Merge pull request #12 from jdebacker/macro_params

Merging

MANIFEST.in

@@ -1 +1,2 @@
 include ogidn/ogidn_default_parameters.json
+include ogidn/data/002_IFPRI_SAM_IDN_2018_SAM.csv

docs/create_doc_figures.py

@@ -146,7 +146,7 @@
 """
 # IDN profiles
 pp.plot_ability_profiles(
-    p, p2=None, t=None, log_scale=True, include_title=False, path=plot_path
+    p, p2=None, t=None, log_scale=False, include_title=False, path=plot_path
 )
 # Plotting with USA also is too busy, so do separately
 pp.plot_ability_profiles(

examples/run_og_idn.py

@@ -6,20 +6,19 @@
 import json
 import time
 import copy
+import importlib.resources
 import matplotlib.pyplot as plt
-from ogidn.calibrate import Calibration
+import ogcore
 from ogcore.parameters import Specifications
 from ogcore import output_tables as ot
 from ogcore import output_plots as op
 from ogcore.execute import runner
 from ogcore.utils import safe_read_pickle, param_dump_json
+from ogidn.calibrate import Calibration
+from ogidn.utils import is_connected
 
 # Use a custom matplotlib style file for plots
-style_file_url = (
-    "https://raw.githubusercontent.com/PSLmodels/OG-Core/"
-    + "master/ogcore/OGcorePlots.mplstyle"
-)
-plt.style.use(style_file_url)
+plt.style.use("ogcore.OGcorePlots")
 
 
 def main():
@@ -30,8 +29,9 @@ def main():
 
     # Directories to save data
     CUR_DIR = os.path.dirname(os.path.realpath(__file__))
-    base_dir = os.path.join(CUR_DIR, "OG-IDN-Example", "OUTPUT_BASELINE")
-    reform_dir = os.path.join(CUR_DIR, "OG-IDN-Example", "OUTPUT_REFORM")
+    save_dir = os.path.join(CUR_DIR, "OG-IDN-Example")
+    base_dir = os.path.join(save_dir, "OUTPUT_BASELINE")
+    reform_dir = os.path.join(save_dir, "OUTPUT_REFORM")
 
     """
     ---------------------------------------------------------------------------
@@ -46,31 +46,16 @@ def main():
         output_base=base_dir,
     )
     # Update parameters for baseline from default json file
-    p.update_specifications(
-        json.load(
-            open(
-                os.path.join(
-                    CUR_DIR, "..", "ogidn", "ogidn_default_parameters.json"
-                )
-            )
-        )
-    )
+    with importlib.resources.open_text(
+        "ogidn", "ogidn_default_parameters.json"
+    ) as file:
+        defaults = json.load(file)
+    p.update_specifications(defaults)
     # Update parameters from calibrate.py Calibration class
-    c = Calibration(p)
-    updated_params = c.get_dict()
-    p.update_specifications(updated_params)
-    # set tax rates
-    p.update_specifications(
-        {
-            "cit_rate": [[0.27]],
-            "tax_func_type": "linear",
-            "age_specific": False,
-            # "etr_params": [[[0.22]]],
-            # "mtrx_params": [[[0.31]]],
-            # "mtry_params": [[[0.25]]],
-            "tau_c": [[0.15]],
-        }
-    )
+    if is_connected():  # only update if connected to internet
+        c = Calibration(p)
+        updated_params = c.get_dict()
+        p.update_specifications(updated_params)
 
     # Run model
     start_time = time.time()
@@ -88,7 +73,7 @@ def main():
     p2.baseline = False
     p2.output_base = reform_dir
 
-    # additional parameters to change
+    # Parameter change for the reform run
     updated_params_ref = {
         "cit_rate": [[0.30]],
     }
@@ -126,12 +111,12 @@ def main():
 
     # create plots of output
     op.plot_all(
-        base_dir, reform_dir, os.path.join(CUR_DIR, "OG-IDN_example_plots")
+        base_dir, reform_dir, os.path.join(save_dir, "OG-IDN_example_plots")
     )
 
     print("Percentage changes in aggregates:", ans)
     # save percentage change output to csv file
-    ans.to_csv("ogidn_example_output.csv")
+    ans.to_csv(os.path.join(save_dir, "OG-IDN_example_output.csv"))
 
 
 if __name__ == "__main__":

ogidn/calibrate.py

@@ -2,17 +2,20 @@
 from ogidn import input_output as io
 import os
 import numpy as np
+import datetime
 from ogcore import demographics
 
+UN_COUNTRY_CODE = "360"
+
 
 class Calibration:
     """OG-IDN calibration class"""
 
     def __init__(
         self,
         p,
-        estimate_tax_functions=False,
-        estimate_chi_n=False,
+        macro_data_start_year=datetime.datetime(1947, 1, 1),
+        macro_data_end_year=datetime.date.today(),
         demographic_data_path=None,
         output_path=None,
     ):
@@ -21,9 +24,6 @@ def __init__(
 
         Args:
             p (OG-Core Specifications object): model parameters
-            estimate_tax_functions (bool): whether to estimate tax
-                function parameters
-            estimate_chi_n (bool): whether to estimate chi_n
             demographic_data_path (str): path to save demographic data
             output_path (str): path to save output to
 
@@ -35,11 +35,11 @@ def __init__(
         if output_path is not None:
             if not os.path.exists(output_path):
                 os.makedirs(output_path)
-        self.estimate_tax_functions = estimate_tax_functions
-        self.estimate_chi_n = estimate_chi_n
 
         # Macro estimation
-        self.macro_params = macro_params.get_macro_params()
+        self.macro_params = macro_params.get_macro_params(
+            macro_data_start_year, macro_data_end_year
+        )
 
         # io matrix and alpha_c
         if p.I > 1:  # no need if just one consumption good
@@ -64,7 +64,7 @@ def __init__(
             p.T,
             0,
             99,
-            country_id="360",
+            country_id=UN_COUNTRY_CODE,
             initial_data_year=p.start_year - 1,
             final_data_year=p.start_year + 1,
             GraphDiag=False,
@@ -78,26 +78,25 @@ def __init__(
             p.T,
             0,
             99,
-            country_id="360",
+            country_id=UN_COUNTRY_CODE,
             initial_data_year=p.start_year - 1,
             final_data_year=p.start_year + 1,
             GraphDiag=False,
         )
 
         # earnings profiles
         self.e = income.get_e_interp(
+            p.E,
             p.S,
-            self.demographic_params["omega_SS"],
-            demog80["omega_SS"],
+            p.J,
             p.lambdas,
+            demog80["omega_SS"],
             plot_path=output_path,
         )
 
     # method to return all newly calibrated parameters in a dictionary
     def get_dict(self):
         dict = {}
-        # if self.estimate_chi_n:
-        #     dict["chi_n"] = self.chi_n
         dict.update(self.macro_params)
         dict["e"] = self.e
         dict["alpha_c"] = self.alpha_c