import numpy as np
import pandas as pd
import holoviews as hv
from holoviews import opts

hv.extension('bokeh', 'matplotlib')

opts.defaults(opts.Scatter(size=10))

xs = np.linspace(0, 10, 11)
ys = np.sin(xs)

table = hv.Table((xs, ys), 'x', 'y')
table

(hv.Scatter(table) + hv.Curve(table) + hv.Area(table) + hv.Bars(table)).cols(2)

print(hv.Scatter({'x': xs, 'y': ys}) +
      hv.Scatter(np.column_stack([xs, ys])) +
      hv.Scatter(pd.DataFrame({'x': xs, 'y': ys})))

print(hv.Scatter(ys) + hv.Scatter((xs, ys)) + hv.Scatter(zip(xs, ys)))

df = pd.DataFrame({'x': xs, 'y': ys, 'z': ys*2})
print(type(hv.Scatter(df).data))

hv.Dataset.datatype

print(type(hv.Scatter((xs.astype('float64'), ys), datatype=['array']).data))
print(type(hv.Scatter((xs, ys), datatype=['dictionary']).data))
print(type(hv.Scatter((xs, ys), datatype=['dataframe']).data))

overlay = hv.Scatter(df, 'x', 'y') * hv.Scatter(df, 'x', 'z')
overlay

overlay.Scatter.I.data is overlay.Scatter.II.data

table.array()

table.dframe().head()

table.columns()

xs = np.arange(10)
curve = hv.Curve(zip(xs, np.sin(xs)))
curve * hv.Scatter(curve) + hv.Table(curve)

curve.dframe()

obs_hmap = hv.HoloMap({i: hv.Image(np.random.randn(10, 10), bounds=(0,0,3,3))
                       for i in range(3)}, kdims='Observation')
obs_hmap

hv.output(backend='matplotlib', size=150)

collapsed = obs_hmap.collapse()
scatter_layout = collapsed.to.scatter3d() + hv.Table(collapsed)
scatter_layout.opts(
    opts.Scatter3D(color='z', cmap='hot', edgecolor='black', s=50))

extents = (0, 0, 3, 3)

error_hmap = hv.HoloMap({
    (i, j): hv.Image(j*np.random.randn(3, 3), bounds=extents)
    for i in range(3) for j in np.linspace(0, 1, 3)},
    ['Observation', 'noise'])

noise_layout = error_hmap.layout('noise')
noise_layout

hv.Table(noise_layout.collapse())

hv.output(backend='bokeh')

bars = hv.Bars((['C', 'A', 'B', 'D'], [2, 7, 3, 4]))
(bars +
 bars.sort().relabel('sorted') +
 bars.sort(['y']).relabel('y-sorted') +
 bars.sort(reverse=True).relabel('reverse sorted')
).opts(shared_axes=False).cols(2)

n = np.arange(1000)
xs = np.repeat(range(2), 500)
ys = n%4
zs = np.random.randn(1000)
table = hv.Table((xs, ys, zs), ['x', 'y'], 'z')
table

hv.BoxWhisker(table)

hv.Bars(table).aggregate('x', function=np.mean) + hv.Bars(table).reduce(x=np.mean)

hmap = hv.HoloMap({i: hv.Curve(np.arange(10)*i) for i in range(10)})
collapsed = hmap.collapse(function=np.mean, spreadfn=np.std)
hv.Spread(collapsed) * hv.Curve(collapsed) + hv.Table(collapsed)