def calc_transformation_rates(pop_fname):
p = pop_model.POPFile(pop_fname, hconst=50.)
p.initialize_gradient_operator()
Nt = len(p.nc.variables['time'])
result = dict()
for rname in region_dict:
result[rname] = 0.
for n in range(Nt):
print n
# calculate density fluxes
T, S, FT_forc, FT_mix, FS_forc, FS_mix = p.ts_forcing[n]
FS_mix = numpy.ma.masked_greater(numpy.ma.masked_invalid(FS_mix),1.)
FT_mix = numpy.ma.masked_greater(numpy.ma.masked_invalid(FT_mix),1.)
for rname, reg in region_dict.iteritems():
AT, AS = reg.calculate_transformation_rate(
T, S, [FT_forc, FT_mix], [FS_forc, FS_mix])
result[rname] += numpy.array([AT, AS])
for rname in region_dict:
result[rname] /= Nt
return result
def calc_transformation_rates(pop_fname):
p = pop_model.POPFile(pop_fname, hconst=50., pref=2000.)
p.initialize_gradient_operator()
Nt = len(p.nc.variables['time'])
result = dict()
for rname in region_dict:
result[rname] = 0.
for n in range(Nt):
print n
# calculate density fluxes
rho, Fheat, Fsalt, Fmix = p.dens_forcing[n]
Fmix = numpy.ma.masked_greater(numpy.ma.masked_invalid(Fmix),1.)
for rname, reg in region_dict.iteritems():
A = reg.calculate_transformation_rate(
rho,Fheat, Fsalt, Fmix)
result[rname] += A
for rname in region_dict:
result[rname] /= Nt
return result
def calc_Fd(pop_fname):
"""Worker function for Nakamura analysis.
Takes a single file name as argument.
The following variables MUST be set in globals:
region_dict - a dictionary of water mass regions
hconst - the assumed surface layer depth (in m)
pref - the reference pressure for EOS
"""
assert 'region_dict' in globals()
assert 'hconst' in globals()
assert 'pref' in globals()
assert 'monthly_mean' in globals()
p = pop_model.POPFile(pop_fname, hconst=hconst, pref=pref)
p.initialize_gradient_operator()
Nt = len(p.nc.variables['time'])
result = dict()
for rname in region_dict:
result[rname] = 0.
if monthly_mean:
rho_mm = p.rho[:Nt]
Nt = 1
# calculate transformation for each daily snapshot
for n in range(Nt):
print n
if monthly_mean:
rho = rho_mm
else:
rho = p.rho[n]
# realized I can use a simpler formula
#diss = p._ah * p._ahf * p.laplacian(rho)**2
diss = p.biharmonic_tendency(rho)
for rname, reg in region_dict.iteritems():
dA, diss_sum = reg.sum_in_rholevs(rho, numpy.ones_like(diss), diss)
A = numpy.cumsum(dA)
Fd = numpy.cumsum(diss_sum)
dq = numpy.diff(reg.rholevs)
dq = numpy.hstack([dq[0], dq])
dq_dA = numpy.ma.masked_invalid(dq / dA)
Keff = numpy.ma.masked_invalid(Fd / dq_dA).filled(0.)
#dDiss_dA = diss_sum[1:] / dA[1:]
#dq_dA = numpy.ma.masked_invalid(numpy.diff(reg.rholevs) / dA[1:])
#Fd = numpy.ma.masked_invalid(dDiss_dA / dq_dA).filled(0.)
result[rname] += numpy.array([A, Fd, Keff])
for rname in region_dict:
result[rname] /= Nt
return result
def calc_transformation_rates(pop_fname):
"""Worker function for transformation analysis.
Takes a single file name as argument.
The following variables MUST be set in globals:
region_dict - a dictionary of water mass regions
hconst - the assumed surface layer depth (in m)
pref - the reference pressure for EOS
"""
assert 'region_dict' in globals()
assert 'hconst' in globals()
assert 'pref' in globals()
assert 'monthly_mean' in globals()
p = pop_model.POPFile(pop_fname, hconst=hconst, pref=pref)
p.initialize_gradient_operator()
Nt = len(p.nc.variables['time'])
result = dict()
for rname in region_dict:
result[rname] = 0.
if monthly_mean:
# take the monthly mean of T, S, fluxes first
rho, Fheat, Fsalt, Fmix, Fcab = p.dens_forcing[:Nt]
for rname, reg in region_dict.iteritems():
A = reg.calculate_transformation_rate(rho, Fheat, Fsalt, Fmix,
Fcab)
result[rname] += A
else:
# calculate transformation for each daily snapshot
for n in range(Nt):
print n
rho, Fheat, Fsalt, Fmix, Fcab = p.dens_forcing[n]
for rname, reg in region_dict.iteritems():
A = reg.calculate_transformation_rate(rho, Fheat, Fsalt, Fmix,
Fcab)
result[rname] += A
for rname in region_dict:
result[rname] /= Nt
return result
ddir = '/glade/scratch/enewsom/HR_ANALYSIS/DATA/HRfiles/'
fprefix = 'HRC06.br.pop.h1'
fnames = []
for year in xrange(147,168):
for month in xrange(1,13):
fnames.append(
'%s/%s.%04d-%02d.nc' % (ddir, fprefix, year, month)
)
print fnames
# referene pressure
pref = 2000.
# load a test file
p = pop_model.POPFile(fnames[0], pref=pref)
# this doesn't actually get used
rho = p.mask
# define basins
natl = transformation.WaterMassRegion(
basin_names=['Atlantic Ocean', 'GIN Seas', 'Labrador Sea'], latmin=15)
natl.initialize_mask(p)
natl.calculate_rholevs(rho, rhomin=1028, rhomax=1037.8, nlevs=120, linear=True)
npac = transformation.WaterMassRegion(
basin_names=['Pacific Ocean'], latmin=15)
npac.initialize_mask(p)
npac.calculate_rholevs(rho, rhomin=1027, rhomax=1036.5, nlevs=120, linear=True)
so = transformation.WaterMassRegion(
from watermasstools import pop_model, transformation
#####################################
## Define Regions for Calculation ###
#####################################
# where to find the data
ddir = '/glade/p/ncgd0001/hybrid_v5_rel04_BC5_ne120_t12_pop62/ocn-hist'
fprefix = 'hybrid_v5_rel04_BC5_ne120_t12_pop62.pop.h.nday1'
fnames = []
for year in xrange(47, 86):
for month in xrange(1, 13):
fnames.append('%s/%s.%04d-%02d-01.nc' % (ddir, fprefix, year, month))
# load a test file
p = pop_model.POPFile(fnames[0])
# this doesn't actually get used
rho = p.rho[0]
# define basins
natl = transformation.WaterMassRegion(
basin_names=['Atlantic Ocean', 'GIN Seas', 'Labrador Sea'], latmin=15)
natl.initialize_mask(p)
natl.calculate_rholevs(rho, rhomin=1022, rhomax=1028.5, nlevs=120, linear=True)
npac = transformation.WaterMassRegion(basin_names=['Pacific Ocean'], latmin=15)
npac.initialize_mask(p)
npac.calculate_rholevs(rho, rhomin=1020, rhomax=1027, nlevs=120, linear=True)
so = transformation.WaterMassRegion(basin_names=['Southern Ocean'], latmax=30)
so.initialize_mask(p)
def wmt_rho(aname,
ddir,
fprefix,
years,
pref=0,
hconst=50.,
task='calc_transformation_rates',
monthly_mean=False,
fsuffix=''):
"""Perform water mass analysis on a specific POP model run.
aname - (string) the nickname of this specific analysis,
used when saving data
ddir - the directory where the run lives
fprefix - the string that begins the file names
(e.g. hybrid_v5_rel04_BC5_ne120_t12_pop62.pop.h.nday1)
years - a list of years to analyze
fsuffix - a trailing suffix (before .nc)
pref - reference pressure for analysis
hconst - depth of assumed surface layer
monthly_mean - whether to calculate the
transformation rate on the monthly mean instead of
of the daily snapshots
"""
##############################
## Set Up Parallel Engines ###
##############################
# give engines time to load
time.sleep(20)
c = Client()
dview = c.direct_view()
lview = c.load_balanced_view()
with dview.sync_imports():
import numpy
from watermasstools import pop_model, transformation
#####################################
## Define Regions for Calculation ###
#####################################
fnames = []
for year in years:
for month in xrange(1, 13):
fname = '%s/%s.%04d-%02d%s.nc' % (ddir, fprefix, year, month,
fsuffix)
fnames.append(fname)
for f in fnames:
print f
# load a test file
p = pop_model.POPFile(fnames[0], pref=pref)
# define basins
if pref == 0:
natl = transformation.WaterMassRegion(
basin_names=['Atlantic Ocean', 'GIN Seas', 'Labrador Sea'],
latmin=15)
natl.initialize_mask(p)
natl.calculate_rholevs(rhomin=1022,
rhomax=1028.5,
nlevs=120,
linear=True)
npac = transformation.WaterMassRegion(basin_names=['Pacific Ocean'],
latmin=15)
npac.initialize_mask(p)
npac.calculate_rholevs(rhomin=1020,
rhomax=1027,
nlevs=120,
linear=True)
so = transformation.WaterMassRegion(basin_names=['Southern Ocean'],
latmax=30)
so.initialize_mask(p)
so.calculate_rholevs(rhomin=1022, rhomax=1030, nlevs=120, linear=True)
globe = transformation.WaterMassRegion()
globe.initialize_mask(p)
globe.calculate_rholevs(rhomin=1018,
rhomax=1030,
nlevs=120,
linear=True)
elif pref == 2000:
natl = transformation.WaterMassRegion(
basin_names=['Atlantic Ocean', 'GIN Seas', 'Labrador Sea'],
latmin=15)
natl.initialize_mask(p)
natl.calculate_rholevs(rhomin=1028,
rhomax=1037.8,
nlevs=120,
linear=True)
npac = transformation.WaterMassRegion(basin_names=['Pacific Ocean'],
latmin=15)
npac.initialize_mask(p)
npac.calculate_rholevs(rhomin=1027,
rhomax=1036.5,
nlevs=120,
linear=True)
so = transformation.WaterMassRegion(basin_names=['Southern Ocean'],
latmax=30)
so.initialize_mask(p)
so.calculate_rholevs(rhomin=1030,
rhomax=1037.8,
nlevs=120,
linear=True)
globe = transformation.WaterMassRegion()
globe.initialize_mask(p)
globe.calculate_rholevs(rhomin=1026,
rhomax=1037.8,
nlevs=120,
linear=True)
else:
raise ValueError('Invalid pressure level %g specified' % pref)
region_dict = {'natl': natl, 'npac': npac, 'so': so, 'globe': globe}
# push to engines
dview.push(dict(hconst=hconst, pref=pref, monthly_mean=monthly_mean))
dview.push(region_dict)
dview.execute(
"region_dict = {'natl': natl, 'npac': npac, 'so': so, 'globe': globe}")
# check
dview.execute('a = region_dict.keys()[0]')
a = dview.gather('a')
for r in a.get():
assert r == 'npac'
#######################
## Apply on Engines ###
#######################
if task == 'calc_transformation_rates':
mapfunc = calc_transformation_rates
prefix = 'WMT'
elif task == 'calc_Fd':
mapfunc = calc_Fd
prefix = 'FD'
res = lview.map(mapfunc, fnames)
while not res.ready():
print 'progress %3.2f%%' % (100 * res.progress / float(len(res)))
time.sleep(60)
assert res.successful()
###################
## Save Results ###
###################
all_res = dict()
for k in region_dict:
all_res[k] = []
for r in res:
for k in all_res:
all_res[k].append(r[k])
for k in all_res:
all_res[k] = numpy.array(all_res[k])
numpy.savez('../data/%s_%s_sigma%1d_hconst%03d_%s.npz' %
(prefix, aname, pref / 1000, hconst, k),
A=all_res[k],
rholevs=region_dict[k].rholevs)