wip

config.yml

@@ -7,6 +7,7 @@
 
 # criteria for file selection in case of multiple channels/slices per position
 file_selection:
+    mode: standard
     channel: C01
 
 # choose method how to segment, filter, and sample the objects

pyproject.toml

@@ -19,16 +19,17 @@ authors = [
 classifiers = [
   "Development Status :: 4 - Beta",
   "Programming Language :: Python",
-  "Programming Language :: Python :: 3.8",
   "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
   "Programming Language :: Python :: Implementation :: CPython",
   "Programming Language :: Python :: Implementation :: PyPy",
 ]
 dependencies = [
   "cellpose",
   "confuse",
+  "faim-ipa",
   "rich",
   "scikit-image",
   "tqdm",
@@ -55,7 +56,7 @@ cov = "pytest --cov-report=term-missing --cov-config=pyproject.toml --cov=faim_w
 no-cov = "cov --no-cov {args}"
 
 [[tool.hatch.envs.test.matrix]]
-python = ["38", "39", "310", "311"]
+python = ["39", "310", "311", "312"]
 
 [tool.coverage.run]
 branch = true

src/faim_wako_searchfirst/main.py

@@ -56,9 +56,16 @@ def run(folder: Union[str, Path], configfile: Union[str, Path]):
     config_copy.write_text(config.dump())
 
     # Select files
+    file_selection_config = config["file_selection"].get()
+    if "mode" in file_selection_config:
+        mode = file_selection_config.pop("mode")
+        if mode == "stitched" and "field" not in file_selection_config:
+            file_selection_config["field"] = "F0001"
+    else:
+        mode = "standard"
     tif_files = _select_files(
         folder=folder_path,
-        **(config["file_selection"].get()),
+        **file_selection_config,
     )
 
     logger.info(f"Found {len(tif_files)} matching files.")
@@ -123,10 +130,13 @@ def _process_tif(tif_file, config, logger):
 
 def _select_files(
     folder: Path,
+    time: str = "T[0-9][0-9][0-9][0-9]",
+    field: str = "F[0-9][0-9][0-9]",
+    plane: str = "Z[0-9][0-9]",
     channel: str = "C01",
 ) -> List[Path]:
     """Filter all TIFs in folder starting with folder name - and containing channel ID."""
-    return sorted(folder.rglob(folder.name + "*" + channel + ".tif"))
+    return sorted(folder.glob(folder.name + "_*_" + time + field + "L[0-9][0-9]A[0-9][0-9]" + plane + channel + ".tif"))
 
 
 def _save_segmentation_image(folder_path, filename, img, labels):