semvec/pypi-packages huggingface dataset:
pydivide 0.0.1
A tool to plot MAVEN Key Parameter data
License: MIT
Keywords: tplot maven lasp idl divide

##########
pydivide
##########

Pydivide is a python package which allows the user to quickly plot MAVEN key parameter data.  This toolkit uses the "pytplot" library.     

Pydivide can be used in python scripts, or interactively through IPython and the Jupyter notebook.  

Install Python
=============

You will need Python version 3.5 to run pydivide.  

It is recommended that you install `Anaconda <https://www.continuum.io/downloads/>`_, as it comes with a suite of packages that are useful for data science. 

You could also install python directly from `python.org <https://www.python.org/download/>`_.

Install pydivide
=============

Open up a terminal, and type::

	pip install pydivide
	
This will install pydivide and all of it's dependencies.  

Running Pydivide
=============

To start using pytplot in a similar manner to IDL tplot, start up an interactive environment through the terminal command::

	ipython 
	
or, if you prefer the jupyter interactive notebook::

	jupyter notebook
	
then, just import the package by typing the command::

	import pydivide

	
Contact
=============

If you have any suggestions or notice any problems, don't hesitate to contact Bryan Harter: harter@lasp.colorado.edu
dogpile_cache_native_redis 0.1
backend for dogpile.cache with reading and writing the native value
License: MIT
Keywords: caching

==========================
dogpile_cache_native_redis
==========================

Install
=======

Use pip::

   pip install dogpile_cache_native_redis

Usage
=====

See the example.

Code::

   from dogpile.cache import make_region

   region = make_region().configure(
       'native_redis',
       # arguments are same as redis backend.
       arguments = {
           'host': "127.0.0.1",
       }
   )

   region.set("test", b"value")
   region.set_multi({"key1": b"value1", "key2": 100})
   print(region.get("test"))
   print(region.get_multi(["key1", "key2"]))

Result::

   $ python main.py
   b'value'
   [b'value1', b'100']

See with redis-cli::

   $ redis-cli
   127.0.0.1:6379> get test
   "value"
   127.0.0.1:6379> mget key1 key2
   1) "value1"
   2) "100"
blueprint-decr 0.1.1
UNKNOWN
License: MIT

blueprint-decr
==============

A tiny python lib for decorating all view functions of a flask
blueprint.

install
-------

::

    pip install blueprint-decr

usage
-----

::

    # api_blueprint is a blueprint object

    import functools
    def dummy_decr(func):
        @functools.wraps(func)
        def wrapper(*sub, **kw):
            print(func.__name__)
            return func(*sub, **kw)
        return wrapper


    import blueprint_decr

    new_api_blueprint = blueprint_decr.attach(api_blueprint, dummy_decr)

    # now, you may register new_api_blueprint with flask app object.
    # app.register_blueprint(new_api_blueprint, url_prefix='/api')
ipymol 0.5
IPyMOL allows you to control PyMOL sessions via IPython.
License: MIT
Keywords: ipython notebook pymol protein molecular visualization

.. image:: https://badge.fury.io/py/ipymol.svg
    :target: https://pypi.python.org/pypi/ipymol/
    :alt: Latest PyPI version

IPyMOL
======

IPyMOL allows you to control `PyMOL <https://www.pymol.org>`_ sessions via IPython. This tool is ideal for situations where you'd like to present your work neatly in a `Jupyter Notebook <https://jupyter.org/>`_ or conveniently prototype PyMOL scripts.

Before You Begin
----------------
Please ensure that PyMOL is in your ``$PATH`` as ``pymol`` or you can start PyMOL in server mode:

.. code:: shell

    $ pymol -R #-cKRQ to run it without a GUI

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


.. code:: shell

    pip install ipymol



Example Usage
--------------
You can fire up an IPython or IPython Notebook session and start using IPyMOL. For example:

.. code:: python

    from ipymol import viewer as pymol
    pymol.start()   # Start PyMOL RPC server
    pymol.fetch('3odu') # Fetch PDB
    pymol.show_as('cartoon') # Show as cartoon
    pymol.bg_color('white') # Set background color to white
    pymol.display() # Show current display

This series of commands will define a variable ``pymol`` of class ``MolViewer``, which can pass commands to PyMOL, and then create an image of ``PDBID:3odu`` in your IPython session.
Additional commands can be invoked via ``pymol.do("[ENTER YOUR COMMAND HERE]")``.

Here's an `example notebook <http://nbviewer.ipython.org/urls/raw.github.com/cxhernandez/iPyMol/master/examples/Example1.ipynb>`_ with more information.

Enjoy!
django-mssql 1.8
Django backend database support for MS SQL Server.
License: MIT

Django MSSQL Database Backend
=============================

`Django-mssql`_ provies a Django database backend for Microsoft SQL Server.

Documentation is available at `django-mssql.readthedocs.org`_.

Requirements
------------

    * Python 2.7, 3.4
    * PyWin32_
    * SQL Server Management Studio or Microsoft Data Access Components (MDAC)

SQL Server Versions
-------------------

Supported Versions:
    * 2012

SQL 2008/2008r2 are support by django-mssql 1.6.x.

Django Version
--------------

	* Django 1.8

django-mssql 1.7 supports Django 1.7.
django-mssql 1.6 supports Django 1.6.
django-mssql 1.4 supports Django 1.4 and 1.5.


References
----------

    * Django-mssql on PyPi: http://pypi.python.org/pypi/django-mssql
    * DB-API 2.0 specification: http://www.python.org/dev/peps/pep-0249/


.. _`Django-mssql`: https://bitbucket.org/Manfre/django-mssql
.. _django-mssql.readthedocs.org: https://django-mssql.readthedocs.io/
.. _PyWin32: http://sourceforge.net/projects/pywin32/
RealThreading 0.0.1
'A python's parallel processing module.
License: MIT
Keywords: threading

RealThreading:
    The multi-processing module for Python 3.5.

To use:
>>>import rt
>>>t=rt.IsoThread(target_function,args=arguments,kwargs=keyword arguments)
>>>t.start()
>>>t.wait()
>>>print(t.result())
>>>t.clean()

    Since the only multi-threading module in python that truly take
the advantage of multi-core computers are multi-processing and MP
can only be used when not imported; also, parallel python(pp) only
work for python 2.X. So, I developed RealThreading which works pretty
much the same way as pp and does the same job but it works for python
3.X.
    Contact me at G.Mpydev@gmail.com