np = numpy++: numpy with added convenience functionality

Project Description
## np – create numpy arrays as `np[1,3,5]`, and more

## Getting Started

### Requirements

### Installation

## Basic Usage

## Changelog

### 0.2.0 (2016-03-29)

### 0.1.4 (2016-01-26)

### 0.1.2 (2015-06-17)

### 0.1.1 (2015-06-17)

### 0.1.0 (2015-06-17)

Release History
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`np` = `numpy` + handy tools

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.

- 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)

np can be installed with pip:

$ pip install np

or directly from the source code:

```
$ git clone https://github.com/k7hoven/np.git
$ cd np
$ python setup.py install
```

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]]

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 np package also provides a convenient way of ensuring something is a numpy array, that is, a shortcut to `numpy.asanyarray()`:

>>> 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])

- 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.

- Bug fix

- Improved experimental dtype shortcuts: np.f[1,2], np.i32[1,2], etc.

- PyPI-friendly readme

- 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]]

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File Name & Checksum SHA256 Checksum Help | Version | File Type | Upload Date |
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np-0.2.0.tar.gz (5.2 kB) Copy SHA256 Checksum SHA256 | – | Source | Mar 28, 2016 |