def test_bivariate_colorbar(self): bivariate = Bivariate(([3, 2, 1], [0, 1, 2]))\ bivariate.opts(colorbar=True) state = self._get_plot_state(bivariate) trace = state['data'][0] self.assertTrue(trace['showscale']) bivariate.opts(colorbar=False) state = self._get_plot_state(bivariate) trace = state['data'][0] self.assertFalse(trace['showscale'])
def test_bivariate_dframe_constructor(self): dist = Bivariate( pd.DataFrame({ 'x': [0, 1, 2], 'y': [0, 1, 2] }, columns=['x', 'y'])) self.assertEqual(dist.kdims, [Dimension('x'), Dimension('y')]) self.assertEqual(dist.vdims, [Dimension('Density')])
def bivariate(self, x, y, data=None): data = self.data if data is None else data if not x: x = data.columns[0] if not y: y = data.columns[1] opts = dict(plot=self._plot_opts, norm=self._norm_opts, style=self._style_opts) return Bivariate(data, [x, y]).redim(**self._redim).opts(**opts)
def test_bivariate_state(self): bivariate = Bivariate(([3, 2, 1], [0, 1, 2])) state = self._get_plot_state(bivariate) self.assertEqual(state['data'][0]['type'], 'histogram2dcontour') self.assertEqual(state['data'][0]['x'], np.array([3, 2, 1])) self.assertEqual(state['data'][0]['y'], np.array([0, 1, 2])) self.assertEqual(state['layout']['xaxis']['range'], [1, 3]) self.assertEqual(state['layout']['yaxis']['range'], [0, 2]) self.assertEqual(state['data'][0]['contours']['coloring'], 'lines')
def test_bivariate_dframe_constructor(self): if pd is None: raise SkipTest("Test requires pandas, skipping.") dist = Bivariate( pd.DataFrame({ 'x': [0, 1, 2], 'y': [0, 1, 2] }, columns=['x', 'y'])) self.assertEqual(dist.kdims, [Dimension('x'), Dimension('y')]) self.assertEqual(dist.vdims, [Dimension('Density')])
def test_bivariate_composite_filled(self): dist = Bivariate(np.random.rand(10, 2)).opts(plot=dict(filled=True)) contours = Compositor.collapse_element(dist) self.assertIsInstance(contours, Polygons) self.assertEqual(contours.vdims, [Dimension('Density')])
def test_bivariate_composite_empty_not_filled(self): dist = Bivariate([]).opts(plot=dict(filled=True)) contours = Compositor.collapse_element(dist) self.assertIsInstance(contours, Contours) self.assertEqual(contours.vdims, [Dimension('Density')]) self.assertEqual(len(contours), 0)
def test_bivariate_composite_transfer_opts_with_group(self): dist = Bivariate(np.random.rand(10, 2), group='Test').opts(style=dict(cmap='Blues')) contours = Compositor.collapse_element(dist) opts = Store.lookup_options('matplotlib', contours, 'style').kwargs self.assertEqual(opts.get('cmap', None), 'Blues')
def test_bivariate_composite_custom_vdim(self): dist = Bivariate(np.random.rand(10, 2), vdims=['Test']) contours = Compositor.collapse_element(dist) self.assertIsInstance(contours, Contours) self.assertEqual(contours.vdims, [Dimension('Test')])
def test_bivariate_array_range_vdims(self): dist = Bivariate(np.array([[0, 1, 2], [0, 1, 3]])) dmin, dmax = dist.range(2) self.assertFalse(np.isfinite(dmin)) self.assertFalse(np.isfinite(dmax))
def test_bivariate_ncontours(self): bivariate = Bivariate(([3, 2, 1], [0, 1, 2])).options(ncontours=5) state = self._get_plot_state(bivariate) self.assertEqual(state['data'][0]['ncontours'], 5) self.assertEqual(state['data'][0]['autocontour'], False)
def test_visible(self): element = Bivariate(([3, 2, 1], [0, 1, 2])).options(visible=False) state = self._get_plot_state(element) self.assertEqual(state['data'][0]['visible'], False)
def test_bivariate_array_constructor_custom_vdim(self): dist = Bivariate(np.array([[0, 1, 2], [0, 1, 2]]), vdims=['Test']) self.assertEqual(dist.kdims, [Dimension('x'), Dimension('y')]) self.assertEqual(dist.vdims, [Dimension('Test')])
def test_bivariate_array_kdim_type(self): dist = Bivariate(np.array([[0, 1], [1, 2], [2, 3]])) self.assertTrue(np.issubdtype(dist.get_dimension_type(0), np.int_)) self.assertTrue(np.issubdtype(dist.get_dimension_type(1), np.int_))
def test_bivariate_array_vdim_type(self): dist = Bivariate(np.array([[0, 1], [1, 2], [2, 3]])) self.assertEqual(dist.get_dimension_type(2), np.float64)
def test_bivariate_array_range_kdims(self): dist = Bivariate(np.array([[0, 1], [1, 2], [2, 3]])) self.assertEqual(dist.range(0), (0, 2)) self.assertEqual(dist.range(1), (1, 3))
def test_bivariate_composite_filled(self): dist = Bivariate(np.random.rand(10, 2)).opts(plot=dict(filled=True)) contours = Compositor.collapse_element(dist, backend='matplotlib') self.assertIsInstance(contours, Polygons) self.assertEqual(contours.vdims[0].name, 'Density')
def test_bivariate_array_constructor(self): dist = Bivariate(np.array([[0, 1, 2], [0, 1, 2]])) self.assertEqual(dist.kdims, [Dimension('x'), Dimension('y')]) self.assertEqual(dist.vdims, [Dimension('Density')])
def test_bivariate_dict_constructor(self): dist = Bivariate({'x': [0, 1, 2], 'y': [0, 1, 2]}, ['x', 'y']) self.assertEqual(dist.kdims, [Dimension('x'), Dimension('y')]) self.assertEqual(dist.vdims, [Dimension('Density')])
def test_bivariate_composite(self): dist = Bivariate(np.random.rand(10, 2)) contours = Compositor.collapse_element(dist, backend='matplotlib') self.assertIsInstance(contours, Contours) self.assertEqual(contours.vdims, [Dimension('Density')])
def test_bivariate_from_points(self): points = Points(np.array([[0, 1], [1, 2], [2, 3]])) dist = Bivariate(points) self.assertEqual(dist.kdims, points.kdims)
def test_bivariate_filled(self): bivariate = Bivariate(([3, 2, 1], [0, 1, 2])).options(filled=True) state = self._get_plot_state(bivariate) self.assertEqual(state['data'][0]['contours']['coloring'], 'fill')