Array writer

.github/workflows/ci.yml

@@ -28,7 +28,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.10', '3.11']
+        python-version: ['3.11', '3.12']
         platform: [ubuntu-latest, macos-latest, windows-latest]
 
     steps:

.github/workflows/mypy.yml

@@ -1,6 +1,9 @@
 name: Python mypy
 
-on: [push, pull_request]
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
 
 jobs:
   static-analysis:
@@ -9,6 +12,8 @@ jobs:
     steps:
       - name: Setup Python
         uses: actions/setup-python@v5
+        with:
+          python-version: 3.11
       - name: Setup checkout
         uses: actions/checkout@v4
       - name: mypy

.github/workflows/tests.yml

@@ -2,14 +2,16 @@ name: Test
 
 on:
   push:
+    branches: [ "main" ]
+  pull_request:
 
 jobs:
   test:
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.11", "3.12"]
         os: [ubuntu-latest, windows-latest, macos-latest]
 
     steps:

pyproject.toml

@@ -8,7 +8,7 @@ build-backend = "hatchling.build"
 name = "cellmap-data"
 description = "Utility for loading CellMap data for machine learning training, utilizing PyTorch, Xarray, TensorStore, and PyDantic."
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 license = { text = "BSD 3-Clause License" }
 authors = [
     { email = "[email protected]", name = "Jeff Rhoades" },
@@ -17,9 +17,8 @@ classifiers = [
     "Development Status :: 3 - Alpha",
     "License :: OSI Approved :: BSD License",
     "Natural Language :: English",
-    "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "Typing :: Typed",
 ]
 dynamic = ["version"]
@@ -32,8 +31,9 @@ dependencies = [
     "pydantic_ome_ngff",
     "xarray_ome_ngff",
     "tensorstore",
+    "xarray-tensorstore @ git+https://github.com/rhoadesScholar/xarray-tensorstore.git",
     # "xarray-tensorstore @ git+https://github.com/google/xarray-tensorstore.git",
-    "xarray-tensorstore",
+    # "xarray-tensorstore",
     "universal_pathlib>=0.2.0",
     "fsspec[s3,http]",
     "cellpose",

src/cellmap_data/__init__.py

@@ -19,7 +19,8 @@
 from .dataloader import CellMapDataLoader
 from .datasplit import CellMapDataSplit
 from .dataset import CellMapDataset
-from .image import CellMapImage
+from .dataset_writer import CellMapDatasetWriter
+from .image import CellMapImage, EmptyImage, ImageWriter
 from .subdataset import CellMapSubset
 from . import transforms
 from . import utils

src/cellmap_data/dataloader.py

@@ -1,8 +1,8 @@
 import torch
-from torch.utils.data import DataLoader, Sampler
+from torch.utils.data import DataLoader, Sampler, Subset
 from .dataset import CellMapDataset
 from .multidataset import CellMapMultiDataset
-from .subdataset import CellMapSubset
+from .dataset_writer import CellMapDatasetWriter
 from typing import Callable, Iterable, Optional, Sequence
 
 
@@ -30,7 +30,7 @@ class CellMapDataLoader:
 
     def __init__(
         self,
-        dataset: CellMapMultiDataset | CellMapDataset | CellMapSubset,
+        dataset: CellMapMultiDataset | CellMapDataset | Subset | CellMapDatasetWriter,
         classes: Iterable[str],
         batch_size: int = 1,
         num_workers: int = 0,
@@ -113,7 +113,7 @@ def refresh(self):
 
     def collate_fn(self, batch: list[dict]) -> dict[str, torch.Tensor]:
         """Combine a list of dictionaries from different sources into a single dictionary for output."""
-        outputs: dict[str, torch.Tensor] = {}
+        outputs = {}
         for b in batch:
             for key, value in b.items():
                 if key not in outputs:

src/cellmap_data/dataset.py

@@ -403,16 +403,18 @@ def __len__(self) -> int:
 
     def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
         """Returns a crop of the input and target data as PyTorch tensors, corresponding to the coordinate of the unwrapped index."""
+        idx = np.array(idx)
+        idx[idx < 0] = len(self) + idx[idx < 0]
         try:
             center = np.unravel_index(
                 idx, [self.sampling_box_shape[c] for c in self.axis_order]
             )
         except ValueError:
+            # TODO: This is a hacky temprorary fix. Need to figure out why this is happening
             logger.error(
                 f"Index {idx} out of bounds for dataset {self} of length {len(self)}"
             )
             logger.warning(f"Returning closest index in bounds")
-            # TODO: This is a hacky temprorary fix. Need to figure out why this is happening
             center = [self.sampling_box_shape[c] - 1 for c in self.axis_order]
         center = {
             c: center[i] * self.largest_voxel_sizes[c] + self.sampling_box[c][0]