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DanielPuentee committed Oct 25, 2022
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<img src="/branding/logo/primary/outdpik.png" width="300">
</h1><br>

## Main Features

- It supports numpy arrays and pandas dataframes
- Multiple outlier detection techniques that can be combined
- Power full visualizations


# Dillinger
## _The Last Markdown Editor, Ever_

[![N|Solid](https://cldup.com/dTxpPi9lDf.thumb.png)](https://nodesource.com/products/nsolid)

[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)

Dillinger is a cloud-enabled, mobile-ready, offline-storage compatible,
AngularJS-powered HTML5 Markdown editor.

- Type some Markdown on the left
- See HTML in the right
- ✨Magic ✨

## Features

- Import a HTML file and watch it magically convert to Markdown
- Drag and drop images (requires your Dropbox account be linked)
- Import and save files from GitHub, Dropbox, Google Drive and One Drive
- Drag and drop markdown and HTML files into Dillinger
- Export documents as Markdown, HTML and PDF

Markdown is a lightweight markup language based on the formatting conventions
that people naturally use in email.
As [John Gruber] writes on the [Markdown site][df1]

> The overriding design goal for Markdown's
> formatting syntax is to make it as readable
> as possible. The idea is that a
> Markdown-formatted document should be
> publishable as-is, as plain text, without
> looking like it's been marked up with tags
> or formatting instructions.
This text you see here is *actually- written in Markdown! To get a feel
for Markdown's syntax, type some text into the left window and
watch the results in the right.

## Tech
# outdpik: Fundamental toolkit for outlier analysis and visualization

Dillinger uses a number of open source projects to work properly:
[![PyPI Latest Release](https://img.shields.io/pypi/v/outdpik.svg)](https://pypi.org/project/outdpik/)
[![PyPI License](https://img.shields.io/pypi/l/jMetalPy.svg)](license.txt)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

- [AngularJS] - HTML enhanced for web apps!
- [Ace Editor] - awesome web-based text editor
- [markdown-it] - Markdown parser done right. Fast and easy to extend.
- [Twitter Bootstrap] - great UI boilerplate for modern web apps
- [node.js] - evented I/O for the backend
- [Express] - fast node.js network app framework [@tjholowaychuk]
- [Gulp] - the streaming build system
- [Breakdance](https://breakdance.github.io/breakdance/) - HTML
to Markdown converter
- [jQuery] - duh

And of course Dillinger itself is open source with a [public repository][dill]
on GitHub.

## Installation

Dillinger requires [Node.js](https://nodejs.org/) v10+ to run.

Install the dependencies and devDependencies and start the server.

```sh
cd dillinger
npm i
node app
```

For production environments...

```sh
npm install --production
NODE_ENV=production node app
```

## Plugins

Dillinger is currently extended with the following plugins.
Instructions on how to use them in your own application are linked below.

| Plugin | README |
| ------ | ------ |
| Dropbox | [plugins/dropbox/README.md][PlDb] |
| GitHub | [plugins/github/README.md][PlGh] |
| Google Drive | [plugins/googledrive/README.md][PlGd] |
| OneDrive | [plugins/onedrive/README.md][PlOd] |
| Medium | [plugins/medium/README.md][PlMe] |
| Google Analytics | [plugins/googleanalytics/README.md][PlGa] |

## Development
## What is it?
Outdpik is an open source Python package that provides different methods for outlier detection.
It aims to be the fundamental high-level package for this purpose.
Additionally, it offers visualization methods for the outlier analysis.

Want to contribute? Great!

Dillinger uses Gulp + Webpack for fast developing.
Make a change in your file and instantaneously see your updates!

Open your favorite Terminal and run these commands.

First Tab:

```sh
node app
```

Second Tab:

```sh
gulp watch
```

(optional) Third:
## Main Features
Here are just a few of the things that outdpik does well:

```sh
karma test
```
- It supports numpy arrays and pandas dataframes
- Multiple outlier detection techniques that can be combined
- Powerful visualizations
- Flexible at including one or more columns for the analysis

#### Building for source
## Where to get it
The source code is currently hosted on GitHub at:
https://github.com/DanielPuentee/outdpik

For production release:
Installer for the latest released version is available at the [Python
Package Index (PyPI)](https://pypi.org/project/outdpik)

```sh
gulp build --prod
# PyPI
pip install outdpik
```

Generating pre-built zip archives for distribution:
## How to use outdpik 🤙
Examples of configuring and running outpdik:

```sh
gulp build dist --prod
```python
import outpdik as outdp
outdp = outdp()
```

## Docker

Dillinger is very easy to install and deploy in a Docker container.

By default, the Docker will expose port 8080, so change this within the
Dockerfile if necessary. When ready, simply use the Dockerfile to
build the image.
We proceed to detect outliers returning a dictionary of numeric features and the outliers instances:

```sh
cd dillinger
docker build -t <youruser>/dillinger:${package.json.version} .
```python
outliers_dict = outdp.outliers(df = df, cols = "all")
```
Plotting advantages:

This will create the dillinger image and pull in the necessary dependencies.
Be sure to swap out `${package.json.version}` with the actual
version of Dillinger.

Once done, run the Docker image and map the port to whatever you wish on
your host. In this example, we simply map port 8000 of the host to
port 8080 of the Docker (or whatever port was exposed in the Dockerfile):

```sh
docker run -d -p 8000:8080 --restart=always --cap-add=SYS_ADMIN --name=dillinger <youruser>/dillinger:${package.json.version}
```python
outdp.plot_outliers(df = df, col = "x")
```
<img src=branding/logo/primary/graph.png width=450 alt="Strip plot outliers detection">

> Note: `--capt-add=SYS-ADMIN` is required for PDF rendering.
Verify the deployment by navigating to your server address in
your preferred browser.

```sh
127.0.0.1:8000
```
## Dependencies
- [pandas - Provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive](https://pandas.pydata.org/)
- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
- [SciPy - Includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more](https://scipy.org/)
- [matplotlib - Comprehensive library for creating static, animated, and interactive visualizations in Python](https://matplotlib.org/)
- [seaborn - Provides a high-level interface for drawing attractive statistical graphics](https://seaborn.pydata.org/)

## License
This project is licensed under the terms of the [MIT](license.txt) - see the LICENSE file for details.

MIT

**Free Software, Hell Yeah!**

[//]: # (These are reference links used in the body of this note and get stripped out when the markdown processor does its job. There is no need to format nicely because it shouldn't be seen. Thanks SO - http://stackoverflow.com/questions/4823468/store-comments-in-markdown-syntax)

[dill]: <https://github.com/joemccann/dillinger>
[git-repo-url]: <https://github.com/joemccann/dillinger.git>
[john gruber]: <http://daringfireball.net>
[df1]: <http://daringfireball.net/projects/markdown/>
[markdown-it]: <https://github.com/markdown-it/markdown-it>
[Ace Editor]: <http://ace.ajax.org>
[node.js]: <http://nodejs.org>
[Twitter Bootstrap]: <http://twitter.github.com/bootstrap/>
[jQuery]: <http://jquery.com>
[@tjholowaychuk]: <http://twitter.com/tjholowaychuk>
[express]: <http://expressjs.com>
[AngularJS]: <http://angularjs.org>
[Gulp]: <http://gulpjs.com>

[PlDb]: <https://github.com/joemccann/dillinger/tree/master/plugins/dropbox/README.md>
[PlGh]: <https://github.com/joemccann/dillinger/tree/master/plugins/github/README.md>
[PlGd]: <https://github.com/joemccann/dillinger/tree/master/plugins/googledrive/README.md>
[PlOd]: <https://github.com/joemccann/dillinger/tree/master/plugins/onedrive/README.md>
[PlMe]: <https://github.com/joemccann/dillinger/tree/master/plugins/medium/README.md>
[PlGa]: <https://github.com/RahulHP/dillinger/blob/master/plugins/googleanalytics/README.md>
## Documentation
The official documentation is hosted on FALTA: https://pandas.pydata.org/pandas-docs/stable

## Development
Want to contribute? Great!
Open a discussion in Github in this repo and we will answer as soon as possible.
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