from bokeh.plotting import ColumnDataSource, figure, gridplot, output_file, show
from bokeh.sampledata.autompg import autompg
output_file("brushing.html")
# Load some Automobile data into a data source. Interesting columns are:
# "yr" - Year manufactured
# "mpg" - miles per gallon
# "displ" - engine displacement
# "hp" - engine horsepower
# "cyl" - number of cylinders
source = ColumnDataSource(autompg.to_dict("list"))
source.add(autompg["yr"], name="yr")
# define some tools to add
TOOLS = "pan,wheel_zoom,box_zoom,box_select,lasso_select"# Let's set up some plot options in a dict that we can re-use on multiple plots
# Let's set up some plot options in a dict that we can re-use on multiple plots
plot_config = dict(plot_width=300, plot_height=300, tools=TOOLS)
# First let's plot the "yr" vs "mpg" using the plot config above
# Note that we are supplying our our data source to the renderer explicitly
p1 = figure(title="MPG by Year", **plot_config)
p1.circle("yr", "mpg", color="blue", source=source)
# EXERCISE: make another figure p2 with circle renderer, for "hp" vs "displ" with
# color "green". This renderer should use the same data source as the renderer
# above, that is what will cause the plots selections to be linked
p2 = figure(title="HP vs. Displacement", **plot_config)
p2.circle("hp", "displ", color="green", source=source)
from bokeh.plotting import *
from bokeh.sampledata.autompg import autompg
output_file("panning.html")
# Load some Automobile data into a data source. Interesting columns are:
# "yr" - Year manufactured
# "mpg" - miles per gallon
# "displ" - engine displacement
# "hp" - engine horsepower
# "cyl" - number of cylinders
source = ColumnDataSource(autompg.to_dict("list"))
source.add(autompg["yr"], name="yr")
# Let's set up some plot options in a dict that we can re-use on multiple plots
plot_config = dict(plot_width=300,
plot_height=300,
tools="pan,wheel_zoom,box_zoom,select")
# gridplot(...) accepts nested lists of plot objects
gridplot([[
# First let's plot the "yr" vs "mpg" using the plot config above
# Note that we are supplying our our data source explicitly
circle("yr",
"mpg",
color="blue",
title="MPG by Year",
source=source,
**plot_config),
def home(request):
########################## Plot >> 1 << ##########################
from bokeh.plotting import *
from bokeh.resources import CDN
from bokeh.embed import components
from random import randint
n = 200
x_s = range(0, 7*n, 7)
y_s = [randint(-10, 10) for _ in range(n)]
radius = [randint(1,6) for _ in range(n)]
plot = circle(x_s, y_s, radius=radius, tools=['pan', 'wheel_zoom', 'box_zoom', 'resize', 'hover', 'reset', 'previewsave'])
script, div = components(plot, CDN)
########################## Plot >> 2 << ##########################
from bokeh.sampledata.autompg import autompg
source = ColumnDataSource(autompg.to_dict("list"))
source.add(autompg["yr"], name="yr")
plot_config = dict(plot_width=300, plot_height=300, tools="pan,wheel_zoom,box_zoom,resize,reset,previewsave")
grid_plot_ = gridplot([
# First let's plot the "yr" vs "mpg" using the plot config above
# Note that we are supplying our our data source explicitly
[circle("yr", "mpg", color="blue", title="MPG by Year", source=source, **plot_config),
# EXERCISE: add another circle renderer for "hp" vs "displ" with color "green" to this
# list of plots. This renderer should use the same data source as the renderer above,
# that is what will cause the plots selections to be linked
circle("hp", "displ", color="green", title="HP vs. Displacement", source=source, **plot_config)],
[scatter("yr", "mpg", color="blue", title="MPG by Year", source=source, **plot_config),
# EXERCISE: add another circle renderer for "hp" vs "displ" with color "green" to this
# list of plots. This renderer should use the same data source as the renderer above,
# that is what will cause the plots selections to be linked
scatter("hp", "displ", color="green", title="HP vs. Displacement", source=source, **plot_config)],
[circle("mpg", "displ", size="cyl", line_color="red", title="MPG vs. Displacement",
fill_color=None, source=source, **plot_config),
circle("mpg", "displ", size="cyl", line_color="red", title="MPG vs. Displacement",
fill_color=None, source=source, **plot_config)],
[scatter("mpg", "displ", size="cyl", line_color="red", title="MPG vs. Displacement",
fill_color=None, source=source, **plot_config),
scatter("mpg", "displ", size="cyl", line_color="red", title="MPG vs. Displacement",
fill_color=None, source=source, **plot_config)],
# EXERCISE: add another circle renderer for "mpg" vs "displ", size proportional to "cyl"
# Set the the line color to be "red" with no fill, and use the same data source again
# to link selections
[line("mpg", "displ", size="cyl", line_color="red", title="MPG vs. Displacement",
fill_color=None, source=source, **plot_config),
line("mpg", "displ", size="cyl", line_color="red", title="MPG vs. Displacement",
fill_color=None, source=source, **plot_config)]
])
script_gridplot, div_gridplot = components(grid_plot_, CDN)
########################## Plot >> 3 << ##########################
import numpy as np
from six.moves import zip
from collections import OrderedDict
from bokeh.objects import HoverTool
# Create a set of tools to use
TOOLS="pan,wheel_zoom,box_zoom,reset,hover"
xx, yy = np.meshgrid(np.arange(0, 101, 4), np.arange(0, 101, 4))
x = xx.flatten()
y = yy.flatten()
N = len(x)
inds = [str(i) for i in np.arange(N)]
radii = np.random.random(size=N)*0.4 + 1.7
colors = [
"#%02x%02x%02x" % (r, g, 150) for r, g in zip(np.floor(50+2*x), np.floor(30+2*y))
]
# EXERCISE: create a new data field for the hover tool to interrogate. It can be
# anything you like, but it needs to have the same length as x, y, etc.
foo = list(itertools.permutations("abcdef"))[:N]
bar = np.random.normal(size=N)
# We need to put these data into a ColumnDataSource
source = ColumnDataSource(
data=dict(
x=x,
y=y,
radius=radii,
colors=colors,
foo=foo,
bar=bar,
)
)
hover = curplot().select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
# add to this
("index", "$index"),
("(x,y)", "($x, $y)"),
("radius", "@radius"),
("fill color", "$color[hex, swatch]:fill_color"),
("foo", "@foo"),
("bar", "@bar"),
])
plot3 = gridplot([[
# This is identical to the scatter exercise, but adds the 'source' parameter
circle(x, y, radius=radii, source=source, tools=TOOLS,
fill_color=colors, fill_alpha=0.6,
line_color=None, Title="Hoverful Scatter"),
# EXERCISE (optional) add a `text` renderer to display the index of each circle
# inside the circle
text(x, y, text=inds, alpha=0.5, text_font_size="5pt",
text_baseline="middle", text_align="center", angle=0),
# We want to add some fields for the hover tool to interrogate, but first we
# have to get ahold of the tool. We can use the 'select' method to do that.
]])
script_plot3, div_plot3 = components(plot3, CDN)
return render_to_response('home.html', {
'script': script,
'div': div,
'script_gridplot': script_gridplot,
'div_gridplot': div_gridplot,
'script_plot3': script_plot3,
'div_plot3': div_plot3,
})
