def plot_adj(adj):
N, _ = adj.shape
plt.output_file("adjacency.html", title="Adjacency")
TOOLS="pan,wheel_zoom,box_zoom,reset,click,previewsave"
plt.figure(plot_width=N+100, plot_height=N+100, tools=TOOLS)
plt.image(image=[adj], x =[0], y=[0], dw=[N], dh=[N],
x_range=[0, N], y_range=[0, N],palette=["YlGnBu-3"])
plt.show()
def plot_adj(adj, sparcity):
N, _ = adj.shape
TOOLS="pan,wheel_zoom,box_zoom,reset,click,previewsave"
plt.figure(plot_width=N+100, plot_height=N+100, tools=TOOLS)
plt.image(image=[adj], x =[0], y=[0], dw=[N], dh=[N],
x_range=[0, N], y_range=[0, N],
palette=["Blues-3"],
title="Adjacency of top %d nodes (%.2f%% sparcity)" % (N, sparcity),
x_axis_type=None, y_axis_type=None)
return plt.curplot()
def build_plot(slice=None):
if not slice:
slice = data.shape[0] / 2
sliced = data[slice, :, :].T
p = image(image=[sliced],
x=list(range(0, sliced.shape[0])),
y=list(range(0, sliced.shape[1])),
dw=[sliced.shape[1]],
dh=[sliced.shape[0]],
palette=['Greys-9'],
dilate=True)
return p.create_html_snippet(server=True)
def build_plot(slice=None):
if not slice:
slice = data.shape[0] / 2
sliced = data[slice, :, :].T
p = image(image=[sliced],
x=list(range(0, sliced.shape[0])),
y=list(range(0, sliced.shape[1])),
dw=[sliced.shape[1]],
dh=[sliced.shape[0]],
palette=['Greys-9'],
dilate=True)
return p.create_html_snippet(server=True)
import numpy as np
from bokeh.plotting import square, output_server, image, show
from bokeh.objects import ServerDataSource
import bokeh.transforms.ar_downsample as ar
#from bokeh.transforms import line_downsample
output_server("Census")
#2010 US Census tracts
source = ServerDataSource(data_url="/defaultuser/CensusTracts.hdf5", owner_username="******")
plot = square( 'LON','LAT',source=source)
heatmap = ar.source(plot, palette=["Reds-9"], points=True)
image(source=heatmap, title="Census Tracts", reserve_val=0, plot_width=600, plot_height=400, **ar.mapping(heatmap))
show()
import numpy as np
from bokeh.plotting import square, output_server, image, show
from bokeh.objects import ServerDataSource
import bokeh.transforms.ar_downsample as ar
#from bokeh.transforms import line_downsample
output_server("abstractrender")
source = ServerDataSource(data_url="fn://gauss", owner_username="******")
plot = square('oneA', 'oneB', color='#FF00FF', source=source)
# Simple heat-map: bin the counts ('tis the default configuration....)
heatmap =ar.source(plot, palette=["Reds-9"])
image(source=heatmap, title="Heatmap", reserve_val=0, **ar.mapping(heatmap))
#Perceptually corrected heat-map. Cube-root then bin
percepmap = ar.source(plot, shader=ar.Cuberoot(), palette=["Reds-9"])
image(source=percepmap, title="Perceptually corrected", reserve_val=0, **ar.mapping(percepmap))
# Contours come in the same framework, but since the results of the shader are lines you use a different plotting function...
colors = ["#C6DBEF", "#9ECAE1", "#6BAED6", "#4292C6", "#2171B5", "#08519C", "#08306B"]
ar.replot(plot, title="ISO Contours", shader=ar.Contour(levels=len(colors)), line_color=colors)
#"""
#In order to run the 'stocks' example, you have to execute
#./bokeh-server -D remotedata
#
#The remote data directory in the bokeh checkout has the sample data for this example
def create(cls):
gbounds = cls.gbounds
xmin, xmax, ymin, ymax = gbounds
app = cls()
data = ARDataSource(
data_url="/bokeh/taxidata/pickup/",
data=dict(
x=[0], y=[0], dw=[xmax-xmin], dh=[ymax-ymin], palette=["Greys-256"]
)
)
app.pickup_ar_plot_source = data
plot = image(source=data,
image="image",
x="x",
y="y",
dw="dw",
dh="dh",
plot_width=400,
plot_height=400,
palette='palette',
x_range=[xmin, xmax], y_range=[ymin, ymax],
tools="pan,wheel_zoom,box_zoom,select,reset",
title='pickup'
)
plot.title_text_font='12pt'
app.pickup_plot = plot
app.pickup_raw_plot_source = plot.select({'type' : ColumnDataSource})[0]
data = ARDataSource(
data_url="/bokeh/taxidatavsregular/pickup/",
data=dict(
x=[0], y=[0], dw=[xmax-xmin], dh=[ymax-ymin], palette=["Greys-256"]
)
)
app.pickup_comparison_ar_plot_source = data
plot = image(source=data,
image="image",
x="x",
y="y",
dw="dw",
dh="dh",
plot_width=400,
plot_height=400,
palette='palette',
x_range=[xmin, xmax], y_range=[ymin, ymax],
tools="pan,wheel_zoom,box_zoom,select,reset",
title='pickup comparison plot'
)
plot.title_text_font='12pt'
app.pickup_comparison_plot = plot
app.pickup_comparison_raw_plot_source = plot.select({'type' : ColumnDataSource})[0]
data = ARDataSource(
data_url="/bokeh/taxidatavsregular/dropoff/",
data=dict(
x=[0], y=[0], dw=[xmax-xmin], dh=[ymax-ymin], palette=["Greys-256"]
)
)
app.dropoff_comparison_ar_plot_source = data
plot = image(source=data,
image="image",
x="x",
y="y",
dw="dw",
dh="dh",
plot_width=400,
plot_height=400,
palette='palette',
x_range=[xmin, xmax], y_range=[ymin, ymax],
tools="pan,wheel_zoom,box_zoom,select,reset",
title='dropoff comparison plot'
)
plot.title_text_font='12pt'
app.dropoff_comparison_plot = plot
app.dropoff_comparison_raw_plot_source = plot.select({'type' : ColumnDataSource})[0]
data = ARDataSource(
data_url="/bokeh/taxidata/dropoff/",
data=dict(
x=[0], y=[0], dw=[xmax-xmin], dh=[ymax-ymin], palette=["Greys-256"]
)
)
app.dropoff_ar_plot_source = data
plot = image(source=data,
image="image",
plot_width=400,
plot_height=400,
x="x",
y="y",
dw="dw",
dh="dh",
palette='palette',
x_range=[xmin, xmax], y_range=[ymin, ymax],
tools="pan,wheel_zoom,box_zoom,reset,select,reset",
title='dropoff'
)
plot.title_text_font='12pt'
app.dropoff_plot = plot
app.dropoff_raw_plot_source = plot.select({'type' : ColumnDataSource})[0]
app.make_trip_distance_histogram()
app.make_trip_time_histogram()
app.widgets = VBoxForm()
app.day_of_week_selector = Select.create(
options=["-----", 'Weekday', 'Friday/Saturday/Sunday', 'Saturday/Sunday'],
name='Day Of Week'
)
app.date_slider = DateRangeSlider(value=(dt.datetime(2012, 1, 1),
dt.datetime(2013, 1, 28)),
bounds=(dt.datetime(2012, 12, 31),
dt.datetime(2013, 1, 31)),
step={'days' : 1},
range=({'days' : 1},{'days':30}),
name='period',
title='period'
)
app.hour_selector = Select.create(options=["-----",
'8am-12pm',
'12pm-4pm',
'4pm-8pm',
'8pm-12am',
'12am-4am'],
name='Hour of the Day'
)
title = Paragraph(text="NYC Taxi Cab Data", width=250, height=50)
app.widgets.children=[title, app.date_slider,
Paragraph(width=250, height=10),
app.hour_selector,
app.day_of_week_selector,
Paragraph(width=250, height=10),
app.distance_histogram,
Paragraph(text="",
width=250, height=50),
app.time_histogram]
app.images = VBox()
app.regular = HBox()
app.filtered = HBox()
app.regular.children = [app.pickup_plot, app.dropoff_plot]
app.filtered.children = [app.pickup_comparison_plot,
app.dropoff_comparison_plot]
app.images.children = [app.regular]
app.children = [app.widgets, app.images]
return app
#aggregator = ar.source(ar.count(), ar.const(1), ar.touches()) ### Aggregator is incomplete without shader and glyphs. Can add either to it
#shader = ar.Cuberoot()+ar.Interpolate(0,9) + ar.Floor()
#image(source=plot+aggregator+shader, palette=["reds-9"]) ###Implement aggregator.__radd__ to get a plot and .__add__ to get a shader
"""
In order to run the 'stocks' example, you have to execute
./bokeh-server -D remotedata
The remote data directory in the bokeh checkout has the sample data for this example
In addition, you must install ArrayManagement from this branch (soon to be master)
https://github.com/ContinuumIO/ArrayManagement
"""
#Stock-data plotting
#source = ServerDataSource(data_url="/defaultuser/AAPL.hdf5", owner_username="******")
#plot = square('volume','close',color='#FF00FF',source=source)
#percepmap = ar.source(plot, shader=ar.Cuberoot(), palette=["Reds-9"])
#image(source=percepmap, title="Perceptually corrected (Stocks)", reserve_val=0, **ar.mapping(percepmap))
#2010 US Census tracts
source = ServerDataSource(data_url="/defaultuser/CensusTracts.hdf5", owner_username="******")
plot = square( 'INTPTLONG','INTPTLAT',source=source)
heatmap = ar.source(plot, palette=["Reds-9"])
image(source=heatmap, title="Census Tracts", reserve_val=0, **ar.mapping(heatmap))
show()
