HBV-Export noch einmal angepasst

planner/HBV/HB0014432021000103351024032021112656.dat

@@ -263,7 +263,7 @@ I0155010335107250003002021        55000.0003
 I0155010335107310003002021            0.0003
 I0155010335107330003002021      1010000.0003
 I0155010335107400003002021         5100.0003
-I0155010335107410003002021       250000.0003
+I0155010335107410003002021      -250000.0003
 I0155010335107420003002021        16000.0003
 I0155010335107430003002021       150000.0003
-I0155010335107440003002021      -130000.0003
+I0155010335107440003002021       130000.0003

planner/HBV/HB0014432021000103353024032021112656.dat

@@ -263,7 +263,7 @@ I0155010335307250003002021         4500.0003
 I0155010335307310003002021            0.0003
 I0155010335307330003002021        96000.0003
 I0155010335307400003002021         1000.0003
-I0155010335307410003002021        45000.0003
+I0155010335307410003002021       -45000.0003
 I0155010335307420003002021         1600.0003
 I0155010335307430003002021        24000.0003
-I0155010335307440003002021        -1000.0003
+I0155010335307440003002021         1000.0003

planner/HBV/HB0014432021000103354024032021112656.dat

@@ -263,7 +263,7 @@ I0155010335407250003002021        32000.0003
 I0155010335407310003002021            0.0003
 I0155010335407330003002021      1334000.0003
 I0155010335407400003002021         6000.0003
-I0155010335407410003002021       310000.0003
+I0155010335407410003002021      -310000.0003
 I0155010335407420003002021        17000.0003
 I0155010335407430003002021       120000.0003
-I0155010335407440003002021       -15000.0003
+I0155010335407440003002021        15000.0003

planner/HBV/HB0014432021000103355024032021112656.dat

@@ -263,7 +263,7 @@ I0155010335507250003002021        24000.0003
 I0155010335507310003002021            0.0003
 I0155010335507330003002021       603200.0003
 I0155010335507400003002021         3000.0003
-I0155010335507410003002021       160000.0003
+I0155010335507410003002021      -160000.0003
 I0155010335507420003002021         2600.0003
 I0155010335507430003002021        70000.0003
-I0155010335507440003002021       -15000.0003
+I0155010335507440003002021        15000.0003

planner/HBV/HB0014432021000103627024032021112656.dat

@@ -263,7 +263,7 @@ I0155010362707250003002021        16000.0003
 I0155010362707310003002021            0.0003
 I0155010362707330003002021       386400.0003
 I0155010362707400003002021         1800.0003
-I0155010362707410003002021        90000.0003
+I0155010362707410003002021       -90000.0003
 I0155010362707420003002021         1400.0003
 I0155010362707430003002021        42000.0003
-I0155010362707440003002021        -2000.0003
+I0155010362707440003002021         2000.0003

planner/HBV/HB0307782021000108481024032021112656.dat

@@ -263,7 +263,7 @@ I0155010848107250003002021        56000.0003
 I0155010848107310003002021            0.0003
 I0155010848107330003002021      3078000.0003
 I0155010848107400003002021        65000.0003
-I0155010848107410003002021       280000.0003
+I0155010848107410003002021      -280000.0003
 I0155010848107420003002021       384000.0003
 I0155010848107430003002021       510000.0003
-I0155010848107440003002021      -362000.0003
+I0155010848107440003002021       362000.0003

planner/hbv_export.py

@@ -28,13 +28,17 @@ def main():
     # Planwerte importieren
     values_converter = {i: str for i in range(0, 200)}
     values_converter[4] = lambda x: np.float64(x.replace(',', '.'))
-    df_values = pd.read_csv(plan_values, decimal=',', sep=';', encoding='latin-1', converters=values_converter)
+    df_values = pd.read_csv(plan_values, decimal=',', sep=';', converters=values_converter)   # encoding='latin-1',
     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)
+    df_amount = pd.read_csv(plan_amount, decimal=',', sep=';', converters=values_converter)   # , encoding='latin-1'
     df_amount['type'] = '1'
     df: pd.DataFrame = df_values.append(df_amount)
 
+    # Planwerte alle positiv
+    df['Minus1'] = np.where(df['Vstufe 1'].isin(['Umsatzerlöse', 'Verk. Stückzahlen']) | df['Zeile'].isin(['7410', '7440']), 1, -1)
+    df['Gesamt'] = df['Gesamt'] * df['Minus1']
+
     # Planwerte übersetzen
     df = df.merge(df_department, how='inner', left_on='Betrieb Nr', right_on='department_id')
     df = df.merge(df_translation, how='left', left_on=['Zeile', 'type'], right_on=['from', 'type'])