semvec/pypi-packages huggingface dataset:
stocal 1.2
simple rule-based stochastic simulation
License: MIT

# stocal

A python framework for regular and rule-based stochastic simulations.


## What is Stocal?

Stocal is a framework for stochastic simulation of continuous
time Markov processes (also known as [Gillespie simulations](https://en.wikipedia.org/wiki/Gillespie_algorithm)). 

Features of stocal:
* support for reactions of any order
* support for unique and periodic (deterministic) events
* support for time-dependent reaction rates
* support for rules that generate novel reactions on the fly
* support for complex chemical states

Stocal works with python version 2.7 as well as version 3.5.


### Basic Usage

Running a simple stochastic simulation is straight forward:
```python
import stocal

# Define a stochastic process
process = stocal.Process([
	stocal.MassAction({'A': 2}, {'A2': 1}, 1.0),
	stocal.MassAction({'A2': 1}, {'A': 2}, 10.0),
	stocal.Event({}, {'A': 100}, 0.0, frequency=10.0),
])

# Sample a stochastic trajectory of the process
initial_state = {}
trajectory = process.sample(initial_state, tmax=100) :
for dt, transitions in trajectory:
	print trajectory.time, trajectory.state['A2']
```


### API / Tutorial

A tutorial on how to use stocal can be found [here](https://github.com/harfel/stocal/wiki).
Various usage examples are provided in stocal/examples.
The package API is thoroughly documented and can be accessed through
pydoc. The behavior of stocal is specified via tests. The test suite
can be run with `python setup.py test`.


### Installation

The latest stable release of stocal is available from the python package index:
```shell
pip install stocal
```
The development version can be obtained from github using the following commands:
```shell
git clone https://github.com/harfel/stocal.git
cd stocal
git checkout develop
sudo python setup.py install
```


## Issue Reporting and Contributing

Please post any issues that might occur with stocal on the
<a href="https://github.com/harfel/stocal/issues">github issue
tracker</a>.
If you are interested in contributing to stocal, pull requests
and any other inquiries will be dealt with as soon as possible.


## License

Stocal is distributed under the MIT Software license.
(c) 2018 Harold Fellermann
text-autoencoder 0.0.1
Yoctol Natural Language Text Autoencoder
License: MIT
Requires Python: >=3.6

# text-autoencoder

[![travis][travis-image]][travis-url]
[![pypi][pypi-image]][pypi-url]

[travis-image]: https://img.shields.io/travis/Yoctol/text-autoencoder.svg?style=flat
[travis-url]: https://travis-ci.org/Yoctol/text-autoencoder
[pypi-image]: https://img.shields.io/pypi/v/text-autoencoder.svg?style=flat
[pypi-url]: https://pypi.python.org/pypi/text-autoencoder

Various autoencoder for text data.

## Usage

### Grab one autoencoder first
```python
from text_autoencoder.variational_autoencoders import VAEXXX
model = VAEXXX(n_steps=..., latent_size=..., state_size=..., ...)
```

### How to train
- Warning: please preprocess your data to be a numpy array with shape (data_size, maxlen, embedding_size)
```python
model.fit(x=..., mask=..., epochs=10)
```

### How to save model
```python
model.save(output_path)
```

### How to get latent vector `z`
```python
model.get_latent_vector(x=..., mask=..., batch_size=1)
```

### How to get output of encoder
```python
model.encode(x=..., mask=..., batch_size=1)
```

### How to load a trained model
```python
model.load(path)
```

### How to monitor the training process
- get the output_dir you input when calling `model.fit`
- monitor training loss
```shell
> tensorboard --logdir="<output_dir>/summary/subtrain/"
```

- monitor validation loss
```shell
> tensorboard --logdir="<output_dir>/summary/valid/"
```
rest-accounts 0.1
Django Rest Authentication System
License: MIT

=====
Rest-Accounts
=====

Rest-Accounts is a simple app to handle REST implementation of `Django <https://www.djangoproject.com/>`_ authentication
system. **rest-accounts** library provides a set of `Django Rest Framework <http://www.django-rest-framework.org/>`_
views to handle basic actions such as create-user, create-token,password
reset and account activation.

Detailed documentation is in the "docs" directory.

Requirements
============

To be able to run **rest-accounts** you have to meet following requirements:

- Python (3.5, 3.6)
- Django (2.0,2.1)
- Django REST Framework (3.7, 3.8)


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

Simply install using ``pip``:

.. code-block:: bash

    $ pip install rest-accounts


Quick start
-----------


1. Add "polls" to your INSTALLED_APPS setting like this::

    INSTALLED_APPS = [
        ...
        'accounts',
    ]


2. Include the polls URLconf in your project urls.py like this::

    path('accounts/', include('accounts.urls')),


3. Start the development server::

	python manage.py runserver,


4. Manage your accounts