semvec/pypi-packages huggingface dataset:
one-data-processing 0.0.14
Data Processing is used for data processing through MinIO, databases, Web APIs, etc.
Requires Python: >=3.8
Keywords: PDF WORD WEB parsing preprocessing

# Features

Data Processing is used for data processing through MinIO, databases, Web APIs, etc. The data types handled include:
- txt
- json  
- doc
- html
- excel
- csv
- pdf
- markdown
- ppt

## Text Type Processing  

The data processing process includes: cleaning abnormal data, filtering, de-duplication, and anonymization.
tinyscaf 0.0.2
A tiny app to scafold individual files.
Requires Python: >=3.12,<4.0

# Tinyscaf 

Tinyscaf is a micro scaffolder for simple files you produce all the time.
It is an easy way to standardize the basic structure of files you use all the time
in projects.

### Installation
    pip install tinyscaf

### Configuration
- Create a directory called **templates**
- Create a configuration file called **tinyscaf.json** inside the templates directory
- Create a jinja2 template file using tinyscaf.<variable_name> for the items you want replaced
- In tinyscaf.json, create a list of objects, one per jinja template
- Populate the json object with the **variable_name** as the key and the value is the question you want to prompt the user with.
- Any matching tinyscaf.<variable_name> will be replaced the in output file

### Example

#### templates/tinyscaf.json

    [
        {
        "template_name": "view.jinja",
        "file_name": "{view_name}.sql",
        "view_name": "What is the name of this view?"
        }
    ]

#### templates/view.jinja

    CREATE VIEW { tinyscaf.view_name } AS
    SELECT order_id, customer_name, total_amount
    FROM orders limit 100;


The **template_name** key in each json object is required to identify the associated template.
If you want to have a file naming standard based on the answer of one of your
questions, you can use the key **file_name** and the value pointing to the variable_name
of the question you want to use in jinja templating formatting.

    [
        {
            "template_name": "function.jinja",
            "function_name": "What is the name of the function?",
            "file_name": "{function_name}.sql"
        }
    ]

### Useage:

From the parent folder of templates run tinyscaf
Tinyscaf will automatically search the templates directorys for the tinyscaf.json file
and any jinja files in the template directory

    $> tinyscaf
    Choices: 
     
    0 - function.jinja 
    1 - view.jinja
    Choose a template file:  1
    What is the name of this view? test_view
    File name will be: test_view.sql - Specify the directory to save to. Leave blank for working dir. 
    $> 2024-02-02 00:32:42,918 [INFO] [tinyscaf:188] - Finished

If you are not in the parent folder of templates, you can override the auto search for tinyscaf.json
and specify the path to the config file manually. Tinyscaf will then search the directory
where the config file lives for any jinja template files.

    $> tinyscaf --config_file ../../templates/tinyscaf.json
    Choices: 
     
    0 - function.jinja 
    1 - view.jinja
    Choose a template file:  1
    What is the name of this view? test_view
    File name will be: test_view.sql - Specify the directory to save to. Leave blank for working dir. 
    $> 2024-02-02 00:32:42,918 [INFO] [tinyscaf:188] - Finished
Physic 1.2
A programming library specialized in the rules of physics in general, and more precisely the rules of mechanics and electrical physics and their rates, and it is used to shorten complex rates into simple-to-use functions

# Physic Library

Physic is a programming library specialized in the rules of physics, focusing on mechanics and electrical physics. It provides functions to handle various aspects of these fields, making it easy to work with complex physics calculations through simple-to-use functions.

## Overview

Physic aims to simplify physics-related calculations, providing a set of functions for mechanics and electrical physics. It is designed to be user-friendly, allowing developers to perform common physics calculations with ease.

## Installation

You can install Physic using pip:

```bash
pip install physic
NarrativeForge 0.1.7
A package for adding text and ai generated voice to videos.

# NarrativeForge

NarrativeForge is a Python package that enhances videos by adding text and synthesized speech over a background video. It's designed to simplify the process of creating engaging videos with customizable text and voice narration.

## Features

- Add text and synthesized speech to background videos.
- Customize font, color, and other text parameters.
- Easily integrate into existing video editing workflows.

## Installation

You can install NarrativeForge using pip:

```bash
pip install NarrativeForge
```

Additionally, you need to set up your OpenAI API key. Obtain your API key by following the instructions on the OpenAI website. Once you have the API key, you can set it as an environment variable:

```bash
export OPENAI_API_KEY="your-api-key-here"
```

Make sure to replace "your-api-key-here" with your actual OpenAI API key.

## Usage

use in command line as

```bash
NarrativeForge -i "input.mp4" -t "Hello, World!, Enhance your videos with NarrativeForge"
```

```python
from NarrativeForge import TextToVideo

# Create an instance of TextToVideo
text_to_video_instance = TextToVideo()

# Specify input video, text, and output filename
input_filename = "input_video.mp4"
texts = "Hello, World!, Enhance your videos with NarrativeForge"
output_filename = "output_video.mp4"

# Generate the enhanced video
text_to_video_instance.generate_video(
    input_filename, texts, output_filename, caption_padding=100
)

```

Replace input_video.mp4 with the path to your input video and customize the texts list accordingly. The enhanced video will be saved as output_video.mp4.

## Configuration

You can customize various parameters using the generate_video function. Refer to the function signature and documentation for options.

## Contributing

Contributions are welcome! If you find any issues or have suggestions, please open an issue or submit a pull request.
ab-data-processing 0.0.1
Data Processing is used for data processing through MinIO, databases, Web APIs, etc.
Requires Python: >=3.11
Keywords: PDF WORD WEB parsing preprocessing

# Features

Data Processing is used for data processing through MinIO, databases, Web APIs, etc. The data types handled include:
- txt
- json  
- doc
- html
- excel
- csv
- pdf
- markdown
- ppt

## Text Type Processing  

The data processing process includes: cleaning abnormal data, filtering, de-duplication, and anonymization.
funix-cloud 0.1.2
Funix cloud tool
Requires Python: >=3.10
Keywords: funix,cloud

# Funix-Cloud

> It's only in the development stage right now, and development may be behind Kumo (Funix-Cloud).

Funix Cloud Tool, help you deploy your local or git repository to Funix Cloud.

## Requirements

- Python 3.10+
- Internet Connection

## Installation

Currently, we only provide Git installation:

```bash
git clone https://github.com/TexteaInc/funix-cloud
cd funix-cloud
pip install -e .
```

In the future, we will support Pip installation:

```bash
pip install 
```

## Registration

```plaintext
> funix-cloud register
What is a user name you preferred: myusername      
What is your email: myemail@gmail.com
Password: ********
Confirm Password: ******** 
Login successful! Your token is saved.
Sending verification email...
Your email myemail@gmail.com will receive a verification link, please check your inbox.
```

Funix will then email you a link to click to complete your registration.

## Deployment

### Single file

```bash
funix-cloud deploy main.py my-first-app
```

We need you to provide a `requirement.txt` file to determine which dependencies to install. If `requirement.txt` does not exist, we will prompt you whether to create a `requirement.txt` with just funix.

### Folder

```bash
funix-cloud deploy my-project my-first-app --file main.py
```

For local folder, we also need a `requirement.txt`. And the `--file` option specifies the program entry file, which defaults to `main.py`.

### Git

```bash
funix-cloud deploy https://github.com/myusername/myrepo.git my-git-app --file main.py
```

Deploying a git project is similar to deploying a local folder, just from a different source.

## Other operations

```bash
# list deployed instances
funix-cloud list
# delete an instance, the 1 is instance id,
# you can query it through the list command above.
funix-cloud delete 1
```
honyx 0.1.1
Python package for creating and analyzing Higher-order networks from sequential dataset
Requires Python: >=3.8
Keywords: Networks,Graph Theory,Mathematics,network,graph,discrete mathematics,math

# HONyx

HONyx is a Python package for generating and analyzing Higher-Order Networks models

- **Source:** https://gitlab.univ-nantes.fr/queyroi-f/honyx
- **Bug reports:** https://gitlab.univ-nantes.fr/queyroi-f/honyx/-/issues


Usage
-----
```python
>>> import honyx
>>> import networkx as nx

>>> sequences = [['a','b','r','a','c','a','d','a','b','r','a']]
>>> fon2 = honyx.generate_hon(sequences, "fix-order", max_order = 2)

>>> pos = nx.spring_layout(fon2)
>>> node_cols = ['blue' if len(v)==1 else 'green' for v in fon2.nodes()]
>>> nx.draw(fon2, pos = pos, with_labels=True, node_size=1000, node_color = node_cols, alpha=0.5)
>>> nx.draw_networkx_edge_labels(fon2, pos, edge_labels = nx.get_edge_attributes(fon2,'weight'))
```
![Order 2 Network](https://gitlab.univ-nantes.fr/queyroi-f/honyx/-/raw/main/docs/ex_fon2_readme.png "Order 2 Network")

```python
>>> honyx.pagerank_hon(fon2)
{'a': 0.40849881734049087,
 'd': 0.14318783763461537,
 'c': 0.1478373488011564,
 'r': 0.15427265837422358,
 'b': 0.14620333784951386}
```

Citing
------

To cite HONyx, please use the following publication:

    Julie Queiros, François Queyroi (2023). 
    Construction de Réseaux d'Ordre Supérieur à partir de Traces : Méthodes et Outils. 
    https://hal.science/hal-04085138 

[PDF](https://hal.science/hal-04085138/) [BibTeX](https://hal.science/hal-04085138v1/bibtex)


Install
-------
Using  Python 3.8, 3.9, or 3.10.

Install the latest version of HONyx:

    $ pip install honyx

Required dependancies include [NetworkX](https://networkx.org/), [Numpy](https://numpy.org/) and [Scipy](https://scipy.org/).

License
-------

Released under the MIT license (see `LICENSE.txt`)::

    Copyright (C) 2004-2023 HONyx Developers
    Julie Queiros <julie.queiros@univ-nantes.fr>
    François Queyroi <francois.queyroi@univ-nantes.fr>
    Simon Artus <simon.artus@etu.univ-nantes.fr>