C Programming Cookbook
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C Programming Cookbook
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Author : B. M. Harwani
language : en
Publisher:
Release Date : 2019-03-29
C Programming Cookbook written by B. M. Harwani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-29 with Computers categories.
A comprehensive guide with curated recipes to help you gain a deeper understanding of modern C. Key Features Learn how to make your applications swift and robust by leveraging powerful features of C Understand the workings of arrays, strings, functions, and more down to how they operate in memory Master process synchronization during multi-tasking and server-client process communication Book Description C is a high-level language that's popular among developers. It enables you to write drivers for different devices, access machine-level hardware, apply dynamic memory allocation, and much more. With self-contained tutorials, known as recipes, this book will guide you in dealing with C and its idiosyncrasies and help you benefit from its latest features. Beginning with common tasks, each recipe addresses a specific problem followed by explaining the solution to get you acquainted with what goes on under the hood. You will explore core concepts of the programming language, including how to work with strings, pointers, and single and multi-dimensional arrays. You will also learn how to break a large application into small modules by creating functions, handling files, and using a database. Finally, the book will take you through advanced concepts such as concurrency and interprocess communication. By the end of this book, you'll have a clear understanding and deeper knowledge of C programming, which will help you become a better developer. What you will learn Manipulate single and multi-dimensional arrays Perform complex operations on strings Understand how to use pointers and memory optimally Discover how to use arrays, functions, and strings to make large applications Implement multitasking using threads and process synchronization Establish communication between two or more processes using different techniques Store simple text in files and store data in a database Who this book is for If you're a programmer with basic experience in C and want to leverage its features through modern programming practices, then this book is for you.
Modern C Programming Cookbook
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Author : Marius Bancila
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-02-29
Modern C Programming Cookbook written by Marius Bancila and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Computers categories.
Meta Description: With a wealth of practical recipes, this third edition isn't just a guide to C++23; it's a complete resource covering key features and techniques from C++11 through to C++23. It's designed to keep you ahead of the curve in C++. Key Features Updated and packed with new recipes, including sync streams, std:expected and std:mdspan, and C++20/23 range adaptors Covers all significant features from all modern versions of the standard, providing comprehensive insights into modern C++ Learn through a series of hands-on, self-contained recipes Purchase of the print or Kindle book includes a free eBook in PDF format Book DescriptionThe updated third edition of Modern C++ Programming Cookbook addresses the latest features of C++23, such as the stacktrace library, std::expected and std::mdspan classes, the header, formatting library improvements, and updates to the ranges library. It also gets into more C++20 topics not previously covered, such as sync streams and source_location. The book is organized into practical recipes covering a wide range of real-world problems, helping you find the solutions you need quickly. You’ll find coverage of all the core concepts of modern C++ programming and features and techniques from C++11 through to C++23, meaning you’ll stay ahead of the curve by learning to incorporate the newest language and library improvements. Beyond the core concepts and new features, you’ll explore recipes related to performance and best practices, how to implement useful patterns and idioms, like pimpl, named parameter, attorney-client, and the factory pattern, and how to complete unit testing with the widely used C++ libraries: Boost.Test, Google Test, and Catch2. With the comprehensive coverage this C++ programming guide offers, by the end of the book you’ll have everything you need to build performant, scalable, and efficient applications in C++. What you will learn Explore the new C++23 language and library features Go deep into the most useful C++20 features Learn to handle threading and concurrency for better performance Solve complex string manipulation tasks efficiently with regex Leverage the standard library for faster development Master the filesystem library to work with files and directories Work with different types of strings and understand compilation See how you can use CRTP, mixins and other patterns in C++ Who this book is for This book is designed for entry- and intermediate-level programmers who already have a foundational understanding of the C++ programming language, but who are looking to master the language, implement the newest features, and become proficient modern C++ developers. Experienced C++ programmers can leverage the recipes in this book to quickly get up to speed on all the most important language and library features of C++11/14/17/20 and 23.
Practical C Programming
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Author : B. M. Harwani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-02-14
Practical C Programming written by B. M. Harwani and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-14 with Computers categories.
A comprehensive guide with practical instructions for learning data structures, low-level programming, high-performance computing, networking and IoT to help you understand the latest standards in C programming such as C11 and C18 Key FeaturesTackle various challenges in C programming by making the most of its latest featuresUnderstand the workings of arrays, strings, functions, pointers, advanced data structures, and algorithmsBecome well-versed with process synchronization during multitasking and server-client process communicationBook Description Used in everything from microcontrollers to operating systems, C is a popular programming language among developers because of its flexibility and versatility. This book helps you get hands-on with various tasks, covering the fundamental as well as complex C programming concepts that are essential for making real-life applications. You’ll start with recipes for arrays, strings, user-defined functions, and pre-processing directives. Once you’re familiar with the basic features, you’ll gradually move on to learning pointers, file handling, concurrency, networking, and inter-process communication (IPC). The book then illustrates how to carry out searching and arrange data using different sorting techniques, before demonstrating the implementation of data structures such as stacks and queues. Later, you’ll learn interesting programming features such as using graphics for drawing and animation, and the application of general-purpose utilities. Finally, the book will take you through advanced concepts such as low-level programming, embedded software, IoT, and security in coding, as well as techniques for improving code performance. By the end of this book, you'll have a clear understanding of C programming, and have the skills you need to develop robust apps. What you will learnDiscover how to use arrays, functions, and strings to make large applicationsPerform preprocessing and conditional compilation for efficient programmingUnderstand how to use pointers and memory optimallyUse general-purpose utilities and improve code performanceImplement multitasking using threads and process synchronizationUse low-level programming and the inline assembly languageUnderstand how to use graphics for animationGet to grips with applying security while developing C programsWho this book is for This intermediate-level book is for developers who want to become better C programmers by learning its modern features and programming practices. Familiarity with C programming is assumed to get the most out of this book.
Herb Schildt S C Programming Cookbook
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Author : Herbert Schildt
language : en
Publisher: McGraw Hill Professional
Release Date : 2008-05-22
Herb Schildt S C Programming Cookbook written by Herbert Schildt and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-05-22 with Computers categories.
Your Ultimate "How-To" Guide to C++ Programming! Legendary programming author Herb Schildt shares some of his favorite programming techniques in this high-powered C++ "cookbook." Organized for quick reference, each "recipe" shows how to accomplish a practical programming task. A recipe begins with a list of key ingredients (classes, functions, and headers) followed by step-by-step instructions that show how to assemble them into a complete solution. Detailed discussions explain the how and why behind each step, and a full code example puts the recipe into action. Each recipe ends with a list of options and alternatives that suggest ways to adapt the technique to fit a variety of situations. Whether you're a beginner or an experienced pro, you'll find recipes that are sure to satisfy your C++ programming appetite! Topics include: String Handling · Standard Template Library (STL) Containers · Algorithms · Function Objects · Binders · Negators · Adaptors · Iterators · I/O · Formatting Data Learn how to: Tokenize a null-terminated string Create a search and replace function for strings Implement subtraction for string objects Use the vector, deque, and list sequence containers Use the container adaptors stack, queue, and priority_queue Use the map, multimap, set, and multiset associative containers Reverse, rotate, and shuffle a sequence Create a function object Use binders, negators, and iterator adapters Read and write files Use stream iterators to handle file I/O Use exceptions to handle I/O errors Create custom inserters and extractors Format date, time, and numeric data Use facets and the localization library Overload the [ ], ( ), and -> operators Create an explicit constructor And much, much more
Herb Schildt S Java Programming Cookbook
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Author : Herbert Schildt
language : en
Publisher: McGraw Hill Professional
Release Date : 2007-11-26
Herb Schildt S Java Programming Cookbook written by Herbert Schildt and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-26 with Computers categories.
Your Ultimate "How-To" Guide to Java Programming! Legendary programming author Herb Schildt shares some of his favorite programming techniques in this high-powered Java "cookbook." Organized for quick reference, each recipe shows how to accomplish a practical programming task. A recipe begins with a list of key ingredients (the classes, methods, and interfaces) followed by step-by-step instructions that show how to assemble them into a complete solution. Detailed discussions explain the how and why behind each step, and a full code example puts the recipe into action. Each recipe ends with a list of options and alternatives that suggest ways to adapt the technique to fit a variety of situations. Whether you're a beginner or an experienced pro, you'll find recipes that are sure to satisfy your Java programming appetite! Topics include: String Handling Regular Expressions File Handling Applets Servlets Swing The Collections Framework Multithreading Formatting Data Learn how to: Find substrings that match a regular expression Use regular expressions to tokenize a string Compress and decompress data files Create a ZIP file Format time and date Use printf( ) to display formatted data Reverse, rotate, and shuffle a list Use a Comparator with a collection Create a simple Swing application Create a Swing-based applet Create a simple servlet Use a cookie with a servlet Synchronize threads Monitor a thread's state Set and obtain a thread's priority Use JScrollPane to automate scrolling Access a resource using HttpURLConnection Schedule a task for future execution Use reflection to dynamically create an object and call methods Much, much more
Full Source Code Sqlite For Students And Programmers With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-07-26
Full Source Code Sqlite For Students And Programmers With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-26 with Computers categories.
In this project, we provide you with a SQLITE version of an Oracle sample database named OT which is based on a global fictitious company that sells computer hardware including storage, motherboard, RAM, video card, and CPU. You can find the detailed structures of the database: https://www.oracletutorial.com/getting-started/oracle-sample-database/. The company maintains the product information such as name, description standard cost, list price, and product line. It also tracks the inventory information for all products including warehouses where products are available. Because the company operates globally, it has warehouses in various locations around the world. The company records all customer information including name, address, and website. Each customer has at least one contact person with detailed information including name, email, and phone. The company also places a credit limit on each customer to limit the amount that customer can owe. Whenever a customer issues a purchase order, a sales order is created in the database with the pending status. When the company ships the order, the order status becomes shipped. In case the customer cancels an order, the order status becomes canceled. In addition to the sales information, the employee data is recorded with some basic information such as name, email, phone, job title, manager, and hire date. In this project, you will write Python script to create every table and insert rows of data into each of them. You will develop GUI with PyQt5 to each table in the database. You will also create GUI to plot: case distribution of order date by year, quarter, month, week, and day; the distribution of amount by year, quarter, month, week, day, and hour; the distribution of bottom 10 sales by product, top 10 sales by product, bottom 10 sales by customer, top 10 sales by customer, bottom 10 sales by category, top 10 sales by category, bottom 10 sales by status, top 10 sales by status, bottom 10 sales by customer city, top 10 sales by customer city, bottom 10 sales by customer state, top 10 sales by customer state, average amount by month with mean and EWM, average amount by every month, amount feature over June 2016, amount feature over 2017, and amount payment in all years.
Full Source Code Practical Data Science With Sqlite And Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-08-08
Full Source Code Practical Data Science With Sqlite And Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-08 with Computers categories.
In this project, we provide you with the SQLite sample database named chinook. The chinook sample database is a good database for practicing with SQL, especially SQLite. The detailed description of the database can be found on: https://www.sqlitetutorial.net/sqlite-sample-database/. There are 11 tables in the chinook sample database:The employee table stores employees data such as employee id, last name, first name, etc. It also has a field named ReportsTo to specify who reports to whom; customers table stores customers data; invoices & invoice_items tables: these two tables store invoice data. The invoice table stores invoice header data and the invoice_items table stores the invoice line items data; The artist table stores artists data. It is a simple table that contains only the artist id and name; The album table stores data about a list of tracks. Each album belongs to one artist. However, one artist may have multiple albums; The media_type table stores media types such as MPEG audio and AAC audio files; genre table stores music types such as rock, jazz, metal, etc; The track table stores the data of songs. Each track belongs to one album; playlist & playlist_track tables: The playlist table store data about playlists. Each playlist contains a list of tracks. Each track may belong to multiple playlists. The relationship between the playlist table and track table is many-to-many. The playlist_track table is used to reflect this relationship. In this project, you will write Python script to create every table and insert rows of data into each of them. You will develop GUI with PyQt5 to each table in the database. You will also create GUI to plot: case distribution of order date by year, quarter, month, week, and day; the distribution of amount by year, quarter, month, week, day, and hour; the bottom/top 10 sales by employee, the bottom/top 10 sales by customer, the bottom/top 10 sales by customer, the bottom/top 10 sales by artist, the bottom/top 10 sales by genre, the bottom/top 10 sales by play list, the bottom/top 10 sales by customer city, the bottom/top 10 sales by customer city, the bottom/top 10 sales by customer city, the payment amount by month with mean and EWM, the average payment amount by every month, and amount payment in all years.
Data Science For Programmer A Project Based Approach With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2021-08-19
Data Science For Programmer A Project Based Approach With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-19 with Computers categories.
Book 1: Practical Data Science Programming for Medical Datasets Analysis and Prediction with Python GUI In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Project 1, you will learn how to use Scikit-Learn, NumPy, Pandas, Seaborn, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset provided by Kaggle. This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. In Project 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict breast cancer using Breast Cancer Prediction Dataset provided by Kaggle. Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. You will develop a GUI using PyQt5 to plot distribution of features, pairwise relationship, test scores, prediced values versus true values, confusion matrix, and decision boundary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. Book 2: Step by Step Tutorials For Data Science With Python GUI: Traffic And Heart Attack Analysis And Prediction In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset provided by Kaggle. This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. In Chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset provided by Kaggle. Book 3: BRAIN TUMOR: Analysis, Classification, and Detection Using Machine Learning and Deep Learning with Python GUI In this project, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset provided by Kaggle. This dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). It also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. The deep learning models used in this project are MobileNet and ResNet50. In this project, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy.
Hands On Guide To Image Classification Using Scikit Learn Keras And Tensorflow With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-20
Hands On Guide To Image Classification Using Scikit Learn Keras And Tensorflow With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-20 with Computers categories.
In this book, implement deep learning on detecting face mask, classifying weather, and recognizing flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle (https://www.kaggle.com/omkargurav/face-mask-dataset/download). Here's an overview of the steps involved in detecting face masks using the Face Mask Detection Dataset: Import the necessary libraries: Import the required libraries like TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, and NumPy.; Load and preprocess the dataset: Load the dataset and perform any necessary preprocessing steps, such as resizing images and converting labels into numeric representations.; Split the dataset: Split the dataset into training and testing sets using the train_test_split function from Scikit-Learn. This will allow us to evaluate the model's performance on unseen data.; Data augmentation (optional): Apply data augmentation techniques to artificially increase the size and diversity of the training set. Techniques like rotation, zooming, and flipping can help improve the model's generalization.; Build the model: Create a Convolutional Neural Network (CNN) model using TensorFlow and Keras. Design the architecture of the model, including the number and type of layers.; Compile the model: Compile the model by specifying the loss function, optimizer, and evaluation metrics. This prepares the model for training. Train the model: Train the model on the training dataset. Adjust the hyperparameters, such as the learning rate and number of epochs, to achieve optimal performance.; Evaluate the model: Evaluate the trained model on the testing dataset to assess its performance. Calculate metrics such as accuracy, precision, recall, and F1 score.; Make predictions: Use the trained model to make predictions on new images or video streams. Apply the face mask detection algorithm to identify whether a person is wearing a mask or not.; Visualize the results: Visualize the predictions by overlaying bounding boxes or markers on the images or video frames to indicate the presence or absence of face masks. In chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle (https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download). To classify weather using the Multi-class Weather Dataset from Kaggle, you can follow these general steps: Load the dataset: Use libraries like Pandas or NumPy to load the dataset into memory. Explore the dataset to understand its structure and the available features.; Preprocess the data: Perform necessary preprocessing steps such as data cleaning, handling missing values, and feature engineering. This may include resizing images (if the dataset contains images) or encoding categorical variables.; Split the data: Split the dataset into training and testing sets. The training set will be used to train the model, and the testing set will be used for evaluating its performance.; Build a model: Utilize TensorFlow and Keras to define a suitable model architecture for weather classification. The choice of model depends on the type of data you have. For image data, convolutional neural networks (CNNs) often work well.; Train the model: Train the model using the training data. Use appropriate training techniques like gradient descent and backpropagation to optimize the model's weights.; Evaluate the model: Evaluate the trained model's performance using the testing data. Calculate metrics such as accuracy, precision, recall, or F1-score to assess how well the model performs.; Fine-tune the model: If the model's performance is not satisfactory, you can experiment with different hyperparameters, architectures, or regularization techniques to improve its performance. This process is called model tuning.; Make predictions: Once you are satisfied with the model's performance, you can use it to make predictions on new, unseen data. Provide the necessary input (e.g., an image or weather features) to the trained model, and it will predict the corresponding weather class. In chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize flower using Flowers Recognition dataset provided by Kaggle (https://www.kaggle.com/alxmamaev/flowers-recognition/download). Here are the general steps involved in recognizing flowers: Data Preparation: Download the Flowers Recognition dataset from Kaggle and extract the contents. Import the required libraries and define the dataset path and image dimensions.; Loading and Preprocessing the Data: Load the images and their corresponding labels from the dataset. Resize the images to a specific dimension. Perform label encoding on the flower labels and split the data into training and testing sets. Normalize the pixel values of the images.; Building the Model: Define the architecture of your model using TensorFlow's Keras API. You can choose from various neural network architectures such as CNNs, ResNet, or InceptionNet. The model architecture should be designed to handle image inputs and output the predicted flower class..; Compiling and Training the Model: Compile the model by specifying the loss function, optimizer, and evaluation metrics. Common choices include categorical cross-entropy loss and the Adam optimizer. Train the model using the training set and validate it using the testing set. Adjust the hyperparameters, such as the learning rate and number of epochs, to improve performance.; Model Evaluation: Evaluate the trained model on the testing set to measure its performance. Calculate metrics such as accuracy, precision, recall, and F1-score to assess how well the model is recognizing flower classes.; Prediction: Use the trained model to predict the flower class for new images. Load and preprocess the new images in a similar way to the training data. Pass the preprocessed images through the trained model and obtain the predicted flower class labels.; Further Improvements: If the model's performance is not satisfactory, consider experimenting with different architectures, hyperparameters, or techniques such as data augmentation or transfer learning. Fine-tuning the model or using ensembles of models can also improve accuracy.
Full Source Code Postgresql And Data Science For Programmers With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-09-19
Full Source Code Postgresql And Data Science For Programmers With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-19 with Computers categories.
This project uses the PostgreSQL version of MySQL-based Sakila sample database which is a fictitious database designed to represent a DVD rental store. The tables of the database include film, film_category, actor, film_actor, customer, rental, payment and inventory among others. You can download the database from https://dev.mysql.com/doc/sakila/en/. In this project, you will write Python script to create every table and insert rows of data into each of them. You will develop GUI with PyQt5 to each table in the database. You will also create GUI to plot case distribution of film release year, film rating, rental duration, and categorize film length; plot rating variable against rental_duration variable in stacked bar plots; plot length variable against rental_duration variable in stacked bar plots; read payment table; plot case distribution of Year, Day, Month, Week, and Quarter of payment; plot which year, month, week, days of week, and quarter have most payment amount; read film list by joining five tables: category, film_category, film_actor, film, and actor; plot case distribution of top 10 and bottom 10 actors; plot which film title have least and most sales; plot which actor have least and most sales; plot which film category have least and most sales; plot case distribution of top 10 and bottom 10 overdue costumers; plot which store have most sales; plot average payment amount by month with mean and EWM; and plot payment amount over June 2005.