Hooke's joint and type of Hooke's joint.  


https://vk.com/doc242471809_363263648
https://vk.com/doc242471809_363264824

Type_of_Hooke.mw

    Intersection of surfaces:

x3-.25*(sin(4*x1)+sin(3*x2+x3)+sin(2*x2))=0;  (1)

(x1-xx1)^4+(x2-xx2)^4+(x3-xx3)^4-1=0;          (2)   

   Surface (1) and a set of surfaces (2). Point (xx1, xx2, xx3) belongs to (1). Moving along the surface (1), we compute its intersection with the surface (2).
   The program is very simple and its algorithm can be used for many other combinations of equations.

intersection_of_surfaces.mw  

There seems to be a bug in the CodeGeneration package for Python which leads to a deletion of braces in some cases.

# E.g.

CodeGeneration[Python](Pi*(a+2));

# leads to

cg5 = math.pi * a + 2

which is obviously wrong.

Hey Everyone:

I was wondering what are your favorite and most useful code snippets that you often use. Maybe ones that are in your initialization code? Maybe ones that speed up something that you often do in maple? Maybe ones that you've seen on this and other websites and adopted for your own purposes?

For fun, I attach my init.mpl (.txt here, as .mpl attachments are not allowed by mapleprimes) here init.txt

Some of the snippets that I use the most are also listed below:

#rearrange curves inside an already created plot, so that certain curves are "on top" of the other ones.
#discussed here:
#http://www.mapleprimes.com/questions/201626-Order-Of-Curves-In-A-Plot
rearrangeCurves:= proc(
     v_items::specfunc(anything, PLOT),
     v_reorder::list([integer,integer]):= []
)
local p, curves, rest;
     (curves,rest):= selectremove(type, v_items, specfunc(anything, CURVES));
     curves:= < op(curves) >:         
     for p in v_reorder do
          curves[p]:= curves[p[[2,1]]]
     end do;
     PLOT(convert(curves,list)[], op(rest))
end proc:


#for numerical differentiation
#based on the idea from:
#http://www.mapleprimes.com/posts/119554-Data-Interpolation
#example use:
#alist:=[seq(i, i=0..10, 0.1)]:
#data:=map(x->evalf(sin(x)), alist):
#plot(alist, data);
#plot([cos(x), 'num_diff(x, LinearAlgebra:-Transpose(Matrix([alist,data])))'], x=1..10, thickness=5, linestyle=[solid, dot], color=[blue, red]);
num_diff:=proc(x, v_data, v_options:=[method=spline, degree=3])
 #v_data is a matrix
 #TODO: let the data be in a more arbitrary format that ArrayInterpolation understands, but keep x as first var
 evalf(D[1](x->CurveFitting:-ArrayInterpolation(v_data, [x],v_options[])[])(x));
end:

#extract nth columns/rows from a matrix
#these only work if have a 2d object... should be updated to also work
#with 1d row/column vectors
#Example use cases
#A := LinearAlgebra:-RandomMatrix(20, 20, outputoptions = [datatype = float[8]]);
#nthColumns(A, 2); #Every other column
#nthRows(A, 10)[.., 1..3]; #Every 10th row, but show only first 3 columns
nthColumns:=proc(v_m, v_n)
  v_m[..,[seq(i, i=1..rtable_size(v_m)[2], v_n)]]
end:
nthRows:=proc(v_m, v_n)
  v_m[[seq(i, i=1..rtable_size(v_m)[1], v_n)],..]
end:


#saves a png plot
savePlot:=proc(v_p, v_fileName, v_w:="800", v_h:="500")
    plotsetup("png", plotoutput=v_fileName, plotoptions=cat("quality=100,portrait,noborder,width=",v_w,",height=",v_h));
    print(plots[display](v_p));
    Threads[Sleep](2):
    fclose(v_fileName):
    plotsetup(default);
end:

The well-known  combinat[composition]  command computes and returns a list containing all distinct ordered  k-tuples of positive integers whose elements sum equals  n . These are known as the compositions of  n .  For some applications, additional constraints are required for the elements of these k-tuples, for example, that they are within a certain range.

The  Composition  procedure solves this problem. Required parameters:  n - a nonnegative integer, k - a positive integer. The parameter  res  is the optional parameter (by default  res is  0 ). If  res  is a number, all elements of  k-tuples must be greater than or equal  res .  If  res  is a range  a .. b ,   all elements of  k-tuples must be greater than or equal  a  and  less than or equal  b .  Composition(n,k,1)  is equivalent to  combinat[composition](n,k) .

The code of the procedure:

Composition := proc (n::nonnegint, k::posint, res::{range, nonnegint} := 0)

local a, b, It, L0; 

if res::nonnegint then a := res; b := n-(k-1)*a  else a := lhs(res); b := rhs(res) fi;

if b < a or b*k < n then return `No solutions` fi; 

It := proc (L)

local m, j, P, R, i, N;

m := nops(L[1]); j := k-m; N := 0;

for i to nops(L) do

R := n-`+`(op(L[i]));

if R <= b*j and a*j <= R then N := N+1;

P[N] := [seq([op(L[i]), s], s = max(a, R-b*(j-1)) .. min(R, b))] fi;

od;

[seq(op(P[s]), s = 1 .. N)];

end proc;

L0 := [[]];

(It@@k)(L0); 

end proc:

Three simple examples:

Composition(10,3); ``;   # All terms greater than or equal 0

Composition(10,3, 2);   # All terms greater than or equal 2

Composition(10,3, 2..4);   # All terms greater than or equal 2 and less than or equal to 4 

A more complex example. The problem - to find all the numbers in the range  1 .. 99999999  whose digits sum is equal to 21 .

Each number is represented by a list of digits from left to right, replacing missing digits at the left with zeros.

M:=Composition(21,8, 0..9):  

nops(M);  # The number of solutions

[seq(M[1+100000*i], i=0..9)]; # 10 solutions from the list M starting the first one

seq(add(%[i,k]*10^(8-k), k=1..8),i=1..nops(%));  # Conversion into numbers

Composition.mws

Greetings to all.

I am writing to alert MaplePrimes users to a Maple package that makes an remarkable contribution to combinatorics and really ought to be part of your discrete math / symbolic combinatorics class if you teach one. The combstruct package was developed at INRIA in Paris, France, by the algorithmics research team of P. Flajolet during the mid 1990s. This software package features a parser for grammars involving combinatorial operators such as sequence, set or multiset and it can derive functional equations from the grammar as well as exponential and ordinary generating functions for labeled and unlabeled enumeration. Coefficients of these generating functions can be computed. All of it easy to use and very powerful. If you are doing research on some type of combinatorial structure definitely check with combstruct first.

My purpose in this message is to advise you of the existence of this package and encourage you to use it in your teaching and research. With this in mind I present five applications of the combstruct package. These are very basic efforts that admit improvement that can perhaps serve as an incentive to deploy combstruct nonetheless. Here they are:

• A parity bias for trees
• Number of nodes with even offspring
• Trees with odd degree sequence
• Ordered rooted trees with all nodes having at least two child nodes
• Average depth of a leaf in an ordered binary tree

I hope you enjoy reading these and perhaps you might want to feature combstruct as well, which presented the first complete implementation in a computer algebra system of the symbolic method, sometimes called the folklore theorem of combinatorial enumeration, when it initially appeared.

Best regards,

Marko Riedel.

We are happy to announce the first results of a partnership between Maplesoft and the University of Waterloo to provide effective, engaging online education for technical courses.

Combining rich course materials developed by the University with Maple T.A. and Maplesoft technology for developing, managing, and displaying dynamic content, the Secondary School Courseware project supports high school students and teachers from around the world in their Precalculus and Calculus courses. The site includes interactive investigations, videos, and self-assessment questions that provide immediate feedback.

Feel free to take a look. The site is free, and no login is required.  

For more information about the project, see Online Mathematical Courseware.

eithne

D_Method.mw

The classical Draghilev’s method.  Example of solving the system of two transcendental equations. For a single the initial approximation are searched 9 approximate solutions of the system.
(4*(x1^2+x2-11))*x1+2*x1+2*x2^2-14+cos(x1)=0;
2*x1^2+2*x2-22+(4*(x1+x2^2-7))*x2-sin(x2)=0; 
x01 := -1.; x02 := 1.;

Equation: ((x1+.25)^2+(x2-.2)^2-1)^2+(x3-.1)^2-.999=0;



a_cam_3D.mw

Cam mechanism animation.   Equation:  (xx2-1.24)^10+5*(xx1-.66)^10-9.=0
a_cam.mw

Transformation and rotation.

transformation.mw

The William Lowell Putnam Mathematical Competition, often abbreviated to the Putnam Competition, is an annual mathematics competition for undergraduate college students enrolled at institutions of higher learning in the world (regardless of the students' nationalities). One can see some problems and answers here. I find it remarkable that a lot of these problems can be done with Maple. Here is a sample (The DirectSearch package should be downloaded from http://www.maplesoft.com/applications/view.aspx?SID=101333 and installed in your Maple.).

Hope the reader will try to continue the above.

Download Putnam_done_with_Maple.mw

Ever year about this time, somewhat geeky holiday-themed content makes the rounds on the internet.  And I realized that even though I have seen much of this content already, I still enjoy seeing it again. (Why wouldn’t I want to see a Dalek Christmas tree every year?)

So in the spirit of internet recycling, here are a couple of older-but-still-fun Maple applications with a Christmas/holiday theme for your enjoyment.

Talkin’ Turkey

The Physics of Santa Claus

Other examples (old or new) are most welcome, if anyone wants to share.

eithne

Hi
Two new things recently added to the latest version of Physics available on Maplesoft's R&D Physics webpage are worth mentioning outside the framework of Physics.

• automaticsimplification. This means that after "Physics:-Setup(automaticsimplification=true)", the output corresponding to every single input (literally) gets automatically simplified in size before being returned to the screen. This is fantastically convenient for interactive work in most situations.
  
  
• Add Physics:-Library:-Assume, to perform the same operations one typically performs with the  assume command, but without the side effect that the variables get redefined. So the variables do not get redefined, they only receive assumptions.

This new Assume implements the concept of an "extended assuming". It permits re-using expressions involving the variables being assumed, expressions that were entered before the assumptions were placed, as well as reusing all the expressions computed while the variables had assumptions, even after removing the variable's assumptions. None of this is possible when placing assumptions using the standard assume. The new routine also permits placing assumptions on global variables that have special meaning, that cannot be redefined, e.g. the cartesian, cylindrical or spherical coordinates sets, or the coordinates of a coordinate spacetime system within the Physics package, etc.

Examples:

Download AutomaticSimplificationAndAssume.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

Download FermiCreationAnnihilation.mw