def make_plot(): source = ColumnDataSource( dict( dates=[date(2014, 3, i) for i in [1, 2, 3, 4, 5]], downloads=[100, 27, 54, 64, 75], )) xdr = DataRange1d(sources=[source.columns("dates")]) ydr = DataRange1d(sources=[source.columns("downloads")]) plot = Plot(title="Product downloads", data_sources=[source], x_range=xdr, y_range=ydr, width=400, height=400) line = Line(x="dates", y="downloads", line_color="blue") line_glyph = Glyph(data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=line) plot.renderers.append(line_glyph) circle = Circle(x="dates", y="downloads", fill_color="red") circle_glyph = Glyph(data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=circle) plot.renderers.append(circle_glyph) hover = HoverTool(plot=plot, tooltips=dict(downloads="@downloads")) plot.tools.append(hover) xaxis = DatetimeAxis(plot=plot, dimension=0) yaxis = LinearAxis(plot=plot, dimension=1) xgrid = Grid(plot=plot, dimension=0, axis=xaxis) ygrid = Grid(plot=plot, dimension=1, axis=yaxis) return plot, source
def make_plot(): xdr = DataRange1d(sources=[source.columns("dates")]) ydr = DataRange1d(sources=[source.columns("downloads")]) plot = Plot(title="Product downloads", x_range=xdr, y_range=ydr, plot_width=400, plot_height=400) line = Line(x="dates", y="downloads", line_color="blue") plot.add_glyph(source, line) circle = Circle(x="dates", y="downloads", fill_color="red") plot.add_glyph(source, circle) xaxis = DatetimeAxis() plot.add_layout(xaxis, 'below') yaxis = LinearAxis() plot.add_layout(yaxis, 'left') plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker)) plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker)) plot.add_tools(HoverTool(tooltips=dict(downloads="@downloads"))) return plot, source
def make_downloads_plot(self, source): xdr = DataRange1d(sources=[source.columns("dates")]) ydr = DataRange1d(sources=[source.columns("downloads")]) title = "%s downloads" % self.modelform.installer plot = Plot(title=title, data_sources=[source], x_range=xdr, y_range=ydr, width=600, height=400) line = Line(x="dates", y="downloads", line_color="blue") line_glyph = Glyph(data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=line) plot.renderers.append(line_glyph) circle = Circle(x="dates", y="downloads", fill_color="red") circle_glyph = Glyph(data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=circle) plot.renderers.append(circle_glyph) hover = HoverTool(plot=plot, tooltips=dict(downloads="@downloads")) plot.tools.append(hover) xformatter = DatetimeTickFormatter(formats=dict(months=["%b %Y"])) yformatter = BasicTickFormatter(precision=None, use_scientific=False) xaxis = DatetimeAxis(plot=plot, dimension=0, formatter=xformatter) yaxis = LinearAxis(plot=plot, dimension=1, formatter=yformatter) xgrid = Grid(plot=plot, dimension=0, axis=xaxis) ygrid = Grid(plot=plot, dimension=1, axis=yaxis) return plot
def trail_map(data): lon = (min(data.lon) + max(data.lon))/2 lat = (min(data.lat) + max(data.lat))/2 map_options = GMapOptions(lng=lon, lat=lat, zoom=13) plot = GMapPlot(title="%s - Trail Map" % title, map_options=map_options, plot_width=800, plot_height=800) xaxis = LinearAxis() plot.add_layout(xaxis, 'below') yaxis = LinearAxis() plot.add_layout(yaxis, 'left') xgrid = Grid(plot=plot, dimension=0, ticker=xaxis.ticker, grid_line_dash="dashed", grid_line_color="gray") ygrid = Grid(plot=plot, dimension=1, ticker=yaxis.ticker, grid_line_dash="dashed", grid_line_color="gray") plot.renderers.extend([xgrid, ygrid]) hover = HoverTool(tooltips=dict(distance="@dist")) plot.add_tools(hover, PanTool(), WheelZoomTool(), ResetTool(), BoxSelectTool()) line_source = ColumnDataSource(dict(x=data.lon, y=data.lat, dist=data.dist)) line = Line(x="x", y="y", line_color="blue", line_width=2) plot.add_glyph(line_source, line) plot.x_range = DataRange1d(sources=[line_source.columns("x")]) plot.y_range = DataRange1d(sources=[line_source.columns("y")]) return plot
def line_advanced(): source = ColumnDataSource(data=dict(x=x,y=y,z=z,widths=widths, heights=heights)) xdr = DataRange1d(sources=[source.columns("x")]) xdr2 = DataRange1d(sources=[source.columns("x")]) ydr = DataRange1d(sources=[source.columns("y")]) ydr2 = DataRange1d(sources=[source.columns("y")]) line_glyph = Line(x="x", y="y", line_color="blue") renderer = GlyphRenderer(data_source = source, xdata_range = xdr, ydata_range = ydr, glyph = line_glyph) pantool = PanTool(dataranges = [xdr, ydr], dimensions=["width","height"]) zoomtool = ZoomTool(dataranges=[xdr,ydr], dimensions=("width","height")) plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], border=50) plot.tools = [pantool, zoomtool] plot.renderers.append(renderer) #notice that these two have a differen y data range renderer2 = GlyphRenderer(data_source = source, xdata_range = xdr, ydata_range = ydr2, glyph = line_glyph) plot2 = Plot(x_range=xdr, y_range=ydr2, data_sources=[source], border=50) plot2.renderers.append(renderer2) #notice that these two have a differen y data range renderer3 = GlyphRenderer(data_source = source, xdata_range = xdr2, ydata_range = ydr, glyph = line_glyph) plot3 = Plot(x_range=xdr2, y_range=ydr, data_sources=[source], border=50) plot3.renderers.append(renderer3) #this is a dummy plot with no renderers plot4 = Plot(x_range=xdr2, y_range=ydr, data_sources=[source], border=50) sess = session.HTMLFileSession("line_linked_advanced.html") sess.add(plot, renderer, source, xdr, ydr, pantool, zoomtool) sess.add(plot2, renderer2, ydr2, xdr2, renderer3, plot3, plot4) grid = GridPlot(children=[[plot, plot2], [plot3, plot4 ]], name="linked_advanced") sess.add(grid) sess.plotcontext.children.append(grid) sess.save(js="relative", css="relative", rootdir=os.path.abspath(".")) print "Wrote line_linked_advanced.html" webbrowser.open("file://" + os.path.abspath("line_linked_advanced.html"))
def job_loc_plot_builder(): xdr = FactorRange(factors=countries) ydr = DataRange1d(sources=[source_country.columns("data_range")]) plot = Plot(title="Postings by Job Location (Country)", data_sources=[source_country], x_range=xdr, y_range=ydr, plot_width=760, plot_height=500) xaxis = CategoricalAxis(plot=plot, dimension=0, major_label_orientation=pi / 4.0) yaxis = LinearAxis(plot=plot, dimension=1) yaxis.major_tick_in = 0 ygrid = Grid(plot=plot, dimension=1, axis=yaxis) quad = Rect(x="country", y="count_half", height="count", width=0.9, fill_color="#483D8B") bars = Glyph(data_source=source_country, xdata_range=xdr, ydata_range=ydr, glyph=quad) plot.renderers.append(bars) plot.background_fill = '#333333' return plot
def weekday_builder(): dow = [ "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday" ] xdr = FactorRange(factors=dow) ydr = DataRange1d(sources=[source_dow.columns("data_range")]) plot = Plot(title="Weekday of Job Posting", data_sources=[source_dow], x_range=xdr, y_range=ydr, plot_width=760, plot_height=500) xaxis = CategoricalAxis(plot=plot, dimension=0, major_label_orientation=pi / 4.0) yaxis = LinearAxis(plot=plot, dimension=1) yaxis.major_tick_in = 0 ygrid = Grid(plot=plot, dimension=1, axis=yaxis) quad = Rect(x="weekday", y="weekday_half", height="count", width=0.9, fill_color="#D9301A") bars = Glyph(data_source=source_dow, xdata_range=xdr, ydata_range=ydr, glyph=quad) plot.renderers.append(bars) plot.background_fill = '#686975' return plot
def population(): xdr = FactorRange(factors=years) ydr = DataRange1d( sources=[source_known.columns("y"), source_predicted.columns("y")]) plot = Plot(title=None, x_range=xdr, y_range=ydr, plot_width=800, plot_height=200) plot.add_layout(CategoricalAxis(major_label_orientation=pi / 4), 'below') line_known = Line(x="x", y="y", line_color="violet", line_width=2) line_known_glyph = plot.add_glyph(source_known, line_known) line_predicted = Line(x="x", y="y", line_color="violet", line_width=2, line_dash="dashed") line_predicted_glyph = plot.add_glyph(source_predicted, line_predicted) plot.add_layout( Legend(orientation="bottom_right", legends=dict(known=[line_known_glyph], predicted=[line_predicted_glyph]))) return plot
def jobtype_builder(): jtypes = ["Half Time", "Full Time", "Hourly", "Temporary"] xdr = FactorRange(factors=jtypes) ydr = DataRange1d(sources=[source_jobtype.columns("data_range")]) plot = Plot(title="Job Type", data_sources=[source_jobtype], x_range=xdr, y_range=ydr, plot_width=760, plot_height=500) xaxis = CategoricalAxis(plot=plot, dimension=0, major_label_orientation=pi / 4.0) yaxis = LinearAxis(plot=plot, dimension=1) yaxis.major_tick_in = 0 ygrid = Grid(plot=plot, dimension=1, axis=yaxis) quad = Rect(x="jobtypes", y="jobtype_half", height="count", width=0.9, fill_color="#33A6A4") bars = Glyph(data_source=source_jobtype, xdata_range=xdr, ydata_range=ydr, glyph=quad) plot.renderers.append(bars) plot.background_fill = '#686975' return plot
def large_plot(n): from bokeh.objects import (Plot, PlotContext, LinearAxis, Grid, Glyph, ColumnDataSource, DataRange1d, PanTool, WheelZoomTool, BoxZoomTool, BoxSelectTool, BoxSelectionOverlay, ResizeTool, PreviewSaveTool, ResetTool) from bokeh.glyphs import Line context = PlotContext() objects = set([context]) for i in xrange(n): source = ColumnDataSource(data=dict(x=[0, i + 1], y=[0, i + 1])) xdr = DataRange1d(sources=[source.columns("x")]) ydr = DataRange1d(sources=[source.columns("y")]) plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source]) xaxis = LinearAxis(plot=plot, dimension=0) yaxis = LinearAxis(plot=plot, dimension=1) xgrid = Grid(plot=plot, dimension=0) ygrid = Grid(plot=plot, dimension=1) tickers = [ xaxis.ticker, xaxis.formatter, yaxis.ticker, yaxis.formatter ] renderer = Glyph(data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=Line(x='x', y='y')) plot.renderers.append(renderer) pan = PanTool(plot=plot) wheel_zoom = WheelZoomTool(plot=plot) box_zoom = BoxZoomTool(plot=plot) box_select = BoxSelectTool(plot=plot) box_selection = BoxSelectionOverlay(tool=box_select) resize = ResizeTool(plot=plot) previewsave = PreviewSaveTool(plot=plot) reset = ResetTool(plot=plot) tools = [ pan, wheel_zoom, box_zoom, box_select, box_selection, resize, previewsave, reset ] plot.tools.append(tools) context.children.append(plot) objects |= set( [source, xdr, ydr, plot, xaxis, yaxis, xgrid, ygrid, renderer] + tickers + tools) return context, objects
def pyramid_plot(self): from bokeh.objects import (Plot, DataRange1d, LinearAxis, Grid, Legend, SingleIntervalTicker) from bokeh.glyphs import Quad xdr = DataRange1d(sources=[ self.source_pyramid.columns("male"), self.source_pyramid.columns("female") ]) ydr = DataRange1d(sources=[self.source_pyramid.columns("groups")]) self.plot = Plot(title=None, x_range=xdr, y_range=ydr, plot_width=600, plot_height=600) xaxis = LinearAxis() self.plot.add_layout(xaxis, 'below') yaxis = LinearAxis(ticker=SingleIntervalTicker(interval=5)) self.plot.add_layout(yaxis, 'left') self.plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker)) self.plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker)) male_quad = Quad(left="male", right=0, bottom="groups", top="shifted", fill_color="#3B8686") male_quad_glyph = self.plot.add_glyph(self.source_pyramid, male_quad) female_quad = Quad(left=0, right="female", bottom="groups", top="shifted", fill_color="#CFF09E") female_quad_glyph = self.plot.add_glyph(self.source_pyramid, female_quad) self.plot.add_layout( Legend(legends=dict(Male=[male_quad_glyph], Female=[female_quad_glyph])))
def make_plot(source, xname, yname, line_color, xdr=None, ydr=None): """ Returns a tuple (plot, [obj1...objN]); the former can be added to a GridPlot, and the latter is added to the plotcontext. """ if xdr is None: xdr = DataRange1d(sources=[source.columns(xname)]) if ydr is None: ydr = DataRange1d(sources=[source.columns(yname)]) plot = Plot(x_range=xdr, y_range=ydr, min_border=50) plot.add_layout(LinearAxis(), 'below') plot.add_layout(LinearAxis(), 'left') plot.add_glyph(source, Line(x=xname, y=yname, line_color=line_color)) plot.add_tools(PanTool(), WheelZoomTool()) return plot
def pyramid(): xdr = DataRange1d(sources=[ source_pyramid.columns("male"), source_pyramid.columns("female") ]) ydr = DataRange1d(sources=[source_pyramid.columns("groups")]) plot = Plot(title=None, x_range=xdr, y_range=ydr, plot_width=600, plot_height=600) xaxis = LinearAxis() plot.add_layout(xaxis, 'below') yaxis = LinearAxis(ticker=SingleIntervalTicker(interval=5)) plot.add_layout(yaxis, 'left') plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker)) plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker)) male_quad = Quad(left="male", right=0, bottom="groups", top="shifted", fill_color="#3B8686") male_quad_glyph = plot.add_glyph(source_pyramid, male_quad) female_quad = Quad(left=0, right="female", bottom="groups", top="shifted", fill_color="#CFF09E") female_quad_glyph = plot.add_glyph(source_pyramid, female_quad) plot.add_layout( Legend( legends=dict(Male=[male_quad_glyph], Female=[female_quad_glyph]))) return plot
def altitude_profile(data): plot = Plot(title="%s - Altitude Profile" % title, plot_width=800, plot_height=400) xaxis = LinearAxis(axis_label="Distance (km)") plot.add_layout(xaxis, 'below') yaxis = LinearAxis(axis_label="Altitude (m)") plot.add_layout(yaxis, 'left') xgrid = Grid(plot=plot, dimension=0, ticker=xaxis.ticker) ygrid = Grid(plot=plot, dimension=1, ticker=yaxis.ticker) plot.renderers.extend([xgrid, ygrid]) plot.add_tools(PanTool(), WheelZoomTool(), ResetTool(), BoxSelectTool()) X, Y = data.dist, data.alt y0 = min(Y) patches_source = ColumnDataSource( dict(xs=[[X[i], X[i + 1], X[i + 1], X[i]] for i in range(len(X[:-1]))], ys=[[y0, y0, Y[i + 1], Y[i]] for i in range(len(Y[:-1]))], color=data.colors[:-1])) patches = Patches(xs="xs", ys="ys", fill_color="color", line_color="color") plot.add_glyph(patches_source, patches) line_source = ColumnDataSource(dict( x=data.dist, y=data.alt, )) line = Line(x='x', y='y', line_color="black", line_width=1) plot.add_glyph(line_source, line) plot.x_range = DataRange1d(sources=[line_source.columns("x")]) plot.y_range = DataRange1d(sources=[line_source.columns("y")]) return plot
def make_plot(source, xname, yname, line_color, xdr=None, ydr=None): """ Returns a tuple (plot, [obj1...objN]); the former can be added to a GridPlot, and the latter is added to the plotcontext. """ if xdr is None: xdr = DataRange1d(sources=[source.columns(xname)]) if ydr is None: ydr = DataRange1d(sources=[source.columns(yname)]) plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], min_border=50) xaxis = LinearAxis(plot=plot, dimension=0, location="bottom") yaxis = LinearAxis(plot=plot, dimension=1, location="left") pantool = PanTool(dimensions=["width", "height"]) wheelzoomtool = WheelZoomTool(dimensions=["width", "height"]) renderer = Glyph( data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=Line(x=xname, y=yname, line_color=line_color), ) plot.renderers.append(renderer) plot.tools = [pantool, wheelzoomtool] return plot
def pyramid(): xdr = DataRange1d(sources=[source_pyramid.columns("male"), source_pyramid.columns("female")]) ydr = DataRange1d(sources=[source_pyramid.columns("groups")]) plot = Plot(title=None, data_sources=[source_pyramid], x_range=xdr, y_range=ydr, plot_width=600, plot_height=600) xaxis = LinearAxis(plot=plot, dimension=0) yaxis = LinearAxis(plot=plot, dimension=1, ticker=SingleIntervalTicker(interval=5)) xgrid = Grid(plot=plot, dimension=0, axis=xaxis) ygrid = Grid(plot=plot, dimension=1, axis=yaxis) male_quad = Quad(left="male", right=0, bottom="groups", top="shifted", fill_color="blue") male_quad_glyph = Glyph(data_source=source_pyramid, xdata_range=xdr, ydata_range=ydr, glyph=male_quad) plot.renderers.append(male_quad_glyph) female_quad = Quad(left=0, right="female", bottom="groups", top="shifted", fill_color="violet") female_quad_glyph = Glyph(data_source=source_pyramid, xdata_range=xdr, ydata_range=ydr, glyph=female_quad) plot.renderers.append(female_quad_glyph) legend = Legend(plot=plot, legends=dict(Male=[male_quad_glyph], Female=[female_quad_glyph])) plot.renderers.append(legend) return plot
def population(): xdr = FactorRange(factors=years) ydr = DataRange1d(sources=[source_known.columns("y"), source_predicted.columns("y")]) plot = Plot(title=None, data_sources=[source_known, source_predicted], x_range=xdr, y_range=ydr, plot_width=800, plot_height=200) xaxis = CategoricalAxis(plot=plot, dimension=0, major_label_orientation=pi/4) # yaxis = LinearAxis(plot=plot, dimension=1, ...) line_known = Line(x="x", y="y", line_color="violet", line_width=2) line_known_glyph = Glyph(data_source=source_known, xdata_range=xdr, ydata_range=ydr, glyph=line_known) plot.renderers.append(line_known_glyph) line_predicted = Line(x="x", y="y", line_color="violet", line_width=2, line_dash="dashed") line_predicted_glyph = Glyph(data_source=source_predicted, xdata_range=xdr, ydata_range=ydr, glyph=line_predicted) plot.renderers.append(line_predicted_glyph) legend = Legend(plot=plot, orientation="bottom_right", legends=dict(known=[line_known_glyph], predicted=[line_predicted_glyph])) plot.renderers.append(legend) return plot
from bokeh import session colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'} flowers['color'] = flowers['species'].map(lambda x: colormap[x]) source = ColumnDataSource(data=dict(petal_length=flowers['petal_length'], petal_width=flowers['petal_width'], sepal_length=flowers['sepal_length'], sepal_width=flowers['sepal_width'], color=flowers['color'])) text_source = ColumnDataSource(data=dict(center=[125])) xdr = DataRange1d(sources=[ source.columns("petal_length", "petal_width", "sepal_length", "sepal_width") ]) ydr = DataRange1d(sources=[ source.columns("petal_length", "petal_width", "sepal_length", "sepal_width") ]) pan = PanTool(dataranges=[xdr, ydr], dimensions=["x", "y"]) zoom = ZoomTool(dataranges=[xdr, ydr], dimensions=["x", "y"]) def make_plot(xname, yname, xax=False, yax=False, text=None): plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], background_fill="#ffeedd",
def generate_embed_test(): """this generates a new plot and uses the script inject to put it into a page running this repeatedly will fill up your redis DB quickly, but it allows quick iteration """ from numpy import pi, arange, sin, cos import numpy as np from bokeh.objects import ( Plot, DataRange1d, LinearAxis, Rule, ColumnDataSource, GlyphRenderer, PanTool, ZoomTool, PreviewSaveTool) from bokeh.glyphs import Circle from bokeh import session x = arange(-2*pi, 2*pi, 0.1) y = sin(x) z = cos(x) widths = np.ones_like(x) * 0.02 heights = np.ones_like(x) * 0.2 source = ColumnDataSource(data=dict(x=x,y=y,z=z,widths=widths, heights=heights)) xdr = DataRange1d(sources=[source.columns("x")]) ydr = DataRange1d(sources=[source.columns("y")]) circle = Circle(x="x", y="y", fill="red", radius=5, line_color="black") glyph_renderer = GlyphRenderer( data_source = source, xdata_range = xdr, ydata_range = ydr, glyph = circle) pantool = PanTool(dataranges = [xdr, ydr], dimensions=["width","height"]) #zoomtool = ZoomTool(dataranges=[xdr,ydr], dimensions=("width","height")) previewtool = PreviewSaveTool(dataranges=[xdr,ydr], dimensions=("width","height")) plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], border= 80) xaxis = LinearAxis(plot=plot, dimension=0) yaxis = LinearAxis(plot=plot, dimension=1) xgrid = Rule(plot=plot, dimension=0) ygrid = Rule(plot=plot, dimension=1) plot.renderers.append(glyph_renderer) plot.tools = [pantool, previewtool] sess = session.PlotServerSession( username="******", serverloc="http://localhost:5006", userapikey="nokey") sess.use_doc("glyph2") sess.add(plot, glyph_renderer, xaxis, yaxis, xgrid, ygrid, source, xdr, ydr, pantool, previewtool) sess.plotcontext.children.append(plot) sess.plotcontext._dirty = True # not so nice.. but set the model doens't know # that we appended to children sess.store_all() if app.debug: slug = hemlib.slug_json() static_js = hemlib.slug_libs(app, slug['libs']) hemsource = os.path.join(app.static_folder, "coffee") hem_js = hemlib.coffee_assets(hemsource, "localhost", 9294) hemsource = os.path.join(app.static_folder, "vendor", "bokehjs", "coffee") hem_js += hemlib.coffee_assets(hemsource, "localhost", 9294) else: static_js = ['/bokeh/static/js/application.js'] hem_js = [] return render_template("generate_embed_test.html", jsfiles=static_js, hemfiles=hem_js, plot_scr=plot.script_inject())
except KeyError: county_colors.append("black") county_source = ColumnDataSource( data=dict(county_xs=[ us_counties[code]["lons"] for code in us_counties if us_counties[code] ["state"] not in ["ak", "hi", "pr", "gu", "vi", "mp", "as"] ], county_ys=[ us_counties[code]["lats"] for code in us_counties if us_counties[code]["state"] not in ["ak", "hi", "pr", "gu", "vi", "mp", "as"] ], county_colors=county_colors)) xdr = DataRange1d(sources=[state_source.columns("state_xs")]) ydr = DataRange1d(sources=[state_source.columns("state_ys")]) plot = Plot(x_range=xdr, y_range=ydr, min_border=0, border_fill="white", title="2009 Unemployment Data", plot_width=1300, plot_height=800, toolbar_location="left") county_patches = Patches(xs="county_xs", ys="county_ys", fill_color="county_colors", fill_alpha=0.7,
from bokeh.objects import (Plot, DataRange1d, LinearAxis, DatetimeAxis, ColumnDataSource, Glyph, PanTool, WheelZoomTool) from bokeh.glyphs import Circle from bokeh import session x = arange(-2 * pi, 2 * pi, 0.1) y = sin(x) # Create an array of times, starting at the current time, and extending # for len(x) number of hours. times = np.arange(len(x)) * 3600000 + time.time() source = ColumnDataSource(data=dict(x=x, y=y, times=times)) xdr = DataRange1d(sources=[source.columns("times")]) ydr = DataRange1d(sources=[source.columns("y")]) circle = Circle(x="times", y="y", fill_color="red", size=5, line_color="black") glyph_renderer = Glyph( data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=circle, ) plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], border=80) xaxis = DatetimeAxis(plot=plot, dimension=0, location="min") yaxis = LinearAxis(plot=plot, dimension=1, location="min")
from bokeh import session from bokeh import document document = document.Document() session = session.Session() session.use_doc('line_animate') session.load_document(document) x = np.linspace(-2 * pi, 2 * pi, 1000) x_static = np.linspace(-2 * pi, 2 * pi, 1000) y = sin(x) z = cos(x) source = ColumnDataSource(data=dict(x=x, y=y, z=z, x_static=x_static)) xdr = DataRange1d(sources=[source.columns("x")]) xdr_static = DataRange1d(sources=[source.columns("x_static")]) ydr = DataRange1d(sources=[source.columns("y")]) line_glyph = Line(x="x", y="y", line_color="blue") line_glyph2 = Line(x="x", y="z", line_color="red") renderer = Glyph(data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=line_glyph) renderer2 = Glyph(data_source=source, xdata_range=xdr_static, ydata_range=ydr, glyph=line_glyph2) plot = Plot(x_range=xdr_static,
GlyphRenderer, ObjectArrayDataSource, PanTool, ZoomTool) from bokeh.glyphs import Circle from bokeh import session colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'} flowers['color'] = flowers['species'].map(lambda x: colormap[x]) source = ColumnDataSource(data=dict(petal_length=flowers['petal_length'], petal_width=flowers['petal_width'], sepal_length=flowers['sepal_length'], sepal_width=flowers['sepal_width'], color=flowers['color'])) xdr = DataRange1d(sources=[source.columns("petal_length")]) ydr = DataRange1d(sources=[source.columns("petal_width")]) circle = Circle(x="petal_length", y="petal_width", fill_color="color", fill_alpha=0.2, radius=5, line_color="color") glyph_renderer = GlyphRenderer( data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=circle, )
def make_plot(): from numpy import pi, arange, sin, cos import numpy as np from bokeh.objects import (Plot, DataRange1d, LinearAxis, ColumnDataSource, GlyphRenderer, PanTool, PreviewSaveTool) from bokeh.glyphs import Circle from bokeh import session x = arange(-2 * pi, 2 * pi, 0.1) y = sin(x) z = cos(x) widths = np.ones_like(x) * 0.02 heights = np.ones_like(x) * 0.2 source = ColumnDataSource( data=dict(x=x, y=y, z=z, widths=widths, heights=heights)) xdr = DataRange1d(sources=[source.columns("x")]) ydr = DataRange1d(sources=[source.columns("y")]) circle = Circle(x="x", y="y", fill="red", radius=5, line_color="black") glyph_renderer = GlyphRenderer(data_source=source, xdata_range=xdr, ydata_range=ydr, glyph=circle) pantool = PanTool(dataranges=[xdr, ydr], dimensions=["width", "height"]) previewtool = PreviewSaveTool(dataranges=[xdr, ydr], dimensions=("width", "height")) plot = Plot(x_range=xdr, y_range=ydr, data_sources=[source], border=80) xaxis = LinearAxis(plot=plot, dimension=0) yaxis = LinearAxis(plot=plot, dimension=1) plot.renderers.append(glyph_renderer) plot.tools = [pantool, previewtool] sess = session.PlotServerSession(username="******", serverloc="http://localhost:5006", userapikey="nokey") sess.use_doc("glyph2") sess.add( plot, glyph_renderer, xaxis, yaxis, # xgrid, ygrid, source, xdr, ydr, pantool, previewtool) sess.plotcontext.children.append(plot) sess.plotcontext._dirty = True # not so nice.. but set the model doens't know # that we appended to children sess.store_all() return plot
def create(self): manufacturers = sorted(mpg["manufacturer"].unique()) models = sorted(mpg["model"].unique()) transmissions = sorted(mpg["trans"].unique()) drives = sorted(mpg["drv"].unique()) classes = sorted(mpg["class"].unique()) manufacturer_select = Select(title="Manufacturer:", value="All", options=["All"] + manufacturers) manufacturer_select.on_change('value', self.on_manufacturer_change) model_select = Select(title="Model:", value="All", options=["All"] + models) model_select.on_change('value', self.on_model_change) transmission_select = Select(title="Transmission:", value="All", options=["All"] + transmissions) transmission_select.on_change('value', self.on_transmission_change) drive_select = Select(title="Drive:", value="All", options=["All"] + drives) drive_select.on_change('value', self.on_drive_change) class_select = Select(title="Class:", value="All", options=["All"] + classes) class_select.on_change('value', self.on_class_change) columns = [ TableColumn(field="manufacturer", header="Manufacturer", type="autocomplete", source=manufacturers), TableColumn(field="model", header="Model", type="autocomplete", source=models), TableColumn(field="displ", header="Displacement", type="numeric", format="0.00"), TableColumn(field="year", header="Year", type="numeric"), TableColumn(field="cyl", header="Cylinders", type="numeric"), TableColumn(field="trans", header="Transmission", type="dropdown", strict=True, source=transmissions), TableColumn(field="drv", header="Drive", type="autocomplete", strict=True, source=drives), TableColumn(field="class", header="Class", type="autocomplete", strict=True, source=classes), TableColumn(field="cty", header="City MPG", type="numeric"), TableColumn(field="hwy", header="Highway MPG", type="numeric"), ] handson_table = HandsonTable(source=self.source, columns=columns, sorting=True) xdr = DataRange1d(sources=[self.source.columns("index")]) #xdr = FactorRange(factors=manufacturers) ydr = DataRange1d( sources=[self.source.columns("cty"), self.source.columns("hwy")]) plot = Plot(title=None, data_sources=[self.source], x_range=xdr, y_range=ydr, plot_width=800, plot_height=300) xaxis = LinearAxis(plot=plot) plot.below.append(xaxis) yaxis = LinearAxis(plot=plot) ygrid = Grid(plot=plot, dimension=1, ticker=yaxis.ticker) plot.left.append(yaxis) cty = Glyph(data_source=self.source, glyph=Circle(x="index", y="cty", fill_color="#396285", size=8, fill_alpha=0.5, line_alpha=0.5)) hwy = Glyph(data_source=self.source, glyph=Circle(x="index", y="hwy", fill_color="#CE603D", size=8, fill_alpha=0.5, line_alpha=0.5)) select_tool = BoxSelectTool(renderers=[cty, hwy], select_y=False) plot.tools.append(select_tool) overlay = BoxSelectionOverlay(tool=select_tool) plot.renderers.extend([cty, hwy, ygrid, overlay]) controls = VBox(children=[ manufacturer_select, model_select, transmission_select, drive_select, class_select ], width=200) top_panel = HBox(children=[controls, plot]) layout = VBox(children=[top_panel, handson_table]) return layout
x, fy, ty = taylor(expr, xs, order, (-2 * sy.pi, 2 * sy.pi), 200) plot.title = "%s vs. taylor(%s, n=%d)" % (expr, expr, order) legend.legends = { "%s" % expr: [line_f_glyph], "taylor(%s)" % expr: [line_t_glyph], } source.data = dict(x=x, fy=fy, ty=ty) slider.value = order session.store_document(document) source = ColumnDataSource(data=dict(x=[], fy=[], ty=[])) xdr = DataRange1d(sources=[source.columns("x")]) ydr = DataRange1d(sources=[source.columns("fy")]) plot = Plot(x_range=xdr, y_range=ydr, plot_width=800, plot_height=400) line_f = Line(x="x", y="fy", line_color="blue", line_width=2) line_f_glyph = plot.add_glyph(source, line_f) plot.add_layout(line_f_glyph) line_t = Line(x="x", y="ty", line_color="red", line_width=2) line_t_glyph = plot.add_glyph(source, line_t) plot.add_layout(line_t_glyph) xaxis = LinearAxis() plot.add_layout(xaxis, 'below')
summer_start = df.Date.irow(summer_start) summer_end = df.Date.irow(summer_end) calendar_end = df.Date.irow(-1) d1 = calendar_start + (summer_start - calendar_start) / 2 d2 = summer_start + (summer_end - summer_start) / 2 d3 = summer_end + (calendar_end - summer_end) / 2 text_source = ColumnDataSource( dict( dates=[d1, d2, d3], times=[dt.time(11, 30)] * 3, texts=["CST (UTC+1)", "CEST (UTC+2)", "CST (UTC+1)"], )) xdr = DataRange1d(sources=[source.columns("dates")]) ydr = DataRange1d(sources=[source.columns("sunrises", "sunsets")]) title = "Daylight Hours - Warsaw, Poland" plot = Plot(title=title, data_sources=[source, patch1_source, patch2_source, text_source], x_range=xdr, y_range=ydr, width=800, height=400) patch1 = Patch(x="dates", y="times", fill_color="skyblue", fill_alpha=0.8) patch1_glyph = Glyph(data_source=patch1_source, xdata_range=xdr, ydata_range=ydr, glyph=patch1)
def make_box_violin_plot(data, num_bins, maxwidth=0.9): """ data: dict[Str -> List[Number]] maxwidth: float Maximum width of tornado plot within each factor/facet Returns the plot object """ df = pd.DataFrame(columns=["group", "centers", "width", "height", "texts"]) bar_height = 50 bins = [0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6] # Compute histograms, while keeping track of max Y values and max counts for i, (group, vals) in enumerate(data.iteritems()): hist, edges = np.histogram(vals, bins) df = df.append( pd.DataFrame( dict( group=group, centers=np.arange(len(hist)) * bar_height, width=np.log10(hist), height=np.ones(hist.shape) * bar_height, texts=map(str, hist), ))) df.replace(-np.inf, 0) # Normalize the widths df["width"] *= (maxwidth / df["width"].max()) ds = ColumnDataSource(df) xdr = FactorRange(factors=sorted(df["group"].unique())) ydr = DataRange1d(sources=[ds.columns("centers")]) plot = Plot(data_sources=[ds], x_range=xdr, y_range=ydr, title="Degree Distribution", plot_width=750, plot_height=600, tools=[]) xaxis = CategoricalAxis(plot=plot, location="bottom", axis_label="number of nodes") #yaxis = LogAxis(plot=plot, location="left", axis_label="degree") plot.below.append(xaxis) #plot.above.append(yaxis) #xgrid = Grid(plot=plot, dimension=0, axis=xaxis) #ygrid = Grid(plot=plot, dimension=1, axis=yaxis) glyph = Rect(x="group", y="centers", width="width", height="height") text_glyph = Text(x="group", y="centers", text="texts", text_baseline="middle", text_align="center") plot.renderers.append( Glyph(data_source=ds, xdata_range=xdr, ydata_range=ydr, glyph=glyph)) plot.renderers.append( Glyph(data_source=ds, xdata_range=xdr, ydata_range=ydr, glyph=text_glyph)) return plot