improve pydoc for SituImage

situr/image/situ_image.py

@@ -1,4 +1,3 @@
-from situr.transformation.transformation import Transform
 import numpy as np
 from PIL import Image
 from typing import List
@@ -6,33 +5,69 @@ from typing import List
 from situr.transformation import Transform, IdentityTransform
 
 
-def extend_dim(array: np.ndarray):
+def extend_dim(array: np.ndarray) -> np.ndarray:
+    """Extends the dimensions of a numpy array where ever value is 1.
+
+    Args:
+        array (np.ndarray): The array to extend the dimension from
+
+    Returns:
+        (np.ndarray): Slice of the modified array
+    """
     ones = np.ones((array.shape[0], 1))
     return np.append(array, ones, axis=1)
 
 
-def remove_dim(array: np.ndarray):
+def remove_dim(array: np.ndarray) -> np.ndarray:
+    """Removes the last dimension of the numpy array
+
+    Args:
+        array (np.ndarray): The array to remove the last dimension from.
+
+    Returns:
+        (np.ndarray): Slice of the modified array
+    """
     return array[:, :-1]
 
 
 class SituImage:
     """
-    A class to representing one situ image with different focus levels.
+    A class to representing one situ image with different
+    channels and focus levels. Another term for this is round.
 
     ...
 
     Attributes
     ----------
     data : numpy.array
-        the image data containing all the channels of shape (channels, focus_levels, image_size_y, image_size_x)
-    files :  List[List[str]] 
-        A list of lists. Each inner list corresponds to one focus level. Its contents correspons to a file for each channel.
+        the image data containing all the channels of
+        shape (channels, focus_levels, image_size_y, image_size_x)
+    files :  List[List[str]]
+        A list of lists. Each inner list corresponds to one focus level.
+        Its contents correspons to a file for each channel.
     nucleaus_channel : int
         tells which channel is used for showing where the cell nucleuses are.
-    peak_finder : 
+    channel_transformations : List[Transform]
+        A list of transformations for each channel,
+        wherby the index corresponds to the channel
     """
 
     def __init__(self, file_list: List[List[str]], nucleaus_channel: int = 4):
+        """Initializes a situ image.
+
+        Args:
+            file_list (List[List[str]]): A list of Lists that specifies
+                channels and focus levels. The inner list is for focus levels
+                and each outer list is for the rounds. So if we have an image
+                with two focus levels and 3 channels it would look like this:
+                    [
+                        ['ch0_f0.tif', 'ch0_f1.tif'],
+                        [ch1_f0.tif', 'ch1_f1.tif'],
+                        [ch2_f0.tif', 'ch2_f1.tif']
+                    ]
+            nucleaus_channel (int, optional): The channel that is showing the nucleai
+                and not other data. Defaults to 4.
+        """
         self.files = file_list
         self.data = None
         self.nucleaus_channel = nucleaus_channel
@@ -41,29 +76,58 @@ class SituImage:
         ]
 
     def get_data(self) -> np.ndarray:
+        """Returns the image data (also loads it if not yet in memory).
+
+        Returns:
+            np.ndarray: the image data in the shape (channel, focus_level, image_dim, image_dim)
+        """
         if self.data is None:
             self._load_image()
         return self.data
 
     def apply_transformations(self):
+        """Applies the stored transformations to the image.
+        """
         for i, transformation in enumerate(self.channel_transformations):
             for focus_level in range(self.get_focus_level_count()):
                 img = self.get_focus_level(i, focus_level)
                 transformation.apply_tranformation(img)
 
     def apply_transform_to_whole_image(self, transform: Transform):
+        """Applies an external transformation to every channel and focus level of an image.
+
+        Args:
+            transform (Transform): the transformation that will be applied
+        """
         for channel in range(self.get_channel_count()):
             for focus_level in range(self.get_focus_level_count()):
                 img = self.get_focus_level(channel, focus_level)
                 transform.apply_tranformation(img)
 
     def set_channel_transformation(self, channel: int, transformation: Transform):
+        """Sets a transformation for a channel, however does not apply it.
+
+        Args:
+            channel (int): The channel that the transformation should be applied to
+            transformation (Transform): The transformation that will be applied to
+                the specified channel
+        """
         self.channel_transformations[channel] = transformation
 
     def get_channel_count(self) -> int:
+        """Method to get the number of channels an image has.
+
+        Returns:
+            int: the number of channels
+        """
         return self.get_data().shape[0]
 
     def get_focus_level_count(self) -> int:
+        """Method to get the number of focus levels an image has.
+
+        Returns:
+            int: the number of focus levels
+        """
         return self.get_data().shape[1]
 
     def get_focus_level(self, channel: int, focus_level: int) -> np.ndarray:
@@ -111,7 +175,8 @@ class SituImage:
         Args:
             channel (int): The channel that should be used when printing
             focus_level (int, optional): The focus level that should be used. Defaults to 0.
-            img_show (bool, optional): Specifies if img.show is to be called or if just the image should be returned. Defaults to True.
+            img_show (bool, optional): Specifies if img.show is to be called or if just the image
+                should be returned. Defaults to True.
 
         Returns:
             Image: The image of the specified focus level and channel