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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 tangled (ITC), Binghamton University85 Murray Hill Road, Vestal, NY 13850Credentials: A micro-credential (digital badge) will breathe issued to students who pass a group assignment at the discontinuance of the day. Participation certificates will breathe issued to utter participants.Who can Take this course: This course is open to utter engineers, professionals and students.


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


    The following topics will breathe covered:

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


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

  • Director, seat for Collective Dynamics of tangled Systems
  • Director, Advanced Graduate Certificate Program in tangled Systems Science and Engineering
  • Professor, Faculty of Commerce, Waseda University, Japan
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  • Chancellor's Award for Excellence in Teaching (2015–2016)

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    Register before Wednesday Feb. 27, 2019.Confirmation and course information will breathe emailed to registrants. involve your email address on your registration form. If you beget not received confirmation seven days before the start of the program or beget a question about the registration process convoke the Office of Industrial Outreach at 607-777-6241.


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    Thread Carefully: An Introduction To Concurrent Python | true questions and Pass4sure dumps

    The aptitude 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.

    It’s sadly the case that writing trait concurrent code can breathe a true headache, but this article aims to demonstrate how facile it is to come by started writing threaded programs in Python. Due to the large number of modules available in the benchmark library which are there to abet out with this kindhearted of thing, it’s often the case that simple concurrent tasks are surprisingly quick to implement.

    We’ll walk through the dissimilarity between threads and processes in a Python context, before reviewing some of the different approaches you can Take and what they’re best suited for.

    (Python 3 is used for the duration of the article.)

    It’s impossible to talk about concurrent programming in Python without mentioning the Global Interpreter Lock, or GIL. This is because of the large impact it has on which approach you select when writing asynchronous Python. The most considerable 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, beget 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 practicable for threads to Take handicap of multi-core systems. Note that if there are blocking operations which befall outside Python, long-wait tasks like I/O for instance, then the GIL is not a bottleneck and writing a threaded program will soundless breathe 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 memory 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 utter this is that if you requisite dependable parallelism and requisite to leverage multi-core CPUs, threads won’t gash it and you requisite to exhaust processes. A sever process means a sever interpreter with sever memory, its own GIL, and dependable parallelism. This sheperd will give examples of both thread and process architectures.

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

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

    """Download webpages in threads.""" import requests from concurrent.futures import ThreadPoolExecutor download_list = [ {'name': 'google', 'url': ""}, {'name': 'reddit', 'url': ""}, {'name': 'ebay', 'url': ""}, {'name': 'bbc', 'url': ""} ] def download_page(page_info): """Download and redeem 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 facile 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:, 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 ideal solution. However, if the operation in question was something which was CPU intensive within Python, processes would likely breathe more arrogate because of the restrictions of the GIL. In that case, they could beget simply switched out ThreadPoolExecutor with  ProcessPoolExecutor.

    Whilst the concurrent.futures module offers a noteworthy course to come by 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': ""}, {'name': 'reddit', 'url': ""}, {'name': 'ebay', 'url': ""}, {'name': 'bbc', 'url': ""} ] 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 redeem 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 beget 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 furthermore added a status update thread. The purpose of this is to repeatedly print “Still downloading” until we’ve finished fetching utter the web pages. Unfortunately, since Python waits for utter threads to finish executing before it exits, the program will never exit and the status updater thread will never discontinue printing.

    This is an sample of when the threading module’s multitude of options could breathe useful: they can track 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 utter 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 breathe 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 breathe working on when they start them. However, it’s often the case that they requisite 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 track tasks as done is as simple as this:

    from queue import Queue # maxsize=0 means eternal 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 exhaust it to create a tasks queue for their website downloader. We’ll create a group of worker threads which can utter access the queue and wait for tasks to arrive 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 redeem 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': ""}) tasks.put({'name': 'reddit', 'url': ""}) tasks.put({'name': 'ebay', 'url': ""}) tasks.put({'name': 'bbc', 'url': ""}) tasks.join()

    Note that in this sample utter the tasks were added in one fade 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 noteworthy for detailed control of threads, but what if they want this finer flush of control for processes? You might judge that this would breathe more challenging since once a process is launched, it’s completely sever and independent – harder to control than a new thread which remains within the current interpreter and memory 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 same 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': ""}, {'name': 'reddit', 'url': ""}, {'name': 'ebay', 'url': ""}, {'name': 'bbc', 'url': ""} ] 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 redeem 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 judge it’s awesome that Python manages to maintain the same syntax between the threading and multiprocessing modules, when the action taking plot 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 breathe a lot of fun due to the inbuilt language features that abstract away a lot of problems. This doesn’t express that a detailed flush of control cannot breathe 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 | true questions and Pass4sure dumps

    Jennifer Campbell et al


    The Pragmatic Bookshelf





    Reviewed by

    Patrick Hill, CEng MBCS CITP


    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, beget gained a sturdy 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 offer introductions to subjects such as protest 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 furthermore to animate 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 breathe explored. I establish the engage to breathe an piquant read and an inspiration to delve deeper into Python.

    Further information: The Pragmatic Bookshelf

    November 2009 

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