a better name and a path!
store_provenance(writer)
elif format=="JSON file":
logger.error("JSON file not implemented yet")
elif format=="JSON string":
return json.dumps(self,cls=DiagsEncoder)
writer.source = "UV-CDAT Diagnostics"
writer.presentation = self.ptype
plot_these = []
for zax in self.vars:
try:
if not hasattr(zax,'filetableid'):
zax.filetableid = zax.filetable.id()
del zax.filetable # we'll write var soon, and can't write a filetable
if hasattr(zax,'filetable2'):
zax.filetable2id = zax.filetable2.id()
del zax.filetable2 # we'll write var soon, and can't write a filetable
except:
pass
try:
zax._filetableid= zax.filetableid # and the named tuple ids aren't writeable as such
zax.filetableid= str(zax.filetableid) # and the named tuple ids aren't writeable as such
except:
pass
try:
zax._filetable2id= zax.filetable2id # and the named tuple ids aren't writeable as such
zax.filetable2id= str(zax.filetable2id) # and the named tuple ids aren't writeable as such
except:
pass
for ax in zax.getAxisList():
try:
del ax.filetable
except:
pass
writer.write( zax )
plot_these.append( str(seqgetattr(zax,'id','')) )
writer.plot_these = ' '.join(plot_these)
# Once the finalized method guarantees that varmax,varmin are numbers...
#if self.finalized==True:
# writer.varmax = self.varmax
# writer.varmin = self.varmin
writer.close()
return [filename]
variant = variant + '_'
logger.info('case: %s', case)
logger.info('variant: %s', variant)
logger.info('season: %s', season)
filename = case + variant + season + "_climo.nc"
# ...actually we want to write this to a full directory structure like
# root/institute/model/realm/run_name/season/
value = 0
cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1
cdms2.setNetcdfDeflateLevelFlag(
value) ## where value is a integer between 0 and 9 included
g = cdms2.open(os.path.join(path, filename),
'w') # later, choose a better name and a path!
store_provenance(g)
for key in varkeys:
if key in reduced_variables:
var = reduced_variables[key]
if varvals[key] is not None:
varvals[key].id = var.variableid
varvals[key].reduced_variable = varvals[key].id
if hasattr(var, 'units'):
varvals[key].units = var.units + '*' + var.units
g.write(varvals[key])
for attr, val in var._file_attributes.items():
if not hasattr(g, attr):
setattr(g, attr, val)
g.season = season
g.close()
return varvals, case
if vars == []:
vars=f.variables.keys()
if not "lat" in vars:
vars.append("lat")
if not "lon" in vars:
vars.append("lon")
## Now we will open the grid file
fg=cdms2.open(args.grid)
## Preparing output file
if args.out is None:
args.out = args.file[:-3]+"_with_grid_info.nc"
fo=cdms2.open(args.out,"w")
store_provenance(fo)
for v in vars:
print "Dealing with:",v
try:
V=f(v)
try:
axIds = V.getAxisIds()
except:
axIds = [] # num variable
if v=="lat":
if not hasattr(V,"bounds"):
V.bounds="grid_corner_lat"
## Ok now we need to read in the vertices
b = fg(V.bounds)
b.setAxis(0,V.getAxis(-1))
parser.add_argument("--fix-esmf-bounds",dest="fix_bounds",action="store_true",default=False,help="fix esmf first and last longitudes being half width")
parser.add_argument("-q","--quiet",action="store_true",default=False,help="quiet mode (no output printed to screen)")
args = parser.parse_args(sys.argv[1:])
# Read the weights file
regdr = WeightFileRegridder(args.weights,True,fix_bounds=args.fix_bounds)
f = cdms2.open(args.file)
if args.out is None:
onm = ".".join(args.file.split(".")[:-1])+"_regrid.nc"
else:
onm = args.out
fo = cdms2.open(onm, "w")
store_provenance(fo,script_file_name=__file__)
history = ""
# Ok now let's start by copying the attributes back onto the new file
for a in f.attributes:
if a != "history":
setattr(fo, a, getattr(f, a))
history = fo.history+"\n"
history += "%s: weights applied via acme_regrid (git commit: %s), " % (
str(datetime.datetime.utcnow()), metrics.git.commit,
)
fo.history = history
dirnm = os.path.dirname(args.file)
basenm = os.path.basename(args.file)
if dirnm == '': # no dirname using current dir
dirnm = os.getcwd()