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    Introduction to Programming Using Python

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    Instructor: Hiroki SayamaDate: Friday, March 1, 2019Time: 9 a.m. - 4 p.m. (Registration/coffee/light breakfast at 8:45 a.m., Lunch 12-1 p.m.)Location: Symposium Hall, Innovative Technologies complicated (ITC), Binghamton University85 Murray Hill Road, Vestal, NY 13850Credentials: A micro-credential (digital badge) will exist issued to students who pass a group assignment at the discontinuance of the day. Participation certificates will exist issued to complete participants.Who can Take this course: This course is open to complete engineers, professionals and students.

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    This course introduces computer programming in Python for those whohave petite or no prior computer programming experience. The coursewill exist heavily based on hands-on exercises, so participants arestrongly encouraged to bring their own laptops. The following topicswill exist covered:

    TOPICS

    The following topics will exist covered:

    1. Software installation and language basics2. Variables and data structures (strings, lists, matrices, dictionaries)3. current control and duty definition4. Plotting and visualization5. Using modules6. Object-oriented programming

    ABOUT THE INSTRUCTOR

    Hiroki Sayama is a Professor in the Department of Systems Science and Industrial Engineering. His research interests embrace complicated dynamical networks, collective behaviors, companionable systems modeling, synthetic life/chemistry, mathematical biology, computer and information sciences.

  • Director, headquarters for Collective Dynamics of complicated Systems
  • Director, Advanced Graduate Certificate Program in complicated Systems Science and Engineering
  • Professor, Faculty of Commerce, Waseda University, Japan
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    Thread Carefully: An Introduction To Concurrent Python | killexams.com true questions and Pass4sure dumps

    The ability to execute code in parallel is crucial in a wide variety of scenarios. Concurrent programming is a key asset for web servers, producer/consumer models, batch number-crunching and pretty much any time an application is bottlenecked by a resource.

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    It’s impossible to talk about concurrent programming in Python without mentioning the Global Interpreter Lock, or GIL. This is because of the great repercussion it has on which approach you select when writing asynchronous Python. The most valuable thing to note is that it is only a feature of CPython (the widely used “reference” Python implementation), it’s not a feature of the language. Jython and IronPython, among other implementations, Enjoy no GIL.

    The GIL is controversial because it only allows one thread at a time to access the Python interpreter. This means that it’s often not possible for threads to Take edge of multi-core systems. Note that if there are blocking operations which chance outside Python, long-wait tasks like I/O for instance, then the GIL is not a bottleneck and writing a threaded program will still exist a benefit. However, if the blocking operations are largely crunching through CPython bytecode, then the GIL becomes a bottleneck.

    Why was the GIL introduced at all? It makes remembrance management much simpler with no possibility of simultaneous access or race conditions, and it makes C extensions easier to write and easier to wrap.

    The upshot of complete this is that if you need honorable parallelism and need to leverage multi-core CPUs, threads won’t slit it and you need to exercise processes. A divide process means a divide interpreter with divide memory, its own GIL, and honorable parallelism. This guide will give examples of both thread and process architectures.

    The concurrent.futures module is a well-kept surreptitious in Python, but provides a uniquely simple way to implement threads and processes. For many basic applications, the simple to exercise Pool interface offered here is sufficient.

    Here’s an specimen where they want to download some webpages, which will exist much quicker if done in parallel.

    """Download webpages in threads.""" import requests from concurrent.futures import ThreadPoolExecutor download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def download_page(page_info): """Download and deliver webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': pool = ThreadPoolExecutor(max_workers=10) for download in download_list: pool.submit(download_page, download)

    Most of the code is just setting up their downloader example; it’s only the final obscure which contains the threading-specific code. Note how simple it is to create a dynamic pool of workers using ThreadPoolExecutor and submit a task. They could even simplify the final two lines to one using map:

    pool.map(download_page, download_list)

    Using threads works well in this case since the blocking operation that benefits from concurrency is the act of fetching the webpage. This means that the GIL is not an issue and threading is an pattern solution. However, if the operation in question was something which was CPU intensive within Python, processes would likely exist more arrogate because of the restrictions of the GIL. In that case, they could Enjoy simply switched out ThreadPoolExecutor with  ProcessPoolExecutor.

    Whilst the concurrent.futures module offers a powerful way to regain off the ground quickly, sometimes more control is needed over different threads, which is where the ubiquitous threading module comes in.

    Let’s re-implement the website downloader they made above, this time using the threading module.

    """Download webpages in threads, using `threading`.""" import requests import time import threading download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and deliver webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = threading.Thread(target=download_page, args=(download,)) downloader.start() status = threading.Thread(target=status_update) status.start()

    For each thread they want to create, they obtain an instance of the threading.Thread class, specifying what they would like their worker duty to be, and the arguments required.

    Note that we’ve besides added a status update thread. The purpose of this is to repeatedly print “Still downloading” until we’ve finished fetching complete the web pages. Unfortunately, since Python waits for complete threads to finish executing before it exits, the program will never exit and the status updater thread will never quit printing.

    This is an specimen of when the threading module’s host of options could exist useful: they can imprint the updater thread as a daemon thread, which means that Python will exit when only daemon threads are left running.

    status = threading.Thread(target=status_update) status.daemon = True status.start()

    The program now successfully stops printing and exits when complete downloader threads are finished.

    Daemon threads are generally most useful for background tasks and repetitive functions which are only required when the main program is running, since a daemon can exist killed at any moment, causing data loss.

    So far we’ve only looked at cases where they know exactly what they want the threads to exist working on when they start them. However, it’s often the case that they need to start a group of worker threads, then feed them tasks as they arrive.

    The best data structure for dealing with these tasks is, of course, a queue, and Python provides a queue module which is especially geared towards threading applications. FIFO, LIFO and priority queues are available.

    Using a queue.Queue object to add, get, and imprint tasks as done is as simple as this:

    from queue import Queue # maxsize=0 means boundless size limit tasks = Queue(maxsize=0) tasks.put("a task") tasks.put("another task") while not tasks.empty(): print(tasks.get()) # execute task tasks.task_done()

    Ok, that’s pretty basic so far. Now let’s exercise it to create a tasks queue for their website downloader. We’ll create a group of worker threads which can complete access the queue and wait for tasks to further in.

    """Download webpages in threads, using `threading` and `queue`.""" import requests import threading from queue import Queue NUM_WORKER_THREADS = 3 def download_page(page_info): """Download and deliver webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) def handle_tasks(tasks_queue): """Monitor tasks queue and execute tasks as appropriate.""" while True: download_page(tasks_queue.get()) tasks_queue.task_done() if __name__ == '__main__': tasks = Queue(maxsize=0) # Create and start worker threads for i in range(NUM_WORKER_THREADS): worker = threading.Thread(target=handle_tasks, args=(tasks,)) worker.daemon = True worker.start() # Add some tasks to the queue tasks.put({'name': 'google', 'url': "http://google.com"}) tasks.put({'name': 'reddit', 'url': "http://reddit.com"}) tasks.put({'name': 'ebay', 'url': "http://ebay.com"}) tasks.put({'name': 'bbc', 'url': "http://bbc.co.uk"}) tasks.join()

    Note that in this specimen complete the tasks were added in one meander for the sake of brevity, but in a true application the tasks could trickle in at any rate. Here they exit the program when the tasks queue has been fully completed, using the .join() method.

    The threading module is powerful for detailed control of threads, but what if they want this finer flat of control for processes? You might speculate that this would exist more challenging since once a process is launched, it’s completely divide and independent – harder to control than a new thread which remains within the current interpreter and remembrance space.

    Fortunately for us, the Python developers worked difficult to create a multiprocessing module which has an interface that is almost identical to the threading module. This means that launching processes follows the exact very syntax as their examples above. Their simple downloader would become this:

    """Download webpages in threads, using `multiprocessing`.""" import requests import time import multiprocessing download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and deliver webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = multiprocessing.Process(target=download_page, args=(download,)) downloader.start() status = multiprocessing.Process(target=status_update) status.daemon = True status.start()

    We speculate it’s awesome that Python manages to maintain the same syntax between the threading and multiprocessing modules, when the action taking site under the hood is so different.

    When it comes to distributing data between processes, the queue.Queue that they used for threading will not travail between processes. This is because a queue.Queue is fundamentally just a data structure within the current process – albeit one which is cleverly locked and mutexed. Thankfully there exists a multiprocessing.Queue, which is specifically designed for inter-process communication. Behind the scenes, this will serialize your data and send it through a pipe between processes – a very convenient abstraction.

    Writing concurrent code in Python can exist a lot of fun due to the inbuilt language features that abstract away a lot of problems. This doesn’t influence that a detailed flat of control cannot exist achieved either, but rather that the barrier to getting started with simple tasks is lowered. So when you’re stuck waiting for one process to finish before starting the next, give one of these techniques a try.


    Practical Programming: An Introduction to Computer Science Using Python | killexams.com true questions and Pass4sure dumps

    Jennifer Campbell et al

    Publisher

    The Pragmatic Bookshelf

    ISBN

    978-1-93435-627-2

    RRP

    £25.50

    Reviewed by

    Patrick Hill, CEng MBCS CITP

    Score

    8 out of 10

    Practical ProgrammingNot long ago, computer programming was often taught using strongly-typed, compiled languages such as Pascal, Modula and Ada. More recently, dynamic interpreted languages, such as Python and Ruby, Enjoy gained a tough following among software practitioners.

    This engage is an introduction to computer programming using the Python language. It is primarily aimed at first year computer science students and at professionals from non-IT fields who may find a grounding in computer programming techniques useful.

    The engage is broadly divided into two sections. The first section introduces elementary computer programming concepts and their corresponding Python implementations. In this section the reader is introduced to the fundamental components of imperative computer programs such as data types, variables, conditionals, iteration, collection types, files, functions and so forth. In addition the section introduces topics such as algorithms and efficiency, common programming patterns, exception handling and debugging techniques.

    The second section builds on these topics to provide standalone chapters that present introductions to subjects such as remonstrate orientation, simple GUI implementation and relational databases.

    This is a very practical, hands-on kindhearted of book. Each chapter contains illustrative in-line examples and concludes with a useful bullet-point summary and a set of exercises. Best-practice tips, common Python idioms and beginner’s ‘gotchas’ are scattered throughout the text, and source code for the examples is available from the book’s website.

    The authors avoid abstract concepts that are typical of many computer science texts. Rather, real-world applications are explored. The exercises are designed not only to review material that has been presented, but besides to inspirit the reader to further explore the Python language, documentation and tools.

    This is, by design, an introductory text. Readers will find no mention of more advanced topics such as reflection, multi-threading, networking etc. Nonetheless, the engage provides a solid foundation from which these topics may exist explored. I found the engage to exist an racy read and an inspiration to delve deeper into Python.

    Further information: The Pragmatic Bookshelf

    November 2009 



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