diff --git a/src/cal_tools/agipdlib.py b/src/cal_tools/agipdlib.py
index ecf4766bdc9f3f1243fdc01683386a2adfa4ed62..76bbc1cf84cb3d48a82cf43c71baf71b76d124dc 100644
--- a/src/cal_tools/agipdlib.py
+++ b/src/cal_tools/agipdlib.py
@@ -28,7 +28,7 @@ from cal_tools.agipdutils import (
 )
 from cal_tools.enums import AgipdGainMode, BadPixels, SnowResolution
 from cal_tools.h5_copy_except import h5_copy_except_paths
-
+from warnings import warn
 
 @dataclass
 class AgipdCtrl:
@@ -1048,10 +1048,17 @@ class AgipdCorrections:
             del rel_corr
 
         # Adjust medium gain baseline to match highest high gain value
+        # This correction is disabled in case CS gain correction is selected.
         if self.corr_bools.get("adjust_mg_baseline"):
-            mgbc = self.md_additional_offset[module_idx][cellid, ...]
-            data[gain == 1] += mgbc[gain == 1]
-            del mgbc
+            if self.rel_gain == "CS":
+                warn(
+                    "`adjust_mg_baseline` correction is not allowed with"
+                    " Current Source correction."
+                    " Skipping `adjust_mg_baseline` correction step")
+            else:
+                mgbc = self.md_additional_offset[module_idx][cellid, ...]
+                data[gain == 1] += mgbc[gain == 1]
+                del mgbc
 
         # Set negative values for medium gain to 0
         # TODO: Probably it would be better to add it to badpixel maps,
@@ -1422,11 +1429,20 @@ class AgipdCorrections:
             rel_gain = np.ones((128, 512, self.max_cells, 3), np.float32)
             if "SlopesCS" in cons_data:
                 # No Chance to have both SlopesPC and SlopesCS used at the same time?
-                # What to do for SlopesCS
-                # Switch between SlopesCS and SlopesPC based on rel_gain_mode.
                 rel_gain[..., 1] = rel_gain[..., 0] * cons_data["SlopesCS"][..., self.max_cells, 6]  # noqa
                 rel_gain[..., 2] = rel_gain[..., 1] * cons_data["SlopesCS"][..., self.max_cells, 7]  # noqa
-            
+                # TODO: Decide between:
+                #   - Median of ratio of high gain and medium gain slope
+                #   - Ratio of medians of high gain slope and medium gain slope
+                frac_high_med_ofratio = np.median(
+                    cons_data["SlopesCS"][..., self.max_cells, 6])
+                frac_high_med = (
+                    np.median(cons_data["SlopesCS"][..., self.max_cells, 0]) /
+                    np.median(cons_data["SlopesCS"][..., self.max_cells, 1]))
+                # TODO: DECIDE AND REMOVE THIS PRINT AFTER TESTING
+                print(
+                    "CHECK IF BOTH MEDIAN FRACTIONS ARE THE SAME:",
+                    np.allclose(frac_high_med, frac_high_med_ofratio))
             elif "SlopesPC" in cons_data:
                 slopesPC = cons_data["SlopesPC"].astype(np.float32, copy=False)