from collections import defaultdict
import numpy as np
import param
from bokeh.models import CategoricalColorMapper, CustomJS, FactorRange, Range1d, Whisker
from bokeh.models.tools import BoxSelectTool
from bokeh.transform import jitter
from ...core.data import Dataset
from ...core.dimension import dimension_name
from ...core.util import dimension_sanitizer, isfinite
from ...operation import interpolate_curve
from ...util.transform import dim
from ..mixins import AreaMixin, BarsMixin, SpikesMixin
from ..util import compute_sizes, get_min_distance
from .element import ColorbarPlot, ElementPlot, LegendPlot, OverlayPlot
from .selection import BokehOverlaySelectionDisplay
from .styles import (
base_properties,
expand_batched_style,
fill_properties,
line_properties,
mpl_to_bokeh,
rgb2hex,
)
from .util import categorize_array
[docs]class PointPlot(LegendPlot, ColorbarPlot):
jitter = param.Number(default=None, bounds=(0, None), doc="""
The amount of jitter to apply to offset the points along the x-axis.""")
selected = param.List(default=None, doc="""
The current selection as a list of integers corresponding
to the selected items.""")
# Deprecated parameters
color_index = param.ClassSelector(default=None, class_=(str, int),
allow_None=True, doc="""
Deprecated in favor of color style mapping, e.g. `color=dim('color')`""")
size_index = param.ClassSelector(default=None, class_=(str, int),
allow_None=True, doc="""
Deprecated in favor of size style mapping, e.g. `size=dim('size')`""")
scaling_method = param.ObjectSelector(default="area",
objects=["width", "area"],
doc="""
Deprecated in favor of size style mapping, e.g.
size=dim('size')**2.""")
scaling_factor = param.Number(default=1, bounds=(0, None), doc="""
Scaling factor which is applied to either the width or area
of each point, depending on the value of `scaling_method`.""")
size_fn = param.Callable(default=np.abs, doc="""
Function applied to size values before applying scaling,
to remove values lower than zero.""")
selection_display = BokehOverlaySelectionDisplay()
style_opts = (['cmap', 'palette', 'marker', 'size', 'angle'] +
base_properties + line_properties + fill_properties)
_plot_methods = dict(single='scatter', batched='scatter')
_batched_style_opts = line_properties + fill_properties + ['size', 'marker', 'angle']
def _get_size_data(self, element, ranges, style):
data, mapping = {}, {}
sdim = element.get_dimension(self.size_index)
ms = style.get('size', np.sqrt(6))
if sdim and ((isinstance(ms, str) and ms in element) or isinstance(ms, dim)):
self.param.warning(
"Cannot declare style mapping for 'size' option and "
"declare a size_index; ignoring the size_index.")
sdim = None
if not sdim or self.static_source:
return data, mapping
map_key = 'size_' + sdim.name
ms = ms**2
sizes = element.dimension_values(self.size_index)
sizes = compute_sizes(sizes, self.size_fn,
self.scaling_factor,
self.scaling_method, ms)
if sizes is None:
eltype = type(element).__name__
self.param.warning(
f'{sdim.pprint_label} dimension is not numeric, cannot use to scale {eltype} size.')
else:
data[map_key] = np.sqrt(sizes)
mapping['size'] = map_key
return data, mapping
[docs] def get_data(self, element, ranges, style):
dims = element.dimensions(label=True)
xidx, yidx = (1, 0) if self.invert_axes else (0, 1)
mapping = dict(x=dims[xidx], y=dims[yidx])
data = {}
if not self.static_source or self.batched:
xdim, ydim = dims[:2]
data[xdim] = element.dimension_values(xdim)
data[ydim] = element.dimension_values(ydim)
self._categorize_data(data, dims[:2], element.dimensions())
cdata, cmapping = self._get_color_data(element, ranges, style)
data.update(cdata)
mapping.update(cmapping)
sdata, smapping = self._get_size_data(element, ranges, style)
data.update(sdata)
mapping.update(smapping)
if 'angle' in style and isinstance(style['angle'], (int, float)):
style['angle'] = np.deg2rad(style['angle'])
if self.jitter:
if self.invert_axes:
mapping['y'] = jitter(dims[yidx], self.jitter,
range=self.handles['y_range'])
else:
mapping['x'] = jitter(dims[xidx], self.jitter,
range=self.handles['x_range'])
self._get_hover_data(data, element)
return data, mapping, style
def get_batched_data(self, element, ranges):
data = defaultdict(list)
zorders = self._updated_zorders(element)
# Angles need special handling since they are tied to the
# marker in certain cases
has_angles = False
for (key, el), zorder in zip(element.data.items(), zorders):
el_opts = self.lookup_options(el, 'plot').options
self.param.update(**{k: v for k, v in el_opts.items()
if k not in OverlayPlot._propagate_options})
style = self.lookup_options(element.last, 'style')
style = style.max_cycles(len(self.ordering))[zorder]
eldata, elmapping, style = self.get_data(el, ranges, style)
style = mpl_to_bokeh(style)
for k, eld in eldata.items():
data[k].append(eld)
# Skip if data is empty
if not eldata:
continue
# Apply static styles
nvals = len(next(iter(eldata.values())))
sdata, smapping = expand_batched_style(style, self._batched_style_opts,
elmapping, nvals)
if 'angle' in sdata and '__angle' not in data and 'marker' in data:
data['__angle'] = [np.zeros(len(d)) for d in data['marker']]
has_angles = True
elmapping.update(smapping)
for k, v in sorted(sdata.items()):
if k == 'angle':
k = '__angle'
has_angles = True
data[k].append(v)
if has_angles and 'angle' not in sdata:
data['__angle'].append(np.zeros(len(v)))
if 'hover' in self.handles:
for d, k in zip(element.dimensions(), key):
sanitized = dimension_sanitizer(d.name)
data[sanitized].append([k]*nvals)
data = {k: np.concatenate(v) for k, v in data.items()}
if '__angle' in data:
elmapping['angle'] = {'field': '__angle'}
return data, elmapping, style
[docs]class VectorFieldPlot(ColorbarPlot):
arrow_heads = param.Boolean(default=True, doc="""
Whether or not to draw arrow heads.""")
magnitude = param.ClassSelector(class_=(str, dim), doc="""
Dimension or dimension value transform that declares the magnitude
of each vector. Magnitude is expected to be scaled between 0-1,
by default the magnitudes are rescaled relative to the minimum
distance between vectors, this can be disabled with the
rescale_lengths option.""")
padding = param.ClassSelector(default=0.05, class_=(int, float, tuple))
pivot = param.ObjectSelector(default='mid', objects=['mid', 'tip', 'tail'],
doc="""
The point around which the arrows should pivot valid options
include 'mid', 'tip' and 'tail'.""")
rescale_lengths = param.Boolean(default=True, doc="""
Whether the lengths will be rescaled to take into account the
smallest non-zero distance between two vectors.""")
# Deprecated parameters
color_index = param.ClassSelector(default=None, class_=(str, int),
allow_None=True, doc="""
Deprecated in favor of dimension value transform on color option,
e.g. `color=dim('Magnitude')`.
""")
size_index = param.ClassSelector(default=None, class_=(str, int),
allow_None=True, doc="""
Deprecated in favor of the magnitude option, e.g.
`magnitude=dim('Magnitude')`.
""")
normalize_lengths = param.Boolean(default=True, doc="""
Deprecated in favor of rescaling length using dimension value
transforms using the magnitude option, e.g.
`dim('Magnitude').norm()`.""")
selection_display = BokehOverlaySelectionDisplay()
style_opts = base_properties + line_properties + ['scale', 'cmap']
_nonvectorized_styles = base_properties + ['scale', 'cmap']
_plot_methods = dict(single='segment')
def _get_lengths(self, element, ranges):
size_dim = element.get_dimension(self.size_index)
mag_dim = self.magnitude
if size_dim and mag_dim:
self.param.warning(
"Cannot declare style mapping for 'magnitude' option "
"and declare a size_index; ignoring the size_index.")
elif size_dim:
mag_dim = size_dim
elif isinstance(mag_dim, str):
mag_dim = element.get_dimension(mag_dim)
(x0, x1), (y0, y1) = (element.range(i) for i in range(2))
if mag_dim:
if isinstance(mag_dim, dim):
magnitudes = mag_dim.apply(element, flat=True)
else:
magnitudes = element.dimension_values(mag_dim)
_, max_magnitude = ranges[dimension_name(mag_dim)]['combined']
if self.normalize_lengths and max_magnitude != 0:
magnitudes = magnitudes / max_magnitude
if self.rescale_lengths:
base_dist = get_min_distance(element)
magnitudes = magnitudes * base_dist
else:
magnitudes = np.ones(len(element))
if self.rescale_lengths:
base_dist = get_min_distance(element)
magnitudes = magnitudes * base_dist
return magnitudes
def _glyph_properties(self, *args):
properties = super()._glyph_properties(*args)
properties.pop('scale', None)
return properties
[docs] def get_data(self, element, ranges, style):
input_scale = style.pop('scale', 1.0)
# Get x, y, angle, magnitude and color data
rads = element.dimension_values(2)
if self.invert_axes:
xidx, yidx = (1, 0)
rads = np.pi/2 - rads
else:
xidx, yidx = (0, 1)
lens = self._get_lengths(element, ranges)/input_scale
cdim = element.get_dimension(self.color_index)
cdata, cmapping = self._get_color_data(element, ranges, style,
name='line_color')
# Compute segments and arrowheads
xs = element.dimension_values(xidx)
ys = element.dimension_values(yidx)
# Compute offset depending on pivot option
xoffsets = np.cos(rads)*lens/2.
yoffsets = np.sin(rads)*lens/2.
if self.pivot == 'mid':
nxoff, pxoff = xoffsets, xoffsets
nyoff, pyoff = yoffsets, yoffsets
elif self.pivot == 'tip':
nxoff, pxoff = 0, xoffsets*2
nyoff, pyoff = 0, yoffsets*2
elif self.pivot == 'tail':
nxoff, pxoff = xoffsets*2, 0
nyoff, pyoff = yoffsets*2, 0
x0s, x1s = (xs + nxoff, xs - pxoff)
y0s, y1s = (ys + nyoff, ys - pyoff)
color = None
if self.arrow_heads:
arrow_len = (lens/4.)
xa1s = x0s - np.cos(rads+np.pi/4)*arrow_len
ya1s = y0s - np.sin(rads+np.pi/4)*arrow_len
xa2s = x0s - np.cos(rads-np.pi/4)*arrow_len
ya2s = y0s - np.sin(rads-np.pi/4)*arrow_len
x0s = np.tile(x0s, 3)
x1s = np.concatenate([x1s, xa1s, xa2s])
y0s = np.tile(y0s, 3)
y1s = np.concatenate([y1s, ya1s, ya2s])
if cdim and cdim.name in cdata:
color = np.tile(cdata[cdim.name], 3)
elif cdim:
color = cdata.get(cdim.name)
data = {'x0': x0s, 'x1': x1s, 'y0': y0s, 'y1': y1s}
mapping = dict(x0='x0', x1='x1', y0='y0', y1='y1')
if cdim and color is not None:
data[cdim.name] = color
mapping.update(cmapping)
return (data, mapping, style)
[docs]class CurvePlot(ElementPlot):
padding = param.ClassSelector(default=(0, 0.1), class_=(int, float, tuple))
interpolation = param.ObjectSelector(objects=['linear', 'steps-mid',
'steps-pre', 'steps-post'],
default='linear', doc="""
Defines how the samples of the Curve are interpolated,
default is 'linear', other options include 'steps-mid',
'steps-pre' and 'steps-post'.""")
selection_display = BokehOverlaySelectionDisplay()
style_opts = base_properties + line_properties
_batched_style_opts = line_properties
_nonvectorized_styles = base_properties + line_properties
_plot_methods = dict(single='line', batched='multi_line')
[docs] def get_data(self, element, ranges, style):
xidx, yidx = (1, 0) if self.invert_axes else (0, 1)
x = element.get_dimension(xidx).name
y = element.get_dimension(yidx).name
if self.static_source and not self.batched:
return {}, dict(x=x, y=y), style
if 'steps' in self.interpolation:
element = interpolate_curve(element, interpolation=self.interpolation)
data = {x: element.dimension_values(xidx),
y: element.dimension_values(yidx)}
self._get_hover_data(data, element)
self._categorize_data(data, (x, y), element.dimensions())
return (data, dict(x=x, y=y), style)
def _hover_opts(self, element):
if self.batched:
dims = list(self.hmap.last.kdims)
line_policy = 'prev'
else:
dims = list(self.overlay_dims.keys())+element.dimensions()
line_policy = 'nearest'
return dims, dict(line_policy=line_policy)
def get_batched_data(self, overlay, ranges):
data = defaultdict(list)
zorders = self._updated_zorders(overlay)
for (key, el), zorder in zip(overlay.data.items(), zorders):
el_opts = self.lookup_options(el, 'plot').options
self.param.update(**{k: v for k, v in el_opts.items()
if k not in OverlayPlot._propagate_options})
style = self.lookup_options(el, 'style')
style = style.max_cycles(len(self.ordering))[zorder]
eldata, elmapping, style = self.get_data(el, ranges, style)
# Skip if data empty
if not eldata:
continue
for k, eld in eldata.items():
data[k].append(eld)
# Apply static styles
sdata, smapping = expand_batched_style(style, self._batched_style_opts,
elmapping, nvals=1)
elmapping.update(smapping)
for k, v in sdata.items():
data[k].append(v[0])
for d, k in zip(overlay.kdims, key):
sanitized = dimension_sanitizer(d.name)
data[sanitized].append(k)
data = {opt: vals for opt, vals in data.items()
if not any(v is None for v in vals)}
mapping = {{'x': 'xs', 'y': 'ys'}.get(k, k): v
for k, v in elmapping.items()}
return data, mapping, style
[docs]class HistogramPlot(ColorbarPlot):
selection_display = BokehOverlaySelectionDisplay(color_prop=['color', 'fill_color'])
style_opts = base_properties + fill_properties + line_properties + ['cmap']
_nonvectorized_styles = base_properties + ['line_dash']
_plot_methods = dict(single='quad')
[docs] def get_data(self, element, ranges, style):
if self.invert_axes:
mapping = dict(top='right', bottom='left', left=0, right='top')
else:
mapping = dict(top='top', bottom=0, left='left', right='right')
if self.static_source:
data = dict(top=[], left=[], right=[])
else:
x = element.kdims[0]
values = element.dimension_values(1)
edges = element.interface.coords(element, x, edges=True)
if hasattr(edges, 'compute'):
edges = edges.compute()
data = dict(top=values, left=edges[:-1], right=edges[1:])
self._get_hover_data(data, element)
return (data, mapping, style)
[docs] def get_extents(self, element, ranges, range_type='combined', **kwargs):
ydim = element.get_dimension(1)
s0, s1 = ranges[ydim.name]['soft']
s0 = min(s0, 0) if isfinite(s0) else 0
s1 = max(s1, 0) if isfinite(s1) else 0
ranges[ydim.name]['soft'] = (s0, s1)
return super().get_extents(element, ranges, range_type)
[docs]class SideHistogramPlot(HistogramPlot):
style_opts = HistogramPlot.style_opts + ['cmap']
height = param.Integer(default=125, doc="The height of the plot")
width = param.Integer(default=125, doc="The width of the plot")
show_title = param.Boolean(default=False, doc="""
Whether to display the plot title.""")
default_tools = param.List(default=['save', 'pan', 'wheel_zoom',
'box_zoom', 'reset'],
doc="A list of plugin tools to use on the plot.")
_callback = """
color_mapper.low = cb_obj['geometry']['{axis}0'];
color_mapper.high = cb_obj['geometry']['{axis}1'];
source.change.emit()
main_source.change.emit()
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.invert_axes:
self.default_tools.append('ybox_select')
else:
self.default_tools.append('xbox_select')
[docs] def get_data(self, element, ranges, style):
data, mapping, style = HistogramPlot.get_data(self, element, ranges, style)
color_dims = [d for d in self.adjoined.traverse(lambda x: x.handles.get('color_dim'))
if d is not None]
dimension = color_dims[0] if color_dims else None
cmapper = self._get_colormapper(dimension, element, {}, {})
if cmapper:
cvals = None
if isinstance(dimension, dim):
if dimension.applies(element):
dim_name = dimension.dimension.name
cvals = [] if self.static_source else dimension.apply(element)
elif dimension in element.dimensions():
dim_name = dimension.name
cvals = [] if self.static_source else element.dimension_values(dimension)
if cvals is not None:
data[dim_name] = cvals
mapping['fill_color'] = {'field': dim_name,
'transform': cmapper}
return (data, mapping, style)
def _init_glyph(self, plot, mapping, properties):
"""
Returns a Bokeh glyph object.
"""
ret = super()._init_glyph(plot, mapping, properties)
if "field" not in mapping.get("fill_color", {}):
return ret
dim = mapping['fill_color']['field']
sources = self.adjoined.traverse(lambda x: (x.handles.get('color_dim'),
x.handles.get('source')))
sources = [src for cdim, src in sources if cdim == dim]
tools = [t for t in self.handles['plot'].tools
if isinstance(t, BoxSelectTool)]
if not tools or not sources:
return
main_source = sources[0]
handles = {'color_mapper': self.handles['color_mapper'],
'source': self.handles['source'],
'cds': self.handles['source'],
'main_source': main_source}
callback = self._callback.format(axis='y' if self.invert_axes else 'x')
self.state.js_on_event("selectiongeometry", CustomJS(args=handles, code=callback))
return ret
[docs]class ErrorPlot(ColorbarPlot):
selected = param.List(default=None, doc="""
The current selection as a list of integers corresponding
to the selected items.""")
selection_display = BokehOverlaySelectionDisplay()
style_opts = ([
p for p in line_properties if p.split('_')[0] not in
('hover', 'selection', 'nonselection', 'muted')
] + ['lower_head', 'upper_head'] + base_properties)
_nonvectorized_styles = base_properties + ['line_dash']
_mapping = dict(base="base", upper="upper", lower="lower")
_plot_methods = dict(single=Whisker)
[docs] def get_data(self, element, ranges, style):
mapping = dict(self._mapping)
if self.static_source:
return {}, mapping, style
x_idx, y_idx = (1, 0) if element.horizontal else (0, 1)
base = element.dimension_values(x_idx)
mean = element.dimension_values(y_idx)
neg_error = element.dimension_values(2)
pos_idx = 3 if len(element.dimensions()) > 3 else 2
pos_error = element.dimension_values(pos_idx)
lower = mean - neg_error
upper = mean + pos_error
if element.horizontal ^ self.invert_axes:
mapping['dimension'] = 'width'
else:
mapping['dimension'] = 'height'
data = dict(base=base, lower=lower, upper=upper)
self._categorize_data(data, ('base',), element.dimensions())
return (data, mapping, style)
def _init_glyph(self, plot, mapping, properties):
"""
Returns a Bokeh glyph object.
"""
properties = {k: v for k, v in properties.items() if 'legend' not in k}
for prop in ['color', 'alpha']:
if prop not in properties:
continue
pval = properties.pop(prop)
line_prop = f'line_{prop}'
fill_prop = f'fill_{prop}'
if line_prop not in properties:
properties[line_prop] = pval
if fill_prop not in properties and fill_prop in self.style_opts:
properties[fill_prop] = pval
properties = mpl_to_bokeh(properties)
plot_method = self._plot_methods['single']
glyph = plot_method(**dict(properties, **mapping))
plot.add_layout(glyph)
return None, glyph
[docs]class SpreadPlot(ElementPlot):
padding = param.ClassSelector(default=(0, 0.1), class_=(int, float, tuple))
selection_display = BokehOverlaySelectionDisplay()
style_opts = base_properties + fill_properties + line_properties
_no_op_style = style_opts
_nonvectorized_styles = style_opts
_plot_methods = dict(single='patch')
_stream_data = False # Plot does not support streaming data
def _split_area(self, xs, lower, upper):
"""
Splits area plots at nans and returns x- and y-coordinates for
each area separated by nans.
"""
xnan = np.array([np.datetime64('nat') if xs.dtype.kind == 'M' else np.nan])
ynan = np.array([np.datetime64('nat') if lower.dtype.kind == 'M' else np.nan])
split = np.where(~isfinite(xs) | ~isfinite(lower) | ~isfinite(upper))[0]
xvals = np.split(xs, split)
lower = np.split(lower, split)
upper = np.split(upper, split)
band_x, band_y = [], []
for i, (x, l, u) in enumerate(zip(xvals, lower, upper)):
if i:
x, l, u = x[1:], l[1:], u[1:]
if not len(x):
continue
band_x += [np.append(x, x[::-1]), xnan]
band_y += [np.append(l, u[::-1]), ynan]
if len(band_x):
xs = np.concatenate(band_x[:-1])
ys = np.concatenate(band_y[:-1])
return xs, ys
return [], []
[docs] def get_data(self, element, ranges, style):
mapping = dict(x='x', y='y')
xvals = element.dimension_values(0)
mean = element.dimension_values(1)
neg_error = element.dimension_values(2)
pos_idx = 3 if len(element.dimensions()) > 3 else 2
pos_error = element.dimension_values(pos_idx)
lower = mean - neg_error
upper = mean + pos_error
band_x, band_y = self._split_area(xvals, lower, upper)
if self.invert_axes:
data = dict(x=band_y, y=band_x)
else:
data = dict(x=band_x, y=band_y)
return data, mapping, style
[docs]class AreaPlot(AreaMixin, SpreadPlot):
padding = param.ClassSelector(default=(0, 0.1), class_=(int, float, tuple))
selection_display = BokehOverlaySelectionDisplay()
_stream_data = False # Plot does not support streaming data
[docs] def get_data(self, element, ranges, style):
mapping = dict(x='x', y='y')
xs = element.dimension_values(0)
if len(element.vdims) > 1:
bottom = element.dimension_values(2)
else:
bottom = np.zeros(len(element))
top = element.dimension_values(1)
band_xs, band_ys = self._split_area(xs, bottom, top)
if self.invert_axes:
data = dict(x=band_ys, y=band_xs)
else:
data = dict(x=band_xs, y=band_ys)
return data, mapping, style
[docs]class SpikesPlot(SpikesMixin, ColorbarPlot):
spike_length = param.Number(default=0.5, doc="""
The length of each spike if Spikes object is one dimensional.""")
position = param.Number(default=0., doc="""
The position of the lower end of each spike.""")
show_legend = param.Boolean(default=True, doc="""
Whether to show legend for the plot.""")
# Deprecated parameters
color_index = param.ClassSelector(default=None, class_=(str, int),
allow_None=True, doc="""
Deprecated in favor of color style mapping, e.g. `color=dim('color')`""")
selection_display = BokehOverlaySelectionDisplay()
style_opts = base_properties + line_properties + ['cmap', 'palette']
_nonvectorized_styles = base_properties + ['cmap']
_plot_methods = dict(single='segment')
[docs] def get_data(self, element, ranges, style):
dims = element.dimensions()
data = {}
pos = self.position
opts = self.lookup_options(element, 'plot').options
if len(element) == 0 or self.static_source:
data = {'x': [], 'y0': [], 'y1': []}
else:
data['x'] = element.dimension_values(0)
data['y0'] = np.full(len(element), pos)
if len(dims) > 1 and 'spike_length' not in opts:
data['y1'] = element.dimension_values(1)+pos
else:
data['y1'] = data['y0']+self.spike_length
if self.invert_axes:
mapping = {'x0': 'y0', 'x1': 'y1', 'y0': 'x', 'y1': 'x'}
else:
mapping = {'x0': 'x', 'x1': 'x', 'y0': 'y0', 'y1': 'y1'}
cdata, cmapping = self._get_color_data(element, ranges, dict(style))
data.update(cdata)
mapping.update(cmapping)
self._get_hover_data(data, element)
return data, mapping, style
[docs]class SideSpikesPlot(SpikesPlot):
"""
SpikesPlot with useful defaults for plotting adjoined rug plot.
"""
selected = param.List(default=None, doc="""
The current selection as a list of integers corresponding
to the selected items.""")
xaxis = param.ObjectSelector(default='top-bare',
objects=['top', 'bottom', 'bare', 'top-bare',
'bottom-bare', None], doc="""
Whether and where to display the xaxis, bare options allow suppressing
all axis labels including ticks and xlabel. Valid options are 'top',
'bottom', 'bare', 'top-bare' and 'bottom-bare'.""")
yaxis = param.ObjectSelector(default='right-bare',
objects=['left', 'right', 'bare', 'left-bare',
'right-bare', None], doc="""
Whether and where to display the yaxis, bare options allow suppressing
all axis labels including ticks and ylabel. Valid options are 'left',
'right', 'bare' 'left-bare' and 'right-bare'.""")
border = param.Integer(default=5, doc="Default borders on plot")
height = param.Integer(default=50, doc="Height of plot")
width = param.Integer(default=50, doc="Width of plot")
[docs]class BarPlot(BarsMixin, ColorbarPlot, LegendPlot):
"""
BarPlot allows generating single- or multi-category
bar Charts, by selecting which key dimensions are
mapped onto separate groups, categories and stacks.
"""
multi_level = param.Boolean(default=True, doc="""
Whether the Bars should be grouped into a second categorical axis level.""")
stacked = param.Boolean(default=False, doc="""
Whether the bars should be stacked or grouped.""")
# Deprecated parameters
color_index = param.ClassSelector(default=None, class_=(str, int),
allow_None=True, doc="""
Deprecated in favor of color style mapping, e.g. `color=dim('color')`""")
selection_display = BokehOverlaySelectionDisplay()
style_opts = (base_properties + fill_properties + line_properties +
['bar_width', 'cmap'])
_nonvectorized_styles = base_properties + ['bar_width', 'cmap']
_plot_methods = dict(single=('vbar', 'hbar'))
# Declare that y-range should auto-range if not bounded
_x_range_type = FactorRange
_y_range_type = Range1d
def _axis_properties(self, axis, key, plot, dimension=None,
ax_mapping=None):
if ax_mapping is None:
ax_mapping = {"x": 0, "y": 1}
props = super()._axis_properties(axis, key, plot, dimension, ax_mapping)
if (not self.multi_level and not self.stacked and self.current_frame.ndims > 1 and
((not self.invert_axes and axis == 'x') or (self.invert_axes and axis =='y'))):
props['separator_line_width'] = 0
props['major_tick_line_alpha'] = 0
# The major_label_text_* is a workaround for 0pt font size not working in Safari.
# See: https://github.com/holoviz/holoviews/issues/5672
props['major_label_text_font_size'] = '1px'
props['major_label_text_alpha'] = 0
props['major_label_text_line_height'] = 0
props['group_text_color'] = 'black'
props['group_text_font_style'] = "normal"
if axis == 'x':
props['group_text_align'] = "center"
if 'major_label_orientation' in props:
props['group_label_orientation'] = props.pop('major_label_orientation')
elif axis == 'y':
props['group_label_orientation'] = 0
props['group_text_align'] = 'right'
props['group_text_baseline'] = 'middle'
return props
def _get_factors(self, element, ranges):
xvals, gvals = self._get_coords(element, ranges)
if gvals is not None:
xvals = [(x, g) for x in xvals for g in gvals]
return ([], xvals) if self.invert_axes else (xvals, [])
[docs] def get_stack(self, xvals, yvals, baselines, sign='positive'):
"""
Iterates over a x- and y-values in a stack layer
and appropriately offsets the layer on top of the
previous layer.
"""
bottoms, tops = [], []
for x, y in zip(xvals, yvals):
baseline = baselines[x][sign]
if sign == 'positive':
bottom = baseline
top = bottom+y
baseline = top
else:
top = baseline
bottom = top+y
baseline = bottom
baselines[x][sign] = baseline
bottoms.append(bottom)
tops.append(top)
return bottoms, tops
def _glyph_properties(self, *args, **kwargs):
props = super()._glyph_properties(*args, **kwargs)
return {k: v for k, v in props.items() if k not in ['width', 'bar_width']}
def _add_color_data(self, ds, ranges, style, cdim, data, mapping, factors, colors):
cdata, cmapping = self._get_color_data(ds, ranges, dict(style),
factors=factors, colors=colors)
if 'color' not in cmapping:
return
# Enable legend if colormapper is categorical
cmapper = cmapping['color']['transform']
legend_prop = 'legend_field'
if ('color' in cmapping and self.show_legend and
isinstance(cmapper, CategoricalColorMapper)):
mapping[legend_prop] = cdim.name
if not self.stacked and ds.ndims > 1 and self.multi_level:
cmapping.pop(legend_prop, None)
mapping.pop(legend_prop, None)
# Merge data and mappings
mapping.update(cmapping)
for k, cd in cdata.items():
if isinstance(cmapper, CategoricalColorMapper) and cd.dtype.kind in 'uif':
cd = categorize_array(cd, cdim)
if k not in data or len(data[k]) != next(len(data[key]) for key in data if key != k):
data[k].append(cd)
else:
data[k][-1] = cd
[docs] def get_data(self, element, ranges, style):
# Get x, y, group, stack and color dimensions
group_dim, stack_dim = None, None
if element.ndims == 1:
grouping = None
elif self.stacked:
grouping = 'stacked'
stack_dim = element.get_dimension(1)
if stack_dim.values:
stack_order = stack_dim.values
elif stack_dim in ranges and ranges[stack_dim.name].get('factors'):
stack_order = ranges[stack_dim]['factors']
else:
stack_order = element.dimension_values(1, False)
stack_order = list(stack_order)
else:
grouping = 'grouped'
group_dim = element.get_dimension(1)
xdim = element.get_dimension(0)
ydim = element.vdims[0]
no_cidx = self.color_index is None
color_index = (group_dim or stack_dim) if no_cidx else self.color_index
color_dim = element.get_dimension(color_index)
if color_dim:
self.color_index = color_dim.name
# Define style information
width = style.get('bar_width', style.get('width', 1))
if 'width' in style:
self.param.warning("BarPlot width option is deprecated "
"use 'bar_width' instead.")
cmap = style.get('cmap')
hover = 'hover' in self.handles
# Group by stack or group dim if necessary
if group_dim is None:
grouped = {0: element}
else:
grouped = element.groupby(group_dim, group_type=Dataset,
container_type=dict,
datatype=['dataframe', 'dictionary'])
y0, y1 = ranges.get(ydim.name, {'combined': (None, None)})['combined']
if self.logy:
bottom = (ydim.range[0] or (0.01 if y1 > 0.01 else 10**(np.log10(y1)-2)))
else:
bottom = 0
# Map attributes to data
if grouping == 'stacked':
mapping = {'x': xdim.name, 'top': 'top',
'bottom': 'bottom', 'width': width}
elif grouping == 'grouped':
mapping = {'x': 'xoffsets', 'top': ydim.name, 'bottom': bottom,
'width': width}
else:
mapping = {'x': xdim.name, 'top': ydim.name, 'bottom': bottom, 'width': width}
# Get colors
cdim = color_dim or group_dim
style_mapping = [v for k, v in style.items() if 'color' in k and
(isinstance(v, dim) or v in element)]
if style_mapping and not no_cidx and self.color_index is not None:
self.param.warning("Cannot declare style mapping for '%s' option "
"and declare a color_index; ignoring the color_index."
% style_mapping[0])
cdim = None
cvals = element.dimension_values(cdim, expanded=False) if cdim else None
if cvals is not None:
if cvals.dtype.kind in 'uif' and no_cidx:
cvals = categorize_array(cvals, color_dim)
factors = None if cvals.dtype.kind in 'uif' else list(cvals)
if cdim is xdim and factors:
factors = list(categorize_array(factors, xdim))
if cmap is None and factors:
styles = self.style.max_cycles(len(factors))
colors = [styles[i]['color'] for i in range(len(factors))]
colors = [rgb2hex(c) if isinstance(c, tuple) else c for c in colors]
else:
colors = None
else:
factors, colors = None, None
# Iterate over stacks and groups and accumulate data
data = defaultdict(list)
baselines = defaultdict(lambda: {'positive': bottom, 'negative': 0})
for k, ds in grouped.items():
k = k[0] if isinstance(k, tuple) else k
if group_dim:
gval = k if isinstance(k, str) else group_dim.pprint_value(k)
# Apply stacking or grouping
if grouping == 'stacked':
for sign, slc in [('negative', (None, 0)), ('positive', (0, None))]:
slc_ds = ds.select(**{ds.vdims[0].name: slc})
stack_inds = [stack_order.index(v) if v in stack_order else -1
for v in slc_ds[stack_dim.name]]
slc_ds = slc_ds.add_dimension('_stack_order', 0, stack_inds).sort('_stack_order')
xs = slc_ds.dimension_values(xdim)
ys = slc_ds.dimension_values(ydim)
bs, ts = self.get_stack(xs, ys, baselines, sign)
data['bottom'].append(bs)
data['top'].append(ts)
data[xdim.name].append(xs)
data[stack_dim.name].append(slc_ds.dimension_values(stack_dim))
if hover:
data[ydim.name].append(ys)
for vd in slc_ds.vdims[1:]:
data[vd.name].append(slc_ds.dimension_values(vd))
if not style_mapping:
self._add_color_data(slc_ds, ranges, style, cdim, data,
mapping, factors, colors)
elif grouping == 'grouped':
xs = ds.dimension_values(xdim)
ys = ds.dimension_values(ydim)
xoffsets = [(x if xs.dtype.kind in 'SU' else xdim.pprint_value(x), gval)
for x in xs]
data['xoffsets'].append(xoffsets)
data[ydim.name].append(ys)
if hover: data[xdim.name].append(xs)
if group_dim not in ds.dimensions():
ds = ds.add_dimension(group_dim, ds.ndims, gval)
data[group_dim.name].append(ds.dimension_values(group_dim))
else:
data[xdim.name].append(ds.dimension_values(xdim))
data[ydim.name].append(ds.dimension_values(ydim))
if hover and grouping != 'stacked':
for vd in ds.vdims[1:]:
data[vd.name].append(ds.dimension_values(vd))
if grouping != 'stacked' and not style_mapping:
self._add_color_data(ds, ranges, style, cdim, data,
mapping, factors, colors)
# Concatenate the stacks or groups
sanitized_data = {}
for col, vals in data.items():
if len(vals) == 1:
sanitized_data[dimension_sanitizer(col)] = vals[0]
elif vals:
sanitized_data[dimension_sanitizer(col)] = np.concatenate(vals)
for name, val in mapping.items():
sanitized = None
if isinstance(val, str):
sanitized = dimension_sanitizer(mapping[name])
mapping[name] = sanitized
elif isinstance(val, dict) and 'field' in val:
sanitized = dimension_sanitizer(val['field'])
val['field'] = sanitized
if sanitized is not None and sanitized not in sanitized_data:
sanitized_data[sanitized] = []
# Ensure x-values are categorical
xname = dimension_sanitizer(xdim.name)
if xname in sanitized_data:
sanitized_data[xname] = categorize_array(sanitized_data[xname], xdim)
# If axes inverted change mapping to match hbar signature
if self.invert_axes:
mapping.update({'y': mapping.pop('x'), 'left': mapping.pop('bottom'),
'right': mapping.pop('top'), 'height': mapping.pop('width')})
return sanitized_data, mapping, style