from __future__ import absolute_import, division, unicode_literals
from itertools import product
import numpy as np
import param
from matplotlib.patches import Wedge, Circle
from matplotlib.collections import LineCollection, PatchCollection
from ...core.data import GridInterface
from ...core.util import dimension_sanitizer, is_nan
from ...core.spaces import HoloMap
from ..mixins import HeatMapMixin
from .element import ColorbarPlot
from .raster import QuadMeshPlot
from .util import filter_styles
[docs]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.')
[docs] 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)
return (xvals, yvals, data), style, {'xticks': xticks, 'yticks': yticks}
[docs]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
[docs] def get_extents(self, view, ranges, range_type='combined'):
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)
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
[docs] 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
