def populated_column_map(self):
'''Return the _column_map without unused optional fields'''
column_map = []
cls = self.model
for csv_name, field_pattern in cls._column_map:
# Separate the local field name from foreign columns
if '__' in field_pattern:
field_name = field_pattern.split('__', 1)[0]
else:
field_name = field_pattern
# Handle point fields
point_match = re_point.match(field_name)
if point_match:
field = None
else:
field = cls._meta.get_field(field_name)
# Only add optional columns if they are used in the records
if field and field.blank and not field.has_default():
kwargs = {field_name: get_blank_value(field)}
if self.exclude(**kwargs).exists():
column_map.append((csv_name, field_pattern))
else:
column_map.append((csv_name, field_pattern))
return column_map
def populated_column_map(self):
'''Return the _column_map without unused optional fields'''
column_map = []
cls = self.model
for csv_name, field_pattern in cls._column_map:
# Separate the local field name from foreign columns
if '__' in field_pattern:
field_name = field_pattern.split('__', 1)[0]
else:
field_name = field_pattern
# Handle point fields
point_match = re_point.match(field_name)
if point_match:
field = None
else:
field = cls._meta.get_field(field_name)
# Only add optional columns if they are used in the records
if field and field.blank and not field.has_default():
kwargs = {field_name: get_blank_value(field)}
if self.exclude(**kwargs).exists():
column_map.append((csv_name, field_pattern))
else:
column_map.append((csv_name, field_pattern))
return column_map