diff --git a/.gitignore b/.gitignore index adadd6b063e0402a39073438f65fb1360dafc573..67b99dd926aa939abf740203cf51c2c7996b0c94 100644 --- a/.gitignore +++ b/.gitignore @@ -161,3 +161,6 @@ cython_debug/ #inputs + +#outputs +test_result/ diff --git a/workflows/loading_custom_atlases.py b/workflows/basic_example_custom_atlas.py similarity index 66% rename from workflows/loading_custom_atlases.py rename to workflows/basic_example_custom_atlas.py index ca4fb162d75434499cc603e4b1073c8d9af01a80..a4f83357ffb5211a8430d41ae495774b11c46630 100644 --- a/workflows/loading_custom_atlases.py +++ b/workflows/basic_example_custom_atlas.py @@ -9,15 +9,17 @@ from PyNutil import PyNutil ###The segmentations should be images which come out of ilastix, segmenting an object of interest ###The alignment json should be out of DeepSlice, QuickNII, or VisuAlign, it defines the sections position in an atlas ###The colour says which colour is the object you want to quantify in your segmentation. It is defined in RGB -###Finally the atlas name is the relevant atlas from brainglobe_atlasapi you wish to use in Quantification. +###The atlas_path is the path to the relevant atlas.nrrd +###The label_path is the path to the corresponding atlas .csv +###The object_cutoff is a cut-off for min object size pnt = PyNutil( segmentation_folder="../tests/test_data/nonlinear_allen_mouse/", alignment_json="../tests/test_data/nonlinear_allen_mouse/alignment.json", colour=[0, 0, 0], - atlas_path="/mnt/z/HBP_analytics/PyNutil/metadata/annotation_volumes/annotation_25_reoriented_2017.nrrd", - label_path="/mnt/z/HBP_analytics/PyNutil/metadata/annotation_volumes/allen2017_colours.csv", + atlas_path="../tests/test_data/allen_mouse_2017_atlas/annotation_25_reoriented_2017.nrrd", + label_path="../tests/test_data/allen_mouse_2017_atlas//allen2017_colours.csv", ) pnt.get_coordinates(object_cutoff=0) pnt.quantify_coordinates() -pnt.save_analysis("../demo_data/PyNutil_nonlinear_noflat_custom") +pnt.save_analysis("../demo_data/test_nonlinear_allen_mouse")