resolve merge conflicts

.github/workflows/python-package.yml

@@ -0,0 +1,31 @@
+name: Run tests
+
+on:
+  push:
+    branches: ["main"]
+  pull_request:
+    branches: ["main"]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.11"
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: uv sync --all-extras --dev
+
+      - name: Run tests
+        # For example, using `pytest`
+        run: uv run pytest

docs/index.md

@@ -7,7 +7,9 @@ It aims to be a reference implementation of the OME-zarr specification.
 
 This package is designed with the following guiding principles:
 
-- Slim, with as few dependencies as possible
 - Strict adherence to the OME-zarr specification
+- A usable set of Python classes for reading and writing and interacting with OME-zarr metadata
 - A separate object for each version of the OME-zarr specification
 - Data operations are out of scope
+
+We are trying to make this as usable and useful as possible while still complying with the OME-zarr specification.

docs/tutorial.py

@@ -1,6 +1,55 @@
 # # Tutorial
 
-from ome_zarr_models.v04 import Image as OMEZarrImage
+# from ome_zarr_models.v04 import Image as OMEZarrImage
 
-my_image = OMEZarrImage(path="path/to/ome/zarr/directory.ome.zarr")
-print(my_image.multiscales)
+# my_image = OMEZarrImage(path="path/to/ome/zarr/directory.ome.zarr")
+# print(my_image.multiscales)
+
+# ## Creating
+#
+# TODO: exmaple of creating a model from a remote store
+# TODO: example of
+
+import gcsfs
+import zarr.storage
+import zarr
+
+bucket = "ucl-hip-ct-35a68e99feaae8932b1d44da0358940b"
+fs = gcsfs.GCSFileSystem(project=bucket, token="anon", access="read_only")
+store = zarr.storage.FSStore(url=bucket, fs=fs)
+group = zarr.open_group(
+    store=store, path="S-20-28/heart/25.27um_complete-organ_bm05.ome.zarr"
+)
+print(group)
+exit()
+
+# ## Updating models
+#
+# All the fields in the models can be updated in place.
+# When you do this, any validation on the individual field
+# you are updating will take place.
+#
+# For example, we can [do something valid]:
+
+
+# but if you try and [do something invalid] it raises an error:
+
+# This means validation happens early, allowing you to catch errors
+# before getting too far.
+
+# ## Writing metadata
+#
+# TODO: example of writing out the metadata again
+
+# ## Accessing data
+#
+# Although these models do not handle reading or writing data,
+# they do expose the zarr arrays.
+
+# ## Not using validation
+#
+# If you *really* want to create models that are not validated against
+# the OME-zarr specifciation, you can use the ``model_construct`` method.
+# For example:
+
+# Put some bad code here

pyproject.toml

@@ -27,6 +27,8 @@ docs = [
     "mkdocstrings-python>=1.12.2",
     "mkdocs-material",
     "mkdocs-jupyter",
+    "gcsfs",
+    "zarr<3",
 ]
 dev = [
     "jupyter[notebook]>=1.1.1",

src/ome_zarr_models/utils.py

@@ -11,7 +11,6 @@ def _unique_items_validator(values: list[T]) -> list[T]:
             raise ValueError(f"Non-unique values in {values}.")
     return values
 
-
 def dataclass_to_pydantic(dataclass_type: type) -> type[pydantic.BaseModel]:
     """Convert a dataclass to a Pydantic model.
 
@@ -39,4 +38,8 @@ def dataclass_to_pydantic(dataclass_type: type) -> type[pydantic.BaseModel]:
             # No default value
             field_definitions[_field.name] = (_field.type, Ellipsis)
 
-    return create_model(dataclass_type.__name__, **field_definitions)
+<<<<<<< HEAD
+    return create_model(dataclass_type.__name__, **field_definitions)
+=======
+    return create_model(dataclass_type.__name__, **field_definitions)
+>>>>>>> 470f4f1a33aef4ecf2ebf0906c912a3621c8957b