merged with main

.github/workflows/test_pinned_deps.yml

@@ -23,6 +23,11 @@ jobs:
         shell: bash -l {0}
 
     steps:
+      - name: Set environment variables
+        # needed for testing the napari plugin
+        if: matrix.os == 'ubuntu-latest'
+        run: echo "QT_QPA_PLATFORM=offscreen" >> $GITHUB_ENV
+
       - uses: actions/checkout@main
 
       - uses: conda-incubator/setup-miniconda@v3

.github/workflows/test_unpinned_deps.yml

@@ -18,6 +18,11 @@ jobs:
     runs-on: ${{ matrix.os }}
 
     steps:
+      - name: Set environment variables
+        # needed for testing the napari plugin
+        if: matrix.os == 'ubuntu-latest'
+        run: echo "QT_QPA_PLATFORM=offscreen" >> $GITHUB_ENV
+
       - uses: actions/checkout@v4
 
       - uses: actions/setup-python@v5

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["setuptools>=61.0", "setuptools_scm>=6.2"]
 build-backend = "setuptools.build_meta"
 
 [project]
-name = "paste"
+name = "paste3"
 authors = [
     {name="Max Land", email="[email protected]"}
 ]
@@ -14,6 +14,7 @@ classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: BSD License",
     "Operating System :: OS Independent",
+    "Framework :: Napari",
 ]
 
 dependencies = [
@@ -26,7 +27,8 @@ dependencies = [
     "IPython",
     "statsmodels",
     "torch",
-    "torchnmf"
+    "torchnmf",
+    "pooch"
 ]
 dynamic = ["version"]
 
@@ -42,7 +44,9 @@ dev = [
     "coveralls",
     "ruff",
     "pre-commit",
-    "napari"
+    "napari",
+    "pytest-qt",
+    "PyQt5"
 ]
 
 docs = [

requirements.txt

@@ -32,6 +32,7 @@ MarkupSafe==2.1.5
 matplotlib==3.9.2
 matplotlib-inline==0.1.7
 mpmath==1.3.0
+napari==0.5.4
 natsort==8.4.0
 networkx==3.3
 nodeenv==1.9.1
@@ -45,6 +46,7 @@ pexpect==4.9.0
 pillow==10.4.0
 platformdirs==4.3.6
 pluggy==1.5.0
+pooch==1.8.2
 POT==0.9.5
 pre-commit==3.8.0
 prompt_toolkit==3.0.48
@@ -55,9 +57,11 @@ pynndescent==0.5.13
 pyparsing==3.1.4
 pyproject_hooks==1.2.0
 pytest==8.3.3
+pytest-qt==4.4.0
 pytest-xdist==3.6.1
 python-dateutil==2.9.0.post0
 pytz==2024.2
+PyQt5==5.15.11
 PyYAML==6.0.2
 requests==2.32.3
 ruff==0.6.8

src/paste3/experimental.py → src/paste3/dataset.py

@@ -1,4 +1,5 @@
 import logging
+import os.path
 from functools import cached_property
 from glob import glob
 from pathlib import Path
@@ -33,11 +34,11 @@ def __init__(self, filepath: Path | None = None, adata: AnnData | None = None):
     def __str__(self):
         if self.filepath is not None:
             return Path(self.filepath).stem
-        return "Slice with adata: " + str(self.adata)
+        return "Slice with adata: " + str(self.adata).split("\n")[0]
 
     @cached_property
     def adata(self):
-        return self._adata or sc.read_h5ad(self.filepath)
+        return self._adata or sc.read_h5ad(str(self.filepath))
 
     @cached_property
     def obs(self):
@@ -53,12 +54,8 @@ def obs(self):
     def get_obs_values(self, which, coordinates=None):
         assert which in self.obs.columns, f"Unknown column: {which}"
         if coordinates is None:
-            assert (
-                self.has_spatial_data
-            ), "Slice has no `spatial` obsm. Don't know which coordinates to query"
-            coordinates = self.adata.obsm["spatial"]
-
-        return self.obs.loc[coordinates.tolist()][which].tolist()
+            coordinates = self.obs.index.values
+        return self.obs.loc[coordinates][which].tolist()
 
     def set_obs_values(self, which, values):
         self.obs[which] = values
@@ -86,20 +83,33 @@ def cluster(
 class AlignmentDataset:
     def __init__(
         self,
+        file_paths: list[Path] | None = None,
         glob_pattern: str | None = None,
         slices: list[Slice] | None = None,
         max_slices: int | None = None,
+        name: str | None = None,
     ):
         if slices is not None:
             self.slices = slices[:max_slices]
-        else:
+        elif glob_pattern is not None:
             self.slices = [
                 Slice(filepath)
                 for filepath in sorted(glob(glob_pattern))[:max_slices]  # noqa: PTH207
             ]
+        else:
+            self.slices = [Slice(filepath) for filepath in file_paths[:max_slices]]
+
+        if name is not None:
+            self.name = name
+        else:
+            # Take common prefix of slice names, but remove the word "slice"
+            # and any trailing underscores
+            name = os.path.commonprefix([str(slice_) for slice_ in self])
+            name = name.replace("slice", "").rstrip("_")
+            self.name = name
 
     def __str__(self):
-        return f"Data with {len(self.slices)} slices"
+        return self.name
 
     def __iter__(self):
         return iter(self.slices)
@@ -118,7 +128,7 @@ def align(
         self,
         center_align: bool = False,
         pis: np.ndarray | None = None,
-        overlap_fraction: float | None = None,
+        overlap_fraction: float | list[float] | None = None,
         max_iters: int = 1000,
     ):
         if center_align:
@@ -136,31 +146,47 @@ def align(
             overlap_fraction=overlap_fraction, pis=pis, max_iters=max_iters
         )
 
-    def find_pis(self, overlap_fraction: float, max_iters: int = 1000):
+    def find_pis(self, overlap_fraction: float | list[float], max_iters: int = 1000):
+        # If multiple overlap_fraction values are specified
+        # ensure that they are |slices| - 1 in length
+        try:
+            iter(overlap_fraction)
+        except TypeError:
+            overlap_fraction = [overlap_fraction] * (len(self) - 1)
+        assert (
+            len(overlap_fraction) == len(self) - 1
+        ), "Either specify a single overlap_fraction or one for each pair of slices"
+
         pis = []
         for i in range(len(self) - 1):
             logger.info(f"Finding Pi for slices {i} and {i+1}")
-            pis.append(
-                pairwise_align(
-                    self.slices[i].adata,
-                    self.slices[i + 1].adata,
-                    overlap_fraction=overlap_fraction,
-                    numItermax=max_iters,
-                    maxIter=max_iters,
-                )
+            pi, _ = pairwise_align(
+                self.slices[i].adata,
+                self.slices[i + 1].adata,
+                overlap_fraction=overlap_fraction[i],
+                numItermax=max_iters,
+                maxIter=max_iters,
             )
+            pis.append(pi)
         return pis
 
     def pairwise_align(
         self,
-        overlap_fraction: float,
+        overlap_fraction: float | list[float],
         pis: list[np.ndarray] | None = None,
         max_iters: int = 1000,
     ):
         if pis is None:
             pis = self.find_pis(overlap_fraction=overlap_fraction, max_iters=max_iters)
-        new_slices = stack_slices_pairwise(self.slices_adata, pis)
-        return AlignmentDataset(slices=[Slice(adata=s) for s in new_slices])
+        new_slices, rotation_angles, translations = stack_slices_pairwise(
+            self.slices_adata, pis
+        )
+        aligned_dataset = AlignmentDataset(
+            slices=[Slice(adata=s) for s in new_slices],
+            name=self.name + "_pairwise_aligned",
+        )
+
+        return aligned_dataset, rotation_angles, translations
 
     def find_center_slice(
         self,
@@ -210,16 +236,25 @@ def center_align(
             center_slice, pis = self.find_center_slice(initial_slice=initial_slice)
 
         logger.info("Stacking slices around center slice")
-        _, new_slices = stack_slices_center(
-            center_slice=center_slice.adata, slices=self.slices_adata, pis=pis
+        new_center, new_slices, rotation_angles, translations = stack_slices_center(
+            center_slice=center_slice.adata,
+            slices=self.slices_adata,
+            pis=pis,
+        )
+        aligned_dataset = AlignmentDataset(
+            slices=[Slice(adata=s) for s in new_slices],
+            name=self.name + "_center_aligned",
         )
-        return AlignmentDataset(slices=[Slice(adata=s) for s in new_slices])
 
-    def all_points(self) -> np.ndarray:
+        return aligned_dataset, rotation_angles, translations
+
+    def all_points(self, translation: np.ndarray | None = None) -> np.ndarray:
         layers = []
         for i, slice in enumerate(self.slices):
             adata = slice.adata
             points = adata.obsm["spatial"]
+            if translation is not None:
+                points = points + translation
             layer_data = np.pad(
                 points, pad_width=((0, 0), (1, 0)), mode="constant", constant_values=i
             )

src/paste3/napari/__init__.py

@@ -1,6 +1,12 @@
 __version__ = "0.0.1"
 
 from ._reader import napari_get_reader
+from ._sample_data import make_sample_data
 from ._widget import CenterAlignContainer, PairwiseAlignContainer
 
-__all__ = ("napari_get_reader", "CenterAlignContainer", "PairwiseAlignContainer")
+__all__ = (
+    "make_sample_data",
+    "napari_get_reader",
+    "CenterAlignContainer",
+    "PairwiseAlignContainer",
+)

src/paste3/napari/_reader.py

@@ -1,14 +1,7 @@
-"""
-This module is an example of a barebones numpy reader plugin for napari.
+from pathlib import Path
 
-It implements the Reader specification, but your plugin may choose to
-implement multiple readers or even other plugin contributions. see:
-https://napari.org/stable/plugins/guides.html?#readers
-"""
-
-import seaborn as sns
-
-from paste3.experimental import AlignmentDataset, Slice
+from paste3.dataset import AlignmentDataset, Slice
+from paste3.napari._widget import init_widget
 
 
 def napari_get_reader(path):
@@ -32,7 +25,7 @@ def napari_get_reader(path):
         path = path[0]
 
     # if we know we cannot read the file, we immediately return None.
-    if not path.endswith(".h5ad"):
+    if not str(path).endswith(".h5ad"):
         return None
 
     # otherwise we return the *function* that can read ``path``.
@@ -62,48 +55,12 @@ def reader_function(path):
         default to layer_type=="image" if not provided
     """
     # handle both a string and a list of strings
-    paths = [path] if isinstance(path, str) else path
+    paths = [path] if isinstance(path, str | Path) else path
     slices = [Slice(filepath) for filepath in paths]
     dataset = AlignmentDataset(slices=slices)
 
-    face_color_cycle = sns.color_palette("Paired", 20)
-
-    layer_data = []
-    all_clusters = []
-    for slice in dataset.slices:
-        points = slice.adata.obsm["spatial"]
-        clusters = slice.get_obs_values("original_clusters")
-        all_clusters.extend(clusters)
-
-        layer_data.append(
-            (
-                points,
-                {
-                    "features": {"cluster": clusters},
-                    "face_color": "cluster",
-                    "face_color_cycle": face_color_cycle,
-                    "size": 1,
-                    "metadata": {"slice": slice},
-                    "name": f"{slice}",
-                },
-                "points",
-            )
-        )
-
-    layer_data.append(
-        (
-            dataset.all_points(),
-            {
-                "features": {"cluster": all_clusters},
-                "face_color": "cluster",
-                "face_color_cycle": face_color_cycle,
-                "ndim": 3,
-                "size": 1,
-                "scale": [3, 1, 1],
-                "name": "paste3_volume",
-            },
-            "points",
-        )
-    )
+    init_widget(alignment_dataset=dataset)
 
-    return layer_data
+    # We let the initialized widget handle the rest of the logic
+    # and add the layers to the viewer
+    return [(None,)]