semvec/pypi-packages huggingface dataset:
pyipa_dist 1.0.3
This python module to creates plist, html file for Adhoc distribution
License: MIT
Keywords: pyipa,dist,distribution,adhoc,ipa

pyipa_dist: a lib for creates plist and html from IPA file
-----------------------------------------------------------

This is a library to easily creates plist and html from IPA file for Adhoc distribution, and also a command line utility to do that

Install
_______

.. code:: sh

    $ pip install pyipa_dist

Usage
-----
::

    Usage:
        pyipa_dist [-d --destUrlPath=<DestinationUrl>] IPA [FILE_OUT_DIR]
        pyipa_dist (-h | --help)
        pyipa_dist --version

    Options:
        -d --destUrlPath=<DestinationUrl> Path is the output files(html, plist, ipa) location in distribution server.
                                          (eg. "https://qa.ncsoft.com/QADeploy/CredentialSDK/20151210/")
        -h --help               Show this screen.
        --version               Show version.



Contribute
__________
- Fork the project on github to start making your changes
- Send pull requests with your bug fixes or features
- Submit and create issues on github
thingpin 3.0.2
Raspberry Pi pin monitor that reports to AWS IoT
License: MIT
Keywords: aws iot thing mqtt sensor gpio raspberry pi rpi

|Build Status| |Coverage Status| |Downloads| |Flux Cap|

thingpin
========

`thingpin`_ monitors Raspberry Pi inputs and reports them to AWS IoT. Use it
to build an door sensor, water sensor, motion detector, etc. that is IoT
enabled.

`See Documentation for Installation and Usage details`_

.. _See Documentation for Installation and Usage details: https://github.com/mgk/thingpin/blob/master/README.md

.. |Build Status| image:: https://img.shields.io/travis/mgk/thingpin.svg
   :target: https://travis-ci.org/mgk/thingpin

.. |Coverage Status| image:: https://img.shields.io/coveralls/mgk/thingpin.svg
   :target: https://coveralls.io/github/mgk/thingpin?branch=master

.. |Downloads| image:: https://img.shields.io/pypi/dm/thingpin.svg
   :target: https://pypi.python.org/pypi/thingpin

.. |Flux Cap| image:: https://img.shields.io/badge/flux%20capacitor-1.21%20GW-orange.svg
pelican-provisioner 0.1.5
A system for provisioning blogs managed by Pelican.
License: MIT

pelican-provisioner
===================

This package creates a working environment for a static blog that is generated by Pelican.

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

::

    mkvirtualenv pelican-provisioner
    pip install pelican-provisioner

Create a blog
-------------

::

    workon pelican-provisioner
    new-blog.sh ~/Work/my-blog

Using the blog
--------------

::

    workon my-blog
    cd ~/Work/my-blog
    make regenerate # to develop locally
    make serve # to test it locally at http://localhost:8000
    make deploy # to make the changes public

Rationale
---------

- I want a blog that is rock-solid.
- When I sit down to do some writing, I don't want to spend any time on maintenance.
- I want to leave the blog alone, come back to it after 1 year, I expect everything to work.
- I want to install this from scratch without depending upon online packages.
- I don't want future work to be contingent upon the any third party.

To meet these goals, I am bundling everything; this is a self-contained static blog generator without any external dependencies.  I can create any number of blogs, which are managed as independent packages, and I can generally count upon them to keep working.
sqoopy 0.0.9.1
UNKNOWN
License: MIT
Keywords: sqoop,mysql,import,hadoop

sqoopy
======

Python CLI to generate custom [sqoop](http://sqoop.apache.org/) import statements. 
Modified from [https://github.com/wikimedia/sqoopy/](https://github.com/wikimedia/sqoopy/).


## Installation

You can install `sqoopy` via `pip`:

```bash
$ pip install sqoopy
```

## Usage 

`sqoopy` will generate custom [sqoop](http://sqoop.apache.org/) import statements given a few simple options:

```bash
usage: sqoopy [-h] [-c CONNECT] [-d TARGET_DIR] [-t TABLES] [-x EXCL_TABLES]
              [--generate] [--pool-size POOL_SIZE]
              [--max-pool-maps MAX_POOL_MAPS] [--min-mbs MIN_MBS]
              [--max-mbs MAX_MBS]

Python CLI to generate custom sqoop import statements.

optional arguments:
  -h, --help            show this help message and exit
  -c CONNECT, --connect CONNECT
                        A jdbc connection string.
  -d TARGET_DIR, --target-dir TARGET_DIR
                        The directory to send output to. If sending to s3, use
                        "{table}" to insert the table name into the directory.
                        EG: s3://my-bucket/{table}/
  -t TABLES, --tables TABLES
                        (Optional) comma-separated list of tables that need to
                        be inspected. If not supplied, all tables will be
                        imported.
  -x EXCL_TABLES, --excl-tables EXCL_TABLES
                        (Optional) comma-separated list of tables to exclude.
                        If not supplied and --tables not specified, all tables
                        will be imported.
  --generate            Just generate the sqoop commands and print them to the
                        console.
  --pool-size POOL_SIZE
                        The number of commands to execute concurrently
  --max-pool-maps MAX_POOL_MAPS
                        The number of mappers at which the import of a table
                        will occur serially, after all other pooled imports
                        are complete
  --min-mbs MIN_MBS     The minimim chunk size (in MBs). Used to determine the
                        number of mappers needed for a given table
  --max-mbs MAX_MBS     The maximum chunk size (in MBs). Used to determine the
                        number of mappers needed for a given table
```

You can also passthrough any other `sqoop import` arguments:

```bash
$ sqoopy --connect=sqlite:///tests/test.db --target-dir=s3://foo-bar/{table} --tables=test --split-by id --num-mappers 4
```

This will output:

```
sqoop import --connect=sqlite:///tests/test.db --table=test --target-dir=s3://foo-bar/test/ --split-by id --num-mappers 4
```

## Tests

You can run tests by first installing `nose`:

```
$ pip install nose
$ nosetests
```
django-recommend 0.1.dev9
Generate recommendations in Django.
License: MIT

django_recommend
================

.. image:: https://travis-ci.org/dan-passaro/django-recommend.svg
    :target: https://travis-ci.org/dan-passaro/django-recommend

Use ``pyrecommend`` in Django projects. 

**Warning:** not yet suitable for production.

``django_recommend/`` is the actual Django app intended for use in projects.

``simplerec/`` is a Django project used for testing.

**Important note:** Model classes/database schemas are only supported if they
have unique integer primary keys.


Quickstart
----------

* Add ``django_recommend`` to your ``INSTALLED_APPS``, and run
  ``python manage.py migrate``.

* Set ``SESSION_SAVE_EVERY_REQUEST=True`` in your settings, to ensure anonymous
  users can be tracked.

* In your views, call ``django_recommend.set_score(request, object, score)`` to
  start recording user scores. (Currently this is assumed to be implicit
  feedback.) **Note:** This will use session keys to store scores for users who
  aren't authenticated.

* In your templates, use ``{% load django_recommend %}`` and
  ``{{ obj|similar_objects }}`` to show similar objects to visitors. This
  filter returns a list, so you may also do, for example:

  .. code:: html+django

      {% load django_recommend %}
      {% with similar_products as product|similar_objects %}
          {% if similiar_products %}
              <h2>Other users also liked:</h2>
              <ul>
              {% for product in similar_products %}
                  <li><a href="{{ product.get_absolute_url }}">{{ product }}</a></li>
              {% endfor %}
              </ul>
          {% endif %}
      {% endwith %}



Notes
-----

There can be some potentially confusing behavior from this library if your
application involves objects that get deleted/deactivated. Currently I am not
sure how feasible it is to filter by a flag like 'deactivated'; tests for that
kind of functionality will be put into a future version.

If your data is ever deleted, by default, ``django_recommend`` will attempt to
delete all recommendation information about it at the next available chance.
This most notably can occur in ``ObjectSimilarityQueryset.get_instances_for``,
where you may, for example, have a queryset like::

    suggested_books = similar_books.order_by(
        '-score')[:5].get_instances_for(curr_book)

If one of the books that *would* be suggested for this book has since been
deleted, the default behavior will be to in fact give you a list of *four*
instances. This is because retrieving the deleted instances raises an
``ObjectDoesNotExist`` error in the Django ORM; the default behavior is to
simply delete that object's information from the recommendation data and skip
the object for now.

This can be controlled by the ``RECOMMEND_PURGE_MISSING_DATA`` boolean setting.
If this is ``False``, the exception will be propagated, so you may handle it in
a different way.
memail 0.72
Personalized Email Project for Princeton COS518
License: MIT

# cos518
Course Project for COS518 - Advanced Computer Systems

Required libraries
------------------

|Library|
|:-----:|
|[numpy](http://www.numpy.org/)|
|[sklearn](http://scikit-learn.org/stable/)|
|[nltk](http://www.nltk.org/)|
|[gensim](https://radimrehurek.com/gensim/)|

The easiest way to install the above is by using [anaconda](https://www.continuum.io/why-anaconda), and then lastly
``pip install --upgrade gensim``