semvec/pypi-packages huggingface dataset:
megatemp 0.1.1
A utility for agm.
Keywords: automation

# AGM library

[![Build Status](https://travis-ci.com/Brian-Williams/actagm.svg?token=z7gYD5GwEhzpEFmQxsP2&branch=master)](https://travis-ci.com/Brian-Williams/actagm)

### Example usage
```
>>> from actagm.agm_helper import AgmHelper
>>> ah = AgmHelper(<dns name or ip address>, <user>, <password>)
>>> ah.login()
>>> r = ah.get('/cluster')
>>> print(r.json())
```

### Platform Dependant Requirements
AgmHelper requires openssl version 1.0.1+ for TLSv1.2. We do not backport ssl so python 2.7.9+ or 3.4+ is required.
nept 0.1.0
Neuroelectrophysiology tools

.. image:: https://img.shields.io/travis/vandermeerlab/nept/master.svg
  :target: https://travis-ci.org/vandermeerlab/nept
  :alt: Travis-CI build status

.. image:: https://img.shields.io/codecov/c/github/vandermeerlab/nept/master.svg
  :target: https://codecov.io/gh/vandermeerlab/nept/branch/master
  :alt: Test coverage

.. image:: https://img.shields.io/badge/docs-latest-blue.svg
  :target: https://vandermeerlab.github.io/nept/
  :alt: Documentation Status

**********************************
nept: Neuroelectrophysiology tools
**********************************

Formerly ``vdmlab``, renamed to emphasize general abilities of this library.

Getting started
===============

If you don't already have python 3, we recommend you download it using Miniconda 
from `Continuum Analytics <http://conda.pydata.org/miniconda.html>`_.

We recommend using a separate python environment.

Open a **new** terminal, create and activate a new conda environment::

  conda create -n yourenv python=3.5
  activate yourenv [Windows] or source activate yourenv [Linux]

Install package dependencies::

  conda install matplotlib jupyter scipy numpy pandas seaborn pytest coverage

For Shapely, try::

  pip install shapely

If that fails, in Windows, download the most recent wheel file 
`here <http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely>`_.
Once downloaded, install with wheel.

.. code:: bash

  pip install yourshapelyinstall.whl

Installation
============

Clone nept from Github and use a developer installation::

  git clone https://github.com/vandermeerlab/nept.git
  cd nept
  python setup.py develop

Documentation
=============

Users
-----

Check `GitHub Pages <https://vandermeerlab.github.io/nept/>`_
for the latest version of the nept documentation.

Developers
----------

Ensure you have sphinx, numpydic, and mock::

  conda install ghp-import sphinx numpydoc sphinx_rtd_theme

Install nbsphinx so notebooks in the documentations can be executed::
  
  pip install nbsphinx --user

Build the latest version of the documentation using 
in the nept directory prior to pushing it to Github::

  sphinx-build docs docs/_build

And push it to Github::

  docs/update.sh

Testing
=======

Run tests with `pytest <http://docs.pytest.org/en/latest/usage.html>`_.

Check coverage with `codecov <https://codecov.io/gh/vandermeerlab/nept>`_.

License
=======

The nept codebase is made available under made available 
under the `MIT license <LICENSE.rst>`_
that allows using, copying and sharing.

The file ``nept/neuralynx_loaders.py`` contains code from 
`nlxio <https://github.com/bwillers/nlxio>`_ by Bernard Willers, 
used with permission. 

Projects using nept
===================

`emi_shortcut <https://github.com/vandermeerlab/emi_shortcut>`_

`emi_biconditional <https://github.com/vandermeerlab/emi_biconditional>`_