README.rst

Python piper

From the genial idea by Toby Rosen, pyp: http://code.google.com/p/pyp/

Pyper.py is a shell command which allows to process line-based data files using python expressions.

How to install? Copy pyper.py on a server and make it executable, or install it in a virtualenv, or in your system if you have root permissions:

pip install git+https://github.com/danse/pyper.git

Pyper works with python 2.6+ (python 3 included). How to play? Given a test file containing:

2
3
4

Process it line by line with piped python evaluable statements (no assignments). p has always the result returned by the previous pipe stage, pp is the whole list:

pyper.py ' int(p) | sum(pp) ' < test
9

Feed it with unix pipes, and forget awk! For example, the following will ist the ten top-level directories containing more files, excluding hidden files and dirs (this requires python 2.7 for the Counter):

find | pyper.py ' "/." not in p & p.split("/") | p[1] | collections.Counter(pp).most_common(10) '

The same, but at any level:

find | pyper.py ' "/." not in p & p.split("/") | p[:-2] | "/".join(p) | collections.Counter(pp).most_common(10) '

A quite contorted example, find the file with the oldest change time in a directory:

find | pyper.py 'p, os.stat(p).st_ctime | p[0], datetime.datetime.fromtimestamp(p[1]) | min(pp, key=lambda x:x[1]) | [str(i) for i in p]'

You can wrap it up in a good old bash function, and play with it around:

oldest_in () { find $1 | pyper.py 'p, os.stat(p).st_ctime | p[0], datetime.datetime.fromtimestamp(p[1]) | min(pp, key=lambda x:x[1]) | [str(i) for i in p]'; }
oldest_in <that_dir>

If you get exceptions, they are going to standard output. They are not going to your next unix pipe stage and you can filter them out using 2> /dev/null.

Other examples

The following examples can be run like the one before. They are written this way to work also like actual automatic tests.

>>> from pyper import test
>>> matrix_ = '''
... 234 4 6
... 2323 5 2
... 6546 7 5
... 675
... '''
>>> test('p.split() | p[2] | int(p) - 3', matrix_)
3
-1
2

Other kinds of processing steps

Aside from |, other kinds of pipe serialization are made up with the bitwise comparison operators ^ and &.

Produce

The result of ^ is iterated, so it acts like a multiplier. Use it instead of | to make the downstream pipe stage iterate the result of a preceding pipe stage:

>>> test('p |', 'iterable')
iterable
>>> test('p ^', 'iterable')
i
t
e
r
a
b
l
e
>>> test('p.split() ^', 'iter on words')
iter
on
words
>>> test('p.split() ^ "flattened "+p', matrix_)
flattened 234
flattened 4
flattened 6
flattened 2323
flattened 5
flattened 2
flattened 6546
flattened 7
flattened 5
flattened 675

Reduce

The result of & is converted to boolean, and it feeds the downstream stage just if turns to be True. It doesn't feeds the downstream stage with its result (after all, it's just True or False), but with it's original p value.

>>> test('p.split() ^  2 < int(p) < 10 &', matrix_)
4
6
5
7
5

Filter blank lines:

>>> input_ = '''
...
... this matters
...
... '''
>>> test(' p &', input_)
this matters

Act like grep:

>>> input_ = '''
... just noise
... use it like grep
... if you want
... '''
>>> test(' "grep" in p &', input_)
use it like grep

Complex cases and mixing pp with p

The pyper pipeline is made up from generators. This makes it extremely efficient (you can feed it with gigabyte files without taking system memory), but it causes some unintuitive behaviour when using pp. pp is a generator and it cannot be used directly as a list. You will have to convert it to a list before:

>>> letters = '''a
... b
... c
... d
... e
... '''
>>> test('pp[3:-1]', letters)            # This won't work (pp is a generator)
>>> test('list(pp) | p[2:-1]', letters)  # This will
['c', 'd']

When using p together with pp, the latter will dominate over the number of results, and so the whole expression will be evaluated just one time for all input values.

>>> test('p, p, p + p, list(pp)', letters)
('a', 'a', 'aa', ['a', 'b', 'c', 'd', 'e'])