def test_heatmap_recipe(self):
source = ServerDataSource(data_url="fn://bivariate",
owner_username="******")
plot = square('A',
'B',
source=source,
plot_width=600,
plot_height=400,
title="Test Title")
plot2 = ar_downsample.heatmap(plot,
palette="Reds9",
reserve_val=0,
points=True,
client_color=True,
title="Test Title 2")
source2 = self._find_source(plot2)
self.assertEquals("Test Title 2", plot2.title)
self.assertEquals(type(source2), ServerDataSource)
transform = source2.transform
self.assertEquals(type(transform['info']), ar_downsample.Const)
self.assertEquals(type(transform['agg']), ar_downsample.Count)
self.assertEquals(type(transform['shader']), ar_downsample.Seq)
self.assertEquals(transform['shader'].out, "image")
def test_replot_property_transfer(self):
self.assertIsNotNone(self.bokeh_server, "Server failed to start, cannot tests")
ar_downsample._loadAR()
output_server("Census")
source = ServerDataSource(data_url="fn://bivariate", owner_username="******")
plot_width = 612
plot_height = 408
plot_title = "Test title"
plot = figure(lot_width=plot_width, plot_height=plot_height, title=plot_title)
plot.square('A', 'B', source=source)
ar_plot = ar_downsample.replot(plot)
self.assertEquals(ar_plot.plot_width, plot_width, "Plot width not transfered")
self.assertEquals(ar_plot.plot_height, plot_height, "Plot height not transfered")
self.assertEquals(ar_plot.title, plot_title, "Plot title not transfered")
plot_width = 612
plot_height = 408
plot_title = "Test title"
ar_plot = ar_downsample.replot(plot, title=plot_title, plot_width=plot_width, plot_height=plot_height)
self.assertEquals(ar_plot.plot_width, plot_width, "Plot width override failed")
self.assertEquals(ar_plot.plot_height, plot_height, "Plot height override failed")
self.assertEquals(ar_plot.title, plot_title, "Plot title override failed")
def test_replot_result_type(self):
ar_downsample._loadAR()
source = ServerDataSource(data_url="fn://bivariate",
owner_username="******")
plot = square('A', 'B', source=source)
expected = {
"image": "Image",
"image_rgb": "ImageRGBA",
"multi_line": "MultiLine"
}
shaders = dict()
for name in dir(ar_downsample):
item = getattr(ar_downsample, name)
if isinstance(item, ar_downsample.Shader):
shaders[item] = item.out
for shader_class in shaders:
shader = shader_class()
rslt = ar_downsample.replot(plot, shader=shader)
self.assertEquals(
expected[shader.out],
self._glyphspec(rslt)['type'],
"Error with {0}. Expected {1}, recieved {2}".format(
str(shader_class), expected[shader.out],
self._glyphspec(rslt)))
def test_replot_remove(self):
self.assertIsNotNone(self.bokeh_server, "Server failed to start, cannot tests")
ar_downsample._loadAR()
output_server("Census")
source = ServerDataSource(data_url="fn://bivariate", owner_username="******")
plot = figure()
plot.square('A', 'B', source=source)
ar_downsample.replot(plot, remove_original=False)
self.assertTrue(plot in curdoc().context.children, "Not retained")
ar_downsample.replot(plot, remove_original=True)
self.assertTrue(plot not in curdoc().context.children, "Not removed")
try:
ar_downsample.replot(plot, remove_original=True)
except:
self.assertTrue(False, "Error reploting plot not in curdoc")
def test_contour_recipe(self):
source = ServerDataSource(data_url="fn://bivariate", owner_username="******")
plot = figure(plot_width=600,
plot_height=400,
title="Test Title")
plot.square('A', 'B', source=source)
plot2 = ar_downsample.contours(plot, title="Contour")
source2 = self._find_source(plot2)
self.assertEquals("Contour", plot2.title)
self.assertEquals(type(source2), ServerDataSource)
transform = source2.transform
self.assertEquals(type(transform['info']), ar_downsample.Const)
self.assertEquals(type(transform['agg']), ar_downsample.Count)
self.assertEquals(type(transform['shader']), ar_downsample.Seq)
self.assertEquals(transform['shader'].out, "multi_line")
def test_source(self):
ar_downsample._loadAR()
source = ServerDataSource(data_url="fn://bivariate", owner_username="******")
plot = figure()
plot.square('A', 'B', source=source)
agg = ar_downsample.CountCategories()
info = ar_downsample.Const(val=1)
shader = ar_downsample.InterpolateColor()
new_source = ar_downsample.source(plot, agg=agg, info=info, shader=shader)
self.assertIsNotNone(new_source.transform)
trans = new_source.transform
self.assertEquals(trans['resample'], 'abstract rendering')
self.assertEquals(trans['agg'], agg)
self.assertEquals(trans['info'], info)
self.assertEquals(trans['shader'], shader)
self.assertEquals(trans['glyphspec'], self._glyphspec(plot))
self.assertEquals(trans['points'], False)
from bokeh.plotting import figure, output_server, show
from bokeh.models import ServerDataSource
import bokeh.transforms.ar_downsample as ar
from blaze.server.client import Client
from blaze import Data
output_server("abstractrender")
c = Client('http://localhost:5006')
d = Data(c)
source = ServerDataSource()
source.from_blaze(d.gauss, local=True)
plot = figure()
plot.square('oneA', 'oneB', color='#FF00FF', source=source)
#Server-side colored heatmap
arplot = ar.heatmap(plot, spread=3, transform=None, title="Server-rendered, uncorrected")
arplot = ar.heatmap(plot, spread=3, transform="Log", title="Server-rendered, log transformed")
arplot = ar.heatmap(plot, spread=3, title="Server-rendered, perceptually corrected")
ar.replot(plot,
agg=ar.Count(),
info=ar.Const(val=1),
shader=ar.Spread(factor=3) + ar.Cuberoot() + ar.InterpolateColor(low=(255,200,200), high=(255,0,0)),
points=True,
title="Manually process: perceptually corrected",
reserve_val=0)
import numpy as np
from bokeh.plotting import square, output_server, image, show
from bokeh.models import ServerDataSource
import bokeh.transforms.ar_downsample as ar
output_server("abstractrender")
source = ServerDataSource(data_url="fn://gauss", owner_username="******")
plot = square('oneA', 'oneB', color='#FF00FF', source=source)
#Server-side colored heatmap
ar.heatmap(plot,
spread=3,
transform=None,
title="Server-rendered, uncorrected")
ar.heatmap(plot,
spread=3,
transform="Log",
title="Server-rendered, log transformed")
ar.heatmap(plot, spread=3, title="Server-rendered, perceptually corrected")
ar.replot(plot,
agg=ar.Count(),
info=ar.Const(val=1),
shader=ar.Spread(factor=3) + ar.Cuberoot() +
ar.InterpolateColor(low=(255, 200, 200), high=(255, 0, 0)),
points=True,
title="Manually process: perceptually corrected",
reserve_val=0)
# Client-side colored heatmap
from bokeh.plotting import figure, output_server, show
from bokeh.models import ServerDataSource
import bokeh.transforms.ar_downsample as ar
from blaze.server.client import Client
from blaze import Data
output_server("Census")
# 2010 US Census tracts
c = Client('http://localhost:5006')
d = Data(c)
source = ServerDataSource()
source.from_blaze(d.census, local=True)
plot = figure()
arplot = plot.square(
'LON', 'LAT',
source=source,
plot_width=600,
plot_height=400,
title="Census Tracts")
ar.heatmap(arplot, palette="Reds9", reserve_val=0, points=True, client_color=True, title="Census Tracts (Client Colors)")
ar.heatmap(arplot, low=(255, 200, 200), points=True, title="Census Tracts (Server Colors)")
ar.contours(arplot, title="ISO Contours")
show(plot)
from bokeh.plotting import figure, output_server, show
from bokeh.models import ServerDataSource
import bokeh.transforms.ar_downsample as ar
from blaze.server.client import Client
from blaze import Data
output_server("Census")
# 2010 US Census tracts
c = Client('http://localhost:5006')
d = Data(c)
source = ServerDataSource()
source.from_blaze(d.census, local=True)
plot = figure()
arplot = plot.square('LON',
'LAT',
source=source,
plot_width=600,
plot_height=400,
title="Census Tracts")
ar.heatmap(arplot,
palette="Reds9",
reserve_val=0,
points=True,
client_color=True,
title="Census Tracts (Client Colors)")
ar.heatmap(arplot,
low=(255, 200, 200),
points=True,
title="Census Tracts (Server Colors)")
from bokeh.plotting import square, output_server, show
from bokeh.models import ServerDataSource
import bokeh.transforms.ar_downsample as ar
"""
In order to run this 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
"""
output_server("Census")
# 2010 US Census tracts
source = ServerDataSource(data_url="/defaultuser/CensusTracts.hdf5",
owner_username="******")
plot = square('LON',
'LAT',
source=source,
plot_width=600,
plot_height=400,
title="Census Tracts")
ar.heatmap(plot,
palette="Reds9",
reserve_val=0,
points=True,
client_color=True,
title="Census Tracts (Client Colors)")
ar.heatmap(plot,
low=(255, 200, 200),
