semvec/pypi-packages huggingface dataset:
cis-checks-10 0.0.11
Provides AWS compliance details
Requires Python: >=3.7

# OBP_security_pillar

OBP_security_Pillar package can be used to get the compliance details of Operational Best Practices for AWS Well Architected Security Pillar.

Release Notes:
# v0.0.1
1. Added following compliance checks:
   - RdsAutomaticMinorVersionUpgradeEnabled
   - RdsInstancePublicAccessCheck
   - RdsSnapshotEncrypted
   - RdsSnapshotsPublicProhibited
   - RdsStorageEncrypted
   - S3BucketLoggingEnabled
   - S3BucketVersioningEnabled
surfwaver 0.0.1
SurfWaveR
Requires Python: >=3.6.0
Keywords: MASW,Multichannel Analysis of Surface Waves,Dispersion,Inversion,seismic,active and passive surface wave,SurfWaveR

# SurfWaveR - a GUI for Multichannel Analysis of Surface Waves (Rayleigh waves).
SurfWaveR is an open source project, initiated by SIIP lab IIT Bombay. It can perform dispersion and inversion (2D) of active and passive seismic data. 
I have used pyQT5 to build the GUI, the "swprocess" module to generate dispersion curves using various transform methods, and the "evodcinv" and "srfpython" modules to do inversion with evolutionary and Monte-Carlo Markov Chain algorithms, respectively. <br>
It is not completed yet, but I'm working on it.
bitte 0.0.1
A way to quickly preprocess common NLP tasks. Specialised for english tasks requiring lightweight compute that you just want done
Requires Python: >=3.7

# Bitte
Please, somebody get me a beer instead of another 4 hours preprocessing NLP!

This module speeds you up so you can get to the exciting tasks.

It is a quirky collection of the makeshift-tasks most NLP tools run into, but are either too recently relevant,
too small, or too monolingual to have made it to the common NLP modules like spacy, nltk, huggingface transformers, spark etc.


# You hit walls processing data with NLP
This module helps you overcome annoying wastes of time. Now you can glue together the jagged OCR output from some strange .epub file
that once had a life as a .pdf file exported frrom .pptx, into your pristine and beautiful BERT acronym
recognition model which ONLY WORKS WITH FULL SENTENCES AND NORMAL GRAMMAR[*1]


# Quick starts
## Repunctuate
```
repunctuated = bitte.repunctuate([list])
```
- Combined functionality of modules like rpunct, NNSplitter(sentence splitting) and transformer models performing CoLA
task. Runs quickly, you don't have to deal with the hassle of rpunct difficulties on windows or fast execution of a quantized DistilBERT model: It'll just work.

This tool has 0 reliance on external APIs. It does not use a large language model API under the hood. That's why it's fast and cheap.

## Semantic Chunk Text
```
chunks: List[List[str]] = bitte.chunk_semantically(input_string)
```
Semantic search is amazing. There's so much. But the second or third time you run it, you'll get some weird disappointing half sentence returned as a result.
Oh no, you realise. The real world isn't like Wikipedia. We aren't in Kansas. The OCR parser is in Kan sa5.

You're going to need the big guns. Semantic Chunk Text can deal with any string of text and make it reasonably semantically chunked together,
ideal for retrieval tasks for semantic search and for embedding tasks.

This API functions similarly to the Autochapter API of Audacity, but it works for text and does not use transcript timing information.

This API isn't super fast as it runs transformer models often multiple times. It is cheap and quality.

## Wholesome
```
sentence_classifications = bitte.are_full_sentences(sentences)
# Filter for full sentenecs only:
full_sentences = [sentence for ind, sentence in enumerate(sentences) if sentence_classifications[ind]]
```
Have you ever felt your sentence was incompl

Now, you can check if an english sentence is complete. This is useful for grammar merging decisions, detecting conversational interupts,
and deciding whether to display questionable quality content to users or not.

This API isn't super fast, as it needs a transformer model too. It is cheap and quality.

# Why now?
It's a fact that lots of NLP right now works better for english. Large language models; medium ones like BERT;
OCR trained on english datasets... you get the point. If you speak english, you'll be tempted to take advantage
of this somewhere in your pipeline. These tools are for you.
wireguard-lib 0.1.10
Библиотека для работы с wireguard
Requires Python: >=3.6

# wireguard
## Описание

Библиотека для работы с wireguard

## Installation

    python3 -m pip install wireguard-lib

## Пример использования:

Сгенерируем конфиг сервера.  
Для этого нужен приватный ключ.  
Воспользуемся консолью Python для получения приватного ключа:  

```
>>> from wireguard import Wireguard
>>> Wireguard().get_private_key
'sBd8jkAY9Ht7wn+q5iGbW4MfShgjdxB1s3oTJsttaHc='
```
Используя полученый ключ сгенерируем конфиг сервера:
```
from wireguard import Wireguard

wg = Wireguard(
    server_private_key = 'sBd8jkAY9Ht7wn+q5iGbW4MfShgjdxB1s3oTJsttaHc=',
    server_addres='wireguard.example.org',
    server_ip='10.10.10.1',
    server_port=51821,
)

server_config = wg.get_config_server
print(server_config)
```
Получим следующий конфиг:  
```
[Interface]
PrivateKey = sBd8jkAY9Ht7wn+q5iGbW4MfShgjdxB1s3oTJsttaHc=
Address = 10.10.10.1
ListenPort = 51821
```

## License

[MIT License (MIT)](LICENSE)
blob-storage-jatin 0.0.2
A Blob Storage Package for configuring Azure Blob Storage Account and access it.
Requires Python: >=3.7
Keywords: blob,storage,blobstorage,azure

# blob_storage

Under construction! Not ready for use yet! Currently experimenting and planning!

Developed by Jatin Talati

## Examples of How To Use (Buggy Alpha Version)

Creating A Server

```python
from src.blob_storage_jatin.blob_conn import BlobStorageAPI

blob = BlobStorageAPI()

# Other Code
```
Explore the BlobStorageAPI class to use other functions
tg-ip-lookup 0.0.1
Find out which public cloud an IP address originates from.
Requires Python: >=3.6

# Lookup an IP address to find out which public cloud it originates from

This repository contains python3 code to find out which public cloud an IP address originates from.

## Requirements
* Python3 (3.11)
* MaxMind license key (for [GeoLite2 ASN](https://dev.maxmind.com/geoip/docs/databases/asn) database). *This database is free subject to license restrictions by MaxMind*. Sign up [here](https://www.maxmind.com/en/geolite2/signup).

## Supported Clouds
* Amazon Web Services (AWS)
* Google Cloud Platform (GCP)
* Microsoft Azure
* Oracle Cloud Infrastructure (OCI)
* Hetzner
* DigitalOcean
* Linode
* Tencent
* OVH
* IBM Cloud
* Vultr
* Scaleway
* Fly.io
* Starlink (yes ok, not a cloud but it is up in the clouds right?)

### How it works
For AWS, GCP, Azure and OCI this looks up the IPs using the publicly published IP ranges by these providers.

For the other providers it uses the MaxMind ASN database to lookup IP information.

Internally this is using the `ipaddress` module in python to do a search through the various CIDR blocks. In future this could be improved if the need arises.

### Setup
1. pip install tg_ip_lookup
2. Prepare the database, execute `python -c "exec(\"import tg_ip_lookup\ntg_ip_lookup.cloudlookup.loadCloudProviderData.main(\'*MaxMind License Key*\')\")"`
3. Try an IP lookup `python -c "exec(\"import tg_ip_lookup\ntg_ip_lookup.main(\'XXX.XXX.XXX.XXX\')\")"`
cis-checks-9 0.0.10
Provides AWS compliance details
Requires Python: >=3.7

# OBP_security_pillar

OBP_security_Pillar package can be used to get the compliance details of Operational Best Practices for AWS Well Architected Security Pillar.

Release Notes:
# v0.0.1
1. Added following compliance checks:
   - RdsAutomaticMinorVersionUpgradeEnabled
   - RdsInstancePublicAccessCheck
   - RdsSnapshotEncrypted
   - RdsSnapshotsPublicProhibited
   - RdsStorageEncrypted
   - S3BucketLoggingEnabled
   - S3BucketVersioningEnabled