HBV-Export 2023

.gitignore

@@ -1,4 +1,5 @@
 save/
 __pycache__
 .~lock.*
-export/hbv/
+export/hbv/
+config/gnupg/random_seed

tox.ini

@@ -0,0 +1,5 @@
+[flake8]
+ignore = E712, W504
+max-line-length = 140
+# exclude = tests/*
+# max-complexity = 10

webservice/gnupg_encrypt.py

@@ -1,20 +1,17 @@
 import gnupg
+from pathlib import Path
 
 
 def encrypt(source_file):
-    base_dir = '/home/robert/projekte/python/planner/gnupg/'
+    config_dir = Path().cwd() / 'config/gnupg'
 
-    gpg = gnupg.GPG(homedir=base_dir)
+    gpg = gnupg.GPG(homedir=config_dir)
     # gpg.import_keys(base_dir + 'export.gpg')
     # public_keys = gpg.list_keys()
     # print(public_keys)
 
     with open(source_file, 'rb') as frh:
-        result = gpg.encrypt(frh, '942170BF95EA785E8D4A5C94D1839587F0E1C01C', output=source_file + '.gpg')
+        result = gpg.encrypt(frh, '942170BF95EA785E8D4A5C94D1839587F0E1C01C', output=str(source_file) + '.gpg')
         # 'BV_IFC; BMW Group <BV_IFC@softlab.de>'
         print(result.stderr)
         #  , verbose=True
-
-
-if __name__ == '__main__':
-    encrypt('/home/robert/projekte/python/planner/HBV/HB0014432021000103351024032021112656.dat')

webservice/hbv_export.py

@@ -3,38 +3,48 @@ import numpy as np
 from datetime import datetime
 from gnupg_encrypt import encrypt
 import os
+from pathlib import Path
 
 
-base_dir = '/home/robert/projekte/python/planner/HBV/'
-hb_format = base_dir + 'hb_format.csv'
-hb_department = base_dir + 'hb_department.csv'
-hb_translation = base_dir + 'hb_translation.csv'
-plan_amount = base_dir + '../export/Planner_2022_V2_Stk.csv'
-plan_values = base_dir + '../export/Planner_2022_V2_Plan.csv'
+current_year = '2023'
+current_version = 'V1'
 
-hb_ignored = base_dir + 'ignoriert.csv'
+base_dir = Path().cwd()
+config_dir = base_dir / 'config/hbv'
+export_dir = base_dir / 'export'
+hb_format = config_dir / 'hb_format.csv'
+hb_department = config_dir / 'hb_department.csv'
+hb_translation = config_dir / 'hb_translation.csv'
+plan_amount = export_dir / f'Planner_{current_year}_{current_version}_Stk.csv'
+plan_values = export_dir / f'Planner_{current_year}_{current_version}_Plan.csv'
+
+hb_ignored = export_dir / 'hbv/ignoriert.csv'
 
-current_year = '2022'
 current_date = datetime.now().strftime('%d%m%Y%H%M%S')
 # current_date = '24032021112656'
 
 
 def main():
     # Übersetzungstabelle importieren
-    df_translation = pd.read_csv(hb_translation, decimal=',', sep=';', encoding='latin-1', converters={i: str for i in range(0, 200)})
-    # df_translation['column_no_join'] = np.where(df_translation['column_no'].isin(['1', '3', '4']), df_translation['column_no'], '0')
+    df_translation = pd.read_csv(hb_translation, decimal=',', sep=';',
+                                 encoding='latin-1', converters={i: str for i in range(0, 200)})
+    # df_translation['column_no_join'] = np.where(df_translation['column_no']
+    # .isin(['1', '3', '4']), df_translation['column_no'], '0')
     # Department-Zuordnung importieren
-    df_department = pd.read_csv(hb_department, decimal=',', sep=';', encoding='latin-1', converters={i: str for i in range(0, 200)})
+    df_department = pd.read_csv(hb_department, decimal=',', sep=';',
+                                encoding='latin-1', converters={i: str for i in range(0, 200)})
 
     # Planwerte importieren
     values_converter = {i: str for i in range(0, 200)}
     values_converter[4] = lambda x: np.float64(x.replace(',', '.') if x != '' else 0.0)
     values_converter[5] = values_converter[4]
-    df_values = pd.read_csv(plan_values, decimal=',', sep=';', encoding='latin-1', converters=values_converter)   # encoding='latin-1',
+    df_values = pd.read_csv(plan_values, decimal=',', sep=';',
+                            encoding='latin-1', converters=values_converter)
     df_values['Gesamt'] = df_values['Gesamt'] + df_values['Periode13']
     df_values['type'] = '2'
     df_values['type'] = np.where(df_values['Vstufe 1'].isin(['Materialaufwand']), '3', df_values['type'])
-    df_amount = pd.read_csv(plan_amount, decimal=',', sep=';', encoding='latin-1', converters=values_converter)   # , encoding='latin-1'
+    df_amount = pd.read_csv(plan_amount, decimal=',', sep=';', 
+                            encoding='latin-1', converters=values_converter)
     df_amount['type'] = '1'
     df: pd.DataFrame = df_values.append(df_amount)
 
@@ -59,8 +69,8 @@ def main():
 
     for group in df_valid.groups:
         g = dict(zip(group_by, group))
-        filename = base_dir + f"{current_year}/{g['BV_NUMMER']}_{g['FILIAL_NR']}/HB{g['BM_CODE']}{current_year}00{g['BV_NUMMER']}{g['FILIAL_NR']}0{current_date}.dat"
-        os.makedirs(os.path.dirname(filename), exist_ok=True)
+        filename = export_dir / f"hbv/{current_year}/{g['BV_NUMMER']}_{g['FILIAL_NR']}/HB{g['BM_CODE']}{current_year}00{g['BV_NUMMER']}{g['FILIAL_NR']}0{current_date}.dat"
+        os.makedirs(filename.parent, exist_ok=True)
         df_group = df_valid.get_group(group).groupby(rename_to[:-1]).sum().reset_index()
         with open(filename, 'w') as fwh:
             for row in df_group.to_dict(orient='records'):