semvec/pypi-packages huggingface dataset:
hivecode 0.0.0.8.36
Hivecode is a versatile and comprehensive Python library, with a focus on efficiency and reusability, Hivecode empowers developers and data enthusiasts alike to streamline their projects.
Requires Python: >=3.8.0

.. |PyPI version Hivecode| image:: https://img.shields.io/pypi/v/hivecode.svg
   :target: https://pypi.org/project/hivecode/

.. |Documentation Status| image:: https://readthedocs.org/projects/hivecode/badge/?version=latest
   :target: http://hivecode.readthedocs.io/?badge=latest

.. |MIT license| image:: https://img.shields.io/badge/License-MIT-blue.svg
   :target: https://lbesson.mit-license.org/

.. |PyPI license| image:: https://img.shields.io/pypi/l/hivecode.svg
   :target: https://pypi.org/project/hivecode/

Hivecode
========
|PyPI version Hivecode| |Documentation Status| |MIT license|

Hivecode is a versatile and comprehensive Python library, with a focus on efficiency and reusability, Hivecode empowers developers and data enthusiasts alike to streamline their projects. This library boasts a wide array of practical and innovative functions, ranging from seamless integration with Azure Databricks to implementing essential design patterns, data preprocessing, visualization, exploration, and more. Whether you're looking to supercharge your data analysis or accelerate your Python projects, Hivecode is the go-to toolkit for unlocking the full potential of your endeavors.

Packages
========
- Hivecore: A set of many vanilla python tools.
- Hiveadb: A set of tools to work in Azure Databricks.
- Hivesignal: A set of tools for signal analysis.

Installation
============
To install the library, you will need pip.

|

.. code-block::

    pip install hivecode

|

.. note::
     Hivecode is constantly changing as new versions try to make it a more stable library. It is highly recommended to specify the version you want to import.

|

Requirements
------------
Hivecode requires Python 3.8.0+ to run.

Development
===========
Requirements
------------
Build is used to build the packages that will be deployed into pypi.

|

.. code-block::

    pip install build

|

Twine is used to deploy packages to pypi.

|

.. code-block::

    pip install twine

|
    
Sphinx is used to build the documentation.

|

.. code-block::

    pip install Sphinx

|

Develop
-------
You can build the project by running the following console command.

|

.. code-block::

    python -m build

|

You can then use twine to upload the newest version to Pypi.

|

.. code-block::

    py -m twine upload --skip-existing --repository pypi dist/* -u {User} -p {password}

|

I personally recommend to cascade both commands using a pipe, like this.

|

.. code-block::

    python -m build | py -m twine upload --skip-existing --repository pypi dist/* -u {User} -p {password}

|

When working with documentations, you will need to run it like this.

|

.. code-block::

    .\doc\make.bat html

|
obsidian-traverse 0.0.1
Recursively list all files linked by Obsidian note.

# obsidian-traverse

Recursively list all files linked by Obsidian note.

## Installation
```sh
pip install obsidian-traverse
```

## Usage
```
usage: obsidian-traverse [-h] -d DIRECTORY [--abspath] NOTES [NOTES ...]

Recursively list all files linked by Obsidian note.

positional arguments:
  NOTES                 Notes to traverse

options:
  -h, --help            show this help message and exit
  -d DIRECTORY, --directory DIRECTORY
                        Vault directory
  --abspath             Output absolute paths of linked files
```

Fixes for emergent parsing problems are welcome!
SEScore2 1.0.0
SEScore2: Retrieval Augmented Pretraining for Text Generation Evaluation
Requires Python: >=3.8.0,<4.0.0

<h1>SEScore2: Retrieval Augmented Pretraining for Text Generation Evaluation</h1>

SESCORE2, is a SSL method to train a metric for general text generation tasks without human ratings. We develop a technique to synthesize candidate sentences with varying levels of mistakes for training. To make these self-constructed samples realistic, we introduce retrieval augmented synthesis on anchor text; It outperforms SEScore in four text generation tasks with three languages (The overall kendall correlation improves 14.3%).

<h3>Paper: https://arxiv.org/abs/2212.09305</h3>

<h3>Author Email: wendaxu@cs.ucsb.edu</h3>

<h3>Maintainer Email: zihan_ma@ucsb.edu</h3>

<h3>Install all dependencies:</h3>

````
```
pip install SEScore2
```
````

<h3>Instructions to score sentences using SEScore2:</h3>

Currently, the PyPI version only support English Checkpoint. To run SEScore2 for text generation evaluation:

````
```
from SEScore2 import download_from_drive
download_from_drive() # Download the model checkpoint


from SEScore2 import SEScore2
from SEScore2.train.regression import *
scorer = SEScore2('en') # load in metric with specified language, en (English), de (German), ja ('Japanese')

refs = ["SEScore is a simple but effective next generation text generation evaluation metric", "SEScore it really works"]

outs = ["SEScore is a simple effective text evaluation metric for next generation", "SEScore is not working"]

scores_ls = scorer.score(refs, outs, 1)
```
````
turbobit-api 1.0.0
Unofficial python api wrapper from https://turbobit.net/
Keywords: api turbobit file video hosting unlimited

# Unofficial StreamTape API wrapper

It is a simple API wrapper for the streaming service streamtape.com. The API documentation can be found on the [docs page](https://devcraftclub.github.io/TurboBitApi/). The whole structure of the API has been split into different classes for easy overview and usage.

## Installation

Install with

```python3
pip install turbobit_api
```

## Usage

### General usage

Every class starts with following initialization:

```python3
my_var = selectedClass()
```

Or

```python3
my_var = selectedClass("API Key")
```

### Variables

Put in your environment variable `TURBOBIT_API` like you can see in .env file

Alternatively you can pass the key when class is initialized



## Changelog

### 1.0.0

- Initial release
qsharp-lang-jupyterlab 1.0.0.dev4
Q# extension for JupyterLab
Requires Python: >=3.7

# Q# extension for JupyterLab

## Prerequisites

You will need to install `jupyterlab` to build and develop the extension.

```bash
pip install jupyterlab==4.0.0
```

## A note about `yarn`

The `jlpm` command used below is an alias for `yarn` which comes bundled with Jupyter.

This folder is not part of the `npm` workspace that is defined at the root of this repo.
This is intentional, since Jupyter extension tooling depends on `jlpm`, which expects a
`yarn`-style workspace and a `yarn.lock` file.

## Building

To build and install the extension in development mode, from the `jupyterlab` directory run:

```bash
pip install -e .
# The below command creates a symlink from JupyterLab's
# extensions directory to the current source directory.
jupyter labextension develop . --overwrite
```

To start JupyterLab and use the extension, run:

```bash
jupyter lab
```

To rebuild after making source changes, run:

```bash
jlpm build
```

Then refresh the browser.

## Uninstalling

Remove the `pip` package by running:

```bash
pip uninstall qsharp-lang-jupyterlab
```

You will also need to remove the symlink created by `jupyter labextension develop`
command. To find its location, you can run `jupyter labextension list` to figure out where the `labextensions` folder is located. Then you can remove the symlink named `qsharp-lang-jupyterlab` within that folder.

## Releasing

The extension can be published to `PyPI` and `npm` manually or using the [Jupyter Releaser](https://github.com/jupyter-server/jupyter_releaser).
hbc-random-number 0.2.0
Python Boilerplate contains all the boilerplate you need to create a Python package.
Requires Python: >=3.9.13
Keywords: hbc_random_number

ProvablyFairRandomNumberGenerator Library
=========================================



.. image:: https://pyup.io/repos/github/sarthakvijayvergiya/hbc_random_number/shield.svg
     :target: https://pyup.io/repos/github/sarthakvijayvergiya/hbc_random_number/
     :alt: Updates

The **ProvablyFairRandomNumberGenerator** is a Python library that provides a provably fair mechanism for generating random numbers. This library uses cryptographic techniques to ensure transparency and fairness in generating random numbers, making it suitable for applications where unbiased randomness is essential.

Features
--------

- Generates random numbers within a specified range using provably fair methods.
- Uses os specific randomness source.

Installation
------------

.. code-block:: bash

   pip install provablyfairrandomnumbergenerator

Usage
-----

.. code-block:: python

   from provablyfairrandomnumbergenerator import generate_random_number

   # Generate a random number within a range
   min_value = 1
   max_value = 100
   random_number = generate_random_number(min_value, max_value)

   print(f"Generated Random Number: {random_number}")

Dependencies
------------

- Python 3.x


Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.1.0 (2023-08-29)
------------------

* First release on PyPI.