Move to BDV h5

.github/lint/action.yml

@@ -10,7 +10,7 @@ runs:
   using: "composite"
   steps:
     - run: |
-        pip install fawltydeps==0.12.0
+        pip install fawltydeps
         fawltydeps --check --detailed --verbose
       shell: bash -l {0}
       working-directory: ${{ inputs.directory }}

.github/workflows/test_and_deploy.yml

@@ -21,7 +21,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8","3.9","3.10"] #not compatible with py 3.10 and 3.12 yet
+        python-version: ["3.10", "3.11", "3.12"]
 
     env:
       DISPLAY: ":99.0"
@@ -46,7 +46,7 @@ jobs:
         with:
           # Install a specific version of uv.
           version: "0.6.17"
-        
+
       - name: Install core
         timeout-minutes: 10
         run: |
@@ -94,9 +94,9 @@ jobs:
     # github secrets (see readme for details)
     needs: [test]
     runs-on: ubuntu-latest
-    environment: 
+    environment:
       name: github-pages
-    permissions: 
+    permissions:
       id-token: write
       pages: write
       contents: write
@@ -136,7 +136,7 @@ jobs:
         uses: actions/deploy-pages@v4
 
       - uses: ncipollo/release-action@v1.18.0
-        name: Create GitHub release 
+        name: Create GitHub release
         if: contains(github.ref, 'tags')
         with:
           skipIfReleaseExists: true

core/lls_core/deconvolution.py

@@ -7,9 +7,9 @@
 import logging
 import importlib.util
 from typing import Collection, Iterable,Union,Literal, Optional, TYPE_CHECKING
-from aicsimageio.aics_image import AICSImage
+from bioio import BioImage
+import bioio_czi
 from skimage.io import imread
-from aicspylibczi import CziFile
 from numpy.typing import NDArray
 import os
 import numpy as np
@@ -62,12 +62,10 @@ def read_psf(psf_paths: Collection[Path],
     for psf in psf_paths:
         if psf.exists() and psf.is_file():
             if psf.suffix == ".czi":
-                psf_czi = CziFile(psf.__str__())
-                psf_aics = psf_czi.read_image()
+                psf_czi = BioImage(psf.__str__(), reader=bioio_czi.Reader)
+                psf_aics = psf_czi.data
                 # make sure shape is 3D
-                psf_aics = psf_aics[0][0]  # np.expand_dims(psf_aics[0],axis=0)
-                # if len(psf_aics[0])>=1:
-                #psf_channels = len(psf_aics[0])
+                psf_aics = psf_aics[0][0]  
                 assert len(
                     psf_aics.shape) == 3, f"PSF should be a 3D image (shape of 3), but got {psf_aics.shape}"
                 # pad psf to multiple of 16 for decon
@@ -79,8 +77,8 @@ def read_psf(psf_paths: Collection[Path],
                    if len(psf_aics_data.shape) != 3:
                        raise ValueError(f"PSF should be a 3D image (shape of 3), but got {psf_aics.shape}")
                 else:
-                    #Use AICSImageIO
-                    psf_aics = AICSImage(str(psf))
+                    #Use BioIO
+                    psf_aics = BioImage(str(psf))
                     psf_aics_data = psf_aics.data[0][0]
                     psf_aics_data = pad_image_nearest_multiple(
                         img=psf_aics_data, nearest_multiple=16)           

core/lls_core/models/deskew.py

@@ -16,7 +16,7 @@
 import numpy as np
 
 if TYPE_CHECKING:
-    from aicsimageio.types import PhysicalPixelSizes
+    from bioio import PhysicalPixelSizes
 
 class DefinedPixelSizes(FieldAccessModel):
     """
@@ -161,7 +161,7 @@ def convert_skew(cls, v: Any):
 
     @validator("physical_pixel_sizes", pre=True, always=True)
     def convert_pixels(cls, v: Any, values: dict[Any, Any]):
-        from aicsimageio.types import PhysicalPixelSizes
+        from bioio import PhysicalPixelSizes
         if isinstance(v, PhysicalPixelSizes):
             v = DefinedPixelSizes.from_physical(v)
         elif isinstance(v, tuple) and len(v) == 3:
@@ -176,15 +176,16 @@ def convert_pixels(cls, v: Any, values: dict[Any, Any]):
 
     @root_validator(pre=True)
     def read_image(cls, values: dict):
-        from aicsimageio import AICSImage
+        print("READING IMAGE DESKEW")
+        from bioio import BioImage
         from os import fspath
 
         img = values["input_image"]
 
-        aics: AICSImage | None = None
+        aics: BioImage | None = None
         if is_pathlike(img):
-            aics = AICSImage(fspath(img))
-        elif isinstance(img, AICSImage):
+            aics = BioImage(fspath(img))
+        elif isinstance(img, BioImage):
             aics = img
         elif isinstance(img, DataArray):
             if set(img.dims) >= {"Z", "Y", "X"}:
@@ -198,9 +199,9 @@ def read_image(cls, values: dict):
             else:
                 raise ValueError("Only 3D numpy arrays are currently supported. If you have a different shape, please use a DataArray and name your dimensions C, T, Z, Y and/or Z.")
         else:
-            raise ValueError("Value of input_image was neither a path, an AICSImage, or array-like.")
+            raise ValueError("Value of input_image was neither a path, a BioImage, or array-like.")
 
-        # If the image was convertible to AICSImage, we should use the metadata from there
+        # If the image was convertible to BioImage, we should use the metadata from there
         if aics:
             values["input_image"] = aics.xarray_dask_data 
             # Take pixel sizes from the image metadata, but only if they're defined

core/lls_core/models/lattice_data.py

@@ -58,6 +58,7 @@ class LatticeData(OutputParams, DeskewParams):
 
     @root_validator(pre=True)
     def read_image(cls, values: dict):
+        print("READING IMAGE LATTICE")
         from lls_core.types import is_pathlike
         from pathlib import Path
         input_image = values.get("input_image")
@@ -499,7 +500,7 @@ def process_into_image(self) -> ArrayLike:
 
     def get_writer(self) -> Type[Writer]:
         from lls_core.writers import BdvWriter, TiffWriter, OMEZarrWriter
-        if self.save_type == SaveFileType.h5:
+        if self.save_type == SaveFileType.bdv_h5:
             return BdvWriter
         elif self.save_type == SaveFileType.tiff:
             return TiffWriter

core/lls_core/models/output.py

@@ -12,7 +12,7 @@ class SaveFileType(StrEnum):
     """
     Choice of File extension to save
     """
-    h5 = "h5"
+    bdv_h5 = "BDV h5"
     tiff = "tiff"
     omezarr = "omezarr"
 
@@ -30,7 +30,7 @@ class OutputParams(FieldAccessModel):
         default=None
     )
     save_type: SaveFileType = Field(
-        default=SaveFileType.h5,
+        default=SaveFileType.bdv_h5.value,
         description=f"The data type to save the result as. This will also be used to determine the file extension of the output files. Choices: `{enum_choices(SaveFileType)}`. Choices can alternatively be specifed as `str`, for example `'tiff'`."
     )
     time_range: range = Field(
@@ -58,7 +58,7 @@ def add_save_suffix(cls, v: str, values: dict):
 
     @property
     def file_extension(self):
-        if self.save_type == SaveFileType.h5:
+        if self.save_type == SaveFileType.bdv_h5:
             return "h5"
         elif self.save_type == SaveFileType.omezarr:
             return "ome.zarr"

core/lls_core/models/utils.py

@@ -16,7 +16,7 @@ def enum_choices(enum: Type[Enum]) -> str:
     """
     Returns a human readable list of enum choices
     """
-    return "{" +  ", ".join([f'"{it.name}"' for it in enum]) + "}"
+    return "{" +  ", ".join([f'"{it.value}"' for it in enum]) + "}"
 
 @contextmanager
 def ignore_keyerror():

core/lls_core/types.py

@@ -6,7 +6,7 @@
 import numpy as np
 from numpy.typing import NDArray
 from xarray import DataArray
-from aicsimageio import AICSImage
+from bioio import BioImage
 from os import fspath, PathLike as OriginalPathLike
 
 # This is a superset of os.PathLike
@@ -19,20 +19,20 @@ def is_pathlike(x: Any) -> TypeGuard[PathLike]:
 def is_arraylike(arr: Any) -> TypeGuard[ArrayLike]:
     return isinstance(arr, (DaskArray, np.ndarray, OCLArray, DataArray))
 
-ImageLike: TypeAlias = Union[PathLike, AICSImage, ArrayLike]
-def image_like_to_image(img: ImageLike) -> DataArray:
-    """
-    Converts an image in one of many formats to a DataArray
-    """
-    # First try treating it as a path
-    try:
-        img = AICSImage(fspath(img))
-    except TypeError:
-        pass
-    if isinstance(img, AICSImage):
-        return img.xarray_dask_data
-    else:
-        for required_key in ("shape", "dtype", "ndim", "__array__", "__array_ufunc__"):
-            if not hasattr(img, required_key):
-                raise ValueError(f"The provided object {img} is not array like!")
+ImageLike: TypeAlias = Union[PathLike, BioImage, ArrayLike]
+def image_like_to_image(img: ImageLike) -> DataArray:
+    """
+    Converts an image in one of many formats to a DataArray
+    """
+    # First try treating it as a path
+    try:
+        img = BioImage(fspath(img))
+    except TypeError:
+        pass
+    if isinstance(img, BioImage):
+        return img.xarray_dask_data
+    else:
+        for required_key in ("shape", "dtype", "ndim", "__array__", "__array_ufunc__"):
+            if not hasattr(img, required_key):
+                raise ValueError(f"The provided object {img} is not array like!")
         return DataArray(img)

core/lls_core/writers.py

@@ -118,7 +118,8 @@ def flush(self):
                 str(path),
                 data = images_array,
                 bigtiff=True,
-                resolution=(1./self.lattice.dx, 1./self.lattice.dy, "MICROMETER"),
+                resolution=(1./self.lattice.dx, 1./self.lattice.dy),
+                resolutionunit="MICROMETER",
                 metadata={'spacing': self.lattice.new_dz, 'unit': 'um', 'axes': 'TZCYX'},
                 imagej=True
             )