Cleanup the get_regionprops_measurements() function in feature_wrapper.py

[enricotagliavini]

This function has a lot of branching and it start being hard to read. Let's make it nicer and easier to read.

[jluethi]

We could split it up into 3 functions:

1 for calculating positions:

gliberal-scMultipleX/src/scmultiplex/features/feature_wrapper.py

Lines 108 to 122 in c564c54

	# Always include xy positions
	coordinate_measurements = {
	"x_pos_pix": labeled_obj["centroid"][-1],
	"y_pos_pix": labeled_obj["centroid"][-2],
	}
	label_info.update(coordinate_measurements)
	if not is_2D:
	coordinate_measurements_3D = {
	"z_pos_pix_scaled": labeled_obj["centroid"][-3],
	"z_pos_pix_img": labeled_obj["centroid"][-3]
	/ spacing_anisotropy_scalar(spacing)
	}
	label_info.update(coordinate_measurements_3D)

1 for the morphology calculations:

gliberal-scMultipleX/src/scmultiplex/features/feature_wrapper.py

Lines 124 to 185 in c564c54

	if measure_morphology:
	morphology_measurements = {
	"is_touching_border_xy": is_touching_border_xy(
	labeled_obj, img_shape=img.shape
	),
	"imgdim_x": img.shape[-1],
	"imgdim_y": img.shape[-2],
	"area_bbox": labeled_obj["area_bbox"],
	"area_convhull": labeled_obj["area_convex"],
	"equivDiam": labeled_obj["equivalent_diameter_area"],
	"extent": labeled_obj["extent"],
	"solidity": labeled_obj["solidity"],
	}
	# Sometimes, major & minor axis calculations fail with a
	# ValueError: math domain error
	try:
	morphology_measurements["majorAxisLength"] = labeled_obj[
	"major_axis_length"
	]
	morphology_measurements["minorAxisLength"] = labeled_obj[
	"minor_axis_length"
	]
	morphology_measurements["minmajAxisRatio"] = minor_major_axis_ratio(
	labeled_obj
	)
	morphology_measurements[
	"aspectRatio_equivalentDiameter"
	] = aspect_ratio(labeled_obj)
	except ValueError:
	morphology_measurements["majorAxisLength"] = np.NaN
	morphology_measurements["minorAxisLength"] = np.NaN
	morphology_measurements["minmajAxisRatio"] = np.NaN
	morphology_measurements["aspectRatio_equivalentDiameter"] = np.NaN
	if is_2D:
	spacing_2d = spacing_to2d(spacing)
	morphology_2D_only = {
	"area_pix": labeled_obj["area"],
	"perimeter": labeled_obj["perimeter"],
	"concavity": convex_hull_area_resid(labeled_obj),
	"asymmetry": convex_hull_centroid_dif(labeled_obj, spacing_2d),
	"eccentricity": labeled_obj["eccentricity"],
	"circularity": circularity(labeled_obj),
	"concavity_count": concavity_count(
	labeled_obj, min_area_fraction=min_area_fraction
	),
	"disconnected_components": disconnected_component(
	labeled_obj.image
	),
	}
	morphology_measurements.update(morphology_2D_only)
	else:
	morphology_3d_only = {
	"imgdim_z": img.shape[-3],
	"is_touching_border_z": is_touching_border_z(
	labeled_obj, img_shape=img.shape
	),
	"volume_pix": labeled_obj["area"],
	"surface_area": labeled_obj["surface_area_marchingcube"],
	}
	morphology_measurements.update(morphology_3d_only)

and 1 for the intensity calculations:

gliberal-scMultipleX/src/scmultiplex/features/feature_wrapper.py

Lines 187 to 227 in c564c54

	if img is not None:
	intensity_measurements = {
	"mean_intensity": labeled_obj["mean_intensity"],
	"max_intensity": labeled_obj["max_intensity"],
	"min_intensity": labeled_obj["min_intensity"],
	"percentile25": labeled_obj["fixed_percentiles"][0],
	"percentile50": labeled_obj["fixed_percentiles"][1],
	"percentile75": labeled_obj["fixed_percentiles"][2],
	"percentile90": labeled_obj["fixed_percentiles"][3],
	"percentile95": labeled_obj["fixed_percentiles"][4],
	"percentile99": labeled_obj["fixed_percentiles"][5],
	"stdev": labeled_obj["stdv"],
	"skew": labeled_obj["skewness"],
	"kurtosis": labeled_obj["kurtos"],
	"x_pos_weighted_pix": labeled_obj["weighted_centroid"][-1],
	"y_pos_weighted_pix": labeled_obj["weighted_centroid"][-2],
	"x_massDisp_pix": labeled_obj["weighted_centroid"][-1]
	- labeled_obj["centroid"][-1],
	"y_massDisp_pix": labeled_obj["weighted_centroid"][-2]
	- labeled_obj["centroid"][-2],
	}
	# add 3D-specific intensity measurements
	if not is_2D:
	intensity_3D_only = {
	"z_pos_weighted_pix": labeled_obj["weighted_centroid"][-3],
	"z_massDisp_pix": labeled_obj["weighted_centroid"][-3]
	- labeled_obj["centroid"][-3],
	}
	intensity_measurements.update(intensity_3D_only)
	# channel prefix addition is optional
	if channel_prefix is not None:
	intensity_measurements_pref = {
	channel_prefix + "." + str(key): val
	for key, val in intensity_measurements.items()
	}
	else:
	intensity_measurements_pref = intensity_measurements

Each function then returns one dictionary with updates and the main function just calls them & updates the dictionaries.

[jluethi]

Each of those functions could just take the labeled_obj and a few args (e.g. min_area_fraction, channel_prefix, the img.shape for morphology)

[jluethi]

Do you want to tackle this @enricotagliavini or shall I have a go at it (as described above) while working on the Fractal task part?