Merge branch 'main' into actions-updates

Author: elevans

dev-environment.yml

@@ -20,7 +20,7 @@ name: pyimagej-dev
 channels:
   - conda-forge
 dependencies:
-  - python >= 3.8
+  - python >= 3.9
   # Project dependencies
   - imglyb >= 2.1.0
   - jgo >= 1.0.3

doc/06-Working-with-Images.ipynb

@@ -48,16 +48,15 @@ If installing via pip, we recommend using a
 [virtualenv](https://virtualenv.pypa.io/) to avoid cluttering up or mangling
 your system-wide or user-wide Python environment. Alternately, you can use
 mamba just for its virtual environment feature (`mamba create -n pyimagej
-python=3.8; mamba activate pyimagej`) and then simply `pip install` everything
+python=3.9; mamba activate pyimagej`) and then simply `pip install` everything
 into that active environment.
 
 There are several ways to install things via pip, but we will not enumerate
 them all here; these instructions will assume you know what you are doing if
 you chose this route over conda/mamba above.
 
-1. Install [Python 3](https://python.org/). As of this writing, PyImageJ has
-   been tested with Python 3.6, 3.7, 3.8, 3.9, and 3.10.
-   You might have issues with Python 3.10 on Windows.
+1. Install [Python 3](https://python.org/). As of this writing,
+   PyImageJ has been tested with Python up through 3.13.
 
 2. Install OpenJDK 8 or OpenJDK 11. PyImageJ should work with whichever
    distribution of OpenJDK you prefer; we recommend
@@ -176,7 +175,7 @@ USER root
 RUN apt-get update
 RUN apt-get install -y wget unzip > /dev/null && rm -rf /var/lib/apt/lists/* > /dev/null
 RUN micromamba install -y -n base -c conda-forge \
-        python=3.8\
+        python=3.9\
         pyimagej  \
         openjdk=11 && \
     micromamba clean --all --yes

doc/Install.md

@@ -1,12 +1,12 @@
 [build-system]
-requires = [ "setuptools>=61.2" ]
+requires = [ "setuptools>=77.0.0" ]
 build-backend = "setuptools.build_meta"
 
 [project]
 name = "pyimagej"
-version = "1.6.1.dev0"
+version = "1.7.0.dev0"
 description = "Python wrapper for ImageJ"
-license = {text = "Apache-2.0"}
+license = "Apache-2.0"
 authors = [{name = "ImageJ2 developers", email = "ctrueden@wisc.edu"}]
 readme = "README.md"
 keywords = ["java", "imagej", "imagej2", "fiji"]

pyproject.toml

@@ -437,11 +437,7 @@ def to_dataset(self, data, dim_order=None) -> "jc.Dataset":
         :return: A net.imagej.Dataset.
         """
         if sj.isjava(data):
-            if dim_order:
-                _logger.warning(
-                    f"Dimension reordering is not supported for {type(data)}."
-                )
-            return convert.java_to_dataset(self._ij, data)
+            return convert.java_to_dataset(self._ij, data, dim_order)
 
         if images.is_xarraylike(data):
             return convert.xarray_to_dataset(

src/imagej/__init__.py

@@ -27,33 +27,35 @@
 ###############
 
 
-def java_to_dataset(ij: "jc.ImageJ", jobj) -> "jc.Dataset":
+def java_to_dataset(ij: "jc.ImageJ", jobj, dim_order=None) -> "jc.Dataset":
     """
     Convert the given Java image data into an ImageJ2 Dataset.
 
     :param ij: The ImageJ2 gateway (see imagej.init)
     :param jobj: The Java image (e.g. RandomAccessibleInterval)
+    :param dim_order: Sequence of desired dimensions for the Dataset.
     :return: The converted ImageJ2 Dataset
     """
     assert sj.isjava(jobj)
     if isinstance(jobj, jc.Dataset):
-        return jobj
+        return _rename_dataset_dims(jobj, dim_order)
 
     # NB: This try checking is necessary because the set of ImageJ2 converters is
     # not complete. E.g., there is no way to directly go from Img to Dataset,
     # instead you need to chain the Img->ImgPlus->Dataset converters.
     try:
         if ij.convert().supports(jobj, jc.Dataset):
-            return ij.convert().convert(jobj, jc.Dataset)
+            ds = ij.convert().convert(jobj, jc.Dataset)
         if ij.convert().supports(jobj, jc.ImgPlus):
             imgplus = ij.convert().convert(jobj, jc.ImgPlus)
-            return ij.dataset().create(imgplus)
+            ds = ij.dataset().create(imgplus)
         if ij.convert().supports(jobj, jc.Img):
             img = ij.convert().convert(jobj, jc.Img)
-            return ij.dataset().create(jc.ImgPlus(img))
+            ds = ij.dataset().create(jc.ImgPlus(img))
         if ij.convert().supports(jobj, jc.RandomAccessibleInterval):
             rai = ij.convert().convert(jobj, jc.RandomAccessibleInterval)
-            return ij.dataset().create(rai)
+            ds = ij.dataset().create(rai)
+        return _rename_dataset_dims(ds, dim_order)
     except Exception as exc:
         _log_exception(_logger, exc)
         raise exc
@@ -237,8 +239,7 @@ def java_to_xarray(ij: "jc.ImageJ", jobj) -> xr.DataArray:
     xr_dims.reverse()
     xr_dims = dims._convert_dims(xr_dims, direction="python")
     xr_coords = dims._get_axes_coords(xr_axes, xr_dims, narr.shape)
-    name = jobj.getName() if isinstance(jobj, jc.Named) else None
-    name = ij.py.from_java(name)
+    name = str(jobj.getName()) if isinstance(jobj, jc.Named) else None
     return xr.DataArray(narr, dims=xr_dims, coords=xr_coords, attrs=xr_attrs, name=name)
 
 
@@ -642,7 +643,38 @@ def _permute_rai_to_python(rich_rai: "jc.RandomAccessibleInterval"):
     return permuted_rai
 
 
+def _rename_dataset_dims(ds, new_dims: Sequence[str]):
+    """
+    Rename, without reshaping, a Dataset's dimension labels.
+
+    :param ds: Input Dataset.
+    :param new_dims: Dimension labels to apply
+    :return: Dataset with dimension labels renamed.
+    """
+    # return the dataset if no new_dims
+    if not new_dims:
+        return ds
+
+    # validate dim sequence
+    new_dims = dims._validate_dim_order(new_dims, ds.shape)
+
+    # set axis type for each axis
+    for i in range(ds.ndim):
+        new_axis_str = dims._convert_dim(new_dims[i], "java")
+        new_axis_type = jc.Axes.get(new_axis_str)
+        ds.dim_axes[i].setType(new_axis_type)
+
+    return ds
+
+
 def _rename_xarray_dims(xarr, new_dims: Sequence[str]):
+    """
+    Rename, without reshaping, an xarray's dimension labels.
+
+    :param xarr: Input xarray.
+    :param new_dims: Dimension labels to apply.
+    :return: xarray with dimension labels renamed.
+    """
     curr_dims = xarr.dims
     if not new_dims:
         return xarr

src/imagej/convert.py

@@ -521,15 +521,15 @@ def test_index_image_converts_to_imglib_roi(ij):
     (
         get_img,
         "java",
-        ["a", "b", "c", "d", "e"],
-        ("X", "Y", "Unknown", "Unknown", "Unknown"),
+        ["X", "Y", "monday", "green", "Time"],
+        ("X", "Y", "monday", "green", "Time"),
         (1, 2, 3, 4, 5),
     ),
     (
         get_imgplus,
         "java",
-        ["a", "b", "c", "d", "e", "f", "g"],
-        ("X", "Y", "foo", "bar", "Channel", "Time", "Z"),
+        ["Y", "X", "Channel", "gray", "e", "foo", "gold"],
+        ("Y", "X", "Channel", "gray", "e", "foo", "gold"),
         (7, 8, 4, 2, 3, 5, 6),
     ),
     (
@@ -650,6 +650,8 @@ def test_direct_to_xarray_conversion(
         im_data = im_req()
     # convert the image data to xarray
     xarr_out = ij.py.to_xarray(im_data, dim_order=new_dims)
+    name = xarr_out.name
+    assert name is None or isinstance(name, str)
     assert xarr_out.dims == exp_dims
     assert xarr_out.shape == exp_shape
     if hasattr(im_data, "dim_axes") and obj_type == "java":