semvec/pypi-packages huggingface dataset:
wikiWrapper 0.0.1a0
A few helpful classes for working with mediawiki data
Requires Python: >=3.7

# wikiWrapper
## About
wikiWrapper is a basic python module designed to make working with the mediawiki action API easier.
## Usage
Just place wikiWrapper.py in your working directory and import it into your program. It will be available on PyPi soon.
### Creating a search:
```py
import wikiWrapper
query = wikiWrapper.SearchQuery("Penguin")
```
SearchQuery parameters:

```def __init__(self, search_q, host="wikipedia", srnamespace=0, srlimit=10, sroffset=0, srqiprofile="classic", _format="json")```

```search_q```: This should be a string containing the search you are enacting. It is the only mandatory parameter

```host```: This should be a string. It chooses which member of the WikiMedia foundation you wish to make the search to. The default value of this parameter is "wikipedia". Accepted values are ```("wikipedia", "wikibooks", "wikidata")```

```srnamespace```: This should be an integer or a string. It chooses which namespace on the wikimedia pages you wish to execute your search on. For all namespaces, use ```"*"```. The default value of this parameter is ```0```. 

```srlimit```: This should be an integer or a string. The amount of pages to return from a search. The default value of this parameter is ```10```.

```sroffset```: This should be an integer or a string. Shifts results forward by specified amount. The default value of this parameter is ```0```

```srqiprofile```: This should be a string. Chooses the algorithm used by the MediaWiki API to find your search results. The default value of this parameter is ```"classic"```. Accepted values are ```("classic", "classic_noboostlinks", "empty", "wsum_inclinks", "wsum_inclinks_pv", "popular_inclinks_pv", "popular_inclinks", "engine_autoselect")```

```_format```: The format you want your data back in. Default value is ```"json"```

### Methods

```query.search()```:
Returns raw HTTP request from the wikipedia page.

```query.content_summary()```: 
Returns the content summary of the search_q parameter's wiki page.
wikiwrapper-git-shr4pnel 0.0.1a0
A few helpful classes for working with mediawiki data
Requires Python: >=3.7

# wikiWrapper
## About
wikiWrapper is a basic python module designed to make working with the mediawiki action API easier.
## Usage
Just place wikiWrapper.py in your working directory and import it into your program. It will be available on PyPi soon.
### Creating a search:
```py
import wikiWrapper
query = wikiWrapper.SearchQuery("Penguin")
```
SearchQuery parameters:

```def __init__(self, search_q, host="wikipedia", srnamespace=0, srlimit=10, sroffset=0, srqiprofile="classic", _format="json")```

```search_q```: This should be a string containing the search you are enacting. It is the only mandatory parameter

```host```: This should be a string. It chooses which member of the WikiMedia foundation you wish to make the search to. The default value of this parameter is "wikipedia". Accepted values are ```("wikipedia", "wikibooks", "wikidata")```

```srnamespace```: This should be an integer or a string. It chooses which namespace on the wikimedia pages you wish to execute your search on. For all namespaces, use ```"*"```. The default value of this parameter is ```0```. 

```srlimit```: This should be an integer or a string. The amount of pages to return from a search. The default value of this parameter is ```10```.

```sroffset```: This should be an integer or a string. Shifts results forward by specified amount. The default value of this parameter is ```0```

```srqiprofile```: This should be a string. Chooses the algorithm used by the MediaWiki API to find your search results. The default value of this parameter is ```"classic"```. Accepted values are ```("classic", "classic_noboostlinks", "empty", "wsum_inclinks", "wsum_inclinks_pv", "popular_inclinks_pv", "popular_inclinks", "engine_autoselect")```

```_format```: The format you want your data back in. Default value is ```"json"```

### Methods

```query.search()```:
Returns raw HTTP request from the wikipedia page.

```query.content_summary()```: 
Returns the content summary of the search_q parameter's wiki page.
python-alis 0.1.3
A collection of algorithms for scalable data science
Keywords: scalable,data science,networks,hash functions

# ALIS: Algorithmic Library for Scalability

ALIS: Algorithmic Library for Scalability is a Python package and a good accompanying reference for Leskovec, Rajaraman, and Ullman's [Mining of Massive Datasets](http://www.mmds.org/). This book currently covers topics from Chapters 3 through 5, and Chapter 10 of the reference.

This book provides:

1. Additional mathematical and code examples
2. Additional exercises both theoretical and practical
3. Re-useable API for scalable data mining

ALIS is inspired by the [Dive into Deep Learning](https://d2l.ai/index.html) interactive book with code, math, and discussion.

## Installation

ALIS was built using a Python version of 3.8.12. To install the package, perform the following steps:

1. Clone the `alis` github repository

```
https://github.com/phdinds-aim/alis.git
```

2. Install the environment or the requirements file via conda or pip

Installation of required libraries via `pip`

```
pip install -r requirements.txt
```

Installation of required libraries via `conda`

```
conda env create -f environment.yml
conda activate alis
```

3. Install the `alis` package as an editable source.

```
pip install -e .
```

Done! 🎉 The `alis` package is now installed in your machine.


## Authors

ALIS is proudly made by the Asian Institute of Management's PhDinDS batch 2024

- Leodegario Lorenzo II
- Michael Dorosan
- Joseph Christian Noel
- Antonio Briza
- Ranzivelle Marianne Roxas-Villanueva

with supervision of our Professor Christian Alis, PhD.
vr-idt-ajpkim 0.0.3
Virtual reality eye tracking event classification algorithm
Requires Python: >=3.8

# vr-idt

This package provides a VR/AR focused eye-tracking classification algorithm for identifying fixations and saccades in data gathered from VR/AR headsets.

## Background

The algorithm was proposed and initially implemented by XXX...

## Overview

How algorithm handles VR/AR data better...

## Usage

How to install and import...
phxshellutils 1.3
various useful shell utilities
Requires Python: >=3.6

# shellutils

This is going to be a handy little repo where I maintain some of useful scripts
that I use on a regular basis.  I have a private repo where I have maintained
most of these scripts for a long time, but some of them could prove useful to the
general public, and half the time I forget what they do and end up re-writing them.

This repo will be a place for me to keep them up-to-date and document what they do.

The structure that I'm going to try to create will be an individual folder for each
script with a `README.md` documenting what the script does.  Additionally, there
will be a `bin` directory where I will try to symlink all of the actual scripts
for easy importing into your `$PATH`.

For more information, browse to the subfolders to view the individual README files.

I will continue to add scripts to this repo as I have time and as the need for them arises.

## Requirements:

In order to fully-utilize all of the scripts included in `shellutils`, Linux is a pre-requisite, but many will work on MacOS (but some may need to be tweaked a bit).

To view individual requirements, feel free to view each package's README file.

The following additional requirements are necessary:

- `/bin/sh` (many scripts)
- `bash` (some scripts)
- `python3` (some scripts)

## Simple Installation via pip

Installation is extremely simple using [`pip`](https://pip.pypa.io/en/stable/installation/):

Install from Pypi:

`pip3 install --user phxshellutils`

or install latest version from GitHub:

`pip3 install --user git+https://github.com/phx/shellutils`

For what it's worth, most of the `phxshellutils` scripts use `sh` and `bash`.

The only Python scripts at the time of this writing are Python3, and are executable with hashbangs pointing to `/usr/bin/env python3` and only use the standard library.

This means, you won't clutter your native Python intallation by installing a bunch of third party libraries outside of virtual environments.

### Upgrading via pip

`pip3 install --user --upgrade phxshellutils`

or

`pip3 install --user --upgrade git+https://github.com/phx/shellutils`

### Uninstall

`pip3 uninstall phxshellutils`

## Advanced Installation

To add these programs to your `$PATH` without using `pip3`, I would do something like the following:

Note: replace `.bashrc` with `.zshrc` or `.bash_profile`, etc., as necessary.

```
mkdir -p "$HOME/bin"
cd "$HOME/bin"
git clone https://github.com/phx/shellutils
echo 'SHELL_UTILS="$HOME/bin/shellutils/bin"' >> ~/.bashrc
echo 'export PATH="$SHELL_UTILS:$MY_BIN:$PATH"' >> ~/.bashrc
source ~/.bashrc
```

The benefits to this method are an easier upgrade.

### Easy Upgrade via Advanced Install Method

`cd "$HOME/bin/shellutils" && git pull`

### Uninstall

`sed -i '/SHELL_UTILS/d' ~/.bashrc && rm -rf "$HOME/bin/shellutils"`

Note: replace `.bashrc` with `.zshrc` or `.bash_profile`, etc., as necessary in the above command.