from __future__ import absolute_import, division, unicode_literals
import param
from .selection import PlotlyOverlaySelectionDisplay
from .chart import ChartPlot
from .element import ElementPlot, ColorbarPlot
[docs]class BivariatePlot(ChartPlot, ColorbarPlot):
filled = param.Boolean(default=False)
ncontours = param.Integer(default=None)
trace_kwargs = {'type': 'histogram2dcontour'}
style_opts = ['visible', 'cmap', 'showlabels', 'labelfont', 'labelformat', 'showlines']
_style_key = 'contours'
selection_display = PlotlyOverlaySelectionDisplay()
def graph_options(self, element, ranges, style):
opts = super(BivariatePlot, self).graph_options(element, ranges, style)
copts = self.get_color_opts(element.vdims[0], element, ranges, style)
if self.ncontours:
opts['autocontour'] = False
opts['ncontours'] = self.ncontours
# Make line width a little wider (default is less than 1)
opts['line'] = {'width': 1}
# Configure contours
opts['contours'] = {
'coloring': 'fill' if self.filled else 'lines',
'showlines': style.get('showlines', True)
}
# Add colorscale
opts['colorscale'] = copts['colorscale']
# Add colorbar
if 'colorbar' in copts:
opts['colorbar'] = copts['colorbar']
opts['showscale'] = copts.get('showscale', False)
# Add visible
opts['visible'] = style.get('visible', True)
return opts
[docs]class DistributionPlot(ElementPlot):
bandwidth = param.Number(default=None, doc="""
The bandwidth of the kernel for the density estimate.""")
cut = param.Number(default=3, doc="""
Draw the estimate to cut * bw from the extreme data points.""")
filled = param.Boolean(default=True, doc="""
Whether the bivariate contours should be filled.""")
style_opts = ['visible', 'color', 'dash', 'line_width']
trace_kwargs = {'type': 'scatter', 'mode': 'lines'}
_style_key = 'line'
selection_display = PlotlyOverlaySelectionDisplay()
[docs]class MultiDistributionPlot(ElementPlot):
def _get_axis_dims(self, element):
return element.kdims, element.vdims[0]
def get_data(self, element, ranges, style):
if element.kdims:
groups = element.groupby(element.kdims).items()
else:
groups = [(element.label, element)]
plots = []
axis = 'x' if self.invert_axes else 'y'
for key, group in groups:
if element.kdims:
label = ','.join([d.pprint_value(v) for d, v in zip(element.kdims, key)])
else:
label = key
data = {axis: group.dimension_values(group.vdims[0]), 'name': label}
plots.append(data)
return plots
[docs] def get_extents(self, element, ranges, range_type='combined'):
return super(MultiDistributionPlot, self).get_extents(
element, ranges, range_type, 'categorical', element.vdims[0]
)
[docs]class BoxWhiskerPlot(MultiDistributionPlot):
boxpoints = param.ObjectSelector(objects=["all", "outliers",
"suspectedoutliers", False],
default='outliers', doc="""
Which points to show, valid options are 'all', 'outliers',
'suspectedoutliers' and False""")
jitter = param.Number(default=0, doc="""
Sets the amount of jitter in the sample points drawn. If "0",
the sample points align along the distribution axis. If "1",
the sample points are drawn in a random jitter of width equal
to the width of the box(es).""")
mean = param.ObjectSelector(default=False, objects=[True, False, 'sd'],
doc="""
If "True", the mean of the box(es)' underlying distribution
is drawn as a dashed line inside the box(es). If "sd" the
standard deviation is also drawn.""")
style_opts = ['visible', 'color', 'alpha', 'outliercolor', 'marker', 'size']
trace_kwargs = {'type': 'box'}
_style_key = 'marker'
selection_display = PlotlyOverlaySelectionDisplay()
def graph_options(self, element, ranges, style):
options = super(BoxWhiskerPlot, self).graph_options(element, ranges, style)
options['boxmean'] = self.mean
options['jitter'] = self.jitter
return options
[docs]class ViolinPlot(MultiDistributionPlot):
box = param.Boolean(default=True, doc="""
Whether to draw a boxplot inside the violin""")
meanline = param.Boolean(default=False, doc="""
If "True", the mean of the box(es)' underlying distribution
is drawn as a dashed line inside the box(es). If "sd" the
standard deviation is also drawn.""")
style_opts = ['visible', 'color', 'alpha', 'outliercolor', 'marker', 'size']
trace_kwargs = {'type': 'violin'}
_style_key = 'marker'
def graph_options(self, element, ranges, style):
options = super(ViolinPlot, self).graph_options(element, ranges, style)
options['meanline'] = {'visible': self.meanline}
options['box'] = {'visible': self.box}
return options
