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

%%opts Curve  [height=200 width=900 xaxis=None tools=['hover']]
%%opts Curve (color='red' line_width=1.5)
%%opts Spikes [height=150 width=900 yaxis=None] (color='grey' line_width=0.25)

spike_train = pd.read_csv('../assets/spike_train.csv.gz')
curve  = hv.Curve( spike_train, 'milliseconds', vdims='Hertz')
spikes = hv.Spikes(spike_train, 'milliseconds', vdims=[])
layout = (curve+spikes).cols(1)
layout

%opts HeatMap (cmap='Summer') [colorbar=True, toolbar='above']

data = [(chr(65+i), chr(97+j),  i*j) for i in range(5) for j in range(5) if i!=j]
hv.HeatMap(data).sort()

%output backend='matplotlib', fig='svg'

lin = np.linspace(0, np.pi*2, 200)

def lissajous(t, a, b, delta):
    return (np.sin(a * t + delta), np.sin(b * t), t)

path = hv.Path([lissajous(lin, 3, 5, np.pi/2)])
path.opts(opts.Path(linewidth=2, color='red', linestyle='dotted'))

%output backend='bokeh'

%%output backend='matplotlib' fig='svg' size=50
%%opts Path (linewidth=3 color='blue')
lin = np.linspace(0, np.pi*2, 200)

def lissajous(t, a, b, delta):
    return (np.sin(a * t + delta), np.sin(b * t), t)

hv.Path([lissajous(lin, 3, 5, np.pi/2)])