add the new normalisation for multi pos excitation

ditb/commands/darkwhite.py

@@ -77,11 +77,14 @@ def darkwhite(images, dark, white, darkofwhite, white_z, force, lower, smooth, v
         dark = smoothed_names[1]
         darkofwhite = smoothed_names[2]
 
+
     print(f'🪩 Normalize images with:')
     print(f'- dark: {dark}')
     print(f'- white: {white}')
     if version == 2:
         print(f'- white for power estimation: {original_white}')
+        assert 'smooth' not in original_white.name.lower(), "Need to use a non smoothed white to estimate the power of the beam"
+
     print(f'- dark of white: {darkofwhite}')
 
     darkwhite_names = [Path(dark).name, Path(white).name, Path(darkofwhite).name]

ditb/exposure/darkwhite_correction.py

@@ -282,13 +282,31 @@ def correct_dark_white(images: str, dark: str = '', white: str = '' ,
             dow_stack = dow_stack.map_blocks(remove_cosmic, 2, dtype=dow_stack.dtype)
 
         diffwhite = white_stack - dow_stack
+
+        if version == 2:
+            print('Two steps normalisation: Spectral power from the mean, then spatial variation of the white')
+            assert original_white is not None, "Please provide a non-smoothed white"
+            # Estimation of the power
+            owhite = mmzarr_read(str(original_white))[0, 0, :, zpos].map_blocks(img_as_float)
+            whiteMean = owhite.mean(axis=(-2,-1))
+            powerEstimation = whiteMean / whiteMean.max()
+            powerEstimation = powerEstimation.compute()
+
+            # Create a normed white with a maximum of 1
+            # for each excitation wavelength
+            max_white = diffwhite.max(axis=(-2,-1))
+            min_ratio = white_stack.min() / max_white
+            normed_white = diffwhite / max_white[:, None, None]
+            # Cut at the maximum of the minimum ratio
+            normed_white[normed_white < min_ratio.max()] = min_ratio.max()
+        else:
+            print("On step normalisation: spectral and sptatial from the white normed by it's stack maximum")
             max_white = diffwhite.max()
             min_ratio = white_stack.min() / max_white
-
             normed_white = diffwhite / max_white
             normed_white[normed_white < min_ratio] = min_ratio
 
-        final_white = normed_white.compute()[None, None, :, None]
+        final_white = normed_white.compute()[None, None, :, :]
 
         darks = mmzarr_read(str(darkin))
         darks_meta = read_mmzarr_metadata(str(darkin))
@@ -340,6 +358,10 @@ def correct_dark_white(images: str, dark: str = '', white: str = '' ,
         diff_im_darks = images - darks
         diff_im_darks[diff_im_darks < 0 ] = 0
 
+        if version == 2:
+            # divide the data by the power estimation
+            diff_im_darks = diff_im_darks / powerEstimation[None, None, None, :, None, None]
+
         # Compute the transfert function
 
         # The transfer function is just the inverse of the white

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "DITB"
-version = "20250108rc1"
+version = "20250108rc2"
 readme = "README.md"
 requires-python = ">=3.11"
 authors = [{ name = "Hugo Chauvet", email = "hugo.chauvet@synchrotron-soleil.fr" }]