# We must use a special json encoder to get Bokeh to work in sagemath
# from https://github.com/thomasfricke/sage-json-encoder
# Thanks Thomas! 
import sage_json_encoder

from bokeh.plotting import figure, output_file, show

# prepare some data
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

# output to static HTML file
output_file("examples/lines.html")

# create a new plot with a title and axis labels
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')

# add a line renderer with legend and line thickness
p.line(x, y, legend="Temp.", line_width=2)

# show the results
show(p)

%%HTML
<iframe src='examples/lines.html' width='100%' height=600px>

from bokeh.plotting import figure, output_file, show

# prepare some data
x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
y0 = [i**2 for i in x]
y1 = [10**i for i in x]
y2 = [10**(i**2) for i in x]

# output to static HTML file
output_file("examples/log_lines.html")

# create a new plot
p = figure(
   tools="pan,box_zoom,reset,save",
   y_axis_type="log", y_range=[0.001, 10**11], title="log axis example",
   x_axis_label='sections', y_axis_label='particles'
)

# add some renderers
p.line(x, x, legend="y=x")
p.circle(x, x, legend="y=x", fill_color="white", size=8)
p.line(x, y0, legend="y=x^2", line_width=3)
p.line(x, y1, legend="y=10^x", line_color="red")
p.circle(x, y1, legend="y=10^x", fill_color="red", line_color="red", size=6)
p.line(x, y2, legend="y=10^x^2", line_color="orange", line_dash="4 4")

# show the results
show(p)

%%HTML
<iframe src='examples/log_lines.html' width='100%' height=600px>

import numpy as np

from bokeh.plotting import figure, output_file, show

# prepare some data
N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = [
    "#%02x%02x%02x" % (int(r), int(g), 150) for r, g in zip(50+2*x, 30+2*y)
]

# output to static HTML file (with CDN resources)
output_file("examples/color_scatter.html", title="color_scatter.py example", mode="cdn")

TOOLS="resize,crosshair,pan,wheel_zoom,box_zoom,reset,box_select,lasso_select"

# create a new plot with the tools above, and explicit ranges
p = figure(tools=TOOLS, x_range=(0,100), y_range=(0,100))

# add a circle renderer with vectorized colors and sizes
p.circle(x,y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)

# show the results
show(p)

%%HTML
<iframe src='examples/color_scatter.html' width='100%' height=600px>

import numpy as np

from bokeh.layouts import gridplot
from bokeh.plotting import figure, output_file, show

# prepare some data
N = 100
x = np.linspace(0, 4*np.pi, N)
y0 = np.sin(x)
y1 = np.cos(x)
y2 = np.sin(x) + np.cos(x)

# output to static HTML file
output_file("examples/linked_panning.html")

# create a new plot
s1 = figure(width=250, plot_height=250, title=None)
s1.circle(x, y0, size=10, color="navy", alpha=0.5)

# NEW: create a new plot and share both ranges
s2 = figure(width=250, height=250, x_range=s1.x_range, y_range=s1.y_range, title=None)
s2.triangle(x, y1, size=10, color="firebrick", alpha=0.5)

# NEW: create a new plot and share only one range
s3 = figure(width=250, height=250, x_range=s1.x_range, title=None)
s3.square(x, y2, size=10, color="olive", alpha=0.5)

# NEW: put the subplots in a gridplot
p = gridplot([[s1, s2, s3]], toolbar_location=None)

# show the results
show(p)

%%HTML
<iframe src='examples/linked_panning.html' width='100%' height=250px>

import numpy as np
from bokeh.plotting import *
from bokeh.models import ColumnDataSource

# prepare some date
N = 300
x = np.linspace(0, 4*np.pi, N)
y0 = np.sin(x)
y1 = np.cos(x)

# output to static HTML file
output_file("examples/linked_brushing.html")

# NEW: create a column data source for the plots to share
source = ColumnDataSource(data=dict(x=x, y0=y0, y1=y1))

TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select,lasso_select"

# create a new plot and add a renderer
left = figure(tools=TOOLS, width=350, height=350, title=None)
left.circle('x', 'y0', source=source)

# create another new plot and add a renderer
right = figure(tools=TOOLS, width=350, height=350, title=None)
right.circle('x', 'y1', source=source)

# put the subplots in a gridplot
p = gridplot([[left, right]])

# show the results
show(p)

%%HTML
<iframe src='examples/linked_brushing.html' width='100%' height=400px>

import numpy as np

from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.stocks import AAPL

# prepare some data
aapl = np.array(AAPL['adj_close'])
aapl_dates = np.array(AAPL['date'], dtype=np.datetime64)

window_size = 30
window = np.ones(window_size)/float(window_size)
aapl_avg = np.convolve(aapl, window, 'same')

# output to static HTML file
output_file("examples/stocks.html", title="stocks.py example")

# create a new plot with a a datetime axis type
p = figure(width=800, height=350, x_axis_type="datetime")

# add renderers
p.circle(aapl_dates, aapl, size=4, color='darkgrey', alpha=0.2, legend='close')
p.line(aapl_dates, aapl_avg, color='navy', legend='avg')

# NEW: customize by setting attributes
p.title.text = "AAPL One-Month Average"
p.legend.location = "top_left"
p.grid.grid_line_alpha=0
p.xaxis.axis_label = 'Date'
p.yaxis.axis_label = 'Price'
p.ygrid.band_fill_color="olive"
p.ygrid.band_fill_alpha = 0.1

# show the results
show(p)

%%HTML
<iframe src='examples/stocks.html' width='100%' height=350px>

# Lorenz example taken from Gallery
# http://bokeh.pydata.org/en/latest/docs/gallery/lorenz.html

import numpy as np
from scipy.integrate import odeint

from bokeh.plotting import figure, show, output_file

sigma = 10
rho = 28
beta = 8.0/3
theta = 3 * np.pi / 4

def lorenz(xyz, t):
    x, y, z = xyz
    x_dot = sigma * (y - x)
    y_dot = x * rho - x * z - y
    z_dot = x * y - beta* z
    return [x_dot, y_dot, z_dot]

initial = (-10, -7, 35)
t = np.arange(0, 100, 0.006)

solution = odeint(lorenz, initial, t)

x = solution[:, 0]
y = solution[:, 1]
z = solution[:, 2]
xprime = np.cos(theta) * x - np.sin(theta) * y

colors = ["#C6DBEF", "#9ECAE1", "#6BAED6", "#4292C6", "#2171B5", "#08519C", "#08306B",]

p = figure(title="lorenz example")

p.multi_line(np.array_split(xprime, 7), np.array_split(z, 7),
             line_color=colors, line_alpha=0.8, line_width=1.5)

output_file("examples/lorenz.html", title="lorenz.py example")

show(p)  # open a browser

%%HTML
<iframe src='examples/lorenz.html' width='100%' height=600px>