Add region name and colours to area reports

PyNutil/folder_of_segmentations_to_meshview_multithreaded.py

@@ -10,7 +10,7 @@ import json
 from datetime import datetime
 
 #import json, use to define input parameters
-with open('../test/test1.json', 'r') as f:
+with open('../test/test5_NOP_s037.json', 'r') as f:
   input = json.load(f)
 #print(input)
 

PyNutil/load_workflow.py

@@ -7,7 +7,7 @@ import numpy as np
 
 from read_and_write import FlattoArray, LabeltoArray
 
-base= r"../test_data/ttA_2877_NOP_atlasmaps/2877_NOP_tTA_lacZ_Xgal_s037_nl.flat"
+base= r"../test_data/tTA_2877_NOP_s037_atlasmap/2877_NOP_tTA_lacZ_Xgal_s037_nl.flat"
 label = r"../annotation_volumes\allen2017_colours.csv"
 
 image_arr = FlattoArray(base)
@@ -33,18 +33,40 @@ area_per_label = list(zip(unique_ids,counts))
 df_area_per_label = pd.DataFrame(area_per_label, columns=["idx","area_count"])
 # create a pandas df with regions and pixel counts
 
-print(df_area_per_label)
-df_area_per_label.to_csv("../outputs/s037_area_per_idx.csv", sep=";", na_rep='', index= False)
 
+"""add region name and colours corresponding to each idx into dataframe.
+This could be a separate function"""
 
+df_label_colours =pd.read_csv(label, sep=",")
+# find colours corresponding to each region ID and add to the pandas dataframe
 
+#look up name, r, g, b in df_allen_colours in df_area_per_label based on "idx"
+new_rows = []
+for index, row in df_area_per_label.iterrows():
+    mask = df_label_colours["idx"] == row["idx"] 
+    current_region_row = df_label_colours[mask]
+    current_region_name = current_region_row["name"].values
+    current_region_red = current_region_row["r"].values
+    current_region_green = current_region_row["g"].values
+    current_region_blue = current_region_row["b"].values
 
-# Count area per unique label
-# Scale up to size of corresponding segmentation
-# calculate segmentation value per slice
-# divide segmentation value per idx per slice by area per idx per slice
-# output reports
-# also do for whole brain
+    row["name"]  = current_region_name[0]
+    row["r"] = current_region_red[0]
+    row["g"] = current_region_green[0]
+    row["b"] = current_region_blue[0]
+    
+    new_rows.append(row)
 
-#Also to do:
-# fix position of clear label in label files
+df_area_per_label_name = pd.DataFrame(new_rows)
+
+print(df_area_per_label_name)
+df_area_per_label_name.to_csv("../outputs/test5_s037_area_per_idx.csv", sep=";", na_rep='', index= False)
+
+
+
+
+# Count area per unique label in one flat file - done.
+# Scale up to size of corresponding segmentation/ or size of reference atlas if points are already scaled?
+# divide "segmentation value per idx per slice" by "area per idx per slice"
+# also do for whole brain - need to loop through and match up section with corresponding atlasmap
+# output reports per brain and per slice

test/test1.json

 {   "volume_path": "../annotation_volumes/annotation_10_reoriented_2017.nrrd",
     "label_path": "../annotation_volumes/allen2017_colours.csv",
-    "segmentation_folder": "../test_data/ttA_2877_NOP",
+    "segmentation_folder": "../test_data/ttA_2877_NOP_segmentations",
     "alignment_json": "../test_data/ttA_2877_NOP_horizontal_final_2017.json",
     "nonlinear": true,
     "colour": [0, 0, 255],

test/test5_NOP_s037.json

+{   "volume_path": "../annotation_volumes/annotation_10_reoriented_2017.nrrd",
+    "label_path": "../annotation_volumes/allen2017_colours.csv",
+    "segmentation_folder": "../test_data/ttA_2877_NOP_s037_seg",
+    "alignment_json": "../test_data/ttA_2877_NOP_horizontal_final_2017.json",
+    "nonlinear": true,
+    "colour": [0, 0, 255],
+    "points_json_path": "../outputs/test5_points.json",
+    "counts_per_label_name": "../outputs/test5_counts_per_allenID_2017.csv"
+}
+