associatedlocation finished

prepared_steps/10_upsert_associated_location/ValueMapping.csv

@@ -0,0 +1,5 @@
+ObjectName,FieldName,RawValue,Value
+AssociatedLocation,Type,Installer (installation),Installer
+AssociatedLocation,Type,Installer (first ignition),Installer
+AssociatedLocation,Type,Installer (service),Installer
+AssociatedLocation,Type,Installer (Timex),Installer

prepared_steps/10_upsert_associated_location/command.txt

@@ -0,0 +1 @@
+sf sfdmu run --sourceusername rene.kasseboehmer@vaillant.de.devrene --targetusername rene.kasseboehmer@vaillant.de.devrene

prepared_steps/10_upsert_associated_location/export.json

@@ -0,0 +1,16 @@
+{
+    "allOrNone": true,
+    "excludeIdsFromCSVFiles": true,
+    
+    "objects": [
+{
+            "query": "SELECT Type,ActiveFrom,ActiveTo,ParentRecordId$Account,PKey__c, LocationId FROM AssociatedLocation",
+            "operation": "Insert",
+            "externalId": "PKey__c",
+            "useSourceCSVFile": true,
+            "master": true,
+            "useValuesMapping": true,
+            "excludedFields": ["PKey__c"]
+        }
+    ]
+}

prepared_steps/1_extract_data/queries.json

@@ -2,10 +2,10 @@
     [
         {
             "sobject": "SCInstalledBaseLocation__c",
-            "query": "SELECT Id, City__c, Country__c, GeoY__c, GeoX__c, PostalCode__c, Street__c, Extension__c, HouseNo__c, FlatNo__c, Floor__c FROM SCInstalledBaseLocation__c WHERE Country__c = 'NL' limit 1"
+            "query": "SELECT Id, City__c, Country__c, GeoY__c, GeoX__c, PostalCode__c, Street__c, Extension__c, HouseNo__c, FlatNo__c, Floor__c FROM SCInstalledBaseLocation__c WHERE Country__c = 'NL' AND Id = 'a1B1r0000099EsfEAE' limit 1"
         },{
             "sobject": "SCInstalledBase__c",
-            "query": "SELECT Id, Name, CommissioningDate__c,InstallationDate__c,ProductEnergy__c, ProductUnitClass__c,ArticleNo__c,SerialNo__c, SerialNoException__c, ProductUnitType__c, InstalledBaseLocation__c  FROM SCInstalledBase__c WHERE Country__c = 'NL' limit 1"
+            "query": "SELECT Id, Name, CommissioningDate__c,InstallationDate__c,ProductEnergy__c, ProductUnitClass__c,ArticleNo__c,SerialNo__c, SerialNoException__c, ProductUnitType__c, InstalledBaseLocation__c  FROM SCInstalledBase__c WHERE Country__c = 'NL' AND InstalledBaseLocation__c = 'a1B1r0000099EsfEAE'"
         },{
             "sobject": "Asset",
             "query": "SELECT Id, Serialnumber FROM Asset WHERE Location.ParentLocation.Name LIKE '%NL'"
@@ -21,6 +21,9 @@
         },{
             "sobject": "Product2",
             "query": "SELECT Id, Main_Product_Group__c, Family, MaterialType__c, Name, Product_Code__c, ProductCode, EAN_Product_Code__c FROM Product2"
+        },{
+            "sobject": "SCInstalledBaseRole__c",
+            "query": "SELECT Id, InstalledBaseLocation__c, Role__c, ValidFrom__c, ValidTo__c, Account__c  FROM SCInstalledBaseRole__c WHERE InstalledBaseLocation__r.Country__c = 'NL' AND InstalledBaseLocation__c = 'a1B1r0000099EsfEAE'"
         }
     ]
 }

prepared_steps/1_extract_data/results/SCInstalledBaseRole__c.csv

@@ -0,0 +1,11 @@
+"Id","InstalledBaseLocation__c","Role__c","ValidFrom__c","ValidTo__c","Account__c"
+"a0a1r00000JtNV5AAN","a1B1r0000099EsfEAE","Installer (installation)","2018-03-27","","0012000000eCN6TAAW"
+"a0a1r00000JtNV6AAN","a1B1r0000099EsfEAE","Owner","2018-04-06","","0011r00001mmKQ8AAM"
+"a0a1r00000JtNVUAA3","a1B1r0000099EsfEAE","Installer (installation)","2018-03-27","","0012000000eCN6TAAW"
+"a0a1r00000JtNVVAA3","a1B1r0000099EsfEAE","Owner","2018-04-06","","0011r00001mmKQ8AAM"
+"a0a1r00000JtNVtAAN","a1B1r0000099EsfEAE","Installer (installation)","2018-03-27","","0012000000eCN6TAAW"
+"a0a1r00000JtNVuAAN","a1B1r0000099EsfEAE","Owner","2018-04-06","","0011r00001mmKQ8AAM"
+"a0a1r00000KpzPNAAZ","a1B1r0000099EsfEAE","Installer (installation)","2018-03-27","","0012000000eCN6TAAW"
+"a0a1r00000KpzPOAAZ","a1B1r0000099EsfEAE","Owner","2018-03-28","","0011r00001mmKQ8AAM"
+"a0a1r00000KpzQQAAZ","a1B1r0000099EsfEAE","Installer (installation)","2018-03-27","","0012000000eCN6TAAW"
+"a0a1r00000KpzQRAAZ","a1B1r0000099EsfEAE","Owner","2018-03-28","","0011r00001mmKQ8AAM"

prepared_steps/2_transform_via_script/LocationScript.py

@@ -9,6 +9,7 @@ country_mapping = {
 read_df = pd.read_csv('../1_extract_data/results/SCInstalledBaseLocation__c.csv', header=0, keep_default_na=False, dtype=str)
 read_df_ib = pd.read_csv('../1_extract_data/results/SCInstalledBase__c.csv', header=0, keep_default_na=False, dtype=str)
 read_df_product2 = pd.read_csv('../1_extract_data/results/Product2.csv', header=0, keep_default_na=False, dtype=str)
+read_df_ibr = pd.read_csv('../1_extract_data/results/SCInstalledBaseRole__c.csv', header=0, keep_default_na=False, dtype=str)
 
 for row in read_df.to_dict('records'):
     try:
@@ -28,11 +29,14 @@ reindex_columns_ib = ['Id','Name','CommissioningDate__c','InstallationDate__c','
 #"Id","Main_Product_Group__c","Family","MaterialType__c","Name","Product_Code__c","ProductCode","EAN_Product_Code__c"
 reindex_columns_product2 = ['Id','Main_Product_Group__c','Family','MaterialType__c','Name','Product_Code__c','ProductCode','EAN_Product_Code__c']
 #reindex_columns_product2 = ['EAN_Product_Code__c','Family','Id','Main_Product_Group__c','MaterialType__c','Name','Product_Code__c','ProductCode']
+#"Id","InstalledBaseLocation__c","Role__c","ValidFrom__c","ValidTo__c","Account__c"
+reindex_columns_ibr = ['Id', 'InstalledBaseLocation__c', 'Role__c', 'ValidFrom__c', 'ValidTo__c', 'Account__c']
 
 # Reindex the columns to match the desired format
 df = read_df.reindex(reindex_columns, axis=1)
 df_ib = read_df_ib.reindex(reindex_columns_ib, axis=1)
 df_product2 = read_df_product2.reindex(reindex_columns_product2, axis=1)
+df_ibr = read_df_ibr.reindex(reindex_columns_ibr, axis=1)
 
 df['Street'] = (
     df['Street__c'].astype(str) + ' ' +
@@ -117,7 +121,7 @@ parent_df['IsMobile'] = 'false'
 parent_df['LocationType'] = 'Site'
 
 ## 3. Child_Location.csv
-child_columns = ['Extension__c', 'FlatNo__c', 'Floor__c', 'City__c', 'Country__c', 
+child_columns = ['Id', 'Extension__c', 'FlatNo__c', 'Floor__c', 'City__c', 'Country__c', 
                    'PostalCode__c', 'Street', 'PKey__c']
 # Modify child_df by explicitly creating a new DataFrame
 child_df = df[child_columns].copy()  # Add .copy() to create an explicit copy
@@ -142,7 +146,7 @@ child_df['ExternalReference'] = (
 )
 
 # Rename columns to match the desired format
-child_df.columns = ['Extension__c', 'Flat__c', 'Floor__c', 'City', 'Country',
+child_df.columns = ['Id', 'Extension__c', 'Flat__c', 'Floor__c', 'City', 'Country',
                      'PostalCode', 'Street', 'PKey__c', 'Name', 'ExternalReference']
 
 child_df = child_df.drop_duplicates(subset=['Extension__c', 'Flat__c', 'Floor__c','City', 'Country', 'PostalCode', 'Street'], keep='first')
@@ -180,11 +184,33 @@ merged_df_ib = merged_df_ib.drop('Product_Code__c', axis=1)
 
 merged_df_ib = merged_df_ib.drop_duplicates(subset=['Name','SerialNumber'], keep='first')
 
+# Merging LPG into one Value for Assets
+merged_df_ib = merged_df_ib.replace({'Kind_of_Energy__c': {'4': '3', '5': '3'}})
+
+## 5. SCInstalledBaseRole__c.csv
+
+df_ibr = pd.merge(df_ibr, 
+                    child_df[['Id', 'ExternalReference']],
+                    left_on='InstalledBaseLocation__c',
+                    right_on='Id',
+                    how='left') 
+
+df_ibr = df_ibr.drop_duplicates(subset=['InstalledBaseLocation__c', 'Role__c', 'Account__c'], keep='first')
+
+df_ibr = df_ibr.drop('Id_x', axis=1)
+df_ibr = df_ibr.drop('Id_y', axis=1)
+df_ibr = df_ibr.drop('InstalledBaseLocation__c', axis=1)
+
+print(df_ibr.columns)
+
+df_ibr.columns = ['Type', 'ActiveFrom', 'ActiveTo', 'ParentRecordId', 'Location.ExternalReference']
+
 #remove kind_of_energy__c and kind_of_installation if field dependency to main product group is not correct
 # Create the mapping dictionary
 kind_of_energy_mapping = {
     '1': ['A2', 'A1', 'B2', 'B1', 'E1', '14', 'E3', '17'],
     '2': ['A2', 'A1', 'B2', 'B1', 'E1', '14', 'E3', '17'],
+    '3': ['A2', 'A1', 'B2', 'B1', 'E1', '14', 'E3', '17'],
     'G': ['A2', 'A1', 'B2', 'B1', 'E1', 'E3', '17'],
     '6': ['B3', '11', '13', '14', '17'],
     '8': ['B4'],
@@ -227,6 +253,7 @@ address_df.to_csv('../3_upsert_address_and_parent_location/Address.csv', index=F
 parent_df.to_csv('../3_upsert_address_and_parent_location/Location.csv', index=False)
 child_df.to_csv('../5_upsert_child_location/Location.csv', index=False)
 merged_df_ib.to_csv('../7_upsert_assets/Asset.csv', index=False)
+df_ibr.to_csv('../9_upsert_associated_location/AssociatedLocation.csv', index=False)
 
 ## end mapping
 

prepared_steps/7_upsert_assets/ValueMapping.csv

@@ -1,3 +1,8 @@
 ObjectName,FieldName,RawValue,Value
 Asset,Kind_of_Energy__c,4,3
 Asset,Kind_of_Energy__c,5,3
+Asset,Serialnumber_Exception__c,350001,Missing
+Asset,Serialnumber_Exception__c,350002,Not Readable
+Asset,Serialnumber_Exception__c,350003,Not Readable
+Asset,Serialnumber_Exception__c,350004,Rejected by the system
+Asset,Serialnumber_Exception__c,350005,Missing

prepared_steps/7_upsert_assets/export.json

@@ -15,7 +15,8 @@
         },{
             "query": "SELECT Product2Id,InstallDate,Name,Kind_of_Energy__c,Main_Product_Group__c,SerialNumber,Serialnumber_Exception__c,LocationId FROM Asset",
             "operation": "Insert",
-            "master": true
+            "master": true,
+            "useValuesMapping": true
         }
     ]
 }

prepared_steps/9_fill_location_id_for_associated_location/FillLocationId.py

@@ -0,0 +1,31 @@
+import pandas as pd
+
+read_df_al = pd.read_csv('../10_upsert_associated_location/AssociatedLocation.csv', header=0, keep_default_na=False, dtype=str)
+read_df_l = pd.read_csv('../5_upsert_child_location/target/Location_insert_target.csv', header=0, keep_default_na=False, dtype=str)
+
+#Type,ActiveFrom,ActiveTo,ParentRecordId,PKey__c
+reindex_columns_al = ['Type','ActiveFrom','ActiveTo','ParentRecordId','PKey__c']
+#Errors,ExternalReference,Id,IsInventoryLocation,IsMobile,LocationType,Name,PKey__c
+reindex_columns_l = ['Errors','ExternalReference','Id','IsInventoryLocation','IsMobile','LocationType','Name','PKey__c']
+
+# Reindex the columns to match the desired format
+df_al = read_df_al.reindex(reindex_columns_al, axis=1)
+df_l = read_df_l.reindex(reindex_columns_l, axis=1)
+
+# Merge df_al with df_l including Id abse on PKey__c
+merged_df_al = pd.merge(df_al, 
+                        df_l[['Id', 'ExternalReference']], 
+                        left_on='PKey__c',
+                        right_on='ExternalReference',
+                        how='left')
+
+#drop External Reference
+merged_df_al = merged_df_al.drop('ExternalReference', axis=1)
+
+#Rename columns
+merged_df_al.columns = ['Type','ActiveFrom','ActiveTo','ParentRecordId','PKey__c','LocationId'] 
+
+
+#safe csv
+merged_df_al.to_csv('../10_upsert_associated_location/AssociatedLocation.csv', index=False)
+

prepared_steps/9_fill_location_id_for_associated_location/command.txt

@@ -0,0 +1 @@
+python .\FillLocationId.py