ars02bahadr-mock-data-generator 0.1.0

Mock data generator for Django, SQLAlchemy, and Pydantic models

MockDataGenerator 🚀

A powerful, developer-friendly Python library for generating realistic mock data from complex Python models. Supports FastAPI (Pydantic), Django ORM, and Flask (SQLAlchemy) frameworks while preserving intricate relationships like One-to-One, One-to-Many, Many-to-Many, and recursive self-references.

✨ Key Features

• 🔧 Framework Support: Full compatibility with FastAPI (Pydantic models), Django ORM, and SQLAlchemy declarative models
• 🔗 Relationship Management: Automatically resolves foreign key relationships, ensuring data consistency across tables
• 🎯 Smart Data Types: Recognizes and generates appropriate data for fields like Turkish phone numbers, IBAN, UUID, EmailStr, HttpUrl, and more
• 🔄 Recursive Relationships: Handles self-referencing structures (e.g., Manager → Employee, Parent → Category)
• 📤 Flexible Output: Exports generated data directly to JSON files
• 🌍 Localization: Supports multiple locales (tr_TR, en_US, etc.) for culturally appropriate data
• 🎮 Interactive Mode: CLI with interactive prompts for customization
• ⚡ Fast & Efficient: Optimized generation algorithms for large datasets

📦 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Install from Source

git clone https://github.com/yourusername/mock-data-generator.git
cd mock-data-generator
pip install -r requirements.txt

Optional Dependencies

For full framework support, install optional packages:

pip install django sqlalchemy  # For Django and SQLAlchemy models

Project Structure

mock-data-generator/
├── mock_generator/
│   ├── __init__.py
│   ├── cli.py                 # Command-line interface
│   ├── scanner.py             # Model file scanning
│   ├── analyzer.py            # Model analysis
│   ├── generator.py           # Mock data generation
│   ├── relationship_resolver.py # Relationship handling
│   ├── exporters.py           # Data export utilities
│   └── field_mappers/         # Framework-specific mappers
│       ├── django_mapper.py
│       ├── pydantic_mapper.py
│       └── sqlalchemy_mapper.py
├── examples/                  # Usage examples
│   ├── django_example/
│   ├── fastapi_example/
│   └── sqlalchemy_example/
├── pyproject.toml             # Project configuration
├── requirements.txt           # Dependencies
└── README.md

🚀 Usage

Command Line Interface

Generate mock data from your model files:

python -m mock_generator.cli generate --file path/to/your/models.py --locale tr_TR --interactive

CLI Options

• --file: Path to your model file (required)
• --output: Output directory (default: ./output)
• --interactive: Enable interactive mode for customization
• --locale: Data locale (default: en_US, supports tr_TR, etc.)

Example Usage Scenarios

1. FastAPI (Pydantic) Models

models.py

from pydantic import BaseModel, EmailStr
from typing import Optional

class Department(BaseModel):
    id: int
    name: str
    budget: float

class Employee(BaseModel):
    id: int
    name: str
    email: EmailStr
    department_id: int
    salary: Optional[float] = None

Generate Data:

python -m mock_generator.cli generate --file models.py --locale tr_TR

2. Django ORM Models

models.py

from django.db import models

class OrgDivision(models.Model):
    name = models.CharField(max_length=100)
    parent = models.ForeignKey('self', null=True, on_delete=models.CASCADE)

class StaffAssociate(models.Model):
    name = models.CharField(max_length=100)
    division = models.ForeignKey(OrgDivision, on_delete=models.CASCADE)
    supervisor = models.ForeignKey('self', null=True, on_delete=models.CASCADE)

class EnterpriseTask(models.Model):
    title = models.CharField(max_length=200)
    assignees = models.ManyToManyField(StaffAssociate)
    division = models.ForeignKey(OrgDivision, on_delete=models.CASCADE)

Generate Data:

python -m mock_generator.cli generate --file models.py --locale tr_TR --interactive

This handles complex relationships: StaffAssociate belongs to OrgDivision, has a supervisor (self-reference), and participates in Many-to-Many relationships with EnterpriseTask.

3. Flask/SQLAlchemy Models

models.py

from sqlalchemy import Column, Integer, String, ForeignKey, Float
from sqlalchemy.orm import declarative_base, relationship

Base = declarative_base()

class CloudServer(Base):
    __tablename__ = 'servers'
    id = Column(Integer, primary_key=True)
    name = Column(String(100))
    ip_address = Column(String(15))

class Tag(Base):
    __tablename__ = 'tags'
    id = Column(Integer, primary_key=True)
    name = Column(String(50))

class BackupLog(Base):
    __tablename__ = 'backup_logs'
    id = Column(Integer, primary_key=True)
    server_id = Column(Integer, ForeignKey('servers.id'))
    tag_id = Column(Integer, ForeignKey('tags.id'))
    size_gb = Column(Float)

Generate Data:

python -m mock_generator.cli generate --file models.py --locale tr_TR

📊 Generated Output

The tool generates JSON files for each model, preserving relationships:

{
  "OrgDivision": [
    {"id": 1, "name": "Engineering", "parent_id": null},
    {"id": 2, "name": "Backend", "parent_id": 1}
  ],
  "StaffAssociate": [
    {"id": 1, "name": "Ahmet Yılmaz", "division_id": 1, "supervisor_id": null},
    {"id": 2, "name": "Mehmet Kaya", "division_id": 2, "supervisor_id": 1}
  ],
  "EnterpriseTask": [
    {"id": 1, "title": "Implement API", "division_id": 1, "assignees": [1, 2]}
  ]
}

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Faker for realistic data generation
• Click for CLI framework
• Rich for beautiful terminal output
• Pydantic for data validation

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Türkçe Özet

MockDataGenerator, FastAPI (Pydantic), Django ve Flask (SQLAlchemy) modellerinden otomatik olarak ilişkisel mock veriler üreten güçlü bir Python kütüphanesidir. One-to-One, One-to-Many, Many-to-Many ve kendi kendine referanslı yapıları koruyarak JSON formatında veri üretir. CLI arayüzü ile kolay kullanım sağlar ve Türkçe dahil çeşitli dillerde lokalize veri desteği sunar.