diff --git a/clean_python/analysis.py b/clean_python/analysis.py
index 119c733..82eecf1 100644
--- a/clean_python/analysis.py
+++ b/clean_python/analysis.py
@@ -36,7 +36,6 @@ def merge(files):
         plt.plot(out[2, :], "g")
 
     all = sum(merge)
-   
     summe = np.max(np.sum(all, axis=0))
     all = all / summe
 
@@ -45,7 +44,6 @@ def merge(files):
     plt.plot(all[2, :], "k")
     percentage = 1-percentage
     return percentage, all
->>>>>>> e1a921c2eb7fb8f51d860e28b81ff3a41af21abc
 
 
 def debug(percentage, out):
@@ -87,15 +85,10 @@ def time_scale(p, o):
     mono_perc = mono_perc - np.min(mono_perc)
     mono_perc /= np.max(mono_perc)
 
-<<<<<<< HEAD
-    cs_rut = ip.CubicSpline(p[::-1], rut_perc[::-1])
-    cs_mono = ip.CubicSpline(p[::-1], mono_perc[::-1])
-=======
     # cs_rut = ip.CubicSpline(p[::-1], rut_perc[::-1])
     # cs_mono = ip.CubicSpline(p[::-1], mono_perc[::-1])
     cs_rut = ip.interp1d(p[::-1], rut_perc[::-1])
     cs_mono = ip.interp1d(p[::-1], mono_perc[::-1])
->>>>>>> e1a921c2eb7fb8f51d860e28b81ff3a41af21abc
 
     plt.figure()
     ph = np.linspace(0.01, 0.99, 100)
@@ -118,6 +111,7 @@ def time_scale(p, o):
     plt.savefig("timescale.png")
     plt.savefig("timescale.pdf")
 
+
 def read_file(file):
     files = np.load("./merged.npz")
     p = files["p"]
@@ -127,13 +121,8 @@ def read_file(file):
 
 if __name__ == "__main__":
     p, o = merge(sys.argv[1:])
-<<<<<<< HEAD
     np.savez("merged.npz", p=p, o=o)
     # eval_data_print(f)
     stacked_plot(p, o)
-=======
->>>>>>> e1a921c2eb7fb8f51d860e28b81ff3a41af21abc
-    # debug(p, o)
-    stacked_plot(p, o)
     time_scale(p, o)
     plt.show()
diff --git a/clean_python/extractors.py b/clean_python/extractors.py
index 00e8066..972ed2b 100644
--- a/clean_python/extractors.py
+++ b/clean_python/extractors.py
@@ -79,8 +79,8 @@ class Rect_Evaluator(Evaluator):
         new_eval_points = np.arange(len(self.eval_points))
         mask = self.mask.copy()
         for nc, ev_points in zip(new_eval_points, self.eval_points):
-            maske_low = np.min(ev_points) >= self.mask
-            maske_high = np.max(ev_points) <= self.mask
+            maske_low = np.min(ev_points) <= self.mask
+            maske_high = np.max(ev_points) >= self.mask
             mask[np.logical_and(maske_high, maske_low)] = nc
 
         plt.figure()
@@ -103,65 +103,3 @@ class Rect_Evaluator(Evaluator):
                 count += 1
         return mask
 
-#
-# def main():
-#     np.random.seed(10)
-#     points = (np.random.rand(100, 2)-0.5) * 2
-#     voro = Voronoi_Evaluator(points, [[1],[2]])
-#     rect = Rect_Evaluator(points, [[1], [2]])
-#     Z = np.ones((1000, 1000))
-#     img = Image_Wrapper(Z, -5, .01, -5, .01)
-#     voro.extract(img)
-#     rect.extract(img)
-#
-#     plt.scatter(points[[1], 0], points[[1], 1])
-#     plt.scatter(points[[2], 0], points[[2], 1])
-#     plt.imshow(img.img, extent=img.ext(), origin="lower")
-#     #plt.imshow(img.img, origin="lower")
-#     plt.show()
-#
-#
-# if __name__ == "__main__":
-#     main()
-# class Voronoi_Evaluator(Evaluator):
-#    def __init__(self, list_points):
-#        points = np.concatenate(list_points, axis=0)
-#        self.eval_points = []
-#        start = 0
-#        for l in list_points:
-#            stop = l.shape[0]
-#            self.eval_points.append(np.arange(start, start + stop))
-#            start += stop
-#        self.vor = Voronoi(points)
-#
-#    @persist_to_file("cache_merge_voro")
-#    def merge_mask_helper(self):
-#        new_eval_points = np.arange(len(self.eval_points))
-#        mask = self.mask
-#        for nc, ev_points in zip(new_eval_points, self.eval_points):
-#            for num in ev_points:
-#                mask[self.mask == num] = nc
-#        return mask
-#
-#    @persist_to_file("cache_voro")
-#    def gen_mask_helper(self, img: Image_Wrapper):
-#        mask = np.full_like(img.img, -1)
-#
-#        counter = -1
-#        region_mask = self.vor.point_region
-#        for i in np.array(self.vor.regions, dtype=list)[region_mask]:
-#            counter += 1
-#            if -1 in i:
-#                continue
-#            if len(i) == 0:
-#                continue
-#            pts = self.vor.vertices[i]
-#            pts = np.stack(img.val2pos(
-#                pts[:, 0], pts[:, 1])).astype(np.int32).T
-#            if np.any(pts < 0):
-#                continue
-#            mask_2 = np.zeros_like(img.img)
-#            cv2.fillConvexPoly(mask_2, pts, 1)
-#            mask_2 = mask_2 > 0  # To convert to Boolean
-#            mask[mask_2] = counter
-#        return mask