def make_plot():
# prepare data stuff - take the keys from the data in order of likelihood
categories = list(reversed(probly.keys()))
palette = [cc.rainbow[i * 15] for i in range(17)]
x = linspace(-20, 110, 500)
source = ColumnDataSource(data=dict(x=x))
p = figure(y_range=categories, plot_width=900, x_range=(-5, 105), toolbar_location=None)
for i, cat in enumerate(reversed(categories)):
pdf = gaussian_kde(probly[cat])
y = ridge(cat, pdf(x))
source.add(y, cat)
p.patch('x', cat, color=palette[i], alpha=0.6, line_color="black", source=source)
p.outline_line_color = None
p.background_fill_color = "#efefef"
p.xaxis.ticker = FixedTicker(ticks=list(range(0, 101, 10)))
p.xaxis.formatter = PrintfTickFormatter(format="%d%%")
p.ygrid.grid_line_color = None
p.xgrid.grid_line_color = "#dddddd"
p.xgrid.ticker = p.xaxis[0].ticker
p.axis.minor_tick_line_color = None
p.axis.major_tick_line_color = None
p.axis.axis_line_color = None
p.y_range.range_padding = 0.12
return p
def colorce():
import colorcet as cc
from numpy import linspace
from scipy.stats.kde import gaussian_kde
from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource, FixedTicker, PrintfTickFormatter
from bokeh.plotting import figure
from bokeh.sampledata.perceptions import probly
def ridge(category, data, scale=20):
return list(zip([category]*len(data), scale*data))
cats = list(reversed(probly.keys()))
palette = [cc.rainbow[i*15] for i in range(17)]
x = linspace(-20,110, 500)
source = ColumnDataSource(data=dict(x=x))
p = figure(y_range=cats, plot_width=900, x_range=(-5, 105), toolbar_location=None)
for i, cat in enumerate(reversed(cats)):
pdf = gaussian_kde(probly[cat])
y = ridge(cat, pdf(x))
source.add(y, cat)
p.patch('x', cat, color=palette[i], alpha=0.6, line_color="black", source=source)
p.outline_line_color = None
p.background_fill_color = "#efefef"
p.xaxis.ticker = FixedTicker(ticks=list(range(0, 101, 10)))
p.xaxis.formatter = PrintfTickFormatter(format="%d%%")
p.ygrid.grid_line_color = None
p.xgrid.grid_line_color = "#dddddd"
p.xgrid.ticker = p.xaxis.ticker
p.axis.minor_tick_line_color = None
p.axis.major_tick_line_color = None
p.axis.axis_line_color = None
p.y_range.range_padding = 0.12
return p
from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource, FixedTicker, PrintfTickFormatter
from bokeh.plotting import figure
from bokeh.sampledata.perceptions import probly
import colorcet as cc
output_file("ridgeplot.html")
def ridge(category, data, scale=20):
return list(zip([category] * len(data), scale * data))
cats = list(reversed(probly.keys()))
palette = [cc.rainbow[i * 15] for i in range(17)]
x = linspace(-20, 110, 500)
source = ColumnDataSource(data=dict(x=x))
p = figure(y_range=cats,
plot_width=900,
x_range=(-5, 105),
toolbar_location=None)
for i, cat in enumerate(reversed(cats)):
pdf = gaussian_kde(probly[cat])
y = ridge(cat, pdf(x))
from numpy import linspace
from scipy.stats.kde import gaussian_kde
from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource, FixedTicker, PrintfTickFormatter
from bokeh.plotting import figure
from bokeh.sampledata.perceptions import probly
import colorcet as cc
output_file("ridgeplot.html")
def ridge(category, data, scale=20):
return list(zip([category]*len(data), scale*data))
cats = list(reversed(probly.keys()))
palette = [cc.rainbow[i*15] for i in range(17)]
x = linspace(-20,110, 500)
source = ColumnDataSource(data=dict(x=x))
p = figure(y_range=cats, plot_width=700, x_range=(-5, 105), toolbar_location=None)
for i, cat in enumerate(reversed(cats)):
pdf = gaussian_kde(probly[cat])
y = ridge(cat, pdf(x))
source.add(y, cat)
p.patch('x', cat, color=palette[i], alpha=0.6, line_color="black", source=source)
