add type hints to tile

situr/image/situ_tile.py

@@ -1,8 +1,11 @@
+from situr.transformation.round_transformation import RoundTransform
 import numpy as np
 
 from situr.image.situ_image import SituImage
 from situr.transformation import IdentityRoundTransform
 
+from typing import List
+
 
 class Tile:
     '''
@@ -13,7 +16,7 @@ class Tile:
     X 2048
     '''
 
-    def __init__(self, file_list, nucleaus_channel=4):
+    def __init__(self, file_list: List[List[List[str]]], nucleaus_channel: int = 4):
         self.images = []
         self.round_transformations = []
         for situ_image_list in file_list:
@@ -33,35 +36,31 @@ class Tile:
         # TODO: implement
         pass
 
-    def get_image_round(self, round):
-        return self.images[round]
-
-    def set_round_transformation(self, round, transformation):
+    def set_round_transformation(self, round, transformation: RoundTransform):
         self.round_transformations[round] = transformation
 
-    def get_round_count(self):
+    def get_round_count(self) -> int:
         return len(self.images)
 
-    def get_channel_count(self):
+    def get_channel_count(self) -> int:
         return self.images[0].get_channel_count()
 
-    def get_round(self, round_number):
-        """This 
+    def get_round(self, round_number: int) -> SituImage:
+        """This methods returns the round based on round number
 
         Args:
-            round_number (integer): The round number starting with 0
+            round_number (int): The round number (starting with index 0)
 
         Returns:
             SituImage: The image corresponding to the requested round number.
         """
-        return self.images[id]
+        return self.images[round_number]
 
-    def to_numpy_array(self):
+    def to_numpy_array(self) -> np.ndarray:
         tmp_list = []
         for image in self.images:
             tmp_list.append(image.get_data())
-
         return np.array(tmp_list)
 
-    def get_channel(self, round, channel, focus_level=0):
+    def get_channel(self, round: int, channel: int, focus_level: int = 0) -> np.ndarray:
         return self.images[round].get_channel(channel, focus_level=focus_level)