PYME.Analysis.Colocalisation.edtColoc module¶
- PYME.Analysis.Colocalisation.edtColoc.imageDensityAtDistance(A, mask, voxelsize=None, bins=100, roi_mask=None)¶
Calculates the distribution of a label at varying distances from a mask. Negative distances are on the inside of the mask.
- Parameters
- A - intensity image
- mask - binary mask
- voxelsize - size of the pixels/voxels - should be either a constant, or an iterable
with a length equal to the number of dimensions in the data
- bins - either a number of bins, or an array of bin edges
- Returns
- bn - number of pixels in distance bin
- bm - mean intensity in distance bin
- bins - the bin edges
- PYME.Analysis.Colocalisation.edtColoc.image_enrichment_and_fraction_at_distance(A, mask, voxelsize=None, bins=100, roi_mask=None)¶
returns the relative enrichment of label of a label at a given distance from a mask, along with the total signal enclosed within that distance.
-ve distances correspond to points in the interior of the mask.
- PYME.Analysis.Colocalisation.edtColoc.plot_image_dist_coloc_figure(bins, enrichment_BA, enrichment_AA, enclosed_BA, enclosed_AA, enclosed_area, pearson=None, MA=None, MB=None, nameA='A', nameB='B')¶
- PYME.Analysis.Colocalisation.edtColoc.pointDensityAtDistance(points, mask, voxelsize, maskOffset, bins=100)¶
Calculates the distribution of a label at varying distances from a mask. Negative distances are on the inside of the mask.
- Parameters
- pointsnp.ndarray
array containing point coordinates
- masknp.ndarray
binary mask
- voxelsizeiterable
size of the pixels/voxels in mask - should be an iterable with a length equal to the number of dimensions in the data
- maskOffsetiterable
iterable with lengh equal to number of dims giving coordinates (in point space) or the 0th pixel in the mask
- binsint or ndarray, default=100
either a number of bins, or an array of bin edges
- Returns
- bnndarray
integrated intensity in distance bin
- bmndarray
mean intensity in distance bin
- binsndarray
the bin edges