autoformat code and add missing imports

situr/image/situ_image.py

@@ -8,8 +8,9 @@ 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]
+    return array[:, :-1]
 
 
 class SituImage:
@@ -32,7 +33,7 @@ class SituImage:
         self.files = file_list
         self.data = None
         self.nucleaus_channel = nucleaus_channel
-        
+
     def get_data(self):
         if self.data is None:
             self._load_image()
@@ -45,8 +46,8 @@ class SituImage:
         Returns: 
             numpy.array: The loaded image of shape (focus_level, width, height)
         '''
-        return self.get_data()[channel,:,:,:]
+        return self.get_data()[channel, :, :, :]
-    
+
     def _load_image(self):
         '''
         Loads the channels of an image from seperate files and returns them as numpy array.
@@ -64,7 +65,7 @@ class SituImage:
                 channels.append(np.array(Image.open(file)))
             image_list.append(channels)
         self.data = np.array(image_list)
-    
+
     def unload_image(self):
         '''
         Unloads the image data to free up memory
@@ -83,7 +84,7 @@ class SituImage:
         Returns:
             image: The image of the specified focus level and channel
         '''
-        img = Image.fromarray(self.get_data()[0,0,:,:])
+        img = Image.fromarray(self.get_data()[0, 0, :, :])
         img.show()
         return img
 
@@ -107,9 +108,10 @@ 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):
         '''
         Returns and shows the found. Uses get_channel_peaks internally.
@@ -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]):
+        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
+        return img

situr/registration/channel_registration.py

@@ -1,14 +1,25 @@
+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
 
-    def do_registration(self, situ_img , reference_channel=0):
+    def do_registration(self, situ_img, reference_channel=0):
         # 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):
@@ -17,7 +28,7 @@ class FilterregChannelRegistration(ChannelRegistration):
         target = o3.geometry.PointCloud()
         target.points = o3.utility.Vector3dVector(extend_dim(reference_peaks))
 
-        registration_method=filterreg.registration_filterreg
+        registration_method = filterreg.registration_filterreg
         tf_param, _, _ = filterreg.registration_filterreg(source, target)
 
         return ScaleRotateTranslateChannelTransform(transform_matrix=tf_param.rot[0:2, 0:2], scale=tf_param.scale, offset=tf_param.t[0:2])

situr/transformation/__init__.py

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

situr/transformation/channel_transformation.py

@@ -1,28 +1,35 @@
+import abc
+import numpy as np
+import scipy
+
+
 class ChannelTransform:
     __metaclass__ = abc.ABCMeta
 
     @abc.abstractmethod
-    def apply_transformation(self, situ_img , channel):
+    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):
     def __init__(self, transform_matrix, scale=1, offset=np.array([0, 0])):
-        # TODO: check 
+        # TODO: check
         #   * transform matrix is 2x2
         #   * offset is array (2,)
         self.transform_matrix = transform_matrix
         self.offset = offset
         self.scale = scale
 
-    def apply_tranformation(self, situ_img , channel):
+    def apply_tranformation(self, situ_img, channel):
         channel_img = situ_img.get_channel(channel)
         focus_levels = channel_img.shape[0]
 
         for focus_level in range(focus_levels):
-            img = channel_img [focus_level, :, :]
+            img = channel_img[focus_level, :, :]
 
-            img [:, :] = scipy.ndimage.affine_transform(img, self.transform_matrix)
+            img[:, :] = scipy.ndimage.affine_transform(
-            img [:, :] = scipy.ndimage.zoom(img, self.scale)
+                img, self.transform_matrix)
-            img [:, :] = scipy.ndimage.shift(img, self.offset)
+            img[:, :] = scipy.ndimage.zoom(img, self.scale)
+            img[:, :] = scipy.ndimage.shift(img, self.offset)