semvec/pypi-packages huggingface dataset:
Flask-Generic-Views 0.1.1
A set of generic class-based views for flask
License: BSD

Flask-Generic-Views
===================

Flask-Generic-Views is a set of generic class-based views for the Flask
microframework inspired by the ones in Django.

.. code:: python

    from flask import Flask
    from flask.ext.generic_views import TemplateView

    app = Flask(__name__)

    index_view = TemplateView.as_view('index', template_file='index.html')

    app.add_url_rule('/', index_view)

    if __name__ == '__main__':
        app.run()

Database Support
----------------

Currently Flask-Generic-Views supports use of Models created with SQLAlchemy.

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

To install the basic views:

.. code:: bash

    $ pip install flask-generic-views

To install optional SQLAlchemy support:

.. code:: bash

    $ pip install flask-generic-views[sqlalchemy]

To install all optional packages:

.. code:: bash

    $ pip install flask-generic-views[all]

Links
-----

* `Documentation <https://flask-generic-views.readthedocs.org/>`_
* `GitHub <https://github.com/artisanofcode/flask-generic-views>`_
motmot.FastImage 0.7.0
Pythonic API for a subset of the Intel Integrated Performance Primitives (Intel IPP) library
License: BSD

# motmot.FastImage -- SIMD image processing

Description
===========

FastImage implements low-level image processing operations designed to
operate very quickly by using SIMD instructions. This is achieved by
calling the Intel IPP library. A bridge to numpy is made through the
`array interface`__.

__ https://docs.scipy.org/doc/numpy/reference/arrays.interface.html

Allocation of aligned memory
============================

For the SIMD instructions to perform at maximal speed, images must be
aligned on 32-byte boundaries. FastImage relies on the underlying
IPP library to allocate the memory, trusting that it
knows best::

  import motmot.FastImage.FastImage as FastImage
  import numpy as np

  # Allocate the image
  fi_im1 = FastImage.FastImage8u( FastImage.Size(4,5) ) # width, height

  # Get a numpy view
  im1 = np.asarray( fi_im1 )
  assert im1.shape == (5,4) # height, width

In the above example, im1.strides will be (32,1), indicating that each
row is aligned on a 32 byte boundary.
cython_ipp 0.2.0
Cython definitions for the Intel Integrated Performance Primitives (Intel IPP) library
License: BSD

cython_ipp - Cython definitions for Intel IPP library
=====================================================

This package contains `Cython <http://cython.org/>`_ includes (``*.pxd``) files
required to use the `Intel Integrated Performance Primitives
(Intel IPP) <https://software.intel.com/en-us/intel-ipp>`_ library from Cython.