semvec/pypi-packages huggingface dataset:
websauna.j2secret 1.0.0a1
Websauna Jinja2 extension that adds a tag to generate secrets.
License: MIT
Keywords: jinja2,websauna,pyramid

=========================
Websauna Jinja2 Extension
=========================

.. |ci| image:: https://travis-ci.org/websauna/websauna.j2secret.svg
    :target: https://travis-ci.org/websauna/websauna.j2secret/

.. |latest| image:: https://img.shields.io/pypi/v/websauna.j2secret.svg
    :target: https://pypi.python.org/pypi/websauna.j2secret/
    :alt: Latest Version

.. |license| image:: https://img.shields.io/pypi/l/websauna.j2secret.svg
    :target: https://pypi.python.org/pypi/websauna.j2secret/
    :alt: License

.. |cov| image:: https://codecov.io/github/websauna/websauna.j2secret/coverage.svg?branch=master
    :target: https://codecov.io/github/websauna/websauna.j2secret?branch=master


.. |versions| image:: https://img.shields.io/pypi/pyversions/websauna.j2secret.svg
    :target: https://pypi.python.org/pypi/websauna.j2secret/
    :alt: Supported Python versions


This is a Jinja2 extension required by Cookiecutter Websauna templates.

+-----------+-----------+-----------+
| |ci|      ||cov|      |           |
+-----------+-----------+-----------+
| |versions|| |latest|  ||license|  |
+-----------+-----------+-----------+

.. contents:: :local:


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

Using the same virtualenv where cookiecutter is installed install this extension executing pip install:

.. code-block:: shell

    pip install websauna.j2secret


Usage
=====

Extend the Cookiecutter environment with this custom `Jinja2 extensions`_,
that adds a tag named **secret** , specifing the **websauna.j2secret** extension in ``cookiecutter.json`` as follows:

.. code-block:: json

    {
        "authentication_random": "{% secret() %}",
        "authomatic_random": "{% secret() %}",
        "session_random": "{% secret() %}",
        "_extensions": ["websauna.j2secret.SecretExtension"]
    }


Read more about `Template Extensions for Cookiecutter`_.


Running the test suite
======================

Install test and dev dependencies (run in the folder with ``setup.py``)::

    pip install -e ".[test]"

Run test suite using py.test running::

    py.test

More information
================

Please see https://websauna.org/


.. _`Jinja2 extensions`: http://jinja2.readthedocs.io/en/latest/extensions.html#extensions
.. _`Template Extensions for Cookiecutter`: http://cookiecutter.readthedocs.io/en/latest/advanced/template_extensions.html


Changelog
=========

1.0.0a1 (2017-10-16)
--------------------

- Implement Jinja2 tag **secret** to be used with Cookiecutter templates.
dempy 0.0.4
A Python project to work with raster, shapefile, and lidar data
License: MIT
Keywords: Plotting

Dempy: a python package to work with GIS data types
===================================================

Simon Filhol, October 2017

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

.. code:: sh

    pip install dempy

WARNING: Use at your own risk.

Objective
---------

This package is a collection of useful tool to work with geospatial type
of data within Python. Tools for working with rasters, shapefile,
pointcloud data, and more advanced dem analysis tool such as fourier
decomposition.

Some of the tools included are:

-  geoTIFF import
-  DEM detrend with linear plane
-  DEM filtering (FFT or Convolution)
-  Generating random DEM with diamondSquare (implemented in Cython)
-  Derive morphometric data
-  plotting DEM
-  derive Blowing snow terrain parameter:
-  Tabler's drift profile (Tabler, R.D., 1975. Predicting Profiles of
   Snowdrifts in Topographic Catchments. In *Proceedings of the 43rd
   Annual Western Snow Conference*. San Diego, CA, pp. 87–97.)
-  Winstral Sx (Winstral, A., Elder, K. & Davis, R.E., 2002. Spatial
   Snow Modeling of Wind-Redistributed Snow Using Terrain-Based
   Parameters. *Journal of Hydrometeorology*, 3(5), pp.524–538.)

Requirements
------------

Requires a few libraries such as laspy, gdal, cython, geopandas ...
banditvis 0.9
A library for simulating and visualizing bandit algorithms.
License: MIT
Keywords: bandit machine learning

Intro
=====

This Bandit Visualization module provides an easy-to-use library for
implementing and visualizing various Bandit algorithms and environments
in machine learning. If you are looking for the following, this library
is not for you:

-  You want something fast and efficient
-  You want a tool to run very large simulations

It is good to use if you want to

-  Visualize your algorithms in various ways
-  Quickly make nice looking graphs
-  Have the support of a super-straightforward minilanguage
-  Have a command-line tool and a module you can import

The library is implemented in python3. This library is dependent also on
the following modules, and you must have them installed to run this:

-  pyyaml (text parsing)
-  numpy (math)
-  scipy (stats)
-  matplotlib (creating graphs)

Run files using

::

    banditvis path/to/your/file.txt

Get syntax help with

::

    banditvis -h