from bokeh.plotting import *
output_notebook()

from bokeh.sampledata.autompg import autompg
autompg.head(10)

source = ColumnDataSource(autompg.to_dict("list"))
#help(source)
#help(source.data)

plot = figure(plot_width=400, plot_height=400, title="MPG by Year",  tools="pan,wheel_zoom,box_zoom,box_select,reset")

plot.circle("yr", "mpg", size="cyl",color="blue", source=source),
show(plot)

fig1 = figure(plot_width=300, plot_height=300, title="MPG by Year", tools="pan,wheel_zoom,box_zoom,box_select,reset")
fig1.circle("yr", "mpg", color="blue", source=source)
fig2 = figure(plot_width=300, plot_height=300, title="HP vs. Displacement", tools="pan,wheel_zoom,box_zoom,box_select,reset")
fig2.circle("hp", "displ", color="green", source=source)
fig3 = figure(plot_width=300, plot_height=300, title="MPG vs. Displacement", tools="pan,wheel_zoom,box_zoom,box_select,reset")
fig3.circle("mpg", "displ", size="cyl", line_color="red", fill_color=None, source=source)
p = gridplot([[fig1, fig2], [None, fig3]])
show(p)

fig4 = figure(plot_width=300, plot_height=300, tools="pan,wheel_zoom,box_zoom,box_select,reset")
fig5 = figure(plot_width=300, plot_height=300, tools="pan,wheel_zoom,box_zoom,box_select,reset")
fig6 = figure(plot_width=300, plot_height=300, tools="pan,wheel_zoom,box_zoom,box_select,reset")
fig7 = figure(plot_width=300, plot_height=300, tools="pan,wheel_zoom,box_zoom,box_select,reset")
# include some other plots here

from bokeh.sampledata import download
download()

from bokeh.sampledata import us_counties, unemployment
print(us_counties.data.items()[0])
print(unemployment.data.items()[0])

from collections import OrderedDict

county_xs=[
    us_counties.data[county_id]['lons'] for county_id in us_counties.data
    if us_counties.data[county_id]['state'] == 'ca'
]
county_ys=[
    us_counties.data[county_id]['lats'] for county_id in us_counties.data
    if us_counties.data[county_id]['state'] == 'ca'
]

# D3 category10 colors
colors =["#1f77b4","#ff7f0e","#2ca02c","#d62728","#9467bd","#8c564b","#e377c2","#7f7f7f","#bcbd22"]

# light-to-dark encoding (try it)
# colors = ["#f7fcfd","#e5f5f9","#ccece6","#99d8c9","#66c2a4","#41ae76","#238b45","#006d2c","#00441b"]


rates=[]
names = []
for county_id in us_counties.data:
    if us_counties.data[county_id]['state'] != 'ca':
        continue
    try:
        rate = unemployment.data[county_id]
        rates.append(rate)
        names.append(us_counties.data[county_id]['name'])
    except KeyError:
        rates.append(0)
min_rate=min(rates)
max_rate=max(rates)        
idx = map(lambda rate:min(int(rate-min_rate), 8),rates)
county_colors = map(lambda id: colors[id],idx)
alldata=ColumnDataSource({"name":names,"rate":rates})


fig8 = figure(plot_width=500, plot_height=500, title="California Unemployment 2009")

fig8.patches(county_xs, county_ys, fill_color=county_colors, fill_alpha=0.7,
        line_color="white", line_width=0.5, source=alldata)


#output_file("california.html")
#save()
show(fig8)

%matplotlib inline 
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

import mpld3
from mpld3 import plugins, utils
mpld3.enable_notebook()

fig, ax = plt.subplots(3, 3, sharex="col", sharey="row", figsize=(8, 8))
fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95,
                    hspace=0.1, wspace=0.1)

name=["mpg","hp","displ"]
data=autompg
#data=autompg.head(100)
for i in range(3):
    for j in range(3):
        points = ax[2 - i, j].scatter(data[name[j]], data[name[i]],
                                      c=data["yr"],s=40, alpha=0.6)

# Here we connect the linked brush plugin
plugins.connect(fig, plugins.LinkedBrush(points))

mpld3.display(fig)


fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))

N=len(data)
scatter = ax.scatter(data["hp"],
                     data["mpg"],
                     c=data["yr"],
                     s=data["cyl"]*10,
                     alpha=0.3,
                     cmap=plt.cm.jet)
ax.grid(color='white', linestyle='solid')

ax.set_title("Scatter Plot (with tooltips!)", size=20)

labels = ['point {0}'.format(i + 1) for i in range(N)]
#labels = [data["name"][i] for i in range(N)]
tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)
mpld3.plugins.connect(fig, tooltip)

mpld3.display(fig)


mpld3.save_json(fig,"viz.json")
mpld3.save_html(fig,"viz.html")

%%html
<div id=canvas>
</script src="d3js.org/d3.v3.min.js">
<script>
    var svg=d3.select("#canvas").append("svg").attr("width",400).attr("height",200)
    svg.selectAll("circle").data([1,2,3])
    .enter().append("circle")
    .attr("cx",function(d){return d*100})
    .attr("cy",100)
    .attr("r",function(d){return d*20})
    .attr("fill","red")
    
</script>

%%html
<script>
    d3.selectAll("circle")
    .transition().duration(2000)
    .attr("r",function(d){return d*10})
    .attr("cx",function(d){return 50+50*d})
</script>

%%html
<script>
    d3.selectAll("circle")
    .on("mouseover",function(d){d3.select(this).attr("fill","blue")})
    .on("mouseout",function(d){d3.select(this).attr("fill","red")})
</script>