import numpy as np
import holoviews as hv
from holoviews import opts
import xarray as xr
hv.extension('bokeh')

mri_xr = xr.open_dataset('../data/mri.nc')
mri_xr

mri = hv.Dataset(mri_xr)
mri

mri.to(hv.Image, groupby='z', dynamic=True)

# Exercise: Display transverse (x,z) or frontal (z,y) sections of the data by declaring the kdims in the .to method


layout = (mri.to(hv.Image, ['z', 'y'], dynamic=True) +
          mri.to(hv.Image, ['z', 'x'], dynamic=True) +
          mri.to(hv.Image, ['x', 'y'], dynamic=True)).redim.range(MR=(0, 255))

layout.opts(
    opts.Image(xaxis=None, yaxis=None, width=225, height=225))

hv.Image(mri.reduce(z=np.mean))

# Exercise: Recreate the plot above using the aggregate method
# Hint: The aggregate and reduce methods are inverses of each other
# Try typing "hv.Image(mri.aggregate(" and press shift-Tab to see the signature of `aggregate`.


im = hv.Image(mri.reduce(z=np.mean))
im.data