npstructures 0.2.19
Simple data structures that augments the numpy library
License: MIT license
Requires Python: >=3.6
Keywords: npstructures

================
Numpy Structures
================


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Simple data structures that augments the numpy library


* Free software: MIT license
* Documentation: https://npstructures.readthedocs.io.


Features
--------
The main feature is the `RaggedArray` class which enables `numpy`-like behaviour and performance for arrays where
the length of the rows differ.

`RaggedArray` is meant as a drop-in replacement for `numpy` when you have arrays with differing row lengths.
As such, familiarity with `numpy` is assumed. The simplest way to construct a `RaggedArray` is from a list of lists::

    >>> from npstructures import RaggedArray
    >>> ra = RaggedArray([[1, 2], [4, 1, 3, 7], [9], [8, 7, 3, 4]])

A `RaggedArray` can be indexed much like a `numpy` array::

    >>> ra[1]
    array([4, 1, 3, 7])
    >>> ra[1, 3]
    7
    >>> ra[1:3]
    RaggedArray([[4, 1, 3, 7], [9]])
    >>> ra[[0, 3]]
    RaggedArray([[1, 2], [8, 7, 3, 4]])
    >>> ra[0] = [0, 0]
    >>> ra
    RaggedArray([[0, 0], [4, 1, 3, 7], [9], [8, 7, 3, 4]])
    >>> ra[1:3] = [[10], [20]]
    >>> ra
    RaggedArray([[0, 0], [10, 10, 10, 10], [20], [8, 7, 3, 4]])
    >>> ra[[0, 2, 3]] = RaggedArray([[2, 2], [3], [5, 5, 5, 5]])
    >>> ra
    RaggedArray([[2, 2], [10, 10, 10, 10], [3], [5, 5, 5, 5]])

`numpy ufuncs` can be applied to `RaggedArray` objects::

    >>> ra + 1
    RaggedArray([[2, 3], [5, 2, 4, 8], [10], [9, 8, 4, 5]])
    >>> ra*2
    RaggedArray([[2, 4], [8, 2, 6, 14], [18], [16, 14, 6, 8]])
    >>> ra + [[1], [10], [100], [1000]]
    RaggedArray([[2, 3], [14, 11, 13, 17], [109], [1008, 1007, 1003, 1004]])
    >>> ra - (ra*2)
    RaggedArray([[-1, -2], [-4, -1, -3, -7], [-9], [-8, -7, -3, -4]])

Some `numpy` functions can be applied to `RaggedArray` objects::

    >>> import numpy as np
    >>> ra = RaggedArray([[1, 2], [4, 1, 3, 7], [9], [8, 7, 3, 4]])
    >>> np.concatenate((ra, ra*10))
    RaggedArray([[1, 2], [4, 1, 3, 7], [9], [8, 7, 3, 4], [10, 20], [40, 10, 30, 70], [90], [80, 70, 30, 40]])
    >>> np.nonzero(ra>3)
    (array([1, 1, 2, 3, 3, 3]), array([0, 3, 0, 0, 1, 3]))
    >>> np.ones_like(ra)
    RaggedArray([[1, 1], [1, 1, 1, 1], [1], [1, 1, 1, 1]])


In addition to this. `HashTable` and `Counter` provides simple `dict`-like behaviour for `numpy` arrays:

`HashTable` can be used for `dict`-like functionality of `numpy` arrays. The simplest way to construct a `HashTable` is from an array of keys and an array of values (note that the set of keys cannot be modified after the initialization of the object)::

    >>> table = HashTable([11, 113, 1191, 11199], [2, 3, 5, 7])
    >>> table[11]
    array([2])
    >>> table[[113, 11199]]
    array([3, 7])
    >>> table[11]=1000
    >>> table
    HashTable([  113  1191    11 11199], [   3    5 1000    7])
    >>> table[[113, 1191]]=2000
    >>> table
    HashTable([  113  1191    11 11199], [2000 2000 1000    7])
    >>> table[[113, 1191, 11, 11191]] = [1, 2, 3, 4]
    >>> table[[113, 1191, 11, 11199]] = [1, 2, 3, 4]
    >>> table
    HashTable([  113  1191    11 11199], [1 2 3 4])

`Counter` objects supports counting the occurances of a predefined set of keys in a set of samples. For instance, to count the occurances of `3` and `1` in the list ``[3, 2, 1, 3, 4, 1, 1]``::

    >>> from npstructures import Counter
    >>> counter = Counter([3, 1])
    >>> counter.count([3, 2, 1, 3, 4, 1, 1])
    >>> counter
    Counter([3 1], [2 3])

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.2.0 (2022-06-17)
------------------
* Tested indexing, ufuncs and arrayfunctions with hypothesis


0.1.0 (2021-12-27)
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* First release on PyPI.