#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Common Utilities
================
Defines common utilities objects that don't fall in any specific category.
"""
from __future__ import division, unicode_literals
import functools
import numpy as np
import warnings
from colour.constants import INTEGER_THRESHOLD
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['handle_numpy_errors',
'ignore_numpy_errors',
'raise_numpy_errors',
'print_numpy_errors',
'warn_numpy_errors',
'ignore_python_warnings',
'batch',
'is_openimageio_installed',
'is_scipy_installed',
'is_iterable',
'is_string',
'is_numeric',
'is_integer']
[docs]def handle_numpy_errors(**kwargs):
"""
Decorator for handling *Numpy* errors.
Parameters
----------
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
object
References
----------
.. [1] Kienzle, P., Patel, N., & Krycka, J. (2011). refl1d.numpyerrors -
Refl1D v0.6.19 documentation. Retrieved January 30, 2015, from
http://www.reflectometry.org/danse/docs/refl1d/_modules/refl1d/numpyerrors.html # noqa
Examples
--------
>>> import numpy
>>> @handle_numpy_errors(all='ignore')
... def f():
... 1 / numpy.zeros(3)
>>> f()
"""
context = np.errstate(**kwargs)
def wrapper(object):
@functools.wraps(object)
def wrapped(*args, **kwargs):
with context:
return object(*args, **kwargs)
return wrapped
return wrapper
ignore_numpy_errors = handle_numpy_errors(all='ignore')
raise_numpy_errors = handle_numpy_errors(all='raise')
print_numpy_errors = handle_numpy_errors(all='print')
warn_numpy_errors = handle_numpy_errors(all='warn')
[docs]def ignore_python_warnings(object):
"""
Decorator for ignoring *Python* warnings.
Parameters
----------
object : object
Object to decorate.
Returns
-------
object
Examples
--------
>>> @ignore_python_warnings
... def f():
... warnings.warn('This is an ignored warning!')
>>> f()
"""
@functools.wraps(object)
def wrapped(*args, **kwargs):
with warnings.catch_warnings():
warnings.simplefilter('ignore')
return object(*args, **kwargs)
return wrapped
[docs]def batch(iterable, k=3):
"""
Returns a batch generator from given iterable.
Parameters
----------
iterable : iterable
Iterable to create batches from.
k : integer
Batches size.
Returns
-------
bool
Is *string_like* variable.
Examples
--------
>>> batch(tuple(range(10))) # doctest: +ELLIPSIS
<generator object batch at 0x...>
"""
for i in range(0, len(iterable), k):
yield iterable[i:i + k]
[docs]def is_openimageio_installed(raise_exception=False):
"""
Returns if *OpenImageIO* is installed and available.
Parameters
----------
raise_exception : bool
Raise exception if *OpenImageIO* is unavailable.
Returns
-------
bool
Is *OpenImageIO* installed.
Raises
------
ImportError
If *OpenImageIO* is not installed.
"""
try:
import OpenImageIO
return True
except ImportError as error:
if raise_exception:
raise ImportError(('"OpenImageIO" related Api features '
'are not available: "{0}".').format(error))
return False
[docs]def is_scipy_installed(raise_exception=False):
"""
Returns if *scipy* is installed and available.
Parameters
----------
raise_exception : bool
Raise exception if *scipy* is unavailable.
Returns
-------
bool
Is *scipy* installed.
Raises
------
ImportError
If *scipy* is not installed.
"""
try:
# Importing *scipy* Api features used in *Colour*.
import scipy.interpolate
import scipy.ndimage
import scipy.spatial
return True
except ImportError as error:
if raise_exception:
raise ImportError(('"scipy" or specific "scipy" Api features '
'are not available: "{0}".').format(error))
return False
[docs]def is_iterable(x):
"""
Returns if given :math:`x` variable is iterable.
Parameters
----------
x : object
Variable to check the iterability.
Returns
-------
bool
:math:`x` variable iterability.
Examples
--------
>>> is_iterable([1, 2, 3])
True
>>> is_iterable(1)
False
"""
try:
for _ in x:
break
return True
except TypeError:
return False
[docs]def is_string(data):
"""
Returns if given data is a *string_like* variable.
Parameters
----------
data : object
Data to test.
Returns
-------
bool
Is *string_like* variable.
Examples
--------
>>> is_string('I`m a string!')
True
>>> is_string(['I`m a string!'])
False
"""
return True if isinstance(data, basestring) else False
[docs]def is_numeric(x):
"""
Returns if given :math:`x` variable is a number.
Parameters
----------
x : object
Variable to check.
Returns
-------
bool
Is :math:`x` variable a number.
See Also
--------
is_integer
Examples
--------
>>> is_numeric(1)
True
>>> is_numeric((1,))
False
"""
return isinstance(x, (int, float, complex,
np.integer, np.floating, np.complex))
[docs]def is_integer(x):
"""
Returns if given :math:`x` variable is an integer under given threshold.
Parameters
----------
x : object
Variable to check.
Returns
-------
bool
Is :math:`x` variable an integer.
Notes
-----
- The determination threshold is defined by the
:attr:`colour.algebra.common.INTEGER_THRESHOLD` attribute.
See Also
--------
is_numeric
Examples
--------
>>> is_integer(1)
True
>>> is_integer(1.01)
False
"""
return abs(x - round(x)) <= INTEGER_THRESHOLD
Colour 0.3