acer

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These are answers submitted by acer

Your attempts at forming a Matrix try to use Ans[k], but your loop simply assigns (repeatedly) to just Ans. It looks like you meant to assign to Ans[j] and Ans1[j] in the loop.

In your systems of equations you have terms like Theta(eta,t), but in your attempts at forming a Matrix you have the arguments of Theta reversed, like Theta(0.5,eta). Did you mean Theta(eta,0.5) instead? The same happens for the other functions.

When you write,
   eval(diff(Theta(0.5,eta),eta),Ans[k])(0)
are you wanting instead to take from Ans[k] the derivative of Theta(eta,0.5) w.r.t. eta, and then evaluate that very close to eta=0? If so then this is essentially the same as your query in your previous Question. You are mistakenly using technique appropriate for a result from dsolve(...,numeric) rather than from pdsolve(...,numeric). Have a look at this: error_in_table_value_ac.mw

You could avoid duplication of conputation by computing the Matrix entries (involving the other terms that I omitted) in a loop.

Are you trying to get something like the following?

restart

randomize()

M := proc () local A, B, operation, str; A := RandomTools:-Generate(integer(range = -5 .. 5, exclude = {0})); B := RandomTools:-Generate(integer(range = -5 .. 5, exclude = {0, A})); operation := combinat:-randcomb([`%+`, `%-`, `%*`], 1)[]; str := sprintf("%a(%a,%a)", operation, A, B); InertForm:-Display(InertForm:-Parse(str), 'inert' = false) = value(operation(A, B)) end proc

M()

0, "%1 is not a command in the %2 package", _Hold, Typesetting

M()

0, "%1 is not a command in the %2 package", _Hold, Typesetting

M()

0, "%1 is not a command in the %2 package", _Hold, Typesetting

M()

0, "%1 is not a command in the %2 package", _Hold, Typesetting

M()

0, "%1 is not a command in the %2 package", _Hold, Typesetting

M()

0, "%1 is not a command in the %2 package", _Hold, Typesetting

M()

0, "%1 is not a command in the %2 package", _Hold, Typesetting

Download Qop2_ac.mw

Those are actually equations, and you could use or access their lhs and rhs separately.

Note that formation of the string may not be strictly necessary, compared with simply calling the operation on the arguments directly to get the inert expression. (But there seemed to be a few corner cases, where using the inert operators with prefix form helped.) Did you really need the string representation, using infix form, or is the above result from InertForm:-Display enough?

I notice that you had B set up to avoid being A. Is that because you didn't want A-B to produce 0? If so perhaps you'd also want to avoid A+B=0 when B=-A? And perhaps you'd prefer neither A nor B to be -1 or 1 when multiplying?  Qop2_ac2.mw

It sounds as if your preferences were not migrated to your new installation. (There's usually a dialogue that asks about that, during installation.)

You can set the preference by using the main menubar,

   Tools->Options->Display

and then set the drop-menu box "Input display" to "Maple Notation".  Then press the "Apply Globally" button to set it as default.

@zenterix You could use the terser,

   m::{Matrix,list(Matrix)}

instead of using `Or` in the type specification.

The LinearSolve command in the LinearAlgebra package can handle this.

Let's write out the coefficients (of x) for your polynomials, as column Vectors. Form a Matrix of the ones from S.

Then call LinearSolve.

restart;

with(LinearAlgebra,LinearSolve):

 

S:={x+4, 3*x-7}: f:=-5*x+10:

T := max(degree~(S,x))+1:

A:=Matrix(T,nops(S),(i,j)->coeff(S[j],x,i-1));

Matrix(2, 2, {(1, 1) = 4, (1, 2) = -7, (2, 1) = 1, (2, 2) = 3})

B:=Vector(T,(i)->coeff(f,x,i-1));

Vector(2, {(1) = 10, (2) = -5})

LinearSolve(A,B);

Vector(2, {(1) = -5/19, (2) = -30/19})

 

SC := {x+4, 2*x+8}:

T := max(degree~(SC,x))+1:

AC:=Matrix(T,nops(SC),(i,j)->coeff(SC[j],x,i-1));

Matrix(2, 2, {(1, 1) = 4, (1, 2) = 8, (2, 1) = 1, (2, 2) = 2})

LinearSolve(AC,B);

Error, (in LinearAlgebra:-LinearSolve) inconsistent system

 

S:={x^2+x+4, 7*x^2+3*x, x+1}: f:=-5*x+10:

T := max(degree~(S,x))+1:

A:=Matrix(T,nops(S),(i,j)->coeff(S[j],x,i-1));

Matrix(3, 3, {(1, 1) = 1, (1, 2) = 0, (1, 3) = 4, (2, 1) = 1, (2, 2) = 3, (2, 3) = 1, (3, 1) = 0, (3, 2) = 7, (3, 3) = 1})

B:=Vector(T,(i)->coeff(f,x,i-1));

Vector(3, {(1) = 10, (2) = -5, (3) = 0})

LinearSolve(A,B);

Vector(3, {(1) = -15/2, (2) = -5/8, (3) = 35/8})

Download LS_ex2.mw

note: You wrote, "corresponding linear system", which indicates that you recognize that this problem can be expressed as a linear system. Using LinearSolve might then not be unexpected. Reformulating the polynomial relationships as a linear system, programmatically, might then be useful for you.

Notice that you were using the %d format code for this item, in printf. That's for integers. But you mentioned wanting them as 1.0,1.2,...2.0, which are floats. So you could use the %2.1f format, in printf.

Now, you already have the float values you mentioned wanting, available directly via t[i] .

If you didn't already have them using t[i] then you could also get n values evenly spaced between a and b by using the formula,
   a + (b-a)*(i-1)/(n-1)
which for this example would be,
   1 + (2-1)*(i-1)/(n-1)
which can be shortened to,
   1 + (i-1)/(n-1)

restart;

actual:=<2,3.5136,5.8016,9.1136,13.7376,20>:
t:=<1,1.2,1.4,1.6,1.8,2>:
n:=6:

P:=Statistics:-LinearFit([1,x],t,actual,x):

printf("t    Actual P  Model P  Error\n");
for i from 1 to n do

   model:=subs(x=t[i],P):
   err:=actual[i]-model:

   #printf("%2.1f %7.4f% 10.4f% 9.4f\n",
   #        1+(i-1)/(n-1),actual[i],model,err);

   printf("%2.1f %7.4f% 10.4f% 9.4f\n",
          t[i],actual[i],model,err);

end do:

t    Actual P  Model P  Error
1.0  2.0000    0.1717   1.8283
1.2  3.5136    3.7141  -0.2005
1.4  5.8016    7.2565  -1.4549
1.6  9.1136   10.7989  -1.6853
1.8 13.7376   14.3413  -0.6037
2.0 20.0000   17.8837   2.1163

Download frm.mw


For future programming, you might also care to think about how the following work. These kinds of construction occur pretty often in programming. It can be a useful thing to get familiar with.

<seq(1.0..2.0,0.2)>;
n:=6:
<seq(1.0..2.0,numelems=n)>;
<seq(1.0..2.0,(2-1)/(n-1))>;
<seq(1.0+(i-1)/(n-1),i=1..n)>;
Vector(n,i->1.0+(i-1)/(n-1));

You are using module locals like Chevron2A as the keys of some of your tables.

That will prevent you from accessing table entries if you subsequently try and use (say) the global names like Chevron2A as the key, trying that access outside the module.

You could, instead, use global name :-Chevron2A as the table key when creating the entry. Then you can use that as key, for access elsewhere. [edit] I would prefer this, over a module export. Using strings is also a good possibility, as Carl has mentioned after I posted this Answer. If you use the global name as key then you ought to quote it (and protect from evaluation), in case it is also assigned some value, eg. ':-Chevon2A'.

Or you could instead declare Chevron2A as an export of the module. Then, later, you could use it as key in its long-form (fully qualified name),  calculateblahcubitblah:-Chevron2A. Or you could make the module a package, so after doing with(calculateblahcubitblah) its exports become the new bindings for reference via the short-form Chevron2A. [edit] This is not my favorite choice, and I can only think of convoluted scenarios in which it'd provide extra contextual usefulness.

The code snippets that you showed above indicate that this problem that I've described occurs in your code. I cannot tell whether you also have other issues, eg. last-name-eval of tables assigned to names. I suggest using eval(nameoftable) when putting into lists or if returning them straight from procedures.

Some of your procedures will throw an error for certains ranges of inputs alpha and delta.

The plots:-inequal command appears to get slightly confused by that situation, and in consequence can misrepresent the feasible region..

Below and use short wrappers for those three procedures, so that when failing to produce a result (throwing an error) they instead return either a large positive or large negative value. Those can be used appropriately for the inequality tests, so that a more clear/robust representation of the feasible region is attained.

Double check, for correctness.

restartNULL

with(plots); with(RealDomain)

NULL

c := 1; cr := 0.3e-1*c; u := 1; sExp := 0.6e-1*c; s := .65*c; v := 3*cNULL

NULL

FirmModelPP := proc (alpha, beta, delta) local p0, xi0, q0, Firmpf0, G01, Recpf0, Unsold0, Environ0; option remember; xi0 := 1; p0 := min(s+sqrt((v-s)*(c-s)), delta*v+sExp); q0 := u*(v-p0)/(v-s); f(N) := 1/u; F(N) := N/u; G01 := int(F(N), N = 0 .. q0); Firmpf0 := (p0-c)*q0-(p0-s)*G01; Recpf0 := (sExp-cr)*xi0*q0; Unsold0 := G01; Environ0 := q0+Unsold0; return p0, q0, Firmpf0, Recpf0, Environ0, Unsold0 end proc

NULL

NULLNULLNULL

FirmModelFC := proc (alpha, beta, delta) local p00, xi00, q00, Firmpf00, G001, G002, Recpf00, Unsold00, Environ00, pr00; option remember; xi00 := 1; p00 := s+sqrt((v-s)*(c-s)); q00 := alpha*u*(v-p00)/(v-s); f(N) := 1/u; F(N) := N/u; G001 := int(F(N), N = 0 .. q00/alpha); G002 := int(F(N), N = 0 .. beta*xi00*q00/(1-alpha)); pr00 := p00-delta*v; Firmpf00 := (p00-c)*q00-alpha*(p00-s)*G001; Recpf00 := xi00*q00*(sExp-cr)+(pr00-sExp)*(beta*xi00*q00-(1-alpha)*G002); Unsold00 := alpha*G001; Environ00 := q00+Unsold00; return p00, q00, Firmpf00, Recpf00, Environ00, Unsold00 end proc

NULLNULLNULL

NULLNULL

NULL

FirmModelHmax := proc (alpha, beta, delta) local q, p, pr, FirmpfSiS, F1, G1, G2, G3, RecpfSiS, sol, UnsoldSiS, EnvironSiS, p0, OldSoldPrim, xi, h, ps, qs, prs, prof1m, prof2m; option remember; xi := 1; prs := ps-delta*v; prof1m := (ps-c)*qs+((1/2)*beta^2*xi^2*qs^2/(u*(1-alpha))-(1/2)*(1+beta*xi)^2*qs^2/u)*(ps-s)+(prs-sExp)*(beta*xi*qs-(1/2)*beta^2*xi^2*qs^2/(u*(1-alpha))); prof2m := (ps-c)*qs-(1/2)*(ps-s)*qs^2/(alpha*u)+(prs-sExp)*(beta*xi*qs-(1/2)*beta^2*xi^2*qs^2/(u*(1-alpha))); if alpha <= 1/(1+beta*xi) then p, q := (eval([ps, qs], solve({diff(prof1m, qs) = 0, qs = alpha*u*(v-ps)/(v-s), c < ps, sExp+delta*v < ps, qs < 2*u*(1-alpha)/(beta*xi)}, [ps, qs])[1]))[]; `h&Assign;`*(p-delta*v-sExp)/(p-delta*v); FirmpfSiS := eval(prof1m, [ps = p, qs = q, prs = p-delta*v]); RecpfSiS := (sExp-cr)*xi*q; UnsoldSiS := (1/2)*(1+beta*xi)^2*q^2/u-(1/2)*beta^2*xi^2*q^2/(u*(1-alpha)); EnvironSiS := q+UnsoldSiS else p, q := (eval([ps, qs], solve({diff(prof2m, qs) = 0, qs = alpha*u*(v-ps)/(v-s), c < ps, sExp+delta*v < ps, qs < 2*u*(1-alpha)/(beta*xi)}, [ps, qs])[1]))[]; h := (p-delta*v-sExp)/(p-delta*v); FirmpfSiS := eval(prof2m, [ps = p, qs = q, prs = p-delta*v]); RecpfSiS := (sExp-cr)*xi*q; UnsoldSiS := (1/2)*q^2/(alpha*u); EnvironSiS := q+UnsoldSiS end if; return p, q, FirmpfSiS, RecpfSiS, EnvironSiS, UnsoldSiS, h, OldSoldPrim, xi end proc

NULL

NULLNULL

pltPP3A := plot('FirmModelPP(alpha, .35, .40)[3]', alpha = 0. .. 1.0, color = red, legend = "", style = pointline, labels = [alpha, "Firm Profit"], labeldirections = ["horizontal", "vertical"], symbolsize = 10, axes = boxed, symbol = box, numpoints = 10, adaptive = false, thickness = 1.0, view = [0 .. 1, 0 .. .18])

NULL

pltPP3B := plot([[0., eval('FirmModelPP(0., .35, .40)[3]', alpha = 0.)]], color = red, legend = ["PP"], style = point, symbol = box, symbolsize = 10, axes = boxed, view = [0 .. 1, 0 .. .18])

NULL

pltFC3A := plot('FirmModelFC(alpha, .35, .40)[3]', alpha = 0. .. 1, linestyle = [solid], color = black, legend = "FC", labels = [alpha, "Firm Profit"], labeldirections = ["horizontal", "vertical"], axes = boxed, adaptive = false, thickness = .7)

NULL

NULLNULLNULLNULLNULL

pltHmax3A := plot('FirmModelHmax(alpha, .35, .40)[3]', alpha = 0. .. 1.0, linestyle = [dashdot], color = brown, legend = [SiS(h__max)], labels = [alpha, "Firm Profit"], labeldirections = ["horizontal", "vertical"], symbolsize = 10, numpoints = 50, adaptive = false, thickness = 1.0, axes = boxed)

NULL

#H:=plot3d((a,d)->FirmModelHmax(a, 0.35, d)[3]-FirmModelFC(a, 0.35, d)[3],
#       0..1,0..0.5,grid=[49,49],style=surface,orientation=[-90,0,0],lightmodel=none,
#       color=((a,d)->1/3*(1+signum(FirmModelHmax(a, 0.35, d)[3]-FirmModelFC(a, 0.35, d)[3]))),
#       adaptmesh=true
#      );

FMHmax := proc (a, d, lowhigh::(identical("low", "high"))) local res; option remember; if not [a, d]::(list(numeric)) then return 'procname(args)' end if; try res := FirmModelHmax(a, .35, d)[3] catch: res := undefined end try; if res::numeric then res elif lowhigh = "low" then -199 else 199 end if end proc; FMFC := proc (a, d, lowhigh::(identical("low", "high"))) local res; option remember; if not [a, d]::(list(numeric)) then return 'procname(args)' end if; try res := FirmModelFC(a, .35, d)[3] catch: res := undefined end try; if res::numeric then res elif lowhigh = "low" then -199 else 199 end if end proc; FMPP := proc (a, d, lowhigh::(identical("low", "high"))) local res; option remember; if not [a, d]::(list(numeric)) then return 'procname(args)' end if; try res := FirmModelPP(a, .35, d)[3] catch: res := undefined end try; if res::numeric then res elif lowhigh = "low" then -199 else 199 end if end proc

 

 

P1 := plots:-inequal(FMFC(a, d, "high") <= FMHmax(a, d, "low"), a = 0 .. 1, d = 0 .. .5, optionsfeasible = [[color = "Spring 1"]])
NULL

P2 := plots:-inequal(FMFC(a, d, "low") >= FMHmax(a, d, "high"), a = 0 .. 1, d = 0 .. .5, optionsfeasible = [[color = "Spring 3"]])

plots:-display(P1, P2)

P3 := plots:-inequal(FMPP(a, d, "high") <= FMHmax(a, d, "low"), a = 0 .. 1, d = 0 .. .5, optionsfeasible = [[color = "Spring 2"]])
NULL

P4 := plots:-inequal(FMPP(a, d, "low") >= FMHmax(a, d, "high"), a = 0 .. 1, d = 0 .. .5, optionsfeasible = [[color = "Spring 4"]])

plots:-display(P3, P4)

Download ConflcitInequal_acc.mw

These results for n=2..15 are computed exactly.

restart;

str:="1/(3 n Sqrt[Pi] Gamma[2 - n] Gamma[2 + n])*2^(-7 - 4 n) (3 256^
  n Gamma[2 - n] Gamma[-(1/2) +
    n] (8 n (-3 + 5 n + 2 n^2) Hypergeometric2F1[1 - n,
      2 - n, -2 n, 3/
      4] - (-1 +
       n) (4 (-4 + 7 n + 2 n^2) Hypergeometric2F1[2 - n, 2 - n,
         1 - 2 n, 3/4] - (-10 + 3 n + n^2) Hypergeometric2F1[
         2 - n, 3 - n, 2 - 2 n, 3/4])) -
 9^n Gamma[-(1/2) - n] Gamma[
   2 + n] (8 n (5 + 11 n + 2 n^2) Hypergeometric2F1[1 + n, 2 + n,
      2 n, 3/4] -
    3 (1 + n) (4 (4 + 9 n + 2 n^2) Hypergeometric2F1[2 + n, 2 + n,
          1 + 2 n, 3/4] -
       3 (6 + 5 n + n^2) Hypergeometric2F1[2 + n, 3 + n,
         2 (1 + n), 3/4])))":

ee := unapply(simplify(MmaTranslator:-FromMma(str)),n);

proc (n) options operator, arrow; -(1/24)*(-48*2^(-4+4*n)*(n+1)*GAMMA(3/2+n)^2*cos(Pi*n)*(n-1/2)*hypergeom([1-n, 1-n], [1-2*n], 3/4)-6*2^(-4+4*n)*GAMMA(3/2+n)^2*cos(Pi*n)*(n-1)^2*hypergeom([2-n, 2-n], [2-2*n], 3/4)+((-(1/32)*3^(2+2*n)*n^2-(3/8)*9^n*(n^4+2*n^3-(1/2)*n-1/4))*hypergeom([2+n, 2+n], [2+2*n], 3/4)+9^n*(1/2+n)^2*hypergeom([n+1, n+1], [1+2*n], 3/4)*(3+n)*(n-1/2))*16^(-n)*sin(Pi*n)*n*GAMMA(n)^2)/(Pi^(1/2)*(1/2+n)*(n-1/2)*GAMMA(3/2+n)*n^2*GAMMA(n)*cos(Pi*n)) end proc

seq(simplify(ee(i)), i=2..15);

14, 106, 838, 6802, 56190, 470010, 3968310, 33747490, 288654574, 2480593546, 21400729382, 185239360178, 1607913963614, 13991107041306

evalf(limit(ee(n),n=1));

2.000000000

Download hg_ex.mw

@RaySierra 

Your worksheet's code is missing `+` as the first argument passed to Continue.

If I simply add that missing argument then your worksheet runs ok in my Maple 2019.2.

restart

 

F := proc (q) options operator, arrow; 100*factorial(400)*(1/100)^q*(99/100)^(400-q)/(factorial(400-q)*factorial(q)) end proc

 

F(5.1); evalhf(F(5.1))

15.19780218

Float(undefined)

 

So, F fails under evalhf, since not all the intermediate quantities
get represented as finite values when using hardware double precision.

There are various workarounds which avoid evalhf mode under plot. Here are a few.

 

plot('evalf[Digits](F(q))', q = 0 .. 20)

UseHardwareFloats := false

plot(F, 0 .. 20); UseHardwareFloats := deduced

Since UseHardwareFloats is an environment variable its
assignment inside the proc does not propagate back to
the higher level at which is was called.
F := proc (q) UseHardwareFloats := false; 100*factorial(400)*(1/100)^q*(99/100)^(400-q)/(factorial(400-q)*factorial(q)) end proc

plot(F, 0 .. 20)

UseHardwareFloats

deduced

Download failed_plot_ac.mw

I don't understand what that Matlab colormap is doing, so I'm not sure how to reproduce that exact coloring.

But you could look at this old Post, and adjust the formula for generating the hue component.

Less fast and a bit more clunky are results from complexplot3d, or densityplot (of argument). Eg,

plots:-complexplot3d(ln((exp(1/z))), z=-0.5-0.5*I..0.5+0.5*I,
                     style=surface, orientation=[-90,0,0], grid=[501,501],
                     lightmodel=none, size=[600,600]);
plots:-densityplot(argument(exp(1/(x+I*y))), x=-0.5..0.5, y=-0.5..0.5,
                   axes=boxed, colorbar=false, grid=[301,301],
                   #colorstyle=HUE
                   colorscheme=["zgradient",["Yellow","Blue"],colorspace="HSV"]
                 );

You might compute the derivatives by using the D operator.

When plotted, evalf of such D calls may go through fdiff to perform numeric differentiation. Such approximation of a derivative -- by a finite difference scheme -- of a pde solution numerically approximated via finite difference methods, may need care. I would not expect high accuracy, even if you do try and adjust the step-size(s).

Double-check everything, and adjust as required.

  restart;

  inf:=10:

  pdes:= diff(u(y,t),t)-xi*diff(u(y,t),y)=diff(u(y,t),y$2)/(1+lambda__t)+Gr*theta(y,t)+Gc*C(y,t)-M*u(y,t)-K*u(y,t),
         diff(theta(y,t),t)-xi*diff(theta(y,t),y)=1/Pr*diff(theta(y,t),y$2)+phi*theta(y,t),
         diff(C(y,t),t)-xi*diff(C(y,t),y)=1/Sc*diff(C(y,t),y$2)-delta*C(y,t)+nu*theta(y,t):
  conds:= u(y,0)=0, theta(y,0)=0, C(y,0)=0,
          u(0,t)=0, D[1](theta)(0,t)=-1, D[1](C)(0,t)=-1,
          u(inf,t)=0, theta(inf,t)=0, C(inf,t)=0:
  pars:= { Gr=1, Gc=1, M=1, nu=1, lambda__t=0.5,
           Sc=0.78, delta=0.1, phi=0.5, K=0.5, xi=0.5
         }        

{Gc = 1, Gr = 1, K = .5, M = 1, Sc = .78, delta = .1, nu = 1, phi = .5, xi = .5, lambda__t = .5}

  PrVals:=[0.71, 1.00, 3.00, 7.00]:
  colors:=[red, green, blue, black]:
  for j from 1 to numelems(PrVals) do
      pars1:=`union`( pars, {Pr=PrVals[j]}):
      pdSol:= pdsolve( eval([pdes], pars1),
                       eval([conds], pars1),
                       numeric, timestep=0.01
                     );
      plt[j]:=pdSol:-plot( diff(u(y,t),y), y=0, t=0..2, numpoints=200, color=colors[j]);
  od:

U := (Y,T)->eval(u(y,t),pdSol:-value(t=T)(Y));
U(1,0.5);

Let's check that U produces the same 3D surface as u(y,t)

plots:-display(
  plot3d(U, 0..1, 0..2, style=point, color=orange),
  pdSol:-plot3d(u(y,t), y=0..1, t=0..2, color=blue)
);

proc (Y, T) options operator, arrow; eval(u(y, t), (pdSol:-value(t = T))(Y)) end proc

HFloat(0.0362341204880735)

Plot the derivative of U with respect to its first procedure-parameter (ie, y),
evaluated at y=0.8, for t from 0 to 2.

plot(D[1](U)(0.8,t), t=0..2, color=green)

plots:-display(
  pdSol:-plot3d( u(y,t), y=0..1, t=0..2, numpoints=200, color=colors[1]),
  plottools:-transform((t,dudy)->[0.8,t,dudy])(plot(D[1](U)(0.8,t), t=0..2, color=green)),
  plottools:-transform((t,dudy)->[0.8,t,dudy])(plot(D[2](U)(0.8,t), t=1e-5..2, color=blue)),
  plottools:-transform((y,dudt)->[y,1.8,dudt])(plot(D[2](U)(y,1.8), y=0..1, color=green)),
  plottools:-transform((y,dudt)->[y,1.8,dudt])(plot(D[1](U)(y,1.8), y=0..1, color=blue))
);

## You might play with the space- and time-step, etc.

  PrVals:=[0.71, 1.00, 3.00, 7.00]:
  colors:=[red, green, blue, black]:
  plt:='plt':
  for j from 1 to numelems(PrVals) do
      pars1:=`union`( pars, {Pr=PrVals[j]}):
      pdSol:= pdsolve( eval([pdes], pars1),
                       eval([conds], pars1),
                       numeric, timestep=0.01
                     );
      U := (Y,T)->eval(u(y,t),pdSol:-value(t=T)(Y));
      try
        plt[j]:=plot(D[1](U)(0.00001,t), t=0..2, color=colors[j]);
      catch: end try;
  od:
plots:-display(convert(plt,list));

 

Download badPDE_ac.mw

Try,

   X := Sample(roll,[n,n]):

For Maple 2023 you could use the new colorbar option for 2D contour plots, as well as colorscheme to get that spread of hue values.

For Maple 2022 you could use one of the following approaches:

1) Use DocumentTools:-Tabluate, like in this old response of mine. Adjust weights and dimensions and borders, to taste. You could use the colorscheme option with densityplot, to get the exact colorbar you want, eg. blue to red in hue, etc.

2) Use this old Post of mine, for getting a colored legend along with contourplot (and densityplot too if you want that kind of graded filled effect), or use a specialized form of the legend option with the contourplot command.

3) Do something like the following, using polygons for that filled legend effect. Naturally, you could also merge/display the following with added contour lines.

restart;

with(plots): with(plottools,polygon): with(ColorTools,Color):

 

f := 11-((x-4)^2+(y-3)^2)/25;

11-(1/25)*(x-4)^2-(1/25)*(y-3)^2

(a,b,c,d) := -5,11,-5,11;

-5, 11, -5, 11

Q := plot3d(f, x=a..b, y=c..d):
(zmin,zmax) := (min,max)(op([1,3],Q));

HFloat(5.199999999999999), HFloat(11.0)

display(densityplot(f, x=a..b, y=c..d, 'style'=':-surface',
                        'colorscheme'=["zgradient",["Blue","Red"],
                                       'colorspace'="HSV"]),
  seq(polygon([[0,0],[0,0]],'thickness'=0.3, 'legend'=sprintf("%5.3f",z),
              'color'=Color("HSV",[:-Hue(Color("Blue"))*(zmax-z)/(zmax-zmin),1,1])),
      z=zmax..zmin, 'numelems'=24),
  'legendstyle'=['location'=':-right'], 'size'=[580,500], labels=["",""],
  'axis[2]'=['location'=':-high','tickmarks'=[],'color'="White"],
  'axis[1]'=['location'=':-low','tickmarks'=[],'color'="White"]);

Download M2022_colorbar_fun.mw

I don't know the full details of your example (all ranges, etc), but here's another:

restart;
with(plots): with(plottools,polygon): with(ColorTools,Color):
f := cos(t)*sin(x)-sin(x)+(1/2)*sin(x)*t^2-(1/24)*sin(x)*t^4:
(a,b,c,d) := -Pi,0,-4,1:
Q := plot3d(f, x=a..b, t=c..d):
(zmin,zmax) := (min,max)(op([1,3],Q)):
display(densityplot(f, x=a..b, t=c..d, 'style'=':-surface',
                        'colorscheme'=["zgradient",["Blue","Red"],
                                       'colorspace'="HSV"]),
  seq(polygon([[0,0],[0,0]],'thickness'=0.3, 'legend'=sprintf("%5.3f",z),
              'color'=Color("HSV",[:-Hue(Color("Blue"))*(zmax-z)/(zmax-zmin),1,1])),
      z=zmax..zmin, 'numelems'=24),
  'legendstyle'=['location'=':-right'], 'size'=[580,500], labels=["",""],
  'axis[2]'=['location'=':-high','tickmarks'=[],'color'="White"],
  'axis[1]'=['location'=':-low','tickmarks'=[],'color'="White"]);
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