stouputils 1.19.4

Stouputils is a collection of utility modules designed to simplify and enhance the development process. It includes a range of tools for tasks such as execution of doctests, display utilities, decorators, as well as context managers, and many more.

🛠️ Project Badges

📚 Project Overview

Stouputils is a collection of utility modules designed to simplify and enhance the development process.
It includes a range of tools for tasks such as execution of doctests, display utilities, decorators, as well as context managers.
Start now by installing the package: pip install stouputils.

📖 Want to see examples? Check out our Google Colab notebook with practical usage examples!

🚀 Project File Tree

<style> .code-tree { border-radius: 6px; padding: 16px; font-family: monospace; line-height: 1.45; overflow: auto; white-space: pre; background-color:rgb(43, 43, 43); color: #d4d4d4; } .code-tree a { color: #569cd6; text-decoration: none; } .code-tree a:hover { text-decoration: underline; } .code-tree .comment { color:rgb(231, 213, 48); } .code-tree .paren { color: orange; } </style>

stouputils/
├── print.py         # 🖨️ Utility functions for printing (info, debug, warning, error, whatisit, breakpoint, colored_for_loop, ...)
├── decorators.py    # 🎯 Decorators (measure_time, handle_error, timeout, retry, simple_cache, abstract, deprecated, silent)
├── ctx.py           # 🔇 Context managers (LogToFile, MeasureTime, Muffle, DoNothing, SetMPStartMethod)
├── io.py            # 💾 Utilities for file management (json_dump, json_load, csv_dump, csv_load, read_file, super_copy, super_open, clean_path, ...)
├── parallel.py      # 🔀 Utility functions for parallel processing (multiprocessing, multithreading, run_in_subprocess)
├── image.py         # 🖼️ Little utilities for image processing (image_resize, auto_crop, numpy_to_gif, numpy_to_obj)
├── collections.py   # 🧰 Utilities for collection manipulation (unique_list, at_least_n, sort_dict_keys, upsert_in_dataframe, array_to_disk)
├── typing.py        # 📝 Utilities for typing enhancements (IterAny, JsonDict, JsonList, ..., convert_to_serializable)
├── all_doctests.py  # ✅ Run all doctests for all modules in a given directory (launch_tests, test_module_with_progress)
├── backup.py        # 💾 Utilities for backup management (delta backup, consolidate)
├── archive.py       # 📦 Functions for creating and managing archives
│
├── applications/
│   ├── automatic_docs.py    # 📚 Documentation generation utilities (used to create this documentation)
│   ├── upscaler/            # 🔎 Image & Video upscaler (configurable)
│   └── ...
│
├── continuous_delivery/
│   ├── cd_utils.py          # 🔧 Utilities for continuous delivery
│   ├── github.py            # 📦 Utilities for continuous delivery on GitHub (upload_to_github)
│   ├── pypi.py              # 📦 Utilities for PyPI (pypi_full_routine)
│   ├── pyproject.py         # 📝 Utilities for reading, writing and managing pyproject.toml files
│   ├── stubs.py             # 📝 Utilities for generating stub files using stubgen
│   └── ...
│
├── data_science/
│   ├── config/              # ⚙️ Configuration utilities for data science
│   ├── dataset/             # 📊 Dataset handling (dataset, dataset_loader, grouping_strategy)
│   ├── data_processing/     # 🔄 Data processing utilities (image augmentation, preprocessing)
│   │   ├── image/           # 🖼️ Image processing techniques
│   │   └── ...
│   ├── models/              # 🧠 ML/DL model interfaces and implementations
│   │   ├── keras/           # 🤖 Keras model implementations
│   │   ├── keras_utils/     # 🛠️ Keras utilities (callbacks, losses, visualizations)
│   │   └── ...
│   ├── scripts/             # 📜 Data science scripts (augment, preprocess, routine)
│   ├── metric_utils.py      # 📏 Static methods for calculating various ML metrics
│   ├── mlflow_utils.py      # 📊 Utility functions for working with MLflow
│   └── ...
│
├── installer/
│   ├── common.py            # 🔧 Common functions used by the Linux and Windows installers modules
│   ├── downloader.py        # ⬇️ Functions for downloading and installing programs from URLs
│   ├── linux.py             # 🐧 Linux/macOS specific implementations for installation
│   ├── main.py              # 🚀 Core installation functions for installing programs from zip files or URLs
│   ├── windows.py           # 💻 Windows specific implementations for installation
│   └── ...
└── ...

🔧 Installation

pip install stouputils

✨ Enable Tab Completion on Linux (Optional)

For a better CLI experience, enable bash tab completion:

# Option 1: Using argcomplete's global activation
activate-global-python-argcomplete --user

# Option 2: Manual setup for bash
register-python-argcomplete stouputils >> ~/.bashrc
source ~/.bashrc

After enabling completion, you can use <TAB> to autocomplete commands:

stouputils <TAB>        # Shows: --version, -v, all_doctests, backup
stouputils all_<TAB>    # Completes to: all_doctests

Note: Tab completion works best in bash, zsh, Git Bash, or WSL on Windows.

⭐ Star History