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Special bounded partial sums

by: Marvin Ray Burns 550
constant
September 10 2014
0  1

For all real a, the partial sums sn= sum((-1)^k (k^(1/k) -a), k=1..n) are bounded so that their limit points form an interval [-1.+  the MRB constant +a, MRB constant] of length 1-a, where the MRB constant is limit(sum((-1)^k*(k^(1/k)), k = 1 ..2*N),N=infinity).

For all complex z, the upper limit point of  sn= sum((-1)^k (k^(1/k) -z), k=1..n) is the  the MRB constant.

We see that maple knows the basics of this because when we enter sum((-1)^k*(k^(1/k)-z), k = 1 .. n) 

maple gives

sum((-1)^k*(k^(1/k)-z), k = 1 .. n). 

 

marvinrayburns.com

Banque de questions MapleTA en français – plus...

by: jzivku 640
September 05 2014
1  0

Aujourd’hui, je suis ravis d’annoncer la disponibilité d’une large banque de questions françaises supportant les enseignements du secondaire et de l’enseignement supérieur. Ce contenu didactique est disponible via le MapleTA Cloud, et également grâce au lien de téléchargement ci-dessous.

Lien de téléchargement de la banque de questions françaises

Ces questions sont librement et gratuitement accessibles, et vous pouvez les utiliser directement sur vos propres évaluations et exercices dans MapleTA, ou les éditer et modifier pour les adapter à vos besoins.

Le contenu de cette banque de questions françaises traite de sujets pour les classes et enseignements pré-bac, post-bac pour en majorité les matières scientifiques.

Les matières traitées par niveaux et domaines sont:

Lycées :

  • Electricité
  • Équations Différentielles
  • Gravitation universelle
  • Langues
  • Maths I
  • Maths II
  • Physique
  • Chimie
  • Mécanique

Enseignement supérieur (Post-Bac) :

  • Astrobiologie
  • Introduction au Calcul pour la Biologie
  • Chimie
  • Déplacement d'onde
  • Electricité & Magnétisme
  • Maths pour l’économie
  • Maths Post-Bac
  • Mécanique Angulaire
  • Mécanique des Fluides
  • Mécanique linéaire
  • Physique Post-Bac
  • Electrocinétique
  • Matériau
  • Mécanique des Fluides
  • Thermodynamique

Jonny Zivku
Maplesoft Product Manager, Maple T.A.

Maple T.A. course materials – Introductory Calculus ...

by: jzivku 640
August 27 2014
1  0

Several Maple T.A. users have developed comprehensive sets of question content and assignments to support full courses in Maple T.A. These questions are available through the Maple T.A. Cloud, and we have decided to also post the associated course modules on Maple Primes as an alternative way of accessing this content.

Below you will find a link to the Introductory Calculus Maple T.A.. course module developed by Keele University.

This testing content is freely distributed, and can be used in your own Maple T.A. tests either as-is, or with edits.

These questions are designed to accompany the first semester of an introductory honours calculus course. The course is intended primarily for students who need or expect to pursue further studies in mathematics, physics, chemistry, engineering and computer science. With over 250 question, topics include: basic material about functions, polynomials, logs and exponentials, the concept of the derivative, and lots of practise exercises for finding derivatives and integrals, and material about series.

Jonny Zivku
Maplesoft Product Manager, Maple T.A.

Maple T.A. course materials – Introductory Calculus...

by: jzivku 640
August 27 2014
3  4

Several Maple T.A. users have developed comprehensive sets of question content and assignments to support full courses in Maple T.A. These questions are available through the Maple T.A. Cloud, and we have decided to also post the associated course modules on Maple Primes as an alternative way of accessing this content.

Below you will find a link to the Introductory Calculus for Biological Sciences Maple T.A.. course module developed by the University of Guelph.

This testing content is freely distributed, and can be used in your own Maple T.A. tests either as-is, or with edits.

The Introductory Calculus for Biological Sciences course module is designed to cover a single-semester introductory calculus course for biological sciences students at the first-year university level. The questions are designed to span the topics listed below, allowing for practice, homework or testing throughout the semester.

Topics include:

  • Introduction to Functions
  • Composite and Inverse Functions
  • Trigonometric Functions
  • Logarithms and Exponents
  • Sequences and Finite Series
  • Limits and Continuity
  • Derivatives
  • Curve Sketching
  • Differentials
  • Linear Approximation
  • Taylor Polynomials
  • Difference Equations
  • Log-Log Graphs
  • Anti-Differentiation
  • Definite Integrals

Jonny Zivku
Maplesoft Product Manager, Maple T.A.

Maple T.A. course materials – Introductory Mathema...

by: jzivku 640
mapleta
August 22 2014
0  0

Several Maple T.A. users have developed comprehensive sets of question content and assignments to support full courses in Maple T.A. These questions are available through the Maple T.A. Cloud, and we have decided to also post the associated course modules on Maple Primes as an alternative way of accessing this content.

Below you will find a link to the Introductory Mathematical Economics Maple T.A.. course module developed by the University of Guelph.

This testing content is freely distributed, and can be used in your own Maple T.A. tests either as-is, or with edits.

The Introductory Mathematical Economics course module is designed to cover a single-semester course in mathematical economics for economics and commerce students at the second-year university level. The questions are designed to span the topics listed below, allowing for practice, homework or testing throughout the semester.

Topics include:

  • Rules of Differentiation
  • First Order Differential Equations
  • Higher Order Derivatives
  • Optimization in One Variable
  • Second Order Conditions for Optimization
  • Systems of Linear Equations
  • Optimization with Direct Restrictions on Variables
  • Over Determined and Under Determined Systems
  • Matrix Representation of Systems
  • Gauss Jordan
  • Matrix Operations
  • Types of Matrices
  • Determinants and Inverses
  • Partial Differentiation
  • Second Order Partial Derivatives
  • Multivariate Optimization
  • Second Order Conditions for Multivariate Optimization
  • Multivariate Optimization with Direct Restrictions of Variables
  • Constrained Optimization and the Lagrangean Method
  • Second Order Conditions for Constrained Optimization

Jonny Zivku
Maplesoft Product Manager, Maple T.A.

Maple T.A. course materials – Introductory Electricity...

by: jzivku 640
mapleta
August 22 2014
1  0

Several Maple T.A. users have developed comprehensive sets of question content and assignments to support full courses in Maple T.A. These questions are available through the Maple T.A. Cloud, and we have decided to also post the associated course modules on Maple Primes as an alternative way of accessing this content.

Below you will find a link to the Introductory Electricity & Magnetism Maple T.A.. course module developed by the University of Guelph.

This testing content is freely distributed, and can be used in your own Maple T.A. tests either as-is, or with edits.

The Introductory Electricity & Magnetism course module is designed to cover a single-semester course in electricity and magnetism for physical sciences students at the first-year university level. The questions are designed to span the topics listed below, allowing for practice, homework or testing throughout the semester. Using the Maple engine that is part of Maple TA, a custom grading engine has been developed to provide even more flexible grading of scalar and vector responses. This partial grading engine can be configured to, among other things, assign part marks for missing units, transposed or missing vector components or missing algebraic terms.


Topics include:

  • Cross Products
  • Coulomb’s Law
  • Electric Fields
  • Point Charge Distributions
  • Continuous Charge Distributions (Integration)
  • Electric Potential
  • Electric Potential Energy
  • Electromotive Force
  • Resistance
  • Capacitance
  • Kirchhoff’s Laws
  • Magnetic Fields
  • Magnetic Fields Due to Current Carrying Wires
  • Forces on Wires in Magnetic Fields
  • Forces on Charges in Electric and/or Magnetic Fields
  • EM Waves
  • Two Source Interference
  • Double Slit Interference
  • Single Slit Diffraction
  • Diffraction Gratings

Jonny Zivku
Maplesoft Product Manager, Maple T.A.

Maple T.A. course materials – Statistics (University...

by: jzivku 640
August 20 2014
1  0

Several Maple T.A. users have developed comprehensive sets of question content and assignments to support full courses in Maple T.A. These questions are available through the Maple T.A. Cloud, and we have decided to also post the associated course modules on Maple Primes as an alternative way of accessing this content.

Below you will find a link to the Statistics Maple T.A.. course module developed by the University of Guelph.

This testing content is freely distributed, and can be used in your own Maple T.A. tests either as-is, or with edits.

The Statistics course module is designed to cover a single-semester course in statistics for science students at the second-year university level. The questions are designed to span the topics listed below, allowing for practice, homework or testing throughout the semester. The questions are mainly of an applied nature and do not delve very deeply into the underlying mathematical theory.

Topics:

  • Introduction to Statistics
  • Descriptive Statistics
  • Basic Probability
  • Discrete Random Variables
  • Continuous Random Variables
  • Sampling Distributions
  • Inference for Means
  • Inference for Proportions
  • Inference for Variances
  • Chi-square Tests for Count Data
  • One-Way ANOVA
  • Simple Linear Regression and Correlation

Jonny Zivku
Maplesoft Product Manager, Maple T.A.

Maple T.A. course materials – Statistics (University...

by: jzivku 640
August 20 2014
0  0

Several Maple T.A. users have developed comprehensive sets of question content and assignments to support full courses in Maple T.A. These questions are available through the Maple T.A. Cloud, and we have decided to also post the associated course modules on Maple Primes as an alternative way of accessing this content.

Below you will find a link to the Statistics Maple T.A.. course module developed by the University of Waterloo.

This testing content is freely distributed, and can be used in your own Maple T.A. tests either as-is, or with edits.

The Statistics content is used in introductory statistics courses at the University of Waterloo, and has been used regularly over several years. The over 700 questions are clearly organized by topic, and provide extensive feedback to students.


Topics include:

  • Basics
  • Confidence Intervals
  • Continuous Distribution
  • Discrete Multivariate
  • Discrete Probability
  • Graphical Analysis
  • Hypothesis Testing
  • Numerical Analysis for Statistics
  • Probability
  • Sampling Distributions

Jonny Zivku
Maplesoft Product Manager, Maple T.A.

Maple T.A. course materials – Calculus 1 and 2

by: jzivku 640
August 15 2014
1  0

Several Maple T.A. users have developed comprehensive sets of question content and assignments to support full courses in Maple T.A. These questions are available through the Maple T.A. Cloud, and we have decided to also post the associated course modules on Maple Primes as an alternative way of accessing this content.

Below you will find a link to the Calculus 1 Maple T.A.. course module developed by the University of Guelph. This course material also forms part of Teaching Calculus with Maple: A Complete Kit, which provides lectures notes, Maple demonstrations, Maple T.A. assignments, and more for teaching both Calculus 1 and Calculus 2.

This testing content is freely distributed, and can be used in your own Maple T.A. tests either as-is, or with edits.

The Calculus 1 course module is designed to accompany the first semester of an introductory honours calculus course. The course is intended primarily for students who need or expect to pursue further studies in mathematics, physics, chemistry, engineering and computer science.

Topics include:

  • trigonometry including the compound angle formulas
  • inequalities and absolute values
  • limits and continuity using rigorous definitions, the derivative and various applications (extreme, related rates, graph sketching)
  • Rolle's Theorem and the Mean Value Theorem for derivatives
  • the differential and anti-differentiation
  • the definite integral with application to area problems
  • the Fundamental Theorem of Calculus
  • logarithmic and exponential functions
  • the Mean Value Theorem for Integrals

The Calculus 2 course module is designed to accompany the second semester of an introductory honours calculus course.

Topics include:

  • inverse trigonometric functions
  • hyperbolic functions
  • L'Hôpital's Rule
  • techniques of integration
  • parametric equations
  • polar coordinates
  • Taylor and MacLaurin series
  • functions or two or more variables
  • partial derivatives
  • multiple integration

Jonny Zivku
Maplesoft Product Manager, Maple T.A.

Packing of circles: touchstone for global optimize...

by: Markiyan Hirnyk 7809 Maple , Maple Toolboxes
application globaloptimization
August 13 2014
3  1

I would like to pay attention to a series of applications by Samir Khan
http://www.maplesoft.com/applications/view.aspx?SID=153600
http://www.maplesoft.com/applications/view.aspx?SID=153599
http://www.maplesoft.com/applications/view.aspx?SID=153596
http://www.maplesoft.com/applications/view.aspx?SID=153598
My congratulations to the author on his work well done. New capacities of Global Optimization Toolbox are spectacular. For example, in the first application  an optimization
problem in 101 variables under 5050 nonlinear  constraints
(other than 202 bounds) is solved.
I think it requires a very powerful comp and much time.
I tried that  problem for n=20 with the good old DirectSearch
on my comp (4 GB RAM, Pentium Dual-Core CPU E5700@3GHz) by

soln2 := DirectSearch:-GlobalSearch(rc, {cons1, cons2, rc >= 0,
seq(`and`(vars[i] >= -70, vars[i] <= 70), i = 1 .. 2*n), rc <= 70},
variables = vars, method = quadratic, number = 140, solutions = 1,
evaluationlimit = 20000)

and obtained not so bad rc=69.9609360106765 (whereas www.packomania.com gives rc=58.4005674790451137175957) in about one hour.

Packing_by_DS.mw
For n=50 the memory of my comp cannot allocate calculations or the obtained result by the Search command is far away from the one in packomania.

 

Pixel Conversion

by: John Dolese 125 Maple 18
array convert adobe
August 09 2014
3  1

Here is an example of manipulating an Array of pixels. I chose the x-rite ColorChecker as a model so there would be published results to check my work. A number of details about color spaces have become clear through this exercise. The color adaptation process was modeled by converting betweenXYZ and LMS. Different black points may be selected depending on how close to zero illuminance one would accept as a good model. 

I look forward to extending this work to verify and improve the color calibration of my photography. Also some experimentation with demosaicing should be possible.

Initialization
 

restart

with(LinearAlgebra):

unprotect(gamma):``

NULL

x-rite Colorchecker xyY Matrix

  CCxyY_D50 := Matrix(4, 6, {(1, 1) = Vector(3, {(1) = .4316, (2) = .3777, (3) = .1008}), (1, 2) = Vector(3, {(1) = .4197, (2) = .3744, (3) = .3495}), (1, 3) = Vector(3, {(1) = .2760, (2) = .3016, (3) = .1836}), (1, 4) = Vector(3, {(1) = .3703, (2) = .4499, (3) = .1325}), (1, 5) = Vector(3, {(1) = .2999, (2) = .2856, (3) = .2304}), (1, 6) = Vector(3, {(1) = .2848, (2) = .3911, (3) = .4178}), (2, 1) = Vector(3, {(1) = .5295, (2) = .4055, (3) = .3118}), (2, 2) = Vector(3, {(1) = .2305, (2) = .2106, (3) = .1126}), (2, 3) = Vector(3, {(1) = .5012, (2) = .3273, (3) = .1938}), (2, 4) = Vector(3, {(1) = .3319, (2) = .2482, (3) = 0.637e-1}), (2, 5) = Vector(3, {(1) = .3984, (2) = .5008, (3) = .4446}), (2, 6) = Vector(3, {(1) = .4957, (2) = .4427, (3) = .4357}), (3, 1) = Vector(3, {(1) = .2018, (2) = .1692, (3) = 0.575e-1}), (3, 2) = Vector(3, {(1) = .3253, (2) = .5032, (3) = .2318}), (3, 3) = Vector(3, {(1) = .5686, (2) = .3303, (3) = .1257}), (3, 4) = Vector(3, {(1) = .4697, (2) = .4734, (3) = .5981}), (3, 5) = Vector(3, {(1) = .4159, (2) = .2688, (3) = .2009}), (3, 6) = Vector(3, {(1) = .2131, (2) = .3023, (3) = .1930}), (4, 1) = Vector(3, {(1) = .3469, (2) = .3608, (3) = .9131}), (4, 2) = Vector(3, {(1) = .3440, (2) = .3584, (3) = .5894}), (4, 3) = Vector(3, {(1) = .3432, (2) = .3581, (3) = .3632}), (4, 4) = Vector(3, {(1) = .3446, (2) = .3579, (3) = .1915}), (4, 5) = Vector(3, {(1) = .3401, (2) = .3548, (3) = 0.883e-1}), (4, 6) = Vector(3, {(1) = .3406, (2) = .3537, (3) = 0.311e-1})})

NULL

NULL

M := RowDimension(CCxyY_D50) = 4NULL

N := ColumnDimension(CCxyY_D50) = 6

NULL

Convert xyY to XYZ
   

NULL

CCXYZ_D50 := C_xyY_to_XYZ(CCxyY_D50):

CCXYZ_D50 = Matrix(4, 6, {(1, 1) = Vector(3, {(1) = .1152, (2) = .1008, (3) = 0.509e-1}), (1, 2) = Vector(3, {(1) = .3918, (2) = .3495, (3) = .1922}), (1, 3) = Vector(3, {(1) = .1680, (2) = .1836, (3) = .2571}), (1, 4) = Vector(3, {(1) = .1091, (2) = .1325, (3) = 0.529e-1}), (1, 5) = Vector(3, {(1) = .2419, (2) = .2304, (3) = .3344}), (1, 6) = Vector(3, {(1) = .3042, (2) = .4178, (3) = .3462}), (2, 1) = Vector(3, {(1) = .4071, (2) = .3118, (3) = 0.500e-1}), (2, 2) = Vector(3, {(1) = .1232, (2) = .1126, (3) = .2988}), (2, 3) = Vector(3, {(1) = .2968, (2) = .1938, (3) = .1015}), (2, 4) = Vector(3, {(1) = 0.852e-1, (2) = 0.637e-1, (3) = .1078}), (2, 5) = Vector(3, {(1) = .3537, (2) = .4446, (3) = 0.895e-1}), (2, 6) = Vector(3, {(1) = .4879, (2) = .4357, (3) = 0.606e-1}), (3, 1) = Vector(3, {(1) = 0.686e-1, (2) = 0.575e-1, (3) = .2138}), (3, 2) = Vector(3, {(1) = .1498, (2) = .2318, (3) = 0.790e-1}), (3, 3) = Vector(3, {(1) = .2164, (2) = .1257, (3) = 0.385e-1}), (3, 4) = Vector(3, {(1) = .5934, (2) = .5981, (3) = 0.719e-1}), (3, 5) = Vector(3, {(1) = .3108, (2) = .2009, (3) = .2356}), (3, 6) = Vector(3, {(1) = .1360, (2) = .1930, (3) = .3094}), (4, 1) = Vector(3, {(1) = .8779, (2) = .9131, (3) = .7397}), (4, 2) = Vector(3, {(1) = .5657, (2) = .5894, (3) = .4894}), (4, 3) = Vector(3, {(1) = .3481, (2) = .3632, (3) = .3029}), (4, 4) = Vector(3, {(1) = .1844, (2) = .1915, (3) = .1592}), (4, 5) = Vector(3, {(1) = 0.846e-1, (2) = 0.883e-1, (3) = 0.759e-1}), (4, 6) = Vector(3, {(1) = 0.299e-1, (2) = 0.311e-1, (3) = 0.269e-1})})NULL

XYZ D50 to XYZ D65

   

NULL

CCXYZ_D65 := XYZ_D50_to_D65(CCXYZ_D50):

CCXYZ_D65 = Matrix(4, 6, {(1, 1) = Vector(3, {(1) = .1110, (2) = 0.996e-1, (3) = 0.670e-1}), (1, 2) = Vector(3, {(1) = .3785, (2) = .3459, (3) = .2533}), (1, 3) = Vector(3, {(1) = .1726, (2) = .1861, (3) = .3403}), (1, 4) = Vector(3, {(1) = .1045, (2) = .1318, (3) = 0.690e-1}), (1, 5) = Vector(3, {(1) = .2470, (2) = .2329, (3) = .4430}), (1, 6) = Vector(3, {(1) = .3030, (2) = .4206, (3) = .4556}), (2, 1) = Vector(3, {(1) = .3850, (2) = .3044, (3) = 0.651e-1}), (2, 2) = Vector(3, {(1) = .1340, (2) = .1165, (3) = .3966}), (2, 3) = Vector(3, {(1) = .2855, (2) = .1895, (3) = .1347}), (2, 4) = Vector(3, {(1) = 0.867e-1, (2) = 0.642e-1, (3) = .1431}), (2, 5) = Vector(3, {(1) = .3334, (2) = .4409, (3) = .1142}), (2, 6) = Vector(3, {(1) = .4600, (2) = .4275, (3) = 0.777e-1}), (3, 1) = Vector(3, {(1) = 0.777e-1, (2) = 0.606e-1, (3) = .2839}), (3, 2) = Vector(3, {(1) = .1428, (2) = .2315, (3) = .1022}), (3, 3) = Vector(3, {(1) = .2063, (2) = .1216, (3) = 0.512e-1}), (3, 4) = Vector(3, {(1) = .5578, (2) = .5888, (3) = 0.906e-1}), (3, 5) = Vector(3, {(1) = .3073, (2) = .1990, (3) = .3131}), (3, 6) = Vector(3, {(1) = .1451, (2) = .1976, (3) = .4092}), (4, 1) = Vector(3, {(1) = .8646, (2) = .9129, (3) = .9759}), (4, 2) = Vector(3, {(1) = .5579, (2) = .5895, (3) = .6458}), (4, 3) = Vector(3, {(1) = .3434, (2) = .3633, (3) = .3997}), (4, 4) = Vector(3, {(1) = .1818, (2) = .1915, (3) = .2100}), (4, 5) = Vector(3, {(1) = 0.836e-1, (2) = 0.884e-1, (3) = .1002}), (4, 6) = Vector(3, {(1) = 0.296e-1, (2) = 0.311e-1, (3) = 0.355e-1})})

NULL

NULLConvert XYZ to Lab (D50 or D65 White Point)
 

NULLNULL

Reference White Point for D50

NULL

X_D50wht := XYZ_D50wht[1] = .96422NULL

Y_D50wht := XYZ_D50wht[2] = 1NULL

Z_D50wht := XYZ_D50wht[3] = .82521

NULL

Lab Conversion Constants;

`&epsilon;` := 216/24389:

kappa := 24389/27:

NULL

fx_D50 := proc (XYZ) options operator, arrow; piecewise(`&epsilon;` < XYZ[1]/X_D50wht, (XYZ[1]/X_D50wht)^(1/3), XYZ[1]/X_D50wht <= `&epsilon;`, (1/116)*kappa*XYZ[1]/X_D50wht+4/29) end proc
                

NULLNULL

NULL

 
fy_D50 := proc (XYZ) options operator, arrow; piecewise(`&epsilon;` < XYZ[2]/Y_D50wht, (XYZ[2]/Y_D50wht)^(1/3), XYZ[2]/Y_D50wht <= `&epsilon;`, (1/116)*kappa*XYZ[2]/Y_D50wht+4/29) end proc
NULLNULL

NULLNULL

fz_D50 := proc (XYZ) options operator, arrow; piecewise(`&epsilon;` < XYZ[3]/Z_D50wht, (XYZ[3]/Z_D50wht)^(1/3), XYZ[3]/Z_D50wht <= `&epsilon;`, (1/116)*kappa*XYZ[3]/Z_D50wht+4/29) end proc
NULL

XYZ_to_Lab_D50 := proc (XYZ) options operator, arrow; `<,>`(116*fy_D50(XYZ)-16, 500*fx_D50(XYZ)-500*fy_D50(XYZ), 200*fy_D50(XYZ)-200*fz_D50(XYZ)) end proc:

NULL

Reference White Point for D65

NULL

X_D65wht := XYZ_D65wht[1] = .95047NULL

Y_D65wht := XYZ_D65wht[2] = 1NULL

Z_D65wht := XYZ_D65wht[3] = 1.08883 

NULL

NULL

NULL

NULL

NULL

NULL

NULL

fx_D65 := proc (XYZ) options operator, arrow; piecewise(`&epsilon;` < XYZ[1]/X_D65wht, (XYZ[1]/X_D65wht)^(1/3), XYZ[1]/X_D65wht <= `&epsilon;`, (1/116)*kappa*XYZ[1]/X_D65wht+4/29) end proc
                

NULLNULL

NULL

 
fy_D65 := proc (XYZ) options operator, arrow; piecewise(`&epsilon;` < XYZ[2]/Y_D65wht, (XYZ[2]/Y_D65wht)^(1/3), XYZ[2]/Y_D65wht <= `&epsilon;`, (1/116)*kappa*XYZ[2]/Y_D65wht+4/29) end proc
NULLNULL

NULLNULL

fz_D65 := proc (XYZ) options operator, arrow; piecewise(`&epsilon;` < XYZ[3]/Z_D65wht, (XYZ[3]/Z_D65wht)^(1/3), XYZ[3]/Z_D65wht <= `&epsilon;`, (1/116)*kappa*XYZ[3]/Z_D65wht+4/29) end proc
NULL

XYZ_to_Lab_D65 := proc (XYZ) options operator, arrow; `<,>`(116*fy_D65(XYZ)-16, 500*fx_D65(XYZ)-500*fy_D65(XYZ), 200*fy_D65(XYZ)-200*fz_D65(XYZ)) end proc:

NULL

NULL
 

NULL

C_XYZ_to_Lab := proc (XYZ, L) options operator, arrow; piecewise(evalb(L = D50), Array([`$`('[`$`('XYZ_to_Lab_D50(XYZ[m, n])', n = 1 .. N)]', m = 1 .. M)]), evalb(L = D65), Array([`$`('[`$`('XYZ_to_Lab_D65(XYZ[m, n])', n = 1 .. N)]', m = 1 .. M)])) end proc
 NULL

NULL

NULLNULL

NULL

CCLab_D50 := C_XYZ_to_Lab(CCXYZ_D50, D50): NULL

CCLab_D50 = Matrix(4, 6, {(1, 1) = Vector(3, {(1) = 37.99, (2) = 13.55, (3) = 14.06}), (1, 2) = Vector(3, {(1) = 65.71, (2) = 18.14, (3) = 17.82}), (1, 3) = Vector(3, {(1) = 49.93, (2) = -4.91, (3) = -21.92}), (1, 4) = Vector(3, {(1) = 43.14, (2) = -13.10, (3) = 21.89}), (1, 5) = Vector(3, {(1) = 55.11, (2) = 8.84, (3) = -25.39}), (1, 6) = Vector(3, {(1) = 70.72, (2) = -33.39, (3) = -.21}), (2, 1) = Vector(3, {(1) = 62.66, (2) = 36.06, (3) = 57.08}), (2, 2) = Vector(3, {(1) = 40.01, (2) = 10.42, (3) = -45.98}), (2, 3) = Vector(3, {(1) = 51.13, (2) = 48.24, (3) = 16.26}), (2, 4) = Vector(3, {(1) = 30.33, (2) = 23.00, (3) = -21.59}), (2, 5) = Vector(3, {(1) = 72.53, (2) = -23.70, (3) = 57.27}), (2, 6) = Vector(3, {(1) = 71.94, (2) = 19.37, (3) = 67.86}), (3, 1) = Vector(3, {(1) = 28.77, (2) = 14.17, (3) = -50.30}), (3, 2) = Vector(3, {(1) = 55.26, (2) = -38.32, (3) = 31.36}), (3, 3) = Vector(3, {(1) = 42.11, (2) = 53.38, (3) = 28.20}), (3, 4) = Vector(3, {(1) = 81.73, (2) = 4.03, (3) = 79.85}), (3, 5) = Vector(3, {(1) = 51.94, (2) = 50.00, (3) = -14.57}), (3, 6) = Vector(3, {(1) = 51.04, (2) = -28.65, (3) = -28.63}), (4, 1) = Vector(3, {(1) = 96.54, (2) = -.46, (3) = 1.19}), (4, 2) = Vector(3, {(1) = 81.26, (2) = -.64, (3) = -.35}), (4, 3) = Vector(3, {(1) = 66.76, (2) = -.72, (3) = -.51}), (4, 4) = Vector(3, {(1) = 50.86, (2) = -.14, (3) = -.28}), (4, 5) = Vector(3, {(1) = 35.65, (2) = -.44, (3) = -1.23}), (4, 6) = Vector(3, {(1) = 20.48, (2) = -0.7e-1, (3) = -.98})})NULL

NULL

Convert XYZ to aRGB (XYZ D50 or D65 to aRGB D65)
 

XYZ Scaling for aRGB Ymax,Ymin (Ref. Adobe RGB (1998) Color Image Encoding Section 4.3.2.2 and 4.3.8)

NULL

White Point (Luminance=160Cd/m^2) D65

Black Point (Luminance=0.5557Cd/m^2) D65

White Point (Luminance=160Cd/m^2) D50

Black Point (Luminance=0.5557Cd/m^2) D50

XW_D65 := 152.07*(1/160) = .9504375000NULL

YW_D65 := 160*(1/160) = 1``

ZW_D65 := 174.25*(1/160) = 1.089062500``

NULL

xXK_D65 := .5282*(1/160) = 0.3301250000e-2``

xYK_D65 := .5557*(1/160) = 0.3473125000e-2``

xZK_D65 := .6025*(1/160) = 0.3765625000e-2``

XK_D65 := 0:

YK_D65 := 0:

ZK_D65 := 0:

``

``

XW_D50 := .9462:NULL

YW_D50 := 1.0000:

ZW_D50 := .8249:

``

NULL

xXK_D50 := 0.33488e-2:

xYK_D50 := 0.34751e-2:

xZK_D50 := 0.28650e-2:

``

XK_D50 := 0:

YK_D50 := 0:

ZK_D50 := 0:

NULL
 

NULL

XYZD65_to_aXYZ := proc (XYZ) options operator, arrow; `<,>`((XYZ[1]-XK_D65)*XW_D65/((XW_D65-XK_D65)*YW_D65), (XYZ[2]-YK_D65)/(YW_D65-YK_D65), (XYZ[3]-ZK_D65)*ZW_D65/((ZW_D65-ZK_D65)*YW_D65)) end proc:

XYZD50_to_aXYZ := proc (XYZ) options operator, arrow; `<,>`((XYZ[1]-XK_D50)*XW_D50/((XW_D50-XK_D50)*YW_D50), (XYZ[2]-YK_D50)/(YW_D50-YK_D50), (XYZ[3]-ZK_D50)*ZW_D50/((ZW_D50-ZK_D50)*YW_D50)) end proc:

 

NULL

(ref. Adobe RGB(1998) section 4.3.6.1, Bradford Matrix includes D50 to D65 adaptation)

M_XYZtoaRGB_D50 := Matrix(3, 3, {(1, 1) = 1.96253, (1, 2) = -.61068, (1, 3) = -.34137, (2, 1) = -.97876, (2, 2) = 1.91615, (2, 3) = 0.3342e-1, (3, 1) = 0.2869e-1, (3, 2) = -.14067, (3, 3) = 1.34926})

  aXYZ_to_RGB_D50 := proc (aXYZ) options operator, arrow; `<,>`(Typesetting:-delayDotProduct(M_XYZtoaRGB_D50, aXYZ)) end proc: NULL

 

(ref. Adobe RBG(1998) section 4.3.4.1, Bradford Matrix assumes XYZ is D65)

M_XYZtoaRGB_D65 := Matrix(3, 3, {(1, 1) = 2.04159, (1, 2) = -.56501, (1, 3) = -.34473, (2, 1) = -.96924, (2, 2) = 1.87597, (2, 3) = 0.4156e-1, (3, 1) = 0.1344e-1, (3, 2) = -.11836, (3, 3) = 1.01517})

  NULL

aXYZ_to_RGB_D65 := proc (aXYZ) options operator, arrow; `<,>`(Typesetting:-delayDotProduct(M_XYZtoaRGB_D65, aXYZ)) end proc:

NULL

  aRGB Expansion for 8bits

 

`&gamma;a` := 2.19921875:

RGB_to_aRGB := proc (RGB) options operator, arrow; `<,>`(round(255*Norm(RGB[1])^(1/`&gamma;a`)), round(255*Norm(RGB[2])^(1/`&gamma;a`)), round(255*Norm(RGB[3])^(1/`&gamma;a`))) end proc:
NULL

 

Combine Steps

NULL

XYZ_to_aRGB := proc (XYZ, L) options operator, arrow; piecewise(evalb(L = D50), Array([`$`('[`$`('RGB_to_aRGB(aXYZ_to_RGB_D50(XYZD50_to_aXYZ(XYZ[m, n])))', n = 1 .. N)]', m = 1 .. M)]), evalb(L = D65), Array([`$`('[`$`('RGB_to_aRGB(aXYZ_to_RGB_D65(XYZD65_to_aXYZ(XYZ[m, n])))', n = 1 .. N)]', m = 1 .. M)])) end proc

NULLNULL

NULLNULL

Note: The aRGB values published for ColorChecker assume a black point of 0cd/m^2.

````

aRGB_D50in := XYZ_to_aRGB(CCXYZ_D50, D50):

aRGB_D50in = Matrix(4, 6, {(1, 1) = Vector(3, {(1) = 107, (2) = 82, (3) = 70}), (1, 2) = Vector(3, {(1) = 184, (2) = 146, (3) = 128}), (1, 3) = Vector(3, {(1) = 101, (2) = 122, (3) = 153}), (1, 4) = Vector(3, {(1) = 95, (2) = 107, (3) = 69}), (1, 5) = Vector(3, {(1) = 128, (2) = 127, (3) = 173}), (1, 6) = Vector(3, {(1) = 129, (2) = 188, (3) = 171}), (2, 1) = Vector(3, {(1) = 201, (2) = 123, (3) = 56}), (2, 2) = Vector(3, {(1) = 77, (2) = 92, (3) = 166}), (2, 3) = Vector(3, {(1) = 174, (2) = 83, (3) = 97}), (2, 4) = Vector(3, {(1) = 86, (2) = 61, (3) = 104}), (2, 5) = Vector(3, {(1) = 167, (2) = 188, (3) = 75}), (2, 6) = Vector(3, {(1) = 213, (2) = 160, (3) = 55}), (3, 1) = Vector(3, {(1) = 49, (2) = 65, (3) = 143}), (3, 2) = Vector(3, {(1) = 99, (2) = 148, (3) = 80}), (3, 3) = Vector(3, {(1) = 155, (2) = 52, (3) = 59}), (3, 4) = Vector(3, {(1) = 227, (2) = 197, (3) = 52}), (3, 5) = Vector(3, {(1) = 169, (2) = 85, (3) = 147}), (3, 6) = Vector(3, {(1) = 61, (2) = 135, (3) = 167}), (4, 1) = Vector(3, {(1) = 245, (2) = 245, (3) = 242}), (4, 2) = Vector(3, {(1) = 200, (2) = 201, (3) = 201}), (4, 3) = Vector(3, {(1) = 160, (2) = 161, (3) = 162}), (4, 4) = Vector(3, {(1) = 120, (2) = 120, (3) = 121}), (4, 5) = Vector(3, {(1) = 84, (2) = 85, (3) = 86}), (4, 6) = Vector(3, {(1) = 52, (2) = 53, (3) = 54})})NULL

  

NULL

aRGB_D65in := XYZ_to_aRGB(CCXYZ_D65, D65):

aRGB_D65in = Matrix(4, 6, {(1, 1) = Vector(3, {(1) = 107, (2) = 82, (3) = 70}), (1, 2) = Vector(3, {(1) = 184, (2) = 146, (3) = 128}), (1, 3) = Vector(3, {(1) = 101, (2) = 122, (3) = 153}), (1, 4) = Vector(3, {(1) = 95, (2) = 107, (3) = 69}), (1, 5) = Vector(3, {(1) = 128, (2) = 127, (3) = 173}), (1, 6) = Vector(3, {(1) = 129, (2) = 188, (3) = 171}), (2, 1) = Vector(3, {(1) = 201, (2) = 123, (3) = 56}), (2, 2) = Vector(3, {(1) = 77, (2) = 92, (3) = 166}), (2, 3) = Vector(3, {(1) = 174, (2) = 83, (3) = 97}), (2, 4) = Vector(3, {(1) = 86, (2) = 61, (3) = 104}), (2, 5) = Vector(3, {(1) = 167, (2) = 188, (3) = 75}), (2, 6) = Vector(3, {(1) = 213, (2) = 160, (3) = 55}), (3, 1) = Vector(3, {(1) = 49, (2) = 65, (3) = 143}), (3, 2) = Vector(3, {(1) = 99, (2) = 148, (3) = 80}), (3, 3) = Vector(3, {(1) = 155, (2) = 52, (3) = 59}), (3, 4) = Vector(3, {(1) = 227, (2) = 197, (3) = 52}), (3, 5) = Vector(3, {(1) = 169, (2) = 85, (3) = 147}), (3, 6) = Vector(3, {(1) = 61, (2) = 135, (3) = 167}), (4, 1) = Vector(3, {(1) = 245, (2) = 245, (3) = 242}), (4, 2) = Vector(3, {(1) = 200, (2) = 201, (3) = 201}), (4, 3) = Vector(3, {(1) = 160, (2) = 161, (3) = 162}), (4, 4) = Vector(3, {(1) = 120, (2) = 120, (3) = 121}), (4, 5) = Vector(3, {(1) = 84, (2) = 85, (3) = 86}), (4, 6) = Vector(3, {(1) = 52, (2) = 53, (3) = 54})})

Convert XYZ to ProPhoto RGB (D50)
   

NULL

CC_PPhoto := XYZ_to_PPhoto(CCXYZ_D50):

NULL

CC_PPhoto = Matrix(4, 6, {(1, 1) = Vector(3, {(1) = 81, (2) = 67, (3) = 54}), (1, 2) = Vector(3, {(1) = 159, (2) = 135, (3) = 113}), (1, 3) = Vector(3, {(1) = 94, (2) = 102, (3) = 133}), (1, 4) = Vector(3, {(1) = 75, (2) = 86, (3) = 55}), (1, 5) = Vector(3, {(1) = 118, (2) = 111, (3) = 154}), (1, 6) = Vector(3, {(1) = 127, (2) = 168, (3) = 157}), (2, 1) = Vector(3, {(1) = 167, (2) = 118, (3) = 54}), (2, 2) = Vector(3, {(1) = 79, (2) = 74, (3) = 145}), (2, 3) = Vector(3, {(1) = 141, (2) = 83, (3) = 80}), (2, 4) = Vector(3, {(1) = 68, (2) = 49, (3) = 82}), (2, 5) = Vector(3, {(1) = 144, (2) = 170, (3) = 74}), (2, 6) = Vector(3, {(1) = 181, (2) = 152, (3) = 60}), (3, 1) = Vector(3, {(1) = 57, (2) = 50, (3) = 120}), (3, 2) = Vector(3, {(1) = 85, (2) = 123, (3) = 69}), (3, 3) = Vector(3, {(1) = 120, (2) = 59, (3) = 46}), (3, 4) = Vector(3, {(1) = 199, (2) = 188, (3) = 66}), (3, 5) = Vector(3, {(1) = 143, (2) = 85, (3) = 127}), (3, 6) = Vector(3, {(1) = 78, (2) = 111, (3) = 148}), (4, 1) = Vector(3, {(1) = 242, (2) = 243, (3) = 240}), (4, 2) = Vector(3, {(1) = 189, (2) = 190, (3) = 191}), (4, 3) = Vector(3, {(1) = 145, (2) = 146, (3) = 146}), (4, 4) = Vector(3, {(1) = 102, (2) = 102, (3) = 102}), (4, 5) = Vector(3, {(1) = 66, (2) = 66, (3) = 68}), (4, 6) = Vector(3, {(1) = 37, (2) = 37, (3) = 38})})NULL

Convert XYZ to sRGB (XYZ D50 or D65 to sRGB D65)
   

NULL

Note: The sRGB values published for ColorChecker assume a black point of 0cd/m^2.

``

CCsRGB_D65in := XYZ_to_sRGB(CCXYZ_D65, D65):

NULL

CCsRGB_D65in = Matrix(4, 6, {(1, 1) = Vector(3, {(1) = 115, (2) = 81, (3) = 67}), (1, 2) = Vector(3, {(1) = 199, (2) = 147, (3) = 129}), (1, 3) = Vector(3, {(1) = 91, (2) = 122, (3) = 156}), (1, 4) = Vector(3, {(1) = 90, (2) = 108, (3) = 64}), (1, 5) = Vector(3, {(1) = 130, (2) = 128, (3) = 176}), (1, 6) = Vector(3, {(1) = 92, (2) = 190, (3) = 172}), (2, 1) = Vector(3, {(1) = 224, (2) = 124, (3) = 47}), (2, 2) = Vector(3, {(1) = 68, (2) = 91, (3) = 170}), (2, 3) = Vector(3, {(1) = 198, (2) = 82, (3) = 97}), (2, 4) = Vector(3, {(1) = 94, (2) = 58, (3) = 106}), (2, 5) = Vector(3, {(1) = 159, (2) = 189, (3) = 63}), (2, 6) = Vector(3, {(1) = 230, (2) = 162, (3) = 39}), (3, 1) = Vector(3, {(1) = 35, (2) = 63, (3) = 147}), (3, 2) = Vector(3, {(1) = 67, (2) = 149, (3) = 74}), (3, 3) = Vector(3, {(1) = 180, (2) = 49, (3) = 57}), (3, 4) = Vector(3, {(1) = 238, (2) = 198, (3) = 20}), (3, 5) = Vector(3, {(1) = 193, (2) = 84, (3) = 151}), (3, 6) = Vector(3, {(1) = 54, (2) = 136, (3) = 170}), (4, 1) = Vector(3, {(1) = 245, (2) = 245, (3) = 243}), (4, 2) = Vector(3, {(1) = 200, (2) = 202, (3) = 202}), (4, 3) = Vector(3, {(1) = 161, (2) = 163, (3) = 163}), (4, 4) = Vector(3, {(1) = 121, (2) = 121, (3) = 122}), (4, 5) = Vector(3, {(1) = 82, (2) = 84, (3) = 86}), (4, 6) = Vector(3, {(1) = 49, (2) = 49, (3) = 51})})NULL

``

CCsRGB_D50in := XYZ_to_sRGB(CCXYZ_D50, D50):

``

CCsRGB_D50in = Matrix(4, 6, {(1, 1) = Vector(3, {(1) = 115, (2) = 81, (3) = 67}), (1, 2) = Vector(3, {(1) = 199, (2) = 148, (3) = 129}), (1, 3) = Vector(3, {(1) = 91, (2) = 123, (3) = 156}), (1, 4) = Vector(3, {(1) = 90, (2) = 108, (3) = 64}), (1, 5) = Vector(3, {(1) = 130, (2) = 129, (3) = 176}), (1, 6) = Vector(3, {(1) = 92, (2) = 190, (3) = 172}), (2, 1) = Vector(3, {(1) = 224, (2) = 125, (3) = 47}), (2, 2) = Vector(3, {(1) = 68, (2) = 92, (3) = 170}), (2, 3) = Vector(3, {(1) = 198, (2) = 83, (3) = 97}), (2, 4) = Vector(3, {(1) = 94, (2) = 59, (3) = 106}), (2, 5) = Vector(3, {(1) = 159, (2) = 190, (3) = 63}), (2, 6) = Vector(3, {(1) = 230, (2) = 163, (3) = 39}), (3, 1) = Vector(3, {(1) = 35, (2) = 64, (3) = 147}), (3, 2) = Vector(3, {(1) = 67, (2) = 149, (3) = 74}), (3, 3) = Vector(3, {(1) = 180, (2) = 51, (3) = 57}), (3, 4) = Vector(3, {(1) = 238, (2) = 199, (3) = 20}), (3, 5) = Vector(3, {(1) = 193, (2) = 85, (3) = 151}), (3, 6) = Vector(3, {(1) = 54, (2) = 137, (3) = 170}), (4, 1) = Vector(3, {(1) = 245, (2) = 246, (3) = 243}), (4, 2) = Vector(3, {(1) = 200, (2) = 203, (3) = 202}), (4, 3) = Vector(3, {(1) = 161, (2) = 164, (3) = 163}), (4, 4) = Vector(3, {(1) = 121, (2) = 122, (3) = 122}), (4, 5) = Vector(3, {(1) = 82, (2) = 84, (3) = 86}), (4, 6) = Vector(3, {(1) = 49, (2) = 50, (3) = 51})})``

NULL

``

NULL

NULL

``

 

 

 

 

 

 

 

 

``

 

Corrections to the original version of theis document;
• Make the scaling for a nonzero black point the same for all RGB color spaces.
• Clip negative RGB values to zero.
• Remove the redundant Array container from matrix multiplications.
Use map in place of the $ to apply a function to each element of an Array.

Pixel_Conversion_B.mw

 

Interactive Embedded Components in Physics

by: Lenin Araujo Castillo 1790
August 06 2014
2  0

The Interactive Embedded Components in Physics are of great importance today and will be even more in the future. Hereand leave a small tutorial of Embedded Components in Physics applied to physics. I hope that somehow you motives to continue the development of science.

 

  Interactive_Embedded_Components_in_Physics.mw      (in spanish)                 

 Ponencia_CRF.pdf

Atte.

Lenin Araujo Castillo

Physics Pure

Computer Science

 

Stem and Leaf Plot

by: Daniel Skoog 1771 Maple
July 04 2014
2  0

Following @acer 's challenge to create some more examples for the Rosetta Code project, I've put together some code that constructs Stem-And-Leaf plots here.

I've also attached a new mathapp ( StemAndLeafDisplay.mw ) that contains the code as well as an interactive example for Stem-Plots. This MathApp is also viewable online on the MapleCloud here.

This older post may also be of interest for anyone looking to make a stem and leaf plot with decimals.

DEs and Mathematical Functions Updates 2014 (Jan - Jun...

by: ecterrab 14540
physics differential-equations
June 23 2014
3  13

Hi,
The FunctionAdvisor project is currently developing at full speed. During the last two months, a significant amount of new conversion routines and mathematical information for Jacobi elliptic and Jacobi Theta functions, on identities, periodicity, transformations, etc. got added to the conversion network for mathematical functions and to the FunctionAdvisor. The previous months was the turn of the set of complex components, added to the network. Developments regarding the simplification and integration of special functions (e.g SphericalY for computing spherical harmonics or Dirac), as well as fixes to the numerical evaluation of JacobiAM, `assuming` and to differential equation subroutines are also part of the update.

These developments are available to everybody as usual in the Maplesoft R&D Differential Equations and Mathematical Functions webpage. Below there is a list of the latest developments as seen in the worksheet that comes in the zip with the DEsAndMathematicalFunctions update.

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

Power Group Enumeration (update).

by: Marko Riedel 390
June 19 2014
4  0

Greetings to all.

As some of you may remember I have posted several announcements concerning Power Group Enumeration and the Polya Enumeration Theorem this past year, e.g. at this MaplePrimes link: Power Group Enumeration.

I have continued to work in this field and for those of you who have followed the earlier threads I would like to present some links to my more recent work using the Burnside lemma. Of course all of these are programmed in Maple and include the Maple code and it is with the demonstration of Maple's group theory capabilities in mind that I present them to you (math.stackexchange links).

The third and fourth to last link in particular include advanced Maple code.

The second entry is new as of October 30 2015.

With my best wishes for happy group theory computing with Maple,

Regards,

Marko Riedel

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