import numpy as np import param from ...core.ndmapping import sorted_context from ..mixins import MultiDistributionMixin from .chart import AreaPlot, ChartPlot from .path import PolygonPlot from .plot import AdjoinedPlot class DistributionPlot(AreaPlot): """ DistributionPlot visualizes a distribution of values as a KDE. """ bandwidth = param.Number(default=None, doc=""" The bandwidth of the kernel for the density estimate.""") cut = param.Number(default=3, doc=""" Draw the estimate to cut * bw from the extreme data points.""") filled = param.Boolean(default=True, doc=""" Whether the bivariate contours should be filled.""") class BivariatePlot(PolygonPlot): """ Bivariate plot visualizes two-dimensional kernel density estimates. Additionally, by enabling the joint option, the marginals distributions can be plotted alongside each axis (does not animate or compose). """ bandwidth = param.Number(default=None, doc=""" The bandwidth of the kernel for the density estimate.""") cut = param.Number(default=3, doc=""" Draw the estimate to cut * bw from the extreme data points.""") filled = param.Boolean(default=False, doc=""" Whether the bivariate contours should be filled.""") levels = param.ClassSelector(default=10, class_=(list, int), doc=""" A list of scalar values used to specify the contour levels.""") class BoxPlot(MultiDistributionMixin, ChartPlot): """ BoxPlot plots the ErrorBar Element type and supporting both horizontal and vertical error bars via the 'horizontal' plot option. """ style_opts = ['notch', 'sym', 'whis', 'bootstrap', 'conf_intervals', 'widths', 'showmeans', 'show_caps', 'showfliers', 'boxprops', 'whiskerprops', 'capprops', 'flierprops', 'medianprops', 'meanprops', 'meanline'] _nonvectorized_styles = style_opts _plot_methods = dict(single='boxplot') def get_data(self, element, ranges, style): if element.kdims: with sorted_context(False): groups = element.groupby(element.kdims).data.items() else: groups = [(element.label, element)] data, labels = [], [] for key, group in groups: if element.kdims: label = ','.join([d.pprint_value(v) for d, v in zip(element.kdims, key)]) else: label = key d = group[group.vdims[0]] data.append(d[np.isfinite(d)]) labels.append(label) style['labels'] = labels style = {k: v for k, v in style.items() if k not in ['zorder', 'label']} style['vert'] = not self.invert_axes format_kdims = [kd.clone(value_format=None) for kd in element.kdims] return (data,), style, {'dimensions': [format_kdims, element.vdims[0]]} def init_artists(self, ax, plot_args, plot_kwargs): artists = ax.boxplot(*plot_args, **plot_kwargs) artists['artist'] = artists['boxes'] return artists def teardown_handles(self): for g in ('whiskers', 'fliers', 'medians', 'boxes', 'caps', 'means'): for v in self.handles.get(g, []): v.remove() class SideBoxPlot(AdjoinedPlot, BoxPlot): bgcolor = param.Parameter(default=(1, 1, 1, 0), doc=""" Make plot background invisible.""") border_size = param.Number(default=0, doc=""" The size of the border expressed as a fraction of the main plot.""") xaxis = param.ObjectSelector(default='bare', objects=['top', 'bottom', 'bare', 'top-bare', 'bottom-bare', None], doc=""" Whether and where to display the xaxis, bare options allow suppressing all axis labels including ticks and xlabel. Valid options are 'top', 'bottom', 'bare', 'top-bare' and 'bottom-bare'.""") yaxis = param.ObjectSelector(default='bare', objects=['left', 'right', 'bare', 'left-bare', 'right-bare', None], doc=""" Whether and where to display the yaxis, bare options allow suppressing all axis labels including ticks and ylabel. Valid options are 'left', 'right', 'bare' 'left-bare' and 'right-bare'.""") def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.adjoined: self.invert_axes = not self.invert_axes class ViolinPlot(BoxPlot): """ BoxPlot plots the ErrorBar Element type and supporting both horizontal and vertical error bars via the 'horizontal' plot option. """ bandwidth = param.Number(default=None, doc=""" Allows supplying explicit bandwidth value rather than relying on scott or silverman method.""") inner = param.ObjectSelector(objects=['box', 'medians', None], default='box', doc=""" Inner visual indicator for distribution values: * box - A small box plot * stick - Lines indicating each sample value * quartiles - Indicates first, second and third quartiles """) _plot_methods = dict(single='violinplot') style_opts = ['showmeans', 'facecolors', 'showextrema', 'bw_method', 'widths', 'stats_color', 'box_color', 'alpha', 'edgecolors'] _nonvectorized_styles = [ s for s in style_opts if s not in ('facecolors', 'edgecolors', 'widths') ] def init_artists(self, ax, plot_args, plot_kwargs): box_color = plot_kwargs.pop('box_color', 'black') stats_color = plot_kwargs.pop('stats_color', 'black') facecolors = plot_kwargs.pop('facecolors', []) edgecolors = plot_kwargs.pop('edgecolors', 'black') labels = plot_kwargs.pop('labels') alpha = plot_kwargs.pop('alpha', 1.) showmedians = self.inner == 'medians' bw_method = self.bandwidth or 'scott' artists = ax.violinplot(*plot_args, bw_method=bw_method, showmedians=showmedians, **plot_kwargs) if self.inner == 'box': box = ax.boxplot(*plot_args, positions=plot_kwargs['positions'], showfliers=False, showcaps=False, patch_artist=True, boxprops={'facecolor': box_color}, medianprops={'color': 'white'}, widths=0.1, labels=labels) artists.update(box) for body, color in zip(artists['bodies'], facecolors): body.set_facecolors(color) body.set_edgecolors(edgecolors) body.set_alpha(alpha) for stat in ['cmedians', 'cmeans', 'cmaxes', 'cmins', 'cbars']: if stat in artists: artists[stat].set_edgecolors(stats_color) artists['bodies'] = artists['bodies'] return artists def get_data(self, element, ranges, style): if element.kdims: with sorted_context(False): groups = element.groupby(element.kdims).data.items() else: groups = [(element.label, element)] data, labels, colors = [], [], [] elstyle = self.lookup_options(element, 'style') for i, (key, group) in enumerate(groups): if element.kdims: label = ','.join([d.pprint_value(v) for d, v in zip(element.kdims, key)]) else: label = key d = group[group.vdims[0]] data.append(d[np.isfinite(d)]) labels.append(label) colors.append(elstyle[i].get('facecolors', 'blue')) style['positions'] = list(range(len(data))) style['labels'] = labels style['facecolors'] = colors if element.ndims > 0: element = element.aggregate(function=np.mean) else: element = element.clone([(element.aggregate(function=np.mean),)]) new_style = self._apply_transforms(element, ranges, style) style = {k: v for k, v in new_style.items() if k not in ['zorder', 'label']} style['vert'] = not self.invert_axes format_kdims = [kd.clone(value_format=None) for kd in element.kdims] ticks = {'yticks' if self.invert_axes else 'xticks': list(enumerate(labels))} return (data,), style, dict(dimensions=[format_kdims, element.vdims[0]], **ticks) def teardown_handles(self): box_artists = ('cmedians', 'cmeans', 'cmaxes', 'cmins', 'cbars', 'bodies') violin_artists = ('whiskers', 'fliers', 'medians', 'boxes', 'caps', 'means') for group in box_artists+violin_artists: for v in self.handles.get(group, []): v.remove()