persistent-rectangle.py

import numpy as np
import pandas as pd
from holoviews import opts
import holoviews as hv
from holoviews import streams
import datashader
import xarray as xr
import hvplot.xarray 
hv.extension('bokeh','matplotlib')

## this is the callback for when a rectangle is created
## it plots the mean intensity over time for the recangle 
def roi_curves(data):
    ## data contains all of the rectangles on the image/canvas
    ## rectangles are defined by (x0, y0), (x1, y1)
    ## all rectangles x0s are in a list, all y0s are in a list, ...
    ## if using in a notebook, open developer tools to see the print
    print(data)
    
    ## if data is empty return
    if not data or not any(len(d) for d in data.values()):
        return hv.NdOverlay({0:hv.Curve([], 't', 'intensity')})
    
    ## empty dictionary to of curves
    curves = {}
    data = zip(data['x0'], data['x1'], data['y0'], data['y1'])
    for i, (x0, x1, y0, y1) in enumerate(data):
        ## selection is the area in the recangle 
        selection = dxp.loc[{'x':slice(x0,x1), 'y':slice(y0,y1), 'c':'gfp'}]
        ## aggregate with mean over remaining dimension
        ## will be time in this case
        cagg = selection.mean(dim=['x', 'y'])
        print(i, (x0, x1, y0, y1))
        ## add 
        curves[i] = hv.Curve(cagg)
    
    return hv.NdOverlay(curves)
  
### dxp is an xarray DataArray 
z =dxp.hvplot.image(x='x', y='y', cmap='viridis', colorbar=True,dynamic=True) 

## polys holds the rectangle objects
polys = hv.Polygons([])
box_stream = streams.BoxEdit(source=polys)

### dynamic map to update the intensity vs time plots
dmap = hv.DynamicMap(roi_curves, streams=[box_stream])

## set default holoviews/bokeh options
opts.defaults(
    opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
    opts.Image(cmap='viridis', width=562, height=512),
    opts.Labels(text_color='white', text_font_size='8pt', text_align='bottom_right', text_baseline='bottom'),
    opts.Path(color='white'),
    opts.Spread(width=700),
    opts.Overlay(show_legend=False))

### create layout
### z*polys overlays the image and the rectangle plots
### + dmap adds the time plots to the side
### i think the some of the options are redundant
layout = (z*polys + dmap).opts(
    opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
    opts.Image(cmap='viridis', width=562, height=512),
    opts.Labels(text_color='white', text_font_size='8pt', text_align='left', text_baseline='bottom'),
    opts.Path(color='white'),
    opts.Spread(width=1700),
    opts.Curve(width=600, framewise=True),
    opts.Polygons(fill_alpha=0.25, line_color='white'),
   )
  
## set the layout to one column so the time plot is underneath  
layout.cols(1)