Added opt_SNR as returned variable in pca_grid

vip_hci/metrics/contrcurve.py

@@ -207,16 +207,20 @@ def contrast_curve(
     else:
         fwhm_med = fwhm
 
+    # Retrieve ncomp info
+    ncomp = algo_dict["ncomp"]
+
     if verbose:
         start_time = time_ini()
         if isinstance(starphot, float) or isinstance(starphot, int):
-            msg0 = "ALGO : {}, FWHM = {}, # BRANCHES = {}, SIGMA = {},"
-            msg0 += " STARPHOT = {}"
-            print(msg0.format(algo.__name__, fwhm_med, nbranch, sigma,
-                              starphot))
+            msg0 = "ALGO : {}, ncomp = {}, FWHM = {}, \n"
+            msg0 += "# BRANCHES = {}, SIGMA = {}, STARPHOT = {}"
+            print(msg0.format(algo.__name__, ncomp, fwhm_med, nbranch,
+                              sigma, starphot))
         else:
-            msg0 = "ALGO : {}, FWHM = {}, # BRANCHES = {}, SIGMA = {}"
-            print(msg0.format(algo.__name__, fwhm_med, nbranch, sigma))
+            msg0 = "ALGO : {}, ncomp = {}, FWHM = {}, \n"
+            msg0 += "# BRANCHES = {}, SIGMA = {}, "
+            print(msg0.format(algo.__name__, ncomp, fwhm_med, nbranch, sigma))
 
     # throughput
     verbose_thru = False
@@ -496,8 +500,7 @@ def contrast_curve(
 
         # Give a title to the contrast curve plot
         if object_name is not None and frame_size is not None:
-            # Retrieve ncomp and pca_type info to use in title
-            ncomp = algo_dict["ncomp"]
+            # Retrieve pca_type info to use in title
             if algo_dict["cube_ref"] is None:
                 pca_type = "ADI"
             else:

vip_hci/objects/pppca.py

@@ -243,6 +243,7 @@ def run(
                         self.frame_final,
                         self.dataframe,
                         self.opt_number_pc,
+                        self.opt_SNR,
                     ) = res
                     if self.results is not None:
                         self.results.register_session(

vip_hci/psfsub/pca_fullfr.py

@@ -655,7 +655,7 @@ def pca(*all_args: List, **all_kwargs: dict):
                 # PCA grid, computing S/Ns
                 if isinstance(algo_params.ncomp, (tuple, list)):
                     if algo_params.full_output:
-                        final_residuals_cube, frame, table, _ = res_pca
+                        final_residuals_cube, frame, table, _, _ = res_pca
                     else:
                         # returning only the optimal residual
                         frame = res_pca[1]

vip_hci/psfsub/utils_pca.py

@@ -185,6 +185,9 @@ def pca_grid(cube, angle_list, fwhm=None, range_pcs=None, source_xy=None,
         S/Ns for ``source_xy``.
     opt_npc : int
         [source_xy is not None] Optimal number of PCs for ``source_xy``.
+    opt_SNR : float
+        [source_xy is not None] Optimal signal-to-noise ratio for ``source_xy`` 
+        at opt_npc
 
     """
     from ..metrics import snr, frame_report
@@ -371,14 +374,15 @@ def get_snr(frame, y, x, fwhm, fmerit):
     if x is not None and y is not None and fwhm is not None:
         argmax = np.argmax(snrlist)
         opt_npc = pclist[argmax]
+        opt_SNR = snrlist[argmax]
         df = DataFrame({'PCs': pclist, 'S/Ns': snrlist, 'fluxes': fluxlist})
         if debug:
             print(df, '\n')
 
         if verbose:
             print('Number of steps', len(pclist))
             msg = 'Optimal number of PCs = {}, for S/N={:.3f}'
-            print(msg.format(opt_npc, snrlist[argmax]))
+            print(msg.format(opt_npc, opt_SNR))
 
             # Plot of SNR and flux as function of PCs
             if plot:
@@ -411,7 +415,7 @@ def get_snr(frame, y, x, fwhm, fmerit):
         finalfr = cubeout[argmax]
         _ = frame_report(finalfr, fwhm, (x, y), verbose=verbose)
 
-        return cubeout, finalfr, df, opt_npc
+        return cubeout, finalfr, df, opt_npc, opt_SNR
 
     else:
         if verbose: