Clean up for example scripts and macro_params.py, pass macro data date ranges,

MANIFEST.in

@@ -1 +1,2 @@
 include ogzaf/ogzaf_default_parameters.json
+include ogzaf/ogzaf_default_parameters_multisector.json

examples/run_og_zaf.py

@@ -6,20 +6,18 @@
 import json
 import time
 import copy
+import importlib.resources
 import matplotlib.pyplot as plt
 from ogzaf.calibrate import Calibration
 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 ogcore.utils import safe_read_pickle
+from ogzaf.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 +28,9 @@ def main():
 
     # Directories to save data
     CUR_DIR = os.path.dirname(os.path.realpath(__file__))
-    base_dir = os.path.join(CUR_DIR, "OG-ZAF-Example", "OUTPUT_BASELINE")
-    reform_dir = os.path.join(CUR_DIR, "OG-ZAF-Example", "OUTPUT_REFORM")
+    save_dir = os.path.join(CUR_DIR, "OG-ZAF-Example")
+    base_dir = os.path.join(save_dir, "OUTPUT_BASELINE")
+    reform_dir = os.path.join(save_dir, "OUTPUT_REFORM")
 
     """
     ---------------------------------------------------------------------------
@@ -46,33 +45,16 @@ 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, "..", "ogzaf", "ogzaf_default_parameters.json"
-                )
-            )
-        )
-    )
+    with importlib.resources.open_text(
+        "ogzaf", "ogzaf_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.tax_func_type = "linear"
-    p.age_specific = False
-    p.update_specifications(updated_params)
-    # set underlying growth rate to zero, as value from data is negative
-    p.g_y = 0.0
-    # set tax rates
-    p.update_specifications(
-        {
-            "cit_rate": [[0.27]],
-            "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()
@@ -90,7 +72,7 @@ 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]],
     }
@@ -128,12 +110,12 @@ def main():
 
     # create plots of output
     op.plot_all(
-        base_dir, reform_dir, os.path.join(CUR_DIR, "OG-ZAF_example_plots")
+        base_dir, reform_dir, os.path.join(save_dir, "OG-ZAF_example_plots")
     )
 
     print("Percentage changes in aggregates:", ans)
     # save percentage change output to csv file
-    ans.to_csv("ogzaf_example_output.csv")
+    ans.to_csv(os.path.join(save_dir, "OG-ZAF_example_output.csv"))
 
 
 if __name__ == "__main__":

examples/run_og_zaf_multiple_industry.py

@@ -6,27 +6,29 @@
 import json
 import time
 import copy
-import numpy as np
-
-# from taxcalc import Calculator
-from ogzaf.calibrate import Calibration
+import importlib.resources
+import matplotlib.pyplot as plt
 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
 
+# Use a custom matplotlib style file for plots
+plt.style.use("ogcore.OGcorePlots")
+
 
 def main():
     # Define parameters to use for multiprocessing
-    client = Client()
     num_workers = min(multiprocessing.cpu_count(), 7)
+    client = Client(n_workers=num_workers, threads_per_worker=1)
     print("Number of workers = ", num_workers)
 
     # Directories to save data
     CUR_DIR = os.path.dirname(os.path.realpath(__file__))
-    base_dir = os.path.join(CUR_DIR, "OG-ZAF-CIT_Example", "OUTPUT_BASELINE")
-    reform_dir = os.path.join(CUR_DIR, "OG-ZAF-CIT_Example", "OUTPUT_REFORM")
+    save_dir = os.path.join(CUR_DIR, "OG-ZAF-MultipleIndustry-example")
+    base_dir = os.path.join(save_dir, "OUTPUT_BASELINE")
+    reform_dir = os.path.join(save_dir, "OUTPUT_REFORM")
 
     """
     ---------------------------------------------------------------------------
@@ -41,32 +43,11 @@ 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, "..", "ogzaf", "ogzaf_default_parameters.json"
-                )
-            )
-        )
-    )
-    # Update parameters from calibrate.py Calibration class
-    p.M = 4
-    p.I = 5
-    c = Calibration(p)
-    updated_params_tax = {
-        # order of industries is primary, energy, tertiary, secondary ex energy
-        "Z": [[0.5, 0.4, 1.7, 1.0]],
-        "epsilon": [1.0, 1.0, 1.0, 1.0],
-        "gamma": [0.67, 0.50, 0.45, 0.53],
-        "gamma_g": [0.0, 0.0, 0.0, 0.0],
-        "alpha_c": c.alpha_c,
-        "io_matrix": c.io_matrix,
-    }
-    p.update_specifications(updated_params_tax)
-
-    # dump params to json
-    param_dump_json(p, os.path.join(base_dir, "model_params.json"))
+    with importlib.resources.open_text(
+        "ogzaf", "ogzaf_default_parameters_multisector.json"
+    ) as file:
+        defaults = json.load(file)
+    p.update_specifications(defaults)
 
     # Run model
     start_time = time.time()
@@ -84,12 +65,14 @@ def main():
     p2.baseline = False
     p2.output_base = reform_dir
 
-    # additional parameters to change
+    # Example reform is a corp tax rate cut (phased in) for all
+    # industries EXCEPT for secondary ex energy, which has a one point
+    # increae in the CIT rate
     updated_params_ref = {
         "cit_rate": [
-            [0.28, 0.28, 0.28, 0.28],
-            [0.28, 0.28, 0.28, 0.28],
-            [0.27, 0.27, 0.27, 0.27],
+            [0.27, 0.27, 0.27, 0.28],
+            [0.26, 0.26, 0.26, 0.28],
+            [0.25, 0.25, 0.25, 0.28],
         ],
         "baseline_spending": True,
     }
@@ -129,12 +112,14 @@ def main():
     op.plot_all(
         base_dir,
         reform_dir,
-        os.path.join(CUR_DIR, "OG-ZAF_CIT_multi_industry_plots"),
+        os.path.join(save_dir, "OG-ZAF-MultipleIndustry-example_plots"),
     )
 
     print("Percentage changes in aggregates:", ans)
     # save percentage change output to csv file
-    ans.to_csv("ogzaf_CIT_multi_industry_output.csv")
+    ans.to_csv(
+        os.path.join(save_dir, "OG-ZAF-MultipleIndustry-example_output.csv")
+    )
 
 
 if __name__ == "__main__":

ogzaf/calibrate.py

@@ -2,6 +2,7 @@
 from ogzaf import input_output as io
 import os
 import numpy as np
+import datetime
 from ogcore import demographics
 
 
@@ -11,8 +12,8 @@
     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 +22,6 @@
 
         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 +33,11 @@
         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
@@ -96,8 +94,6 @@
     # 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

ogzaf/income.py

@@ -313,7 +313,7 @@ def get_e_interp(S, age_wgts, age_wgts_80, abil_wgts, plot_path=None):
 
 
 def get_e_orig(age_wgts, abil_wgts, plot_path=None):
-    """
+    r"""
     This function generates the 80 x 7 matrix of lifetime earnings
     ability profiles, corresponding to annual ages from 21 to 100 and to
     paths based on income percentiles 0-25, 25-50, 50-70, 70-80, 80-90,

ogzaf/input_output.py

@@ -1,5 +1,6 @@
 import pandas as pd
 import numpy as np
+from ogzaf.utils import is_connected
 from ogzaf.constants import CONS_DICT, PROD_DICT
 
 """
@@ -8,13 +9,16 @@
 # Read in SAM file
 storage_options = {"User-Agent": "Mozilla/5.0"}
 SAM_path = "https://www.wider.unu.edu/sites/default/files/Data/SASAM-2015-Data-Resource.xlsx"
-SAM = pd.read_excel(
-    SAM_path,
-    sheet_name="Micro SAM 2015",
-    skiprows=6,
-    index_col=0,
-    storage_options=storage_options,
-)
+if is_connected():
+    SAM = pd.read_excel(
+        SAM_path,
+        sheet_name="Micro SAM 2015",
+        skiprows=6,
+        index_col=0,
+        storage_options=storage_options,
+    )
+else:
+    SAM = None
 
 
 def get_alpha_c(sam=SAM, cons_dict=CONS_DICT):