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

hv.extension('bokeh')

from holoviews.plotting.bokeh.element import (line_properties, fill_properties,
                                              text_properties)
print("""
      Line properties: %s\n
      Fill properties: %s\n
      Text properties: %s
      """ % (line_properties, fill_properties, text_properties))

curve_opts = dict(line_width=10, line_color='indianred', line_dash='dotted', line_alpha=0.5)
point_opts = dict(fill_color='#00AA00', fill_alpha=0.5, line_width=1, line_color='black', size=5)
text_opts  = dict(text_align='center', text_baseline='middle', text_color='gray', text_font='Arial')
xs = np.linspace(0, np.pi*4, 100)
data = (xs, np.sin(xs))

(hv.Curve(data).options(**curve_opts) +
 hv.Points(data).options(**point_opts) +
 hv.Text(6, 0, 'Here is some text').options(**text_opts))

%%opts Points.A [width=300 height=300] Points.B [width=600 height=300]
hv.Points(data, group='A') + hv.Points(data, group='B')

points = hv.Points(np.exp(xs)) 
axes_opts = [('Plain', {}),
             ('Log', {'logy': True}),
             ('None', {'yaxis': None}),
             ('Rotate', {'xrotation': 90}),
             ('N Ticks', {'xticks': 3}),
             ('List Ticks', {'xticks': [0, 20, 50, 90]})]

hv.Layout([points.relabel(group=group).options(**opts)
           for group, opts in axes_opts]).cols(3)

%%opts Overlay [width=600 legend_position='top_left'] Curve (muted_alpha=0.5 muted_color='black')
try:
    import bokeh.sampledata.stocks
except:
    import bokeh.sampledata
    bokeh.sampledata.download()

from bokeh.sampledata.stocks import GOOG, AAPL
goog_dates = np.array(GOOG['date'], dtype=np.datetime64)
aapl_dates = np.array(AAPL['date'], dtype=np.datetime64)
hv.Curve((goog_dates, GOOG['adj_close']), 'Date', 'Stock Index', label='Google') *\
hv.Curve((aapl_dates, AAPL['adj_close']), 'Date', 'Stock Index', label='Apple')

%%opts Points [size_index='z' tools=['hover']] HeatMap [toolbar='above' tools=['hover']]
points = hv.Points([(chr(i+65), chr(j+65), i*j) for i in range(10) for j in range(10)], vdims='z')
hv.HeatMap(points) * points

%%opts Overlay [show_legend=False height=400 width=600] 
%%opts ErrorBars (line_width=5) Scatter [jitter=0.2] (alpha=0.2 size=6)

overlay = hv.NdOverlay({group: hv.Scatter(([group]*100, np.random.randn(100)*(i+1)+i))
                        for i, group in enumerate(['A', 'B', 'C', 'D', 'E'])})

errorbars = hv.ErrorBars([(k, el.reduce(function=np.mean), el.reduce(function=np.std))
                          for k, el in overlay.items()])

global_mean = hv.Text('A', 12, 'Global mean: %.3f' % overlay.dimension_values('y').mean(), halign='left')

errorbars * overlay * hv.Curve(errorbars) * global_mean

grid_style = {'grid_line_color': 'black', 'grid_line_width': 1.5, 'ygrid_bounds': (0.3, 0.7),
              'minor_xgrid_line_color': 'lightgray', 'xgrid_line_dash': [4, 4]}

hv.Points(np.random.rand(10, 2)).options(gridstyle=grid_style, show_grid=True, size=5, width=600)

%%opts Overlay [tabs=True] Curve Image [width=400 height=400]
x,y = np.mgrid[-50:51, -50:51] * 0.1

img = hv.Image(np.sin(x**2+y**2), bounds=(-1,-1,1,1))
img.relabel('Image') * img.sample(x=0).relabel('Cross-section')

%%opts Histogram [width=600] (alpha=0.8 muted_fill_alpha=0.1)
hv.NdOverlay({i: hv.Histogram(np.histogram(np.random.randn(100)+i*2)) for i in range(5)})

points = hv.Points(np.random.randn(500,2))
points.hist(num_bins=51, dimension=['x','y'])

img.hist(num_bins=100, dimension=['x', 'y'], weight_dimension='z', mean_weighted=True) +\
img.hist(dimension='z')

hmap = hv.HoloMap({phase: img.clone(np.sin(x**2+y**2+phase))
                   for phase in np.linspace(0, np.pi*2, 6)}, kdims='Phase')
hmap.hist(num_bins=100, dimension=['x', 'y'], weight_dimension='z', mean_weighted=True)

%%opts Points [tools=['hover']] (size=5) HeatMap [tools=['hover']] 
%%opts Histogram [tools=['hover']] Layout [shared_axes=False]
error = np.random.rand(100, 3)
heatmap_data = {(chr(65+i),chr(97+j)):i*j for i in range(5) for j in range(5) if i!=j}
data = [np.random.normal() for i in range(10000)]
hist = np.histogram(data, 20)
hv.Points(error) + hv.HeatMap(heatmap_data).sort() + hv.Histogram(hist)

%%opts Points [tools=['box_select', 'lasso_select', 'tap']] (size=10 nonselection_color='red' color='blue')
hv.Points(error)

macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\t')

%%opts Scatter [tools=['box_select', 'lasso_select']] Layout [shared_axes=True shared_datasource=True]
hv.Scatter(macro_df, 'year', 'gdp') +\
hv.Scatter(macro_df, 'gdp',  'unem')

%%opts Scatter [tools=['box_select', 'lasso_select', 'hover'] border=0] Histogram {+axiswise}
table = hv.Table(macro_df, kdims=['year', 'country'])
matrix = hv.operation.gridmatrix(table.groupby('country'))
matrix.select(country=['West Germany', 'United Kingdom', 'United States'])

from bokeh.themes.theme import Theme

theme = Theme(
    json={
'attrs' : {
    'Figure' : {
        'background_fill_color': '#2F2F2F',
        'border_fill_color': '#2F2F2F',
        'outline_line_color': '#444444',
    },
    'Grid': {
        'grid_line_dash': [6, 4],
        'grid_line_alpha': .3,
    },
    
    'Axis': {
        'major_label_text_color': 'white',
        'axis_label_text_color': 'white',
        'major_tick_line_color': 'white',
        'minor_tick_line_color': 'white',
        'axis_line_color': "white"
    }
  }
})

hv.renderer('bokeh').theme = theme

%%opts Curve [bgcolor='grey']
xs = np.linspace(0, np.pi*4, 100)
hv.Curve((xs, np.sin(xs)), label='foo')