from __future__ import absolute_import, division, unicode_literals
import param
import numpy as np
from bokeh.models.glyphs import AnnularWedge
from ...core.data import GridInterface
from ...core.util import is_nan, dimension_sanitizer
from ...core.spaces import HoloMap
from .element import ColorbarPlot, CompositeElementPlot
from .selection import BokehOverlaySelectionDisplay
from .styles import (
base_properties, line_properties, fill_properties, text_properties
)
[docs]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(HeatMapPlot, self)._get_factors(element.gridded, ranges)
[docs] 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(HeatMapPlot, self)._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(HeatMapPlot, self)._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')
[docs]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(RadialHeatMapPlot, self).__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(RadialHeatMapPlot, self)._postprocess_hover(renderer, source)
[docs] def get_extents(self, view, ranges, range_type='combined'):
"""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 {}
[docs] 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)
[docs] 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(RadialHeatMapPlot, self)._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
