Functions

validate Check if a data type is well-formed.
resolve Retrieve the properties for a given data type.
update Update a data type with different values.
get_data Retrieve the NumPy data from an array view generated by Nani.
get_element_view Retrieve the element view from an array view generated by Nani.
nani.validate(data_type)[source]

Check if a data type is well-formed.

Parameters:data_type (nani data type) – Data type.
Returns:True if the data type is well-formed.
Return type:bool
Raises:TypeError or ValueError – The data type isn’t well-formed.
nani.resolve(data_type, name=None, listify_default=False)[source]

Retrieve the properties for a given data type.

This is the main routine where most of the work is done. It converts Nani’s data types into properties that can be used to define a new NumPy array and to wrap it into a view object.

Use validate() to check if the input data type is well-formed.

Parameters:
  • data_type (nani data type) – Type of the array elements.
  • name (str) – Name for the view to be generated for the array.
  • listify_default (bool) – True to output the default values with lists in place of tuples. This might cause the output to be incompatible with array creation routines such as numpy.array but it should still work for element assignment.
Returns:

The properties to use to initalize a NumPy array around the data type.
Return type:

nani.Nani

Examples

Create a NumPy array where each element represents a color:

>>> 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] * element_count, dtype=dtype)
>>> v = view(a)
>>> type(v)
<class 'nani.Color'>
>>> for color in v:
...     color
[255, 255, 255]
[255, 255, 255]
nani.update(data_type, **kwargs)[source]

Update a data type with different values.

The operation is not made in place, instead a copy is returned.

Parameters:
  • data_type (nani data type) – Data type.
  • kwargs – Keyword arguments to update.
Returns:

The updated version of the data type.
Return type:

nani data type

Examples

Update the shape of an array data type and the default value of its elements:

>>> import nani
>>> data_type = nani.Array(
...     element_type=nani.Number(),
...     shape=2)
>>> new_data_type = nani.update(
...     data_type,
...     element_type=nani.update(data_type.element_type, default=123),
...     shape=3)
nani.get_data(view)[source]

Retrieve the NumPy data from an array view generated by Nani.

Parameters:view (nani.AtomicArrayView or nani.CompositeArrayView) – Array view.
Returns:The NumPy array, None otherwise.
Return type:type

Examples

>>> import numpy
>>> import nani
>>> data_type = nani.Number(type=numpy.int32)
>>> dtype, _, view = nani.resolve(data_type)
>>> a = numpy.a_range(10, dtype=dtype)
>>> v = view(a)
>>> nani.get_data(v) is a
True
nani.get_element_view(view)[source]

Retrieve the element view from an array view generated by Nani.

Parameters:view (nani.AtomicArrayView or nani.CompositeArrayView) – Array view.
Returns:The element view, None otherwise.
Return type:type

Examples

>>> import numpy
>>> import nani
>>> vector2_type = nani.Array(
...     element_type=nani.Number(),
...     shape=2,
...     name='Vector2')
>>> dtype, default, view = nani.resolve(vector2_type, name='Positions')
>>> a = numpy.zeros(3, dtype=dtype)
>>> v = view(a)
>>> type(v)
<class 'nani.Positions'>
>>> nani.get_element_view(v)
<class 'nani.Vector2'>