def set_value(self, series, x, y):
series_title = self.get_series_title(series)
GNUPlot.set_value(self, series_title, x, y)
L = iogen.get_L()
Y = iogen.get_Y() # mixed units
Y_standard = iogen_standard.get_Y(use_exchange_rate=True)
dirname = "usa-pce-energy"
hybrid_vector = Y.get_total()
gdp_value = Y_standard.get_total().sum() / 1000 # result is millions
use_vector = L.matrix_mult(hybrid_vector)
for i in range(len(energy_codes)):
code = energy_codes[i]
if eia.is_fossil_fuel(code):
name = eia.name_for_naics(code)
value = use_vector.get_element(rowname=code) # billion Btu
# divide billion Btu by 1k for tBtu
ff_plot.set_value(year, name, value / 1000)
# div billion Btu by MM$ for kBtu/$
ff_iplot.set_value(year, name, value / gdp_value)
# manually generate electricity figures
stmt = db.prepare(
"select source, use_btu from %s.seds_short_%d where sector = 'EI'" %
(config.EIA_SCHEMA, year))
result = stmt()
for row in result:
source = row[0]
btu = row[1] # billion Btu
elec_plot.set_value(elec_sources[source], year, btu / 1000) # tBtu
elec_iplot.set_value(elec_sources[source], year, btu / gdp_value)
stmt = db.prepare(
def graph_table(filename, title, vector,
base_year, is_intensities, collapse=[]):
plot = GNUPlot(filename, title, "usa")
data = {}
sortby = {}
other_category = "Other"
max_year = max(config.STUDY_YEARS)
other_category_numer = dict((year, 0) for year in config.STUDY_YEARS)
other_category_denom = dict((year, 0) for year in config.STUDY_YEARS)
for group in vector.keys():
numerator = vector[group]
if is_intensities:
denominator = pce_dollars[group]
else:
denominator = dict((year, 1) for year in config.STUDY_YEARS)
base_value = numerator[base_year] / denominator[base_year]
if group in collapse:
for year in config.STUDY_YEARS:
other_category_numer[year] += numerator[year]
# adding 1 for each year is fine since it's relative to the base
other_category_denom[year] += denominator[year]
else:
data[group] = {}
for year in config.STUDY_YEARS:
data[group][year] = numerator[year] / denominator[year]
sortby[ data[group][max_year] / data[group][base_year] ] = group
if len(other_category_numer):
data[other_category] = dict(
(year, other_category_numer[year] / other_category_denom[year]) \
for year in config.STUDY_YEARS)
sortby[ data[other_category][max_year] /\
data[other_category][base_year] ] = other_category
sorted_groups = [sortby[key] for key in sorted(sortby.keys(), reverse=True)]
plot.add_custom_setup("set style data linespoints")
plot.add_custom_setup("unset colorbox")
plot.add_custom_setup("set grid")
min_x = 5 * math.floor(min(config.STUDY_YEARS) / 5)
max_x = 5 * math.ceil(max(config.STUDY_YEARS) / 5) + 5 # room for labels
plot.add_custom_setup("set xrange [ %d : %d ]" % (min_x, max_x))
interval = 1 / (len(sorted_groups) - 1)
for i in range(len(sorted_groups)):
plot.add_custom_setup("set style lines %d lc palette frac %.2f lw 2"
% (i + 1, i * interval))
series_values = []
for i in range(len(sorted_groups)):
group = sorted_groups[i]
if group in bea.short_nipa:
group_name = bea.short_nipa[group]
else:
group_name = group
series_values.append(group_name)
plot.suppress_title(group_name)
base_value = data[group][base_year]
for year in config.STUDY_YEARS:
print(group, year, data[group][year], base_value, data[group][year] / base_value)
plot.set_value(group_name, year,
data[group][year] / base_value * 100)
plot.set_series_style(group_name, "linestyle %d" % (i + 1))
plot.series_values = series_values
plot.write_tables()
plot.get_axis_specs() # make sure max_y is adjusted
prev_pos = None
for i in range(len(sorted_groups)):
group = sorted_groups[i]
if group in bea.short_nipa:
group_name = bea.short_nipa[group]
else:
group_name = group
ending_value = data[group][max_year] / data[group][base_year] * 100
position = ending_value / plot.max_y + 0.01 # line up baseline
# space labels out by at least 0.04 so they don't overlap
if prev_pos is not None and prev_pos - position < 0.03:
position = prev_pos - 0.03
plot.add_custom_setup(
"set label %d '%s' at graph 0.81, %.2f font 'Arial,8'"
% (i + 1, group_name, position))
prev_pos = position
plot.generate_plot()
L = iogen.get_L()
Y = iogen.get_Y() # mixed units
Y_standard = iogen_standard.get_Y(use_exchange_rate=True)
dirname = "usa-pce-energy"
hybrid_vector = Y.get_total()
gdp_value = Y_standard.get_total().sum() / 1000 # result is millions
use_vector = L.matrix_mult(hybrid_vector)
for i in range(len(energy_codes)):
code = energy_codes[i]
if eia.is_fossil_fuel(code):
name = eia.name_for_naics(code)
value = use_vector.get_element(rowname=code) # billion Btu
# divide billion Btu by 1k for tBtu
ff_plot.set_value(year, name, value / 1000)
# div billion Btu by MM$ for kBtu/$
ff_iplot.set_value(year, name, value / gdp_value)
# manually generate electricity figures
stmt = db.prepare(
"select source, use_btu from %s.seds_short_%d where sector = 'EI'"
% (config.EIA_SCHEMA, year))
result = stmt()
for row in result:
source = row[0]
btu = row[1] # billion Btu
elec_plot.set_value(elec_sources[source], year, btu / 1000) # tBtu
elec_iplot.set_value(elec_sources[source], year, btu / gdp_value)
stmt = db.prepare(
def graph_table(filename, title, vector, base_year, intensity_divisor,
sector_groups):
plot = GNUPlot(filename, title, "usa")
data = {}
sortby = {}
numerators = {}
denominators = {}
for key in sector_groups.keys():
data[key] = {}
denominators[key] = dict((year, 0) for year in config.STUDY_YEARS)
numerators[key] = dict((year, 0) for year in config.STUDY_YEARS)
max_year = max(config.STUDY_YEARS)
for sector in vector.keys():
print(sector)
numerator = vector[sector]
for (key, sectors) in sector_groups.items():
if sector in sectors:
for year in config.STUDY_YEARS:
if intensity_divisor is not None:
denominators[key][year] += \
intensity_divisor[sector][year]
else:
denominators[key][year] += 1
numerators[key][year] += numerator[year]
break
for key in sector_groups.keys():
print(key, denominators[key])
data[key] = dict(
(year, numerators[key][year] / denominators[key][year])
for year in config.STUDY_YEARS)
sortby[data[key][max_year] / data[key][base_year]] = key
sorted_groups = [
sortby[key] for key in sorted(sortby.keys(), reverse=True)
]
plot.add_custom_setup("set style data linespoints")
plot.add_custom_setup("unset colorbox")
plot.add_custom_setup("set grid")
min_x = 5 * math.floor(min(config.STUDY_YEARS) / 5)
max_x = 5 * math.ceil(max(config.STUDY_YEARS) / 5) + 5 # room for labels
plot.add_custom_setup("set xrange [ %d : %d ]" % (min_x, max_x))
interval = 1 / (len(sorted_groups) - 1)
for i in range(len(sorted_groups)):
plot.add_custom_setup("set style lines %d lc palette frac %.2f lw 2" %
(i + 1, i * interval))
series_values = []
def name_for_group(group):
if group in bea.short_nipa:
group_name = bea.short_nipa[group]
else:
group_name = group
return group_name
for i in range(len(sorted_groups)):
group = sorted_groups[i]
group_name = name_for_group(group)
series_values.append(group_name)
plot.suppress_title(group_name)
base_value = data[group][base_year]
for year in config.STUDY_YEARS:
plot.set_value(group_name, year,
data[group][year] / base_value * 100)
plot.set_series_style(group_name, "linestyle %d" % (i + 1))
plot.series_values = series_values
plot.write_tables()
plot.get_axis_specs() # make sure max_y is adjusted
prev_pos = None
for i in range(len(sorted_groups)):
group = sorted_groups[i]
group_name = name_for_group(group)
ending_value = data[group][max_year] / data[group][base_year] * 100
position = ending_value / plot.max_y + 0.01 # line up baseline
# space labels out by at least 0.04 so they don't overlap
if prev_pos is not None and prev_pos - position < 0.03:
position = prev_pos - 0.03
plot.add_custom_setup(
"set label %d '%s' at graph 0.81, %.2f font 'Arial,8'" %
(i + 1, group_name, position))
prev_pos = position
plot.generate_plot()
def set_value(self, series, x, y):
series_title = self.get_series_title(series)
GNUPlot.set_value(self, series_title, x, y)
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()
def graph_table(filename, title, vector,
base_year, intensity_divisor, sector_groups):
plot = GNUPlot(filename, title, "usa")
data = {}
sortby = {}
numerators = {}
denominators = {}
for key in sector_groups.keys():
data[key] = {}
denominators[key] = dict((year, 0) for year in config.STUDY_YEARS)
numerators[key] = dict((year, 0) for year in config.STUDY_YEARS)
max_year = max(config.STUDY_YEARS)
for sector in vector.keys():
print(sector)
numerator = vector[sector]
for (key, sectors) in sector_groups.items():
if sector in sectors:
for year in config.STUDY_YEARS:
if intensity_divisor is not None:
denominators[key][year] += \
intensity_divisor[sector][year]
else:
denominators[key][year] += 1
numerators[key][year] += numerator[year]
break
for key in sector_groups.keys():
print(key, denominators[key])
data[key] = dict(
(year, numerators[key][year] / denominators[key][year])
for year in config.STUDY_YEARS)
sortby[ data[key][max_year] / data[key][base_year] ] = key
sorted_groups = [sortby[key] for key in sorted(sortby.keys(), reverse=True)]
plot.add_custom_setup("set style data linespoints")
plot.add_custom_setup("unset colorbox")
plot.add_custom_setup("set grid")
min_x = 5 * math.floor(min(config.STUDY_YEARS) / 5)
max_x = 5 * math.ceil(max(config.STUDY_YEARS) / 5) + 5 # room for labels
plot.add_custom_setup("set xrange [ %d : %d ]" % (min_x, max_x))
interval = 1 / (len(sorted_groups) - 1)
for i in range(len(sorted_groups)):
plot.add_custom_setup("set style lines %d lc palette frac %.2f lw 2"
% (i + 1, i * interval))
series_values = []
def name_for_group(group):
if group in bea.short_nipa:
group_name = bea.short_nipa[group]
else:
group_name = group
return group_name
for i in range(len(sorted_groups)):
group = sorted_groups[i]
group_name = name_for_group(group)
series_values.append(group_name)
plot.suppress_title(group_name)
base_value = data[group][base_year]
for year in config.STUDY_YEARS:
plot.set_value(group_name, year,
data[group][year] / base_value * 100)
plot.set_series_style(group_name, "linestyle %d" % (i + 1))
plot.series_values = series_values
plot.write_tables()
plot.get_axis_specs() # make sure max_y is adjusted
prev_pos = None
for i in range(len(sorted_groups)):
group = sorted_groups[i]
group_name = name_for_group(group)
ending_value = data[group][max_year] / data[group][base_year] * 100
position = ending_value / plot.max_y + 0.01 # line up baseline
# space labels out by at least 0.04 so they don't overlap
if prev_pos is not None and prev_pos - position < 0.03:
position = prev_pos - 0.03
plot.add_custom_setup(
"set label %d '%s' at graph 0.81, %.2f font 'Arial,8'"
% (i + 1, group_name, position))
prev_pos = position
plot.generate_plot()