Extract timecourse of one channel from a labels layer in napari

extract_timecourse_napari.py

# import packages
import numpy as np
import pandas as pd
from skimage.measure import label

# assign arrays to simpler variables
labels = label(viewer.layers["Labels"].data[0])
red_data = viewer.layers["NAME_OF_RED_CHANNEL"].data

# intialise empty list
timecourses = []

# for each ROI drawn
for roi_index in range(labels.max()):
    
    # add 1 due to 0 based indexing
    mask_2d = labels == roi_index + 1
    
    # create a 3D mask by repeating the 2D ROI
    mask_3d = np.broadcast_to(mask_2d, red_data.shape)
    
    # calculate mean of red channel in ROI over time
    timecourse = (red_data * mask_3d).mean(axis=(1, 2))
    
    # add single timecourse to list
    timecourses.append(np.squeeze(timecourse))

# convert to dataframe
df = pd.DataFrame(timecourses)
df.to_csv("/path/to/save/file.csv")