semvec/pypi-packages huggingface dataset:
sqlitestorage 1.1.1
SQLite storage for aiogram bots.
Requires Python: >=3.8

# SQLiteStorage
SQLite storage is a small module that allows you to use SQLite as aiogram storage.

# Install
```pip install sqlitestorage```

# Usage
```from sqlitestorage import SQLiteStorage```

....

```dp = Dispatcher(storage = SQLiteStorage('users.db'))```


Thanks [KurimuzonAkuma](https://github.com/KurimuzonAkuma) for his [gist](https://gist.github.com/KurimuzonAkuma/683eec4d62e111578a42608d4485fc27)
cipher-craft 0.1.0
A simple password generator tool.
Requires Python: >=3.6

# CipherCraft
CipherCraft is a versatile and secure password generator designed to help you create strong, memorable passwords with ease. With a range of customizable features and unique tools, CipherCraft ensures your passwords are not only secure but also tailored to your needs.

 ## Key Features
- **Passphrase Generation**: Generate easy-to-remember passphrases from a customizable wordlist, perfect for both security and memorability.
- **Password Expiry Checker**: Track the age of your passwords and receive reminders to update them regularly to maintain security.
- **Entropy Calculation**: Evaluate the strength of your passwords with entropy metrics, providing a numeric value for better security assessment.
- **Customizable Wordlists**: Use your own wordlists or choose from various pre-defined ones to create unique passphrases.
- **Password Obfuscation**: Enhance security by obfuscating your passphrases with common letter-to-symbol substitutions.
- **Data Breach Checker**: Check if your passwords have been compromised in data breaches to avoid using vulnerable passwords.
- **Secure Password Sharing**: Share passwords securely with time-limited, expiring links to ensure safe transmission.
- **Password History Management**: Maintain a secure history of generated passwords to prevent reuse and enhance security.
- **Custom Password Policies**: Define and apply custom password policies to ensure compliance with specific security requirements.
- **Password Pronunciation Guide**: Generate passwords that are easy to pronounce, making them simpler to share verbally.
  
## Installation
You can install CipherCraft via PyPI:

```bash
pip install ciphercraft
```
## Usage
- To generate a password or passphrase, use the command-line interface:

```bash
generate-password --length 16 --no-digits --obfuscate
```
- To generate a passphrase:

```bash
generate-password --generate-passphrase --wordlist path/to/wordlist.txt --num-words 5 --separator '-'
```

- To check password expiry:

```bash
generate-password --check-expiry your-password
```

- To record the creation date of a new password:

```bash
generate-password --record-password your-new-password
```

## License
CipherCraft is licensed under the MIT License. See the LICENSE file for details.
pythOnmarg 0.0.1
A package for accessing and working with the Ontario Marginalization Index.
Requires Python: >=3.9

# pythOnmarg

The Python version of the onmaRg package.

pythOnmarg makes Public Health Ontario's "Ontario Marginalization Index" (OnMarg) datasets accessible as a geo dataframe using spatial files from Statistics Canada.  OnMarg data can be loaded for the 2011, 2016, and 2021 data sets.  The inclusion of all levels of Stats Canada geography means data can be filtered by CD, CSD, etc.
sum-pts 0.0.4
counts assignment points
Requires Python: <4.0,>=3.10

# sum_pts

Reads an assignment file (source code or latex source) and counts how many points each problem is worth.

## Installation

    pip3 install sum-pts

## Quick-Start

Given an input text file, like [ex_hw0.py](test/ex/case0/ex_hw0.py) or [ex_hw0.ipynb](test/ex/case0/ex_hw0.ipynb):

    # Part 1 (10 points)
    # some text or code here
    
    # Part 2 (14 + 2 auto pt)
    # some text or code here
    
    # part 3 (14 pt + 3 extra credit really long name pts)

Run this module from its command line interface:

    python3 -m sum_pts ex_hw0.py

to generate a markdown table which sums all points in assignment:

    | part   |    | auto   | extra credit really long name   |   part total |
    |:-------|---:|:-------|:--------------------------------|-------------:|
    | Part 1 | 10 |        |                                 |           10 |
    | Part 2 | 14 | 2.0    |                                 |           16 |
    | part 3 | 14 |        | 3.0                             |           17 |
    | total  | 38 | 2.0    | 3.0                             |           43 |

you can write this to a markdown or csv file with the `-m` or `-c` flags if desired.  This example shows more than one type of point in a problem (e.g. 'auto', 'extra credit really long name') though the final 3 columns above would be removed if no other point type existed.

## Advanced Usage
You can customize the behavior to support more use cases, such as [LaTeX files](test/ex/case2) by modifying the default regular expression configurations, available via the command line interface.

There are a few regular expression strings which specify how points are identified.

    # Part 2 (14 + 2 auto pt)

- **prefix** matches the beginning of a line which contains points to be counted. default `'#'`
- **left** and **right** mark the beginning and end of the "point block" (e.g. `(14 + 2 auto pt)` above).   default `'\\('` and `'\\)'`
- **points** must match at least once inside the point block for the problem to be included.  default `'([Pp]ts?|[Pp]oints?)'`
- **pt_split** a point block is split between different types of points (e.g. 'auto' and '' in the example) whenever this is matched. default `'[+&,]'`
- **rm_list** is a list of regex strings which are deleted from problem names (e.g. part 1) and point types.  by default it is empty, users can pass strings here via the `-r` command line option.  see [case 2](test/ex/case2) for motivating use case

### Parsing:
1. Find lines matching `'{prefix}.*{left}.*{points}.*{right}.*'`
2. The name of the problem is the whole match above, after removing **prefix**, **left**, **right** and **points** and **rm_list**
3. The `{left}.*{points}.*{right}` string is split according to **pt_split**
4. Point types have **left**, **right**, **points** and **rm_list** removed
drpg-elasticache-redis 0.0.1
Elasticache Redis for AWS CDK
Requires Python: >=3.9

# Requirements

Requires packages installing:

```
aws-cdk-lib==2.110.0
constructs>=10.0.0,<11.0.0
cdk_nag>=2.0.0
cdklabs.cdk-validator-cfnguard>=0.0.52
python-dotenv>=1.0.0
```
etiq-spark 1.6.0
This is an optional, extension to the etiq library to provide spark datasets
Requires Python: <3.13,>=3.8
Keywords: ai, bias, etiq, fairness

# ETIQ AI

Etiq helps companies test and monitor ML models. For data science & ML teams,
Etiq tests ML algorithms, identifying issues and preventing accuracy loss in
both building & production stages. Etiq helps identify the following issue
types in the dataset/model: Bias, Data Leakage, Accuracy Issues, Drift, across
both pre-production and production stages. Additionally, Etiq helps identify
which segments are impacted by each issue type and provides modular tests
that you can plug at different points in your pipeline.

## Getting Started

To get started with this package, view our quickstart guide at
https://docs.etiq.ai/


## Licence

This library is distributed under the Creative Commons "Attribution-NoDerivatives 4.0
International (CC BY-ND 4.0)":

https://creativecommons.org/licenses/by-nd/4.0/
etiq 1.6.0
ETIQ.ai ML Testing library
Requires Python: <3.13,>=3.8
Keywords: ai, bias, etiq, fairness

# ETIQ AI

Etiq helps companies test and monitor ML models. For data science & ML teams, Etiq tests ML algorithms, identifying issues and preventing accuracy loss in both development & production stages. Etiq helps identify the following issue types in the dataset/model: Bias, Data Leakage, Data Fingerprinting, Accuracy Issues, Drift, across both pre-production and production stages. Additionally, Etiq helps identify which segments are impacted by each issue type and provides modular tests that you can plug at different points in your pipeline.

With the release of Etiq 1.6.0 the package is now compatible with Apple Silicon processors. Due to dependencies (xgboost)
in the package you may need to install libomp via homebrew

If you haven't already, install homebrew on your computer: **https://brew.sh/**

Then run the following in your terminal: **brew install libomp**

## Getting Started

To get started with this package, view our quickstart guide at https://docs.etiq.ai/

## Licence

The current licensing terms are available on this page;

https://docs.etiq.ai/faq-and-other/faq#licensing-terms
iacob 1.0.7
Development of an interactive brain connectivity data mining tool
Requires Python: <3.13,>=3.10

<h1 align="center"> <ins>PROJECT</ins> : Development of an interactive brain connectivity data mining tool </H1>


<p align="justify"> This project has been realized during a 4th year internship in order to develop a python software helping the brain connectivity research. </p>

<p align="justify"> To install and execute this software, you can use PyPI or Github method. The Python version expected are 3.10/3.11/3.12. (Either way, verify that python is already being present in the PATH.) </p>

<h2> INSTALL AND EXECUTION WITH PyPI : </h2>

1) Run <code> pip install iacob </code>
2) Run <code> iacob-app </code>

<h2> INSTALL AND EXECUTION WITH GitHub Download : </h2>

1) Run <code> git clone https&#65279;://scm.univ-tours.fr/projetspublics/lifat/iacob.git IACOB_APP && cd IACOB_APP </code>
2) Run <code> pip install -r requirements.txt </code>
3) Run <code> cd iacob </code>
4) Run (Windows) <code> python -m src.IACOB </code>
4) Run (Unix/MacOS) <code> python3 -m src.IACOB </code>


# <ins>Authors</ins> :

- <ins>Developers</ins> :

    * Thibaud SCRIBE (thibaud.scribe@etu.univ-tours.fr)
    * Enzo CREUZET (enzo.creuzet@etu.univ-tours.fr)

- <ins>Supervisors</ins> :

    * BarthÃ©lÃ©my SERRES (barthelemy.serres@univ-tours.fr)
    * Frederic ANDERSSON (frederic.andersson@univ-tours.fr)