typos

proxtoolbox/experiments/orbitaltomography/orthogonal_orbits.py

@@ -103,7 +103,7 @@ class OrthogonalOrbitals(PlanarMolecule):
 
         self.createRandomGuess()
 
-        # some variables wich are necessary for the algorithm:
+        # some variables which are necessary for the algorithm:
         self.data_sq = self.data ** 2
         self.data_zeros = np.where(self.data == 0)
 
@@ -182,7 +182,7 @@ class OrthogonalOrbitals(PlanarMolecule):
         u_hat = prop.eval(u)
         fourier_intensity = np.sqrt(np.sum(abs(u_hat) ** 2, axis=0))
         if interpolate_and_zoom:
-            u_show = self.interp_zoom_field(u)
+            u_show = interp_zoom_field(u)
         else:
             u_show = u
         fig, ax = plt.subplots(2, len(u) + 1, figsize=figsize, num=name)
@@ -203,24 +203,6 @@ class OrthogonalOrbitals(PlanarMolecule):
             plt.show()
         return fig
 
-    def interp_zoom_field(self, u, interpolation=2, zoom=0.5):
-        """
-        interpolate a field and zoom in to the center
-        """
-        nt, ny, nx = u.shape
-
-        cm = center_of_mass(np.sum(abs(u) ** 2, axis=0))
-        to_shift = (0, -1*int(np.round(cm[0] - ny / 2)), -1*int(np.round(cm[1] - nx / 2)))
-        centered = np.roll(u, to_shift, axis=(0, 1, 2))
-
-        zmy = int(ny * zoom) // 2
-        zmx = int(nx * zoom) // 2
-        zoomed = centered[:, zmy:ny - zmy, zmx:nx - zmx]
-
-        interpolated = np.array([fourier_interpolate(u_i, factor=interpolation) for u_i in zoomed])
-
-        return interpolated
-
 
 def support_from_stack(input_array: np.ndarray,
                        threshold: float = 0.1,
@@ -234,7 +216,7 @@ def support_from_stack(input_array: np.ndarray,
     Args:
         input_array: either the measured diffraction patterns (arpes patterns) or guesses of the objects
         threshold: support is everywhere where the autocorrelation is higher than the threshold
-        relative_threshold: If true, threshold at threshold*np.amax(autocorrelation)
+        relative_threshold: If true, threshold at threshold * np.max(autocorrelation)
         input_in_fourier_domain: False if a guess of the object is given in input_array
         absolute_autocorrelation: Take the absolute value of the autocorrelation? (Generally a
             good idea for objects which are not non-negative)
@@ -257,7 +239,7 @@ def support_from_stack(input_array: np.ndarray,
     # Take the sum along the first axis to get the average of the autocorrelations
     autocorrelation = np.sum(autocorrelation, axis=0)
 
-    # Detetmine thresholding
+    # Determine thresholding
     maxval = np.amax(autocorrelation)
     if relative_threshold:
         threshold_val = threshold * maxval
@@ -270,3 +252,22 @@ def support_from_stack(input_array: np.ndarray,
         support = binary_dilation(support, iterations=binary_dilate_support).astype(np.uint)
 
     return support
+
+
+def interp_zoom_field(u, interpolation=2, zoom=0.5):
+    """
+    interpolate a field and zoom in to the center
+    """
+    nt, ny, nx = u.shape
+
+    cm = center_of_mass(np.sum(abs(u) ** 2, axis=0))
+    to_shift = (0, -1*int(np.round(cm[0] - ny / 2)), -1*int(np.round(cm[1] - nx / 2)))
+    centered = np.roll(u, to_shift, axis=(0, 1, 2))
+
+    zmy = int(ny * zoom) // 2
+    zmx = int(nx * zoom) // 2
+    zoomed = centered[:, zmy:ny - zmy, zmx:nx - zmx]
+
+    interpolated = np.array([fourier_interpolate(u_i, factor=interpolation) for u_i in zoomed])
+
+    return interpolated