[Numpy-discussion] looking for code advice
Wed Sep 29 08:19:23 CDT 2010
On Wed, Sep 29, 2010 at 8:25 AM, Gordon Wrigley <email@example.com> wrote:
> First the disclaimer: This is my first numpy experience, so I have next to
> no idea what I'm doing.
> I've muddled through and managed to put together some code for my current
> problem and now that I have it going I'd like to hear any comments people
> may have on both my solution and other ways of approaching the problem.
> I have two goals here, I'd like to make the process run faster and I'd like
> to broaden my understanding of numpy as I can see from my brief use of it
> that it is a remarkably powerful tool.
> Now to the problem at hand. I find this difficult to explain but will try as
> best I can.
> The best word I have for the process is decimation. The input and output are
> both 3 dimensional arrays of uint8's. The output is half the size of the
> input along each dimension. Each cell [x,y,z] in the output corresponds to
> the 2x2x2 block [2*x:2*x+2, 2*y:2*y+2, 2*z:2*z+2] in the input. The tricky
> bit is in how the correspondence works. If all the cells in the input block
> have the same value then the cell in the output block will also have that
> value. Otherwise the output cell will have the value MIXED.
> Here is my current solution, from my limited testing it seems to produce the
> result I'm after.
> def decimate(data_in):
> in_x, in_y, in_z = data_in.shape
> out_x = in_x / 2
> out_y = in_y / 2
> out_z = in_z / 2
> out_shape = out_x, out_y, out_z
> out_size = product(out_shape)
> # figure out which chunks are homogeneous
> reshaped_array = data_in.reshape(out_x, 2, out_y, 2, out_z,
> 2).transpose(0,2,4,1,3,5).reshape(out_x, out_y, out_z, 8)
> min_array = numpy.amin(reshaped_array, axis=3)
> max_array = numpy.amax(reshaped_array, axis=3)
> equal_array = numpy.equal(min_array, max_array)
> # select the actual value for the homogeneous chunks and MIXED for the
> decimated_array = data_in[::2,::2,::2]
> mixed_array = numpy.tile(MIXED, out_size).reshape(out_shape)
> data_out = numpy.where(equal_array, decimated_array, mixed_array)
data_out = numpy.where(equal_array, decimated_array, MIXED)
I don't see anything else, unless there is something in scipy.ndimage.
I have to remember your reshape trick for 3d. (I don't know how many
temporary arrays this creates.)
> return data_out
> For the curious this is will be used to build a voxel octtree for a 3d
> graphics application. The final setup will be more complicated, this is the
> minimum that will let me get up and running.
> P.S. congrats on numpy, it is a very impressive tool, I've only scraped the
> surface and it's already impressed me several times over.
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