diff --git a/py4DSTEM/process/phase/iterative_parallax.py b/py4DSTEM/process/phase/iterative_parallax.py
index dcfd8f504..6ebb9962e 100644
--- a/py4DSTEM/process/phase/iterative_parallax.py
+++ b/py4DSTEM/process/phase/iterative_parallax.py
@@ -1341,7 +1341,18 @@ def aberration_fit(
         ### First pass
 
         # Convert real space shifts to Angstroms
-        self._xy_shifts_Ang = self._xy_shifts * xp.array(self._scan_sampling)
+
+        if force_transpose is None:
+            self.transpose_detected = False
+        else:
+            self.transpose_detected = force_transpose
+
+        if force_transpose is True:
+            self._xy_shifts_Ang = xp.flip(self._xy_shifts, axis=1) * xp.array(
+                self._scan_sampling
+            )
+        else:
+            self._xy_shifts_Ang = self._xy_shifts * xp.array(self._scan_sampling)
 
         # Solve affine transformation
         m = asnumpy(
@@ -1362,11 +1373,6 @@ def aberration_fit(
         self.aberration_A1x = (m_aberration[0, 0] - m_aberration[1, 1]) / 2.0
         self.aberration_A1y = (m_aberration[1, 0] + m_aberration[0, 1]) / 2.0
 
-        if force_transpose is None:
-            self.transpose_detected = False
-        else:
-            self.transpose_detected = force_transpose
-
         ### Second pass
 
         # Aberration coefs
@@ -1590,8 +1596,15 @@ def score_CTF(coefs):
                 )
             )
 
-            if force_transpose is None or force_transpose is True:
-                # Transposed fit
+            # (Relative) untransposed fit
+            tf = AffineTransform(angle=self.rotation_Q_to_R_rads)
+            rotated_shifts = tf(self._xy_shifts_Ang, xp=xp).T.ravel()
+            aberrations_coefs, res = xp.linalg.lstsq(
+                gradients, rotated_shifts, rcond=None
+            )[:2]
+
+            if force_transpose is None:
+                # (Relative) transposed fit
                 transposed_shifts = xp.flip(self._xy_shifts_Ang, axis=1)
                 m_T = asnumpy(
                     xp.linalg.lstsq(self._probe_angles, transposed_shifts, rcond=None)[
@@ -1615,19 +1628,10 @@ def score_CTF(coefs):
                     gradients, rotated_shifts_T, rcond=None
                 )[:2]
 
-            if force_transpose is None or force_transpose is False:
-                # Untransposed fit
-                tf = AffineTransform(angle=self.rotation_Q_to_R_rads)
-                rotated_shifts = tf(self._xy_shifts_Ang, xp=xp).T.ravel()
-                aberrations_coefs, res = xp.linalg.lstsq(
-                    gradients, rotated_shifts, rcond=None
-                )[:2]
-
-            if force_transpose is None:
                 # Compare fits
                 if res_T.sum() < res.sum():
                     self.rotation_Q_to_R_rads = rotation_Q_to_R_rads_T
-                    self.transpose_detected = True
+                    self.transpose_detected = not self.transpose_detected
                     self._aberrations_coefs = asnumpy(aberrations_coefs_T)
                     self._rotated_shifts = rotated_shifts_T
 
@@ -1638,15 +1642,6 @@ def score_CTF(coefs):
                         ),
                         UserWarning,
                     )
-                else:
-                    self._aberrations_coefs = asnumpy(aberrations_coefs)
-                    self._rotated_shifts = rotated_shifts
-
-            elif force_transpose is True:
-                self.rotation_Q_to_R_rads = rotation_Q_to_R_rads_T
-                self._aberrations_coefs = asnumpy(aberrations_coefs_T)
-                self._rotated_shifts = rotated_shifts_T
-
             else:
                 self._aberrations_coefs = asnumpy(aberrations_coefs)
                 self._rotated_shifts = rotated_shifts