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chart.py
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chart.py
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from collections import defaultdict
import numpy as np
import param
try:
from bokeh.charts import Bar, BoxPlot as BokehBoxPlot
except:
Bar, BokehBoxPlot = None, None
from bokeh.models import Circle, GlyphRenderer, ColumnDataSource, Range1d
from ...element import Raster, Points, Polygons, Spikes
from ...core.util import max_range, basestring, dimension_sanitizer
from ...core.options import abbreviated_exception
from ..util import compute_sizes, get_sideplot_ranges, match_spec, map_colors
from .element import ElementPlot, ColorbarPlot, line_properties, fill_properties
from .path import PathPlot, PolygonPlot
from .util import get_cmap, mpl_to_bokeh, update_plot, rgb2hex, bokeh_version
class PointPlot(ColorbarPlot):
color_index = param.ClassSelector(default=3, class_=(basestring, int),
allow_None=True, doc="""
Index of the dimension from which the color will the drawn""")
size_index = param.ClassSelector(default=2, class_=(basestring, int),
allow_None=True, doc="""
Index of the dimension from which the sizes will the drawn.""")
scaling_method = param.ObjectSelector(default="area",
objects=["width", "area"],
doc="""
Determines whether the `scaling_factor` should be applied to
the width or area of each point (default: "area").""")
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.""")
style_opts = (['cmap', 'palette', 'marker', 'size', 's', 'alpha', 'color',
'unselected_color'] +
line_properties + fill_properties)
_plot_methods = dict(single='scatter', batched='scatter')
def get_data(self, element, ranges=None, empty=False):
style = self.style[self.cyclic_index]
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 = {}
cdim = element.get_dimension(self.color_index)
if cdim:
mapper = self._get_colormapper(cdim, element, ranges, style)
data[cdim.name] = [] if empty else element.dimension_values(cdim)
mapping['color'] = {'field': cdim.name,
'transform': mapper}
sdim = element.get_dimension(self.size_index)
if sdim:
map_key = 'size_' + sdim.name
mapping['size'] = map_key
if empty:
data[map_key] = []
else:
ms = style.get('size', np.sqrt(6))**2
sizes = element.dimension_values(self.size_index)
data[map_key] = np.sqrt(compute_sizes(sizes, self.size_fn,
self.scaling_factor,
self.scaling_method, ms))
data[dims[xidx]] = [] if empty else element.dimension_values(xidx)
data[dims[yidx]] = [] if empty else element.dimension_values(yidx)
self._get_hover_data(data, element, empty)
return data, mapping
def get_batched_data(self, element, ranges=None, empty=False):
data = defaultdict(list)
style = self.style.max_cycles(len(self.ordering))
for key, el in element.items():
self.set_param(**self.lookup_options(el, 'plot').options)
eldata, elmapping = self.get_data(el, ranges, empty)
for k, eld in eldata.items():
data[k].append(eld)
if 'color' not in elmapping:
zorder = self.get_zorder(element, key, el)
val = style[zorder].get('color')
elmapping['color'] = 'color'
if isinstance(val, tuple):
val = rgb2hex(val)
data['color'].append([val]*len(data[k][-1]))
data = {k: np.concatenate(v) for k, v in data.items()}
return data, elmapping
def _init_glyph(self, plot, mapping, properties):
"""
Returns a Bokeh glyph object.
"""
properties = mpl_to_bokeh(properties)
unselect_color = properties.pop('unselected_color', None)
if (any(t in self.tools for t in ['box_select', 'lasso_select'])
and unselect_color is not None):
source = properties.pop('source')
color = properties.pop('color', None)
color = mapping.pop('color', color)
properties.pop('legend', None)
unselected = Circle(**dict(properties, fill_color=unselect_color, **mapping))
selected = Circle(**dict(properties, fill_color=color, **mapping))
renderer = plot.add_glyph(source, selected, selection_glyph=selected,
nonselection_glyph=unselected)
else:
plot_method = self._plot_methods.get('batched' if self.batched else 'single')
renderer = getattr(plot, plot_method)(**dict(properties, **mapping))
if self.colorbar and 'color_mapper' in self.handles:
self._draw_colorbar(plot, self.handles['color_mapper'])
return renderer, renderer.glyph
class CurvePlot(ElementPlot):
style_opts = ['color'] + line_properties
_plot_methods = dict(single='line', batched='multi_line')
_mapping = {p: p for p in ['xs', 'ys', 'color', 'line_alpha']}
def get_data(self, element, ranges=None, empty=False):
xidx, yidx = (1, 0) if self.invert_axes else (0, 1)
x = element.get_dimension(xidx).name
y = element.get_dimension(yidx).name
return ({x: [] if empty else element.dimension_values(xidx),
y: [] if empty else element.dimension_values(yidx)},
dict(x=x, y=y))
def get_batched_data(self, overlay, ranges=None, empty=False):
style = self.style.max_cycles(len(self.ordering))
data = defaultdict(list)
for key, el in overlay.items():
zorder = self.get_zorder(overlay, key, el)
for opt in self._mapping:
if opt in ['xs', 'ys']:
index = {'xs': 0, 'ys': 1}[opt]
val = el.dimension_values(index)
else:
val = style[zorder].get(opt)
if opt == 'color' and isinstance(val, tuple):
val = rgb2hex(val)
data[opt].append(val)
data = {opt: vals for opt, vals in data.items()
if not any(v is None for v in vals)}
return data, {k: k for k in data}
class AreaPlot(PolygonPlot):
def get_extents(self, element, ranges):
vdims = element.vdims
vdim = vdims[0].name
if len(vdims) > 1:
ranges[vdim] = max_range([ranges[vd.name] for vd in vdims])
else:
vdim = vdims[0].name
ranges[vdim] = (np.nanmin([0, ranges[vdim][0]]), ranges[vdim][1])
return super(AreaPlot, self).get_extents(element, ranges)
def get_data(self, element, ranges=None, empty=False):
mapping = dict(self._mapping)
if empty: return {'xs': [], 'ys': []}
xs = element.dimension_values(0)
x2 = np.hstack((xs[::-1], xs))
if len(element.vdims) > 1:
bottom = element.dimension_values(2)
else:
bottom = np.zeros(len(element))
ys = np.hstack((bottom[::-1], element.dimension_values(1)))
if self.invert_axes:
data = dict(xs=[ys], ys=[x2])
else:
data = dict(xs=[x2], ys=[ys])
return data, mapping
class SpreadPlot(PolygonPlot):
style_opts = ['color'] + line_properties + fill_properties
def get_data(self, element, ranges=None, empty=None):
if empty:
return dict(xs=[], ys=[]), dict(self._mapping)
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 = np.append(xvals, xvals[::-1])
band_y = np.append(lower, upper[::-1])
if self.invert_axes:
data = dict(xs=[band_y], ys=[band_x])
else:
data = dict(xs=[band_x], ys=[band_y])
return data, dict(self._mapping)
class HistogramPlot(ElementPlot):
style_opts = ['color'] + line_properties + fill_properties
_plot_methods = dict(single='quad')
def get_data(self, element, ranges=None, empty=None):
if self.invert_axes:
mapping = dict(top='left', bottom='right', left=0, right='top')
else:
mapping = dict(top='top', bottom=0, left='left', right='right')
if empty:
data = dict(top=[], left=[], right=[])
else:
data = dict(top=element.values, left=element.edges[:-1],
right=element.edges[1:])
self._get_hover_data(data, element, empty)
return (data, mapping)
class SideHistogramPlot(HistogramPlot, ColorbarPlot):
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.""")
def get_data(self, element, ranges=None, empty=None):
if self.invert_axes:
mapping = dict(top='left', bottom='right', left=0, right='top')
else:
mapping = dict(top='top', bottom=0, left='left', right='right')
if empty:
data = dict(top=[], left=[], right=[])
else:
data = dict(top=element.values, left=element.edges[:-1],
right=element.edges[1:])
dim = element.get_dimension(0)
main = self.adjoined.main
range_item, main_range, _ = get_sideplot_ranges(self, element, main, ranges)
if isinstance(range_item, (Raster, Points, Polygons, Spikes)):
style = self.lookup_options(range_item, 'style')[self.cyclic_index]
else:
style = {}
if 'cmap' in style or 'palette' in style:
main_range = {dim.name: main_range}
mapper = self._get_colormapper(dim, element, main_range, style)
data[dim.name] = [] if empty else element.dimension_values(dim)
mapping['fill_color'] = {'field': dim.name,
'transform': mapper}
self._get_hover_data(data, element, empty)
return (data, mapping)
class ErrorPlot(PathPlot):
horizontal = param.Boolean(default=False)
style_opts = ['color'] + line_properties
def get_data(self, element, ranges=None, empty=False):
if empty:
return dict(xs=[], ys=[]), dict(self._mapping)
data = element.array(dimensions=element.dimensions()[0:4])
err_xs = []
err_ys = []
for row in data:
x, y = row[0:2]
if len(row) > 3:
neg, pos = row[2:]
else:
neg, pos = row[2], row[2]
if self.horizontal:
err_xs.append((x - neg, x + pos))
err_ys.append((y, y))
else:
err_xs.append((x, x))
err_ys.append((y - neg, y + pos))
if self.invert_axes:
data = dict(xs=err_ys, ys=err_xs)
else:
data = dict(xs=err_xs, ys=err_ys)
return (data, dict(self._mapping))
class SpikesPlot(PathPlot, ColorbarPlot):
color_index = param.ClassSelector(default=1, class_=(basestring, int), doc="""
Index of the dimension from which the color will the drawn""")
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.""")
style_opts = (['color', 'cmap', 'palette'] + line_properties)
def get_extents(self, element, ranges):
l, b, r, t = super(SpikesPlot, self).get_extents(element, ranges)
if len(element.dimensions()) == 1:
b, t = self.position, self.position+self.spike_length
return l, b, r, t
def get_data(self, element, ranges=None, empty=False):
style = self.style[self.cyclic_index]
dims = element.dimensions(label=True)
pos = self.position
mapping = dict(xs='xs', ys='ys')
if empty:
xs, ys = [], []
elif len(dims) > 1:
xs, ys = zip(*(((x, x), (pos+y, pos))
for x, y in element.array(dims[:2])))
else:
height = self.spike_length
xs, ys = zip(*(((x[0], x[0]), (pos+height, pos))
for x in element.array(dims[:1])))
if not empty and self.invert_axes: xs, ys = ys, xs
data = dict(zip(('xs', 'ys'), (xs, ys)))
cdim = element.get_dimension(self.color_index)
if cdim:
mapper = self._get_colormapper(cdim, element, ranges, style)
data[cdim.name] = [] if empty else element.dimension_values(cdim)
mapping['color'] = {'field': cdim.name,
'transform': mapper}
if 'hover' in self.tools+self.default_tools and not empty:
for d in dims:
data[dimension_sanitizer(d)] = element.dimension_values(d)
return data, mapping
class SideSpikesPlot(SpikesPlot):
"""
SpikesPlot with useful defaults for plotting adjoined rug plot.
"""
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=30 if bokeh_version < '0.12' else 5,
doc="Default borders on plot")
height = param.Integer(default=100 if bokeh_version < '0.12' else 50,
doc="Height of plot")
width = param.Integer(default=100 if bokeh_version < '0.12' else 50,
doc="Width of plot")
class ChartPlot(ElementPlot):
"""
ChartPlot creates and updates Bokeh high-level Chart instances.
The current implementation requires creating a new Chart for each
frame and updating the existing Chart. Once Bokeh supports updating
Charts directly this workaround will no longer be required.
"""
def initialize_plot(self, ranges=None, plot=None, plots=None, source=None):
"""
Initializes a new plot object with the last available frame.
"""
# Get element key and ranges for frame
element = self.hmap.last
key = self.keys[-1]
ranges = self.compute_ranges(self.hmap, key, ranges)
ranges = match_spec(element, ranges)
self.current_ranges = ranges
self.current_frame = element
self.current_key = key
# Initialize plot, source and glyph
if plot is not None:
raise Exception("Can't overlay Bokeh Charts based plot properties")
init_element = element.clone(element.interface.concat(self.hmap.values()))
with abbreviated_exception():
plot = self._init_chart(init_element, ranges)
self.handles['plot'] = plot
self.handles['glyph_renderers'] = [r for r in plot.renderers
if isinstance(r, GlyphRenderer)]
self._update_chart(key, element, ranges)
# Update plot, source and glyph
self.drawn = True
return plot
def update_frame(self, key, ranges=None, plot=None, element=None):
"""
Updates an existing plot with data corresponding
to the key.
"""
element = self._get_frame(key)
if not element:
if self.dynamic and self.overlaid:
self.current_key = key
element = self.current_frame
else:
element = self._get_frame(key)
else:
self.current_key = key
self.current_frame = element
self.style = self.lookup_options(element, 'style')
self.set_param(**self.lookup_options(element, 'plot').options)
ranges = self.compute_ranges(self.hmap, key, ranges)
ranges = match_spec(element, ranges)
self.current_ranges = ranges
self._update_chart(key, element, ranges)
def _update_chart(self, key, element, ranges):
with abbreviated_exception():
new_chart = self._init_chart(element, ranges)
old_chart = self.handles['plot']
update_plot(old_chart, new_chart)
properties = self._plot_properties(key, old_chart, element)
old_chart.update(**properties)
@property
def current_handles(self):
plot = self.handles['plot']
sources = plot.select(type=ColumnDataSource)
return sources
class BoxPlot(ChartPlot):
"""
BoxPlot generates a box and whisker plot from a BoxWhisker
Element. This allows plotting the median, mean and various
percentiles. Displaying outliers is currently not supported
as they cannot be consistently updated.
"""
style_opts = ['color', 'whisker_color'] + line_properties
def _init_chart(self, element, ranges):
properties = self.style[self.cyclic_index]
dframe = element.dframe()
label = element.dimensions('key', True)
if len(element.dimensions()) == 1:
dframe[''] = ''
label = ['']
plot = BokehBoxPlot(dframe, label=label,
values=element.dimensions('value', True)[0],
**properties)
# Disable outliers for now as they cannot be consistently updated.
plot.renderers = [r for r in plot.renderers
if not (isinstance(r, GlyphRenderer) and
isinstance(r.glyph, Circle))]
return plot
class BarPlot(ChartPlot):
"""
BarPlot allows generating single- or multi-category
bar Charts, by selecting which key dimensions are
mapped onto separate groups, categories and stacks.
"""
group_index = param.Integer(default=0, doc="""
Index of the dimension in the supplied Bars
Element, which will be laid out into groups.""")
stack_index = param.Integer(default=2, doc="""
Index of the dimension in the supplied Bars
Element, which will stacked.""")
style_opts = ['bar_width', 'max_height', 'color', 'fill_alpha']
def _init_chart(self, element, ranges):
kdims = element.dimensions('key', True)
vdim = element.dimensions('value', True)[0]
kwargs = self.style[self.cyclic_index]
if self.group_index < element.ndims:
kwargs['label'] = kdims[self.group_index]
if self.stack_index < element.ndims:
kwargs['stack'] = kdims[self.stack_index]
crange = Range1d(*ranges.get(vdim))
plot = Bar(element.dframe(), values=vdim,
continuous_range=crange, **kwargs)
if not self.show_legend:
plot.legend[0].legends[:] = []
return plot