PYME.Analysis.angleFilter module

To be run by invoking:

python angleFilter.py <file1> <file2> <file3> etc ....

note that running python angleFilter.py <adirectory>/*.tif will expand out to the above.

expects files to be in tiff format and inverted (ie skeleton is 0, other pixels 255).

The calculated histograms are output as tab formatted txt, with the columns being left hand bin edge (in degrees), raw count, and normalised count respectively.

PYME.Analysis.angleFilter.angHist(theta)
PYME.Analysis.angleFilter.angHist2(theta)
PYME.Analysis.angleFilter.angle_filter(data, FILT_SIZE=5)
PYME.Analysis.angleFilter.angle_filter_m(mask, FILT_SIZE=5)
PYME.Analysis.angleFilter.fold(thet)
PYME.Analysis.angleFilter.genCoords(FILT_SIZE)
PYME.Analysis.angleFilter.procSkelFile(filename, disp=True)
PYME.Analysis.angleFilter.roi_at(data, x0, y0, FILT_SIZE, x, y, b, ang)
PYME.Analysis.angleFilter.th(data, FILT_SIZE, x, y, b, ang)
PYME.Analysis.angleFilter.th2(data, FILT_SIZE, x, y, b, ang)

calculate principle axis of ROI using SVD

PYME.Analysis.angleFilter.width(data, FILT_SIZE, x, y, b, ang)

calculate orthogonal width of data segment using data itself to define principle axis

PYME.Analysis.angleFilter.width_filter(data, angles=None, FILT_SIZE=5)
PYME.Analysis.angleFilter.width_filter_m(data, mask, angles=None, FILT_SIZE=5)
PYME.Analysis.angleFilter.width_o(data, FILT_SIZE, x, y, b, ang)

calculate orthogonal width of data segment based on 3D data where first slice is the intensities, and second slice is the angle in each pixel.