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The site contains free books in English and Russian along with software created under the guidance of the main author prof. V. Aladjev in such areas as general theory of statistics, theory of cellular automata, programming in Maple and Mathematica systems. Each book is archived, including its cover and book block in pdf format. The software with freeware license is designed for Maple and Mathematica.

Hello everyone,

There is a strange behaviour with code-edit. Namely, when hiding code-edit window and recovering it, sometimes Maple create a very narrow window. It is very hard to see the code that way. After a while the code-edit region window readjusts itself though. However it takes time.

Also another thing, it would be useful when searching in a worksheet, to have an option that allows to search in code-edit region also.

Best regards,

When this question was asked here earlier, I neglected to suggest or to emphasize two further items.  Now, on revising Mathematics for Chemistry with [Maple], I recognise that I should have included these two objectives for inclusion in Maple 2021.

- an extended and improved spreadsheet with symbolic capability; I suspect that Maple was the only software for symbolic computation to include such a facility, which sadly has become deprecated, for no obvious reason.

- a much extended capability to solve integral equations; publications dating from 1976 -- i.e. before Maple! -- have shown what is possible; Maple's capabilities for differential equations might still be superior, although the competition is becoming close, so further efforts in the development of both differential and integral equations are timely and appropriate.  Related to differential equations is naturally the extension of capabilities of special functions, both to extend present functions and to produce new functions, such as those of Lame.

We recently published a paper on multiscale-multidomain simulation of battery models.

Some challenges are listed at

Maple and symbolic math can play a critical role in solving many challenging problems. For example, consider a seemingly-trivial problem

uxx+uyy = 0

x = 0 and x = 1, ux = 0 for all y

y = 1, u = 0, for all x

y = 0, u =1, 0<=x<=0.5

y = 0, uy = 0, 0.5<x<=1

There is a singularity at (0.5,0) and most numerical methods will have trouble there. In these equations, uxx means the second derivative of u with respect to x.

Maple can help solve this problem with conformal mapping to achieve arbitrary precision. As of today, machine precision is not possible with any numerical method even with millions of Degrees of Freedom. The Maple code is given below. A FEM code is given below as well. Models like this can benefit from Maple adding parallel sparse direct and iterative solvers.





Digits := 15


 The domain is tranformed from Z = X+IY to w. The points tranformed are


Zdomain := [[0, 0], [1, 0], [1, 1], [0, 1]]



wdomain := [0, 1, 1+a, a+2]



eq1 := diff(Z(w), w) = -I*K1/(w^(1/2)*(w-1)^(1/2)*(w-a-1)^(1/2)*(w-a-2)^(1/2))


 a is not known apriori. The value of a should be found to make sure [1,1] in the Z coordinate is transformed to [1+a] in the w coordinate.


a := 2^(1/2)-1


 Value of K1 is found using the transformation of [1,0] to 1 in the w coordinate


eq11 := 1 = -K1*2^(1/2)*EllipticK(2^(1/2)/2)



K1 := -(1/2)*(2^(1/2)/EllipticK(2^(1/2)/2))


 The height Y in the Z coordinate is found by integrating from 1 to 1+a in the w coordinate.







 The choice of a = sqrt(2)-1 gives the height of 1 for Z coordinate.







 integration from 0 to 1+a in the w coordinate gives 1,1 in the Z coordinate.


EllipticF(2^(1/2)/(2+2^(1/2))^(1/2), 2^(1/2)/2)/EllipticK(2^(1/2)/2)





 Integrating w from 0 to wmid =1/sqrt(2) gives the point 0.5,0 in the Z coordinate


wmid := 2^(1/2)/2


 Next w domain is transformed to Z2 domain Z2 = X2+IY2


Z2domain := [[0, 0], [1, 0], [1, H], [0, H]]



wdomain2 := [0, 2^(1/2)/2, 2^(1/2), 2^(1/2)+1]



eq2 := diff(Z2(w), w) = -(2*I)*K2/(w^(1/2)*(4*w-2*2^(1/2))^(1/2)*(w-2^(1/2))^(1/2)*(w-2^(1/2)-1)^(1/2))


 K2 is found based on the transformation of wmid to [1,0] in Z2 coordinate.


eq21 := 1 = -2*K2*EllipticK(1/(((2+2^(1/2))^(1/2))))/(2^(1/2)+1)^(1/2)



K2 := -(1/2)*((2^(1/2)+1)^(1/2)/EllipticK(1/(((2+2^(1/2))^(1/2)))))


 The corner 0,1 in the Z coordinate is mapped by integrating eq2 from 0 to 1 in the w coordinate




1+EllipticF((2-2^(1/2))^(1/2), ((2^(1/2)+1)/(2+2^(1/2)))^(1/2))*I/EllipticK(1/(((2+2^(1/2))^(1/2))))



corner := 1.+.559419351518322*I


 This is the point 1,0 in the original coordinate.

 The height in the Z2 coorinate is found by integrating eq2 from wmid to 1+a.


ytot := EllipticK(((2^(1/2)+1)/(2+2^(1/2)))^(1/2))*I/EllipticK(1/(((2+2^(1/2))^(1/2))))





 The magnitude in the Y direction is given by the coefficient of I, the imaginary number


Ytot := EllipticK(((2^(1/2)+1)/(2+2^(1/2)))^(1/2))/EllipticK(1/(((2+2^(1/2))^(1/2))))


 The analytial solution in the Z2 corordinate is a line in Y2 to satisfy simple zero flux conditions at X2 = 0 and X2 = 1.


phianal := 1+b*Y2


 The value of phi is zero at Y2 = Ytot (originally the cathode domain in the Z domain)


bc := 1+b*EllipticK(((2^(1/2)+1)/(2+2^(1/2)))^(1/2))/EllipticK(1/(((2+2^(1/2))^(1/2)))) = 0



b := -EllipticK(1/(((2+2^(1/2))^(1/2))))/EllipticK(((2^(1/2)+1)/(2+2^(1/2)))^(1/2))


 The analytical solution is given by







 The potential at the corner is given by substituting the imaginary value of corner for Y2 in phinanal)


phicorner := 1-.559419351518322*EllipticK(1/(((2+2^(1/2))^(1/2))))/EllipticK(((2^(1/2)+1)/(2+2^(1/2)))^(1/2))





local flux/current density calculation, written in terms of w is


curr := -(2^(1/2)+1)^(1/2)*2^(1/2)*EllipticK(2^(1/2)/2)*(w-1)^(1/2)/(EllipticK(((2^(1/2)+1)/(2+2^(1/2)))^(1/2))*(4*w-2*2^(1/2))^(1/2))


average flux/current density calculation for the anode


currave := -(1/2)*((2^(1/2)+1)^(1/2)*(2^(1/2)-1)*EllipticK(2^(1/2)/2)*(2^(3/4)+2^(1/4)+arctanh(2^(3/4)/2))*2^(1/2)/EllipticK(2^(3/4)/2))



 The average current density at Y =0, local current density at X = 0,Y=0 and potential at X=1,Y=0 (Corner) can be used to study convergence of FEM and other numerical methods


-1.656507648777793388522396, -1.161311530233258689567781, .5414751796044734741869534




Download Conformalmapping.mws


This FEM code is for solving Laplace's equation with primary current distribution considered in Model 1.
This code is based on FEM weak-form. Biquadratic Lagrange shape functions (9nodes in an element) are used.



Lx:=1: #length in X

Ly:=1: #length in Y

nx:=10: #number of elements in X (even numbers only)

ny:=10: #number of elements in Y, to be kept same as nx in this version

hx:=Lx/nx: #element size x

hy:=Ly/ny: #element size y

Procedure to perform numerical integration on shape functions to obtain local matrices (can be replaced with analytical integration for this particular problem)
  -Shape functions are also used as weight functions in applying weak formulation. Numerical integration is done using Simpson's rule.
  -Local cartesian coordinates x,y are converted to natural coordinates zeta and eta. This transformation is not required for this simple geomerty but useful in general. zeta and eta are obtained by scaling x and y with hx/2 and hy/2, respectively, in this code.


global Kx,Ky,Nx,Ny,zeta,eta,c;
local A,dAdzeta,dAdeta,y,x,J,terms,i,j,k,l,dx,dy,fx,fy,fxy,fyy,dzeta,deta,J1,J2;

A:=[(1-zeta)*zeta*(1-eta)*eta/4,-(1-(zeta)^2)*(1-eta)*eta/2,-(1+zeta)*zeta*(1-eta)*eta/4,(1-(eta)^2)*(1+zeta)*zeta/2,(1+zeta)*zeta*(1+eta)*eta/4,(1-(zeta)^2)*(1+eta)*eta/2,-(1-zeta)*zeta*(1+eta)*eta/4,-(1-(eta)^2)*(1-zeta)*(zeta)/2,(1-(zeta)^2)*(1-(eta)^2)]; #bi quadratic langrange shape functions









terms:=20:#number of terms for numerical integration

for i from 1 to nops(Nx) do #loop to obtain local matrices      

for j from 1 to nops(Ny) do

for k from 0 to terms do #outer loop double integration, integration in zeta

if k = 0 then fx[k]:= subs(zeta=-1,Nx[i]*Nx[j]*J); fy[k]:= subs(zeta=-1,Ny[i]*Ny[j]*J);  
elif k = terms then
fx[k]:= subs(zeta=-1+(k*dzeta),Nx[i]*Nx[j]*J);
fy[k]:= subs(zeta=-1+(k*dzeta),Ny[i]*Ny[j]*J);  
elif irem(k,2) = 0 then
fx[k]:= 2*subs(zeta=-1+(k*dzeta),Nx[i]*Nx[j]*J);
fy[k]:=     2*subs(zeta=-1+(k*dzeta),Ny[i]*Ny[j]*J);
else fx[k]:= 4*subs(zeta=-1+(k*dzeta),Nx[i]*Nx[j]*J);
fy[k]:=     4*subs(zeta=-1+(k*dzeta),Ny[i]*Ny[j]*J);  end if;

for l from 0 to terms do #inner loop double integration, integration in eta

if l = 0 then fxy[l]:= subs(eta=-1,fx[k]); fyy[l]:= subs(eta=-1,fy[k]);
elif l = terms then fxy[l]:= subs(eta=-1+(l*deta),fx[k]); fyy[l]:= subs(eta=-1+(l*deta),fy[k]);
elif irem(l,2) = 0 then fxy[l]:= 2*subs(eta=-1+(l*deta),fx[k]); fyy[l]:= 2*subs(eta=-1+(l*deta),fy[k]);
else fxy[l]:=4*subs(eta=-1+(l*deta),fx[k]); fyy[l]:=4*subs(eta=-1+(l*deta),fy[k]); end if;

end do;
end do;
end do;
end do:

end proc:

n:=nx*ny; #total number of elements

n := 100


Nx1:=nx*2+1: #number of nodes in x in one row

N:=Nx1*(2*ny+1); # total number of nodes/equations

N := 441


K:=Matrix(N,N,storage=sparse): # global K matrix

C:=Vector(N,storage=sparse): # global c matrix

L2G:=Matrix(n,9):  #mapping matrix - each row has node numbers for each element

localmatrices(hy/2,hy/2,hy/2,hx/2,hx/2,hx/2,0,0): kx:=copy(Kx):ky:=copy(Ky):c0:=copy(c):

for i from 1 to n do #modifying,adding and assembling matrices to get global matrix
if i<=nx/2 then  
a1:=copy(kx); a2:=copy(ky); a3:=0; a1[1..3,1..9]:=IdentityMatrix(3,9); a2[1..3,1..9]:=Matrix(3,9,shape=zero); a4:=a1+a2; c:=copy(c0); c[1..3]:=1.0;

elif i=nx/2+1 then  a1:=copy(kx); a2:=copy(ky); a3:=0; a1[1,1..9]:=IdentityMatrix(1,9); a2[1,1..9]:=Matrix(1,9,shape=zero); a4:=a1+a2; c:=copy(c0); c[1]:=1.0;

elif i>nx*(ny-1) then a1:=copy(kx); a2:=copy(ky); a3:=0; a1[5..7,5..7]:=IdentityMatrix(3,3);
a1[5..7,8..9]:=ZeroMatrix(3,2); a2[5..7,1..9]:=Matrix(3,9,shape=zero); a4:=a1+a2; c:=copy(c0); c[5..7]:=0;

else a1:=kx; a2:=ky; a3:=0;a4:=a1+a2; c:=c0;  end if;


if k>nx then k:=1; l:=l+Nx1+3;

else l:=l+2; end if:



for i1 from 1 to 9 do
end do:

end do:

phi:=LinearSolve(K,C,method=SparseLU): #linear set of equations solved using Sparse LU solver


phi_at(1, 0) := .546587799122513





current_at(0, 0) := -1.15773815354626


if irem(nx/2,2)=0
then current_at(0,0.25):=add(dNdy_bottom_left[i]*phi[L2G[nx/4+1,i]],i=1..nops(Ny));
end if;

dNdy_bottom_center := [0, -30, 0, 0, 0, -10, 0, 0, 40]

current_at(0, .25) := -1.26989097821724





Download FEM_2D.mws

What are the things you most like to see improved/add to next version of Maple? 

This is my list for a starter:

1.  Improve the debugger. Debugger is very useful but needs more work. At least be able to see code listing in larger view as one steps in for example. See Matlab debugger for inspiration.

2.  Improve Latex. It still does not do fractions well. Posted about this before.

3. Eliminate hangs when using timelimit(). On long runs, random hangs happen when timelimit() do not expire as requested. Posted about this before.



I don't stand by all I said here, so I'm deleting. I think there was valid criticism to be made, but I didn't make it any valid way.

Hello all ,

It's been years and years that I have been using Maple (since Maple V.2 in 1992 if my memory is still

correct. Now we are at Maple 2020 and the brilliant scientific library GMP is still in Maple at version



The relatively new GMP is 6.2.0 :

I wonder if in a not too far version of Maple we could have GMP updated. It is really an impressive mathematical library alongside with GSL.

Voilà !


Kind regards to all,





This is more of an open discussion than a real question. Maybe it would gain to be displaced in the post section?

Working with discrete random variables I found several inconsistencies or errors.
In no particular order: 

  • The support of a discrete RV is not defined correctly (a real range instead of a countable set)
  • The plot of the probability function (which, in my opinion, would gain to be renamed "Probability Mass Function, see is not correct.
  • The  ProbabiliytFunction of a discrte rv of EmpiricalDistribution can be computed at any point, but its formal expression doesn't exist (or at least is not accessible).
  • Defining the discrete rv "toss of a fair dice"  with EmpiricalDistribution and DiscreteUniform gives different results.

The details are given in the attached file and I do hope that the companion text is clear enough to point the issues.
I believe there is no major issues here, but that Maple suffers of some lack of consistencies in the treatment of discrete (at least some) rvs. Nothing that could easily be fixed.

As I said above, if some think this question has no place here and ought to me moved to the post section, please feel free to do it.

Thanks for your attention.




Two alternate ways to define a discrete random variable on a finite set
of equally likely outcomes.

Universe    := [$1..6]:
toss_1_dice := RandomVariable(EmpiricalDistribution(Universe));
TOSS_1_DICE := RandomVariable(DiscreteUniform(1, 6));





Let's look to the ProbabilityFunction of each RV

ProbabilityFunction(toss_1_dice, x);
ProbabilityFunction(TOSS_1_DICE, x);

"_ProbabilityFunction[Typesetting:-mi("x",italic = "true",mathvariant = "italic")]"


piecewise(x < 1, 0, x <= 6, 1/6, 6 < x, 0)


It looks like the procedure ProbabilityFunction is not an attribute of RV with EmpiticalDistribution.
Let's verify

law := [attributes(toss_1_dice)][3]:

Conditions, ParentName, Parameters, CDF, DiscreteValueMap, Mean, Median, Mode, ProbabilityFunction, Quantile, Specialize, Support, RandomSample, RandomVariate


Clearly ProbabilityFunction is an attribute of toss_1_dice.

In fact it appears the explanation of the difference of behaviours relies upon different definitions
of the set of outcomes of toss_1_dice and TOSS_1_DICE

LAW := [attributes(TOSS_1_DICE)][3]:


[(Vector(6, {(1) = 1, (2) = 2, (3) = 3, (4) = 4, (5) = 5, (6) = 6}))::rtable]


[1 < 6]


From :-Conditions one can see that toss_1_dice is realy a discrete RV defined on a countable set of outcomes,
but that nothing is said about the set over which TOSS_1_DICE is defined.

The truly discrete definition of toss_1_dice is confirmed here :
(the second result is correct

ProbabilityFinction(toss_1_dice, x) = {0 if x < 1, 0 if x > 6, 1/6 if x::integer, 0 otherwise

ProbabilityFunction~(toss_1_dice, Universe);
ProbabilityFunction~(toss_1_dice, [seq(0..7, 1/2)]);

[1/6, 1/6, 1/6, 1/6, 1/6, 1/6]


[0, 0, 1/6, 0, 1/6, 0, 1/6, 0, 1/6, 0, 1/6, 0, 1/6, 0, 0]


One can also see that the Support of both of these RVs are wrong

(see for instance

There should be {1, 2, 3, 4, 5, 6}, not a RealRange.


RealRange(1, 6)


RealRange(1, 6)





{1, 2, 3, 4, 5, 6}



Now this is the surprising ProbabilityFunction of TOSS_1_DICE.
This obviously wrong result probably linked to the weak definition of the conditions for this RB.

# plot(ProbabilityFunction(TOSS_1_DICE, x), x=0..7);
plot(ProbabilityFunction(TOSS_1_DICE, x), x=0..7, discont=true)


These differences of treatments raise a lot of questions :
    -  Why is a DiscreteUniform RV not defined on a countable set?
    -  Why does the ProbabilityFunction of an EmpiricalDistribution return no result
        if its second parameter is not set to one  its outcomes.

 All this without even mentioning the wrong plot shown above.

I believe something which would work like the module below would be much better than what is done

right now


EmpiricalRV := module()
export MassDensityFunction, PlotMassDensityFunction, Support:

MassDensityFunction := proc(rv, x)
  local u, v, N:
  u := [attributes(rv)][3]:
  if u:-ParentName = EmpiricalDistribution then
    v := op([1, 1], u:-Conditions);
    N := numelems(v):
    return piecewise(op(op~([seq([x=v[n], 1/N], n=1..N)])), 0)
    error "The random variable does not have an EmpiricalDistribution"
  end if
end proc:

PlotMassDensityFunction := proc(rv, x1, x2)
  local u, v, a, b:
  u := [attributes(rv)][3]:
  if u:-ParentName = EmpiricalDistribution then
    v := op([1, 1], u:-Conditions);
    a := select[flatten](`>=`, v, x1);
    b := select[flatten](`<=`, a, x2);
    PLOT(seq(CURVES([[n, 0], [n, 1/numelems(v)]], COLOR(RGB, 0, 0, 1), THICKNESS(3)), n in b), VIEW(x1..x2, default))
    error "The random variable does not have an EmpiricalDistribution"
  end if
end proc:

Support := proc(rv, x1, x2)
  local u, v, a, b:
  u := [attributes(rv)][3]:
  if u:-ParentName = EmpiricalDistribution then
    v := op([1, 1], u:-Conditions);
    return {entries(v, nolist)}
    error "The random variable does not have an EmpiricalDistribution"
  end if
end proc:

end module:

EmpiricalRV:-MassDensityFunction(toss_1_dice, x);

piecewise(x = 1, 1/6, x = 2, 1/6, x = 3, 1/6, x = 4, 1/6, x = 5, 1/6, x = 6, 1/6, 0)


f := unapply(EmpiricalRV:-MassDensityFunction(toss_1_dice, x), x):





EmpiricalRV:-PlotMassDensityFunction(toss_1_dice, 0, 7);







I'm only just hearing (haven't experienced) about some serious issues with the 2019.2 updates.  I would recommend waiting for Maplesoft to release an emergency 2019.3 fix update - Maplesoft can NOT leave the last update of 2019 in this state.

I have noticed that there exists a Stack Exchange site for mathematica, and not for maple. My discussions with the part of Stack Exchange that handle the creation of a new Stack Exchange community have said that I must accrue a certain level of interest in the subject in order for it to be approved, and so I thought I would begin here to see if there is suffice level of interest.

This would not diminish the use of the Maple Primes forum, and an additional proposal, in consideration of the years of dedication that have gone into this domain, be to pool the data between the two, make reputation points the same on both, perhaps even user profiles and questions answered already linkable, and all of the questions already addressed here showing up in the search on both domains.

I am proposing this simply because I want to encourage the use of maple, and have noted that Stack Exchange is very popular. 

So I am posting this to get overall feedback from other Maple users, as to what their opinion is regarding this proposal, and advice on whether it should and how it ought to be pursued.

We are currently in the process of updating the support FAQs at 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 at, 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!

There have been several posts, over the years, related to visual cues about the values associated with particular 2D contours in a plot.

Some people ask or post about color-bars [1]. Some people ask or post about inlined labelling of the curves [1, 2, 3, 4, 5, 6, 7]. And some post about mouse popup/hover-over functionality [1]., which got added as general new 2D plot annotation functionality in Maple 2017 and is available for the plots:-contourplot command via its contourlabels option.

Another possibility consists of a legend for 2D contour plots, with distinct entries for each contour value. That is not currently available from the plots:-contourplot command as documented. This post is about obtaining such a legend.

Aside from the method used below, a similar effect may be possible (possibly with a little effort) using contour-plotting approaches based on individual plots:-implicitplot calls for each contour level. Eg. using Kitonum's procedure, or an undocumented, alternate internal driver for plots:-contourplot.

Since I like the functionality provided by the contourlabels option I thought that I'd highjack that (and the _HOVERCONTENT plotting substructure that plot-annotations now generate) and get a relatively convenient way to get a color-key via the 2D plotting legend.  This is not supposed to be super-efficient.

Here below are some examples. I hope that it illustrates some useful functionality that could be added to the contourplot command. It can also be used to get a color-key for use with densityplot.


contplot:=proc(ee, rng1, rng2)
  local clabels, clegend, i, ncrvs, newP, otherdat, others, tcrvs, tempP;
  (clabels,others):= selectremove(type,others,identical(:-contourlabels)=anything);
  if nops(clegend)>0 then
    if nops(clabels)>0 then
      return ':-PLOT'(seq(':-CURVES'(op(ncrvs[i]),op(indets(tcrvs[i],'specfunc(:-LEGEND)'))),
      return tempP;
    end if;
  elif nops(clabels)>0 then
    return plots:-contourplot(ee,rng1,rng2,others[],
    return plots:-contourplot(ee,rng1,rng2,others[]);
  end if;
end proc:

contplot(x^2+y^2, x=-2..2, y=-2..2,
      contours = 9,
      legendstyle = [location = right],

contplot(x^2+y^2, x=-2..2, y=-2..2,
      contours = 17,
      legendstyle = [location = right],

# Apparently legend items must be unique, to persist on document re-open.

contplot(x^2+y^2, x=-2..2, y=-2..2,
      contours = 11,
      legendstyle = [location = right],
      legend=['contourvalue',seq(cat($(` `,i)),i=2..5),
              'contourvalue',seq(cat($(` `,i)),i=6..9),

contplot(x^2+y^2, x=-2..2, y=-2..2,
      contours = 8,

contplot(x^2+y^2, x=-2..2, y=-2..2,
      contours = 13,

conts:=[seq(low..high*1.01, (high-low)/(N-1))]:
contplot(x^2+y^2, x=-2..2, y=-2..2,
      contours = conts,

  subsindets(contplot((x^2+y^2)^(1/2), x=-2..2, y=-2..2,
                      contours = 7,
  contplot((x^2+y^2)^(1/2), x=-2..2, y=-2..2,
      contours = 7, #grid=[50,50],
      legendstyle = [location=right],


  contplot(x^2+y^2, x=-2..2, y=-2..2,
      contours = 5,
      thickness=0, filledregions),
  contplot(x^2+y^2, x=-2..2, y=-2..2,
      contours = 5,
      legendstyle = [location=right],

  contplot(sin(x)*y, x=-2*Pi..2*Pi, y=-1..1,
      contours = [seq(-1+(i-1)*(1-(-1))/(N-1),i=1..N)],
      legendstyle = [location=right],
   plots:-densityplot(sin(x)*y, x=-2*Pi..2*Pi, y=-1..1,
      style=surface, restricttoranges),

  contplot(sin(x)*y, x=-2*Pi..2*Pi, y=-1..1,
      contours = [seq(-1+(i-1)*(1-(-1))/(N-1),i=1..N)],
      legendstyle = [location=right],
      legend=['contourvalue',seq(cat($(` `,i)),i=2..3),
              'contourvalue',seq(cat($(` `,i)),i=5..6),
              'contourvalue',seq(cat($(` `,i)),i=8..9),
              'contourvalue',seq(cat($(` `,i)),i=11..12),
   plots:-densityplot(sin(x)*y, x=-2*Pi..2*Pi, y=-1..1,
      style=surface, restricttoranges),






Error when running 'm_model.m' to generate simulink function block when using 'Group all parameters into nested structure'. No errors when using 'Do not group parameters'.

Error using m_fullSwing (line 125)
Invalid setting in 'MapleSim_fullSwing/MapleSim_fullSwing/MapleSimParameters' for parameter 'Value'.
Caused by:
    Error using m_fullSwing (line 125)
    Expression '[Param.Address.ArmPlane, Param.Address.ClubRelHand,  ... ]' for
    parameter 'Value' in 'MapleSim_fullSwing/MapleSim_fullSwing/MapleSimParameters' cannot be evaluated.
        Error using m_fullSwing (line 125)
        Error: Invalid expression. When calling a function or indexing a variable, use parentheses. Otherwise, check for
        mismatched delimiters.

Starting the Maple 2019 wishlist.  Anything in particular users would like to see added or upgraded to maple can add it here. 


Let us consider

plots:-inequal(max(1, min(x, 2))+max(1, min(y, 2)) <= 3, x = -4 .. 4, y = -4 .. 4);

and compare it with

plots:-implicitplot(max(1, min(x, 2))+max(1, min(y, 2)) = 3, x = -4 .. 4, y = -4 .. 4, gridrefine = 2);

The latter plot must be a subset of the former plot, but it isn't so.

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