[Numpy-discussion] looking for code advice

josef.pktd@gmai... josef.pktd@gmai...
Wed Sep 29 11:36:10 CDT 2010

On Wed, Sep 29, 2010 at 9:19 AM,  <josef.pktd@gmail.com> wrote:
> On Wed, Sep 29, 2010 at 8:25 AM, Gordon Wrigley <gordon@tolomea.com> wrote:
>> Hi
>> 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)

maybe ptp==0 is faster

numpy.ptp(a, axis=None, out=None)
Range of values (maximum - minimum) along an axis.


>>     # select the actual value for the homogeneous chunks and MIXED for the
>> heterogeneous
>>     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)
> should work
> 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.)
> Josef
>>     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.
>> Regards
>> Gordon
>> 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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