Metadata-Version: 2.1
Name: tinyarray
Version: 1.2.2
Summary: Arrays of numbers for Python, optimized for small sizes
Home-page: https://gitlab.kwant-project.org/kwant/tinyarray
Author: Christoph Groth (CEA) and others
Author-email: christoph.groth@cea.fr
License: Simplified BSD license
Download-URL: http://downloads.kwant-project.org/tinyarray/
Platform: Unix
Platform: Linux
Platform: Mac OS-X
Platform: Windows
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: C++
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows

Tinyarrays are similar to NumPy arrays, but optimized for small sizes.
Common operations on very small arrays are to 3-7 times faster than with
NumPy (with NumPy 1.6 it used to be up to 35 times), and 3 times less
memory is used to store them.  Tinyarrays are useful if you need many
small arrays of numbers, and cannot combine them into a few large ones.
(The resulting code is still much slower than C, but it may now be fast
enough.)

Unlike Python's built-in tuples, Tinyarrays support mathematical
operations like element-wise addition and matrix multiplication.  Unlike
Numpy arrays, Tinyarrays can be used as dictionary keys because they are
hashable and immutable.

The module's interface is a subset of that of NumPy and thus should be
familiar to many.  Whenever an operation is missing from Tinyarray,
NumPy functions can be used directly with Tinyarrays.

