NiBabel

Access a cacophony of neuro-imaging file formats

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deprecator

Reggie -- the one

deprecated

Module to help with deprecating objects and classes

FutureWarningMixin(*args, **kwargs) Insert FutureWarning for object creation
ModuleProxy(module_name) Proxy for module that may not yet have been imported
VisibleDeprecationWarning Deprecation warning that will be shown by default

FutureWarningMixin

class nibabel.deprecated.FutureWarningMixin(*args, **kwargs)

Bases: object

Insert FutureWarning for object creation

Examples

>>> class C(object): pass
>>> class D(FutureWarningMixin, C):
...     warn_message = "Please, don't use this class"

Record the warning

>>> with warnings.catch_warnings(record=True) as warns:
...     d = D()
...     warns[0].message
FutureWarning("Please, don't use this class",)
__init__(*args, **kwargs)
warn_message = 'This class will be removed in future versions'

ModuleProxy

class nibabel.deprecated.ModuleProxy(module_name)

Bases: object

Proxy for module that may not yet have been imported

Parameters:

module_name : str

Full module name e.g. nibabel.minc

Examples

::
arr = np.arange(24).reshape((2, 3, 4)) minc = ModuleProxy(‘nibabel.minc’) minc_image = minc.Minc1Image(arr, np.eye(4))

So, the minc object is a proxy that will import the required module when you do attribute access and return the attributes of the imported module.

__init__(module_name)

VisibleDeprecationWarning

class nibabel.deprecated.VisibleDeprecationWarning

Bases: exceptions.UserWarning

Deprecation warning that will be shown by default

Python >= 2.7 does not show standard DeprecationWarnings by default:

http://docs.python.org/dev/whatsnew/2.7.html#the-future-for-python-2-x

Use this class for cases where we do want to show deprecations by default.

__init__()

x.__init__(...) initializes x; see help(type(x)) for signature