finish the switch of the mean intensity measurement name to camel cas…

…e to replace snake case

spotfishing/detection_result.py

@@ -32,21 +32,16 @@ def to_list(cls) -> list[str]:
 # TODO: consider making this configurable, see: https://github.com/gerlichlab/spotfishing/issues/1
 SPOT_DETECTION_COLUMN_RENAMING: tuple[str, str] = tuple(
     (f"{ROI_CENTROID_KEY}-{i}", c) for i, c in enumerate(RoiCenterKeys.to_list())
-) + (ROI_MEAN_INTENSITY_KEY, ROI_MEAN_INTENSITY_KEY_CAMEL_CASE)
-
-# fields to pull from skimage.measure.regionprops_table result, besides centroid coordinates and label
-ROI_MEASUREMENT_KEYS = [ROI_AREA_KEY, ROI_MEAN_INTENSITY_KEY]
+) + (
+    (ROI_AREA_KEY, ROI_AREA_KEY),
+    (ROI_MEAN_INTENSITY_KEY, ROI_MEAN_INTENSITY_KEY_CAMEL_CASE), 
+)
 
 # the sequence of columns of fields extracted from skimage.measure.regionprops_table, after accounting for expansion in multiple dimensions (e.g., centroid_weighted)
-SKIMAGE_REGIONPROPS_TABLE_COLUMNS_EXPANDED = [
-    old for old, _ in SPOT_DETECTION_COLUMN_RENAMING
-] + ROI_MEASUREMENT_KEYS
+SKIMAGE_REGIONPROPS_TABLE_COLUMNS_EXPANDED = [old for old, _ in SPOT_DETECTION_COLUMN_RENAMING]
 
 # the expected column names in a detection result table, after extraction and renaming
-DETECTION_RESULT_TABLE_COLUMNS = [
-    dict(SPOT_DETECTION_COLUMN_RENAMING).get(c, c)
-    for c in SKIMAGE_REGIONPROPS_TABLE_COLUMNS_EXPANDED
-]
+DETECTION_RESULT_TABLE_COLUMNS = [new for _, new in SPOT_DETECTION_COLUMN_RENAMING]
 
 
 @doc(

spotfishing/detectors.py

@@ -18,7 +18,6 @@
 from ._types import *
 from .detection_result import (
     SPOT_DETECTION_COLUMN_RENAMING,
-    ROI_MEASUREMENT_KEYS,
     SKIMAGE_REGIONPROPS_TABLE_COLUMNS_EXPANDED,
     DetectionResult,
 )
@@ -125,7 +124,7 @@ def _build_props_table(
             regionprops_table(
                 label_image=labels,
                 intensity_image=input_image,
-                properties=(ROI_CENTROID_KEY, *ROI_MEASUREMENT_KEYS),
+                properties=(ROI_CENTROID_KEY, ROI_AREA_KEY, ROI_MEAN_INTENSITY_KEY),
             )
         )
     spot_props = spot_props.rename(

tests/data/outputs__original_settings/expect__spots_table__diff_gauss__threshold_10__p0_t57_c0.csv

@@ -1,4 +1,4 @@
-,zc,yc,xc,intensity_mean
+,zc,yc,xc,intensityMean
 0,2.4580271876546713,125.05243771893812,189.95460283233513,140.35164243403338
 1,3.215275611393135,165.8262102606279,338.60801357496007,142.0859637340497
 2,2.9371662355253076,174.2444293945887,343.5015376758335,135.34335086401202

tests/data/outputs__original_settings/expect__spots_table__diff_gauss__threshold_10__p13_t57_c0.csv

@@ -1,4 +1,4 @@
-,zc,yc,xc,intensity_mean
+,zc,yc,xc,intensityMean
 0,1.464823275885825,381.0047398327831,256.9925375190833,150.19950331125827
 1,5.64082522600635,21.70851874992031,177.53797926734416,158.95216862586136
 2,5.645954517203461,334.42413956132106,246.54587324100783,158.50154364299746

tests/data/outputs__original_settings/expect__spots_table__diff_gauss__threshold_15__p0_t57_c0.csv

@@ -1,4 +1,4 @@
-,zc,yc,xc,intensity_mean
+,zc,yc,xc,intensityMean
 0,2.900904569286373,478.08907046083226,672.0878758101751,143.69342359767893
 1,3.4613367258090584,206.96659488938826,769.1328018686423,119.6487835308796
 2,3.3685636416728855,557.7280560872998,563.5415814974639,150.34634146341463

tests/data/outputs__original_settings/expect__spots_table__diff_gauss__threshold_15__p13_t57_c0.csv

@@ -1,4 +1,4 @@
-,zc,yc,xc,intensity_mean
+,zc,yc,xc,intensityMean
 0,5.338708099413269,21.623742874490315,177.42528322192456,160.0732381391064
 1,5.4162750969712805,334.3139437667062,246.38533874630704,159.93892639022823
 2,6.183825840659056,343.4778794839467,238.8895252240894,161.87028235951914

tests/data/outputs__original_settings/expect__spots_table__intensity__threshold_200__p0_t57_c0.csv

@@ -1,4 +1,4 @@
-,zc,yc,xc,intensity_mean
+,zc,yc,xc,intensityMean
 0,3.3902772597289568,125.08864851423598,190.01392515230637,187.04651162790697
 1,4.863298083935557,126.35459623417093,210.9740933501293,266.3664459161148
 2,2.74294559405134,134.9870391491757,311.8039430611308,201.5253731343284

tests/data/outputs__original_settings/expect__spots_table__intensity__threshold_200__p13_t57_c0.csv

@@ -1,4 +1,4 @@
-,zc,yc,xc,intensity_mean
+,zc,yc,xc,intensityMean
 0,3.658232714432168,22.889665679462357,178.80805758059003,221.90205371248024
 1,0.5710688944838636,250.68390936140997,486.9258411535821,189.95652173913044
 2,4.348300667423207,340.4061183666404,242.91498528591487,226.78735005452563

tests/data/outputs__original_settings/expect__spots_table__intensity__threshold_300__p0_t57_c0.csv

@@ -1,4 +1,4 @@
-,zc,yc,xc,intensity_mean
+,zc,yc,xc,intensityMean
 0,0.2320966350301984,477.7818809318378,672.2436583261432,275.95238095238096
 1,2.3448733193085154,557.9810207628282,563.80988749657,283.97402597402595
 2,5.223356373573381,126.48178213577668,210.59382200075012,358.90384615384613

tests/data/outputs__original_settings/expect__spots_table__intensity__threshold_300__p13_t57_c0.csv

@@ -1,4 +1,4 @@
-,zc,yc,xc,intensity_mean
+,zc,yc,xc,intensityMean
 0,3.315825788780396,22.067221443954253,177.91419072908607,298.91875
 1,3.748072232760957,336.2526422113276,245.102830676751,302.9179104477612
 2,4.329418330331549,341.85340300854875,240.40516475989955,323.12295081967216

tests/spotfishing_looptrace/test_accord_with_original_settings.py

@@ -11,7 +11,7 @@
 
 import spotfishing_looptrace
 from spotfishing import (
-    ROI_MEAN_INTENSITY_KEY,
+    ROI_MEAN_INTENSITY_KEY_CAMEL_CASE,
     RoiCenterKeys,
     detect_spots_dog,
     detect_spots_int,
@@ -82,7 +82,7 @@ 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)
-    obs_table = obs.table[RoiCenterKeys.to_list() + [ROI_MEAN_INTENSITY_KEY]]
+    obs_table = obs.table[RoiCenterKeys.to_list() + [ROI_MEAN_INTENSITY_KEY_CAMEL_CASE]]
 
     print("EXPECTED (below):")
     print(exp_table)