<p> Utilities .... </p>

72a0dcd9-bda2-47a8-b4c4-b910e88c0cd8 14 4 5 decimal

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

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 :

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

M scale M M Min - M Max m Min - / 255 * vectorize :

A B r c replace U A : i 1 B rows range j 1 B cols range U r i + c j + el B i j el : for for U 31 line :

M γ scale_γ M M Min - M Max m Min - / γ * vectorize :

M γ0 γ1 Gamma M M Min - M Max m Min - / γ0 * vectorize (γ1 - :

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 :

M Center Cyclic permutation: center/recenter a spectral 2D rowmax M rows : colmax M cols : 12 mat rowctr rowmax 2 / Floor : colctr colmax 2 / Floor : 12 mat p 1 rowmax range q 1 colmax range J p rowctr + M rows mod 1 + q colctr + M cols mod 1 + el M p q el : for for J 51 line :

Discrete compactor L 1 - : H 1 : N 9 : U L H N xd : V U : 51 sys

x y f 255 x 2^y 2^+ (- 0.875 / exp * : Pic x y f U V Sym : 21 sys

Kernel convolution is infinite wrt infinite "Kernel design". To make things simpler, we convolve zoomed 10 'Pic' over 'Ks' which is adjustable ... observe the quality of !N!

Ks 0.1 0.1 0.1 0.1 k 0.1 0.1 0.1 0.1 33 mat :

ortho Ks 22 zoomfract 0255 Scale :

k 0.25 :

iVBORw0KGgoAAAANSUhEUgAAAAYAAAAGCAIAAABvrngfAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAAWSURBVBhXYyAF/AMDKAcCsAjhBQwMAKc4C+n7n2k6AAAAAElFTkSuQmCC ortho iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAIAAAACUFjqAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABwSURBVChThdA5FUMhEAVQbIwDLFBR0qbFBQYiBQ8xgYzoSPvvGQHhNbyNYSkRUWttrY0xXgmEZIqKpfc+51xrvRMIyRQVRYK79/4kEJIpKkaps8453wRCMkXFYbo2CX4JhGSKbvFl+OVql4f9+5aIB7LIiI16ziX7AAAAAElFTkSuQmCC Pic

Z Pic 1010 zoomfract : m Z rows : n Z cols : 12 mat r m 2 / Floor 2 - : c n 2 / Floor 2 - : 12 mat A m n matrix : K A ortho r c replace : 51 line

iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABASURBVHhe7dCxCQBACATB679oeRBj4wdnsk03AAAAAAAAAAAAAAAAAABwSbUJdmYBAAAAAAAAAAAAAAAAAD9KHm9WC+lvoAQrAAAAAElFTkSuQmCC K 0 255 Scale iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAIgSURBVHhe7dyhUS1BAAXRTeNlQAoo5LdYsiABQiEHkvhhEAcW06ar6DFr79HUzvR1+4B3PY6ejp6P/h293sAjAscHrh7IDhtLyA4bS8gOG0vIDhtLyA4bS8gOG0vIDhtLyA4bS8gOG0vIDhc/FV6O3o7ejz5u4BGB4wNXD2SHjSVkh40lZIeNJWSHjSVkh40lZIeNJWSHjSVkh40lZIeNJWSHi7eAwAmBu4fPo68beETg+MDVA9lhYwnZYWMJ2WFjCdlhYwnZYWMJ2WFjCdlhYwnZYWMJ2WFjCdnh4lP+wBtE4Hbh/9H3DTwicHzg6oHssLGE7LCxhOywsYTssLGE7LCxhOywsYTssLGE7LCxhOywsYTscPFXPIF3hMDbR6As/NzAIwLHB64eyA4bS8gOG0vIDhtLyA4bS8gOG0vIDhtLyA4bS8gOG0vIDhtLyA4bS8gOG0vIDhtLyA4bS8gOG0vIDhtLyA4bS8gOG0vIDhtLyA4bS8gO+4WFkB02lpAdNpaQHTaWkB02lpAdNpaQHTaWkB02lpAdNpaQHTaWkB32TwNCdthYQnbYWEJ22FhCdthYQnbYWEJ22FhCdthYQnbYWEJ22FhCdtgX9wjZYWMJ2WFjCdlhYwnZYWMJ2WFjCdlhYwnZYWMJ2WFjCdlhYwnZYV+eL2SHjSVkh40lZIeNJWSHjSVkh40lZIeNJWSHjSVkh40lZIeNJWT/6fH4BYMiWcz+XrayAAAAAElFTkSuQmCC Z

x y convol x mwfft2 y mwfft2 * vectorize mwifft2 :

KZ K Z convol : Convol KZ mwabs eval : Convol Convol 0255 Scale : Convol Convol mwround : 41 line

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 Convol

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Convol 11 mat

Inconvenience/alternative:

Algorithm Center(M) does NOT support:

1. calculated matrix 'Convol',

2. 'Convol' spooled to file,

3. assigned N_i,j from dual "forloop"

Center(M) takes ONLY an existing matrix

Expand Convol => paste in collapsed

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Convol 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mat :

N Convol Center :

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