diff --git a/src/vip_hci/fm/negfc_fmerit.py b/src/vip_hci/fm/negfc_fmerit.py
index b1d9668a..1e6f3485 100644
--- a/src/vip_hci/fm/negfc_fmerit.py
+++ b/src/vip_hci/fm/negfc_fmerit.py
@@ -740,7 +740,7 @@ def get_mu_and_sigma(cube, angs, ncomp, annulus_width, aperture_radius, fwhm,
             planet_parameter = (r_guess, theta_guess, f_guess[0])
         else:
             r_all = [r_guess]*len(f_guess)
-            theta_all = [r_guess]*len(f_guess)
+            theta_all = [theta_guess]*len(f_guess)
             planet_parameter = np.array([r_all, theta_all, f_guess])
         array = cube_planet_free(planet_parameter, cube, angs, psfn,
                                  imlib=imlib, interpolation=interpolation,
diff --git a/src/vip_hci/greedy/ipca_fullfr.py b/src/vip_hci/greedy/ipca_fullfr.py
index e2159138..fd87f15a 100644
--- a/src/vip_hci/greedy/ipca_fullfr.py
+++ b/src/vip_hci/greedy/ipca_fullfr.py
@@ -44,7 +44,7 @@
 """
 
 __author__ = 'Valentin Christiaens, Sandrine Juillard'
-__all__ = ['ipca']
+__all__ = ['ipca', 'IPCA_Params']
 
 from dataclasses import dataclass
 import numpy as np
diff --git a/src/vip_hci/metrics/contrcurve.py b/src/vip_hci/metrics/contrcurve.py
index d70c45d7..d30e7c10 100644
--- a/src/vip_hci/metrics/contrcurve.py
+++ b/src/vip_hci/metrics/contrcurve.py
@@ -84,7 +84,7 @@ def contrast_curve(
         non-coronagraphic PSF which we use to scale the contrast. If a vector
         is given it must contain the photometry correction for each frame.
     algo : callable or function
-        The post-processing algorithm, e.g. vip_hci.pca.pca.
+        The post-processing algorithm, e.g. vip_hci.psfsub.pca.
     sigma : int
         Sigma level for contrast calculation. Note this is a "Gaussian sigma"
         regardless of whether Student t correction is performed (set by the
@@ -175,7 +175,7 @@ def contrast_curve(
         pixels, the noise and the sigma corrected (if Student is True).
 
     If full_output is True then the function returns:
-        datafr, cube_fc_all, frame_fc_all, frame_nofc and fc_map_all.
+        datafr, frame_fc_all, frame_nofc and fc_map_all.
 
     frame_fc_all : numpy ndarray
         3d array with the 3 frames of the 3 (patterns) processed cubes with