Coverage for backend/django/core/auxiliary/views/UploadMSSData.py: 88%

1from rest_framework.response import Response 

2from core.auxiliary.models.DataCell import DataCell 

3from core.auxiliary.models.DataRow import DataRow 

4from drf_spectacular.utils import extend_schema 

5from core.auxiliary.models.DataColumn import DataColumn 

6from rest_framework.decorators import api_view 

7from drf_spectacular.utils import extend_schema 

8from rest_framework import serializers 

9from core.validation import api_view_validate 

10 

11class UploadDataSerializer(serializers.Serializer): 

12 # THe data format is e.g: 

13 # { 

14 # "data": { 

15 # "heater_enthalpy": [1, 2, 3, 4, 5], 

16 # "heater_temperature": [1, 2, 3, 4, 5] 

17 # } 

18 # "flowsheet": 1 

19 # } 

20 flowsheet = serializers.IntegerField() 

21 scenario=serializers.IntegerField() 

22 data = serializers.DictField( # column name 

23 child=serializers.ListField( # List of values 

24 child=serializers.FloatField() # Value 

25 ) 

26 ) 

27 

28 

29@api_view_validate 

30@extend_schema(request=UploadDataSerializer, responses=None) 

31@api_view(['POST']) 

32def upload_data(request) -> Response: 

33 try: 

34 serializer = UploadDataSerializer(data=request.data) 

35 serializer.is_valid(raise_exception=True) 

36 validated_data = serializer.validated_data 

37 data = validated_data.get('data') 

38 flowsheet_id = validated_data.get('flowsheet') 

39 scenario_id = validated_data.get('scenario') 

40 except Exception as e: 

41 return Response(status=400, data=f"Invalid csv data: {e}") 

42 

43 # Step 1: Create any missing data column under the given optimization 

44 data_columns = [] 

45 for key in data: 

46 data_columns.append(DataColumn(name=key, scenario_id=scenario_id,flowsheet_id=flowsheet_id)) 

47 

48 DataColumn.objects.bulk_create(data_columns, ignore_conflicts=True) 

49 # Step 2: Determine number of rows 

50 num_rows = len(list(next(iter(data.values())))) 

51 

52 # Step 3: Get existing data rows by optimization 

53 existing_rows = list(DataRow.objects.filter(scenario_id=scenario_id).order_by("index")) 

54 existing_indices = {r.index for r in existing_rows} 

55 

56 # Step 4: Create missing data rows 

57 new_rows = [ 

58 DataRow(index=i, flowsheet_id=flowsheet_id, scenario_id=scenario_id) 

59 for i in range(num_rows) 

60 if i not in existing_indices 

61 ] 

62 if new_rows: 62 ↛ 66line 62 didn't jump to line 66 because the condition on line 62 was always true

63 DataRow.objects.bulk_create(new_rows) 

64 

65 # Refresh data row list 

66 data_rows = list(DataRow.objects.filter(scenario_id=scenario_id).order_by("index")) 

67 data_row_map = {r.index: r for r in data_rows} 

68 

69 # Step 5: Get updated data columns 

70 data_columns = DataColumn.objects.filter(scenario_id=scenario_id).prefetch_related("dataCells") 

71 column_map = {column.name: column for column in data_columns} 

72 

73 # Step 6: Build mapping of existing values 

74 existing_values = { 

75 column.name: {sv.data_row.index: sv for sv in column.dataCells.all()} 

76 for column in data_columns 

77 } 

78 

79 # Step 7: Insert or update DataCells 

80 for i in range(num_rows): 

81 data_row = data_row_map[i] 

82 for column_name, values in data.items(): 

83 value = values[i] 

84 column = column_map[column_name] 

85 existing = existing_values.get(column_name, {}).get(i) 

86 

87 if existing: 87 ↛ 88line 87 didn't jump to line 88 because the condition on line 87 was never true

88 existing.value = value 

89 existing.save() 

90 data_row = data_row_map[i] 

91 for column_name, values in data.items(): 

92 value = values[i] 

93 column = column_map[column_name] 

94 existing = existing_values.get(column_name, {}).get(i) 

95 

96 if existing: 96 ↛ 97line 96 didn't jump to line 97 because the condition on line 96 was never true

97 existing.value = value 

98 existing.save() 

99 else: 

100 DataCell.objects.create(value=value, data_column=column, data_row=data_row, flowsheet_id=flowsheet_id) 

101 

102 return Response(status=200, data="Data uploaded successfully")