import numpy as np
import holoviews as hv
from holoviews import opts
hv.extension('bokeh', 'matplotlib')

data = np.load('../assets/twophoton.npz')
calcium_array = data['Calcium']
calcium_array.shape

ds = hv.Dataset((np.arange(50), np.arange(111), np.arange(62), calcium_array),
                ['Time', 'x', 'y'], 'Fluorescence')
ds

type(ds.data), list(ds.data.keys())

opts.defaults(
    opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
    opts.Image(cmap='viridis', width=400, height=400),
    opts.Labels(text_color='white', text_font_size='8pt', text_align='left', text_baseline='bottom'),
    opts.Path(color='white'),
    opts.Spread(width=600),
    opts.Overlay(show_legend=False))

ds.to(hv.Image, ['x', 'y']).hist()

ROIs = data['ROIs']
roi_bounds = hv.Path([hv.Bounds(tuple(roi)) for roi in ROIs])
print(ROIs.shape)

labels = hv.Labels([(roi[0], roi[1], i) for i, roi in enumerate(ROIs)])
(ds[21].to(hv.Image, ['x', 'y']) * roi_bounds * labels).relabel('Time: 21')

x0, y0, x1, y1 = ROIs[60]
roi = ds.select(x=(x0, x1), y=(y0, y1), time=(250, 280)).relabel('ROI #60')
roi.to(hv.Image, ['x', 'y'])

roi.to(hv.Curve, 'Time').grid()

agg = roi.aggregate('Time', np.mean, spreadfn=np.std)
hv.Spread(agg) * hv.Curve(agg)