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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.

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๐Ÿ“š 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.

๐Ÿ“– Want to see examples? Check out our Google Colab notebook with practical usage examples!

๐Ÿš€ Project File Tree

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stouputils/
โ”œโ”€โ”€ print.py         # ๐Ÿ–จ๏ธ Utility functions for printing (info, debug, warning, error, whatisit, breakpoint, colored_for_loop, ...)
โ”œโ”€โ”€ decorators.py    # ๐ŸŽฏ Decorators (measure_time, handle_error, timeout, retry, simple_cache, abstract, deprecated, silent)
โ”œโ”€โ”€ ctx.py           # ๐Ÿ”‡ Context managers (LogToFile, MeasureTime, Muffle, DoNothing, SetMPStartMethod)
โ”œโ”€โ”€ io.py            # ๐Ÿ’พ Utilities for file management (json_dump, json_load, csv_dump, csv_load, read_file, super_copy, super_open, clean_path, ...)
โ”œโ”€โ”€ parallel.py      # ๐Ÿ”€ Utility functions for parallel processing (multiprocessing, multithreading, run_in_subprocess)
โ”œโ”€โ”€ image.py         # ๐Ÿ–ผ๏ธ Little utilities for image processing (image_resize, auto_crop, numpy_to_gif, numpy_to_obj)
โ”œโ”€โ”€ collections.py   # ๐Ÿงฐ Utilities for collection manipulation (unique_list, at_least_n, sort_dict_keys, upsert_in_dataframe, array_to_disk)
โ”œโ”€โ”€ typing.py        # ๐Ÿ“ Utilities for typing enhancements (IterAny, JsonDict, JsonList, ..., convert_to_serializable)
โ”œโ”€โ”€ all_doctests.py  # โœ… Run all doctests for all modules in a given directory (launch_tests, test_module_with_progress)
โ”œโ”€โ”€ backup.py        # ๐Ÿ’พ Utilities for backup management (delta backup, consolidate)
โ”œโ”€โ”€ archive.py       # ๐Ÿ“ฆ Functions for creating and managing archives
โ”‚
โ”œโ”€โ”€ applications/
โ”‚   โ”œโ”€โ”€ automatic_docs.py    # ๐Ÿ“š Documentation generation utilities (used to create this documentation)
โ”‚   โ”œโ”€โ”€ upscaler/            # ๐Ÿ”Ž Image & Video upscaler (configurable)
โ”‚   โ””โ”€โ”€ ...
โ”‚
โ”œโ”€โ”€ continuous_delivery/
โ”‚   โ”œโ”€โ”€ cd_utils.py          # ๐Ÿ”ง Utilities for continuous delivery
โ”‚   โ”œโ”€โ”€ git.py               # ๐Ÿ“œ Utilities for local git changelog generation
โ”‚   โ”œโ”€โ”€ github.py            # ๐Ÿ“ฆ Utilities for continuous delivery on GitHub (upload_to_github)
โ”‚   โ”œโ”€โ”€ pypi.py              # ๐Ÿ“ฆ Utilities for PyPI (pypi_full_routine)
โ”‚   โ”œโ”€โ”€ pyproject.py         # ๐Ÿ“ Utilities for reading, writing and managing pyproject.toml files
โ”‚   โ”œโ”€โ”€ stubs.py             # ๐Ÿ“ Utilities for generating stub files using stubgen
โ”‚   โ””โ”€โ”€ ...
โ”‚
โ”œโ”€โ”€ data_science/
โ”‚   โ”œโ”€โ”€ config/              # โš™๏ธ Configuration utilities for data science
โ”‚   โ”œโ”€โ”€ dataset/             # ๐Ÿ“Š Dataset handling (dataset, dataset_loader, grouping_strategy)
โ”‚   โ”œโ”€โ”€ data_processing/     # ๐Ÿ”„ Data processing utilities (image augmentation, preprocessing)
โ”‚   โ”‚   โ”œโ”€โ”€ image/           # ๐Ÿ–ผ๏ธ Image processing techniques
โ”‚   โ”‚   โ””โ”€โ”€ ...
โ”‚   โ”œโ”€โ”€ models/              # ๐Ÿง  ML/DL model interfaces and implementations
โ”‚   โ”‚   โ”œโ”€โ”€ keras/           # ๐Ÿค– Keras model implementations
โ”‚   โ”‚   โ”œโ”€โ”€ keras_utils/     # ๐Ÿ› ๏ธ Keras utilities (callbacks, losses, visualizations)
โ”‚   โ”‚   โ””โ”€โ”€ ...
โ”‚   โ”œโ”€โ”€ scripts/             # ๐Ÿ“œ Data science scripts (augment, preprocess, routine)
โ”‚   โ”œโ”€โ”€ metric_utils.py      # ๐Ÿ“ Static methods for calculating various ML metrics
โ”‚   โ”œโ”€โ”€ mlflow_utils.py      # ๐Ÿ“Š Utility functions for working with MLflow
โ”‚   โ””โ”€โ”€ ...
โ”‚
โ”œโ”€โ”€ installer/
โ”‚   โ”œโ”€โ”€ common.py            # ๐Ÿ”ง Common functions used by the Linux and Windows installers modules
โ”‚   โ”œโ”€โ”€ downloader.py        # โฌ‡๏ธ Functions for downloading and installing programs from URLs
โ”‚   โ”œโ”€โ”€ linux.py             # ๐Ÿง Linux/macOS specific implementations for installation
โ”‚   โ”œโ”€โ”€ main.py              # ๐Ÿš€ Core installation functions for installing programs from zip files or URLs
โ”‚   โ”œโ”€โ”€ windows.py           # ๐Ÿ’ป Windows specific implementations for installation
โ”‚   โ””โ”€โ”€ ...
โ””โ”€โ”€ ...

๐Ÿ”ง Installation

pip install stouputils

โœจ 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.

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