ó ÇÅ>Yc @`sLdZddlmZmZmZddlmZddlmZm Z m Z m Z ddl m Z mZddlmZddlmZmZdd lmZdd lmZdd lmZmZdd lmZmZdd lmZddlm Z defd„ƒYZ!e e!ƒdddddddde#e$dd„ ƒZ%dS(sThis is the Bokeh charts interface. It gives you a high level API to build complex plot is a simple way. This is the Bar class which lets you build your Bar charts just passing the arguments to the Chart class and calling the proper functions. It also add a new chained stacked method. i(tabsolute_importtprint_functiontdivision(t Aggregation(tFloattEnumtBooltOverride(t FactorRangetRange1d(tColumnDataSourcei(tBuildertcreate_and_build(tBarGlyph(t Dimension(t ColorAttrtCatAttr(tStacktDodge(tstats(thelpt BarBuildercB`seZdZedƒZdgZieƒd6eƒd6eddƒd6eƒd6eƒd6Ze e dd ƒZ e d ƒZ e d ƒZe dd ƒZe dd ƒZeZedegƒZddgZeeƒZeeƒZeZeZd „Zd„Zd„Zd„Zd„Z RS(seThis is the Bar builder and it is in charge of plotting Bar chart (grouped and stacked) in an easy and intuitive way. Essentially, it utilizes a standardized way to ingest the data, make the proper calculations and generate renderers. The renderers reference the transformed data, which represent the groups of data that were derived from the inputs. We additionally make calculations for the ranges. The x_range is categorical, and is made either from the label argument or from the `pandas.DataFrame.index`. The y_range can be supplied as the parameter continuous_range, or will be calculated as a linear range (Range1d) based on the supplied values. The bar builder is and can be further used as a base class for other builders that might also be performing some aggregation across derived groups of data. tvaluestlabeltcolortdefaulttwhitet line_colortstacktgrouptsumgð?ggš™™™™™é?cC`s|jdjdkr|jdjdk rP|jdjd|jdjƒn|jdjdk r|jdjd|jdjƒqn|jdjdk r¯t|_n|jdjdk rÑt|_n|jjj dkr!t |j ƒdkr!d|_ |jdj |jjƒn|jƒ|jdkr“|jdjdk rtd j|jdjƒjƒƒjƒ|_q“|jj|_n|jdkr|jsÜd |j jƒt|jjƒjƒf|_qd |j jƒd j|jdjƒjƒf|_ndS( NRRtcolumnsRtobjectitcountRs, s%s( %s )(t attributesRtNonetsetuptTruet_perform_stackt_perform_groupRtdtypetnametlentattribute_columnstaggt set_columnst selectiont_apply_inferred_indextxlabeltstrtjointtitletylabelt label_only(tself((s<lib/python2.7/site-packages/bkcharts/builders/bar_builder.pyR$Zs0$'  *  . .cC`s“|jdjdkr|jjdk r|jdjdk rGd}nd}||jd<|jdjdt|jjƒd|ƒd|_ ndS( sFConfigure chart when labels are provided as index instead of as kwarg.RRtunitytindextdataRtN( R"RR#RR.t_dataR$R tdfR0(R6tspecial_column((s<lib/python2.7/site-packages/bkcharts/builders/bar_builder.pyR/ƒs  "cC`sô|jdj}|d kr%d}ng}x0|D](}|j|ƒ}|jt|ƒƒq2Wtd|ƒ|_td|j |j dƒ}|j dkr­|j |}nd}|j dkrÒ|j |}nd}t d|d |ƒ|_ d S( s]Push the Bar data into the ColumnDataSource and calculate the proper ranges. RR:tfactorsgš™™™™™¹?iigtstarttendN( R"titemsR#t _get_labeltappendR1Rtx_rangetabst min_heightt max_heightR ty_range(R6tx_itemstx_labelstitemty_shiftR?R@((s<lib/python2.7/site-packages/bkcharts/builders/bar_builder.pyt set_ranges•s    c`s=ˆjtk r5ˆjdtƒ}‡fd†|DƒSiSdS(Nt with_basesc`s"i|]}tˆ|ƒ|“qS((tgetattr(t.0tattr(R6(s<lib/python2.7/site-packages/bkcharts/builders/bar_builder.pys ¸s (t __class__Rt propertiestFalse(R6tattrs((R6s<lib/python2.7/site-packages/bkcharts/builders/bar_builder.pytget_extra_argsµsc c`s4|jƒ}|jƒ}xZ|jj|jD]C}|j||ƒ}|jƒ}|j|ƒ|jj ƒj t dgƒƒ}x"ddgD]}||||    !      (((!t__name__t __module__t__doc__RRt dimensionsRRtdefault_attributesRRR,RRGRFReRYR RaRtcomp_glyph_typestlabel_attributesRRTR5t values_onlyR&R'R$R/RMRVRv(((s<lib/python2.7/site-packages/bkcharts/builders/bar_builder.pyR(s2            )  Rt categoricaltlinearc K`s-| r%t| tƒ r%tdƒ‚n|dk rq|dkrqt| d<|dks_|dkrqd}|}qqn| } || d<|| d<|| d<|| d <|| d <|| d <|| d <|| d <| | d<| | d<| | d` render the geometry from values, cat and stacked. 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 bars Examples: .. bokeh-plot:: :source-position: above from bkcharts import Bar, 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 bar = Bar(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 bar2 = Bar(data, values='timing', label=['interpreter', 'sample'], agg='mean', title="Python Interpreters", plot_width=400) output_file("stacked_bar.html") show(row(bar, bar2)) sCcontinuous_range must be an instance of bokeh.models.ranges.Range1dR5RtmeanR!RRRRRR,txscaletyscaletxgridtygridRHiiN( t isinstanceR t ValueErrorR#R%R RR*RDR>RTtbelowtvisible(R9RRRRRR,R‚RƒR„R…tcontinuous_rangetkwRHtchart((s<lib/python2.7/site-packages/bkcharts/builders/bar_builder.pytBarïs0:              N(&Ryt __future__RRRtbokeh.core.enumsRtbokeh.core.propertiesRRRRt bokeh.modelsRR tbokeh.models.sourcesR tbuilderR R tglyphsR RSRR"RRt operationsRRRtutilsRRR#RTR%R(((s<lib/python2.7/site-packages/bkcharts/builders/bar_builder.pyts" "Ç