Spot

5c4e1484-8a11-4623-9fdb-38b6aa14fb57 11 4 5 decimal

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

Vectorize for a scalar vector

A 30 : a A 2 / : 12 mat R 1.5 : (outer ring ≡ r 1 : (inner ring ≡ d 0.125 : (holes dimension ≡ size 100 : (canvas size ≡ 51 line

Part 1: is "scalar", it imitates Mathcad. It needs the 'stack' function to collect the two circles. Being "scalar", it won't plot. The plot needs arguments be vectors instead of scalars. The "vector plots" are given in the collapsed area.

outer holes position i 0 a range : x R 2 π * i * 15 / sin * vectorize : y R 2 π * i * 15 / cos * vectorize : y 1 el 0 : 41 line

inner holes position j 16 A range : vx r 2 π * j * 15 / sin * vectorize : vy r 2 π * j * 15 / cos * vectorize : 31 line

Coordinates 
space wave function k 0 size range : xx 2 R * - 4 R * k * size / + vectorize : yy 2 R * - 4 R * k * size / + vectorize : 31 line

x x vx stack : y y vy stack : 21 line

scalar x y augment :

scalar 00 0.61 1.37 1.115 1.004 1.427 0.464 1.492 0.157 - 1.299 0.75 - 62 mat

Analyse matrix via truth table

W 1.22 7.36 7.96 7.94 1.26 2.36 4.74 3.07 5.83 4.11 3.78 8.14 3.64 9.93 8.95 9.73 5.13 3.71 5.78 3.16 54 mat :

x c x 2 ≥ (x 4 ≤ (* :

Truth(M,a,b) ... truth table within [a, b] M a b Truth count 0 : i 1 M rows range j 1 M cols range M i j el a ≥ (M i j el b ≤ (* Z i j el M i j el : 11 line count count 1 + : if for for Z 31 line :

W 24 Truth 00000 2.36 0 3.07 00 3.78 0 3.64 0000 3.71 0 3.16 54 mat

W c W * vectorize 00000 2.36 0 3.07 00 3.78 0 3.64 0000 3.71 0 3.16 54 mat

You need that one for scaling grayscale image M lo hi Scale hi lo - (M M Min - M Max M Min - 106 -^+ / (* lo + :

g 32 :

m 1 g range n 1 g range I g g el 0 : for for

1. 'g' sets the size of the kernel. 2. in 'I' & 'A' you can shift the kernels up/down left <=> right in the dual for loops. 3. Experiment: [p, m, n]

m 1 g range n 1 g range A g g el 0 : for for

p 4 :

m 16 : n 1 : 12 mat

Vertical i m - m range j n - n range I i g 2 / + j g 2 / + el 1 - : for for

Horizontal i m - m range j n - n range I j g 2 / + i g 2 / + el 1 - : for for

Square centered size 2 p i p - p range j p - p range A j g 2 / + (i g 2 / + (el 255 : for for

iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAIAAAD8GO2jAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABOSURBVEhL7ZExCgAgDMT6/0/rEseDCgoiydbhkqHVZyy4j4PeQAa9gQx6Axn0BjLodwOMrvFBoA+L557cB72BDHoDGfQGMugNZNBvBaom6BAi+pTpxLEAAAAASUVORK5CYII= I iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAIAAAD8GO2jAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABGSURBVEhL7cyhEQAwDMPATJL9t2yJoItECvxYp9ndqaqyzhuFxCyhkJglFBKzhEJillBIzBIKiVlCITFLKCRmCUVV1edmLl0H8mh0eyOQAAAAAElFTkSuQmCC A Spot

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 :

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 :

Discrete compactor L 1 - : H 1 : N 16 : U L H N xd : V U : 51 sys

u v f λ u 2^v 2^+ (- s / exp * : M u v f U V Sym : 21 sys

s 0.05 : (spread ≡ λ 255 : (contrast ≡ 21 line

iVBORw0KGgoAAAANSUhEUgAAABEAAAARCAIAAAC0D9CtAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABrSURBVDhP5ZCxDQAhCEW9hM6ChlUcws2YieW8l6itxPbuFeSDgF/LL3g2K08RkVqrqhLRq3qAJjNrrfXeiehkDDPsptXdI4KIpnIyyRmWuIGBMQYRTSWZub4Hrt8zoYndF/82wcxk5V+mlBc8Ox2qgzAO9AAAAABJRU5ErkJggg== M

x y N Ω x mwfft2 y mwfft2 * vectorize mwifft2 : Convol Ω mwabs eval : Convol Convol 0255 Scale : Fo Convol mwround : 41 line Fo Fo sys2mat : 21 line :

<= Vectorize over an algorithm

M s Center Cyclic permutation: center/recenter a Fourier 2D ..... check 'Floor/Ceil' ..... rowmax M rows : colmax M cols : 12 mat rowctr rowmax 2 / Ceil : colctr colmax 2 / Ceil : 12 mat p 1 rowmax range q 1 colmax range J p rowctr + M rows mod s + q colctr + M cols mod s + el M p q el : for for J 61 line :

t0 1 time :

Center(M) takes only a "vector matrix". The Matlab convolution is a scalar matrix. It is converted to a "vector matrix" via M:=sys2mat(M) ... Alternately: Export image 'N' to file and read back. An image is a resident object in the Smath home system ... in other words: an exported image is the isolated "vector matrix" Center(M) needs.

N I A N (:

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 N

Export

Ce N C:\SmathFile\convol_KM.png READBMP : Ce N 2 - Center : 21 line :

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 N - 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 Ce 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 Ce -

1 time t0 - 11.5 s *

ConvCenter(x,y,s)

This program convolves an image with a kernel of same size , then recenters the "convolved". x y s ConvCenter Ω x mwfft2 y mwfft2 * vectorize mwifft2 : Convol Ω mwabs eval : Convol Convol 0255 Scale : M Convol mwround : 41 line M M sys2mat : Cyclic permutation: center/recenter a Fourier 2D rowmax M rows : colmax M cols : 12 mat rowctr rowmax 2 / Ceil : colctr colmax 2 / Ceil : 12 mat p 1 rowmax range q 1 colmax range J p rowctr + M rows mod s + q colctr + M cols mod s + el M p q el : for for 41 line J 41 line :

t0 1 time :

Uk I A 2 - ConvCenter :

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 Uk 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 Uk -

1 time t0 - 5 s *

Pad[M,nc,nr], Scale[M,lo,hi], zoomfract[M,a,b]

You need that one for scaling grayscale image M lo hi Scale hi lo - (M M Min - M Max M Min - 1012 -^+ / vectorize (* lo + :

M nc nr Pad A M mwfft2 : insert 'nc' cols in 'A' i 1 A rows range j 1 nc range vert i j el 0 : for for a A 1 A rows 1 A cols 2 / Floor submatrix : b A 1 A rows A cols 2 / Floor 1 + A cols submatrix : S a vert b augment transpose : insert 'nr' rows in the transpose 'S' i 1 S rows range j 1 nr range horiz i j el 0 : for for a S 1 S rows 1 S cols 2 / Floor submatrix : b S 1 S rows S cols 2 / Floor 1 + S cols submatrix : S a horiz b augment transpose : 111 line :

Implicit 'zoom fractional' Up/Down M a b zoomfract a b ≡ (a b | (1 ≠ (& (implicit conditions ≡ i 1 M rows a * (range j 1 M cols b * (range N i j el M i a / Ceil j b / Ceil el : a 1 ≥ N i j el M i a / Floor j b / Floor el : cases for for N 31 line :

Vectorize a matrix over a function

Constrain the plot x u 0 x ≤ (x 255 ≤ (& :

255 x 2^x 2^160 + / * 255 x 160 - (2^50 / - exp * 255 0.375 π * x * 160 / cos * 255 0.5 π * x * 160 / sin * 255 x 255 / 9 * 1 + log10 * 255 x 255 / sqrt * 255 x 255 / (2^* 25510 x 255 - 200 / (^* 255 x - 255 + 255 / (* 91 mat

pic Uk :

plug your mapping f(x) x Φ 255 x 2^x 2^160 + / * 0 x ≤ 0 cases :

Zoom as desired ... slow pic pic 22 zoomfract :

x Φ x u *

fmap pic Φ vectorize :

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 fmap -

NTSC => 0.299*Red+0.587*Green+0.114*Blue = 1

image pic - :

RGB 0.229 image (* 0.587 image (* 0.114 image (* 13 mat :

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