17 
ability to emit/track/record variables into dynamically resized 
ability to emit/track/record variables into dynamically resized 
18 
runtime output buffer 
runtime output buffer 
19 


20 
tensor fields: convolution on general tensor images (order > 1) 
[GLK:4] tensor fields from tensor images: Initially need at least 
21 

convolution on tensor[2,2] and tensor[3,3] (the same componentwise 
22 

convolution as for vectors). 
23 


24 
======================== 
======================== 
25 
SHORTISH TERM ========= (to make using Diderot less annoying to 
SHORTISH TERM ========= (to make using Diderot less annoying to 
33 


34 
[GLK:1] Proper handling of stabilize method 
[GLK:1] Proper handling of stabilize method 
35 


36 

Convolution on general tensor images (order > 2) 
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 
40 
sequences are implemented this should be removed: the operation is not 
sequences are implemented this should be removed: the operation is not 
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:4] Save Diderot output to nrrd, instead of "mip.txt" 
[Nick working on this] 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:5] ability to declare a field so that probe positions are 
[GLK:6] ability to declare a field so that probe positions are 
54 
*always* "inside"; with various ways of mapping the known image values 
*always* "inside"; with various ways of mapping the known image values 
55 
to nonexistant index locations. One possible syntax emphasizes that 
to nonexistant index locations. One possible syntax emphasizes that 
56 
there is a index mapping function that logically precedes convolution: 
there is a index mapping function that logically precedes convolution: 
81 
MEDIUM TERM ================== (*needed* for particles) 
MEDIUM TERM ================== (*needed* for particles) 
82 
============================== 
============================== 
83 


84 
runtime birth of strands 
[Lamont working on this] runtime birth of strands 
85 


86 
"initially" supports lists 
"initially" supports lists 
87 


90 
e.g. using output of iso2d.diderot for one isovalue to seed the input 
e.g. using output of iso2d.diderot for one isovalue to seed the input 
91 
to another invocation of the same program) 
to another invocation of the same program) 
92 


93 
Communication between strands: they have to be able to learn each 
[Lamont working on this] Communication between strands: they have to 
94 
other's state (at the previous iteration). Early version of this can 
be able to learn each other's state (at the previous iteration). 
95 
have the network of neighbors be completely static (for running one 
Early version of this can have the network of neighbors be completely 
96 
strand/pixel image computations). Later version with strands moving 
static (for running one strand/pixel image computations). Later 
97 
through the domain will require some spatial data structure to 
version with strands moving through the domain will require some 
98 
optimize discovery of neighbors. 
spatial data structure to optimize discovery of neighbors. 
99 


100 
============================ 
============================ 
101 
MEDIUMISH TERM ============ (to make Diderot more useful/effective) 
MEDIUMISH TERM ============ (to make Diderot more useful/effective) 
102 
============================ 
============================ 
103 


104 

[GLK:5] Want codegeneration working for tensors of order three. 
105 

Order three matters for edge detection in scalar fields (to get 
106 

second derivatives of gradient magnitude), second derivatives 
107 

of vector fields (for some feature extraction), and first 
108 

derivatives of diffusion tensor fields. 
109 


110 
Python/ctypes interface to runtime 
Python/ctypes interface to runtime 
111 


112 
support for Python interop and GUI 
support for Python interop and GUI 
138 
LONG TERM ==================== (make Diderot more interesting/attractive from 
LONG TERM ==================== (make Diderot more interesting/attractive from 
139 
============================== a research standpoint) 
============================== a research standpoint) 
140 



[GLK:6] Want codegeneration working for tensors of order three. 


Order three matters for edge detection in scalar fields (to get 


second derivatives of gradient magnitude), second derivatives 


of vector fields (for some feature extraction), and first 


derivatives of diffusion tensor fields. 




141 
IL support for higherorder tensor values (matrices, etc). 
IL support for higherorder tensor values (matrices, etc). 
142 
tensor construction [DONE] 
tensor construction [DONE] 
143 
tensor indexing [DONE] 
tensor indexing [DONE] 
208 
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 
209 
vec2 grad = ∇F([x,y]); // 2D gradient 
vec2 grad = ∇F([x,y]); // 2D gradient 
210 



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


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


tensor fields). 




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 

