SciCookie user guide¤

SciCookie is a package that offers a template for your Python project. The team has created this guide to help you understand every option and its functionality.

The structure of the guide is as follows:

SciCookie user guide
Notes about the text-based user interface (TUI)
The author of project
Information about the project
Project settings
Project tools
Integration with DevOps tools
Automation tools
Project team
Version control

Notes about the text-based user interface (TUI)¤

A TUI, or text-based user interface, is a type of user interface that uses text and keyboard input to display and interact with information on a computer screen.

When you run the scicookie command in your terminal, a text-based user interface (TUI) is displayed where you can provide information about the author or the project itself. You can also choose from several options that are presented to you, with default values in all cases. To switch from one option to another, simply press "Enter".

In some cases, you may see a list of sub-options within a main option. Use the arrow keys to move up and down between the sub-options and the > symbol will appear before the sub-option you have selected. Make sure you press "Enter" to confirm your selection when the required sub-option has been selected.

You will also see a list of options for the project tools, this is presented in the following way:

 > [ ] bandit
   [ ] black
   [ ] conda
   [ ] coverage
   [ ] flake8
   [ ] ruff_linter
   [ ] ruff_formatter
   [ ] isort
   [ ] make
   [ ] makim
   [ ] mccabe
   [ ] mypy
   [ ] pre-commit
   [ ] prettier
   [ ] pydocstyle
   [ ] pytest
   [ ] hypothesis
   [ ] shellcheck
   [ ] vulture

Use the right arrow key to select the tools you want, and use the up and down arrow keys to navigate through the list. If you accidentally select an option you don't want, simply use the left arrow key to deselect it.

The following is a description of the options in the TUI.

The author of project¤

With SciCookie, you can easily add information about the author of the project by filling in the author_full_name and author_email fields, which refer to the name and email address of the person or organization that authored the project.

The default values give you an idea of what you can enter in these fields. This information can be found in the pyproject.toml file, the documentation and the github configuration, to name a few.

Information about the project¤

Every project needs specific information for both development and maintenance. SciCookie allows you to customise this information, which is displayed in several parts, such as: documentation, git, configuration files, the project repository, PyPI (Python Package Index) and more.

Below are the fields within the TUI that relate to project information:

project_name: This is the name of the project according to the creator, i.e. the main title of the project, as shown on the README page, for example.
project_slug: This is the name of the project, probably abbreviated using characters suitable for naming a Python package. Also, it is the name used for the repository and the one registered with PyPI. In simpler terms, it refers to the name of the python package to use when you want to install it. In SciCookie, this name can include hyphens (-) or underscores (_).
package_slug: This refers to the name used when importing the package. This name allows the usage of (_) instead of (-), which is a particularity of SciCookie. Consequently, the project_slug and the package_slug can match (when both use underscores).
project_version: This is where you can enter the version of your python package, most Python packages use semantic versioning. SciCookie offers you to manage the release workflow with semantic release and github actions. For more information on releasing and versioning, please click here.
project_url: This is the URL of the project's website used for example, in the documentation and the pyproject.toml file. On this website you will generally find information about the project, the team, events, the blog, etc. It is used to promote the project.
project_license: This field allows you to specify the license of the project. In SciCookie you can find and choose from the following well-known open source software licenses: MIT, BSD 3 Clauses, ISC license, Apache Software License 2.0 and GNU General Public License v3.
MIT option: is a free software license that allows users to use, modify and redistribute the software without significant restrictions. It is a permissive license that allows users to do whatever they want with the software, as long as a copy of the license is included and the original authorship of the software is acknowledged. In SciCookie this is the license type for default. If you want to know the content of this license, you can visit https://mit-license.org/.
BSD 3 Clauses option: also allows users to use, modify and redistribute the software without significant restrictions. It is similar to the MIT license in terms of permissibility, but includes three additional clauses that require you to include a copy of the license, acknowledge original authorship of the software, and release the author from any liability for damages. For more information, see https://opensource.org/license/bsd-3-clause/.
ISC license option: is mainly used to distribute software related to networking and Internet protocols. It is permissive and known to be short and easy to understand. The author is not responsible for any damage or problems caused by the use of the software. You can learn more about the contents of this license at https://opensource.org/license/isc-license-txt/.
Apache Software License 2.0 option: is a permissive license, compatible with the GNU General Public License (GPL). It contains special provisions relating to patents. It also requires that copyright and attribution notices of the original authors be maintained on all copies of the software, and allows users to make modifications to the software and distribute modified versions. You can find out more about this license at https://www.apache.org/licenses/LICENSE-2.0.
GNU General Public License v3 option: includes the freedom to use, modify and redistribute the software in accordance with the terms of the license. Compared to previous versions, it addresses concerns about interoperability with other systems and the use of technological protection measures. If you modify or distribute the software under the GPL v3, you must make the source code available to end users. More information is available at https://www.gnu.org/licenses/gpl-3.0.html.

If you want to include another type of license according to the needs of your project, you can do so by selecting the option Other.

Project settings¤

Setting up some configurations in the project structure is important because it allows you to get an orderly workflow. With SciCookie you can configure elements such as project layout, build system, command-line interface and documentation engine. This can help streamline the development process by providing a consistent and standardized framework for the project.

Project layout¤

The organization of your code is important, which is why at SciCookie we offer two alternatives: src and flat.

src (Source) layout: The src layout is a common approach where the source code files are organized within a dedicated "src" directory. This directory serves as the root of the project's source code and typically contains subdirectories that represent different modules or packages. Each module or package within the "src" directory focuses on a specific aspect of the project's functionality.

For example, a project following the src structure might have the following layout:

project_slug/
├─ src/
│  └─ package_slug/
│     ├─ __init__.py
|     ├─ __main__.py
│     ├─ module.py
│     └─ ...
├─ tests
│  └─ ...
├── pyproject.toml
└── README.md

The src structure helps in organizing the codebase and makes it easier to understand and maintain as the project grows larger. It also allows for better separation of concerns and promotes modularity.

Flat layout: The flat layout involves placing all the project's source code files directly in the project's root directory without any subdirectories. This means that all modules and packages are at the same level and there is no explicit separation between different aspects of the project.

Here's an example of a flat structure:

project_slug/
│ └─ package_slug/
│ __init__.py
| __main__.py
│ module.py
│ ...
├─ tests
│  └─ ...
├── pyproject.toml
└── README.md

Because all source files are on the same level in this layout, they are easy to find and access. However, as the project becomes larger and more complex, it can become more difficult to maintain and navigate the codebase.

The flat structure is simpler and may be suitable for smaller projects or scripts where the codebase is relatively small and does not require extensive organization or separation of concerns.

Both src and flat layouts have their own advantages and disadvantages. The choice between them depends on your specific requirements and the complexity of your project, as well as your personal preferences and team conventions.

If you want to find more information about it you can visit:

Build system¤

There are several build system options available to development of Python packages. SciCookie support the following:

Poetry (default): It's a Python package manager that streamlines dependency management and package distribution. It provides a simple and intuitive syntax for defining project dependencies and allows you to easily create and manage virtual environments for your projects. With Poetry, you can easily install, update, and remove dependencies, and it automatically handles conflicts and resolution between packages. Additionally, Poetry provides a comprehensive toolset for packaging and publishing your projects to PyPI, including support for building source distributions and wheel packages, as well as generating and uploading documentation. Overall, Poetry simplifies the development and distribution of Python projects, making it a popular tool for many Python developers.
Flit: It's a Python package and a lightweight tool for creating and distributing Python packages. It automates the processes involved in packaging and submitting a project to PyPI, and provides a straightforward interface for managing a project's dependencies. With just a few commands, you can use Flit to quickly create source distributions and wheel packages and submit them to PyPI.
meson-python: It's a Python build backend built on top of the Meson build-system. It enables you to use Meson for your Python packages. With meson-python, you can easily define project dependencies, specify build options, generate configuration files and build scripts, among other things. Meson-python is primarily focused on improving speed and ease of use compared to other build systems. It is designed to be fast and scalable, making it suitable for both small and large projects.
Setuptools: It's a package that facilitates the distribution and installation of Python packages. Setuptools provides a way to define metadata about your project, such as its name, version, dependencies, and other details. It also provides functionality for building and distributing packages, creating distribution archives, and installing packages with their dependencies. "It helps developers to easily share reusable code (in the form of a library) and programs (e.g., CLI/GUI tools implemented in Python), that can be installed with pip and uploaded to PyPI."
PDM: It's a modern Python package and dependency manager supporting the latest PEP standards. But it is more than a package manager. It boosts your development workflow in various aspects. It has very powerful features, including easy and fast dependency resolution, especially for large binary distributions, a PEP 517 compilation backend, PEP 621 project metadata, a flexible and powerful plugin system. It also offers, among other things, versatile user scripting, PyPI integration and version management.
Hatch: It's a PEP 517/PEP 660 compatible build backend used by Hatch, a modern, extensible Python project manager. It provides a standardized build system with reproducible builds by default, robust environment management with support for custom scripts, easy publishing to PyPI or other indexes, version management, and configurable project generation with sane defaults. Hatchling ensures that your builds are reproducible, so you can be confident that they will always produce the same results. It also helps you manage your Python environments, so you can be sure that your projects have the correct dependencies.
Maturin: It's build system designed to create Python bindings from Rust projects. It allows Rust code to be seamlessly integrated into Python applications, providing efficient builds and cross-platform support for various Python versions. Maturin automates the generation of Python modules that directly access Rust functions, harnessing Rust's high performance and low-level capabilities within Python. Its user-friendly interface and compatibility with setuptools and Cargo make it an easy-to-use tool, offering developers a simple solution to combine the strengths of Python and Rust within a unified project.
scikit-build-core: It's build system designed for Python packaging tool, serving as an enhanced build system generator for CPython C extensions greatly improves package management within the scientific Python ecosystem. It offers superior support for diverse compilers, build systems, cross-compilation, and efficient dependency locating with their associated build requirements. With its capabilities, it facilitates cross-platform builds using CMake and effortless integration with C/C++ libraries, making it a valuable asset for research software engineers.
setuptools + pybind11: It's build system designed for C++ library that simplifies the creation of Python bindings for C++ code, enabling easy integration of C++ functions and classes into Python scripts. It acts as a bridge between the two languages, allowing C++ algorithms and functionality to be directly called from Python as if they were native Python modules. Pybind11's user-friendly syntax reduces boilerplate code, making binding generation more straightforward, while standard C++ build systems like CMake or Make facilitate the compilation of projects using pybind11. Its efficiency, ease of use, and strong community support have made it a popular choice for projects requiring seamless interoperability between C++ and Python, ranging from scientific computing to game development and automation. Staying up-to-date with the latest pybind11 documentation ensures the best practices are followed.

The idea behind the options in SciCookie is that you can choose from some of the most popular system compilers to suit your needs and preferences for developing Python packages. If you think we should add more options, you can submit your suggestion as a issue at https://github.com/osl-incubator/scicookie/issues/new/choose.

Command-line interfaces (CLIs)¤

A command line interface (CLI) is a type of text-based interface that allows the user to interact with a program or system using text commands rather than a graphical interface. It allows automation of tasks, access to advanced functions, and execution of complex commands with additional options.

In addition to the operating system's native CLI, many programs and tools provide their own command line interfaces to facilitate interaction with their functionality. With SciCookie you have two CLI options: Click and Argparse.

Click: The Command Line Interface Creation Kit is a Python package that allows you to create beautiful command line interfaces in a composable way with as little code as necessary. It is highly configurable, it aims to make the process of writing command line tools quick and fun.
Argparse: This is the recommended command line parsing module in the Python standard library, and provides an easy way to create command line interfaces with custom arguments and options. It helps you parse user-supplied arguments and options, and automatically generate help and usage messages.

If you do not want to include a CLI in your project, select No command-line interface (this is the default option).

Documentation engine¤

One of the most important elements of a package (apart from the functionality of the code) is its documentation. If there is no quality documentation, it is very likely that people will not understand and therefore not use your package.

To generate documentation for any project, tools have been developed that include functions such as content creation and editing, organization and classification of information, generation of output formats and integration with other development and project management tools, known as a documentation engine. This tool automates the creation of technical documentation for software projects by extracting information from source code, comments and project metadata.

Depending on your taste and the needs of your project, you should choose the documentation engine that best suits your needs. SciCookie offers you four documentation engine options for your Python package: mkdocs, sphinx-rst, sphinx-myst, jupyter-book and quarto. Additionally styling options (themes) are provided for your documentation.

mkdocs: is a fast, simple, and downright gorgeous static site generator for creating project documentation. Documentation source files are written in Markdown and configured with a single YAML configuration file. It is designed to allow users to quickly create clear and well-structured documentation without requiring advanced programming skills. mkdocs can be easily integrated with version control systems such as Git, supports a variety of predefined themes that allow you to customise the visual appearance of your documentation. In SciCookie you can choose between the MkDocs (default), Material, Cinder and readthedocs themes.
Sphinx: Sphinx makes it easy to create intelligent and attractive documentation. It provides various output formats such as HTML, LaTeX, ePub, Texinfo, manual pages, plain text. It allows the use of built-in extensions for automatic code snippet checking, the inclusion of docstrings from Python modules, and third-party extensions to include many more features. To work with Sphinx you can select either of the two available options: sphinx-rst and sphinx-myst. Sphinx-rst uses the reStructuredText markup language by default and Sphinx-myst(Markedly Structured Text - Parser) is a Sphinx and Docutils extension to parse MyST, a rich and extensible flavour of Markdown for authoring technical and scientific documentation. Also, SciCookie offers themes to customize the look and feel of your documentation : a clean and minimalist theme (default - Alabaster), one that is very popular among scientists for its documentation – Read the Docs and another collection of themes for PyData made for a particular kind of data science projects.
Jupyter Book: allows you to create engaging, publication-quality books and documents from computational content. Jupyter Book uses Jupyter notebooks as the basis for creating interactive content. It allows you to structure and organise the notebooks into a cohesive, navigable book. It also offers customisation options to adapt the look and feel of the book to your needs and extensions to add additional features. It can generate books in a variety of output formats, including HTML, PDF and static web pages. It integrates well with version control systems such as Git. If you want to customise the look or style of the documentation, SciCookie lets you choose between the default (Sphinx Book Theme) theme or one derived from sphinx such as Readthedocs or PyData.
Quarto: It is a versatile open-source platform designed for scientific and technical publishing. It offers the unique feature of embedding Python code directly into your documentation, enabling interactive and dynamic content creation. With Quarto, you can easily render your documents in multiple formats such as HTML, PDF, and websites, making it convenient for sharing and presenting your work. In addition, Quarto offers customisation options to personalise your content according to your preferences and needs. Through SciCookie, you can choose from some of the themes available in Quarto: Default, Cosmo, Cerulean, Materia.

If you think we should add more options of documentation engines or themes, you can submit your suggestion as a issue at https://github.com/osl-incubator/scicookie/issues/new/choose.

Project tools¤

SciCookie allows you to choose between several tools that can improve your project in different ways. Some of these tools can help you automate tasks, others allow you to format your code consistently, and others can help you find errors and vulnerabilities in your code, and much more.

The tools are described below according to their functionality.

Virtual environment¤

When working with Python, you should always have a virtual environment. This is an isolated space where you can install and run applications and software packages independently, without interfering with other environments or versions on your system. You control which package versions are installed and when they are updated. Virtual environments are disposable; you can delete and create as many as you like. In Python there are many tools to manage virtual environments, in SciCookie we offer you the option to use conda as your environment management system, which is a very popular option in the Python scientific community.

Conda: is an open source package and environment management system that runs on Windows, MacOS and Linux. It quickly installs, runs and updates packages and their dependencies. It also makes it easy to create, save, load and switch between environments on your local machine, which is why we use conda as an option in SciCookie. If you want an easy way to choose a specific version of Python, and if your project relies on complex software libraries that are not easily installed with pip, using conda is a good option. It is included in all versions of Anaconda and Miniconda.

If you select this option, a conda folder will appear in the project directory, containing a dev.yaml file that you can use to manage the development project's dependencies. If you do not select this option, a requirements.txt will be added for you to manage with virtualenv.

Code formatter¤

A code formatter is a tool that automatically reformats code to conform to a set of coding standards, such as PEP 8 guidelines. It helps us ensure consistency in our code, saves time by automating the formatting process, reduces errors by enforcing coding standards, and facilitates collaboration by making it easier for multiple developers to work on the same code base. In the options of SciCookie, you will find: Black.

Black: It is a popular code formatter tool for Python that automatically formats code to conform to PEP 8 guidelines. It provides a simple and opinionated way to format code, making it easy to use. The advantages of using Black include its ability to ensure consistent formatting, save time by automating the process, and reduce errors by enforcing coding standards.

You can read the black documentation if you want to know more about it.

Ruff: Ruff is a versatile tool for Python that offers both linting and auto-formatting capabilities. As an auto-formatter, Ruff ensures consistent code formatting, fixes import sorting, and adheres to best practices. Notably, Ruff is significantly faster than tools like Black, making it ideal for large codebases. Its benefits include enhancing code readability, saving time on manual formatting, and allowing for fine-tuned configurations to meet specific project needs.

Using code formatters such as Black and Ruff in your project helps ensure consistent and readable code, making it easier to maintain and collaborate on.

Code security vulnerabilities¤

The code security vulnerabilities are errors or weaknesses in code that can be exploited by attackers to gain unauthorized access, steal data, or cause damage to a system.

To verify and prevent these vulnerabilities, there are several tools available in Python. One such tool available in SciCookie is Bandit.

Bandit: It is a tool specifically designed to identify security issues in Python code. It scans code for common security problems such as hard-coded passwords and insecure file permissions. Some of the key features of Bandit are its ease of use, its ability to integrate with other tools and support for multiple versions of Python. To learn more about Bandit, you can read its documentation.

It is important to use code vulnerability detection tools because they can help identify security vulnerabilities in software and fix them before they can be exploited by attackers. This helps to ensure the security and reliability of the project.

Code coverage testing¤

Code coverage testing is a software testing method that measures the percentage of code that is executed during a test run. This can be used to identify areas of code that are not being tested and to improve the overall quality of the software.

There are a number of different code coverage tools available in Python. In SciCookie you have coverage available.

Coverage: It is an open source tool for measuring the code coverage of a program. This means that it measures what percentage of the program code was executed when the tests were run. Coverage can be useful for identifying parts of the code that are not being tested and may be vulnerable to bugs. This will show you the percentage of code covered by your tests, as well as detailed information about which lines were executed and which were not. If you want to know more about how it works, you can read the Coverge documentation.

By using code coverage testing in Python, you can ensure that your code has been thoroughly tested and is free of bugs.

Code style and logic (code quality)¤

Code style refers to the way in which your code is written. It includes things like indentation, line breaks, and variable names. Code logic refers to the way in which your code works. It includes things like the flow of your code, the use of data structures, and the implementation of algorithms.

There are a number of tools that can help you to improve the code style and logic of your Python code; analyzing and verification of the code. In SciCookie you can choose and include in your project flake8, Ruff, isort, mccabe, pydocstyle and/or vulture.

Flake8: A tool that helps you find potential performance issues in your code. Flake8 can detect errors in syntax, indentation, naming conventions, and other types of style errors. It can be useful for improving the quality of Python code. Flake8 enforces coding style conventions defined in the Python Enhancement Proposal 8 (PEP 8). Additionally, flake8 integrates well with popular code editors like Visual Studio Code, PyCharm, and Sublime Text. It can highlight style violations directly in your editor, making it easy to spot and fix issues as you write code. Flake8 can also be integrated into continuous integration and deployment (CI/CD) pipelines to ensure code quality standards are met before merging changes.
Ruff: This is an extremely fast Python linter written in Rust. Linting is the process of inspect code for potential bugs or style issues, and Ruff is designed to perform this analysis quickly and efficiently. It is particularly useful for large codebases where traditional Python linters can be slow and resource intensive.

Ruff is designed to be easy to use and can be integrated into existing Python development workflows. It can be used as a standalone command line tool, or integrated into editors and IDEs using plugins or extensions. It is also able to detect a wide range of potential errors and style issues, and provides clear and detailed error messages to help developers resolve these issues quickly.

isort: is a Python utility/library for automatically sorting imports alphabetically and separating them into sections and by type. This can help maintain a consistent import style and make the code easier to read. isort provides a command-line utility, a Python library and plugins for various editors to quickly sort all your imports. It offers a number of configuration options to suit each project's import style preferences, has official pre-commit support and is black-compatible.
McCabe: is a code complexity checker that automatically detects code complexity based on cyclomatic complexity, which is roughly equal to one plus the number of loops and if statements. Simply put, it provides an upper bound on the number of test cases needed to obtain branch coverage of the code. It is recommended to run it during the git hook pre-commit.
Vulture: finds unused code in Python programs. This is useful for cleaning up and finding bugs in large code bases. Due to the dynamic nature of Python, static code analysers such as Vulture are likely to miss some dead code. However, it can be a very useful tool for improving code quality. It uses static code analysis, it is self-testing and has full test coverage, it complements pyflakes and has the same output syntax, it sorts classes and functions by size and it supports Python >= 3.6, these are some of the main features of Vulture.
pydocstyle: is a static analysis tool for checking compliance with Python docstring conventions. pydocstyle analyses source code for docstrings and verifies that they comply with the guidelines set out in PEP 257. This includes rules such as the formatting of docstrings, the presence of required sections, and the appropriate use of punctuation and structure. pydocstyle supports Python 3.7 to 3.11 and can be integrated into development workflows as part of code review or automated validation processes.

When writing your code, you should always try to make it as clear and concise as possible. This will make it easier for other developers to understand your code and make changes if necessary. It is also important to use data structures and algorithms that are appropriate for the task at hand. This will help ensure that your code is efficient and scalable.

Testing framework¤

A test framework is a software tool that provides a set of guidelines, conventions, and utilities for writing and executing automated tests. It provides a structure for organizing tests, and typically includes features for test discovery, test execution, and test reporting.

Test frameworks help to ensure the quality of software by automating the testing process. They allow developers to write tests that can be easily executed and repeated, and provide feedback on the success or failure of the tests. There are several options available for developing Python packages. SciCookie supports pytest and hypothesis.

Pytest: It is a popular testing framework for Python. It simplifies the process of writing and running tests by providing a concise syntax and powerful features. With Pytest, you can automatically discover and collect test cases, use fixtures for test setup and resource management, and write test functions with assert statements to check expected outcomes. It offers various options for test execution, including running specific tests, parallel execution, and generating test reports. Pytest also has a thriving ecosystem of plugins that extend its capabilities, such as code coverage analysis and test parameterization. Overall, Pytest is widely adopted for its simplicity, flexibility, and community support, making it an effective tool for ensuring the quality and reliability of Python code. You can know more about it in its documentation.
Hypothesis: is a property-based testing library for Python. It focuses on generating diverse input data and exploring different scenarios to thoroughly test code. Instead of relying on specific examples, Hypothesis allows you to define general properties that your code should satisfy. It automatically generates random inputs, including edge cases, to uncover potential bugs and unexpected behaviors. Hypothesis integrates well with popular testing frameworks like Pytest and promotes comprehensive testing to improve code reliability. If you want to know more, check out the documentation here.

Static analysis of shell scripts¤

This is the process of analyzing the code of a shell script without actually executing it. This is done by using specialized software tools that can scan the script and identify potential issues such as syntax errors, coding standards violations, security vulnerabilities, and performance problems. In SciCookie you have ShellCheck available.

ShellCheck: This is a static analysis tool for shell scripts. It checks shell scripts for common errors and potential bugs, such as syntax errors, variable misuse, and command substitution issues. Shellcheck supports various shells, including Bash, Dash, and Zsh, and it can be integrated into various text editors and IDEs. Some advantages of using Shellcheck may include its ability to catch potential bugs before they cause issues, its support for multiple shells, and its ease of integration with various development environments. For more information, you can visit its website or its repository on GitHub.

The static analysis of shell scripts is an important tool in your project, it ensure that your code is of high quality, secure, and efficient.

Each tool has its own purpose and can be useful in different situations. When choosing among these tools, it's important to consider your specific needs and which tools may be most useful for your project.

Pre-commit verification¤

It is a code quality control tool that runs automatically before commits are made to a version control repository. When a commit is made, pre-commit executes the configured hooks. If you select the pre-commit option offered by SciCookie, we have configured the following hooks for you: end-of-file-fixer, black, flake8, ruff, isort, mypy, shellcheck, bandit, pydocstyle, vulture, mccabe and prettier (will be available in your project according to the tools you have selected in TUI).

pre-commit: It is a framework for managing and maintaining multi-language pre-commit hooks. A list of desired hooks must be specified, and pre-commit manages the installation and execution of all hooks written in any language before each commit. You will be notified before the end of the commit if validation fails, as configured.

This is configured using a .pre-commit-config.yaml file in the repository, which sets the hooks to be executed before each commit. You can check the supported hooks using this link https://pre-commit.com/hooks.html.

Integration with DevOps tools¤

Integrating your Python project with DevOps tools can bring a number of benefits to the development and deployment process. DevOps tools are designed to automate and streamline the development pipeline, from code development to deployment and maintenance, and can help you improve the speed, quality and reliability of the development process. In SciCookie you can choose between Docker, Podman and Kubernetes. These are all containerization technologies used to deploy and manage applications.

Docker: This is a containerization platform that allows developers to package their applications and dependencies into a portable container. Containers are lightweight, efficient, and provide a consistent runtime environment, making it easier to deploy and run applications across different environments. In Python projects, Docker can be used to package and deploy Python applications and dependencies, providing a consistent runtime environment and making it easier to manage dependencies and configurations. You can read more about this on the Docker website and in the Docker documentation.
Podman: It is a container engine without the need for a daemon running as root. With Podman, you can easily create and run containers, as well as manage their lifecycle and resources. This integration improves development and deployment processes, making them more efficient and streamlined. Podman in Python project helps to achieve a more secure, efficient and flexible containerization strategy and gives more control over application dependencies and configurations. As Podman allows containers to be managed without the need for a daemon, it provides a more secure and lightweight solution. You can read more about this on the Podman website and in the Podman documentation.
Kubernetes: It is a container orchestration platform that automates the deployment, scaling, and management of containerized applications. It provides a platform for managing and scaling containerized applications across multiple hosts and environments, and offers advanced features such as automatic scaling, rolling updates, and self-healing. In Python projects, Kubernetes can be used to manage and orchestrate containerized Python applications, providing a scalable and reliable platform for running and deploying applications. You can read more about this on the Kubernetes website and in the Kubernetes documentation.

Overall, Docker, Podman, and Kubernetes are powerful tools for managing and deploying containerized applications, and can provide a streamlined and efficient platform for running Python projects.

In case you do not want to include DevOps in your project, you can do so by selecting the option None (this is the default option).

Automation tools¤

An automation tool is software or a platform designed to automate repetitive tasks, processes, or workflows that are traditionally performed manually. These tools are used to streamline and optimize various operations, reduce human intervention, increase efficiency, and minimize errors. Currently, SciCookie allows you to use Make and/or Makim.

Makim: Makim is an innovative tool inspired by make, designed to simplify target and dependency definition through YAML format. It introduces advanced features such as conditionals, arguments, and dependencies with targeted parameters. It also facilitates organized grouping of targets and supports user-defined variables and environment variables. Makim empowers users to streamline documentation and parameterize targets effectively. This free and open-source tool offers improved target management while maintaining compatibility with familiar YAML syntax.
Make: Make is a versatile build automation tool that uses Makefiles to define rules and dependencies for compiling code and building projects. It automates the process, intelligently rebuilding only changed components, streamlining software development workflows.

Project team¤

The project team refers to the group of people responsible for developing and maintaining the project. This includes developers, designers, testers and other stakeholders involved in the project. It is important that they are governed by some rules and regulations so that they become a friendly group with standards that others can follow and form a wider community. In SciCookie, we have some options that are linked to the code of conduct, the governance document and the roadmap.

Code of conduct¤

A code of conduct is a set of guidelines that outlines the expected behavior of individuals participating in a community or organization. It typically specifies the types of behavior that are considered acceptable and unacceptable, as well as the consequences for violating the code of conduct. In SciCookie you can find and choose between three adaptations of well-known codes of conduct accepted by a large part of the community: A Code of Conduct for Open Source Communities by Contributors Covenant,The Citizen Code of Conduct,and an adapted version of the NumFOCUS and Python Codes of Conduct.

contributor-covenant option: The Contributor Covenant aims to create a safe and inclusive environment for all contributors to open source projects. By promoting inclusive language, respectful communication, and a zero-tolerance policy for harassment and discrimination, it helps to ensure that everyone can participate in open source communities without fear of discrimination or mistreatment. If you want to know more about this, you can visit the full text of this code of conduct.
citizen-code-of-conduct option: The Citizen Code of Conduct is intended to create a safe and welcoming environment for all members of the community. By promoting respectful communication, inclusive behavior, and collaboration, it helps to ensure that everyone can participate in the community without fear of discrimination or mistreatment. If you would like to know more, you can read the full text of this code of conduct.
numfocus-adapted-coc option: It is an adaptation of the NumFocus Code of Conduct, promotes kindness, respect, professionalism, good communication, among others. It does not tolerate harassment, regardless of gender, sexual orientation, gender identity and expression, disability, physical appearance, body size, race or religion of the members; nor sexist, racist or exclusionary jokes. If you wish to read the full text, please visit this link code of conduct
python-adapted-coc option: This option is an adaptation of the Python Code of Conduct prioritizing inclusivity, respect, and a welcoming community. It emphasizes kindness, discourages discrimination, and encourages positive contributions, consideration, and collaboration among all members. If you would like to know more, you can read the full text of this code of conduct.

In case you do not want to include this file in your project, you can do so by selecting the option None (this is the default option).

Governance document¤

A governance document is a formal document that outlines the structure, policies, and procedures of an organization or community. It typically describes how decisions are made, how resources are allocated, and how conflicts are resolved. This can take many different forms, depending on the needs and goals of the organization or community. SciCookie offers two adaptations for your project governance document: NumPy Governance and SciML Scientific Machine Learning Governance.

numpy-governance option: NumPy is a widely used open source project for scientific computing in Python. Its governance document outlines how the project is managed and how decisions are made. The NumPy governance document describes the roles and responsibilities of the project's contributors, maintainers, and steering council, as well as the decision-making processes and procedures for contributing to the project. It emphasizes the importance of transparency, inclusivity, and participation in the development of the project. The NumPy Governance Document provides a clear framework for the management of the project and helps to ensure that it remains a vibrant and sustainable community-driven project. If you want to read the full text of Numpy's governance document, click here.
sciml-governance option: SciML Scientific Machine Learning Governance is a governance document that outlines the structure and decision-making processes of the SciML community, which focuses on the development of scientific machine learning tools and methods. The document describes the roles and responsibilities of community members, including the Steering Committee and Technical Leaders. It also outlines the community's decision-making processes, including how new projects are proposed and accepted, and how conflicts and disputes are handled. The governance document emphasizes the importance of collaboration, inclusivity and transparency in the development of scientific machine learning tools and methods. It also provides a clear framework for the governance of the community, helping to ensure that it remains a vibrant and effective force in the development of scientific machine learning. If you want to read the full text of SciML Scientific Machine Learning Governance, click here.

In case you do not want to include this file in your project, you can do so by selecting the option None (this is the default option).

Roadmap document¤

A roadmap is a strategic plan that outlines the objectives, milestones and actions required to achieve a specific goal or vision. It is typically used in business, project management and product development to provide a high-level overview of the steps required to achieve a desired outcome. A roadmap can include timelines, budgets, resource requirements and key performance indicators to track progress and measure success. The purpose is to provide a clear and actionable plan for achieving a goal and to communicate that plan to stakeholders and team members. SciCookie offers an adaptation for your project roadmap document: PyTorch-Ignite roadmap.

pytorch-ignite-roadmap option: PyTorch Ignite is an open-source library for high-level training and evaluation of neural networks in PyTorch. The PyTorch Ignite roadmap outlines the development plans for the library, including new features, bug fixes, and performance improvements. The roadmap may include timelines for releasing new versions, as well as details on specific features that are planned for each release. If you want to read the full text of PyTorch Ignite Roadmap, click here.

In case you do not want to include this file in your project, you can do so by selecting the option None (this is the default option).

Version control¤

Version control is essential for any project because it enables us to track and store all versions and changes made to our codebase. With this in mind, we have added some functionalities to SciCookie that allow you to easily add your Git account information and project URLs.

By filling in the following fields, you can integrate your codebase with Git and take advantage of its powerful version control features:

git_username: This is where you can enter your Git username, which will be used only in the text of the documentation.

git_https_origin: This is the Git https origin URL, which is used for configuring your repository in your local environment.

git_https_upstream: This is the Git https upstream URL, which is used for adding on text of documentation installation and the contributing file, as well as configuring your repository in your local environment.

git_main_branch: This field allows you to specify the name of your main branch. By default, it is named "main".

By providing this information, you can easily integrate your codebase with GitHub and take advantage of its powerful version control features. These functionalities in SciCookie make it easy to manage your project and collaborate with others, while ensuring that your code is properly versioned and tracked.