def do_kuznets_plot():
minyear = min(config.STUDY_YEARS)
maxyear = max(config.STUDY_YEARS)
plot = ScatterPlot("gdp vs emissions change", None, "wiod")
for country in config.countries:
gdp_pop = common.get_national_value(country, minyear, "ppppc")
(env_i, denom_i, intensity_i) = common.get_efficiency(
country, minyear, "env", "gdp")
(env_f, denom_f, intensity_f) = common.get_efficiency(
country, maxyear, "env", "gdp")
# numbers are just for sorting which goes on x axis
plot.set_value("1 ppp per capita", country, gdp_pop)
plot.set_value("2 emiss change", country, intensity_f - intensity_i)
plot.write_tables()
plot.generate_plot()
for year in (minyear, maxyear):
plot = ScatterPlot("gdp vs emissions %d" % year, None, "wiod")
for country in config.countries:
gdp_pop = common.get_national_value(country, year, "ppppc")
env_pop = common.get_efficiency(country, year, "env", "gdp")
plot.set_value("1 gdp per capita", country, gdp_pop)
plot.set_value("2 emissions per capita", country, env_pop[2])
plot.write_tables()
plot.generate_plot()
def do_overview_table(sortby):
minyear = min(config.STUDY_YEARS)
maxyear = max(config.STUDY_YEARS)
data = {}
reverse_data = {}
for (country, name) in config.countries.items():
(env_i, gdp_i, intensity_i) = common.get_efficiency(
country, minyear, "env", "gdp")
(env_f, gdp_f, intensity_f) = common.get_efficiency(
country, maxyear, "env", "gdp")
if sortby == "growth":
pop_i = common.get_national_value(country, minyear, "pop")
pop_f = common.get_national_value(country, maxyear, "pop")
ppp_i = common.get_national_value(country, minyear, "ppppc")
ppp_f = common.get_national_value(country, maxyear, "ppppc")
percap_i = env_i / pop_i * 1000
percap_f = env_f / pop_f * 1000
growth = intensity_f - intensity_i
pgrowth = percap_f - percap_i
reverse_data[ppp_i] = name
data[name] = [
utils.add_commas(val).rjust(10) for val in (ppp_i, ppp_f)]
data[name] += [
"%.2f" % val for val in (intensity_i, intensity_f, growth,
percap_i, percap_f, pgrowth)]
else: # end year intensity
reverse_data[intensity_f] = name
data[name] = [
utils.add_commas(val).rjust(10)
for val in (gdp_i, gdp_f, env_i, env_f)]
data[name] += ["%.2f" % val for val in (intensity_i, intensity_f)]
for key in sorted(reverse_data.keys()):
country = reverse_data[key]
vals = data[country]
print(country.ljust(18) + " & " + " & ".join(vals) + " \\NN")
