Data Structures

Werkzeug provides some subclasses of common Python objects to extend themwith additional features. Some of them are used to make them immutable, othersare used to change some semantics to better work with HTTP.

General Purpose

Changed in version 0.6: The general purpose classes are now pickleable in each protocol as longas the contained objects are pickleable. This means that theFileMultiDict won’t be pickleable as soon as it contains afile.

  • class werkzeug.datastructures.TypeConversionDict
  • Works like a regular dict but the get() method can performtype conversions. MultiDict and CombinedMultiDictare subclasses of this class and provide the same feature.

New in version 0.5.

  • get(key, default=None, type=None)
  • Return the default value if the requested data doesn’t exist.If type is provided and is a callable it should convert the value,return it or raise a ValueError if that is not possible. Inthis case the function will return the default as if the value was notfound:
  1. >>> d = TypeConversionDict(foo='42', bar='blub')
  2. >>> d.get('foo', type=int)
  3. 42
  4. >>> d.get('bar', -1, type=int)
  5. -1

Parameters:

  1. - **key** – The key to be looked up.
  2. - **default** – The default value to be returned if the key can’tbe looked up. If not further specified _None_ isreturned.
  3. - **type** – A callable that is used to cast the value in the[<code>MultiDict</code>]($7800771996c024cd.md#werkzeug.datastructures.MultiDict). If a [<code>ValueError</code>](https://docs.python.org/3/library/exceptions.html#ValueError) is raisedby this callable the default value is returned.
  • class werkzeug.datastructures.ImmutableTypeConversionDict
  • Works like a TypeConversionDict but does not supportmodifications.

New in version 0.5.

  • copy()
  • Return a shallow mutable copy of this object. Keep in mind thatthe standard library’s copy() function is a no-op for this classlike for any other python immutable type (eg: tuple).
  • class werkzeug.datastructures.MultiDict(mapping=None)
  • A MultiDict is a dictionary subclass customized to deal withmultiple values for the same key which is for example used by the parsingfunctions in the wrappers. This is necessary because some HTML formelements pass multiple values for the same key.

MultiDict implements all standard dictionary methods.Internally, it saves all values for a key as a list, but the standard dictaccess methods will only return the first value for a key. If you want togain access to the other values, too, you have to use the list methods asexplained below.

Basic Usage:

  1. >>> d = MultiDict([('a', 'b'), ('a', 'c')])
  2. >>> d
  3. MultiDict([('a', 'b'), ('a', 'c')])
  4. >>> d['a']
  5. 'b'
  6. >>> d.getlist('a')
  7. ['b', 'c']
  8. >>> 'a' in d
  9. True

It behaves like a normal dict thus all dict functions will only return thefirst value when multiple values for one key are found.

From Werkzeug 0.3 onwards, the KeyError raised by this class is also asubclass of the BadRequest HTTP exception and willrender a page for a 400 BAD REQUEST if caught in a catch-all for HTTPexceptions.

A MultiDict can be constructed from an iterable of(key, value) tuples, a dict, a MultiDict or from Werkzeug 0.2onwards some keyword parameters.

Parameters:mapping – the initial value for the MultiDict. Either aregular dict, an iterable of (key, value) tuplesor None.

  • add(key, value)
  • Adds a new value for the key.

New in version 0.6.

Parameters:

  1. - **key** – the key for the value.
  2. - **value** – the value to add.
  • clear() → None. Remove all items from D.
  • copy()

  • Return a shallow copy of this object.

  • deepcopy(memo=None)

  • Return a deep copy of this object.

  • fromkeys()

  • Create a new dictionary with keys from iterable and values set to value.

  • get(key, default=None, type=None)

  • Return the default value if the requested data doesn’t exist.If type is provided and is a callable it should convert the value,return it or raise a ValueError if that is not possible. Inthis case the function will return the default as if the value was notfound:
  1. >>> d = TypeConversionDict(foo='42', bar='blub')
  2. >>> d.get('foo', type=int)
  3. 42
  4. >>> d.get('bar', -1, type=int)
  5. -1

Parameters:

  1. - **key** – The key to be looked up.
  2. - **default** – The default value to be returned if the key can’tbe looked up. If not further specified _None_ isreturned.
  3. - **type** – A callable that is used to cast the value in the[<code>MultiDict</code>]($7800771996c024cd.md#werkzeug.datastructures.MultiDict). If a [<code>ValueError</code>](https://docs.python.org/3/library/exceptions.html#ValueError) is raisedby this callable the default value is returned.
  • getlist(key, type=None)
  • Return the list of items for a given key. If that key is not in theMultiDict, the return value will be an empty list. Just as get__getlist accepts a type parameter. All items will be convertedwith the callable defined there.

Parameters:

  1. - **key** – The key to be looked up.
  2. - **type** – A callable that is used to cast the value in the[<code>MultiDict</code>]($7800771996c024cd.md#werkzeug.datastructures.MultiDict). If a [<code>ValueError</code>](https://docs.python.org/3/library/exceptions.html#ValueError) is raisedby this callable the value will be removed from the list.Returns:

a list of all the values for the key.

  • items(multi=False)
  • Return an iterator of (key, value) pairs.

Parameters:multi – If set to True the iterator returned will have a pairfor each value of each key. Otherwise it will onlycontain pairs for the first value of each key.

  • keys() → a set-like object providing a view on D's keys
  • lists()

  • Return a iterator of (key, values) pairs, where values is the listof all values associated with the key.

  • listvalues()

  • Return an iterator of all values associated with a key. Zippingkeys() and this is the same as calling lists():
  1. >>> d = MultiDict({"foo": [1, 2, 3]})
  2. >>> zip(d.keys(), d.listvalues()) == d.lists()
  3. True
  • pop(key, default=no value)
  • Pop the first item for a list on the dict. Afterwards thekey is removed from the dict, so additional values are discarded:
  1. >>> d = MultiDict({"foo": [1, 2, 3]})
  2. >>> d.pop("foo")
  3. 1
  4. >>> "foo" in d
  5. False

Parameters:

  1. - **key** – the key to pop.
  2. - **default** – if provided the value to return if the key wasnot in the dictionary.
  • popitem()

  • Pop an item from the dict.

  • popitemlist()

  • Pop a (key, list) tuple from the dict.

  • poplist(key)

  • Pop the list for a key from the dict. If the key is not in the dictan empty list is returned.

Changed in version 0.5: If the key does no longer exist a list is returned instead ofraising an error.

  • setdefault(key, default=None)
  • Returns the value for the key if it is in the dict, otherwise itreturns default and sets that value for key.

Parameters:

  1. - **key** – The key to be looked up.
  2. - **default** – The default value to be returned if the key is notin the dict. If not further specified it’s _None_.
  • setlist(key, new_list)
  • Remove the old values for a key and add new ones. Note that the listyou pass the values in will be shallow-copied before it is inserted inthe dictionary.
  1. >>> d = MultiDict()
  2. >>> d.setlist('foo', ['1', '2'])
  3. >>> d['foo']
  4. '1'
  5. >>> d.getlist('foo')
  6. ['1', '2']

Parameters:

  1. - **key** – The key for which the values are set.
  2. - **new_list** – An iterable with the new values for the key. Old valuesare removed first.
  • setlistdefault(key, default_list=None)
  • Like setdefault but sets multiple values. The list returnedis not a copy, but the list that is actually used internally. Thismeans that you can put new values into the dict by appending itemsto the list:
  1. >>> d = MultiDict({"foo": 1})
  2. >>> d.setlistdefault("foo").extend([2, 3])
  3. >>> d.getlist("foo")
  4. [1, 2, 3]

Parameters:

  1. - **key** – The key to be looked up.
  2. - **default_list** – An iterable of default values. It is either copied(in case it was a list) or converted into a listbefore returned.Returns:

a list

  • todict(_flat=True)
  • Return the contents as regular dict. If flat is True thereturned dict will only have the first item present, if flat isFalse all values will be returned as lists.

Parameters:flat – If set to False the dict returned will have listswith all the values in it. Otherwise it will onlycontain the first value for each key.Returns:a dict

  • update(other_dict)
  • update() extends rather than replaces existing key lists:
  1. >>> a = MultiDict({'x': 1})
  2. >>> b = MultiDict({'x': 2, 'y': 3})
  3. >>> a.update(b)
  4. >>> a
  5. MultiDict([('y', 3), ('x', 1), ('x', 2)])

If the value list for a key in other_dict is empty, no new valueswill be added to the dict and the key will not be created:

  1. >>> x = {'empty_list': []}
  2. >>> y = MultiDict()
  3. >>> y.update(x)
  4. >>> y
  5. MultiDict([])
  • values()
  • Returns an iterator of the first value on every key’s value list.
  • class werkzeug.datastructures.OrderedMultiDict(mapping=None)
  • Works like a regular MultiDict but preserves theorder of the fields. To convert the ordered multi dict into alist you can use the items() method and pass it multi=True.

In general an OrderedMultiDict is an order of magnitudeslower than a MultiDict.

note

Due to a limitation in Python you cannot convert an orderedmulti dict into a regular dict by using dict(multidict).Instead you have to use the to_dict() method, otherwisethe internal bucket objects are exposed.

  • class werkzeug.datastructures.ImmutableMultiDict(mapping=None)
  • An immutable MultiDict.

New in version 0.5.

  • copy()
  • Return a shallow mutable copy of this object. Keep in mind thatthe standard library’s copy() function is a no-op for this classlike for any other python immutable type (eg: tuple).
  • class werkzeug.datastructures.ImmutableOrderedMultiDict(mapping=None)
  • An immutable OrderedMultiDict.

New in version 0.6.

  • copy()
  • Return a shallow mutable copy of this object. Keep in mind thatthe standard library’s copy() function is a no-op for this classlike for any other python immutable type (eg: tuple).
  • class werkzeug.datastructures.CombinedMultiDict(dicts=None)
  • A read only MultiDict that you can pass multiple MultiDictinstances as sequence and it will combine the return values of all wrappeddicts:
  1. >>> from werkzeug.datastructures import CombinedMultiDict, MultiDict
  2. >>> post = MultiDict([('foo', 'bar')])
  3. >>> get = MultiDict([('blub', 'blah')])
  4. >>> combined = CombinedMultiDict([get, post])
  5. >>> combined['foo']
  6. 'bar'
  7. >>> combined['blub']
  8. 'blah'

This works for all read operations and will raise a TypeError formethods that usually change data which isn’t possible.

From Werkzeug 0.3 onwards, the KeyError raised by this class is also asubclass of the BadRequest HTTP exception and willrender a page for a 400 BAD REQUEST if caught in a catch-all for HTTPexceptions.

  • class werkzeug.datastructures.ImmutableDict
  • An immutable dict.

New in version 0.5.

  • copy()
  • Return a shallow mutable copy of this object. Keep in mind thatthe standard library’s copy() function is a no-op for this classlike for any other python immutable type (eg: tuple).
  • class werkzeug.datastructures.ImmutableList
  • An immutable list.

New in version 0.5.

Private:

  • class werkzeug.datastructures.FileMultiDict(mapping=None)
  • A special MultiDict that has convenience methods to addfiles to it. This is used for EnvironBuilder and generallyuseful for unittesting.

New in version 0.5.

  • addfile(_name, file, filename=None, content_type=None)
  • Adds a new file to the dict. file can be a file name ora file-like or a FileStorage object.

Parameters:

  1. - **name** – the name of the field.
  2. - **file** – a filename or <code>file</code>-like object
  3. - **filename** – an optional filename
  4. - **content_type** – an optional content type
  • class werkzeug.datastructures.Headers([defaults])
  • An object that stores some headers. It has a dict-like interfacebut is ordered and can store the same keys multiple times.

This data structure is useful if you want a nicer way to handle WSGIheaders which are stored as tuples in a list.

From Werkzeug 0.3 onwards, the KeyError raised by this class isalso a subclass of the BadRequest HTTP exceptionand will render a page for a 400 BAD REQUEST if caught in acatch-all for HTTP exceptions.

Headers is mostly compatible with the Python wsgiref.headers.Headersclass, with the exception of getitem. wsgiref will returnNone for headers['missing'], whereas Headers will raisea KeyError.

To create a new Headers object pass it a list or dict of headerswhich are used as default values. This does not reuse the list passedto the constructor for internal usage.

Parameters:defaults – The list of default values for the Headers.

Changed in version 0.9: This data structure now stores unicode values similar to how themulti dicts do it. The main difference is that bytes can be set aswell which will automatically be latin1 decoded.

Changed in version 0.9: The linked() function was removed without replacement as itwas an API that does not support the changes to the encoding model.

  • add(key_, value, **kw_)
  • Add a new header tuple to the list.

Keyword arguments can specify additional parameters for the headervalue, with underscores converted to dashes:

  1. >>> d = Headers()
  2. >>> d.add('Content-Type', 'text/plain')
  3. >>> d.add('Content-Disposition', 'attachment', filename='foo.png')

The keyword argument dumping uses dump_options_header()behind the scenes.

New in version 0.4.1: keyword arguments were added for wsgiref compatibility.

  • addheader(__key, _value, **_kw)
  • Add a new header tuple to the list.

An alias for add() for compatibility with the wsgirefadd_header() method.

  • clear()

  • Clears all headers.

  • extend(iterable)

  • Extend the headers with a dict or an iterable yielding keys andvalues.

  • get(key, default=None, type=None, as_bytes=False)

  • Return the default value if the requested data doesn’t exist.If type is provided and is a callable it should convert the value,return it or raise a ValueError if that is not possible. Inthis case the function will return the default as if the value was notfound:
  1. >>> d = Headers([('Content-Length', '42')])
  2. >>> d.get('Content-Length', type=int)
  3. 42

If a headers object is bound you must not add unicode stringsbecause no encoding takes place.

New in version 0.9: Added support for as_bytes.

Parameters:

  1. - **key** – The key to be looked up.
  2. - **default** – The default value to be returned if the key can’tbe looked up. If not further specified _None_ isreturned.
  3. - **type** – A callable that is used to cast the value in the[<code>Headers</code>]($7800771996c024cd.md#werkzeug.datastructures.Headers). If a [<code>ValueError</code>](https://docs.python.org/3/library/exceptions.html#ValueError) is raisedby this callable the default value is returned.
  4. - **as_bytes** – return bytes instead of unicode strings.
  • getall(_name)
  • Return a list of all the values for the named field.

This method is compatible with the wsgirefget_all() method.

  • getlist(key, type=None, as_bytes=False)
  • Return the list of items for a given key. If that key is not in theHeaders, the return value will be an empty list. Just asget()getlist() accepts a type parameter. All items willbe converted with the callable defined there.

New in version 0.9: Added support for as_bytes.

Parameters:

  1. - **key** – The key to be looked up.
  2. - **type** – A callable that is used to cast the value in the[<code>Headers</code>]($7800771996c024cd.md#werkzeug.datastructures.Headers). If a [<code>ValueError</code>](https://docs.python.org/3/library/exceptions.html#ValueError) is raisedby this callable the value will be removed from the list.
  3. - **as_bytes** – return bytes instead of unicode strings.Returns:

a list of all the values for the key.

  • haskey(_key)

  • Check if a key is present.

  • pop(key=None, default=no value)

  • Removes and returns a key or index.

Parameters:key – The key to be popped. If this is an integer the item atthat position is removed, if it’s a string the value forthat key is. If the key is omitted or None the lastitem is removed.Returns:an item.

  • popitem()

  • Removes a key or index and returns a (key, value) item.

  • remove(key)

  • Remove a key.

Parameters:key – The key to be removed.

  • set(key_, value, **kw_)
  • Remove all header tuples for key and add a new one. The newlyadded key either appears at the end of the list if there was noentry or replaces the first one.

Keyword arguments can specify additional parameters for the headervalue, with underscores converted to dashes. See add() formore information.

Changed in version 0.6.1: set() now accepts the same arguments as add().

Parameters:

  1. - **key** – The key to be inserted.
  2. - **value** – The value to be inserted.
  • setdefault(key, default)
  • Returns the value for the key if it is in the dict, otherwise itreturns default and sets that value for key.

Parameters:

  1. - **key** – The key to be looked up.
  2. - **default** – The default value to be returned if the key is notin the dict. If not further specified it’s _None_.
  • tolist(_charset='iso-8859-1')
  • Convert the headers into a list suitable for WSGI.

Deprecated since version 0.9.

  • to_wsgi_list()
  • Convert the headers into a list suitable for WSGI.

The values are byte strings in Python 2 converted to latin1 and unicodestrings in Python 3 for the WSGI server to encode.

Returns:list

  • class werkzeug.datastructures.EnvironHeaders(environ)
  • Read only version of the headers from a WSGI environment. Thisprovides the same interface as Headers and is constructed froma WSGI environment.

From Werkzeug 0.3 onwards, the KeyError raised by this class is also asubclass of the BadRequest HTTP exception and willrender a page for a 400 BAD REQUEST if caught in a catch-all forHTTP exceptions.

  • class werkzeug.datastructures.HeaderSet(headers=None, on_update=None)
  • Similar to the ETags class this implements a set-like structure.Unlike ETags this is case insensitive and used for vary, allow, andcontent-language headers.

If not constructed using the parse_set_header() function theinstantiation works like this:

  1. >>> hs = HeaderSet(['foo', 'bar', 'baz'])
  2. >>> hs
  3. HeaderSet(['foo', 'bar', 'baz'])
  • add(header)

  • Add a new header to the set.

  • asset(_preserve_casing=False)

  • Return the set as real python set type. When calling this, allthe items are converted to lowercase and the ordering is lost.

Parameters:preserve_casing – if set to True the items in the set returnedwill have the original case like in theHeaderSet, otherwise they willbe lowercase.

  • clear()

  • Clear the set.

  • discard(header)

  • Like remove() but ignores errors.

Parameters:header – the header to be discarded.

  • find(header)
  • Return the index of the header in the set or return -1 if not found.

Parameters:header – the header to be looked up.

  • index(header)
  • Return the index of the header in the set or raise anIndexError.

Parameters:header – the header to be looked up.

  • remove(header)
  • Remove a header from the set. This raises an KeyError if theheader is not in the set.

Changed in version 0.5: In older versions a IndexError was raised instead of aKeyError if the object was missing.

Parameters:header – the header to be removed.

  • to_header()

  • Convert the header set into an HTTP header string.

  • update(iterable)

  • Add all the headers from the iterable to the set.

Parameters:iterable – updates the set with the items from the iterable.

  • class werkzeug.datastructures.Accept(values=())
  • An Accept object is just a list subclass for lists of(value, quality) tuples. It is automatically sorted by specificityand quality.

All Accept objects work similar to a list but provide extrafunctionality for working with the data. Containment checks arenormalized to the rules of that header:

  1. >>> a = CharsetAccept([('ISO-8859-1', 1), ('utf-8', 0.7)])
  2. >>> a.best
  3. 'ISO-8859-1'
  4. >>> 'iso-8859-1' in a
  5. True
  6. >>> 'UTF8' in a
  7. True
  8. >>> 'utf7' in a
  9. False

To get the quality for an item you can use normal item lookup:

  1. >>> print a['utf-8']
  2. 0.7
  3. >>> a['utf7']
  4. 0

Changed in version 0.5: Accept objects are forced immutable now.

  • best

  • The best match as value.

  • bestmatch(_matches, default=None)

  • Returns the best match from a list of possible matches basedon the specificity and quality of the client. If two items have thesame quality and specificity, the one is returned that comes first.

Parameters:

  1. - **matches** – a list of matches to check for
  2. - **default** – the value that is returned if none match
  • find(key)
  • Get the position of an entry or return -1.

Parameters:key – The key to be looked up.

  • index(key)
  • Get the position of an entry or raise ValueError.

Parameters:key – The key to be looked up.

Changed in version 0.5: This used to raise IndexError, which was inconsistentwith the list API.

  • quality(key)
  • Returns the quality of the key.

New in version 0.6: In previous versions you had to use the item-lookup syntax(eg: obj[key] instead of obj.quality(key))

  • to_header()

  • Convert the header set into an HTTP header string.

  • values()

  • Iterate over all values.
  • class werkzeug.datastructures.MIMEAccept(values=())

  • Like Accept but with special methods and behavior formimetypes.

    • accept_html

    • True if this object accepts HTML.

    • accept_json

    • True if this object accepts JSON.

    • accept_xhtml

    • True if this object accepts XHTML.
  • class werkzeug.datastructures.CharsetAccept(values=())
  • Like Accept but with normalization for charsets.
  • class werkzeug.datastructures.LanguageAccept(values=())
  • Like Accept but with normalization for languages.
  • class werkzeug.datastructures.RequestCacheControl(values=(), on_update=None)
  • A cache control for requests. This is immutable and gives accessto all the request-relevant cache control headers.

To get a header of the RequestCacheControl object again you canconvert the object into a string or call the to_header() method. Ifyou plan to subclass it and add your own items have a look at the sourcecodefor that class.

New in version 0.5: In previous versions a CacheControl class existed that was usedboth for request and response.

  • no_cache

  • accessor for ‘no-cache’

  • no_store

  • accessor for ‘no-store’

  • max_age

  • accessor for ‘max-age’

  • no_transform

  • accessor for ‘no-transform’

  • max_stale

  • accessor for ‘max-stale’

  • min_fresh

  • accessor for ‘min-fresh’

  • no_transform

  • accessor for ‘no-transform’

  • only_if_cached

  • accessor for ‘only-if-cached’
  • class werkzeug.datastructures.ResponseCacheControl(values=(), on_update=None)
  • A cache control for responses. Unlike RequestCacheControlthis is mutable and gives access to response-relevant cache controlheaders.

To get a header of the ResponseCacheControl object again you canconvert the object into a string or call the to_header() method. Ifyou plan to subclass it and add your own items have a look at the sourcecodefor that class.

New in version 0.5: In previous versions a CacheControl class existed that was usedboth for request and response.

  • no_cache

  • accessor for ‘no-cache’

  • no_store

  • accessor for ‘no-store’

  • max_age

  • accessor for ‘max-age’

  • no_transform

  • accessor for ‘no-transform’

  • must_revalidate

  • accessor for ‘must-revalidate’

  • private

  • accessor for ‘private’

  • proxy_revalidate

  • accessor for ‘proxy-revalidate’

  • public

  • accessor for ‘public’

  • s_maxage

  • accessor for ‘s-maxage’
  • class werkzeug.datastructures.ETags(strong_etags=None, weak_etags=None, star_tag=False)

  • A set that can be used to check if one etag is present in a collectionof etags.

    • asset(_include_weak=False)

    • Convert the ETags object into a python set. Per default all theweak etags are not part of this set.

    • contains(etag)

    • Check if an etag is part of the set ignoring weak tags.It is also possible to use the in operator.

    • containsraw(_etag)

    • When passed a quoted tag it will check if this tag is part of theset. If the tag is weak it is checked against weak and strong tags,otherwise strong only.

    • containsweak(_etag)

    • Check if an etag is part of the set including weak and strong tags.

    • isstrong(_etag)

    • Check if an etag is strong.

    • isweak(_etag)

    • Check if an etag is weak.

    • to_header()

    • Convert the etags set into a HTTP header string.
  • class werkzeug.datastructures.Authorization(auth_type, data=None)
  • Represents an Authorization header sent by the client. You shouldnot create this kind of object yourself but use it when it’s returned bythe parse_authorization_header function.

This object is a dict subclass and can be altered by setting dict itemsbut it should be considered immutable as it’s returned by the client andnot meant for modifications.

Changed in version 0.5: This object became immutable.

  • cnonce

  • If the server sent a qop-header in the WWW-Authenticateheader, the client has to provide this value for HTTP digest auth.See the RFC for more details.

  • nc

  • The nonce count value transmitted by clients if a qop-header isalso transmitted. HTTP digest auth only.

  • nonce

  • The nonce the server sent for digest auth, sent back by the client.A nonce should be unique for every 401 response for HTTP digestauth.

  • opaque

  • The opaque header from the server returned unchanged by the client.It is recommended that this string be base64 or hexadecimal data.Digest auth only.

  • password

  • When the authentication type is basic this is the passwordtransmitted by the client, else None.

  • qop

  • Indicates what “quality of protection” the client has applied tothe message for HTTP digest auth. Note that this is a single token,not a quoted list of alternatives as in WWW-Authenticate.

  • realm

  • This is the server realm sent back for HTTP digest auth.

  • response

  • A string of 32 hex digits computed as defined in RFC 2617, whichproves that the user knows a password. Digest auth only.

  • uri

  • The URI from Request-URI of the Request-Line; duplicated becauseproxies are allowed to change the Request-Line in transit. HTTPdigest auth only.

  • username

  • The username transmitted. This is set for both basic and digestauth all the time.
  • class werkzeug.datastructures.WWWAuthenticate(auth_type=None, values=None, on_update=None)

  • Provides simple access to WWW-Authenticate headers.

    • algorithm

    • A string indicating a pair of algorithms used to producethe digest and a checksum. If this is not present it is assumedto be “MD5”. If the algorithm is not understood, the challengeshould be ignored (and a different one used, if there is morethan one).

    • static authproperty(_name, doc=None)

    • A static helper function for subclasses to add extra authenticationsystem properties onto a class:
  1. class FooAuthenticate(WWWAuthenticate):
  2.     special_realm = auth_property('special_realm')

For more information have a look at the sourcecode to see how theregular properties (realm etc.) are implemented.

  • domain

  • A list of URIs that define the protection space. If a URIis an absolute path, it is relative to the canonical root URL ofthe server being accessed.

  • nonce

  • A server-specified data string which should be uniquely generatedeach time a 401 response is made. It is recommended that thisstring be base64 or hexadecimal data.

  • opaque

  • A string of data, specified by the server, which shouldbe returned by the client unchanged in the Authorization headerof subsequent requests with URIs in the same protection space.It is recommended that this string be base64 or hexadecimaldata.

  • qop

  • A set of quality-of-privacy directives such as auth andauth-int.

  • realm

  • A string to be displayed to users so they know whichusername and password to use. This string should contain atleast the name of the host performing the authentication andmight additionally indicate the collection of users who mighthave access.

  • setbasic(_realm='authentication required')

  • Clear the auth info and enable basic auth.

  • setdigest(_realm, nonce, qop=('auth', ), opaque=None, algorithm=None, stale=False)

  • Clear the auth info and enable digest auth.

  • stale

  • A flag, indicating that the previous request from the clientwas rejected because the nonce value was stale.

  • to_header()

  • Convert the stored values into a WWW-Authenticate header.

  • type

  • The type of the auth mechanism. HTTP currently specifiesBasic and Digest.
  • class werkzeug.datastructures.IfRange(etag=None, date=None)
  • Very simple object that represents the If-Range header in parsedform. It will either have neither a etag or date or one of either butnever both.

New in version 0.7.

  • date = None

  • The date in parsed format or None.

  • etag = None

  • The etag parsed and unquoted. Ranges always operate on strongetags so the weakness information is not necessary.

  • to_header()

  • Converts the object back into an HTTP header.
  • class werkzeug.datastructures.Range(units, ranges)
  • Represents a Range header. All methods only support onlybytes as the unit. Stores a list of ranges if given, but the methodsonly work if only one range is provided.

Raises:ValueError – If the ranges provided are invalid.

Changed in version 0.15: The ranges passed in are validated.

New in version 0.7.

  • makecontent_range(_length)

  • Creates a ContentRange objectfrom the current range and given content length.

  • rangefor_length(_length)

  • If the range is for bytes, the length is not None and there isexactly one range and it is satisfiable it returns a (start, stop)tuple, otherwise None.

  • ranges = None

  • A list of (begin, end) tuples for the range header provided.The ranges are non-inclusive.

  • tocontent_range_header(_length)

  • Converts the object into Content-Range HTTP header,based on given length

  • to_header()

  • Converts the object back into an HTTP header.

  • units = None

  • The units of this range. Usually “bytes”.
  • class werkzeug.datastructures.ContentRange(units, start, stop, length=None, on_update=None)
  • Represents the content range header.

New in version 0.7.

  • length

  • The length of the range or None.

  • set(start, stop, length=None, units='bytes')

  • Simple method to update the ranges.

  • start

  • The start point of the range or None.

  • stop

  • The stop point of the range (non-inclusive) or None. Can only beNone if also start is None.

  • units

  • The units to use, usually “bytes”

  • unset()

  • Sets the units to None which indicates that the header shouldno longer be used.

Others

  • class werkzeug.datastructures.FileStorage(stream=None, filename=None, name=None, content_type=None, content_length=None, headers=None)

  • The FileStorage class is a thin wrapper over incoming files.It is used by the request object to represent uploaded files. All theattributes of the wrapper stream are proxied by the file storage soit’s possible to do storage.read() instead of the long formstorage.stream.read().

    • stream

    • The input stream for the uploaded file. This usually points to anopen temporary file.

    • filename

    • The filename of the file on the client.

    • name

    • The name of the form field.

    • headers

    • The multipart headers as Headers object. This usually containsirrelevant information but in combination with custom multipart requeststhe raw headers might be interesting.

New in version 0.6.

  • close()

  • Close the underlying file if possible.

  • content_length

  • The content-length sent in the header. Usually not available

  • content_type

  • The content-type sent in the header. Usually not available

  • mimetype

  • Like content_type, but without parameters (eg, withoutcharset, type etc.) and always lowercase. For example if the contenttype is text/HTML; charset=utf-8 the mimetype would be'text/html'.

New in version 0.7.

  • mimetype_params
  • The mimetype parameters as dict. For example if the contenttype is text/html; charset=utf-8 the params would be{'charset': 'utf-8'}.

New in version 0.7.

  • save(dst, buffer_size=16384)
  • Save the file to a destination path or file object. If thedestination is a file object you have to close it yourself after thecall. The buffer size is the number of bytes held in memory duringthe copy process. It defaults to 16KB.

For secure file saving also have a look at secure_filename().

Parameters:

  1. - **dst** – a filename or open file object the uploaded fileis saved to.
  2. - **buffer_size** – the size of the buffer. This works the same asthe _length_ parameter of[<code>shutil.copyfileobj()</code>](https://docs.python.org/3/library/shutil.html#shutil.copyfileobj).