from collections import defaultdict from functools import partial import numpy as np import param from bokeh.models import Circle, FactorRange, HBar, VBar from ...core import NdOverlay from ...core.dimension import Dimension, Dimensioned from ...core.ndmapping import sorted_context from ...core.util import ( dimension_sanitizer, is_cupy_array, is_dask_array, isfinite, unique_iterator, wrap_tuple, ) from ...operation.stats import univariate_kde from ...util.transform import dim from ..mixins import MultiDistributionMixin from .chart import AreaPlot from .element import ColorbarPlot, CompositeElementPlot, LegendPlot from .path import PolygonPlot from .selection import BokehOverlaySelectionDisplay from .styles import base_properties, fill_properties, line_properties from .util import decode_bytes 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.""") selection_display = BokehOverlaySelectionDisplay() 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.""") selection_display = BokehOverlaySelectionDisplay(color_prop='cmap', is_cmap=True) class BoxWhiskerPlot(MultiDistributionMixin, CompositeElementPlot, ColorbarPlot, LegendPlot): show_legend = param.Boolean(default=False, doc=""" Whether to show legend for the plot.""") # Deprecated options color_index = param.ClassSelector(default=None, class_=(str, int), allow_None=True, doc=""" Deprecated in favor of color style mapping, e.g. `box_color=dim('color')`""") # X-axis is categorical _x_range_type = FactorRange # Map each glyph to a style group _style_groups = {'segment': 'whisker', 'vbar': 'box', 'hbar': 'box', 'circle': 'outlier'} style_opts = (['whisker_'+p for p in base_properties+line_properties] + ['box_'+p for p in base_properties+fill_properties+line_properties] + ['outlier_'+p for p in fill_properties+line_properties] + ['box_width', 'cmap', 'box_cmap']) _nonvectorized_styles = base_properties + ['box_width', 'whisker_width', 'cmap', 'box_cmap'] _stream_data = False # Plot does not support streaming data selection_display = BokehOverlaySelectionDisplay(color_prop='box_color') def _glyph_properties(self, plot, element, source, ranges, style, group=None): properties = dict(style, source=source) if self.show_legend and not element.kdims and self.overlaid: legend_prop = 'legend_label' properties[legend_prop] = element.label return properties def _apply_transforms(self, element, data, ranges, style, group=None): if element.ndims > 0: element = element.aggregate(function=np.mean) else: agg = element.aggregate(function=np.mean) if isinstance(agg, Dimensioned): element = agg else: element = element.clone([(agg,)]) return super()._apply_transforms(element, data, ranges, style, group) def _get_factors(self, element, ranges): """ Get factors for categorical axes. """ if not element.kdims: xfactors, yfactors = [element.label], [] else: factors = [key for key in element.groupby(element.kdims).data.keys()] if element.ndims > 1: factors = sorted(factors) factors = [tuple(d.pprint_value(k) for d, k in zip(element.kdims, key)) for key in factors] factors = [f[0] if len(f) == 1 else f for f in factors] xfactors, yfactors = factors, [] return (yfactors, xfactors) if self.invert_axes else (xfactors, yfactors) def _postprocess_hover(self, renderer, source): if not isinstance(renderer.glyph, (Circle, VBar, HBar)): return super()._postprocess_hover(renderer, source) def _box_stats(self, vals): is_finite = isfinite is_dask = is_dask_array(vals) is_cupy = is_cupy_array(vals) if is_cupy: import cupy percentile = cupy.percentile is_finite = cupy.isfinite elif is_dask: import dask.array as da percentile = da.percentile else: percentile = np.percentile vals = vals[is_finite(vals)] if is_dask or len(vals): q1, q2, q3 = (percentile(vals, q=q) for q in range(25, 100, 25)) iqr = q3 - q1 upper = max(vals[vals <= q3 + 1.5*iqr].max(), q3) lower = min(vals[vals >= q1 - 1.5*iqr].min(), q1) else: q1, q2, q3 = 0, 0, 0 upper, lower = 0, 0 outliers = vals[(vals > upper) | (vals < lower)] if is_cupy: return (q1.item(), q2.item(), q3.item(), upper.item(), lower.item(), cupy.asnumpy(outliers)) elif is_dask: return da.compute(q1, q2, q3, upper, lower, outliers) else: return q1, q2, q3, upper, lower, outliers def get_data(self, element, ranges, style): if element.kdims: with sorted_context(False): groups = element.groupby(element.kdims).data else: groups = dict([(element.label, element)]) vdim = dimension_sanitizer(element.vdims[0].name) # Define CDS data r1_data, r2_data = ({'index': [], 'top': [], 'bottom': []} for i in range(2)) s1_data, s2_data = ({'x0': [], 'y0': [], 'x1': [], 'y1': []} for i in range(2)) w1_data, w2_data = ({'x0': [], 'y0': [], 'x1': [], 'y1': []} for i in range(2)) out_data = defaultdict(list, {'index': [], vdim: []}) # Define glyph-data mapping width = style.get('box_width', 0.7) whisker_width = style.pop('whisker_width', 0.4)/2. if 'width' in style: self.param.warning("BoxWhisker width option is deprecated " "use 'box_width' instead.") if self.invert_axes: vbar_map = {'y': 'index', 'left': 'top', 'right': 'bottom', 'height': width} seg_map = {'y0': 'x0', 'y1': 'x1', 'x0': 'y0', 'x1': 'y1'} out_map = {'y': 'index', 'x': vdim} else: vbar_map = {'x': 'index', 'top': 'top', 'bottom': 'bottom', 'width': width} seg_map = {'x0': 'x0', 'x1': 'x1', 'y0': 'y0', 'y1': 'y1'} out_map = {'x': 'index', 'y': vdim} vbar2_map = dict(vbar_map) # Get color values if self.color_index is not None: cdim = element.get_dimension(self.color_index) cidx = element.get_dimension_index(self.color_index) else: cdim, cidx = None, None factors = [] vdim = element.vdims[0].name for key, g in groups.items(): # Compute group label if element.kdims: label = tuple(d.pprint_value(v) for d, v in zip(element.kdims, key)) if len(label) == 1: label = label[0] else: label = key hover = 'hover' in self.handles # Add color factor if cidx is not None and cidx=element.ndims: cdim = Dimension('index') else: r1_data[dimension_sanitizer(cdim.name)] = factors r2_data[dimension_sanitizer(cdim.name)] = factors factors = list(unique_iterator(factors)) if self.show_legend: vbar_map['legend_field'] = cdim.name return data, mapping, style class ViolinPlot(BoxWhiskerPlot): bandwidth = param.Number(default=None, doc=""" Allows supplying explicit bandwidth value rather than relying on scott or silverman method.""") clip = param.NumericTuple(default=None, length=2, doc=""" A tuple of a lower and upper bound to clip the violin at.""") cut = param.Number(default=5, doc=""" Draw the estimate to cut * bw from the extreme data points.""") inner = param.ObjectSelector(objects=['box', 'quartiles', 'stick', 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 """) split = param.ClassSelector(default=None, class_=(str, dim), doc=""" The dimension to split the Violin on.""") violin_width = param.Number(default=0.8, doc=""" Relative width of the violin""") # Deprecated options color_index = param.ClassSelector(default=None, class_=(str, int), allow_None=True, doc=""" Deprecated in favor of color style mapping, e.g. `violin_color=dim('color')`""") # Map each glyph to a style group _style_groups = {'patches': 'violin', 'multi_line': 'outline', 'segment': 'stats', 'vbar': 'box', 'scatter': 'median', 'hbar': 'box'} _draw_order = ['patches', 'multi_line', 'segment', 'vbar', 'hbar', 'circle', 'scatter'] style_opts = ([glyph+p for p in base_properties+fill_properties+line_properties for glyph in ('violin_', 'box_')] + [glyph+p for p in base_properties+line_properties for glyph in ('stats_', 'outline_')] + [f'{glyph}_{p}' for p in ('color', 'alpha') for glyph in ('box', 'violin', 'stats', 'median')] + ['cmap', 'box_cmap', 'violin_cmap']) _stat_fns = [partial(np.percentile, q=q) for q in [25, 50, 75]] selection_display = BokehOverlaySelectionDisplay(color_prop='violin_fill_color') def _get_axis_dims(self, element): split_dim = dim(self.split) if isinstance(self.split, str) else self.split kdims = [kd for kd in element.kdims if not split_dim or kd != split_dim.dimension] return kdims, element.vdims[0] def _get_factors(self, element, ranges): """ Get factors for categorical axes. """ split_dim = dim(self.split) if isinstance(self.split, str) else self.split kdims = [kd for kd in element.kdims if not split_dim or kd != split_dim.dimension] if not kdims: xfactors, yfactors = [element.label], [] else: factors = [key for key in element.groupby(kdims).data.keys()] if element.ndims > 1: factors = sorted(factors) factors = [tuple(d.pprint_value(k) for d, k in zip(kdims, key)) for key in factors] factors = [f[0] if len(f) == 1 else f for f in factors] xfactors, yfactors = factors, [] return (yfactors, xfactors) if self.invert_axes else (xfactors, yfactors) def _kde_data(self, element, el, key, split_dim, split_cats, **kwargs): vdims = el.vdims vdim = vdims[0] if self.clip: vdim = vdim(range=self.clip) el = el.clone(vdims=[vdim]) if split_dim is not None: el = el.clone(kdims=element.kdims) all_cats = split_dim.apply(el) if len(split_cats) > 2: raise ValueError( 'The number of categories for split violin plots cannot be ' 'greater than 2. Found {} categories: {}'.format( len(split_cats), ', '.join(split_cats))) el = el.add_dimension(repr(split_dim), len(el.kdims), all_cats) kdes = univariate_kde(el, dimension=vdim.name, groupby=repr(split_dim), **kwargs) scale = 4 else: split_cats = [None, None] kdes = {None: univariate_kde(el, dimension=vdim.name, **kwargs)} scale = 2 x_range = el.range(vdim) xs, fill_xs, ys, fill_ys = [], [], [], [] for i, cat in enumerate(split_cats): kde = kdes.get(cat) if kde is None: _xs, _ys = np.array([]), np.array([]) else: _xs, _ys = (kde.dimension_values(idim) for idim in range(2)) mask = isfinite(_ys) & (_ys>0) # Mask out non-finite and zero values _xs, _ys = _xs[mask], _ys[mask] if i == 0: _ys *= -1 else: _ys = _ys[::-1] _xs = _xs[::-1] if split_dim: if len(_xs): fill_xs.append([x_range[0]]+list(_xs)+[x_range[-1]]) fill_ys.append([0]+list(_ys)+[0]) else: fill_xs.append([]) fill_ys.append([]) x_range = x_range[::-1] xs += list(_xs) ys += list(_ys) xs = np.array(xs) ys = np.array(ys) # this scales the width if split_dim: fill_xs = [np.asarray(x) for x in fill_xs] fill_ys = [[key + (y,) for y in (fy/np.abs(ys).max())*(self.violin_width/scale)] if len(fy) else [] for fy in fill_ys] ys = (ys/np.nanmax(np.abs(ys)))*(self.violin_width/scale) if len(ys) else [] ys = [key + (y,) for y in ys] line = {'ys': xs, 'xs': ys} if split_dim: kde = {'ys': fill_xs, 'xs': fill_ys} else: kde = line if isinstance(kdes, NdOverlay): kde[repr(split_dim)] = [str(k) for k in split_cats] bars, segments, scatter = defaultdict(list), defaultdict(list), {} values = el.dimension_values(vdim) values = values[isfinite(values)] if not len(values): pass elif self.inner == 'quartiles': if len(xs): for stat_fn in self._stat_fns: stat = stat_fn(values) sidx = np.argmin(np.abs(xs-stat)) sx, sy = xs[sidx], ys[sidx] segments['x'].append(sx) segments['y0'].append(key+(-sy[-1],)) segments['y1'].append(sy) elif self.inner == 'stick': if len(xs): for value in values: sidx = np.argmin(np.abs(xs-value)) sx, sy = xs[sidx], ys[sidx] segments['x'].append(sx) segments['y0'].append(key+(-sy[-1],)) segments['y1'].append(sy) elif self.inner == 'box': xpos = key+(0,) q1, q2, q3, upper, lower, _ = self._box_stats(values) segments['x'].append(xpos) segments['y0'].append(lower) segments['y1'].append(upper) bars['x'].append(xpos) bars['bottom'].append(q1) bars['top'].append(q3) scatter['x'] = xpos scatter['y'] = q2 return kde, line, segments, bars, scatter def get_data(self, element, ranges, style): split_dim = dim(self.split) if isinstance(self.split, str) else self.split kdims = [kd for kd in element.kdims if not split_dim or split_dim.dimension != kd] if kdims: with sorted_context(False): groups = element.groupby(kdims).data else: groups = dict([((element.label,), element)]) if split_dim: split_name = split_dim.dimension.name if split_name in ranges and not split_dim.ops and 'factors' in ranges[split_name]: split_cats = ranges[split_name].get('factors') elif split_dim: split_cats = list(unique_iterator(split_dim.apply(element))) else: split_cats = None # Define glyph-data mapping if self.invert_axes: bar_map = {'y': 'x', 'left': 'bottom', 'right': 'top', 'height': 0.1} kde_map = {'xs': 'ys', 'ys': 'xs'} if self.inner == 'box': seg_map = {'x0': 'y0', 'x1': 'y1', 'y0': 'x', 'y1': 'x'} else: seg_map = {'x0': 'x', 'x1': 'x', 'y0': 'y0', 'y1': 'y1'} scatter_map = {'x': 'y', 'y': 'x'} bar_glyph = 'hbar' else: bar_map = {'x': 'x', 'bottom': 'bottom', 'top': 'top', 'width': 0.1} kde_map = {'xs': 'xs', 'ys': 'ys'} if self.inner == 'box': seg_map = {'x0': 'x', 'x1': 'x', 'y0': 'y0', 'y1': 'y1'} else: seg_map = {'y0': 'x', 'y1': 'x', 'x0': 'y0', 'x1': 'y1'} scatter_map = {'x': 'x', 'y': 'y'} bar_glyph = 'vbar' kwargs = {'bandwidth': self.bandwidth, 'cut': self.cut} mapping, data = {}, {} kde_data, line_data, seg_data, bar_data, scatter_data = ( defaultdict(list) for i in range(5) ) for key, g in groups.items(): key = decode_bytes(key) if element.kdims: key = tuple(d.pprint_value(k) for d, k in zip(element.kdims, key)) kde, line, segs, bars, scatter = self._kde_data( element, g, key, split_dim, split_cats, **kwargs ) for k, v in segs.items(): seg_data[k] += v for k, v in bars.items(): bar_data[k] += v for k, v in scatter.items(): scatter_data[k].append(v) for k, v in line.items(): line_data[k].append(v) for k, vals in kde.items(): if split_dim: for v in vals: kde_data[k].append(v) else: kde_data[k].append(vals) data['multi_line_1'] = line_data mapping['multi_line_1'] = kde_map data['patches_1'] = kde_data mapping['patches_1'] = kde_map if seg_data: data['segment_1'] = {k: v if isinstance(v[0], tuple) else np.array(v) for k, v in seg_data.items()} mapping['segment_1'] = seg_map if bar_data: data[bar_glyph+'_1'] = {k: v if isinstance(v[0], tuple) else np.array(v) for k, v in bar_data.items()} mapping[bar_glyph+'_1'] = bar_map if scatter_data: data['scatter_1'] = {k: v if isinstance(v[0], tuple) else np.array(v) for k, v in scatter_data.items()} mapping['scatter_1'] = scatter_map if split_dim: factors = [str(v) for v in split_cats] cmapper = self._get_colormapper( split_dim, element, ranges, dict(style), name='violin_color_mapper', group='violin', factors=factors) style['violin_fill_color'] = {'field': repr(split_dim), 'transform': cmapper} if self.show_legend: kde_map['legend_field'] = repr(split_dim) for k in style.copy(): if k.startswith('violin_line'): style[k.replace('violin', 'outline')] = style.pop(k) style['violin_line_width'] = 0 return data, mapping, style