semvec/pypi-packages huggingface dataset:
ibooks 0.3
iBooks Author cover and metadata extraction
License: BSD
Keywords: ibooks author metadata cover extraction epub epub3

============================================================
ibooks - Extract cover and metadata from iBooks Author files
============================================================

*"ibooks" is a small python module that can be used to extract cover images
and basic metadata from iBooks Author files.*

- **Author:** *Titusz <tp at py7 de>*
- **PyPi:** http://pypi.python.org/pypi/ibooks
- **Source Code**: http://github.com/titusz/ibooks
- **License**: BSD

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

Use easy_install or pip::

    pip install ibooks


Commandline Usage
-----------------
With the commandline script you can print metadata to the console and extract
the cover from .ibooks files::

    ibooks my-ibooks-author-file.ibooks


Library Usage
-------------
Here is how you can use 'ibooks' as a lib in your code::

    >>>from ibooks import iBooks
    >>>ibook = iBooks('my-ibooks-author.ibooks')

    # Get some metadata
    >>> ibook.title
    "Title of iBooks file"
    >>>ibook.author
    "Authors Name"

    # Extract cover
    >>>ibook.cover
    /temp-path-to/OPS/assets/images/cover-image.jpg
    # Be sure to process the image file before the "ibook" object is released



0.3
---

*Release date: 2013-07-29*

* added wheel package and other packaging fixes
* fix error when calling as script without argument

0.2
---

*Release date: 2013-07-26*

* custom exceptions
* added .is_preview()

0.1
---

*Release date: 2013-06-19*

* Initial release
ftz 0.1
ftz: flush denormal numbers in numpy arrays to zero
License: BSD

Denormal numbers ("denorms") are extremely nonzero small numbers (less than
1.2-38 for single precision or 2.2e-308 in double precision) which are
handled poorly on most modern archicectures. Instructions involving denormal 
operands may run as much as 100 times slower than those involving standard
floating point operands [1], giving rise to very odd performance bugs.

This package contains optimized SSE code to flush denorms in numpy arrays
to zero.

[1] http://en.wikipedia.org/wiki/Denormal_number