autoformat code and add missing imports

situr/image/situ_image.py

@@ -8,6 +8,7 @@ def extend_dim(array):
     ones = np.ones((array.shape[0], 1))
     return np.append(array, ones, axis=1)
 
+
 def remove_dim(array):
     return array[:, :-1]
 
@@ -107,7 +108,8 @@ class SituImage:
             np.array: The peaks found by this method as np.array of shape (n, 2)
         '''
         img = img_as_float(self.get_data()[channel, focus_level, :, :])
-        peaks = blob_dog(img, min_sigma=min_sigma, max_sigma=max_sigma, threshold=threshold)
+        peaks = blob_dog(img, min_sigma=min_sigma,
+                         max_sigma=max_sigma, threshold=threshold)
         return peaks[:, 0:2]
 
     def show_channel_peaks(self, channel, focus_level=0, min_sigma=0.75, max_sigma=3, threshold=0.1):
@@ -128,11 +130,13 @@ class SituImage:
         Returns:
             image: The image of the specified focus level and channel with encircled peaks.
         '''
-        peaks = self.get_channel_peaks(channel, focus_level, min_sigma, max_sigma, threshold)
+        peaks = self.get_channel_peaks(
+            channel, focus_level, min_sigma, max_sigma, threshold)
 
         img = Image.fromarray(self.get_data()[channel, focus_level, :, :])
         draw = ImageDraw.Draw(img)
         for x, y in zip(peaks[:, 0], peaks[:, 1]):
-            draw.ellipse((x - 5, y - 5, x + 5, y + 5), outline ='white', width = 3)
+            draw.ellipse((x - 5, y - 5, x + 5, y + 5),
+                         outline='white', width=3)
         img.show()
         return img

situr/registration/channel_registration.py

@@ -1,3 +1,11 @@
+import abc
+import open3d as o3
+from probreg import filterreg
+from situr.image import extend_dim
+
+from situr.transformation import ScaleRotateTranslateChannelTransform
+
+
 class ChannelRegistration:
     __metaclass__ = abc.ABCMeta
 
@@ -5,10 +13,13 @@ class ChannelRegistration:
         # For each channel (except nucleus) compute transform compared to reference_channel
         # Add Channel transformation to Channel
         pass
+
     @abc.abstractmethod
     def register_single_channel(self, peaks_data, reference_peaks):
         """Performs the channel registration on an image. Expects the peaks in each image as input."""
-        raise NotImplementedError(self.__class__.__name__ + '.register_single_channel')
+        raise NotImplementedError(
+            self.__class__.__name__ + '.register_single_channel')
+
 
 class FilterregChannelRegistration(ChannelRegistration):
     def register_single_channel(self, data_peaks, reference_peaks):

situr/transformation/__init__.py

@@ -0,0 +1 @@
+from .channel_transformation import ChannelTransform, ScaleRotateTranslateChannelTransform

situr/transformation/channel_transformation.py

@@ -1,10 +1,16 @@
+import abc
+import numpy as np
+import scipy
+
+
 class ChannelTransform:
     __metaclass__ = abc.ABCMeta
 
     @abc.abstractmethod
     def apply_transformation(self, situ_img, channel):
         """Performs a transformation on one channel, all focus_levels are transformed the same way"""
-        raise NotImplementedError(self.__class__.__name__ + '.apply_transformation')
+        raise NotImplementedError(
+            self.__class__.__name__ + '.apply_transformation')
 
 
 class ScaleRotateTranslateChannelTransform(ChannelTransform):
@@ -23,6 +29,7 @@ class ScaleRotateTranslateChannelTransform(ChannelTransform):
         for focus_level in range(focus_levels):
             img = channel_img[focus_level, :, :]
 
-            img [:, :] = scipy.ndimage.affine_transform(img, self.transform_matrix)
+            img[:, :] = scipy.ndimage.affine_transform(
+                img, self.transform_matrix)
             img[:, :] = scipy.ndimage.zoom(img, self.scale)
             img[:, :] = scipy.ndimage.shift(img, self.offset)