import numpy as np import param from bokeh.models.glyphs import AnnularWedge from ...core.data import GridInterface from ...core.spaces import HoloMap from ...core.util import dimension_sanitizer, is_nan from .element import ColorbarPlot, CompositeElementPlot from .selection import BokehOverlaySelectionDisplay from .styles import base_properties, fill_properties, line_properties, text_properties class HeatMapPlot(ColorbarPlot): 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 either (1) a color hex string of the form #FFFFFF or #FFFFFFFF, (2) a length-3 or length-4 tuple specifying values in the range 0-1, or (3) a named HTML color.""") padding = param.ClassSelector(default=0, class_=(int, float, tuple)) show_legend = param.Boolean(default=False, doc=""" Whether to show legend for the plot.""") radial = param.Boolean(default=False, doc=""" Whether the HeatMap should be radial""") 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.""") _plot_methods = dict(single='rect') style_opts = (['cmap', 'color', 'dilate'] + base_properties + line_properties + fill_properties) selection_display = BokehOverlaySelectionDisplay() @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 _get_factors(self, element, ranges): return super()._get_factors(element.gridded, ranges) def _element_transform(self, transform, element, ranges): return transform.apply(element.gridded, ranges=ranges, flat=False).T.flatten() def get_data(self, element, ranges, style): x, y, z = (dimension_sanitizer(d) for d in element.dimensions(label=True)[:3]) if self.invert_axes: x, y = y, x cmapper = self._get_colormapper(element.vdims[0], element, ranges, style) if 'line_alpha' not in style and 'line_width' not in style: style['line_alpha'] = 0 style['selection_line_alpha'] = 0 style['nonselection_line_alpha'] = 0 elif 'line_color' not in style: style['line_color'] = 'white' 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.') if self.static_source: return {}, {'x': x, 'y': y, 'fill_color': {'field': 'zvalues', 'transform': cmapper}}, style aggregate = element.gridded xdim, ydim = aggregate.dimensions()[:2] xtype = aggregate.interface.dtype(aggregate, xdim) widths = None if xtype.kind in 'SUO': xvals = aggregate.dimension_values(xdim) width = 1 else: xvals = aggregate.dimension_values(xdim, flat=False) if xvals.shape[1] > 1: edges = GridInterface._infer_interval_breaks(xvals, axis=1) widths = np.diff(edges, axis=1).T.flatten() else: widths = [self.default_span]*xvals.shape[0] if len(xvals) else [] xvals = xvals.T.flatten() width = 'width' ytype = aggregate.interface.dtype(aggregate, ydim) heights = None if ytype.kind in 'SUO': yvals = aggregate.dimension_values(ydim) height = 1 else: yvals = aggregate.dimension_values(ydim, flat=False) if yvals.shape[0] > 1: edges = GridInterface._infer_interval_breaks(yvals, axis=0) heights = np.diff(edges, axis=0).T.flatten() else: heights = [self.default_span]*yvals.shape[1] if len(yvals) else [] yvals = yvals.T.flatten() height = 'height' zvals = aggregate.dimension_values(2, flat=False) zvals = zvals.T.flatten() if self.invert_axes: width, height = height, width data = {x: xvals, y: yvals, 'zvalues': zvals} if widths is not None: data['width'] = widths if heights is not None: data['height'] = heights if 'hover' in self.handles and not self.static_source: for vdim in element.vdims: sanitized = dimension_sanitizer(vdim.name) data[sanitized] = ['-' if is_nan(v) else vdim.pprint_value(v) for v in aggregate.dimension_values(vdim)] # Filter radial heatmap options style = {k: v for k, v in style.items() if not any(g in k for g in RadialHeatMapPlot._style_groups.values())} return (data, {'x': x, 'y': y, 'fill_color': {'field': 'zvalues', 'transform': cmapper}, 'height': height, 'width': width}, style) def _draw_markers(self, plot, element, marks, axis='x'): if marks is None or self.radial: return self.param.warning('Only radial HeatMaps supports marks, to make the' 'HeatMap quads for distinguishable set a line_width') def _init_glyphs(self, plot, element, ranges, source): super()._init_glyphs(plot, element, ranges, source) self._draw_markers(plot, element, self.xmarks, axis='x') self._draw_markers(plot, element, self.ymarks, axis='y') def _update_glyphs(self, element, ranges, style): super()._update_glyphs(element, ranges, style) plot = self.handles['plot'] self._draw_markers(plot, element, self.xmarks, axis='x') self._draw_markers(plot, element, self.ymarks, axis='y') class RadialHeatMapPlot(CompositeElementPlot, ColorbarPlot): 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 either (1) a color hex string of the form #FFFFFF or #FFFFFFFF, (2) a length-3 or length-4 tuple specifying values in the range 0-1, or (3) a named HTML color.""") start_angle = param.Number(default=np.pi/2, doc=""" Define starting angle of the first annulus segment. By default, begins at 12 o'clock.""") 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.""") 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.""") max_radius = param.Number(default=0.5, doc=""" Define the maximum radius which is used for the x and y range extents. """) radial = param.Boolean(default=True, doc=""" Whether the HeatMap should be radial""") show_frame = param.Boolean(default=False, doc=""" Whether or not to show a complete frame around the plot.""") 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.""") yrotation = param.Number(default=90, doc=""" Define angle along which yticks/annulars are shown. By default, yticks are drawn like a regular y-axis.""") # Map each glyph to a style group _style_groups = {'annular_wedge': 'annular', 'text': 'ticks', 'multi_line': 'xmarks', 'arc': 'ymarks'} _draw_order = ['annular_wedge', 'multi_line', 'arc', 'text'] style_opts = (['xmarks_' + p for p in base_properties + line_properties] + \ ['ymarks_' + p for p in base_properties + line_properties] + \ ['annular_' + p for p in base_properties + fill_properties + line_properties] + \ ['ticks_' + p for p in text_properties] + ['cmap']) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.xaxis = None self.yaxis = None def _get_bins(self, kind, order, reverse=False): """ Map elements from given `order` array to bins of start and end values for radius or angle dimension. """ if kind == "radius": start = self.max_radius * self.radius_inner end = self.max_radius elif kind == "angle": start = self.start_angle end = self.start_angle + 2 * np.pi bounds = np.linspace(start, end, len(order) + 1) bins = np.vstack([bounds[:-1], bounds[1:]]).T if reverse: bins = bins[::-1] return dict(zip(order, bins)) @staticmethod def _get_bounds(mapper, values): """ Extract first and second value from tuples of mapped bins. """ array = np.array([mapper.get(x) for x in values]) return array[:, 0], array[:, 1] def _postprocess_hover(self, renderer, source): """ Limit hover tool to annular wedges only. """ if isinstance(renderer.glyph, AnnularWedge): super()._postprocess_hover(renderer, source) def get_extents(self, view, ranges, range_type='combined', **kwargs): """Supply custom, static extents because radial heatmaps always have the same boundaries. """ if range_type not in ('data', 'combined'): return (None,)*4 lower = -self.radius_outer upper = 2 * self.max_radius + self.radius_outer return (lower, lower, upper, upper) def _get_axis_dims(self, element): return (None, None) def _axis_properties(self, *args, **kwargs): """Overwrite default axis properties handling due to clashing categorical input and numerical output axes. Axis properties are handled separately for radial heatmaps because of missing radial axes in bokeh. """ return {} def get_default_mapping(self, z, cmapper): """Create dictionary containing default ColumnDataSource glyph to data mappings. """ map_annular = dict(x=self.max_radius, y=self.max_radius, inner_radius="inner_radius", outer_radius="outer_radius", start_angle="start_angle", end_angle="end_angle", fill_color={'field': z, 'transform': cmapper}) map_seg_label = dict(x="x", y="y", text="text", angle="angle", text_align="center") map_ann_label = dict(x="x", y="y", text="text", angle="angle", text_align="center", text_baseline="bottom") map_xmarks = dict(xs="xs", ys="ys") map_ymarks = dict(x= self.max_radius, y=self.max_radius, start_angle=0, end_angle=2*np.pi, radius="radius") return {'annular_wedge_1': map_annular, 'text_1': map_seg_label, 'text_2': map_ann_label, 'multi_line_1': map_xmarks, 'arc_1': map_ymarks} def _pprint(self, element, dim_label, vals): """ Helper function to convert values to corresponding dimension type. """ if vals.dtype.kind not in 'SU': dim = element.gridded.get_dimension(dim_label) return [dim.pprint_value(v) for v in vals] return vals def _compute_tick_mapping(self, kind, order, bins): """ Helper function to compute tick mappings based on `ticks` and default orders and bins. """ if kind == "angle": ticks = self.xticks reverse = True elif kind == "radius": ticks = self.yticks reverse = False if callable(ticks): text_nth = [x for x in order if ticks(x)] elif isinstance(ticks, (tuple, list)): bins = self._get_bins(kind, ticks, reverse) text_nth = ticks elif ticks: nth_label = np.ceil(len(order) / float(ticks)).astype(int) text_nth = order[::nth_label] return {x: bins[x] for x in text_nth} def _get_seg_labels_data(self, order_seg, bins_seg): """ Generate ColumnDataSource dictionary for segment labels. """ if self.xticks is None: return dict(x=[], y=[], text=[], angle=[]) mapping = self._compute_tick_mapping("angle", order_seg, bins_seg) values = [(text, ((end - start) / 2) + start) for text, (start, end) in mapping.items()] labels, radiant = zip(*values) radiant = np.array(radiant) y_coord = np.sin(radiant) * self.max_radius + self.max_radius x_coord = np.cos(radiant) * self.max_radius + self.max_radius return dict(x=x_coord, y=y_coord, text=labels, angle=1.5 * np.pi + radiant) def _get_ann_labels_data(self, order_ann, bins_ann): """ Generate ColumnDataSource dictionary for annular labels. """ if self.yticks is None: return dict(x=[], y=[], text=[], angle=[]) mapping = self._compute_tick_mapping("radius", order_ann, bins_ann) values = [(label, radius[0]) for label, radius in mapping.items()] labels, radius = zip(*values) radius = np.array(radius) y_coord = np.sin(np.deg2rad(self.yrotation)) * radius + self.max_radius x_coord = np.cos(np.deg2rad(self.yrotation)) * radius + self.max_radius return dict(x=x_coord, y=y_coord, text=labels, angle=[0]*len(labels)) @staticmethod def _get_markers(marks, order, bins): """ Helper function to get marker positions depending on mark type. """ if callable(marks): markers = [x for x in order if marks(x)] elif isinstance(marks, list): markers = [order[x] for x in marks] elif isinstance(marks, tuple): markers = marks else: nth_mark = np.ceil(len(order) / marks).astype(int) markers = order[::nth_mark] return np.array([bins[x][1] for x in markers]) def _get_xmarks_data(self, order_seg, bins_seg): """ Generate ColumnDataSource dictionary for segment separation lines. """ if not self.xmarks: return dict(xs=[], ys=[]) angles = self._get_markers(self.xmarks, order_seg, bins_seg) inner = self.max_radius * self.radius_inner outer = self.max_radius y_start = np.sin(angles) * inner + self.max_radius y_end = np.sin(angles) * outer + self.max_radius x_start = np.cos(angles) * inner + self.max_radius x_end = np.cos(angles) * outer + self.max_radius xs = zip(x_start, x_end) ys = zip(y_start, y_end) return dict(xs=list(xs), ys=list(ys)) def _get_ymarks_data(self, order_ann, bins_ann): """ Generate ColumnDataSource dictionary for segment separation lines. """ if not self.ymarks: return dict(radius=[]) radius = self._get_markers(self.ymarks, order_ann, bins_ann) return dict(radius=radius) 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 # color mapper cmapper = self._get_colormapper(element.vdims[0], element, ranges, style) # default CDS data mapping mapping = self.get_default_mapping(z, cmapper) if self.static_source: return {}, mapping, style # get raw values aggregate = element.gridded xvals = aggregate.dimension_values(x) yvals = aggregate.dimension_values(y) zvals = aggregate.dimension_values(2, flat=True) # get orders order_seg = aggregate.dimension_values(x, expanded=False) order_ann = aggregate.dimension_values(y, expanded=False) # pretty print if necessary xvals = self._pprint(element, x, xvals) yvals = self._pprint(element, y, yvals) order_seg = self._pprint(element, x, order_seg) order_ann = self._pprint(element, y, order_ann) # annular wedges bins_ann = self._get_bins("radius", order_ann) if len(bins_ann): inner_radius, outer_radius = self._get_bounds(bins_ann, yvals) data_text_ann = self._get_ann_labels_data(order_ann, bins_ann) else: inner_radius, outer_radius = [], [] data_text_ann = dict(x=[], y=[], text=[], angle=[]) bins_seg = self._get_bins("angle", order_seg, True) if len(bins_seg): start_angle, end_angle = self._get_bounds(bins_seg, xvals) data_text_seg = self._get_seg_labels_data(order_seg, bins_seg) else: start_angle, end_angle = [], [] data_text_seg = dict(x=[], y=[], text=[], angle=[]) # create ColumnDataSources data_annular = {"start_angle": start_angle, "end_angle": end_angle, "inner_radius": inner_radius, "outer_radius": outer_radius, z: zvals, x: xvals, y: yvals} if 'hover' in self.handles: for vdim in element.vdims: sanitized = dimension_sanitizer(vdim.name) values = ['-' if is_nan(v) else vdim.pprint_value(v) for v in aggregate.dimension_values(vdim)] data_annular[sanitized] = values data_xmarks = self._get_xmarks_data(order_seg, bins_seg) data_ymarks = self._get_ymarks_data(order_ann, bins_ann) data = {'annular_wedge_1': data_annular, 'text_1': data_text_seg, 'text_2': data_text_ann, 'multi_line_1': data_xmarks, 'arc_1': data_ymarks} return data, mapping, style def _init_glyph(self, plot, mapping, properties, key): ret = super()._init_glyph(plot, mapping, properties, key) if self.colorbar and 'color_mapper' in self.handles: self._draw_colorbar(plot, self.handles['color_mapper']) return ret