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import warnings
import functools
import sys
import numpy as np
import types
import numbers
from ..util import img_as_float
from ._warnings import all_warnings, warn
__all__ = ['deprecated', 'get_bound_method_class', 'all_warnings',
'safe_as_int', 'assert_nD', 'warn']
class skimage_deprecation(Warning):
"""Create our own deprecation class, since Python >= 2.7
silences deprecations by default.
class deprecated(object):
"""Decorator to mark deprecated functions with warning.
Adapted from <>.
alt_func : str
If given, tell user what function to use instead.
behavior : {'warn', 'raise'}
Behavior during call to deprecated function: 'warn' = warn user that
function is deprecated; 'raise' = raise error.
removed_version : str
The package version in which the deprecated function will be removed.
def __init__(self, alt_func=None, behavior='warn', removed_version=None):
self.alt_func = alt_func
self.behavior = behavior
self.removed_version = removed_version
def __call__(self, func):
alt_msg = ''
if self.alt_func is not None:
alt_msg = ' Use ``%s`` instead.' % self.alt_func
rmv_msg = ''
if self.removed_version is not None:
rmv_msg = (' and will be removed in version %s' %
msg = ('Function ``%s`` is deprecated' % func.__name__ +
rmv_msg + '.' + alt_msg)
def wrapped(*args, **kwargs):
if self.behavior == 'warn':
func_code = func.__code__
warnings.simplefilter('always', skimage_deprecation)
lineno=func_code.co_firstlineno + 1)
elif self.behavior == 'raise':
raise skimage_deprecation(msg)
return func(*args, **kwargs)
# modify doc string to display deprecation warning
doc = '**Deprecated function**.' + alt_msg
if wrapped.__doc__ is None:
wrapped.__doc__ = doc
wrapped.__doc__ = doc + '\n\n ' + wrapped.__doc__
return wrapped
def get_bound_method_class(m):
"""Return the class for a bound method.
return m.im_class if sys.version < '3' else m.__self__.__class__
def safe_as_int(val, atol=1e-3):
Attempt to safely cast values to integer format.
val : scalar or iterable of scalars
Number or container of numbers which are intended to be interpreted as
integers, e.g., for indexing purposes, but which may not carry integer
atol : float
Absolute tolerance away from nearest integer to consider values in
``val`` functionally integers.
val_int : NumPy scalar or ndarray of dtype `np.int64`
Returns the input value(s) coerced to dtype `np.int64` assuming all
were within ``atol`` of the nearest integer.
This operation calculates ``val`` modulo 1, which returns the mantissa of
all values. Then all mantissas greater than 0.5 are subtracted from one.
Finally, the absolute tolerance from zero is calculated. If it is less
than ``atol`` for all value(s) in ``val``, they are rounded and returned
in an integer array. Or, if ``val`` was a scalar, a NumPy scalar type is
If any value(s) are outside the specified tolerance, an informative error
is raised.
>>> safe_as_int(7.0)
>>> safe_as_int([9, 4, 2.9999999999])
array([9, 4, 3])
>>> safe_as_int(53.1)
Traceback (most recent call last):
ValueError: Integer argument required but received 53.1, check inputs.
>>> safe_as_int(53.01, atol=0.01)
mod = np.asarray(val) % 1 # Extract mantissa
# Check for and subtract any mod values > 0.5 from 1
if mod.ndim == 0: # Scalar input, cannot be indexed
if mod > 0.5:
mod = 1 - mod
else: # Iterable input, now ndarray
mod[mod > 0.5] = 1 - mod[mod > 0.5] # Test on each side of nearest int
np.testing.assert_allclose(mod, 0, atol=atol)
except AssertionError:
raise ValueError("Integer argument required but received "
"{0}, check inputs.".format(val))
return np.round(val).astype(np.int64)
def assert_nD(array, ndim, arg_name='image'):
Verify an array meets the desired ndims and array isn't empty.
array : array-like
Input array to be validated
ndim : int or iterable of ints
Allowable ndim or ndims for the array.
arg_name : str, optional
The name of the array in the original function.
array = np.asanyarray(array)
msg_incorrect_dim = "The parameter `%s` must be a %s-dimensional array"
msg_empty_array = "The parameter `%s` cannot be an empty array"
if isinstance(ndim, int):
ndim = [ndim]
if array.size == 0:
raise ValueError(msg_empty_array % (arg_name))
if not array.ndim in ndim:
raise ValueError(msg_incorrect_dim % (arg_name, '-or-'.join([str(n) for n in ndim])))
def copy_func(f, name=None):
"""Create a copy of a function.
f : function
Function to copy.
name : str, optional
Name of new function.
return types.FunctionType(f.__code__, f.__globals__, name or f.__name__,
f.__defaults__, f.__closure__)
def check_random_state(seed):
"""Turn seed into a `np.random.RandomState` instance.
seed : None, int or np.random.RandomState
If `seed` is None, return the RandomState singleton used by `np.random`.
If `seed` is an int, return a new RandomState instance seeded with `seed`.
If `seed` is already a RandomState instance, return it.
If `seed` is of the wrong type.
# Function originally from scikit-learn's module sklearn.utils.validation
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (numbers.Integral, np.integer)):
return np.random.RandomState(seed)
if isinstance(seed, np.random.RandomState):
return seed
raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
' instance' % seed)
def convert_to_float(image, preserve_range):
"""Convert input image to double image with the appropriate range.
image : ndarray
Input image.
preserve_range : bool
Determines if the range of the image should be kept or transformed
using img_as_float. Also see
image : ndarray
Transformed version of the input.
if preserve_range:
image = image.astype(np.double)
image = img_as_float(image)
return image