lead_pipe

Lead Pipe allows values to be piped from one function to the next without nesting the calls. For example, the following blocks of code are equivalent.

a = foo(bar(baz(8, 2), a=3), 4)

from lead_pipe import Pipe
a = ~Pipe(8)(baz, 2)(bar, a=3)(foo, 4)

Basic Use

Creating a Pipe

Pipelines begin with a base value passed to the Pipe constructor.

>>> from lead_pipe import Pipe
>>> Pipe(3)
Pipe(3)
>>> Pipe('a')
Pipe('a')

Piping Results

Each instance of Pipe is callable and takes any number of arguments (at least one). The first argument is a function that gets called with the pipeline's value followed by the additional arguments and keyword arguments, if any.

>>> Pipe(3)(lambda x: x + 0.5)(lambda x: x ** 2)(int)('{} - {x} + {}'.format, 1, x=8)(eval)
Pipe(5)

Obtaining Result

Once your pipeline is finished, you can retrieve the result with the tilde (~) operator or through the pipe's 'value' attribute.

>>> ~Pipe(11)
11
>>> Pipe('foo').value
'foo'
>>> ~Pipe(2)(pow, 3)(str)
'8'

Advanced Features

Intermediate Pipes

Since each step along the pipeline is its own instance of Pipe, an intermediate pipe can be saved to pipe to multiple functions.

>>> p = Pipe(4)(range)(zip, range(2, 6))
>>> p
Pipe([(0, 2), (1, 3), (2, 4), (3, 5)])
>>> p(dict).value[2]
4
>>> ~p(lambda x: x[1][1])
3

Apply

Sometimes a function may return another function rather than a value to be piped to another function. Apply is a helper function that continues the pipeline with the function returned.

>>> from lead_pipe import apply
>>> ~Pipe('{} foo{a} {}'.format)(apply, 'bar', 'baz', a=3)
'bar foo3 baz'

Reflect

Sometimes one may want to call a member function of a value in the pipeline. One way would be to pipe the value to getattr then to apply, but the reflect function is the combination of the two.

>>> from lead_pipe import reflect
>>> ~Pipe({'a': 1, 'b': 2})(reflect, 'get', 'a')
1

In this specific example, one could pipe the dictionary to dict.get, but reflect is more general.