Fields

Scheme fields are intended to describe the desired result. When you create a schema class, fields are declared as its attributes.

Field initialization arguments

Parameters accepted by all field types and their default values:

• allow_none = True – Allow null values

• required = True – Is the field required

• default = True – Any value that will be used by default

• validate = None – None type or validator

• positive_data_from = None – A function object that will return a list of positive values or None

• negative_data_from = None – A function object that will return a list of negative values or None

Note

Some field types such as Integer, Float, Collection and Nested accept an additional argument. For Integer and Float this argument is precision. It is responsible for the minimum number step that will be used when generating negative values validator Range. For the Collection and Nested types, this parameter is data_type. It accepts a nested data type. For Collection - the data type inside the collection, for Nested - nested schema (class instance inherited from SGen)

Note

When using the positive_data_from and negative_data_from parameters their values will completely replace the result of generating the positive() and negative methods

String field

Represents a string data type. Generates random strings with the ability to install minimum/maximum length by using the Length validator

from sgen import SGen, validate, fields

class User(SGen):
    name = fields.String(
        validate=validate.Length(
            min=10,
            max=100,
            min_inclusive=True,
            max_inclusive=False,
        )
    )

user_sgen = User()
user_sgen.positive()  # Returns positive data generator
user_sgen.negative()  # Returns negative data generator

The min_inclusive and max_inclusive parameters are responsible for including min/max in the range of acceptable values

Integer Field

Represents an integer data type. Generates random numbers with the ability to set min/max value by using Range validator

Additionally, it includes the precision parameter, which is responsible for the step when generating negative values or positive, provided that at least one of the ...inclusive parameters is False.

Note

I don’t see any obvious cases of using the precision parameter for the Integer type, I left it for flexibility.

from sgen import SGen, validate, fields

class User(SGen):
    age = fields.Integer(
        validate=validate.Range(
            min=10,
            max=100,
            min_inclusive=True,
            max_inclusive=False,
        )
    )

The min_inclusive and max_inclusive parameters are responsible for including min/max in the range of acceptable values

Float field

Represents floating point numbers. Generates settable random floating point numbers min/max value by using Range validator

Additionally, it includes the precision parameter, which is responsible for the step when generating negative values or positive, provided that at least one of the ...inclusive parameters is False.

from sgen import SGen, validate, fields

class User(SGen):
    balance = fields.Float(
        validate=validate.Range(
            min=10,
            max=100,
            min_inclusive=True,
            max_inclusive=False,
        )
    )

The min_inclusive and max_inclusive parameters are responsible for including min/max in the range of acceptable values

Boolean field

Represents a Boolean data type.

from sgen import SGen, validate, fields

class User(SGen):
    age = fields.Float(
        validate=validate.Equal(
            comparable=True
        )
    )

DateTime and Date field

Represents time data types.

from sgen import SGen, validate, fields

class User(SGen):
    created_at = fields.DateTime()
    birth_date = fields.Date()

Collection field

Represents lists.

from sgen import SGen, validate, fields

class User(SGen):
    numbers = fields.List(
        data_type=fields.Integer()
    )

Accepts the argument data_type which must be an inheritor of the Field class

Nested field

Represents nested schemas.

from sgen import SGen, validate, fields


class Pet(SGen):
    name = fields.String()


class User(SGen):
    pet = fields.Nested(
        Pet(),
        required=True,
    )

Accepts the argument data_type which must be an inheritor of the class SGen