tomleslie

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10 years, 107 days

MaplePrimes Activity


These are answers submitted by tomleslie

You need to supply the name of the dependent variable (ie 'y(t)'), as well as a range for the independent variable ('t'). See the att

  restart;
  with(DEtools):
  DEplot( diff(y(t), t$2)-3*(diff(y(t), t))+2*y(t) = exp(t),
          y(t),
          t=0..1,
          [[y(0) = 0, (D(y))(0) = 2]],
          stepsize = .1,
          linecolor = black,
          thickness = 2
        );

 

 


 

Download deplt.mw

ached

 

you are trying to achieve, but maybe(?) something in the attached will help.

NB the final "really pretty" table does not render on this site, but it does in the actua worksheet - which you will have to download to verify

NULL

 

Example: Verifying Inverse Functions Numerically

 

"ex17f6(x):=(x-5)/(2):"

"ex17g6(x):=2 x+5:"

y3:

ex17f6(ex17g6(x))

x

(1.1)

y4:

ex17g6(ex17f6(x))

x

(1.2)

````

 

Table 1: Table of Values

x

x

(1.3)

y3

y4

-2

-2

(1.4)

-2

-2

(1.5)

-2

-2

(1.6)

-1

-1

(1.7)

-1

-1

(1.8)

-1

-1

(1.9)

0

0

(1.10)

0

0

(1.11)

0

0

(1.12)

1

1

(1.13)

1

1

(1.14)

1

1

(1.15)

2

2

(1.16)

2

2

(1.17)

2

2

(1.18)

3

3

(1.19)

3

3

(1.20)

3

3

(1.21)

4

4

(1.22)

4

4

(1.23)

4

4

(1.24)

 

``

#
# If you just want to know the results
# then this will suffice
#
  ex17g6~(ex17f6~([$-2..5]));
  ex17f6~(ex17g6~([$-2..5]))

[-2, -1, 0, 1, 2, 3, 4, 5]

 

[-2, -1, 0, 1, 2, 3, 4, 5]

(1.25)

#
# On the other hand if you want to make
# the output look "pretty", then you could
# use
#
  DF:=DataFrame( < ex17g6~(ex17f6~(<$-2..5>)) | ex17f6~(ex17g6~(<$-2..5>)) >,
               columns=[y3,y4],
               rows=[$-2..5]
             );
#
# Or if you are desperate to have the output
# look even "prettier" then maybe this will
# help
#
  Tabulate(DF, width=50):

DataFrame(Matrix(8, 2, {(1, 1) = -2, (1, 2) = -2, (2, 1) = -1, (2, 2) = -1, (3, 1) = 0, (3, 2) = 0, (4, 1) = 1, (4, 2) = 1, (5, 1) = 2, (5, 2) = 2, (6, 1) = 3, (6, 2) = 3, (7, 1) = 4, (7, 2) = 4, (8, 1) = 5, (8, 2) = 5}), rows = [-2, -1, 0, 1, 2, 3, 4, 5], columns = [y3, y4])

(1.26)

 


 

Download dataTab.mw

as in the attached

f:=(x,y)->sin(x)*cos(y);
g:=(x,y)->sin(y)*cos(x);
v:=unapply( combine((f-g)(x,y), trig), [x,y]);

proc (x, y) options operator, arrow; sin(x)*cos(y) end proc

 

proc (x, y) options operator, arrow; sin(y)*cos(x) end proc

 

proc (x, y) options operator, arrow; sin(x-y) end proc

(1)

 

Download combFunc.mw

 

I fixed a lot of syntax errors - mainly

  1. consistent use of subscripts eg alpha[1] and alpha__1 may "look" the same inboth 2-D input and pretty-printed output, but represent two completely differetn quantities. I have change all of your subscripted variables to use inert subscripts (ie the 'name__number' approach
  2. bcs and ics have to use 'D' operator notation.

I didn't understand the initial conditions which use C(0,x)=C0(x) and then C(0)=13.0 so in the attached I have used C(0,,x)=13.0. Similar problem/solution with B(0,x), so check these ICS carefully

With these changes I get the attached - and the function B(t,x) doesn't seem to do anything interesting.

restart

`&sigma;__1` := .1*10^(-5)`&alpha;__1` := 3.0k__1 := 1.1k__2 := 0.1e-1`&sigma;__2` := .1*10^(-5)`&alpha;__2` := 4.0k__3 := 0.1e-1k__4 := .12`&beta;__1` := .2`&beta;__2` := 0.2e-1

0.1000000000e-5

 

3.0

 

1.1

 

0.1e-1

 

0.1000000000e-5

 

4.0

 

0.1e-1

 

.12

 

.2

 

0.2e-1

(1)

pdes := [diff(C(t, x), t) = `&sigma;__1`*(diff(C(t, x), x, x))+`&alpha;__1`*C(t, x)^k__1+`&alpha;__1`*C(t, x)^k__2*B(t, x)^k__3-`&beta;__1`*C(t, x), diff(B(t, x), t) = `&sigma;__2`*(diff(B(t, x), x, x))+`&alpha;__2`*B(t, x)^k__3+`&alpha;__2`*C(t, x)^k__2*B(t, x)^k__4-`&beta;__2`*B(t, x)]

[diff(C(t, x), t) = 0.1000000000e-5*(diff(diff(C(t, x), x), x))+3.0*C(t, x)^1.1+3.0*C(t, x)^0.1e-1*B(t, x)^0.1e-1-.2*C(t, x), diff(B(t, x), t) = 0.1000000000e-5*(diff(diff(B(t, x), x), x))+4.0*B(t, x)^0.1e-1+4.0*C(t, x)^0.1e-1*B(t, x)^.12-0.2e-1*B(t, x)]

(2)

bcs := [(D[2](C))(t, 0) = 0, (D[2](C))(t, 1) = 0, (D[2](B))(t, 0) = 0, (D[2](B))(t, 1) = 0, C(0, x) = 13.0, B(0, x) = 300]

[(D[2](C))(t, 0) = 0, (D[2](C))(t, 1) = 0, (D[2](B))(t, 0) = 0, (D[2](B))(t, 1) = 0, C(0, x) = 13.0, B(0, x) = 300]

(3)

sol := pdsolve(pdes, bcs, numeric)

_m686543712

(4)

sol:-plot3d([C(t, x), B(t, x)], t = 0 .. 1, x = 0 .. 1)

 

 

 


 

Download pde_prob.mw

about the answer you quote?  ie  m*diff(x,t)+m__1/2 

note that your "variable" , ie diff(x,t) appears quadratically in both of the terms in your definition of 'T', so should appear linearly in both terms of the answer.

Consider the attached, which works by substituing a "dummy" variable for diff(x,t), performing the differetniation with respect to the "dummy" variable, then reversing the substitution

restart:

with(DEtools):

local D:

alias(x=x(t),theta=theta(t)):

x__1,y__1:=x+l*sin(theta),l*(1-cos(theta)):

T:=1/2*m*(diff(x,t))^2+1/2*m__1*((diff(x__1,t))^2+(diff(y__1,t))^2);

(1/2)*m*(diff(x, t))^2+(1/2)*m__1*((diff(x, t)+l*(diff(theta, t))*cos(theta))^2+l^2*(diff(theta, t))^2*sin(theta)^2)

(1)

T:=subs( Z=diff(x,t), diff(algsubs(diff(x,t)=Z,T),Z));

(1/2)*m__1*(2*l*(diff(theta, t))*cos(theta)+2*(diff(x, t)))+m*(diff(x, t))

(2)

 

Download trickDiff.mw

I have no idea what this code is supposed to do, but

  1. if I make a few guesses,
  2. remove stuff which is evaluated and never used (why is it there?)
  3. and  fix some syntax errors

then I come up with the attached - which at least executes!

Note that the answer you get (ie the graph for the variable 'hh') will vary depending on the value of the Digits setting. Increasing 'Digits' will make the plotted values of 'hh' smaller and smaller.

This may (or may not?!) be what you are trying to prove!

restart

with(plots); Digits := 20

pr := .5; ec := .5; N := 7; re := 2; ta := .5; H := 1

f[0] := unapply(-2*x^3+3*x^2, x)

proc (x) options operator, arrow; -2*x^3+3*x^2 end proc

(1)

for m to N do CHI[m] := `if`(m > 1, 1, 0); f[m] := unapply(int(int(int(int(CHI[m]*x^3*(diff(f[m-1](x), x, x, x, x))+h*H*x^3*(diff(f[m-1](x), x, x, x, x))-2*h*H*x^2*(diff(f[m-1](x), x, x, x))+3*h*H*x*(diff(f[m-1](x), x, x))-3*h*H*(diff(f[m-1](x), x))-re*h*H*x^2*(sum((diff(f[m-1-n](x), x, x, x))*(diff(f[n](x), x)), n = 0 .. m-1))-re*h*H*x*(sum((diff(f[m-1-n](x), x))*(diff(f[n](x), x)), n = 0 .. m-1))+re*h*H*x^2*(sum((diff(f[m-1-n](x), x, x, x))*f[n](x), n = 0 .. m-1))-3*re*x*h*H*(sum((diff(f[m-1-n](x), x, x))*f[n](x), n = 0 .. m-1))+3*re*h*H*(sum((diff(f[m-1-n](x), x))*f[n](x), n = 0 .. m-1))+ta*x^3*h*H*(diff(f[m-1](x), x, x))-ta*x^2*h*H*(diff(f[m-1](x), x)), x), x)+_C1*x, x)+_C2*x, x)+_C3*x+_C4, x); s := solve([f[m](0), eval(diff(f[m](x), x), x = 0), f[m](1), eval(diff(f[m](x), x), x = 1)], {_C1, _C2, _C3, _C4}); f[m] := unapply(expand(eval(f[m](x), s)), x) end do

F := unapply(add(f[l](x), l = 0 .. N), x); hh := eval(diff(F(x), x), x = 1); plot(hh, h = -5 .. 5)

0.1362531e-18*h^6-0.36e-19*h^2+0.178e-20*h^4-0.333238e-18*h^5-0.203e-19*h+0.7e-19*h^3-0.249e-19*h^7

 

 

``

Download ham.mw

If you have two functions f() and g(), then you can "combine" these (with the same argument) as

f(x)+g(x) or (f+g)(x)
f(x)-g(x) or (f-g)(x)
f(x)*g(x) or (f*g)(x)
f(x)/g(x) or (f/g)(x)

Similarly if you want to do function "composition", you have a choice between

f(g(x)) or (f@g)(x)

The obvious question - which of the alternatives is "better". As a general(?) rule, I would say that it is safer to combine the functions before applying the argument - so (f+g)(x) is "safer" than f(x)+g(x).

The reason is best illustrated by considering the case where the argument is a floating point number. (f+g)(x) will combine the function definitions for the symbol 'x', and then evaluate the result for a (floating-point number) 'x' - so there is only one floating point evaluation. On the other hand f(x)+g(x) will evaluate f() as a floating point number g() as a floating point number and then do a floating point addition - so three floating point computations. So will the increase in the number of floating point computations ever result in some kind of floating point rounding "issue"? Maybe or maybe not! But minimising the number of floating point computations is almost certainly a good way to avoid (as far as possible) any potential floating point rounding issues.

In the attached, I have inserted additional execution groups, which illustrate the function combinations above - They all give the "same" answers as you have already obtained - so are they better??? I'd say yes - but it's not something I'd be that interested in having an argument about

Section 1.6: Combinations of Functions

 

Example 1: - Sum Rule

"ex1f(x):= 2 x+1;"

proc (x) options operator, arrow, function_assign; 2*x+1 end proc

(1)

"ex1g(x):=x^(2)+2 x-1;"

proc (x) options operator, arrow, function_assign; x^2+2*x-1 end proc

(2)

ex1f(x)+ex1g(x)

x^2+4*x

(3)

eval(x^2+4*x, [x = 2])

12

(4)

(ex1f+ex1g)(2)

12

(5)

Example 2: - Difference Rule

"ex2f(x):=2 x+1"

proc (x) options operator, arrow, function_assign; 2*x+1 end proc

(6)

"ex2g(x):=x^(2)+2 x-1"

proc (x) options operator, arrow, function_assign; x^2+2*x-1 end proc

(7)

ex2f(x)-ex2g(x)

-x^2+2

(8)

eval(-x^2+2, [x = 2])

-2

(9)

(ex2f-ex2g)(2)

-2

(10)

Example 3: - Product Rule

"ex3f(x):=x^(2)"

proc (x) options operator, arrow, function_assign; x^2 end proc

(11)

"ex3g(x):= x-3"

proc (x) options operator, arrow, function_assign; x-3 end proc

(12)

ex3f(x)*ex3g(x)

x^2*(x-3)

(13)

collect(x^2*(x-3), x)

x^3-3*x^2

(14)

eval(x^3-3*x^2, [x = 4])

16

(15)

(ex3f*ex3g)(4)

16

(16)

Example 4: - Quotient Rule

"ex4f(x):=sqrt(x)"

proc (x) options operator, arrow, function_assign; sqrt(x) end proc

(17)

"ex4g(x):=sqrt(4-x^(2))"

proc (x) options operator, arrow, function_assign; sqrt(4-x^2) end proc

(18)

ex4f(x)/ex4g(x)

x^(1/2)/(-x^2+4)^(1/2)

(19)

(ex4f/ex4g)(x)

x^(1/2)/(-x^2+4)^(1/2)

(20)

Example 5: - Composition - Chain Rule

 

This is an example of the chain rule.

"ex5t1(x):=sqrt(x):"

"ex5t2(x):=x-1:"

Function in another function:

 

ex5t1(ex5t2(x))

(x-1)^(1/2)

(1.1)

eval((x-1)^(1/2), [x = 2])

1

(1.2)

(`@`(ex5t1, ex5t2))(2)

1

(1.3)

plot((x-1)^(1/2))

 

Example 6: - Composition - Chain Rule

 

Insert*two*functions

 

"ex6f(x):=x+2:"

"ex6g(x):=4-x^(2):"

 

ex6f(ex6g(x))

-x^2+6

(2.1)

(`@`(ex6f, ex6g))(x)

-x^2+6

(2.2)

eval(-x^2+6, [x = 0])

6

(2.3)

(`@`(ex6f, ex6g))(0)

6

(2.4)

eval(-x^2+6, [x = 1])

5

(2.5)

(`@`(ex6f, ex6g))(1)

5

(2.6)

ex6g(ex6f(x))

4-(x+2)^2

(2.7)

(`@`(ex6g, ex6f))(x)

4-(x+2)^2

(2.8)

eval(4-(x+2)^2, [x = 0])

0

(2.9)

(`@`(ex6g, ex6f))(0)

0

(2.10)

eval(4-(x+2)^2, [x = 1])

-5

(2.11)

(`@`(ex6g, ex6f))(1)

-5

(2.12)

NULL

Example 7: Find Domain with Chain Rule

 

#Insert two Functions:

"ex7f(x):=x^(2)-9:"

"ex7g(x):=sqrt(9-x^(2)):"

ex7f(ex7g(x))

-x^2

(3.1)

(`@`(ex7f, ex7g))(x)

-x^2

(3.2)

plot(-x^2)

 

plot(ex7f(ex7g(x)))

 

plot((`@`(ex7f, ex7g))(x))

 

[-3, 3]

[-3, 3]

(3.3)

NULL

Download funcomb.mw

 

of which one is to use the VolumeOfRevolution() command, with appropriate options - as shown in the attached.

Another obvious alternative is to use implicitplot3d()

NB the graphics in the attached render far better in the Maple worksheet than they do on this site (honest!)
 

restart

with(plottools)

with(plots)

a := 2; b := 3; x0 := 0; y0 := 0

 

eq := (x-x0)^2/a^2+(y-y0)^2/b^2 = 1

implicitplot(eq, x = -4 .. 4, y = -4 .. 4, scaling = constrained)

 

  Student[Calculus1]:-VolumeOfRevolution
                      ( [ solve(eq,y) ][1],
                        x=-4..4,
                        output=plot,
                        showfunction=false,
                        showrotationline=false,
                        caption="",
                        volumeoptions=[color=blue, style=surface]
                      );

 

 

plots:-implicitplot3d
       ( x^2/a^2+y^2/b^2+z^2/b^2=1,
         x=-4..4,
         y=-4..4,
         z=-4..4,
         numpoints=100000,
         color=red,
         style=surface,
         axes=normal,
         transparency=0.5
       );

 

NULL

Download ellplt.mw

( I often am!), but there are so many "easier" ways!

For example - isn't convert(..,base,..) more or less designed for this?

See this with a couple of alternative versions in the attached

#
# The really easy way
#
  convert(3657, base, 8);

[1, 1, 1, 7]

(1)

#
# And if you prefer "most-significant" first
# (a matter of personal taste, I admit) then
# you could simply reverse the above
#
  ListTools:-Reverse(convert(3657, base, 8))

[7, 1, 1, 1]

(2)

#
# Or if you feel under some sort of "obligation"
# to write "code", then you could use
#
  q:=3657:
  j:=1:
  while q>0 do
        a[j]:=irem(q, 8,'q');
        j:=j+1;
  od:
  convert(a, list);;

[1, 1, 1, 7]

(3)

 

Download base.mw

 

The attached files show the returned results for the last four versions of Maple, so I'm guessing you upgraded from Maple2017 to Maple2019

  restart;
  interface(version);
  solve(0.6*10*35000^a+0.4*10*15000^a=1*10*26000^a);

`Standard Worksheet Interface, Maple 2017.3, Windows 7, September 13 2017 Build ID 1262472`

 

.5008768259, 0.

(1)

 

  restart;
  interface(version);
  solve(0.6*10*35000^a+0.4*10*15000^a=1*10*26000^a);

`Standard Worksheet Interface, Maple 2018.2, Windows 7, November 16 2018 Build ID 1362973`

 

.5008768259, -43.40059992, 0.

(1)

 

  restart;
  interface(version);
  solve(0.6*10*35000^a+0.4*10*15000^a=1*10*26000^a);

`Standard Worksheet Interface, Maple 2019.2, Windows 7, November 26 2019 Build ID 1435526`

 

1.957277984-41.90244490*I, 0., 1.957277984+41.90244490*I, 0., -1.556330153-46.39571460*I, 0., -1.556330153+46.39571460*I, 0., -2.096312657-33.97912684*I, 0., -2.096312657+33.97912684*I, 0., -1.969625215-56.64504935*I, 0., -1.969625215+56.64504935*I, 0., 1.581147374-20.57802676*I, 0., 1.581147374+20.57802676*I, 0., -1.104743096-23.57386309*I, 0., -1.104743096+23.57386309*I, 0., -2.156722523-11.32518016*I, 0., -2.156722523+11.32518016*I, 0., .5008768259, -43.40059992, 0., 0., 0., 0., 0.

(1)

 

  restart;
  interface(version);
  solve(0.6*10*35000^a+0.4*10*15000^a=1*10*26000^a);

`Standard Worksheet Interface, Maple 2020.0, Windows 7, March 4 2020 Build ID 1455132`

 

1.957277984-41.90244490*I, 0., 1.957277984+41.90244490*I, 0., -1.556330153-46.39571460*I, 0., -1.556330153+46.39571460*I, 0., -2.096312657-33.97912684*I, 0., -2.096312657+33.97912684*I, 0., -1.969625215-56.64504935*I, 0., -1.969625215+56.64504935*I, 0., 1.581147374-20.57802676*I, 0., 1.581147374+20.57802676*I, 0., -1.104743096-23.57386309*I, 0., -1.104743096+23.57386309*I, 0., -2.156722523-11.32518016*I, 0., -2.156722523+11.32518016*I, 0., .5008768259, -43.40059992, 0., 0., 0., 0., 0.

(1)

 

Download M2020.mw

Download M2019.mw

Download M2018.mw

Download M2017.mw

Your data results in a 9*4 matrix. I make the "radical" assumption that row1 and column1 correspond to 'x' and 'y' values. If this is the case then the best(?) method is probably to ocnvert the data to a list of lists which comprise [x,y,z] triples, then use surfdata() - as in the attached.

(Various colorings and caling of the data are possible - the most important thing is to determine whether my interpretation of the original 9x4 matrix is correct)

restart:
M:=ExcelTools:-Import("C:/Users/TomLeslie/Desktop/PlotTest.xlsx"):
L1:=[seq([seq( [M[1,j],M[i,1],M[i,j]],i=2..op([1,1],M))],j=2..op([1,2],M))]:
plots:-surfdata(L1, style=surface, shading=zhue);

 

 


Download surfplot.mw

 

  1. By default, when you specify ranges for the dependent variables, the DETools integrator "stops" if the dependent variables exceed the range limits - whihc is what is happening on the "spikes" in the graph. You can override this behaviour either by
    1. not specifying the ranges on the dependent variables, or,
    2. setting the option obsrange=false
    3. both of these options are illustrated in the attached
  2. The resulting output graph still looks a little ragged because (again by default), the step size is equal to the range of the independent variable divided by 48 - so in your case 1/24. Using a smaller step size improves things. I have used 1/100 in the attached

restart; with(DEtools); with(plots)

NULL

``

eqn := diff(x(tau), tau) = y(tau), diff(y(tau), tau) = 215/2+4*y(tau)^2*x(tau)^3-33*y(tau)^2*x(tau)^2+78*y(tau)^2*x(tau)-54*y(tau)^2-4*x(tau)^3

ld := dsolve({eqn, x(0) = 0, y(0) = 1}, numeric, range = -1 .. 1); p1 := odeplot(ld, [tau, y(tau)], refine = 2, color = black); ld := dsolve({eqn, x(0) = 2, y(0) = 1}, numeric, range = -1 .. 1); p2 := odeplot(ld, [tau, y(tau)], refine = 2, color = red)

display(p1, p2, axes = box)

 

DEplot([eqn], [x(tau), y(tau)], tau = -1 .. 1, x = 0 .. 4, y = -60 .. 60, [[0, 0, 1], [0, 2, 1]], axes = box, linecolor = [black, red], scene = [tau, y])

 

DEplot([eqn], [x(tau), y(tau)], tau = -1 .. 1, x = 0 .. 4, y = -60 .. 60, [[0, 0, 1], [0, 2, 1]], axes = box, linecolor = [black, red], scene = [tau, y], obsrange = false)

 

DEplot([eqn], [x(tau), y(tau)], tau = -1 .. 1, [[0, 0, 1], [0, 2, 1]], axes = box, linecolor = [black, red], scene = [tau, y])

 

DEplot([eqn], [x(tau), y(tau)], tau = -1 .. 1, [[0, 0, 1], [0, 2, 1]], axes = box, linecolor = [black, red], scene = [tau, y], stepsize = 1/100)

 

 

Download deplt.mw

I actually fixed part of this yesterday - but by the time the calculation had finished, someone had removed the question!!

The attached shows the solution for just one of the conditions, since this took about 30mins, I haven't got the time/patience to do the others

I also have no idea what you are trying to achieve with this calculation, but I feel certain there is a much better way to do it!


 

restart

with(plottools)

with(plots)

with(CurveFitting)

Digits := 10

NULL

````

v := .7

disp := 15

NULL

NULL

f := [Vector[row](252, {(1) = 0, (2) = 5000, (3) = 10000, (4) = 10040.16064, (5) = 10080.32129, (6) = 10120.48193, (7) = 10160.64257, (8) = 10200.80321, (9) = 10240.96386, (10) = 10281.1245, (11) = 10321.28514, (12) = 10361.44578, (13) = 10401.60643, (14) = 10441.76707, (15) = 10481.92771, (16) = 10522.08835, (17) = 10562.249, (18) = 10602.40964, (19) = 10642.57028, (20) = 10682.73092, (21) = 10722.89157, (22) = 10763.05221, (23) = 10803.21285, (24) = 10843.37349, (25) = 10883.53414, (26) = 10923.69478, (27) = 10963.85542, (28) = 11004.01606, (29) = 11044.17671, (30) = 11084.33735, (31) = 11124.49799, (32) = 11164.65863, (33) = 11204.81928, (34) = 11244.97992, (35) = 11285.14056, (36) = 11325.3012, (37) = 11365.46185, (38) = 11405.62249, (39) = 11445.78313, (40) = 11485.94378, (41) = 11526.10442, (42) = 11566.26506, (43) = 11606.4257, (44) = 11646.58635, (45) = 11686.74699, (46) = 11726.90763, (47) = 11767.06827, (48) = 11807.22892, (49) = 11847.38956, (50) = 11887.5502, (51) = 11927.71084, (52) = 11967.87149, (53) = 12008.03213, (54) = 12048.19277, (55) = 12088.35341, (56) = 12128.51406, (57) = 12168.6747, (58) = 12208.83534, (59) = 12248.99598, (60) = 12289.15663, (61) = 12329.31727, (62) = 12369.47791, (63) = 12409.63855, (64) = 12449.7992, (65) = 12489.95984, (66) = 12530.12048, (67) = 12570.28112, (68) = 12610.44177, (69) = 12650.60241, (70) = 12690.76305, (71) = 12730.92369, (72) = 12771.08434, (73) = 12811.24498, (74) = 12851.40562, (75) = 12891.56627, (76) = 12931.72691, (77) = 12971.88755, (78) = 13012.04819, (79) = 13052.20884, (80) = 13092.36948, (81) = 13132.53012, (82) = 13172.69076, (83) = 13212.85141, (84) = 13253.01205, (85) = 13293.17269, (86) = 13333.33333, (87) = 13373.49398, (88) = 13413.65462, (89) = 13453.81526, (90) = 13493.9759, (91) = 13534.13655, (92) = 13574.29719, (93) = 13614.45783, (94) = 13654.61847, (95) = 13694.77912, (96) = 13734.93976, (97) = 13775.1004, (98) = 13815.26104, (99) = 13855.42169, (100) = 13895.58233, (101) = 13935.74297, (102) = 13975.90361, (103) = 14016.06426, (104) = 14056.2249, (105) = 14096.38554, (106) = 14136.54618, (107) = 14176.70683, (108) = 14216.86747, (109) = 14257.02811, (110) = 14297.18876, (111) = 14337.3494, (112) = 14377.51004, (113) = 14417.67068, (114) = 14457.83133, (115) = 14497.99197, (116) = 14538.15261, (117) = 14578.31325, (118) = 14618.4739, (119) = 14658.63454, (120) = 14698.79518, (121) = 14738.95582, (122) = 14779.11647, (123) = 14819.27711, (124) = 14859.43775, (125) = 14899.59839, (126) = 14939.75904, (127) = 14979.91968, (128) = 15020.08032, (129) = 15060.24096, (130) = 15100.40161, (131) = 15140.56225, (132) = 15180.72289, (133) = 15220.88353, (134) = 15261.04418, (135) = 15301.20482, (136) = 15341.36546, (137) = 15381.5261, (138) = 15421.68675, (139) = 15461.84739, (140) = 15502.00803, (141) = 15542.16867, (142) = 15582.32932, (143) = 15622.48996, (144) = 15662.6506, (145) = 15702.81124, (146) = 15742.97189, (147) = 15783.13253, (148) = 15823.29317, (149) = 15863.45382, (150) = 15903.61446, (151) = 15943.7751, (152) = 15983.93574, (153) = 16024.09639, (154) = 16064.25703, (155) = 16104.41767, (156) = 16144.57831, (157) = 16184.73896, (158) = 16224.8996, (159) = 16265.06024, (160) = 16305.22088, (161) = 16345.38153, (162) = 16385.54217, (163) = 16425.70281, (164) = 16465.86345, (165) = 16506.0241, (166) = 16546.18474, (167) = 16586.34538, (168) = 16626.50602, (169) = 16666.66667, (170) = 16706.82731, (171) = 16746.98795, (172) = 16787.14859, (173) = 16827.30924, (174) = 16867.46988, (175) = 16907.63052, (176) = 16947.79116, (177) = 16987.95181, (178) = 17028.11245, (179) = 17068.27309, (180) = 17108.43373, (181) = 17148.59438, (182) = 17188.75502, (183) = 17228.91566, (184) = 17269.07631, (185) = 17309.23695, (186) = 17349.39759, (187) = 17389.55823, (188) = 17429.71888, (189) = 17469.87952, (190) = 17510.04016, (191) = 17550.2008, (192) = 17590.36145, (193) = 17630.52209, (194) = 17670.68273, (195) = 17710.84337, (196) = 17751.00402, (197) = 17791.16466, (198) = 17831.3253, (199) = 17871.48594, (200) = 17911.64659, (201) = 17951.80723, (202) = 17991.96787, (203) = 18032.12851, (204) = 18072.28916, (205) = 18112.4498, (206) = 18152.61044, (207) = 18192.77108, (208) = 18232.93173, (209) = 18273.09237, (210) = 18313.25301, (211) = 18353.41365, (212) = 18393.5743, (213) = 18433.73494, (214) = 18473.89558, (215) = 18514.05622, (216) = 18554.21687, (217) = 18594.37751, (218) = 18634.53815, (219) = 18674.6988, (220) = 18714.85944, (221) = 18755.02008, (222) = 18795.18072, (223) = 18835.34137, (224) = 18875.50201, (225) = 18915.66265, (226) = 18955.82329, (227) = 18995.98394, (228) = 19036.14458, (229) = 19076.30522, (230) = 19116.46586, (231) = 19156.62651, (232) = 19196.78715, (233) = 19236.94779, (234) = 19277.10843, (235) = 19317.26908, (236) = 19357.42972, (237) = 19397.59036, (238) = 19437.751, (239) = 19477.91165, (240) = 19518.07229, (241) = 19558.23293, (242) = 19598.39357, (243) = 19638.55422, (244) = 19678.71486, (245) = 19718.8755, (246) = 19759.03614, (247) = 19799.19679, (248) = 19839.35743, (249) = 19879.51807, (250) = 19919.67871, (251) = 19959.83936, (252) = 20000}), Vector[row](252, {(1) = 0, (2) = 0, (3) = 0.26009649e-1, (4) = 0.27405651e-1, (5) = 0.32915758e-1, (6) = 0.3847215e-1, (7) = 0.41741693e-1, (8) = 0.50980077e-1, (9) = 0.56559004e-1, (10) = 0.6737613e-1, (11) = 0.71652446e-1, (12) = 0.88300553e-1, (13) = 0.92166583e-1, (14) = .105724783, (15) = .126642598, (16) = .145334895, (17) = .15850865, (18) = .177720409, (19) = .202307687, (20) = .217910454, (21) = .258592873, (22) = .270692986, (23) = .301039668, (24) = .346783846, (25) = .372651, (26) = .447603963, (27) = .478582315, (28) = .530755984, (29) = .60857978, (30) = .646110622, (31) = .71205847, (32) = .808982637, (33) = .828815424, (34) = .923596285, (35) = 1.05251647, (36) = 1.164245202, (37) = 1.183972154, (38) = 1.277325642, (39) = 1.439621765, (40) = 1.477300689, (41) = 1.631992348, (42) = 1.768440321, (43) = 1.88397491, (44) = 2.082548507, (45) = 2.231652698, (46) = 2.490478886, (47) = 2.647224719, (48) = 2.707613414, (49) = 2.955129772, (50) = 2.99105143, (51) = 3.467517126, (52) = 3.518568609, (53) = 3.728151488, (54) = 3.87291072, (55) = 3.948881559, (56) = 4.163670645, (57) = 4.50761056, (58) = 4.659870014, (59) = 4.971024606, (60) = 5.458301117, (61) = 5.313306637, (62) = 5.700074112, (63) = 6.368157069, (64) = 6.180671049, (65) = 6.345326505, (66) = 6.884994338, (67) = 7.395430521, (68) = 7.358920818, (69) = 7.835875889, (70) = 8.035588291, (71) = 8.348022516, (72) = 8.543578771, (73) = 8.35799688, (74) = 9.047350243, (75) = 9.236507255, (76) = 9.079728514, (77) = 9.520366068, (78) = 9.261457352, (79) = 9.255716685, (80) = 10.11545734, (81) = 10.02218178, (82) = 10.0416674, (83) = 10.32242125, (84) = 10.67790031, (85) = 10.23370472, (86) = 10.51490115, (87) = 10.28914278, (88) = 10.29342465, (89) = 10.91952415, (90) = 10.41843223, (91) = 11.27709844, (92) = 10.66522866, (93) = 10.51922279, (94) = 10.74447001, (95) = 10.34144847, (96) = 10.65913883, (97) = 11.01231833, (98) = 10.98942763, (99) = 11.24148259, (100) = 10.88397521, (101) = 10.56742818, (102) = 10.30325829, (103) = 11.01807741, (104) = 10.10336832, (105) = 10.6418855, (106) = 10.49271006, (107) = 10.36676132, (108) = 10.47324166, (109) = 10.63377854, (110) = 10.11315422, (111) = 10.82971097, (112) = 10.78528602, (113) = 10.70613288, (114) = 10.42840544, (115) = 10.03494507, (116) = 10.77013817, (117) = 9.887696383, (118) = 10.62037816, (119) = 10.4649, (120) = 10.31700926, (121) = 9.998855124, (122) = 10.00025779, (123) = 10.98064791, (124) = 10.85487602, (125) = 11.195142, (126) = 11.00751786, (127) = 11.01871271, (128) = 11.29924388, (129) = 10.78951699, (130) = 10.72167335, (131) = 11.80070955, (132) = 11.79201946, (133) = 11.71403158, (134) = 11.71587708, (135) = 11.77557241, (136) = 11.79819841, (137) = 11.75737481, (138) = 12.07898735, (139) = 12.15471646, (140) = 13.11361193, (141) = 12.80944971, (142) = 12.60645724, (143) = 12.41728943, (144) = 12.35861782, (145) = 12.47224373, (146) = 13.37754875, (147) = 12.79229788, (148) = 13.51496015, (149) = 13.26071017, (150) = 13.12581524, (151) = 13.25835085, (152) = 13.17751591, (153) = 13.8992729, (154) = 13.38971017, (155) = 12.66749292, (156) = 12.77264034, (157) = 13.44493393, (158) = 13.63457066, (159) = 13.40058658, (160) = 12.33653781, (161) = 13.20335893, (162) = 13.27340233, (163) = 12.43130022, (164) = 12.08947005, (165) = 12.59770502, (166) = 11.80610178, (167) = 12.22122134, (168) = 11.89225845, (169) = 10.91517744, (170) = 11.69790167, (171) = 10.96795856, (172) = 11.26062606, (173) = 9.998841513, (174) = 10.04782599, (175) = 10.44414762, (176) = 9.480867681, (177) = 9.069403944, (178) = 9.265712295, (179) = 8.775010952, (180) = 8.200938934, (181) = 8.274448378, (182) = 8.275229651, (183) = 7.877728004, (184) = 7.421724119, (185) = 7.479375339, (186) = 6.895186702, (187) = 6.468926653, (188) = 6.366823421, (189) = 5.904431744, (190) = 5.624362456, (191) = 5.504526875, (192) = 5.188560014, (193) = 4.893882628, (194) = 4.822889125, (195) = 4.592528773, (196) = 4.478493578, (197) = 3.910074951, (198) = 3.981032969, (199) = 3.562177243, (200) = 3.432273988, (201) = 3.189218902, (202) = 3.016436682, (203) = 2.806795872, (204) = 2.677455331, (205) = 2.435336873, (206) = 2.437125159, (207) = 2.183004263, (208) = 2.071738749, (209) = 1.835507659, (210) = 1.777696386, (211) = 1.675778511, (212) = 1.49761719, (213) = 1.363122415, (214) = 1.378815355, (215) = 1.173951508, (216) = 1.085739562, (217) = 1.097045525, (218) = .9204646, (219) = .895294741, (220) = .852611661, (221) = .767938182, (222) = .716089119, (223) = .615480156, (224) = .567177029, (225) = .546414668, (226) = .471604031, (227) = .443186957, (228) = .386202619, (229) = .380203952, (230) = .332020319, (231) = .297588509, (232) = .28422842, (233) = .256511308, (234) = .22738596, (235) = .209794731, (236) = .174682073, (237) = .173423169, (238) = .148272131, (239) = .140763924, (240) = .116456294, (241) = .107866637, (242) = 0.92609537e-1, (243) = 0.90995883e-1, (244) = 0.74533307e-1, (245) = 0.69766087e-1, (246) = 0.59640566e-1, (247) = 0.56126176e-1, (248) = 0.48286231e-1, (249) = 0.43761338e-1, (250) = 0.38423946e-1, (251) = 0.35691675e-1, (252) = 0.31284434e-1})]

f := unapply(spline(f[1], f[2], x, 3), x)

esp := 800000

k := 0

0

(1)

E := proc (x, t) options operator, arrow; Int(exp((-esp*w^4+disp*w^2+k)*t)*cos(w*(x+v*t))/Pi, w = 0 .. infinity, epsilon = 0.1e-6) end proc

proc (x, t) options operator, arrow; Int(exp((-esp*w^4+disp*w^2+k)*t)*cos(w*(x+v*t))/Pi, w = 0 .. infinity, epsilon = 0.1e-6) end proc

(2)

uu1 := [seq(evalf(Int(E(i-xi, 1)*f(xi), xi = 0 .. 20000, method = _NCrule, epsilon = 10^(-6))), i = 0 .. 20000, 100)]; xx := [seq(i, i = 0 .. 20000, 100)]; p2 := plot(xx, uu1, color = red, legend = [''inverse, t = 1''])

 

 

 

``

NULL

``

NULL


 

Download plotfit.mw

A .maple file extension represents a Maple "workbook"

A Maple workbook is a collection of files which are necessary for a given problem. It will "usually" comprise a Maple worksheet (ie a .mw file), maybe some input data files (eg .csv or .xlsx),  possibly some Maple procedures (written as .mpl files) and indeed any other files which might be required.

The capability to generate Maple workbooks was introduced in Maple 2016. I can think of no way that such .maple files could be read/executed/whatever in any Maple version earlier than Maple 2016 :-(

As the name suggests, Maple 2016 was released in 2016, so the .maple file format has been around/usable for five years. You state that you are using Maple 15,which was released in 2011: so you are using ten year-old software and you expect to be able to read/execute file formats which were developed five years after your was released?? Really?

Worth trying (since it will take about 10 secs) but pretty much guaranteed to fail.

  1. If you have the file "filename.maple", change the file extension to ".mw" and try opening it it the usual way
  2. change the file extension to ".mpl", then from within Maple use the command  read("filename.mpl")

 

 to the Geogebra executable.

The attached shows success/failure for launhing my (more or less default) editor

  restart:

#
# This doesn't work
#
  system[launch]("notepad++.exe");

Error, could not execute command, notepad++.exe; The system cannot find the file specified.

 

#
# This does
#
  system[launch]("C:/Program Files (x86)/Notepad++/notepad++.exe");

1248

(1)

 

Download launch.mw

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