![]() vbar_stack ( regions, x = 'x', width = 0.9, alpha = 0.5, color =, source = source, legend_label = regions ) p. orientation = "horizontal" show ( p )įrom bokeh.models import ColumnDataSource, FactorRange from otting import figure, show factors = regions = source = ColumnDataSource ( data = dict ( x = factors, east =, west =, )) p = figure ( x_range = FactorRange ( * factors ), height = 250, toolbar_location = None, tools = "" ) p. vbar ( x = dodge ( 'fruits', 0.25, range = p. vbar ( x = dodge ( 'fruits', 0.0, range = p. vbar ( x = dodge ( 'fruits', - 0.25, range = p. The example below shows a sequence of simpleįrom bokeh.models import ColumnDataSource from bokeh.palettes import GnBu3, OrRd3 from otting import figure, show fruits = years = exports = source = ColumnDataSource ( data = data ) p = figure ( x_range = fruits, y_range = ( 0, 10 ), title = "Fruit Counts by Year", height = 350, toolbar_location = None, tools = "" ) p. To create a basic bar chart, use the hbar() (horizontal bars) or vbar() ![]() This section will demonstrate how to draw a variety ofĭifferent categorical bar charts. The length of this bar along the continuous axis corresponds toīar charts may also be stacked or grouped together according to hierarchical The values associated with each category are represented by drawing a bar for BarĬharts are useful when there is one value to plot for each category. Bar charts have one categorical axis and one continuous axis. One of the most common ways to handle categorical data is to present it in aīar chart. Present several kinds of common plot types for categorical data. Slider_layer = _by_quarter = ĭepending on the structure of your data, you can use different kinds of charts:īar charts, categorical heatmaps, jitter plots, and others. Plot1.image(image=], x=0, y=image_neurons.shape,ĭw=image_neurons.shape, dh=image_neurons.shape, palette=grayp) Plot1 = bpl.figure(x_range=xr, y_range=yr, plot_width=300, plot_height=300) Yr = Range1d(start=image_neurons.shape if max_projection else d2, end=0) Xr = Range1d(start=0, end=image_neurons.shape if max_projection else d3) Title="Neuron Number", callback=callback) Slider = (start=1, end=Y_r.shape, value=1, step=1, Plot.line('x', 'y2', source=source, line_width=1, line_alpha=0.6, color=denoised_color) Plot.line('x', 'y', source=source, line_width=1, line_alpha=0.6) Plot = bpl.figure(plot_width=600, plot_height=300) No_title_slider = Slider(title= None, value= 50, start= 0, end= 96, step= 5) def color_picker(): def color_slider( title, color): return Slider(title=title, show_value= False, value= 127, start= 0, end= 255, step= 1, orientation= "vertical", bar_color=color)ĭiv = Div(width= 100, height= 100, background= "rgb(127, 127, 127)")Ĭb = CustomJS(args= dict(red=red, green=green, blue=blue, div=div), code= """ ![]() Only_value_slider = Slider(value= 50, start= 0, end= 96, step= 5) Slider = Slider(title= "Numerical", value= 50, start= 0, end= 96, step= 5)ĭisabled_slider = Slider(title= "Disabled", value= 50, start= 0, end= 96, step= 5, disabled= True) 1, title= "Offset")Ĭallback = CustomJS(args= dict(source=source, amp=amp_slider, freq=freq_slider, phase=phase_slider, offset=offset_slider), Offset_slider = Slider(start=- 5, end= 5, value= 0, step=. Phase_slider = Slider(start= 0, end= 6.4, value= 0, step=. ![]() 1, title= "Amplitude")įreq_slider = Slider(start= 0.1, end= 10, value= 1, step=. Plot.line( 'x', 'y', source=source, line_width= 3, line_alpha= 0.6)Īmp_slider = Slider(start= 0.1, end= 10, value= 1, step=. Source = ColumnDataSource(data= dict(x=x, y=y)) From bokeh.models import CustomJS, Sliderįrom otting import ColumnDataSource, figure, output_file, show
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