-  how to simplify this expression to zero by any commands?

Error, module member referencing is not supported in evalhf

What is the solution for this error. 

Also when I run job in command prompt I also getting following errors

Warning, the restart command only works at the top level. It cannot be executed within a procedure, or from a file being read by the read statement. 

But the same code runs fine in GUI.

Hello everybody and thankyou in advance to answer my question.

I am trying to answer this question:

“Find the solutions for x so that the sine of x degrees be equal to the sine of x radians in the interval [0, Pi].” I wrote this formula to solve it but without success:

solve(sin(x*degree)=sin(x),x) assuming 0<=x<=Pi

I expected a solution like:

{x=0, x=(180*Pi)/(180+Pi)} but not,

I somehow get a numeric solution for the first positive real number within the referred interval with this:

fsolve(sin(x*Pi/180) = sin(x), x, 0 .. Pi);

But if I change the interval for example [0, 3*Pi] I still only get one answer, not the four I spect.

Hi, 

I am struggling with statistical analysis of a dataset. I would appreciate help from the community. Let me describe what I am looking for.

1. The linear regression model that I am trying to use is Y=b1X1 + b2x2 + e. I am interested in estimating b1 and b2 so that the estimated error is the least.

2. I am trying to randomly select 25 percent of observations from the dataset and estimate b1 and b2. 

3. The estimated coefficients are used to predict the Y values for the remainder of the 75 percent of the data and calculate the error. The idea is to use a subset to estimate coefficients, and check for the robustness of the estimates.

4. The process is iterated for say 100 times.

5. Each time, I would like to store the statistical results and export them to an excel file.

For some reason, each time I struggle with one or two steps mentioned above. Could anyone help me with the approach?

Thank you,

Omkar

I have a circle equation: F: = x^2+y^2 = 1.
I need to check: x^2+y^2 <= 1.
I tried to get this expression from F like this: lhs(F) <= rhs (F).
The content matches what I need but get: Error, cannot determine if this expression is true or false: x^2+y^2 <= 1.
When I specify just the if x^2+y^2 <= 1 then everything works fine. What am I doing wrong?

New to maple here. Having this problem for evaluating my function. If its relevant it is to solve the Ising model analytically. Whats the problem and how do I fix it? Thanks.

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!

Hi all

We denote the collecction of sets determined by the first k coin tosses

Suppose the imitial stock price is ,with up and down facter being and .

Up : S1(H)=u S0 and S1(T)=d S0

S_{N+1}= alpha S_N

where alpha =u or d

Let the probability of each and be and and   the sigma-lgebra generated by the coin tosses up to (and inchudling) time t:

After three coin tosses.

Can we propose a code computing the element of the filtration F1 and F3 and sigma(S3) (the sigma algebra generated by S3).

For example by hand we have F1={ emptyset, Omega, AH, AT}

Where AH={ w: w1=H}

AT={w: w1=T}

Can we compute

restart;
with(Finance);
S := [7.9, 7.5, 7.1, 6.5, 5., 3.7, 3.3, 2.95, 2.8];
         [7.9, 7.5, 7.1, 6.5, 5., 3.7, 3.3, 2.95, 2.8]
T := BinomialTree(3, S, .3);
TreePlot(T, thickness = 2, axes = BOXED, gridlines = true);

many thanks

Hi! For a process control exercise I'm trying to obtain the coefficients of different powers of the variable to design a PID controller. I have obtained the following equation :

Gc := (s^2*t^2+2*s*t*x+1)*(-b*s+1)/(k(-b*s+1)*s(tc+b))

I want to simplify it into an equation of the following form :

Gc = a( 1 + 1/(b*s) + c*s )

where a, b and c would be determined by Maple from the previous equation. I've tried using simplify() expand(simplify()) in different ways from the documentation and other threads, but to no avail. Any help would be greatly appreciated!

Thanks a lot!

Antoine.

Hello all,

I'm trying to do kinetic modeling of sequential dissociations with DE. I'm hitting a snag when modeling the third dissociation. The population should start at zero at t=0, but some of my model functions are non-zero at t=0. Is there anyway to fix this to force the funtions to go through zero?

Scheme:
PPPP -> intermediates -> PPP -> intermediates -> PP -> intermediates -> P  
(where P is a subunit and intermediates are confirmational changes before dissociation of a subunit)

a'..d' is the first dissociation
e' is the second dissociation
f'..l' is the third dissociation
Fits are evaluated by the residual sum of squares.

sol := dsolve([a' = -k1*a(x), b' = k1*a(x)-k1*b(x), c' = k1*b(x)-k1*c(x), d' = k1*c(x)-k1*d(x),
e' = k1*d(x)-k2*e(x), 
f' = k2*e(x)-k3*f(x), g' = k3*f(x)-k3*g(x), h' = k3*g(x)-k3*h(x), i' = k3*h(x)-k3*i(x), j' = k3*i(x)-k3*j(x), k' = k3*j(x)-k3*k(x), l' = k3*k(x)-k3*l(x), 
a(0) = 1, b(0) = 0, c(0) = 0, d(0) = 0, e(0) = 0, f(0) = 0, g(0) = 0, h(0) = 0, i(0) = 0, j(0) = 0, k(0) = 0, l(0) = 0],
{a(x), b(x), c(x), d(x), e(x), f(x), g(x), h(x), i(x), j(x), k(x), l(x)}, method = laplace);

f1 := sol[6];
f1 := rhs(f1);
g1 := sol[7];
g1 := rhs(g1);
h1 := sol[8];
h1 := rhs(h1);
i1 := sol[9];
i1 := rhs(i1);
j1 := sol[10];
j1 := rhs(j1);
kk := sol[11];
kk := rhs(kk);
l1 := sol[12];
l1 := rhs(l1);

xdata := Vector([0,10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,200,210,220,230,240,250,260,270,280,290,300,310,320,330,340,350,360,370,380,390,400], datatype = float);
ydata := Vector([0.0034,0.00392,0.00184,0.00782,0.01873,0.03683,0.11016,0.09838,0.18402,0.24727,0.20901,0.2972,0.37635,0.49235,0.57845,0.4457,0.50285,0.5672,0.62783,0.57264,0.54918,0.44792,0.49795,0.55218,0.47512,0.46473,0.37989,0.32236,0.3323,0.20894,0.28473,0.21273,0.19855,0.13548,0.12725,0.13277,0.0784,0.07969,0.06162,0.03855], datatype = float);

k1 := 0.391491454107626e-1; 
k2 := 0.222503562261129e-1; 

z1:=f1;
z2:=f1+g1;
z3:=f1+g1+h1;
z4:=f1+g1+h1+i1;
z5:=f1+g1+h1+i1+j1;
z6:=f1+g1+h1+i1+j1+kk;
z7:=f1+g1+h1+i1+j1+kk+l1;

Statistics[NonlinearFit](z1,xdata, ydata, x, initialvalues = [k3=0.1], output = [parametervalues, residualsumofsquares]); 
A:=plot(xdata, ydata, style=point, symbol=solidcircle, color=blue, symbolsize=12,labels = ["time (minutes)", "Relative Abundance"], labeldirections = [horizontal, vertical]):
F:=Statistics[NonlinearFit](z1,xdata, ydata, x,initialvalues = [k3=0.1]):
B:=plot(F, x=xdata[1]..xdata[-1], color=red):
plots[display](A, B);

Statistics[NonlinearFit](z2,xdata, ydata, x, initialvalues = [k3=0.1], output = [parametervalues, residualsumofsquares]); 
A:=plot(xdata, ydata, style=point, symbol=solidcircle, color=blue, symbolsize=12,labels = ["time (minutes)", "Relative Abundance"], labeldirections = [horizontal, vertical]):
F:=Statistics[NonlinearFit](z2,xdata, ydata, x,initialvalues = [k3=0.1]):
B:=plot(F, x=xdata[1]..xdata[-1], color=red):
plots[display](A, B);

Statistics[NonlinearFit](z3,xdata, ydata, x, initialvalues = [k3=0.1], output = [parametervalues, residualsumofsquares]); 
A:=plot(xdata, ydata, style=point, symbol=solidcircle, color=blue, symbolsize=12,labels = ["time (minutes)", "Relative Abundance"], labeldirections = [horizontal, vertical]):
F:=Statistics[NonlinearFit](z3,xdata, ydata, x,initialvalues = [k3=0.1]):
B:=plot(F, x=xdata[1]..xdata[-1], color=red):
plots[display](A, B);

Statistics[NonlinearFit](z4,xdata, ydata, x, initialvalues = [k3=0.1], output = [parametervalues, residualsumofsquares]); 
A:=plot(xdata, ydata, style=point, symbol=solidcircle, color=blue, symbolsize=12,labels = ["time (minutes)", "Relative Abundance"], labeldirections = [horizontal, vertical]):
F:=Statistics[NonlinearFit](z4,xdata, ydata, x,initialvalues = [k3=0.1]):
B:=plot(F, x=xdata[1]..xdata[-1], color=red):
plots[display](A, B);

Statistics[NonlinearFit](z5,xdata, ydata, x, initialvalues = [k3=0.1], output = [parametervalues, residualsumofsquares]); 
A:=plot(xdata, ydata, style=point, symbol=solidcircle, color=blue, symbolsize=12,labels = ["time (minutes)", "Relative Abundance"], labeldirections = [horizontal, vertical]):
F:=Statistics[NonlinearFit](z5,xdata, ydata, x,initialvalues = [k3=0.1]):
B:=plot(F, x=xdata[1]..xdata[-1], color=red):
plots[display](A, B);

Statistics[NonlinearFit](z6,xdata, ydata, x, initialvalues = [k3=0.1], output = [parametervalues, residualsumofsquares]); 
A:=plot(xdata, ydata, style=point, symbol=solidcircle, color=blue, symbolsize=12,labels = ["time (minutes)", "Relative Abundance"], labeldirections = [horizontal, vertical]):
F:=Statistics[NonlinearFit](z6,xdata, ydata, x,initialvalues = [k3=0.1]):
B:=plot(F, x=xdata[1]..xdata[-1], color=red):
plots[display](A, B);

Statistics[NonlinearFit](z7,xdata, ydata, x, initialvalues = [k3=0.1], output = [parametervalues, residualsumofsquares]); 
A:=plot(xdata, ydata, style=point, symbol=solidcircle, color=blue, symbolsize=12,labels = ["time (minutes)", "Relative Abundance"], labeldirections = [horizontal, vertical]):
F:=Statistics[NonlinearFit](z7,xdata, ydata, x,initialvalues = [k3=0.1]):
B:=plot(F, x=xdata[1]..xdata[-1], color=red):
plots[display](A, B);

3rd_diss.mw

Hi,

I have a private license of Maple 2018, and am interested in advances in terms of tensor calculus in Maple 2019, in particular concerning applications in general relativity. Three questions:

(1) Is there a way for me to buy the package without having to buy Maple 2019 in full, or would I have to upgrade?

(2) I have seen in the preview video that the features can for instance calculate the Christoffel symbols, the Rieman tensor, etc., from a prescribed metric in a coordinate basis, correct? Can the formalism also handle Expressions expressed in a non-coordinate frame though? So a frame field, for which the commutaror of the basis vector fields does not vanish?

(3) Is there somewhere a nice documentation, with exaples, where I can read on what I can do with the package? The documentation in the "what's new" section on the website is mainly concerned with applications for quantum mechanics, which is not what I am interested in.

Cheers!

If I create  and save a worksheet in Maple 2019, then try to up load it here, it uploads but contents won't display

No issue if I have an "identical" worksheet in Maple 2018.

See the attachment attempts below

#### Result of trying to upload Maple 2019 file here

Maple Worksheet - Error

Failed to load the worksheet /maplenet/convert/fibon2019.mw .
 

Download fibon2019.mw

But "same" file from Maple 2018 "works"

Download fibon2018.mw

Hello,

I have a matrix of size m*n and I want to apply a procedure "f" to each entry of the matrix. But I want to do this in parallel to reduce the computation time.

I used: Matrix(n, m, (i,j) -> Grid:-Seq( f (M (i,j) ) ,i=1..n, j=1..m) ); 

but I'm not satisfied with the result, I want the calculation to be faster. Are there any other methods?

Dear All,

Gretting from me. I have faced a problem in Maple program due to some errors. Could you please help me about the solution of that problem?
Thanks

@Preben Alsholm
@tomleslie
Sourse File: solution.mw

I've always had problems installing Physics package from inside Maple.

I thought by buying Maple 2019 this problem will go away, but I am still not able to install this package.

I get this error

Fetching package "Physics Updates" from MapleCloud...
ID: 5137472255164416
Version: 326
URL: https://maple.cloud

File size is 10764288 bytes(10 MB). 

Installing package...
PackageTools:-Install("C:/Users/me/AppData/Local/Temp/cloudDownload3648614516733692025/Physics+Updates.maple",overwrite=true,pkgname="Physics Updates");

ERROR: The package could not be installed.
error PackageTools:-Install, "this package is intended to work 
with Maple %1; it can not be installed in the version you 
are using -- %2", "2018", "2019"

Here is screen shot

I am on windows 10 professional. Maple 2019 installed with no problems.

any suggestion to try (other what was suggeted in link at top, which is to manually download the physics package each time and update maple.ini to point to the new version)?

Is Physics package updated to work/install in Maple 2019 or does one need to wait few more days untill this happens?