Automatically scale ome-tiff and ome-zarr data

.pre-commit-config.yaml

@@ -39,12 +39,12 @@ repos:
     -   id: yamllint
         exclude: pre-commit-config.yaml
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.14.8"
+    rev: "v0.14.10"
     hooks:
     -   id: ruff-format
     -   id: ruff-check
 -   repo: https://github.com/rhysd/actionlint
-    rev: v1.7.9
+    rev: v1.7.10
     hooks:
     -   id: actionlint
 -   repo: https://gitlab.com/vojko.pribudic.foss/pre-commit-update

CITATION.cff

@@ -129,3 +129,29 @@ references:
       JUMP (cpg0000-jump-pilot) was used to help demonstrate CytoDataFrame performance
       with large data. See here for more information:
       https://github.com/broadinstitute/cellpainting-gallery
+  - type: article
+    authors:
+      - family-names: Blin
+        given-names: Guillaume
+      - family-names: Sadurska
+        given-names: Dominika
+      - family-names: Portero Migueles
+        given-names: Rafael
+      - family-names: Chen
+        given-names: Ni
+      - family-names: Watson
+        given-names: James A.
+      - family-names: Lowell
+        given-names: Sally
+    title: "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures"
+    journal: PLoS Biology
+    volume: "17"
+    issue: "8"
+    pages: e3000388
+    year: 2019
+    doi: "10.1371/journal.pbio.3000388"
+    url: "https://doi.org/10.1371/journal.pbio.3000388"
+    notes: >
+      This work used the file "6001240_labels.zarr" from the DISCEPTS imaging
+      dataset, available through the Image Data Resource (IDR) under accession
+      number idr0062.

docs/src/examples/learning_to_fly_with_ome-arrow.py

@@ -44,7 +44,10 @@
 stack
 
 # we can visualize the stack using pyvista for 3D rendering
-stack.view(how="pyvista")
+# note: we use manually specified scaling values here
+# and can also default to what the image metadata provides
+# with `scaling_values=None` (the default).
+stack.view(how="pyvista", scaling_values=(1, 0.1, 0.1))
 
 # here we demonstrate that the data can be exported again
 # into numpy format and re-imported
@@ -95,3 +98,8 @@
 )
 # by default, the image and metadata are shown
 oa_image
+
+# read a 3d zarr image from IDR
+oa_image = OMEArrow(data="../../../tests/data/idr0062A/6001240_labels.zarr")
+# show the image using pyvista
+oa_image.view(how="pyvista")

pyproject.toml

@@ -84,6 +84,15 @@ lint.select = [
   "C4",
   # mccabe
   "C90",
+  # pydocstyle (docstring presence)
+  "D100",
+  "D101",
+  "D102",
+  "D103",
+  "D104",
+  "D105",
+  "D106",
+  "D107",
   # pycodestyle
   "E",
   # pyflakes
@@ -102,6 +111,8 @@ lint.select = [
 lint.per-file-ignores."*" = [ "ANN401", "C901", "PLC0415", "PLR0912", "PLR0913", "PLR0915", "PLR2004" ]
 # Ignore `F401` (unused imports) for `__init__.py` file
 lint.per-file-ignores."__init__.py" = [ "F401" ]
+# ignore docstring presence checks for docs
+lint.per-file-ignores."docs/*" = [ "D100", "D101", "D102", "D103", "D104", "D105", "D106", "D107" ]
 # ignore typing rules for tests
 lint.per-file-ignores."tests/*" = [ "ANN201", "E501", "PLR0913", "PLR2004" ]
 

src/ome_arrow/core.py

@@ -299,7 +299,7 @@ def view(
         opacity: str | float = "sigmoid",
         clim: tuple[float, float] | None = None,
         show_axes: bool = True,
-        scaling_values: tuple[float, float, float] | None = (1.0, 0.1, 0.1),
+        scaling_values: tuple[float, float, float] | None = None,
     ) -> matplotlib.figure.Figure | "pyvista.Plotter":
         """
         Render an OME-Arrow record using Matplotlib or PyVista.
@@ -337,7 +337,10 @@ def view(
         clim: Contrast limits (``(low, high)``) for PyVista rendering.
         show_axes: If ``True``, display axes in the PyVista scene.
         scaling_values: Physical scale multipliers for the (x, y, z) axes used by
-            PyVista, typically to express anisotropy. Defaults to ``(1.0, 0.1, 0.1)``.
+            PyVista, typically to express anisotropy. If ``None``, uses metadata
+            scaling from the OME-Arrow record (pixels_meta.physical_size_x/y/z).
+            These scaling values will default to 1µm if metadata is missing in
+            source image metadata.
 
         Returns:
         matplotlib.figure.Figure | pyvista.Plotter:

src/ome_arrow/ingest.py

@@ -3,6 +3,7 @@
 """
 
 import itertools
+import json
 import re
 import warnings
 from datetime import datetime, timezone
@@ -20,6 +21,129 @@
 from ome_arrow.meta import OME_ARROW_STRUCT, OME_ARROW_TAG_TYPE, OME_ARROW_TAG_VERSION
 
 
+def _normalize_unit(unit: str | None) -> str | None:
+    if not unit:
+        return None
+    u = unit.strip().lower()
+    if u in {"micrometer", "micrometre", "micron", "microns", "um", "µm"}:
+        return "µm"
+    if u in {"nanometer", "nanometre", "nm"}:
+        return "nm"
+    return unit
+
+
+def _read_physical_pixel_sizes(
+    img: BioImage,
+) -> tuple[float, float, float, str | None, bool]:
+    pps = getattr(img, "physical_pixel_sizes", None)
+    if pps is None:
+        return 1.0, 1.0, 1.0, None, False
+
+    vx = getattr(pps, "X", None) or getattr(pps, "x", None)
+    vy = getattr(pps, "Y", None) or getattr(pps, "y", None)
+    vz = getattr(pps, "Z", None) or getattr(pps, "z", None)
+
+    if vx is None and vy is None and vz is None:
+        return 1.0, 1.0, 1.0, None, False
+
+    try:
+        psize_x = float(vx or 1.0)
+        psize_y = float(vy or 1.0)
+        psize_z = float(vz or 1.0)
+    except Exception:
+        return 1.0, 1.0, 1.0, None, False
+
+    unit = getattr(pps, "unit", None) or getattr(pps, "units", None)
+    unit = _normalize_unit(str(unit)) if unit is not None else None
+
+    return psize_x, psize_y, psize_z, unit, True
+
+
+def _load_zarr_attrs(zarr_path: Path) -> dict:
+    zarr_json = zarr_path / "zarr.json"
+    if zarr_json.exists():
+        try:
+            data = json.loads(zarr_json.read_text())
+            return data.get("attributes") or data.get("attrs") or {}
+        except Exception:
+            return {}
+    zattrs = zarr_path / ".zattrs"
+    if zattrs.exists():
+        try:
+            return json.loads(zattrs.read_text())
+        except Exception:
+            return {}
+    return {}
+
+
+def _extract_multiscales(attrs: dict) -> list[dict]:
+    if not isinstance(attrs, dict):
+        return []
+    ome = attrs.get("ome")
+    if isinstance(ome, dict) and isinstance(ome.get("multiscales"), list):
+        return ome["multiscales"]
+    if isinstance(attrs.get("multiscales"), list):
+        return attrs["multiscales"]
+    return []
+
+
+def _read_ngff_scale(zarr_path: Path) -> tuple[float, float, float, str | None] | None:
+    zarr_root = zarr_path
+    for parent in [zarr_path, *list(zarr_path.parents)]:
+        if parent.suffix.lower() in {".zarr", ".ome.zarr"}:
+            zarr_root = parent
+            break
+
+    for candidate in (zarr_path, zarr_root):
+        attrs = _load_zarr_attrs(candidate)
+        multiscales = _extract_multiscales(attrs)
+        if multiscales:
+            break
+    else:
+        return None
+
+    ms = multiscales[0]
+    axes = ms.get("axes") or []
+    datasets = ms.get("datasets") or []
+    if not axes or not datasets:
+        return None
+
+    ds = next((d for d in datasets if str(d.get("path")) == "0"), datasets[0])
+    cts = ds.get("coordinateTransformations") or []
+    scale_ct = next((ct for ct in cts if ct.get("type") == "scale"), None)
+    if not scale_ct:
+        return None
+
+    scale = scale_ct.get("scale") or []
+    if len(scale) != len(axes):
+        return None
+
+    axis_scale: dict[str, float] = {}
+    axis_unit: dict[str, str] = {}
+    for i, ax in enumerate(axes):
+        name = str(ax.get("name", "")).lower()
+        if name in {"x", "y", "z"}:
+            try:
+                axis_scale[name] = float(scale[i])
+            except Exception:
+                continue
+            unit = _normalize_unit(ax.get("unit"))
+            if unit:
+                axis_unit[name] = unit
+
+    if not axis_scale:
+        return None
+
+    psize_x = axis_scale.get("x", 1.0)
+    psize_y = axis_scale.get("y", 1.0)
+    psize_z = axis_scale.get("z", 1.0)
+
+    units = [axis_unit.get(a) for a in ("x", "y", "z") if axis_unit.get(a)]
+    unit = units[0] if units and len(set(units)) == 1 else None
+
+    return psize_x, psize_y, psize_z, unit
+
+
 def to_ome_arrow(
     type_: str = OME_ARROW_TAG_TYPE,
     version: str = OME_ARROW_TAG_VERSION,
@@ -338,13 +462,8 @@ def from_tiff(
     if size_x <= 0 or size_y <= 0:
         raise ValueError("Image must have positive Y and X dims.")
 
-    pps = getattr(img, "physical_pixel_sizes", None)
-    try:
-        psize_x = float(getattr(pps, "X", None) or 1.0)
-        psize_y = float(getattr(pps, "Y", None) or 1.0)
-        psize_z = float(getattr(pps, "Z", None) or 1.0)
-    except Exception:
-        psize_x = psize_y = psize_z = 1.0
+    psize_x, psize_y, psize_z, unit, _pps_valid = _read_physical_pixel_sizes(img)
+    psize_unit = unit or "µm"
 
     # --- NEW: coerce top-level strings --------------------------------
     img_id = str(image_id or p.stem)
@@ -394,7 +513,7 @@ def from_tiff(
         physical_size_x=psize_x,
         physical_size_y=psize_y,
         physical_size_z=psize_z,
-        physical_size_unit="µm",
+        physical_size_unit=psize_unit,
         channels=channels,
         planes=planes,
         masks=None,
@@ -410,6 +529,20 @@ def from_stack_pattern_path(
     image_id: Optional[str] = None,
     name: Optional[str] = None,
 ) -> pa.StructScalar:
+    """Build an OME-Arrow record from a filename pattern describing a stack.
+
+    Args:
+        pattern_path: Path or pattern string describing the stack layout.
+        default_dim_for_unspecified: Dimension to use when tokens lack a dim.
+        map_series_to: Dimension to map series tokens to (e.g., "T"), or None.
+        clamp_to_uint16: Whether to clamp pixel values to uint16.
+        channel_names: Optional list of channel names to apply.
+        image_id: Optional image identifier override.
+        name: Optional display name override.
+
+    Returns:
+        A validated OME-Arrow StructScalar describing the stack.
+    """
     path = Path(pattern_path)
     folder = path.parent
     line = path.name.strip()
@@ -741,13 +874,15 @@ def from_ome_zarr(
     if size_x <= 0 or size_y <= 0:
         raise ValueError("Image must have positive Y and X dimensions.")
 
-    pps = getattr(img, "physical_pixel_sizes", None)
-    try:
-        psize_x = float(getattr(pps, "X", None) or 1.0)
-        psize_y = float(getattr(pps, "Y", None) or 1.0)
-        psize_z = float(getattr(pps, "Z", None) or 1.0)
-    except Exception:
-        psize_x = psize_y = psize_z = 1.0
+    psize_x, psize_y, psize_z, unit, pps_valid = _read_physical_pixel_sizes(img)
+    psize_unit = unit or "µm"
+
+    if not pps_valid:
+        ngff_scale = _read_ngff_scale(p)
+        if ngff_scale is not None:
+            psize_x, psize_y, psize_z, unit = ngff_scale
+            if unit:
+                psize_unit = unit
 
     img_id = str(image_id or p.stem)
     display_name = str(name or p.name)
@@ -805,7 +940,7 @@ def from_ome_zarr(
         physical_size_x=psize_x,
         physical_size_y=psize_y,
         physical_size_z=psize_z,
-        physical_size_unit="µm",
+        physical_size_unit=psize_unit,
         channels=channels,
         planes=planes,
         masks=None,

src/ome_arrow/view.py

@@ -33,6 +33,23 @@ def view_matplotlib(
     cmap: str = "gray",
     show: bool = True,
 ) -> tuple[Figure, Axes, AxesImage]:
+    """Render a single (t, c, z) plane with Matplotlib.
+
+    Args:
+        data: OME-Arrow row or dict containing pixels_meta and planes.
+        tcz: (t, c, z) indices of the plane to render.
+        autoscale: If True, infer vmin/vmax from the image data.
+        vmin: Explicit lower display limit for intensity scaling.
+        vmax: Explicit upper display limit for intensity scaling.
+        cmap: Matplotlib colormap name.
+        show: Whether to display the plot immediately.
+
+    Returns:
+        A tuple of (figure, axes, image) from Matplotlib.
+
+    Raises:
+        ValueError: If the requested plane is missing or pixel sizes mismatch.
+    """
     if isinstance(data, pa.StructScalar):
         data = data.as_py()
 

tests/data/idr0062A/6001240_labels.zarr/0/zarr.json

@@ -0,0 +1 @@
+{"chunk_grid":{"configuration":{"chunk_shape":[1,10,512,512]},"name":"regular"},"chunk_key_encoding":{"name":"default"},"codecs":[{"configuration":{"chunk_shape":[1,1,256,256],"codecs":[{"configuration":{"endian":"little"},"name":"bytes"},{"configuration":{"blocksize":0,"clevel":5,"cname":"zstd","shuffle":"shuffle","typesize":2},"name":"blosc"}],"index_codecs":[{"configuration":{"endian":"little"},"name":"bytes"},{"name":"crc32c"}]},"name":"sharding_indexed"}],"data_type":"uint16","dimension_names":["c","z","y","x"],"fill_value":0,"node_type":"array","shape":[2,236,275,271],"zarr_format":3}

tests/data/idr0062A/6001240_labels.zarr/1/zarr.json

@@ -0,0 +1 @@
+{"chunk_grid":{"configuration":{"chunk_shape":[1,10,512,512]},"name":"regular"},"chunk_key_encoding":{"name":"default"},"codecs":[{"configuration":{"chunk_shape":[1,1,256,256],"codecs":[{"configuration":{"endian":"little"},"name":"bytes"},{"configuration":{"blocksize":0,"clevel":5,"cname":"zstd","shuffle":"shuffle","typesize":2},"name":"blosc"}],"index_codecs":[{"configuration":{"endian":"little"},"name":"bytes"},{"name":"crc32c"}]},"name":"sharding_indexed"}],"data_type":"uint16","dimension_names":["c","z","y","x"],"fill_value":0,"node_type":"array","shape":[2,236,137,135],"zarr_format":3}

tests/data/idr0062A/6001240_labels.zarr/2/zarr.json

@@ -0,0 +1 @@
+{"chunk_grid":{"configuration":{"chunk_shape":[1,10,512,512]},"name":"regular"},"chunk_key_encoding":{"name":"default"},"codecs":[{"configuration":{"chunk_shape":[1,1,256,256],"codecs":[{"configuration":{"endian":"little"},"name":"bytes"},{"configuration":{"blocksize":0,"clevel":5,"cname":"zstd","shuffle":"shuffle","typesize":2},"name":"blosc"}],"index_codecs":[{"configuration":{"endian":"little"},"name":"bytes"},{"name":"crc32c"}]},"name":"sharding_indexed"}],"data_type":"uint16","dimension_names":["c","z","y","x"],"fill_value":0,"node_type":"array","shape":[2,236,68,67],"zarr_format":3}

tests/data/idr0062A/6001240_labels.zarr/6001240_labels.zarr/ro-crate-metadata.json

@@ -0,0 +1,39 @@
+{
+  "@context": [
+    "https://w3id.org/ro/crate/1.1/context",
+    {
+      "organism_classification": "https://schema.org/taxonomicRange",
+      "BioChemEntity": "https://schema.org/BioChemEntity",
+      "channel": "https://www.openmicroscopy.org/Schemas/Documentation/Generated/OME-2016-06/ome_xsd.html#Channel",
+      "obo": "http://purl.obolibrary.org/obo/",
+      "FBcv": "http://ontobee.org/ontology/FBcv/",
+      "acquisiton_method": {
+        "@reverse": "https://schema.org/result",
+        "@type": "@id"
+      },
+      "biological_entity": "https://schema.org/about",
+      "biosample": "http://purl.obolibrary.org/obo/OBI_0002648",
+      "preparation_method": "https://www.wikidata.org/wiki/Property:P1537",
+      "specimen": "http://purl.obolibrary.org/obo/HSO_0000308"
+    }
+  ],
+  "@graph": [
+    {
+      "@id": "./",
+      "@type": "Dataset",
+      "name": "dataset name",
+      "description": "dataset description",
+      "licence": "https://creativecommons.org/licenses/by/4.0/"
+    },
+    {
+      "@id": "ro-crate-metadata.json",
+      "@type": "CreativeWork",
+      "conformsTo": {
+        "@id": "https://w3id.org/ro/crate/1.1"
+      },
+      "about": {
+        "@id": "./"
+      }
+    }
+  ]
+}

tests/data/idr0062A/6001240_labels.zarr/labels/0/0/zarr.json

@@ -0,0 +1 @@
+{"chunk_grid":{"configuration":{"chunk_shape":[1,10,512,512]},"name":"regular"},"chunk_key_encoding":{"name":"default"},"codecs":[{"configuration":{"chunk_shape":[1,1,256,256],"codecs":[{"name":"bytes"},{"configuration":{"blocksize":0,"clevel":5,"cname":"zstd","shuffle":"bitshuffle","typesize":1},"name":"blosc"}],"index_codecs":[{"configuration":{"endian":"little"},"name":"bytes"},{"name":"crc32c"}]},"name":"sharding_indexed"}],"data_type":"int8","dimension_names":["c","z","y","x"],"fill_value":0,"node_type":"array","shape":[1,236,275,271],"zarr_format":3}