moapy 1.3.4
Midas Open API for Python
License: MIT


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## Midas API: Revolutionizing Engineering 🚀
Welcome to the future of engineering with Midas API! 🌟 This cutting-edge repository is designed to transcend traditional engineering methodologies. A goldmine of innovation, Midas API eagerly awaits your contributions. Let's push the boundaries of possibility together! 😊

## Table of Contents
- [Introduction](#introduction)
- [Getting Started](#getting-started)
- [Coding Conventions](#coding-conventions)
- [Rules](#rules)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

## Introduction
Midas API is a state-of-the-art tool designed to revolutionize the field of engineering. By providing robust, scalable, and efficient solutions, Midas API empowers engineers to tackle complex challenges with ease. Whether you're a seasoned professional or a newcomer, Midas API is here to support your engineering journey.

## Getting Started
### Prerequisites
1. **Install VSCode**: Download and install Visual Studio Code from the [official Visual Studio Code website](https://code.visualstudio.com/download).
2. **Install Python**: Download and install Python 3.12.1 from the [official Python website](https://www.python.org/downloads/release/python-3121/). Ensure Python is added to your PATH during installation.
3. **Install Pipenv**: Use pip to install Pipenv, a tool that helps manage dependencies.
    ```bash
    pip install pipenv
    ```
    - Documentation: [Pipenv: Python Dev Workflow for Humans](https://pipenv.pypa.io/en/latest/)
4. **Install Project Dependencies**: Navigate to your project directory and install the necessary modules using Pipenv.
    - For running environment:
    ```bash
    pipenv install
    ```
    - For development environment:
    ```bash
    pipenv install --dev
    ```
5. **Prepare to Use Midas API**: Visit the [Midas API Documentation](https://midas-support.atlassian.net/wiki/spaces/MAW/overview) for more details.

## Coding Conventions
### Project Structure
- **project/**: Develop each plugin within this folder.
- **base/**: Contains shared utility functions and base modules like MidasAPI. Use this folder for any calculation base functions.
- **tests/**: Contains test files for regression testing.
- **rod/**: Extract JSON schema from this repository.
- **docs/**: Sphinx document folder.

### Style Guide
Adhere to the [Google Python Style Guide](https://yosseulsin-job.github.io/styleguide/pyguide.html) for comprehensive coding conventions.

- **Using Flake8**: Install `flake8` to automatically check and enforce coding conventions during development.
    ```bash
    pip install flake8
    ```
    ![flake8 Visual Studio Code extension](./resources/flake8_install.png)
- **Development Environment**: Configure your development environment to use `flake8`. Ensure any issues flagged by `flake8` are corrected promptly.

### Documentation Guide
Proper documentation is key to maintainability. Ensure that all functions, especially those in the `base` module, are well-documented.

- **Using autoDocstring**: Install `autoDocstring` to assist with writing docstrings. (ctrl + shift + 2)
    ![autoDocstring Visual Studio Code extension](./resources/autoDocstring_install.png)
- **Docstrings**: Use Google-style docstrings. Install the necessary extensions to assist with writing docstrings.
    - **Example**: Data class with docstring:
        ```python
        from dataclasses import dataclass, field as dataclass_field

        @dataclass
        class Person:
            """
            Person details.
            """
            name: str = dataclass_field(default="", metadata={"description": "The person's full name."})
            age: int = dataclass_field(default=0, metadata={"description": "The person's age."})
            email: str = dataclass_field(default='', metadata={"description": "The person's email address."})
        ```

    - **Example**: Pydantic model with docstring:
        ```python
        from pydantic import BaseModel, Field
        from typing import List, Dict, Union

        class Contact(BaseModel):
            """
            Contact details.
            """
            phone: str = Field(default="", description="The contact's phone number.")
            address: str = Field(default='', description="The contact's address.")

        def my_function(person: Person, contacts: List[Contact], settings: Dict[str, Union[int, str]] = {}) -> bool:
            """
            Processes person and their contacts with given settings.

            Args:
                person: The person details.
                contacts: List of contact details.
                settings: Miscellaneous settings.

            Returns:
                bool: True if successful, False otherwise.
            """
            return True
        ```

### Testing
Ensure the reliability of new code by writing comprehensive tests.

- **Regression Tests**: For any additions to the `base` module, add corresponding regression tests using `pytest`.
- **Test Files**: Save test files in the `tests` folder with the `test_*.py` naming convention.
  
  - **Example Test**:
    ```python
    import json
    import pytest
    import moapy.plugins.baseplate_KDS41_30_2022.baseplate_KDS41_30_2022_calc

    def test_baseplate_KDS41_30_2022_calc():
        input = {  
            'B' : 240, 'H' : 240, 'Fc' : 24, 'Fy' : 400,  
            'Ec' : 25811.006260943130, 'Es' : 210000,  
            'bolt' : [  
              { 'X' : 90, 'Y' : 0, 'Area' : 314.15926535897933 },  
              { 'X' : -90, 'Y' : 0, 'Area' : 314.15926535897933 } ],  
            'P' : -3349.9999999999964, 'Mx' : 0, 'My' : 51009999.999999985 
        }

        JsonData = json.dumps(input)
        result = moapy.plugins.baseplate_KDS41_30_2022.baseplate_KDS41_30_2022_calc.calc_ground_pressure(JsonData)
        
        assert pytest.approx(result['bolt'][0]['Load']) == 0.0
        assert pytest.approx(result['bolt'][1]['Load']) == -269182.84245616524
    ```

### Summary
- **Coding Conventions**: Follow the Google Python Style Guide and use `flake8` for enforcement.
- **Documentation**: Write and maintain proper docstrings for all functions.
- **Testing**: Develop test cases for new functions to enable regression testing.

By adhering to these rules and conventions, we can ensure our codebase remains high-quality, consistent, and reliable.