def do_plots():
for (name, measurements) in config.env_series_names.items():
data = {}
for year in config.STUDY_YEARS:
strings = {
"schema": config.WIOD_SCHEMA,
"year": year,
"fd_sectors": sqlhelper.set_repr(config.default_fd_sectors),
"measurements": sqlhelper.set_repr(measurements),
"nipa_schema": usa.config.NIPA_SCHEMA,
}
stmt = db.prepare(
"""SELECT a.country, a.series, b.gdp,
a.series / b.gdp as intensity
FROM (SELECT country, sum(value) as series
FROM %(schema)s.env_%(year)d
WHERE industry = 'total'
AND measurement in %(measurements)s
GROUP BY country) a,
(SELECT aa.country, sum(value) * deflator as gdp
FROM %(schema)s.indbyind_%(year)d aa,
(SELECT 100 / gdp as deflator
FROM %(nipa_schema)s.implicit_price_deflators
WHERE year = $1) bb
WHERE to_ind in %(fd_sectors)s
GROUP BY aa.country, deflator) b
WHERE a.country = b.country
AND a.series is not null
ORDER BY a.series / b.gdp""" % strings)
for row in stmt(year):
country = row[0]
intensity = row[3]
if country not in data:
data[country] = {}
data[country][year] = intensity
slopes = {}
for (country, country_data) in data.items():
n = len(country_data.keys())
if n < 2:
continue
sum_y = sum(country_data.values())
sum_x = sum(country_data.keys())
slope = (n * sum([k * v for (k, v) in country_data.items()]) \
- sum_x * sum_y) / \
(n * sum([k * k for k in country_data.keys()]) - sum_x)
slopes[country] = slope * 1000000
years = "%d-%d" % (config.STUDY_YEARS[0], config.STUDY_YEARS[-1])
i = 0
binsize = 8
plot = None
for (country, slope) in sorted(slopes.items(), key=lambda x: x[1]):
if i % binsize == 0:
if plot is not None:
plot.write_tables()
plot.generate_plot()
tier = i / binsize + 1
plot = GNUPlot("tier%d" % tier, "",
#"%s intensity from %s, tier %d" \
# % (name, years, tier),
"wiod-%s" % name.replace(" ", "-"))
plot.legend("width -5")
for year in config.STUDY_YEARS:
if year in data[country]:
plot.set_value(
"%s (%.2f)" % (config.countries[country], slope),
year,
data[country][year])
i += 1
if plot is not None:
plot.write_tables()
plot.generate_plot()
