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A lens library for python

Project description

# Lenses

Lenses is a python library that helps you to manipulate large data-structures without mutating them. It is inspired by the lenses in Haskell, although it’s much less principled and the api is more suitable for python.

## Installation

You can install the latest github version using pip like so:

pip install git+git://

You can uninstall similarly:

pip uninstall lenses

## How to Use

The lenses library makes liberal use of docstrings, which you can access as normal with the pydoc shell command, the help function in the repl, or by reading the source yourself.

Most users will only need the docs from lenses.Lens. If you want to add hooks to allow parts of the library to work with custom objects then you should check out the lenses.setters module. Most of the fancy lens code is in the lenses.baselens module for those who are curious how everything works.

An example is given in the examples folder.

### The Basics

For most users, the lenses library exports only one thing worth knowing about; a lens function:

>>> from lenses import lens

If you have a large data structure that you want to manipulate, you can pass it to this function and you will receive a bound Lens object, which is a lens that has been bound to that specific object. The lens can then be walked to focus it down on a particular part of the data-structure. You walk the lens by getting attributes and items from it (anything that would call __getattr__ or __getitem__):

>>> data = [1, 2, 3]
>>> my_lens = lens(data)[1]

Once you arrive at the data you want, you can get hold of it with the get method:

>>> my_lens.get()

Just getting data using the lens isn’t very impressive. Better is the set method, which allows you to set that particular piece of data within the larger data structure. It returns a copy of the original data structure with that one single piece of data changed. Note that the lens never mutates the original data structure:

>>> my_lens.set(5)
[1, 5, 3]
>>> data
[1, 2, 3]

Lenses allow you to manipulate arbitrarily nested objects:

>>> data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> lens(data)[1][0].set(20)
[[1, 2, 3], [20, 5, 6], [7, 8, 9]]
>>> lens(data)[2].set(20)
[[1, 2, 3], [4, 5, 6], 20]

And they support more than just lists. Any mutable python object that can by copied with copy.copy will work. Immutable objects need special support, but support for any python object can be added so long as you know how to construct a new version of that object with the appropriate data changed. tuples and namedtuples are supported out of the box.

>>> class MyClass:
...     def __init__(self, attribute):
...         self.attr = attribute
...     def __repr__(self):
...         return 'MyClass(' + repr(self.attr) + ')'
>>> data = (0, MyClass({'hello': 'world'}))
>>> lens(data)[1].attr['hello'].set('everyone')
(0, MyClass({'hello': 'everyone'}))

If you wish to apply a function using a lens you can use the modify method:

>>> lens([1, 2, 3])[0].modify(lambda a: a + 10)
[11, 2, 3]

You can call methods on the data using call. Note that this method should return new data to include in the data-structure:

>>> lens([1, {2}, 3])[1].call('union', {4, 5})
[1, {2, 4, 5}, 3]

Lenses will also pass most operators through to the data they’re focused on. This makes using lenses in your code much more readable:

>>> lens([1, 2, 3])[0] + 10
[11, 2, 3]

Lenses work best when you have to manipulate highly nested data structures that hold a great deal of state, such as when programming games:

>>> from collections import namedtuple
>>> GameState = namedtuple('GameState', 'worlds current_world current_level')
>>> World = namedtuple('World', 'levels theme')
>>> Level = namedtuple('Level', 'map enemies')
>>> Enemy = namedtuple('Enemy', 'x y')
>>> old_state = GameState({
...     1: World({}, 'grassland'),
...     2: World({
...         1: Level({}, {
...             'goomba1': Enemy(100, 45),
...             'goomba2': Enemy(130, 45),
...             'goomba3': Enemy(160, 45),
...         }),
...     }, 'desert'),
... }, 1, 1)
>>> new_state = lens(old_state).worlds[2].levels[1].enemies['goomba3'].x + 1

With the structure above, that last line of code produces a new GameState object where the third enemy on the first level of the second world has been moved across by one pixel without any of the objects in the original state being mutated. Without lenses this would take a rather large amount of plumbing to achieve.

Note that the lens does not make a deep copy of the entire state. Objects in the state that do not need to change are reused and no new copies are made. This makes lenses more memory efficient than using copy.deepcopy for sufficiently large states:

>>> old_state.worlds[1] is new_state.worlds[1]
>>> old_state.worlds[2] is new_state.worlds[2]

### Unbound Lenses

If you pass no arguments to the lens function then you will get an unbound Lens object. An unbound lens can be manipulated in all the ways that a bound lens can except that you can’t call any of the methods that manipulate the state (such as get and set).

>>> unbound_lens = lens()
>>> key_one = unbound_lens['one']

You can then attach a state to the lens using the bind method and call state manipulating methods as normal:

>>> key_one.bind({'one': 1, 'two': 2}).get()

Alternatively, you can call the state manipulating method as normal and pass in a keyword-only state argument for the method to act on:

>>> key_one.get(state={'one': 1, 'two': 2})

You can use unbound Lens objects as descriptors. That is, if you set a lens as a class attribute and you access that attribute from an instance, you will get a lens that has been bound to that instance. This allows you to conveniently store and access lenses that are likely to be used with particular classes as attributes of those classes. Attribute access is much more readable than requiring the user of a class to construct a lens themselves.

>>> class ClassWithLens:
...     def __init__(self, items):
...         self._private_items = items
...     def __repr__(self):
...         return 'ClassWithLens({!r})'.format(self._private_items)
...     first = lens()._private_items[0]
>>> my_instance = ClassWithLens([1, 2, 3])
>>> my_instance.first.set(4)
ClassWithLens([4, 2, 3])

If you ever end up focusing an object with a lens as one of its attributes then you can use that lens by accessing the attribute with an extra _l at the end:

>>> data = [ClassWithLens([1, 2, 3]), ClassWithLens([4, 5, 6])]
>>> lens(data)[1].first_l.set(7)
[ClassWithLens([1, 2, 3]), ClassWithLens([7, 5, 6])]

### Composing Lenses

If you have two lenses, you can join them together using the add_lens method. Joining lenses means that one of the lenses is placed “inside” of the other so that the focus of one lens is fed into the other one as its state:

>>> first = lens()[0]
>>> second = lens()[1]
>>> first_then_second = first.add_lens(second)
>>> first_then_second.bind([[2, 3], [4, 5]]).get()
>>> second_then_first = second.add_lens(first)
>>> second_then_first.bind([[2, 3], [4, 5]]).get()

When you call a.add_lens(b), b must be an unbound lens and the resulting lens will be bound to the same object as a, if any.

### Lenses that do computation

So far we’ve seen lenses that extract data out of data-structures, but lenses are more powerful than that. Lenses can actually perform arbitrary computation on the data passing through them as long as that computation can be reversed.

A simple example is that of the item_ method which returns a lens that focuses on a single key of a dictionary but returns both the key and the value:

>>> l = lens({'one': 1})
>>> l.item_('one').get()
('one', 1)
>>> l.item_('one').set(('three', 3))
{'three': 3}

There are a number of such more complicated lenses defined on Lens. To help avoid collision with accessing attributes on the state, their names all end with a single underscore. See help(lenses.Lens) in the repl for more. If you need to access an attribute on the state that has been shadowed by Lens’ methods then you can use Lens.getattr_(attribute).

At their heart, lenses are really just souped-up getters and setters. If you have a getter and a setter for some data then you can turn those into a lens using the getter_setter_ method. Here is a lens that focuses some text and interprets it as json data:

>>> import json
>>> def setter(state, value):
...     return json.dumps(value)
>>> json_lens = lens().getter_setter_(json.loads, setter)
>>> my_data = json_lens.bind('{"numbers":[1, 2, 3]}')
>>> my_data.get()
{'numbers': [1, 2, 3]}
>>> my_data['numbers'][1].set(4)
'{"numbers": [1, 4, 3]}'

This is just an example; the json lens defined above is already available with the json_ method. See the docstrings for both these methods for details on how to use them.

### Traversals

All the lenses so far have focused a single object inside a state, but it is possible for a lens to have more than one focus. A lens with multiple foci is usually referred to as a traversal. A simple traversal can be made with the _both method. Lens.both_ focuses the two objects at indices 0 and 1 within the state. It is intended to be used with tuples of length 2, but will work on any indexable object.

One issue with multi-focus lenses is that the get method only ever returns a single focus. It will return the _first_ item focused by the traversal. If you want to get all the items focused by a lens then you can use the get_all method which will return those objects in a list:

>>> lens([0, 1, 2, 3]).both_().get_all()
[0, 1]

Setting works with a traversal, though all foci will be set to the same object.

>>> lens([0, 1, 2, 3]).both_().set(4)
[4, 4, 2, 3]

Modifying is the most useful operation you can perform. The modification will be applied to all the foci independently. All the foci must be of the same type (or at least be of a type that supports the modification that you want to make).

>>> lens([0, 1, 2, 3]).both_().modify(lambda a: a + 10)
[10, 11, 2, 3]
>>> lens([0, 1.0, 2, 3]).both_().modify(str)
['0', '1.0', 2, 3]

You can of course use the same shortcut for operators that single-focus lenses allow:

>>> lens([0, 1, 2, 3]).both_() + 10
[10, 11, 2, 3]

Traversals can be composed with normal lenses. The result is a traversal with the lens applied to each of its original foci:

>>> both_first = lens([[0, 1], [2, 3]]).both_()[0]
>>> both_first.get_all()
[0, 2]
>>> both_first + 10
[[10, 1], [12, 3]]

Traversals can also be composed with other traversals just fine. They will simply increase the number of foci targeted. Note that get_all returns a flat list of foci; none of the structure of the state is preserved.

>>> both_twice = lens([[0, 1], [2, 3]]).both_().both_()
>>> both_twice.get_all()
[0, 1, 2, 3]
>>> both_twice + 10
[[10, 11], [12, 13]]

A slightly more useful traversal method is each_. each_ will focus all of the items in a data-structure analogous to iterating over it using python’s iter and next. It supports most of the built-in iterables out of the box, but if you want to use it on your own objects then you will need to add a hook yourself.

>>> lens([1, 2, 3]).each_() + 10
[11, 12, 13]

The values_ method returns a traversal that focuses all of the values in a dictionary. If we return to our GameState example from earlier, we can use values_ to move _every_ enemy in the same level 1 pixel over to the right in one line of code:

>>> _ = lens(old_state).worlds[2].levels[1].enemies.values_().x + 1

Or you could do the same thing to every enemy in the entire game (assuming that there were other enemies on other levels in the GameState):

>>> _ = (lens(old_state).worlds.values_()
...                     .levels.values_()
...                     .enemies.values_().x) + 1

## License

python-lenses is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see

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