fh = open(path, "r")
csvf = csv.reader(fh)
io_codes = common.io_codes_for_year(year)
data = {}
row = next(csvf)
for row in csvf:
if len(row) == 6:
sector = row[0]
btu = row[1] # total
#btu = row[2] # coal
#btu = row[3] # natural gas
#btu = row[5] # PA-nontrans
data[sector] = float(btu)
pce_vector = common.pce_bridge_vector(
year, "Food and beverages purchased for off-premises consumption")
#pce_vector = common.pce_bridge_vector(
# year, "Clothing and footwear")
total_expenditure = 0
meat_expenditure = 0
energy_data = {}
for row in pce_vector.get_rows():
expenditure = pce_vector.get_element(row)
if expenditure != 0:
intensity = data[row]
if row in combined_meat_codes:
sector = io_codes[row]
print(row, sector, intensity)
for sector in bea.scrap_used_codes[year]:
imp_to_cons.set_element(sector, colname, 0)
# create matrix to collapse split petroleum sectors when multiplying
# by the import ratio
rows = iogen_standard.get_sectors()
cols = iogen.get_sectors()
pa_collapser = NamedMatrix(False, None, rows, cols)
for col in cols:
if col in rows:
pa_collapser.set_element(col, col, 1)
elif col == eia.source_naics_map["PA-trans"][year] or \
col == eia.source_naics_map["PA-nontrans"][year]:
pa_collapser.set_element(eia.source_naics_map["PA"][year], col, 1)
conven_pce = common.pce_bridge_vector(year) # all in dollars
conversions = {} # get btu per dollar which we will use to convert subvectors
for code in energy_codes:
conversions[code] = hybrid_pce.get_element(code) / \
conven_pce.get_element(code)
for group in bea.nipa_groups:
Y = common.pce_bridge_vector(year, group)
deflator = deflators.get_pce_deflator(year)
pce = Y.sum() * deflator
if group not in pce_imports:
pce_imports[group] = {}
# pce gets /1000 and /1000 again in "is_intensity" version
fh = open(path, "r")
csvf = csv.reader(fh)
io_codes = common.io_codes_for_year(year)
data = {}
row = next(csvf)
for row in csvf:
if len(row) == 6:
sector = row[0]
btu = row[1] # total
#btu = row[2] # coal
#btu = row[3] # natural gas
#btu = row[5] # PA-nontrans
data[sector] = float(btu)
pce_vector = common.pce_bridge_vector(
year, "Food and beverages purchased for off-premises consumption")
#pce_vector = common.pce_bridge_vector(
# year, "Clothing and footwear")
total_expenditure = 0
meat_expenditure = 0
energy_data = {}
for row in pce_vector.get_rows():
expenditure = pce_vector.get_element(row)
if expenditure != 0:
intensity = data[row]
if row in combined_meat_codes:
sector = io_codes[row]
print(row, sector, intensity)