A more complex plugin

An interesting feature of intensity signals, particularly in fluorescence microscopy, is how uniform or heterogeneous they are. See for example (Zulueta-Coarasa, 2018). This below is a plugin to measure the heterogeneity of the pixel values under a mask, calculated as the ratio of the standard deviation to the mean of the pixel values. The thickness of the mask can be adjusted setting the brush size under the Options menu in PyJAMAS. The plugin also introduces some PyJAMAS measurement tools, the use of Pandas dataframes to store data and Matplotlib to display results.

from typing import List

import matplotlib.pyplot as plt
import numpy
import pandas as pd

from pyjamas.rannotations.rpolyline import RPolyline
from pyjamas.rplugins.base import PJSPluginABC

class PJSPlugin(PJSPluginABC):
    def name(self) -> str:
        return 'Heterogeneity'

    def run(self, parameters: dict) -> bool:
        # Array with an index per slice in the image stack.
        slices = numpy.arange(self.pjs.n_frames, dtype=numpy.int)

        # Find the maximum number of polylines in a slice.
        max_n_polylines = 0
        for i in slices:
            max_n_polylines = max(max_n_polylines, len(self.pjs.polylines[i]))

        # Create a pandas data frame to store the measurements.
        # The data frame will have one column per polyline, and one row per slice in the image.
        n_columns: int = max_n_polylines
        n_rows: int = slices.shape[0]

        column_names: List[str] = ['heterogeneity_' + str(i) for i in range(1, max_n_polylines+1)]

        heterogeneity_df: pd.DataFrame = pd.DataFrame(numpy.nan * numpy.zeros((n_rows, n_columns)), columns=column_names, index=slices+1)

        # For every slice ...
        for i in slices:
            theimage = self.pjs.slices[i]

            # Retrieve the polylines in this slice.
            polygon_slice = self.pjs.polylines[i]

            n_polylines = len(polygon_slice)

            # For every polyline ...
            for j in range(n_polylines):
                # We take advantage of the RPolyline class in PyJAMAS, which contains methods to measure
                # morphology and pixel values for the polyline.
                thepolyline = RPolyline(polygon_slice[j])

                intensities = thepolyline.pixel_values(theimage, self.pjs.brush_size)
                mean_poly = intensities[0]
                std_poly = intensities[2]
                heterogeneity_df.loc[i + 1, 'heterogeneity_' + str(j + 1)] = std_poly / mean_poly

        # Plot results.
        heterogeneity_df.plot()
        plt.xlabel('slice index')
        plt.ylabel('heterogeneity')
        plt.show()

        # Print full results in the terminal.
        with pd.option_context('display.max_rows', None, 'display.max_columns', None):
            print(heterogeneity_df)

        return True