<p> Contour utilities </p>

9ebbb865-0d06-4679-be46-29ce106f5c0b 3 15 12 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 :

M Unwrap V M 1 col : i 2 M cols range V V M i col stack : for V 31 line :

t0 1 time :

M Low High Scale High Low - (M M Min - M Max M Min - 106 -^+ / (* Low + :

Generate the symmetric square [U,V] 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 :

The contour extractor , pair wise in X direction M ε level CONTR count 1 : i 1 M rows range j 1 M cols range M i j el level - abs ε ≤ u count el i : v count el j : 21 line count count 1 + : if for for u v augment 31 line :

data Strip2 nest striped in 'R' u data 1 col : v data 2 col : k 0 : i 1 data rows range u i el v i el * 0 ≠ R k k 1 + : el u i el v i el 12 mat : 11 line Undefined if 11 line for 21 line Un-nest R v R 1 el : j 2 R rows range v v R j el stack : for v 81 line :

M n Mround i 1 M rows range j 1 M cols range m i j el M i j el n round : for for m 21 line :

Explore an hypersurface

c 1 :

λ 1.414214 :

f 2 c * y * 3 x 2^* y 2^* y 4^- c 2^x 2^y 2^+ (* - 4 c 4^* + (* x cos * y sinh * 2 c * y * x 4^3 y 2^* x 2^* - c 2^x 2^y 2^+ (* - 4 c 4^* - (* x sin * y cosh * x y * x 2^y 2^+ (y 2^x 2^- 2 c 2^* + (* (* 2 c * sin * 31 mat :

x y f f 1 el f 2 el + f 3 el + :

M Unwrap V M 1 col : i 2 M cols range V V M i col stack : for V 31 line :

L λ - : H λ : N 49 : 13 mat U L H N xd : V U : 12 mat M x y f U V Sym : 31 sys

V r c Pack i 1 r range v i el V i el : for j 1 c range i 1 r range i r j * + V rows ≤ m i j el V i r j * + el : m i j el 0 : if for for v m augment 31 line eval :

1 time t0 - min 0.4

m N 1 + : U M Unwrap : θ π - 4 / : N U θ * m m Pack : 41 sys

You can have larger size 'N' at the expense of timing.

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 M 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 N

1 time t0 - min 10

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 M 0 M * M 1 3 mat