B Y @sdZddlmZddlmZmZddlmZddlm Z ddl m Z dd l m Z dd lmZmZmZdddZGddde Zd S)aThis is the Bokeh charts interface. It gives you a high level API to build complex plot is a simple way. This is the Dot class which lets you build your Dot plots just passing the arguments to the Chart class and calling the proper functions. It also add a new chained stacked method. )absolute_import)BoolFloat)Range1d)create_and_build) BarBuilder)DotGlyph) ColorAttrCatAttr MarkerAttrNsum categoricallinearFTc Ks| rt| tstd|dk rF|dkrFd| d<|dks>|dkrFd}|}| } || d<|| d <|| d <|| d <|| d <|| d <|| d<|| d<| | d<| | d<| | d<tt|f| S)a Create a Dot chart using :class:`DotBuilder ` to render the geometry from the inputs. Args: data (:ref:`userguide_charts_data_types`): the data source for the chart. label (list(str) or str, optional): list of string representing the categories. (Defaults to None) values (str, optional): iterable 2d representing the data series values matrix. color (str or list(str) or `~bkcharts._attributes.ColorAttr`): string color, string column name, list of string columns or a custom `ColorAttr`, which replaces the default `ColorAttr` for the builder. stack (list(str) or str, optional): columns to use for stacking. (Defaults to False, so grouping is assumed) group (list(str) or str, optional): columns to use for grouping. agg (str): how to aggregate the `values`. (Defaults to 'sum', or only label is provided, then performs a `count`) continuous_range(Range1d, optional): Custom continuous_range to be used. (Defaults to None) In addition to the parameters specific to this chart, :ref:`userguide_charts_defaults` are also accepted as keyword parameters. Returns: :class:`Chart`: includes glyph renderers that generate dots Examples: .. bokeh-plot:: :source-position: above from bkcharts import Dot, output_file, show from bokeh.layouts import row # best support is with data in a format that is table-like data = { 'sample': ['1st', '2nd', '1st', '2nd', '1st', '2nd'], 'interpreter': ['python', 'python', 'pypy', 'pypy', 'jython', 'jython'], 'timing': [-2, 5, 12, 40, 22, 30] } # x-axis labels pulled from the interpreter column, stacking labels from sample column dot = Dot(data, values='timing', label='interpreter', stack='sample', agg='mean', title="Python Interpreter Sampling", legend='top_right', plot_width=400) # table-like data results in reconfiguration of the chart with no data manipulation dot2 = Dot(data, values='timing', label=['interpreter', 'sample'], agg='mean', title="Python Interpreters", plot_width=400) output_file("Dot.html") show(row(dot, dot2)) zCcontinuous_range must be an instance of bokeh.models.ranges.Range1dNTZ label_onlyrZmeancountlabelvaluescolorstackgroupaggxscaleyscalexgridygridy_range) isinstancer ValueErrorr DotBuilder)datarrrrrrrrrrZcontinuous_rangekwrr"s      T