- support some sequences of files in tifread

ndbioimage/readers/seqread.py

@@ -192,4 +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]
@@ -149,7 +155,7 @@ class Reader(AbstractReader, ABC):
 
         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."
@@ -161,19 +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, mod_z, mod_t), "czt")  # noqa
+
+    def close(self) -> None:
         self.reader.close()
 
-    def __frame__(self, c: int, z: int, t: int):
+    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, "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)]
+                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, "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)
+                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 = "2026.7.0"
+version = "2026.7.1"
 description = "Bio image reading, metadata and some affine registration."
 authors = [
     { name = "W. Pomp", email = "w.pomp@nki.nl" }