pydata2015 berlin bokeh population server

14 February 2016

code

  1. population_server

    1. original

       from __future__ import print_function

 from math import pi

 from bokeh.browserlib import view
 from bokeh.document import Document
 from bokeh.models.glyphs import Line, Quad
 from bokeh.models import (
     Plot, ColumnDataSource, DataRange1d, FactorRange,
     LinearAxis, CategoricalAxis, Grid, Legend,
     SingleIntervalTicker
 )
 from bokeh.sampledata.population import load_population
 from bokeh.session import Session
 from bokeh.models.widgets import Select, HBox, VBox

 document = Document()
 session = Session()
 session.use_doc('population_server')
 session.load_document(document)

 df = load_population()
 revision = 2012

 year = 2010
 location = "World"

 years = [str(x) for x in sorted(df.Year.unique())]
 locations = sorted(df.Location.unique())

 source_pyramid = ColumnDataSource(data=dict())

 def pyramid():
     xdr = DataRange1d()
     ydr = DataRange1d()

     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=[("Male", [male_quad_glyph]), ("Female", [female_quad_glyph])]))

     return plot

 source_known = ColumnDataSource(data=dict(x=[], y=[]))
 source_predicted = ColumnDataSource(data=dict(x=[], y=[]))

 def population():
     xdr = FactorRange(factors=years)
     ydr = DataRange1d()

     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=[("known", [line_known_glyph]), ("predicted", [line_predicted_glyph])],
         )
     )

     return plot

 def update_pyramid():
     pyramid = df[(df.Location == location) & (df.Year == 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]

     source_pyramid.data = dict(
         groups=groups,
         shifted=shifted,
         male=male_percent,
         female=female_percent,
     )

 def update_population():
     population = df[df.Location == location].groupby(df.Year).Value.sum()
     aligned_revision = revision//10 * 10

     known = population[population.index <= aligned_revision]
     predicted = population[population.index >= aligned_revision]

     source_known.data = dict(x=known.index.map(str), y=known.values)
     source_predicted.data = dict(x=predicted.index.map(str), y=predicted.values)

 def update_data():
     update_population()
     update_pyramid()
     session.store_document(document)

 def on_year_change(obj, attr, old, new):
     global year
     year = int(new)
     update_data()

 def on_location_change(obj, attr, old, new):
     global location
     location = new
     update_data()

 def layout():
     year_select = Select(title="Year:", value="2010", options=years)
     location_select = Select(title="Location:", value="World", options=locations)

     year_select.on_change('value', on_year_change)
     location_select.on_change('value', on_location_change)

     controls = HBox(children=[year_select, location_select])
     layout = VBox(children=[controls, pyramid(), population()])

     return layout

 document.add(layout())
 update_data()

 if __name__ == "__main__":
     link = session.object_link(document.context)
     print("Please visit %s to see the plots" % link)
     view(link)
     print("\npress ctrl-C to exit")
     session.poll_document(document)

    2. convert to bokeh 0.11

       from math import pi

 from bokeh.io import curdoc
 from bokeh.client import push_session
 from bokeh.models.glyphs import Line, Quad
 from bokeh.models.widgets import Select, HBox, VBox
 from bokeh.sampledata.population import load_population
 from bokeh.models import (
     Plot, ColumnDataSource, DataRange1d, FactorRange,
     LinearAxis, CategoricalAxis, Grid, Legend,
     SingleIntervalTicker
 )

 document = curdoc()
 session = push_session(document)


 df = load_population()
 revision = 2012

 year = 2010
 location = "World"

 years = [str(x) for x in sorted(df.Year.unique())]
 locations = sorted(df.Location.unique())

 source_pyramid = ColumnDataSource(data=dict())


 def pyramid():
     xdr = DataRange1d()
     ydr = DataRange1d()

     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=[("Male", [male_quad_glyph]), ("Female", [female_quad_glyph])]))

     return plot

 source_known = ColumnDataSource(data=dict(x=[], y=[]))
 source_predicted = ColumnDataSource(data=dict(x=[], y=[]))


 def population():
     xdr = FactorRange(factors=years)
     ydr = DataRange1d()

     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(
             location="bottom_right",
             legends=[("known", [line_known_glyph]), ("predicted", [line_predicted_glyph])],
         )
     )

     return plot


 def update_pyramid():
     pyramid = df[(df.Location == location) & (df.Year == 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]

     source_pyramid.data = dict(
         groups=groups,
         shifted=shifted,
         male=male_percent,
         female=female_percent,
     )


 def update_population():
     population = df[df.Location == location].groupby(df.Year).Value.sum()
     aligned_revision = revision//10 * 10

     known = population[population.index <= aligned_revision]
     predicted = population[population.index >= aligned_revision]

     source_known.data = dict(x=known.index.map(str), y=known.values)
     source_predicted.data = dict(x=predicted.index.map(str), y=predicted.values)


 def update_data():
     update_population()
     update_pyramid()


 def on_year_change(attr, old, new):
     global year
     year = int(new)
     update_data()


 def on_location_change(attr, old, new):
     global location
     location = new
     update_data()


 def layout():
     year_select = Select(title="Year:", value="2010", options=years)
     location_select = Select(title="Location:", value="World", options=locations)

     year_select.on_change('value', on_year_change)
     location_select.on_change('value', on_location_change)

     controls = HBox(children=[year_select, location_select])
     layout = VBox(children=[controls, pyramid(), population()])

     return layout

 document.add_root(layout())
 update_data()

 if __name__ == "__main__":
     # open the doc in browser
     session.show()
     # run forever
     session.loop_until_closed()

class Select(**kwargs)

  1. base bokeh.models.widgets.inputs.InputWidget

  2. description

    1. single select widget

       location_select = Select(title="Location:", value="World", options=locations)

  3. attr

    1. callback a callback to run in the browser whenever the current select dropdown value changes

       location_select.on_change('value', on_location_change)

    2. options available selection options

    3. value initial or selected value

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