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Python wrapper for stdlib (and your) objects to give them a fluent interface.

Project Description
The Fluent python library
=========================

Fluent helps you write more object-oriented and concise python code.

It is inspired by jQuery and underscore / lodash from the javascript
world. It also takes some inspiration from Ruby / SmallTalk -- in
particluar, collections and how to work with them.

Please Note: **This library is an experiment.** It is based on a wrapper
that aggressively wraps anything it comes in contact with and tries to
stay invisible. We'll address this in section `Caveats <#caveats>`__
below.

Introduction: Why use fluent?
-----------------------------

The Python standard library includes many useful, time-saving
convenience methods such as ``map``, ``zip``, ``filter`` and ``join``.
The problem that motivated me to write fluent is that these convenience
methods are often available as free functions or on (arguably) the wrong
object.

For example, ``map``, ``zip``, and ``filter`` all operate on iterable
objects but they are implemented as free functions. This not only goes
against my sense of how object oriented code should behave, but more
importantly, writing python code using these free functions requires
that the reader must often mentally skip back and forth in a line of
code to understand what it does, making the code more difficult to
understand.

Let's use the following simple example to analyse this problem:

::

>>> map(print, map(str.upper, sys.stdin.read().split('\n')))

How many backtrackings did you have to do? I read this code as follows:

I start in the middle at ``sys.stdin.read().split('\n')``, then I
backtrack to ``map(str.upper, …)``, then to ``map(print, …)``. I also
have to make sure that the parenthesis all match up.

I find code like this hard to write and hard to understand, as it
doesn't follow the way I think about this statement. I don't like to
have to write or read statements from the inside out and wrap them using
my editor as I write them. As demonstrated above, it's also hard to read
- requiring quite a bit of backtracking.

One alternative to the above approach is to use list comprehension /
generator statements like this:

::

>>> [print(line.upper()) for line in sys.stdin.read().split('\n')]

This is clearly better: I only have to skip back and forth once instead
of twice.

This approach still leaves room for improvement though because I have to
find where the statement starts and to then backtrack to the beginning
to see what is happening. Adding filtering to list comprehensions
doesn't help:

::

>>> [print(line.upper()) for line in sys.stdin.read().split('\n') if line.upper().startswith('FNORD')]

The backtracking problem persists. Additionally, if the filtering has to
be done on the processed version (here artificially on
``line.upper().startswith()``) then the operation has to be applied
twice - which sucks because you have to write it twice, but also because
it is computed twice.

The solution? Nest them!

::

>>> [print(line) for line in \
>>> (line.upper() for line in sys.stdin.read().split('\n')) \
>>> if line.startswith('FNORD')]

Which gets us back to all the initial problems with nested statements
and manually having to check for the right ammount of closing parens.

Compare it to this:

::

>>> for line in sys.stdin.read().split('\n'):
>>> uppercased = line.upper()
>>> if uppercased.startswith('FNORD'):
>>> print(uppercased)

Almost all my complaints are gone. It reads and writes almost completely
in order it is computed.

Easy to read, easy to write. So that is usually what I end up doing.

But it has a huge drawback: It's not an expression - it's a bunch of
statements.

Why is that bad? Because it means, that it's not easily combinable and
abstractable with higher order methods or generators. Because I have to
invent variable names for things that are completely clear from context
and that just serve as grease to express the flow of data through the
program.

Don't get me wrong, this is the most important function of variables in
programs. But in this case, it just makes the code longer and makes it
harder to see how data flows through the expressions.

Plus (drumroll): parsing this still requires some backtracking and
buildup of mental state to read.

Oh well.

Lets see this in action:

::

>>> cross_product_of_dependency_labels = \
>>> set(map(frozenset, itertools.product(*map(attrgetter('_labels'), dependencies))))

That certainly is hard to read (and write). Pulling out explaining
variables, makes it better. Like so:

::

>>> labels = map(attrgetter('_labels'), dependencies)
>>> cross_product_of_dependency_labels = set(map(frozenset, itertools.product(*labels)))

Better, but still hard to read. Sure, those explaining variables are
nice and sometimes essential to understand the code. - but it does take
up space in lines, and space in my head while parsing this code. The
question would be - is this really easier to read than something like
this?

::

>>> cross_product_of_dependency_labels = (
... _(dependencies)
... .map(_.each._labels)
... .star_call(itertools.product)
... .map(frozenset)
... .call(set)
... ._
... )

Sure you are not used to this at first, but consider the advantages. The
intermediate variable names are abstracted away - the data flows through
the methods completely naturally. No jumping back and forth to parse
this at all. It just reads and writes exactly in the order it is
computed.

To me this means, that what I think that I want to accomplish, I can
write down directly in order. And I don't have to keep track of extra
closing parantheses at the end of the expression.

So what is the essence of all of this?

Python is an object oriented language - but it doesn't really use what
object orientation has tought us about how we can work with collections
and higher order methods in the languages that came before it (I think
of SmallTalk here, but more recently also Ruby). Why can't I make those
beautiful fluent call chains that SmallTalk could do 20 years ago in
Python today?

Well, now I can and you can too.

Features
--------

To enable this style of coding this library has some features that might
not be so obvious at first.

Importing the library
~~~~~~~~~~~~~~~~~~~~~

It is recomended to import and use the library by renaming it to
something locally unique.:

::

>>> import fluentpy as _f

or

::

>>> import fluentpy as _

I prefer ``_`` for small projects and ``_f`` for larger projects where
gettext is used.

If you want you can also import the library in the classic way:

::

>>> from fluentpy import _, lib, each

But it is not required to import all these symbols, as they are all also
available as attributes on ``_``. Also, the library exposes itself as an
executable module, i.e. the module ``fluentpy`` itself is the central
wrapper function and can be used directly by renaming it to what you
need locally.

Aggressive (specialized) wrapping
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``_`` is actually the function ``wrap`` in the fluent module, which is a
factory function that returns a subclass of Wrapper, the basic and main
object of this library.

This does two things: First it ensures that every attribute access, item
access or method call off of the wrapped object will also return a
wrapped object. This means that once you wrap something, unless you
unwrap it explicitly via ``.unwrap`` or ``._`` it stays wrapped - pretty
much no matter what you do with it. The second thing this does is that
it returns a subclass of Wrapper that has a specialized set of methods,
depending on the type of what is wrapped. I envision this to expand in
the future, but right now the most usefull wrappers are: Iterable, where
we add all the python collection functions (map, filter, zip, reduce, …)
as well as a good batch of methods from itertools and a few extras for
good measure. Callable, where we add ``.curry()`` and ``.compose()`` and
Text, where most of the regex methods are added. `Explore the method
documentation for what you can do <>`__).

TODO add link!

Easy Shell Filtering with Python
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

It could often be super easy, to achieve somethign on the shell, with a
bit of python. But, the backtracking (while writing) as well as the
tendency of python commands to span many lines, makes this often just
impractical enough that you won't do it.

That's why fluent is an executable module, so that you can use it on the
shell like this:

::

$ python3 -m fluentpy "lib.sys.stdin.readlines().map(str.lower).map(print)"

In this mode, the variables 'lib', '\_' and 'each' are injected into the
namespace of of the python commands given as the first positional
argument.

Imports as expressions
~~~~~~~~~~~~~~~~~~~~~~

Import statements are (ahem) statements in python. This is fine, but can
be really annoying at times.

Consider this shell text filter written in python:

::

$ curl -sL 'https://www.iblocklist.com/lists.php' | egrep -A1 'star_[345]' \
> | python3 -c "import sys, re; from xml.sax.saxutils import unescape; \
> print('\n'.join(map(unescape, re.findall(r'value=\'(.*)\'', sys.stdin.read()))))"

Sure it has all the backtracking problems I talked about already. Using
fluent this could be much shorter.

::

$ curl -sL 'https://www.iblocklist.com/lists.php' \
> | egrep -A1 'star_[345]' \
> | python3 -c "import fluentpy as _; import sys, re; from xml.sax.saxutils import unescape; \
> _(sys.stdin.read()).findall(r'value=\'(.*)\'').map(unescape).map(print)"

This still leaves the problem that it has to start with this fluff

::

import fluentpy as _; import sys, re; from xml.sax.saxutils import unescape;

This doesn't really do anything to make it easier to read and write and
is almost half the characters it took to achieve the wanted effect.
Wouldn't it be nice if you could have some kind of object (lets call it
``lib`` for lack of a better word), where you could just access the
whole python library via attribute access and let it's machinery handle
importing behind the scenes?

Like this:

::

$ curl -sL 'https://www.iblocklist.com/lists.php' | egrep -A1 'star_[345]' \
> | python3 -m fluentpy "lib.sys.stdin.read().findall(r'value=\'(.*)\'') \
> .map(lib.xml.sax.saxutils.unescape).map(print)"

How's that for reading and writing if all the imports are inlined? Oh,
and of course everything imported via ``lib`` comes already pre-wrapped,
so your code becomes even shorter.

More formally:The ``lib`` object, which is a wrapper around the python
import machinery, allows to import anything that is accessible by import
to be imported as an expression for inline use.

So instead of

::

>>> import sys
>>> input = sys.stdin.read()

You can do

::

>>> input = _.lib.sys.stdin.read()

As a bonus, everything imported via lib is already pre-wrapped, so you
can chain off of it immediately.

Generating lambda's from expressions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``lambda`` is great - it's often exactly what the doctor ordered. But it
can also be annyoing if you have to write it down everytime you just
want to get an attribute or call a method on every object in a
collection.

::

>>> _([dict(fnord='foo'), dict(fnord='bar')]).map(lambda each: each['fnord']) == ['foo', 'bar]
>>> class Foo(object):
>>> attr = 'attrvalue'
>>> def method(self, arg): return 'method+'+arg
>>> _([Foo(), Foo()]).map(lambda each: each.attr) == ['attrvalue', 'attrvalue']
>>> _([Foo(), Foo()]).map(lambda each: each.method('arg')) == ['method+arg', 'method+arg']

Sure it works, but wouldn't it be nice if we could save a variable and
do this a bit shorter?

Python does have attrgetter, itemgetter and methodcaller - they are just
a bit inconvenient to use:

::

>>> from operator import itemgetter, attrgetter, methodcaller
>>> _([dict(fnord='foo'), dict(fnord='bar')]).map(itemgetter('fnord')) == ['foo', 'bar]
>>> class Foo(object):
>>> attr = 'attrvalue'
>>> def method(self, arg): return 'method+'+arg
>>> _([Foo(), Foo()]).map(attrgetter(attr)) == ['attrvalue', 'attrvalue']
>>> _([Foo(), Foo()]).map(methodcaller(method, 'arg')) == ['method+arg', 'method+arg']

To ease this the object ``_.each`` is provided, that just exposes a bit
of syntactic shugar for these (and the other operators). Basically,
everything you do to ``_.each`` it will do to each object in the
collection:

::

>>> _([1,2,3]).map(_.each + 3) == [4,5,6]
>>> _([1,2,3]).filter(_.each < 3) == [1,2]
>>> _([1,2,3]).map(- _.each) == [-1,-2,-3]
>>> _([dict(fnord='foo'), dict(fnord='bar')]).map(_.each['fnord']) == ['foo', 'bar]
>>> class Foo(object):
>>> attr = 'attrvalue'
>>> def method(self, arg): return 'method+'+arg
>>> _([Foo(), Foo()]).map(_.each.attr) == ['attrvalue', 'attrvalue']
>>> _([Foo(), Foo()]).map(_.each.call.method('arg')) == ['method+arg', 'method+arg']

I know ``_.each.call.*()`` is crude - but I haven't found a good syntax
to get rid of the .call yet. Feedback welcome.

Chaining off of methods that return None
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A major nuissance for using fluent interfaces are methods that return
None. Sadly, many methods in python return None, if they mostly exhibit
a side effect on the object. Consider for example ``list.sort()``.

This is a feature of python, where methods that don't have a return
statement return None.

While this is way better than e.g. Ruby where that will just return the
value of the last expression - which means objects constantly leak
internals - it is very annoying if you want to chain off of one of these
method calls.

Fear not though, fluent has you covered. :)

Fluent wrapped objects will have a ``self`` property, that allows you to
continue chaining off of the previous self.

::

>>> _([3,2,1]).sort().self.reverse().self.call(print)

Even though both sort() and reverse() return None

Of course, if you unwrap at any point with ``.unwrap`` or ``._`` you
will get the true return value of ``None``.

Caveats
-------

If you do not end each fluent statement with a ``.unwrap`` or ``._``
operation to get a normal python object back, the wrapper will spread in
your runtime image like a virus, 'infecting' more and more objects
causing strange side effects. So remember: Always religiously unwrap
your objects at the end of a fluent statement, when using fluent in
bigger projects.

::

>>> _("foo").uppercase().match('(foo)').group(0)._

That being said, ``str()`` and ``repr()`` output is clearly marked, so
this is easy to debug. Also, not having to unwrap is perfect for short
scripts and especially 'one-off' shell commands. Use fluents power
wisely!

Famous Last Words
-----------------

This library tries to do a little of what libraries like underscore or
lodash or jQuery do for Javascript. Just provide the missing glue to
make the standard library nicer and easier to use - especially for short
oneliners or short script. Have fun!

I envision this to be very usefull in quick python scripts and shell one
liners and filters, where python was previously just that little bit too
hard to use, that 'overflowed the barrel' and prevented you from doing
so.


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