- bugfix in tifread reading files missing a dimension in metadata

- tiff: read spacing

.gitignore

@@ -11,3 +11,4 @@
 /poetry.lock
 /dist/
 /uv.lock
+.agentbridge

README.md

@@ -1,4 +1,4 @@
-[![Pytest](https://github.com/wimpomp/ndbioimage/actions/workflows/pytest.yml/badge.svg)](https://github.com/wimpomp/ndbioimage/actions/workflows/pytest.yml)
+[![Pytest](https://git.wimpomp.nl/wim/ndbioimage/actions/workflows/pytest.yml/badge.svg)](https://git.wimpomp.nl/wim/ndbioimage/actions?workflow=pytest.yml)
 
 # ndbioimage
 

ndbioimage/__init__.py

@@ -87,6 +87,7 @@ def find(obj: Sequence[Any], **kwargs: Any) -> Any:
                 return item
         except AttributeError:
             pass
+    return None
 
 
 R = TypeVar("R")
@@ -154,23 +155,26 @@ class OmeCache(DequeDict):
     def __reduce__(self) -> tuple[type, tuple]:
         return self.__class__, ()
 
-    def __getitem__(self, path: Path | str | tuple) -> OME:
+    def __getitem__(self, path_and_series: tuple[Path | str | tuple, int]) -> OME:
+        path, series = path_and_series
         if isinstance(path, tuple):
-            return super().__getitem__(path)
+            return super().__getitem__((path, series))
         else:
-            return super().__getitem__(self.path_and_lstat(path))
+            return super().__getitem__((self.path_and_lstat(path), series))
 
-    def __setitem__(self, path: Path | str | tuple, value: OME) -> None:
+    def __setitem__(self, path_and_series: tuple[Path | str | tuple, int], value: OME) -> None:
+        path, series = path_and_series
         if isinstance(path, tuple):
-            super().__setitem__(path, value)
+            super().__setitem__((path, series), value)
         else:
-            super().__setitem__(self.path_and_lstat(path), value)
+            super().__setitem__((self.path_and_lstat(path), series), value)
 
-    def __contains__(self, path: Path | str | tuple) -> bool:
+    def __contains__(self, path_and_series: tuple[Path | str | tuple, int]) -> bool:
+        path, series = path_and_series
         if isinstance(path, tuple):
-            return super().__contains__(path)
+            return super().__contains__((path, series))
         else:
-            return super().__contains__(self.path_and_lstat(path))
+            return super().__contains__((self.path_and_lstat(path), series))
 
     @staticmethod
     def path_and_lstat(path: str | Path) -> tuple[Path, Optional[os.stat_result], Optional[os.stat_result]]:
@@ -187,7 +191,8 @@ def get_positions(path: str | Path) -> Optional[list[int]]:
     return subclass.get_positions(AbstractReader.split_path_series(path)[0])
 
 
-class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
+# noinspection PyAbstractClass
+class Imread(np.lib.mixins.NDArrayOperatorsMixin):
     """class to read image files, while taking good care of important metadata,
     currently optimized for .czi files, but can open anything that bioformats can handle
         path: path to the image file
@@ -1025,7 +1030,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
         return [self.get_channel(c) for c in czt[0]], *czt[1:3]  # type: ignore
 
     @staticmethod
-    def bioformats_ome(path: [str, Path]) -> OME:
+    def bioformats_ome(path: str | Path) -> OME:
         """Use java BioFormats to make an ome metadata structure."""
         with multiprocessing.get_context("spawn").Pool(1) as pool:
             return pool.map(bioformats_ome, (path,))[0]
@@ -1057,10 +1062,11 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
         return ome
 
     @staticmethod
-    def read_ome(path: [str, Path]) -> Optional[OME]:
+    def read_ome(path: str | Path) -> Optional[OME]:
         path = Path(path)
         if path.with_suffix(".ome.xml").exists():
             return OME.from_xml(path.with_suffix(".ome.xml"))
+        return None
 
     def get_ome(self) -> OME:
         """overload this"""
@@ -1069,12 +1075,12 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
     @cached_property
     def ome(self) -> OME:
         cache = OmeCache()
-        if self.path not in cache:
+        if (self.path, self.series) not in cache:
             ome = self.read_ome(self.path)
             if ome is None:
                 ome = self.get_ome()
-            cache[self.path] = self.fix_ome(ome)
-        return cache[self.path]
+            cache[self.path, self.series] = self.fix_ome(ome)
+        return cache[self.path, self.series]
 
     def is_noise(self, volume: ArrayLike = None) -> bool:
         """True if volume only has noise"""
@@ -1306,9 +1312,11 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
     @staticmethod
     def split_path_series(path: Path | str) -> tuple[Path, int]:
         if isinstance(path, str):
-            path = Path(path)
+            path = Path(path).absolute()
         if isinstance(path, Path) and path.name.startswith("Pos") and path.name.lstrip("Pos").isdigit():
-            return path.parent, int(path.name.lstrip("Pos"))
+            return path.absolute().parent, int(path.name.lstrip("Pos"))
+        if isinstance(path, Path):
+            return path.absolute(), 0
         return path, 0
 
     def view(self, *args: Any, **kwargs: Any) -> View:
@@ -1521,6 +1529,12 @@ class AbstractReader(Imread, metaclass=ABCMeta):
             self.immersionN = 1
 
         p = re.compile(r"(\d+):(\d+)$")
+        if self.ome.structured_annotations is not None:
+            tirf_angles = {}
+            for annotation in self.ome.structured_annotations:
+                if annotation.description is not None and annotation.description.lower() == "tirfangle":
+                    tirf_angles[int(annotation.id.split(":")[1])] = annotation.value
+            self.tirfangle = [angle for track, angle in sorted(tirf_angles.items(), key=lambda x: x[0])]
         try:
             self.track, self.detector = zip(
                 *[

ndbioimage/readers/cziread.py

@@ -14,7 +14,7 @@ from lxml import etree
 from ome_types import OME, model
 from tifffile import repeat_nd
 
-from .. import AbstractReader
+from .. import AbstractReader, ureg
 
 try:
     # TODO: use zoom from imagecodecs implementation when available
@@ -152,6 +152,14 @@ def data(self, raw: bool = False, resize: bool = True, order: int = 0) -> np.nda
 czifile.czifile.SubBlockSegment.data = data
 
 
+def xml_walk(tree, elements):
+    element, *elements = elements
+    if elements:
+        return [j for i in tree.findall(element) for j in xml_walk(i, elements)]
+    else:
+        return tree.findall(element)
+
+
 class Reader(AbstractReader, ABC):
     priority = 0
     do_not_pickle = "reader", "filedict"
@@ -163,16 +171,55 @@ class Reader(AbstractReader, ABC):
     def open(self) -> None:
         self.reader = czifile.CziFile(self.path)
         filedict = {}
+        syx = set()
+        si = self.reader.axes.index("S") if "S" in self.reader.axes else None
+        ci = self.reader.axes.index("C") if "C" in self.reader.axes else None
+        zi = self.reader.axes.index("Z") if "Z" in self.reader.axes else None
+        ti = self.reader.axes.index("T") if "T" in self.reader.axes else None
+        yi = self.reader.axes.index("Y") if "Y" in self.reader.axes else None
+        xi = self.reader.axes.index("X") if "X" in self.reader.axes else None
+        if si is None and self.series > 0:
+            raise FileNotFoundError(f"Series {self.series} not found in {self.path}.")
+
         for directory_entry in self.reader.filtered_subblock_directory:
             idx = self.get_index(directory_entry, self.reader.start)
-            if "S" not in self.reader.axes or self.series in range(*idx[self.reader.axes.index("S")]):
-                for c in range(*idx[self.reader.axes.index("C")]):
-                    for z in range(*idx[self.reader.axes.index("Z")]):
-                        for t in range(*idx[self.reader.axes.index("T")]):
-                            if (c, z, t) in filedict:
-                                filedict[c, z, t].append(directory_entry)
-                            else:
-                                filedict[c, z, t] = [directory_entry]
+            syx.add((0 if si is None else idx[si], idx[yi], idx[xi]))
+
+        if self.tiles != (1, 1):
+            assert len({s for s, *_ in list(syx)}) == 1, "multiple tiled series not supported"
+            x, y = np.array(list(syx))[:, 1:, 0].T
+            a, b = np.min(x), np.max(x)
+            n = self.tiles[0]
+            bx = np.linspace(a - (b - a) / (n - 1) / 2, b + (b - a) / (n - 1) / 2, n + 1)
+            a, b = np.min(y), np.max(y)
+            n = self.tiles[1]
+            by = np.linspace(a - (b - a) / (n - 1) / 2, b + (b - a) / (n - 1) / 2, n + 1)
+            b = list(product([(i, j) for i, j in zip(by, by[1:])], [(i, j) for i, j in zip(bx, bx[1:])]))
+            if self.series < len(b):
+                by, bx = b[self.series]
+            else:
+                raise FileNotFoundError(f"Series {self.series} not found in {self.path}.")
+            for directory_entry in self.reader.filtered_subblock_directory:
+                idx = self.get_index(directory_entry, self.reader.start)
+                if bx[0] < idx[xi][0] < bx[1] and by[0] < idx[yi][0] < by[1]:
+                    for cj in (0,) if ci is None else range(*idx[ci]):
+                        for zj in (0,) if zi is None else range(*idx[zi]):
+                            for tj in (0,) if ti is None else range(*idx[ti]):
+                                if (cj, zj, tj) in filedict:
+                                    filedict[cj, zj, tj].append(directory_entry)
+                                else:
+                                    filedict[cj, zj, tj] = [directory_entry]
+        else:
+            for directory_entry in self.reader.filtered_subblock_directory:
+                idx = self.get_index(directory_entry, self.reader.start)
+                if si is None or self.series == idx[si][0]:
+                    for cj in (0,) if ci is None else range(*idx[ci]):
+                        for zj in (0,) if zi is None else range(*idx[zi]):
+                            for tj in (0,) if ti is None else range(*idx[ti]):
+                                if (cj, zj, tj) in filedict:
+                                    filedict[cj, zj, tj].append(directory_entry)
+                                else:
+                                    filedict[cj, zj, tj] = [directory_entry]
         if len(filedict) == 0:
             raise FileNotFoundError(f"Series {self.series} not found in {self.path}.")
         self.filedict = filedict  # noqa
@@ -187,17 +234,11 @@ class Reader(AbstractReader, ABC):
         f = np.zeros(self.base_shape["yx"], self.dtype)
         if (c, z, t) in self.filedict:
             directory_entries = self.filedict[c, z, t]
-            x_min = min([f.start[f.axes.index("X")] for f in directory_entries])
-            y_min = min([f.start[f.axes.index("Y")] for f in directory_entries])
-            xy_min = {"X": x_min, "Y": y_min}
+            start = np.min([directory_entry.start for directory_entry in directory_entries], 0)
             for directory_entry in directory_entries:
                 subblock = directory_entry.data_segment()
                 tile = subblock.data(resize=True, order=0)
-                axes_min = [xy_min.get(ax, 0) for ax in directory_entry.axes]
-                index = [
-                    slice(i - j - m, i - j + k)
-                    for i, j, k, m in zip(directory_entry.start, self.reader.start, tile.shape, axes_min)
-                ]
+                index = [slice(i - j, i - j + k) for i, j, k in zip(directory_entry.start, start, tile.shape)]
                 index = tuple(index[self.reader.axes.index(i)] for i in "YX")
                 f[index] = tile.squeeze()
         return f
@@ -206,6 +247,38 @@ class Reader(AbstractReader, ABC):
     def get_index(directory_entry: czifile.DirectoryEntryDV, start: tuple[int]) -> list[tuple[int, int]]:
         return [(i - j, i - j + k) for i, j, k in zip(directory_entry.start, start, directory_entry.shape)]
 
+    @cached_property
+    def tiles(self) -> tuple[int, int]:
+        columns = 1
+        rows = 1
+        xml = self.reader.metadata()
+        tree = etree.fromstring(xml)
+        tile_regions = xml_walk(
+            tree,
+            (
+                "Metadata",
+                "Experiment",
+                "ExperimentBlocks",
+                "AcquisitionBlock",
+                "SubDimensionSetups",
+                "RegionsSetup",
+                "SampleHolder",
+                "TileRegions",
+                "TileRegion",
+            ),
+        )
+        for tile_region in tile_regions:
+            used = tile_region.find("IsUsedForAcquisition")
+            if used is not None and used.text.lower() == "true":
+                c = tile_region.find("Columns")
+                if c is not None:
+                    columns = int(c.text)
+                r = tile_region.find("Rows")
+                if r is not None:
+                    rows = int(r.text)
+                break
+        return columns, rows
+
 
 class OmeParse:
     size_x: int
@@ -262,6 +335,7 @@ class OmeParse:
         self.get_light_sources()
         self.get_filters()
         self.get_pixels()
+        self.get_tirf_angle()
         self.get_channels()
         self.get_planes()
         self.get_annotations()
@@ -429,7 +503,7 @@ class OmeParse:
         self.size_x = x_max - x_min
         self.size_y = y_max - y_min
         self.size_c, self.size_z, self.size_t = (
-            self.reader.shape[self.reader.axes.index(directory_entry)] for directory_entry in "CZT"
+            self.reader.shape[self.reader.axes.index(axis)] if axis in self.reader.axes else 1 for axis in "CZT"
         )
         image = self.information.find("Image")
         pixel_type = self.text(image.find("PixelType"), "Gray16")
@@ -482,6 +556,18 @@ class OmeParse:
             elif self.size_z > 1 and distance.attrib["Id"] == "Z":
                 self.ome.images[0].pixels.physical_size_z = float(self.text(distance.find("Value"))) * 1e6
 
+    def get_tirf_angle(self) -> None:
+        if self.version == "1.0":
+            for track_setup in self.multi_track_setup:
+                tirf_angle = track_setup.find("TirfAngle")
+                if tirf_angle is not None:
+                    self.ome.structured_annotations.append(
+                        model.DoubleAnnotation(
+                            description="TirfAngle", id="Annotation:0", value=50 * float(tirf_angle.text)
+                        )
+                    )
+                    self.ome.images[0].annotation_refs.append(model.AnnotationRef(id="Annotation:0"))
+
     @cached_property
     def positions(self) -> tuple[float, float, Optional[float]]:
         if self.version == "1.0":
@@ -495,6 +581,8 @@ class OmeParse:
             except AttributeError:
                 center_position = [0, 0]
             return center_position[0], center_position[1], None
+        else:
+            raise NotImplementedError(f"unknown czi version: {self.version}")
 
     @cached_property
     def channels_im(self) -> dict:
@@ -602,28 +690,30 @@ class OmeParse:
                 else:
                     light_source_settings = None
 
-                self.ome.images[0].pixels.channels.append(
-                    model.Channel(
-                        id=f"Channel:{idx}",
-                        name=channel.attrib["Name"],
-                        acquisition_mode=self.text(channel.find("AcquisitionMode")).replace(  # type: ignore
-                            "SingleMoleculeLocalisation", "SingleMoleculeImaging"
-                        ),
-                        color=color,
-                        detector_settings=model.DetectorSettings(
-                            id=detector.attrib["Id"].replace(" ", ""), binning=binning
-                        ),
-                        emission_wavelength=emission_wavelength,
-                        excitation_wavelength=self.try_default(
-                            float, None, self.text(channel.find("ExcitationWavelength"))
-                        ),
-                        # filter_set_ref=model.FilterSetRef(id=ome.instruments[0].filter_sets[filterset_idx].id),
-                        illumination_type=self.text(channel.find("IlluminationType")),  # type: ignore
-                        light_source_settings=light_source_settings,
-                        samples_per_pixel=samples_per_pixel,
-                    )
+                channel = dict(
+                    id=f"Channel:{idx}",
+                    name=channel.attrib["Name"],
+                    acquisition_mode=self.text(channel.find("AcquisitionMode")).replace(  # type: ignore
+                        "SingleMoleculeLocalisation", "SingleMoleculeImaging"
+                    ),
+                    detector_settings=model.DetectorSettings(
+                        id=detector.attrib["Id"].replace(" ", ""), binning=binning
+                    ),
+                    emission_wavelength=emission_wavelength,
+                    excitation_wavelength=self.try_default(
+                        float, None, self.text(channel.find("ExcitationWavelength"))
+                    ),
+                    # filter_set_ref=model.FilterSetRef(id=ome.instruments[0].filter_sets[filterset_idx].id),
+                    illumination_type=self.text(channel.find("IlluminationType")),  # type: ignore
+                    light_source_settings=light_source_settings,
+                    samples_per_pixel=samples_per_pixel,
                 )
 
+                if color is not None:
+                    channel["color"] = color
+
+                self.ome.images[0].pixels.channels.append(model.Channel(**channel))
+
     def get_planes(self) -> None:
         try:
             exposure_times = [
@@ -639,7 +729,16 @@ class OmeParse:
             delta_ts = dt * np.arange(len(delta_ts))
             warnings.warn(f"delta_t is inconsistent, using median value: {dt}")
 
+        pxsize_x = self.ome.images[0].pixels.physical_size_x_quantity.to(ureg.um).magnitude
+        pxsize_y = self.ome.images[0].pixels.physical_size_y_quantity.to(ureg.um).magnitude
+
         for t, z, c in product(range(self.size_t), range(self.size_z), range(self.size_c)):
+            x_min = (
+                min([f.start[f.axes.index("X")] for f in self.filedict[c, z, t]]) if (c, z, t) in self.filedict else 0
+            )
+            y_min = (
+                min([f.start[f.axes.index("Y")] for f in self.filedict[c, z, t]]) if (c, z, t) in self.filedict else 0
+            )
             self.ome.images[0].pixels.planes.append(
                 model.Plane(
                     the_c=c,
@@ -647,9 +746,9 @@ class OmeParse:
                     the_t=t,
                     delta_t=delta_ts[t],
                     exposure_time=exposure_times[min(c, len(exposure_times) - 1)] if len(exposure_times) > 0 else None,
-                    position_x=self.positions[0],
+                    position_x=self.positions[0] + x_min * pxsize_x,
                     position_x_unit=self.um,
-                    position_y=self.positions[1],
+                    position_y=self.positions[1] + y_min * pxsize_y,
                     position_y_unit=self.um,
                     position_z=self.positions[2],
                     position_z_unit=self.um,

ndbioimage/readers/seqread.py

@@ -1,4 +1,5 @@
 import re
+import warnings
 from abc import ABC
 from datetime import datetime
 from itertools import product
@@ -59,9 +60,10 @@ class Reader(AbstractReader, ABC):
         ome = model.OME()
         with tifffile.TiffFile(self.filedict[0, 0, 0]) as tif:
             metadata = {key: yaml.safe_load(value) for key, value in tif.pages[0].tags[50839].value.items()}
-        ome.experimenters.append(
-            model.Experimenter(id="Experimenter:0", user_name=metadata["Info"]["Summary"]["UserName"])
-        )
+        if "Summary" in metadata["Info"] and "UserName" in metadata["Info"]["Summary"]:
+            ome.experimenters.append(
+                model.Experimenter(id="Experimenter:0", user_name=metadata["Info"]["Summary"]["UserName"])
+            )
         objective_str = metadata["Info"]["ZeissObjectiveTurret-Label"]
         ome.instruments.append(model.Instrument())
         ome.instruments[0].objectives.append(
@@ -112,7 +114,9 @@ class Reader(AbstractReader, ABC):
                     type=pixel_type,
                     physical_size_x=pxsize,
                     physical_size_y=pxsize,
-                    physical_size_z=metadata["Info"]["Summary"]["z-step_um"],
+                    physical_size_z=metadata["Info"]["Summary"]["z-step_um"]
+                    if "Summary" in metadata["Info"]
+                    else None,
                 ),
                 objective_settings=model.ObjectiveSettings(id="Objective:0"),
             )
@@ -164,7 +168,15 @@ class Reader(AbstractReader, ABC):
             metadata = {key: yaml.safe_load(value) for key, value in tif.pages[0].tags[50839].value.items()}
 
         # compare channel names from metadata with filenames
-        cnamelist = metadata["Info"]["Summary"]["ChNames"]
+        if "Summary" in metadata["Info"] and "ChNames" in metadata["Info"]["Summary"]:
+            cnamelist = metadata["Info"]["Summary"]["ChNames"]
+        elif (self.path.parent / "display_and_comments.txt").exists():
+            warnings.warn(f"{self.path} is missing some metadata")
+            with open(self.path.parent / "display_and_comments.txt") as f:
+                cnamelist = [channel["Name"] for channel in yaml.safe_load(f)["Channels"]]
+        else:
+            raise ValueError("could not find metadata describing the order of the channels")
+
         cnamelist = [c for c in cnamelist if any([c in f.name for f in filelist])]
 
         pattern_c = re.compile(r"img_\d{3,}_(.*)_\d{3,}$", re.IGNORECASE)
@@ -180,4 +192,4 @@ class Reader(AbstractReader, ABC):
         }
 
     def __frame__(self, c=0, z=0, t=0):
-        return tifffile.imread(self.path / self.filedict[(c, z, t)])
+        return tifffile.imread(self.path / self.filedict[c, z, t])

ndbioimage/readers/tifread.py

@@ -1,16 +1,18 @@
 import re
 import warnings
 from abc import ABC
+from contextlib import ExitStack
 from functools import cached_property
 from itertools import product
 from pathlib import Path
+from typing import Any
 
 import numpy as np
 import tifffile
 import yaml
-from ome_types import from_xml, model
+from ome_types import OME, from_xml, model
 
-from .. import AbstractReader, try_default
+from .. import AbstractReader, Shape, try_default
 
 
 class Reader(AbstractReader, ABC):
@@ -18,7 +20,7 @@ class Reader(AbstractReader, ABC):
     do_not_pickle = "reader"
 
     @staticmethod
-    def _can_open(path):
+    def _can_open(path) -> bool:
         if isinstance(path, Path) and path.suffix in (".tif", ".tiff"):
             with tifffile.TiffFile(path) as tif:
                 return tif.is_imagej and tif.pages[-1]._nextifd() == 0  # noqa
@@ -26,13 +28,13 @@ class Reader(AbstractReader, ABC):
             return False
 
     @cached_property
-    def metadata(self):
+    def metadata(self) -> dict[str, Any]:
         return {
             key: try_default(yaml.safe_load, value, value) if isinstance(value, str) else value
             for key, value in self.reader.imagej_metadata.items()
         }
 
-    def get_ome(self):
+    def get_ome(self) -> OME:
         if self.reader.is_ome:
             pos_number_pat = re.compile(r"\d+")
 
@@ -88,13 +90,17 @@ class Reader(AbstractReader, ABC):
         page = self.reader.pages[0]
         size_y = page.imagelength
         size_x = page.imagewidth
-        if self.p_ndim == 3:
-            size_c = page.samplesperpixel
-            size_t = self.metadata.get("frames", 1)  # // C
-        else:
-            size_c = self.metadata.get("channels", 1)
-            size_t = self.metadata.get("frames", 1)
-        size_z = self.metadata.get("slices", 1)
+        max_c = max_z = max_t = 0
+        if self.file_dict:
+            for ci, zi, ti in self.file_dict.keys():
+                max_c = max(max_c, ci)
+                max_z = max(max_z, zi)
+                max_t = max(max_t, ti)
+
+        size_c: int = self.mod_shape["c"] * (max_c + 1)  # type: ignore
+        size_z: int = self.mod_shape["z"] * (max_z + 1)  # type: ignore
+        size_t: int = self.mod_shape["t"] * (max_t + 1)  # type: ignore
+
         if 282 in page.tags and 296 in page.tags and page.tags[296].value == 1:
             f = page.tags[282].value
             pxsize = f[1] / f[0]
@@ -142,9 +148,18 @@ class Reader(AbstractReader, ABC):
         )
         for c, z, t in product(range(size_c), range(size_z), range(size_t)):
             ome.images[0].pixels.planes.append(model.Plane(the_c=c, the_z=z, the_t=t, delta_t=interval_t * t))
+        if (spacing := self.metadata.get("spacing")) is not None:
+            ome.images[0].pixels.physical_size_z = spacing
+        if (unit := self.metadata.get("unit")) is not None and unit.lower() != "micron":
+            raise ValueError(f"cannot parse unit {unit}")
+
         return ome
 
-    def open(self):
+    def open(self) -> None:
+        if self.series != 0:
+            raise FileNotFoundError(
+                f"Series {self.series} not found in {self.path}. Tifread only supports one series."
+            )
         self.reader = tifffile.TiffFile(self.path)
         page = self.reader.pages.first
         self.p_ndim = page.ndim  # noqa
@@ -152,19 +167,68 @@ class Reader(AbstractReader, ABC):
             self.p_transpose = [i for i in [page.axes.find(j) for j in "SYX"] if i >= 0]  # noqa
         else:
             self.p_transpose = [i for i in [page.axes.find(j) for j in "YX"] if i >= 0]  # noqa
+        self.file_dict = {}  # noqa
+        pat = re.compile(r"^.*?((?:_[czt]\d+)*)$", re.IGNORECASE)
+        if (m := pat.match(self.path.stem)) is not None:
+            m = m.group(1)
+            cpat = re.compile(r"_c(\d+)", re.IGNORECASE)
+            zpat = re.compile(r"_z(\d+)", re.IGNORECASE)
+            tpat = re.compile(r"_t(\d+)", re.IGNORECASE)
+            for file in self.path.parent.glob(
+                re.sub(m, re.sub(r"\d+", "*", m), self.path.stem, re.IGNORECASE) + self.path.suffix
+            ):
+                m = pat.match(file.stem)
+                if m:
+                    m = m.group(0)
+                    c = cpat.findall(m)
+                    z = zpat.findall(m)
+                    t = tpat.findall(m)
+                    c = int(c[0]) if len(c) else 0
+                    z = int(z[0]) if len(z) else 0
+                    t = int(t[0]) if len(t) else 0
+                    self.file_dict[c, z, t] = file.name
 
-    def close(self):
+        page = self.reader.pages[0]
+        if self.p_ndim == 3:
+            mod_c = page.samplesperpixel
+            mod_t = self.metadata.get("frames", 1)  # // C
+        else:
+            mod_c = self.metadata.get("channels", 1)
+            mod_t = self.metadata.get("frames", 1)
+
+        mod_z = self.metadata.get("slices")
+        if mod_z is None:
+            if self.metadata.get("spacing") is None:
+                mod_t = len(self.reader.pages)
+            else:
+                mod_z = len(self.reader.pages)
+
+        self.mod_shape = Shape((mod_c or 1, mod_z or 1, mod_t or 1), "czt")  # noqa
+
+    def close(self) -> None:
         self.reader.close()
 
-    def __frame__(self, c: int, z: int, t: int):
-        dimension_order = self.ome.images[0].pixels.dimension_order.value
-        if self.p_ndim == 3:
-            axes = "".join([ax.lower() for ax in dimension_order if ax.lower() in "zt"])
-            ct = {"z": z, "t": t}
-            n = sum([ct[ax] * np.prod(self.base_shape[axes[:i]]) for i, ax in enumerate(axes)])
-            return np.transpose(self.reader.asarray(int(n)), self.p_transpose)[int(c)]
-        else:
-            axes = "".join([ax.lower() for ax in dimension_order if ax.lower() in "czt"])
-            czt = {"c": c, "z": z, "t": t}
-            n = sum([czt[ax] * np.prod(self.base_shape[axes[:i]]) for i, ax in enumerate(axes)])
-        return np.transpose(self.reader.asarray(int(n)), self.p_transpose)
+    def __frame__(self, c: int, z: int, t: int) -> np.ndarray:
+        with ExitStack() as stack:
+            if self.file_dict:
+                filename = self.file_dict[
+                    c - c % self.mod_shape["c"], z - z % self.mod_shape["z"], t - t % self.mod_shape["t"]
+                ]
+                if self.reader.filename == filename:
+                    reader = self.reader
+                else:
+                    reader = stack.enter_context(tifffile.TiffFile(self.path.parent / filename))
+            else:
+                reader = self.reader
+
+            dimension_order = self.ome.images[0].pixels.dimension_order.value
+            if self.p_ndim == 3:
+                axes = "".join([ax.lower() for ax in dimension_order if ax.lower() in "zt"])
+                ct = {"z": z % self.mod_shape["z"], "t": t % self.mod_shape["t"]}
+                n = sum([ct[ax] * np.prod(self.mod_shape[axes[:i]]) for i, ax in enumerate(axes)])
+                return np.transpose(reader.asarray(int(n)), self.p_transpose)[int(c % self.mod_shape["c"])]
+            else:
+                axes = "".join([ax.lower() for ax in dimension_order if ax.lower() in "czt"])
+                czt = {"c": c % self.mod_shape["c"], "z": z % self.mod_shape["z"], "t": t % self.mod_shape["t"]}
+                n = sum([czt[ax] * np.prod(self.mod_shape[axes[:i]]) for i, ax in enumerate(axes)])
+                return np.transpose(reader.asarray(int(n)), self.p_transpose)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ndbioimage"
-version = "2026.1.2"
+version = "2026.9.0"
 description = "Bio image reading, metadata and some affine registration."
 authors = [
     { name = "W. Pomp", email = "w.pomp@nki.nl" }
@@ -14,7 +14,7 @@ exclude = ["ndbioimage/jars"]
 
 dependencies = [
     "czifile == 2019.7.2",
-    "imagecodecs",
+    "imagecodecs <= 2026.1.14",
     "lxml",
     "numpy >= 1.20",
     "ome-types",
@@ -24,8 +24,8 @@ dependencies = [
     "pyyaml",
     "SimpleITK-SimpleElastix; sys_platform != 'darwin'",
     "scikit-image",
-    "tifffile <= 2025.1.10",
-    "tiffwrite >= 2024.12.1",
+    "tifffile >= 2024.1.30, <= 2025.1.10",
+    "tiffwrite >= 2026.5.0",
     "tqdm",
 ]
 
@@ -35,7 +35,7 @@ write = ["matplotlib", "scikit-video"]
 bioformats = ["JPype1"]
 
 [project.urls]
-repository = "https://github.com/wimpomp/ndbioimage"
+repository = "https://git.wimpomp.nl/wim/ndbioimage"
 
 [project.scripts]
 ndbioimage = "ndbioimage:main"