Overview

Upon getting started with NumPy, the rules to define numpy.dtype objects tend to quickly become confusing. Not only different syntaxes can create a same data type, but it also seems arbitrary and hence difficult to remember that sub-array data types can only be defined as tuples while structured data types exclusively require lists made of field tuples, and so on.

To address this point, Nani takes the stance of offering one—and only one—way of constructing numpy.dtype objects. Although this syntax makes the code more verbose, it also makes it easier to read and to reason about.

Nani’s approach allows type introspection which brings additional benefits in the form of dynamically generated default values and view types. Default values facilitate the definition of new array elements while view types are useful to encapsulate interactions with NumPy and to expose a different public interface to your library users instead of the one provided with numpy.ndarray.

Features

• explicit syntax for defining numpy.dtype objects.
• generates default values and view types.
• allows for type introspection.

Usage

>>> import numpy
>>> import nani
>>> color_type = nani.Array(
...     element_type=nani.Number(type=numpy.uint8, default=255),
...     shape=3,
...     view=None)
>>> dtype, default, view = nani.resolve(color_type, name='Color')
>>> a = numpy.array([default] * 2, dtype=dtype)
>>> v = view(a)
>>> for color in v:
...     print(color)
[255, 255, 255]
[255, 255, 255]

The color_type above defines an array of 3 numpy.uint8 elements having each a default value of 255. The resulting dtype and default objects are used to initialize a new NumPy array of 10 color elements, while the view type is used to wrap that array into a standard collection interface.

See also

The Tutorial section for more detailed examples and explanations on how to use Nani.