Merge pull request PSLmodels#962 from jdebacker/debt_gdp_labels

Merging

ogcore/constants.py

@@ -4,7 +4,7 @@
 BASELINE_DIR = "OUTPUT_BASELINE"
 
 # Default year for model runs
-DEFAULT_START_YEAR = 2021
+DEFAULT_START_YEAR = 2025
 
 
 VAR_LABELS = {
@@ -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 = {

ogcore/output_plots.py

@@ -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
@@ -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:
@@ -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]

ogcore/parameter_tables.py

@@ -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 = (
                     "["

ogcore/pensions.py

@@ -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
 ):
@@ -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
@@ -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
 ):
@@ -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
@@ -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.
@@ -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.
@@ -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,
@@ -962,7 +962,7 @@ def DB_1dim_loop(
     return DB
 
 
-@numba.jit
+@numba.jit(nopython=True)
 def DB_2dim_loop(
     w,
     e,
@@ -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
@@ -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
 ):

ogcore/utils.py

@@ -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.
@@ -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: