<p>Quickly learn how to automate unit testing of Python 3 code with Python 3 automation libraries, such as doctest, unittest, nose, nose2, and pytest. </p> <p>This book explores the important concepts in software testing and their implementation in Python 3 and shows you how to automate, organize, and execute unit tests for this language. This knowledge is often acquired by reading source code, manuals, and posting questions on community forums, which tends to be a slow and painful process.</p> <p><i>Python Unit Test Automation</i> will allow you to quickly ramp up your understanding of unit test libraries for Python 3 through the practical use of code examples and exercises. All of which makes this book a great resource for software developers and testers who want to get started with unit test automation in Python 3 and compare the differences with Python 2.</p> <p>This short work is your must-have quick start guide to mastering the essential concepts of software testing in Python.</p>
<br>What You'll Learn: <br><ul>
<li>Essential concepts in software testing<br>
</li>
<li>Various test automation libraries for Python, such as doctest, unittest, nose, nose2, and pytest<br>
</li>
<li>Test-driven development and best practices for test automation in Python<br>
</li>
<li>Code examples and exercises<br>
</li>
</ul>
<br>Who This Book Is For:<br>Python developers, software testers, open source enthusiasts, and contributors to the Python community<br><br>

Python Unit Test Automation: Practical Techniques for Python Developers and Testers

Pajankar, Ashwin

Apress

<p>Write your own Digital Image Processing programs with the use of pillow, scipy.ndimage, and matplotlib in Python 3 with Raspberry Pi 3 as the hardware platform. This concise quick-start guide provides working code examples and exercises. Learn how to interface Raspberry Pi with various image sensors.<br><b>What You'll Learn</b><br></p>
<ul>
<li>Understand Raspberry Pi concepts and setup<br>
</li>
<li>Understand digital image processing concepts<br>
</li>
<li>Study pillow, the friendly PIL fork<br>
</li>
<li>Explore scipy.ndimage and matplotlib<br>
</li>
<li>Master use of the Pi camera and webcam<br>
</li>
</ul>
<b>Who This Book Is For</b><br>Raspberry Pi and IoT enthusiasts, digital image processing enthusiasts, Python and Open Source enthusiasts and professionals

Raspberry Pi Image Processing Programming: Develop Real-Life Examples with Python, Pillow, and SciPy

Computers & Technology

Computer Science

<p>Build an inexpensive cluster of multiple Raspberry Pi computers and install all the required libraries to write parallel and scientific programs in Python 3. This book covers setting up your Raspberry Pis, installing the necessary software, and making a cluster of multiple Pis. <br>Once the cluster is built, its power has to be exploited by means of programs to run on it. So, Raspberry Pi Supercomputing and Scientific Programming teaches you to code the cluster with the MPI4PY library of Python 3. Along the way, you will learn the concepts of the Message Passing Interface (MPI) standards and will explore the fundamentals of parallel programming on your inexpensive cluster. This will make this book a great starting point for supercomputing enthusiasts who want to get started with parallel programming. <br>The book finishes with details of symbolic mathematics and scientific and numerical programming in Python, using SymPi, SciPy, NumPy, and Matplotlib. You’ll see how to process signals and images, carry out calculations using linear algebra, and visualize your results, all using Python code. With the power of a Raspberry Pi supercomputer at your fingertips, data-intensive scientific programming becomes a reality at home.<br>What You Will Learn  </p>
<ul>
<li>Discover the essentials of supercomputing<br>
</li>
<li>Build a low-cost cluster of Raspberry Pis at home<br>
</li>
<li>Harness the power of parallel programming and the Message Passing Interface (MPI)<br>
</li>
<li>Use your Raspberry Pi for symbolic, numerical, and scientific programming<br>
</li>
</ul>
<br>Who This Book Is For<br>Python 3 developers who seek the knowledge of parallel programming, Raspberry Pi enthusiasts, researchers, and the scientific Python community.<br><br>

Raspberry Pi Supercomputing and Scientific Programming: MPI4PY, NumPy, and SciPy for Enthusiasts

<p><p>Get a quick start to learn, understand, and apply GNU Octave using a math- and programming-friendly approach. This book focuses on an end-to-end track to teach mathematical programming, data science, signal processing, and image processing with GNU Octave.</p> <p><i>GNU Octave by Example</i> starts with an introduction to GNU Octave, a free and open-source alternative to MATLAB. Next, it explains the processes to install GNU Octave on popular operating systems such as Windows, Ubuntu, Raspberry Pi, and other platforms. Further, it covers hands-on exercises with GNU Octave exploring the basic functionality and command line in interactive mode. This is followed by covering matrices and various operations including how to read and analyze data from various sources. Moving forward, it introduces commonly used programming constructs in data visualization. It explains 2D and 3D data visualization along with data analysis. It also demonstrates the concepts related to geometry and its application with GNU Octave. It concludes with coverage of signal processing followed by image, video, and audio processing techniques.</p> <p>After reading this book, you will be able to write your own programs for scientific and numerical applications.</p> <p><b>What&nbsp;You Will Learn&nbsp;&nbsp;</b></p> <p>&#9679;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Understand the practical aspects of GNU Octave with math and programming-friendly abstractions</p> <p>&#9679;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Install GNU Octave on multiple platforms including Windows, Raspberry Pi, and Ubuntu</p> <p>&#9679;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Work with GNU Octave using the GUI, the command line, and Jupyter notebooks</p> <p>&#9679;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Implement 2D and 3D data visualization and&nbsp;analysis with GNU Octave</p> <p><b>Who This Book Is For</b></p> Software engineers, data engineers, data science enthusiasts, and computer vision professionals.</p>

GNU Octave by Example: A Fast and Practical Approach to Learning GNU Octave

Chandu, Sharvani

Operating Systems

<p>Product Description      Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations.You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets.The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. With Practical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface.What You'll LearnReview practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with PandasWho This Book Is ForData Science enthusiasts and professionals, Business analysts and managers, software engineers, data engineers.       From the Back Cover      Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations.You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets.The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. With Practical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface.You will:Review practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with Pandas      About the Author      Ashwin Pajankar holds a Master of Technology from IIIT Hyderabad, and has over 25 years of programming experience. He started his journey in programming and electronics at the tender age of 7 with BASIC programming language and is now proficient in Assembly programming, C, C++, Java, Shell Scripting, and Python. Other technical experience includes single board computers such as Raspberry Pi and Banana Pro, and Arduino. He is currently a freelance online instructor teaching programming bootcamps to more than 60,000 students from tech companies and colleges. His Youtube channel has an audience of 10000 subscribers and he has published more than 15 books on programming and electronics with many international publications.</p>

Practical Python Data Visualization: A Fast Track Approach To Learning Data Visualization With Python

<p>Quickly start programming with Linux while learning the Raspberry Pi OS―the Linux distribution designed specifically for low-cost Raspberry Pis. This short guide reviews Linux commands, GUI, and shell scripting in a holistic manner by diving into both advanced and day-to-day tasks using the Raspberry Pi OS.<br />
You'll comfortably work with the Linux command prompt, and explore the RPi OS GUI and all its base applications. Then move into writing your own programs with shell-programming and using high-level languages such as C, C++, and Python 3. You’ll also study hardware and GPIO programming. Use Python 3 for GPIO programming to drive LEDs and pushbuttons.<br />
Examples are written in Shell, C, C++, and Python 3. Graphical output is displayed in helpful screenshots that capture just what you’ll see when working in this environment. All code examples are well tested on actual Raspberry Pi boards. After reading this book and following the examples, you’ll be able to write programs for demonstration in your academic/industrial research work, business environment, or just your circle of friends for fun!<br />
What You'll Learn<br />
Navigate the core aspects of Linux and programming on a Linux platform<br />
Install Raspberry Pi OS on a Raspberry Pi<br />
Program in Shell, C, C++, and Python Redirect Io and work with the crontab\nWho This Book Is For<br />
Linux enthusiasts, software engineers, researchers, business analysts, and managers working with the low-cost Raspberry Pi.\nFrom the Back Cover\nQuickly start programming with Linux while learning the Raspberry Pi OS―the Linux distribution designed specifically for low-cost Raspberry Pis. This short guide reviews Linux commands, GUI, and shell scripting in a holistic manner by diving into both advanced and day-to-day tasks using the Raspberry Pi OS.<br />
You'll comfortably work with the Linux command prompt, and explore the RPi OS GUI and all its base applications. Then move into writing your own programs with shell-programming and using high-level languages such as C, C++, and Python 3. You’ll also study hardware and GPIO programming. Use Python 3 for GPIO programming to drive LEDs and pushbuttons.<br />
Examples are written in Shell, C, C++, and Python 3. Graphical output is displayed in helpful screenshots that capture just what you’ll see when working in this environment. All code examples are well tested on actual Raspberry Pi boards. After reading this book and following the examples, you’ll be able to write programs for demonstration in your academic/industrial research work, business environment, or just your circle of friends for fun!<br />
You will:<br />
Navigate the core aspects of Linux and programming on a Linux platform<br />
Install Raspberry Pi OS on a Raspberry Pi<br />
Program in Shell, C, C++, and Python Redirect Io and work with the crontab</p>

Practical Linux with Raspberry Pi OS: Quick Start

<p>Learn the core aspects of NumPy, Matplotlib, and Pandas, and use them to write programs with Python 3. This book focuses heavily on various data visualization techniques and will help you acquire expert-level knowledge of working with Matplotlib, a MATLAB-style plotting library for Python programming language that provides an object-oriented API for embedding plots into applications.&nbsp; <div><br /> <div>You'll begin with an introduction to Python 3 and the scientific Python ecosystem. Next, you'll explore NumPy and ndarray data structures, creation routines, and data visualization. You'll examine useful concepts related to style sheets, legends, and layouts, followed by line, bar, and scatter plots. Chapters then cover recipes of histograms, contours, streamplots, and heatmaps, and how to visualize images and audio with pie and polar charts.&nbsp;</div> <div><br /></div> <div>Moving forward, you'll learn how to visualize with pcolor, pcolormesh, and colorbar, and how to visualize in 3D in Matplotlib, create simple animations, and embed Matplotlib with different frameworks. The concluding chapters cover how to visualize data with Pandas and Matplotlib, Seaborn, and how to work with the real-life data and visualize it. After reading <i>Hands-on Matplotlib</i> you'll be proficient with Matplotlib and able to comfortably work with ndarrays in NumPy and data frames in Pandas.&nbsp;</div> <div><br /></div> <div><b>What You'll Learn</b></div> <div> <div> <ul> <li> <div>Understand Data Visualization and Python using Matplotlib</div> </li> <li> <div>Review the fundamental data structures in NumPy and Pandas&nbsp;</div> </li> <li> <div>Work with 3D plotting, visualizations, and animations</div> </li> <li> <div>Visualize images and audio data</div> </li> </ul> <div><b>Who This Book Is For</b></div> <div><b><br /></b></div> <div>Data scientists, machine learning engineers and software professionals&nbsp;with basic programming skills.<br /></div> </div> </div> <div><br /> <br /></div> </div></p>

Hands-on Matplotlib: Learn Plotting and Visualizations with Python 3

<p>Learn how to automate unit tests of Python 3 with automation libraries, such as doctest, unittest, nose, nose2, pytest, and selenium. This book explores important concepts in software test automation and demonstrates how to automate, organize, and execute unit tests with Python. It also introduces readers to the concepts of web browser automation and logging.<br />
This new edition starts with an introduction to Python 3. Next, it covers doctest and pydoc. This is followed by a discussion on unittest, a framework that comes packaged with Python 3 itself. There is a dedicated section on creating test suites, followed by an explanation of how nose2 provides automatic test module discovery. Moving forward, you will learn about pytest, the most popular third-party library and testrunner for Python. You will see how to write and execute tests with pytest. You’ll also learn to discover tests automatically with pytest.<br />
This edition features two brand new chapters, the first of which focuses on the basics of web browser automation with Selenium. You’ll learn how to use Selenium with unittest to write test cases for browser automation and use the Selenium IDE with web browsers such as Chrome and Firefox. You’ll then explore logging frameworks such as Python’s built-in logger and the third-party framework loguru.<br />
The book concludes with an exploration of test-driven development with pytest, during which you will execute a small project using TDD methodology.<br />
What You Will Learn<br />
Start testing with doctest and unittest Understand the idea of unit testing Get started with nose 2 and pytest Learn how to use logger and loguru Work with Selenium and test driven development\nWho This Book Is ForPython developers, software testers, open source enthusiasts, and contributors to the Python community.\nFrom the Back Cover\nLearn how to automate unit tests of Python 3 with automation libraries, such as doctest, unittest, nose, nose2, pytest, and selenium. This book explores important concepts in software test automation and demonstrates how to automate, organize, and execute unit tests with Python. It also introduces readers to the concepts of web browser automation and logging.<br />
This new edition starts with an introduction to Python 3. Next, it covers doctest and pydoc. This is followed by a discussion on unittest, a framework that comes packaged with Python 3 itself. There is a dedicated section on creating test suites, followed by an explanation of how nose2 provides automatic test module discovery. Moving forward, you will learn about pytest, the most popular third-party library and testrunner for Python. You will see how to write and execute tests with pytest. You’ll also learn to discover tests automatically with pytest.<br />
This edition features two brand new chapters, the first of which focuses on the basics of web browser automation with Selenium. You’ll learn how to use Selenium with unittest to write test cases for browser automation and use the Selenium IDE with web browsers such as Chrome and Firefox. You’ll then explore logging frameworks such as Python’s built-in logger and the third-party framework loguru.<br />
The book concludes with an exploration of test-driven development with pytest, during which you will execute a small project using TDD methodology.<br />
You will:<br />
Start testing with doctest and unittest Understand the idea of unit testing Get started with nose 2 and pytest Learn how to use logger and loguru Work with Selenium and test driven development</p>

Python Unit Test Automation: Automate, Organize, and Execute Unit Tests in Python

Programming

<p>Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. <div><br /></div> <div>The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.</div> <div><br /></div> <div>After completing <i>Hands-on Machine Learning with Python</i>, you will be able to implement machine learning and neural network solutions and extend them to your advantage.&nbsp;</div> <div><br /></div> <div><b>What You'll Learn</b></div> <div> <ul> <li>Review data structures in NumPy and Pandas&nbsp;</li> <li>Demonstrate machine learning techniques and algorithm</li> <li>Understand supervised learning and unsupervised learning&nbsp;</li> <li>Examine convolutional neural networks and Recurrent neural networks</li> <li>Get acquainted with scikit-learn and PyTorch</li> <li>Predict sequences in recurrent neural networks and long short term memory&nbsp;<br /></li> </ul> <div><br /></div> <div><b>Who This Book Is For</b></div> </div> <div><br /></div> <div>Data scientists, machine learning engineers, and software professionals with basic skills in Python programming.<br /></div></p>

Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch

Joshi, Aditya

<p><div>Understand the concepts of image processing with Python 3 and create applications using Raspberry Pi 4. This book covers image processing with the latest release of Python 3, using Raspberry Pi OS and Raspberry Pi 4B with the 8 GB RAM model as the preferred computing platform.</div> <div><br /></div> <div>This second edition begins with the installation of Raspberry Pi OS on the latest model of Raspberry Pi and then introduces Python programming language, IDEs for Python, and digital image processing. It also illustrates the theoretical foundations of Image processing followed by advanced operations in image processing. You'll then review image processing with NumPy, and Matplotlib followed by transformations, interpolation, and measurements of images.&nbsp;</div> <div><br /></div> <div>Different types of filters such as Kernels convolution filters, low pass filters, high pass filters, and Fourier filters are discussed in a clear, methodical manner. Additionally, the book examines various image processing techniques such as Morphology, Thresholding, and Segmentation, followed by a chapter on&nbsp;live webcam input with OpenCV, an image processing library with Python. The book concludes with an appendix covering a new library for image processing with Python, pgmagik, followed by a few important tips and tricks relevant to RPi.</div> <div><br /></div> <div><b>What You'll Learn</b><br /></div> <div> <ul> <li>Get started with Raspberry Pi and Python</li> <li>Understand Image Processing with Pillow</li> <li>See how image processing is processed using Numpy and Matplotlib</li> <li>Use Pi camera and webcam</li> </ul> </div> <div><b>Who This Book Is For</b></div> <div><br /></div> <div>Raspberry Pi and IoT enthusiasts, and Python and Open Source professionals&nbsp;</div> <div> <p><br /></p> <br /></div></p>

Raspberry Pi Image Processing Programming: With NumPy, SciPy, Matplotlib, and OpenCV

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