semvec/pypi-packages huggingface dataset:
aesthetic-text 0.2.0
to stylized terminal text

# Aesthetic TEXT

Give a beautiful look of your text in the terminal.

**link to access to the pypi page :** https://pypi.org/project/aesthetic-text/0.2.0/

## Installation

```zsh
$ pip install aesthetic_text
```

### Usage

```python
from aesthetic_text import aesthetic_text as aesthetic

# print a text with a style
# style: bold, italic, underline, strikethrough, reverse, conceal, crossed
print(f"{aesthetic.style.bold}{aesthetic.style.underline}" + "Hello World" + f"{aesthetic.reset}")

# print a text with a color
# colors: black, red, green, yellow, blue, magenta, cyan, white...
print(f"{aesthetic.color.red}" + "Hello World" + f"{aesthetic.reset}")

# print a text with style and color
print(f"{aesthetic.style.bold}{aesthetic.color.red}" + "Hello World" + f"{aesthetic.reset}

# print a text with icon
print(f"{aesthetic.icon.check}" + "the task has been completed successfully" + f"{aesthetic.reset}")
```
signalstickers-client 3.3.0
A client for the Signal stickers API
Requires Python: >=3.6

# Python client for Signal stickers

A client to interact with the [Signal](https://signal.org/) stickers API.

+ Fetch sticker packs
+ Get images files
+ Upload sticker packs
+ etc.


> Note: despite its name, this client does not interacts with
> `signalstickers.com`, so information defined there (tags, etc.) will **not**
> be fetched.

> This client connects to the Signal sticker API. Please **do not flood it**
> (you'll be blocked anyway).

## Installation

```bash
pip3 install --user signalstickers-client
```

This module requires `cryptography`, `protobuf`, `anyio`, and `httpx` (but they
should be installed with the previous command).
 
## Usage

If you are not familiar with Signal stickers, read
[STICKERS_INTERNALS.md](STICKERS_INTERNALS.md) first.

### Downloading a pack
The `StickerPack` object returned by `await
StickersClient().get_pack(<pack_id>, <pack_key>)` exposes the following
attributes:

+ `id: str` the pack id. Equals to `pack_id`;
+ `key: str` the pack key. Equals to `pack_key`;
+ `title: str` the title of the pack;
+ `author: str` the author of the pack;
+ `nb_stickers: int` the number of stickers in the pack;
+ `cover: Sticker` the cover sticker;
+ `stickers: list[Sticker]` the list of stickers in the pack

A `Sticker` object exposes the following attributes:

+ `id: int` the id of the sticker in the pack;
+ `emoji: str` the emoji mapped to this sticker;
+ `image_data: bytes` the webp image of the sticker.


### Uploading a pack

Same thing, but use `LocalStickerPack` (that does not contains `id` and `key`)
instead of `StickerPack`.

**You will need your Signal credentials!**

To obtain them, run the Signal Desktop app with the flag `--enable-dev-tools`,
open the Developer Tools, **change the JavaScript context from `top` to
`Electron Isolated Context`** (cf. video below), and type
`window.reduxStore.getState().items.uuid_id` to get your USER, and
`window.reduxStore.getState().items.password` to get your PASSWORD.

<details>
  <summary><i>Video: how to set JavaScript context</i></summary>

https://github.com/signalstickers/signalstickers-client/assets/7778898/ca3f1fec-e908-49d9-88a8-e33d0ee9a453

</details>


## Example usage

[See `examples/`](examples/)

## Development

+ Create a `pipenv` with `pipenv install --dev`;
+ Edit the code you want;
+ Don't forget to launch tests with `pipenv run py.test`.

## License

See
[LICENSE](https://github.com/romainricard/signalstickers-client/blob/master/LICENSE)


## Legal

This is not an official Signal project. This is an independant project.  
Signal is a registered trademark in the United States and other countries.


## Author

Romain Ricard <contact+stickerclient@romainricard.fr>
jupyter-docx-bundler 0.4.0
Jupyter bundler extension to export notebook as a docx file
Requires Python: >=3.6

# Jupyter docx bundler extension

[![PyPi Version](https://img.shields.io/pypi/v/jupyter-docx-bundler.svg)](https://pypi.org/project/jupyter-docx-bundler/)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/jupyter-docx-bundler.svg)](https://anaconda.org/conda-forge/jupyter-docx-bundler)
[![Conda Version](https://img.shields.io/conda/vn/mrossi/jupyter-docx-bundler.svg)](https://anaconda.org/mrossi/jupyter-docx-bundler)
[![CI](https://github.com/m-rossi/jupyter-docx-bundler/workflows/CI/badge.svg)](https://github.com/m-rossi/jupyter-docx-bundler/actions)
[![codecov](https://codecov.io/gh/m-rossi/jupyter-docx-bundler/branch/main/graph/badge.svg)](https://codecov.io/gh/m-rossi/jupyter-docx-bundler)

Jupyter bundler extension to export notebook as a docx file

## Installation

### Using conda

```
conda install -c conda-forge jupyter-docx-bundler
```

### Using pip

Make sure you have [Pandoc](https://pandoc.org) installed, see [installing-pandoc](https://github.com/bebraw/pypandoc#installing-pandoc) for instructions.

```
pip install jupyter-docx-bundler
```

## Usage

### Adding Metadata

The bundle extension uses metadata of the notebook, if you you provide it.

* `"title": "Notebook title"`
* `"authors": [{"name": "author1"}, {"name": "author2"}]`
* `"subtitle": "Notebook subtitle"`
* `"date": "Notebook date"`

The notebook metadata can be edited under _Edit_ -> _Edit Notebook Metadata_.

### Hiding inputs or complete code cells

You can hide individual code cells or just their inputs by defining cell tags:

* `nbconvert-remove-cell`: Remove the entire cell
* `nbconvert-remove-input`: Remove the input code of the cell

_(Currently there are no default values configured for these tags, the ones listed above are defined in my code and not in [nbconvert](https://github.com/jupyter/nbconvert). This may will change in the future.)_

Cell tags can be shown by activating the cell toolbar under _View_ -> _Cell Toolbar_ -> _Tags_.

#### Hiding all inputs

It is also possible to hide all inputs. To achive this you need to add the following lines to your notebook metadata:

```json
{
    "jupyter-docx-bundler": {
        "exclude_input": "True"
    }
}
```

The notebook metadata can be edited under _Edit_ -> _Edit Notebook Metadata_.

### Direct call from console (nbconvert)

To use the bundler direct from console the nbconvert utility can be used with target format docx:

* `jupyter nbconvert --execute --to=docx <source notebook>.ipynb --output <target document>.docx`

The `--execute` option should be used to ensure that the notebook is run before generation.

## Development

See [CONTRIBUTING](CONTRIBUTING.md)
file-cycle 0.0.2
The FileCycle is a Python library designed to manage file rotation in a specified directory.
Requires Python: >=3.7

# FileCycle: Python File Rotation Manager

**FileCycle** is a Python library designed to manage file rotation in a specified directory. It automatically organizes files into dated folders and applies a retention policy to remove old files. This ensures that you keep only the data you need for a specified period.

## Key Features:

- **Automated File Rotation**: Automatically moves files into dated folders based on the current date.
  
- **Retention Policy**: Cleans up old files based on a user-defined retention period, specified in days.

- **Customizable Working Directory**: Allows you to set the working directory where the file rotation and retention will occur.

- **Version Listing**: Provides a method to list all available versions (dated folders) in the working directory.

This library is particularly useful for applications that generate time-sensitive data files that need to be retained for a specific period.


## Installation

Copy the `FileCycle` class into your project.

## Features

- **Automated File Rotation**: Moves files into dated folders based on the current date.
- **Retention Policy**: Removes old files based on a user-defined retention period.
- **Customizable Working Directory**: Allows you to set the working directory for file rotation.
- **Version Listing**: Lists all available versions (dated folders) in the working directory.

## Usage

### Initialization

PIP:
`pip install file-cycle`


Manually:
Initialize the `FileCycle` class by specifying the working directory prefix, the working directory, and the retention days.

```python
from FileCycle import FileCycle  # Assuming FileCycle is in a file named FileCycle.py

# Initialize with default settings
file_cycle = FileCycle()

# Or specify custom settings
file_cycle = FileCycle(workdir_prefix="/your/prefix", workdir="/your/dir", retention_days=15)