rendercv/docs/developer_guide.md

Developer Guide

This document provides everything you need to know about the development of RenderCV.

Getting Started

1. Ensure that you have Python version 3.10 or higher.

2. Then, 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

3. Go to the rendercv directory.

cd rendercv

4. Create a virtual environment.

python -m venv .venv

5. Activate the virtual environment.

   === "Windows (PowerShell)" powershell .venv\Scripts\Activate.ps1 === "MacOS/Linux" bash source .venv/bin/activate

6. Install the dependencies.

pip install --editable .[docs,tests,dev]

How RenderCV works?

The flowchart below illustrates the general operations of RenderCV. A detailed documentation of the source code is available in the reference.

flowchart TD
    A[YAML Input File] --parsing with ruamel.yaml package--> B(Python Dictionary)
    B --validation with pydantic package--> C((Pydantic Object))
    C --> D[LaTeX File]
    C --> E[Markdown File]
    E --markdown package--> K[HTML FIle]
    D --TinyTeX--> L[PDF File]
    L --PyMuPDF package--> Z[PNG Files]
    AA[(Jinja2 Templates)] --> D
    AA[(Jinja2 Templates)] --> E

Writing Documentation

The documentation's source files are located in the docs directory and it is built using the mkdocs package. To work on the documentation and see the changes in real-time, run the following command.

mkdocs serve

Updating the examples and the JSON Schema

The example entry images found in the Structure of the YAML input file, the examples folder, and the JSON Schema schema.json are generated using the script docs/update_rendercv_files.py. To update these files, run update_rendercv_files.py with the following command.

python docs/update_rendercv_files.py

Testing

After updating the code, all tests should pass. To run the tests, use the following command.

pytest

A note about testdata folder

In some of the tests:

• RenderCV generates an output with a sample input.
• Then, the output is compared with a reference output, which has been manually generated and stored in testdata. If the files differ, the tests fail.

When the testdata folder needs to be updated, it can be manually regenerated by setting update_testdata to True in conftest.py and running the tests.

Whenever the testdata folder is generated, the files should be reviewed manually to ensure everything works as expected.