semvec/pypi-packages huggingface dataset:
pryvacy 0.1.4
A set of access control descriptors for methods and nested classes
Requires Python: <4.0,>=3.10

# pryvacy - a set of access control decorators for python

## Philosophy

* Incur as least overhead as possible when using the access control decorators
* Only classes that use the decorators need to be changed, other related classes do not need to -> Can be opted-in easily

## Installation

```bash
pip install pryvacy
```

## Usage

The package provides 3 access control decorators: `@private`, `@public`, `@protected` that can be used on methods and nested classes (currently only `@private` can be used on nested classes)

```python
from pryvacy import pryvacy, private, public, protected

@pryvacy
class Foo():
  @public
  def public_method(self):
    pass

  @protected
  def protected_method(self):
    pass

  @privaye
  def private_method(self):
    pass
```

Access control rules:
* Methods inside `Foo` are able to access `public_method`, `protected_method`, `private_method`.
* Code outside `Foo` and not inside any `Foo`'s subclass methods can only access `public_method`.
* Methods inside `Foo`'s subclasses (either decorated with `@pryvacy` or not) can access `public_method` and `protected_method`.
* Nested classes methods can access `public_method`, `protected_method`, `private_method`.

## Pitfalls & Bugs

Disclaimer: The package has not been tested thoroughly! Use with caution! Any contributions are appreciated~

Currently, class-level code cannot access `protected_method` and `private_method`.

Example:
```python
  class Foo():
    ...
    class Bar(Foo):
      Foo().public_method() # OK!
      Foo().protected_method() # Exception!
      Foo().private_method() # Exception!
```

## Current limitations

* `@private` and `@protected` are not supported on nested classes yet.

* No way to enforce access policy on class and instance attributes.

## Roadmap

* Benchmark decorated classes

* Test comprehensively the decorators interaction with the whole ecosystem

* Implement @private and @protected on nested classes
simple-nlp-library 4.0.0
Simple NLP library
Requires Python: >=3.7

# simple_nlp_library
Simple NLP library

## usage

### installation
```
python3 -m pip install --upgrade simple-nlp-library
```

### api

#### semantic_tokens

```python
stop_words = preprocessing.stop_words()
preprocessing.semantic_tokens(stop_words, "The quick brown fox jumps over the lazy dog")
```

#### cosine_similarity
```python
metrics.cosine_similarity([1.0, 1.0, 1.0], [4.0, 4.0, 4.0])
```

#### inserting_distance
```python
metrics.inserting_distance("simple", "simpler")
```

#### inserting_similarity
```python
metrics.inserting_similarity("simple", "simpler")
```

#### jaccard_similarity
```python
metrics.jaccard_similarity([1, 2, 3], [1, 2, 3, 4])
```

#### embeddings
```python
vectors = embeddings.vectors()
vector = vectors["facebook"]
```

#### sentence similarity
```python
vectors = embeddings.vectors()
stop_words = preprocessing.stop_words()
metrics.cosine_similarity(
    embeddings.tokens_vector(vectors, preprocessing.semantic_tokens(stop_words, "the slow brown bear jumped over the lazy dog")),
    embeddings.tokens_vector(vectors, preprocessing.semantic_tokens(stop_words, "the quick red fox jumped over the lazy cat")),
)
```
pysofa2 23.10.11.2
A wrapper of the International Astronomical Union's SOFA lbrary.
Requires Python: >=3.7
Keywords: some, keywords

| Testing | Coverage | PyPi |
| :-----: | :------: | :--: |
| [![Build Status](https://travis-ci.org/duncaneddy/pysofa2.svg?branch=master)](https://travis-ci.org/duncaneddy/pysofa2) | [![Coverage Status](https://coveralls.io/repos/github/duncaneddy/pysofa2/badge.svg?branch=master)](https://coveralls.io/github/duncaneddy/pysofa2?branch=master) | [![PyPI version](https://badge.fury.io/py/pysofa2.svg)](https://badge.fury.io/py/pysofa2) |

# pysofa2

*pysofa2* is a module for using International Astronomical
 Union's (<http://www.iau.org/>) SOFA library (<http://www.iausofa.org/>) in 
python. It is inspired by original [pysofa](https://pypi.org/project/pysofa/)
implementation of Frederic Grollier. It extends the original pysofa module by
distributing and building the SOFA C library with the package, eliminiting the
need for the end-user to build the libraries separaetly from the package 
installtion.

Like *pysofa*, *pysofa2* is not a port of SOFA routines but a wrapper around the 
SOFA C library. Thus, no calculations are made in the pysofa software, this package
simply calls the underlying SOFA_C library.

## Documentation

For package documentation please refer to the underlying SOFA documentation at:
[http://www.iausofa.org/](http://www.iausofa.org/)

## Versioning

This package diverges from semantic versioning, and instead is versioned based on
the underlying SOFA release. SOFA versions based on YEAR-MONTH-DAY of the release.
For pysofa2

## Licensing

*pysofa2* not endorsed by the International Astronomical Union. 
In addition to *pysofa2*'s MIT license, any use of this module should comply 
with SOFA's license and [terms of use](http://www.iausofa.org/copyr.pdf). 
Especially, but not exclusively, any published work or commercial products 
which includes results achieved by using *pysofa* shall acknowledge that the 
SOFA software was used in obtaining those results.
miband2 0.1.1
Python API for communicating with MiBand2

# MiBand2 Python API based on bleak

The library provides convenient APIs to control and read data of MiBand2.

## Supported features

- sending notifications
- date/time setup and reading
- onetime alarm setup
- reading battery level

## Usage

```
import asyncio
import bleak
from miband2 import Band2, Key, NotificationType as NT
from datetime import datetime

KEY = Key(
    key=b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x05',
    reset=True # note *
)

async def main():
    located_band = await scan_bands(timeout=15)
    client = bleak.BleakClient(located_band, timeout=15)
    
    async with Band2(client) as band:
        auth_status = await band.auth(KEY)
        print(f"auth status: {auth_status}")

        battery_level = await band.get_battery()
        print(f"battery: {battery_level}")

        await band.ring(NT.SINGLE)
        
        now = datetime.now()
        await band.set_datetime(now)

async def scan_bands(timeout):
    def bands_only(dev, adv):
        return "MI Band" in dev.name
    return await bleak.BleakScanner.find_device_by_filter(timeout=timeout, filterfunc=bands_only)

asyncio.run(main())
```
_Note: key reset is required for the first time only. Later, one can auth with the same key setting the `reset=False`_

## Why?

This project initially helped me to learn BLE.
My old Band2 got its second life when I started to learn BLE and became curious if I can communicate with the device using a Linux machine. This Band2 device acted as an oracle visually confirming if I craft packets correctly. The fact of a visual feedback in a form of a notification or displaying a newly set time provided some incentive to me and made learning funnier than it could be if relying solely on reading books.
pythonpackage-morteza 1.0.0
A package for weight predict based on height

<!-- برای فعال کردن گیت هاب در محیط vscode این مراحل را انجام دهید: -->
- [ ] in this work we run a machine learning task for predict weight based on height a person from scratch

##for active push code in vscod via terminal vscode follow these steps:

1. Check for existing keys-
a. ls -al ~/.ssh

2. Create key if does not exist-
Paste the text below, substituting in your GitHub email address.
a. ssh-keygen -t ed25519 -C "your_email@example.com"
b. When you're prompted to "Enter a file in which to save the key," press Enter. This accepts the default file location.
c. At the prompt, type a secure passphrase.

3. Adding your SSH key to the ssh-agent-
Fire up the SSH agent and add the key
1. eval `ssh-agent -s`
2. ssh-add ~/.ssh/id_ed25519

4. Adding key to Github account-
Pull up the key and add to Github account
1. cat ~/.ssh/id_ed25519.pub
2. Navigate to Github account and add key

Hooray! now you should be able to push files to Github account.


<!-- اگر خطای نبودن ریپازیتوری را داد از این دستور استفاده کنید: -->
if get this error:(you have not repository)  so you type this command in terminal:
git remote set-url origin https://github.com/<user_name>/<repo_name>.git
py-distance-transforms 0.2.3
Add your description here
Requires Python: >=3.8

# py-distance-transforms
`py_distance_transforms` is a Python package that provides efficient distance transform operations on arrays. It is a wrapper around the Julia package [DistanceTransforms.jl](https://github.com/Dale-Black/DistanceTransforms.jl), bringing its high-performance capabilities to the Python ecosystem.


## Documentation
| Docs | Description |
|------|-------------|
| Getting Started: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1YNcN0sTk4pu1f79KZLK9dnd4BBqKGqSv?usp=sharing) | A quickstart guide to using `py_distance_transforms` for efficient distance transform operations on arrays. |
| Deep Learning (Hausdorff Loss): [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1YNou2N6cywlosHSuBP1Yjj6RLUl-SfLV?usp=sharing) | A [MONAI](https://github.com/Project-MONAI/tutorials) tutorial adjusted to show how to use the Hausdorff loss function and the corresponding `py_distance_transforms`|

## Features

- Fast distance transform computations on CPU and GPU
- Support for 1D, 2D, and 3D arrays
- Multi-threading for enhanced CPU performance
- GPU acceleration for NVIDIA GPUs (CUDA)
- Simple and intuitive API

## Installation

Install `py_distance_transforms` using pip:

```bash
pip install py_distance_transforms
```

## Basic Usage

```python
from py_distance_transforms import transform
import numpy as np

arr = np.random.choice([0, 1], size=(10, 10)).astype(np.float32)
result = transform(arr)
```

## GPU Acceleration

```python
import torch
from py_distance_transforms import transform_cuda

x_gpu = torch.rand((100, 100), device='cuda')
x_gpu = (x_gpu > 0.5).float()

gpu_transformed = transform_cuda(x_gpu)
```

## Acknowledgments

- `py_distance_transforms` is a Python wrapper around the Julia package [DistanceTransforms.jl](https://github.com/MolloiLab/DistanceTransforms.jl).
- Huge thanks to @pabloferz for getting DLPack.jl to work with PythonCall/juliacall and PyTorch. Massive thanks to @cjdoris and all of the contributors to PythonCall.jl as well.
goit-phone-book-bot 0.2.2
Phone Book bot developed by team #5 - CREATORS
Requires Python: >=3.6

# Installing and launching the application

- `git clone https://github.com/VasylMartyniv/GoIT_Phone_Book_bot.git`
- `python -m venv venv`
- `venv\Scripts\activate`
- `pip install -r requirements.txt`
- `py -m src.main`

# PyPi

Package is accessible on PyPi: https://pypi.org/project/goit-phone-book-bot/
Can be installed with `pip install goit-phone-book-bot`
to run the package simply use `goit_phone_book_bot` CLI command

#### To select a command, a letter followed by a TAB and ENTER
iisignature_tensorflow_2 0.1.0
Tensorflow 2 implementation of the iisignature package.
Requires Python: <4.0,>=3.10

# iisignature-tensorflow-2

Simple tensorflow layer wrapper over the [iisignature library](https://github.com/bottler/iisignature/tree/master). 

The iisignature library examples for tensorflow covered only tensorflow1.X versions, this package only recreate the same examples for tensorflow 2.X versions.

A pip package can be installed directly using

```bash
pip install iisignature_tensorflow_2
```

The package implements SigLayer, LogSigLayer, SigScaleLayer and SigJoinLayer.

The SigJoinLayer not implements FixedGrad method for the moment.

For the first two they can be directly used in keras Sequential models, for the last two they need to be used in a functional model.

The package also includes a simple example of how you can use them (not specially smartly, just for the example).

For more details on how to use this refers to the original [iisignature library](https://github.com/bottler/iisignature/tree/master) or [paper](https://arxiv.org/abs/1802.08252)

Shield: [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]

This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg