semvec/pypi-packages huggingface dataset:
cassette 0.3.8
Cassette stores and replays HTTP requests.
License: MIT
Keywords: http,tests,mock

Cassette
========

Cassette stores and replays HTTP requests made in your Python app.

.. code:: python

    import urllib2

    import cassette

    with cassette.play("data/responses.yaml"):

        # If the request is not already stored in responses.yaml, cassette
        # will request the URL and store its response in the file.
        r = urllib2.urlopen("http://www.internic.net/domain/named.root")

        # This time, the request response must be in the file. The external
        # request is not made. cassette retrieves the response from the
        # file.
        r = urllib2.urlopen("http://www.internic.net/domain/named.root")

    assert "A.ROOT-SERVERS.NET" in r.read(10000)

Cassette also supports the `requests <https://github.com/kennethreitz/requests>`_
library.

.. code:: python

    import requests

    with cassette.play("data/responses.yaml"):
        r = requests.get("http://www.internic.net/domain/named.root")

Note that requests stored between different libraries may not be compatible with
each other. That is, a request stored with ``urllib2`` might still trigger an external
request is the same URL is requested with ``requests``.



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

.. code-block:: sh

    $ pip install cassette

Documentation
-------------

Latest documentation: `cassette.readthedocs.org <http://cassette.readthedocs.org/>`_


License
-------

cassette is available under the MIT License.

Copyright Uber 2013, Charles-Axel Dein <charles@uber.com>
djhcup_core 0.20150403
A django-based interface for warehousing HCUP data
License: MIT
Keywords: HCUP SAS healthcare analysis pandas

=====
HCUP Hachoir: Core module
=====

Part of the Django-HCUP Hachoir set of Python packages. Provides core functionality, including automated detection and configuration of other installed djhcup components.

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

0. HCUP Hachoir uses Celery (http://www.celeryproject.org/) as a tasking layer by default. Be sure you have properly configured your Django installation to use Celery, including a message broker and result backend. Typically this invovles creating a celery.py file as described in http://celery.readthedocs.org/en/latest/django/first-steps-with-django.html, and adding configuration items to your settings.py file.

For example, with a local rabbitmq-server back-end, add these lines::

    BROKER_URL = 'amqp://'
    CELERY_RESULT_BACKEND = 'amqp://'

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

      INSTALLED_APPS = (
          ...
          'djhcup_core',
      )

2. Modify your urls.py to include the following pattern entry::

    url(r'', include('djhcup_core.urls'))
    
If you want all djhcup addresses to appear as a subdirectory instead, such as www.example.com/your_prefix/, change r'' to r'your_prefix'::

    url(r'your_prefix', include('djhcup_core.urls'))

3. Modify your settings.py to include a separate database dictionary entry for djhcup named 'djhcup'. For example::

    'djhcup': {
            'ENGINE': 'django.db.backends.postgresql_psycopg2',
            'NAME': 'your_db_name',
            'USER': 'your_db_user',
            'PASSWORD': 'your_db_password',
            'HOST': 'your_db_host',
            'PORT': '5432',
        },
    
Note that while this could use the same database and credentials as your default entry, we recommend you set it up in a separate database for easier management.

4. Tell djhcup where to look for your HCUP data by adding these lines to your settings.py::

    import os, pyhcup
    # Look in here for data and loadfiles
    DJHCUP_IMPORT_PATHS = [
                        '/path/to/your/hcup/data/', # update this to point at your raw data
                        os.path.dirname(pyhcup.__file__), # loadfiles included with PyHCUP
                        ]
    
5. Run `python manage.py syncdb` to create the djhcup_staging models. Or, if using South, migrate forward to build database objects for this package's models.

6. Start your server per normal (for test servers, use `python manage.py runserver`).
memcache_clone 1.0.3
Clone memcache data to othor memcahe .
License: MIT
Keywords: memcache clone package

clone memcache data to other memcache
Usage Example:
    from memcache_clone import memcache
    s = memcache('127.0.0.1',11211)
    s.con()
    #set(key,value,len,flags,express)
    s.set("key","value",5,0,0)
    #get(key) return list : list[value,value_len,flags]
    s.get("key")[0]
    #clone
    s.clone('127.0.0.1',11212)
exreporter 0.1.3
Report Internal Server Errors as Issues
License: MIT
Keywords: exreporter

=========================================
Exreporter: Report Internal Server Errors
=========================================

.. image:: https://badge.fury.io/py/exreporter.png
    :target: http://badge.fury.io/py/exreporter

.. image:: https://travis-ci.org/vedarthk/exreporter.png?branch=master
        :target: https://travis-ci.org/vedarthk/exreporter

Exreporter is an MIT Licensed library, written in Python to report internal server errors and exceptions in background applications to issue trackers such as Github.

This will help in reducing the dependency on error emails for internal server error notifications and to manage them in a single, right place. Reported issues contain everything a developer needs, debugging got a whole lot simpler.

Also it should be easy to use:

.. code-block:: python

    from exreporter.credentials import GithubCredentials
    from exreporter.stores import GithubStore
    from exreporter import ExReporter
    gc = GithubCredentials(user="username", repo="repo-name", auth_token="personal-token")
    gs = GithubStore(credentials=gc)
    reporter = ExReporter(store=gs, labels=['Bug'])

    reporter.report()


Features
--------

- Creates issues in issue trackers
- Aggregate same kind of issues
- Handle multiple occurrence


Usage
--------

Usage reference available at https://exreporter.readthedocs.org/en/latest/usage.html


Documentation
-------------

Documentation is available at https://exreporter.readthedocs.org/.


TODOS
-----

#. Add support for Bitbucket




History
-------

0.1.0 (2014-12-11)
---------------------

* First release on PyPI.

0.1.1 (2014-12-15)
---------------------

* Change development status to Alpha.
* Also add contrib module - django_middlewares

0.1.2 (2015-04-04)
---------------------

* Change public API
* Refactor github issue search
lets_be_rational 1.0.9
Peter Jäckel's LetsBeRational is an extremely fast and accurate method for obtaining Black's implied volatility from option prices with as little as two iterations to maximum attainable precision on standard(64 bit floating point) hardware for all possible inputs.
License: MIT

lets_be_rational is a Python SWIG wrapper for the functions in "Let's Be Rational" by Peter Jäckel.

About "Let's be Rational":


"Let's Be Rational" is a paper by Peter Jäckel showing "how Black's volatility can be implied from option prices with as little as two iterations to maximum attainable precision on standard (64 bit floating point) hardware for all possible inputs."

The paper is accompanied by the full C source code, which resides at www.jaeckel.org/LetsBeRational.7z .

===========================================================
Copyright © 2013-2014 Peter Jäckel.

Permission to use, copy, modify, and distribute this software is freely granted,
provided that this notice is preserved.

WARRANTY DISCLAIMER
The Software is provided "as is" without warranty of any kind, either express or implied,
including without limitation any implied warranties of condition, uninterrupted use,
merchantability, fitness for a particular purpose, or non-infringement.
===========================================================
patroltrigger 0.3
Trigger custom commands from filesystem events.
License: MIT
Keywords: development environment tool pyuv epoll build

PatrolTrigger
=============

Trigger custom commands from filesystem events.

Patrol uses libuv, which creates event driven hooks to filesystem events using epoll, kqueue or IOCP.

You can use it to selectively build your project when files change and run tests as soon as you hit the save button.


Use
===

To install::

    sudo pip install patroltrigger


Create a patrol.py file in your project's root directory:

.. code-block:: python

    #!/usr/bin/python
    from patroltrigger import runnable, run, trigger

    @trigger(["*.py", ], exclude=['venv/*',])
    def trigger1(filenames):
        print "trigger1"
        run("""echo first command triggered""")
        the_output_is_in_this_variable_though = run("echo this command is run, but the output is not displayed", silent=True)

    @trigger(["*.py", ], exclude=['venv/*',])
    def trigger2(filenames):
        print "trigger2 command triggered with filename: {}".format(', '.join(filenames))
        run("false", ignore_errors=True)    # If ignore_errors=False, nothing further will be executed.
        run("""echo if ignore_errors is False or not specified, you wont ever see this command *or* the results of trigger3.""")

    @trigger(["*.py", ], exclude=['venv/*',])
    def trigger3(filenames):
        print "trigger3 command triggered with filename: {}".format(', '.join(filenames))

    runnable(__name__)


Run like so::

    $ python patrol.py


Or get help::

    $ python patrol.py --help
    Usage: patrol.py [options]

    Options:
    -h, --help            show this help message and exit
    -r RUN, --run=RUN     Specify a method in patrol.py to run directly.
    -a, --all             Run all methods in patrol.py in priority order.
    -p POST, --post=POST  Command to run after a trigger (e.g. guake)
    -d DIRECTORY, --directory=DIRECTORY
                            Directory to run patrol.py in (default:
                            /home/user/yourproject).



Features
========

* If multiple methods are triggered by a changed file, they will run *in the order they appear* in patrol.py.
* Queue up triggers by putting a file named 'lock' in the directory patrol.py is run from. Once 'lock' is removed, the pent up triggers will all be fired.
* Each time a set of methods are triggered, the total run time is timed.
* If a command fails (e.g. a unit test), the method is aborted by default at that point to shorten feedback time. Subsequent methods will not run.
* Run a custom command after each method is finished (e.g. guake, notify-send).
* Ability to run commands manually.