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https://github.com/povilasb/pycollection-pipelines

Collection pipeline pattern implementation in Python
https://github.com/povilasb/pycollection-pipelines

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Collection pipeline pattern implementation in Python

Lists

README 

        
=====

About

=====



.. image:: https://travis-ci.org/povilasb/pycollection-pipelines.svg?branch=master

.. image:: https://www.quantifiedcode.com/api/v1/project/7913d23626d3406fa334a88e962d8529/badge.svg

  :target: https://www.quantifiedcode.com/app/project/7913d23626d3406fa334a88e962d8529

  :alt: Code issues



Experimental `collection pipeline `_

pattern implementation in python.



.. code-block:: python



    cat('/tmp/file.txt') | filter('some line') | filter('some line 2') | out()



.. contents:: :local:



.. image:: basic_samples.gif



.. note::



    Library only works on Python 3.  There are no plans to support previous

    python versions.



Installation

============



::



    $ pip install collection-pipelines



Usage

=====



Every pipeline has a data source (**cat()**, **http()**, etc.) and an optional

data transformation/filtering and output processors.



::



         Source        Transformations      Output

    +---------------+----------------------+-------+

    |               |                      |       |

    v               v                      v       V

     echo('1.2.3.4') | split('.') | count() | out()



You can save a partial pipe and reuse it later.



.. code-block:: python



    from collection_pipelines import *



    word_list = echo('word1 word2 word3') | words()

    word_list | out() # will print the words to stdout



    word_list | filter(word2) | freq() | bar() # will draw a bar chart for word frequencies



Reference

=========



Sources

-------



**cat(file_name)**

  Reads the specified file. Sends items to the pipe line by line.



**echo(text)**

  Sends the specified text to the pipe.



**http(url)**

  Sends HTTP GET method to the specified URL and puts the response body to pipe.



Transformers, Filters

---------------------



**filter(value)**

  Filters out the items that match the specified value.



**head(N)**

  Passes only the first N items through the pipe.



**tail(N)**

  Passes only the last N items through the pipe.



**count()**

  Calculates the incoming items. When the pipeline source signals the end

  of the items, *count()* sends a single item - numbers of items, to the

  pipe.



**freq()**

  Calculates how many times each unique item appears on the pipe.

  When the source signals the end of the items, every unique item is

  sent together with it's repetition count.

  *freq()* outputs tuples: *('item_x', 8)*.



**unique()**

  Filters out the duplicate items.



**json(path)**

  Parses incoming items as json strings, extracts elements with the

  specified json path and sends them trough the pipe.

  E.g. let's say we have json '{"name": {"first": "Bob"}}' and we want to

  extract the first name.

  Then *path* would be "name.first".



**split(delimiter)**

  Splits the incoming items by the specified delimiter.

  Sends the resulting items through the pipeline one by one.



**words(text)**

  Splits the incoming text items into words and sends each word through

  the pipe.



Output Processors

-----------------



**out()**

  Outputs items to stdandard output.



**value()**

  Returns the collected items rather than outputting them somewhere.

  Useful when you want to store resulting pipeline items to variable.

  If more than one item passes the pipeline, the array of those items is

  returned.



**line()**

  Collects all items and draws a line chart.

  Items must be tuples where first item is X axis value, and second item

  is Y axis value.

  Chart is plotted using matplotlib.



**bar()**

  Collects all items and draws a bar chart.

  Items must be tuples where first item is X axis value, and second item

  is Y axis value.

  Chart is plotted using matplotlib.



**wordcloud()**

  Collects all text items and draws a word cloud.

  See: https://github.com/amueller/word_cloud



Development

===========



If you want to write your own sources, transformers or outputs there's

couple of base classes you should get familiar with.



Let's implement a very basic filter that forwards only even numbers.



.. code-block:: python



    from collection_pipelines import *



    class even(CollectionPipelineProcessor):

        def process(self, item):

            if isinstance(item, int):

                if item % 2 == 0:

                    self.receiver.send(item)



    echo([1, 2, 3]) | even() | out()



Sources

-------



Every source object must extend the *CollectionPipelineSource* class and

implement the *on_begin()* method.



E.g. this source will send random integer to a pipeline:



.. code-block:: python



    import random



    class rand_int(CollectionPipelineSource):

        def on_begin(self):

            self.receiver.send(random.randint(0, 1000))

            self.receiver.close()



Transformers, Filters

---------------------



Every transformer and filter is a python object that instantiates a class

that extends *CollectionPipelineProcessor* class.

All the work is done in *process()* method.

This methods receives an item passing the pipeline.



You might either ignore, transform or simply pass forward the items.

To send item further to the pipe use *self.receiver.send(item)*.



E.g. if you wanted to multiply all items, you could implement the method

like this



.. code-block:: python



    def process(self, item):

        self.receiver.send(item * 2)



Output processors

-----------------



Pipeline output processors must extend the *CollectionPipelineOutput* class.

Output processors are special in a way that they don't forwards the items

any further. They trigger the pipeline execution.



Implementing an output processor is very similar to implementing a transformer.



.. code-block:: python



    class stdout(CollectionPipelineOutput):

        def process(self, item):

            print(item)



Such processor would print an item as soon as it received one.

There's also a special method *on_done()*, which is called when all items

in the pipeline are processed.



E.g. if you wanted an output processor to print items only when you received

all of them, the class would look like



.. code-block:: python



    class stdout(CollectionPipelineOutput):

        def __init__(self):

            self.items = []



        def process(self, item):

            self.items.append(item)



        def on_done(self):

            for item in self.items:

                print(item)



More Samples

============



Bar Chart

---------



.. code-block:: python



    echo([('apples', 2), ('bananas', 5), ('oranges', 3)]) | bar()



.. image:: bar.png



Line Chart

----------



.. code-block:: python



    echo([(1, 10), (2, 7), (3, 5), (4, 5), (5, 8)]) | line()



.. image:: line.png



Word Cloud

----------



.. code-block:: python



    cat('README.rst') | wordcloud()



.. image:: wordcloud.png