added todos for future release

Author: JensBlack

experiments/custom/classifier.py

@@ -319,11 +319,11 @@ def pure_simba_classifier_pool_run(input_q: mp.Queue, output_q: mp.Queue):
             last_prob = classifier.classify(features)
             output_q.put((last_prob, feature_id))
             end_time = time.time()
-            print(
-                "Classification time: {:.2f} msec".format(
-                    (end_time - start_time) * 1000
-                )
-            )
+            # print(
+            #     "Classification time: {:.2f} msec".format(
+            #         (end_time - start_time) * 1000
+            #     )
+            # )
         else:
             pass
 

experiments/custom/featureextraction.py

@@ -111,6 +111,7 @@ def EuclidianDistCalc(inArr):
 ### EUCLIDEAN DISTANCES BETWEEN CENTROIDS IN ROLLING WINDOWS
 @jit(nopython=True, cache=True)
 def distancesBetweenBps(bpArray, bp):
+    #TODO: adapt to flexible window
     """provided by Simon Nilsson from Golden Lab; Main developer of SiMBA https://github.com/sgoldenlab/simba"""
 
     frames2Process = bpArray.shape[0]
@@ -127,6 +128,7 @@ def distancesBetweenBps(bpArray, bp):
         int(np.sum(outputArray)),
     )
     if bp == "centroid":
+        #currently hardcoded to 15 frames
         msArr200, msArr166, msArr133, msArr66 = (
             outputArray[8:15],
             outputArray[10:15],
@@ -326,6 +328,7 @@ class SimbaFeatureExtractor:
     def __init__(self, input_array_length):
         self.currPixPerMM = PIXPERMM
         self.input_array_length = input_array_length
+        #TODO: Collect bodypart position in input array from skeleton automatically (this will make this much easier!)
 
     def get_currPixPerMM(self):
         return self.currPixPerMM

experiments/custom/triggers.py

@@ -709,7 +709,9 @@ def fill_time_window(self, skeleton: dict):
             k: skeleton[k] for k in skeleton.keys() if k not in key_selection
         }
         flat_values = transform_2pose(skeleton_selection).flatten()
+
         # if not enough animals are present, padd the rest with default value "0,0"
+        # TODO: Padd automatically; remove hardcoding
         if flat_values.shape[0] < 28:
             flat_values = np.pad(
                 flat_values,