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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.

Project description

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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.

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.

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, sort_dict_keys, upsert_in_dataframe, array_to_disk)
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”€ 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”€ ...

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