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Allow specifying Bivariate levels #2474

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merged 2 commits into from Mar 26, 2018
Merged

Allow specifying Bivariate levels #2474

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cf71c67
Allow setting levels as plot option on Bivariate
philippjfr Mar 22, 2018
21da6c8
Ensure bivariate_kde computes specified number of levels
philippjfr Mar 22, 2018
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18 changes: 10 additions & 8 deletions holoviews/operation/element.py
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Expand Up @@ -439,7 +439,7 @@ def _process(self, element, key=None):
element.data[2])

if isinstance(self.p.levels, int):
levels = self.p.levels+1 if self.p.filled else self.p.levels
levels = self.p.levels+2 if self.p.filled else self.p.levels+3
zmin, zmax = element.range(2)
levels = np.linspace(zmin, zmax, levels)
else:
Expand All @@ -448,23 +448,25 @@ def _process(self, element, key=None):
xdim, ydim = element.dimensions('key', label=True)
fig = Figure()
ax = Axes(fig, [0, 0, 1, 1])
contour_set = QuadContourSet(ax, *data, filled=self.p.filled, extent=extent, levels=levels)
contour_set = QuadContourSet(ax, *data, filled=self.p.filled,
extent=extent, levels=levels)
if self.p.filled:
contour_type = Polygons
levels = np.convolve(levels, np.ones((2,))/2, mode='valid')
else:
contour_type = Contours
vdims = element.vdims[:1]

paths = []
for level, cset in zip(levels, contour_set.collections):
empty = np.full((1, 2), np.NaN)
for level, cset in zip(contour_set.get_array(), contour_set.collections):
subpaths = []
for path in cset.get_paths():
if path.codes is None:
subpaths = [path.vertices]
subpaths.append(path.vertices)
else:
subpaths = np.split(path.vertices, np.where(path.codes==1)[0][1:])
for p in subpaths:
paths.append({(xdim, ydim): p, element.vdims[0].name: level})
subpaths += np.split(path.vertices, np.where(path.codes==1)[0][1:])
subpath = np.concatenate([p for sp in subpaths for p in (sp, empty)][:-1])
paths.append({(xdim, ydim): subpath, element.vdims[0].name: level})
contours = contour_type(paths, label=element.label, kdims=element.kdims, vdims=vdims)
if self.p.overlaid:
contours = element * contours
Expand Down
5 changes: 4 additions & 1 deletion holoviews/operation/stats.py
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Expand Up @@ -150,6 +150,9 @@ class bivariate_kde(Operation):
filled = param.Boolean(default=False, doc="""
Controls whether to return filled or unfilled contours.""")

levels = param.ClassSelector(default=10, class_=(list, int), doc="""
A list of scalar values used to specify the contour levels.""")

n_samples = param.Integer(default=100, doc="""
Number of samples to compute the KDE over.""")

Expand Down Expand Up @@ -219,6 +222,6 @@ def _process(self, element, key=None):

img = Image((xs, ys, f.T), kdims=element.dimensions()[:2], vdims=[vdim], **params)
if self.p.contours:
cntr = contours(img, filled=self.p.filled)
cntr = contours(img, filled=self.p.filled, levels=self.p.levels)
return cntr.clone(cntr.data[1:], **params)
return img
4 changes: 4 additions & 0 deletions holoviews/plotting/bokeh/stats.py
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Expand Up @@ -49,6 +49,10 @@ class BivariatePlot(PolygonPlot):
filled = param.Boolean(default=False, doc="""
Whether the bivariate contours should be filled.""")

levels = param.ClassSelector(default=10, class_=(list, int), doc="""
A list of scalar values used to specify the contour levels.""")




class BoxWhiskerPlot(CompositeElementPlot, ColorbarPlot, LegendPlot):
Expand Down
4 changes: 4 additions & 0 deletions holoviews/plotting/mpl/stats.py
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Expand Up @@ -38,6 +38,10 @@ class BivariatePlot(PolygonPlot):
filled = param.Boolean(default=False, doc="""
Whether the bivariate contours should be filled.""")

levels = param.ClassSelector(default=10, class_=(list, int), doc="""
A list of scalar values used to specify the contour levels.""")




class BoxPlot(ChartPlot):
Expand Down
9 changes: 5 additions & 4 deletions tests/operation/testoperation.py
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Expand Up @@ -57,17 +57,18 @@ def test_image_gradient(self):
def test_image_contours(self):
img = Image(np.array([[0, 1, 0], [3, 4, 5.], [6, 7, 8]]))
op_contours = contours(img, levels=[0.5])
contour = Contours([[(-0.5, 0.416667, 0.5), (-0.25, 0.5, 0.5)],
[(0.25, 0.5, 0.5), (0.5, 0.45, 0.5)]],
contour = Contours([[(-0.5, 0.416667, 0.5), (-0.25, 0.5, 0.5),
(np.NaN, np.NaN, 0.5), (0.25, 0.5, 0.5),
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I don't quite understand where the NaNs come from...

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contours now returns a Contours element with one array per level, if there's multiple contours at a level they will be NaN separated.

(0.5, 0.45, 0.5)]],
vdims=img.vdims)
self.assertEqual(op_contours, contour)

@attr(optional=1) # Requires matplotlib
def test_image_contours_filled(self):
img = Image(np.array([[0, 1, 0], [3, 4, 5.], [6, 7, 8]]))
op_contours = contours(img, filled=True, levels=[2, 2.5])
data = [[(0., 0.333333, 2.25), (0.5, 0.3, 2.25), (0.5, 0.25, 2.25), (0., 0.25, 2.25),
(-0.5, 0.08333333, 2.25), (-0.5, 0.16666667, 2.25), (0., 0.33333333, 2.25)]]
data = [[(0., 0.333333, 2), (0.5, 0.3, 2), (0.5, 0.25, 2), (0., 0.25, 2),
(-0.5, 0.08333333, 2), (-0.5, 0.16666667, 2), (0., 0.33333333, 2)]]
polys = Polygons(data, vdims=img.vdims)
self.assertEqual(op_contours, polys)

Expand Down
16 changes: 15 additions & 1 deletion tests/operation/teststatsoperations.py
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Expand Up @@ -7,7 +7,7 @@

import numpy as np

from holoviews import Distribution, Bivariate, Area, Image
from holoviews import Distribution, Bivariate, Area, Image, Contours, Polygons
from holoviews.element.comparison import ComparisonTestCase
from holoviews.operation.stats import (univariate_kde, bivariate_kde)

Expand Down Expand Up @@ -48,6 +48,20 @@ def test_bivariate_kde(self):
bounds=(-2, -2, 6, 6), vdims=['Density'])
self.assertEqual(kde, img)

def test_bivariate_kde_contours(self):
bivariate = Bivariate(np.random.rand(100, 2))
kde = bivariate_kde(bivariate, n_samples=100, x_range=(0, 1),
y_range=(0, 1), contours=True, levels=10)
self.assertIsInstance(kde, Contours)
self.assertEqual(len(kde.data), 10)

def test_bivariate_kde_contours_filled(self):
bivariate = Bivariate(np.random.rand(100, 2))
kde = bivariate_kde(bivariate, n_samples=100, x_range=(0, 1),
y_range=(0, 1), contours=True, filled=True, levels=10)
self.assertIsInstance(kde, Polygons)
self.assertEqual(len(kde.data), 10)

def test_bivariate_kde_nans(self):
kde = bivariate_kde(self.bivariate_nans, n_samples=2, x_range=(0, 4),
y_range=(0, 4), contours=False)
Expand Down