import inspect import warnings import functools import sys import numpy as np 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', 'check_nD', 'check_shape_equality', 'warn'] class skimage_deprecation(Warning): """Create our own deprecation class, since Python >= 2.7 silences deprecations by default. """ pass class change_default_value: """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 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): 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=2) return func(*args, **kwargs) return fixed_func class deprecate_kwarg: """Decorator ensuring backward compatibility when argument names are modified in a function definition. Parameters ---------- arg_mapping: dict Mapping between the function's old argument names and the new ones. 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, 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 += ("It will be removed in version {}. " .format(removed_version)) self.warning_msg += "Please use '{new_arg}' instead." else: self.warning_msg = warning_msg def __call__(self, func): @functools.wraps(func) def fixed_func(*args, **kwargs): 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=2) # Substitute new_arg to old_arg kwargs[new_arg] = kwargs.pop(old_arg) # Call the function with the fixed arguments return func(*args, **kwargs) return fixed_func class deprecated(object): """Decorator to mark deprecated functions with warning. Adapted from . 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 = ' Use ``%s`` instead.' % self.alt_func rmv_msg = '' if self.removed_version is not None: rmv_msg = (' and will be removed in version %s' % self.removed_version) msg = ('Function ``%s`` is deprecated' % func.__name__ + 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("Integer argument required but received " "{0}, check inputs.".format(val)) return np.round(val).astype(np.int64) def check_shape_equality(im1, im2): """Raise an error if the shape do not match.""" if not im1.shape == im2.shape: raise ValueError('Input images must have the same dimensions.') return 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 not array.ndim in ndim: raise ValueError(msg_incorrect_dim % (arg_name, '-or-'.join([str(n) for n in ndim]))) def check_random_state(seed): """Turn seed into a `np.random.RandomState` instance. Parameters ---------- 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. Raises ------ ValueError 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 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 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: 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: 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