Metadata-Version: 1.0
Name: spout
Version: 0.1.1
Summary: A simple framework that makes it easy to work with data streams in Python.
Home-page: http://github.com/daviesjamie/spout
Author: Jamie Davies
Author-email: jamie@jamiedavies.me
License: MIT
Description: Spout
        ====
        
        Spout is a small and simple framework that makes it easy to work with data
        streams in Python. In particular, Spout was designed with the processing and
        consumption of live data sources in mind.
        
        
        How it works
        ------------
        
        At the heart of Spout is the concept of a Stream (which is defined in an
        abstract `Stream` class). This defines the basic operations that can be
        performed upon a data stream:
        
        ### Mapping
        The items in one stream can me "mapped" to another stream. This is done by
        applying a supplied `Function` to each item in the input stream, to produce
        another output stream.
        
        ```python
        stream.map(Function)
        ```
        
        ### Filtering
        The items in a stream can be "filtered", so that the resultant stream only
        contains items that match a given criteria. This is done by using a supplied
        `Predicate` to test each item in the input stream, and copies it to the output
        stream if it passes the test criteria.
        
        ```python
        stream.filter(Predicate)
        ```
        
        ### Processing (Consuming)
        The items in a stream are used in some calculations or functionality that
        provides no further output to the stream. This is done by applying the supplied
        Operation to each item in the stream.
        
        ```python
        stream.for_each(Operation)
        ```
        
        
        Usage
        -----
        
        To use Spout, you first need to create an input data stream. A data stream is simply an
        instantiation of a `Stream` or any of its children (which can be found in the
        [`streams.py`](https://github.com/daviesjamie/spout/blob/master/spout/streams.py) file). The Stream class has been specifically designed so that it
        is easy to extend and wrap around currently existing data sources that you might
        have, such as files or databases.
        
        Some existing examples of stream data sources can by found in [`sources.py`](https://github.com/daviesjamie/spout/blob/master/spout/sources.py).
        
        For example, to create a Stream out of the lines in a plain text file:
        
        ```python
        from spout.sources import FileInputStream
        s = FileInputStream("test.txt")
        ```
        
        Now that you have your data in a stream, you simply have to process it! This can
        be done by creating and using your own Functions, Predicates or Operations
        (see above).
        
        For example, to print out all the lines in a text file that start with a digit,
        but with the digit stripped, we can create our own Predicate and Function
        and pass these to the `.filter()` and `.map()` functions:
        
        ```python
        from spout.sources import FileInputStream
        from spout.structs import Function, Predicate
        from spout.utils import PrintOperation
        
        
        class StartsWithDigit(Predicate):
            def test(self, obj):
                return obj[0].is_digit()
        
        
        class StripFirstChar(Function):
            def apply(self, input):
                return input[1:]
        
        
        s = FileInputStream("test.txt")
        s \
            .filter(StartsWithDigit()) \
            .map(StripFirstChar()) \
            .for_each(PrintOperation())
        ```
        
        Installation
        ------------
        
        Spout is available in the Python Package Index (PyPI), and so the easiest way to
        install it is through `pip`:
        
        ```
        $ pip install spout
        ```
        
        However, it is also possible to install the repository from the source, through
        the `setup.py` utility:
        
        ```
        $ python setup.py install
        ```
        
Platform: UNKNOWN
