projektAI/venv/Lib/site-packages/np-1.0.2-py3.8.egg-info/PKG-INFO

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Metadata-Version: 1.1
Name: np
Version: 1.0.2
Summary: np = numpy++: numpy with added convenience functionality
Home-page: https://github.com/k7hoven/np
Author: Koos Zevenhoven
Author-email: koos.zevenhoven@aalto.fi
License: BSD
Description: np -- create numpy arrays as ``np[1,3,5]``, and more
====================================================
``np`` = ``numpy`` + handy tools
It's easy: start by importing ``np`` (the alias for numpy):
.. code-block:: python
import np
Create a 1-D array:
.. code-block:: python
np[1, 3, 5]
Create a 2-D matrix:
.. code-block:: python
np.m[1, 2, 3:
:4, 5, 6:
:7, 8, 9]
For the numerical Python package ``numpy`` itself, see http://www.numpy.org/.
The idea of ``np`` is to provide a way of creating numpy arrays with a compact syntax and without an explicit function call. Making the module name ``np`` subscriptable, while still keeping it essentially an alias for numpy, does this in a clean way.
Any feedback is very welcome: ``koos.zevenhoven@aalto.fi``.
Getting Started
===============
Requirements
------------
* Works best with Python 3.5+ (Tested also with 3.4 and 2.7)
* numpy (you should install this using your python package manager like ``conda`` or ``pip``)
Installation
------------
``np`` can be installed with ``pip`` or ``pip3``:
.. code-block:: bash
$ pip install np
or directly from the source code:
.. code-block:: bash
$ git clone https://github.com/k7hoven/np.git
$ cd np
$ python setup.py install
Basic Usage
===========
Even before the ``np`` tool, a popular style of using ``numpy`` has been to import it as ``np``:
.. code-block:: python
>>> import numpy as np
>>> my_array = np.array([3, 4, 5])
>>> my_2d_array = np.array([[1, 2], [3, 4]])
The most important feature of ``np`` is to make the creation of arrays less verbose, while everything else works as before. The above code becomes:
.. code-block:: python
>>> import np
>>> my_array = np[3, 4, 5]
>>> my_2d_array = np[[1, 2], [3, 4]]
>>> my_matrix = np.m[1, 2: 3, 4]
>>> my_matrix2 = np.m[1, 2, 3:
... :4, 5, 6:
... :7, 8, 9]
>>> my_row_vector = np.m[1, 2, 3]
As you can see from the above example, you can create numpy arrays by subscripting the ``np`` module. Since most people would have numpy imported as ``np`` anyway, this requires no additional names to clutter the namespace. Also, the syntax ``np[1,2,3]`` resembles the syntax for ``bytes`` literals, ``b"asd"``.
The above also shows how you can use ``np.m`` and colons to easily create matrices (NxM) or row vectors (1xM).
The `np` package also provides a convenient way of ensuring something is a numpy array, that is, a shortcut to ``numpy.asarray()``:
.. code-block:: python
>>> import np
>>> mylist = [1, 3, 5]
>>> mylist + [7, 9, 11]
[1, 3, 5, 7, 9, 11]
>>> np(mylist) + [7, 9, 11]
array([8, 12, 16])
As an experimental feature, there are also shortcuts for giving the arrays a specific data type (numpy dtype):
.. code-block:: python
>>> np[1, 2, 3]
array([1, 2, 3])
>>> np.f[1, 2, 3]
array([ 1., 2., 3.])
>>> np.f2[1, 2, 3]
array([ 1., 2., 3.], dtype=float16)
>>> np.u4[1, 2, 3]
array([1, 2, 3], dtype=uint32)
>>> np.c[1, 2, 3]
array([ 1.+0.j, 2.+0.j, 3.+0.j])
Changelog
=========
1.0.0 (2017-09-20)
------------------
- Creating matrices is now even simpler::
np.m[1, 2: 3, 4] == np.array([[1, 2], [3, 4]])
np.m[1, 2:
:3, 4] == np.array([[1, 2], [3, 4]])
np.m[1, 2] == np.array([[1, 2]])
np.m[1, 2].T == np.array([[1],
[2]])
- ``np(...)`` corresponds to ``np.asarray(...)``
- Many improvements to error handling
- Some more cleanups to type shortcuts
0.2.0 (2016-03-29)
------------------
- Quick types are now ``np.i``, ``np.f``, ``np.u``, ``np.c``, or with the
number of *bytes* per value appended:
``np.i4`` -> int32, ``np.u2`` -> uint16, ``np.c16`` -> complex128, ...
(still somewhat experimental)
- Removed the old np.i8 and np.ui8 which represented 8-bit types, which
was inconsistent with short numpy dtype names which correspond to numbers of
bytes. The rest of the bit-based shortcuts are deprecated and will be removed
later.
- Handle Python versions >=3.5 better; now even previously imported plain numpy
module objects become the exact same object as np.
- Tests for all np functionality
- Ridiculously slow tests that runs the numpy test suite several times to
make sure that np does not affect numpy functionality.
- Remove numpy from requirements and give a meaningful error instead if numpy
is missing (i.e. install it using your package manager like conda or pip)
- Better reprs for subscriptable array creator objects and the np/numpy module.
0.1.4 (2016-01-26)
------------------
- Bug fix
0.1.2 (2015-06-17)
------------------
- Improved experimental dtype shortcuts: np.f[1,2], np.i32[1,2], etc.
0.1.1 (2015-06-17)
------------------
- PyPI-friendly readme
0.1.0 (2015-06-17)
------------------
- First distributable version
- Easy arrays such as np[[1,2],[3,4]]
- Shortcut for np.asanyarray(obj): np(obj)
- Experimental dtype shortcuts: np.f64[[1,2],[3,4]]
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3