Python-Object-Oriented-Programming---4th-edition

Code Repository for Python Object-Oriented Programming - 4th edition, Published by Packt

The case study relies on a number of external packages. It's often best to start with a tool like conda to build virtual environments and download packages. This can also be done with other virtual environment managers.

The following steps will help you build a working Python environment. Later, we'll add the additional packages used by the case study.

1. Get miniconda. Find it here: https://docs.conda.io/en/latest/miniconda.html.

2. Install miniconda. Find the instructions here: https://conda.io/projects/conda/en/latest/user-guide/install/index.html. For the most part, the instructions can be summarized as "double-click the installer." There are potential complications, so it can help to read through the the instructions.

3. Use the conda tool to create a virtual environment that has Python. We'll add the required packages for this case study.

   % conda create -n CaseStudy python=3.9
   % conda activate CaseStudy

4. Now that this is available, you can run Python. Try the following:

   % python
   >>> print("Hello, world!")
   Hello, world!
   >>> exit()

Installing Other Components

Now that you have a working Python environment, we can add some of packages we'll be using.

    % conda install bs4 pytest pillow

This will ask if you want to proceed. The answer is "y", for "yes." It will then download and install the four packages listed above, plus the packages they depend on.

Not every package can be installed by conda, so PIP is sometimes needed. Specifically, we want to automate our testing with the tox tool, which isn't easily installed by conda.

    % python -m pip install tox

The file environment.yml has the exported environment used to produce this example.

Once PIP has been run, conda can lose track of the extra installations. To make changes, it's often helpful to create a new conda environment with the packages available from conda, and then add PIP packages.

Your versions may be slightly newer than the ones used by the author.

Multiple Python Version Setup

The full test suite requires multiple versions of Python and the tox utility.

There is some complexity when using Windows. See https://tox.readthedocs.io/en/latest/developers.html?highlight=windows#multiple-python-versions-on-windows

The easiest way to do this is to create an additional conda environment,

    conda create --name=tox-py38 python=3.8

This environment will have the needed Python run-time.

For Windows, only, edit the python3.8.bat file to point to this environment's executables. Generally, the name supplied will be correct.

Running the Test Suite

The test suite requires tox and Python 3.8 (see above for additional installs.)

Use the following command to run all of the tests.

    % make test

To run tests for a specific chapter, you can change the working director and run tox. Here's an exception for Chapter 2.

    % (cd ch_02; tox)

This will change to the chapter 2 directory, ch_02, and run tox in that directory.

For Windows, use cmd:

    C:\path\to\repo> cmd /c "CHDIR ch_02&&tox"

An Integrated Development Environment

You can edit Python code with any text editor. It can be easier to use a sophisticated IDE, but some developers are happy with simple text editors. There's no "best" IDE for Python. While the author uses PyCharm and Komodo Edit, some people prefer VS Code, or Spyder.

Good UML diagrams can be created with the http://draw.io diagram editor. This creates drawio text files that can saved as part of a project. It can export PNG files for publication. This is very easy to install and use.

Another choice is to use plantuml. See https://plantuml.com. This can be incorporated into a markdown processing plug-in used by the PyCharm IDE. The plugin depends on graphviz, making the installation fairly complex.

• Add the Markdown tool to PyCharm.

• In the preferences for Markdown, install and enable PlantUML.

• Use conda to install graphviz as well as installing the plantuml-markdown tools. The markdown_py application can create an HTML draft of a Markdown doc. It needs to be installed separately, if this is needed.

• Update the OS environment settings to set the GRAPHVIZ_DOT environment variable to name the conda virtual environment. where graphviz was installed. The macOS and Linux users should update their ~/.zshrc or ~/.bashrc file, depending on which shell is in use. Windows users should set their system environment variables.

• It may be necessary to create a plantuml shell script in /usr/local/bin. See https://github.com/mikitex70/plantuml-markdown for details on installation.

The plantuml can be used to tranform UML files to PNG images.

The Project Structure

Each chapter's code is in a separate directory, ch_01, ch_02, etc.

Within the chapter, there's some combination of docs, src, and tests folders. There will also be a pyproject.toml file with parameters used to control tools like tox.

Chapters and Case Study Content

1. Object-Oriented Design. Creating the 4+1 views of the problem domain.

2. Objects in Python. Core data model of samples and training data.

3. When Objects Are Alike. Algorithm Alternatives for k-NN -- euclidean, manhattan, chebyshev, minkowski

4. Expecting the Unexpected. Central authentication and authorization for a web service.

5. When to Use Object-Oriented Programming. Input validations for training data as well as requests. Properties. Context Managers.

6. Abstract Base Classes (abc’s) and Operator Overloading. Filtering and subsetting the training data to create test sets. Shuffling. Sorting. Random Selection. Filters.

7. Python Data Structures. The @dataclass definitions and NamedTuple implementation choices.

8. Functional Techniques. The essential k-NN algorithm as a functional design. Computing test results for different K values and distance algorithms.

9. Strings and Serialization. JSON serialization and deserialization of training data, requests and responses.

10. The Iterator Pattern. Revisiting the k-NN design to permit future flexibility.

11. Common Design Patterns. (No case study.)

12. Advanced Design Patterns. (No case study.)

13. Testing Object-Oriented Programs. Using Test-Driven Develoment on a small ciphering algorithm.

14. Concurrency. Compressing image files using Run-Length Encoding.

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