Hi All,
I'm finding myself dealing with n-dimensional grids quite a lot, and
trying to do some 'tricky' index manipulation. The main problem is
manipulating arrays when I don't know a priori the number of dimensions;
in essence I need to be able to iterate across dimensions.
first, I've got three arrays of lower-bounds (param_min), upper-bounds
(param_max) and numbers of steps (nstep). I'm using the following
incantations to produce the association mgrid an ogrid arrays:
args = tuple(slice(p1, p2, n*1j)
for p1,p2,n in zip(param_min, param_max, nstep))
param_grid = N.mgrid.__getitem__(args)
Is this the easiest way to do this?
Second, from the mgrid object, param_grid, I want to recover the step
sizes (assume I've thrown away the args so I can't make an ogrid
object). This seems to work:
deltas = numpy.empty(npar)
for i in xrange(npar):
idxtup = (i,)+(0,)*i + (1,) + (0,)*(npar-1-i)
deltas[i] = param_grid[idxtup] - param_grid[((i,)+(0,)*npar )]
(Or I could compress this into a single somewhat complicated list
comprehension). Again, this seems a bit overly complicated. Any ideas
for simplifying it?
But at least I can work out how to do these things.
Finally, however, I need to reconstruct the individual
param_min:param_max:(nstep*1j) 1-d arrays (i.e., the flattened versions
of the ogrid output). These are effectively param_grid[i,0,,,,:,,,,]
where ':' is in slot i. But I don't know how to emulate ':' in either a
slice object or tuple-indexing notation. Obviously I could just do a
more complicated version of the deltas[] calculation, or direct
manipulation on param_grid.flat, but both seem like too much work on my
part...
Thanks in advance!
Andrew
p.s. thanks to travis for all his hard work, especially in the run-up to
1.0b (although test() crashes on my PPC Mac... more on that later when
I've had time to play).
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