semvec/pypi-packages huggingface dataset:
PyWENO 0.11.2
Weighted Essentially Non-oscillatory (WENO) reconstructions.
License: BSD
Keywords: weno,interpolate,interpolation,finite,volume

PyWENO
======

PyWENO is a Python module for computing high-order Weighted
Essentially Non-oscillatory (WENO) reconstructions of cell-averaged
data arrays.

The basic interface provides a simple routine to compute 1D
reconstructions at various points within each grid cell.  The points
at which the basic interface can reconstruct the original function at
include: left edge, right edge, Gauss-Legendre quadrature points,
Gauss-Lobatto quadrature points, and Guass-Radau quadrature points.

PyWENO can also be used as a code generator to build custom WENO
reconstructors in C, Fortran, and OpenCL on uniform grids.

Please see

  http://readthedocs.org/docs/pyweno/en/latest/

for more information.


Important links
---------------

  * Official project page: https://github.com/memmett/PyWENO
  * Documentation: http://readthedocs.org/docs/pyweno/en/latest/


Prerequisites
-------------

To install PyWENO, you need:

  * NumPy
  * SymPy    (optional if you avoid pyweno.symbolic)


Testing
-------

To build PyWENO and create symlinks to the extension modules:

  $ python setup.py build
  $ cd pyweno
  $ for so in ../build/lib*/pyweno/*.so; do ln -s $so; done

Now, nosetests should work:

  $ nosetests


License
-------

Please see LICENSE for copyright information.


Contributors
------------

* Matthew Emmett
* Michael Welter
* Ben Thompson
django-mstats 0.1.2
Simple, re-usable, stateless Django app for visualizing and browsing statistics, mainly based on your existing Django models.
License: BSD

=============
Django MStats
=============

**Please note**: *Django MStats is in early development and the API is very likely to change.*

MStats is a super simple, re-usable, stateless Django app for visualizing and browsing statistics, mainly 
based on existing Django models.

My motivation for creating MStats is to have a dead simple way, with as little effort as possible, to get 
visualization of key metrics in different Django projects. 

The goal of Django MStats is *not* to be the ultimate metrics/statistics solution™. It will not support 
different backends for different Metrics services and databases. MStats makes all queries in real-time, 
and does not store any permanent data itself, even though Django's cache might be used.

In other words, Django MStats is a reusable app for those who want to get basic statistics browsing with 
minimum effort. Since MStats is stateless, it can easily be tested out, and thrown away in favor of 
something more advanced, if a project grow out of it.


What does M in Mstats stand for?
================================

Model or Mini. Whichever you like best.


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

Currently MStats depends on PostgreSQL, because it uses a Postgres specific SQL functions for retrieving 
stats. 


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

1. Install from PyPI::

    pip install django-mstats

2. Add django_mstats to INSTALLED_APPS

3. Add URL route to your urls.py::

    url(r"^mstats/", include("django_mstats.urls")),

4. Create **mstats.py** file(s) in your Django apps (see below).


Defining different metrics
==========================

Once you have added django_mstats to your INSTALLED_APPS, you can create mstats.py files within your 
Django apps. In those files you should create classes that inherits from ModelStats. Below are some 
examples.

Statistics for newly registered users::

    from django_mstats.models import ModelStats
    from django.contrib.auth.models import User
    
    class NewUsers(ModelStats):
        model = User
        datetime_field = "date_joined"

Specifying a name::

    class NewUsers(ModelStats):
        model = User
        datetime_field = "date_joined"
        name = "User registrations"


Author
======

Django-MStats is developed by `Jonatan Heyman <http://heyman.info>`_.


License
=======

BSD License