On Mon, Jun 21, 2010 at 1:51 PM, Skipper Seabold <jsseab...@gmail.com>wrote:
> On Mon, Jun 21, 2010 at 2:44 PM, Benjamin Root <ben.r...@ou.edu> wrote:
> > Hello,
> >
> > I ran into a somewhat counter-intuitive situation that probably should be
> > documented somewhere with respect to record (structured?) arrays. I
> wanted
> > to stack multiple arrays together that had the same names for the
> columns.
> > Since I was imagining the columns as having the names (as opposed to
> rows),
> > I figured vstack() would do the job, but it merely created a sequence of
> > record arrays. Turns out that I had to use hstack() to get what I
> wanted.
> > Here is an example of what I am running into:
> >
> >>>> tracks[0]
> > array([('M', 87.087318420410156, 223.30305480957031, 1),
> > ('M', 76.440017700195312, 227.68635559082031, 2),
> > ('M', 66.116767883300781, 233.32769775390625, 3),
> > ('M', 53.614784240722656, 239.75303649902344, 4),
> > ('M', 40.807880401611328, 245.34136962890625, 5),
> > ('M', 28.479558944702148, 250.30470275878906, 6),
> > ('S', 0.0, 0.0, -9)],
> > dtype=[('types', '|S1'), ('xLocs', '<f4'), ('yLocs', '<f4'),
> > ('frameNums', '<i4')])
> >
> >>>> tracks[1]
> > array([('M', 184.38957214355469, 114.406494140625, 1),
> > ('M', 197.28269958496094, 133.10012817382812, 2),
> > ('M', 209.65650939941406, 151.74812316894531, 3),
> > ('M', 223.28224182128906, 171.2159423828125, 4),
> > ('M', 238.75798034667969, 190.44374084472656, 5),
> > ('M', 254.47175598144531, 211.06010437011719, 6),
> > ('S', 0.0, 0.0, -9)],
> > dtype=[('types', '|S1'), ('xLocs', '<f4'), ('yLocs', '<f4'),
> > ('frameNums', '<i4')])
> >
> >
> >>>> np.vstack((tracks[0], tracks[1]))
> > array([[('M', 87.087318420410156, 223.30305480957031, 1),
> > ('M', 76.440017700195312, 227.68635559082031, 2),
> > ('M', 66.116767883300781, 233.32769775390625, 3),
> > ('M', 53.614784240722656, 239.75303649902344, 4),
> > ('M', 40.807880401611328, 245.34136962890625, 5),
> > ('M', 28.479558944702148, 250.30470275878906, 6),
> > ('S', 0.0, 0.0, -9)],
> > [('M', 184.38957214355469, 114.406494140625, 1),
> > ('M', 197.28269958496094, 133.10012817382812, 2),
> > ('M', 209.65650939941406, 151.74812316894531, 3),
> > ('M', 223.28224182128906, 171.2159423828125, 4),
> > ('M', 238.75798034667969, 190.44374084472656, 5),
> > ('M', 254.47175598144531, 211.06010437011719, 6),
> > ('S', 0.0, 0.0, -9)]],
> > dtype=[('types', '|S1'), ('xLocs', '<f4'), ('yLocs', '<f4'),
> > ('frameNums', '<i4')])
> >
> >>>> np.hstack((tracks[0], tracks[1]))
> > array([('M', 87.087318420410156, 223.30305480957031, 1),
> > ('M', 76.440017700195312, 227.68635559082031, 2),
> > ('M', 66.116767883300781, 233.32769775390625, 3),
> > ('M', 53.614784240722656, 239.75303649902344, 4),
> > ('M', 40.807880401611328, 245.34136962890625, 5),
> > ('M', 28.479558944702148, 250.30470275878906, 6),
> > ('S', 0.0, 0.0, -9), ('M', 184.38957214355469, 114.406494140625,
> 1),
> > ('M', 197.28269958496094, 133.10012817382812, 2),
> > ('M', 209.65650939941406, 151.74812316894531, 3),
> > ('M', 223.28224182128906, 171.2159423828125, 4),
> > ('M', 238.75798034667969, 190.44374084472656, 5),
> > ('M', 254.47175598144531, 211.06010437011719, 6),
> > ('S', 0.0, 0.0, -9)],
> > dtype=[('types', '|S1'), ('xLocs', '<f4'), ('yLocs', '<f4'),
> > ('frameNums', '<i4')])
> >
> > By the way, both methods will return record arrays as expected. However,
> it
> > would be a 2-d array for vstack and 1-d for hstack.
> > Maybe I have been seeing this wrong, but I hope this helps out anyone
> else
> > who might have been confused.
> >
>
> For a little more control, you might try
>
> import numpy.lib.recfunctions as nprf
> nprf.stack_arrays((arr1,arr2),usemask=False)
>
> If you're working with structured arrays a lot, the functions in nprf
> become pretty handy. It's still not imported into the numpy namespace
> by default, but hopefully there will be some time for a review and
> inclusion at some point, because it's pretty useful.
>
> Skipper
>
Oooh! Nice, I never noticed that before. Thanks!
Ben Root
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