e2dd233a-1551-4302-8782-5cb62a2aa2cd 18 6 5 auto
&[DATE] &[TIME] - &[FILENAME]
&[PAGENUM] / &[COUNT]

Explode mirror Matrix

M mirror v M M cols col : ct 1 : j M cols ct range N 1 ct el M j col : ct ct 1 + : 2 1 line for N Unest N v N 1 el : j 2 N cols range v v N j el augment : for v 8 1 line :

Mesh U, V within the bounds L, H

L H N xd N intervals U 0 : dx H L - N / : i 1 N 1 + range U i el L dx i * dx - ( + : for U 2 1 line 4 1 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 + : 2 1 line for r r 1 + : 3 1 line for M 3 1 line :

Is this Gaussian spot symmetric ? Answer: YES ... test for N:= 16

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Highlight by negative pixelation

experiment around A = -1.75 xo = 127 α = 1

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JAAAAAElFTkSuQmCC fmap 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7iPw/3/4H5xVSelNmurNAAAAAElFTkSuQmCC N fmap * 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 fmap N *

Nice pattern ............ A = -4 xo = 127 α = 1.25 or > 1.25

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

 x q 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 NTSC

Dotted circle in spot A = -1.75 xo = 64 α = -1.1

Ring in spot A = -1.75 xo = 127 α = 1

Darken ring in spot A = -2 xo = 127 α = 1

Owl eye ......... A = -4 xo = 127 α = 1

Pattern styles ... avatar

xo A α 3 1 mat 127 2 - 2.24 2.25 2.26 2.27 2.27 # # # # # # # 1 12 mat 3 1 mat ≡ 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vPMo3XhC7QlNTqhzQrM71LtDUyfUdUJTMdYVY3N2qLfy5hRZnyKb5wl1TmieLNVPlppnjPUzxuZpc/20ufncoQ6H5hOo+hOo5rPI+rPI5lPp+ijdfD+h/n5C802V+psqzXeW6u8sNd9eq7+91nyPsf5qUPON1vobrc13m+vvNjffcq8PUM3vHerfOzS/fKl/+dL8Bmp182u4/DVc87vI+neRzS9k61/INr+Vrn8r3fxqvq4Tmv+fsCj+B0avm3m7dpvcAAAAAElFTkSuQmCC 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