def test_PolyLine(self): source = _SourceShim(ar_downsample.Contour) result = ar_downsample.mapping(source) expected = {} self.assertEquals(len(expected), len(result)) self.assertEquals(sorted(expected.keys()), sorted(result.keys())) source = _SourceShim(ar_downsample.Contour, "A", "B", "C") result = ar_downsample.mapping(source) expected['A'] = source.defVal expected['B'] = source.defVal expected['C'] = source.defVal self.assertEquals(sorted(expected.keys()), sorted(result.keys()))
def test_PolyLine(self): source = _SourceShim(ar_downsample.Contour) result = ar_downsample.mapping(source) expected = {'line_color': []} self.assertEquals(len(expected), len(result)) self.assertEquals(sorted(expected.keys()), sorted(result.keys())) source = _SourceShim(ar_downsample.Contour, "A", "B", "C") result = ar_downsample.mapping(source) expected['A'] = source.defVal expected['B'] = source.defVal expected['C'] = source.defVal self.assertEquals(sorted(expected.keys()), sorted(result.keys()))
def test_ImageRGB(self): source = _SourceShim(ar_downsample.InterpolateColor) result = ar_downsample.mapping(source) expected = {'x_range': Range1d(start=0, end=0), 'y_range': Range1d(start=0, end=0)} self.assertEquals(len(expected), len(result)) self.assertEquals(sorted(expected.keys()), sorted(result.keys())) source = _SourceShim(ar_downsample.InterpolateColor, "A", "B", "C") result = ar_downsample.mapping(source) expected['A'] = source.defVal expected['B'] = source.defVal expected['C'] = source.defVal self.assertEquals(sorted(expected.keys()), sorted(result.keys()))
def test_ImageRGB(self): source = _SourceShim(ar_downsample.InterpolateColor) result = ar_downsample.mapping(source) expected = {'x_range': Range1d(start=0, end=0), 'y_range': Range1d(start=0, end=0)} self.assertEquals(len(expected), len(result)) self.assertEquals(sorted(expected.keys()), sorted(result.keys())) source = _SourceShim(ar_downsample.InterpolateColor, "A", "B", "C") result = ar_downsample.mapping(source) expected['A'] = source.defVal expected['B'] = source.defVal expected['C'] = source.defVal self.assertEquals(sorted(expected.keys()), sorted(result.keys()))
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
#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()