tubo 0.1.1

Installation

pip install tubo

Usage

You have a source of iterable items and you want to perform various operations on them. In a Unix-like system you would write something like this:

cat foo.txt | op1 | op2 | op3

Using Tubo, instead, you would use Python and you would write something like this:

>>> output = tubo.pipeline(file('foo.txt'), op1, op2, op3)

And the output would be available for you, to print it or to further transform it as you prefer. The advantage is that you can write the operations in Python, giving you a lot of flexibility.

Create a pipeline

The central part of Tubo is the method tubo.pipeline. It accepts an arbitrary number of arguments, the first being a data source and the following being operations on iterable data, defined using python generators.

Each operation should `yield` something, so that the following operation can work.

Example: capitalize words that contain a i letter.

text = ['italy', 'germany', 'brazil', 'france', 'england',
    'argentina', 'peru', 'united states', 'australia',
    'sweden', 'china', 'poland', 'portugal']

def capitalize(lines):
    for line in lines:
        for word in line.split(","):
            yield word.capitalize()

def filter_wordwith_i(words):
    for word in words:
        if 'i' in word:
          yield word

output = tubo.pipeline(
    text,
    filter_wordwith_i,
    capitalize,
)

At this point, output is an iterable, and we can do anything we want with it. We can print it or further transform it.

Merge two or more iterables

Sometimes, you need to write functions that take two or more inputs, and process them. In this case, you need to write an operation that accepts a list of iterables.

Example: interleave lines from two or more files (such as the utility paste)

def interleave(listoflines):
    for lines in itertools.izip(*listoflines):
        yield ''.join(lines)

output = tubo.pipeline(
    (file('file1.txt'), file('file2.txt')),
    interleave
)

Consume iterators at C-speed

Once you have your pipeline, it’s time to consume it.

tubo.consume(output)

# Equivalent to:
#
# for element in output:
#     pass

This consumes the iterator at C-speed, and uses this recipe.

Examples

def uniq(lines):
    seen = set()
    for line in lines:
        if line not in seen:
            seen.add(line)
            yield line

def reverse_string(lines):
    for line in lines:
        yield ''.join(reversed(line))

def append_nlines(lines):
    for nlines, line in enumerate(lines):
        yield line
    yield "\nTotal Number of lines: {}".format(nlines+1)

output = tubo.pipeline(
    open(filename),
    uniq,
    reverse_string,
    append_nlines,
)

def select_Nth_word(N, lines):
    for line in lines:
        yield line.split(' ')[N]
select_first_word = functools.partial(select_Nth_word, 0)
select_second_word = functools.partial(select_Nth_word, 1)

def concatenate(words):
    for word1, word2 in words:
        yield "{} {}".format(word1, word2)

pipeline1 = tubo.pipeline(
    open(fname1),
    select_first_word,
)
pipeline2 = tubo.pipeline(
    open(fname2),
    select_second_word,
)
output = tubo.pipeline(
    tubo.merge(
        pipeline1,
        pipeline2,
    ),
    concatenate
)

Credits

The library was inspired from a post by Christoph Rauch.

History