import numpy as np
import pandas as pd
import holoviews as hv
hv.extension('bokeh', 'matplotlib')

xs = np.arange(10)
ys = np.exp(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.exp(xs)))
curve * hv.Scatter(curve) + curve.table()

curve.dframe()

%%opts Points (s=200) [size_index=None]
extents = (-1.6,-2.7,2.0,3)
np.random.seed(42)
mat = np.random.rand(3, 3)

img = hv.Image(mat, bounds=extents)
raster = hv.Raster(mat)

img    * hv.Points(img)    + img.table() + \
raster * hv.Points(raster) + raster.table()

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

%%output backend='matplotlib'
%%opts Layout [fig_size=150] Scatter3D [color_index=3 size_index=None] (cmap='hot' edgecolor='k' s=50)
obs_hmap.table().to.scatter3d() + obs_hmap.table()

%%opts Image [width=225 height=225]
from itertools import product
extents = (0,0,3,3)
error_hmap = hv.HoloMap({(i, j): hv.Image(j*np.random.randn(3, 3), bounds=extents)
                         for i, j in product(range(3), np.linspace(0, 1, 3))},
                        kdims=['Observation', 'noise'])
noise_layout = error_hmap.layout('noise')
noise_layout

noise_layout.table()

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')).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)

%%opts Bars {+axiswise}
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) + collapsed.table()