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Mastering Object-oriented Python

By : Steven F. Lott, Steven F. Lott
Book Image

Mastering Object-oriented Python

By: Steven F. Lott, Steven F. Lott

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Mastering Object-oriented Python
Steven F. Lott | Steven F. Lott
Apr, 2014 | 634 pages
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Table of Contents (26 chapters)
Mastering Object-oriented Python
Mastering Object-oriented Python
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Some Preliminaries
Some Preliminaries
Free Chapter
1
The __init__() Method
The __init__() Method
The implicit superclass – object
The base class object __init__() method
Implementing __init__() in a superclass
Using __init__() to create manifest constants
Leveraging __init__() via a factory function
Implementing __init__() in each subclass
Simple composite objects
Complex composite objects
Stateless objects without __init__()
Some additional class definitions
Multi-strategy __init__()
Yet more __init__() techniques
Summary
2
Integrating Seamlessly with Python Basic Special Methods
Integrating Seamlessly with Python Basic Special Methods
The __repr__() and __str__() methods
The __format__() method
The __hash__() method
The __bool__() method
The __bytes__() method
The comparison operator methods
The __del__() method
The __new__() method and immutable objects
The __new__() method and metaclasses
Summary
3
Attribute Access, Properties, and Descriptors
Attribute Access, Properties, and Descriptors
Basic attribute processing
Creating properties
Using special methods for attribute access
The __getattribute__() method
Creating descriptors
Summary, design considerations, and trade-offs
4
The ABCs of Consistent Design
The ABCs of Consistent Design
Abstract base classes
Base classes and polymorphism
Callables
Containers and collections
Numbers
Some additional abstractions
The abc module
Summary, design considerations, and trade-offs
5
Using Callables and Contexts
Using Callables and Contexts
Designing with ABC callables
Improving performance
Using functools for memoization
Complexities and the callable API
Managing contexts and the with statement
Defining the __enter__() and __exit__() methods
Context manager as a factory
Summary
6
Creating Containers and Collections
Creating Containers and Collections
ABCs of collections
Examples of special methods
Using the standard library extensions
Creating new kinds of collections
Defining a new kind of sequence
Creating a new kind of mapping
Creating a new kind of set
Summary
7
Creating Numbers
Creating Numbers
ABCs of numbers
The arithmetic operator's special methods
Creating a numeric class
Computing a numeric hash
Implementing other special methods
Optimization with the in-place operators
Summary
8
Decorators and Mixins – Cross-cutting Aspects
Decorators and Mixins – Cross-cutting Aspects
Class and meaning
Using built-in decorators
Using standard library mixin classes
Writing a simple function decorator
Parameterizing a decorator
Creating a method function decorator
Creating a class decorator
Adding method functions to a class
Using decorators for security
Summary
9
Serializing and Saving – JSON, YAML, Pickle, CSV, and XML
Serializing and Saving – JSON, YAML, Pickle, CSV, and XML
Understanding persistence, class, state, and representation
Filesystem and network considerations
Defining classes to support persistence
Dumping and loading with JSON
Dumping and loading with YAML
Dumping and loading with pickle
Dumping and loading with CSV
Dumping and loading with XML
Summary
10
Storing and Retrieving Objects via Shelve
Storing and Retrieving Objects via Shelve
Analyzing persistent object use cases
Creating a shelf
Designing shelvable objects
Searching, scanning, and querying
Designing an access layer for shelve
Creating indexes to improve efficiency
Adding yet more index maintenance
The writeback alternative to index updates
Summary
11
Storing and Retrieving Objects via SQLite
Storing and Retrieving Objects via SQLite
SQL databases, persistence, and objects
Processing application data with SQL
Mapping Python objects to SQLite BLOB columns
Mapping Python objects to database rows manually
Improving performance with indices
Adding an ORM layer
Querying post objects given a tag string
Improving performance with indices
Summary
12
Transmitting and Sharing Objects
Transmitting and Sharing Objects
Class, state, and representation
Using HTTP and REST to transmit objects
Implementing a REST server – WSGI and mod_wsgi
Using Callable classes for WSGI applications
Creating a secure REST service
Implementing REST with a web application framework
Using a message queue to transmit objects
Summary
13
Configuration Files and Persistence
Configuration Files and Persistence
Configuration file use cases
Representation, persistence, state, and usability
Storing the configuration in the INI files
Handling more literals via the eval() variants
Storing the configuration in PY files
Why is exec() a nonproblem?
Using ChainMap for defaults and overrides
Storing the configuration in JSON or YAML files
Storing the configuration in property files
Storing the configuration in XML files – PLIST and others
Summary
14
The Logging and Warning Modules
The Logging and Warning Modules
Creating a basic log
Configuration gotcha
Specializing logging for control, debug, audit, and security
Using the warnings module
Advanced logging – the last few messages and network destinations
Summary
15
Designing for Testability
Designing for Testability
Defining and isolating units for testing
Using doctest to define test cases
Using setup and teardown
The TestCase class hierarchy
Using externally defined expected results
Automated integration or performance testing
Summary
16
Coping With the Command Line
Coping With the Command Line
The OS interface and the command line
Parsing the command line with argparse
Integrating command-line options and environment variables
Customizing the help output
Creating a top-level main() function
Programming In The Large
Additional composite command design patterns
Integrating with other applications
Summary
17
The Module and Package Design
The Module and Package Design
Designing a module
Whole module versus module items
Designing a package
Designing a main script and the __main__ module
Designing long-running applications
Organizing code into src, bin, and test
Installing Python modules
Summary
18
Quality and Documentation
Quality and Documentation
Writing docstrings for the help() function
Using pydoc for documentation
Better output via the RST markup
Writing effective docstrings
Writing file-level docstrings, including modules and packages
More sophisticated markup techniques
Using Sphinx to produce the documentation
Writing the documentation
Literate programming
Summary
Index
Index

Storing the configuration in JSON or YAML files

We can store configuration values in JSON or YAML files with relative ease. The syntax is designed to be user friendly. We can represent a wide variety of things in YAML. We're somewhat restricted to a narrower variety of object classes in JSON. We can use a JSON configuration file that looks like the following code:

{
    "table":{
        "dealer":"Hit17",
        "split":"NoResplitAces",
        "decks":6,
        "limit":50,
        "payout":[3,2]
    },
    "player":{
        "play":"SomeStrategy",
        "betting":"Flat",
        "rounds":100,
        "stake":50
    },
    "simulator":{
        "samples":100,
        "outputfile":"p2_c13_simulation.dat"
    }
}

The JSON document looks like a dictionary of dictionaries. This is precisely the object that will be built when we load this file. We can load a single configuration file with the following code:

import json
config= json.load( "config.json" )

This allows us to use config['table']...

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