forked from 170010011/fr
418 lines
14 KiB
Python
418 lines
14 KiB
Python
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import inspect
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import warnings
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import functools
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import sys
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import numpy as np
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import numbers
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from ..util import img_as_float
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from ._warnings import all_warnings, warn
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__all__ = ['deprecated', 'get_bound_method_class', 'all_warnings',
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'safe_as_int', 'check_nD', 'check_shape_equality', 'warn']
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class skimage_deprecation(Warning):
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"""Create our own deprecation class, since Python >= 2.7
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silences deprecations by default.
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"""
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pass
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class change_default_value:
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"""Decorator for changing the default value of an argument.
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Parameters
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----------
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arg_name: str
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The name of the argument to be updated.
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new_value: any
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The argument new value.
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changed_version : str
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The package version in which the change will be introduced.
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warning_msg: str
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Optional warning message. If None, a generic warning message
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is used.
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"""
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def __init__(self, arg_name, *, new_value, changed_version,
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warning_msg=None):
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self.arg_name = arg_name
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self.new_value = new_value
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self.warning_msg = warning_msg
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self.changed_version = changed_version
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def __call__(self, func):
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parameters = inspect.signature(func).parameters
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arg_idx = list(parameters.keys()).index(self.arg_name)
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old_value = parameters[self.arg_name].default
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if self.warning_msg is None:
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self.warning_msg = (
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f"The new recommended value for {self.arg_name} is "
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f"{self.new_value}. Until version {self.changed_version}, "
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f"the default {self.arg_name} value is {old_value}. "
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f"From version {self.changed_version}, the {self.arg_name} "
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f"default value will be {self.new_value}. To avoid "
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f"this warning, please explicitly set {self.arg_name} value.")
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@functools.wraps(func)
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def fixed_func(*args, **kwargs):
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if len(args) < arg_idx + 1 and self.arg_name not in kwargs.keys():
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# warn that arg_name default value changed:
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warnings.warn(self.warning_msg, FutureWarning, stacklevel=2)
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return func(*args, **kwargs)
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return fixed_func
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class remove_arg:
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"""Decorator to remove an argument from function's signature.
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Parameters
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----------
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arg_name: str
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The name of the argument to be removed.
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changed_version : str
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The package version in which the warning will be replaced by
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an error.
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help_msg: str
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Optional message appended to the generic warning message.
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"""
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def __init__(self, arg_name, *, changed_version, help_msg=None):
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self.arg_name = arg_name
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self.help_msg = help_msg
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self.changed_version = changed_version
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def __call__(self, func):
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parameters = inspect.signature(func).parameters
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arg_idx = list(parameters.keys()).index(self.arg_name)
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warning_msg = (
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f"{self.arg_name} argument is deprecated and will be removed "
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f"in version {self.changed_version}. To avoid this warning, "
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f"please do not use the {self.arg_name} argument. Please "
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f"see {func.__name__} documentation for more details.")
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if self.help_msg is not None:
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warning_msg += f" {self.help_msg}"
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@functools.wraps(func)
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def fixed_func(*args, **kwargs):
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if len(args) > arg_idx or self.arg_name in kwargs.keys():
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# warn that arg_name is deprecated
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warnings.warn(warning_msg, FutureWarning, stacklevel=2)
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return func(*args, **kwargs)
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return fixed_func
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class deprecate_kwarg:
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"""Decorator ensuring backward compatibility when argument names are
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modified in a function definition.
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Parameters
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----------
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arg_mapping: dict
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Mapping between the function's old argument names and the new
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ones.
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warning_msg: str
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Optional warning message. If None, a generic warning message
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is used.
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removed_version : str
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The package version in which the deprecated argument will be
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removed.
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"""
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def __init__(self, kwarg_mapping, warning_msg=None, removed_version=None):
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self.kwarg_mapping = kwarg_mapping
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if warning_msg is None:
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self.warning_msg = ("'{old_arg}' is a deprecated argument name "
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"for `{func_name}`. ")
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if removed_version is not None:
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self.warning_msg += ("It will be removed in version {}. "
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.format(removed_version))
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self.warning_msg += "Please use '{new_arg}' instead."
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else:
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self.warning_msg = warning_msg
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def __call__(self, func):
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@functools.wraps(func)
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def fixed_func(*args, **kwargs):
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for old_arg, new_arg in self.kwarg_mapping.items():
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if old_arg in kwargs:
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# warn that the function interface has changed:
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warnings.warn(self.warning_msg.format(
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old_arg=old_arg, func_name=func.__name__,
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new_arg=new_arg), FutureWarning, stacklevel=2)
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# Substitute new_arg to old_arg
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kwargs[new_arg] = kwargs.pop(old_arg)
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# Call the function with the fixed arguments
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return func(*args, **kwargs)
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return fixed_func
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class deprecated(object):
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"""Decorator to mark deprecated functions with warning.
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Adapted from <http://wiki.python.org/moin/PythonDecoratorLibrary>.
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Parameters
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----------
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alt_func : str
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If given, tell user what function to use instead.
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behavior : {'warn', 'raise'}
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Behavior during call to deprecated function: 'warn' = warn user that
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function is deprecated; 'raise' = raise error.
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removed_version : str
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The package version in which the deprecated function will be removed.
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"""
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def __init__(self, alt_func=None, behavior='warn', removed_version=None):
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self.alt_func = alt_func
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self.behavior = behavior
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self.removed_version = removed_version
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def __call__(self, func):
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alt_msg = ''
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if self.alt_func is not None:
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alt_msg = ' Use ``%s`` instead.' % self.alt_func
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rmv_msg = ''
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if self.removed_version is not None:
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rmv_msg = (' and will be removed in version %s' %
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self.removed_version)
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msg = ('Function ``%s`` is deprecated' % func.__name__ +
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rmv_msg + '.' + alt_msg)
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@functools.wraps(func)
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def wrapped(*args, **kwargs):
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if self.behavior == 'warn':
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func_code = func.__code__
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warnings.simplefilter('always', skimage_deprecation)
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warnings.warn_explicit(msg,
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category=skimage_deprecation,
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filename=func_code.co_filename,
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lineno=func_code.co_firstlineno + 1)
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elif self.behavior == 'raise':
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raise skimage_deprecation(msg)
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return func(*args, **kwargs)
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# modify doc string to display deprecation warning
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doc = '**Deprecated function**.' + alt_msg
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if wrapped.__doc__ is None:
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wrapped.__doc__ = doc
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else:
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wrapped.__doc__ = doc + '\n\n ' + wrapped.__doc__
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return wrapped
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def get_bound_method_class(m):
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"""Return the class for a bound method.
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"""
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return m.im_class if sys.version < '3' else m.__self__.__class__
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def safe_as_int(val, atol=1e-3):
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"""
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Attempt to safely cast values to integer format.
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Parameters
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----------
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val : scalar or iterable of scalars
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Number or container of numbers which are intended to be interpreted as
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integers, e.g., for indexing purposes, but which may not carry integer
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type.
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atol : float
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Absolute tolerance away from nearest integer to consider values in
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``val`` functionally integers.
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Returns
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-------
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val_int : NumPy scalar or ndarray of dtype `np.int64`
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Returns the input value(s) coerced to dtype `np.int64` assuming all
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were within ``atol`` of the nearest integer.
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Notes
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-----
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This operation calculates ``val`` modulo 1, which returns the mantissa of
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all values. Then all mantissas greater than 0.5 are subtracted from one.
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Finally, the absolute tolerance from zero is calculated. If it is less
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than ``atol`` for all value(s) in ``val``, they are rounded and returned
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in an integer array. Or, if ``val`` was a scalar, a NumPy scalar type is
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returned.
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If any value(s) are outside the specified tolerance, an informative error
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is raised.
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Examples
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--------
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>>> safe_as_int(7.0)
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7
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>>> safe_as_int([9, 4, 2.9999999999])
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array([9, 4, 3])
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>>> safe_as_int(53.1)
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Traceback (most recent call last):
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...
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ValueError: Integer argument required but received 53.1, check inputs.
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>>> safe_as_int(53.01, atol=0.01)
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53
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"""
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mod = np.asarray(val) % 1 # Extract mantissa
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# Check for and subtract any mod values > 0.5 from 1
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if mod.ndim == 0: # Scalar input, cannot be indexed
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if mod > 0.5:
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mod = 1 - mod
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else: # Iterable input, now ndarray
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mod[mod > 0.5] = 1 - mod[mod > 0.5] # Test on each side of nearest int
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try:
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np.testing.assert_allclose(mod, 0, atol=atol)
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except AssertionError:
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raise ValueError("Integer argument required but received "
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"{0}, check inputs.".format(val))
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return np.round(val).astype(np.int64)
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def check_shape_equality(im1, im2):
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"""Raise an error if the shape do not match."""
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if not im1.shape == im2.shape:
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raise ValueError('Input images must have the same dimensions.')
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return
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def check_nD(array, ndim, arg_name='image'):
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"""
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Verify an array meets the desired ndims and array isn't empty.
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Parameters
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----------
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array : array-like
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Input array to be validated
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ndim : int or iterable of ints
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Allowable ndim or ndims for the array.
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arg_name : str, optional
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The name of the array in the original function.
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"""
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array = np.asanyarray(array)
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msg_incorrect_dim = "The parameter `%s` must be a %s-dimensional array"
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msg_empty_array = "The parameter `%s` cannot be an empty array"
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if isinstance(ndim, int):
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ndim = [ndim]
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if array.size == 0:
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raise ValueError(msg_empty_array % (arg_name))
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if not array.ndim in ndim:
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raise ValueError(msg_incorrect_dim % (arg_name, '-or-'.join([str(n) for n in ndim])))
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def check_random_state(seed):
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"""Turn seed into a `np.random.RandomState` instance.
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Parameters
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----------
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seed : None, int or np.random.RandomState
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If `seed` is None, return the RandomState singleton used by `np.random`.
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If `seed` is an int, return a new RandomState instance seeded with `seed`.
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If `seed` is already a RandomState instance, return it.
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Raises
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------
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ValueError
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If `seed` is of the wrong type.
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"""
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# Function originally from scikit-learn's module sklearn.utils.validation
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if seed is None or seed is np.random:
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return np.random.mtrand._rand
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if isinstance(seed, (numbers.Integral, np.integer)):
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return np.random.RandomState(seed)
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if isinstance(seed, np.random.RandomState):
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return seed
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raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
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' instance' % seed)
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def convert_to_float(image, preserve_range):
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"""Convert input image to float image with the appropriate range.
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Parameters
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----------
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image : ndarray
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Input image.
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preserve_range : bool
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Determines if the range of the image should be kept or transformed
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using img_as_float. Also see
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https://scikit-image.org/docs/dev/user_guide/data_types.html
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Notes:
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------
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* Input images with `float32` data type are not upcast.
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Returns
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-------
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image : ndarray
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Transformed version of the input.
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"""
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if preserve_range:
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# Convert image to double only if it is not single or double
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# precision float
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if image.dtype.char not in 'df':
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image = image.astype(float)
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else:
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image = img_as_float(image)
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return image
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def _validate_interpolation_order(image_dtype, order):
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"""Validate and return spline interpolation's order.
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Parameters
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----------
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image_dtype : dtype
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Image dtype.
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|
|
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.
|
||
|
|
|
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|
|
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:
|
||
|
|
warn("Input image dtype is bool. Interpolation is not defined "
|
||
|
|
"with bool data type. Please set order to 0 or explicitely "
|
||
|
|
"cast input image to another data type. Starting from version "
|
||
|
|
"0.19 a ValueError will be raised instead of this warning.",
|
||
|
|
FutureWarning, stacklevel=2)
|
||
|
|
|
||
|
|
return order
|