semvec/pypi-packages huggingface dataset:
django-postgrespool 0.3.1
Postgres Connection Pooling for Django.
License: MIT

Django-PostgresPool
===================

This is a simple Postgres Connection Pooling backend for Django 1.4+, powered by the lovely and beautiful SQLAlchemy.


Usage
-----

Using Django-PostgresPool is simple, just set ``django_postgrespool`` as your connection engine:

::

    DATABASES = {
        'default': {
            'ENGINE': 'django_postgrespool'


If you're using the `dj-database-url <https://crate.io/packages/dj-database-url/>`_ module:

::

    import dj_database_url

    DATABASES = {'default': dj_database_url.config(engine='django_postgrespool')}

If you're using `south <http://south.aeracode.org>`_:

::

    SOUTH_DATABASE_ADAPTERS = {
        'default': 'south.db.postgresql_psycopg2'
    }


Everything should work as expected.


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

Installing Django-PostgresPool is simple, with pip::

    $ pip install django-postgrespool

Configuration
-------------

Optionally, you can provide additional options to pass to SQLAlchemy's pool creation::

    DATABASE_POOL_ARGS = {
        'max_overflow': 10,
        'pool_size': 5,
        'recycle': 300
    }

Here's a basic explanation of two of these options:

* **pool_size** – The *minimum* number of connections to maintain in the pool.
* **max_overflow** – The maximum *overflow* size of the pool. This is not the maximum size of the pool.

The total number of "sleeping" connections the pool will allow is ``pool_size``.
The total simultaneous connections the pool will allow is ``pool_size + max_overflow``.

As an example, databases in the `Heroku Postgres <https://postgres.heroku.com>`_ starter tier have a maximum connection limit of 20. In that case your ``pool_size`` and ``max_overflow``, when combined, should not exceed 20.

Check out the official `SQLAlchemy Connection Pooling <http://docs.sqlalchemy.org/en/latest/core/pooling.html#sqlalchemy.pool.QueuePool.__init__>`_ docs to learn more about the optoins that can be defined in ``DATABASE_POOL_ARGS``.

Django 1.3 Support
------------------

django-postgrespool currently supports Django 1.4 and greater. See `this ticket <https://github.com/kennethreitz/django-postgrespool/pull/9>`_ for 1.3 support.
CGEA 0.919
Chemo Genomic Enrichment Analysis
License: MIT

To use CGEA:
Install: pip install CGEA

CGEA offers two primary classes:
drugListCGEA(druglist, saveAs = "json", sort = 'up', outputDir = None, prefix = 'CGEAResults', 
			 data = {}, numWorkers = cpu_count(), verbose = False, numPermutations = 1000):
geneListCGEA(upgenes, downgenes, saveAs = "json", sort = 'up', outputDir = None, prefix = 'CGEAResults', 
			 data = {}, numWorkers = cpu_count(), verbose = False, numPermutations = 1000):

arguments:
	upgenes = list of up regulated genes in your gene set.
	downgenes = list of down regulated genes in your gene set. Input None if you don't discriminate between up and down regulation.
	druglist = list of drugs in quuery set.
	outputDir: if you want to have the function save the outputs for you, supply an output directory here.
	prefix: all the file outputs will have this as a prefix if outputDir is defined
	async: set this to True if you want the program to run asynchronously
	numPermutations: increasing this number will increase accuracy at the cost of speed
	verbose: set this to True to have the program give status updates about its progress
	numWorkers: how many cores to use
	data: you can change what data is used by the program. Advanced users only.

methods:
	run - run the process
	
objects:
	results - holds the result data
	
Example operation:
	myUpgenes = [1,2,3]
	myDowngenes = [4,5,6]
	myCGEA = geneListCGEA(myUpgenes, myDowngenes)
	myCGEA.run()
	results = myCGEA.results