semvec/pypi-packages huggingface dataset:
py-file-conf-gui 0.2.0
Web GUI for pyfileconf
License: MIT


[![](https://codecov.io/gh/nickderobertis/py-file-conf-gui/branch/master/graph/badge.svg)](https://codecov.io/gh/nickderobertis/py-file-conf-gui)

# py-file-conf-gui

## Overview

Web GUI for pyfileconf

## Getting Started

Install `py-file-conf-gui`:

```
pip install py-file-conf-gui
```

A simple example:

```python
import pyfileconfgui

# Do something with pyfileconfgui
```

## Links

See the
[documentation here.](
https://nickderobertis.github.io/py-file-conf-gui/
)

## Author

Created by Nick DeRobertis. MIT License.
cirkel 0.2
Automata plotting circles on canvas
License: MIT
Requires Python: >=3.5



# CIRKEL

<img src="https://raw.githubusercontent.com/zmic/cirkel/master/examples/test_0.jpg" width=400px height=400px>

## How to install it

On Windows:
`pip install cirkel`

OR

clone this repository, build the binary with Visual Studio 2017/2019 and run setup.py

## How to  use it

See example.py

## Disclaimer
Disclaimer: I wrote this code in a hurry during a two week period in 2014, for quick experimentation. It was never refactored or cleaned up as it should be. It works, and it's not inscrutable, but does NOT meet my professional standards for deliverable source code.
bandicoot 0.6.0
A toolbox to analyze mobile phone metadata.
License: MIT

=========
bandicoot
=========

.. image:: https://img.shields.io/pypi/v/bandicoot.svg
    :target: https://pypi.python.org/pypi/bandicoot
    :alt: Version
     
.. image:: https://img.shields.io/pypi/l/bandicoot.svg
    :target: https://github.com/computationalprivacy/bandicoot/blob/master/LICENSE
    :alt: MIT License

.. image:: https://img.shields.io/pypi/dm/bandicoot.svg
    :target: https://pypi.python.org/pypi/bandicoot
    :alt: PyPI downloads

.. image:: https://img.shields.io/travis/computationalprivacy/bandicoot.svg
    :target: https://travis-ci.org/computationalprivacy/bandicoot
    :alt: Continuous integration

.. begin

**bandicoot** (http://bandicoot.mit.edu) is Python toolbox to analyze mobile phone metadata. It provides a complete, easy-to-use environment for data-scientist to analyze mobile phone metadata. With only a few lines of code, load your datasets, visualize the data, perform analyses, and export the results.

.. image:: https://raw.githubusercontent.com/computationalprivacy/bandicoot/master/docs/_static/bandicoot-dashboard.png
    :alt: Bandicoot interactive visualization

---------------
Where to get it
---------------

The source code is currently hosted on Github at https://github.com/computationalprivacy/bandicoot. Binary installers for the latest released version are available at the Python package index:

    http://pypi.python.org/pypi/bandicoot/

And via `easy_install`:

.. code-block:: sh

    easy_install bandicoot

or  `pip`:

.. code-block:: sh

    pip install bandicoot

------------
Dependencies
------------

bandicoot has no dependencies, which allows users to easily compute indicators on a production machine. To run tests and compile the visualization, optional dependencies are needed:

- `nose <http://nose.readthedocs.io/en/latest/>`_, `numpy <http://www.numpy.org/>`_, `scipy <https://www.scipy.org/>`_, and `networkx <https://networkx.github.io/>`_ for tests,
- `npm <http://npmjs.com>`_ to compile the js and css files of the dashboard.

-------
Licence
-------

MIT

-------------
Documentation
-------------

The official documentation is hosted on http://bandicoot.mit.edu/docs. It includes a quickstart tutorial, a detailed reference for all functions, and guides on how to use and extend bandicoot. You can also check out our `interactive training notebooks <https://github.com/yvesalexandre/bandicoot-training>`_ to learn how to download your own data from your mobile phone and load it into bandicoot to visualize it or to learn how to use bandicoot indicators in *scikit-learn*.