5 
SHORT TERM ============= (*needed* for streamlines & tractography) 
SHORT TERM ============= (*needed* for streamlines & tractography) 
6 
======================== 
======================== 
7 


8 
[GLK:3] Add sequence types (needed for evals & evecs) 
Remove CL from compiler 
9 


10 

[GLK:2] Add sequence types (needed for evals & evecs) 
11 
syntax 
syntax 
12 
types: ty '{' INT '}' 
types: ty '{' INT '}' 
13 
value construction: '{' e1 ',' … ',' en '}' 
value construction: '{' e1 ',' … ',' en '}' 
14 
indexing: e '{' e '}' 
indexing: e '{' e '}' 
15 


16 
[GLK:4] evals & evecs for symmetric tensor[2,2] and 
[GLK:3] evals & evecs for symmetric tensor[2,2] and 
17 
tensor[3,3] (requires sequences) 
tensor[3,3] (requires sequences) 
18 


19 
ability to emit/track/record variables into dynamically resized 
ability to emit/track/record variables into dynamically resized 
25 
SHORTISH TERM ========= (to make using Diderot less annoying to 
SHORTISH TERM ========= (to make using Diderot less annoying to 
26 
======================== program in, and slow to execute) 
======================== program in, and slow to execute) 
27 


28 
valuenumbering optimization 
valuenumbering optimization [DONE] 
29 


30 

Allow ".ddro" file extensions in addition to ".diderot" 
31 


32 
[GLK:1] Add a clamp function, which takes three arguments; either 
Be able to output values of type tensor[2,2] and tensor[3,3]; 
33 
three scalars: 
(currently only scalars & vectors). Want to add some regression tests 
34 
clamp(lo, hi, x) = max(lo, min(hi, x)) 
based on this and currently can't 

or three vectors of the same size: 


clamp(lo, hi, [x,y]) = [max(lo[0], min(hi[0], x)), 


max(lo[1], min(hi[1], y))] 


This would be useful in many current Diderot programs. 


One question: clamp(x, lo, hi) is the argument order used in OpenCL 


and other places, but clamp(lo, hi, x) is much more consistent with 


lerp(lo, hi, x), hence GLK's preference 

35 


36 
[GLK:2] Proper handling of stabilize method 
[GLK:1] Proper handling of stabilize method 
37 


38 
allow "*" to represent "modulate": percomponent multiplication of 
allow "*" to represent "modulate": percomponent multiplication of 
39 
vectors, and vectors only (not tensors of order 2 or higher). Once 
vectors, and vectors only (not tensors of order 2 or higher). Once 
43 
implicit type promotion of integers to reals where reals are 
implicit type promotion of integers to reals where reals are 
44 
required (e.g. not exponentiation "^") 
required (e.g. not exponentiation "^") 
45 


46 
[GLK:5] Save Diderot output to nrrd, instead of "mip.txt" 
[GLK:4] Save Diderot output to nrrd, instead of "mip.txt" 
47 
For grid of strands, save to similarlyshaped array 
For grid of strands, save to similarlyshaped array 
48 
For list of strands, save to long 1D (or 2D for nonscalar output) list 
For list of strands, save to long 1D (or 2D for nonscalar output) list 
49 
For ragged things (like tractography output), will need to save both 
For ragged things (like tractography output), will need to save both 
50 
complete list of values, as well as list of start indices and lengths 
complete list of values, as well as list of start indices and lengths 
51 
to index into complete list 
to index into complete list 
52 


53 
[GLK:6] Use of Teem's "hest" commandline parser for getting 
[GLK:5] Use of Teem's "hest" commandline parser for getting 
54 
any input variables that are not defined in the source file 
any "input" variables that are not defined in the source file. 
55 


56 
[GLK:7] ability to declare a field so that probe positions are 
[GLK:6] ability to declare a field so that probe positions are 
57 
*always* "inside"; with various ways of mapping the known image values 
*always* "inside"; with various ways of mapping the known image values 
58 
to nonexistant index locations. One possible syntax emphasizes that 
to nonexistant index locations. One possible syntax emphasizes that 
59 
there is a index mapping function that logically precedes convolution: 
there is a index mapping function that logically precedes convolution: 
84 


85 
"initially" supports lists 
"initially" supports lists 
86 


87 
"initially" supports lists of positions output from 
"initially" supports lists of positions output from different 
88 
different initalization Diderot program 
initalization Diderot program (or output from the same program; 
89 

e.g. using output of iso2d.diderot for one isovalue to seed the input 
90 

to another invocation of the same program) 
91 


92 
Communication between strands: they have to be able to learn each 
Communication between strands: they have to be able to learn each 
93 
other's state (at the previous iteration). Early version of this can 
other's state (at the previous iteration). Early version of this can 
107 
Allow integer exponentiation ("^2") to apply to square matrices, 
Allow integer exponentiation ("^2") to apply to square matrices, 
108 
to represent repeated matrix multiplication 
to represent repeated matrix multiplication 
109 



Alow X *= Y, X /= Y, X += Y, X = Y to mean what they do in C, 


provided that X*Y, X/Y, X+Y, XY are already supported. 


Nearly every Diderot program would be simplified by this. 




110 
Put small 1D and 2D fields, when reconstructed specifically by tent 
Put small 1D and 2D fields, when reconstructed specifically by tent 
111 
and when differentiation is not needed, into faster texture buffers. 
and when differentiation is not needed, into faster texture buffers. 
112 
test/illustvr.diderot is good example of program that uses multiple 
test/illustvr.diderot is good example of program that uses multiple 
113 
such 1D fields basically as lookuptablebased function evaluation 
such 1D fields basically as lookuptablebased function evaluation 
114 


115 
expand trace in mid to low translation 
expand trace in mid to low translation [DONE] 
116 


117 
extend norm (exp) to all tensor types [DONE for vectors and matrices] 
extend norm (exp) to all tensor types [DONE for vectors and matrices] 
118 


151 
(but we should only duplicate over the liverange of the result of the 
(but we should only duplicate over the liverange of the result of the 
152 
conditional. 
conditional. 
153 


154 
[GLK:8] Want: nontrivial field expressions & functions. 
[GLK:7] Want: nontrivial field expressions & functions. 
155 
scalar fields from scalar fields F and G: 
scalar fields from scalar fields F and G: 
156 
field#0(2)[] X = (sin(F) + 1.0)/2; 
field#0(2)[] X = (sin(F) + 1.0)/2; 
157 
field#0(2)[] X = F*G; 
field#0(2)[] X = F*G; 
170 
There is value in having these, even if the differentiation of them is 
There is value in having these, even if the differentiation of them is 
171 
not supported (hence the indication of "field#0" for these above) 
not supported (hence the indication of "field#0" for these above) 
172 


173 

Introduce region types (syntax region(d), where d is the dimension of the 
174 

region. One useful operator would be 
175 

dom : field#k(d)[s] > region(d) 
176 

Then the inside test could be written as 
177 

pos ∈ dom(F) 
178 

We could further extend this approach to allow geometric definitions of 
179 

regions. It might also be useful to do inside tests in world space, 
180 

instead of image space. 
181 


182 
co vs contra index distinction 
co vs contra index distinction 
183 


184 
Permit field composition: 
Permit field composition: 
194 
field#2(3)[] F = bspln3 ⊛ img; 
field#2(3)[] F = bspln3 ⊛ img; 
195 
or, as a tensor product of kernels, one for each axis, e.g. 
or, as a tensor product of kernels, one for each axis, e.g. 
196 
field#0(3)[] F = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; 
field#0(3)[] F = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; 
197 
This is especially important for things like timevarying data, or 
This is especially important for things like timevarying fields 
198 
other multidimensional fields where one axis of the domain is very 
and the use of scalespace in field visualization: one axis of the 
199 
different from the rest, and hence must be treated separately when 
must be convolved with a different kernel during probing. 
200 
it comes to convolution. What is very unclear is how, in such cases, 
What is very unclear is how, in such cases, we should notate the 
201 
we should notate the gradient, when we only want to differentiate with 
gradient, when we only want to differentiate with respect to some 
202 
respect to some subset of the axes. One ambitious idea would be: 
subset of the axes. One ambitious idea would be: 
203 
field#0(3)[] Ft = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; // 2D timevarying field 
field#0(3)[] Ft = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; // 2D timevarying field 
204 
field#0(2)[] F = lambda([x,y], Ft([x,y,42.0])) // restriction to time=42.0 
field#0(2)[] F = lambda([x,y], Ft([x,y,42.0])) // restriction to time=42.0 
205 
vec2 grad = ∇F([x,y]); // 2D gradient 
vec2 grad = ∇F([x,y]); // 2D gradient 
206 


207 

Tensors of order 3 (e.g. gradients of diffusion tensor fields, or 
208 

hessians of vector fields) and order 4 (e.g. Hessians of diffusion 
209 

tensor fields). 
210 


211 
representation of tensor symmetry 
representation of tensor symmetry 
212 
(have to identify the group of index permutations that are symmetries) 
(have to identify the group of index permutations that are symmetries) 
213 


215 


216 
outer works on all tensors 
outer works on all tensors 
217 


218 

Help for debugging Diderot programs: need to be able to uniquely 
219 

identify strands, and for particular strands that are known to behave 
220 

badly, do something like printf or other logging of their computations 
221 

and updates. 
222 


223 

Permit writing dimensionally general code: Have some statement of the 
224 

dimension of the world "W" (or have it be learned from one particular 
225 

field of interest), and then able to write "vec" instead of 
226 

"vec2/vec3", and perhaps "tensor[W,W]" instead of 
227 

"tensor[2,2]/tensor[3,3]" 
228 


229 

Traits: all things things that have boilerplate code (especially 
230 

volume rendering) should be expressed in terms of the unique 
231 

computational core. Different kinds of streamline/tractography 
232 

computation will be another example, as well as particle systems. 
233 


234 
Einstein summation notation 
Einstein summation notation 
235 


236 
"tensor comprehension" (like list comprehension) 
"tensor comprehension" (like list comprehension) 
237 


238 

Fields coming from different sources of data: 
239 

* triangular or tetrahedral meshes over 2D or 3D domains (of the 
240 

source produced by finiteelement codes; these will come with their 
241 

own specialized kinds of reconstruction kernels, called "basis 
242 

functions" in this context) 
243 

* Large point clouds, with some radial basis function around each point, 
244 

which will be tuned by parameters of the point (at least one parameter 
245 

giving some notion of radius) 
246 


247 
====================== 
====================== 
248 
BUGS ================= 
BUGS ================= 
249 
====================== 
====================== 
253 
// uncaught exception Size [size] 
// uncaught exception Size [size] 
254 
// raised at ctarget/ctarget.sml:47.1547.19 
// raised at ctarget/ctarget.sml:47.1547.19 
255 
//field#4(3)[] F = img ⊛ bspln5; 
//field#4(3)[] F = img ⊛ bspln5; 
256 

