Merge pull request PSLmodels#1053 from jdebacker/ss_replace

Merging

Author: rickecon

run_examples/example-diffs examples/example-diffsrun_examples/example-diffs renamed to examples/example-diffs

@@ -21,10 +21,6 @@
 # Use a custom matplotlib style file for plots
 plt.style.use("ogcore.OGcorePlots")
 
-# Set verbosity
-ogcore.SS.VERBOSE = False
-ogcore.TPI.VERBOSE = False
-
 
 def main():
     # Define parameters to use for multiprocessing

run_examples/example-diffs.bat examples/example-diffs.batrun_examples/example-diffs.bat renamed to examples/example-diffs.bat

@@ -11,7 +11,6 @@
 import warnings
 import logging
 
-
 if not SHOW_RUNTIME:
     warnings.simplefilter("ignore", RuntimeWarning)
 
@@ -1226,27 +1225,25 @@ def SS_fsolve(guesses, *args):
             + list(error_BQ)
             + [error_TR]
         )
-    logging.info(f"GE loop errors = ", [f"{error:.3e}" for error in errors])
+    error_string = [f"{error:.3e}" for error in errors]
+    logging.info(f"GE loop errors = {error_string}")
 
     return errors
 
 
-def run_SS(p, client=None, verbose=False):
+def run_SS(p, client=None):
     """
     Solve for steady-state equilibrium of OG-Core.
 
     Args:
         p (OG-Core Specifications object): model parameters
         client (Dask client object): client
-        verbose (bool): if True, set logging to INFO level
 
     Returns:
         output (dictionary): dictionary with steady-state solution
             results
 
     """
-    # Configure logging level based on verbose parameter
-    config.set_logging_level(verbose)
     # Create list of deviation factors for initial guesses of r and TR
     dev_factor_list = [
         [1.00, 1.0],

…mples/expected_ogcore_example_output.csv …mples/expected_ogcore_example_output.csvrun_examples/expected_ogcore_example_output.csv renamed to examples/expected_ogcore_example_output.csv run_examples/expected_ogcore_example_output.csv renamed to examples/expected_ogcore_example_output.csv

@@ -543,28 +543,22 @@ def inner_loop(guesses, outer_loop_vars, initial_values, ubi, j, ind, p):
         n_vec = solutions.x[p.S :]
         n_mat[t + ind, ind] = n_vec
 
-    # print('Type ', j, ' max euler error = ',
-    #       np.absolute(euler_errors).max())
-
     return euler_errors, b_mat, n_mat
 
 
-def run_TPI(p, client=None, verbose=False):
+def run_TPI(p, client=None):
     """
     Solve for transition path equilibrium of OG-Core.
 
     Args:
         p (OG-Core Specifications object): model parameters
         client (Dask client object): client
-        verbose (bool): if True, set logging to INFO level
 
     Returns:
         output (dictionary): dictionary with transition path solution
             results
 
     """
-    # Configure logging level based on verbose parameter
-    config.set_logging_level(verbose)
     # unpack tuples of parameters
     initial_values, ss_vars, theta, baseline_values = get_initial_SS_values(p)
     (B0, b_sinit, b_splus1init, factor, initial_b, initial_n) = initial_values
@@ -1145,7 +1139,7 @@ def run_TPI(p, client=None, verbose=False):
         #         print 'New Value of nu:', nu
         TPIiter += 1
         logging.info(f"Iteration: {TPIiter}")
-        logging.info(f"\tDistance: {TPIdist}")
+        logging.info(f"Distance: {TPIdist}")
 
     # Compute effective and marginal tax rates for all agents
     num_params = len(p.mtrx_params[0][0])

run_examples/multi_industry_example.py examples/multi_industry_example.pyrun_examples/multi_industry_example.py renamed to examples/multi_industry_example.py

@@ -7,10 +7,10 @@
 
 import logging
 
-VERBOSE = False  # Default value
+VERBOSE = True  # Default verbosity setting
 
 
-def set_logging_level(verbose=False):
+def set_logging_level(verbose=True):
     """
     Set the logging level for OG-Core modules.
 
@@ -26,13 +26,14 @@ def set_logging_level(verbose=False):
     level = logging.INFO if verbose else logging.WARNING
 
     # Configure the root logger
-    logging.basicConfig(
-        level=level,
-        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-    )
+    logging.basicConfig(level=level, format="%(message)s")
 
     # Also set the ogcore logger specifically
     logger = logging.getLogger("ogcore")
     logger.setLevel(level)
 
     return level
+
+
+# Auto-configure when module is imported
+set_logging_level(verbose=VERBOSE)

run_examples/run_ogcore_example.py examples/run_ogcore_example.pyrun_examples/run_ogcore_example.py renamed to examples/run_ogcore_example.py

@@ -8,13 +8,7 @@
 import numpy as np
 from ogcore import tax, utils
 import logging
-
-# Configure logging
-VERBOSE = False
-log_level = logging.INFO if VERBOSE else logging.WARNING
-logging.basicConfig(
-    level=log_level, format="%(message)s"  # Only show the message itself
-)
+from ogcore import config
 
 """
 ------------------------------------------------------------------------
@@ -775,28 +769,27 @@ def constraint_checker_SS(bssmat, nssmat, cssmat, ltilde):
     logging.info("Checking constraints on capital, labor, and consumption.")
 
     if (bssmat < 0).any():
-        logging.info("\tWARNING: There is negative capital stock")
+        logging.info("WARNING: There is negative capital stock")
     flag2 = False
     if (nssmat < 0).any():
         logging.info(
-            "\tWARNING: Labor supply violates nonnegativity ", "constraints."
+            "WARNING: Labor supply violates nonnegativity constraints."
         )
         flag2 = True
     if (nssmat > ltilde).any():
-        logging.info("\tWARNING: Labor supply violates the ltilde constraint.")
+        logging.info("WARNING: Labor supply violates the ltilde constraint.")
         flag2 = True
     if flag2 is False:
         logging.info(
-            "\tThere were no violations of the constraints on labor",
-            " supply.",
+            "There were no violations of the constraints on labor supply.",
         )
     if (cssmat < 0).any():
         logging.info(
-            "\tWARNING: Consumption violates nonnegativity", " constraints."
+            "WARNING: Consumption violates nonnegativity constraints."
         )
     else:
         logging.info(
-            "\tThere were no violations of the constraints on", " consumption."
+            "There were no violations of the constraints on consumption."
         )
 
 
@@ -821,13 +814,11 @@ def constraint_checker_TPI(b_dist, n_dist, c_dist, t, ltilde):
     """
     if (b_dist <= 0).any():
         logging.info(
-            "\tWARNING: Aggregate capital is less than or equal to ",
-            "zero in period %.f." % t,
+            f"WARNING: Aggregate capital is less than or equal to zero in period {t}."
         )
     if (n_dist < 0).any():
         logging.info(
-            "\tWARNING: Labor supply violates nonnegativity",
-            " constraints in period %.f." % t,
+            f"WARNING: Labor supply violates nonnegativity constraints in period {t}."
         )
     if (n_dist > ltilde).any():
         logging.info(

ogcore/SS.py

@@ -148,10 +148,14 @@ def SS_amount(w, n, theta, t, j, shift, method, e, p):
         # entry for retirement will change (it shifts back one).
         # The shift boolean makes sure we start replacement rates
         # at the correct age.
+        if j is None:
+            replace_rate_adjust = p.replacement_rate_adjust[-1, :]
+        else:
+            replace_rate_adjust = p.replacement_rate_adjust[-1, j]
         if shift is False:
-            pension[p.retire[-1] :] = theta * w
+            pension[p.retire[-1] :] = replace_rate_adjust * theta * w
         else:
-            pension[p.retire[-1] - 1 :] = theta * w
+            pension[p.retire[-1] - 1 :] = replace_rate_adjust * theta * w
     elif method == "TPI":
         length = w.shape[0]
         if not shift:
@@ -734,8 +738,8 @@ def deriv_DB_loop(
             another unit of labor supply
     """
     d_theta = np.zeros(per_rmn)
-    print("Year contribution: ", yr_contr)
-    print("Average earnings years: ", avg_earn_num_years)
+    # print("Year contribution: ", yr_contr)
+    # print("Average earnings years: ", avg_earn_num_years)
     num_per_retire = S - S_ret
     for s in range(per_rmn):
         d_theta[s] = w[s] * e[s] * alpha_db * (yr_contr / avg_earn_num_years)