AHHHHHHH

clean_python/analysis.py

@@ -15,6 +15,7 @@ def merge(files):
     merge = []
     plt.figure()
     for file in files:
+        print(file)
         data = np.load(file, allow_pickle=True)
         old_percentage = data["percentage"]
         w_percentage = data["w_percentage"]
@@ -23,6 +24,7 @@ def merge(files):
         out = []
         for o in ["out_1", "out_2", "out_3", "out_4"]:
             out.append(np.array(data[o]))
+        print(out)
         out = np.array(out)[:, :, 0]
 
         summe = np.max(np.sum(out, axis=0))
@@ -43,6 +45,7 @@ def merge(files):
     plt.plot(all[2, :], "k")
     percentage = 1-percentage
     return percentage, all
+>>>>>>> e1a921c2eb7fb8f51d860e28b81ff3a41af21abc
 
 
 def debug(percentage, out):
@@ -84,10 +87,15 @@ 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)
@@ -119,6 +127,12 @@ 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)

clean_python/extractors.py

@@ -79,10 +79,14 @@ 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 = np.logical_and(maske_high, maske_low)
-            mask[maske] = nc
+            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()
+        plt.imshow(mask)
+        plt.figure()
+        plt.imshow(self.mask)
         return mask
 
     def gen_mask_helper(self, img: FFT):