Tame an explosive rule of your choice (space fabrication)

shouldsee · Post by **shouldsee** » May 12th, 2016, 9:28 pm

As you know, my current favorite is 23/2/4.

Here are some fabrics useful for taming (as validated in 23/2/4), tile-clip each and fill with rfill_mash.py.

Code: Select all

x = 87, y = 106, rule = BGRainbowR2
4$.3C.C$C.C$4C.C$C.4C$3.C.C$C.4C11$6.4C.C$6.3C.C$6.4C.C$6.C.4C$7.C.3C
$6.C.4C2$81.3C$48.3C29.2C.2C$48.C.C29.C.C.C$18.2C28.C.C29.2C.2C$17.3C
61.3C$17.2C4$9.C8.2C28.3C14.3C$10.C6.C.C29.C15.C.2C$17.2C2.C.C24.3C
14.C2.C$21.2C42.4C$22.2C3$48.C.C$49.C$48.3C5$49.2C$48.3C$48.C.C$48.2C
5$49.2C$48.C.C$48.C.C$9.C.C36.2C$9.2C5$49.2C$48.C.C$48.C.C$48.C.C$48.
2C22.3C$71.2C.2C$71.C.C.C$71.2C.2C$72.3C4$47.3C$47.C.C$47.C.C$47.C.C$
47.3C5$47.4C$47.C2.C$47.C2.C$47.C2.C$47.4C5$47.5C$47.C3.C$47.C3.C$47.
C3.C$47.5C!

One of the self-organising example

Code: Select all

x = 167, y = 158, rule = BGRainbowR2
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3C3.3C3.3C3.3C3.3C3.3C3.3C3.3C3.3C3.3C3.3C3.3C3.3C!

BTW, you need to extend your rule of choice to include a fabric/medium state that constricts cell life inside it. This could be done with the rule-table generator that came with golly.

Good luck!

blah · Post by **blah** » May 13th, 2016, 8:24 am

I guess that one example of a ruletable that allows you to do this is Wireworld, interestingly. The CA inside a large wire (/12/3) is incredibly explosive:

Code: Select all

x = 1, y = 1, rule = /12/3
A!

I wonder if it would be possible to create circuits of the same kind of functioning you find in Wireworld, but with a less explosive rule. For one, it would mean stable memory; Wireworld requires all RAM be unstable in at least one state, and you always must contend with timing. Although I guess that's all unrelated to the subject of tilings, so to get to that topic I guess it's interesting a checkerboard tiling makes /12/3 create simple waves:

Code: Select all

x = 16, y = 16, rule = WireWorld
.C.C.C.C.C.C.C.C$C.C.C.C.C.C.C.C$.C.C.C.C.C.C.C.C$C.C.C.C.C.C.C.C$.C.
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shouldsee · Post by **shouldsee** » May 13th, 2016, 9:21 am

blah wrote:I guess that one example of a ruletable that allows you to do this is Wireworld, interestingly. The CA inside a large wire (/12/3) is incredibly explosive:
Code: Select all
x = 1, y = 1, rule = /12/3
A!

Indeed, it's almost explosive/random as ice-9.

shouldsee · Post by **shouldsee** » May 13th, 2016, 11:16 am

I tried my fabrics so far (in fab1.mc) under WireWorld

I wonder if it would be possible to create circuits of the same kind of functioning you find in Wireworld, but with a less explosive rule. For one, it would mean stable memory; Wireworld requires all RAM be unstable in at least one state, and you always must contend with timing. Although I guess that's all unrelated to the subject of tilings, so to get to that topic I guess it's interesting a checkerboard tiling makes /12/3 create simple waves:

I don't quite understand about "stable memory" yet.

blah · Post by **blah** » May 13th, 2016, 4:06 pm

Stable as in not oscillating, so you don't have to worry about timing (spartan, I guess).
What I find interesting about these fabrics is that they effectively simulate different neighbourhoods and tilings. For example, a checkerboard is von neumann neighbourhoods, but I wonder if others are possible with this. Especially the chickenwire fabric:

Code: Select all

x = 64, y = 64, rule = WireWorld
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2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C$2C2.2C2.2C2.2C2.2C2.2C2.
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2C2.2C2.2C2.2C2.2C$2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.
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2.2C2.2C2.2C2.2C2.2C2.2C2.2C!

I'm pretty sure this actually simulates a triangular tiling, which is interesting. I don't believe it's possible to emulate a triangular neighbourhood simply using a square lattice and specific neighbourhood, like it is with hexagons. As such, this fabric allows you to run a triangular rule in golly, with the fitting ruletable (wireworld simulates /12/3T here). I guess the way the cells are organised in the chickenwire fabric makes waves formed by B1 rules into hexagons.

If anyone can confirm mathematically that this does infact simulate a triangular tiling, that would be nice.

shouldsee · Post by **shouldsee** » May 13th, 2016, 10:32 pm

An interesting frequent puffer

Code: Select all

x = 29, y = 15, rule = BGRainbowR2
.C.C.C.C.C.C.C.C.C.C.C.C.C.C$C.C.C.C.C.C.C.C.C.C.C.C.C.C.C$20CE8C$.C.
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.D.C.D.C.C.D.C.C.C.C.C.C.C$C.D.C.D.D.C.D.D.C.D.C.C.E.D.C$3C4D2C4D2C4D
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.C.C.C$29C!

Cyclic behavior with distinct phases

Code: Select all

x = 121, y = 99, rule = BGRainbowR2
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.3C.3C.3C.3C.3C.C$2C.3C.3C.3C.3C.3C.3C.3C.3D.3C.3C.3C.3C.3C.3C.3C.3C.
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3C.C$2C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.
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3C.3C.3C.3C.3C.2C$2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.
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2C$C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.
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.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.C$2C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.
3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.2C$2.2C2.2C
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2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C$C.3C.3C.3C.3C.3C.3C.3C.
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3C.3C.3C.3C.3C.3C.3C.C$2C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.
3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.2C$2.2C2.2C2.2C2.2C2.2C
2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C
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3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.2C$2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C
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2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C$C.3C
.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C
.3C.3C.3C.3C.3C.3C$3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C
.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.3C.C!

A manual assertion trying to categorise tiles according to their patterns.

Graphical: 1D/DG(1 dimensional/diagonal) 1D(1 dimensional,orthogonal),2D(2 dimensional), T(timed life), O(other)
class 1 for replicators, class 2 for patterns that stablise into SS/osci, class 3 for explosive/chaotic

it was quite useful to rearrange the shape to spot similarity

Code: Select all

x = 409, y = 99, rule = BGRainbowR2
3.2A4.2A2.2A.A13.2A2.2A.A9.A.2A2.2A$4.A5.A2.A.2A14.A2.A.2A9.2A.A2.A$
3.A5.A20.A23.A88.2A4.2A2.2A.A$3.2A4.2A6.2A11.2A6.2A5.2A6.2A89.A5.A2.A
.2A$18.A20.A5.A97.A5.A$3.2A4.2A6.A12.2A6.A7.A96.2A4.2A6.2A$4.A5.A6.2A
12.A6.2A5.2A111.A$3.A5.A20.A20.A.2A88.2A4.2A6.A$3.2A4.2A19.2A19.2A.A
89.A5.A6.2A$17.2A19.2A5.2A96.A5.A$3.2A4.2A7.A11.2A7.A5.A7.2A88.2A4.2A
97.2A.A$4.A5.A6.A13.A6.A7.A6.A103.2A89.A.2A4.2A2.2A.A$3.A5.A7.2A11.A
7.2A5.2A7.A88.2A4.2A7.A93.2A3.A2.A.2A$3.2A4.2A.2A.A14.2A.2A.A11.A.2A.
2A89.A5.A6.A94.A3.A83.2A.A.2A.A.2A.A$12.A.2A17.A.2A11.2A.A91.A5.A7.2A
94.A2.2A6.2A74.A.2A.A.2A.A.2A43.2A.A$143.2A4.2A.2A.A96.2A11.A131.A.2A
$152.A.2A92.2A.A4.2A6.A81.2A$248.A.2A5.A6.2A81.A45.2A6.2A$246.2A8.A
89.A47.A7.A$246.A9.2A88.2A45.A7.A$247.A16.2A127.2A6.2A$246.2A8.2A7.A$
248.2A.A5.A6.A81.2A45.2A$248.A.2A4.A7.2A81.A46.A6.2A$256.2A.2A.A83.A
46.A8.A$259.A.2A83.2A45.2A6.A$401.2A$396.2A.A$396.A.2A3$4C13.4C14.3C$
2C.C13.2C.C15.C.C$C2.C13.C2.C14.C.2C$3C14.3C15.2C.C$385.C.2C6.C.2C6.
4C$385.2C11.C6.C.C$385.4C6.C.2C6.4C$385.2C.C6.4C6.3C3$100.2A48.2A.A$
101.A48.A.2A34.2A.A$100.A53.2A32.A.2A32.2A$100.2A52.A37.2A31.A30.2A.A
$155.A36.A31.A31.A.2A34.2A.A87.4C$100.2A52.2A37.A30.2A34.2A32.A.2A88.
C.C$101.A48.2A.A38.2A66.A37.2A85.C.2C$100.A49.A.2A34.2A.A32.2A35.A36.
A86.3C$100.2A46.2A38.A.2A33.A34.2A37.A$148.A43.2A30.A31.2A.A38.2A$
100.2A47.A42.A31.2A30.A.2A34.2A.A$101.A46.2A43.A60.2A38.A.2A$100.A49.
2A.A38.2A30.2A28.A43.2A$100.2A48.A.2A34.2A.A33.A29.A42.A$188.A.2A32.A
29.2A43.A$224.2A30.2A.A38.2A$256.A.2A34.2A.A$294.A.2A2$135.3C$135.C.
2C18.4C$100.2C.C5.3C23.C2.C8.3C7.2C.C$88.C.C9.C.2C5.C25.3C9.C.C7.C2.C
$88.C.C9.C2.C5.C.C35.C.C7.2C.C$89.2C9.C.2C6.2C73.4C57.3C6.4C6.4C26.4C
7.3C26.4C7.2C8.2C$185.C59.3C7.3C7.3C17.4C6.2C.C7.3C26.C2.C7.C.C6.2C.C
$185.2C.C56.C2.C6.C2.C6.C2.C16.C.C9.2C7.C.C26.C.C7.C.2C6.C.2C$186.3C
56.4C6.3C7.4C17.C.C6.4C7.C.C27.3C6.3C7.C.2C3$135.3C$135.C.C7.2C11.4C$
135.2C.C6.C2.C9.2C$136.3C6.C2.C9.C.2C$145.4C10.3C83.C.2C6.4C27.C8.2C.
C8.4C25.2C7.2C.C6.3C$245.4C6.C2.C26.2C8.4C8.C.2C26.2C6.C2.C7.3C$245.C
2.C6.C.2C26.2C8.2C.C8.2C.C25.2C7.C.C7.2C.C$245.4C6.4C26.2C8.4C36.2C.C
6.4C6.3C7$135.4C7.2C97.4C6.3C7.4C$135.C2.C7.2C.C6.C.2C85.C2.C6.C.2C6.
4C15.4C7.4C$135.C.C8.C2.C6.C.2C85.C2.C6.2C.C6.C2.C15.C2.C7.C2.C$136.
2C8.4C6.C2.C85.4C7.C.C6.2C17.C2.C7.C.2C$156.3C125.3C9.3C6$136.3C$135.
C2.C$135.4C$135.4C!

Assertion in text

Code: Select all

notepad topo_area16_hash1926950739


1D DG

x = 4, y = 4, rule = BGRainbowR2
2C.C$.2C$3C$4C!
1D asymmetric diagonal ss,stable

x = 4, y = 4, rule = BGRainbowR2
4C$C.2C$2.2C$2C.C!
1D dg as ss/puf, stable

x = 4, y = 4, rule = BGRainbowR2
C.C$2C.C$3C$.3C!
1D diagonal ss, stable


1D
x = 3, y = 3, rule = BGRainbowR2
C.C$3C$.C!
1D replicator,stable

x = 4, y = 4, rule = BGRainbowR2
.3C$3C$.2C$3C!
1D rep ,stable

x = 3, y = 4, rule = BGRainbowR2
3C$.C$2C$C.C!
1d replicator, crystal

x = 4, y = 4, rule = BGRainbowR2
4C$2C$2C.C$2.2C!
1D bias,  stable,7.37437459213

x = 3, y = 3, rule = BGRainbowR2
3C$.2C$.2C!
1D SS stable

x = 4, y = 4, rule = BGRainbowR2
.2C$.3C$4C$.3C!
1D ss stable

x = 4, y = 4, rule = BGRainbowR2
2C$.3C$2C.C$C.2C!
1D SS,bias,stable

x = 4, y = 4, rule = BGRainbowR2
2.2C$.2C$.2C$4C!
1D SS bias slow stable

x = 4, y = 4, rule = BGRainbowR2
4C$2C$C.2C$.3C!
1D as SS's ,stable

x = 4, y = 4, rule = BGRainbowR2
4C$2.2C$.C.C$2C!
1D SS's, stable

x = 4, y = 4, rule = BGRainbowR2
.2C$3C$2C$4C!
1D SS, kind of slow,stable

x = 4, y = 4, rule = BGRainbowR2
2C.C$3.C$4C$3C!
1D 	unidirectional SS, stable

x = 4, y = 4, rule = BGRainbowR2
.3C$C2.C$4C$4C!
1D SS stable-chaos



x = 4, y = 4, rule = BGRainbowR2
.3C$2C.C$4C$2.C!
1D line puffer




2D

x = 4, y = 4, rule = BGRainbowR2
C.C$.3C$.3C$4C!
!!!very interesting!!!
2D bias,ordered,stable

x = 4, y = 4, rule = BGRainbowR2
C.C$3C$4C$4C!
bd as ss/puf, stable

x = 4, y = 4, rule = BGRainbowR2
C.2C$.3C$2C$4C!
dg orth puffer n ss, stable

x = 4, y = 4, rule = BGRainbowR2
4C$.3C$.3C$C.2C!
2D SS biased,stable

x = 4, y = 4, rule = BGRainbowR2
4C$4C$C.C$C.2C!
2D SS stable

x = 4, y = 4, rule = BGRainbowR2
2C.C$3C$C.C$.3C!
2D ss bias stable

x = 4, y = 4, rule = BGRainbowR2
.2C$4C$4C$.2C!
2D SS stable

x = 4, y = 4, rule = BGRainbowR2
4C$4C$C2.C$2C!
2D ss stable

x = 4, y = 4, rule = BGRainbowR2
3C$4C$4C$.2C!
2D SS stable,18.6285949619



x = 2, y = 3, rule = BGRainbowR2
.C$2C$C!
2D,biased frequent ring puffer, 16.815104285

x = 4, y = 4, rule = BGRainbowR2
4C$4C$.3C$2C!
2D bias, ss cluster,longlive



x = 3, y = 4, rule = BGRainbowR2
3C$3C$2C$2C!
2D explosive, 21.5303593556

x = 2, y = 4, rule = BGRainbowR2
.C$2C$2C$2C!
2D explosive line puf

x = 4, y = 4, rule = BGRainbowR2
2C.C$4C$2C.C$4C!
2D bias, pop-oscillation, chaotic,25.1342910681

x = 4, y = 3, rule = BGRainbowR2
4C$.3C$C.2C!	
2D bias explosive ,26.4041994751

x = 4, y = 4, rule = BGRainbowR2
C.2C$4C$.2C$.2C!
2D bias, explosive

x = 4, y = 4, rule = BGRainbowR2
C.2C$.3C$4C$C2.C!
,2D exposive,17.1930142069



!!timed life!!, form short-lived ss-like structure

x = 4, y = 4, rule = BGRainbowR2
4C$C2.C$C.C$.3C!
timed transient structure

x = 4, y = 4, rule = BGRainbowR2
.2C$.C.C$C.2C$3C!

x = 4, y = 4, rule = BGRainbowR2
.2C$2C.C$C.2C$C.2C!

x = 4, y = 4, rule = BGRainbowR2
.2C$2.2C$.2C$2C.C!

x = 4, y = 4, rule = BGRainbowR2
2C.C$C2.C$C.C$4C!

x = 4, y = 4, rule = BGRainbowR2
3C$.3C$2C.C$3C!




Oscillators
x = 4, y = 4, rule = BGRainbowR2
C.2C$2C$4C$2C.C!
osci

x = 4, y = 4, rule = BGRainbowR2
C.2C$3.C$C.2C$4C!
stab osci



MISC:
x = 4, y = 4, rule = BGRainbowR2
4C$C.C$4C$3C!
transient secondary random cell

x = 4, y = 4, rule = BGRainbowR2
4C$.C.C$C.2C$3C!
explosive, transient line structure

I made a collection of population plot of 20,000 tiling with a size <=4, no hash collision, but still much redundancy

Attachment is a demo of some interesting fabrics out of manual assertion

shouldsee · Post by **shouldsee** » May 14th, 2016, 4:43 am

blah wrote: I'm pretty sure this actually simulates a triangular tiling, which is interesting. I don't believe it's possible to emulate a triangular neighbourhood simply using a square lattice and specific neighbourhood, like it is with hexagons. As such, this fabric allows you to run a triangular rule in golly, with the fitting ruletable (wireworld simulates /12/3T here). I guess the way the cells are organised in the chickenwire fabric makes waves formed by B1 rules into hexagons.

If anyone can confirm mathematically that this does infact simulate a triangular tiling, that would be nice.

If you think about the connectivity, every cell in that pattern has exactly 3 cell around it, thus I don't see why it shouldn't be a triangular neighborhood. One way to validate it is to emulate an iconic triangular rule.

I suspect this is a 5-cell neighborhood, apparently every cell has 5 neighbors here, but since pentagon can't fill a plane, I am not sure about its geometric equivalent.

Code: Select all

x = 74, y = 75, rule = BGRainbowR2
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BlinkerSpawn · Post by **BlinkerSpawn** » May 14th, 2016, 8:57 am

It is indeed a representation of a pentagonal tiling; it's equivalent to a 45-degree rotation of this tiling, with region adjacency only along edges:

Code: Select all

x = 41, y = 41, rule = B/S012345678
41o$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo$o5bobo
3bobo5bo5bobo3bobo5bo$o4bo3bobo3bo4bo4bo3bobo3bo4bo$o3bo5bo5bo3bo3bo5b
o5bo3bo$4o6bo6b7o6bo6b4o$o3bo5bo5bo3bo3bo5bo5bo3bo$o4bo4bo4bo4bo4bo4bo
4bo4bo$o5b9o5bo5b9o5bo$o4bo4bo4bo4bo4bo4bo4bo4bo$o3bo5bo5bo3bo3bo5bo5b
o3bo$4o6bo6b7o6bo6b4o$o3bo5bo5bo3bo3bo5bo5bo3bo$o4bo3bobo3bo4bo4bo3bob
o3bo4bo$o5bobo3bobo5bo5bobo3bobo5bo$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo
5bo6bo$o6bo5bo6bo6bo5bo6bo$41o$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo
$o6bo5bo6bo6bo5bo6bo$o5bobo3bobo5bo5bobo3bobo5bo$o4bo3bobo3bo4bo4bo3bo
bo3bo4bo$o3bo5bo5bo3bo3bo5bo5bo3bo$4o6bo6b7o6bo6b4o$o3bo5bo5bo3bo3bo5b
o5bo3bo$o4bo4bo4bo4bo4bo4bo4bo4bo$o5b9o5bo5b9o5bo$o4bo4bo4bo4bo4bo4bo
4bo4bo$o3bo5bo5bo3bo3bo5bo5bo3bo$4o6bo6b7o6bo6b4o$o3bo5bo5bo3bo3bo5bo
5bo3bo$o4bo3bobo3bo4bo4bo3bobo3bo4bo$o5bobo3bobo5bo5bobo3bobo5bo$o6bo
5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo$41o!

Fun fact: This tiling is 3-colorable.

shouldsee · Post by **shouldsee** » May 14th, 2016, 11:51 am

BlinkerSpawn wrote:It is indeed a representation of a pentagonal tiling; it's equivalent to a 45-degree rotation of this tiling, with region adjacency only along edges:

Code: Select all

x = 41, y = 41, rule = B/S012345678
41o$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo$o5bobo
3bobo5bo5bobo3bobo5bo$o4bo3bobo3bo4bo4bo3bobo3bo4bo$o3bo5bo5bo3bo3bo5b
o5bo3bo$4o6bo6b7o6bo6b4o$o3bo5bo5bo3bo3bo5bo5bo3bo$o4bo4bo4bo4bo4bo4bo
4bo4bo$o5b9o5bo5b9o5bo$o4bo4bo4bo4bo4bo4bo4bo4bo$o3bo5bo5bo3bo3bo5bo5b
o3bo$4o6bo6b7o6bo6b4o$o3bo5bo5bo3bo3bo5bo5bo3bo$o4bo3bobo3bo4bo4bo3bob
o3bo4bo$o5bobo3bobo5bo5bobo3bobo5bo$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo
5bo6bo$o6bo5bo6bo6bo5bo6bo$41o$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo
$o6bo5bo6bo6bo5bo6bo$o5bobo3bobo5bo5bobo3bobo5bo$o4bo3bobo3bo4bo4bo3bo
bo3bo4bo$o3bo5bo5bo3bo3bo5bo5bo3bo$4o6bo6b7o6bo6b4o$o3bo5bo5bo3bo3bo5b
o5bo3bo$o4bo4bo4bo4bo4bo4bo4bo4bo$o5b9o5bo5b9o5bo$o4bo4bo4bo4bo4bo4bo
4bo4bo$o3bo5bo5bo3bo3bo5bo5bo3bo$4o6bo6b7o6bo6b4o$o3bo5bo5bo3bo3bo5bo
5bo3bo$o4bo3bobo3bo4bo4bo3bobo3bo4bo$o5bobo3bobo5bo5bobo3bobo5bo$o6bo
5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo$o6bo5bo6bo6bo5bo6bo$41o!

Fun fact: This tiling is 3-colorable.

1.What is 3-colorable?
2.This equivalency you spotted is indeed a nice example of graph duality
3.The tiling problem will now connect to the space-filling problem from time to time.

BTW, I made an adaptation of your graph to allow for signalling.

Code: Select all

x = 60, y = 60, rule = BGRainbowR2
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4.2C4.4C2.4C4.C$C3.2C2.4C2.3C2.2C3.2C2.4C2.3C2.2C3.2C2.4C2.3C2.C$5C4.
2C4.10C4.2C4.10C4.2C4.5C$5C4.2C4.10C4.2C4.10C4.2C4.5C$C3.2C3.2C3.2C3.
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4.2C4.10C4.2C4.10C4.C$C3.2C3.2C3.2C3.2C3.2C3.2C3.2C3.2C3.2C3.2C3.2C3.
C$5C4.2C4.10C4.2C4.10C4.2C4.5C$5C4.2C4.10C4.2C4.10C4.2C4.5C$C2.3C2.4C
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2.4C4.C$C5.2C4.3C4.2C5.2C4.3C4.2C5.2C4.3C4.C$C5.2C4.2C5.2C5.2C4.2C5.
2C5.2C4.2C5.C$C5.2C4.2C5.2C5.2C4.2C5.2C5.2C4.2C5.C$60C$60C$C5.2C4.2C
5.2C5.2C4.2C5.2C5.2C4.2C5.C$C5.2C4.2C5.2C5.2C4.2C5.2C5.2C4.2C5.C$C4.
3C4.2C5.2C4.3C4.2C5.2C4.3C4.2C5.C$C4.4C2.4C4.2C4.4C2.4C4.2C4.4C2.4C4.
C$C3.2C2.4C2.3C2.2C3.2C2.4C2.3C2.2C3.2C2.4C2.3C2.C$5C4.2C4.10C4.2C4.
10C4.2C4.5C$5C4.2C4.10C4.2C4.10C4.2C4.5C$C3.2C3.2C3.2C3.2C3.2C3.2C3.
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shouldsee · Post by **shouldsee** » May 14th, 2016, 5:03 pm

EDIT: Obviously not all graph can be emulated on a cellular automata, but let's not worry about it for now.

EDIT: Had another thought, the original reason I treated it as a torus is to allow recongnition between equivalent tiles. And such configuration is indeed useful for such purpose. Now if we want to compare the topologies of two seemingly different tiles, we just need to compare their adjacency matrix. However, it's worth noticing "adjacency within tile" and "adjacency across tile" should be treated differently.

However I don't have a clue about how to write a such function.
Clue: May eigenvalue+size of the matrix is possible.
Or it seems possible to hash

Code: Select all

hash = 0
for i=1..n
  for j=1..n
    hash=p*hash+A[i][j]

EDIT:Had a thought, I probably shouldn't view the tile to be connected to itself. This tile is indeed equivalent to pentagonal if put in a 3*3 torus (This is still problematic to me at the moment, will update later). See the image below for its connectivity on a larger bounded grid.

: AXIrlrU.jpg (38.61 KiB) Viewed 414 times

As to the pentagon suspect I posted before (I now call it pentagonA fabric), here is its connectivity graph, which is dual to the fabric posted by @BlinkerSpawn.

: aexOj3z.jpg (20.92 KiB) Viewed 414 times

Dual graph

: TbB4IsH.jpg (17.06 KiB) Viewed 414 times

In case anyone want to reproduce the analysis, I attach this export_mathematica_sel.py
After obtaining the adjacency matrix, use AdjacencyGraph[] in mathematic to visualise.

Code: Select all

# Output an adjacency matrix for current selection to clipborad in a "Mathematica" list fomart 
# Use AdjacencyGraph[] in Mathematica for downstream processing.
# Author: Feng Geng(shouldsee.gem@gmail.com), May 2016.

import golly as g
from glife import *

	
def parse_list(clist):
	newlist=[]
	for i in range(len(clist[0::3])):
		if 3*i+3 <= len(clist):
			temp=clist[3*i:3*i+3]
			newlist.append(temp)
		else:
			break
	return(newlist)

	
def full_clist(parsed_clist,pbox):
	# if parsed_clist[0][0:2]!=pbox[0:2]:
		# parsed_clist.insert(0,[pbox[0],pbox[1],0])
	
	# left=pbox[0]
	# top=pbox[1]
	# right=left+pbox[2]-1
	# bottom=top+pbox[1]-1
	
	
	output=[]
	
	for i in [[x,y] for x in range(pbox[2]) for y in range(pbox[3])]:
		i[0]+=pbox[0]
		i[1]+=pbox[1]
		match=i[:]
		match.append(state)
		if match in parsed_clist:
			output.append(match)
		else:
			i.append(0)
			output.append(i)
	return output
	
	
	# for i in range(len(parsed_clist)):
		# c1=parsed_clist[i]
		# c2=parsed_clist[i+1]
		
		
		# if c1[:2]==[right,bottom]:
		
		# c1p=c1[:]
		
		
		# if c1[0]==pbox[0]+pbox[2]-1
		# c1p[0]=c1[0]

		# if y[0]==x[0]+1 or (y[0]==x[0]-pbox[2]+1 and y[1]==x[1]+1):
			# continue
		# else:
			# parsed_clist.insert(i+1,[x[0])
			
def adjacencymatrix(parsed_clist,pbox,torus=0):
	adjacencymatrix=[]
	if torus:
		adjacency=[0,1,pbox[2]-1]
	else:
		adjacency=[0,1]
	# fclist=full_clist(parsed_clist,pbox)
	for i in parsed_clist:
		row=[]
		for j in parsed_clist:
			if 	i!=j and \
			(abs(i[0]-j[0]) in adjacency) and \
			(abs(i[1]-j[1]) in adjacency) and \
			[i[2],j[2]]==[state,state]:
				row.append(1)
			else:
				row.append(0)
				
		adjacencymatrix.append(row)
	return adjacencymatrix

def mathematica(s):
	s=s.replace('[','{')
	s=s.replace(']','}')
	return s

state=int(g.getstring('what cell state to screen for','%s'%g.getoption("drawingstate")))

pbox=g.getselrect()
clist=g.getcells(pbox)
g.setclipstr(str(clist))

parsed_clist=parse_list(g.getcells(pbox))
g.show('clist %i %s,  parsed_clist %i %s'%(len(clist),str(clist),len(parsed_clist),str(parsed_clist))	)

fclist=full_clist(parsed_clist,pbox)		
adjmat=adjacencymatrix(parsed_clist,pbox)
s=mathematica(str(adjmat))
g.setclipstr(s)

Original post

I was examining the connectivity of tiles and it came out that adjacency matrix does not seem to uniquely identify a tile. I'm making this conjecture based on the following case:(EDIT: that conjecture was a bit irrelevant, and the graph is torus-based for a confined space, so ineffective to illustrate the property of a tiled fabric)
Code: Select all
x = 3, y = 3, rule = BGRainbowR2
C.C$3C$.C!
If you plot the adjacency matrix into a graph, you will get, which bear an apparent symmetry:

EfZBLgC.gif (8.71 KiB) Viewed 414 times

However, on a closer look, I checked the number of neighbors with a distance<2 for 2 positions, and identified cell I and cell II as non-equivalents.
1=neighbor with a nearest distance(nd) of 1, 1 possible route to achieve such distance
2a=neighbor of nd2, 2 possible routes
2b=neighbor of nd2, 1 possible route

KlAOmTE.png (28.47 KiB) Viewed 414 times

Thus, I am still in search for a unique notation to identify the connectivity of a tile.

BlinkerSpawn · Post by **BlinkerSpawn** » May 14th, 2016, 5:07 pm

3-colorable graphs are sets of regions that can be colored with 3 colors so that no two regions sharing an edge share a color. Alternatively, they are sets of nodes that can be colored such that no two connected nodes share a color.
See the Wikipedia pages for "graph coloring" or "four color theorem" for more details.

shouldsee · Post by **shouldsee** » May 15th, 2016, 8:40 am

“crawl hard, tough head”(This is an accidental boundar phenomena)

Code: Select all

x = 348, y = 32, rule = BGRainbowR2
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343C!

A stable medium for the pattern above.

Code: Select all

x = 4, y = 7, rule = BGRainbowR2
.C.C$.C.C$C.C$4C$.C.C$C.C$C.C!

This chaotic fabric lives quite long and oscillates near 8.3%

Code: Select all

x = 4, y = 4, rule = BGRainbowR2
2.2C$.3C$2C.C$2C.C!

patterns here die really slowly

Code: Select all

x = 256, y = 87, rule = BGRainbowR2
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shouldsee · Post by **shouldsee** » May 15th, 2016, 11:55 pm

I tried to embed a dynamic topology into the rule, but it turns out particularly hard to find the right balance. Maybe I should try to embed in rules other than 23-2-2 (maybe in 23-2-4 but it's gonna be 7 states......).

DM23-2-2_b5d4.table

Code: Select all

# rules: 32
#
# Golly rule-table format.
# Each rule: C,N,NE,E,SE,S,SW,W,NW,C'
# N.B. Where the same variable appears multiple times in a transition,
# it takes the same value each time.
#
# Default for transitions not listed: no change
#
n_states:3
neighborhood:Moore
symmetries:rotate8
var a={0,2}
var b={0,2}
var c={0,2}
var d={0,2}
var e={0,2}
var f={0,1}
var g={0,1}
var h={0,1}
var i={0,1}
var j={0,1}
var k={0,1}
var l={0,1}
var m={0,1,2}
var n={0,1,2}
var o={0,1,2}
var p={0,1,2}
0,a,b,c,d,e,2,2,2,1
0,0,a,b,c,2,0,2,2,1
0,0,a,0,0,2,2,0,2,1
0,0,0,a,2,0,0,2,2,1
0,0,a,0,2,0,2,0,2,1
0,0,0,0,2,2,0,0,2,1
0,0,0,2,0,0,2,0,2,1
1,f,g,h,i,j,k,2,2,2
1,f,g,h,i,j,2,k,2,2
1,f,g,h,i,2,j,k,2,2
1,a,b,c,d,2,2,2,2,0
1,f,g,h,2,i,j,k,2,2
1,0,a,b,2,0,2,2,2,0
1,0,a,b,2,2,0,2,2,0
1,0,0,0,2,2,2,0,2,0
1,0,0,2,0,0,2,2,2,0
1,0,0,2,0,2,0,2,2,0
1,0,0,2,0,2,2,0,2,0
1,0,0,2,2,0,0,2,2,0
1,0,0,2,2,0,2,0,2,0
1,0,2,0,2,0,2,0,2,0
2,f,g,h,i,j,k,l,m,1
2,m,n,o,p,2,2,2,2,1
2,f,m,n,2,g,2,2,2,1
2,f,m,n,2,2,g,2,2,1
2,f,g,h,2,2,2,i,2,1
2,f,g,2,h,i,2,2,2,1
2,f,g,2,h,2,i,2,2,1
2,f,g,2,h,2,2,i,2,1
2,f,g,2,2,h,i,2,2,1
2,f,g,2,2,h,2,i,2,1
2,f,2,g,2,h,2,i,2,1

b=bfnum
d=dfnum
transition function:

Code: Select all

switch(c)
{
case 0:
if ((state2 neighbors+state3 neighbors>=8-bfnum) and (((nw==0)+(n==0)+(ne==0)+(w==0)+(e==0)+(sw==0)+(s==0)+(se==0)) + +state1 neighbors+state2 neighbors + state3 neighbors+state1 neighbors == 8) and (state1 neighbors<=state2 neighbors + state3 neighbors ))

return 1;
else return 0;


//23-2-2 medium(off)
case 1: 
if ((state2 neighbors)==2) return 2;
else if ((state2 neighbors+state3 neighbors)>=dfnum and (state1 neighbors ==0)) return 0;
else return 1;

//23-2-2 on
case 2:
if (((state2 neighbors + state3 neighbors)|1) == 3) return 2;
else return 1;



default:
	return 0;
}

shouldsee · Post by **shouldsee** » May 16th, 2016, 4:27 pm

As it turns out manual construction of fabrics is equally efficient when there it not a simple parameter to screen for.

Interesting behavior:
1.Spaceship/puffer that are unstable due to inherent uncertainty, yet can partially escape from randomness spontaneously. These unstable forms are not bound to hold the form,which means the movement is an emergent property that does not rely heavily on the initial state.
This phenomena can be explained by the fact that the repeating unit of the fabric forms secondary cell but communicate by a seemingly uncertain (pseudo random) transition function. I take this effect as a manifestation of entropy.
Such behavior is also characterised with frequent death.
(f12,f13,f14)

2.Mashup between orthogonal and diagonal. (fabric 16)

3.Highly dynamic elementary spaceship (f8, f17)

4. proof of construction potential(f9,f10,f11)

other:f13, chaotic oscillator

Here is an this collection of 2D stable fabrics (2Dordered.mc)

Code: Select all

x = 772, y = 193, rule = BGRainbowR2
7$622.3C.C7.3C.C$621.2C.2C7.2C.2C$621.C3.2C6.C3.2C$621.2C.2C7.2C.2C$
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5C.5C.5C.5C.C!

shouldsee · Post by **shouldsee** » May 18th, 2016, 1:48 pm

I generated a bigger random fabric that shows probabilistic hopping between lattice.

Code: Select all

x = 201, y = 141, rule = BGRainbowR2
.4C.C4.2D18C4.C.4C.C4.20C4.C.4C.C4.3C3D14C4.C.4C.C4.18C2D4.C.4C.C4.
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4C$9C.3C2.3C6.3C2.3C.12C.3C2.3C6.2FC2.3C.12C.3C2.3C6.3C2.ECE.12C.3C2.
3C6.3C2.3C.12C.3C2.3C6.3C2.3C.12C.3C2.3C$.4C2.3C.2C.C.2C2.2C2.2C.C.2C
.3C2.4C2.3C.2C.C.2C2.2C2.2C.C.2C.3C2.4C2.3C.2C.C.2C2.2C2.2C.C.DF.D2C
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.C.2C.2C2.C2.C2.2C.2C.C.4C.2C.4C.C.2C.2C2.C2.C2.2C.2C.C.4C.C$2C2.6C.
9C2.9C.6C2.6C.7C2F2.F2DCE4C.6C2.6C.9C2.7C2D.6C2.6C.9C2.9C.6C2.6C.9C2.
9C.6C2.6C.9C$.4C.C4.20C4.C.4C.C4.3CDEFCE3CECD6C4.C.4C.C4.8CEDE9C4.C.
4C.C4.20C4.C.4C.C4.20C4.C.4C.C4.10C$9C.3C2.12C2.3C.12C.3C2.EF10C2.3C.
12C.3C2.3CFCFCF4C2.3C.12C.3C2.12C2.3C.12C.3C2.12C2.3C.12C.3C2.6C$C.2C
.8C3.10C3.8C.2C.8C3.8CFC3.8C.2C.8C3.10C3.8C.2C.8C3.10C3.8C.2C.8C3.10C
3.8C.2C.8C3.5C$7C.4C2.2C.8C.2C2.4C.8C.4C2.2C.8C.2C2.4C.8C.4C2.2C.8C.
2C2.4C.8C.4C2.2C.8C.2C2.4C.8C.4C2.2C.8C.2C2.4C.8C.4C2.2C.4C!

Here is a population plot,apparently it's not easy for fall into attractors. It's possible to see consecutive hopping, but till now there isn't a pattern hopping non-randomly.

EDIT: This pattern dies between 40M to 45M

shouldsee · Post by **shouldsee** » June 28th, 2016, 6:39 pm

Recurrence Plot Has long been applied to analyse chaos and complexity. I am wondering how feasible is it for a harmonic oscillator to produce a recurrent plot that contains the space fabrication within which it exists?

Attached is some recurrence plot generated by oscillators in BGRainbow2 that resembles space fabrics.

My current script to make recurrence plot:
RecurrencePlot.py

Code: Select all

# RecurrencePlot.py
# By Feng
import golly as g
from glife import *
from glife.text import make_text
from time import time
from random import random
import math

clist=[]
fill = g.getoption("drawingstate")
def rp_plot(input,fill=3):
	m=len(input);
	for i in range(m):
		for j in range(m):
			d=norm(input[i],input[j])
			if d<=dmax:
				g.setcell(i,-j,fill)
			
	#		clist.append(i)
	#		clist.append(j)
	#		clist.append(input[i]==input[j])
	#g.putcells(clist,0,0)

def norm(a,b):
	return abs(a-b)


def popcount(sel=0):
	dict_lc={'BDRainbow':[2,4],'BGRainbowR2':[2,4]}
	live_cells=dict_lc[g.getrule().split(':')[0]]	
	if not sel:
		clist=g.getcells(g.getrect())
	else:
		clist=g.getcells(g.getselrect())		
	return sum(clist[2::3].count(x) for x in live_cells)
	

dict_lc={'BDRainbow':[2,4],'BGRainbowR2':[2,4]}

input=(g.getstring('How many steps?/similarity distance?','2000/1'))
numsteps=int(input.split('/')[0])
dmax=int(input.split('/')[1])

poplist=[]
hashlist=[int(g.hash(g.getrect()))]
popold=int(g.getpop())
dead=0

if g.getrule().split(':')[0] in dict_lc:
	popfunc=lambda:popcount()

else:
	popfunc=lambda:g.getpop()

for i in range(numsteps):
	g.run(1)	
	if g.empty():
		break	
	poplist.append(int(popfunc()))
	h=int(g.hash(g.getrect()))
	if h in hashlist:
		break
	hashlist.append(h)

layername = "recurrent plot"
poplayer = -1

for i in xrange(g.numlayers()):
    if g.getname(i) == layername:
        poplayer = i
        break
    if poplayer == -1 and g.numlayers() == g.maxlayers():
    	g.exit("You need to delete a layer.")


if poplayer == -1:
    poplayer = g.addlayer()
else:
    g.setlayer(poplayer)
g.new(layername)

rp_plot(poplist)

shouldsee · Post by **shouldsee** » July 2nd, 2016, 5:02 pm

Code: Select all

x = 100, y = 100, rule = BGRainbowR2:T100,100
44C2F18CDEF33C$44C2E18CDEF33C$2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C
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2D54C$92C2F6C$2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2D2.2C2.2C2.2C2.2C
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2D2.2C$36C2F18C2D42C$56C2E42C$2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C
2.2C2.2C2.2C2.2C2.2F2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C$2C2.2C2.
2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.
2C2.2C2.2C2.2C2.2C2.2C!

To reiterate, the aforementioned fabric allows very complex interpretable dynamics if placed in a torus, which is supported in 2 ways :
1.Every lattice is excitable to a photon gun if two photons have suitably collided here.
2.Collisions between photons can only produce objects from a limited selection, in contrast to glider synthesis. In other words, the result landscape of photon collision is fairly smooth without many sharp changes, which reduces the apparent chaos.

The excitability here somewhat resembles that in greenberg-hastings model (thank PHPBB12345 for spotting). The biggest difference is that the photon gun can communicate via photon whereas cells in GH model can only communicate to its neighbours.

(The dynamics would be more interesting if a bigger torus is allowed)
Another property is the rare emergence of still obstacles from photon reaction obstacles from rare photon reactions, which reduces the strength of the second statement. A further modification may be applied to prevent such reaction where the distance between lattices is increased, as follows:
(However, it might be advantageous to keep some obstacles in order to diversify the dynamics.)
(Random soup automatically generate tough still lives, thus you might want to randomly place photons to start a pattern)

Code: Select all

x = 120, y = 120, rule = BGRainbowR2:T120,120
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rput is a somewhat relevant script I made place random photons under StarWars. It needs some modification before possible usage. StarWars is also a potential target for such "limited" dynamics where only an inter-convertible set of objects are allowed, but the current photon reactions seem to produce results too diversified to be interpretable.

rput.py

Code: Select all

# A randomized rectangle filler for use with Golly.
# Author: Nathaniel Johnston (nathaniel@nathanieljohnston.com), June 2009.
#
# Even though Golly comes built-in with the golly.randfill() function, there
# does not seem to be a way to seed that function, so you get the same sequence
# of random rectangles every time you reload Golly. This script eliminates that
# problem.


import golly as g
import random
from glife import *
from utils import *
random.seed()

# --------------------------------------------------------------------

def randfill(rectcoords, amt):
   newstate = g.getoption("drawingstate")
   for i in range(rectcoords[0],rectcoords[0]+rectcoords[2]):
      for j in range(rectcoords[1],rectcoords[1]+rectcoords[3]):
         if(100*random.random()<amt):
            g.setcell(i, j, newstate)
         else:
            # g.setcell(i, j, 0)
			continue

def randput(rectcoords,clist,symlist,amt=50):
   xlist=range(rectcoords[0],rectcoords[0]+rectcoords[2])
   ylist=range(rectcoords[1],rectcoords[1]+rectcoords[3])

   size=int(len(clist)/3)
   base=pattern(clist)
   box=getminbox(base)
   num=int(len(xlist)*len(ylist)/size)
#   num=10
   for i in range(num):
      if (100*random.random())<amt:
         n=0
         while True:
            x=random.choice(xlist)
            y=random.choice(ylist)
            sym=random.choice(symlist)
            tpbox=multiply(sym,[box.wd,0,box.ht,0])
            wd=tpbox[0]
            ht=tpbox[2]


            tpbox=[min(x-1,x+wd),min(y-1,y+ht),abs(wd)+2,abs(ht)+2]
            g.select(tpbox)
            pop=popcount(sel=1)
            
            if pop==0:
               base.put(x,y,sym)
            #   g.note('pop=%i \n wd=%i \n ht=%i'%(pop,wd,ht) )
               break
            else:
               n=n+1
               if n>1000:
                  g.note('Too many attemps'+str(tpbox))
                  g.exit()

      else:
         continue



def multiply(a,b):
   b1=[b[i] for i in [0,2]]
   b2=[b[i] for i in [1,3]]
   a1=[a[0],a[1]]
   a2=[a[2],a[3]]
   return [vmt(a,b)  for a in [a1,a2] for b in [b1,b2] ]
def vmt(a,b):
   ma=len(a)
   mb=len(b)
   if ma==mb:
      return sum([a[i]*b[i] for i in range(ma)])
   else:
      g.show('Vector not of same length')
      return NaN

# --------------------------------------------------------------------

if len(g.getselrect()) == 0: g.exit("No selection.")
s = g.getstring("Enter the fill percentage (0.0 - 100.0):","50", "Random filler")
try:
   amt = float(s)
   if amt < 0 or amt > 100:
      g.exit('Bad number: %s' % s)
except ValueError:
   g.exit('Bad number: %s' % s)
 

base=g.parse(g.getstring("RLE string for your seed","CBA$CBA!"))
symlist=[identity,rcw,rccw,flip_x]

# base=pattern(base)

# for sym in symlist:
#    base.put(1,1,sym)

l1=[5,6,7,8]
l2=[4,3,2,1]
#g.note(str(multiply(l1,l2)))
randput(g.getselrect(),base,symlist,amt)

It require utils.py and glife folder under the same directory to work

Code: Select all

import random
import golly as g

def popcount(sel=0):
	dict_lc={'BDRainbow':[2,4],'BGRainbowR2':[2,4]}
	rule=g.getrule().split(':')[0]
	try:
		live_cells=dict_lc[rule]
	except:
		live_cells=range(1,g.numstates())
	if not sel:
		clist=g.getcells(g.getrect())
	else:
		clist=g.getcells(g.getselrect())		
		return sum(clist[2::3].count(x) for x in live_cells)
	


def randfill(rectcoords, amt,amt2=100,secondary_state=1):
   statelist=[ g.getoption("drawingstate"),secondary_state]
   for i in range(rectcoords[0],rectcoords[0]+rectcoords[2]):
      for j in range(rectcoords[1],rectcoords[1]+rectcoords[3]):
         if(100*random.random()<amt):
     		newstate=(100*random.random()>amt2)
        	g.setcell(i, j, statelist[newstate])
         else:
            # g.setcell(i, j, 0)
			continue


def randtopo( num , low , high ):
	hashlist=[]
	topolist=[]
	arealist=[]
	i=0
	while i<num:
		size=random.choice(range(low,high+1))
		area=size*size
		sel=[size,size,size,size]
		g.new('')
		randfill(sel,(1+random.randrange(area))*int(100/area))
		if g.empty():
			continue
		h=g.hash(g.getrect())
		if h in hashlist:
			continue
		else:
			hashlist.append(h)
			pbox=g.getrect()
			actarea=pbox[2]*pbox[3]
			clist=g.getcells(g.getrect())
			g.store(clist,dir+'topo_area%i_hash%i'%(actarea,h))

			clist.insert(0,pbox[2])			#prepare for tiling
			clist.insert(1,pbox[3])
			topolist.append(clist)
			arealist.append(actarea)
			i=i+1
			continue
			return [topolist,hashlist,arealist]

def parse_list(clist):
	newlist=[]
	for i in range(len(clist[0::3])):
		if 3*i+3 <= len(clist):
			temp=clist[3*i:3*i+3]
			newlist.append(temp)
		else:
			break
	return(newlist)

	
def parse_list(clist):
	newlist=[]
	for i in range(len(clist[0::3])):
		if 3*i+3 <= len(clist):
			temp=clist[3*i:3*i+3]
			newlist.append(temp)
		else:
			break
	return(newlist)


def full_clist(parsed_clist,pbox):
	# if parsed_clist[0][0:2]!=pbox[0:2]:
		# parsed_clist.insert(0,[pbox[0],pbox[1],0])
	
	# left=pbox[0]
	# top=pbox[1]
	# right=left+pbox[2]-1
	# bottom=top+pbox[1]-1
	
	
	output=[]
	
	for i in [[x,y] for x in range(pbox[2]) for y in range(pbox[3])]:
		i[0]+=pbox[0]
		i[1]+=pbox[1]
		match=i[:]
		match.append(state)
		if match in parsed_clist:
			output.append(match)
		else:
			i.append(0)
			output.append(i)
	return output
	
	
	# for i in range(len(parsed_clist)):
		# c1=parsed_clist[i]
		# c2=parsed_clist[i+1]
		
		
		# if c1[:2]==[right,bottom]:
		
		# c1p=c1[:]
		
		
		# if c1[0]==pbox[0]+pbox[2]-1
		# c1p[0]=c1[0]

		# if y[0]==x[0]+1 or (y[0]==x[0]-pbox[2]+1 and y[1]==x[1]+1):
			# continue
		# else:
			# parsed_clist.insert(i+1,[x[0])
			
def adjacencymatrix(parsed_clist,pbox,torus=0):
	adjacencymatrix=[]
	if torus:
		adjacency_x=[0,1,pbox[2]-1]
		adjacency_y=[0,1,pbox[3]-1]
	else:
		adjacency_x=[0,1]
		adjacency_y=[0,1]
	# fclist=full_clist(parsed_clist,pbox)
	for i in parsed_clist:
		row=[]
		for j in parsed_clist:
			if 	i!=j and \
			(abs(i[0]-j[0]) in adjacency_x) and \
			(abs(i[1]-j[1]) in adjacency_y) and \
			[i[2],j[2]]==[state,state]:
				row.append(1)
			else:
				row.append(0)
				
		adjacencymatrix.append(row)
	return adjacencymatrix

def mathematica(s):
	s=s.replace('[','{')
	s=s.replace(']','}')
	return s

shouldsee · Post by **shouldsee** » July 2nd, 2016, 5:19 pm

Photon reaction can even produce a puffer that produces obstacles in the less stringent topology.

But since the topology is implemented by tricking rule table, it's not easy to run scripts that fetch population or position in such rules. It would be desirable if golly gang can allow a parallel input of topology file in the mapping algorithm in the future to allow more systematic examination of dynamics on non-ordinary topology.

Code: Select all

x = 56, y = 142, rule = BGRainbowR2
C2.2C2.2C2.2C2.2C2.2C2.2D2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.C$23C2E31C$23C
2F31C$C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.C$C2.2C
2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.C$56C$55CF$C2.2C2.
2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.E$C2.2C2.2C2.2C2.2C2.
2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.D$56C$56C$C2.2C2.2C2.2C2.2C2.2C2.
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2C2.2C2.2C2.2C2.2C2.C$56C$56C$C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.
2C2.2C2.2C2.2C2.C$C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.
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2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.C$56C$56C$C2.2C2.
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2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.C$23C2F31C!

shouldsee · Post by **shouldsee** » July 17th, 2016, 11:35 am

These interacting photons do not destroy each other

Code: Select all

x = 152, y = 88, rule = BGRainbowR2:T640,640
152C$152C$2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C2.2C
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2.2C!

Bullet51 · Post by **Bullet51** » July 17th, 2016, 12:47 pm

shouldsee wrote:These interacting photons do not destroy each other

Since photons travel with the speed of light, they can't interfere each other, and it turned out that there is no interaction.

shouldsee · Post by **shouldsee** » July 18th, 2016, 8:28 am

Bullet51 wrote:
shouldsee wrote:These interacting photons do not destroy each other
Since photons travel with the speed of light, they can't interfere each other, and it turned out that there is no interaction.

If photons are perturbed at some point to establish an entanglement, this entanglement will carry on and exhibit observable impact. That was the point I wanted to make with such patterns.

shouldsee · Post by **shouldsee** » July 18th, 2016, 12:41 pm

I dug up a vonNeumman rule from DDLab's presentation tab that exhibits similar photon dynamics.

v3k4_202200222012210V.rule

Code: Select all

@RULE v3k4_202200222012210V

@COLORS

0 0 0 0
1 0 155 155
2 200 200 0
3 200 0 200



@TABLE
n_states:3
neighborhood:vonNeumann
symmetries:permute

var a={1}
var b={2}

var d={0}

var j={0,1,2}



j,0,0,0,0,0
j,0,0,0,1,1
j,0,0,1,1,2
j,0,1,1,1,2
j,1,1,1,1,1
j,2,0,0,0,0
j,2,1,0,0,2
j,2,1,1,0,2
j,2,1,1,1,2
j,2,2,0,0,0
j,2,2,1,0,0
j,2,2,1,1,2
j,2,2,2,0,2
j,2,2,2,1,0
j,2,2,2,2,2

Bullet51 · Post by **Bullet51** » July 18th, 2016, 12:55 pm

shouldsee wrote:I dug up a vonNeumman rule from DDLab's presentation tab that exhibits similar photon dynamics.

Two photons can pass each other:

Code: Select all

x = 5, y = 6, rule = v3k4_202200222012210V
B.B$.A2$4.B$3.A$4.B!

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