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")