better definition and sharing of keys for ROI center

spotfishing/__init__.py

@@ -1,18 +1,20 @@
 """Package-level members"""
 
-from ._constants import ROI_MEAN_INTENSITY_KEY
+from ._constants import ROI_AREA_KEY, ROI_MEAN_INTENSITY_KEY
 from ._exceptions import *
-from .detection_result import DetectionResult
+from .detection_result import DetectionResult, RoiCenterKeys
 from .detectors import detect_spots_dog, detect_spots_int
 from .dog_transform import DifferenceOfGaussiansTransformation
 
 __author__ = "Vince Reuter"
 __credits__ = ["Vince Reuter", "Kai Sandoval Beckwith"]
 
 __all__ = [
+    "ROI_AREA_KEY",
     "ROI_MEAN_INTENSITY_KEY",
     "DifferenceOfGaussiansTransformation",
     "DimensionalityError",
+    "RoiCenterKeys",
     "DetectionResult",
     "detect_spots_dog",
     "detect_spots_int",

spotfishing/_constants.py

@@ -10,14 +10,15 @@
     "ROI_MEAN_INTENSITY_KEY",
 ]
 
-# the key for an ROI's label, coming back from skimage.measure.regionprops_table
-ROI_LABEL_KEY = "label"
 
 # the key for an ROI's area, coming back from skimage.measure.regionprops_table
 ROI_AREA_KEY = "area"
 
 # the key for an ROI's centroid, coming back from skimage.measure.regionprops_table
 ROI_CENTROID_KEY = "centroid_weighted"
 
+# the key for an ROI's label, coming back from skimage.measure.regionprops_table
+ROI_LABEL_KEY = "label"
+
 # the key for an ROI's average intensity, coming back from skimage.measure.regionprops_table
 ROI_MEAN_INTENSITY_KEY = "intensity_mean"

spotfishing/detection_result.py

@@ -1,6 +1,7 @@
 """Abstraction over the result of application of a spot detection procedure to an input image"""
 
 from dataclasses import dataclass
+from enum import Enum
 from typing import TYPE_CHECKING, Iterable
 
 from numpydoc_decorator import doc  # type: ignore[import-untyped]
@@ -17,13 +18,20 @@
 __all__ = ["DetectionResult"]
 
 
-# column names corresponding to the coordinates of the ROI centroid
-ROI_CENTER_KEYS = ["zc", "yc", "xc"]
+class RoiCenterKeys(Enum):
+    Z = "zc"
+    Y = "yc"
+    X = "xc"
+
+    @classmethod
+    def to_list(cls) -> list[str]:
+        return [m.value for m in cls]
+
 
 # how to rename columns arising from extraction from skimage.measure.regionprops_table, to better suit downstream analysis
 # TODO: consider making this configurable, see: https://github.com/gerlichlab/spotfishing/issues/1
 ROI_CENTROID_COLUMN_RENAMING = tuple(
-    (f"{ROI_CENTROID_KEY}-{i}", c) for i, c in enumerate(ROI_CENTER_KEYS)
+    (f"{ROI_CENTROID_KEY}-{i}", c) for i, c in enumerate(RoiCenterKeys.to_list())
 )
 
 # fields to pull from skimage.measure.regionprops_table result, besides centroid coordinates and label

tests/spotfishing_looptrace/test_accord_with_original_settings.py

@@ -9,8 +9,7 @@
 from pandas.testing import assert_frame_equal
 
 from helpers import ORIGINAL_SPECIFICATION, load_image_file
-from spotfishing import ROI_MEAN_INTENSITY_KEY, detect_spots_dog, detect_spots_int
-from spotfishing.detection_result import ROI_CENTER_KEYS
+from spotfishing import ROI_MEAN_INTENSITY_KEY, RoiCenterKeys, detect_spots_dog, detect_spots_int
 import spotfishing_looptrace
 from spotfishing_looptrace.transformation_specification import (
     DifferenceOfGaussiansSpecificationForLooptrace,
@@ -76,7 +75,7 @@ def test_output_is_correct_with_original_settings(
     arr_name = f"img__{data_name}__smaller.npy"
     input_image = load_image_file(arr_name)
     obs = detect(input_image, spot_threshold=threshold, expand_px=expand_px)  # type: ignore[call-arg]
-    obs_table = obs.table[ROI_CENTER_KEYS + [ROI_MEAN_INTENSITY_KEY]]
+    obs_table = obs.table[RoiCenterKeys.to_list() + [ROI_MEAN_INTENSITY_KEY]]
 
     print("EXPECTED (below):")
     print(exp_table)