Contour utilities

0d08eb1f-d8d1-4abf-beaf-5482fd0e99ab 21 4 5 decimal

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

Contour utilities

Mesh U, V  within the bounds L, H L H N xd U 0 : dx H L - N / : i 1 N 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 :

M Low High Scale High Low - (M M Min - M Max M Min - / (* 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 :

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 :

The contour extractor , pairwise 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 :

Explore an hypersurface

Source: Mathsoft Collaboratory Unexplained/unknown application

The governing hypersurface parameter 1 ≤ 'c' c 1 :

t0 1 time :

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 :

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

λ 2 sqrt :

L λ - : H λ : N 100 : 13 mat U L H N xd : V U : 12 mat M x y f U V Sym : 31 sys

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bXLLly9ftmzZ0qVLlyxZsnjxYrVbsGCBwKmazFokk40ZRyx66XmfDJceW4oV92AuZZniKOVD1AiUG7Vhkn0Xv9PVM23cuHH9+vVuzZo1q1evXrVq1cqVK1esWCF2AqdqOmtKJlOmG1NHzHrpea/DlWDpd0Pdg3EDqpQdqGgU5ygHynUkCxTnKDeyvyeoYdKzKWHasGGDMK1bt27t2rX/BRYj2YoJR/0uAAAAAElFTkSuQmCC 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40a3Z8+evXv37tu3b//+/QcOHFC7w4cPC5yqyaxFMtmYccSil573yXDpsaVYcQ/mUpYpjlI+RI1AuVEbJtl38TtdPdOmTZu2bNnitm/fvmPHjp07d+7atWv37t1iJ3CqprOmZDJlujF1xKyXnvc6XAmWfjfUPRg3oErZgYpGcY5yoFxHskBxjnIj+3uCGiY9mxKmzZs3C9PWrVu3bdv2X2itYL7Vxk33AAAAAElFTkSuQmCC M - Lobe data from Contour Matrix Extractor

Lobe 49 - 846 - 745 - 740 - 639 - 638 - 637 - 636 - 635 - 634 - 633 - 632 - 631 - 630 - 629 - 628 - 627 - 626 - 625 - 620 - 719 - 718 - 717 - 714 - 813 - 812 - 811 - 32 - 11 - 25 - 10 - 34 - 10 - 22 - 10 - 99 - 20 - 9 - 98 - 19 - 7 - 18 - 7 - 106 - 17 - 6 - 105 - 16 - 4 - 15 - 4 - 113 - 14 - 3 - 112 - 41 - 2 - 121 - 13 - 1 - 12042 - 012 - 013112 - 113143211 - 214311 - 342410 - 415510 - 51661779 - 71889 - 819920108 - 1021118 - 1123113512261233137 - 147 - 157 - 186 - 196 - 206 - 216 - 265 - 275 - 285 - 295 - 305 - 315 - 325 - 335 - 345 - 355 - 365 - 375 - 385 - 395 - 405 - 415 - 466 - 476 - 507 - 1002 mat :

u v char size clr plotG n u length : plot u 1 el v 1 el char size clr augment : i 2 n range plot plot u i el v i el char size clr augment stack : for plot 41 line :

p 49 - 32 - 015 23.5 07 2.5 5.75 16334326 mat transpose :

vx Lobe 1 col : vy Lobe 2 col : 21 line

Bezier-Bernstein

n p rows 1 - : 5

k 0 :

ρ n k - (n ≤ n k - (0 if :

ρ 5

Bézier-Bernstein procedure j n t Q n! j! (n j - (! (* / t j^* 1 t - (n j -^* :

t QX j n t Q ctrlX j 1 + el * (j 0 ρ sum eval :

t QY j n t Q ctrlY j 1 + el * (j 0 ρ sum eval :

Bern t 1015 -^1 0.01 range : i 1 t rows range LX i el t i el QX eval : LY i el t i el QY eval : 21 line for LX LY augment 31 line :

ctrlX p 1 col : ctrlY p 2 col : 12 mat Polygon p 1 col p 2 col augment : Cntrl ctrlX ctrlY . 14 red plotG : data vx vy o 5 black plotG : 41 sys

Polygon Bern Cntrl data Bern - 71 sys

Lobe rows Bern rows 21 mat 10010121 mat

Polygon Bern Cntrl data Bern - 7 1 sys

OBSERVE ... The matrix extractor alternates. One possible way to smooth/populate is via a control point system. Bézier/Bernstein is offered.

x y axes x y / - :

IsoScale Bern 2 sqrt - 2 sqrt Scale :

Bern Bern - 2 1 sys IsoScale IsoScale - 2 1 sys Bern Bern - axes 3 1 sys sol(f,L,H,N,level)

f L H N level sol Roots range typical ± 1.5 X 0 : dx H L - N / : 12 mat i 1 N range X i el L dx - (dx i * + : for j 1 X rows range Sol j el y X j el f level - y roots eval : j j 1 + : 21 line for X Sol augment 51 line :

collapse in utilities at top of work sheet.

Suite explore this hypersurface

The governing hypersurface parameter 1 ≤ 'c' c 1 :

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 :

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

Iso_125 f 1.5 - 1.5 99 0.125 sol : Iso_250 f 1.5 - 1.5 99 0.25 sol : Iso_1000 f 1.5 - 1.5 99 0.75 sol : 31 sys

Dual solver AlgoStyle . . .

1. They don't solve by themselves, you have to help from trial/error.

2. Depending upon the mathematical difficulty they reply red error.

3. This example may fly in the blue otherwise than demanded .

]]> Iso_125 Iso_250 Iso_1000 3 1 sys

reverse the solver x y f f 1 el f 2 el + f 3 el + :

Iso_1 f 1.5 - 1.5 99 0.25 sol :

Iso_250 Iso_1 2 1 sys

Iso_2 f 2 sqrt - 2 sqrt 1990 sol :

Iso_3 f 1 - 11990 sol :

Iso_3 Iso_2 2 1 sys

sol(f,L,H,N,level) solves either for 'x or y' as set in f(y,x) or f(x,y)

1 time t0 - min 5.4

M

M 75.991 - 69.944 - 64.339 - 59.146 - 54.338 - 49.888 - 45.773 - 41.968 - 38.452 - 35.205 - 32.209 - 29.445 - 26.898 - 24.551 - 22.39 - 20.402 - 18.574 - 16.894 - 15.352 - 13.937 - 12.64 - 11.452 - 10.364 - 9.369 - 8.459 - 7.629 - 6.871 - 6.18 - 5.551 - 4.978 - 4.458 - 3.985 - 3.556 - 3.166 - 2.814 - 2.494 - 2.204 - 1.941 - 1.703 - 1.487 - 1.291 - 1.111 - 0.947 - 0.796 - 0.656 - 0.526 - 0.402 - 0.284 - 0.169 - 0.056 - 0.057 0.172 0.29 0.415 0.547 0.689 0.843 1.012 1.196 1.4 1.626 1.877 2.154 2.463 2.805 3.185 3.607 4.074 4.591 5.162 5.793 6.489 7.255 8.097 9.023 10.038 11.15 12.368 13.698 15.151 16.735 18.46 20.338 22.379 24.595 26.999 29.605 32.427 35.48 38.78 42.344 46.191 50.338 54.807 59.618 64.795 70.36 76.34 82.759 89.647 73.21 - 67.398 - 62.012 - 57.025 - 52.408 - 48.136 - 44.186 - 40.534 - 37.161 - 34.047 - 31.172 - 28.521 - 26.077 - 23.825 - 21.752 - 19.843 - 18.088 - 16.475 - 14.993 - 13.633 - 12.385 - 11.24 - 10.192 - 9.232 - 8.354 - 7.551 - 6.817 - 6.148 - 5.537 - 4.98 - 4.472 - 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62.439 - 57.47 - 52.871 - 48.618 - 44.686 - 41.052 - 37.695 - 34.595 - 31.735 - 29.095 - 26.661 - 24.417 - 22.35 - 20.446 - 18.694 - 17.081 - 15.598 - 14.234 - 12.982 - 11.831 - 10.775 - 9.805 - 8.916 - 8.101 - 7.354 - 6.67 - 6.044 - 5.471 - 4.946 - 4.467 - 4.028 - 3.627 - 3.26 - 2.925 - 2.618 - 2.337 - 2.079 - 1.843 - 1.625 - 1.424 - 1.238 - 1.065 - 0.903 - 0.75 - 0.605 - 0.466 - 0.331 - 0.198 - 0.066 - 0.067 0.202 0.342 0.488 0.642 0.806 0.982 1.173 1.38 1.607 1.854 2.126 2.425 2.754 3.117 3.516 3.955 4.439 4.971 5.555 6.197 6.902 7.673 8.519 9.443 10.453 11.556 12.757 14.066 15.49 17.038 18.718 20.541 22.516 24.654 26.967 29.466 32.164 35.074 38.211 41.59 45.225 49.134 53.334 57.843 62.68 67.866 73.421 79.369 85.732 65.191 - 60.032 - 55.258 - 50.844 - 46.763 - 42.992 - 39.509 - 36.292 - 33.323 - 30.584 - 28.057 - 25.727 - 23.58 - 21.602 - 19.781 - 18.104 - 16.561 - 15.141 - 13.836 - 12.636 - 11.533 - 10.521 - 9.591 - 8.737 - 7.954 - 7.236 - 6.577 - 5.973 - 5.419 - 4.912 - 4.447 - 4.021 - 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12.222 - 13.279 - 14.436 - 15.704 - 17.093 - 18.616 - 20.285 - 22.114 - 24.118 - 26.314 - 28.719 - 31.354 - 34.238 - 37.396 - 40.851 - 60.869 57.177 53.659 50.312 47.132 44.112 41.248 38.535 35.967 33.54 31.247 29.084 27.045 25.126 23.32 21.622 20.029 18.534 17.134 15.823 14.596 13.45 12.379 11.381 10.45 9.583 8.777 8.026 7.329 6.681 6.079 5.521 5.003 4.523 4.077 3.664 3.28 2.924 2.594 2.286 1.999 1.732 1.482 1.247 1.027 0.819 0.622 0.435 0.256 0.084 0.083 - 0.244 - 0.403 - 0.559 - 0.714 - 0.869 - 1.024 - 1.182 - 1.343 - 1.508 - 1.678 - 1.854 - 2.038 - 2.23 - 2.433 - 2.647 - 2.874 - 3.115 - 3.372 - 3.648 - 3.943 - 4.261 - 4.603 - 4.973 - 5.372 - 5.805 - 6.275 - 6.784 - 7.339 - 7.942 - 8.6 - 9.316 - 10.098 - 10.952 - 11.885 - 12.904 - 14.019 - 15.237 - 16.57 - 18.028 - 19.623 - 21.368 - 23.277 - 25.366 - 27.65 - 30.148 - 32.879 - 35.865 - 39.128 - 42.692 - 63.567 59.605 55.842 52.27 48.884 45.678 42.644 39.777 37.069 34.515 32.108 29.841 27.709 25.706 23.824 22.06 20.406 18.858 17.409 16.055 14.791 13.612 12.513 11.489 10.537 9.651 8.828 8.064 7.355 6.698 6.088 5.524 5.001 4.516 4.068 3.653 3.268 2.912 2.581 2.274 1.988 1.722 1.473 1.24 1.021 0.815 0.619 0.433 0.255 0.084 0.082 - 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14.282 - 15.52 - 16.869 - 18.341 - 19.946 - 21.698 - 23.608 - 25.691 - 27.964 - 30.442 - 33.144 - 36.09 - 39.3 - 42.798 - 46.609 - 68.791 64.298 60.05 56.037 52.248 48.676 45.31 42.14 39.159 36.358 33.727 31.259 28.946 26.779 24.752 22.857 21.087 19.435 17.895 16.46 15.125 13.883 12.729 11.658 10.664 9.744 8.891 8.102 7.372 6.697 6.074 5.499 4.968 4.478 4.026 3.608 3.223 2.867 2.538 2.234 1.951 1.688 1.444 1.215 1.001 0.799 0.607 0.425 0.251 0.082 0.081 - 0.241 - 0.399 - 0.556 - 0.713 - 0.872 - 1.034 - 1.2 - 1.371 - 1.548 - 1.733 - 1.928 - 2.132 - 2.349 - 2.579 - 2.825 - 3.087 - 3.368 - 3.669 - 3.994 - 4.343 - 4.72 - 5.128 - 5.568 - 6.046 - 6.563 - 7.124 - 7.733 - 8.395 - 9.114 - 9.896 - 10.747 - 11.674 - 12.682 - 13.78 - 14.975 - 16.278 - 17.697 - 19.243 - 20.927 - 22.762 - 24.762 - 26.94 - 29.312 - 31.896 - 34.71 - 37.773 - 41.107 - 44.735 - 48.683 - 71.295 66.542 62.057 57.829 53.844 50.094 46.566 43.25 40.137 37.216 34.477 31.912 29.511 27.267 25.17 23.212 21.386 19.685 18.102 16.629 15.26 13.988 12.809 11.716 10.704 9.767 8.901 8.1 7.362 6.68 6.051 5.471 4.937 4.445 3.992 3.575 3.19 2.835 2.508 2.205 1.925 1.665 1.424 1.198 0.986 0.787 0.599 0.419 0.247 0.081 0.08 - 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16.363 - 17.803 - 19.369 - 21.072 - 22.925 - 24.939 - 27.129 - 29.511 - 32.099 - 34.911 - 37.967 - 41.287 - 44.891 - 48.805 - 53.053 - 76.028 70.774 65.832 61.187 56.825 52.732 48.894 45.297 41.93 38.78 35.836 33.086 30.519 28.126 25.897 23.822 21.892 20.099 18.434 16.89 15.459 14.134 12.908 11.776 10.729 9.764 8.875 8.055 7.301 6.607 5.97 5.384 4.846 4.353 3.9 3.484 3.102 2.752 2.429 2.133 1.859 1.606 1.371 1.153 0.949 0.757 0.576 0.404 0.238 0.078 0.078 - 0.231 - 0.384 - 0.537 - 0.691 - 0.849 - 1.012 - 1.181 - 1.357 - 1.542 - 1.738 - 1.945 - 2.167 - 2.403 - 2.657 - 2.93 - 3.223 - 3.54 - 3.883 - 4.253 - 4.654 - 5.089 - 5.561 - 6.073 - 6.628 - 7.232 - 7.887 - 8.6 - 9.374 - 10.216 - 11.131 - 12.126 - 13.208 - 14.385 - 15.664 - 17.055 - 18.567 - 20.21 - 21.997 - 23.938 - 26.048 - 28.34 - 30.83 - 33.534 - 36.469 - 39.656 - 43.114 - 46.865 - 50.934 - 55.347 - 78.235 72.74 67.58 62.736 58.194 53.938 49.952 46.222 42.735 39.477 36.436 33.599 30.955 28.493 26.202 24.072 22.094 20.259 18.557 16.98 15.521 14.172 12.925 11.775 10.714 9.737 8.837 8.01 7.25 6.552 5.911 5.324 4.785 4.292 3.84 3.426 3.047 2.7 2.381 2.088 1.818 1.57 1.339 1.126 0.926 0.739 0.562 0.394 0.233 0.077 0.076 - 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18.428 - 20.091 - 21.897 - 23.859 - 25.988 - 28.299 - 30.807 - 33.528 - 36.478 - 39.676 - 43.143 - 46.899 - 50.967 - 55.372 - 60.139 - 82.272 76.322 70.749 65.532 60.651 56.088 51.826 47.847 44.136 40.678 37.457 34.459 31.672 29.083 26.68 24.451 22.386 20.474 18.706 17.072 15.563 14.172 12.89 11.71 10.625 9.628 8.713 7.873 7.105 6.401 5.758 5.17 4.633 4.142 3.695 3.287 2.915 2.576 2.265 1.982 1.722 1.484 1.264 1.061 0.872 0.695 0.528 0.37 0.219 0.072 0.072 - 0.214 - 0.356 - 0.5 - 0.647 - 0.799 - 0.958 - 1.124 - 1.3 - 1.488 - 1.688 - 1.904 - 2.136 - 2.387 - 2.659 - 2.954 - 3.275 - 3.623 - 4.003 - 4.415 - 4.865 - 5.355 - 5.889 - 6.47 - 7.103 - 7.793 - 8.544 - 9.363 - 10.253 - 11.223 - 12.277 - 13.425 - 14.673 - 16.03 - 17.505 - 19.108 - 20.849 - 22.74 - 24.793 - 27.021 - 29.438 - 32.06 - 34.902 - 37.983 - 41.322 - 44.937 - 48.852 - 53.09 - 57.675 - 62.634 - 84.08 77.918 72.152 66.76 61.722 57.018 52.629 48.536 44.722 41.172 37.869 34.799 31.948 29.302 26.848 24.576 22.472 20.527 18.73 17.071 15.542 14.133 12.836 11.644 10.549 9.545 8.624 7.781 7.01 6.306 5.662 5.076 4.541 4.053 3.61 3.206 2.838 2.504 2.199 1.921 1.667 1.434 1.22 1.023 0.84 0.669 0.509 0.357 0.211 0.069 0.069 - 0.206 - 0.344 - 0.484 - 0.627 - 0.776 - 0.932 - 1.097 - 1.272 - 1.459 - 1.66 - 1.877 - 2.111 - 2.366 - 2.643 - 2.944 - 3.272 - 3.63 - 4.021 - 4.447 - 4.912 - 5.419 - 5.973 - 6.577 - 7.236 - 7.954 - 8.737 - 9.591 - 10.521 - 11.533 - 12.636 - 13.836 - 15.141 - 16.561 - 18.104 - 19.781 - 21.602 - 23.58 - 25.727 - 28.057 - 30.584 - 33.323 - 36.292 - 39.509 - 42.992 - 46.763 - 50.844 - 55.258 - 60.032 - 65.191 - 85.732 79.369 73.421 67.866 62.68 57.843 53.334 49.134 45.225 41.59 38.211 35.074 32.164 29.466 26.967 24.654 22.516 20.541 18.718 17.038 15.49 14.066 12.757 11.556 10.453 9.443 8.519 7.673 6.902 6.197 5.555 4.971 4.439 3.955 3.516 3.117 2.754 2.425 2.126 1.854 1.607 1.38 1.173 0.982 0.806 0.642 0.488 0.342 0.202 0.067 0.066 - 0.198 - 0.331 - 0.466 - 0.605 - 0.75 - 0.903 - 1.065 - 1.238 - 1.424 - 1.625 - 1.843 - 2.079 - 2.337 - 2.618 - 2.925 - 3.26 - 3.627 - 4.028 - 4.467 - 4.946 - 5.471 - 6.044 - 6.67 - 7.354 - 8.101 - 8.916 - 9.805 - 10.775 - 11.831 - 12.982 - 14.234 - 15.598 - 17.081 - 18.694 - 20.446 - 22.35 - 24.417 - 26.661 - 29.095 - 31.735 - 34.595 - 37.695 - 41.052 - 44.686 - 48.618 - 52.871 - 57.47 - 62.439 - 67.808 - 87.218 80.666 74.548 68.839 63.516 58.555 53.936 49.637 45.64 41.927 38.479 35.281 32.317 29.572 27.032 24.684 22.516 20.515 18.67 16.971 15.408 13.972 12.653 11.444 10.336 9.322 8.396 7.55 6.778 6.075 5.436 4.855 4.327 3.848 3.413 3.02 2.664 2.341 2.048 1.783 1.542 1.323 1.122 0.939 0.769 0.612 0.465 0.326 0.192 0.063 0.063 - 0.189 - 0.316 - 0.446 - 0.581 - 0.722 - 0.871 - 1.03 - 1.2 - 1.385 - 1.585 - 1.803 - 2.04 - 2.3 - 2.585 - 2.897 - 3.238 - 3.613 - 4.024 - 4.475 - 4.969 - 5.51 - 6.102 - 6.75 - 7.459 - 8.234 - 9.081 - 10.006 - 11.014 - 12.115 - 13.314 - 14.62 - 16.043 - 17.59 - 19.274 - 21.103 - 23.091 - 25.25 - 27.593 - 30.134 - 32.889 - 35.875 - 39.11 - 42.612 - 46.401 - 50.5 - 54.932 - 59.722 - 64.896 - 70.482 - 88.526 81.799 75.524 69.673 64.223 59.149 54.429 50.04 45.964 42.18 38.67 35.418 32.406 29.619 27.044 24.665 22.47 20.447 18.584 16.87 15.295 13.849 12.523 11.309 10.198 9.182 8.255 7.41 6.64 5.94 5.305 4.728 4.205 3.731 3.303 2.916 2.566 2.25 1.965 1.707 1.473 1.261 1.069 0.892 0.73 0.581 0.441 0.308 0.182 0.06 0.06 - 0.179 - 0.301 - 0.425 - 0.554 - 0.69 - 0.835 - 0.99 - 1.158 - 1.34 - 1.539 - 1.756 - 1.994 - 2.256 - 2.543 - 2.859 - 3.207 - 3.59 - 4.01 - 4.472 - 4.98 - 5.537 - 6.148 - 6.817 - 7.551 - 8.354 - 9.232 - 10.192 - 11.24 - 12.385 - 13.633 - 14.993 - 16.475 - 18.088 - 19.843 - 21.752 - 23.825 - 26.077 - 28.521 - 31.172 - 34.047 - 37.161 - 40.534 - 44.186 - 48.136 - 52.408 - 57.025 - 62.012 - 67.398 - 73.21 - 89.647 82.759 76.34 70.36 64.795 59.618 54.807 50.338 46.191 42.344 38.78 35.48 32.427 29.605 26.999 24.595 22.379 20.338 18.46 16.735 15.151 13.698 12.368 11.15 10.038 9.023 8.097 7.255 6.489 5.793 5.162 4.591 4.074 3.607 3.185 2.805 2.463 2.154 1.877 1.626 1.4 1.196 1.012 0.843 0.689 0.547 0.415 0.29 0.172 0.057 0.056 - 0.169 - 0.284 - 0.402 - 0.526 - 0.656 - 0.796 - 0.947 - 1.111 - 1.291 - 1.487 - 1.703 - 1.941 - 2.204 - 2.494 - 2.814 - 3.166 - 3.556 - 3.985 - 4.458 - 4.978 - 5.551 - 6.18 - 6.871 - 7.629 - 8.459 - 9.369 - 10.364 - 11.452 - 12.64 - 13.937 - 15.352 - 16.894 - 18.574 - 20.402 - 22.39 - 24.551 - 26.898 - 29.445 - 32.209 - 35.205 - 38.452 - 41.968 - 45.773 - 49.888 - 54.338 - 59.146 - 64.339 - 69.944 - 75.991 - 100100 mat :

M lo hi Scale hi lo - (M M Min - M Max M Min - / (* lo + :

N M 0255 Scale :

set for  Max(α*R) > 256 α 2 :

centered 129 lo 128 : hi 130 : α 2 : 13 mat x c lo x ≤ (x hi ≤ (* : 21 sys

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 M 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 N

R N c N * vectorize : G N c N * vectorize : B N c N * vectorize : 13 mat

plot1 α R * α G * α B * 13 mat :

α R * Max 11 mat 26011 mat

centered 100 lo 99 : hi 101 : α 2.6 : 13 mat x c lo x ≤ (x hi ≤ (* : 21 sys

Otherwise IsoContour

R N c N * vectorize : G N c N * vectorize : B N c N * vectorize : 13 mat

plot2 α R * α G * α B * 13 mat :

α R * Max 262

centered 115 lo 114 : hi 116 : α 2.25 : 13 mat x c lo x ≤ (x hi ≤ (* : 21 sys

R N c N * vectorize : G N c N * vectorize : B N c N * vectorize : 13 mat

plot3 α R * α G * α B * 13 mat :

α R * Max 11 mat 26111 mat

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 plot1 - iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAYAAABw4pVUAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAJ7SURBVHhe7dzbbqswEIVhhxze/3lzbFcEW7QlYOyxZ6T9f1Iv6CVLa6akxIeU0uv7x9Tz+UzDMIxX2KPJXXu9zDP+bxBIMAQSjHkg2h8EUs58qT8ej3Q8Hscr7GXeENpRxzQQxlU900AUxvl8Hq9QwiwQhaGGoI7ZUmeZ2zBpCO2wY9IQ2mGnuiFqBu2wYxIIf1nZqRpZjCp7xQ1hkbdRHIjawaiyVzSyGFXt7G6IxpQCQRu7AtHeUBiXy2X8DaztCoS90V72DmFv9JEViPYGr/X0kXWXWeL9bDbkfr+n0+k0XqG11UAYVf2t3m1GVX8fG0I7fHy847TDx2JDaIefxbtOO/xkPRiinz8NoR2+/gSi/QE/jKxgfjSEdvj7EQj7wx8jK5jF5xD4+RcI+yMGAgmGHRIMOyQYAgnmHQjPH3G8A2Ghx8FSD4YdEszAuIrjdrulgYUeh96dZocEww4JhkCC0P4QAglieg+OHRIMDQmGQIIhkAD0pagJgQQwf7GdpR4MDQmGQJxND4QTAnH2+zAGdkgwNMTR73ElBOJo6XucjKxgaIiTpXElBOLk01FXBOLg/TLDh3MACMTB2kFwBNLZWjuEQDrbOiaRQDrS/z3W2iEE0olGVc7JfATSydaomhBIB3pdd2tUTQikMe2N3HYIgTSkvbEnDCGQRq7X6zuMw0Gf3+YjkEYURu7emCOQBvbujTkCMVayN+YIxJjG1N69MUcghmpG1YRAjGx9ipuLQIwoDAIJwqodQiAGahf5HIFUsmyHEEgly3YIgVRQOyzDEAKpoDAsx5UQSAXrdgiBFNLH6wQSSItxJQRSqEU7hEAKEUgwbQJJ6Qs/I+3aAOloNAAAAABJRU5ErkJggs7kCdI0H49UyJmhNEOQAAAAAElFTkSuQmCCAElFTkSuQmCCXfcHHrw3qt57VJkAAAAASUVORK5CYIIWxH6xi2HVghAjTdUgRIlpmj8Op0stpqUgSgAAAABJRU5ErkJgggZCgjGKEDt9Be+4C0FGnlESwkGeNCQkGK5C7IAopBmlZEEaVyFKBwnJ4ypER6YRksc9IRwCzVFVfwCGq/nWlK97CwAAAABJRU5ErkJgglfxsmGwiD80NKQX9Tl1JUP/Y4iuuWbJALYLA7fJ5+fnZpSEUECDr21jbBZYX4YM3CYjSDiFRlohfaCRDFIQJJxUa1k2S9KS/hdJVQhEQAgayEgYqVd7rZDT01MzQnyICWGWhJG6ENxxQYr0WU55JXUhwGYIp63aiAhBdiCkD9jKIyJCMG1ZKcSPiBDADAlDTIg9E9C1w4h8IyYEK45WCnEjJgRgPR5B3IgKQXZQiB/xDMnisMY8QSGRISoEMhBcRXQjKgSlEwjJ4sDGvCAqBEAGhbjJREhWhzbmAXEhWR1pmhcoJDIyEULciAshfjIRksUJmnlBXEhWR5rmBXEh6IpHkOpQSGRkIoQ7dt2IC8FmHuJGVAgyQ/pcwLwhKgQyKMSPqBDpMwHziKgQPGSAQvyIC5E8xjR/KPUPS6g5Y5h9mIsAAAAASUVORK5CYIJdPmcKIZygefz4cXjx4kVxhkyHCvQFyM4O26okgl0+1d846AehqxCeU/v06dOOdqXDAzYddTI4bEBt3bq1+IzTnVohyOBBwjxUmHbFMFc6aFdKh21VVoZE2PLWNTVJIchgdtCuXr58WZw71nEbOzs0yFMyODumpV5v3769+KxTT4cQZHC1hR65TTo4OxbPDtuqrIxYBMdx9Jry7yfdqQg5d+5cuHbtWpGOR48eFcOc2cEwZ3aQDrubiwwdr1EyJIBlXPy6p6Q7LSFnz54Nly9f7kgHu7q3bt1ilRbMgliGEsCG59yxlva1xHQ7QfO3Uwg5ffp0uHTpUisdzI4rV65MrtEFxKg9aWNLAmfI7FJCWHK1hZNm5MSJExMXL14sBHDeGDH9wmWU2vhxcaYsJca/l6QZGx8f/+fChQvh1KlTxf+ZMQjMGjaydoW1B9atjh8/Xn7asYyePHkynDlzpnw7OFevXm1dq2TPOWt32Z7MoZw0owzxYYEQe1UGMnSqExkqhHAo3+lkqLcjcK5Z1y1ZIamEeErSDP3+EAlRu5KQOCE6fO9UGboQvsPYGVKXEBeSppE7qNSyND8omw4Vh/edKo0IUUpIiNJhE0I6KB3Gd9o0IgTsDEmlQ1KcKo0JSV3d5wmZmsaEgG1ZNiGS4QnppFEhNh1xq5IUH+xVGhXCkeM6KcigdAbSmaRRIVA3OySDpdOmcSGkQ8mok+K0yZ4QiZAMF1KlcSHMkTgZEuFCOmlcCKhdpdLBVSxOmyxC6loVMlxIlWxC4nYlGS6kStaEpGS4kCpZhLiM3plWIU4nWYSkRHBJql06k2QRUpcIZLiQKlmEcAbRCpEIF9JJFiEiFsGVji6kSjYhVkJcTpssW4PHbqREUFwH7LTJIqROBMVrp01WIZIQl9MmixA2OrfBaanivd+ZWyWLECshLqdKFiGpG3ZUTpWsQriN2sqgnCpZhPCkB2TYciEpQvgX3UjH7n+4HycAAAAASUVORK5CYII= plot2 - 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 plot3 -

Extract Lobe for Bernstein

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 :

The contour extractor , pairwise 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 :

M

M 75.991 - 69.944 - 64.339 - 59.146 - 54.338 - 49.888 - 45.773 - 41.968 - 38.452 - 35.205 - 32.209 - 29.445 - 26.898 - 24.551 - 22.39 - 20.402 - 18.574 - 16.894 - 15.352 - 13.937 - 12.64 - 11.452 - 10.364 - 9.369 - 8.459 - 7.629 - 6.871 - 6.18 - 5.551 - 4.978 - 4.458 - 3.985 - 3.556 - 3.166 - 2.814 - 2.494 - 2.204 - 1.941 - 1.703 - 1.487 - 1.291 - 1.111 - 0.947 - 0.796 - 0.656 - 0.526 - 0.402 - 0.284 - 0.169 - 0.056 - 0.057 0.172 0.29 0.415 0.547 0.689 0.843 1.012 1.196 1.4 1.626 1.877 2.154 2.463 2.805 3.185 3.607 4.074 4.591 5.162 5.793 6.489 7.255 8.097 9.023 10.038 11.15 12.368 13.698 15.151 16.735 18.46 20.338 22.379 24.595 26.999 29.605 32.427 35.48 38.78 42.344 46.191 50.338 54.807 59.618 64.795 70.36 76.34 82.759 89.647 73.21 - 67.398 - 62.012 - 57.025 - 52.408 - 48.136 - 44.186 - 40.534 - 37.161 - 34.047 - 31.172 - 28.521 - 26.077 - 23.825 - 21.752 - 19.843 - 18.088 - 16.475 - 14.993 - 13.633 - 12.385 - 11.24 - 10.192 - 9.232 - 8.354 - 7.551 - 6.817 - 6.148 - 5.537 - 4.98 - 4.472 - 4.01 - 3.59 - 3.207 - 2.859 - 2.543 - 2.256 - 1.994 - 1.756 - 1.539 - 1.34 - 1.158 - 0.99 - 0.835 - 0.69 - 0.554 - 0.425 - 0.301 - 0.179 - 0.06 - 0.06 0.182 0.308 0.441 0.581 0.73 0.892 1.069 1.261 1.473 1.707 1.965 2.25 2.566 2.916 3.303 3.731 4.205 4.728 5.305 5.94 6.64 7.41 8.255 9.182 10.198 11.309 12.523 13.849 15.295 16.87 18.584 20.447 22.47 24.665 27.044 29.619 32.406 35.418 38.67 42.18 45.964 50.04 54.429 59.149 64.223 69.673 75.524 81.799 88.526 70.482 - 64.896 - 59.722 - 54.932 - 50.5 - 46.401 - 42.612 - 39.11 - 35.875 - 32.889 - 30.134 - 27.593 - 25.25 - 23.091 - 21.103 - 19.274 - 17.59 - 16.043 - 14.62 - 13.314 - 12.115 - 11.014 - 10.006 - 9.081 - 8.234 - 7.459 - 6.75 - 6.102 - 5.51 - 4.969 - 4.475 - 4.024 - 3.613 - 3.238 - 2.897 - 2.585 - 2.3 - 2.04 - 1.803 - 1.585 - 1.385 - 1.2 - 1.03 - 0.871 - 0.722 - 0.581 - 0.446 - 0.316 - 0.189 - 0.063 - 0.063 0.192 0.326 0.465 0.612 0.769 0.939 1.122 1.323 1.542 1.783 2.048 2.341 2.664 3.02 3.413 3.848 4.327 4.855 5.436 6.075 6.778 7.55 8.396 9.322 10.336 11.444 12.653 13.972 15.408 16.971 18.67 20.515 22.516 24.684 27.032 29.572 32.317 35.281 38.479 41.927 45.64 49.637 53.936 58.555 63.516 68.839 74.548 80.666 87.218 67.808 - 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t0 1 time :

level 1 : ε 0.05 : 12 mat Q M ε level CONTR : XY Q Strip2 : (Export the project ≡ 31 line

XY rows 100

re-center the system Xlevel XY 1 col XY rows 2 / - : Ylevel XY 2 col XY rows 2 / - : Lobe Xlevel Ylevel augment : Next fit/smooth Bernstein ≡ 41 line

Lobe 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 M

1 time t0 - min 3.9

Lobe rows 100