c9d80641-b1dc-4177-b5ee-c4a5408d4807 41 15 12 decimal

&[DATE] &[TIME] - &[FILENAME]

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 13 mat for data r3 r4 r5 augment 21 line :

Sequential triplet : U, V, Z x y X x y Y x y Z u v Sr count 1 : i 1 u rows range j 1 v rows range U count el u i el v j el X eval : V count el u i el v j el Y eval : Z count el u i el v j el Z eval : count count 1 + : 41 line for for U V Z augment 31 line :

θ rot θ cos θ sin - θ sin θ cos 22 mat :

Mesh U, V  within the bounds L, H L H N xd U 0 : dx H L - N / : i 1 N 1 + (range U i el L dx i * dx - (+ : for U 41 line :

θ π - 2 / :

θ 1.5708 -

L 1 - : H 1 : N 50 : 13 mat u L H N xd : v u : 12 mat 21 line

x y X x : x y Y y : x y Z 1 x 2^y 2^+ (- 0.1 / exp * : 31 sys

G x y X x y Y x y Z u v Sr : P1 G 1 col G 3 col augment : P2 G 2 col G 3 col augment : 31 sys

P2 rows 2601

idx 1 P2 rows range : traces idx 1 - P2 augment : 21 sys

select a trace n 20 :

Export the selected 'n'  trace sub P2 n 1 - n + (N * + (n 1 N + (* (range 12 range el :

Selected trace

sub

Sub sub . 14 black plot :

Global multitrace over 'N' in the range [L H

P2 Sub 3 1 sys

Stem plot, trace plot

P1 θ rot * P2 θ - rot * 2 1 sys

Global multitrace over 'N' in the range [L H

1 - 0.125 - 1 - 0.125 2 2 mat 1 0.125 - 1 0.125 2 2 mat P1 Sub 5 1 sys

submatrix a range of traces
for neat unicolor publishing P P2 12751975 range 12 range el :

0.125 - 1 0.125 1 2 2 mat P Sub 3 1 sys P sub sub 0.125 - 1 0.125 1 2 2 mat 0 1.125 0 0.125 - 2 2 mat 5 1 sys

Image below illustrates the profitable 'Dot' spline traces. Ordianary lines renders "Horrible Henry" plot NFG4publishing.

image

Otherwise multitrace . . .

1. Plot the matrix, from which you can gray scale image.

2. Plot selected trace from col of matrix.

3. Unwrap the matrix in vector.

4. Develop xd(L,H,N) in vector of same size of Unwrap.

5. At this point, you are back to previous multitrace.

]]>

Generate the symmetric square matrix x y f U V Sym r 1 : i 1 U rows range c 1 : j 1 V rows range M r c el U i el V j el f eval : c c 1 + : 21 line for r r 1 + : 31 line for M 31 line :

Mesh U, V  within the bounds L, H L H N xd U 0 : dx H L - N / : i 1 N 1 + (range U i el L dx i * dx - (+ : for U 41 line :

u v f 1 u 2^v 2^+ (- 0.1 / exp * :

U 0 : V 0 : 12 mat L 1 - : H 1 : N 50 : 13 mat U L H N xd : V U : 12 mat 31 sys

data x y f U V Sym :

data max 1

data 00000000000000000000000000000000000000000 0.00001 00000 0.00001 0.00001 00000 0.00001 0.00002 0000 0.00001 0.00002 0.00003 000 0.00001 0.00001 0.00003 0.00005 00 0.00001 0.00001 0.00002 0.00005 0.00008 00 0.00001 0.00002 0.00004 0.00007 0.00013 0 0.00001 0.00001 0.00003 0.00006 0.00011 0.00021 0 0.00001 0.00002 0.00004 0.00009 0.00017 0.00031 0.00001 0.00001 0.00003 0.00006 0.00012 0.00024 0.00045 157 mat

select col j 26 :

col vector explorer

U data j col augment U data j 5 + col augment 2 1 sys

Otherwise multitrace . . .

1. Plot the matrix, from which you can gray scale image.

2. On the final, you are back to previous multitrace.

3. Does not allow stem style plot.

4. Symmetric Gaussian is not best example.

]]>

Unwrap a matrix in vector M Unwrap V M 1 col : i 2 M cols range V V M i col stack : for V 31 line :

Deploy adjacent 'U' vector Un ct 1 : i 1 N 1 + (range S ct el U : ct ct 1 + : 21 line for v S 1 el : j 2 S rows range v v S j el stack : for v 51 line :

New data Unwrap :

select col j 32 :

New rows 2601

Un rows 2601

U data j col augment Un New augment 2 1 sys

M Low High Scale High Low - (M M Min - M Max M Min - 1012 -^+ / (* Low + :

M data 0255 Scale :

Additive RGB

iVBORw0KGgoAAAANSUhEUgAAADMAAAAzCAIAAAC1w6d9AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAQ1SURBVFhH7da9L6VREAZwu9ZihayPYBMJkU1EIiKEikKCEo2GRqGlEKVSdKJRKJXiP1BJJGq1RKFX6BQK2d++c+6bG2yye91rb3Gf4uSY88zM887MOW5dDTXUUMOH4tOfkRgfjJQ8w+e3kM4yJJ9KI2UrCKov4EsB6e/6+iAkdkX1pQyZpshNR0NDw9e3wJ6rrKy+iJtrCkGNjY1NTU3Nzc3fMrS0tMSGhd1pSAyXXF+KWBZExJClEqFJelJaW1vb2tq+Z2hvb48NC7tTnNAX9SuzuIgVsqJUoUl6Ujo7O7u7u3t6en4UYM/C7hQn9OXFK5u4iJLLUgDNUg+F6erq6u3t7evr6+/vHxwc/FmAPQu7UxxMfF58yynutSxl6OjoUBi5iRgaGhoZGRkbGxsfH5+YmLDas7A7xcHE5/VaXMpRArIP+yRQzFbI0iYtUxW5R0dHp6ampqenZ2dn5+bm5ufnrfYs7E5xMPF5hbh85iJ4yvSv4BkFi9nSFF8vzcDAwPDwsArNzMwsLCwsLS2trKysrq6ura1Z7VnYneJg4vPiK0LM3LvKln3S74JFHw2yidEaNZBscnJSbRYXF0nZ2NjY3Nzc3t7e2dmx2rOwO8XBxOfFVwRx8p5GipTv78GnuGB6YZzNjQaphJTLy8vr6+tbW1u7u7v7+/sHBweHh4dWexZ2pziY+Lz4iiDOe8sWysxEFMwT4K4ZatOjTeohsQrt7e0dHR2dnJycnp6enZ1Z7VnYneJg4vPiK4I4UTaRS1HGAaKVXnPzYYSjYEbbDGmWqkh/fHxM0Pn5+eXl5dXVldWehd0pDiY+ryibOKKJWWJDsV+00uNpVjwH7p0BN0lapjZEXFxcXF9f39zc3N7eWu1Z2J3iYOLz4iuCOO9qaLEyVz1mXzu8VebG7TPm5knjVIiUu7u7+/v7h4cHqz0Lu1McTHxefEWIexDPR+nK8mdMLNfe++4h9WJpkBky7KZK+9SJoMfHx6enJ6s9C7tTHEx8XnxFECdXVsqoFSuL8Q9lbpnn1LvlgXATtcxsaaJqkfX8/Gy1Z2F3ioOJz4tvKItLUAZl1Viz6p2zUFZ1dxN4Vt17Bhx8kFGorv8BEMqKG1pF/zchGhpli3tgVv7zbw3gVlw281EVv88g+6Sq/E0LnKNs0dMQ5+u1xtwYark9B94qD6kKWe1Z2J3iYOKHrLyP7ypYIPuwl+I0xcQYZ3dNblUhwvsesGdhd4qDif9aFqQcJSOi5OJi5gyyMngCtMnjqTBaFrBnYXeKgxmzVWZZgYgV4mLmFCD0qYf0CgOkxIaFPTRh5rNVZlmBiBjiIIonq9dces0CUmLDwh6aolRQEVmBiAu5PpUIia/BHnWCXBOkWJVAypDpyyUCHYH0dyYIEruimoqRsmUIBS+QzjIknw9GSv4WEqOGGmqo4SNQV/cLh0pCxUkoOSoAAAAASUVORK5CYII= M

R M 0 M * 0 M * 13 mat : G 0 M * M 0 M * 13 mat : B 0 M * 0 M * M 13 mat : 31 line

Yw M M 0 M * 13 mat : Cy 0 M * M M 13 mat : Ma M 0 M * M 13 mat : 31 line

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 R 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 G 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 B 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 Yw 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 Cy 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 Ma