Updated Requirements

Author: JBeckwith

src/AnalysisFunctions.py

@@ -1372,7 +1372,9 @@ def threshold_cell_areas_3d(
             cell_mask_new = np.asarray(
                 ski.morphology.binary_closing(
                     cell_mask_new,
-                    footprint=ski.morphology.footprint_rectangle((1, erosionsize, erosionsize)),
+                    footprint=ski.morphology.footprint_rectangle(
+                        (1, erosionsize, erosionsize)
+                    ),
                 ),
                 dtype=bool,
             )

src/Image_Analysis_Functions.py

@@ -191,7 +191,7 @@ def default_SMD_routine(self, image, d=2, pfa=1e-6, localise_in_first_frame=Fals
             image (array): image as numpy array
             d (int): integer for pyRASP intensity analyses
             pfa (float): probability of false alarm for spot detection code
-            
+
         Returns:
             centroids (2D array): centroid positions per oligomer
             estimated_intensity (numpy.ndarray): Estimated sum intensity per oligomer.
@@ -419,14 +419,16 @@ def infocus_indices(self, focus_scores, cluster_size=4):
 
         hdb.fit(focus_scores.reshape(-1, 1))
         if len(np.unique(hdb.labels_)) == 1:
-            return np.array([0, 0]) # no in-focus slices
+            return np.array([0, 0])  # no in-focus slices
         else:
             mean_scores = np.zeros(len(np.unique(hdb.labels_)))
             for i, l in enumerate(np.unique(hdb.labels_)):
                 mean_scores[i] = np.mean(focus_scores[hdb.labels_ == l])
             focus_label = np.unique(hdb.labels_)[np.argmax(mean_scores)]
             infocus_indices = np.where(hdb.labels_ == focus_label)[0]
-            in_focus_indices = np.array([np.min(infocus_indices), np.max(infocus_indices)+1])
+            in_focus_indices = np.array(
+                [np.min(infocus_indices), np.max(infocus_indices) + 1]
+            )
             return in_focus_indices
 
     @staticmethod
@@ -546,7 +548,9 @@ def calculate_offsets(octagon_shape):
 
         small_oct = ski.morphology.octagon(2, 4)
         outer_ind = ski.morphology.octagon(2, 5)
-        inner_ind = np.pad(small_oct, int((outer_ind.shape[0] - small_oct.shape[0])/2))
+        inner_ind = np.pad(
+            small_oct, int((outer_ind.shape[0] - small_oct.shape[0]) / 2)
+        )
         outer_ind -= inner_ind
 
         x_inner, y_inner = calculate_offsets(inner_ind)

src/PlottingFunctions.py

@@ -551,7 +551,7 @@ def image_plot(
             axs.contour(mask, [0.5], linewidths=masklinewidth, colors=maskcolor)
 
         return axs
-    
+
     def image_scatter_plot(
         self,
         axs,

src/RASPRoutines.py

@@ -633,7 +633,9 @@ def analyse_images(
             variance_map=self.variance_map,
         )
 
-        def _analysis_loop(img, k1, k2, img_cell, thres, large_thres, rdl, z_test, i, cell_focus):
+        def _analysis_loop(
+            img, k1, k2, img_cell, thres, large_thres, rdl, z_test, i, cell_focus
+        ):
             if cell_focus is not None:
                 z_planes, _, _, _ = self.get_infocus_planes(img_cell, k1)
                 _, img2, Gx, Gy = self.get_infocus_planes(img, k1)
@@ -710,7 +712,16 @@ def process_files(subfolder, files, cell_files):
                     cell_focus = 1
                 z_test = len(img.shape) > 2
                 _analysis_loop(
-                    img, k1, k2, img_cell, thres, large_thres, rdl, z_test, i, cell_focus,
+                    img,
+                    k1,
+                    k2,
+                    img_cell,
+                    thres,
+                    large_thres,
+                    rdl,
+                    z_test,
+                    i,
+                    cell_focus,
                 )
                 if disp:
                     print(
@@ -837,7 +848,12 @@ def filter_z_planes(to_save, z_planes):
             return z_to_plot[z_to_plot >= 0]
 
         def plot_images(
-            z_to_plot, img, to_save, to_save_largeobjects=None, img_cell=None, cell_mask=None
+            z_to_plot,
+            img,
+            to_save,
+            to_save_largeobjects=None,
+            img_cell=None,
+            cell_mask=None,
         ):
             for i in enumerate(z_to_plot):
                 xpositions = to_save.filter(pl.col("z") == i[1])["x"].to_numpy()