minor az postprocessing for data analysis

Author: SarahMuth

.gitignore

@@ -17,4 +17,6 @@ scripts/portal/upscale_tomo.py
 analysis_results/
 scripts/cooper/revision/evaluation_results/
 scripts/cooper/revision/export_tif_to_h5.py
-scripts/cooper/revision/copy_path.py
+scripts/cooper/revision/copy_path.py
+scripts/cooper/revision/view.py
+scripts/cooper/revision/count_AZ.py

run_sbatch_revision.sbatch

@@ -1,6 +1,6 @@
 #! /bin/bash
 #SBATCH -c 4 #4 #8
-#SBATCH --mem 256G #120G #32G #64G #256G
+#SBATCH --mem 120G #120G #32G #64G #256G
 #SBATCH -p grete:shared #grete:shared #grete-h100:shared
 #SBATCH -t 4:00:00 #6:00:00 #48:00:00 
 #SBATCH -G A100:1 #V100:1 #2 #A100:1  #gtx1080:2 #v100:1 #H100:1
@@ -9,4 +9,7 @@
 
 source ~/.bashrc
 conda activate synapse-net
-python scripts/cooper/revision/surface_dice.py -i /mnt/ceph-hdd/cold/nim00007/AZ_prediction_new/stem_for_eval/ -gt /mnt/ceph-hdd/cold/nim00007/new_AZ_train_data/stem_for_eval/ -v 7
+python /user/muth9/u12095/synapse-net/scripts/cooper/revision/updated_data_analysis/run_data_analysis.py \
+ -i /mnt/lustre-emmy-hdd/projects/nim00007/data/synaptic-reconstruction/cooper/20241102_TOMO_DATA_Imig2014/exported/SNAP25/ \
+ -o /mnt/lustre-emmy-hdd/projects/nim00007/data/synaptic-reconstruction/cooper/20241102_TOMO_DATA_Imig2014/afterRevision_analysis/boundaryT0_9_constantins_presynapticFiltering --store \
+ -s ./analysis_results/man_subset

scripts/cooper/revision/updated_data_analysis/data_analysis.py

@@ -1,3 +1,7 @@
+import numpy as np
+from scipy.ndimage import label
+from skimage.measure import regionprops
+
 from synapse_net.distance_measurements import measure_segmentation_to_object_distances
 from synapse_net.imod.to_imod import convert_segmentation_to_spheres
 
@@ -14,7 +18,23 @@ def calc_AZ_SV_distance(vesicles, az, resolution):
     Returns:
         list of dict: Each dict contains 'seg_id' and 'distance', sorted by seg_id.
     """
-    distances, _, _, seg_ids = measure_segmentation_to_object_distances(vesicles, az, resolution=resolution)
+
+    # Apply connected components to az
+    labeled_az, num_features = label(az)
+    
+    if num_features == 0:
+        raise ValueError("No connected components found in active zone segmentation.")
+
+    # Measure the size of each component
+    props = regionprops(labeled_az)
+    largest_region = max(props, key=lambda r: r.area)
+
+    # Create a mask for the largest component only
+    largest_az_mask = labeled_az == largest_region.label
+
+    # Call the distance function with the filtered AZ mask
+    distances, _, _, seg_ids = measure_segmentation_to_object_distances(vesicles, largest_az_mask, resolution=resolution)
+
 
     # Exclude seg_id == 0
     dist_list = [