Support camel case downstream access

CHANGELOG.md

@@ -4,6 +4,11 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [v0.2.0] - 2024-10-03
+
+### Changed
+* Renaming `mean_intensity` to `meanIntensity` right away after detection, before writing to disk.
+
 ## [v0.1.0] - 2024-04-18
 
 ### Added

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "spotfishing"
-version = "0.1.0"
+version = "0.2.0"
 description = "Detecting spots in FISH images"
 authors = [
     "Vince Reuter <[email protected]>",

spotfishing/__init__.py

@@ -1,6 +1,10 @@
 """Package-level members"""
 
-from ._constants import ROI_AREA_KEY, ROI_MEAN_INTENSITY_KEY
+from ._constants import (
+    ROI_AREA_KEY,
+    ROI_MEAN_INTENSITY_KEY,
+    ROI_MEAN_INTENSITY_KEY_CAMEL_CASE,
+)
 from ._exceptions import *
 from .detection_result import DetectionResult, RoiCenterKeys
 from .detectors import detect_spots_dog, detect_spots_int
@@ -11,7 +15,7 @@
 
 __all__ = [
     "ROI_AREA_KEY",
-    "ROI_MEAN_INTENSITY_KEY",
+    "ROI_MEAN_INTENSITY_KEY_CAMEL_CASE",  # Only export this (not the snake case one).
     "DifferenceOfGaussiansTransformation",
     "DimensionalityError",
     "RoiCenterKeys",

spotfishing/_constants.py

@@ -1,13 +1,13 @@
-"""Constant useful package-wide"""
+"""Constants useful package-wide"""
 
 __author__ = "Vince Reuter"
 __credits__ = ["Vince Reuter"]
 
 __all__ = [
     "ROI_AREA_KEY",
     "ROI_CENTROID_KEY",
-    "ROI_LABEL_KEY",
-    "ROI_MEAN_INTENSITY_KEY",
+    "ROI_MEAN_INTENSITY_KEY",  # Export this for package-internal use.
+    "ROI_MEAN_INTENSITY_KEY_CAMEL_CASE",
 ]
 
 
@@ -17,8 +17,7 @@
 # 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"
+
+ROI_MEAN_INTENSITY_KEY_CAMEL_CASE = "intensityMean"

spotfishing/detection_result.py

@@ -30,23 +30,20 @@ def to_list(cls) -> list[str]:
 
 # 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(
+SPOT_DETECTION_COLUMN_RENAMING: tuple[tuple[str, str], ...] = tuple(
     (f"{ROI_CENTROID_KEY}-{i}", c) for i, c in enumerate(RoiCenterKeys.to_list())
+) + (
+    (ROI_AREA_KEY, ROI_AREA_KEY),
+    (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]
-
 # 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 ROI_CENTROID_COLUMN_RENAMING
-] + ROI_MEASUREMENT_KEYS
+    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(ROI_CENTROID_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

@@ -17,9 +17,8 @@
 from ._exceptions import DimensionalityError
 from ._types import *
 from .detection_result import (
-    ROI_CENTROID_COLUMN_RENAMING,
-    ROI_MEASUREMENT_KEYS,
     SKIMAGE_REGIONPROPS_TABLE_COLUMNS_EXPANDED,
+    SPOT_DETECTION_COLUMN_RENAMING,
     DetectionResult,
 )
 from .dog_transform import DifferenceOfGaussiansTransformation
@@ -125,13 +124,14 @@ def _build_props_table(
             regionprops_table(
                 label_image=labels,
                 intensity_image=input_image,
-                properties=tuple(
-                    [ROI_LABEL_KEY, ROI_CENTROID_KEY] + ROI_MEASUREMENT_KEYS
-                ),
+                properties=(ROI_CENTROID_KEY, ROI_AREA_KEY, ROI_MEAN_INTENSITY_KEY),
             )
         )
-    spot_props = spot_props.drop(["label"], axis=1, errors="ignore")
-    spot_props = spot_props.rename(columns=dict(ROI_CENTROID_COLUMN_RENAMING))
+    spot_props = spot_props.rename(
+        columns=dict(SPOT_DETECTION_COLUMN_RENAMING),
+        inplace=False,
+        errors="raise",
+    )
     spot_props = spot_props.reset_index(drop=True)
     return spot_props, labels
 

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)