def get_mask(quant, rel, trend):
pvalue = emission_diagnostic_batch[quant, "pval"]
# pmask = ij.map_ij(lambda trend, pvalue: 1 if (pvalue > 0.5) else 0,
# trend, pvalue)
avg = absolute_emission_trends_batch.get_avg(quant)
nmask = ij.map_ij(lambda a: 0 if a>=1e5 else 1, avg)
return np.logical_or(h.na_ocean_mask, ij.ij_to_array(nmask))
def totalled_values(spcs, year, iteration):
pattern = search_pattern(spcs, ".+", iteration)
datablocks = data[year].match(pattern).values()
try:
total_ems = map_ij(h.add, *datablocks)
except TypeError as e: #in case of failure, print debugging info
print pattern
print datablocks
raise e
return total_ems
def __process__(self, year):
bc, oc = self.bcoc_emissions(year)
ra, rb = self.ra, self.rb
an, bf = ij.map_ij(self.invert, ra, rb, bc, oc)
return an, bf
def __process__(self, quant):
ems = self.get_ems(quant)
trend = ij.map_ij(self.trend, *ems)
return trend
def __process__(self, quant):
ems = self.get_ems(quant)
trend = ij.map_ij(self.trend, *ems)
avg = self.get_avg(quant)
return ij.map_ij(lambda t,a: 100.0 * t / a, trend, avg)
def get_avg(self, quant):
return ij.map_ij(lambda *a: self.avg(a), *self.get_ems(quant))
def __process__(self, quant, diag):
ems = self.get_ems(quant)
d = ij.map_ij(self.diag_getter(diag), *ems)
return d
import helpers.ij_tools as ij
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 search_pattern(spcs, sector, iteration):
template = "{spcs}_{sector}.{long_spcs}.{iteration}.dat"
vals = {"spcs": spcs, "sector": sector, "iteration": iteration,
"long_spcs": {"bc":"blkc", "oc":"orgc"}[spcs]}
return template.format(**vals)
def log_ratio(opt, prior):
if prior == 0.0:
return 0.0
else:
return np.log(opt/prior)
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)
if __name__ == "__main__":
for year, dd in zip(h.years, h.data_dirs):
for spcs, sector in it.product(species, sectors):
prior = dd.match(search_pattern(spcs, sector, "1")).values()
opt = dd.match(search_pattern(spcs, sector, str(h.iterations[-1]))).values()
assert len(prior)==1 and len(opt)==1
prior, opt = prior[0], opt[0]
ratio = map_ij(log_ratio, opt, prior)
plot(ratio, spcs, sector, year)
