def pop_frac_reimbursement_costs():
refresh_country_comparison()
df = pd.read_csv('output/reverse_engineering/all_policies.csv').set_index(
'Policy').sort_index()
#df = pd.read_csv('output/reverse_engineering/all_policies.csv').set_index(['Policy','Net beneficiaries']).sort_index()
try:
df = df.drop('Median')
except:
pass
try:
df = df.drop('Median', axis=1)
except:
pass
df.columns.name = 'Social spending to offset carbon tax'
df.index.name = '(as % of total carbon revenue)'
df = df.fillna('-')
#df = df.astype('int')
for _c in df.columns:
if _c == 'Net beneficiaries': continue
df = df.rename(columns={_c: iso_to_name[_c.lower()]})
df = df[['Net beneficiaries'] +
sorted([_c for _c in df.columns if _c != 'Net beneficiaries'])]
df = df.sort_index(ascending=True)
#df = df.sort_values(df.columns[0],ascending=False,na_position='last')
df = df.fillna('-')
df = df.round(1)
df.insert(
1, 'Median',
df.median(axis=1, skipna=True).round(0).astype('int').fillna('-'))
for _c in df.columns:
if _c == 'Net beneficiaries' or _c == 'Median': continue
df[_c] = df[_c].apply(lambda x: force_round(x))
#for _c in df.columns:
# df.loc[df[_c].values.dtype=='float',_c].dtype = 'int'
#df = df.sort_values('Median',ascending=True)
df.to_latex('output/reverse_engineering/_all_policies.tex',
column_format='@{}l' + col_form[3:])
with open('output/reverse_engineering/_all_policies.tex', 'r') as f:
with open('output/reverse_engineering/all_policies.tex', 'w') as f2:
f2.write(r'\documentclass[preview=true]{standalone}' + '\n')
f2.write(r'\usepackage{amssymb} %maths' + '\n')
f2.write(r'\usepackage{amsmath} %maths' + '\n')
f2.write(r'\usepackage{booktabs}' + '\n')
f2.write(r'\usepackage{rotating}' + '\n')
f2.write(r'\begin{document}' + '\n')
reading_is_fundamental = f.read()
reading_is_fundamental = reading_is_fundamental.replace(
'Median', r'\rotatebox{90}{Median}').replace(
'(as \% of total carbon revenue) &',
'(as \% of total carbon revenue) & (\% of population)')
for _n in iso_to_name:
if _n == 'chi': continue
reading_is_fundamental = reading_is_fundamental.replace(
iso_to_name[_n],
r'\rotatebox{90}{' + iso_to_name[_n] + '}')
f2.write(reading_is_fundamental)
f2.write(r'\end{document}')
f2.close()
subprocess.call(
'cd ~/Box\ Sync/stranded\ jobs\ and\ distributional\ impacts/output/reverse_engineering/; pdflatex all_policies.tex',
shell=True)
for f in glob.glob('output/reverse_engineering/*.aux'):
os.remove(f)
for f in glob.glob('output/reverse_engineering/*.log'):
os.remove(f)
for f in glob.glob('output/reverse_engineering/_*.tex'):
os.remove(f)
def q12_reimbursement_costs():
refresh_country_comparison()
df = pd.read_csv(
'output/all_countries_sp/redistribution_expenditures_by_country.csv'
).set_index('sp') #.fillna('-')
try:
df = df.drop('Median')
except:
pass
try:
df = df.drop('Median', axis=1)
except:
pass
#df.columns.name = 'Cost to reimburse bottom 40%'
#df.index.name = '(as % of total carbon revenue)'
df.index.name = 'Rebate program'
df = df.fillna(-1)
#df = df.astype('int')
for _c in df.columns:
df = df.rename(columns={_c: iso_to_name[_c.lower()]})
df = df.reindex(sorted(df.columns), axis=1)
df = df.sort_values(df.columns[0], ascending=False, na_position='last')
df = df.fillna('-')
df = df.round(1)
df.insert(
0, 'Median',
df.median(axis=1, skipna=True).round(0).astype('int').fillna('-'))
df = df.round(0).astype('int')
df = df.sort_values('Median', ascending=True)
df.to_latex(
'output/all_countries_sp/_redist_exp_fractional_spending_Q12_compensated.tex',
column_format=col_form)
with open(
'output/all_countries_sp/_redist_exp_fractional_spending_Q12_compensated.tex',
'r') as f:
with open(
'output/all_countries_sp/redist_exp_fractional_spending_Q12_compensated.tex',
'w') as f2:
f2.write(r'\documentclass[preview=true]{standalone}' + '\n')
f2.write(r'\usepackage{amssymb} %maths' + '\n')
f2.write(r'\usepackage{amsmath} %maths' + '\n')
f2.write(r'\usepackage{booktabs}' + '\n')
f2.write(r'\usepackage{rotating}' + '\n')
f2.write(r'\begin{document}' + '\n')
reading_is_fundamental = f.read()
reading_is_fundamental = reading_is_fundamental.replace(
'Median', r'\rotatebox{90}{Median}')
for _n in iso_to_name:
if _n == 'chi': continue
reading_is_fundamental = reading_is_fundamental.replace(
iso_to_name[_n],
r'\rotatebox{90}{' + iso_to_name[_n] + '}')
f2.write(reading_is_fundamental)
f2.write(r'\end{document}')
f2.close()
subprocess.call(
'cd ~/Box\ Sync/stranded\ jobs\ and\ distributional\ impacts/output/all_countries_sp/; pdflatex redist_exp_fractional_spending_Q12_compensated.tex',
shell=True)
for f in glob.glob('output/all_countries_sp/*.aux'):
os.remove(f)
for f in glob.glob('output/all_countries_sp/*.log'):
os.remove(f)
for f in glob.glob('output/all_countries_sp/_*.tex'):
os.remove(f)