diff --git a/src/cdtools/tools/analysis/analysis.py b/src/cdtools/tools/analysis/analysis.py
index 9844edb4..25dcf630 100644
--- a/src/cdtools/tools/analysis/analysis.py
+++ b/src/cdtools/tools/analysis/analysis.py
@@ -1067,7 +1067,7 @@ def remove_phase_ramp(im, window, probe=None):
     Is, Js = np.mgrid[:window.shape[0],:window.shape[1]]
     def zero_freq_component(freq):
         phase_ramp = np.exp(2j * np.pi * (freq[0] * Is + freq[1] * Js))
-        return -np.abs(np.sum(phase_ramp * window))**2
+        return 1/np.abs(np.sum(phase_ramp * window))**2 ##Somehow scipy did not like to minize a negative value, so to circumvent that, we can minimize 1/f instead.
     
     x0 = np.array([0,0])
     result = opt.minimize(zero_freq_component, x0)
@@ -1114,7 +1114,8 @@ def rms_error(x):
     if weights is not None:
         pix_translations = cdtools.tools.interactions.translations_to_pixel(t.as_tensor(basis), t.as_tensor(translations)).numpy()
         pix_translations -= np.min(pix_translations,axis=0)
-        weights = weights * np.exp(growth_rate[0] * pix_translations[:,0] + growth_rate[1] * pix_translations[:,1])
+        scale = np.exp(growth_rate[0] * pix_translations[:,0] + growth_rate[1] * pix_translations[:,1])
+        weights = weights * scale[:,np.newaxis,np.newaxis] # taking into account when dm_rank is not zero. 
         to_return = to_return + (weights,)
     
     if len(to_return) == 1:
@@ -1153,7 +1154,8 @@ def standardize_reconstruction_set(
         correct_phase_offset=True,
         correct_phase_ramp=True,
         correct_amplitude_exponent=False,
-        window=np.s_[:,:],
+        window = np.s_[:,:],
+        window_frc = np.s_[:,:],
         nbins=50,
         frc_limit='side',
 ):
@@ -1213,17 +1215,17 @@ def standardize_reconstruction_set(
         obj_1, probe_1, weights_1 = remove_amplitude_exponent(
             obj_1, window, probe=probe_1,
             weights=half_1['weights'],
-            basis=half_1['basis'],
+            basis=half_1['obj_basis'],
             translations=half_1['translations'])
         obj_2, probe_2, weights_2 = remove_amplitude_exponent(
             obj_2, window, probe=probe_2,
             weights=half_2['weights'],
-            basis=half_2['basis'],
+            basis=half_2['obj_basis'],
             translations=half_2['translations'])
         obj, probe, weights = remove_amplitude_exponent(
             obj, window, probe=probe,
             weights=full['weights'],
-            basis=full['basis'],
+            basis=full['obj_basis'],
             translations=full['translations'])
 
     
@@ -1233,22 +1235,23 @@ def standardize_reconstruction_set(
         obj = np.exp(-1j* np.angle(np.sum(obj[window]))) * obj
 
 
-    # Todo update the translations to account for the determined shift
+    # Todo: update the translations to account for the determined shift
+    # Using a different window for the FRC than for the other function
     shift_1 = ip.find_shift(
-        t.as_tensor(ip.hann_window(np.abs(obj[window]))),
-        t.as_tensor(ip.hann_window(np.abs(obj_1[window]))))
+        t.as_tensor(ip.hann_window(np.abs(obj[window_frc]))),
+        t.as_tensor(ip.hann_window(np.abs(obj_1[window_frc]))))
     obj_1  = ip.sinc_subpixel_shift(
         t.as_tensor(obj_1), shift_1).numpy()
 
     shift_2 = ip.find_shift(
-        t.as_tensor(ip.hann_window(np.abs(obj[window]))),
-        t.as_tensor(ip.hann_window(np.abs(obj_2[window]))))
+        t.as_tensor(ip.hann_window(np.abs(obj[window_frc]))),
+        t.as_tensor(ip.hann_window(np.abs(obj_2[window_frc]))))
     obj_2  = ip.sinc_subpixel_shift(
         t.as_tensor(obj_2), shift_2).numpy()
 
     freqs, frc, threshold = calc_frc(
-        ip.hann_window(obj_1[window]),
-        ip.hann_window(obj_2[window]),
+        ip.hann_window(obj_1[window_frc]),
+        ip.hann_window(obj_2[window_frc]),
         full['obj_basis'], nbins=nbins, limit=frc_limit)