def __process__(self, spcs, sector, year, iteration):
ij = self.get_ij(spcs, sector, year, iteration)
title = self.make_title(spcs, sector, year, iteration)
filename = self.make_filename(spcs, sector, year, iteration)
h.plot_ij(ij, filename=filename, title=title,
cblabel="Emissions", vmin=1, vmax=3e6,
norm=h.LogNorm())
return filename
def __process__(self, quant, diag):
filename = self.filename(quant, diag)
title = self.title(quant, diag)
x = emission_diagnostic_batch[quant, diag]
vmin, vmax = self.vlimits[diag]
cblabel = self.units[diag]
h.plot_ij(x,
filename=filename,
title=title,
cblabel=cblabel,
mask=h.na_ocean_mask,
vmin=vmin, vmax=vmax)
def __process__(self, year):
a, b = sectoral_emissions_batch[year]
title = "Anth Emissions in {}".format(year)
filename = join(plt_dir, "{}_{}.png".format("anth", year))
plot_ij(a, title=title, filename=filename,
vmin=1.0, vmax=1e8, mask=na_ocean_mask,
norm=LogNorm())
title = "Biof Emissions in {}".format(year)
filename = join(plt_dir, "{}_{}.png".format("biof", year))
plot_ij(b, title=title, filename=filename,
vmin=1.0, vmax=1e8, mask=na_ocean_mask,
norm=LogNorm())
def __process__(self, quant, rel):
filename = self.filename(quant, rel)
title = self.title(quant, rel)
trend = self.trend_sources[rel][quant]
vmin, vmax = self.vlims[rel]
mask = self.get_mask(quant, rel, trend)
cbticks = self.cbticks[rel]
h.plot_ij(trend,
filename=filename,
title=title,
cblabel="Change/yr [% of avg.]",
vmin=vmin, vmax=vmax,
mask=mask,
cbticks=cbticks,
norm=h.SymNorm())
def plot_gdt(datafile, ij, plots_dir, year=None):
print "Plotting", datafile
match = gdt_regex.match(datafile)
species, hygr, sector, iteration = match.groups()
title = ' '.join([str(year),
sector_name[sector],
(species+hygr).upper(),
"Gradient, Iteration ",
iteration])
cblabel = ''
filename = os.path.join(plots_dir,
"_".join([(species+hygr), sector, str(year), iteration]))
gch.plot_ij(ij, filename=filename, title=title, cblabel=cblabel, norm=gch.SymLogNorm(1e-2),
vmin=-5e2, vmax=5e2)
def plot_sf(datafile, ij, plots_dir, year=None):
match = sf_regex.match(datafile)
species, hygr, sector, iteration = match.groups()
title = ' '.join([str(year),
(species+hygr).upper(),
sector_name[sector],
", Iteration ",
iteration])
cblabel = "Scale Factor"
filename = os.path.join(plots_dir,
"_".join([(species+hygr), sector, str(year), iteration]) + ".png")
print "Plotting {} for {} in {} ".format(datafile, year, filename)
gch.plot_ij(ij, filename=filename, title=title, cblabel=cblabel,
vmin=0, vmax=1)
def oc_plots(oc_raw, oc_ast, year):
oc_raw_conc = h.map_ij(h.ppbv_to_conc, oc_raw)
oc_ast_conc = h.map_ij(h.ppbv_to_conc, oc_ast)
oc_log_ratio = h.map_ij(np.log, h.map_ij(h.divider(default=1), oc_ast_conc, oc_raw_conc))
h.plot_ij(oc_raw_conc,
filename=os.path.join(plt_dir,
"oc_raw_{}.png".format(year)),
title="GEOS-Chem Raw OC {}".format(year), cblabel="ug/m^3",
vmin=0, vmax=3.5)
h.plot_ij(oc_ast_conc,
filename=os.path.join(plt_dir,
"oc_ast_{}.png".format(year)),
title="GEOS-Chem Assimilated OC {}".format(year), cblabel="ug/m^3",
vmin=0, vmax=3.5)
h.plot_ij(oc_log_ratio,
filename=os.path.join(plt_dir,
"oc_ratio_{}.png".format(year)),
title="OC Ln(opt/prior) {}".format(year),
cblabel='',
vmin=-_oc_lim, vmax=_oc_lim,
cmap="RdBu")
def bc_plots(bc_raw, bc_ast, year):
bc_raw_conc = h.map_ij(h.ppbv_to_conc, bc_raw)
bc_ast_conc = h.map_ij(h.ppbv_to_conc, bc_ast)
bc_log_ratio = h.map_ij(np.log, h.map_ij(h.divider(default=1), bc_ast_conc, bc_raw_conc))
h.plot_ij(bc_raw_conc,
filename=os.path.join(plt_dir,
"bc_raw_{}".format(year)),
title="GEOS-Chem Raw BC {}".format(year), cblabel="ug/m^3",
vmin=0, vmax=0.6)
h.plot_ij(bc_ast_conc,
filename=os.path.join(plt_dir,
"bc_ast_{}.png".format(year)),
title="GEOS-Chem Assimilated BC {}".format(year), cblabel="ug/m^3",
vmin=0, vmax=0.6)
h.plot_ij(bc_log_ratio,
filename=os.path.join(plt_dir,
"bc_ratio_{}.png".format(year)),
title="BC Ln(opt/prior) {}".format(year),
cblabel='', norm=h.SymNorm(),
vmin=-_bc_lim, vmax=_bc_lim,
cmap="RdBu")
#!/usr/bin/python
import gc_helpers as h, re
dd = h.data_dirs[1]
an_ems = h.map_ij(h.add, *dd.match(re.compile("bc_anth.blkc.1.dat")).values())
bf_ems = h.map_ij(h.add, *dd.match(re.compile("bc_biof.blkc.1.dat")).values())
bb_ems = h.map_ij(h.add, *dd.match(re.compile("bc_biob.blkc.1.dat")).values())
def max_frac(a,b,c):
n = max(a,b,c)
d = a+b+c
if d!=0:
return n/d
else:
return -1
f = h.map_ij(max_frac, an_ems, bf_ems, bb_ems)
h.plot_ij(f, "fraction.png", "Fraction emitted by dominant sector",
cblabel= "max(sectors)/sum(sectors)",
vmin=0, vmax=1)
from emissions import opt_ems_batch, get_totalled_ij
# Import bc and oc by:
# [bc, oc]
# [anth, biob, biof, biog, total]
# years
# iterations
raw_anth_bc = [get_totalled_ij("bc", yr, 1) for yr in years]
raw_anth_oc = [get_totalled_ij("oc", yr, 1) for yr in years]
def sub(a,b): return a - b
ast_biof_bc = [ij.map_ij(sub,
opt_ems_batch["bc", yr, 9],
rab) for yr, rab in zip(years, raw_anth_bc)]
ast_biof_oc = [ij.map_ij(sub,
opt_ems_batch["oc", yr, 9],
rab) for yr, rao in zip(years, raw_anth_oc)]
def fname(spcs, yr):
name = "{}.{}.png".format(spcs, yr)
return os.path.join("./plt/const_anth/", name)
if __name__ == "__main__":
for bc, oc, year in zip(ast_biof_bc, ast_biof_oc, years):
plot_ij(bc,
filename=fname("bc", yr))
plot_ij(oc,
filename=fname("oc", yr))
def plot(ij, spcs, sector, year):
title = "Log(opt/prior) for {} {} {}".format(spcs, sector, year)
filename = os.path.join(plt_dir,
"{}_{}_{}.png".format(spcs, sector, year))
h.plot_ij(ij, filename, title, cblabel="log(opt/prior)",
vmin=-2,vmax=2)
