I found an interesting and visually pleasing phenomenon called Domain Coloring. Based on mathematical formulas, I don't know how closely it relates to fractals. I'm a big fan of rainbow coloring and when I stumbled upon these images I was mesmerized. Is there any way to make something like this in UF?

I found an interesting and visually pleasing phenomenon called Domain Coloring. Based on mathematical formulas, I don&#039;t know how closely it relates to fractals. I&#039;m a big fan of rainbow coloring and when I stumbled upon these images I was mesmerized. Is there any way to make something like this in UF? Here&#039;s a link: https://www.dynamicmath.xyz/domain-coloring/dcgallery.html ![63c0c12a46e14.jpg](serve/attachment&amp;path=63c0c12a46e14.jpg) ![63c0c13d4f35d.jpg](serve/attachment&amp;path=63c0c13d4f35d.jpg) ![63c0c13d4fc23.jpg](serve/attachment&amp;path=63c0c13d4fc23.jpg) ![63c0c13d51b92.jpg](serve/attachment&amp;path=63c0c13d51b92.jpg)

0

Yes, UF can absolutely do this, and I wouldn't be surprised if it's already implemented somewhere in the jungle of public coloring algorithms. If not, it should be relatively easy (compared to what other CAs do) to implement this.

The simplest way would be to:

1. use a fractal formula that does nothing (just bails out after 0 iterations)
2. use a coloring algorithm that assigns color based on real and imaginary value (direct coloring algorithm)
3. use whichever function you'd like to examine as a mapping
Yes, UF can absolutely do this, and I wouldn&#039;t be surprised if it&#039;s already implemented somewhere in the jungle of public coloring algorithms. If not, it should be relatively easy (compared to what other CAs do) to implement this. The simplest way would be to: 1. use a fractal formula that does nothing (just bails out after 0 iterations) 2. use a coloring algorithm that assigns color based on real and imaginary value (direct coloring algorithm) 3. use whichever function you&#039;d like to examine as a mapping
edited Jan 13 at 9:17 am

0

Thank you very much for your response Phillip, appreciate the feedback. I'm vague on Direct Coloring formulas...is this the same as Direct Orbit Traps? Every time I try to use it the colors seem so dull. What are some examples of Direct Coloring formulas?

Thank you very much for your response Phillip, appreciate the feedback. I&#039;m vague on Direct Coloring formulas...is this the same as Direct Orbit Traps? Every time I try to use it the colors seem so dull. What are some examples of Direct Coloring formulas?

0

Direct Orbit Traps is a direct coloring algorithm, the difference is merely that a coloring algorithm returns an index for a color in the selected gradient whereas a direct coloring algorithm returns an RGB triplet. To achieve the nice coloring shown in your example you will probably need to work in HSV (hue, saturation, value) space, there are conversion formulas to be found online between the two color spaces.

Direct Orbit Traps is a direct coloring algorithm, the difference is merely that a coloring algorithm returns an index for a color in the selected gradient whereas a direct coloring algorithm returns an RGB triplet. To achieve the nice coloring shown in your example you will probably need to work in HSV (hue, saturation, value) space, there are conversion formulas to be found online between the two color spaces.

0

It reminds me of Carlson Orbit Traps:

`Fractal1 {::MefPghn2NyZWPyNOSC43Ng/PUwvP28mU7g8hpnBexsYc7G2enG7+ihqKV5SdpUZ2KV6j9X/G  8U8SSuBmBuE1HZE8IYEBZqHnafYud4/4lv4u7m7nH6O8q3afE+V39t+jzPdALJo7eqr/LPNf  ATaQ3N0+juprHwamHm6O2PfVT1dsbq/57+4Q/f8c30p2x/K+NY2bIIC9V31OO33O03etf8LH  +R31X+CTtYa3HavM3fe8wr+l2He+LTnvNe8V3d+S7D9z/4AGhu7U38TnPeYofsrd6u5p2xrX  an6Gnt10p2LXgq1WXwT7mO8XQvWiU0GBChE43geNlTlEJhSlN87O1+lRQVow7wpsX+iHPPd6  2QrpCO1+9edNQ4o7esfobs9E0n8x52xjtTHf9tHP9q7g2Y6HHe17gn1Nc/055Xd3lPfduda+  A6NIdtc5zXO/Ndt8GE8vvvtf48Ndvn6lvof8a/xObHuWTeEetxzjdv8FwrUWkTpXElnf4B6r  v9wQQMgH8X+7tTDXPP++p77n/0U7lrvyKFz6/9/GGt09vUKy9oP9jLdH+lb3f/Q3V3je35j6  x++prz39PhOgW9QirWaH/yQ3bvN+A+j9/fdHQvGn8w3dbYu/yQPItm5EPGVm+/TXT4DgmNOX  tMitss2iUrtI5t1lP/H3gpVfsr7I8u0GqkDj2YhSyjbLSucc5zwA9H6aHOwsVz9tDDuWko/z  hz3f9DtH7vdV/Eu/RRtP61cHqugfzMeDPT/ATPbk8Dt1/7BmeSyl+pWf7Qt42n97m1awD18d  tHfLsI8MMRWjfs/0lhujf8yTdTn7P6rYL+jDdfPS4xRPKI/YtU9EMHp7Pv1PmyHeckqRSedb  fLm/G8rJ6OW7YcocfXbon9pfc5sXdwu/2pOhW9S3cbsIb+73/4jX7mjfyf/8w5JtJD6/Aek+  flK86n40Ss7P/0T9P88Y31ru65quXrLF7aWPp+R/5t2puiHc1LT+11wy8hnj1OzD+XdjfJ+J  TxVE8nBglJbLDOwD6GG6vctLueP1+Hnn+bfv/KGegwydqfM6ZxvExNvN8G+HcOX+fu/yBtdg  H0dt/GohQp/juHbhRf9Tvdd+8JtwPe2/SGTD/KYjsdwWHD3gmpd8hu/7LXMT6FxP8f5WJg98  fAWG2d9gyCPe7k/B+X46v1ZfpDGLUQdOfb6e3b9rGbjmBb9De/0R9afClxFyQN8u2vjPwINs  GEuRGa531PqfsijQclyJAeAiHQKQRA6HrYEcDthE92U7bTxNik6pfkaAkciqREBwsACJiFX9  Mb1TBRKTe4QJSFhzZmlreA3jh9BZxqrw/2CMPVeclAb/RRRASX9AbBGX92HjhuBFJVeUaJtR  wbw0YAzjxNEuE7VAzcGgox3jqaYLPvfsxMGgpiGOODBjsMYOXlwYKQxocOSSyhcj1McDnkAZ  GtZoGFXgyhMj3UigiRptkZEnIEwuHicIaYQxO4GgcFgkgDG5QMbZMmAJTgcFggZLscIubiRD  l3kAZKAhI8lRoAkesuBrUycIhRnkYCmVIeS3wEBZnoFg0FoMjSq8hWsyNOhUIVCkyveBk9cI  3ECY2OBlA1YhICYSdGExNjQ7wWSLZKQR5SJM6mDZmRgkgPe0kxJTBKEFRF4CIifwlLoJQuCQ  a3HzhcD8kGOKFi6WJBLZKaJzqehshIzgYhVTM3MijWbxOLawKJlU7D8SBObag6C7FTyhsW10  O2SYJQW7aMFm7NgtAZNuJ12GEJQ0AkfpxCk1AnE8omkKeGdSrtNlQcLkoRISFPzscFGoKh82  6YysWybqjQVF9eSPEI/JQSPEDX0SmJzEGCjyaJVwMpMFCTW27hQlSaSRuxKmADeOxKBDjWw0  pMQSAUQKBdbHhaaUsMwVHzMgutlIgddSGIzuVJWvGqEk7s52Io5gmxOYbLYKeFdU42IkTMbg  FDasSxUQvKWVCKX2QMH0PG2UVHVLrrz7VdFBxxSq0iN2SpNw/lB243GWwrMcY3tCxx4SSTZ6  5OYCq2cHzMEJ4WhUIzRNlhhtm41m9Y3SD1Ig97yHTs7qBiDmkLwiIvlaUYUSR+ZsgDKcErEM  zrpYwYPnKBdzYJYrZ9YQzMWIYdYBJqE0PjtJxOjBk52ufxOdM4yMWBNDk7aRRT+yZR0MWnrb  xg2ZsQZ485PC3MWFo+COWmBaKCBmzZiK9qhZsMOLD0VEi3Ubc0NtkDb0jzAb8bixxVEV3sSz  2s5yaYGbDYGvCK277jkl3x6KT7YmqWr6sqJhO/mcU7MW9WkiatqZWCszBFx55o0g7OVRNThI  C9eV59SmyUUw/PStpCYeqzSJoWX2QKQZ4VQFhISQFrxEBj0NptKhVGLzSR1inJGkk6BeMIpw  J8YQaq7dxg0CH8iBZVtHYAZrZPwAyTd8PGkX4dYMoI1pyYQfvq+/VCKjChLDU6ihjyQVAVph  bEDqKC4IGsJ1V2YwmCnZTmAgqaCyODAVECZCKONOnEUcRoOJokUvoTQJFOSngSTjeNBlGFCb  FUWaMWJosiwsSQ5pewngyLciPBNOA6c04QorgKTjvLBVWEiXCqKN6hEUVRAEJoNpBwng2UEF  fiVBUaslJmFQFhXmgiTjcJBFvWwLWUSaCESQJFZRIBlmGXbCq1WP4xPr2SOCrqBbLKrMAXdW  N+9n66GCOyrjqSY3eKpUntXwiN4oHpKuZ9DUB+w8SxJhdaCJnIFPzp+Mcah50McXAZMZDqCu  1aM8GwE3q4c7ifEEkFqEnb71ZI6K6ubNDYmkUprzWKTAdf175NGnhYPApvCucxP46ddh0TQ5  V0djVaKDWNjpVxbS9xMD3mtCdkzUZ9pNez16sSWZejNzFNSQAr355dpCCnZJ7lRVgrYMGUi6  VgJlFQwHIS1Kg4MQAu776/bHeq909TtK30eYJOFW0ITKyOnXbPDjZoSQn/GcGysONG0FNAlR  qBamqDOyhsrSjBpWPllg3ciSQmfvYY3iMQ/McOhWpFNTgh9uE2OoYQ/OFECiWC683AmYRIZg  CXEzoGvBwYQrL/QwrEj/Gxg+dJA/Cwlge/NkYRenjy5oCL0i33N8V9Rv2pWccUIt5TIqMfcd  EGF59ROBlsBKZbUzImgqn6pyR95JF64VVQ9ZJHJM+5kgazjkCm45X0ngy3AlvNaYvfw/rcU/  e/gP91Q9JPF2EpoVdeRyoiqtqdsjRlSzilE0l8LJaconv9n36mDLPtOIhjLJs6EjD5HICj4N  ayMHEQEGxjhpkCMvncErl4IMaIiSqqAzMSiAHERyMMmdBNmF8rJCzOKKkSJllixd9m6UJWg5  DCA6LFpYC3e8qlVILYuRPmzsTEmN33EGPke0IM3CStfzyUM/YKRwi3Y6jfrf+hniWRiRklTZ  yXqfNpOCSSdc7ZTA/H0TUiTWm1Qqiv4V+iLBL4+xTw9PZVcm30HZ5otWwd5LEmihqL8+YnVN  kKCP3HJLsnVVczIZDo7COvEXYtuCOU1oqinlA4McvFY9WQVxVhIwWOmxFc74ueBk3Xuuv2Nq  Pf6n/RshYwLWUWhe/IBr4uDTPH2vrajEhLgteRKlLJSKF26EJnJEm0tmCbz0GEOqAhrB7SdP  H26lWAbiFD8xRAbUWD2nvN9ykCYr/j6lJ1FbhdPEBnZCWIF2YTGcXQHzbNYfk9ISTZLb9dEC  7X6TwTKsZokzBfDr0brWmm4E7HbHnbv23GPKjsJCbpI/KbF4akPUjYQ3ZRqocTg2xgkQCe8i  bMo1vJEGCxoJD0M2iVwIrsSL6PTSFE+SGoPGb9c1SQrfTKtnn5to9kJFKwv+mSQnJaYFqJrJ  xgWb0YwZUeFdUGOUEj7PxgLnQJtiOq8hgIJ4MQVYrLGxfvZ03TBYk+9TP8U70xoxTtHd41eL  3QrAGc5hO6K1ldIuhDDx81rLr9dY3PIM51rrQuzx+4XqVXU3MH9ZrtedZWiD2VlS1G1Fr4k1  qVXuV8md3XvusG6lmgyXvuMzJAbTUBuZ96ya1HRFhwQrVX+t5phUEUruMTm4Qk+oG+61lynD  PGfj+LlL50SIIP91w58U5mBa32dNUc5uJUgVZoDiXAbtHALFAzq1gtB+pzylxGUKsbiDXvXV  NYakV5CY7u/ImkQrCb3PgwgNYFFw+NDkhkdkCbmUglgjTmEOnCbNdw1HEQVYhdDUY5gJe1UY  zwqAsR0Qr2bHyQHTVK2hd9x+T+IF2lvXIUQTQHXavO3NoCZOSpao+xYfZLxQrXMlAR8QMvDY  LQkojpOByeOIgLVQ4A5Q0ojpOByMWqPbFsfiwCEL66EkA5GDpNCvZnFIe01JIByO2pkswA/C  kI6opTgk+FZhF/LQyorTQCU0YVRvnK66EEK4Dt9j3f+bLLJxY4/jEzGeF3omenG71vooKsJL  HLbU0mVqCirKIYiqWVYTzJTho41qCb2Q4clgVVK85DBMtgWpKcnSKhYPbniqwHINElCblqg7  ChDbPzliqwfip67ryKVhdNL47IEtTtqwMFgQkgbayVqCpLJio4lERVhfCBKcslFVh9CeozQq  xg31nO/t5+TL5OB2NQbM1dXJDl6tR3wl2bxQMozddKIbqSQn9bzRElBatODuWJ9m2jBt57lz  1e0kBGyaSDWWCaGvlUYVGKH06FOSHVdFQu/AIoGnLjBtZFRp1CeJodXZIwAOJHU6CSE6Wxlg  2Ukpzmlx0TMoZwEBb6K9eGFDavFiQoG213XPP01PkkBMkP2KfZ+xQsJi3EIfAXw2sscIrHaI  h2b+EIzoHVH+p/cRXgoud3EsMIfE16oZyhcnXKYY3sNxCEPYozfYxLQ810Jv73Q8zFtkYNdS  GSCIXmDJ9OtTxkEIf6KVLzGDQ2Mvj1zhNp/4p+hhwaNOB485z2UifvSMMWYWj7BcGbhhbjXO  +3eJBIh7cnDwvToylRfHg9x6TgzcZb8vNb5+ExTlHWoFM+m5B4RKQ0bzjk+U5RUXBERKQ0bL  jk+U5RWXBURKQ0brikeX9Mc+2xftfsLJbxYaSRulKSuUiCnUWEod9CjS4mbdSMo1lGIO8QGJ  jBNrZIYIkBWOoPZU0QiijBdrb46A1zAtphC8kE5XdEDyXXH5e7kEWlWUsuOK2UHlrrjyN1R1  66oqQHHa/U30UbU0FgDCyoCCpgAcdyLmLQ+s9DWXxJQE3BMAbZpyho2r3EnSMrNXgo+LshCX  Ie28Cr7/Zptk9wYhw8DpPcBivmOx3QnErpTiN0J5a6kcDdStmOpK0pr6kQ82kDnRfC/xl4zo  AscPoVRYEfmbaIZYRhI0Ug5D9z5+aEmNJgcRT4yuEh59nEmkySxc3hZUjUUqb8V1N+W6mYVd  TsluJXV3kbpbqV1NVpudRHhfiHFYFPqA/VxipjAqAyeTAAXCxGz0LQLXCAvPqLQ2k7J0xbl2  SBbjgl8cIWINbUaCEr4ebtAxXTn4boTi10JxG6kcNdSuhOpWTnK/xGc92pTdTpe+Zc1wXg/6  yT1X2EROkzOox/pEIi/WmESA2C0ypqIwJQ0wd1UWAZjymxaawNJQLeYwKg4rpT8N0Jxa6kYD  dSumOJ3QnUrpTqCd6x2hhUjgG/ysP27vBWfoIko32P8IDLn9A+fOD6LRUEA1PzR1EX9WTeKK  JcObegwtLGH1wenK0Bf4SQ/wQqZu06x7MRhCIiUgo3WEJ9pyjsuCIjUgo3WFJ9pyjKVB+Wv+  3Fd+VEwi4LzvSBiqSYOtxFITvt2ZUikmDZvYg6sAbm5sAZdXgDzdF4cI3BRBmfMhtsAZ74FE  ej3TnFIbCH5Id2OTg4boT810JxG6kYNdSuhOJXTnUboTqUd605/o9rxeLw4I/6OfZ+dekNQo  diEIvbdsgb7LQudeo6selAR3olor1SsNaJ2atEfjWivWLJ2olEr1SyNaJ5atkajWSl2Sf58w  x/zpuux8descRF7GtA7uUmsAlsNKZDU62o0NQZbjy2A1dXpIUs/qslg6DAWRKRFbjK2AVuNq  cDU12oqVQ/bDz7M0aej1HddVAZ9m3VBktlA6uSAdbJgtrEw2WC47KB8tlAxuSgYbJQurEI3W  CU7KBVmH8x+hvW6a5Cuv8qzBWgJ7AT2CmuDMdLY2OwstgL3TNF2GXHDDRTxLgF7AL2CWuDsc  LY1Owq1g3cBf0rsxod0S+1Eho18bJF09lC6OSBbfpgtjUw3XK47IFi9lCxOShcfpQujUo2XK  ymXc520lhu/nuhhlzL0/bx2d0SJvR6PK7KVAZ3KgsdFQ3tCobXBsdrA22VQ50h8KwObgKYK7  RmmVBidrAx2Vgc3KQudFo2tCUrXBRWIWvfM2GR9uycrEVFlY7EbKN0fGphur0w+ZkG2uSD/n  Ra47KNifGpRsr0I/ZkG5uSj6nRaym3c1Yb5XGu1VPm74y9Zqnr/l3gLgJ7AT2CmuDMdLY2Ow  stg5LfKFo1g5pXVlUYxOwitgl7AL3CWtDsaN4c/Hq0nFbboW3Wklh1EhsYGWTKo7LF0dkC2+  SBbHpgvvUw3RKE7LFidkC5+ShcHpQtvUkNv4xz3mmf69P+fdb4H04fR3hDYI9N8nBEXqEm7H  TeFUOpIvCCzJsp9NvCKnSkXB2fGPCzPmEzn1ubdWXkC/yITa/4yXO3eQDlFwlmwShzMhpL83  fqfub9G2Vc12NgWvZDoWnwpMFzkSnH+R747a/S34cbodFCGjE+uGF/CL3qPB3+dKKF3fpgYh  rZbGe4AGsXREdp2NUWvx9lXttXgL9sIFOzpiQkwktz3BZjkdQSy0h/qyngmlmj4tQJ76VFZb  XqI76PVF9eq74najVazv7W3naOXZej1hrs4CU4ODF+olsAZnZJMH7l9z/o5DZofq6yPKt2He  OvE/v1E/HeQ71pp/o9Dy3trdv9XeH8vd5ns96zfeW/18M8N7090DoXj8vwpzHN1T/Ise7aXg  eu77z3m6sVwHaHPe+kWNGP6g7H71f1WP8b9fvbQbw4+TPeb8hD/zlPXnwju+Q7Q3BshA+zlP  4g+Pcnua76c3llPammSMPiKNFf+h52v2d9gc5jn6rt31wZQ1GNfHA1/5jTd/pu1cqwtp7t/1  XmaP2Dyl5zn61TnPP/k+zD7dTnnN9LHw31Pes77QPhp/9ggz56vxf2nKYuHjpUKmY/dxZLCL  U+y0/O1wNSXBEu0Vg5H1n7p6L4j9pcdymcfIbjFLYk0LK+PztE91q7/Hrfvs/A==}`

It reminds me of Carlson Orbit Traps: Fractal1 { ::MefPghn2NyZWPyNOSC43Ng/PUwvP28mU7g8hpnBexsYc7G2enG7+ihqKV5SdpUZ2KV6j9X/G 8U8SSuBmBuE1HZE8IYEBZqHnafYud4/4lv4u7m7nH6O8q3afE+V39t+jzPdALJo7eqr/LPNf ATaQ3N0+juprHwamHm6O2PfVT1dsbq/57+4Q/f8c30p2x/K+NY2bIIC9V31OO33O03etf8LH +R31X+CTtYa3HavM3fe8wr+l2He+LTnvNe8V3d+S7D9z/4AGhu7U38TnPeYofsrd6u5p2xrX an6Gnt10p2LXgq1WXwT7mO8XQvWiU0GBChE43geNlTlEJhSlN87O1+lRQVow7wpsX+iHPPd6 2QrpCO1+9edNQ4o7esfobs9E0n8x52xjtTHf9tHP9q7g2Y6HHe17gn1Nc/055Xd3lPfduda+ A6NIdtc5zXO/Ndt8GE8vvvtf48Ndvn6lvof8a/xObHuWTeEetxzjdv8FwrUWkTpXElnf4B6r v9wQQMgH8X+7tTDXPP++p77n/0U7lrvyKFz6/9/GGt09vUKy9oP9jLdH+lb3f/Q3V3je35j6 x++prz39PhOgW9QirWaH/yQ3bvN+A+j9/fdHQvGn8w3dbYu/yQPItm5EPGVm+/TXT4DgmNOX tMitss2iUrtI5t1lP/H3gpVfsr7I8u0GqkDj2YhSyjbLSucc5zwA9H6aHOwsVz9tDDuWko/z hz3f9DtH7vdV/Eu/RRtP61cHqugfzMeDPT/ATPbk8Dt1/7BmeSyl+pWf7Qt42n97m1awD18d tHfLsI8MMRWjfs/0lhujf8yTdTn7P6rYL+jDdfPS4xRPKI/YtU9EMHp7Pv1PmyHeckqRSedb fLm/G8rJ6OW7YcocfXbon9pfc5sXdwu/2pOhW9S3cbsIb+73/4jX7mjfyf/8w5JtJD6/Aek+ flK86n40Ss7P/0T9P88Y31ru65quXrLF7aWPp+R/5t2puiHc1LT+11wy8hnj1OzD+XdjfJ+J TxVE8nBglJbLDOwD6GG6vctLueP1+Hnn+bfv/KGegwydqfM6ZxvExNvN8G+HcOX+fu/yBtdg H0dt/GohQp/juHbhRf9Tvdd+8JtwPe2/SGTD/KYjsdwWHD3gmpd8hu/7LXMT6FxP8f5WJg98 fAWG2d9gyCPe7k/B+X46v1ZfpDGLUQdOfb6e3b9rGbjmBb9De/0R9afClxFyQN8u2vjPwINs GEuRGa531PqfsijQclyJAeAiHQKQRA6HrYEcDthE92U7bTxNik6pfkaAkciqREBwsACJiFX9 Mb1TBRKTe4QJSFhzZmlreA3jh9BZxqrw/2CMPVeclAb/RRRASX9AbBGX92HjhuBFJVeUaJtR wbw0YAzjxNEuE7VAzcGgox3jqaYLPvfsxMGgpiGOODBjsMYOXlwYKQxocOSSyhcj1McDnkAZ GtZoGFXgyhMj3UigiRptkZEnIEwuHicIaYQxO4GgcFgkgDG5QMbZMmAJTgcFggZLscIubiRD l3kAZKAhI8lRoAkesuBrUycIhRnkYCmVIeS3wEBZnoFg0FoMjSq8hWsyNOhUIVCkyveBk9cI 3ECY2OBlA1YhICYSdGExNjQ7wWSLZKQR5SJM6mDZmRgkgPe0kxJTBKEFRF4CIifwlLoJQuCQ a3HzhcD8kGOKFi6WJBLZKaJzqehshIzgYhVTM3MijWbxOLawKJlU7D8SBObag6C7FTyhsW10 O2SYJQW7aMFm7NgtAZNuJ12GEJQ0AkfpxCk1AnE8omkKeGdSrtNlQcLkoRISFPzscFGoKh82 6YysWybqjQVF9eSPEI/JQSPEDX0SmJzEGCjyaJVwMpMFCTW27hQlSaSRuxKmADeOxKBDjWw0 pMQSAUQKBdbHhaaUsMwVHzMgutlIgddSGIzuVJWvGqEk7s52Io5gmxOYbLYKeFdU42IkTMbg FDasSxUQvKWVCKX2QMH0PG2UVHVLrrz7VdFBxxSq0iN2SpNw/lB243GWwrMcY3tCxx4SSTZ6 5OYCq2cHzMEJ4WhUIzRNlhhtm41m9Y3SD1Ig97yHTs7qBiDmkLwiIvlaUYUSR+ZsgDKcErEM zrpYwYPnKBdzYJYrZ9YQzMWIYdYBJqE0PjtJxOjBk52ufxOdM4yMWBNDk7aRRT+yZR0MWnrb xg2ZsQZ485PC3MWFo+COWmBaKCBmzZiK9qhZsMOLD0VEi3Ubc0NtkDb0jzAb8bixxVEV3sSz 2s5yaYGbDYGvCK277jkl3x6KT7YmqWr6sqJhO/mcU7MW9WkiatqZWCszBFx55o0g7OVRNThI C9eV59SmyUUw/PStpCYeqzSJoWX2QKQZ4VQFhISQFrxEBj0NptKhVGLzSR1inJGkk6BeMIpw J8YQaq7dxg0CH8iBZVtHYAZrZPwAyTd8PGkX4dYMoI1pyYQfvq+/VCKjChLDU6ihjyQVAVph bEDqKC4IGsJ1V2YwmCnZTmAgqaCyODAVECZCKONOnEUcRoOJokUvoTQJFOSngSTjeNBlGFCb FUWaMWJosiwsSQ5pewngyLciPBNOA6c04QorgKTjvLBVWEiXCqKN6hEUVRAEJoNpBwng2UEF fiVBUaslJmFQFhXmgiTjcJBFvWwLWUSaCESQJFZRIBlmGXbCq1WP4xPr2SOCrqBbLKrMAXdW N+9n66GCOyrjqSY3eKpUntXwiN4oHpKuZ9DUB+w8SxJhdaCJnIFPzp+Mcah50McXAZMZDqCu 1aM8GwE3q4c7ifEEkFqEnb71ZI6K6ubNDYmkUprzWKTAdf175NGnhYPApvCucxP46ddh0TQ5 V0djVaKDWNjpVxbS9xMD3mtCdkzUZ9pNez16sSWZejNzFNSQAr355dpCCnZJ7lRVgrYMGUi6 VgJlFQwHIS1Kg4MQAu776/bHeq909TtK30eYJOFW0ITKyOnXbPDjZoSQn/GcGysONG0FNAlR qBamqDOyhsrSjBpWPllg3ciSQmfvYY3iMQ/McOhWpFNTgh9uE2OoYQ/OFECiWC683AmYRIZg CXEzoGvBwYQrL/QwrEj/Gxg+dJA/Cwlge/NkYRenjy5oCL0i33N8V9Rv2pWccUIt5TIqMfcd EGF59ROBlsBKZbUzImgqn6pyR95JF64VVQ9ZJHJM+5kgazjkCm45X0ngy3AlvNaYvfw/rcU/ e/gP91Q9JPF2EpoVdeRyoiqtqdsjRlSzilE0l8LJaconv9n36mDLPtOIhjLJs6EjD5HICj4N ayMHEQEGxjhpkCMvncErl4IMaIiSqqAzMSiAHERyMMmdBNmF8rJCzOKKkSJllixd9m6UJWg5 DCA6LFpYC3e8qlVILYuRPmzsTEmN33EGPke0IM3CStfzyUM/YKRwi3Y6jfrf+hniWRiRklTZ yXqfNpOCSSdc7ZTA/H0TUiTWm1Qqiv4V+iLBL4+xTw9PZVcm30HZ5otWwd5LEmihqL8+YnVN kKCP3HJLsnVVczIZDo7COvEXYtuCOU1oqinlA4McvFY9WQVxVhIwWOmxFc74ueBk3Xuuv2Nq Pf6n/RshYwLWUWhe/IBr4uDTPH2vrajEhLgteRKlLJSKF26EJnJEm0tmCbz0GEOqAhrB7SdP H26lWAbiFD8xRAbUWD2nvN9ykCYr/j6lJ1FbhdPEBnZCWIF2YTGcXQHzbNYfk9ISTZLb9dEC 7X6TwTKsZokzBfDr0brWmm4E7HbHnbv23GPKjsJCbpI/KbF4akPUjYQ3ZRqocTg2xgkQCe8i bMo1vJEGCxoJD0M2iVwIrsSL6PTSFE+SGoPGb9c1SQrfTKtnn5to9kJFKwv+mSQnJaYFqJrJ xgWb0YwZUeFdUGOUEj7PxgLnQJtiOq8hgIJ4MQVYrLGxfvZ03TBYk+9TP8U70xoxTtHd41eL 3QrAGc5hO6K1ldIuhDDx81rLr9dY3PIM51rrQuzx+4XqVXU3MH9ZrtedZWiD2VlS1G1Fr4k1 qVXuV8md3XvusG6lmgyXvuMzJAbTUBuZ96ya1HRFhwQrVX+t5phUEUruMTm4Qk+oG+61lynD PGfj+LlL50SIIP91w58U5mBa32dNUc5uJUgVZoDiXAbtHALFAzq1gtB+pzylxGUKsbiDXvXV NYakV5CY7u/ImkQrCb3PgwgNYFFw+NDkhkdkCbmUglgjTmEOnCbNdw1HEQVYhdDUY5gJe1UY zwqAsR0Qr2bHyQHTVK2hd9x+T+IF2lvXIUQTQHXavO3NoCZOSpao+xYfZLxQrXMlAR8QMvDY LQkojpOByeOIgLVQ4A5Q0ojpOByMWqPbFsfiwCEL66EkA5GDpNCvZnFIe01JIByO2pkswA/C kI6opTgk+FZhF/LQyorTQCU0YVRvnK66EEK4Dt9j3f+bLLJxY4/jEzGeF3omenG71vooKsJL HLbU0mVqCirKIYiqWVYTzJTho41qCb2Q4clgVVK85DBMtgWpKcnSKhYPbniqwHINElCblqg7 ChDbPzliqwfip67ryKVhdNL47IEtTtqwMFgQkgbayVqCpLJio4lERVhfCBKcslFVh9CeozQq xg31nO/t5+TL5OB2NQbM1dXJDl6tR3wl2bxQMozddKIbqSQn9bzRElBatODuWJ9m2jBt57lz 1e0kBGyaSDWWCaGvlUYVGKH06FOSHVdFQu/AIoGnLjBtZFRp1CeJodXZIwAOJHU6CSE6Wxlg 2Ukpzmlx0TMoZwEBb6K9eGFDavFiQoG213XPP01PkkBMkP2KfZ+xQsJi3EIfAXw2sscIrHaI h2b+EIzoHVH+p/cRXgoud3EsMIfE16oZyhcnXKYY3sNxCEPYozfYxLQ810Jv73Q8zFtkYNdS GSCIXmDJ9OtTxkEIf6KVLzGDQ2Mvj1zhNp/4p+hhwaNOB485z2UifvSMMWYWj7BcGbhhbjXO +3eJBIh7cnDwvToylRfHg9x6TgzcZb8vNb5+ExTlHWoFM+m5B4RKQ0bzjk+U5RUXBERKQ0bL jk+U5RWXBURKQ0brikeX9Mc+2xftfsLJbxYaSRulKSuUiCnUWEod9CjS4mbdSMo1lGIO8QGJ jBNrZIYIkBWOoPZU0QiijBdrb46A1zAtphC8kE5XdEDyXXH5e7kEWlWUsuOK2UHlrrjyN1R1 66oqQHHa/U30UbU0FgDCyoCCpgAcdyLmLQ+s9DWXxJQE3BMAbZpyho2r3EnSMrNXgo+LshCX Ie28Cr7/Zptk9wYhw8DpPcBivmOx3QnErpTiN0J5a6kcDdStmOpK0pr6kQ82kDnRfC/xl4zo AscPoVRYEfmbaIZYRhI0Ug5D9z5+aEmNJgcRT4yuEh59nEmkySxc3hZUjUUqb8V1N+W6mYVd TsluJXV3kbpbqV1NVpudRHhfiHFYFPqA/VxipjAqAyeTAAXCxGz0LQLXCAvPqLQ2k7J0xbl2 SBbjgl8cIWINbUaCEr4ebtAxXTn4boTi10JxG6kcNdSuhOpWTnK/xGc92pTdTpe+Zc1wXg/6 yT1X2EROkzOox/pEIi/WmESA2C0ypqIwJQ0wd1UWAZjymxaawNJQLeYwKg4rpT8N0Jxa6kYD dSumOJ3QnUrpTqCd6x2hhUjgG/ysP27vBWfoIko32P8IDLn9A+fOD6LRUEA1PzR1EX9WTeKK JcObegwtLGH1wenK0Bf4SQ/wQqZu06x7MRhCIiUgo3WEJ9pyjsuCIjUgo3WFJ9pyjKVB+Wv+ 3Fd+VEwi4LzvSBiqSYOtxFITvt2ZUikmDZvYg6sAbm5sAZdXgDzdF4cI3BRBmfMhtsAZ74FE ej3TnFIbCH5Id2OTg4boT810JxG6kYNdSuhOJXTnUboTqUd605/o9rxeLw4I/6OfZ+dekNQo diEIvbdsgb7LQudeo6selAR3olor1SsNaJ2atEfjWivWLJ2olEr1SyNaJ5atkajWSl2Sf58w x/zpuux8descRF7GtA7uUmsAlsNKZDU62o0NQZbjy2A1dXpIUs/qslg6DAWRKRFbjK2AVuNq cDU12oqVQ/bDz7M0aej1HddVAZ9m3VBktlA6uSAdbJgtrEw2WC47KB8tlAxuSgYbJQurEI3W CU7KBVmH8x+hvW6a5Cuv8qzBWgJ7AT2CmuDMdLY2OwstgL3TNF2GXHDDRTxLgF7AL2CWuDsc LY1Owq1g3cBf0rsxod0S+1Eho18bJF09lC6OSBbfpgtjUw3XK47IFi9lCxOShcfpQujUo2XK ymXc520lhu/nuhhlzL0/bx2d0SJvR6PK7KVAZ3KgsdFQ3tCobXBsdrA22VQ50h8KwObgKYK7 RmmVBidrAx2Vgc3KQudFo2tCUrXBRWIWvfM2GR9uycrEVFlY7EbKN0fGphur0w+ZkG2uSD/n Ra47KNifGpRsr0I/ZkG5uSj6nRaym3c1Yb5XGu1VPm74y9Zqnr/l3gLgJ7AT2CmuDMdLY2Ow stg5LfKFo1g5pXVlUYxOwitgl7AL3CWtDsaN4c/Hq0nFbboW3Wklh1EhsYGWTKo7LF0dkC2+ SBbHpgvvUw3RKE7LFidkC5+ShcHpQtvUkNv4xz3mmf69P+fdb4H04fR3hDYI9N8nBEXqEm7H TeFUOpIvCCzJsp9NvCKnSkXB2fGPCzPmEzn1ubdWXkC/yITa/4yXO3eQDlFwlmwShzMhpL83 fqfub9G2Vc12NgWvZDoWnwpMFzkSnH+R747a/S34cbodFCGjE+uGF/CL3qPB3+dKKF3fpgYh rZbGe4AGsXREdp2NUWvx9lXttXgL9sIFOzpiQkwktz3BZjkdQSy0h/qyngmlmj4tQJ76VFZb XqI76PVF9eq74najVazv7W3naOXZej1hrs4CU4ODF+olsAZnZJMH7l9z/o5DZofq6yPKt2He OvE/v1E/HeQ71pp/o9Dy3trdv9XeH8vd5ns96zfeW/18M8N7090DoXj8vwpzHN1T/Ise7aXg eu77z3m6sVwHaHPe+kWNGP6g7H71f1WP8b9fvbQbw4+TPeb8hD/zlPXnwju+Q7Q3BshA+zlP 4g+Pcnua76c3llPammSMPiKNFf+h52v2d9gc5jn6rt31wZQ1GNfHA1/5jTd/pu1cqwtp7t/1 XmaP2Dyl5zn61TnPP/k+zD7dTnnN9LHw31Pes77QPhp/9ggz56vxf2nKYuHjpUKmY/dxZLCL U+y0/O1wNSXBEu0Vg5H1n7p6L4j9pcdymcfIbjFLYk0LK+PztE91q7/Hrfvs/A== }

Ultra Fractal author

0

Here's another link in support of the Domain Coloring idea with an interesting collection of pics, my favorite one is the first. The white lines are cool. And down the page look to be some Ducky fractals, go figure! http://blog.hvidtfeldts.net/index.php/2012/03/lifted-domain-coloring/

Here&#039;s another link in support of the Domain Coloring idea with an interesting collection of pics, my favorite one is the first. The white lines are cool. And down the page look to be some Ducky fractals, go figure! http://blog.hvidtfeldts.net/index.php/2012/03/lifted-domain-coloring/

0

Very nice Frederik, thank you! I love Carlson orbit traps, kcc coloring. It is one of my favorites for sure with the beautiful bright colors. One thing I didn't realize is that it actually says Direct Coloring underneath:

Looks like Paul Carlson also did a study on Newton M-set fractals found here:

Very neat pics! You can do a lot with the Newton formula with so many variations. I just don't know how he captured the Mandelbrot set in the middle of these.

Very nice Frederik, thank you! I love Carlson orbit traps, kcc coloring. It is one of my favorites for sure with the beautiful bright colors. One thing I didn&#039;t realize is that it actually says Direct Coloring underneath: ![63c2ba731d1e2.jpg](serve/attachment&amp;path=63c2ba731d1e2.jpg) Looks like Paul Carlson also did a study on Newton M-set fractals found here: https://www.semanticscholar.org/paper/Two-artistic-orbit-trap-rendering-methods-for-M-set-Carlson/0341ad367379fba0700bbd2fde485fa4689c8d40 Very neat pics! You can do a lot with the Newton formula with so many variations. I just don&#039;t know how he captured the Mandelbrot set in the middle of these.

0
119
views
6
replies
3
followers
live preview
Enter at least 10 characters.
WARNING: You mentioned %MENTIONS%, but they cannot see this message and will not be notified
Saving...
Saved
All posts under this topic will be deleted ?
Pending draft ... Click to resume editing