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f0.def @ 754

Last change on this file since 754 was 754, checked in by Maciej Komosinski, 7 years ago
More strict rules for Param type descriptions and "unlimited range value" and "unlimited string length": "d 0 -1", "f 0 -1", "s 0 0", "s 1 0" (see formatspec.html)
Property svn:eol-style set to `native`
File size: 10.9 KB

Rev	Line
[187]	1	CLASS(Model,f0_model,m)
	2	GROUP(Properties)
	3	GROUP(Visual)
	4	PROP(se,0,1024,startenergy,f,,,,startenergy)
[754]	5	PROP(Vstyle,1,0,vis_style,s,0,0,,vis_style)
[187]	6	ENDCLASS
	7
	8	CLASS(Part,f0_part,p)
	9	GROUP(Geometry)
	10	GROUP(Other properties)
	11	GROUP(Visual)
	12	PROP(x,0,1024,position.x,f,,,,p.x)
	13	PROP(y,0,1024,position.y,f,,,,p.y)
	14	PROP(z,0,1024,position.z,f,,,,p.z)
	15	PROP(sh,1,0,shape,d,0,3,0,shape)
	16	PROP(s,1,0,size,f,0.1,10.0,1.0,size)
	17	PROP(sx,1,0,scale.x,f,0.001,1000.0,1.0,scale.x)
	18	PROP(sy,1,0,scale.y,f,0.001,1000.0,1.0,scale.y)
	19	PROP(sz,1,0,scale.z,f,0.001,1000.0,1.0,scale.z)
[528]	20	XPROP(h,1,0,hollow,f,0,1,0,hollow)
[187]	21	XPROP(dn,1,0,density,f,0.2,5.0,1.0,density)
	22	XPROP(fr,1,0,friction,f,0.0,4.0,0.4,friction)
	23	XPROP(ing,1,0,ingestion,f,0.0,1.0,0.25,ingest)
	24	XPROP(as,1,0,assimilation,f,0.0,1.0,0.25,assim)
	25	PROP(rx,0,0,rot.x,f,,,,rot.x)
	26	PROP(ry,0,1024,rot.y,f,,,,rot.y)
	27	PROP(rz,0,1024,rot.z,f,,,,rot.z)
	28	PROP(i,1,0,`info',s,,,,info)
[754]	29	PROP(Vstyle,2,0,vis_style,s,0,0,part,vis_style)
[187]	30	XPROP(vs,2,0,visual thickness,f,0.05,0.7,0.2,vsize)
[507]	31	XPROP(vr,2,0,red component,f,0.0,1.0,1.0,vcolor.x)
	32	XPROP(vg,2,1024,green component,f,0.0,1.0,1.0,vcolor.y)
	33	XPROP(vb,2,1024,blue component,f,0.0,1.0,1.0,vcolor.z)
[187]	34	ENDCLASS
	35
	36	CLASS(Joint,f0_joint,j)
	37	GROUP(Connections)
	38	GROUP(Geometry)
	39	GROUP(Other properties)
	40	GROUP(Visual)
	41	PROP(p1,0,1024,`part1 ref#',d,-1,999999,-1,p1_refno)
	42	PROP(p2,0,1024,`part2 ref#',d,-1,999999,-1,p2_refno)
	43	PROP(rx,1,0,rotation.x,f,,,,rot.x)
	44	PROP(ry,1,1024,rotation.y,f,,,,rot.y)
	45	PROP(rz,1,1024,rotation.z,f,,,,rot.z)
	46	PROP(dx,1,0,delta.x,f,-2,2,0,d.x)
	47	PROP(dy,1,1024,delta.y,f,-2,2,0,d.y)
	48	PROP(dz,1,1024,delta.z,f,-2,2,0,d.z)
[247]	49	PROP(sh,2,0,shape,d,0,1,0,shape)
[187]	50	XPROP(stif,2,0,stiffness,f,0.0,1.0,1.0,stif)
	51	XPROP(rotstif,2,0,rotation stiffness,f,0.0,1.0,1.0,rotstif)
	52	PROP(stam,2,0,stamina,f,0.0,1.0,0.25,stamina)
	53	PROP(i,2,0,`info',s,,,,info)
[754]	54	PROP(Vstyle,3,0,vis_style,s,0,0,joint,vis_style)
[507]	55	XPROP(vr,3,0,red component,f,0.0,1.0,1.0,vcolor.x)
	56	XPROP(vg,3,1024,green component,f,0.0,1.0,1.0,vcolor.y)
	57	XPROP(vb,3,1024,blue component,f,0.0,1.0,1.0,vcolor.z)
[187]	58	ENDCLASS
	59
	60	CLASS(Joint,f0_nodeltajoint,j,NOXML)
	61	GROUP(Connections)
	62	GROUP(Geometry)
	63	GROUP(Other properties)
	64	GROUP(Visual)
	65	PROP(p1,0,1024,`part1 ref#',d,-1,999999,-1,p1_refno)
	66	PROP(p2,0,1024,`part2 ref#',d,-1,999999,-1,p2_refno)
[247]	67	PROP(sh,2,0,shape,d,0,1,0,shape)
[187]	68	XPROP(stif,2,0,stiffness,f,0.0,1.0,1.0,stif)
	69	XPROP(rotstif,2,0,rotation stiffness,f,0.0,1.0,1.0,rotstif)
	70	PROP(stam,2,0,stamina,f,0.0,1.0,0.25,stamina)
	71	PROP(i,2,0,`info',s,,,,info)
[754]	72	PROP(Vstyle,3,0,vis_style,s,0,0,joint,vis_style)
[507]	73	XPROP(vr,3,0,red component,f,0.0,1.0,1.0,vcolor.x)
	74	XPROP(vg,3,1024,green component,f,0.0,1.0,1.0,vcolor.y)
	75	XPROP(vb,3,1024,blue component,f,0.0,1.0,1.0,vcolor.z)
[187]	76	ENDCLASS
	77
	78	CLASS(Neuro,f0_neuro,n)
	79	GROUP(Connections)
	80	GROUP(Other)
	81	GROUP(Visual)
	82	PROP(p,0,0,`part ref#',d,-1,999999,-1,part_refno)
	83	PROP(j,0,0,`joint ref#',d,-1,999999,-1,joint_refno)
[289]	84	PROP(d,1,0,details,s,,,N,details,GETSET)
[187]	85	PROP(i,1,0,`info',s,,,,info)
[754]	86	PROP(Vstyle,2,0,vis_style,s,0,0,neuro,vis_style)
[187]	87	PROP(getInputCount,0,1+2,`input count',d,,,,inputCount,GETONLY)
[656]	88	PROP(getInputNeuroDef,0,0,`get input neuron',p oNeuroDef(d),,,,p_getInputNeuroDef,PROCEDURE)
	89	PROP(getInputNeuroIndex,0,0,`get input neuron index',p d(d),,,,p_getInputNeuroIndex,PROCEDURE)
	90	PROP(getInputWeight,0,0,`get input weight',p f(d),,,,p_getInputWeight,PROCEDURE)
[732]	91	PROP(classObject,0,1+2,`neuron class',oNeuroClass,,,,classObject,GETONLY)
[187]	92	ENDCLASS
	93
	94	CLASS(NeuroConn,f0_neuroconn,c)
	95	GROUP(Connection)
	96	GROUP(Other)
	97	PROP(n1,0,1024,`this neuro ref#',d,-1,999999,-1,n1_refno)
	98	PROP(n2,0,1024,`connected neuro ref#',d,-1,999999,-1,n2_refno)
	99	PROP(w,0,1024,weight,f,-999999,999999,1.0,weight)
	100	PROP(i,1,0,`info',s,,,,info)
	101	ENDCLASS
	102
	103	NEUROCLASS(StdNeuron,N,Neuron,`Standard neuron',-1,1,0)
	104	VISUALHINTS(DontShowClass)
	105	NEUROPROP(in,1,0,Inertia,f,0.0,1.0,0.8,inertia)
	106	NEUROPROP(fo,1,0,Force,f,0.0,999.0,0.04,force)
	107	NEUROPROP(si,1,0,Sigmoid,f,-99999.0,99999.0,2.0,sigmo)
	108	NEUROPROP(s,2,0,State,f,-1.0,1.0,0.0,newstate)
	109	ENDNEUROCLASS
	110
	111	NEUROCLASS(StdUNeuron,Nu,`Unipolar neuron [EXPERIMENTAL!]',`Works like standard neuron (N) but the output value is scaled to 0...+1 instead of -1...+1.\nHaving 0 as one of the saturation states should help in \"gate circuits\", where input signal is passed through or blocked depending on the other singal.',-1,1,0)
	112	NEUROPROP(in,1,0,Inertia,f,0.0,1.0,0.8,inertia)
	113	NEUROPROP(fo,1,0,Force,f,0.0,999.0,0.04,force)
	114	NEUROPROP(si,1,0,Sigmoid,f,-99999.0,99999.0,2.0,sigmo)
	115	NEUROPROP(s,2,0,State,f,-1.0,1.0,0.0,newstate)
	116	ENDNEUROCLASS
	117
	118	NEUROCLASS(Gyro,G,Gyroscope,`Equilibrium sensor.\n0=the stick is horizontal\n+1/-1=the stick is vertical',0,1,2)
	119	VISUALHINTS(ReceptorClass)
	120	SYMBOL(`8,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,12,43,24,48,24,48,19,38,19,38,24,43,24,43,54,48,54,48,64,43,69,38,64,38,54,43,54,5,63,69,58,74,48,79,38,79,28,74,23,69,1,43,79,43,74,1,23,69,26,66,1,63,69,60,66,1,55,76,53,73,1,31,75,33,72')
	121	ENDNEUROCLASS
	122
	123	NEUROCLASS(Touch,T,Touch,`Touch sensor.\n-1=no contact\n0=just touching\n>0=pressing, value depends on the force applied',0,1,1)
	124	VISUALHINTS(ReceptorClass)
	125	SYMBOL(`2,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,11,75,50,65,50,60,55,55,45,50,55,45,45,40,50,35,50,30,45,25,50,30,55,35,50')
	126	NEUROPROP(r,1,0,Range,f,0.0,1.0,1.0,range)
	127	ENDNEUROCLASS
	128
	129	NEUROCLASS(Smell,S,Smell,`Smell sensor. Aggregated \"smell of energy\" experienced from all energy objects (creatures and food pieces).\nClose objects have bigger influence than the distant ones: for each energy source, its partial feeling is proportional to its energy/(distance^2)',0,1,1)
	130	VISUALHINTS(ReceptorClass)
	131	SYMBOL(`5,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,3,10,40,15,45,15,55,10,60,5,20,30,25,35,30,45,30,55,25,65,20,70,4,15,35,20,40,22,50,20,60,15,65,5,75,50,50,50,45,45,40,50,45,55,50,50')
	132	ENDNEUROCLASS
	133
	134	NEUROCLASS(Const,*,Constant,Constant value,0,1,0)
	135	VISUALHINTS(Invisible)
	136	SYMBOL(`4,4,26,27,26,73,73,73,73,27,26,27,1,73,50,100,50,1,56,68,46,68,2,41,47,51,32,51,68')
	137	ENDNEUROCLASS
	138
	139	NEUROCLASS(BendMuscle,\|,Bend muscle,,1,0,2)
	140	VISUALHINTS(DontShowClass+EffectorClass+V1BendMuscle+AtFirstPart)
	141	SYMBOL(`6,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,3,65,56,49,29,57,24,72,50,4,68,53,70,53,70,55,68,55,68,53,5,50,21,60,15,70,14,79,15,87,20,81,10,1,86,20,77,21')
	142	NEUROPROP(p,0,0,power,f,0.01,1.0,0.25,power)
	143	NEUROPROP(r,0,0,bending range,f,0.0,1.0,1.0,bendrange)
	144	ENDNEUROCLASS
	145
	146	NEUROCLASS(RotMuscle,@,Rotation muscle,,1,0,2)
	147	VISUALHINTS(DontShowClass+EffectorClass+V1RotMuscle+AtFirstPart)
	148	SYMBOL(`5,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,1,69,10,77,17,10,59,15,57,17,57,22,60,26,69,27,78,26,82,21,82,16,79,12,69,10,80,6,3,65,50,65,20,75,20,75,50')
	149	NEUROPROP(p,0,0,power,f,0.01,1.0,1.0,power)
	150	ENDNEUROCLASS
	151
	152	NEUROCLASS(Diff,D,Differentiate,Calculate the difference between the current and previous input value. Multiple inputs are aggregated with respect to their weights,-1,1,0)
	153	SYMBOL(`3,3,25,0,25,100,75,50,25,0,1,75,50,100,50,3,44,42,51,57,36,57,44,42')
	154	ENDNEUROCLASS
	155
	156	NEUROCLASS(FuzzyNeuro,Fuzzy,Fuzzy system [EXPERIMENTAL!],Refer to publications to learn more about this neuron.,-1,1,0)
	157	SYMBOL(`5,2,30,65,37,37,44,65,3,37,65,44,37,51,37,58,65,2,51,65,58,37,65,65,6,100,50,70,50,70,25,25,10,25,90,70,75,70,50,1,70,65,25,65')
	158	NEUROPROP(ns,0,0,number of fuzzy sets,d,1,,,fuzzySetsNr)
	159	NEUROPROP(nr,0,0,number of rules,d,1,,,rulesNr)
[419]	160	NEUROPROP(fs,0,0,fuzzy sets,s,0,-1,,fuzzySetString)
	161	NEUROPROP(fr,0,0,fuzzy rules,s,0,-1,,fuzzyRulesString)
[187]	162	ENDNEUROCLASS
	163
	164	NEUROCLASS(VectorEye,VEye,Vector Eye [EXPERIMENTAL!],Refer to publications to learn more about this neuron.,1,1,1)
	165	SYMBOL(`11,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,14,70,50,65,40,60,35,45,30,30,30,15,35,10,40,5,50,10,60,15,65,30,70,45,70,60,65,65,60,70,50,8,38,67,28,62,23,52,28,42,38,37,48,42,53,52,48,62,38,67,4,33,52,38,47,43,52,38,57,33,52,5,70,50,60,40,45,35,30,35,15,40,5,50,1,53,33,58,22,1,62,36,68,26,1,45,30,47,19,1,35,30,35,19,1,27,31,24,20,1,18,34,12,24')
	166	NEUROPROP(tx,0,0,target.x,f,,,,target.x)
	167	NEUROPROP(ty,0,0,target.y,f,,,,target.y)
	168	NEUROPROP(tz,0,0,target.z,f,,,,target.z)
[419]	169	NEUROPROP(ts,0,0,target shape,s,0,-1,,targetShape)
[187]	170	NEUROPROP(p,0,0,perspective,f,0.1,10.0,1.0,perspz)
	171	NEUROPROP(s,0,0,scale,f,0.1,100.0,1.0,perspscale)
	172	NEUROPROP(h,0,0,show hidden lines,d,0,1,0,showhidden)
	173	NEUROPROP(o,0,0,`output lines count (each line needs four channels)',d,0,99,0,outputcount)
	174	NEUROPROP(d,0,0,debug,d,0,1,0,showdebug)
	175	ENDNEUROCLASS
	176
	177	NEUROCLASS(VisualMotorNeuron,VMotor,Visual-Motor Cortex [EXPERIMENTAL!],`Must be connected to the VEye and properly set up. Refer to publications to learn more about this neuron.',-1,1,0)
	178	NEUROPROP(noIF,0,0,number of basic features,d,,,,noIF)
	179	NEUROPROP(noDim,0,0,number of degrees of freedom,d,,,,noDim)
	180	NEUROPROP(params,0,0,parameters,s,,,,params)
	181	ENDNEUROCLASS
	182
	183	NEUROCLASS(Sticky,Sti,Sticky [EXPERIMENTAL!],,1,0,1)
	184	VISUALHINTS(EffectorClass)
	185	ENDNEUROCLASS
	186
	187	NEUROCLASS(LinearMuscle,LMu,Linear muscle [EXPERIMENTAL!],,1,0,2)
	188	VISUALHINTS(EffectorClass+LinearMuscleFlag)
	189	NEUROPROP(p,0,0,power,f,0.01,1.0,1.0,power)
	190	ENDNEUROCLASS
	191
	192	NEUROCLASS(WaterDetect,Water,Water detector,`Output signal:\n0=on or above water surface\n1=under water (deeper than 1)\n0..1=in the transient area just below water surface',0,1,1)
	193	VISUALHINTS(ReceptorClass)
	194	ENDNEUROCLASS
	195
	196	NEUROCLASS(Energy,Energy,Energy level,`The current energy level divided by the initial energy level.\nUsually falls from initial 1.0 down to 0.0 and then the creature dies. It can rise above 1.0 if enough food is ingested',0,1,0)
	197	VISUALHINTS(ReceptorClass)
	198	ENDNEUROCLASS
	199
	200	NEUROCLASS(Channelize,Ch,Channelize,`Combines all input signals into a single multichannel output; Note: ChSel and ChMux are the only neurons which support multiple channels. Other neurons discard everything except the first channel.',-1,1,0)
	201	SYMBOL(`10,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,1,30,80,55,50,1,30,20,55,50,1,30,35,55,50,1,30,45,55,50,1,30,55,55,50,1,61,53,65,47,1,30,65,55,50')
	202	ENDNEUROCLASS
	203
	204	NEUROCLASS(ChMux,ChMux,Channel multiplexer,`Outputs the selected channel from the second (multichannel) input. The first input is used as the selector value (-1=select first channel, .., 1=last channel)',2,1,0)
	205	SYMBOL(`7,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,3,30,67,45,67,45,50,50,50,1,35,70,39,64,2,30,33,53,33,53,48')
	206	ENDNEUROCLASS
	207
	208	NEUROCLASS(ChSel,ChSel,Channel selector,`Outputs a single channel (selected by the \"ch\" parameter) from multichannel input',1,1,0)
	209	SYMBOL(`6,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,1,30,50,50,50,1,35,53,39,47')
	210	NEUROPROP(ch,0,0,channel,d,,,,ch)
	211	ENDNEUROCLASS
	212
	213	NEUROCLASS(Random,Rnd,Random noise,`Generates random noise (subsequent random values in the range of -1..+1)',0,1,0)
	214	ENDNEUROCLASS
	215
	216	NEUROCLASS(Sinus,Sin,Sinus generator,`Output frequency = f0+input',1,1,0)
	217	SYMBOL(`3,12,75,50,71,37,62,28,50,25,37,28,28,37,25,50,28,62,37,71,50,75,62,71,71,62,75,50,1,75,50,100,50,5,35,50,40,35,45,35,55,65,60,65,65,50')
	218	NEUROPROP(f0,0,0,base frequency,f,-1.0,1.0,0.06283185307,f0)
	219	NEUROPROP(t,0,0,time,f,0,6.283185307,0,t)
	220	ENDNEUROCLASS

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