Developer Guide¶

All contributions to RenderCV are welcome!

The source code is thoroughly documented and well-commented, making it an enjoyable read and easy to understand. A detailed documentation of the source code is available in the reference.

Getting Started¶

There are two ways of developing RenderCV: locally or with GitHub Codespaces.

Develop Locally¶

Ensure that you have Python version 3.10 or higher.
Install Hatch, as it is the project manager for RenderCV. The installation guide for Hatch can be found here.
Clone the repository recursively (because TinyTeX is being used as a submodule) with the following command.
```
git clone --recursive https://github.com/sinaatalay/rendercv.git
```
Go to the rendercv directory.
```
cd rendercv
```
RenderCV uses three virtual environments:
- default: For the development. It contains packages like Ruff, Black, etc.
- docs: For building the documentation.
- test: For testing RenderCV.
Create the virtual environments with the following commands.
```
hatch env create default
hatch env create docs
hatch env create test
```
To use the virtual environments, either
- Activate one of the virtual environments with one of the following commands.
```
hatch shell default
```
```
hatch shell docs
```
```
hatch shell test
```
- Select one of the virtual environments in your Integrated Development Environment (IDE).
  Visual Studio CodeOther
  Press Ctrl+Shift+P.
  
  Type Python: Select Interpreter.
  
  Select one of the virtual environments created by Hatch.
  To be added.

Develop with GitHub Codespaces¶

Fork the repository.
Navigate to the forked repository.
Click the <> Code button, then click the Codespaces tab, and then click Create codespace on main.

Then, Visual Studio Code for the Web will be opened with a ready-to-use development environment.

This is done with Development containers, and the environment is defined in the .devcontainer/devcontainer.json file. Dev containers can also be run locally using various supporting tools and editors.

Available Commands¶

These commands are defined in the pyproject.toml file.

Format the code with Black
```
hatch run default:format
```
Lint the code with Ruff
```
hatch run default:lint
```
Sort the imports with isort
```
hatch run default:sort-imports
```
Check the types with Pyright
```
hatch run default:check-types
```
Run the tests
```
hatch run test:run
```
Run the tests and generate a coverage report
```
hatch run test:run-and-report
```
Start the development server for the documentation
```
hatch run docs:serve
```
Build the documentation
```
hatch run docs:build
```
Update schema.json
```
hatch run docs:update-schema
```
Update examples folder
```
hatch run docs:update-examples
```
Update figures of the entry types in the "Structure of the YAML Input File"
```
hatch run docs:update-entry-figures
```

About `pyproject.toml`¶

pyproject.toml contains the metadata, dependencies, and tools required for the project. Please read through the file to understand the project's technical details.