Source code for robot.utils.normalizing

#  Copyright 2008-2015 Nokia Networks
#  Copyright 2016-     Robot Framework Foundation
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  See the License for the specific language governing permissions and
#  limitations under the License.

from import MutableMapping
import re

from .robottypes import is_dict_like, is_string

[docs]def normalize(string, ignore=(), caseless=True, spaceless=True): """Normalizes given string according to given spec. By default string is turned to lower case and all whitespace is removed. Additional characters can be removed by giving them in ``ignore`` list. """ empty = '' if is_string(string) else b'' if isinstance(ignore, bytes): # Iterating bytes in Python3 yields integers. ignore = [bytes([i]) for i in ignore] if spaceless: string = empty.join(string.split()) if caseless: string = string.lower() ignore = [i.lower() for i in ignore] # both if statements below enhance performance a little if ignore: for ign in ignore: if ign in string: string = string.replace(ign, empty) return string
[docs]def normalize_whitespace(string): return re.sub(r'\s', ' ', string, flags=re.UNICODE)
[docs]class NormalizedDict(MutableMapping): """Custom dictionary implementation automatically normalizing keys.""" def __init__(self, initial=None, ignore=(), caseless=True, spaceless=True): """Initialized with possible initial value and normalizing spec. Initial values can be either a dictionary or an iterable of name/value pairs. In the latter case items are added in the given order. Normalizing spec has exact same semantics as with the :func:`normalize` function. """ self._data = {} self._keys = {} self._normalize = lambda s: normalize(s, ignore, caseless, spaceless) if initial: self._add_initial(initial) def _add_initial(self, initial): items = initial.items() if hasattr(initial, 'items') else initial for key, value in items: self[key] = value def __getitem__(self, key): return self._data[self._normalize(key)] def __setitem__(self, key, value): norm_key = self._normalize(key) self._data[norm_key] = value self._keys.setdefault(norm_key, key) def __delitem__(self, key): norm_key = self._normalize(key) del self._data[norm_key] del self._keys[norm_key] def __iter__(self): return (self._keys[norm_key] for norm_key in sorted(self._keys)) def __len__(self): return len(self._data) def __str__(self): return '{%s}' % ', '.join('%r: %r' % (key, self[key]) for key in self) def __eq__(self, other): if not is_dict_like(other): return False if not isinstance(other, NormalizedDict): other = NormalizedDict(other) return self._data == other._data
[docs] def copy(self): copy = NormalizedDict() copy._data = self._data.copy() copy._keys = self._keys.copy() copy._normalize = self._normalize return copy
# Speed-ups. Following methods are faster than default implementations. def __contains__(self, key): return self._normalize(key) in self._data
[docs] def clear(self): self._data.clear() self._keys.clear()