We recently had a question about using some of the plotting commands in Maple to draw things. We were feeling creative and thought why not take it a step further and draw something in 3D.

Using the geom3d, plottools, and plots packages we decided to make a gingerbread house.

To make the base of the house we decided to use 2 cubes, as these would give us additional lines and segments for the icing on the house.

point(p__1,[2,3,2]):
point(p__2,[3,3,2]):
cube(c1,p__1,2):
cube(c2,p__2,2):
base:=draw([c1,c2],color=tan);

Using the same cubes but changing the style to be wireframe and point we made some icing lines and decorations for the gingerbread house.

base_decor1:=draw([c1,c2],style=wireframe,thickness=3,color=red,transparency=0.2):
base_decor2:=draw([c1,c2],style=wireframe,thickness=10,color=green,linestyle=dot):
base_decor3:=draw([c1,c2],style=point,thickness=2,color="Silver",symbol=sphere):
base_decor:=display(base_decor1,base_decor2,base_decor3);

To create the roof we found the vertices of the cubes and used those to find the top corners of the base.

v1:=vertices(c1):
v2:=vertices(c2):
pc1:=seq(point(pc1||i,v1[i]),i=1..nops(v1)):
pc2:=seq(point(pc2||i,v2[i]),i=1..nops(v2)):
topCorners:=[pc1[5],pc1[6],pc2[1],pc2[2]]:
d1:=draw(topCorners):

Using these top corners we found the midpoints (where the peak of the roof would be) and added the roof height to the coordinates.

midpoint(lc1,topCorners[1],topCorners[2]):
detail(lc1);

point(cc1,[-(2*sqrt(3))/3 + 2, (2*sqrt(3))/3 + 3+1, 2]):
d3:=draw(cc1):

midpoint(lc2,topCorners[3],topCorners[4]):
detail(lc2);

point(cc2,[(2*sqrt(3))/3 + 3, (2*sqrt(3))/3 + 3+1, 2]):
d4:=draw(cc2):

With the midpoints and vertices at the front and rear of the house we made two triangles for the attic of the gingerbread house.

triangle(tf,[topCorners[1],topCorners[2],cc1]):
front:=draw(tf,color=brown):

triangle(tb,[topCorners[3],topCorners[4],cc2]):
back:=draw(tb,color=tan):

Using these same points again we made more triangles to be the roof.

triangle(trl,[cc1,cc2,pc1[5]]):
triangle(trh,[pc2[2],pc1[6],cc1]):
triangle(tll,[cc1,cc2,pc2[2]]):
triangle(tlh,[pc2[1],pc1[5],cc2]):
roof:=draw([trl,trh,tll,tlh],color="Chocolate");

Our gingerbread house now had four walls, a roof, and icing, but no door. Creating the door was as easy as making a parallelepiped, but what is a door without more icing?

door:=display(plottools:-parallelepiped([1,0,0],[0,1.2,0],[0,0,0.8],[0.8,1.9,1.6]),color="DarkRed"):
door_decor1:=display(plottools:-parallelepiped([1,0,0],[0,1.2,0],[0,0,0.8],[0.8,1.9,1.6]),color="Gold",style=line):
door_decor2:=display(plottools:-parallelepiped([1,0,0],[0,1.2,0],[0,0,0.8],[0.8,1.9,1.6]),color="Silver", style=line,linestyle=dot,thickness=5):
door_decor:=display(door_decor1,door_decor2):

Now having a door we could have left it like this, but what better way to decorate a gingerbread house than with candy canes? Naturally, if we were going to have one candy cane we were going to have lots of candy canes. To facilitate this we made a candy cane procedure.

candy_pole:=proc(c:=[0,0,0], {segR:=0.1}, {segH:=0.1}, {segn:=7}, {tilt_theta:=0}, {theta:=0}, {arch:=true}, {flip:=false})
local cane1,cane2,cane_s,cane_c,cane0,cane,i,cl,cd,ch, cane_a,tmp,cane_ac,cane_a1,cane00,cane01,cane02,cane_a1s,tmp2,cane_a2s:
uses plots,geom3d:
cl:=c[1]:
cd:=c[2]:
ch:=c[3]:
cane1:=plottools:-cylinder([cd, ch, cl], segR, segH,style=surface):

cane2:=display(plottools:-rotate(cane1,Pi/2,[[cd,ch,cl],[cd+1,ch,cl]])):
cane_s:=[cane2,seq(display(plottools:-translate(cane2,0,segH*i,0)),i=1..segn-1)]:
cane_c:=seq(ifelse(type(i,odd),red,white),i=1..segn):

cane0:=display(cane_s,color=[cane_c]):

if arch then

cane_a:=plottools:-translate(cane2,0,segH*segn-segH/2,0):
tmp:=i->plottools:-rotate(cane_a,i*Pi/24, [ [cd,ch+segH*segn-segH/2,cl+segR*2] , [cd+1,ch+segH*segn-segH/2,cl+segR*2] ] ):

cane_ac:=seq(ifelse(type(i,odd),red,white),i=1..24):

                cane_a1s:=seq(plottools:-translate(tmp(i),0,segH*i/12,segR*i/4),i=1..12):

tmp2:=i->plottools:-rotate(cane_a1s[12],i*Pi/24,[[cd,ch+segH*segn-0.05,cl+segR*2],[cd+1,ch+segH*segn-0.05,cl+segR*2]]):

cane_a2s:=seq(plottools:-translate(tmp2(i),0,-segH*i/500,segR*i/4),i=1..12):
cane_a1:=display(cane_a1s,cane_a2s,color=[cane_ac]):
cane00:=display(cane0,cane_a1);

                if flip then

cane01:=plottools:-rotate(cane00,tilt_theta,[[cd,ch,cl],[cd+1,ch,cl]]):
cane02:=plottools:-rotate(cane01,theta,[[cd,ch,cl],[cd,ch+1,cl]]):
cane:=plottools:-reflect(cane01,[[cd,ch,cl],[cd,ch+1,cl]]):

                else

cane01:=plottools:-rotate(cane00,tilt_theta,[[cd,ch,cl],[cd+1,ch,cl]]):
cane:=plottools:-rotate(cane01,theta,[[cd,ch,cl],[cd,ch+1,cl]]):

                end if:

                return cane:

else

                cane:=plottools:-rotate(cane0,tilt_theta,[[cd,ch,cl],[cd+1,ch,cl]]):

                return cane:

end if:

end proc:

With this procedure we decided to add candy canes to the front, back, and sides of the gingerbread house. In addition we added two candy poles.

Candy Canes in front of the house:

cane1:=candy_pole([1.2,0,2],segn=9,arch=false):
cane2:=candy_pole([2.8,0,2],segn=9,arch=false):
cane3:=candy_pole([2.7,0.8,3.3],segn=9,segR=0.05,tilt_theta=-Pi/7):
cane4:=candy_pole([1.3,0.8,3.3],segn=9,segR=0.05,tilt_theta=-Pi/7,flip=true):
front_canes:=display(cane1,cane2,cane3,cane4):

Candy Canes at the back of the house:

caneb3:=candy_pole([1.5,4.2,2.5],segn=15,segR=0.05,tilt_theta=-Pi/3,flip=true):
caneb4:=candy_pole([2.5,4.2,2.5],segn=15,segR=0.05,tilt_theta=-Pi/3):}
back_canes:=display(caneb3,caneb4):

Candy Canes at the left of the house:

canel1:=candy_pole([0.8,1.5,2.5],segn=15,segR=0.05,tilt_theta=-Pi/7,theta=Pi/2):
canel2:=candy_pole([0.8,2.5,2.5],segn=15,segR=0.05,tilt_theta=-Pi/7,theta=Pi/2):
canel3:=candy_pole([0.8,4,2.5],segn=15,segR=0.05,tilt_theta=-Pi/7,theta=Pi/2):
left_canes:=display(canel1,canel2,canel3):

Candy Canes at the right of the house:

caner1:=candy_pole([3.2,1.5,2.5],segn=15,segR=0.05,tilt_theta=-Pi/7,theta=Pi/2):
caner2:=candy_pole([3.2,2.5,2.5],segn=15,segR=0.05,tilt_theta=-Pi/7,theta=Pi/2):
caner3:=candy_pole([3.2,4,2.5],segn=15,segR=0.05,tilt_theta=-Pi/7,theta=Pi/2):
right_canes:=display(caner1,caner2,caner3):

canes:=display(front_canes,back_canes,right_canes,left_canes):

With these canes in place all that was left was to create the ground and display our Gingerbread House.

ground:=display(plottools:-parallelepiped([5,0,0],[0,0.5,0],[0,0,4],[0,1.35,0]),color="DarkGreen"):

display([door,door_decor,d1,base,base_decor,d3,d4,front,back,roof,ground,canes],orientation=[-100,0,95]);

You can download the full worksheet creating the gingerbread house below:

Geometry_Gingerbread.mw

I am very pleased to announce that we have released a new version of the free Maple Companion app. For those you may have missed it, the first release of this app gave you a way to take a picture of math using your phone’s camera and upload it into Maple. Instructors have told me they’ve found this very useful in their classes, as they no longer have to deal with transcription errors as students enter problems into Maple.

So that’s good. But version 2 is a lot better. The Maple Companion now solves math problems directly on your phone. It can handle problems from algebra, precalculus, calculus, linear algebra, differential equations, and more. No need to upload to Maple – students can solve the problem by hand, and then use the app to check their answer, try new operations on the same expression, and even create plots. And if they want to do even more, they can still upload the expression into Maple for more advanced operations and explorations.

There’s also a built-in math editor, so you can enter problems without the camera, too. And if you use the camera, and it misinterprets part of your expression, you can fix it using the editor instead of having to retake the picture.  Good as the math recognition is, even in the face of some pretty poor handwriting, the ability to tweak the results has proven to be extremely useful.

There’s lots more we’d like to do with the Maple Companion app over time, and we’d like hear your thoughts, as well. How else can it help students learn?  How else can it act as a complement to Maple? Let us know!

Visit Maple Companion to learn more, find links to the app stores so you can download the app, and access the feedback form. And if you already have version 1, you can get the new release simply by updating the app on your phone.

This update fixes the problems inadvertently introduced in Maple 2019.2, namely:

• Maple failed to run the code in the maple.ini/.mapleinit initialization files when loading existing worksheets containing a restart() command
• Installing some packages from the MapleCloud was unsuccessful

For anyone who installed the 2019.2 update, installing 2019.2.1 will fix these problems.

If you are at Maple 2019.1 or earlier, installing this update will bring you straight to Maple 2019.2.1.

This update is available through Tools>Check for Updates in Maple, and is also available from our website on the Maple 2019.2.1 download page.

If you are a MapleSim user, please note that these problems do not affect your use of MapleSim. If you use Maple on its own, and if you use Maple command initialization files and/or you need to install a package from the MapleCloud that does not work, please contact Maplesoft Technical Support for assistance.

We sincerely apologize for the inconvenience and thank you for your patience as we worked through this issue.

We have just released updates to Maple and MapleSim.

Maple 2019.2 includes corrections and improvements to a variety of areas in the product, including a new “Go to page ____” option in print preview (that am personally quite pleased about), sections are expanded by default when printing or exporting, a fix to a problem using non-executable math with text in document mode that sometimes made it impossible to advance to a new line using Enter, improvements to VectorCalculus, select, abs and other math functions, support for macOS Catalina, and more.  We recommend that all Maple 2019 users install these updates.

This update is available through Tools>Check for Updates in Maple, and is also available from our website on the Maple 2019.2 download page, where you can also find more details.

For MapleSim users, the MapleSim 2019.2 family of products includes enhancements in the areas of model development and toolchain connectivity, including substantial enhancements to the MapleSim CAD toolbox.   For more details and download instructions, visit the MapleSim 2019.2 download page.

I’m very pleased to announce that we have just released the Maple Companion mobile app for iOS and Android phones. As its name implies, this free app is a complement to Maple. You can use it to take pictures of math you find out in the wild (e.g. in your handwritten notes, on a blackboard, in a textbook), and bring that math into Maple so you can get to work.

The Maple Companion lets you:

• Avoid the mistakes that can occur when transcribing mathematical expressions into Maple manually
• Save time when entering multiple equations into Maple, such as when you are checking your homework or pulling information from a reference book
• Push math you’ll need later into Maple now, even if you don’t have your computer handy

The Maple Companion is an idea we started playing with recently. We believe it has interesting potential as a tool to help students learn math, and we’d really like your feedback to help shape its future direction. This first release is a step towards that goal, so you can try it out and start thinking about what else you would like to see from an app like this. Should it bring in entire documents? Integrate with tutors and Math Apps? Help students figure out where they went wrong when solving a problem? Let us know what you think!

Visit Maple Companion to learn more, link to the app stores so you can download the app, and access the feedback form. And of course, you are also welcome to give us your ideas in the comment section of this post.

We are currently in the process of updating the support FAQs at https://faq.maplesoft.com. We’ve been working on updating the existing content for clarity, and have added several new articles already.

The majority of our FAQs are from questions people ask us in Technical Support by support request form, but we’d also like to like to add content from other sources.

Since we have such a great community here at MaplePrimes, we wanted to reach out and ask if there are any articles or questions that you'd like to see added to our FAQ.

We look forward to hearing your feedback!

We occasionally get asked questions about methods of Perturbation Theory in Maple, including the Lindstedt-Poincaré Method. Presented here is the most famous application of this method.

Introduction

During the dawn of the 20th Century, one problem that bothered astronomers and astrophysicists was the precession of the perihelion of Mercury. Even when considering the gravity from other planets and objects in the solar system, the equations from Newtonian Mechanics could not account for the discrepancy between the observed and predicted precession.

One of the early successes of Einstein's General Theory of Relativity was that the new model was able to capture the precession of Mercury, in addition to the orbits of all the other planets. The Einsteinian model, when applied to the orbit of Mercury, was in fact a non-negligible perturbation of the old model. In this post, we show how to use Maple to compute the perturbation, and derive the formula for calculating the precession.

In polar coordinates, the Einsteinian model can be written in the following form, where u(theta)=a(1-e^2)/r(theta), with theta being the polar angle, r(theta) being the radial distance, a being the semi-major axis length, and e being the eccentricity of the orbit:
 

# Original system.
f := (u,epsilon) -> -1 - epsilon * u^2;
omega := 1;
u0, du0 := 1 + e, 0;
de1 := diff( u(theta), theta, theta ) + omega^2 * u(theta) + f( u(theta), epsilon );
ic1 := u(0) = u0, D(u)(0) = du0;

The small parameter epsilon (along with the amount of precession) can be found in terms of the physical constants, but for now we leave it arbitrary:
 

# Parameters.
P := [
    a = 5.7909050e10 * Unit(m),
    c = 2.99792458e8 * Unit(m/s),
    e = 0.205630,
    G = 6.6740831e-11 * Unit(N*m^2/kg^2), 
    M = 1.9885e30 * Unit(kg), 
    alpha = 206264.8062, 
    beta = 415.2030758 
];
epsilon = simplify( eval( 3 * G * M / a / ( 1 - e^2 ) / c^2, P ) ); # approximately 7.987552635e-8

Note that c is the speed of light, G is the gravitational constant, M is the mass of the sun, alpha is the number of arcseconds per radian, and beta is the number of revolutions per century for Mercury.

We will show that the radial distance, predicted by Einstein's model, is close to that for an ellipse, as predicted by Newton's model, but the perturbation accounts for the precession (42.98 arcseconds/century). During one revolution, the precession can be determined to be approximately
 

sigma = simplify( eval( 6 * Pi * G * M / a / ( 1 - e^2 ) / c^2, P ) ); # approximately 5.018727337e-7

and so, per century, it is alpha*beta*sigma, which is approximately 42.98 arcseconds/century.
It is worth checking out this question on Stack Exchange, which includes an animation generated numerically using Maple for a similar problem that has a more pronounced precession.

Lindstedt-Poincaré Method in Maple

In order to obtain a perturbation solution to the perturbed differential equation u'+omega^2*u=1+epsilon*u^2 which is periodic, we need to write both u and omega as a series in the small parameter epsilon. This is because otherwise, the solution would have unbounded oscillatory terms ("secular terms"). Using this Lindstedt-Poincaré Method, we substitute arbitrary series in epsilon for u and omega into the initial value problem, and then choose the coefficient constants/functions so that both the initial value problem is satisfied and there are no secular terms. Note that a first-order approximation provides plenty of agreement with the measured precession, but higher-order approximations can be obtained.

To perform this in Maple, we can do the following:
 

# Transformed system, with the new independent variable being the original times a series in epsilon.
de2 := op( PDEtools:-dchange( { theta = phi/b }, { de1 }, { phi }, params = { b, epsilon }, simplify = true ) );
ic2 := ic1;

# Order and series for the perturbation solutions of u(phi) and b. Here, n = 1 is sufficient.
n := 1;
U := unapply( add( p[k](phi) * epsilon^k, k = 0 .. n ), phi );
B := omega + add( q[k] * epsilon^k, k = 1 .. n );

# DE in terms of the series.
de3 := series( eval( de2, [ u = U, b = B ] ), epsilon = 0, n + 1 );

# Successively determine the coefficients p[k](phi) and q[k].
for k from 0 to n do

    # Specify the initial conditions for the kth DE, which involves p[k](phi).
    # The original initial conditions appear only in the coefficient functions with index k = 0,
    # and those for k > 1 are all zero.
    if k = 0 then
        ic3 := op( expand( eval[recurse]( [ ic2 ], [ u = U, epsilon = 0 ] ) ) );
    else
        ic3 := p[k](0), D(p[k])(0);
    end if:

    # Solve kth DE, which can be found from the coefficients of the powers of epsilon in de3, for p[k](phi).
    # Then, update de3 with the new information.
    soln := dsolve( { simplify( coeff( de3, epsilon, k ) ), ic3 } );
    p[k] := unapply( rhs( soln ), phi );
    de3 := eval( de3 );

    # Choose q[k] to eliminate secular terms. To do this, use the frontend() command to keep only the terms in p[k](phi)
    # which have powers of t, and then solve for the value of q[k] which makes the expression zero. 
    # Note that frontend() masks the t-dependence within the sine and cosine terms.
    # Note also that this method may need to be amended, based on the form of the terms in p[k](phi).
    if k > 0 then
        q[1] := solve( frontend( select, [ has, p[k](phi), phi ] ) = 0, q[1] );
        de3 := eval( de3 );
    end if;

end do:

# Final perturbation solution.
'u(theta)' = eval( eval( U(phi), phi = B * theta ) ) + O( epsilon^(n+1) );

# Angular precession in one revolution.
sigma := convert( series( 2 * Pi * (1/B-1), epsilon = 0, n + 1 ), polynom ):
epsilon := 3 * G * M / a / ( 1 - e^2 ) / c^2;
'sigma' = sigma;

# Precession per century.
xi := simplify( eval( sigma * alpha * beta, P ) ); # returns approximately 42.98

Maple Worksheet: Lindstedt-Poincare_Method.mw

I just wanted to let everyone know that the Call for Papers and Extended Abstracts deadline for the Maple Conference has been extended to June 14.

The papers and extended abstracts presented at the 2019 Maple Conference will be published in the Communications in Computer and Information Science Series from Springer. We welcome topics that fall into the following broad categories:

• Maple in Education
• Algorithms and Software
• Applications of Maple

You can learn more about the conference or submit your paper or abstract here: 

https://www.maplesoft.com/mapleconference/Papers-and-Presentations.aspx

Hope to hear from you soon!

We’re excited to announce that we have just released a new version of MapleSim. The MapleSim 2019 family of products helps you get the answers you need from your models, with improved performance, increased modeling scope, and more ways to connect to your existing toolchain. Improvements include:
 

• Faster simulation speeds, both within MapleSim and when using exported MapleSim models in other tools

• More simulation options are now available when running models imported from other systems

• Additional options for FMI connectivity, including support for variable-step solvers with imported FMUs, and exporting models using variable step solvers using the MapleSim FMI Connector add-on

• Improved interactive analysis apps for Monte Carlo analysis, Optimization, and Parameter Sweep

• Expanded modeling scope in hydraulics, electrical, multibody, and more, with new built-in components and support for more external Modelica libraries

• New add-on library: MapleSim Engine Dynamics Library™ from Modelon provides specialized tools for modeling, simulating, and analyzing the performance of combustion engines

• New add-on connector: The B&R MapleSim Connector gives automation projects a powerful, model-based ability to test and visualize control strategies from within B&R Automation Studio
   

See What’s New in MapleSim 2019 for more information about these and other improvements!

Submit your paper or extended abstract to the Maple Conference!

The papers and extended abstracts presented at the 2019 Maple Conference will be published in the Communications in Computer and Information Science Series from Springer. 

The deadline to submit is May 27, 2019. 

This conference is an amazing opportunity to contribute to the development of technology in academics. I hope that you, or your colleagues and associates, will consider making a contribution.

We welcome topics that fall into the following broad categories:

• Maple in Education
• Algorithms and Software
• Applications of Maple

You can learn more about the conference or submit your paper or abstract here: 

https://www.maplesoft.com/mapleconference/Papers-and-Presentations.aspx

Download QCT2019_PrimesV17_05.05.19.mw

While googling around for Season 8 spoilers, I found data sets that can be used to create a character interaction network for the books in the A Song of Ice and Fire series, and the TV show they inspired, Game of Thrones.

The data sets are the work of Dr Andrew Beveridge, an associate professor at Macalaster College (check out his Network of Thrones blog).

You can create an undirected, weighted graph using this data and Maple's GraphTheory package.

Then, you can ask yourself really pressing questions like

• Who is the most influential person in Westeros? How has their influence changed over each season (or indeed, book)?
• How are Eddard Stark and Randyll Tarly connected?
• What do eigenvectors have to do with the battle for the Iron Throne, anyway?

These two applications (one for the TV show, and another for the novels) have the answers, and more.

• Game of Thrones and Graph Theory
• A Song of Ice and Fire and Graph Theory

The graphs for the books tend to be more interesting than those for the TV show, simply because of the far broader range of characters and the intricacy of the interweaving plot lines.

Let’s look at some of the results.

This a small section of the character interaction network for the first book in the A Song of Ice and Fire series (this is the entire visualization - it's big, simply because of the shear number of characters)

The graph was generated by GraphTheory:-DrawGraph (with method = spring, which models the graph as a system of protons repelling each other, connected by springs).

The highlighted vertices are the most influential characters, as determined by their Eigenvector centrality (more on this later).

The importance of a vertex can be described by its centrality, of which there are several variants.

Eigenvector centrality, for example, is the dominant eigenvector of the adjacency matrix, and uses the number and importance of neighboring vertices to quantify influence.

This plot shows the 15 most influential characters in Season 7 of the TV show Game of Thrones. Jon Snow is the clear leader.

Here’s how the Eigenvector centrality of several characters change over the books in the A Song of Ice and Fire series.

A clique is a group of vertices that are all connected to every other vertex in the group. Here’s the largest clique in Season 7 of the TV show.

Game of Thrones has certainly motivated me to learn more about graph theory (yes, seriously, it has). It's such a wide, open field with many interesting real-world applications.

Enjoy tinkering!

I recently had a wonderful and valuable opportunity to meet with some primary school students and teachers at Holbaek by Skole in Denmark to discuss the use of technology in the classroom. The Danish education system has long been an advocate of using technology and digital learning solutions to augment learning for its students. One of the technology solutions they are using is Maple, Maplesoft’s comprehensive mathematics software tool designed to meet the unique and complex needs of STEM courses. It is rare to find Maple being used at the primary school level, so it was fascinating to see first-hand how Maple is being incorporated at the school.

In speaking with some of the students, I asked them what their education was like before Maple was incorporated into their course. They told me that before they had access to Maple, the teacher would put an example problem on the whiteboard and they would have to take notes and work through the solution in their notebooks. They definitely prefer the way the course is taught using Maple. They love the fact that they have a tool that let them work through the solution and provide context to the answer, as opposed to just giving them the solution. It forces them to think about how to solve the problem. The students expressed to me that Maple has transformed their learning and they cannot imagine going back to taking lectures using a whiteboard and notebook.

Here, I am speaking with some students about how they have adapted Maple to meet their needs ... and about football. Their team had just won 12-1.

Mathematics courses, and on a broader level, STEM courses, deal with a lot of complex materials and can be incredibly challenging. If we are able to start laying the groundwork for competency and understanding at a younger age, students will be better positioned for not only higher education, but their careers as well. This creates the potential for stronger ideas and greater innovation, which has far-reaching benefits for society as a whole.

Jesper Estrup and Gitte Christiansen, two passionate primary school teachers, were responsible for introducing Maple at Holbaek by Skole. It was a pleasure to meet with them and discuss their vision for improving mathematics education at the school. They wanted to provide their students experience with a technology tool so they would be better equipped to handle learning in the future. With the use of Maple, the students achieved the highest grades in their school system. As a result of this success, Jesper and Gitte decided to develop primary school level content for a learning package to further enhance the way their students learn and understand mathematics, and to benefit other institutions seeking to do the same. Their efforts resulted in the development of Maple-Skole, a new educational tool, based on Maple, that supports mathematics teaching for primary schools in Denmark.

Maplesoft has a long-standing relationship with the Danish education system. Maple is already used in high schools throughout Denmark, supported by the Maple Gym package. This package is an add-on to Maple that contains a number of routines to make working with Maple more convenient within various topics. These routines are made available to students and teachers with a single command that simplifies learning. Maple-Skole is the next step in the country’s vision of utilizing technology tools to enhance learning for its students. And having the opportunity to work with one tool all the way through their schooling will provide even greater benefit to students.

(L-R) Henrik and Carolyn from Maplesoft meeting with Jesper and Gitte from Holbaek by Skole

It helps foster greater knowledge and competency in primary school students by developing a passion for mathematics early on. This is a big step and one that we hope will revolutionize mathematics education in the country. It is exciting to see both the great potential for the Maple-Skole package and the fact that young students are already embracing Maple in such a positive way.

For us at Maplesoft, this exciting new package provides a great opportunity to not only improve upon our relationships with educational institutions in Denmark, but also to be a part of something significant, enhancing the way students learn mathematics. We strongly believe in the benefits of Maple-Skole, which is why it will be offered to schools at no charge until July 2020. I truly believe this new tool has the potential to revolutionize mathematics education at a young age, which will make them better prepared as they move forward in their education.

Maple users often want to write a derivative evaluated at a point using Leibniz notation, as a matter of presentation, with appropriate variables and coordinates. For instance:

Now, Maple uses the D operator for evaluating derivatives at a point, but this can be a little clunky:

p := D[1,2,2,3](f)(a,b,c);

q := convert( p, Diff );

u := D[1,2,2,3](f)(5,10,15);

v := convert( u, Diff );

How can we tell Maple, programmatically, to print this in a nicer way? We amended the print command (see below) to do this. For example:

print( D[1,2,2,3](f)(a,b,c), [x,y,z] );

print( D[1,2,2,3](f)(5,10,15), [x,y,z] );

print( 'D(sin)(Pi/6)', theta );

Here's the definition of the custom version of print:

# Type to check if an expression is a derivative using 'D', e.g. D(f)(a) and D[1,2](f)(a,b).

TypeTools:-AddType(   

        'Dexpr',      

        proc( f )     

               if op( [0,0], f ) <> D and op( [0,0,0], f ) <> D then

                       return false;

               end if;       

               if not type( op( [0,1], f ), 'name' ) or not type( { op( f ) }, 'set(algebraic)' ) then

                       return false;

               end if;       

               if op( [0,0,0], f ) = D and not type( { op( [0,0,..], f ) }, 'set(posint)' ) then

                       return false;

               end if;       

               return true;          

        end proc      

):


# Create a local version of 'print', which will print expressions like D[1,2](f)(a,b) in a custom way,

# but otherwise print in the usual fashion.

local print := proc()


        local A, B, f, g, L, X, Y, Z;


        # Check that a valid expression involving 'D' is passed, along with a variable name or list of variable names.

        if ( _npassed < 2 ) or ( not _passed[1] :: 'Dexpr' ) or ( not passed[2] :: 'Or'('name','list'('name')) ) then

               return :-print( _passed );

        end if;


        # Extract important variables from the input.

        g := _passed[1]; # expression

        X := _passed[2]; # variable name(s)

        f := op( [0,1], g ); # function name in expression

        A := op( g ); # point(s) of evaluation


        # Check that the number of variables is the same as the number of evaluation points.

        if nops( X ) <> nops( [A] ) then

               return :-print( _passed );

        end if;


        # The differential operator.

        L := op( [0,0], g );


        # Find the variable (univariate) or indices (multivariate) for the derivative(s).

        B := `if`( L = D, X, [ op( L ) ] );


        # Variable name(s) as expression sequence.

        Y := op( X );


        # Check that the point(s) of evaluation is/are distinct from the variable name(s).

        if numelems( {Y} intersect {A} ) > 0 then

               return :-print( _passed );

        end if;


        # Find the expression sequence of the variable names.

        Z := `if`( L = D, X, X[B] );

       

        return print( Eval( Diff( f(Y), Z ), (Y) = (A) ) );


end proc:

Do you use Leibniz Notation often? Or do you have an alternate method? We’d love to hear from you!

Last year, I read a fascinating paper that presented evidence of an exoplanet, inferred through the “wobble” (or radial velocity) of the star it orbits, HD 3651. A periodogram of the radial velocity revealed the orbital period of the exoplanet – about 62.2 days.

I found the experimental data and attempted to reproduce the periodogram. However, the data was irregularly sampled, as is most astronomical data. This meant I couldn’t use the standard Fourier-based tools from the signal processing package.

I started hunting for the techniques used in the spectral analysis of irregularly sampled data, and found that the Lomb Scargle approach was often used for astronomical data. I threw together some simple prototype code and successfully reproduced the periodogram in the paper.

After some (not so) gentle prodding, Erik Postma’s team wrote their own, far faster and far more robust, implementation.

This new functionality makes its debut in Maple 2019 (and the final worksheet is here.)

From a simple germ of an idea, to a finished, robust, fully documented product that we can put in front of our users – that, for me, is incredibly satisfying.

That’s a minor story about a niche I’m interested in, but these stories are repeated time and time again.  Ideas spring from users and from those that work at Maplesoft. They’re filtered to a manageable set that we can work on. Some projects reach completion in under a year, while other, more ambitious, projects take longer.

The result is software developed by passionate people invested in their work, and used by passionate people in universities, industry and at home.

We always pack a lot into each release. Maple 2019 contains improvements for the most commonly used Maple functions that nearly everyone uses – such as solve, simplify and int – as well features that target specific groups (such as those that share my interest in signal processing!)

I’d like to to highlight a few new of the new features that I find particularly impressive, or have just caught my eye because they’re cool.

Of course, this is only a small selection of the shiny new stuff – everything is described in detail on the Maplesoft website.

Edgardo, research fellow at Maplesoft, recently sent me a recent independent comparison of Maple’s PDE solver versus those in Mathematica (in case you’re not aware, he’s the senior developer for that function). He was excited – this test suite demonstrated that Maple was far ahead of its closest competitor, both in the number of PDEs solved, and the time taken to return those solutions.

He’s spent another release cycle working on pdsolve – it’s now more powerful than before. Here’s a PDE that Maple now successfully solves.

Maplesoft tracks visits to our online help pages - simplify is well-inside the top-ten most visited pages. It’s one of those core functions that nearly everyone uses.

For this release, R&D has made many improvements to simplify. For example, Maple 2019 better simplifies expressions that contain powers, exponentials and trig functions.

Everyone who touches Maple uses the same programming language. You could be an engineer that’s batch processing some data, or a mathematical researcher prototyping a new algorithm – everyone codes in the same language.

Maple now supports C-style increment, decrement, and assignment operators, giving you more concise code.

We’ve made a number of improvements to the interface, including a redesigned start page. My favorite is the display of large data structures (or rtables).

You now see the header (that is, the top-left) of the data structure.

For an audio file, you see useful information about its contents.

I enjoy creating new and different types of visualizations using Maple's sandbox of flexible plots and plotting primitives.

Here’s a new feature that I’ll use regularly: given a name (and optionally a modifier), polygonbyname draws a variety of shapes.

In other breaking news, I now know what a Reuleaux hexagon looks like.

Since I can’t resist talking about another signal processing feature, FindPeakPoints locates the local peaks or valleys of a 1D data set. Several options let you filter out spurious peaks or valleys

I’ve used this new function to find the fundamental frequencies and harmonics of a violin note from its periodogram.

Speaking of passionate developers who are devoted to their work, Edgardo has written a new e-book that teaches you how to use tensor computations using Physics. You get this e-book when you install Maple 2019.

The new LeastTrimmedSquares command fits data to an equation while not being signficantly influenced by outliers.

In this example, we:

• Artifically generate a noisy data set with a few outliers, but with the underlying trend Y =5 X + 50
• Fit straight lines using CurveFitting:-LeastSquares and Statistics:-LeastTrimmedSquares

LeastTrimmedSquares function correctly predicts the underlying trend.

We try to make every release faster and more efficient. We sometimes target key changes in the core infrastructure that benefit all users (such as the parallel garbage collector in Maple 17). Other times, we focus on specific functions.

For this release, I’m particularly impressed by this improved benchmark for factor, in which we’re factoring a sparse multivariate polynomial.

On my laptop, Maple 2018 takes 4.2 seconds to compute and consumes 0.92 GiB of memory.

Maple 2019 takes a mere 0.27 seconds, and only needs 45 MiB of memory!

I’m a visualization nut, and I always get a vicarious thrill when I see a shiny new plot, or a well-presented application.

I was immediately drawn to this new Maple 2019 app – it illustrates the transition between day and night on a world map. You can even change the projection used to generate the map. Shiny!

So that’s my pick of the top new features in Maple 2019. Everyone here at Maplesoft would love to hear your comments!