feat: cleanup obsolet address and contact flags (LAN-780)

landa/fixtures/translation.json

@@ -4519,13 +4519,13 @@
   "docstatus": 0,
   "doctype": "Translation",
   "language": "de",
-  "modified": "2021-12-03 17:22:26.449471",
+  "modified": "2023-09-26 15:40:26.449471",
   "name": "2418380635",
   "parent": null,
   "parentfield": null,
   "parenttype": null,
-  "source_text": "Primary Address (Full)",
-  "translated_text": "Prim\u00e4re Adresse (vollst\u00e4ndig)"
+  "source_text": "Full Address",
+  "translated_text": "Vollst\u00e4ndige Adresse"
  },
  {
   "context": null,
@@ -5807,22 +5807,6 @@
   "source_text": "Pincode",
   "translated_text": "Postleitzahl"
  },
- {
-  "context": null,
-  "contributed": 0,
-  "contribution_docname": null,
-  "contribution_status": "",
-  "docstatus": 0,
-  "doctype": "Translation",
-  "language": "de",
-  "modified": "2021-09-02 17:10:49.800353",
-  "name": "6dc383a8a1",
-  "parent": null,
-  "parentfield": null,
-  "parenttype": null,
-  "source_text": "Primary Address (Full)",
-  "translated_text": "Prim\u00e4re Adresse (kommagetrennt)"
- },
  {
   "context": null,
   "contributed": 0,

landa/organization_management/doctype/member_data_import/member_data_import.py

@@ -173,8 +173,6 @@ def create_address(
 	address.pincode = pincode
 	address.city = city
 	address.country = "Germany"
-	address.is_primary_address = 1
-	address.is_shipping_address = 1
 	address.append("links", {"link_doctype": "LANDA Member", "link_name": member})
 	address.organization = organization
 

landa/organization_management/report/external_contacts_addresses_and_contacts/external_contacts_addresses_and_contacts.py

@@ -41,7 +41,7 @@
 	{
 		"fieldname": "full_address",
 		"fieldtype": "Data",
-		"label": "Primary Address (Full)",
+		"label": "Full Address",
 	},
 	{
 		"fieldname": "primary_email_address",
@@ -103,25 +103,22 @@ def get_link_filters(link_names: List[str]):
 	link_filters = get_link_filters(external_contact_ids)
 
 	# load addresses from db
-	address_fields = ["address_line1", "pincode", "city", "is_primary_address"]
+	address_fields = ["address_line1", "pincode", "city"]
 	addresses = frappe.get_list(
 		"Address",
 		filters=link_filters,
 		fields=[link_field] + address_fields,
 	)
 	# convert to pandas dataframe
 	addresses_df = to_df(addresses, address_fields)
-	# remove all duplicate addresses by keeping only the primary address or last existing address if there is no primary address
-	addresses_df = remove_duplicate_indices(addresses_df, sort_by="is_primary_address")
+	# remove all duplicate addresses by keeping only the last existing address
+	addresses_df = remove_duplicate_indices(addresses_df)
 
 	# merge all columns to one address column and add this as the first column
 	addresses_df["full_address"] = (
 		addresses_df["address_line1"] + ", " + addresses_df["pincode"] + " " + addresses_df["city"]
 	)
 
-	# remove column 'is_primary_address'
-	addresses_df.drop("is_primary_address", axis=1, inplace=True)
-
 	# load contacts from db that are linked to the member fucntions loaded before
 	contact_fields = ["email_id", "phone", "mobile_no"]
 	contacts = frappe.get_list(

landa/organization_management/report/magazine_address_list/magazine_address_list.py

@@ -79,7 +79,7 @@ def get_member_filter(frappe_tuple):
 		link_filters = get_link_filters(members)
 
 		# load addresses from db
-		address_fields = ["address_line1", "pincode", "city", "is_primary_address"]
+		address_fields = ["address_line1", "pincode", "city"]
 		addresses = frappe.get_list(
 			"Address",
 			filters=link_filters,
@@ -88,17 +88,14 @@ def get_member_filter(frappe_tuple):
 		)
 		# convert to pandas dataframe
 		addresses_df = frappe_tuple_to_pandas_df(addresses, address_fields + ["member"])
-		# remove all duplicate addresses by keeping only the primary address or last existing address if there is no primary address
-		addresses_df = remove_duplicate_indices(addresses_df, sort_by="is_primary_address")
+		# remove all duplicate addresses by keeping only the last existing address
+		addresses_df = remove_duplicate_indices(addresses_df)
 
 		# merge all columns to one address column and add this as the first column
 		addresses_df["full_address"] = (
 			addresses_df["address_line1"] + ", " + addresses_df["pincode"] + " " + addresses_df["city"]
 		)
 
-		# remove column 'is_primary_address'
-		addresses_df.drop("is_primary_address", axis=1, inplace=True)
-
 		# load addresses from db
 		permit_fields = ["year", "member", "docstatus"]
 		permits = frappe.get_list(
@@ -182,7 +179,7 @@ def get_columns(self):
 			{
 				"fieldname": "full_address",
 				"fieldtype": "Data",
-				"label": "Primary Address (Full)",
+				"label": "Full Address",
 			},
 			{
 				"fieldname": "magazine_active",

landa/organization_management/report/member_address_list/member_address_list.json

@@ -41,7 +41,7 @@
   {
    "fieldname": "full_address",
    "fieldtype": "Data",
-   "label": "Primary Address (Full)",
+   "label": "Full Address",
    "width": 0
   }
  ],

landa/organization_management/report/member_address_list/member_address_list.py

@@ -19,7 +19,7 @@ def get_data(self):
 		link_filters = get_link_filters(self.members)
 
 		# load addresses from db
-		address_fields = ["address_line1", "pincode", "city", "is_primary_address"]
+		address_fields = ["address_line1", "pincode", "city"]
 		addresses = frappe.get_list(
 			"Address",
 			filters=link_filters,
@@ -30,17 +30,14 @@ def get_data(self):
 		addresses_df = pd.DataFrame(addresses, columns=address_fields + ["member"])
 		addresses_df.set_index("member", inplace=True)
 
-		# remove all duplicate addresses by keeping only the primary address or last existing address if there is no primary address
-		addresses_df = remove_duplicate_indices(addresses_df, sort_by="is_primary_address")
+		# remove all duplicate addresses by keeping only the last existing address
+		addresses_df = remove_duplicate_indices(addresses_df)
 
 		# merge all columns to one address column and add this as the first column
 		addresses_df["full_address"] = (
 			addresses_df["address_line1"] + ", " + addresses_df["pincode"] + " " + addresses_df["city"]
 		)
 
-		# remove column 'is_primary_address'
-		addresses_df.drop("is_primary_address", axis=1, inplace=True)
-
 		# merge all dataframes from different doctypes
 		data = pd.concat([self.members_df, addresses_df], axis=1).reindex(self.members_df.index)
 
@@ -83,7 +80,7 @@ def get_columns(self):
 			{
 				"fieldname": "full_address",
 				"fieldtype": "Data",
-				"label": "Primary Address (Full)",
+				"label": "Full Address",
 			},
 		]
 

landa/organization_management/report/members_with_member_functions/members_with_member_functions.py

@@ -53,7 +53,7 @@
 	{
 		"fieldname": "full_address",
 		"fieldtype": "Data",
-		"label": "Primary Address (Full)",
+		"label": "Full Address",
 	},
 	{
 		"fieldname": "address_line1",
@@ -177,22 +177,20 @@ def get_contact_details(
 	awards_df.drop(award_fields[:-1], axis=1, inplace=True)
 
 	# load addresses from db
-	address_fields = ["address_line1", "pincode", "city", "is_primary_address"]
+	address_fields = ["address_line1", "pincode", "city"]
 	addresses = get_contact_details("Address", MEMBERS, address_fields)
 
 	# convert to pandas dataframe
 	addresses_df = frappe_tuple_to_pandas_df(addresses, address_fields + ["member"])
-	# remove all duplicate addresses by keeping only the primary address or last existing address if there is no primary address
-	addresses_df = remove_duplicate_indices(addresses_df, sort_by="is_primary_address")
+	# remove all duplicate addresses by keeping only the last existing address
+	addresses_df = remove_duplicate_indices(addresses_df)
 
 	# merge all columns to one address column and add this as the first column
 	addresses_df["full_address"] = (
 		addresses_df["address_line1"] + ", " + addresses_df["pincode"] + " " + addresses_df["city"]
 	)
 	address_cols = addresses_df.columns.tolist()
 	addresses_df = addresses_df[address_cols[-1:] + address_cols[:-1]]
-	# remove column 'is_primary_address'
-	addresses_df.drop("is_primary_address", axis=1, inplace=True)
 
 	# load contacts from db that are linked to the member fucntions loaded before
 	contact_fields = ["email_id", "phone", "mobile_no"]

landa/patches.txt

@@ -25,3 +25,4 @@ landa.patches.update_system_settings
 landa.patches.delete_customized_workspaces  # 2023-08-14
 landa.patches.build_water_body_cache  # 2023-08-14
 landa.patches.set_hide_custom_in_user_workspaces
+landa.patches.cleanup_addresses_and_contacts

landa/patches/cleanup_addresses_and_contacts.py

@@ -0,0 +1,16 @@
+import frappe
+
+
+def execute():
+	cleanup_addresses()
+	cleanup_contacts()
+
+
+def cleanup_addresses():
+	# the two checkboxes are no longer used and are hidden from now on.
+	frappe.db.sql("""update `tabAddress` set is_shipping_address=0, is_primary_address=0""")
+
+
+def cleanup_contacts():
+	# the two checkboxes are no longer used and are hidden from now on.
+	frappe.db.sql("""update `tabContact` set is_billing_contact=0, is_primary_contact=0""")

landa/patches/set_billing_and_shipping_defaults.py

@@ -8,10 +8,6 @@
 
 def execute():
 	set_billing_and_shipping_defaults()
-	# Cleanup of data not yet now, but in the next release. Ask Samuel why.
-	# TODO: Clean data
-	# cleanup_addresses()
-	# cleanup_contacts()
 
 
 def customize_customer():
@@ -75,13 +71,3 @@ def set_billing_and_shipping_defaults():
 					"default_shipping_address": address_name,
 				},
 			)
-
-
-def cleanup_addresses():
-	# the two checkboxes are no longer used and are hidden from now on.
-	frappe.db.sql("""update `tabAddress` set is_shipping_address=0, is_primary_address=0""")
-
-
-def cleanup_contacts():
-	# the two checkboxes are no longer used and are hidden from now on.
-	frappe.db.sql("""update `tabContact` set is_billing_contact=0, is_primary_contact=0""")

landa/water_body_management/report/members_in_water_body_management/members_in_water_body_management.py

@@ -55,7 +55,7 @@
 	{
 		"fieldname": "full_address",
 		"fieldtype": "Data",
-		"label": "Primary Address (Full)",
+		"label": "Full Address",
 	},
 	{
 		"fieldname": "address_line1",
@@ -127,27 +127,24 @@ def get_link_filters(frappe_tuple, index="member"):
 			"address_line1",
 			"pincode",
 			"city",
-			"is_primary_address",
 			link_field_label,
 		],
 	)
 	# convert to pandas dataframe
 	addresses_df = pd.DataFrame.from_records(
 		addresses,
 		index="member",
-		columns=["address_line1", "pincode", "city", "is_primary_address", "member"],
+		columns=["address_line1", "pincode", "city", "member"],
 	)
-	# remove all duplicate addresses by keeping only the primary address or last existing address if there is no primary address
-	addresses_df = remove_duplicate_indices(addresses_df, sort_by="is_primary_address")
+	# remove all duplicate addresses by keeping only the last existing address
+	addresses_df = remove_duplicate_indices(addresses_df)
 
 	# merge all columns to one address column and add this as the first column
 	addresses_df["full_address"] = (
 		addresses_df["address_line1"] + ", " + addresses_df["pincode"] + " " + addresses_df["city"]
 	)
 	address_cols = addresses_df.columns.tolist()
 	addresses_df = addresses_df[address_cols[-1:] + address_cols[:-1]]
-	# remove column 'is_primary_address'
-	addresses_df.drop("is_primary_address", axis=1, inplace=True)
 
 	# load contacts from db that are linked to the member fucntions loaded before
 	contacts = frappe.get_all(