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to index into complete list 
to index into complete list 
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[GLK:6] Use of Teem's "hest" commandline parser for getting 
[GLK:6] Use of Teem's "hest" commandline parser for getting 
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any input variables that are not defined in the source file 
any "input" variables that are not defined in the source file. 
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[GLK:7] ability to declare a field so that probe positions are 
[GLK:7] ability to declare a field so that probe positions are 
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*always* "inside"; with various ways of mapping the known image values 
*always* "inside"; with various ways of mapping the known image values 
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field#2(3)[] F = bspln3 ⊛ img; 
field#2(3)[] F = bspln3 ⊛ img; 
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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. 
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field#0(3)[] F = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; 
field#0(3)[] F = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; 
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This is especially important for things like timevarying data, or 
This is especially important for things like timevarying fields 
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other multidimensional fields where one axis of the domain is very 
and the use of scalespace in field visualization: one axis of the 
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different from the rest, and hence must be treated separately when 
must be convolved with a different kernel during probing. 
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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 
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we should notate the gradient, when we only want to differentiate with 
gradient, when we only want to differentiate with respect to some 
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respect to some subset of the axes. One ambitious idea would be: 
subset of the axes. One ambitious idea would be: 
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field#0(3)[] Ft = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; // 2D timevarying field 
field#0(3)[] Ft = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; // 2D timevarying field 
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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 
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vec2 grad = ∇F([x,y]); // 2D gradient 
vec2 grad = ∇F([x,y]); // 2D gradient 