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ton
2023-04-06 13:53:47 +07:00
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.CondaPkg/env/Lib/site-packages/skimage/_shared/utils.py vendored
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import inspect
import functools
import sys
import warnings
from collections.abc import Iterable
import numpy as np
from ._warnings import all_warnings, warn
__all__ = ['deprecated', 'get_bound_method_class', 'all_warnings',
'safe_as_int', 'check_shape_equality', 'check_nD', 'warn',
'reshape_nd', 'identity', 'slice_at_axis']
class skimage_deprecation(Warning):
"""Create our own deprecation class, since Python >= 2.7
silences deprecations by default.
"""
pass
def _get_stack_rank(func):
"""Return function rank in the call stack."""
if _is_wrapped(func):
return 1 + _get_stack_rank(func.__wrapped__)
else:
return 0
def _is_wrapped(func):
return "__wrapped__" in dir(func)
def _get_stack_length(func):
"""Return function call stack length."""
return _get_stack_rank(func.__globals__.get(func.__name__, func))
class _DecoratorBaseClass:
"""Used to manage decorators' warnings stacklevel.
The `_stack_length` class variable is used to store the number of
times a function is wrapped by a decorator.
Let `stack_length` be the total number of times a decorated
function is wrapped, and `stack_rank` be the rank of the decorator
in the decorators stack. The stacklevel of a warning is then
`stacklevel = 1 + stack_length - stack_rank`.
"""
_stack_length = {}
def get_stack_length(self, func):
return self._stack_length.get(func.__name__,
_get_stack_length(func))
class change_default_value(_DecoratorBaseClass):
"""Decorator for changing the default value of an argument.
Parameters
----------
arg_name: str
The name of the argument to be updated.
new_value: any
The argument new value.
changed_version : str
The package version in which the change will be introduced.
warning_msg: str
Optional warning message. If None, a generic warning message
is used.
"""
def __init__(self, arg_name, *, new_value, changed_version,
warning_msg=None):
self.arg_name = arg_name
self.new_value = new_value
self.warning_msg = warning_msg
self.changed_version = changed_version
def __call__(self, func):
parameters = inspect.signature(func).parameters
arg_idx = list(parameters.keys()).index(self.arg_name)
old_value = parameters[self.arg_name].default
stack_rank = _get_stack_rank(func)
if self.warning_msg is None:
self.warning_msg = (
f'The new recommended value for {self.arg_name} is '
f'{self.new_value}. Until version {self.changed_version}, '
f'the default {self.arg_name} value is {old_value}. '
f'From version {self.changed_version}, the {self.arg_name} '
f'default value will be {self.new_value}. To avoid '
f'this warning, please explicitly set {self.arg_name} value.')
@functools.wraps(func)
def fixed_func(*args, **kwargs):
stacklevel = 1 + self.get_stack_length(func) - stack_rank
if len(args) < arg_idx + 1 and self.arg_name not in kwargs.keys():
# warn that arg_name default value changed:
warnings.warn(self.warning_msg, FutureWarning,
stacklevel=stacklevel)
return func(*args, **kwargs)
return fixed_func
class remove_arg(_DecoratorBaseClass):
"""Decorator to remove an argument from function's signature.
Parameters
----------
arg_name: str
The name of the argument to be removed.
changed_version : str
The package version in which the warning will be replaced by
an error.
help_msg: str
Optional message appended to the generic warning message.
"""
def __init__(self, arg_name, *, changed_version, help_msg=None):
self.arg_name = arg_name
self.help_msg = help_msg
self.changed_version = changed_version
def __call__(self, func):
parameters = inspect.signature(func).parameters
arg_idx = list(parameters.keys()).index(self.arg_name)
warning_msg = (
f'{self.arg_name} argument is deprecated and will be removed '
f'in version {self.changed_version}. To avoid this warning, '
f'please do not use the {self.arg_name} argument. Please '
f'see {func.__name__} documentation for more details.')
if self.help_msg is not None:
warning_msg += f' {self.help_msg}'
stack_rank = _get_stack_rank(func)
@functools.wraps(func)
def fixed_func(*args, **kwargs):
stacklevel = 1 + self.get_stack_length(func) - stack_rank
if len(args) > arg_idx or self.arg_name in kwargs.keys():
# warn that arg_name is deprecated
warnings.warn(warning_msg, FutureWarning,
stacklevel=stacklevel)
return func(*args, **kwargs)
return fixed_func
def docstring_add_deprecated(func, kwarg_mapping, deprecated_version):
"""Add deprecated kwarg(s) to the "Other Params" section of a docstring.
Parameters
---------
func : function
The function whose docstring we wish to update.
kwarg_mapping : dict
A dict containing {old_arg: new_arg} key/value pairs as used by
`deprecate_kwarg`.
deprecated_version : str
A major.minor version string specifying when old_arg was
deprecated.
Returns
-------
new_doc : str
The updated docstring. Returns the original docstring if numpydoc is
not available.
"""
if func.__doc__ is None:
return None
try:
from numpydoc.docscrape import FunctionDoc, Parameter
except ImportError:
# Return an unmodified docstring if numpydoc is not available.
return func.__doc__
Doc = FunctionDoc(func)
for old_arg, new_arg in kwarg_mapping.items():
desc = [f'Deprecated in favor of `{new_arg}`.',
'',
f'.. deprecated:: {deprecated_version}']
Doc['Other Parameters'].append(
Parameter(name=old_arg,
type='DEPRECATED',
desc=desc)
)
new_docstring = str(Doc)
# new_docstring will have a header starting with:
#
# .. function:: func.__name__
#
# and some additional blank lines. We strip these off below.
split = new_docstring.split('\n')
no_header = split[1:]
while not no_header[0].strip():
no_header.pop(0)
# Store the initial description before any of the Parameters fields.
# Usually this is a single line, but the while loop covers any case
# where it is not.
descr = no_header.pop(0)
while no_header[0].strip():
descr += '\n ' + no_header.pop(0)
descr += '\n\n'
# '\n ' rather than '\n' here to restore the original indentation.
final_docstring = descr + '\n '.join(no_header)
# strip any extra spaces from ends of lines
final_docstring = '\n'.join(
[line.rstrip() for line in final_docstring.split('\n')]
)
return final_docstring
class deprecate_kwarg(_DecoratorBaseClass):
"""Decorator ensuring backward compatibility when argument names are
modified in a function definition.
Parameters
----------
kwarg_mapping: dict
Mapping between the function's old argument names and the new
ones.
deprecated_version : str
The package version in which the argument was first deprecated.
warning_msg: str
Optional warning message. If None, a generic warning message
is used.
removed_version : str
The package version in which the deprecated argument will be
removed.
"""
def __init__(self, kwarg_mapping, deprecated_version, warning_msg=None,
removed_version=None):
self.kwarg_mapping = kwarg_mapping
if warning_msg is None:
self.warning_msg = ("`{old_arg}` is a deprecated argument name "
"for `{func_name}`. ")
if removed_version is not None:
self.warning_msg += (f'It will be removed in '
f'version {removed_version}. ')
self.warning_msg += "Please use `{new_arg}` instead."
else:
self.warning_msg = warning_msg
self.deprecated_version = deprecated_version
def __call__(self, func):
stack_rank = _get_stack_rank(func)
@functools.wraps(func)
def fixed_func(*args, **kwargs):
stacklevel = 1 + self.get_stack_length(func) - stack_rank
for old_arg, new_arg in self.kwarg_mapping.items():
if old_arg in kwargs:
# warn that the function interface has changed:
warnings.warn(self.warning_msg.format(
old_arg=old_arg, func_name=func.__name__,
new_arg=new_arg), FutureWarning,
stacklevel=stacklevel)
# Substitute new_arg to old_arg
kwargs[new_arg] = kwargs.pop(old_arg)
# Call the function with the fixed arguments
return func(*args, **kwargs)
if func.__doc__ is not None:
newdoc = docstring_add_deprecated(func, self.kwarg_mapping,
self.deprecated_version)
fixed_func.__doc__ = newdoc
return fixed_func
class channel_as_last_axis:
"""Decorator for automatically making channels axis last for all arrays.
This decorator reorders axes for compatibility with functions that only
support channels along the last axis. After the function call is complete
the channels axis is restored back to its original position.
Parameters
----------
channel_arg_positions : tuple of int, optional
Positional arguments at the positions specified in this tuple are
assumed to be multichannel arrays. The default is to assume only the
first argument to the function is a multichannel array.
channel_kwarg_names : tuple of str, optional
A tuple containing the names of any keyword arguments corresponding to
multichannel arrays.
multichannel_output : bool, optional
A boolean that should be True if the output of the function is not a
multichannel array and False otherwise. This decorator does not
currently support the general case of functions with multiple outputs
where some or all are multichannel.
"""
def __init__(self, channel_arg_positions=(0,), channel_kwarg_names=(),
multichannel_output=True):
self.arg_positions = set(channel_arg_positions)
self.kwarg_names = set(channel_kwarg_names)
self.multichannel_output = multichannel_output
def __call__(self, func):
@functools.wraps(func)
def fixed_func(*args, **kwargs):
channel_axis = kwargs.get('channel_axis', None)
if channel_axis is None:
return func(*args, **kwargs)
# TODO: convert scalars to a tuple in anticipation of eventually
# supporting a tuple of channel axes. Right now, only an
# integer or a single-element tuple is supported, though.
if np.isscalar(channel_axis):
channel_axis = (channel_axis,)
if len(channel_axis) > 1:
raise ValueError(
"only a single channel axis is currently supported")
if channel_axis == (-1,) or channel_axis == -1:
return func(*args, **kwargs)
if self.arg_positions:
new_args = []
for pos, arg in enumerate(args):
if pos in self.arg_positions:
new_args.append(np.moveaxis(arg, channel_axis[0], -1))
else:
new_args.append(arg)
new_args = tuple(new_args)
else:
new_args = args
for name in self.kwarg_names:
kwargs[name] = np.moveaxis(kwargs[name], channel_axis[0], -1)
# now that we have moved the channels axis to the last position,
# change the channel_axis argument to -1
kwargs["channel_axis"] = -1
# Call the function with the fixed arguments
out = func(*new_args, **kwargs)
if self.multichannel_output:
out = np.moveaxis(out, -1, channel_axis[0])
return out
return fixed_func
class deprecated:
"""Decorator to mark deprecated functions with warning.
Adapted from <http://wiki.python.org/moin/PythonDecoratorLibrary>.
Parameters
----------
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 = f' Use ``{self.alt_func}`` instead.'
rmv_msg = ''
if self.removed_version is not None:
rmv_msg = f' and will be removed in version {self.removed_version}'
msg = f'Function ``{func.__name__}`` is deprecated{rmv_msg}.{alt_msg}'
@functools.wraps(func)
def wrapped(*args, **kwargs):
if self.behavior == 'warn':
func_code = func.__code__
warnings.simplefilter('always', skimage_deprecation)
warnings.warn_explicit(msg,
category=skimage_deprecation,
filename=func_code.co_filename,
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
else:
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.
Parameters
----------
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
type.
atol : float
Absolute tolerance away from nearest integer to consider values in
``val`` functionally integers.
Returns
-------
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.
Notes
-----
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
returned.
If any value(s) are outside the specified tolerance, an informative error
is raised.
Examples
--------
>>> safe_as_int(7.0)
7
>>> 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)
53
"""
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
try:
np.testing.assert_allclose(mod, 0, atol=atol)
except AssertionError:
raise ValueError(f'Integer argument required but received '
f'{val}, check inputs.')
return np.round(val).astype(np.int64)
def check_shape_equality(*images):
"""Check that all images have the same shape"""
image0 = images[0]
if not all(image0.shape == image.shape for image in images[1:]):
raise ValueError('Input images must have the same dimensions.')
return
def slice_at_axis(sl, axis):
"""
Construct tuple of slices to slice an array in the given dimension.
Parameters
----------
sl : slice
The slice for the given dimension.
axis : int
The axis to which `sl` is applied. All other dimensions are left
"unsliced".
Returns
-------
sl : tuple of slices
A tuple with slices matching `shape` in length.
Examples
--------
>>> slice_at_axis(slice(None, 3, -1), 1)
(slice(None, None, None), slice(None, 3, -1), Ellipsis)
"""
return (slice(None),) * axis + (sl,) + (...,)
def reshape_nd(arr, ndim, dim):
"""Reshape a 1D array to have n dimensions, all singletons but one.
Parameters
----------
arr : array, shape (N,)
Input array
ndim : int
Number of desired dimensions of reshaped array.
dim : int
Which dimension/axis will not be singleton-sized.
Returns
-------
arr_reshaped : array, shape ([1, ...], N, [1,...])
View of `arr` reshaped to the desired shape.
Examples
--------
>>> rng = np.random.default_rng()
>>> arr = rng.random(7)
>>> reshape_nd(arr, 2, 0).shape
(7, 1)
>>> reshape_nd(arr, 3, 1).shape
(1, 7, 1)
>>> reshape_nd(arr, 4, -1).shape
(1, 1, 1, 7)
"""
if arr.ndim != 1:
raise ValueError("arr must be a 1D array")
new_shape = [1] * ndim
new_shape[dim] = -1
return np.reshape(arr, new_shape)
def check_nD(array, ndim, arg_name='image'):
"""
Verify an array meets the desired ndims and array isn't empty.
Parameters
----------
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 array.ndim not in ndim:
raise ValueError(
msg_incorrect_dim % (arg_name, '-or-'.join([str(n) for n in ndim]))
)
def convert_to_float(image, preserve_range):
"""Convert input image to float image with the appropriate range.
Parameters
----------
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
https://scikit-image.org/docs/dev/user_guide/data_types.html
Notes
-----
* Input images with `float32` data type are not upcast.
Returns
-------
image : ndarray
Transformed version of the input.
"""
if image.dtype == np.float16:
return image.astype(np.float32)
if preserve_range:
# Convert image to double only if it is not single or double
# precision float
if image.dtype.char not in 'df':
image = image.astype(float)
else:
from ..util.dtype import img_as_float
image = img_as_float(image)
return image
def _validate_interpolation_order(image_dtype, order):
"""Validate and return spline interpolation's order.
Parameters
----------
image_dtype : dtype
Image dtype.
order : int, optional
The order of the spline interpolation. The order has to be in
the range 0-5. See `skimage.transform.warp` for detail.
Returns
-------
order : int
if input order is None, returns 0 if image_dtype is bool and 1
otherwise. Otherwise, image_dtype is checked and input order
is validated accordingly (order > 0 is not supported for bool
image dtype)
"""
if order is None:
return 0 if image_dtype == bool else 1
if order < 0 or order > 5:
raise ValueError("Spline interpolation order has to be in the "
"range 0-5.")
if image_dtype == bool and order != 0:
raise ValueError(
"Input image dtype is bool. Interpolation is not defined "
"with bool data type. Please set order to 0 or explicitly "
"cast input image to another data type.")
return order
def _to_np_mode(mode):
"""Convert padding modes from `ndi.correlate` to `np.pad`."""
mode_translation_dict = dict(nearest='edge', reflect='symmetric',
mirror='reflect')
if mode in mode_translation_dict:
mode = mode_translation_dict[mode]
return mode
def _to_ndimage_mode(mode):
"""Convert from `numpy.pad` mode name to the corresponding ndimage mode."""
mode_translation_dict = dict(constant='constant', edge='nearest',
symmetric='reflect', reflect='mirror',
wrap='wrap')
if mode not in mode_translation_dict:
raise ValueError(
f"Unknown mode: '{mode}', or cannot translate mode. The "
f"mode should be one of 'constant', 'edge', 'symmetric', "
f"'reflect', or 'wrap'. See the documentation of numpy.pad for "
f"more info.")
return _fix_ndimage_mode(mode_translation_dict[mode])
def _fix_ndimage_mode(mode):
# SciPy 1.6.0 introduced grid variants of constant and wrap which
# have less surprising behavior for images. Use these when available
grid_modes = {'constant': 'grid-constant', 'wrap': 'grid-wrap'}
return grid_modes.get(mode, mode)
new_float_type = {
# preserved types
np.float32().dtype.char: np.float32,
np.float64().dtype.char: np.float64,
np.complex64().dtype.char: np.complex64,
np.complex128().dtype.char: np.complex128,
# altered types
np.float16().dtype.char: np.float32,
'g': np.float64, # np.float128 ; doesn't exist on windows
'G': np.complex128, # np.complex256 ; doesn't exist on windows
}
def _supported_float_type(input_dtype, allow_complex=False):
"""Return an appropriate floating-point dtype for a given dtype.
float32, float64, complex64, complex128 are preserved.
float16 is promoted to float32.
complex256 is demoted to complex128.
Other types are cast to float64.
Parameters
----------
input_dtype : np.dtype or Iterable of np.dtype
The input dtype. If a sequence of multiple dtypes is provided, each
dtype is first converted to a supported floating point type and the
final dtype is then determined by applying `np.result_type` on the
sequence of supported floating point types.
allow_complex : bool, optional
If False, raise a ValueError on complex-valued inputs.
Returns
-------
float_type : dtype
Floating-point dtype for the image.
"""
if isinstance(input_dtype, Iterable) and not isinstance(input_dtype, str):
return np.result_type(*(_supported_float_type(d) for d in input_dtype))
input_dtype = np.dtype(input_dtype)
if not allow_complex and input_dtype.kind == 'c':
raise ValueError("complex valued input is not supported")
return new_float_type.get(input_dtype.char, np.float64)
def identity(image, *args, **kwargs):
"""Returns the first argument unmodified."""
return image
def as_binary_ndarray(array, *, variable_name):
"""Return `array` as a numpy.ndarray of dtype bool.
Raises
------
ValueError:
An error including the given `variable_name` if `array` can not be
safely cast to a boolean array.
"""
array = np.asarray(array)
if array.dtype != bool:
if np.any((array != 1) & (array != 0)):
raise ValueError(
f"{variable_name} array is not of dtype boolean or "
f"contains values other than 0 and 1 so cannot be "
f"safely cast to boolean array."
)
return np.asarray(array, dtype=bool)