from unittest import SkipTest import numpy as np import pandas as pd import holoviews as hv from holoviews.core.dimension import Dimension from holoviews.core.options import Compositor, Store from holoviews.element import ( Area, Bivariate, Contours, Curve, Distribution, Image, Points, Polygons, ) from holoviews.element.comparison import ComparisonTestCase class StatisticalElementTest(ComparisonTestCase): 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_dframe_constructor(self): dist = Distribution(pd.DataFrame({'Value': [0, 1, 2]})) self.assertEqual(dist.kdims, [Dimension('Value')]) self.assertEqual(dist.vdims, [Dimension('Density')]) 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_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_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_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_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 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_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_distribution_array_range_kdims(self): dist = Distribution(np.array([0, 1, 2])) self.assertEqual(dist.range(0), (0, 2)) 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_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_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_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_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_distribution_array_vdim_type(self): dist = Distribution(np.array([0, 1, 2])) self.assertEqual(dist.get_dimension_type(1), np.float64) 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_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_bivariate_from_points(self): points = Points(np.array([[0, 1], [1, 2], [2, 3]])) dist = Bivariate(points) self.assertEqual(dist.kdims, points.kdims) class StatisticalCompositorTest(ComparisonTestCase): def setUp(self): try: import scipy # noqa except ImportError: raise SkipTest('SciPy not available') self.renderer = hv.renderer('matplotlib') np.random.seed(42) super().setUp() 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', 'Density'))]) def test_distribution_composite_transfer_opts(self): dist = Distribution(np.array([0, 1, 2])).opts(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_transfer_opts_with_group(self): dist = Distribution(np.array([0, 1, 2]), group='Test').opts(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_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_composite_not_filled(self): dist = Distribution(np.array([0, 1, 2]), ).opts(filled=False) curve = Compositor.collapse_element(dist, backend='matplotlib') self.assertIsInstance(curve, Curve) self.assertEqual(curve.vdims, [Dimension(('Value_density', 'Density'))]) def test_distribution_composite_empty_not_filled(self): dist = Distribution([]).opts(filled=False) curve = Compositor.collapse_element(dist, backend='matplotlib') self.assertIsInstance(curve, Curve) self.assertEqual(curve.vdims, [Dimension(('Value_density', '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_composite_transfer_opts(self): dist = Bivariate(np.random.rand(10, 2)).opts(cmap='Blues') contours = Compositor.collapse_element(dist, backend='matplotlib') opts = Store.lookup_options('matplotlib', contours, 'style').kwargs self.assertEqual(opts.get('cmap', None), 'Blues') def test_bivariate_composite_transfer_opts_with_group(self): dist = Bivariate(np.random.rand(10, 2), group='Test').opts(cmap='Blues') contours = Compositor.collapse_element(dist, backend='matplotlib') 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, backend='matplotlib') self.assertIsInstance(contours, Contours) self.assertEqual(contours.vdims, [Dimension('Test')]) def test_bivariate_composite_filled(self): dist = Bivariate(np.random.rand(10, 2)).opts(filled=True) contours = Compositor.collapse_element(dist, backend='matplotlib') self.assertIsInstance(contours, Polygons) self.assertEqual(contours.vdims[0].name, 'Density') def test_bivariate_composite_empty_filled(self): dist = Bivariate([]).opts(filled=True) contours = Compositor.collapse_element(dist, backend='matplotlib') self.assertIsInstance(contours, Polygons) self.assertEqual(contours.vdims, [Dimension('Density')]) self.assertEqual(len(contours), 0) def test_bivariate_composite_empty_not_filled(self): dist = Bivariate([]).opts(filled=True) contours = Compositor.collapse_element(dist, backend='matplotlib') self.assertIsInstance(contours, Contours) self.assertEqual(contours.vdims, [Dimension('Density')]) self.assertEqual(len(contours), 0)