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Debt to GDP plot labels #962

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merged 8 commits into from
Aug 10, 2024
Merged

Debt to GDP plot labels #962

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b907877
add more labels for tthe to gdp plots
jdebacker Aug 8, 2024
4e8b440
ensure variable passed in variable label list
jdebacker Aug 8, 2024
c5d086b
provide no python arg
jdebacker Aug 8, 2024
8eccd44
allow for markdown table
jdebacker Aug 8, 2024
02f1246
more informative note in parameter table:
jdebacker Aug 8, 2024
b5c2a05
add assertion statement
jdebacker Aug 8, 2024
402a0c3
format
jdebacker Aug 8, 2024
79dcd46
markdown table
jdebacker Aug 8, 2024
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21 changes: 20 additions & 1 deletion ogcore/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
BASELINE_DIR = "OUTPUT_BASELINE"

# Default year for model runs
DEFAULT_START_YEAR = 2021
DEFAULT_START_YEAR = 2025


VAR_LABELS = {
Expand Down Expand Up @@ -83,16 +83,35 @@
}

ToGDP_LABELS = {
"Y": "GDP-to-GDP (=1 ;-))",
"B": "Savings-to-GDP ($B_{t}/Y_t$)",
"C": "Consumption-to-GDP ($C_{t}/Y_t$)",
"D": "Debt-to-GDP ($D_{t}/Y_t$)",
"D_d": "Domestically-owned Debt-to-GDP ($D^d_{t}/Y_t$)",
"D_f": "Foreign-owned Debt-to-GDP ($D^f_{t}/Y_t$)",
"G": "Govt Spending-to-GDP ($G_{t}/Y_t$)",
"K": "Capital-Output Ratio ($K_{t}/Y_t$)",
"K_d": "Domestically-owned Capital-Output Ratio ($K^d_{t}/Y_t$)",
"K_f": "Foreign-owned Capital-Output Ratio ($K^f_{t}/Y_t$)",
"K_g": "Infrastructure-Output Ratio ($K^g_{t}/Y_t$)",
"C": "Consumption-Output Ratio ($C_{t}/Y_t$)",
"I": "Investment-Output Ratio ($I_{t}/Y_t$)",
"I_total": "Investment-Output Ratio ($I_{t}/Y_t$)",
"I_d": "Domestic Investment-Output Ratio ($I^d_{t}/Y_t$)",
"I_g": "Infrastructure Investment-Output Ratio ($I^g_{t}/Y_t$)",
"total_tax_revenue": "Tax Revenue-to-GDP ($REV_{t}/Y_t$)",
"TR": "Transfer Spending-to-GDP ($TR_{t}/Y_t$)",
"BQ": "Bequests-to-GDP ($BQ_{t}/Y_t$)",
"total_tax_revenue": "Tax Revenue-to-GDP ($Revenue_{t}/Y_t$)",
"business_tax_revenue": "Corporate Tax Revenue-to-GDP",
"iit_revenue": "Personal Income Tax Revenue-to-GDP",
"iit_payroll_revenue": "Personal Income + Payroll Tax Revenue-to-GDP",
"payroll_tax_revenue": "Payroll Tax Revenue-to-GDP",
"bequest_tax_revenue": "Bequest Tax Revenue-to-GDP",
"wealth_tax_revenue": "Wealth Tax Revenue-to-GDP",
"cons_tax_revenue": "Consumption Tax Revenue-to-GDP",
"agg_pension_outlays": "Pension Outlays-to-GDP ($Pensions_{t}/Y_t$)",
"UBI_path": "UBI Outlays-to-GDP ($UBI_{t}/Y_t$)",
}

GROUP_LABELS = {
Expand Down
9 changes: 9 additions & 0 deletions ogcore/output_plots.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,6 +76,9 @@ def plot_aggregates(
assert reform_tpi is not None
fig1, ax1 = plt.subplots()
for i, v in enumerate(var_list):
assert (
v in VAR_LABELS.keys()
), "{} is not in the list of variable labels".format(v)
if plot_type == "pct_diff":
if v in ["r_gov", "r", "r_p"]:
# Compute just percentage point changes for rates
Expand Down Expand Up @@ -237,6 +240,9 @@ def plot_industry_aggregates(
assert reform_tpi is not None
fig1, ax1 = plt.subplots()
for i, v in enumerate(var_list):
assert (
v in VAR_LABELS.keys()
), "{} is not in the list of variable labels".format(v)
if len(var_list) == 1:
var_label = ""
else:
Expand Down Expand Up @@ -449,6 +455,9 @@ def plot_gdp_ratio(
start_index = start_year - base_params.start_year
fig1, ax1 = plt.subplots()
for i, v in enumerate(var_list):
assert (
v in ToGDP_LABELS.keys()
), "{} is not in the list of variable labels".format(v)
if plot_type == "levels":
plot_var_base = (
base_tpi[v][: base_params.T] / base_tpi["Y"][: base_params.T]
Expand Down
4 changes: 3 additions & 1 deletion ogcore/parameter_tables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -194,7 +194,9 @@ def param_table(p, table_format="tex", path=None):
value = getattr(p, k)
if hasattr(value, "__len__") & ~isinstance(value, str):
if value.ndim > 1:
report = "See elsewhere"
report = (
"Too large to report here, see default parameters JSON"
)
else:
report = (
"["
Expand Down
20 changes: 10 additions & 10 deletions ogcore/pensions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -712,7 +712,7 @@ def delta_ret(r, Y, p):
return delta_ret


@numba.jit
@numba.jit(nopython=True)
def deriv_DB_loop(
w, e, S, S_ret, per_rmn, avg_earn_num_years, alpha_db, yr_contr
):
Expand Down Expand Up @@ -744,7 +744,7 @@ def deriv_DB_loop(
return d_theta


@numba.jit
@numba.jit(nopython=True)
def deriv_PS_loop(w, e, S, S_ret, per_rmn, d_theta, vpoint, factor):
"""
Change in points system pension benefits for another unit of
Expand Down Expand Up @@ -776,7 +776,7 @@ def deriv_PS_loop(w, e, S, S_ret, per_rmn, d_theta, vpoint, factor):
return d_theta


@numba.jit
@numba.jit(nopython=True)
def deriv_NDC_loop(
w, e, per_rmn, S, S_ret, tau_p, g_ndc_value, delta_ret_value, d_theta
):
Expand Down Expand Up @@ -813,7 +813,7 @@ def deriv_NDC_loop(
return d_theta


@numba.jit
@numba.jit(nopython=True)
def delta_ret_loop(S, S_ret, surv_rates, g_dir_value, dir_delta_s):
"""
Compute conversion coefficient for the NDC pension amount
Expand Down Expand Up @@ -842,7 +842,7 @@ def delta_ret_loop(S, S_ret, surv_rates, g_dir_value, dir_delta_s):
return dir_delta


@numba.jit
@numba.jit(nopython=True)
def PS_1dim_loop(w, e, n, S_ret, S, g_y, vpoint, factor, L_inc_avg_s, PS):
"""
Calculate public pension from a points system.
Expand Down Expand Up @@ -876,7 +876,7 @@ def PS_1dim_loop(w, e, n, S_ret, S, g_y, vpoint, factor, L_inc_avg_s, PS):
return PS


@numba.jit
@numba.jit(nopython=True)
def PS_2dim_loop(w, e, n, S_ret, S, J, g_y, vpoint, factor, L_inc_avg_sj, PS):
"""
Calculate public pension from a points system.
Expand Down Expand Up @@ -912,7 +912,7 @@ def PS_2dim_loop(w, e, n, S_ret, S, J, g_y, vpoint, factor, L_inc_avg_sj, PS):
return PS


@numba.jit
@numba.jit(nopython=True)
def DB_1dim_loop(
w,
e,
Expand Down Expand Up @@ -962,7 +962,7 @@ def DB_1dim_loop(
return DB


@numba.jit
@numba.jit(nopython=True)
def DB_2dim_loop(
w,
e,
Expand Down Expand Up @@ -1010,7 +1010,7 @@ def DB_2dim_loop(
return DB


@numba.jit
@numba.jit(nopython=True)
def NDC_1dim_loop(w, e, n, S_ret, S, g_y, tau_p, g_ndc, delta_ret, NDC_s, NDC):
"""
Calculate public pension from a notional defined contribution
Expand Down Expand Up @@ -1046,7 +1046,7 @@ def NDC_1dim_loop(w, e, n, S_ret, S, g_y, tau_p, g_ndc, delta_ret, NDC_s, NDC):
return NDC


@numba.jit
@numba.jit(nopython=True)
def NDC_2dim_loop(
w, e, n, S_ret, S, g_y, tau_p, g_ndc, delta_ret, NDC_sj, NDC
):
Expand Down
6 changes: 5 additions & 1 deletion ogcore/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -401,7 +401,7 @@ def save_return_table(table_df, output_type, path, precision=2):
Args:
table_df (Pandas DataFrame): table
output_type (string): specifies the type of file to save
table to: 'csv', 'tex', 'excel', 'json'
table to: 'csv', 'tex', 'excel', 'json', 'md'
path (string): specifies path to save file with table to
precision (integer): number of significant digits to print.
Defaults to 0.
Expand Down Expand Up @@ -433,6 +433,10 @@ def save_return_table(table_df, output_type, path, precision=2):
table_df.to_csv(path_or_buf=path, index=False, na_rep="")
elif output_type == "json":
table_df.to_json(path_or_buf=path, double_precision=precision)
elif output_type == "md":
table_df.to_markdown(
buf=path, index=False, floatfmt=".{}f".format(precision)
)
elif output_type == "excel":
table_df.to_excel(excel_writer=path, index=False, na_rep="")
else:
Expand Down
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