source: cpp/frams/config/f0-SDK.def @ 1107

Last change on this file since 1107 was 1107, checked in by Maciej Komosinski, 3 years ago

Unified names of neurons and neuron flags; muscle power range now starts from 0 instead of 0.01

  • Property svn:eol-style set to native
File size: 13.4 KB
RevLine 
[952]1CLASS(Model,f0_model,m,Model)
[109]2GROUP(Properties)
3GROUP(Visual)
4PROP(se,0,1024,startenergy,f,,,,startenergy)
[754]5PROP(Vstyle,1,0,vis_style,s,0,0,,vis_style)
[109]6ENDCLASS
7
[952]8CLASS(Part,f0_part,p,Part)
[109]9GROUP(Geometry)
10GROUP(Other properties)
11GROUP(Visual)
12PROP(x,0,1024,position.x,f,,,,p.x)
13PROP(y,0,1024,position.y,f,,,,p.y)
14PROP(z,0,1024,position.z,f,,,,p.z)
[920]15PROP(sh,0,0,shape,d,0,3,0,shape)
16PROP(s,0,0,size,f,0.1,10.0,1.0,size)
[1012]17PROP(sx,0,0,scale.x,f,0.05,5.0,1.0,scale.x)
18PROP(sy,0,0,scale.y,f,0.05,5.0,1.0,scale.y)
19PROP(sz,0,0,scale.z,f,0.05,5.0,1.0,scale.z)
[528]20XPROP(h,1,0,hollow,f,0,1,0,hollow)
[109]21XPROP(dn,1,0,density,f,0.2,5.0,1.0,density)
22XPROP(fr,1,0,friction,f,0.0,4.0,0.4,friction)
23XPROP(ing,1,0,ingestion,f,0.0,1.0,0.25,ingest)
24XPROP(as,1,0,assimilation,f,0.0,1.0,0.25,assim)
25PROP(rx,0,0,rot.x,f,,,,rot.x)
26PROP(ry,0,1024,rot.y,f,,,,rot.y)
27PROP(rz,0,1024,rot.z,f,,,,rot.z)
28PROP(i,1,0,`info',s,,,,info)
[754]29PROP(Vstyle,2,0,vis_style,s,0,0,part,vis_style)
[507]30XPROP(vr,2,0,red component,f,0.0,1.0,1.0,vcolor.x)
31XPROP(vg,2,1024,green component,f,0.0,1.0,1.0,vcolor.y)
32XPROP(vb,2,1024,blue component,f,0.0,1.0,1.0,vcolor.z)
[109]33ENDCLASS
34
[934]35CLASS(Part_MinMaxDef,f0_part_minmaxdef,p)
36GROUP(Geometry)
[945]37PROP(f,0,0,volume,f,0.83776,20.94395,4.18879,volume,,``Recommended default and min,max range for solid-shape Parts created and modified by genetic operators which are responsible for setting sizex,y,z. Default is the volume of the solid sphere (ball) with default radius=1 (radius is the same as sizex,y,z). Minimum is 5x less, maximum is 5x more.'')
[934]38ENDCLASS
39
[952]40CLASS(Joint,f0_joint,j,Joint)
[109]41GROUP(Connections)
42GROUP(Geometry)
43GROUP(Other properties)
44GROUP(Visual)
45PROP(p1,0,1024,`part1 ref#',d,-1,999999,-1,p1_refno)
46PROP(p2,0,1024,`part2 ref#',d,-1,999999,-1,p2_refno)
47PROP(rx,1,0,rotation.x,f,,,,rot.x)
48PROP(ry,1,1024,rotation.y,f,,,,rot.y)
49PROP(rz,1,1024,rotation.z,f,,,,rot.z)
50PROP(dx,1,0,delta.x,f,-2,2,0,d.x)
51PROP(dy,1,1024,delta.y,f,-2,2,0,d.y)
52PROP(dz,1,1024,delta.z,f,-2,2,0,d.z)
[920]53PROP(sh,1,0,shape,d,0,3,0,shape)
[915]54PROP(hx,1,0,hinge position.x,f,,,0,hinge_pos.x)
55PROP(hy,1,1024,hinge position.y,f,,,0,hinge_pos.y)
56PROP(hz,1,1024,hinge position.z,f,,,0,hinge_pos.z)
57PROP(hrx,1,0,hinge rotation.x,f,,,0,hinge_rot.x)
58PROP(hry,1,1024,hinge rotation.y,f,,,0,hinge_rot.y)
59PROP(hrz,1,1024,hinge rotation.z,f,,,0,hinge_rot.z)
60PROP(hxn,1,0,hinge x negative limit,f,-6.2832,0,-1.5708,hinge_limit_x[0])
61PROP(hxp,1,1024,hinge x positive limit,f,0,6.2832,1.5708,hinge_limit_x[1])
62PROP(hyn,1,0,hinge y negative limit,f,-6.2832,0,-1.5708,hinge_limit_y[0])
63PROP(hyp,1,1024,hinge y positive limit,f,0,6.2832,1.5708,hinge_limit_y[1])
[109]64XPROP(stif,2,0,stiffness,f,0.0,1.0,1.0,stif)
65XPROP(rotstif,2,0,rotation stiffness,f,0.0,1.0,1.0,rotstif)
66PROP(stam,2,0,stamina,f,0.0,1.0,0.25,stamina)
67PROP(i,2,0,`info',s,,,,info)
[754]68PROP(Vstyle,3,0,vis_style,s,0,0,joint,vis_style)
[507]69XPROP(vr,3,0,red component,f,0.0,1.0,1.0,vcolor.x)
70XPROP(vg,3,1024,green component,f,0.0,1.0,1.0,vcolor.y)
71XPROP(vb,3,1024,blue component,f,0.0,1.0,1.0,vcolor.z)
[109]72ENDCLASS
73
[952]74CLASS(Joint,f0_nodeltajoint,j,Joint,NOXML)
[109]75GROUP(Connections)
76GROUP(Geometry)
77GROUP(Other properties)
78GROUP(Visual)
79PROP(p1,0,1024,`part1 ref#',d,-1,999999,-1,p1_refno)
80PROP(p2,0,1024,`part2 ref#',d,-1,999999,-1,p2_refno)
[920]81PROP(sh,1,0,shape,d,0,3,0,shape)
[915]82PROP(hx,1,0,hinge position.x,f,,,0,hinge_pos.x)
83PROP(hy,1,1024,hinge position.y,f,,,0,hinge_pos.y)
84PROP(hz,1,1024,hinge position.z,f,,,0,hinge_pos.z)
85PROP(hrx,1,0,hinge rotation.x,f,,,0,hinge_rot.x)
86PROP(hry,1,1024,hinge rotation.y,f,,,0,hinge_rot.y)
87PROP(hrz,1,1024,hinge rotation.z,f,,,0,hinge_rot.z)
88PROP(hxn,1,0,hinge x negative limit,f,-6.2832,0,-1.5708,hinge_limit_x[0])
89PROP(hxp,1,1024,hinge x positive limit,f,0,6.2832,1.5708,hinge_limit_x[1])
90PROP(hyn,1,0,hinge y negative limit,f,-6.2832,0,-1.5708,hinge_limit_y[0])
91PROP(hyp,1,1024,hinge y positive limit,f,0,6.2832,1.5708,hinge_limit_y[1])
[109]92XPROP(stif,2,0,stiffness,f,0.0,1.0,1.0,stif)
93XPROP(rotstif,2,0,rotation stiffness,f,0.0,1.0,1.0,rotstif)
94PROP(stam,2,0,stamina,f,0.0,1.0,0.25,stamina)
95PROP(i,2,0,`info',s,,,,info)
[754]96PROP(Vstyle,3,0,vis_style,s,0,0,joint,vis_style)
[507]97XPROP(vr,3,0,red component,f,0.0,1.0,1.0,vcolor.x)
98XPROP(vg,3,1024,green component,f,0.0,1.0,1.0,vcolor.y)
99XPROP(vb,3,1024,blue component,f,0.0,1.0,1.0,vcolor.z)
[109]100ENDCLASS
101
[952]102CLASS(Neuro,f0_neuro,n,Neuro)
[109]103GROUP(Connections)
104GROUP(Other)
105GROUP(Visual)
106PROP(p,0,0,`part ref#',d,-1,999999,-1,part_refno)
107PROP(j,0,0,`joint ref#',d,-1,999999,-1,joint_refno)
[289]108PROP(d,1,0,details,s,,,N,details,GETSET)
[109]109PROP(i,1,0,`info',s,,,,info)
[754]110PROP(Vstyle,2,0,vis_style,s,0,0,neuro,vis_style)
[109]111PROP(getInputCount,0,1+2,`input count',d,,,,inputCount,GETONLY)
[932]112PROP(getInputNeuroDef,0,0,`get input neuron',p oNeuroDef(d),,,,p_getInputNeuroDef,PROCEDURE)
113PROP(getInputNeuroIndex,0,0,`get input neuron index',p d(d),,,,p_getInputNeuroIndex,PROCEDURE)
114PROP(getInputWeight,0,0,`get input weight',p f(d),,,,p_getInputWeight,PROCEDURE)
[732]115PROP(classObject,0,1+2,`neuron class',oNeuroClass,,,,classObject,GETONLY)
[109]116ENDCLASS
117
[952]118CLASS(NeuroConn,f0_neuroconn,c,Neuron connection)
[109]119GROUP(Connection)
120GROUP(Other)
121PROP(n1,0,1024,`this neuro ref#',d,-1,999999,-1,n1_refno)
122PROP(n2,0,1024,`connected neuro ref#',d,-1,999999,-1,n2_refno)
123PROP(w,0,1024,weight,f,-999999,999999,1.0,weight)
124PROP(i,1,0,`info',s,,,,info)
125ENDCLASS
126
127NEUROCLASS(StdNeuron,N,Neuron,`Standard neuron',-1,1,0)
128VISUALHINTS(DontShowClass)
129NEUROPROP(in,1,0,Inertia,f,0.0,1.0,0.8,inertia)
130NEUROPROP(fo,1,0,Force,f,0.0,999.0,0.04,force)
131NEUROPROP(si,1,0,Sigmoid,f,-99999.0,99999.0,2.0,sigmo)
132NEUROPROP(s,2,0,State,f,-1.0,1.0,0.0,newstate)
133ENDNEUROCLASS
134
135NEUROCLASS(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)
136NEUROPROP(in,1,0,Inertia,f,0.0,1.0,0.8,inertia)
137NEUROPROP(fo,1,0,Force,f,0.0,999.0,0.04,force)
138NEUROPROP(si,1,0,Sigmoid,f,-99999.0,99999.0,2.0,sigmo)
139NEUROPROP(s,2,0,State,f,-1.0,1.0,0.0,newstate)
140ENDNEUROCLASS
141
[952]142NEUROCLASS(Gyro,G,Gyroscope,`Tilt sensor.\nSignal is proportional to sin(angle) = most sensitive in horizontal orientation.\n0=the stick is horizontal\n+1/-1=the stick is vertical',0,1,2)
143SHAPETYPE(BallAndStickShapeType)
[109]144VISUALHINTS(ReceptorClass)
145SYMBOL(`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')
146ENDNEUROCLASS
147
[976]148NEUROCLASS(GyroP,Gpart,Part Gyroscope,`Tilt sensor. Signal is directly proportional to the tilt angle.\n0=the part X axis is horizontal\n+1/-1=the axis is vertical',0,1,1)
[109]149VISUALHINTS(ReceptorClass)
[952]150SYMBOL(`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')
151NEUROPROP(ry,1,0,rotation.y,f,-6.282,6.282,0,ry)
152NEUROPROP(rz,1,0,rotation.z,f,-6.282,6.282,0,rz)
153ENDNEUROCLASS
154
[1107]155NEUROCLASS(Touch,T,Touch,`Touch and proximity sensor (Tcontact and Tproximity combined)\n-1=no contact\n0=just touching\n>0=pressing, value depends on the force applied (not implemented in ODE mode)',0,1,1)
[952]156VISUALHINTS(ReceptorClass)
[109]157SYMBOL(`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')
158NEUROPROP(r,1,0,Range,f,0.0,1.0,1.0,range)
[952]159NEUROPROP(ry,1,0,rotation.y,f,-6.282,6.282,0,ry)
160NEUROPROP(rz,1,0,rotation.z,f,-6.282,6.282,0,rz)
[109]161ENDNEUROCLASS
162
[976]163NEUROCLASS(TouchC,Tcontact,Touch contact,`Touch sensor.\n-1=no contact\n0=the Part is touching the obstacle\n>0=pressing, value depends on the force applied (not implemented in ODE mode)',0,1,1)
[952]164VISUALHINTS(ReceptorClass)
165SYMBOL(`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')
166ENDNEUROCLASS
167
[1043]168NEUROCLASS(TouchP,Tproximity,Touch proximity,`Proximity sensor detecting obstacles along the X axis.\n-1=distance is \"r\" or more\n0=zero distance',0,1,1)
[952]169VISUALHINTS(ReceptorClass)
170SYMBOL(`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')
171NEUROPROP(r,1,0,Range,f,0.0,1.0,1.0,range)
172NEUROPROP(ry,1,0,rotation.y,f,-6.282,6.282,0,ry)
173NEUROPROP(rz,1,0,rotation.z,f,-6.282,6.282,0,rz)
174ENDNEUROCLASS
175
[109]176NEUROCLASS(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)
177VISUALHINTS(ReceptorClass)
178SYMBOL(`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')
179ENDNEUROCLASS
180
181NEUROCLASS(Const,*,Constant,Constant value,0,1,0)
182VISUALHINTS(Invisible)
183SYMBOL(`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')
184ENDNEUROCLASS
185
186NEUROCLASS(BendMuscle,|,Bend muscle,,1,0,2)
[932]187SHAPETYPE(BallAndStickShapeType)
[1107]188VISUALHINTS(DontShowClass+EffectorClass+IsV1BendMuscle+AtFirstPart)
[109]189SYMBOL(`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')
[1107]190NEUROPROP(p,0,0,power,f,0.0,1.0,0.25,power)
[109]191NEUROPROP(r,0,0,bending range,f,0.0,1.0,1.0,bendrange)
192ENDNEUROCLASS
193
194NEUROCLASS(RotMuscle,@,Rotation muscle,,1,0,2)
[932]195SHAPETYPE(BallAndStickShapeType)
[1107]196VISUALHINTS(DontShowClass+EffectorClass+IsV1RotMuscle+AtFirstPart)
[109]197SYMBOL(`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')
[1107]198NEUROPROP(p,0,0,power,f,0.0,1.0,1.0,power)
[109]199ENDNEUROCLASS
200
[945]201NEUROCLASS(SolidMuscle,M,Muscle for solids,,1,0,2)
[932]202SHAPETYPE(SolidsShapeType)
[975]203JOINTTYPE(SUPPORTED_JOINT_HINGE_X+SUPPORTED_JOINT_HINGE_XY)
[1107]204VISUALHINTS(EffectorClass+AtFirstPart+IsSolidMuscle)
[920]205SYMBOL(`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')
[1107]206NEUROPROP(p,0,0,power,f,0.0,1.0,1.0,power)
[920]207NEUROPROP(a,0,0,axis,d,0,1,0,axis)
208ENDNEUROCLASS
209
[109]210NEUROCLASS(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)
211SYMBOL(`3,3,25,0,25,100,75,50,25,0,1,75,50,100,50,3,44,42,51,57,36,57,44,42')
212ENDNEUROCLASS
213
214NEUROCLASS(FuzzyNeuro,Fuzzy,Fuzzy system [EXPERIMENTAL!],Refer to publications to learn more about this neuron.,-1,1,0)
215SYMBOL(`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')
216NEUROPROP(ns,0,0,number of fuzzy sets,d,1,,,fuzzySetsNr)
217NEUROPROP(nr,0,0,number of rules,d,1,,,rulesNr)
[419]218NEUROPROP(fs,0,0,fuzzy sets,s,0,-1,,fuzzySetString)
219NEUROPROP(fr,0,0,fuzzy rules,s,0,-1,,fuzzyRulesString)
[109]220ENDNEUROCLASS
221
222NEUROCLASS(Sticky,Sti,Sticky [EXPERIMENTAL!],,1,0,1)
[932]223SHAPETYPE(BallAndStickShapeType)
[109]224VISUALHINTS(EffectorClass)
225ENDNEUROCLASS
226
227NEUROCLASS(LinearMuscle,LMu,Linear muscle [EXPERIMENTAL!],,1,0,2)
[932]228SHAPETYPE(BallAndStickShapeType)
[1107]229VISUALHINTS(EffectorClass+IsLinearMuscle)
230NEUROPROP(p,0,0,power,f,0.0,1.0,1.0,power)
[109]231ENDNEUROCLASS
232
233NEUROCLASS(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)
234VISUALHINTS(ReceptorClass)
235ENDNEUROCLASS
236
237NEUROCLASS(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)
238VISUALHINTS(ReceptorClass)
239ENDNEUROCLASS
240
241NEUROCLASS(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)
242SYMBOL(`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')
243ENDNEUROCLASS
244
245NEUROCLASS(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)
246SYMBOL(`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')
247ENDNEUROCLASS
248
249NEUROCLASS(ChSel,ChSel,Channel selector,`Outputs a single channel (selected by the \"ch\" parameter) from multichannel input',1,1,0)
250SYMBOL(`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')
251NEUROPROP(ch,0,0,channel,d,,,,ch)
252ENDNEUROCLASS
253
254NEUROCLASS(Random,Rnd,Random noise,`Generates random noise (subsequent random values in the range of -1..+1)',0,1,0)
255ENDNEUROCLASS
256
257NEUROCLASS(Sinus,Sin,Sinus generator,`Output frequency = f0+input',1,1,0)
258SYMBOL(`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')
259NEUROPROP(f0,0,0,base frequency,f,-1.0,1.0,0.06283185307,f0)
260NEUROPROP(t,0,0,time,f,0,6.283185307,0,t)
261ENDNEUROCLASS
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