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"""Various miscellaneous code required by ConKit"""
__author__ = "Felix Simkovic"
__date__ = "18 May 2018"
__version__ = "2.0"
import numpy as np
import warnings
[docs]def deprecate(version, msg=None):
"""Decorator to deprecate Python classes and functions
Parameters
----------
version : str
A string containing the version with which the callable is removed
msg : str, optional
An additional message that will be displayed alongside the default message
Examples
--------
Enable :obj:`~DeprecationWarning` messages to be displayed.
>>> import warnings
>>> warnings.simplefilter('default')
Decorate a simple Python function without additional message
>>> @deprecate('0.0.0')
... def sum(a, b):
... return a + b
>>> sum(1, 2)
deprecated.py:34: DeprecationWarning: sum has been deprecated and will be removed in version 0.0.0!
warnings.warn(message, DeprecationWarning)
3
Decorate a simple Python function with additional message
>>> @deprecate('0.0.1', msg='Use XXX instead!')
... def sum(a, b):
... return a + b
>>> sum(2, 2)
deprecated.py:34: DeprecationWarning: sum has been deprecated and will be removed in version 0.0.0! - Use XXX instead!
warnings.warn(message, DeprecationWarning)
4
Decorate an entire Python class
>>> @deprecate('0.0.2')
... class Obj(object):
... pass
>>> Obj()
deprecated.py:34: DeprecationWarning: Obj has been deprecated and will be removed in version 0.0.2!
warnings.warn(message, DeprecationWarning)
<__main__.Obj object at 0x7f8ee0f1ead0>
Decorate a Python class method
>>> class Obj(object):
... def __init__(self, v):
... self.v = v
... @deprecate('0.0.3')
... def mul(self, other):
... return self.v * other.v
>>> Obj(2).mul(Obj(3))
deprecated.py:34: DeprecationWarning: mul has been deprecated and will be removed in version 0.0.3!
warnings.warn(message, DeprecationWarning)
6
Decorate a Python class staticmethod
>>> class Obj(object):
... @staticmethod
... @deprecate('0.0.4')
... def sub(a, b):
... return a - b
>>> Obj.sub(2, 1)
deprecated.py:34: DeprecationWarning: sub has been deprecated and will be removed in version 0.0.4!
warnings.warn(message, DeprecationWarning)
1
Decorate a Python class classmethod
>>> class Obj(object):
... CONST = 5
... @classmethod
... @deprecate('0.0.5')
... def sub(cls, a):
... return a - cls.CONST
>>> Obj().sub(5)
deprecated.py:34: DeprecationWarning: sub has been deprecated and will be removed in version 0.0.5!
warnings.warn(message, DeprecationWarning)
0
"""
def deprecate_decorator(callable_):
def warn(*args, **kwargs):
message = "%s has been deprecated and will be removed in version %s!" % (callable_.__name__, version)
if msg:
message += " - %s" % msg
warnings.warn(message, DeprecationWarning)
return callable_(*args, **kwargs)
return warn
return deprecate_decorator
[docs]def normalize(data, vmin=0, vmax=1):
"""Apply a Feature scaling algorithm to normalize the data
This normalization will bring all values into the range [0, 1]. This function
allows range restrictions by values ``vmin`` and ``vmax``.
.. math::
{X}'=\\frac{(X-X_{min})(vmax-vmin)}{X_{max}-X_{min}}
Parameters
----------
data : list, tuple
The data to normalize
vmin : int, float, optional
The minimum value
vmax : int, float, optional
The maximum value
Returns
-------
list
The normalized data
"""
data = np.array(data, dtype=np.float64)
if np.unique(data).size == 1:
data.fill(vmax)
else:
data = vmin + (data - data.min()) * (vmax - vmin) / (data.max() - data.min())
return data.tolist()
