Spectroscopy is both qualitative and quantitative, so one can use spectral data tables of elements to do some fairly accurate Light Engineering.

Some nifty emulation of the spectral distributions of many non-LED popular lamps, which allows for direct utility calculations based on many different parameters including chromaticity, space type, lifetime, occasion, application, cost and efficiency. 7 such parameters are used with constrained weight optimization to fish out some of the more popular lamp types used in many situations today.

References (inline) the following docs:

The Science of Color, the Emission Spectra of the Elements and Some Lamp Engineering Applications

and

The Double Amici Prism Hand-held Spectroscope

First link main Theory, second link experimental verification.

Usage: Maple 18 main document code with library and data files. Download, unzip and run document for some quick results. Don't move library/data files relative to main doc. For further results and particular details, such as particular spectra & lamps, UN-comment the relevant commands in sections and execute individually after you have executed the entire sheet at least once.

Will be modified some time later to deal with LEDs. Based on elemental spectral data published by NIST. Suggestions 4 Improvements/Errors @ followup, here.

Elements.zip

For sample pics generated with the above code, click on the first reference link. All pictures therein were generated using this code.

--

Cheers,

Yiannis

A customer wondered if it was possible to create 2-line tickmarks on Maple plots like so

We achieved this using typeset, fractions, and backticks. Worksheet follows.

2line-tickmarks-mprimes.mw

Hi,
This post is inspired by a recent question maple least square fit error... where the OP was simulating what appeared to be a stochastic process known as the Drunkard's walk (see for instance The_Drunkard's_Walk).

In the case of the PO, the drunk took a step forward or a step backward (say along a narrow, long corridor) with equal probabilities. In addition one assumes that the step the drunkhard takes is independent of all the steps he did before.
His move is what is called a (1D) Random_walk

This little application based on MAPLETS (ok, I know that some people see them as old-fashioned technology).
It draws a sequence of several drunkard's walks, all of identical number of steps, and interactively plots the current histogram of the arrival point (the point where he is at the end of his walk -which should be the door if the same pub he started from if he is an inveterate drunkhard or if he knows a little about statistics- ).

The code contains 2 procedures :

• f_step_by_step (n, Discrete=false/true) 
  n : number of steps
  Discrete = false (default value) plot the histogram of the arrivals point as if these points were realizarions of a continuous random variable
  In this simple model these arrivals can take only integer values between -n and +n included; the Discrete=true option is recommended but it takes more walks for it to converge to the asymptotic distribution (see below).
  
  Once launched, f_step_by_step opens a maplet containing a Plotter and 2 buttons. A first walk is displayed, clic the "Plot" button to draw another and repeat the operation as many times as you want.
   
• f_automatic (n, m, Discrete=false/true) 
  d and Discrete both have the same meaning than for f_step_by_step.
  m is the number of random walk you want to draw
  The code is set to draw 1000 walks of 1000 steps ; this correspong roughly to 250 Mb of memory used.
  
  f_automatic contains a call to Threads:-Sleep to delay the display, the argument of Sleep is set to 0.25 second and must be modified within the procedure (its value could be passed to f_automatic as an argument).
  
  In my opinion it is the more interesting of the two procedures.
   

The values of the current mean and standard deviation are displayed as title.

The purpose is before all educative and can be seen as an illustration of the (one of) Central Limit Theorem(s) (CLT)

A little bit of theory:
Let X[n] the position of the drunkhard after n steps; his position X[n+1] is either X[n]-1 or X[n]+1 with equal probabilities.
The displacement X[n+1]-X[n] is a discrete random variable S with outcomes -1 and +1 and it's easy to find its variance is equal to 1.
The position of the trunkard after n steps is just a realization of the n independant and identically distributed, random variables S₁, ...S_n whose distribibution is equal to the one of S.
Thus :

• Expectation (S₁ + ... +S_n)  = 0 
• Variance (S₁ + ... +S_n)  = n 
  For n=1000 steps, the standard deviation of the arrivals is about 31.6)

CLT says that the distribution of S₁ + ... +S_n  tends to a Gaussian distribution as n tends to infinity.

What is the exact distribution of the arrivals?
Another way to represent S is to write S = 2*B-1 where B is a Bernoulli random variable with parameter 1/2. The random variable "Arrival" is  twice the sum of N indpendent rabdom variables such like S 
and thus its distribution is 2*Binomial(N, 1/2)-N.

What is the rate of convergence of the histogram to the true probability function?
For a sample of size N drawn from a continuous random variables, its histogram has:

• a bias error of order 1/K (K being the number of bins)
• a L_infinity error of order  K*sqrt( log(K) / M )  (M number of drunkhard's walks)
  see for instance Lec2_density.pdf

Using the option Discrete=true corresponds to the choice K=2*N+1, in this case the choice with the highest L_infinity error (the larger K the smaller the bias but the larger the  L_infinity error).
This is the reason I introduced the possibility to graw histograms and bar (column) graphs: for the same value of M the L_infinity error of the histogram (for instance with the default number of bins Maple uses) is nuch slower than the one of the comumn graph.

A few "internal" parameters.
I already spoke about the delay to display txo succesive walks.
Other parameters could be:

• The value of minbins in the case discrete=false (default)
  This value is fixed to 2*sqrt(M).
   
• The width in the "view" option of the plot: it's left part displays the drunkhard's walk and it's right one the histogram of the arrivals (after a rotation of -Pi/2). 
  This value si fixed to 5/4*M.
  Note that the histogram is dynamically rescaled in order it's height is always 1/4*number_of_steps.
   
• The height of the view option is set to -Q..Q where Q is equal to 4 standard deviations of the theoritical distribution of the arrivals.
  The continuous envelope of this distribution is red plotted in red (its height is normalized to M/4, see above). 
  One can show this standard deviation iverifies sqrt(M).
  In my opinion using a full vertical scale (-M..M) doesn't give pretty drunkward's walkes because they seem to more concentrated around the value 0.
   

WATCHOUT
Starting from 0 any walk with an odd number of steps will give an odd arrival and any walk with an even number of steps will give an even arrival. Thus the exact number of outcomes for the arrivals are:

• 2*n if n is odd
• 2*n-1 if n is even
   

Other application
This maplet can be used as illustration of the Galton Board (also known as the Bean_machine)


Why using maplets?
Another solution could have been to use animate. But to draw the M drunkard's walks, you would have had to use M frames. An excessive task that I'm not even sure Maple would have been able to handle.
I guess that imbeded components could do the job too, but I'm not as comfortable with them as I am with maplets.

To illustrate what the code does an image of the final result is given below.

Drunkard_walk.mw

One forum had a topic related to such a platform. You can download a video of the movement of this platform from the picture at this link. The manufacturer calls the three-degrees platform, that is, having three degrees of freedom. Three cranks rotate, and the platform is connected to them by connecting rods through ball joints. The movable beam (rocker arm) has torsion springs.  I counted 4 degrees of freedom, because when all three cranks are locked, the platform remains mobile, which is camouflaged by the springs of the rocker arm. Actually, the topic on the forum arose due to problems with the work of this platform. Neither the designers nor those who operate the platform take into account this additional fourth, so-called parasitic degree of freedom. Obviously, if we will to move the rocker with the locked  cranks , the platform will move.
Based on this parasitic movement and a similar platform design, a very simple device is proposed that has one degree of freedom and is, in fact, a spatial linkage mechanism. We remove 3 cranks, keep the connecting rods, convert the rocker arm into a crank and get such movements that will not be worse (will not yield) to the movements of the platform with 6 degrees of freedom. And by changing the length of the crank, the plane of its rotation, etc., we can create simple structures with the required design trajectories of movement and one degree of freedom.
Two examples (two pictures for each example). The crank rotates in the vertical plane (side view and top view)
PLAT_1.mw


and the crank rotates in the horizontal plane (side view and top view).

The program consists of three parts. 1 choice of starting position, 2 calculation of the trajectory, 3 design of the picture.  Similar to the programm  in this topic.

I created a little procedure to automatically size text areas based on content. It sizes the text area based on wraparound and tab characters, something that the autosize for the code edit region does not do. (Hint to Maple developers)

Enjoy.

    AutosizeTextArea:=proc(TextAreaName, {intMinRows::nonnegint:=5, intMinChars::nonnegint:=50, intMaxChars::nonnegint:=140})
        description "Autosizes the TextArea based on content",
                  "Parameters",
                  "1) TextAreaName__The name of the textarea",
                  "Optional Parameters",
                  "intMinRows________Minimum number of visible rows",
                  "intMinChars_______Minimum character width",
                  "intMaxChars_______Maximum character width";
        uses DocumentTools, StringTools;          
        local strLines, intLongestLine, nLines;
        strLines := Split(GetProperty(TextAreaName,'value'),"\n");
        intLongestLine := max('numelems'~(strLines));
        # Count the characters in each line (add 7 extra characters for each tab). Determine the number of lines to display each line due to wraparound, then add all these together
        #   to determine the number of rows to display.
        nLines := add(ceil~(('numelems'~(strLines) + StringTools:-CountCharacterOccurrences~(strLines, "\t")*~7)/~intMaxChars));
        SetProperty(TextAreaName, 'visibleRows', max(nLines, intMinRows), 'refresh' = true);
        SetProperty(TextAreaName, 'visibleCharacterWidth', min(max(intLongestLine, intMinChars),intMaxChars), 'refresh' = true);
    
    end proc:

A fascinating race is presently running (even if the latest results seem  to have put an end to it).
I'm talking of course about the US presidential elections.

My purpose is not to do politics but to discuss of a point of detail that really left me puzzled: the possibility of an electoral college tie.
I guess that this possibility seems as an aberration for a lot of people living in democratic countries. Just because almost everywhere at World electoral colleges contain an odd number of members to avoid such a situation!

So strange a situation that I did a few things to pass the time (of course with the earphones on the head so I don't miss a thing).
This is done with Maple 2015 and I believe that the amazing Iterator package (that I can't use thanks to the teleworking :-( ) could be used to do much more interesting things.

 

Download ElectoralCollegeTie.mw

Controlled platform with 6 degrees of freedom. It has three rotary-inclined racks of variable length:

and an example of movement parallel to the base:

Perhaps the Stewart platform may not reproduce such trajectories, but that is not the point. There is a way to select a design for those specific functions that our platform will perform. That is, first we consider the required trajectories of the platform movement, and only then we select a driving device that can reproduce them. For example, we can fix the extreme positions of the actuators during the movement of the platform and compare them with the capabilities of existing designs, or simulate your own devices.
In this case, the program consists of three parts. (The text of the program directly for the first figure : PLATFORM_6.mw) In the first part, we select the starting point for the movement of a rigid body with six degrees of freedom. Here three equations f6, f7, f8 are responsible for the six degrees of freedom. The equations f1, f2, f3, f4, f5 define a trajectory of motion of a rigid body. The coordinates of the starting point are transmitted via disk E for the second part of the program. In the second part of the program, the trajectory of a rigid body is calculated using the Draghilev method. Then the trajectory data is transferred via the disk E for the third part of the program.
In the third part of the program, the visualization is executed and the platform motion drive device is modeled.
It is like a sketch of a possible way to create controlled platforms with six degrees of freedom. Any device that can provide the desired trajectory can be inserted into the third part. At the same time, it is obvious that the geometric parameters of the movement of this device with the control of possible emergency positions and the solution of the inverse kinematics problem can be obtained automatically if we add the appropriate code to the program text.
Equations can be of any kind and can be combined with each other, and they must be continuously differentiable. But first, the equations must be reduced to uniform variables in order to apply the Draghilev method.
(These examples use implicit equations for the coordinates of the vertices of the triangle.)

Download Godel_universe_and_Tedrads.mw

The 2020 Maple Conference is coming up fast! It is running from November 2-6 this year, all remotely, and completely free.

The week will be packed with activities, and we have designed it so that it will be valuable for Maple users of all skill and experience levels. The agenda includes 3 keynote presentations, 2 live panel presentations, 8 Maplesoft recorded presentations, 3 Maple workshops, and 68 contributed recorded presentations.

There will be live Q&A’s for every presentation. Additionally, we are hosting what we’re calling “Virtual Tables” at every breakfast (8-9am EST) and almost every lunch (12-1 EST). These tables offer attendees a chance to discuss topics related to the conference streams of the day, as well as a variety of special topics and social discussions. You can review the schedule for these virtual tables here.

Attendance is completely free, and we’re confident that there will be something there for all Maple users. Whether you attend one session or all of them, we’d love to see you there!

You can register for the Maple Conference here.

Download The_metric_[27_37_1]_in_canonical_form.mw

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

When we plot a curve with the option  style=point  , symbols go evenly not along the length of this curve, but along the range of the independent variable. For this reason the plot often looks unattractive. Here are two examples. In the first example, the default option  adaptive=true  is used, in which Maple adds points in some places.

restart;
plot(surd(x,3), x=-2.5..2.5, style=point, scaling=constrained, symbol=solidcircle, symbolsize=8, numpoints=30, size=[800,300]);
plot(surd(x,3), x=-2.5..2.5, style=point, scaling=constrained, symbol=solidcircle, symbolsize=8, numpoints=30, adaptive=false, size=[800,300]);

The  UniformPointPlot  procedure allows you to plot curves by symbols (as for  style=point), and these symbols go from each other at equal distances, measured along this curve. The procedure uses a well-known formula for the length of a curve in two and three dimensions. The procedure parameters are clear from the three examples below.

UniformPointPlot:=proc(F::{algebraic,list},eq::`=`,n::posint:=40,Opt::list:=[symbol=solidcircle, symbolsize=8, scaling=constrained])
local t, R, P, g, L, step, L1, L2;
uses plots;
Digits:=4:
t:=lhs(eq); R:=rhs(eq);
P:=`if`(type(F,algebraic),[t,F],F); 
g:=x->`if`(F::algebraic or nops(F)=2,evalf(Int(sqrt(diff(P[1],t)^2+diff(P[2],t)^2), t=lhs(R)..x, epsilon=0.001)),evalf(Int(sqrt(diff(P[1],t)^2+diff(P[2],t)^2+diff(P[3],t)^2), t=lhs(R)..x, epsilon=0.001))):
L:=g(rhs(R)); step:=L/(n-1);
L1:=[lhs(R),seq(fsolve(g-k*step, fulldigits),k=1..n-2),rhs(R)];
L2:=map(s->`if`(type(F,algebraic),[s,eval(F,t=s)],eval(F,t=s)), L1):
`if`(F::algebraic or nops(F)=2,plot(L2, style=point, Opt[]),pointplot3d(L2, Opt[]));
end proc:

   
Examples of use:

UniformPointPlot(surd(x,3), x=-2.5..2.5, 30);

UniformPointPlot([5*cos(t),3*sin(t)], t=0..2*Pi, [color=red,symbol=solidcircle,scaling=constrained, symbolsize=8,  size=[800,400]]);

UniformPointPlot([cos(t),sin(t),2-2*cos(t)], t=0..2*Pi, 41, [color=red,symbol=solidsphere, symbolsize=8,scaling=constrained, labels=[x,y,z]]);

                             
Here's another example of using the same technique as in the procedure. In this example, we are plotting Archimedean spiral uniformly colored with 7 rainbow colors:

f:=t->[t*cos(t),t*sin(t)]:
g:=t->evalf(Int(sqrt(diff(f(s)[1],s)^2+diff(f(s)[2],s)^2), s=0..t)):
h:=s->fsolve(s=g(t), t):
L:=evalf(g(2*Pi)): step:=L/7:
L1:=[0,seq(h(k*step), k=1..6),2*Pi]:
Colors:=convert~([Red,Orange,Yellow,Green,Blue,Indigo,Violet], string):
plots:-display(seq(plot([f(t)[], t=L1[i]..L1[i+1]], color=Colors[i], thickness=12), i=1..7), scaling=constrained, size=[500,400]);

Uniform_Point_Plot.mw

Examples of mathematical models for the formation of spline curves based on polynomials of various orders that simulate certain trajectories are given.
Mathematical models of the formation of a spline curve, taking into account the extreme derivatives of the initial orders, are presented. The construction of the spline curves of the hermit and bezier of various orders, consisting of different segments, is considered. The presented models are systematic and universal, i.e. can be used to generate any polynomial curves with specified extreme derivatives of the vectors.

100_nodes_not_closed.mwExample_1.mwExample_2.mwExample_3.mwExample_4.mwExample_5.mwExample_6.mwExample_7.mw

«Формирования линий OC и бриджей по линии MAT для области с островами».OC_MAT_MA_bridge.mw

As an addition to the post.
Non-orientable surface in the sequence of orientable surfaces. In the picture we see the equations corresponding to the current surface plot.
Just entertainment.
surfaces.mw

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

https://iopscience.iop.org/article/10.1149/1945-7111/abb37b

Some challenges are listed at 

https://twitter.com/UT_MAPLE/status/1311356957941522438

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.

Download Conformalmapping.mws

Download FEM_2D.mws

Download DataFrames_Example.mw