MaplePrimes Commons General Technical Discussions

The primary forum for technical discussions.

The Joint Mathematics Meetings are taking place next week (January 16 – 19) in Baltimore, Maryland, U.S.A. This will be the 102nd annual winter meeting of the Mathematical Association of America (MAA) and the 125th annual meeting of the American Mathematical Society (AMS).

Maplesoft will be exhibiting at booth #501 as well as in the networking area. Please stop by to chat with me and other members of the Maplesoft team, as well as to pick up some free Maplesoft swag or win some prizes.

This year we will be hosting a hands-on workshop on Maple: A Natural Way to Work with Math

This special event will take place on Thursday, January 17 at 6:00 -8:00 P.M. in the Holiday Ballroom 4 at the Hilton Baltimore.

 

There are also several other interesting Maple related talks:

MYMathApps Tutorials

MAA General Contributed Paper Session on Mathematics and Technology 

Wednesday January 16, 2019, 1:00 p.m.-1:55 p.m.

Room 323, BCC
Matthew Weihing*, Texas A&M University 
Philip B Yasskin, Texas A&M University 

 

The Logic Behind the Turing Bombe's Role in Breaking Enigma. 

MAA General Contributed Paper Session on Mathematics and Technology 

Wednesday January 16, 2019, 1:00 p.m.-1:55 p.m.
Room 323, BCC
Neil Sigmon*, Radford University 
Rick Klima, Appalachian State University 

 

On a software accessible database of faithful representations of Lie algebras. 

MAA General Contributed Paper Session on Algebra, I 

Wednesday January 16, 2019, 2:15 p.m.-6:25 p.m.
Room 348, BCC
Cailin Foster*, Dixie State University 
 

Discussion of Various Technical Strategies Used in College Math Teaching. 

MAA Contributed Paper Session on Open Educational Resources: Combining Technological Tools and Innovative Practices to Improve Student Learning, IV 

Friday January 18, 2019, 8:00 a.m.-10:55 a.m.
Room 303, BCC
Lina Wu*, Borough of Manhattan Community College-The City University of New York 
 

An Enticing Simulation in Ordinary Differential Equations that predict tangible results. 

MAA Contributed Paper Session on The Teaching and Learning of Undergraduate Ordinary Differential Equations 

Friday January 18, 2019, 1:00 p.m.-4:55 p.m.
Room 324, BCC
Satyanand Singh*, New York City College of Technology of CUNY 
 

An Effort to Assess the Impact a Modeling First Approach has in a Traditional Differential Equations Class. 

AMS Special Session on Using Modeling to Motivate the Study of Differential Equations, I 
Saturday January 19, 2019, 8:00 a.m.-11:50 a.m.

Room 336, BCC
Rosemary C Farley*, Manhattan College 
Patrice G Tiffany, Manhattan College 

 

If you are attending the Joint Math meetings this week and plan on presenting anything on Maple, please feel free to let me know and I'll update this list accordingly.


See you in Baltimore!

Daniel

Maple Product Manager

We have just released updates to Maple and MapleSim, which provide corrections and improvements to product functionality.

As usual, the Maple update is available through Tools>Check for Updates in Maple, and is also available from our website on the Maple 2018.2.1 download page, where you can also find more details.

For MapleSim users, use Help>Check for Updates or visit the MapleSim 2018.2.1 download page.

We have just released an update to Maple, Maple 2018.2. This release includes improvements in a variety of areas, including code edit regions, Workbooks, and Physics, as well as support for macOS 10.14.

This update is available through Tools>Check for Updates in Maple, and is also available from our website on the Maple 2018.2 download page, where you can also find more details.

For MapleSim users, the update includes optimizations for handling large models, improvements to model import and export, updates to the hydraulics and pneumatics libraries, and more. For more details and download instructions, visit the MapleSim 2018.2 download page.

If one looks in the profile of Rouben Rostamian , then one sees that any search in his Posts, Questions, Answers, and Replies is linked with MaplePrimes only. I informed  MaplePrimes staff about that disturbance without any effect. Using the opportunity, I'd like to pay attention of MaplePrimes users to many clones in the forum. This is a problem for ages: clones vote up themselves and impose one's opinion on  others. Several years ago some clones were deleted. It would be nice to continue that process.

Hi Primes Users,

We’re back with another tech support challenge that we wanted to share with you!

A customer had been having issues launching Maplets using the standard double-clicking method. This is a known issue that rarely occurs, but is being addressed for a future release. In the meantime, we were able to provide this person with a command-prompt-based way of opening the Maplet, and we thought it would be great to share in case you run into the same kind of problem.

After suggesting a few workarounds, our Team Lead was able to offer a command-prompt based way of solving the problem. Since command prompts are the target of batch scripts, which we had already used as a workaround for another issue, we just needed a way of programmatically creating scripts based on the command prompt code for each file.

Using various commands from the FileTools package (including a technique suggested by our Team Lead), we were able to put together code that takes all files in a particular folder (namely, a folder named “Maplets” on the Desktop), and creates a new batch script from a template that is based on the command prompt code (provided that the template is saved in the same Maplets folder under the file name “Maplet script”):

restart; with(FileTools): username := kernelopts(username):

directory := cat("C:\\Users\\", username, "\\Desktop\\Maplets"):

dir := ListDirectory(directory): dir := Basename~(dir):

main := cat(directory, "\\Maplet script.txt"): body := Import(main):


n := numelems(dir):

for i to n do

script := cat(directory, "\\launch ", dir[i], ".txt");

batch := cat(directory, "\\launch ", dir[i], ".bat");

newbody := StringTools:-Substitute(body, "name", dir[i]);

Export(script, newbody);

Rename(script, batch);

end do:


Script template:


if not "%minimized%"=="" goto :minimized

set minimized=true

start /min cmd /C "%~dpnx0"

goto :EOF

:minimized


"C:\Program Files\Maple 2018\bin.X86_64_WINDOWS\mapletviewer.exe" "C:\Users\%USERNAME%\Desktop\Maplets\name.maplet"

Before using the Maplet script:

After using the Maplet script:

If the appropriate executable is referenced, and the relevant file paths are adjusted accordingly, one should be able to adapt this process to other programs and their corresponding files.  In fact, any batch script that needs to be modified programmatically can be modified using these techniques.  Does anyone have other useful batch scripts that they’ve modified programmatically in Maple using similar techniques?

*(including dragging the files to the executable directly, which only seemed to work when the executable was in its original directory)

Hi MaplePrimes Users!

It’s your friendly, neighborhood tech support team; here to share some tips and tricks from issues we help users with on a daily basis.

A customer contacted us through a Help Page feedback form, asking how to add a row or column in a Matrix. The form came from the Row Operations help page, but the wording of the message suggested that the customer actually wanted to insert a new row or column altogether. Such manipulations can often be accomplished by a command in the ArrayTools package, but the only Insert command currently available is the one for Vectors and 1-D Arrays. Using the Concatenate command from that package, and various commands from the LinearAlgebra package (including the SubMatrix command), we were able to write two custom procedures to perform these manipulations:

InsertRow := proc (A::rtable, n::integer, v::Vector[row])
    local R, C, top, bottom;
    uses LinearAlgebra;
    R := RowDimension(A); C := ColumnDimension(A);
    top := SubMatrix(A, [1 .. n-1], [1 .. C]);
    bottom := SubMatrix(A, [n .. R], [1 .. C]);
    return ArrayTools:-Concatenate(1, top, v, bottom);
end proc:

InsertColumn := proc (A::rtable, n::integer, v::Vector[column])
    local R, C, left, right;
    uses LinearAlgebra;
    R := RowDimension(A); C := ColumnDimension(A);
    left := SubMatrix(A, [1 .. R], [1 .. n-1]);
    right := SubMatrix(A, [1 .. R], [n .. C]);
    return ArrayTools:-Concatenate(2, left, v, right)
end proc:

# test cases:

M := Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]):
v := Vector[row]([2, 2, 2]):
v2 := Vector[column]([2, 2, 2]):
seq(InsertRow(M, i, v), i = 1 .. 4);
seq(InsertColumn(M, i, v2), i = 1 .. 4);

We then reworked this problem using some handy indexing and construction notation that allows our previous procedures to save on the variable constructions and syntax:

InsertRow := proc( A :: rtable, V :: Vector[row], r :: posint )
    return < A[1..r-1,..]; V; A[r..-1,..] >:
end proc:

InsertColumn := proc( A :: rtable, V :: Vector[column], c :: posint )
    return < A[..,1..c-1] | V | A[..,c..-1] >:
end proc:

M := Matrix(3, 3, [seq(i, i = 1 .. 9)]);
A := convert(M, Array);
U := Vector[row]( [ a, b, c ] );
V := convert( U, 'Vector[column]' );
seq(InsertRow( A, U, i ), i=1..4);
seq(InsertColumn( A, V, i ), i=1..4);
seq(InsertRow( M, U, i ), i=1..4);
seq(InsertColumn( M, V, i ), i=1..4);

We have just released a new version of MapleSim.  The MapleSim 2018 family of products offers new tools for developing digital twins, greater connectivity with other modeling tools, and expanded modeling scope. Improvements include:

  • New tools for creating motion profiles
  • FMI  import for FMI 2.0 Fixed-Step Co-Simulation
  • Optimized handling of large models
  • Inclusion of temperature effects in the MapleSim Hydraulics Library from Modelon and MapleSim Pneumatics Library from Modelon
  • Heat transfer through air and water with the MapleSim Heat Transfer Library from CYBERNET

See What’s New in MapleSim 2018 for more information about these and other improvements.

Who should be considered an 'expert'? How does one achieve expert status? In this guest MaplePrimes blog post, 'Understanding Maple' author Ian Thompson discusses his view of what makes an expert, his journey of becoming an expert in Maple, and the process of putting together and perfecting this resource for Maple users.

 

In days of 8-bit computers, one would sometimes encounter individuals who knew everything about a particular device or piece of software. Single programmers wrote entire applications or games, and some could debug their work by looking directly at a core dump (a printout of the numbers stored in the computer’s memory). Some even managed to take computers beyond their specifications by exploiting design loopholes that the manufacturers hadn’t foreseen or intended. It would be fair to classify such individuals as ‘experts’.

Fast forward twenty five years, and the picture is far less clear. The complexity of computers and software has grown to such an extent that even relatively small smartphone applications are created by teams of developers, and nobody understands every aspect of a CPU chip, much less an entire PC or tablet. Who now should be classified as an expert? One possibility is that an expert is a person who may sometimes need to look up the details of a rarely used command or feature, but who is never confused or frustrated by the behavior of the system or software in question (except where there is a bug), and never needs help from anyone, except perhaps on rare occasions from its creators.

This rather stringent definition makes me an expert in only two areas of computing: the Fortran programming language, and the mathematical computation system Maple. An argument could be made for the typesetting system LATEX, but whilst this has a large number of expert users, there is also a much smaller group of more exalted experts, who maintain the system and develop new packages and extensions. It would be fair to say that I fall into the first category, but not the second.*

How does one achieve expert status? Some software actively prevents this, by hiding its workings to such an extent that fully understanding its behavior is impossible. Where it is possible to gain expert status, I have experienced two very different routes, both starting during my time as a research student, when it became clear that Fortran and Maple would be useful in my work. There were several parallels. I knew a little about both, having used them for basic tasks as an undergraduate. However, working out why things went wrong and how to fix them was time-consuming and unrewarding, since it often relied on magic recipes obtained from unreliable sources, and in many cases I didn’t really understand why these worked, any more than I understood why my own attempts had not. I realized then that knowing a little was at the root of these problems. Partial knowledge, supplemented by contradictory, outdated and even downright bad advice from websites and well-meaning individuals (some of whom invariably labor under false pretences of their own expert status) is not an efficient way to approach scientific computing. In fact it’s just a recipe for frustration. In the case of Fortran, fixing this turned out to be easy, because there are lots of good books on the subject. Reading one of these eliminated all of my problems with the language at a stroke. I can’t claim that I remembered every command and its syntax, nor do I know them all now. This is hardly surprising — the Fortran Language Standard (a very terse document that sets out everything the language provides) now extends to more than 600 pages. Instead, the book provided a general picture of how things work in Fortran, and showed the right way to go about tackling a problem. This investment in time has since paid itself back hundreds of times over.

The route to expert status in Maple was far more challenging. Its own help pages give a very comprehensive description of individual commands, but they are intended as a reference guide, and if it’s possible to become an expert using these alone, then I never discovered the correct order in which to read them. I found a number of books on Maple in the university library, but most were too basic to be useful, and others focused on particular applications. None seemed likely to give me the general picture — the feel for how things work — that would make Maple into the time-saving resource it was intended to be.

The picture became clearer after I taught Maple to students in three different courses. Nothing encourages learning better than the necessity to teach someone else! Investigating the problems that students experienced gave me new opportunities to properly understand Maple, and eventually the few remaining gaps were filled in by the Programming Guide. This is a complex document, similar in length to the Fortran Language Standard, but with more examples. Personally I would only recommend it to readers with experience of programming language specifications. Students now started to ask how I came to know so much about Maple, and whether there was a book that would teach them the same. Since no such book existed, I decided to write one myself. As the old adage goes, if you want something doing properly, do it yourself. The project soon began to evolve as I tried to set down everything that the majority of Maple users need to know. I’ve always hated books that skirt around important but difficult topics, so where before I might have used a dirty trick to circumnavigate a problem, now I felt compelled to research exactly what was going on, and to try to explain it in a simple, concise way. When the first draft was complete, I approached Cambridge University Press (CUP). The editor arranged for reviews by four anonymous referees**, and by Maplesoft’s own programming team. This led to several major improvements. My colleague, Dr Martyn Hughes, also deserves a mention for his efforts in reading and commenting on four different drafts. Meanwhile, Maplesoft continued to release new editions of their software, and the drafts had to be revised to keep up with these. The cover was created by one of CUP’s designers, with instructions that it should not look too ‘treeish’ — one might be surprised by the number of books that have been written about Maple syrup, and it would be a shame for Understanding Maple to be mixed up with these by potential readers browsing the internet. Then there were the minor details: how wide should the pages be? What font should be used? Should disk be spelled with a ‘c’ or a ‘k’? Could quotes from other sources be used without the threat of legal action over copyright infringement? One rights holder laughably tried to charge $200 for a fragment of text from one of their books. Needless to say, no greenbacks were forthcoming.

The resulting book is concise, with all the key concepts needed to gain an understanding of Maple, alongside numerous examples, packed into a mere 228 pages. It gives new users a solid introduction, and doesn’t avoid difficult topics. It isn’t perfect (in fact I have already started to list revisions that will be made if a second edition is published in the future) but I’ve seen very few problems that can’t be solved with the material it contains. Only time will tell if Understanding Maple will it create new experts. At the very least, I would certainly like to think it will make Maple far easier to grasp, and help new users to avoid some of the traps that caught me out many years ago.

 

Learn more about Understanding Maple, which is published by Cambridge University Press.

At Maplesoft, we are excited to be celebrating our 30th year of incorporation. This anniversary is a tremendous milestone for us. As a leading provider of mathematics-based software solutions for science, technology, engineering and mathematics (STEM), this longevity attests to our ability to grow along with changing market conditions, to continually enhance the quality of our offerings and strengthen our partnerships with industry leaders.

As a company, it is our goal to actively connect and partner with our users and industry leaders to advance STEM education and continue to revolutionize engineering design processes. When it comes to academics, we believe our partnerships and outreach initiatives help improve STEM education, develop and enhance digital learning tools and foster online education. To that end, Maplesoft is an Affiliate Member of the Fields Institute, Educational Outreach Champion of Perimeter Institute, and Technology Partner of the American Math Society’s “Who Wants to be a Mathematician” student competition. On the commercial side, we work closely with our commercial partners to seamlessly integrate our technology with complementary tools. Our relationships with prominent companies such as Rockwell Automation, B&R, Altair and more, allow us to continue leading this charge.

At Maplesoft, we work continuously to improve our technology offerings by developing new products and enhancing our existing technology. Maple 2018, the newest version of our flagship product Maple, offers new and improved features to benefit all users, no matter what they use Maple for. It provides an environment where students and instructors can enrich the classroom experience, researchers can accelerate their projects and engineers can refine their calculation management processes. Möbius, our online courseware platform, enables instructors to author rich content, explore important STEM concepts using engaging, interactive applications, visualize problems and solutions, and test students’ understanding by answering questions that are graded instantly.

On the engineering side, we are revolutionizing the engineering design process using Digital Twins, which are virtual machine designs created in MapleSim, Maplesoft’s modeling and simulation software. By taking a virtual approach to machine-level system integration, engineers can commission faster, earlier, and with less risk.

Maplesoft has come a long way since our humble beginnings as a research project at the University of Waterloo. Our website features a timeline that provides insights and information on our incredible journey. The company was built on a foundation of creativity and passion for mathematics and we have worked hard to preserve that legacy. The growth experienced over the past 30 years, along with the drive of our global employee and partner base, will ensure Maplesoft continues to be a driving force in the world of online education and design engineering long into the future.

We invite you to join us as we continue our journey towards new and exciting developments and innovations.

The first update to the Maple 2018 Physics, Differential Equations and Mathematical Functions packages is available. As has been the case since 2013, this update contains fixes, enhancements to existing functionality, and new developments in the three areas. 

The webpage for these updates will continue being the Maplesoft R&D Physics webpage. Starting with Maple 2018, however, this update is also available from the MapleCloud.

To install the update: open Maple and click the Cloud icon (upper-right corner), select "Packages" and search for "Physics Updates". Then, in the corresponding "Actions" column, click the third icon (install pop-up).

NOTE May/1: the "Updates" icon of the MapleCloud toolbar (that opens when you click the upper-right icon within a Maple document / worksheet), now works fine, after having installed the Physics Updates version 32 or higher.

These first updates include:

  • New Physics functionality regarding Tensor Products of Quantum States; and Coherent States.
  • Updates to pdsolve regarding PDE & Boundary Conditions (exact solutions);
  • A change in notation: d_(x), the differential of a coordinate in the Physics package, is now displayed as shown in this Mapleprimes post.


Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

Today we are pleased to announce the release of Maple 2018.

For many people, today is just another day in March. It’s not like the release of a new version of a software product is a world-shaking event. But for us here at Maplesoft, these first few days after the latest version of Maple is released are always a bit more special. There’s always a nervous energy whenever we release Maple and everyone else gets to see what we’ve been pouring our efforts into for the past year.

I’m not going to start this post by calling the latest version of Maple “game-changing” or “cutting edge” or any other marketing friendly platitude. I’m well aware that the latest version of Maple isn’t going to change the world.

What I would say though is that with every new release of software comes an opportunity. Every new software release is an opportunity to re-evaluate how that software has evolved to better suit your needs and requirements. So… if you've been sitting on the sidelines and watching version after version go by, assuming that it won't affect you, that's wrong! There's a lot that you could be missing out on.

The way that these release announcements usually work is that we try to amaze and astound you with a long list of features. Don’t worry, I’ll get into that in a bit. But first I wanted to walk through a simple exercise of release arithmetic.

I’ll start with one of those basic truths that has always been hiding in plain sight. The build number # for Maple 2018 is 1298750. Here at Maplesoft, every time our developers make a change to Maple this build number goes up by 1. These changes are sometimes small and sometimes very big; they can be as small as fixing a documentation typo or they can constitute implementing a major feature spread across numerous files in our source tree.

I have come to look at these build numbers in a slightly different way. I look at build numbers as representing all of the small to large sized steps our developers take to get you from one version to the next (or put another way, how many steps behind you are if you are using the older versions). With that in mind, let’s do some quick math:

If you are using Maple 2017 (2017.0 was build # 1231047), there have been 1298750 – 1231047 = 67703 steps since that release (these numerous "steps" are what built the "long list" of features below). If you’re using Maple 2016 (#1113130) this number grows to 185620. And so it goes… Maple 2015 (#1022128) = 276622 steps, Maple 18 (#922027) = 376723, Maple 17 (#813473) = 485277, you get the idea. If you’re using a really old version of Maple – there’s a good chance that we have fixed up a bunch of stuff or added something that you might find interesting in the time since your last upgrade!

 

Every new release of Maple adds functionality that pushes Maple into new domains, rounds out existing packages, fills gaps, and addresses common user requests. Let's explore some additions:

 

Clickable Math - a.k.a. math that looks like math and can be interacted with using your mouse - has evolved. What was once a collection of operations found in the right-click or main menu items or in interactive smart-popups or in many additional dialogs, has been brought together and enhanced to form the new Context Panel.

We can summarize the Context Panel as follows: Enter an expression and relevant operations that you can apply to that expression appear in a panel on the right side of your screen. Easy, right? It's a great change that unlocks a large part of the Maple library for you.

The addition of the Context Panel is important. It represents a shift in the interaction model for Maple – you’ll see more and more interaction being driven through the context panel in future releases. Already, the changes for the Context Panel permeate through to various other parts of Maple too. You’ll see an example in the Units section below and here’s another for coding applications.

The Context Panel also gives you access to embedded component properties – this makes it much easier to modify parts of your application.

There’s much more we can say about the Context Panel but in the interest of keeping this post (somewhat) concise I’ll stop there. If you are interested and want to see more examples, watch this video.

 

Coding in Maple - For many of us, using the Maple coding language is fundamental; it's just what we do. Whether you write a lot of procedures, or modify the start-up code for your worksheet, or put a sequence of commands in a code edit region, or include a button or slider in your application, you’ll find yourself using Maple’s code editing tools.

For Code Edit Regions and the Maple Code Editor, there’s automatic command completion for packages, commands, and even file paths.

maplemint has been integrated into the Code Editor, providing code analysis while you write your code.

mint and maplemint have been unified and upgraded. If you’ve never heard of these before, these are tools for analysing your Maple code. They provide information on procedures, giving parameter naming conflicts, unreachable code, unused parameters or variables, and more. Mint is available for use with external text files and maplemint runs directly inside of Maple.

For more, I’ve got another video.

 

For many engineers and scientists, units are intrinsically linked with calculations. Here's something else in Maple 2018 that will improve your everyday experience – units are now supported in many core routines such as in numeric equation solving, integration, and optimization.

Here’s a quick example of using units in the int command with some thermophysical data:

We define an expression that gives the pressure of methane as a function of the specific volume V.

P := ThermophysicalData:-Property("pressure", "methane", "temperature" = 350*Unit('K'), "density" = 1/V):
-(int(P, V = 1.0*Unit('m'^3/'kg') .. .5*Unit('m'^3/'kg'), numeric));

You'll also find unit formatting in the Context Panel.

Near and dear to my heart, data analysts also have some occasion to rejoice. Maple 2018 finally adds an Interpolate command that supports irregular data! This is one of those items that users have been requesting for a long time and I'm very happy to say that it's finally here.

Furthering the data story, there are new sources for thermochemical data as well as updates to ensure that existing datasets for thermophysical data and scientific constants are up to date.

 

If you're interested in protecting your content in Maple, listen up:

You can now encrypt procedures; anyone can use your code, but they can't see the source!

You can also lock your Maple documents - effectively protecting them from accidental changes or other unintended modifications.

 

 

Of course, I won't leave mathematics out of this. As always, there’s a ton of new and updated stuff here.

There's a new computational geometry package. There are improvements across all fields of mathematics including group theory, graph theory, integration, differential equations and partial differential equations. And there's a ton of new work in Physics (many of you who have been following the Physics project will already know about these).

You can recreate some of the visualizations above as follows:

Here’s an example of the new VoronoiDiagram Command:

m := LinearAlgebra:-RandomMatrix(40, 2):
ComputationalGeometry:-VoronoiDiagram(m, showpoints, symbol = solidcircle, symbolsize = 7,colorregions=ColorTools:-GetPalette("Dalton"));

Here’s another change that I’ve seen mentioned several times on MaplePrimes – the ability to control the  border of arrows:

plots:-display(plottools:-arrow([0, 0], [2, 2], 0.5e-1, .2, .1, border = false, color = "DarkGrey", legend = "A+B"),
                       plottools:-arrow([0, 0], [1, 2], .15, .3, .15, border = false, color = "Crimson", legend = "A"),
                       plottools:-arrow([1, 2], [2, 2], .15, .3, .15, border = false, color = "CornflowerBlue", legend = "B"),
                   size = [600, 400]);

You can rotate Tickmarks in plots using the rotation option. Some plots, such as those in the TimeSeriesAnalysis package, use rotation by default.

ts := TimeSeriesAnalysis:-TimeSeries([7, 23, 21, 19, 13, 46, 42, 30, 31, 26, 19, 9, 16, 26, 17, 33, 31, 46, 42, 35, 45, 30, 11, 17, 23, 20, 15, 36, 31, 55, 49, 39, 36, 28, 12, 11, 21, 23, 27, 33, 36, 49, 42, 37, 33, 45, 12, 7, 23, 32, 25, 42, 27, 52, 50, 34, 41, 40, 16, 14], frequency = monthly, startdate = "2005-09");
TimeSeriesAnalysis:-SeasonalSubseriesPlot(ts, startingperiod = 9, seasonnames = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"], space = .25, size = [800, 400]);

 

I’ll also mention some updates to the Maple language – items that the readers of this forum will likely find useful.

Dates and Times – Maple 2018 adds new data structures that represent dates and times. There are numerous functions that work with dates and times, including fundamental operations such as date arithmetic and more advanced functionality for working with Calendars.

today := Date();

Year( today ), DayOfMonth( today ), Month( today );

Date arithmetic:

One_year_ago := today - 365*Unit(d);

 

Until - An optional until clause has been added to Maple's loop control structure.

Here's an example, the following code finds the next prime number after 37 and then terminates the loop.

n := 37;

do n := n+1 until

    isprime(n):

n;

As always with these posts, we can't cover everything. This post is really just the beginning of the story. I would love to spend another couple of pages describing the inner-workings of every single improvement to Maple 2018 for you; however I'd rather you just try these features yourself, so go ahead, get out there and try out Maple 2018 today. You won't be disappointed that you did.

The Joint Mathematics Meetings are taking place this week (January 10 – 13) in San Diego, California, U.S.A. This will be the 101th annual winter meeting of the Mathematical Association of America (MAA) and the 124nd annual meeting of the American Mathematical Society (AMS).

Maplesoft will be exhibiting at booth #505 as well as in the networking area. Please stop by our booth or the networking area to chat with me and other members of the Maplesoft team, as well as to pick up some free Maplesoft swag or win some prizes.

There are also several interesting Maple-related talks and events happening this week - I would definitely not miss the talk by our own Paulina Chin on grading sketch graphs.

 

Using Symbol-Crunching to find ALL Sucker's Bets (with given deck sizes). 

AMS Special Session on Applied and Computational Combinatorics, II 
Wednesday January 10, 2018, 2:15 p.m.-2:45 p.m.

Shalosh B. Ekhad, Rutgers University, New Brunswick 
Doron Zeilberger*, Rutgers University, New Brunswick 
 

Collaborative Research: Maplets for Calculus. 

MAA Poster Session: Projects Supported by the NSF Division of Undergraduate Education 
Thursday January 11, 2018, 2:00 p.m.-4:00 p.m.

Philip B. Yasskin*, Texas A&M University 
Douglas B. Meade, University of South Carolina 
Matthew Barry, Texas A&M Engineering Extension Service 
Andrew Crenwelge, Texas A&M University 
Joseph Martinson, Texas A&M University 
Matthew Weihing, Texas A&M University

 

Automated Grading of Sketched Graphs in Introductory Calculus Courses. 

AMS Special Session on Visualization in Mathematics: Perspectives of Mathematicians and Mathematics Educators, I 

Friday January 12, 2018, 9:00 a.m.

Dr. Paulina Chin*, Maplesoft 

 

Semantic Preserving Bijective Mappings of Mathematical Expressions between LaTeX and Computer Algebra Systems.

AMS Special Session on Mathematical Information in the Digital Age of Science, III 
Friday January 12, 2018, 9:00 a.m.-9:20 a.m.

Howard S. Cohl*, NIST 

 

Interactive Animations in MYMathApps Calculus. 

MAA General Contributed Paper Session on Mathematics and Technology 
Saturday January 13, 2018, 11:30 a.m.-11:40 a.m.

Philip B. Yasskin*, Texas A&M University 
Andrew Crenwelge, Texas A&M University 
Joseph Martinsen, Texas A&M University 
Matthew Weihing, Texas A&M University 
Matthew Barry, Texas A&M Engineering Experiment Station 

 

Applying Maple Technology in Calculus Teaching To Create Artwork. 

MAA General Contributed Paper Session on Teaching and Learning Calculus, II 
Saturday January 13, 2018, 2:15 p.m.

Lina Wu*, Borough of Manhattan Community College-The City University of New York

 

If you are attending the Joint Math meetings this week and plan on presenting anything on Maple, please feel free to let me know and I'll update this list accordingly.


See you in San Diego!

Daniel

Maple Product Manager


 

Quantum Commutation Rules Basics

 

Pascal Szriftgiser1 and Edgardo S. Cheb-Terrab2 

(1) Laboratoire PhLAM, UMR CNRS 8523, Université Lille 1, F-59655, France

(2) Maplesoft

NULL

NULL

In Quantum Mechanics, in the coordinates representation, the component of the momentum operator along the x axis is given by the differential operator


 "`p__x`=-i `&hbar;`(&PartialD;)/(&PartialD;x)  "

 

The purpose of the exercises below is thus to derive the commutation rules, in the coordinates representation, between an arbitrary function of the coordinates and the related momentum, departing from the differential representation

 

p[n] = -i*`&hbar;`*`&PartialD;`[n]

These two exercises illustrate how to have full control of the computational process by using different elements of the Maple language, including inert representations of abstract vectorial differential operators, Hermitian operators, algebra rules, etc.

 

These exercises also illustrate a new feature of the Physics package, introduced in Maple 2017, that is getting refined (the computation below requires the Maplesoft updates of the Physics package) which is the ability to perform computations algebraically, using the product operator, but with differential operators, and transform the products into the application of the operators only when we want that, as we do with paper and pencil.

 

%Commutator(g(x, y, z), p_) = I*`&hbar;`*Nabla(F(X))

 

restart; with(Physics); with(Physics[Vectors]); interface(imaginaryunit = i)

 

Start setting the problem:

– 

 all ofx, y, z, p__x, p__y, p__z are Hermitian operators

– 

 all of x, y, z commute between each other

– 

 tell the system only that the operators x, y, z are the differentiation variables of the corresponding (differential) operators p__x, p__y, p__z but do not tell what is the form of the operators

 

Setup(mathematicalnotation = true, differentialoperators = {[p_, [x, y, z]]}, hermitianoperators = {p, x, y, z}, algebrarules = {%Commutator(x, y) = 0, %Commutator(x, z) = 0, %Commutator(y, z) = 0}, quiet)

[algebrarules = {%Commutator(x, y) = 0, %Commutator(x, z) = 0, %Commutator(y, z) = 0}, differentialoperators = {[p_, [x, y, z]]}, hermitianoperators = {p, x, y, z}, mathematicalnotation = true]

(1.1)

Assuming F(X) is a smooth function, the idea is to apply the commutator %Commutator(F(X), p_) to an arbitrary ket of the Hilbert space Ket(psi, x, y, z), perform the operation explicitly after setting a differential operator representation for `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))`, and from there get the commutation rule between F(X) and `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))`.

 

Start introducing the commutator, to proceed with full control of the operations we use the inert form %Commutator

alias(X = (x, y, z))

CompactDisplay(F(X))

` F`(X)*`will now be displayed as`*F

(1.2)

%Commutator(F(X), p_)*Ket(psi, X)

Physics:-`*`(%Commutator(F(X), p_), Physics:-Ket(psi, x, y, z))

(1.3)

For illustration purposes only (not necessary), expand this commutator

Physics[`*`](%Commutator(F(X), p_), Physics[Ket](psi, x, y, z)) = expand(Physics[`*`](%Commutator(F(X), p_), Physics[Ket](psi, x, y, z)))

Physics:-`*`(%Commutator(F(X), p_), Physics:-Ket(psi, x, y, z)) = Physics:-`*`(F(X), p_, Physics:-Ket(psi, x, y, z))-Physics:-`*`(p_, F(X), Physics:-Ket(psi, x, y, z))

(1.4)

Note that  `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))`, F(X) and the ket Ket(psi, x, y, z) are operands in the products above and that they do not commute: we indicated that the coordinates x, y, z are the differentiation variables of `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))`. This emulates what we do when computing with these operators with paper and pencil, where we represent the application of a differential operator as a product operation.

 

This representation can be transformed into the (traditional in computer algebra) application of the differential operator when desired, as follows:

Physics[`*`](%Commutator(F(X), p_), Physics[Ket](psi, x, y, z)) = Library:-ApplyProductsOfDifferentialOperators(Physics[`*`](%Commutator(F(X), p_), Physics[Ket](psi, x, y, z)))

Physics:-`*`(%Commutator(F(X), p_), Physics:-Ket(psi, x, y, z)) = Physics:-`*`(F(X), p_(Physics:-Ket(psi, x, y, z)))-p_(Physics:-`*`(F(X), Physics:-Ket(psi, x, y, z)))

(1.5)

Note that, in `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))`(F(X)*Ket(psi, x, y, z)), the application of `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))` is not expanded: at this point nothing is known about  `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))` , it is not necessarily a linear operator. In the Quantum Mechanics problem at hands, however, it is. So give now the operator  `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))` an explicit representation as a linear vectorial differential operator (we use the inert form %Nabla, %Nabla, to be able to proceed with full control one step at a time)

p_ := proc (f) options operator, arrow; -I*`&hbar;`*%Nabla(f) end proc

proc (f) options operator, arrow; -Physics:-`*`(Physics:-`*`(I, `&hbar;`), %Nabla(f)) end proc

(1.6)

The expression (1.5) becomes

Physics[`*`](%Commutator(F(X), p_), Physics[Ket](psi, x, y, z)) = Physics[`*`](F(X), p_(Physics[Ket](psi, x, y, z)))-p_(Physics[`*`](F(X), Physics[Ket](psi, x, y, z)))

Physics:-`*`(%Commutator(F(X), p_), Physics:-Ket(psi, x, y, z)) = -I*`&hbar;`*Physics:-`*`(F(X), %Nabla(Physics:-Ket(psi, x, y, z)))+I*`&hbar;`*%Nabla(Physics:-`*`(F(X), Physics:-Ket(psi, x, y, z)))

(1.7)

Activate now the inert operator VectorCalculus[Nabla] and simplify taking into account the algebra rules for the coordinate operators {%Commutator(x, y) = 0, %Commutator(x, z) = 0, %Commutator(y, z) = 0}

Simplify(value(Physics[`*`](%Commutator(F(X), p_), Physics[Ket](psi, x, y, z)) = -I*`&hbar;`*Physics[`*`](F(X), %Nabla(Physics[Ket](psi, x, y, z)))+I*`&hbar;`*%Nabla(Physics[`*`](F(X), Physics[Ket](psi, x, y, z)))))

Physics:-`*`(Physics:-Commutator(F(X), p_), Physics:-Ket(psi, x, y, z)) = I*`&hbar;`*_i*Physics:-`*`(diff(F(X), x), Physics:-Ket(psi, x, y, z))+I*`&hbar;`*_j*Physics:-`*`(diff(F(X), y), Physics:-Ket(psi, x, y, z))+I*`&hbar;`*_k*Physics:-`*`(diff(F(X), z), Physics:-Ket(psi, x, y, z))

(1.8)

To make explicit the gradient in disguise on the right-hand side, factor out the arbitrary ket Ket(psi, x, y, z)

Factor(Physics[`*`](Physics[Commutator](F(X), p_), Physics[Ket](psi, x, y, z)) = I*`&hbar;`*_i*Physics[`*`](diff(F(X), x), Physics[Ket](psi, x, y, z))+I*`&hbar;`*_j*Physics[`*`](diff(F(X), y), Physics[Ket](psi, x, y, z))+I*`&hbar;`*_k*Physics[`*`](diff(F(X), z), Physics[Ket](psi, x, y, z)))

Physics:-`*`(Physics:-Commutator(F(X), p_), Physics:-Ket(psi, x, y, z)) = I*`&hbar;`*Physics:-`*`((diff(F(X), y))*_j+(diff(F(X), z))*_k+(diff(F(X), x))*_i, Physics:-Ket(psi, x, y, z))

(1.9)

Combine now the expanded gradient into its inert (not-expanded) form

subs((Gradient = %Gradient)(F(X)), Physics[`*`](Physics[Commutator](F(X), p_), Physics[Ket](psi, x, y, z)) = I*`&hbar;`*Physics[`*`]((diff(F(X), y))*_j+(diff(F(X), z))*_k+(diff(F(X), x))*_i, Physics[Ket](psi, x, y, z)))

Physics:-`*`(Physics:-Commutator(F(X), p_), Physics:-Ket(psi, x, y, z)) = I*`&hbar;`*Physics:-`*`(%Gradient(F(X)), Physics:-Ket(psi, x, y, z))

(1.10)

Since (1.10) is true for allKet(psi, x, y, z), this ket can be removed from both sides of the equation. One can do that either taking coefficients (see Coefficients ) or multiplying by the "formal inverse" of this ket, arriving at the (expected) form of the commutation rule between F(X) and `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))`

(Physics[`*`](Physics[Commutator](F(X), p_), Ket(psi, x, y, z)) = I*`&hbar;`*Physics[`*`](%Gradient(F(X)), Ket(psi, x, y, z)))*Inverse(Ket(psi, x, y, z))

Physics:-Commutator(F(X), p_) = I*`&hbar;`*%Gradient(F(X))

(1.11)

Tensor notation, "[`X__m`,P[n]][-]=i `&hbar;` g[m,n]"

 

The computation rule for position and momentum, this time in tensor notation, is performed in the same way, just that, additionally, specify that the space indices to be used are lowercase latin letters, and set the relationship between the differential operators and the coordinates directly using tensor notation.

You can also specify that the metric is Euclidean, but that is not necessary: the default metric of the Physics package, a Minkowski spacetime, includes a 3D subspace that is Euclidean, and the default signature, (- - - +), is not a problem regarding this computation.

 

restart; with(Physics); interface(imaginaryunit = i)

Setup(mathematicalnotation = true, coordinates = cartesian, spaceindices = lowercaselatin, algebrarules = {%Commutator(x, y) = 0, %Commutator(x, z) = 0, %Commutator(y, z) = 0}, hermitianoperators = {P, X, p}, differentialoperators = {[P[m], [x, y, z]]}, quiet)

[algebrarules = {%Commutator(x, y) = 0, %Commutator(x, z) = 0, %Commutator(y, z) = 0}, coordinatesystems = {X}, differentialoperators = {[P[m], [x, y, z]]}, hermitianoperators = {P, p, t, x, y, z}, mathematicalnotation = true, spaceindices = lowercaselatin]

(2.1)

Define now the tensor P[m]

Define(P[m], quiet)

{Physics:-Dgamma[mu], P[m], Physics:-Psigma[mu], Physics:-d_[mu], Physics:-g_[mu, nu], Physics:-gamma_[a, b], Physics:-KroneckerDelta[mu, nu], Physics:-LeviCivita[alpha, beta, mu, nu], Physics:-SpaceTimeVector[mu](X)}

(2.2)

Introduce now the Commutator, this time in active form, to show how to reobtain the non-expanded form at the end by resorting the operands in products

Commutator(X[m], P[n])*Ket(psi, x, y, z)

Physics:-`*`(Physics:-Commutator(Physics:-SpaceTimeVector[m](X), P[n]), Physics:-Ket(psi, x, y, z))

(2.3)

Expand first (not necessary) to see how the operator P[n] is going to be applied

Physics[`*`](Physics[Commutator](Physics[SpaceTimeVector][m](X), P[n]), Ket(psi, x, y, z)) = expand(Physics[`*`](Physics[Commutator](Physics[SpaceTimeVector][m](X), P[n]), Ket(psi, x, y, z)))

Physics:-`*`(Physics:-Commutator(Physics:-SpaceTimeVector[m](X), P[n]), Physics:-Ket(psi, x, y, z)) = Physics:-`*`(Physics:-SpaceTimeVector[m](X), P[n], Physics:-Ket(psi, x, y, z))-Physics:-`*`(P[n], Physics:-SpaceTimeVector[m](X), Physics:-Ket(psi, x, y, z))

(2.4)

Now expand and directly apply in one ago the differential operator P[n]

Physics[`*`](Physics[Commutator](Physics[SpaceTimeVector][m](X), P[n]), Ket(psi, x, y, z)) = Library:-ApplyProductsOfDifferentialOperators(Physics[`*`](Physics[Commutator](Physics[SpaceTimeVector][m](X), P[n]), Ket(psi, x, y, z)))

Physics:-`*`(Physics:-Commutator(Physics:-SpaceTimeVector[m](X), P[n]), Physics:-Ket(psi, x, y, z)) = Physics:-`*`(Physics:-SpaceTimeVector[m](X), P[n](Physics:-Ket(psi, x, y, z)))-P[n](Physics:-`*`(Physics:-SpaceTimeVector[m](X), Physics:-Ket(psi, x, y, z)))

(2.5)

Introducing the explicit differential operator representation for P[n], here again using the inert %d_[n] to keep control of the computations step by step

P[n] := proc (f) options operator, arrow; -I*`&hbar;`*%d_[n](f) end proc

proc (f) options operator, arrow; -Physics:-`*`(Physics:-`*`(I, `&hbar;`), %d_[n](f)) end proc

(2.6)

The expanded and applied commutator (2.5) becomes

Physics[`*`](Physics[Commutator](Physics[SpaceTimeVector][m](X), P[n]), Ket(psi, x, y, z)) = Physics[`*`](Physics[SpaceTimeVector][m](X), P[n](Ket(psi, x, y, z)))-P[n](Physics[`*`](Physics[SpaceTimeVector][m](X), Ket(psi, x, y, z)))

Physics:-`*`(Physics:-Commutator(Physics:-SpaceTimeVector[m](X), P[n]), Physics:-Ket(psi, x, y, z)) = -I*`&hbar;`*Physics:-`*`(Physics:-SpaceTimeVector[m](X), %d_[n](Physics:-Ket(psi, x, y, z)))+I*`&hbar;`*%d_[n](Physics:-`*`(Physics:-SpaceTimeVector[m](X), Physics:-Ket(psi, x, y, z)))

(2.7)

Activate now the inert operators %d_[n] and simplify taking into account Einstein's rule for repeated indices

Simplify(value(Physics[`*`](Physics[Commutator](Physics[SpaceTimeVector][m](X), P[n]), Ket(psi, x, y, z)) = -I*`&hbar;`*Physics[`*`](Physics[SpaceTimeVector][m](X), %d_[n](Ket(psi, x, y, z)))+I*`&hbar;`*%d_[n](Physics[`*`](Physics[SpaceTimeVector][m](X), Ket(psi, x, y, z)))))

Physics:-`*`(Physics:-Commutator(Physics:-SpaceTimeVector[m](X), P[n]), Physics:-Ket(psi, x, y, z)) = I*`&hbar;`*Physics:-g_[m, n]*Physics:-Ket(psi, x, y, z)

(2.8)

Since the ket Ket(psi, x, y, z) is arbitrary, we can take coefficients (or multiply by the formal Inverse  of this ket as done in the previous section). For illustration purposes, we use   Coefficients  and note hwo it automatically expands the commutator

Coefficients(Physics[`*`](Physics[Commutator](Physics[SpaceTimeVector][m](X), P[n]), Ket(psi, x, y, z)) = I*`&hbar;`*Physics[g_][m, n]*Ket(psi, x, y, z), Ket(psi, x, y, z))

Physics:-`*`(Physics:-SpaceTimeVector[m](X), P[n])-Physics:-`*`(P[n], Physics:-SpaceTimeVector[m](X)) = I*`&hbar;`*Physics:-g_[m, n]

(2.9)

One can undo this (frequently undesired) expansion of the commutator by sorting the products on the left-hand side using the commutator between X[m] and P[n]

Library:-SortProducts(Physics[`*`](Physics[SpaceTimeVector][m](X), P[n])-Physics[`*`](P[n], Physics[SpaceTimeVector][m](X)) = I*`&hbar;`*Physics[g_][m, n], [P[n], X[m]], usecommutator)

Physics:-Commutator(Physics:-SpaceTimeVector[m](X), P[n]) = I*`&hbar;`*Physics:-g_[m, n]

(2.10)

And that is the result we wanted to compute.

 

Additionally, to see this rule in matrix form,

TensorArray(-(Physics[Commutator](Physics[SpaceTimeVector][m](X), P[n]) = I*`&hbar;`*Physics[g_][m, n]))

Matrix(%id = 18446744078261558678)

(2.11)

In the above, we use equation (2.10) multiplied by -1 to avoid a minus sign in all the elements of (2.11), due to having worked with the default signature (- - - +); this minus sign is not necessary if in the Setup at the beginning one also sets  signature = `+ + + -`

 

For display purposes, to see this matrix expressed in terms of the geometrical components of the momentum `#mover(mi("p",mathcolor = "olive"),mo("&rarr;"))` , redefine the tensor P[n] explicitly indicating its Cartesian components

Define(P[m] = [p__x, p__y, p__z], quiet)

{Physics:-Dgamma[mu], P[m], Physics:-Psigma[mu], Physics:-d_[mu], Physics:-g_[mu, nu], Physics:-gamma_[a, b], Physics:-KroneckerDelta[mu, nu], Physics:-LeviCivita[alpha, beta, mu, nu], Physics:-SpaceTimeVector[mu](X)}

(2.12)

TensorArray(-(Physics[Commutator](Physics[SpaceTimeVector][m](X), P[n]) = I*`&hbar;`*Physics[g_][m, n]))

Matrix(%id = 18446744078575996430)

(2.13)

Finally, in a typical situation, these commutation rules are to be taken into account in further computations, and for that purpose they can be added to the setup via

"Setup(?)"

[algebrarules = {%Commutator(x, p__x) = I*`&hbar;`, %Commutator(x, p__y) = 0, %Commutator(x, p__z) = 0, %Commutator(x, y) = 0, %Commutator(x, z) = 0, %Commutator(y, p__x) = 0, %Commutator(y, p__y) = I*`&hbar;`, %Commutator(y, p__z) = 0, %Commutator(y, z) = 0, %Commutator(z, p__x) = 0, %Commutator(z, p__y) = 0, %Commutator(z, p__z) = I*`&hbar;`}]

(2.14)

For example, from herein computations are performed taking into account that

(%Commutator = Commutator)(x, p__x)

%Commutator(x, p__x) = I*`&hbar;`

(2.15)

NULL

NULL


 

Download DifferentialOperatorCommutatorRules.mw

 

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

We’ve just released a major new version of MapleSim. The MapleSim 2017 family of products provides new and improved model development and analysis tools, expands modeling scope, introduces new deployment options, and strengthens toolchain connectivity.  Here are some highlights:

  • The new Initialization Diagnostics App further simplifies the initialization task by helping you determine how your initial values are computed and what you need to do to adjust them.
     
  • The new Modal Analysis App helps you explore and understand the natural vibration modes of your mechanism, so you can determine how to reduce the vibration in the final product. 
     
  • Over 100 new components include expansions to the Electrical and Magnetic libraries.
     
  • A new Modelica® code editor makes it easier to create Modelica-based custom components.
     
  • The MapleSim Heat Transfer Library from CYBERNET, a new add-on component library, provides a comprehensive view into heat transfer effects in your model, enabling you to refine your  design to improve performance and avoid overheating.
     
  • The new MapleSim Explorer product provides a cost-effective deployment solution that allows you to make MapleSim models available to more people in your organization.

 

There’s more, of course.  See What’s New in MapleSim for lots more details.

eithne

Try More/GeneratePDF  in  the menu under  a post/question. See screen_26.09.17.docx as an example of a result. Also Adobe Acrobat Reader fails with it. That was submitted to MaplePrimes staff through the Contact  button at the bottom of this page. I obtained no feedback from them.

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