mocksmith 3.0.2

Type-safe data validation and mocking for Python dataclasses and Pydantic models

mocksmith

Type-safe data validation with automatic mock generation for Python dataclasses and Pydantic models. Build robust data models with database-aware validation and generate realistic test data with a single decorator.

Features

• Type-safe database columns: Define database columns with proper validation
• Serialization/Deserialization: Automatic conversion between Python and SQL types
• Dataclass Integration: Full support for Python dataclasses with validation
• Pydantic Integration: First-class Pydantic support with automatic validation
• Clean API: Simple, intuitive interface for both Pydantic AND dataclasses - just name: Varchar(50)
• Comprehensive Types: STRING (VARCHAR, CHAR, TEXT), NUMERIC (INTEGER, DECIMAL, FLOAT), TEMPORAL (DATE, TIME, TIMESTAMP), and more
• Mock Data Generation: Built-in mock/fake data generation for testing with @mockable decorator
• Constrained Types: Support for min/max constraints on numeric types - price: PositiveMoney(), age: Integer(min_value=0, max_value=120)

Why mocksmith?

Before (Traditional Approach)

from typing import Annotated
from pydantic import BaseModel, Field, validator
from decimal import Decimal

class Product(BaseModel):
    name: Annotated[str, Field(max_length=100)]
    price: Annotated[Decimal, Field(decimal_places=2, max_digits=10)]
    in_stock: bool = True

    @validator('price')
    def validate_price(cls, v):
        if v < 0:
            raise ValueError('Price must be non-negative')
        return v

After (With mocksmith)

from pydantic import BaseModel
from mocksmith import Varchar, Money, Boolean

class Product(BaseModel):
    name: Varchar(100)         # Enforces VARCHAR(100) constraint
    price: Money()             # Decimal(19,4) - use PositiveMoney() for price > 0
    in_stock: Boolean() = True # Flexible boolean parsing

✨ Benefits:

• Same clean syntax for both Pydantic and dataclasses
• Automatic SQL constraint validation
• Type conversion (string "99.99" → Decimal)
• Better IDE support and type hints
• Write once, use with either framework

Installation

pip install mocksmith

For Pydantic support:

pip install "mocksmith[pydantic]"

For mock data generation:

pip install "mocksmith[mock]"

Import Structure

The library organizes types into two categories:

Core Database Types

Core database types are available directly from the main package:

from mocksmith import (
    # String types
    VARCHAR, CHAR, TEXT, Varchar, Char, Text,
    # Numeric types
    INTEGER, DECIMAL, FLOAT, Integer, DecimalType, Float,
    # Temporal types
    DATE, TIME, TIMESTAMP, Date, Time, Timestamp,
    # Other types
    BOOLEAN, BINARY, Boolean, Binary,
    # Constrained types
    PositiveInteger, NonNegativeInteger, NegativeInteger, NonPositiveInteger,
    Money, PositiveMoney, NonNegativeMoney, ConstrainedMoney,
    ConstrainedDecimal, ConstrainedFloat
)

Specialized Types

Specialized types for common use cases are available from the specialized submodule:

from mocksmith.specialized import (
    # Geographic types
    CountryCode,  # ISO 3166-1 alpha-2 country codes
    City,         # City names
    State,        # State/province names
    ZipCode,      # Postal codes

    # Contact types
    PhoneNumber,  # Phone numbers
)

Note: For email and web types, use Pydantic's built-in types instead:

• Email → Use pydantic.EmailStr
• URL → Use pydantic.HttpUrl or pydantic.AnyUrl
• IP addresses → Use pydantic.IPvAnyAddress, pydantic.IPv4Address, or pydantic.IPv6Address

This separation keeps the main namespace clean and makes it clear which types are fundamental database types versus application-specific types.

Quick Start

Clean Interface (Works with both Pydantic and Dataclasses!) ✨

from pydantic import BaseModel
from mocksmith import Varchar, Integer, Boolean, Money

class User(BaseModel):
    id: Integer()
    username: Varchar(50)
    email: Varchar(255)
    is_active: Boolean() = True
    balance: Money() = "0.00"

# Automatic validation and type conversion
user = User(
    id=1,
    username="john_doe",
    email="[e-mail]",
    is_active="yes",      # Converts to True
    balance="1234.56"     # Converts to Decimal('1234.56')
)

The same syntax works with dataclasses! See full examples:

• examples/pydantic_example.py - Comprehensive Pydantic examples with all features
• examples/dataclass_example.py - Comprehensive dataclass examples with all features
• examples/pydantic_mock_example.py - Mock data generation with Pydantic models
• examples/dataclass_mock_example.py - Mock data generation with dataclasses
• examples/constrained_types_example.py - Constrained types with validation and mock generation

Common Use Cases

E-commerce Product Model:

from pydantic import BaseModel
from mocksmith import Varchar, Text, Money, Boolean, Timestamp

class Product(BaseModel):
    sku: Varchar(20)
    name: Varchar(100)
    description: Text()
    price: Money()
    in_stock: Boolean() = True
    created_at: Timestamp()

User Account with Constraints:

from mocksmith import Integer, PositiveInteger, NonNegativeInteger

class UserAccount(BaseModel):
    user_id: PositiveInteger()
    age: Integer(min_value=13, max_value=120)
    balance_cents: NonNegativeInteger()

See complete working examples:

• examples/ - All example files with detailed documentation
• examples/pydantic_example.py - All features including constraints
• examples/dataclass_example.py - All features including constraints

Mock Data Generation

Generate realistic test data automatically with the @mockable decorator:

from dataclasses import dataclass
from mocksmith import Varchar, Integer, Date, mockable
from mocksmith.specialized import PhoneNumber, CountryCode

@mockable
@dataclass
class User:
    id: Integer()
    username: Varchar(50)
    phone: PhoneNumber()
    country: CountryCode()
    birth_date: Date()

# Generate mock instances
user = User.mock()
print(user.username)  # "Christina Wells"
print(user.phone)     # "[phone]"
print(user.country)   # "US"

# With overrides
user = User.mock(username="test_user", country="GB")

# Using builder pattern
user = (User.mock_builder()
        .with_username("john_doe")
        .with_country("CA")
        .build())

The same @mockable decorator works with Pydantic models! Mock generation:

• Respects all field constraints (length, format, etc.)
• Generates appropriate mock data for each type
• Supports specialized types with realistic data
• Works with both dataclasses and Pydantic models
• Automatically handles Python Enum types with random value selection

See mock examples:

• examples/dataclass_mock_example.py - Complete mock examples with dataclasses including enum support
• examples/pydantic_mock_example.py - Complete mock examples with Pydantic including enum support and built-in types

Clean Annotation Interface

The library provides a clean, Pythonic interface for defining database types that works with both Pydantic and dataclasses:

# Works with Pydantic
from pydantic import BaseModel
from mocksmith import Varchar, Integer, Money, Date, Boolean, Text

class Product(BaseModel):
    sku: Varchar(20)
    name: Varchar(100)
    description: Text()
    price: Money()  # Alias for Decimal(19, 4)
    in_stock: Boolean()

# Also works with dataclasses!
from dataclasses import dataclass
from mocksmith.dataclass_integration import validate_dataclass

@validate_dataclass
@dataclass
class Product:
    sku: Varchar(20)
    name: Varchar(100)
    description: Text()
    price: Money() = Decimal("0.00")
    in_stock: Boolean() = True

# Instead of the verbose way:
# from typing import Annotated
# from mocksmith.types.string import VARCHAR
# from mocksmith.types.numeric import DECIMAL
# class Product:
#     sku: Annotated[str, VARCHAR(20)]
#     name: Annotated[str, VARCHAR(100)]
#     price: Annotated[Decimal, DECIMAL(19, 4)]

Available Clean Types:

String Types:

• Varchar(length) → Variable-length string
• Char(length) → Fixed-length string
• Text() → Large text field
• String → Alias for Varchar

Numeric Types:

• Integer() → 32-bit integer
• BigInt() → 64-bit integer
• SmallInt() → 16-bit integer
• TinyInt() → 8-bit integer
• DecimalType(precision, scale) → Fixed-point decimal
• Numeric(precision, scale) → Alias for DecimalType
• Money() → Alias for Decimal(19, 4)
• Float() → Floating point (generates FLOAT SQL type)
• Real() → Floating point (generates REAL SQL type, typically single precision in SQL)
• Double() → Double precision

Constrained Numeric Types:

• PositiveInteger() → Integer > 0
• NegativeInteger() → Integer < 0
• NonNegativeInteger() → Integer ≥ 0
• NonPositiveInteger() → Integer ≤ 0
• ConstrainedInteger(min_value=x, max_value=y, multiple_of=z) → Custom constraints
• ConstrainedBigInt(...) → Constrained 64-bit integer
• ConstrainedSmallInt(...) → Constrained 16-bit integer
• ConstrainedTinyInt(...) → Constrained 8-bit integer

Temporal Types:

• Date() → Date only
• Time() → Time only
• Timestamp() → Date and time with timezone
• DateTime() → Date and time without timezone

Other Types:

• Boolean() / Bool() → Boolean with flexible parsing
• Binary(length) → Fixed binary
• VarBinary(max_length) → Variable binary
• Blob() → Large binary object

Pydantic Integration Features

Pydantic Built-in Types Support

Mocksmith now supports automatic mock generation for Pydantic's built-in types:

from pydantic import BaseModel, EmailStr, HttpUrl, IPvAnyAddress, conint, constr
from mocksmith import mockable

@mockable
class ServerConfig(BaseModel):
    hostname: constr(min_length=1, max_length=253)
    ip_address: IPvAnyAddress
    port: conint(ge=1, le=65535)
    api_url: HttpUrl
    admin_email: EmailStr

# Generate mock with Pydantic types
config = ServerConfig.mock()
print(config.ip_address)  # IPv4Address('192.168.1.100')
print(config.api_url)     # https://example.com
print(config.admin_email) # [e-mail]

Tip: For types that have Pydantic equivalents, prefer using Pydantic's built-in types:

• Use EmailStr instead of mocksmith.specialized.Email
• Use HttpUrl or AnyUrl instead of mocksmith.specialized.URL
• Use IPvAnyAddress, IPv4Address, or IPv6Address for IP addresses

Using Pydantic Types in Dataclasses

While Pydantic types can be used as type annotations in dataclasses, there are important limitations:

from dataclasses import dataclass
from pydantic import EmailStr, HttpUrl, conint

@dataclass
class ServerConfig:
    hostname: str
    email: EmailStr  # Works as type hint only
    port: conint(ge=1, le=65535)  # No validation!

# This creates an instance WITHOUT validation
server = ServerConfig(
    hostname="api.example.com",
    email="invalid-email",  # Not validated!
    port=99999  # Out of range but accepted!
)

Key Points:

• Pydantic types in dataclasses serve as type hints only
• No automatic validation occurs
• Mock generation works but produces regular Python types (str, int, etc.)
• For validation, use Pydantic's BaseModel instead

See the Pydantic types limitations section in examples/dataclass_example.py for a complete comparison.

Supported Pydantic Types for Mock Generation

The @mockable decorator supports automatic mock generation for the following Pydantic types:

Network Types

• HttpUrl - Generates valid HTTP/HTTPS URLs
• AnyHttpUrl - Generates any HTTP scheme URLs
• EmailStr - Generates valid email addresses
• IPvAnyAddress - Generates IPv4 or IPv6 addresses (80% IPv4, 20% IPv6)
• IPvAnyInterface - Generates IP addresses with CIDR notation
• IPvAnyNetwork - Generates IP network addresses

Numeric Types

• PositiveInt - Integers > 0
• NegativeInt - Integers < 0
• NonNegativeInt - Integers >= 0
• NonPositiveInt - Integers <= 0
• PositiveFloat - Floats > 0
• NegativeFloat - Floats < 0
• NonNegativeFloat - Floats >= 0
• NonPositiveFloat - Floats <= 0

String/Identifier Types

• UUID1, UUID3, UUID4, UUID5 - Generates UUIDs (currently all as UUID4)
• SecretStr - Generates password-like strings
• Json - Generates valid JSON strings

Date/Time Types

• FutureDate - Generates dates in the future
• PastDate - Generates dates in the past
• FutureDatetime - Generates datetimes in the future
• PastDatetime - Generates datetimes in the past

Constraint Types

• conint(ge=1, le=100) - Integers with min/max constraints
• confloat(ge=0.0, le=1.0) - Floats with min/max constraints
• constr(min_length=1, max_length=50) - Strings with length constraints
• constr(pattern=r"^[A-Z]{3}[0-9]{3}$") - Strings matching regex patterns (limited support)
• conlist(item_type, min_length=1, max_length=10) - Lists with constraints

Example Usage

from pydantic import BaseModel, EmailStr, HttpUrl, conint, PositiveInt
from mocksmith import mockable

@mockable
class UserProfile(BaseModel):
    user_id: PositiveInt
    email: EmailStr
    website: HttpUrl
    age: conint(ge=18, le=120)

# Generate mock data
user = UserProfile.mock()
print(user.email)     # "[e-mail]"
print(user.website)   # "https://example.com"
print(user.age)       # 42 (between 18-120)

Note: When using Pydantic types in dataclasses (not BaseModel), the types work as annotations only without validation. The mock generation still works but produces regular Python types.

Handling Unsupported Types

When @mockable encounters an unsupported type, it attempts to handle it intelligently:

1. Common types (Path, Set, FrozenSet) - Now supported with appropriate mock values
2. Auto-instantiable types - Tries to create instances with (), None, "", or 0
3. Truly unsupported types - Returns None with a warning to help identify gaps in type support

Newly Supported Types

from dataclasses import dataclass
from pathlib import Path
from typing import Set, FrozenSet
from mocksmith import mockable

@mockable
@dataclass
class Config:
    config_path: Path        # ✓ Generates Path('/tmp/mock_file.txt')
    data_dir: Path          # ✓ Smart naming: Path('/tmp/mock_directory')
    tags: Set[str]          # ✓ Generates {'tag1', 'tag2', ...}
    frozen_tags: FrozenSet[int]  # ✓ Generates frozenset({1, 2, 3})

config = Config.mock()
# All fields get appropriate mock values!

Warning System

class CustomType:
    def __init__(self, required_arg):
        # Cannot be auto-instantiated
        pass

@mockable
@dataclass
class Example:
    name: str                              # ✓ Supported
    custom_required: CustomType            # ⚠️ Warning issued, returns None
    custom_optional: Optional[CustomType] = None  # ⚠️ Warning issued (if attempted), returns None

# Console output:
# UserWarning: mocksmith: Unsupported type 'CustomType' for field 'custom_required'.
# Returning None. Consider making this field Optional or providing a mock override.

Important Notes:

• All unsupported types trigger warnings - This helps identify gaps in mocksmith's type support
• Warnings help improve mocksmith - If you encounter warnings, please file an issue on GitHub
• Optional fields - May show warnings ~80% of the time (when generation is attempted)
• Override unsupported types - Use mock() with overrides: Example.mock(custom_required=CustomType('value'))
• Pydantic models - Make unsupported fields Optional to avoid validation errors

Optional Fields Pattern

Python's Optional type indicates fields that can be None:

from typing import Optional
from pydantic import BaseModel
from mocksmith import Varchar, Integer, Text

class Example(BaseModel):
    # Required field
    required_field: Varchar(50)

    # Optional field (can be None)
    optional_field: Optional[Varchar(50)] = None

    # Field with default value
    status: Varchar(20) = "active"

Best Practice: For optional fields, use Optional[Type] with = None:

bio: Optional[Text()] = None           # Clear and explicit
phone: Optional[Varchar(20)] = None    # Optional field with no default

Automatic Type Conversion

from pydantic import BaseModel
from mocksmith import Money, Boolean, Date, Timestamp

class Order(BaseModel):
    # String to Decimal conversion
    total: Money()

    # Flexible boolean parsing
    is_paid: Boolean()

    # String to date conversion
    order_date: Date()

    # String to datetime conversion
    created_at: Timestamp(with_timezone=False)

# All these string values are automatically converted
order = Order(
    total="99.99",           # → Decimal('99.99')
    is_paid="yes",           # → True
    order_date="2023-12-15", # → date(2023, 12, 15)
    created_at="2023-12-15T10:30:00"  # → datetime
)

Field Validation with Pydantic

from pydantic import BaseModel, field_validator
from mocksmith import Varchar, Integer, Money

class Product(BaseModel):
    name: Varchar(50)
    price: Money()
    quantity: Integer()

    @field_validator('price')
    def price_must_be_positive(cls, v):
        if v <= 0:
            raise ValueError('Price must be positive')
        return v

    @field_validator('quantity')
    def quantity_non_negative(cls, v):
        if v < 0:
            raise ValueError('Quantity cannot be negative')
        return v

Model Configuration

from pydantic import BaseModel, ConfigDict
from mocksmith import Varchar, Money, Timestamp

class StrictModel(BaseModel):
    model_config = ConfigDict(
        # Validate on assignment
        validate_assignment=True,
        # Use Enum values
        use_enum_values=True,
        # Custom JSON encoders
        json_encoders={
            Decimal: str,
            datetime: lambda v: v.isoformat()
        }
    )

    name: Varchar(100)
    price: Money()
    updated_at: Timestamp()

Working Examples

For complete working examples, see the examples/ directory:

• dataclass_example.py - Comprehensive dataclass examples including:

  • All data types (String, Numeric, Date/Time, Binary, Boolean)
  • Constrained numeric types (PositiveInteger, NonNegativeInteger, etc.)
  • Custom constraints (min_value, max_value, multiple_of)
  • TINYINT usage for small bounded values
  • REAL vs FLOAT distinction
  • SQL serialization
  • Validation and error handling

• pydantic_example.py - Comprehensive Pydantic examples including:

  • All data types with automatic validation
  • Field validators and computed properties
  • Constrained types with complex business logic
  • JSON serialization with custom encoders

• dataclass_mock_example.py - Mock data generation examples:

  • Using @mockable decorator with dataclasses
  • Generating mock instances with .mock()
  • Override specific fields
  • Type-safe builder pattern
  • Specialized types (Email, CountryCode, etc.)

• pydantic_mock_example.py - Mock data generation with Pydantic:

  • Using @mockable decorator with Pydantic models
  • Same mock API as dataclasses
  • Automatic validation of generated data
  • Specialized types with DBTypeValidator
  • Model configuration and validation on assignment
  • TINYINT and REAL type usage
  • Boolean type conversions

• constrained_types_example.py - Constrained types with validation:

  • PositiveMoney, NonNegativeMoney, ConstrainedMoney usage
  • ConstrainedDecimal with precision and range constraints
  • ConstrainedFloat for percentages and probabilities
  • Mock generation respecting all constraints
  • Validation examples showing error handling
  • Builder pattern with constrained types

Example: E-commerce Order System

from dataclasses import dataclass
from typing import Optional
from datetime import datetime, date
from decimal import Decimal

from mocksmith import Varchar, Integer, Date, DecimalType, Text, BigInt, Timestamp
from mocksmith.dataclass_integration import validate_dataclass

@validate_dataclass
@dataclass
class Customer:
    customer_id: Integer()
    first_name: Varchar(50)
    last_name: Varchar(50)
    email: Varchar(100)
    phone: Optional[Varchar(20)]
    date_of_birth: Optional[Date()]

@validate_dataclass
@dataclass
class Order:
    order_id: BigInt()
    customer_id: Integer()
    order_date: Timestamp(with_timezone=False)
    total_amount: DecimalType(12, 2)
    status: Varchar(20)
    notes: Optional[Text()]

# Create instances
customer = Customer(
    customer_id=1,
    first_name="Jane",
    last_name="Smith",
    email="[e-mail]",
    phone="[phone]",
    date_of_birth=date(1990, 5, 15)
)

order = Order(
    order_id=1001,
    customer_id=1,
    order_date=datetime(2023, 12, 15, 14, 30, 0),
    total_amount=Decimal("299.99"),
    status="pending",
    notes="Rush delivery requested"
)

# Convert to SQL-ready format
print(order.to_sql_dict())

For more complete examples including financial systems, authentication, and SQL testing integration, see the examples/ directory.

Default Value Validation in Dataclasses

When using @validate_dataclass, default values are validated when an instance is created, not when the class is defined:

@validate_dataclass
@dataclass
class Config:
    # This class definition succeeds even with invalid default
    hour: SmallInt(min_value=0, max_value=23) = 24

# But creating an instance fails with validation error
try:
    config = Config()  # Raises ValueError: Value 24 exceeds maximum 23
except ValueError as e:
    print(f"Validation error: {e}")

# You can override with valid values
config = Config(hour=12)  # Works fine

This behavior is consistent with Python's normal evaluation of default values and ensures that validation runs for all values, including defaults.

Advanced Features

Custom Validation

@validate_dataclass
@dataclass
class CustomProduct:
    sku: Annotated[str, VARCHAR(20)]  # Required field
    name: Annotated[str, VARCHAR(100)]  # Required field
    description: Annotated[Optional[str], VARCHAR(500)]  # Optional field

Working with Different Types

# Integer types with range validation
small_value = SMALLINT()
small_value.validate(32767)  # OK
# small_value.validate(32768)  # Raises ValueError - out of range

# Decimal with precision
money = DECIMAL(19, 4)
money.validate("12345.6789")  # OK
# money.validate("12345.67890")  # Raises ValueError - too many decimal places

# Time with precision
timestamp = TIMESTAMP(precision=0)  # No fractional seconds
timestamp.validate("2023-12-15T10:30:45.123456")  # Microseconds will be truncated

# Boolean accepts various formats
bool_type = BOOLEAN()
bool_type.deserialize("yes")    # True
bool_type.deserialize("1")      # True
bool_type.deserialize("false")  # False
bool_type.deserialize(0)        # False

Constrained Numeric Types

The library provides specialized numeric types with built-in constraints for common validation scenarios:

from mocksmith import Integer, PositiveInteger, NonNegativeInteger

# Enhanced Integer functions - no constraints = standard type
id: Integer()                    # Standard 32-bit integer
quantity: Integer(min_value=0)   # With constraints (same as NonNegativeInteger)
discount: Integer(min_value=0, max_value=100)  # Percentage 0-100
price: Integer(positive=True)    # Same as PositiveInteger()

# Specialized constraint types
id: PositiveInteger()            # > 0
quantity: NonNegativeInteger()   # >= 0

For complete examples with both dataclasses and Pydantic, see:

• examples/dataclass_example.py - All constraint examples with dataclasses
• examples/pydantic_example.py - All constraint examples with Pydantic

Available Constraint Options:

# Enhanced Integer functions - no constraints = standard type
Integer()                   # Standard 32-bit integer
Integer(min_value=0)        # With constraints
Integer(positive=True)      # Shortcut for > 0
BigInt()                    # Standard 64-bit integer
BigInt(min_value=0, max_value=1000000)  # With constraints
SmallInt()                  # Standard 16-bit integer
SmallInt(multiple_of=10)    # With constraints

# Specialized constraint types
PositiveInteger()           # > 0
NegativeInteger()           # < 0
NonNegativeInteger()        # >= 0
NonPositiveInteger()        # <= 0

# Full constraint options
Integer(
    min_value=10,          # Minimum allowed value
    max_value=100,         # Maximum allowed value
    multiple_of=5,         # Must be divisible by this
    positive=True,         # Shortcut for min_value=1
    negative=True,         # Shortcut for max_value=-1
)

Constrained Money and Decimal Types

mocksmith provides constrained versions of Money and Decimal types using Pydantic's constraint system:

from mocksmith import (
    ConstrainedMoney, PositiveMoney, NonNegativeMoney,
    ConstrainedDecimal, ConstrainedFloat
)

# Money with constraints
price: PositiveMoney()                          # > 0
balance: NonNegativeMoney()                     # >= 0
discount: ConstrainedMoney(ge=0, le=100)        # 0-100 range
payment: ConstrainedMoney(gt=0, le=10000)       # 0 < payment <= 10000

# Decimal with precision and constraints
weight: ConstrainedDecimal(10, 2, gt=0)         # Positive weight, max 10 digits, 2 decimal places
temperature: ConstrainedDecimal(5, 2, ge=-273.15)  # Above absolute zero

# Float with constraints
percentage: ConstrainedFloat(ge=0.0, le=1.0)    # 0-1 range
rate: ConstrainedFloat(gt=0, lt=0.5)            # 0 < rate < 0.5

These constrained types:

• Work seamlessly with Pydantic validation
• Generate appropriate mock data respecting constraints
• Provide the same clean API as other mocksmith types
• Fall back gracefully if Pydantic is not available

Example Usage:

from pydantic import BaseModel
from mocksmith import mockable, PositiveMoney, NonNegativeMoney, ConstrainedMoney, ConstrainedFloat

@mockable
class Order(BaseModel):
    subtotal: PositiveMoney()                    # Must be > 0
    discount: ConstrainedMoney(ge=0, le=50)      # 0-50 range
    tax: NonNegativeMoney()                      # >= 0
    discount_rate: ConstrainedFloat(ge=0, le=0.3)  # 0-30%

# Validation works
order = Order(
    subtotal="100.00",    # ✓ Converts to Decimal
    discount="25.00",     # ✓ Within 0-50 range
    tax="8.50",          # ✓ Non-negative
    discount_rate=0.15   # ✓ 15% is within 0-30%
)

# Mock generation respects constraints
mock_order = Order.mock()
assert mock_order.subtotal > 0
assert 0 <= mock_order.discount <= 50
assert mock_order.tax >= 0
assert 0 <= mock_order.discount_rate <= 0.3

Development

1. Clone the repository:

git clone https://github.com/gurmeetsaran/mocksmith.git
cd mocksmith

2. Install Poetry (if not already installed):

curl -sSL https://install.python-poetry.org | python3 -

3. Install dependencies:

poetry install

4. Set up pre-commit hooks:

poetry run pre-commit install

5. Run tests:

make test

Development Commands

• make lint - Run linting (ruff + pyright)
• make format - Format code (black + isort + ruff fix)
• make test - Run tests
• make test-cov - Run tests with coverage
• make check-all - Run all checks (lint + format check + tests)
• make check-consistency - Verify pre-commit, Makefile, and CI are in sync

Ensuring Consistency

To ensure your development environment matches CI/CD:

# Check that pre-commit hooks match Makefile and GitHub Actions
make check-consistency

This will verify that all tools (black, isort, ruff, pyright) are configured consistently across:

• Pre-commit hooks (.pre-commit-config.yaml)
• Makefile commands
• GitHub Actions workflows

License

MIT