np 1.0.2

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):

import np

Create a 1-D array:

np[1, 3, 5]

Create a 2-D matrix:

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

$ pip install np

$ 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:

>>> 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:

>>> 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():

>>> 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):

>>> 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])

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