changing file paths and current directories

code/main/parse_GB_raw_data.py

@@ -1,4 +1,5 @@
 import pandas as pd
+import os
 
 # Load the Excel file
 excel_file = pd.ExcelFile('data/raw/GBweb_Row_Format.xlsx') 
@@ -80,6 +81,10 @@
     for column in GB_df.columns[1:]:
         GB_df[column] = GB_df[column].apply(lambda x: f"{x:.{decimal}f}")
 
+current_directory = os.path.dirname(os.path.abspath(__file__))
+file_path = "/../data/output/FRED.csv"
+GB_df.to_csv(current_directory + file_path)
+
 # Uncomment these final lines to get the output of your choice
-GB_df.to_csv('data/output/GB.csv')
+#GB_df.to_csv('data/output/GB.csv')
 # GB_df.to_excel('data/output/GB_parsed.xlsx', index=False)

code/main/parse_SPF_raw_data.py

@@ -1,4 +1,5 @@
 import pandas as pd
+import os
 
 # Load the Excel file with levels first
 excel_file = pd.ExcelFile('data/raw/meanLevel.xlsx') 
@@ -68,6 +69,10 @@
     for column in results_df.columns[1:]:
         results_df[column] = results_df[column].apply(lambda x: f"{x:.{decimal}f}")
 
+current_directory = os.path.dirname(os.path.abspath(__file__))
+file_path = "/../data/output/FRED.csv"
+results_df.to_csv(current_directory + file_path)
+
 # Uncomment these final lines to get the output of your choice
-results_df.to_csv('data/output/SPF.csv')
+# results_df.to_csv('data/output/SPF.csv')
 # SPF_df.to_excel('data/output/SPF_parsed.xlsx', index=False)

code/main/reproduce.py

@@ -8,12 +8,12 @@
 
 # List all files in the current directory
 files_to_run = [
-    "./code/main/parse_GB_raw_data.py",
-    "./code/main/parse_SPF_raw_data.py",
-    "./code/main/scrape_FRED_data.py",
-    "./code/main/annual_forecasts.py",
-    "./code/main/compute_abs_error.py",
-    "./code/main/abse_reg.py"
+    "/parse_GB_raw_data.py",
+    "/parse_SPF_raw_data.py",
+    "/scrape_FRED_data.py",
+    "/annual_forecasts.py",
+    "/compute_abs_error.py",
+    "/abse_reg.py"
 ]
 
 # Measure the start time
@@ -22,7 +22,7 @@
 # Run each Python script in the directory
 for python_file in files_to_run:
     if python_file != "reproduce.py":  # Exclude the launcher script itself
-        subprocess.run(["python", python_file])
+        subprocess.run(["python", current_directory + python_file])
 
 end_time = time.time()
 

code/main/scrape_FRED_data.py

@@ -2,6 +2,7 @@
 import numpy as np
 import fredpy as fp
 import matplotlib.pyplot as plt
+import os
 
 fp.api_key = "c735c085b4e162a17326871bc1d5c625"
 win = ['01-01-1982','12-01-2017']
@@ -29,6 +30,10 @@
     for column in obs_df.columns[1:]:
         obs_df[column] = obs_df[column].apply(lambda x: f"{x:.{decimal}f}")
 
-obs_df.to_csv('data/output/FRED.csv')
+current_directory = os.path.dirname(os.path.abspath(__file__))
+file_path = "/../data/output/FRED.csv"
+obs_df.to_csv(current_directory + file_path)
+
+#obs_df.to_csv('data/output/FRED.csv')
 
 # obs_df.to_excel('data/output/FRED_scraped.xlsx', index=False)

reproduce.sh

@@ -6,7 +6,7 @@ cd "$SCPT_DIR"
 
 ipython *.ipynb
 
-cd code
+cd code/main
 
 python reproduce.py
 

results/abse_reg_1983.txt

@@ -4,8 +4,8 @@ Regression Summary for GB_error_unemp:
 Dep. Variable:         GB_error_unemp   R-squared:                       0.013
 Model:                            OLS   Adj. R-squared:                  0.006
 Method:                 Least Squares   F-statistic:                     1.760
-Date:                Tue, 07 Nov 2023   Prob (F-statistic):              0.187
-Time:                        09:53:44   Log-Likelihood:                -118.32
+Date:                Wed, 21 Feb 2024   Prob (F-statistic):              0.187
+Time:                        08:35:09   Log-Likelihood:                -118.32
 No. Observations:                 144   AIC:                             240.6
 Df Residuals:                     142   BIC:                             246.6
 Df Model:                           1                                         
@@ -32,8 +32,8 @@ Regression Summary for SPF_error_unemp:
 Dep. Variable:        SPF_error_unemp   R-squared:                       0.005
 Model:                            OLS   Adj. R-squared:                 -0.002
 Method:                 Least Squares   F-statistic:                    0.6700
-Date:                Tue, 07 Nov 2023   Prob (F-statistic):              0.414
-Time:                        09:53:44   Log-Likelihood:                -125.71
+Date:                Wed, 21 Feb 2024   Prob (F-statistic):              0.414
+Time:                        08:35:09   Log-Likelihood:                -125.71
 No. Observations:                 144   AIC:                             255.4
 Df Residuals:                     142   BIC:                             261.4
 Df Model:                           1                                         
@@ -60,8 +60,8 @@ Regression Summary for GB_error_cons:
 Dep. Variable:          GB_error_cons   R-squared:                       0.219
 Model:                            OLS   Adj. R-squared:                  0.214
 Method:                 Least Squares   F-statistic:                     30.02
-Date:                Tue, 07 Nov 2023   Prob (F-statistic):           1.89e-07
-Time:                        09:53:44   Log-Likelihood:                -166.53
+Date:                Wed, 21 Feb 2024   Prob (F-statistic):           1.89e-07
+Time:                        08:35:10   Log-Likelihood:                -166.53
 No. Observations:                 144   AIC:                             337.1
 Df Residuals:                     142   BIC:                             343.0
 Df Model:                           1                                         
@@ -88,8 +88,8 @@ Regression Summary for SPF_error_cons:
 Dep. Variable:         SPF_error_cons   R-squared:                       0.133
 Model:                            OLS   Adj. R-squared:                  0.127
 Method:                 Least Squares   F-statistic:                     26.15
-Date:                Tue, 07 Nov 2023   Prob (F-statistic):           1.01e-06
-Time:                        09:53:44   Log-Likelihood:                -177.99
+Date:                Wed, 21 Feb 2024   Prob (F-statistic):           1.01e-06
+Time:                        08:35:10   Log-Likelihood:                -177.99
 No. Observations:                 144   AIC:                             360.0
 Df Residuals:                     142   BIC:                             365.9
 Df Model:                           1