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",
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_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", 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_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 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 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)
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, dataranges=[xdr, ydr])
wheel_zoom = WheelZoomTool(plot=plot, dataranges=[xdr, ydr])
box_zoom = BoxZoomTool(plot=plot)
box_select = BoxSelectTool(plot=plot)
box_selection = BoxSelectionOverlay(tool=box_select)
resize = ResizeTool(plot=plot)
previewsave = PreviewSaveTool(plot=plot, dataranges=[xdr, ydr])
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] + tools)
return context, objects
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 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 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_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
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 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 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
z = (cos(x) + 1) * 6 + 6
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))
#source = ObjectArrayDataSource(
# data = [
# {'x' : 1, 'y' : 5, 'z':3},
# {'x' : 2, 'y' : 4, 'z':3, 'radius':10},
# {'x' : 3, 'y' : 3, 'z':3, 'fill':"blue"},
# {'x' : 4, 'y' : 2, 'z':3},
# {'x' : 5, 'y' : 1, 'z':3},
# ])
xdr = DataRange1d(sources=[source.columns("x")])
ydr = DataRange1d(sources=[source.columns("y")])
circle = Circle(x="x", y="y", fill_color="red", radius="z", 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"))
plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], border=80)
try:
rate = unemployment[county_id]
idx = min(int(rate/2), 5)
county_colors.append(colors[idx])
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")])
county_patches = Patches(xs="county_xs", ys="county_ys", fill_color="county_colors", fill_alpha=0.7, line_color="white", line_width=0.5)
state_patches = Patches(xs="state_xs", ys="state_ys", fill_alpha=0.0, line_color="#884444", line_width=2)
county_renderer = Glyph(
data_source = county_source,
xdata_range = xdr,
ydata_range = ydr,
glyph = county_patches,
)
state_renderer = Glyph(
data_source = state_source,
xdata_range = xdr,
from bokeh.objects import (Plot, DataRange1d, LinearAxis, DatetimeAxis,
ColumnDataSource, GlyphRenderer, PanTool, ZoomTool)
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", radius=5, line_color="black")
glyph_renderer = GlyphRenderer(
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")
class Population(object):
year = 2010
location = "World"
def __init__(self):
from bokeh.objects import ColumnDataSource
from bokeh.document import Document
from bokeh.session import Session
from bokeh.sampledata.population import load_population
self.document = Document()
self.session = Session()
self.session.use_doc('population')
self.session.load_document(self.document)
self.df = load_population()
self.source_pyramid = ColumnDataSource(data=dict())
def render(self):
self.pyramid_plot()
self.create_layout()
self.document.add(self.layout)
self.update_pyramid()
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 on_year_change(self, obj, attr, old, new):
self.year = int(new)
self.update_pyramid()
def on_location_change(self, obj, attr, old, new):
self.location = new
self.update_pyramid()
def create_layout(self):
from bokeh.widgets import Select, HBox, VBox
years = list(map(str, sorted(self.df.Year.unique())))
locations = sorted(self.df.Location.unique())
year_select = Select(title="Year:", value="2010", options=years)
location_select = Select(title="Location:",
value="World",
options=locations)
year_select.on_change('value', self.on_year_change)
location_select.on_change('value', self.on_location_change)
controls = HBox(children=[year_select, location_select])
self.layout = VBox(children=[controls, self.plot])
def update_pyramid(self):
pyramid = self.df[(self.df.Location == self.location)
& (self.df.Year == self.year)]
male = pyramid[pyramid.Sex == "Male"]
female = pyramid[pyramid.Sex == "Female"]
total = male.Value.sum() + female.Value.sum()
male_percent = -male.Value / total
female_percent = female.Value / total
groups = male.AgeGrpStart.tolist()
shifted = groups[1:] + [groups[-1] + 5]
self.source_pyramid.data = dict(
groups=groups,
shifted=shifted,
male=male_percent,
female=female_percent,
)
self.session.store_document(self.document)
class DataTables(object):
def __init__(self):
self.document = Document()
self.session = Session()
self.session.use_doc('data_tables_server')
self.session.load_document(self.document)
self.manufacturer_filter = None
self.model_filter = None
self.transmission_filter = None
self.drive_filter = None
self.class_filter = None
self.source = ColumnDataSource()
self.update_data()
self.document.add(self.create())
self.session.store_document(self.document)
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="green"))
hwy = Glyph(data_source=self.source, glyph=Circle(x="index", y="hwy", fill_color="red"))
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
def on_manufacturer_change(self, obj, attr, _, value):
self.manufacturer_filter = None if value == "All" else value
self.update_data()
def on_model_change(self, obj, attr, _, value):
self.model_filter = None if value == "All" else value
self.update_data()
def on_transmission_change(self, obj, attr, _, value):
self.transmission_filter = None if value == "All" else value
self.update_data()
def on_drive_change(self, obj, attr, _, value):
self.drive_filter = None if value == "All" else value
self.update_data()
def on_class_change(self, obj, attr, _, value):
self.class_filter = None if value == "All" else value
self.update_data()
def update_data(self):
df = mpg
if self.manufacturer_filter:
df = df[df["manufacturer"] == self.manufacturer_filter]
if self.model_filter:
df = df[df["model"] == self.model_filter]
if self.transmission_filter:
df = df[df["trans"] == self.transmission_filter]
if self.drive_filter:
df = df[df["drv"] == self.drive_filter]
if self.class_filter:
df = df[df["class"] == self.class_filter]
self.source.data = ColumnDataSource.from_df(df)
self.session.store_document(self.document)
def run(self, poll_interval=0.5):
link = self.session.object_link(self.document.context)
print("Please visit %s to see the plots (press ctrl-C to exit)" % link)
try:
while True:
self.session.load_document(self.document)
time.sleep(poll_interval)
except KeyboardInterrupt:
print()
except ConnectionError:
print("Connection to bokeh-server was terminated")
class Population(object):
year = 2010
location = "World"
def __init__(self):
from bokeh.objects import ColumnDataSource
from bokeh.document import Document
from bokeh.session import Session
from bokeh.sampledata.population import load_population
self.document = Document()
self.session = Session()
self.session.use_doc('population_reveal')
self.session.load_document(self.document)
self.df = load_population()
self.source_pyramid = ColumnDataSource(data=dict())
def render(self):
self.pyramid_plot()
self.create_layout()
self.document.add(self.layout)
self.update_pyramid()
def pyramid_plot(self):
from bokeh.objects import (Plot, DataRange1d, LinearAxis, Grid, Glyph,
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="Widgets", x_range=xdr, y_range=ydr, plot_width=600, plot_height=600)
xaxis = LinearAxis(plot=self.plot)
self.plot.below.append(xaxis)
yaxis = LinearAxis(plot=self.plot, ticker=SingleIntervalTicker(interval=5))
self.plot.left.append(yaxis)
xgrid = Grid(plot=self.plot, dimension=0, ticker=xaxis.ticker)
ygrid = Grid(plot=self.plot, dimension=1, ticker=yaxis.ticker)
male_quad = Quad(left="male", right=0, bottom="groups", top="shifted", fill_color="blue")
male_quad_glyph = Glyph(data_source=self.source_pyramid,
xdata_range=xdr, ydata_range=ydr, glyph=male_quad)
self.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=self.source_pyramid,
xdata_range=xdr, ydata_range=ydr, glyph=female_quad)
self.plot.renderers.append(female_quad_glyph)
legend = Legend(plot=self.plot, legends=dict(Male=[male_quad_glyph],
Female=[female_quad_glyph]))
self.plot.renderers.append(legend)
def on_year_change(self, obj, attr, old, new):
self.year = int(new)
self.update_pyramid()
def on_location_change(self, obj, attr, old, new):
self.location = new
self.update_pyramid()
def create_layout(self):
from bokeh.widgetobjects import Select, HBox, VBox
years = list(map(str, sorted(self.df.Year.unique())))
locations = sorted(self.df.Location.unique())
year_select = Select(title="Year:", value="2010", options=years)
location_select = Select(title="Location:", value="World", options=locations)
year_select.on_change('value', self.on_year_change)
location_select.on_change('value', self.on_location_change)
controls = HBox(children=[year_select, location_select])
self.layout = VBox(children=[controls, self.plot])
def update_pyramid(self):
pyramid = self.df[(self.df.Location == self.location) & (self.df.Year == self.year)]
male = pyramid[pyramid.Sex == "Male"]
female = pyramid[pyramid.Sex == "Female"]
total = male.Value.sum() + female.Value.sum()
male_percent = -male.Value / total
female_percent = female.Value / total
groups = male.AgeGrpStart.tolist()
shifted = groups[1:] + [groups[-1] + 5]
self.source_pyramid.data = dict(
groups=groups,
shifted=shifted,
male=male_percent,
female=female_percent,
)
self.session.store_document(self.document)
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())
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,
)
calendar_start = df.Date.irow(0)
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, plot_width=800, plot_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)
plot.renderers.append(patch1_glyph)
patch2 = Patch(x="dates", y="times", fill_color="orange", fill_alpha=0.8)
patch2_glyph = Glyph(data_source=patch2_source, xdata_range=xdr, ydata_range=ydr, glyph=patch2)
plot.renderers.append(patch2_glyph)
line1 = Line(x="dates", y="sunrises", line_color="yellow", line_width=2)
line1_glyph = Glyph(data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=line1)
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
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))
#source = ObjectArrayDataSource(
# data = [
# {'x' : 1, 'y' : 5, 'z':3},
# {'x' : 2, 'y' : 4, 'z':3, 'radius':10},
# {'x' : 3, 'y' : 3, 'z':3, 'fill':"blue"},
# {'x' : 4, 'y' : 2, 'z':3},
# {'x' : 5, 'y' : 1, 'z':3},
# ])
xdr = DataRange1d(sources=[source.columns("x")])
ydr = DataRange1d(sources=[source.columns("y")])
circle = Circle(x="x", y="y", fill="red", radius="z", 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"))
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, size=10, line_color="color")
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], min_border=80, title="Iris Data")
xaxis = LinearAxis(plot=plot, dimension=0, location="min",
axis_label="petal length", bounds=(1,7), major_tick_in=0)
yaxis = LinearAxis(plot=plot, dimension=1, location="min",
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())
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)
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")
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,
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(xcenter=[125], ycenter=[145])
)
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 = WheelZoomTool(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",
width=250, height=250, border_fill='white', title="", border_symmetry="", min_border=2)
if xax:
xaxis = LinearAxis(plot=plot, dimension=0, location="bottom")
if yax:
yaxis = LinearAxis(plot=plot, dimension=1, location="left")
xgrid = Grid(plot=plot, dimension=0)
ygrid = Grid(plot=plot, dimension=1)
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 iris_splom():
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",
width=250, height=250, border_fill='white', title="", border_symmetry="", min_border=2)
objs = []
if xax:
xaxis = LinearAxis(plot=plot, dimension=0, location="bottom")
objs.append(xaxis)
if yax:
yaxis = LinearAxis(plot=plot, dimension=1, location="left")
objs.append(yaxis)
xgrid = Grid(plot=plot, dimension=0)
ygrid = Grid(plot=plot, dimension=1)
circle = Circle(x=xname, y=yname, fill_color="color", fill_alpha=0.2, radius=2, line_color="color")
circle_renderer = GlyphRenderer(
data_source = source,
xdata_range = xdr,
ydata_range = ydr,
glyph = circle,
)
plot.renderers.append(circle_renderer)
plot.tools = [pan, zoom]
if text:
text = " ".join(text.split('_'))
text = Text(
x={'field':'center', 'units':'screen'},
y={'field':'center', 'units':'screen'},
text=text, angle=pi/4, text_font_style="bold", text_baseline="top",
text_color="#ffaaaa", text_alpha=0.5, text_align="center", text_font_size="28pt")
text_renderer = GlyphRenderer(
data_source=text_source,
xdata_range = xdr,
ydata_range = ydr,
glyph = text,
)
plot.data_sources.append(text_source)
plot.renderers.append(text_renderer)
objs.append(text_renderer)
objs.append(text_source)
return plot, objs + [circle_renderer, xgrid, ygrid]
sess = session.HTMLFileSession("iris_splom.html")
attrs = ["petal_length", "petal_width", "sepal_width", "sepal_length"]
plots = []
for y in attrs:
row = []
for x in attrs:
xax = (y == attrs[-1])
yax = (x == attrs[0])
text = x if (x==y) else None
plot, objs = make_plot(x, y, xax, yax, text)
sess.add(plot, *objs)
row.append(plot)
plots.append(row)
grid = GridPlot(children=plots, name="iris_splom")
sess.add(source, xdr, ydr, pan, zoom)
sess.add(grid)
sess.plotcontext.children.append(grid)
sess.save(js="relative", css="relative", rootdir=os.path.abspath("."))
return grid
class DataTables(object):
def __init__(self):
self.document = Document()
self.session = Session()
self.session.use_doc('data_tables_server')
self.session.load_document(self.document)
self.manufacturer_filter = None
self.model_filter = None
self.transmission_filter = None
self.drive_filter = None
self.class_filter = None
self.source = ColumnDataSource()
self.update_data()
self.document.add(self.create())
self.session.store_document(self.document)
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
def on_manufacturer_change(self, obj, attr, _, value):
self.manufacturer_filter = None if value == "All" else value
self.update_data()
def on_model_change(self, obj, attr, _, value):
self.model_filter = None if value == "All" else value
self.update_data()
def on_transmission_change(self, obj, attr, _, value):
self.transmission_filter = None if value == "All" else value
self.update_data()
def on_drive_change(self, obj, attr, _, value):
self.drive_filter = None if value == "All" else value
self.update_data()
def on_class_change(self, obj, attr, _, value):
self.class_filter = None if value == "All" else value
self.update_data()
def update_data(self):
df = mpg
if self.manufacturer_filter:
df = df[df["manufacturer"] == self.manufacturer_filter]
if self.model_filter:
df = df[df["model"] == self.model_filter]
if self.transmission_filter:
df = df[df["trans"] == self.transmission_filter]
if self.drive_filter:
df = df[df["drv"] == self.drive_filter]
if self.class_filter:
df = df[df["class"] == self.class_filter]
self.source.data = ColumnDataSource.from_df(df)
self.session.store_document(self.document)
def run(self, do_view=False, poll_interval=0.5):
link = self.session.object_link(self.document.context)
print("Please visit %s to see the plots" % link)
if do_view: view(link)
print("\npress ctrl-C to exit")
self.session.poll_document(self.document)
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,
PanTool, ZoomTool)
from bokeh.glyphs import Line
from bokeh import session
x = np.linspace(-2*pi, 2*pi, 1000)
x_static = np.linspace(-2*pi, 2*pi, 1000)
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, x_static=x_static,
widths=widths, heights=heights))
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,
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
