def kde(self, x, y, data=None):
data = self.data if data is None else data
plot_opts = dict(self._plot_opts)
invert = self.kwds.get('orientation', False) == 'horizontal'
opts = dict(plot=dict(plot_opts, invert_axes=invert),
style=self._style_opts,
norm=self._norm_opts)
opts = {
'Distribution': opts,
'Area': opts,
'NdOverlay': {
'plot': dict(plot_opts, legend_limit=0)
}
}
if y and self.by:
ds = Dataset(data)
return ds.to(Distribution, y, [], self.by).overlay().opts(opts)
elif y:
return Distribution(data, y, []).opts(opts)
if self.columns:
data = data[self.columns]
df = pd.melt(data,
var_name=self.group_label,
value_name=self.value_label)
ds = Dataset(df)
if len(df):
overlay = ds.to(Distribution, self.value_label).overlay()
else:
vdim = self.value_label + ' Density'
overlay = NdOverlay({0: Area([], self.value_label, vdim)},
[self.group_label])
return overlay.relabel(**self._relabel).opts(opts)
def test_dataset_extract_kdims(self):
df = pd.DataFrame({
'x': [1, 2, 3],
'y': [1, 2, 3],
'z': [1, 2, 3]
},
columns=['x', 'y', 'z'])
ds = Distribution(df)
self.assertEqual(ds.kdims, [Dimension('x')])
def test_distribution_composite_empty_not_filled(self):
dist = Distribution([]).opts(plot=dict(filled=False))
curve = Compositor.collapse_element(dist, backend='matplotlib')
self.assertIsInstance(curve, Curve)
self.assertEqual(curve.vdims, [Dimension(('Value_density', 'Value Density'))])
def test_distribution_composite_custom_vdim(self):
dist = Distribution(np.array([0, 1, 2]), vdims=['Test'])
area = Compositor.collapse_element(dist, backend='matplotlib')
self.assertIsInstance(area, Area)
self.assertEqual(area.vdims, [Dimension('Test')])
def test_distribution_array_constructor(self):
dist = Distribution(np.array([0, 1, 2]))
self.assertEqual(dist.kdims, [Dimension('Value')])
self.assertEqual(dist.vdims, [Dimension('Density')])
def test_distribution_array_vdim_type(self):
dist = Distribution(np.array([0, 1, 2]))
self.assertEqual(dist.get_dimension_type(1), np.float64)
def test_distribution_not_filled(self):
dist = Distribution([1, 1.1, 2.1, 3, 2, 1, 2.2]).options(filled=False)
state = self._get_plot_state(dist)
self.assertEqual(state['data'][0]['type'], 'scatter')
self.assertEqual(state['data'][0]['mode'], 'lines')
self.assertEqual(state['data'][0].get('fill'), None)
def test_distribution_dict_constructor(self):
dist = Distribution({'Value': [0, 1, 2]})
self.assertEqual(dist.kdims, [Dimension('Value')])
self.assertEqual(dist.vdims, [Dimension('Density')])
def test_distribution_array_range_kdims(self):
dist = Distribution(np.array([0, 1, 2]))
self.assertEqual(dist.range(0), (0, 2))
def test_distribution_array_kdim_type(self):
dist = Distribution(np.array([0, 1, 2]))
self.assertTrue(np.issubdtype(dist.get_dimension_type(0), np.int_))
def test_distribution_array_vdim_type(self):
dist = Distribution(np.array([0, 1, 2]))
self.assertEqual(dist.get_dimension_type(1), np.float64)
def test_distribution_array_range_kdims(self):
dist = Distribution(np.array([0, 1, 2]))
self.assertEqual(dist.range(0), (0, 2))
def test_distribution_array_range_vdims(self):
dist = Distribution(np.array([0, 1, 2]))
dmin, dmax = dist.range(1)
self.assertFalse(np.isfinite(dmin))
self.assertFalse(np.isfinite(dmax))
def test_distribution_from_image(self):
dist = Distribution(Image(np.arange(5)*np.arange(5)[:, np.newaxis]), 'z')
self.assertEqual(dist.range(0), (0, 16))
def test_distribution_filled(self):
dist = Distribution([1, 1.1, 2.1, 3, 2, 1, 2.2])
state = self._get_plot_state(dist)
self.assertEqual(state['data'][0]['type'], 'scatter')
self.assertEqual(state['data'][0]['mode'], 'lines')
self.assertEqual(state['data'][0]['fill'], 'tozeroy')
def test_visible(self):
element = Distribution([1, 1.1, 2.1, 3, 2, 1,
2.2]).options(visible=False)
state = self._get_plot_state(element)
self.assertEqual(state['data'][0]['visible'], False)
def test_distribution_dframe_constructor(self):
if pd is None:
raise SkipTest("Test requires pandas, skipping.")
dist = Distribution(pd.DataFrame({'Value': [0, 1, 2]}))
self.assertEqual(dist.kdims, [Dimension('Value')])
self.assertEqual(dist.vdims, [Dimension('Density')])
def test_distribution_composite_not_filled(self):
dist = Distribution(np.array([0, 1, 2])).opts(plot=dict(filled=False))
curve = Compositor.collapse_element(dist)
self.assertIsInstance(curve, Curve)
self.assertEqual(curve.vdims,
[Dimension(('Value_density', 'Value Density'))])
def test_distribution_series_constructor(self):
if pd is None:
raise SkipTest("Test requires pandas")
dist = Distribution(pd.Series([0, 1, 2], name='Value'))
self.assertEqual(dist.kdims, [Dimension('Value')])
self.assertEqual(dist.vdims, [Dimension('Density')])
def test_distribution_from_image(self):
dist = Distribution(Image(np.arange(5)*np.arange(5)[:, np.newaxis]), 'z')
self.assertEqual(dist.range(0), (0, 16))
def test_distribution_array_constructor_custom_vdim(self):
dist = Distribution(np.array([0, 1, 2]), vdims=['Test'])
self.assertEqual(dist.kdims, [Dimension('Value')])
self.assertEqual(dist.vdims, [Dimension('Test')])
def test_distribution_series_constructor(self):
dist = Distribution(pd.Series([0, 1, 2], name='Value'))
self.assertEqual(dist.kdims, [Dimension('Value')])
self.assertEqual(dist.vdims, [Dimension('Density')])
def test_distribution_array_range_vdims(self):
dist = Distribution(np.array([0, 1, 2]))
dmin, dmax = dist.range(1)
self.assertFalse(np.isfinite(dmin))
self.assertFalse(np.isfinite(dmax))
def test_distribution_composite_transfer_opts_with_group(self):
dist = Distribution(np.array([0, 1, 2]), group='Test').opts(style=dict(color='red'))
area = Compositor.collapse_element(dist, backend='matplotlib')
opts = Store.lookup_options('matplotlib', area, 'style').kwargs
self.assertEqual(opts.get('color', None), 'red')
def test_distribution_composite(self):
dist = Distribution(np.array([0, 1, 2]))
area = Compositor.collapse_element(dist, backend='matplotlib')
self.assertIsInstance(area, Area)
self.assertEqual(area.vdims, [Dimension(('Value_density', 'Value Density'))])
def test_distribution_array_kdim_type(self):
dist = Distribution(np.array([0, 1, 2]))
self.assertTrue(np.issubdtype(dist.get_dimension_type(0), np.int_))
