Various improvements #166
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Various improvements #166
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- enable custom output formatting via dedicated method
- allow multiple file extensions via `file_ext` tuple/list
- add missing call to read_tar
- allow passing parallel as ('auto', threshold) tuple
- accept dotprops (see new `dp_dist` parameter) - new parameter: `n_largest`
- VoxelNeuron.density = fraction of filled voxels - VoxelNeuron.nnz = number of non-zero voxels - VoxelNeuron.count_nonzero(): see above
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Initially, the purpose of this PR is to improve NAVis' handling of image data. However, over time it has morphed into a collection of more-or-less related improvements:
navis.config.add_units=True(default isFalse) will add units to certain neuron properties; this is a test balloon and could be extended/switched on by defaultread_nrrd,read_meshandread_tiffto useBaseReader(allows reading from URLs and archives, parallel processing, etc)BaseReadernow understands patterns for searching directories (mostly forread_mesh)read_*function that use the reader now have anerrorparameter to determine what happens if file content can't be parsedDotprops:navis.drop_fluffcan now be used to remove dust or get the N largest connected components inDotpropsread_nrrdandread_tiffnow accept athinparameternavis.neuron2nxnow works withDotpropsTreeNeuronscan now be initialised from a(vertices, edges)tuple (see also newnavis.edge2neuronfunction)navis.thin_voxelsuses scikit-image (optional dependency) to thin images to single-voxel widthTreeNeuron.verticesgives quick, read-only access to node positionsVoxelNeuron.nnzandVoxelNeuron.densityTODOs
add tutorial on image transforms/registrationnavis.downsample_neuronuse Poisson disk sampling for dotprops (implemented but not exposed yet - it's expensive)look into whether we can makenavis.skeletonizesmarter for dotpropsaddvoxel2skeletonfunction for VoxelNeurons (using kimimaro) and wrap innavis.skeletonizeImportant
This PR switches a number of
ImportErrorstoModuleNotFoundErrors. Forpykdtreeon OSX that may mean that it fails to import if the wheel was compiled with OMP support butlibompis not installed. We for now pinned thepykdtreeversion.