semvec/pypi-packages huggingface dataset:
snailapi 1.2.0
Asynchronous Python wrapper for Snail API.

# snailapi
Fully Asynchronous Python wrapper for Snail API.

[![Downloads](https://pepy.tech/badge/snailapi)](https://pepy.tech/project/snailapi)
[![Requires: Python 3.x](https://img.shields.io/pypi/pyversions/snailapi.svg)](https://pypi.org/project/snailapi/)
[![BenBot Version: 1.0.1](https://img.shields.io/pypi/v/snailapi.svg)](https://pypi.org/project/snailapi/)

## Installing:
Windows: ``py -3 -m pip install snailapi``<br>
Linux/macOS: ``python3 -m pip install snailapi``

After installing, you can check out the documentation for snailapi [here](https://github.com/xMistt/snailapi/wiki).

### Example:
```
import snailapi
import asyncio

snail = snailapi.APIClient()


async def main() -> None:
    shop = await snail.get_shop()

    print('=== FEATURED ===')
    for item in shop.featured:
        print(f'{item.name}: {item.price} V-Bucks')

    print('=== DAILY ===')
    for item in shop.featured:
        print(f'{item.name}: {item.price} V-Bucks')


loop = asyncio.get_event_loop()
loop.run_until_complete(main())
loop.close()
```
____
### fortnitepy example:
```
import fortnitepy
import BenBotAsync
import snailapi

snail = snailapi.APIClient()

client = fortnitepy.Client(
    auth=fortnitepy.EmailAndPasswordAuth(
        email='example@email.com',
        password='password123'
    )
)


@client.event
async def event_friend_message(message: fortnitepy.FriendMessage) -> None:
    args = message.content.split()
    split = args[1:]
    content = " ".join(split)

    if args[0] == '!skin':
        try:
            skin = await snail.cosmetics.search_cosmetics(
                name=content
            )

            await client.user.party.me.set_outfit(asset=skin[0].id)
            await message.reply('Skin set to: {skin[0].name}.')
        except snailapi.exceptions.NotFound:
            await message.reply('Failed to find skin with the name: {content}.')


client.run()
```
LSH-LINK 0.1
Base on Fast agglomerative hierarchical clustering algorithm using Locality-Sensitive Hashing,  we develop algorithm in Python.

# LSHlink_numba_version

Base on Fast agglomerative hierarchical clustering algorithm using Locality-Sensitive Hashing,  we develop algorithm in Python.



## install method

```
pip3 install LSHlink-ffghcv
```



## example

```python
import LSHlink as LSH
import sklearn
import numpy as np

X = [[i] for i in [2, 8, 0, 4, 1, 9, 9, 0]]
X = np.array(X)
test = LSH.HASH_FUNS(X)
test.set_parameters(4,10,2,11)
test.fit_data()

test2.plot_dendrogram()
```




## important functions

```
set_parameters()
fit_data()
plot_raw_data()
plot_cluster()
plot_dendrogram()
```
pretermgrowth 0.3
Calculate growth measurement z-scores for preterm infants
Requires Python: >=3.6
Keywords: preterm neonatology fenton growth

# fenton-growth-calculator
A python package for calculating preterm infant growth statistics.

Currently you can calculate z-scores for infants between ages 22w4d-50w0d.

```python
from pretermgrowth.fenton import Fenton

Fenton.calc_zscore(
    metric="weight", # or "length" or "hc"
    sex="f", # or "m"
    gestational_age_in_days=158, # range = [158-350]
    measure=2400.0 # g for weight, cm for length or hc
)
```

### fenton calculations
Fenton TR and Kim JH, BMC Pediatrics 2013, 13:59 http://www.ncbi.nlm.nih.gov/pubmed/23601190
sphinx-abcnotation 0.4
A abc directive for Sphinx

# Sphinx ABC Notation

This extension adds an `abc` directive to Sphinx. The content of the directive
will be rendered to `svg` by [abcm2ps](https://github.com/leesavide/abcm2ps)
and included in the rendered document as an image.

## Usage

```rst
Here are 12 notes on a staff.

.. abc::

    X:1
    L:1/4
    K:C
    A, ^A, B, C ^C D ^D E F ^F G ^G
    w: a a♯ b c c♯ d d♯ e f f♯ g g♯
```

Here are 12 notes on a staff.

![](example.svg)
youandme 2.0.0
Simple private data sharing via bytearrays, Tor tunneling and metadata paranoia
Requires Python: >=3.7

![](logo.png)


[![Build Status](https://travis-ci.org/beardog108/youandme.svg?branch=master)](https://travis-ci.org/beardog108/youandme) ![](https://img.shields.io/pypi/wheel/youandme) ![](https://img.shields.io/github/languages/top/beardog108/youandme) ![](https://img.shields.io/badge/metadata%20surveillance-LIMITED-blue) ![](https://img.shields.io/github/languages/code-size/beardog108/youandme)

Only you and the person you're talking to should know the details of the conversation. This includes metadata.

This is a Python library to share data anonymously and securely* with limited traffic metadata. It is designed for generic byte streaming over a private tunnel, using Tor onion services..


This library is meant to be used by other libraries or applications, however a script 'yam' is included to enable basic CLI instant messaging.


\* The ID one connects to a host with must be shared via a secure (private, authenticated) channel.

# install ��

`$ pip install youandme`

You also need a recent Tor daemon in executable path. 0.4 Tor is what is tested. https://www.torproject.org/download/tor/

## basic chat usage ��

`$ yam.py host`

This will start a bare-bones CLI-based chat and output an address to be given to a friend.

The friend connects:

`$ yam.py conn --address <address>`

# purpose ��‍��‍��

In normal socket connections, Eve can see when Alex and Bob communicate and the size of their communications.

This library sends continuous streams of data (null bytes) even when no information is being communicated, in order to increase unobservability of transmission times and packet sizes.

Anonymity and encryption is provided via Tor onion services, though this library could easily be adapted to use plaintext (and encryption by an application) or another relay like I2P.


# security ��

As stated above, this library does no authentication. However, if the ID is shared privately and safely, the tunnel will have roughly the security of a Tor v3 onion service, with increased metadata unobservability.

That said, one should not rely on any software when the stakes are too high.


## what an attacker sees in a normal Tor connection

![](no-dummy.png)


## what an attacker sees in a youandme connection


![](dummy.png)


# Limitations + Road map

This project will forever follow the KISS principle, but these two three will be addressed.

* Multi-byte character support (full utf-8 support)
* Tor bridge support
* Support non-anonymous hidden services. Mainly useful for certain development needs
plsRe 0.0.2
A simple package for Partial Least-Sqaures Regression
Keywords: package models regression

# plsre Documentation
* * *
## Installation
```python
pip install plsre
```
* * *
## Usage
```python
from plsre import PlsRegression as PR
# Perform Partial Least_Squared Regression training

X = np.array([[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]])
Y = np.array([[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]])

P, Q, W, T,B = PR.plsr_train(X,Y)

# Perform Prediction using different optimization
print(PR.plsr_predict(X,Y,P,Q,W,B))
print(PR.plsr_predict_numba(X,Y,P,Q,W,B))
print(PR.plsr_predict_numpy(X,Y,P,Q,W,B))

```