semvec/pypi-packages huggingface dataset:
alamopy 0.2.2
A package that is able to run ALAMO without the use of its GUI
Requires Python: >=3.6

# alamopy

This is an interface for using ALAMO through a Python pip package.

<h2> Preparation : </h2>

<h4> for MacOS users: </h4>

Download the ALAMO application from minlp.com, and unzip the package.

<h4>  for Windows users: </h4>

Download the ALAMO application from minlp.com, and install with the provided installer.

<h2> Data to run ALAMO:</h2>

Add the directory of the folder you have installed the pip package with your Python file to your PATH variable.

Get your ALAMO license from minlp.com and place it inside the same folder as the Python file.

Before running ALAMO, you should decide if you will provide your own data. If so, make sure your data are all in numpy arrays.
Otherwise, select the appropriate simulator to run ALAMO.

pip install alamopy\
python\
import numpy as np

## Example


Test 1 to see if we can generate model for z = x\*\*2
Most notably tests if the given example 1 works from ALAMO UI.

from alamopy import almain as alamo\
import numpy as np

xdata = np.random.rand(11, 1)\
xdata[:, 0] = [-5,-4,-3,-2,-1,0,1,2,3,4,5]

zdata = xdata[:, 0]\*\*2

opts = alamo.doalamo(xdata, zdata, linfcns = 1, logfcns = 1, sinfcns = 1, cosfns = 1, constant = 1, expfcns = 1, monomialpower = [2,3], keep_alm_file=True, keep_lst_file=True)

<h2> Outputs from ALAMO </h2>

You would get the result dictionary with a best-fitted function and other variables when calling ALAMO using this python interface.
neuronZeal-pypi-ANKITZEAL 0.0.2
Its a implementation of perceptron
Requires Python: >=3.7

# neuronZeal_pypi
neuronZeal_pypi


# Reference -
[official python docs](https://packaging.python.org/tutorials/packaging-projects/)

[github docs for github actions](https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries)
protectsql 0.0.8
A small package to track SQL injection in flask app

# PROTECTSQL

# Table of Contents

- [PROTECTSQL](#protectsql)
- [Table of Contents](#table-of-contents)
  - [Installation](#installation)
    - [Installing the repository for contribution purposes](#installing-the-repository-for-contribution-purposes)
    - [Installing the package](#installing-the-package)
  - [Usage](#usage)
    - [Init command](#init-command)
    - [Analyze command](#analyze-command)
  - [Inspiration](#inspiration)
  - [What it does](#what-it-does)
  - [How we built it](#how-we-built-it)
  - [Tech Stack](#tech-stack)
  - [Challenges we ran into](#challenges-we-ran-into)
  - [Accomplishments that we're proud of](#accomplishments-that-were-proud-of)
  - [What's next for Protectsql](#whats-next-for-protectsql)
## Installation

### Installing the repository for contribution purposes
Please refer to [our CONTRIBUTING.md file](CONTRIBUTING.md) to setup protectsql locally

### Installing the package

Our package is publicly available on [PyPi](https://pypi.org/project/protectsql/).
To install using `pip`, run the command:

```bash
pip install protectsql
```

## Usage

### Init command

```bash
protectsql init # initialise the pysa configurations
```

### Analyze command
Runs the static analysis.

```bash
protectsql analyze # analyze your app
```

## Inspiration
We wanted to make a package to check for SQLi vulnerabilities for generic frameworks/specific to Flask as there are quite a few python applications that don't use ORM and are vulnerable to SQLi vulnerabilities.

## What it does
A CLI tool which will help you analyze your python/flask app, using [Pysa](https://github.com/facebook/pyre-check) (a static analysis tool by facebook), In case sqli are found, they're displayed at runtime after running the `analyze` command.

## How we built it
Protectsql is build on top of `pysa`, a part of the `pyre-check` project package (see more about `pysa` [here](https://pyre-check.org/docs/pysa-running)).
Additionally, since it's a CLI tool, we also make use of `click` (see more [here](https://click.palletsprojects.com/en/7.x/)).

## Tech Stack
- `python`
- `pysa`
- `click`
- `flask`

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## Challenges we ran into
- Understanding `pysa` documentation
- Coming up with target frameworks vulnerable to sqli injections and how can we use `pysa` for them

## Accomplishments that we're proud of
- Using `pysa` for static analysis
- Usage of `click`, the python CLI tool
- Uploading our own package to [PyPi](https://pypi.org/project/protectsql/)

## What's next for Protectsql
We plan to add support to more lightweight framework which does not rely on ORM!

As of now, Protectsql is published on [PyPi](https://pypi.org/project/protectsql/) and is ready for use. Anyone can contribute following our contribution [rules and guidelines](CONTRIBUTING.md).

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