Hi Mike, g.step is a 3-tuple giving the grid plane spacing, for example g.step = (3.5, 3.5, 3.5) means the grid planes are separate by 3.5 Angstroms along x, y, and z axes. The line of code you are looking at converts the Gaussian linewidth in Angstroms to grid index units. The g variable holds a Grid_Data object defined in share/VolumeData/griddata.py The code that actually calculates the Gaussian filtering is share/VolumeFilter/gaussian.py It does a convolution with a Gaussian using a Fourier transform to speed it up. It can do cyclic or non-cyclic convolution -- meaning the map is considered to be periodic or not periodic. This effects filter results near the edge of the map. In the cyclic case the Gaussian centered near an edge wraps around to the opposite map box edge. For the non-cyclic case it behaves as if the map is zero outside its box. The code uses the Python numpy library to do the Fourier transforms and uses efficient FFT sizes since this can make an large difference (10x) in the speed with powers of 2 generally being best. Let me know if you want more explanation -- I wrote that code. Tom
Hi,
I've been using the gaussian filter for a number of years to help me display my noisy data. I'd like to understand what it's doing a bit better, and so I need help with the following code:
ijk_linewidths = map(lambda step: linewidth / step, g.step)
what is the resulting length of ijk_linewidths? I can't seem to figure out what type of data type g.step is. Is it a feature of the image itself, or where does it come from?
Best Regards,
mike
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