from itertools import product import numpy as np import param from matplotlib.collections import LineCollection, PatchCollection from matplotlib.patches import Circle, Wedge from ...core.data import GridInterface from ...core.spaces import HoloMap from ...core.util import dimension_sanitizer, is_nan from ..mixins import HeatMapMixin from .element import ColorbarPlot from .raster import QuadMeshPlot from .util import filter_styles class HeatMapPlot(HeatMapMixin, QuadMeshPlot): clipping_colors = param.Dict(default={'NaN': 'white'}, doc=""" Dictionary to specify colors for clipped values, allows setting color for NaN values and for values above and below the min and max value. The min, max or NaN color may specify an RGB(A) color as a color hex string of the form #FFFFFF or #FFFFFFFF or a length 3 or length 4 tuple specifying values in the range 0-1 or a named HTML color.""") padding = param.ClassSelector(default=0, class_=(int, float, tuple)) radial = param.Boolean(default=False, doc=""" Whether the HeatMap should be radial""") show_values = param.Boolean(default=False, doc=""" Whether to annotate each pixel with its value.""") xmarks = param.Parameter(default=None, doc=""" Add separation lines to the heatmap for better readability. By default, does not show any separation lines. If parameter is of type integer, draws the given amount of separations lines spread across heatmap. If parameter is of type list containing integers, show separation lines at given indices. If parameter is of type tuple, draw separation lines at given categorical values. If parameter is of type function, draw separation lines where function returns True for passed heatmap category.""") ymarks = param.Parameter(default=None, doc=""" Add separation lines to the heatmap for better readability. By default, does not show any separation lines. If parameter is of type integer, draws the given amount of separations lines spread across heatmap. If parameter is of type list containing integers, show separation lines at given indices. If parameter is of type tuple, draw separation lines at given categorical values. If parameter is of type function, draw separation lines where function returns True for passed heatmap category.""") xticks = param.Parameter(default=20, doc=""" Ticks along x-axis/segments specified as an integer, explicit list of ticks or function. If `None`, no ticks are shown.""") yticks = param.Parameter(default=20, doc=""" Ticks along y-axis/annulars specified as an integer, explicit list of ticks or function. If `None`, no ticks are shown.""") @classmethod def is_radial(cls, heatmap): heatmap = heatmap.last if isinstance(heatmap, HoloMap) else heatmap opts = cls.lookup_options(heatmap, 'plot').options return ((any(o in opts for o in ('start_angle', 'radius_inner', 'radius_outer')) and not (opts.get('radial') == False)) or opts.get('radial', False)) def _annotate_plot(self, ax, annotations): for a in self.handles.get('annotations', {}).values(): a.remove() handles = {} for plot_coord, text in annotations.items(): handles[plot_coord] = ax.annotate(text, xy=plot_coord, xycoords='data', horizontalalignment='center', verticalalignment='center') return handles def _annotate_values(self, element, xvals, yvals): val_dim = element.vdims[0] vals = element.dimension_values(val_dim).flatten() xpos = xvals[:-1] + np.diff(xvals)/2. ypos = yvals[:-1] + np.diff(yvals)/2. plot_coords = product(xpos, ypos) annotations = {} for plot_coord, v in zip(plot_coords, vals): text = '-' if is_nan(v) else val_dim.pprint_value(v) annotations[plot_coord] = text return annotations def _compute_ticks(self, element, xvals, yvals, xfactors, yfactors): xdim, ydim = element.kdims if self.invert_axes: xdim, ydim = ydim, xdim opts = self.lookup_options(element, 'plot').options xticks = opts.get('xticks') if xticks is None: xpos = xvals[:-1] + np.diff(xvals)/2. if not xfactors: xfactors = element.gridded.dimension_values(xdim, False) xlabels = [xdim.pprint_value(k) for k in xfactors] xticks = list(zip(xpos, xlabels)) yticks = opts.get('yticks') if yticks is None: ypos = yvals[:-1] + np.diff(yvals)/2. if not yfactors: yfactors = element.gridded.dimension_values(ydim, False) ylabels = [ydim.pprint_value(k) for k in yfactors] yticks = list(zip(ypos, ylabels)) return xticks, yticks def _draw_markers(self, ax, element, marks, values, factors, axis='x'): if marks is None or self.radial: return self.param.warning('Only radial HeatMaps supports marks, to make the' 'HeatMap quads more distinguishable set linewidths' 'to a non-zero value.') def init_artists(self, ax, plot_args, plot_kwargs): xfactors = plot_kwargs.pop('xfactors') yfactors = plot_kwargs.pop('yfactors') annotations = plot_kwargs.pop('annotations', None) prefixes = ['annular', 'xmarks', 'ymarks'] plot_kwargs = {k: v for k, v in plot_kwargs.items() if not any(p in k for p in prefixes)} artist = ax.pcolormesh(*plot_args, **plot_kwargs) if self.show_values and annotations: self.handles['annotations'] = self._annotate_plot(ax, annotations) self._draw_markers(ax, self.current_frame, self.xmarks, plot_args[0], xfactors, axis='x') self._draw_markers(ax, self.current_frame, self.ymarks, plot_args[1], yfactors, axis='y') return {'artist': artist} def get_data(self, element, ranges, style): xdim, ydim = element.kdims aggregate = element.gridded if not element._unique: self.param.warning('HeatMap element index is not unique, ensure you ' 'aggregate the data before displaying it, e.g. ' 'using heatmap.aggregate(function=np.mean). ' 'Duplicate index values have been dropped.') data = aggregate.dimension_values(2, flat=False) data = np.ma.array(data, mask=np.logical_not(np.isfinite(data))) if self.invert_axes: xdim, ydim = ydim, xdim data = data.T[::-1, ::-1] xtype = aggregate.interface.dtype(aggregate, xdim) if xtype.kind in 'SUO': xvals = np.arange(data.shape[1]+1)-0.5 else: xvals = aggregate.dimension_values(xdim, expanded=False) xvals = GridInterface._infer_interval_breaks(xvals) ytype = aggregate.interface.dtype(aggregate, ydim) if ytype.kind in 'SUO': yvals = np.arange(data.shape[0]+1)-0.5 else: yvals = aggregate.dimension_values(ydim, expanded=False) yvals = GridInterface._infer_interval_breaks(yvals) xfactors = list(ranges.get(xdim.name, {}).get('factors', [])) yfactors = list(ranges.get(ydim.name, {}).get('factors', [])) xticks, yticks = self._compute_ticks(element, xvals, yvals, xfactors, yfactors) style['xfactors'] = xfactors style['yfactors'] = yfactors if self.show_values: style['annotations'] = self._annotate_values(element.gridded, xvals, yvals) vdim = element.vdims[0] self._norm_kwargs(element, ranges, style, vdim) if 'vmin' in style: style['clim'] = style.pop('vmin'), style.pop('vmax') return (xvals, yvals, data), style, {'xticks': xticks, 'yticks': yticks} class RadialHeatMapPlot(ColorbarPlot): start_angle = param.Number(default=np.pi/2, doc=""" Define starting angle of the first annulars. By default, beings at 12 o clock.""") max_radius = param.Number(default=0.5, doc=""" Define the maximum radius which is used for the x and y range extents. """) radius_inner = param.Number(default=0.1, bounds=(0, 0.5), doc=""" Define the radius fraction of inner, empty space.""") radius_outer = param.Number(default=0.05, bounds=(0, 1), doc=""" Define the radius fraction of outer space including the labels.""") radial = param.Boolean(default=True, doc=""" Whether the HeatMap should be radial""") show_values = param.Boolean(default=False, doc=""" Whether to annotate each pixel with its value.""") xmarks = param.Parameter(default=None, doc=""" Add separation lines between segments for better readability. By default, does not show any separation lines. If parameter is of type integer, draws the given amount of separations lines spread across radial heatmap. If parameter is of type list containing integers, show separation lines at given indices. If parameter is of type tuple, draw separation lines at given segment values. If parameter is of type function, draw separation lines where function returns True for passed segment value.""") ymarks = param.Parameter(default=None, doc=""" Add separation lines between annulars for better readability. By default, does not show any separation lines. If parameter is of type integer, draws the given amount of separations lines spread across radial heatmap. If parameter is of type list containing integers, show separation lines at given indices. If parameter is of type tuple, draw separation lines at given annular values. If parameter is of type function, draw separation lines where function returns True for passed annular value.""") xticks = param.Parameter(default=4, doc=""" Ticks along x-axis/segments specified as an integer, explicit list of ticks or function. If `None`, no ticks are shown.""") yticks = param.Parameter(default=4, doc=""" Ticks along y-axis/annulars specified as an integer, explicit list of ticks or function. If `None`, no ticks are shown.""") projection = param.ObjectSelector(default='polar', objects=['polar']) _style_groups = ['annular', 'xmarks', 'ymarks'] style_opts = ['annular_edgecolors', 'annular_linewidth', 'xmarks_linewidth', 'xmarks_edgecolor', 'cmap', 'ymarks_linewidth', 'ymarks_edgecolor'] @staticmethod def _map_order_to_ticks(start, end, order, reverse=False): """Map elements from given `order` array to bins ranging from `start` to `end`. """ size = len(order) bounds = np.linspace(start, end, size + 1) if reverse: bounds = bounds[::-1] mapping = list(zip(bounds[:-1]%(np.pi*2), order)) return mapping @staticmethod def _compute_separations(inner, outer, angles): """Compute x and y positions for separation lines for given angles. """ return [np.array([[a, inner], [a, outer]]) for a in angles] @staticmethod def _get_markers(ticks, marker): if callable(marker): marks = [v for v, l in ticks if marker(l)] elif isinstance(marker, int) and marker: nth_mark = max([np.ceil(len(ticks) / marker).astype(int), 1]) marks = [v for v, l in ticks[::nth_mark]] elif isinstance(marker, tuple): marks = [v for v, l in ticks if l in marker] else: marks = [] return marks @staticmethod def _get_ticks(ticks, ticker): if callable(ticker): ticks = [(v, l) for v, l in ticks if ticker(l)] elif isinstance(ticker, int): nth_mark = max([np.ceil(len(ticks) / ticker).astype(int), 1]) ticks = ticks[::nth_mark] elif isinstance(ticker, (tuple, list)): nth_mark = max([np.ceil(len(ticks) / len(ticker)).astype(int), 1]) ticks = [(v, tl) for (v, l), tl in zip(ticks[::nth_mark], ticker)] elif ticker: ticks = list(ticker) else: ticks = [] return ticks def get_extents(self, view, ranges, range_type='combined', **kwargs): if range_type == 'hard': return (np.nan,)*4 return (0, 0, np.pi*2, self.max_radius+self.radius_outer) def get_data(self, element, ranges, style): # dimension labels dim_labels = element.dimensions(label=True)[:3] x, y, z = (dimension_sanitizer(d) for d in dim_labels) if self.invert_axes: x, y = y, x # get raw values aggregate = element.gridded xvals = aggregate.dimension_values(x, expanded=False) yvals = aggregate.dimension_values(y, expanded=False) zvals = aggregate.dimension_values(2, flat=False) # pretty print x and y dimension values if necessary def _pprint(dim_label, vals): if vals.dtype.kind not in 'SU': dim = aggregate.get_dimension(dim_label) return [dim.pprint_value(v) for v in vals] return vals xvals = _pprint(x, xvals) yvals = _pprint(y, yvals) # annular wedges start_angle = self.start_angle end_angle = self.start_angle + 2 * np.pi bins_segment = np.linspace(start_angle, end_angle, len(xvals)+1) segment_ticks = self._map_order_to_ticks(start_angle, end_angle, xvals, True) radius_max = 0.5 radius_min = radius_max * self.radius_inner bins_annular = np.linspace(radius_min, radius_max, len(yvals)+1) radius_ticks = self._map_order_to_ticks(radius_min, radius_max, yvals) patches = [] for j in range(len(yvals)): ybin = bins_annular[j:j+2] for i in range(len(xvals))[::-1]: xbin = np.rad2deg(bins_segment[i:i+2]) width = ybin[1]-ybin[0] wedge = Wedge((0.5, 0.5), ybin[1], xbin[0], xbin[1], width=width) patches.append(wedge) angles = self._get_markers(segment_ticks, self.xmarks) xmarks = self._compute_separations(radius_min, radius_max, angles) radii = self._get_markers(radius_ticks, self.ymarks) ymarks = [Circle((0.5, 0.5), r) for r in radii] style['array'] = zvals.flatten() self._norm_kwargs(element, ranges, style, element.vdims[0]) if 'vmin' in style: style['clim'] = style.pop('vmin'), style.pop('vmax') data = {'annular': patches, 'xseparator': xmarks, 'yseparator': ymarks} xticks = self._get_ticks(segment_ticks, self.xticks) if not isinstance(self.xticks, int): xticks = [(v-((np.pi)/len(xticks)), l) for v, l in xticks] yticks = self._get_ticks(radius_ticks, self.yticks) ticks = {'xticks': xticks, 'yticks': yticks} return data, style, ticks def init_artists(self, ax, plot_args, plot_kwargs): # Draw edges color_opts = ['c', 'cmap', 'vmin', 'vmax', 'norm', 'array'] groups = [g for g in self._style_groups if g != 'annular'] edge_opts = filter_styles(plot_kwargs, 'annular', groups) annuli = plot_args['annular'] edge_opts.pop('interpolation', None) annuli = PatchCollection(annuli, transform=ax.transAxes, **edge_opts) ax.add_collection(annuli) artists = {'artist': annuli} paths = plot_args['xseparator'] if paths: groups = [g for g in self._style_groups if g != 'xmarks'] xmark_opts = filter_styles(plot_kwargs, 'xmarks', groups, color_opts) xmark_opts.pop('edgecolors', None) xseparators = LineCollection(paths, **xmark_opts) ax.add_collection(xseparators) artists['xseparator'] = xseparators paths = plot_args['yseparator'] if paths: groups = [g for g in self._style_groups if g != 'ymarks'] ymark_opts = filter_styles(plot_kwargs, 'ymarks', groups, color_opts) ymark_opts.pop('edgecolors', None) yseparators = PatchCollection(paths, facecolor='none', transform=ax.transAxes, **ymark_opts) ax.add_collection(yseparators) artists['yseparator'] = yseparators return artists