- main_channel and default_transform arguments for Imread.with_transform

ndbioimage/__init__.py

@@ -1253,6 +1253,8 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
         drift: bool = False,
         file: Path | str = None,
         bead_files: Sequence[Path | str] = (),
+        main_channel: int = None,
+        default_transform: Sequence[float] = None,
     ) -> View:
         """returns a view where channels and/or frames are registered with an affine transformation
         channels: True/False register channels using bead_files
@@ -1260,6 +1262,11 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
         file: load registration from file with name file, default: transform.yml in self.path.parent
         bead_files: files used to register channels, default: files in self.path.parent,
             with names starting with 'beads'
+        main channel: which channel to transform with default_transform
+        default_transform: parameters of the default transform: [a, b, c, d, e, f] where the affine transform matrix is
+            | a b e |
+            | c d f |
+            | 0 0 1 |
         """
         view = self.view()
         if file is None:
@@ -1278,7 +1285,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
             try:
                 view.transform = Transforms.from_file(file, T=drift)
             except Exception:  # noqa
-                view.transform = Transforms().with_beads(view.cyllens, bead_files)
+                view.transform = Transforms().with_beads(view.cyllens, bead_files, main_channel, default_transform)
                 if drift:
                     view.transform = view.transform.with_drift(view)
                 view.transform.save(file.with_suffix(".yml"))

ndbioimage/transforms.py

@@ -147,9 +147,11 @@ class Transforms(dict):
         else:
             raise TypeError("Not a pandas DataFrame or Series.")
 
-    def with_beads(self, cyllens, bead_files):
+    def with_beads(self, cyllens, bead_files, main_channel=None, default_transform=None):
         assert len(bead_files) > 0, "At least one file is needed to calculate the registration."
-        transforms = [self.calculate_channel_transforms(file, cyllens) for file in bead_files]
+        transforms = [
+            self.calculate_channel_transforms(file, cyllens, main_channel, default_transform) for file in bead_files
+        ]
         for key in {key for transform in transforms for key in transform.keys()}:
             new_transforms = [transform[key] for transform in transforms if key in transform]
             if len(new_transforms) == 1:
@@ -191,7 +193,7 @@ class Transforms(dict):
         return checked_files
 
     @staticmethod
-    def calculate_channel_transforms(bead_file, cyllens):
+    def calculate_channel_transforms(bead_file, cyllens, main_channel=None, default_transform=None):
         """When no channel is not transformed by a cylindrical lens, assume that the image is scaled by a factor 1.162
         in the horizontal direction"""
         from . import Imread
@@ -204,25 +206,30 @@ class Transforms(dict):
             untransformed = [c for c in range(im.shape["c"]) if cyllens[im.detector[c]].lower() == "none"]
 
             good_and_untrans = sorted(set(goodch) & set(untransformed))
+            if main_channel is None:
                 if good_and_untrans:
-                masterch = good_and_untrans[0]
+                    main_channel = good_and_untrans[0]
                 else:
-                masterch = goodch[0]
+                    main_channel = goodch[0]
             transform = Transform()
             if not good_and_untrans:
                 matrix = transform.matrix
+                if default_transform is None:
                     matrix[0, 0] = 0.86
+                else:
+                    for i, t in zip(([0, 0], [0, 1], [1, 0], [1, 1], [0, 2], [1, 2]), default_transform):
+                        matrix[i] = t
                 transform.matrix = matrix
             transforms = Transforms()
             for c in tqdm(goodch, desc="Calculating channel transforms"):  # noqa
-                if c == masterch:
+                if c == main_channel:
                     transforms[im.channel_names[c]] = transform
                 else:
-                    transforms[im.channel_names[c]] = Transform.register(max_ims[masterch], max_ims[c]) * transform
+                    transforms[im.channel_names[c]] = Transform.register(max_ims[main_channel], max_ims[c]) * transform
         return transforms
 
     @staticmethod
-    def save_channel_transform_tiff(bead_files, tiffile):
+    def save_channel_transform_tiff(bead_files, tiffile, default_transform=None):
         from . import Imread
 
         n_channels = 0
@@ -232,7 +239,7 @@ class Transforms(dict):
         with IJTiffFile(tiffile) as tif:
             for t, file in enumerate(bead_files):
                 with Imread(file) as im:
-                    with Imread(file).with_transform() as jm:
+                    with Imread(file).with_transform(default_transform=default_transform) as jm:
                         for c in range(im.shape["c"]):
                             tif.save(np.hstack((im(c=c, t=0).max("z"), jm(c=c, t=0).max("z"))), c, 0, t)
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ndbioimage"
-version = "2026.1.0"
+version = "2026.1.1"
 description = "Bio image reading, metadata and some affine registration."
 authors = [
     { name = "W. Pomp", email = "w.pomp@nki.nl" }