semvec/pypi-packages huggingface dataset:
ipv4mutate 0.9.1
performs various mutations on IPv4 addresses, such as converting to hex, octal, urlencoded, and more

# IPv4Mutate
performs various mutations on IPv4 addresses, such as converting to binary, hex, octal, urlencoded, and more

### Installation
```
pip install ipv4mutate
```

### Usage
```
from ipv4mutate import IPv4Mutate
i = IPv4Mutate("127.0.5.7")
print(i.mutate_hex)
```

### Available Attributes
```
mutate_binary
mutate_dotted_hex
mutate_hex
mutate_octal
mutate_octal_padded
mutate_zero_padded
mutate_zero_stripped
mutate_urlencoded
mutate_decimal
```

### Example Output (via `test-ipv4mutate-usage-example.py`)
```
IP: 127.0.5.7
        mutate_binary            01111111.00000000.00000101.00000111
        mutate_dotted_hex        0x7f.0x0.0x5.0x7
        mutate_hex               0x7f000507
        mutate_octal             0177.00.05.07
        mutate_octal_padded      0000000000000000000177.000000000000000000000.00000005.00000000007
        mutate_zero_padded       127.000.005.007
        mutate_zero_stripped     127.5.7
        mutate_urlencoded        %31%32%37%2e%30%2e%35%2e%37
        mutate_decimal           2130707719
```

### Integration with IPv4Helper [GitHub](https://github.com/bonifield/IPv4Helper) [PyPi](https://pypi.org/project/ipv4helper/)
```
from ipv4helper import IPv4Helper
from ipv4mutate import IPv4Mutate
i = IPv4Helper("127.144.4.9/28")
for x in i.ip_range_generator():
	z = IPv4Mutate(x)
	print(z.mutate_hex)

# snipped output
# ...
# 0x7f.0x90.0x4.0x0
# 0x7f.0x90.0x4.0x1
# 0x7f.0x90.0x4.0x2
# 0x7f.0x90.0x4.0x3
# ...
```
Pyserva 1.5.1.0
The Senserva Python library
Requires Python: >=3.6


![Pyserva](/docs/Pyserva_logo.png)

The Senserva Python Library is provided to help analyze and visualize data. The JupyterNotebookHelper module will assist users who are utilizing Jupyter Notebooks for their needs. The StandAloneApiHelper will be useful for those who want a pure Python experience. Our team has also made a sample report for Azure Connections, using the StandAloneApiHelper, which will automate the acquisition of data and building the report.

# Install

The Pyserva library is available on the [Python Package Index](https://pypi.org/project/Pyserva/).

The install command on Windows for the package is:

    py -m pip install Pyserva

The install command on Unix/Mac for the package is:

    python3 -m pip install "Pyserva"

  
  The import commands for the library and modules are:
  

    import Pyserva
    from Pyserva import JupyterNotebookHelper
    from Pyserva import StandAloneApiHelper
    from Pyserva import SampleReport

# Modules

### JupyterNotebookHelper
This module will focus on helping a user with visualizing [Senserva data](https://azuremarketplace.microsoft.com/marketplace/apps/senservallc.senserva_multitenant?tab=Overview) in a Jupyter Notebook. 

### StandAloneApiHelper
This module will focus on helping a user with visualizing data from the Microsoft Graph API.

### SampleReport
This module is a Python Command Line app, leveraging the StandAloneApiHelper for data.

# Support

Please reach to our team of experts [via email](mailto:support@senserva.com) for questions regarding the library
pltflow 0.0.5
A matplotlib/seaborn wrapper to create beautiful plots with predefined styles using pipelines
Requires Python: >=3.6
Keywords: matplotlib,seaborn,plt,plot,graphs,data,visualization,pandas

# ``pltflow``

## What is ``pltflow``?

* ``pltflow`` is a wrapper for matplotlib/seaborn to create beautiful charts
* It makes use of predifined styles to completely automate the chart creation process
* It also uses the concept of pipelines to create charts as if one is following a recipe

``pltflow`` is not intended for quick and beautiful plots.
You dont need to know anything about styling to make a pretty chart. But of course you can modify any parameter using matplotlib/seaborn arguments.
<br/>


## ✅&nbsp; Installation

``pltflow`` can be installed using pip.

```bash
pip install pltflow
```
<br/>

## 📌&nbsp; Requirements
* python 3.6 or above
* matplotlib 2.2 or above
* seaborn 0.9.0 or above
* pandas 1.0.0 or above

<br/>

## 🔨 Usage&nbsp;

*``pltflow`` works with pandas dataframes as data source
* It uses pipeline to convey the 'recipe' of the chart

 ```bash

(
    flow                                                            # from the pltflow package
    .plot(df, primary="YrSold",secondary ="ppsqm", style = "vox")   # plot the df, define variables and style
    .set_ylabel("PRICE PER M2")                                     # set the y label text
    .set_xlabel("YEAR SOLD")                                        # set the x label text
    .set_title("BOSTON PRICE PER SQUARE METER")                     # set the title text
    .set_subtitle("YEARS 2006 TO 2010 | FUNDA.COM")                 # set the subtitle text
    .color_by("Neighborhood")                       # color the chart by the neighborhood (different categories)
    .focus_on("CollgCr")                            # focus on a specific category (other will be grayed out)
    .set_figsize(8,4)                               # set the figure size
    .set_yticks(np.arange(1000, 1800, 200))         # set the spacing of the y axis labels
    .set_xticks(np.arange(2006, 2011))              # set the spacing of the x axis labels
    .show()                                         # SHOW!!! the graph is ready!
)

```
<br/>

Currently there are 4 predifined styles: base, vox, mkbhd, and innocent.


![styles](https://github.com/ismaelcv/pltflow/blob/main/images/styles_sample.png?raw=true)




But more are coming soon!
<br/>

## 🐜 &nbsp; Found a bug? Missing a specific feature?

Feel free to **file a new issue** 📫&nbsp with a respective title and description issue on the ``pltflow`` repository: [ismaelcv](https://github.com/ismaelcv/pltflow).

<br/>

## 📘&nbsp; License
``pltflow``is released under the GNU Lesser General Public License (LGPL) license. All accompanying documentation and manual are released under the  [Creative Commons BY-SA 4.0 license](https://creativecommons.org/licenses/).
marley 0.5.2
Marley - A framework for multi-agent reinforcment learning
Requires Python: >=3.8

# Marley #

Marley is a framework for multi-agent reinforcement learning. It lets you design a game and run experiments with multiple AI-powered agents playing that game.

**Marley isn't ready for use yet; it works for my research purposes, but I haven't yet made the work needed to make it easy to understand for others.** If you're still interested in using it, feel free to read the code or [email me](mailto:ram@rachum.com) for help.

I hope to make Marley easier to use soon.

## GridRoyale ##

![](https://i.imgur.com/pmxEKnR.gif)

## [View the live version here!](https://grid-royale.herokuapp.com/) ##

**GridRoyale** is a life simulation. It's a case study that's bundled with Marley.

GridRoyale is similar to [Game of Life](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life) or
[GridWorld](https://towardsdatascience.com/reinforcement-learning-implement-grid-world-from-scratch-c5963765ebff),
except I added game mechanics to encourage the players to behave socially. These game mechanics are
similar to those in the [battle royale](https://en.wikipedia.org/wiki/Battle_royale_game) genre of
computer games, which is why it's called GridRoyale.


# How to run GridRoyale #

Installation:

```console
$ pip install marley
```

Run the server:

```console
$ marley grid_royale demo
```

This will automatically open a browser window and show you your simulation.
wordle-ebarbeary 0.1.0
A suite of tools to build, test and run algorithms that solve the popular web-game 'Wordle'
Requires Python: >=3.6

Wordle
========

A suite of tools to build, test and run algorithms that solve the popular web-game 'Wordle'

Installation
------------

```bash
pip install -r requirements.txt
pip install -e
```

To run existing bots:

```bash
python -m pytest -s
```

License
-------

This project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3
py-tf-utils 0.2.1
Various Terraform Utilities
Requires Python: >=3.6

# py-tf-utils

This project contains a command-line tool for various Terraform utilities.

For an example, see the `sample-module` directory.

## Installation

```sh
pip install py-tf-utils
```

## Usage

To test with the sample project:

```sh
# Will show unused variables in `sample-module` and `sub-module`
tf-utils unused sample-module

# Will ONLY show unused variables in `sub-module`
tf-utils unused sample-module/sub-module/
```

## Background

- [Finding unused variables in a Terraform module](https://alexwlchan.net/2019/05/finding-unused-variables-in-a-terraform-module/)
ADB-Easy-Control 1.0.1
A python package that makes it easier to control your android devices with ADB.
Requires Python: >=3.4

# ADB Easy Control

A python package that makes it easier to control your android devices with ADB.  

With ADB Easy Control, you can integrate ADB instructions into Python programs in a more intuitive way, and ADB based scripts are easier to write. </br>
At the same time, ADB Easy Control is much lighter and portable than traditional Android device control software.