a9c307c2-8b3f-4427-bef0-17f28678fc5a 3 4 5 decimal

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

M 12340678933 mat :

M 1 -^0.8 - 0.1 0.2 0.1 0.2 - 0.1 0.5333 0.1 0.1333 - 33 mat

M 12300078933 mat :

M 1 -^#

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

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 : (image size ≡ U L H N xd : V U : 51 sys

size 16 :

Size the spot: size [0.125, 10] x y f 255 x 2^y 2^+ (- size / exp * : M x y f U V Sym : 21 sys

R 1 M * 0 M * 0 M * 31 mat transpose :

G 0 M * 1 M * 0 M * 31 mat transpose :

B 0 M * 0 M * 1 M * 31 mat transpose :

Yw 1 M * 1 M * 0 M * 31 mat transpose :

Cy 0 M * 1 M * 1 M * 31 mat transpose :

Ma 1 M * 0 M * 1 M * 31 mat transpose :

Or 1 M * 0.5 M * 0 M * 31 mat transpose :

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 R 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 G 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 B iVBORw0KGgoAAAANSUhEUgAAABEAAAARCAYAAAA7bUf6AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAACqSURBVDhPjZMBDoUgDENhev87eg+Vb1kaNj4MltQ+t9EYo/m6UjmOlCCRppxVrFJU79v0PCphAHSeKvLMyZQLofqlVV/w6O3Gi73ZrPUHwx33+i54gWyQV+7YDvvFyF0PFxZ511HgL0ththg5uYbwQyLvOlkIqNFi5ChwDcEnDLe8cstih/1i5JZriBV+qB23qiEYjIbRQXtGeGN13/+9qO9CsMTFyMmqlH4yEv7jyY5LMwAAAABJRU5ErkJggh+Vp+h1Cqk2P94FDgkbxzzYc2K+UyGtIjdCZMMFQaNh8LWf4wsHbmh1/v8XUkkvCsPiAwPud4Ov/RxfOHDD4oTR6+Mx67ODPc58Cl0wSkc5JifPK98qnFyU4dlupd3OLGv2o/lc8a8YaTWKZG5ibHEGTsYSFQKlR7n8Yyr3nKU/KBfdufc+kuwAAAAASUVORK5CYIKUnegSSZ23wtcRsQCjbY/IV97Od17x//9PpF8ExxPM4ErtzwAAAABJRU5ErkJggnZd/Rv8f6KUL3gNIsQl7BA0AAAAAElFTkSuQmCCqHqdd60Y7sRs5IPSz8ey/ADM1xvl2qe9oQAAAABJRU5ErkJggtYmcx5nRzQmLB4D3Q2QweKpk5c3bw4m0hAQQm22bsNnBAzw5dTdACYcCLVgi67zOw4GGiIAqkBEY+Zf8Tk+P1QN9cWAnV8RPxiZCTRx49QVRJIr3Tldv068dR9NeAEbbIRsaAydSOf6zNSz3TdYF6xMeILaiwOH1WZViMcjun3Gws1I672JfGN5kk2BA7egcRVjwfWZ953DeqwT3btj4ck/eJPpGlkc4y5Te2xqL+uAvzTnH/DCjtpgRCdma76j629Wuj0wLNzKHRZA3c11XNK3Y2gi0c0HH3iurvnSurKp5/D478hDgrrOeX6r3npW64TnCdc1hibytk5Q1znPb9Vbz2qd8DzhusaOAyqJbj7U/79s1TVfWle29AxN1OZd7sintVp3cx2X9O1YmcjCLSLgEvjoeel8R9ffWPe9CT/wQg6rDSKmwuddj7tM7bGpvawDquadJ70JukYVCzXdh3rwvnNYj3Wi+94Ek67ZBBxYxfATCAs+R7fPdOLDSOu9CSaAAyycbCwk5EcrhrnRM1PPdt9gXYD2o4nOCAdVaGwxVWT4/HmdK905XT8bAeu+/2lH3u4reYesY5Mg52vT32zBDesLgmBTTXkM7OUcWF68OJiIIAuudTKiCcR3JnjlbIBcxSHY4m3CexDu844mzok3jmqiuwFADLli4a4B8cHnLre30x3i/lU8BmzCTdy8CoNzBoINuF6ePTuYGAkPCXIVbwOYyMFkN0VIFRciHDzv9akhZ8Jyc3O6CQt23YXFp45oTGzdRLCoTniH93Mm5y9Pn55MJJyrgYglG8Qnc/DoJhBkgR11PfhMuI7fFF3F7+yu4renV/F77Kv4i4Kr+NuOq/grm///752m6Rc0cNGvNLILQwAAAABJRU5ErkJggrF0GjaxFdbcxn/ZpH42RowFdqKrAdbaQGJaNZEW4fU0qhnEOe/GwsPDMPwFsb6epOxg5EoAAAAASUVORK5CYII= Yw 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 Cy 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 Ma 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 Or

M rows 17

M 1 -^#

M 11 mat 225.04 228.36 231.27 233.77 235.84 237.45 238.62 239.32 239.55 239.32 238.62 237.45 235.84 233.77 231.27 228.36 225.04 228.36 231.73 234.69 237.22 239.32 240.96 242.14 242.85 243.09 242.85 242.14 240.96 239.32 237.22 234.69 231.73 228.36 231.27 234.69 237.69 240.25 242.37 244.04 245.23 245.95 246.19 245.95 245.23 244.04 242.37 240.25 237.69 234.69 231.27 233.77 237.22 240.25 242.85 244.99 246.67 247.88 248.61 248.85 248.61 247.88 246.67 244.99 242.85 240.25 237.22 233.77 235.84 239.32 242.37 244.99 247.15 248.85 250.07 250.8 251.05 250.8 250.07 248.85 247.15 244.99 242.37 239.32 235.84 237.45 240.96 244.04 246.67 248.85 250.56 251.78 252.52 252.77 252.52 251.78 250.56 248.85 246.67 244.04 240.96 237.45 238.62 242.14 245.23 247.88 250.07 251.78 253.02 253.76 254.01 253.76 253.02 251.78 250.07 247.88 245.23 242.14 238.62 239.32 242.85 245.95 248.61 250.8 252.52 253.76 254.5 254.75 254.5 253.76 252.52 250.8 248.61 245.95 242.85 239.32 239.55 243.09 246.19 248.85 251.05 252.77 254.01 254.75 255 254.75 254.01 252.77 251.05 248.85 246.19 243.09 239.55 239.32 242.85 245.95 248.61 250.8 252.52 253.76 254.5 254.75 254.5 253.76 252.52 250.8 248.61 245.95 242.85 239.32 238.62 242.14 245.23 247.88 250.07 251.78 253.02 253.76 254.01 253.76 253.02 251.78 250.07 247.88 245.23 242.14 238.62 237.45 240.96 244.04 246.67 248.85 250.56 251.78 252.52 252.77 252.52 251.78 250.56 248.85 246.67 244.04 240.96 237.45 235.84 239.32 242.37 244.99 247.15 248.85 250.07 250.8 251.05 250.8 250.07 248.85 247.15 244.99 242.37 239.32 235.84 233.77 237.22 240.25 242.85 244.99 246.67 247.88 248.61 248.85 248.61 247.88 246.67 244.99 242.85 240.25 237.22 233.77 231.27 234.69 237.69 240.25 242.37 244.04 245.23 245.95 246.19 245.95 245.23 244.04 242.37 240.25 237.69 234.69 231.27 228.36 231.73 234.69 237.22 239.32 240.96 242.14 242.85 243.09 242.85 242.14 240.96 239.32 237.22 234.69 231.73 228.36 225.04 228.36 231.27 233.77 235.84 237.45 238.62 239.32 239.55 239.32 238.62 237.45 235.84 233.77 231.27 228.36 225.04 1717 mat 11 mat

sub M 711 range 711 range el :

sub 1 -^#

sub 253.0156 253.7579 254.0058 253.7579 253.0156 253.7579 254.5024 254.7511 254.5024 253.7579 254.0058 254.7511 255 254.7511 254.0058 253.7579 254.5024 254.7511 254.5024 253.7579 253.0156 253.7579 254.0058 253.7579 253.0156 55 mat

N M 19 range 19 range el :

N 225.0367 228.3574 231.275 233.7727 235.8364 237.4541 238.6164 239.3165 239.5503 228.3574 231.7271 234.6877 237.2224 239.3165 240.9581 242.1375 242.8479 243.0852 231.275 234.6877 237.6861 240.2532 242.3741 244.0366 245.2311 245.9506 246.1909 233.7727 237.2224 240.2532 242.8479 244.9917 246.6722 247.8796 248.6069 248.8498 235.8364 239.3165 242.3741 244.9917 247.1545 248.8498 250.0679 250.8015 251.0466 237.4541 240.9581 244.0366 246.6722 248.8498 250.5567 251.7832 252.5219 252.7686 238.6164 242.1375 245.2311 247.8796 250.0679 251.7832 253.0156 253.7579 254.0058 239.3165 242.8479 245.9506 248.6069 250.8015 252.5219 253.7579 254.5024 254.7511 239.5503 243.0852 246.1909 248.8498 251.0466 252.7686 254.0058 254.7511 25599 mat

When inverting a matrix, I get a different output between (1) evaluating the inverse of a matrix and (2) defining a new variable as the inverse of the original matrix and evaluating the new variable

Converts the given expression from symbolic to numeric notation. New N 1 -^eval :

NUMERIC New N 1 -^:

N 1 -^11 mat 1.878 1012^* 3.1831 1012^* - 2.2939 1012^* 2.3663 1012^* 1.7253 1012^* - 5.5127 1012^* 7.1739 1011^* - 2.6241 1012^* - 3.6835 1012^* - 8.1305 1011^* - 1.1302 1012^* - 4.7691 1012^* 4.0588 1012^* 4.2408 1012^* - 1.1882 1013^* 1.5541 1012^* - 5.2153 1012^* - 7.5691 1012^* - 5.3243 1011^* - 9.1065 1012^* 1.0203 1013^* - 1.0301 1013^* - 7.995 1012^* 2.6904 1013^* - 2.786 1012^* 1.1493 1013^* 1.6264 1013^* 4.1608 1011^* - 8.2225 1012^* 1.0213 1013^* - 9.5516 1012^* - 8.3626 1012^* 2.5489 1013^* - 4.1023 1012^* 1.0829 1013^* 1.3861 1013^* 3.5477 1011^* 6.4737 1012^* - 6.7463 1012^* 7.0876 1012^* 4.9536 1012^* - 1.8636 1013^* 3.1697 1012^* - 6.82 1012^* - 1.1218 1013^* - 2.0297 1012^* - 2.4548 1013^* 2.8198 1013^* - 2.7035 1013^* - 2.3081 1013^* 7.5122 1013^* - 8.611 1012^* 3.2496 1013^* 4.2813 1013^* 2.3922 1011^* 2.8488 1012^* - 2.5642 1012^* 4.1094 1012^* 3.6378 1012^* - 7.8532 1012^* 2.011 1012^* - 2.1386 1012^* - 4.0584 1012^* - 5.5497 1011^* 1.0722 1013^* - 1.2256 1013^* 1.1615 1013^* 8.6539 1012^* - 3.292 1013^* 2.5998 1012^* - 1.6022 1013^* - 1.8984 1013^* - 8.0089 1011^* 1.7159 1013^* - 1.9628 1013^* 1.7298 1013^* 1.595 1013^* - 4.975 1013^* 5.3525 1012^* - 2.1588 1013^* - 2.6939 1013^* - 99 mat 11 mat

New 11 mat 1.878 1012^* 3.1831 1012^* - 2.2939 1012^* 2.3663 1012^* 1.7253 1012^* - 5.5127 1012^* 7.1739 1011^* - 2.6241 1012^* - 3.6835 1012^* - 8.1305 1011^* - 1.1302 1012^* - 4.7691 1012^* 4.0588 1012^* 4.2408 1012^* - 1.1882 1013^* 1.5541 1012^* - 5.2153 1012^* - 7.5691 1012^* - 5.3243 1011^* - 9.1065 1012^* 1.0203 1013^* - 1.0301 1013^* - 7.995 1012^* 2.6904 1013^* - 2.786 1012^* 1.1493 1013^* 1.6264 1013^* 4.1608 1011^* - 8.2225 1012^* 1.0213 1013^* - 9.5516 1012^* - 8.3626 1012^* 2.5489 1013^* - 4.1023 1012^* 1.0829 1013^* 1.3861 1013^* 3.5477 1011^* 6.4737 1012^* - 6.7463 1012^* 7.0876 1012^* 4.9536 1012^* - 1.8636 1013^* 3.1697 1012^* - 6.82 1012^* - 1.1218 1013^* - 2.0297 1012^* - 2.4548 1013^* 2.8198 1013^* - 2.7035 1013^* - 2.3081 1013^* 7.5122 1013^* - 8.611 1012^* 3.2496 1013^* 4.2813 1013^* 2.3922 1011^* 2.8488 1012^* - 2.5642 1012^* 4.1094 1012^* 3.6378 1012^* - 7.8532 1012^* 2.011 1012^* - 2.1386 1012^* - 4.0584 1012^* - 5.5497 1011^* 1.0722 1013^* - 1.2256 1013^* 1.1615 1013^* 8.6539 1012^* - 3.292 1013^* 2.5998 1012^* - 1.6022 1013^* - 1.8984 1013^* - 8.0089 1011^* 1.7159 1013^* - 1.9628 1013^* 1.7298 1013^* 1.595 1013^* - 4.975 1013^* 5.3525 1012^* - 2.1588 1013^* - 2.6939 1013^* - 99 mat 11 mat

N 1 -^(New - vectorize 1.878 1012^* - 3.1831 1012^* 2.2939 1012^* - 2.3663 1012^* - 8.1305 1011^* 1.1302 1012^* 4.7691 1012^* - 4.0588 1012^* - 5.3243 1011^* 9.1065 1012^* - 1.0203 1013^* 1.0301 1013^* 4.1608 1011^* 8.2225 1012^* - 1.0213 1013^* 9.5516 1012^* 3.5477 1011^* - 6.4737 1012^* 6.7463 1012^* - 7.0876 1012^* - 2.0297 1012^* 2.4548 1013^* - 2.8198 1013^* 2.7035 1013^* 2.3922 1011^* - 2.8488 1012^* 2.5642 1012^* - 4.1094 1012^* - 5.5497 1011^* - 1.0722 1013^* 1.2256 1013^* - 1.1615 1013^* - 8.0089 1011^* - 1.7159 1013^* 1.9628 1013^* - 1.7298 1013^* - 94 mat

i 1 New rows range j 1 New cols range Δ i j el N 1 -^i j el eval New i j el eval - : for for

Δ 0.010000000000000 - 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.019200000000000 - 0.000000000000000 0.010000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000832000000000 0.000000000000000 0.024000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.000000000000000 0.100000000000000 - 93 mat