 trunk/TODO 2011/06/24 17:54:44 1389
+++ trunk/TODO 2011/07/10 08:09:50 1442
@@ 17,7 +17,9 @@
ability to emit/track/record variables into dynamically resized
runtime output buffer
tensor fields: convolution on general tensor images (order > 1)
+[GLK:4] tensor fields from tensor images: Initially need at least
+convolution on tensor[2,2] and tensor[3,3] (the same componentwise
+convolution as for vectors).
========================
SHORTISH TERM ========= (to make using Diderot less annoying to
@@ 31,6 +33,8 @@
[GLK:1] Proper handling of stabilize method
+Convolution on general tensor images (order > 2)
+
allow "*" to represent "modulate": percomponent multiplication of
vectors, and vectors only (not tensors of order 2 or higher). Once
sequences are implemented this should be removed: the operation is not
@@ 39,14 +43,14 @@
implicit type promotion of integers to reals where reals are
required (e.g. not exponentiation "^")
[GLK:4] Save Diderot output to nrrd, instead of "mip.txt"
+[Nick working on this] Save Diderot output to nrrd, instead of "mip.txt"
For grid of strands, save to similarlyshaped array
For list of strands, save to long 1D (or 2D for nonscalar output) list
For ragged things (like tractography output), will need to save both
complete list of values, as well as list of start indices and lengths
to index into complete list
[GLK:5] ability to declare a field so that probe positions are
+[GLK:6] ability to declare a field so that probe positions are
*always* "inside"; with various ways of mapping the known image values
to nonexistant index locations. One possible syntax emphasizes that
there is a index mapping function that logically precedes convolution:
@@ 77,7 +81,7 @@
MEDIUM TERM ================== (*needed* for particles)
==============================
runtime birth of strands
+[Lamont working on this] runtime birth of strands
"initially" supports lists
@@ 86,17 +90,23 @@
e.g. using output of iso2d.diderot for one isovalue to seed the input
to another invocation of the same program)
Communication between strands: they have to be able to learn each
other's state (at the previous iteration). Early version of this can
have the network of neighbors be completely static (for running one
strand/pixel image computations). Later version with strands moving
through the domain will require some spatial data structure to
optimize discovery of neighbors.
+[Lamont working on this] Communication between strands: they have to
+be able to learn each other's state (at the previous iteration).
+Early version of this can have the network of neighbors be completely
+static (for running one strand/pixel image computations). Later
+version with strands moving through the domain will require some
+spatial data structure to optimize discovery of neighbors.
============================
MEDIUMISH TERM ============ (to make Diderot more useful/effective)
============================
+[GLK:5] 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.
+
Python/ctypes interface to runtime
support for Python interop and GUI
@@ 128,12 +138,6 @@
LONG TERM ==================== (make Diderot more interesting/attractive from
============================== a research standpoint)
[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.

IL support for higherorder tensor values (matrices, etc).
tensor construction [DONE]
tensor indexing [DONE]
@@ 204,10 +208,6 @@
field#0(2)[] F = lambda([x,y], Ft([x,y,42.0])) // restriction to time=42.0
vec2 grad = ∇F([x,y]); // 2D gradient
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).

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