semvec/pypi-packages huggingface dataset:
wfepy 0.1.1
Workflow Engine for Python
License: MIT
Requires Python: >=3.5, <4
Keywords: Workflow,Workflow Engine

WfEpy
=====

**WfEpy (Workflow Engine for Python)** is Python library for creating workflows
and automating various processes. It is designed to be as simple as possible so
developers can focus on tasks logic, not how to execute workflow, store state,
etc.

Individual steps in workflow are simply functions with decorator and transitions
between tasks are also defined by decorators. Everything what developer needs to
do is add few decorators to functions that implements tasks logic. This library
is then used to build graph from tasks and transitions and execute tasks in
workflow by traversing graph and calling task functions. Context passed to each
function is arbitrary user object that can be used to store data, connect to
other services or APIs, ...

.. code:: python

    @wfepy.task()
    @wfepy.start_point()
    @wfepy.followed_by('make_coffee')
    def start(context):
        ...

    @wfepy.task()
    @wfepy.followed_by('drink_coffee')
    def make_coffee(context):
        ...

    @wfepy.task()
    @wfepy.followed_by('end')
    def drink_coffee(context):
        ...

    @wfepy.task()
    @wfepy.end_point()
    def end(context):
        ...

WfEpy does not provide any server scheduler or something like that. It was
designed to be used in scripts, that are for example periodically executed by
cron. If workflow have task that cannot be finished in single run library
provides way how to store current state of runner including user data and
restore it on next run.

.. code:: python

    import coffee_workflow

    wf = wfepy.Workflow()
    wf.load_tasks(coffee_workflow)

    runner = wf.create_runner()
    if restore_state:
        runner.load('state-file')

    runner.run()

    runner.dump('state-file')

This simple design provides many options how to execute your workflow and
customize it. This was also reason why this library was created, we don't want
to manage new service/server that executes few simple workflows. We would like
to use service we already have, for example Jenkins, cron, ...


Installation
------------

Install it using pip ::

    pip3 install wfepy

or clone repository ::

    git clone https://github.com/redhat-aqe/wfepy.git
    cd wfepy

and install Python package including dependencies ::

    python3 setup.py install
train 0.0.5
A library to build and train reinforcement learning agents in OpenAI Gym environments.
License: MIT
Requires Python: >=3.6
Keywords: train reinforcement-learning machine-learning gym tensorflow

Train
=====

A library to build and train reinforcement learning agents in OpenAI Gym environments.

.. image:: https://travis-ci.org/marella/train.svg?branch=master
    :target: https://travis-ci.org/marella/train
    :alt: Build Status
.. image:: https://readthedocs.org/projects/train/badge/?version=latest
    :target: https://train.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

Read full documentation `here <https://train.readthedocs.io/>`_.

Getting Started
***************

An agent has to implement the ``act()`` method which takes the current ``state`` as input and returns an action:

.. code:: python

    from train import Agent

    class RandomAgent(Agent):

        def act(self, state):
            return self.env.action_space.sample()


Create an environment using OpenAI Gym_:

.. code:: python

    import gym

    env = gym.make('CartPole-v0')

Initialize your agent using the environment:

.. code:: python

    agent = RandomAgent(env=env)

Now you can start training your agent (in this example, the agent acts randomly always and doesn't learn anything):

.. code:: python

    scores = agent.train(episodes=100)

You can also visualize how the training progresses but it will slow down the process:

.. code:: python

    scores = agent.train(episodes=100, render=True)

Once you are done with the training, you can test it:

.. code:: python

    scores = agent.test(episodes=10)

Alternatively, visualize how it performs:

.. code:: python

    scores = agent.test(episodes=10, render=True)

To learn more about how to build an agent that learns see Agent_ documentation.

See examples_ directory to see implementations of some algorithms (DQN, A3C, PPO etc.) in TensorFlow.

Installation
************

Requirements:

-   Python >= 3.6

Install from PyPI (recommended):

::

    pip install train

Alternatively, install from source:

::

    git clone https://github.com/marella/train.git
    cd train
    pip install -e .

To run examples and tests, install from source.

Other libraries such as Gym_ and TensorFlow_ should be installed separately.

Examples
********

To run examples, install TensorFlow_ and install dependencies:

::

    pip install -e .[examples]

and run an example in examples_ directory:

::

    cd examples
    python PPO.py

Testing
*******

To run tests, install dependencies:

::

    pip install -e .[tests]

and run:

::

    pytest tests

.. _Agent: https://train.readthedocs.io/en/latest/agents.html
.. _examples: https://github.com/marella/train/tree/master/examples
.. _Gym: https://gym.openai.com/docs/
.. _TensorFlow: https://www.tensorflow.org/install
smospy 0.20
strong motion seismic data analysis module for Python
License: MIT
Requires Python: >=3
Keywords: seismology, waveform analysis,geophysics, geophysical inversion

# smospy

[![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)

Smospy is a Python package for strong motion seismic data processing.

### Dependencies

smospy uses a number of open source projects to work properly:

* [NumPy](https://aip.scitation.org/doi/abs/10.1109/MCSE.2011.37) - DOI:10.1109/MCSE.2011.37 
* [Matplotlib](https://aip.scitation.org/doi/abs/10.1109/MCSE.2007.55) - DOI:10.1109/MCSE.2007.55 
* [Cython](https://aip.scitation.org/doi/abs/10.1109/MCSE.2010.118) -  DOI:10.1109/MCSE.2010.118 
* [pandas](http://conference.scipy.org/proceedings/scipy2010/mckinney.html)
* [gdal](https://gdal.org)
* [pyshp](https://pypi.org/project/pyshp/)
* [h5py](http://www.h5py.org/)
* [Armadillo](http://arma.sourceforge.net/)

### Installation
The package is available over the [pip](https://pypi.org/) package manager

```sh
$ pip install smospy
```
read the docs [here](http://alejaege.gitext.gfz-potsdam.de/smospydoc/)

License
----
MIT License

Copyright (c) 2019 Alexander Jäger, Sebastian von Specht, [Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.