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Produce a dotplot similar in style to those in Cleveland's "Visualizing Data" book ([1]_). These are also known as "forest plots". Parameters ---------- points : array_like The quantitative values to be plotted as markers. intervals : array_like The intervals to be plotted around the points. The elements of `intervals` are either scalars or sequences of length 2. A scalar indicates the half width of a symmetric interval. A sequence of length 2 contains the left and right half-widths (respectively) of a nonsymmetric interval. If None, no intervals are drawn. lines : array_like A grouping variable indicating which points/intervals are drawn on a common line. If None, each point/interval appears on its own line. sections : array_like A grouping variable indicating which lines are grouped into sections. If None, everything is drawn in a single section. styles : array_like A grouping label defining the plotting style of the markers and intervals. marker_props : dict A dictionary mapping style codes (the values in `styles`) to dictionaries defining key/value pairs to be passed as keyword arguments to `plot` when plotting markers. Useful keyword arguments are "color", "marker", and "ms" (marker size). line_props : dict A dictionary mapping style codes (the values in `styles`) to dictionaries defining key/value pairs to be passed as keyword arguments to `plot` when plotting interval lines. Useful keyword arguments are "color", "linestyle", "solid_capstyle", and "linewidth". split_names : str If not None, this is used to split the values of `lines` into substrings that are drawn in the left and right margins, respectively. If None, the values of `lines` are drawn in the left margin. section_order : array_like The section labels in the order in which they appear in the dotplot. line_order : array_like The line labels in the order in which they appear in the dotplot. stacked : bool If True, when multiple points or intervals are drawn on the same line, they are offset from each other. styles_order : array_like If stacked=True, this is the order in which the point styles on a given line are drawn from top to bottom (if horizontal is True) or from left to right (if horizontal is False). If None (default), the order is lexical. striped : bool If True, every other line is enclosed in a shaded box. horizontal : bool If True (default), the lines are drawn horizontally, otherwise they are drawn vertically. show_names : str Determines whether labels (names) are shown in the left and/or right margins (top/bottom margins if `horizontal` is True). If `both`, labels are drawn in both margins, if 'left', labels are drawn in the left or top margin. If `right`, labels are drawn in the right or bottom margin. fmt_left_name : callable The left/top margin names are passed through this function before drawing on the plot. fmt_right_name : callable The right/bottom marginnames are passed through this function before drawing on the plot. show_section_titles : bool or None If None, section titles are drawn only if there is more than one section. If False/True, section titles are never/always drawn, respectively. ax : matplotlib.axes The axes on which the dotplot is drawn. If None, a new axes is created. Returns ------- fig : Figure The figure given by `ax.figure` or a new instance. Notes ----- `points`, `intervals`, `lines`, `sections`, `styles` must all have the same length whenever present. References ---------- .. [1] Cleveland, William S. (1993). "Visualizing Data". Hobart Press. .. [2] Jacoby, William G. (2006) "The Dot Plot: A Graphical Display for Labeled Quantitative Values." The Political Methodologist 14(1): 6-14. Examples -------- This is a simple dotplot with one point per line: >>> dot_plot(points=point_values) This dotplot has labels on the lines (if elements in `label_values` are repeated, the corresponding points appear on the same line): >>> dot_plot(points=point_values, lines=label_values) rNc4|dStj|S)N)npasarray)xs aC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\statsmodels/graphics/dotplots.pyzdot_plot..s 1 g?FTrg{Gz?)rrg@g)rgbykpurpleorangecolormarkeroms grey linewidthgffffff?darkgrey)r transformzordergffffff?center)horizontalalignmentverticalalignmentr boldg{Gz?))\(?r&)(\?r')rr r!clip_ongRQ?bottom)r lightgrey)rr)rr)leftbothgright monospace)r#r$r familytop)r-r,g?r+-r None)lsr labelnoneaxes)scaleytight)scalexr9)2matplotlib.transforms transformsr create_mpl_axrrlenarangezerosset set_xmargin set_ymarginlistsortrangeappendget_window_extent transformeddpi_scale_transinvertedwidthheightblended_transform_factory transData transAxesfloatindex fill_betweentextset_fontweightaxhlineaxvlinesplitlower enumerateisscalarplotaddxaxisset_ticks_positionyaxisset_yticklabelsspines set_color set_positionset_ylim set_ticksautoscale_viewset_xticklabelsset_xlim)Gpoints intervalslinessectionsstyles marker_props line_props split_names section_order line_orderstacked styles_orderstriped horizontal show_names fmt_left_namefmt_right_nameshow_section_titlesaxr<figasarray_or_nonenpointsection_title_spacensectdraw_section_titles nsect_titlesection_space_totallines0lines1 lines_mapikybboxawidthaheightnrowsr)r0transtitle_space_axesdpos style_codesnvalstackdr style_codes_mapcolorsjscposlabeledticksk0y0txtrmjrowk1us left_label right_labeljijpyoptlcbucbslsllsG r dot_plotrs3 n/!!"%GCZZ FEO *I E "Ex(H V $F[F } &!88F#~&! 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