def figure_fn(*kargs, **kwargs):
fig = bk_figure(*kargs, **kwargs)
fig.title.text_font_size = '14pt'
fig.xaxis.axis_label_text_font_size = '24pt'
fig.yaxis.axis_label_text_font_size = '24pt'
fig.xaxis.major_label_text_font_size = '24pt'
fig.yaxis.major_label_text_font_size = '24pt'
return fig
def bokeh_plot(histo, jup_url="http://127.0.0.1:8889"):
if not isnotebook():
raise NotImplementedError("Only usable in jupyter notebook")
import bokeh.plotting.figure as bk_figure
from bokeh.io import curdoc, show
from bokeh import palettes
from bokeh.layouts import row, widgetbox, column
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import RadioButtonGroup, CheckboxButtonGroup
from bokeh.models.widgets import RangeSlider, Div
from bokeh.io import output_notebook # enables plot interface in J notebook
import numpy as np
# init bokeh
from bokeh.application import Application
from bokeh.application.handlers import FunctionHandler
from bokeh.core.validation import silence
from bokeh.core.validation.warnings import EMPTY_LAYOUT
silence(EMPTY_LAYOUT, True)
output_notebook()
# Set up widgets
cfg_labels = ["Ghost"]
wi_config = CheckboxButtonGroup(labels=cfg_labels, active=[0])
wi_dense_select = RadioButtonGroup(
labels=[ax.name for ax in histo.dense_axes()], active=0)
wi_sparse_select = RadioButtonGroup(
labels=[ax.name for ax in histo.sparse_axes()], active=0)
# Dense widgets
sliders = {}
for ax in histo.dense_axes():
edge_vals = (histo.axis(ax.name).edges()[0],
histo.axis(ax.name).edges()[-1])
_smallest_bin = np.min(np.diff(histo.axis(ax.name).edges()))
sliders[ax.name] = RangeSlider(title=ax.name,
value=edge_vals,
start=edge_vals[0],
end=edge_vals[1],
step=_smallest_bin,
name=ax.name)
# Cat widgets
togglers = {}
for ax in histo.sparse_axes():
togglers[ax.name] = CheckboxButtonGroup(
labels=[i.name for i in ax.identifiers()],
active=[0],
name=ax.name)
# Toggles for all widgets
configers = {}
for ax in histo.sparse_axes():
configers[ax.name] = CheckboxButtonGroup(labels=["Display", "Ghost"],
active=[0, 1],
name=ax.name)
for ax in histo.dense_axes():
configers[ax.name] = CheckboxButtonGroup(labels=["Display"],
active=[0],
name=ax.name)
# Figure
fig = bk_figure(title="1D Projection",
plot_width=500,
plot_height=500,
min_border=20,
toolbar_location=None)
fig.yaxis.axis_label = "N"
fig.xaxis.axis_label = "Quantity"
# Iterate over possible overlays
_max_idents = 0 # Max number of simultaneou histograms
for ax in histo.sparse_axes():
_max_idents = max(_max_idents, len([i.name for i in ax.identifiers()]))
# Data source list
sources = []
sources_ghost = []
for i in range(_max_idents):
sources.append(
ColumnDataSource(dict(left=[], top=[], right=[], bottom=[])))
sources_ghost.append(
ColumnDataSource(dict(left=[], top=[], right=[], bottom=[])))
# Hist list
hists = []
hists_ghost = []
for i in range(_max_idents):
if _max_idents < 10:
_color = palettes.Category10[min(max(3, _max_idents), 10)][i]
else:
_color = palettes.magma(_max_idents)[i]
hists.append(
fig.quad(left="left",
right="right",
top="top",
bottom="bottom",
source=sources[i],
alpha=0.9,
color=_color))
hists_ghost.append(
fig.quad(left="left",
right="right",
top="top",
bottom="bottom",
source=sources_ghost[i],
alpha=0.05,
color=_color))
def update_data(attrname, old, new):
sparse_active = wi_sparse_select.active
sparse_name = [ax.name for ax in histo.sparse_axes()][sparse_active]
sparse_other = [
ax.name for ax in histo.sparse_axes() if ax.name != sparse_name
]
dense_active = wi_dense_select.active
dense_name = [ax.name for ax in histo.dense_axes()][dense_active]
dense_other = [
ax.name for ax in histo.dense_axes() if ax.name != dense_name
]
# Apply cuts in projections
_h = histo.copy()
for proj_ax in sparse_other:
_idents = histo.axis(proj_ax).identifiers()
_labels = [ident.name for ident in _idents]
if 0 in configers[proj_ax].active:
_h = _h.integrate(
proj_ax, [_labels[i] for i in togglers[proj_ax].active])
else:
_h = _h.integrate(proj_ax)
for proj_ax in dense_other:
_h = _h.integrate(
proj_ax,
slice(sliders[proj_ax].value[0], sliders[proj_ax].value[1]))
for cat_ix in range(_max_idents):
# Update histo for each toggled overlay
if cat_ix in togglers[sparse_name].active:
cat_value = histo.axis(sparse_name).identifiers()[cat_ix]
h1d = _h.integrate(sparse_name, cat_value)
# Get shown histogram
values = h1d.project(dense_name).values()
if values != {}:
h = values[()]
bins = h1d.axis(dense_name).edges()
# Apply cuts on shown axis
bin_los = bins[:-1][
bins[:-1] > sliders[dense_name].value[0]]
bin_his = bins[1:][bins[1:] < sliders[dense_name].value[1]]
new_bins = np.intersect1d(bin_los, bin_his)
bin_ixs = np.searchsorted(bins, new_bins)[:-1]
h = h[bin_ixs]
sources[cat_ix].data = dict(left=new_bins[:-1],
right=new_bins[1:],
top=h,
bottom=np.zeros_like(h))
else:
sources[cat_ix].data = dict(left=[],
right=[],
top=[],
bottom=[])
# Add ghosts
if 0 in wi_config.active:
h1d = histo.integrate(sparse_name, cat_value)
for proj_ax in sparse_other:
_idents = histo.axis(proj_ax).identifiers()
_labels = [ident.name for ident in _idents]
if 1 not in configers[proj_ax].active:
h1d = h1d.integrate(
proj_ax,
[_labels[i] for i in togglers[proj_ax].active])
else:
h1d = h1d.integrate(proj_ax)
values = h1d.project(dense_name).values()
if values != {}:
h = h1d.project(dense_name).values()[()]
bins = h1d.axis(dense_name).edges()
sources_ghost[cat_ix].data = dict(
left=bins[:-1],
right=bins[1:],
top=h,
bottom=np.zeros_like(h))
else:
sources_ghost[cat_ix].data = dict(left=[],
right=[],
top=[],
bottom=[])
else:
sources[cat_ix].data = dict(left=[],
right=[],
top=[],
bottom=[])
sources_ghost[cat_ix].data = dict(left=[],
right=[],
top=[],
bottom=[])
# Cosmetics
fig.xaxis.axis_label = dense_name
for name, slider in sliders.items():
slider.on_change('value', update_data)
for name, toggler in togglers.items():
toggler.on_change('active', update_data)
for name, configer in configers.items():
configer.on_change('active', update_data)
# Button
for w in [wi_dense_select, wi_sparse_select, wi_config]:
w.on_change('active', update_data)
from bokeh.models.widgets import Panel, Tabs
from bokeh.io import output_file, show
from bokeh.plotting import figure
layout = row(
fig,
column(
Div(text="<b>Overlay Axis:</b>",
style={
'font-size': '100%',
'color': 'black'
}), wi_sparse_select,
Div(text="<b>Plot Axis:</b>",
style={
'font-size': '100%',
'color': 'black'
}), wi_dense_select,
Div(text="<b>Categorical Cuts:</b>",
style={
'font-size': '100%',
'color': 'black'
}), *[toggler for name, toggler in togglers.items()],
Div(text="<b>Dense Cuts:</b>",
style={
'font-size': '100%',
'color': 'black'
}), *[slider for name, slider in sliders.items()]))
# Config prep
incl_lists = [[], [], []]
for i, key in enumerate(list(configers.keys())):
incl_lists[i // max(5,
len(list(configers.keys())) / 3)].append(
Div(text="<b>{}:</b>".format(key),
style={
'font-size': '70%',
'color': 'black'
}))
incl_lists[i // max(5,
len(list(configers.keys())) / 3)].append(
configers[key])
layout_cfgs = column(
row(
column(
Div(text="<b>Configs:</b>",
style={
'font-size': '100%',
'color': 'black'
}), wi_config)),
Div(text="<b>Axis togglers:</b>",
style={
'font-size': '100%',
'color': 'black'
}),
row(
column(incl_lists[0]),
column(incl_lists[1]),
column(incl_lists[2]),
))
# Update active buttons
def update_layout(attrname, old, new):
active_axes = [None]
for name, wi in configers.items():
if 0 in wi.active:
active_axes.append(name)
for child in layout.children[1].children:
if child.name not in active_axes:
child.visible = False
else:
child.visible = True
for name, configer in configers.items():
configer.on_change('active', update_layout)
tab1 = Panel(child=layout, title="Projection")
tab2 = Panel(child=layout_cfgs, title="Configs")
tabs = Tabs(tabs=[tab1, tab2])
def modify_doc(doc):
doc.add_root(row(tabs, width=800))
doc.title = "Sliders"
handler = FunctionHandler(modify_doc)
app = Application(handler)
show(app, notebook_url=jup_url)
update_data("", "", "")
def plot():
# Set up initial parameters
x = range(11)
y = get_infections(10,.2, 1, 2)
source = ColumnDataSource(data=dict(x=x, y=y))
# Set up plot
plot = bk_figure(plot_height=400, plot_width=600, title="Probability Distribution for the Number of Infected People",
tools="crosshair,pan,reset,save,wheel_zoom",
x_range=[0, 10], y_range=[0, 1])
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Set up widgets
trans_prob = Slider(title="Person-to-person transmission probability", value=0.2, start=0.0, end=1.0, step=0.01)
population = Slider(title="Community population", value=10, start=0, end=140, step=1)
prop_infected = Slider(title="Initial proportion of population infected", value=.2, start=0.0, end=1, step=.01)
days = Slider(title="Number of days", value=1, start=1, end=14, step=1)
mean = Paragraph(text="Expected number of infected people: "+str(round(E(y),2)))
# Set up callbacks
def update_title(attrname, old, new):
plot.title.text = text.value
def update_data(attrname, old, new):
# Get the current slider values
t = trans_prob.value
pop = population.value
inf = prop_infected.value
d = days.value
# Generate the new curve
x = range(pop+1)
y = get_infections(pop, t, d, int(inf*pop))
# update expectation
mean.text="Expected number of people infected: "+str(round(E(y),2))
#re-scale so that relevant part of x-axis stays in view
plot.x_range.end=0
plot.x_range.end=pop
source.data = dict(x=x, y=y)
for w in [trans_prob,population,prop_infected,days]:
w.on_change('value', update_data)
# Set up layouts and add to document
inputs = widgetbox(population,trans_prob,prop_infected,days,mean)
layout = row(plot,
widgetbox(population,trans_prob,prop_infected,days,mean))
def modify_doc(doc):
doc.add_root(row(layout, width=800))
doc.title = "Sliders"
text.on_change('value', update_title)
return layout