semvec/pypi-packages huggingface dataset:
LambdaGuard 2.4.3
LambdaGuard
Requires Python: >=3.6.3

LambdaGuard is an AWS Serverless Security auditing tool designed to create asset visibility
and provide actionable results. It provides a meaningful overview in terms of
statistical analysis, AWS service dependencies and configuration checks from
the security perspective.

https://github.com/Skyscanner/LambdaGuard
reval 0.1.0
Relative clustering validation to select best number of clusters
Requires Python: >=3.6

# `reval`: stability-based relative clustering validation method to determine the best number of clusters

Determining the number of clusters that best partitions a dataset can be a challenging task because of 1) the lack of a 
priori information within an unsupervised learning framework; and 2) the absence of a unique clustering validation 
approach to evaluate clustering solutions. Here we present `reval`: a Python package that leverages 
stability-based relative clustering validation methods to determine best clustering solutions, as described in [1]. 
    
Statistical software, both in R and Python, usually compute internal validation metrics that can be leveraged
to select the number of clusters that best fit the data and open-source software solutions that easily implement 
relative clustering techniques are lacking. The advantage of a relative approach over internal validation methods 
lies in the fact that internal metrics exploit characteristics of the data itself to produce a result, 
whereas relative validation converts an unsupervised clustering algorithm into a supervised classification problem, 
hence enabling generalizability and replicability of the results.

## Requirements

    python>=3.6
    
## Installing

From github:

    git clone https://github.com/IIT-LAND/reval_clustering
    pip install -r requirements.txt

PyPI alternative:

    pip install reval

## Documentation

Code documentation can be found [here](https://reval.readthedocs.io/en/latest/). Documents include Python code 
descriptions, `reval` usage examples, 
performance on benchmark datasets, and common issues that can be encountered related to a dataset number of features 
and samples.

## Refrences

[1] Lange, T., Roth, V., Braun, M. L., & Buhmann, J. M. (2004). Stability-based validation of clustering solutions. 
*Neural computation*, 16(6), 1299-1323.

## Cite as

    Isotta Landi, Veronica Mandelli, & Michael Vincent Lombardo. (2020, June 29). 
    reval: stability-based relative clustering validation method to determine the best number of clusters 
    (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.3922334
    
BibTeX alternative

    @software{isotta_landi_2020_3922334,
              author       = {Isotta Landi and
                              Veronica Mandelli and
                              Michael Vincent Lombardo},
              title        = {{reval: stability-based relative clustering 
                               validation method to determine the best number of
                               clusters}},
              month        = jun,
              year         = 2020,
              publisher    = {Zenodo},
              version      = {v1.0.0},
              doi          = {10.5281/zenodo.3922334},
              url          = {https://doi.org/10.5281/zenodo.3922334}
            }
covid-tracking-project 0.2
A python wrapper for the Covid Tracking Project API
Requires Python: >=3.6

# covid-tracking-project
![](https://img.shields.io/pypi/v/covid-tracking-project?color=dark%20green) \
A python wrapper for the Covid Tracking Project API.

### Installation

    pip3 install covid-tracking-project

### Importing

    import ctp

### Functions

- **Class**: `us()`
    - **Function**: `history()`
        - Description: Get US historical data
        - Parameters: 
            - day (description: Get data on a specific day, type: string/int, requirement: optional, defaults: all days, example: `20200501`)
    - **Function**: `current()`
        - Description: Get current US data
- **Class**: `states()`
    - **Function**: `metadata()`
        - Description: Get state metadata
        - Parameters:
            - state (description: Get data on a specific state, type: string, requirement: optional, default: all states, example: `ca`)
    - **Function**: `history()`    
        - Description: Get state historical data
        - Parameters:
            - day (description: Get data on a specific day, type: string/int, requirement: optional, defaults: all days, example: `20200501`)
            - state (description: Get data on a specific state, type: string, requirement: optional, default: all states, example: `ca`)
    - **Function**: `current()`
        - Description: Get state current data
        - Parameters:
            - state (description: Get data on a specific state, type: string, requirement: optional, default: all states, example: `ca`)
pelmanism 1.1
Pelmanism Memory Game
Requires Python: >=3.6

Pelmanism, also known as Memory, is a game in which the players try to
find pairs of matching cards. In this one-player version, the goal is to
find all pairs as fast as possible.

![](http://www.rolf-sander.net/software/pelmanism/screenshot.png)

## Installation:

The following code will start pelmanism from within python:
```
import pelmanism
pelmanism.main()
```

If `$HOME/.local/bin` is included in your `$PATH`, you can start the
game directly with typing `pelmanism` in a terminal.

## Usage:

Run the script `pelmanism.py` to start the game.

## Modifications:

- To play the game with your own pictures, simply replace the `png` files
  in the cards directory by your own.
  
- To change the number of cards, adjust `Nx` and `Ny` in `pelmanism.py`.

- To change the size of the cards, adjust `thumbsize` in `pelmanism.py`.