def make_plot():
source = ColumnDataSource(
dict(
dates=[date(2014, 3, i) for i in [1, 2, 3, 4, 5]],
downloads=[100, 27, 54, 64, 75],
))
xdr = DataRange1d(sources=[source.columns("dates")])
ydr = DataRange1d(sources=[source.columns("downloads")])
plot = Plot(title="Product downloads",
data_sources=[source],
x_range=xdr,
y_range=ydr,
width=400,
height=400)
line = Line(x="dates", y="downloads", line_color="blue")
line_glyph = Glyph(data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=line)
plot.renderers.append(line_glyph)
circle = Circle(x="dates", y="downloads", fill_color="red")
circle_glyph = Glyph(data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=circle)
plot.renderers.append(circle_glyph)
hover = HoverTool(plot=plot, tooltips=dict(downloads="@downloads"))
plot.tools.append(hover)
xaxis = DatetimeAxis(plot=plot, dimension=0)
yaxis = LinearAxis(plot=plot, dimension=1)
xgrid = Grid(plot=plot, dimension=0, axis=xaxis)
ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
return plot, source
def make_plot():
xdr = DataRange1d(sources=[source.columns("dates")])
ydr = DataRange1d(sources=[source.columns("downloads")])
plot = Plot(title="Product downloads",
x_range=xdr,
y_range=ydr,
plot_width=400,
plot_height=400)
line = Line(x="dates", y="downloads", line_color="blue")
plot.add_glyph(source, line)
circle = Circle(x="dates", y="downloads", fill_color="red")
plot.add_glyph(source, circle)
xaxis = DatetimeAxis()
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis()
plot.add_layout(yaxis, 'left')
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.add_tools(HoverTool(tooltips=dict(downloads="@downloads")))
return plot, source
def make_downloads_plot(self, source):
xdr = DataRange1d(sources=[source.columns("dates")])
ydr = DataRange1d(sources=[source.columns("downloads")])
title = "%s downloads" % self.modelform.installer
plot = Plot(title=title,
data_sources=[source],
x_range=xdr,
y_range=ydr,
width=600,
height=400)
line = Line(x="dates", y="downloads", line_color="blue")
line_glyph = Glyph(data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=line)
plot.renderers.append(line_glyph)
circle = Circle(x="dates", y="downloads", fill_color="red")
circle_glyph = Glyph(data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=circle)
plot.renderers.append(circle_glyph)
hover = HoverTool(plot=plot, tooltips=dict(downloads="@downloads"))
plot.tools.append(hover)
xformatter = DatetimeTickFormatter(formats=dict(months=["%b %Y"]))
yformatter = BasicTickFormatter(precision=None, use_scientific=False)
xaxis = DatetimeAxis(plot=plot, dimension=0, formatter=xformatter)
yaxis = LinearAxis(plot=plot, dimension=1, formatter=yformatter)
xgrid = Grid(plot=plot, dimension=0, axis=xaxis)
ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
return plot
def trail_map(data):
lon = (min(data.lon) + max(data.lon))/2
lat = (min(data.lat) + max(data.lat))/2
map_options = GMapOptions(lng=lon, lat=lat, zoom=13)
plot = GMapPlot(title="%s - Trail Map" % title, map_options=map_options, plot_width=800, plot_height=800)
xaxis = LinearAxis()
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis()
plot.add_layout(yaxis, 'left')
xgrid = Grid(plot=plot, dimension=0, ticker=xaxis.ticker, grid_line_dash="dashed", grid_line_color="gray")
ygrid = Grid(plot=plot, dimension=1, ticker=yaxis.ticker, grid_line_dash="dashed", grid_line_color="gray")
plot.renderers.extend([xgrid, ygrid])
hover = HoverTool(tooltips=dict(distance="@dist"))
plot.add_tools(hover, PanTool(), WheelZoomTool(), ResetTool(), BoxSelectTool())
line_source = ColumnDataSource(dict(x=data.lon, y=data.lat, dist=data.dist))
line = Line(x="x", y="y", line_color="blue", line_width=2)
plot.add_glyph(line_source, line)
plot.x_range = DataRange1d(sources=[line_source.columns("x")])
plot.y_range = DataRange1d(sources=[line_source.columns("y")])
return plot
def line_advanced():
source = ColumnDataSource(data=dict(x=x,y=y,z=z,widths=widths,
heights=heights))
xdr = DataRange1d(sources=[source.columns("x")])
xdr2 = DataRange1d(sources=[source.columns("x")])
ydr = DataRange1d(sources=[source.columns("y")])
ydr2 = DataRange1d(sources=[source.columns("y")])
line_glyph = Line(x="x", y="y", line_color="blue")
renderer = GlyphRenderer(data_source = source, xdata_range = xdr,
ydata_range = ydr, glyph = line_glyph)
pantool = PanTool(dataranges = [xdr, ydr], dimensions=["width","height"])
zoomtool = ZoomTool(dataranges=[xdr,ydr], dimensions=("width","height"))
plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source],
border=50)
plot.tools = [pantool, zoomtool]
plot.renderers.append(renderer)
#notice that these two have a differen y data range
renderer2 = GlyphRenderer(data_source = source, xdata_range = xdr,
ydata_range = ydr2, glyph = line_glyph)
plot2 = Plot(x_range=xdr, y_range=ydr2, data_sources=[source],
border=50)
plot2.renderers.append(renderer2)
#notice that these two have a differen y data range
renderer3 = GlyphRenderer(data_source = source, xdata_range = xdr2,
ydata_range = ydr, glyph = line_glyph)
plot3 = Plot(x_range=xdr2, y_range=ydr, data_sources=[source],
border=50)
plot3.renderers.append(renderer3)
#this is a dummy plot with no renderers
plot4 = Plot(x_range=xdr2, y_range=ydr, data_sources=[source],
border=50)
sess = session.HTMLFileSession("line_linked_advanced.html")
sess.add(plot, renderer, source, xdr, ydr, pantool, zoomtool)
sess.add(plot2, renderer2, ydr2, xdr2, renderer3, plot3, plot4)
grid = GridPlot(children=[[plot, plot2], [plot3, plot4 ]], name="linked_advanced")
sess.add(grid)
sess.plotcontext.children.append(grid)
sess.save(js="relative", css="relative", rootdir=os.path.abspath("."))
print "Wrote line_linked_advanced.html"
webbrowser.open("file://" + os.path.abspath("line_linked_advanced.html"))
def job_loc_plot_builder():
xdr = FactorRange(factors=countries)
ydr = DataRange1d(sources=[source_country.columns("data_range")])
plot = Plot(title="Postings by Job Location (Country)",
data_sources=[source_country],
x_range=xdr,
y_range=ydr,
plot_width=760,
plot_height=500)
xaxis = CategoricalAxis(plot=plot,
dimension=0,
major_label_orientation=pi / 4.0)
yaxis = LinearAxis(plot=plot, dimension=1)
yaxis.major_tick_in = 0
ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
quad = Rect(x="country",
y="count_half",
height="count",
width=0.9,
fill_color="#483D8B")
bars = Glyph(data_source=source_country,
xdata_range=xdr,
ydata_range=ydr,
glyph=quad)
plot.renderers.append(bars)
plot.background_fill = '#333333'
return plot
def weekday_builder():
dow = [
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
"Sunday"
]
xdr = FactorRange(factors=dow)
ydr = DataRange1d(sources=[source_dow.columns("data_range")])
plot = Plot(title="Weekday of Job Posting",
data_sources=[source_dow],
x_range=xdr,
y_range=ydr,
plot_width=760,
plot_height=500)
xaxis = CategoricalAxis(plot=plot,
dimension=0,
major_label_orientation=pi / 4.0)
yaxis = LinearAxis(plot=plot, dimension=1)
yaxis.major_tick_in = 0
ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
quad = Rect(x="weekday",
y="weekday_half",
height="count",
width=0.9,
fill_color="#D9301A")
bars = Glyph(data_source=source_dow,
xdata_range=xdr,
ydata_range=ydr,
glyph=quad)
plot.renderers.append(bars)
plot.background_fill = '#686975'
return plot
def population():
xdr = FactorRange(factors=years)
ydr = DataRange1d(
sources=[source_known.columns("y"),
source_predicted.columns("y")])
plot = Plot(title=None,
x_range=xdr,
y_range=ydr,
plot_width=800,
plot_height=200)
plot.add_layout(CategoricalAxis(major_label_orientation=pi / 4), 'below')
line_known = Line(x="x", y="y", line_color="violet", line_width=2)
line_known_glyph = plot.add_glyph(source_known, line_known)
line_predicted = Line(x="x",
y="y",
line_color="violet",
line_width=2,
line_dash="dashed")
line_predicted_glyph = plot.add_glyph(source_predicted, line_predicted)
plot.add_layout(
Legend(orientation="bottom_right",
legends=dict(known=[line_known_glyph],
predicted=[line_predicted_glyph])))
return plot
def jobtype_builder():
jtypes = ["Half Time", "Full Time", "Hourly", "Temporary"]
xdr = FactorRange(factors=jtypes)
ydr = DataRange1d(sources=[source_jobtype.columns("data_range")])
plot = Plot(title="Job Type",
data_sources=[source_jobtype],
x_range=xdr,
y_range=ydr,
plot_width=760,
plot_height=500)
xaxis = CategoricalAxis(plot=plot,
dimension=0,
major_label_orientation=pi / 4.0)
yaxis = LinearAxis(plot=plot, dimension=1)
yaxis.major_tick_in = 0
ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
quad = Rect(x="jobtypes",
y="jobtype_half",
height="count",
width=0.9,
fill_color="#33A6A4")
bars = Glyph(data_source=source_jobtype,
xdata_range=xdr,
ydata_range=ydr,
glyph=quad)
plot.renderers.append(bars)
plot.background_fill = '#686975'
return plot
def large_plot(n):
from bokeh.objects import (Plot, PlotContext, LinearAxis, Grid, Glyph,
ColumnDataSource, DataRange1d, PanTool,
WheelZoomTool, BoxZoomTool, BoxSelectTool,
BoxSelectionOverlay, ResizeTool,
PreviewSaveTool, ResetTool)
from bokeh.glyphs import Line
context = PlotContext()
objects = set([context])
for i in xrange(n):
source = ColumnDataSource(data=dict(x=[0, i + 1], y=[0, i + 1]))
xdr = DataRange1d(sources=[source.columns("x")])
ydr = DataRange1d(sources=[source.columns("y")])
plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source])
xaxis = LinearAxis(plot=plot, dimension=0)
yaxis = LinearAxis(plot=plot, dimension=1)
xgrid = Grid(plot=plot, dimension=0)
ygrid = Grid(plot=plot, dimension=1)
tickers = [
xaxis.ticker, xaxis.formatter, yaxis.ticker, yaxis.formatter
]
renderer = Glyph(data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=Line(x='x', y='y'))
plot.renderers.append(renderer)
pan = PanTool(plot=plot)
wheel_zoom = WheelZoomTool(plot=plot)
box_zoom = BoxZoomTool(plot=plot)
box_select = BoxSelectTool(plot=plot)
box_selection = BoxSelectionOverlay(tool=box_select)
resize = ResizeTool(plot=plot)
previewsave = PreviewSaveTool(plot=plot)
reset = ResetTool(plot=plot)
tools = [
pan, wheel_zoom, box_zoom, box_select, box_selection, resize,
previewsave, reset
]
plot.tools.append(tools)
context.children.append(plot)
objects |= set(
[source, xdr, ydr, plot, xaxis, yaxis, xgrid, ygrid, renderer] +
tickers + tools)
return context, objects
def pyramid_plot(self):
from bokeh.objects import (Plot, DataRange1d, LinearAxis, Grid, Legend,
SingleIntervalTicker)
from bokeh.glyphs import Quad
xdr = DataRange1d(sources=[
self.source_pyramid.columns("male"),
self.source_pyramid.columns("female")
])
ydr = DataRange1d(sources=[self.source_pyramid.columns("groups")])
self.plot = Plot(title=None,
x_range=xdr,
y_range=ydr,
plot_width=600,
plot_height=600)
xaxis = LinearAxis()
self.plot.add_layout(xaxis, 'below')
yaxis = LinearAxis(ticker=SingleIntervalTicker(interval=5))
self.plot.add_layout(yaxis, 'left')
self.plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
self.plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
male_quad = Quad(left="male",
right=0,
bottom="groups",
top="shifted",
fill_color="#3B8686")
male_quad_glyph = self.plot.add_glyph(self.source_pyramid, male_quad)
female_quad = Quad(left=0,
right="female",
bottom="groups",
top="shifted",
fill_color="#CFF09E")
female_quad_glyph = self.plot.add_glyph(self.source_pyramid,
female_quad)
self.plot.add_layout(
Legend(legends=dict(Male=[male_quad_glyph],
Female=[female_quad_glyph])))
def make_plot(source, xname, yname, line_color, xdr=None, ydr=None):
""" Returns a tuple (plot, [obj1...objN]); the former can be added
to a GridPlot, and the latter is added to the plotcontext.
"""
if xdr is None:
xdr = DataRange1d(sources=[source.columns(xname)])
if ydr is None:
ydr = DataRange1d(sources=[source.columns(yname)])
plot = Plot(x_range=xdr, y_range=ydr, min_border=50)
plot.add_layout(LinearAxis(), 'below')
plot.add_layout(LinearAxis(), 'left')
plot.add_glyph(source, Line(x=xname, y=yname, line_color=line_color))
plot.add_tools(PanTool(), WheelZoomTool())
return plot
def pyramid():
xdr = DataRange1d(sources=[
source_pyramid.columns("male"),
source_pyramid.columns("female")
])
ydr = DataRange1d(sources=[source_pyramid.columns("groups")])
plot = Plot(title=None,
x_range=xdr,
y_range=ydr,
plot_width=600,
plot_height=600)
xaxis = LinearAxis()
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis(ticker=SingleIntervalTicker(interval=5))
plot.add_layout(yaxis, 'left')
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
male_quad = Quad(left="male",
right=0,
bottom="groups",
top="shifted",
fill_color="#3B8686")
male_quad_glyph = plot.add_glyph(source_pyramid, male_quad)
female_quad = Quad(left=0,
right="female",
bottom="groups",
top="shifted",
fill_color="#CFF09E")
female_quad_glyph = plot.add_glyph(source_pyramid, female_quad)
plot.add_layout(
Legend(
legends=dict(Male=[male_quad_glyph], Female=[female_quad_glyph])))
return plot
def altitude_profile(data):
plot = Plot(title="%s - Altitude Profile" % title,
plot_width=800,
plot_height=400)
xaxis = LinearAxis(axis_label="Distance (km)")
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis(axis_label="Altitude (m)")
plot.add_layout(yaxis, 'left')
xgrid = Grid(plot=plot, dimension=0, ticker=xaxis.ticker)
ygrid = Grid(plot=plot, dimension=1, ticker=yaxis.ticker)
plot.renderers.extend([xgrid, ygrid])
plot.add_tools(PanTool(), WheelZoomTool(), ResetTool(), BoxSelectTool())
X, Y = data.dist, data.alt
y0 = min(Y)
patches_source = ColumnDataSource(
dict(xs=[[X[i], X[i + 1], X[i + 1], X[i]] for i in range(len(X[:-1]))],
ys=[[y0, y0, Y[i + 1], Y[i]] for i in range(len(Y[:-1]))],
color=data.colors[:-1]))
patches = Patches(xs="xs", ys="ys", fill_color="color", line_color="color")
plot.add_glyph(patches_source, patches)
line_source = ColumnDataSource(dict(
x=data.dist,
y=data.alt,
))
line = Line(x='x', y='y', line_color="black", line_width=1)
plot.add_glyph(line_source, line)
plot.x_range = DataRange1d(sources=[line_source.columns("x")])
plot.y_range = DataRange1d(sources=[line_source.columns("y")])
return plot
def make_plot(source, xname, yname, line_color, xdr=None, ydr=None):
""" Returns a tuple (plot, [obj1...objN]); the former can be added
to a GridPlot, and the latter is added to the plotcontext.
"""
if xdr is None:
xdr = DataRange1d(sources=[source.columns(xname)])
if ydr is None:
ydr = DataRange1d(sources=[source.columns(yname)])
plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], min_border=50)
xaxis = LinearAxis(plot=plot, dimension=0, location="bottom")
yaxis = LinearAxis(plot=plot, dimension=1, location="left")
pantool = PanTool(dimensions=["width", "height"])
wheelzoomtool = WheelZoomTool(dimensions=["width", "height"])
renderer = Glyph(
data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=Line(x=xname, y=yname, line_color=line_color),
)
plot.renderers.append(renderer)
plot.tools = [pantool, wheelzoomtool]
return plot
def pyramid():
xdr = DataRange1d(sources=[source_pyramid.columns("male"), source_pyramid.columns("female")])
ydr = DataRange1d(sources=[source_pyramid.columns("groups")])
plot = Plot(title=None, data_sources=[source_pyramid], x_range=xdr, y_range=ydr, plot_width=600, plot_height=600)
xaxis = LinearAxis(plot=plot, dimension=0)
yaxis = LinearAxis(plot=plot, dimension=1, ticker=SingleIntervalTicker(interval=5))
xgrid = Grid(plot=plot, dimension=0, axis=xaxis)
ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
male_quad = Quad(left="male", right=0, bottom="groups", top="shifted", fill_color="blue")
male_quad_glyph = Glyph(data_source=source_pyramid, xdata_range=xdr, ydata_range=ydr, glyph=male_quad)
plot.renderers.append(male_quad_glyph)
female_quad = Quad(left=0, right="female", bottom="groups", top="shifted", fill_color="violet")
female_quad_glyph = Glyph(data_source=source_pyramid, xdata_range=xdr, ydata_range=ydr, glyph=female_quad)
plot.renderers.append(female_quad_glyph)
legend = Legend(plot=plot, legends=dict(Male=[male_quad_glyph], Female=[female_quad_glyph]))
plot.renderers.append(legend)
return plot
def population():
xdr = FactorRange(factors=years)
ydr = DataRange1d(sources=[source_known.columns("y"), source_predicted.columns("y")])
plot = Plot(title=None, data_sources=[source_known, source_predicted], x_range=xdr, y_range=ydr, plot_width=800, plot_height=200)
xaxis = CategoricalAxis(plot=plot, dimension=0, major_label_orientation=pi/4)
# yaxis = LinearAxis(plot=plot, dimension=1, ...)
line_known = Line(x="x", y="y", line_color="violet", line_width=2)
line_known_glyph = Glyph(data_source=source_known, xdata_range=xdr, ydata_range=ydr, glyph=line_known)
plot.renderers.append(line_known_glyph)
line_predicted = Line(x="x", y="y", line_color="violet", line_width=2, line_dash="dashed")
line_predicted_glyph = Glyph(data_source=source_predicted, xdata_range=xdr, ydata_range=ydr, glyph=line_predicted)
plot.renderers.append(line_predicted_glyph)
legend = Legend(plot=plot, orientation="bottom_right", legends=dict(known=[line_known_glyph], predicted=[line_predicted_glyph]))
plot.renderers.append(legend)
return plot
from bokeh import session
colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
flowers['color'] = flowers['species'].map(lambda x: colormap[x])
source = ColumnDataSource(data=dict(petal_length=flowers['petal_length'],
petal_width=flowers['petal_width'],
sepal_length=flowers['sepal_length'],
sepal_width=flowers['sepal_width'],
color=flowers['color']))
text_source = ColumnDataSource(data=dict(center=[125]))
xdr = DataRange1d(sources=[
source.columns("petal_length", "petal_width", "sepal_length",
"sepal_width")
])
ydr = DataRange1d(sources=[
source.columns("petal_length", "petal_width", "sepal_length",
"sepal_width")
])
pan = PanTool(dataranges=[xdr, ydr], dimensions=["x", "y"])
zoom = ZoomTool(dataranges=[xdr, ydr], dimensions=["x", "y"])
def make_plot(xname, yname, xax=False, yax=False, text=None):
plot = Plot(x_range=xdr,
y_range=ydr,
data_sources=[source],
background_fill="#ffeedd",
def generate_embed_test():
"""this generates a new plot and uses the script inject to put it
into a page running this repeatedly will fill up your redis DB
quickly, but it allows quick iteration
"""
from numpy import pi, arange, sin, cos
import numpy as np
from bokeh.objects import (
Plot, DataRange1d, LinearAxis, Rule,
ColumnDataSource, GlyphRenderer,
PanTool, ZoomTool, PreviewSaveTool)
from bokeh.glyphs import Circle
from bokeh import session
x = arange(-2*pi, 2*pi, 0.1)
y = sin(x)
z = cos(x)
widths = np.ones_like(x) * 0.02
heights = np.ones_like(x) * 0.2
source = ColumnDataSource(data=dict(x=x,y=y,z=z,widths=widths,
heights=heights))
xdr = DataRange1d(sources=[source.columns("x")])
ydr = DataRange1d(sources=[source.columns("y")])
circle = Circle(x="x", y="y", fill="red", radius=5, line_color="black")
glyph_renderer = GlyphRenderer(
data_source = source,
xdata_range = xdr,
ydata_range = ydr,
glyph = circle)
pantool = PanTool(dataranges = [xdr, ydr], dimensions=["width","height"])
#zoomtool = ZoomTool(dataranges=[xdr,ydr], dimensions=("width","height"))
previewtool = PreviewSaveTool(dataranges=[xdr,ydr], dimensions=("width","height"))
plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source],
border= 80)
xaxis = LinearAxis(plot=plot, dimension=0)
yaxis = LinearAxis(plot=plot, dimension=1)
xgrid = Rule(plot=plot, dimension=0)
ygrid = Rule(plot=plot, dimension=1)
plot.renderers.append(glyph_renderer)
plot.tools = [pantool, previewtool]
sess = session.PlotServerSession(
username="******",
serverloc="http://localhost:5006", userapikey="nokey")
sess.use_doc("glyph2")
sess.add(plot, glyph_renderer, xaxis, yaxis, xgrid, ygrid, source,
xdr, ydr, pantool, previewtool)
sess.plotcontext.children.append(plot)
sess.plotcontext._dirty = True
# not so nice.. but set the model doens't know
# that we appended to children
sess.store_all()
if app.debug:
slug = hemlib.slug_json()
static_js = hemlib.slug_libs(app, slug['libs'])
hemsource = os.path.join(app.static_folder, "coffee")
hem_js = hemlib.coffee_assets(hemsource, "localhost", 9294)
hemsource = os.path.join(app.static_folder, "vendor",
"bokehjs", "coffee")
hem_js += hemlib.coffee_assets(hemsource, "localhost", 9294)
else:
static_js = ['/bokeh/static/js/application.js']
hem_js = []
return render_template("generate_embed_test.html", jsfiles=static_js, hemfiles=hem_js,
plot_scr=plot.script_inject())
except KeyError:
county_colors.append("black")
county_source = ColumnDataSource(
data=dict(county_xs=[
us_counties[code]["lons"] for code in us_counties if us_counties[code]
["state"] not in ["ak", "hi", "pr", "gu", "vi", "mp", "as"]
],
county_ys=[
us_counties[code]["lats"] for code in us_counties
if us_counties[code]["state"] not in
["ak", "hi", "pr", "gu", "vi", "mp", "as"]
],
county_colors=county_colors))
xdr = DataRange1d(sources=[state_source.columns("state_xs")])
ydr = DataRange1d(sources=[state_source.columns("state_ys")])
plot = Plot(x_range=xdr,
y_range=ydr,
min_border=0,
border_fill="white",
title="2009 Unemployment Data",
plot_width=1300,
plot_height=800,
toolbar_location="left")
county_patches = Patches(xs="county_xs",
ys="county_ys",
fill_color="county_colors",
fill_alpha=0.7,
from bokeh.objects import (Plot, DataRange1d, LinearAxis, DatetimeAxis,
ColumnDataSource, Glyph, PanTool, WheelZoomTool)
from bokeh.glyphs import Circle
from bokeh import session
x = arange(-2 * pi, 2 * pi, 0.1)
y = sin(x)
# Create an array of times, starting at the current time, and extending
# for len(x) number of hours.
times = np.arange(len(x)) * 3600000 + time.time()
source = ColumnDataSource(data=dict(x=x, y=y, times=times))
xdr = DataRange1d(sources=[source.columns("times")])
ydr = DataRange1d(sources=[source.columns("y")])
circle = Circle(x="times", y="y", fill_color="red", size=5, line_color="black")
glyph_renderer = Glyph(
data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=circle,
)
plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], border=80)
xaxis = DatetimeAxis(plot=plot, dimension=0, location="min")
yaxis = LinearAxis(plot=plot, dimension=1, location="min")
from bokeh import session
from bokeh import document
document = document.Document()
session = session.Session()
session.use_doc('line_animate')
session.load_document(document)
x = np.linspace(-2 * pi, 2 * pi, 1000)
x_static = np.linspace(-2 * pi, 2 * pi, 1000)
y = sin(x)
z = cos(x)
source = ColumnDataSource(data=dict(x=x, y=y, z=z, x_static=x_static))
xdr = DataRange1d(sources=[source.columns("x")])
xdr_static = DataRange1d(sources=[source.columns("x_static")])
ydr = DataRange1d(sources=[source.columns("y")])
line_glyph = Line(x="x", y="y", line_color="blue")
line_glyph2 = Line(x="x", y="z", line_color="red")
renderer = Glyph(data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=line_glyph)
renderer2 = Glyph(data_source=source,
xdata_range=xdr_static,
ydata_range=ydr,
glyph=line_glyph2)
plot = Plot(x_range=xdr_static,
GlyphRenderer, ObjectArrayDataSource, PanTool,
ZoomTool)
from bokeh.glyphs import Circle
from bokeh import session
colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
flowers['color'] = flowers['species'].map(lambda x: colormap[x])
source = ColumnDataSource(data=dict(petal_length=flowers['petal_length'],
petal_width=flowers['petal_width'],
sepal_length=flowers['sepal_length'],
sepal_width=flowers['sepal_width'],
color=flowers['color']))
xdr = DataRange1d(sources=[source.columns("petal_length")])
ydr = DataRange1d(sources=[source.columns("petal_width")])
circle = Circle(x="petal_length",
y="petal_width",
fill_color="color",
fill_alpha=0.2,
radius=5,
line_color="color")
glyph_renderer = GlyphRenderer(
data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=circle,
)
def make_plot():
from numpy import pi, arange, sin, cos
import numpy as np
from bokeh.objects import (Plot, DataRange1d, LinearAxis, ColumnDataSource,
GlyphRenderer, PanTool, PreviewSaveTool)
from bokeh.glyphs import Circle
from bokeh import session
x = arange(-2 * pi, 2 * pi, 0.1)
y = sin(x)
z = cos(x)
widths = np.ones_like(x) * 0.02
heights = np.ones_like(x) * 0.2
source = ColumnDataSource(
data=dict(x=x, y=y, z=z, widths=widths, heights=heights))
xdr = DataRange1d(sources=[source.columns("x")])
ydr = DataRange1d(sources=[source.columns("y")])
circle = Circle(x="x", y="y", fill="red", radius=5, line_color="black")
glyph_renderer = GlyphRenderer(data_source=source,
xdata_range=xdr,
ydata_range=ydr,
glyph=circle)
pantool = PanTool(dataranges=[xdr, ydr], dimensions=["width", "height"])
previewtool = PreviewSaveTool(dataranges=[xdr, ydr],
dimensions=("width", "height"))
plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], border=80)
xaxis = LinearAxis(plot=plot, dimension=0)
yaxis = LinearAxis(plot=plot, dimension=1)
plot.renderers.append(glyph_renderer)
plot.tools = [pantool, previewtool]
sess = session.PlotServerSession(username="******",
serverloc="http://localhost:5006",
userapikey="nokey")
sess.use_doc("glyph2")
sess.add(
plot,
glyph_renderer,
xaxis,
yaxis, # xgrid, ygrid,
source,
xdr,
ydr,
pantool,
previewtool)
sess.plotcontext.children.append(plot)
sess.plotcontext._dirty = True
# not so nice.. but set the model doens't know
# that we appended to children
sess.store_all()
return plot
def create(self):
manufacturers = sorted(mpg["manufacturer"].unique())
models = sorted(mpg["model"].unique())
transmissions = sorted(mpg["trans"].unique())
drives = sorted(mpg["drv"].unique())
classes = sorted(mpg["class"].unique())
manufacturer_select = Select(title="Manufacturer:",
value="All",
options=["All"] + manufacturers)
manufacturer_select.on_change('value', self.on_manufacturer_change)
model_select = Select(title="Model:",
value="All",
options=["All"] + models)
model_select.on_change('value', self.on_model_change)
transmission_select = Select(title="Transmission:",
value="All",
options=["All"] + transmissions)
transmission_select.on_change('value', self.on_transmission_change)
drive_select = Select(title="Drive:",
value="All",
options=["All"] + drives)
drive_select.on_change('value', self.on_drive_change)
class_select = Select(title="Class:",
value="All",
options=["All"] + classes)
class_select.on_change('value', self.on_class_change)
columns = [
TableColumn(field="manufacturer",
header="Manufacturer",
type="autocomplete",
source=manufacturers),
TableColumn(field="model",
header="Model",
type="autocomplete",
source=models),
TableColumn(field="displ",
header="Displacement",
type="numeric",
format="0.00"),
TableColumn(field="year", header="Year", type="numeric"),
TableColumn(field="cyl", header="Cylinders", type="numeric"),
TableColumn(field="trans",
header="Transmission",
type="dropdown",
strict=True,
source=transmissions),
TableColumn(field="drv",
header="Drive",
type="autocomplete",
strict=True,
source=drives),
TableColumn(field="class",
header="Class",
type="autocomplete",
strict=True,
source=classes),
TableColumn(field="cty", header="City MPG", type="numeric"),
TableColumn(field="hwy", header="Highway MPG", type="numeric"),
]
handson_table = HandsonTable(source=self.source,
columns=columns,
sorting=True)
xdr = DataRange1d(sources=[self.source.columns("index")])
#xdr = FactorRange(factors=manufacturers)
ydr = DataRange1d(
sources=[self.source.columns("cty"),
self.source.columns("hwy")])
plot = Plot(title=None,
data_sources=[self.source],
x_range=xdr,
y_range=ydr,
plot_width=800,
plot_height=300)
xaxis = LinearAxis(plot=plot)
plot.below.append(xaxis)
yaxis = LinearAxis(plot=plot)
ygrid = Grid(plot=plot, dimension=1, ticker=yaxis.ticker)
plot.left.append(yaxis)
cty = Glyph(data_source=self.source,
glyph=Circle(x="index",
y="cty",
fill_color="#396285",
size=8,
fill_alpha=0.5,
line_alpha=0.5))
hwy = Glyph(data_source=self.source,
glyph=Circle(x="index",
y="hwy",
fill_color="#CE603D",
size=8,
fill_alpha=0.5,
line_alpha=0.5))
select_tool = BoxSelectTool(renderers=[cty, hwy], select_y=False)
plot.tools.append(select_tool)
overlay = BoxSelectionOverlay(tool=select_tool)
plot.renderers.extend([cty, hwy, ygrid, overlay])
controls = VBox(children=[
manufacturer_select, model_select, transmission_select,
drive_select, class_select
],
width=200)
top_panel = HBox(children=[controls, plot])
layout = VBox(children=[top_panel, handson_table])
return layout
x, fy, ty = taylor(expr, xs, order, (-2 * sy.pi, 2 * sy.pi), 200)
plot.title = "%s vs. taylor(%s, n=%d)" % (expr, expr, order)
legend.legends = {
"%s" % expr: [line_f_glyph],
"taylor(%s)" % expr: [line_t_glyph],
}
source.data = dict(x=x, fy=fy, ty=ty)
slider.value = order
session.store_document(document)
source = ColumnDataSource(data=dict(x=[], fy=[], ty=[]))
xdr = DataRange1d(sources=[source.columns("x")])
ydr = DataRange1d(sources=[source.columns("fy")])
plot = Plot(x_range=xdr, y_range=ydr, plot_width=800, plot_height=400)
line_f = Line(x="x", y="fy", line_color="blue", line_width=2)
line_f_glyph = plot.add_glyph(source, line_f)
plot.add_layout(line_f_glyph)
line_t = Line(x="x", y="ty", line_color="red", line_width=2)
line_t_glyph = plot.add_glyph(source, line_t)
plot.add_layout(line_t_glyph)
xaxis = LinearAxis()
plot.add_layout(xaxis, 'below')
summer_start = df.Date.irow(summer_start)
summer_end = df.Date.irow(summer_end)
calendar_end = df.Date.irow(-1)
d1 = calendar_start + (summer_start - calendar_start) / 2
d2 = summer_start + (summer_end - summer_start) / 2
d3 = summer_end + (calendar_end - summer_end) / 2
text_source = ColumnDataSource(
dict(
dates=[d1, d2, d3],
times=[dt.time(11, 30)] * 3,
texts=["CST (UTC+1)", "CEST (UTC+2)", "CST (UTC+1)"],
))
xdr = DataRange1d(sources=[source.columns("dates")])
ydr = DataRange1d(sources=[source.columns("sunrises", "sunsets")])
title = "Daylight Hours - Warsaw, Poland"
plot = Plot(title=title,
data_sources=[source, patch1_source, patch2_source, text_source],
x_range=xdr,
y_range=ydr,
width=800,
height=400)
patch1 = Patch(x="dates", y="times", fill_color="skyblue", fill_alpha=0.8)
patch1_glyph = Glyph(data_source=patch1_source,
xdata_range=xdr,
ydata_range=ydr,
glyph=patch1)
def make_box_violin_plot(data, num_bins, maxwidth=0.9):
"""
data: dict[Str -> List[Number]]
maxwidth: float
Maximum width of tornado plot within each factor/facet
Returns the plot object
"""
df = pd.DataFrame(columns=["group", "centers", "width", "height", "texts"])
bar_height = 50
bins = [0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6]
# Compute histograms, while keeping track of max Y values and max counts
for i, (group, vals) in enumerate(data.iteritems()):
hist, edges = np.histogram(vals, bins)
df = df.append(
pd.DataFrame(
dict(
group=group,
centers=np.arange(len(hist)) * bar_height,
width=np.log10(hist),
height=np.ones(hist.shape) * bar_height,
texts=map(str, hist),
)))
df.replace(-np.inf, 0)
# Normalize the widths
df["width"] *= (maxwidth / df["width"].max())
ds = ColumnDataSource(df)
xdr = FactorRange(factors=sorted(df["group"].unique()))
ydr = DataRange1d(sources=[ds.columns("centers")])
plot = Plot(data_sources=[ds],
x_range=xdr,
y_range=ydr,
title="Degree Distribution",
plot_width=750,
plot_height=600,
tools=[])
xaxis = CategoricalAxis(plot=plot,
location="bottom",
axis_label="number of nodes")
#yaxis = LogAxis(plot=plot, location="left", axis_label="degree")
plot.below.append(xaxis)
#plot.above.append(yaxis)
#xgrid = Grid(plot=plot, dimension=0, axis=xaxis)
#ygrid = Grid(plot=plot, dimension=1, axis=yaxis)
glyph = Rect(x="group", y="centers", width="width", height="height")
text_glyph = Text(x="group",
y="centers",
text="texts",
text_baseline="middle",
text_align="center")
plot.renderers.append(
Glyph(data_source=ds, xdata_range=xdr, ydata_range=ydr, glyph=glyph))
plot.renderers.append(
Glyph(data_source=ds,
xdata_range=xdr,
ydata_range=ydr,
glyph=text_glyph))
return plot
