import numpy as np from holoviews import Dataset, HoloMap from holoviews.core import Dimension from holoviews.core.ndmapping import ( MultiDimensionalMapping, NdMapping, UniformNdMapping, ) from holoviews.core.overlay import Overlay from holoviews.element import Curve from holoviews.element.comparison import ComparisonTestCase class DimensionTest(ComparisonTestCase): def test_dimension_init(self): Dimension('Test dimension') Dimension('Test dimension', cyclic=True) Dimension('Test dimension', cyclic=True, type=int) Dimension('Test dimension', cyclic=True, type=int, unit='Twilight zones') def test_dimension_clone(self): dim1 = Dimension('Test dimension') dim2 = dim1.clone(cyclic=True) self.assertEqual(dim2.cyclic,True) dim3 = dim1.clone('New test dimension', unit='scovilles') self.assertEqual(dim3.name, 'New test dimension') self.assertEqual(dim3.unit, 'scovilles') def test_dimension_pprint(self): dim = Dimension('Test dimension', cyclic=True, type=float, unit='Twilight zones') self.assertEqual(dim.pprint_value_string(3.23451), 'Test dimension: 3.2345 Twilight zones') self.assertEqual(dim.pprint_value_string(4.23441), 'Test dimension: 4.2344 Twilight zones') self.assertEqual(dim.pprint_value(3.23451, print_unit=True), '3.2345 Twilight zones') self.assertEqual(dim.pprint_value(4.23441, print_unit=True), '4.2344 Twilight zones') class NdIndexableMappingTest(ComparisonTestCase): def setUp(self): self.init_items_1D_list = [(1, 'a'), (5, 'b')] self.init_item_list = [((1, 2.0), 'a'), ((5, 3.0), 'b')] self.init_item_odict = dict([((1, 2.0), 'a'), ((5, 3.0), 'b')]) self.dimension_labels = ['intdim', 'floatdim'] self.dim1 = Dimension('intdim', type=int) self.dim2 = Dimension('floatdim', type=float) self.time_dimension = Dimension def test_idxmapping_init(self): MultiDimensionalMapping() def test_idxmapping_init_item_odict(self): MultiDimensionalMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) def test_idxmapping_init_item_list(self): MultiDimensionalMapping(self.init_item_list, kdims=[self.dim1, self.dim2]) def test_idxmapping_init_dimstr(self): MultiDimensionalMapping(self.init_item_odict, kdims=self.dimension_labels) def test_idxmapping_init_dimdict(self): MultiDimensionalMapping( self.init_item_odict, kdims=[dict(name=label) for label in self.dimension_labels], ) def test_idxmapping_init_dimensions(self): MultiDimensionalMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) def test_idxmapping_dimension_labels(self): idxmap = MultiDimensionalMapping(self.init_item_odict, kdims=[self.dim1, 'floatdim']) self.assertEqual([d.name for d in idxmap.kdims], self.dimension_labels) def test_idxmapping_ndims(self): dims = [self.dim1, self.dim2, 'strdim'] idxmap = MultiDimensionalMapping(kdims=dims) self.assertEqual(idxmap.ndims, len(dims)) def test_idxmapping_key_len_check(self): try: MultiDimensionalMapping(initial_items=self.init_item_odict) raise AssertionError('Invalid key length check failed.') except KeyError: pass def test_idxmapping_nested_update(self): data1 = [(0, 'a'), (1, 'b')] data2 = [(2, 'c'), (3, 'd')] data3 = [(2, 'e'), (3, 'f')] ndmap1 = MultiDimensionalMapping(data1, kdims=[self.dim1]) ndmap2 = MultiDimensionalMapping(data2, kdims=[self.dim1]) ndmap3 = MultiDimensionalMapping(data3, kdims=[self.dim1]) ndmap_list = [(0.5, ndmap1), (1.5, ndmap2)] nested_ndmap = MultiDimensionalMapping(ndmap_list, kdims=[self.dim2]) nested_ndmap[(0.5,)].update(dict([(0, 'c'), (1, 'd')])) self.assertEqual(list(nested_ndmap[0.5].values()), ['c', 'd']) nested_ndmap[1.5] = ndmap3 self.assertEqual(list(nested_ndmap[1.5].values()), ['e', 'f']) def test_ndmapping_slice_lower_bound_inclusive_int(self): ndmap = NdMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) self.assertEqual(ndmap[1:].keys(), [(1, 2.0), (5, 3.0)]) def test_ndmapping_slice_lower_bound_inclusive2_int(self): ndmap = NdMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) self.assertEqual(ndmap[1:6].keys(), [(1, 2.0), (5, 3.0)]) def test_ndmapping_slice_upper_bound_exclusive_int(self): ndmap = NdMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) self.assertEqual(ndmap[1:5].keys(), [(1, 2.0)]) def test_ndmapping_slice_upper_bound_exclusive2_int(self): ndmap = NdMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) self.assertEqual(ndmap[:5].keys(), [(1, 2.0)]) def test_ndmapping_slice_lower_bound_inclusive_float(self): ndmap = NdMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) self.assertEqual(ndmap[:, 2.0:].keys(), [(1, 2.0), (5, 3.0)]) def test_ndmapping_slice_lower_bound_inclusive2_float(self): ndmap = NdMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) self.assertEqual(ndmap[:, 2.0:5.0].keys(), [(1, 2.0), (5, 3.0)]) def test_ndmapping_slice_upper_bound_exclusive_float(self): ndmap = NdMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) self.assertEqual(ndmap[:, :3.0].keys(), [(1, 2.0)]) def test_ndmapping_slice_upper_bound_exclusive2_float(self): ndmap = NdMapping(self.init_item_odict, kdims=[self.dim1, self.dim2]) self.assertEqual(ndmap[:, 0.0:3.0].keys(), [(1, 2.0)]) def test_idxmapping_unsorted(self): data = [('B', 1), ('C', 2), ('A', 3)] ndmap = MultiDimensionalMapping(data, sort=False) self.assertEqual(ndmap.keys(), ['B', 'C', 'A']) def test_idxmapping_unsorted_clone(self): data = [('B', 1), ('C', 2), ('A', 3)] ndmap = MultiDimensionalMapping(data, sort=False).clone() self.assertEqual(ndmap.keys(), ['B', 'C', 'A']) def test_idxmapping_groupby_unsorted(self): data = [(('B', 2), 1), (('C', 2), 2), (('A', 1), 3)] grouped = NdMapping(data, sort=False, kdims=['X', 'Y']).groupby('Y') self.assertEqual(grouped.keys(), [2, 1]) self.assertEqual(grouped.values()[0].keys(), ['B', 'C']) self.assertEqual(grouped.last.keys(), ['A']) def test_idxmapping_reindex(self): data = [((0, 0.5), 'a'), ((1, 0.5), 'b')] ndmap = MultiDimensionalMapping(data, kdims=[self.dim1, self.dim2]) reduced_dims = ['intdim'] reduced_ndmap = ndmap.reindex(reduced_dims) self.assertEqual([d.name for d in reduced_ndmap.kdims], reduced_dims) def test_idxmapping_redim(self): data = [((0, 0.5), 'a'), ((1, 0.5), 'b')] ndmap = MultiDimensionalMapping(data, kdims=[self.dim1, self.dim2]) redimmed = ndmap.redim(intdim='Integer') self.assertEqual(redimmed.kdims, [Dimension('Integer', type=int), Dimension('floatdim', type=float)]) def test_idxmapping_redim_range_aux(self): data = [((0, 0.5), 'a'), ((1, 0.5), 'b')] ndmap = MultiDimensionalMapping(data, kdims=[self.dim1, self.dim2]) redimmed = ndmap.redim.range(intdim=(-9,9)) self.assertEqual(redimmed.kdims, [Dimension('intdim', type=int, range=(-9,9)), Dimension('floatdim', type=float)]) def test_idxmapping_redim_type_aux(self): data = [((0, 0.5), 'a'), ((1, 0.5), 'b')] ndmap = MultiDimensionalMapping(data, kdims=[self.dim1, self.dim2]) redimmed = ndmap.redim.type(intdim=str) self.assertEqual(redimmed.kdims, [Dimension('intdim', type=str), Dimension('floatdim', type=float)]) def test_idxmapping_add_dimension(self): ndmap = MultiDimensionalMapping(self.init_items_1D_list, kdims=[self.dim1]) ndmap2d = ndmap.add_dimension(self.dim2, 0, 0.5) self.assertEqual(list(ndmap2d.keys()), [(0.5, 1), (0.5, 5)]) self.assertEqual(ndmap2d.kdims, [self.dim2, self.dim1]) def test_idxmapping_apply_key_type(self): data = dict([(0.5, 'a'), (1.5, 'b')]) ndmap = MultiDimensionalMapping(data, kdims=[self.dim1]) self.assertEqual(list(ndmap.keys()), [0, 1]) def test_setitem_nested_1(self): nested1 = MultiDimensionalMapping([('B', 1)]) ndmap = MultiDimensionalMapping([('A', nested1)]) nested2 = MultiDimensionalMapping([('B', 2)]) ndmap['A'] = nested2 self.assertEqual(ndmap['A'], nested2) def test_setitem_nested_2(self): nested1 = MultiDimensionalMapping([('B', 1)]) ndmap = MultiDimensionalMapping([('A', nested1)]) nested2 = MultiDimensionalMapping([('C', 2)]) nested_clone = nested1.clone() nested_clone.update(nested2) ndmap.update({'A': nested2}) self.assertEqual(ndmap['A'].data, nested_clone.data) class UniformNdMappingTest(ComparisonTestCase): def test_collapse_nested(self): inner1 = UniformNdMapping({1: Dataset([(1, 2)], ['x', 'y'])}, 'Y') inner2 = UniformNdMapping({1: Dataset([(3, 4)], ['x', 'y'])}, 'Y') outer = UniformNdMapping({1: inner1, 2: inner2}, 'X') collapsed = outer.collapse() expected = Dataset([(1, 1, 1, 2), (2, 1, 3, 4)], ['X', 'Y', 'x', 'y']) self.assertEqual(collapsed, expected) class HoloMapTest(ComparisonTestCase): def setUp(self): self.xs = range(11) self.y_ints = [i*2 for i in range(11)] self.ys = np.linspace(0, 1, 11) self.columns = Dataset(np.column_stack([self.xs, self.y_ints]), kdims=['x'], vdims=['y']) def test_holomap_redim(self): hmap = HoloMap({i: Dataset({'x':self.xs, 'y': self.ys * i}, kdims=['x'], vdims=['y']) for i in range(10)}, kdims=['z']) redimmed = hmap.redim(x='Time') self.assertEqual(redimmed.dimensions('all', True), ['z', 'Time', 'y']) def test_holomap_redim_nested(self): hmap = HoloMap({i: Dataset({'x':self.xs, 'y': self.ys * i}, kdims=['x'], vdims=['y']) for i in range(10)}, kdims=['z']) redimmed = hmap.redim(x='Time', z='Magnitude') self.assertEqual(redimmed.dimensions('all', True), ['Magnitude', 'Time', 'y']) def test_columns_collapse_heterogeneous(self): collapsed = HoloMap({i: Dataset({'x':self.xs, 'y': self.ys * i}, kdims=['x'], vdims=['y']) for i in range(10)}, kdims=['z']).collapse('z', np.mean) expected = Dataset({'x':self.xs, 'y': self.ys * 4.5}, kdims=['x'], vdims=['y']) self.compare_dataset(collapsed, expected) def test_columns_sample_homogeneous(self): samples = self.columns.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 10, 20])) def test_holomap_map_with_none(self): hmap = HoloMap({i: Dataset({'x':self.xs, 'y': self.ys * i}, kdims=['x'], vdims=['y']) for i in range(10)}, kdims=['z']) mapped = hmap.map(lambda x: x if x.range(1)[1] > 0 else None, Dataset) self.assertEqual(hmap[1:10], mapped) def test_holomap_hist_two_dims(self): hmap = HoloMap({i: Dataset({'x':self.xs, 'y': self.ys * i}, kdims=['x'], vdims=['y']) for i in range(10)}, kdims=['z']) hists = hmap.hist(dimension=['x', 'y']) self.assertEqual(hists['right'].last.kdims, ['y']) self.assertEqual(hists['top'].last.kdims, ['x']) def test_holomap_collapse_overlay_no_function(self): hmap = HoloMap({ (1,0): Curve(np.arange(8)) * Curve(-np.arange(8)), (2,0): Curve(np.arange(8)**2) * Curve(-np.arange(8)**3) }, kdims=["A","B"]) self.assertEqual(hmap.collapse(), Overlay([ (('Curve', 'I'), Dataset({ 'A': np.concatenate([np.ones(8), np.ones(8)*2]), 'B': np.zeros(16), 'x': np.tile(np.arange(8), 2), 'y': np.concatenate([np.arange(8), np.arange(8)**2]) }, kdims=['A', 'B', 'x'], vdims=['y'])), (('Curve', 'II'), Dataset({ 'A': np.concatenate([np.ones(8), np.ones(8)*2]), 'B': np.zeros(16), 'x': np.tile(np.arange(8), 2), 'y': np.concatenate([-np.arange(8), -np.arange(8)**3]) }, kdims=['A', 'B', 'x'], vdims=['y'])) ])) def test_holomap_collapse_overlay_max(self): hmap = HoloMap({ (1,0): Curve(np.arange(8)) * Curve(-np.arange(8)), (2,0): Curve(np.arange(8)**2) * Curve(-np.arange(8)**3) }, kdims=["A","B"]) self.assertEqual(hmap.collapse(function=np.max), Overlay([ (('Curve', 'I'), Curve({ 'x': np.arange(8), 'y': np.arange(8)**2 }, kdims=['x'], vdims=['y'])), (('Curve', 'II'), Curve({ 'x': np.arange(8), 'y': -np.arange(8) }, kdims=['x'], vdims=['y'])) ]))