On Wed, Sep 29, 2010 at 9:19 AM, <josef.p...@gmail.com> wrote: > On Wed, Sep 29, 2010 at 8:25 AM, Gordon Wrigley <gor...@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. Josef >> # 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. >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
