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README for bokeh histograms

The histogram array is used as an optional argument in the bokehDrawSA to define histograms and histogram statistics on the client side.

The declarative programming used in bokehDrawSA is a type of coding where developers express the computational logic without having to programme the control flow of each process. This can help simplify coding, as developers only need to describe what they want the programme to achieve, rather than explicitly prescribing the steps or commands required to achieve the desired result.

bokehDrawSA.fromArray(df, None, figureArray, widgetParams, layout=figureLayoutDesc, tooltips=tooltips, parameterArray=parameterArray, widgetLayout=widgetLayoutDesc, sizing_mode="scale_width", nPointRender=300, aliasArray=aliasArray, histogramArray=histoArray)

For example usage see also Jupyter notebook tutorial file: https://github.com/miranov25/RootInteractive/blob/master/RootInteractive/tutorial/bokehDraw/test_bokehClientHistogram.ipynb

Parameters each element of the histogram array takes

The RootInteractive histograms parameterization was inspired by the numpy histogram and Root histogram syntax.

  • name - histogram name
  • variables - array of variables used (1D or ND)
  • nbins - number for 1D or array for >1D e.g:
    • nbins=20 in 1D (see )
    • nbins=[20,20] in 2D
  • range - range for the histogram axis
    • for 2D eg. [[0,1],[0,1]]
    • None can be used to enable automatic range (min,max)
    • range can be parameterized using widget parameters (starting on RI v0-01-06)
  • quantiles - quantile array to calculate - used for tableHisto
  • sum_range - integral in range - used for tableHisto output
  • weight - weigh used to fill histogram

Example use case

https://github.com/miranov25/RootInteractive/blob/master/RootInteractive/tutorial/bokehDraw/test_bokehClientHistogram.ipynb

Example with table aggregation:

    histoArray = [
        {"name": "histoA", "variables": ["A"], "nbins":20, "quantiles": [.05, .5, .95], "sum_range": [[.25, .75], [.4, .6]]},
        {"name": "histoB", "variables": ["B"], "nbins":20, "range": [0, 1]},
        {"name": "histoTransform", "variables": ["(A+B)/2"], "nbins": 20, "weights": "A*C"},
    ]
    figureArray = [
        [['A'], ['histoA']],
        [['B'], ['histoB'], {"show_histogram_error": True}],
        [['(A+B)/2'], ['histoTransform'], {"yAxisTitle": "sum A*C"}],
        ["tableHisto", {"rowwise": True}]
    ]

Example with 2D histogram and projections

Showing histogram of A vs (A+B)/2, mean, median, RMS, quantiles of A as a function of (A+B)/2

    histoArray = [
        {"name": "histoAB", "variables": ["A", "(A+B)/2"], "nbins": [20, 20], "weights": "D", "axis": [0, 1], "quantiles": [.1, .5, .9]}
    ]
    figureArray = [
        [['quantile_0', 'quantile_1', 'quantile_2'], ['bin_center_1'], {"source": "histoAB_0"}],
        [['quantile_1', 'mean'], ['bin_center_1'], {"source": "histoAB_0"}],
        [['A'], ['histoAB'], {"yAxisTitle": "(A+B)/2"}],
        [['std'], ['bin_center_1'], {"source": "histoAB_0"}],
        ["tableHisto", {"rowwise": False}]
    ]

Example with projections of a 3D histogram into 2D

  • Uniform distribution in varable A,B,C, weighted 3D histogram of (A+C)/2, B, C with D as weights
  • showing mean and rms in (A+C)/2 as function of B with C bin center as a color
histoArray = [
        {"name": "histoABC", "variables": ["(A+C)/2", "B", "C"], "nbins": [8, 10, 12], "weights": "D", "axis": [0], "sum_range": [[.25, .75]]},
    ]
    figureArray = [
        [['bin_center_1'], ['mean']],
        [['bin_center_1'], ['sum_0']],
        [['bin_center_1'], ['std']],
        {"source": "histoABC_0", "colorZvar": "bin_center_2", "size": 7}
    ]