qdriven / fluentqa-structs

fluentqa-models

fluent-structs

• fluent-structs
• Easy to Handle Structs Data
• Different Structs Data includes:
  • JSON
  • YAML
  • DICT
  • EXCEL
  • [] TOML
  • [] DATABASE
  • [] CSV

Pydantic V2

1. use bump-pydantic to migrate

poetry run bump-pydantic src/fluentstructs

get any value or set any value to json or dict like data

• given different type of data structure:

more_json_dict = """
{
  "characters": {
    "Lonestar": {
      "id": 55923,
      "role": "renegade",
      "items": ["space winnebago", "leather jacket"]
    },
    "Barfolomew": {
      "id": 55924,
      "role": "mawg",
      "items": ["peanut butter jar", "waggy tail"]
    },
    "Dark Helmet": {
      "id": 99999,
      "role": "Good is dumb",
      "items": ["Shwartz", "helmet"]
    },
    "Skroob1": {
      "id": 12345,
      "role": "Spaceballs CEO",
      "items": ["luggage"]
    }
  }
}
"""

more_dict = {
    "characters": {
        "Lonestar": {
            "id": 55923,
            "role": "renegade",
            "items": ["space winnebago", "leather jacket"],
        },
        "Barfolomew": {
            "id": 55924,
            "role": "mawg",
            "items": ["peanut butter jar", "waggy tail"],
        },
        "Dark Helmet": {
            "id": 99999,
            "role": "Good is dumb",
            "items": ["Shwartz", "helmet"],
        },
        "Skroob": {"id": 12345, "role": "Spaceballs CEO", "items": ["luggage"]},
    }
}

• get or set value by json path like expressions

def test_get_value_by_expression():
    result = fluentstructs.get_value(more_dict, "characters.Lonestar")
    assert result == {
        "id": 55923,
        "role": "renegade",
        "items": ["space winnebago", "leather jacket"],
    }


def test_set_value_by_express_json():
    result = fluentstructs.set_value(more_json_dict, "characters.Lonestar", {})
    result = fluentstructs.get_value(result, "characters.Lonestar")
    assert result == {}

• differ: compare to different json

def test_differ():
    result = fluentstructs.differ(more_dict, more_json_dict)
    assert len(result) > 1

Pydantic Model Enhance

• GenericModel: support camelCase and alias field
• YamlGenericModel: support Pydantic to Yaml

    ## Model
    m1 = MyModel(x=2, e="b", m=InnerModel(fld=1.5))

    # This dumps to YAML and JSON respectively
    yml = to_yaml_str(m1)
    jsn = m1.model_dump_json()

    # This parses YAML as the MyModel type
    m2 = parse_yaml_raw_as(MyModel, yml)
    assert m1 == m2

    # JSON is also valid YAML
    m3 = parse_yaml_raw_as(MyModel, jsn)
    assert m1 == m3

Excel Read and Write Tools

• Read excel data to Pydantic Models

def test_load_objects_from_excel():
    result = read_excel_to_objects("./unit_demo.xlsx", UnitExcelModel)
    print(result)
    print(type(result))

• Write to Excel

def test_write_excels():
    u = UnitExcelModel()
    u.unit_name = "质量"
    u.unit_group_name = "kg"
    u1 = UnitExcelModel(unit_name="test1", unit_group_name="group1")
    list_objects = [u, u1]
    exceltools.write_objects_to_excel(list_objects, "unit_demo.xlsx")
    exceltools.write_objects_to_excel(list_objects, "unit_demo.csv")

• Read/Write CSV

    write_objects_to_csv("unit.csv",result)
    read_csv_to_objects("unit.csv",UnitExcelModel)

JSON Yaml 转换

from fluentstructs import jsontools, yamltools


def test_to_yaml_file():
    data = jsontools.load("./example.json")
    yamltools.to_yaml_file("./example.yaml", data)

To Do:

• CSV
• [] DataFrame