class ColumnsDFrameTest(ComparisonTestCase): def setUp(self): self.datatype = Columns.datatype Columns.datatype = ['dataframe'] self.column_data = [('M',10, 15, 0.8), ('M',16, 18, 0.6), ('F',12, 10, 0.8)] self.kdims = ['Gender', 'Age'] self.vdims = ['Weight', 'Height'] self.xs = range(11) self.ys = np.linspace(0, 1, 11) self.zs = np.sin(self.xs) self.columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys}), kdims=['x'], vdims=['y']) def tearDown(self): Columns.datatype = self.datatype def test_columns_range(self): self.assertEqual(self.columns.range('y'), (0., 1.)) def test_columns_shape(self): self.assertEqual(self.columns.shape, (11, 2)) def test_columns_closest(self): closest = self.columns.closest([0.51, 1, 9.9]) self.assertEqual(closest, [1., 1., 10.]) def test_columns_sample(self): samples = self.columns.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 0.5, 1])) def test_columns_df_construct(self): self.assertTrue(isinstance(self.columns.data, pd.DataFrame)) def test_columns_tuple_list_construct(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) self.assertTrue(isinstance(self.columns.data, pd.DataFrame)) def test_columns_slice(self): data = [('x', range(5, 9)), ('y', np.linspace(0.5, 0.8, 4))] columns_slice = Columns(pd.DataFrame.from_items(data), kdims=['x'], vdims=['y']) self.assertEqual(self.columns[5:9], columns_slice) def test_columns_index_row_gender(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) row = columns['F',:] self.assertEquals(type(row), Columns) self.compare_columns(row, Columns(self.column_data[2:], kdims=self.kdims, vdims=self.vdims)) def test_columns_index_rows_gender(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) row = columns['M',:] self.assertEquals(type(row), Columns) self.compare_columns(row, Columns(self.column_data[:2], kdims=self.kdims, vdims=self.vdims)) def test_columns_index_row_age(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) row = columns[:, 12] self.assertEquals(type(row), Columns) self.compare_columns(row, Columns(self.column_data[2:], kdims=self.kdims, vdims=self.vdims)) def test_columns_index_single_row(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) row = columns['F', 12] self.assertEquals(type(row), Columns) self.compare_columns(row, Columns(self.column_data[2:], kdims=self.kdims, vdims=self.vdims)) def test_columns_index_value1(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) self.assertEquals(columns['F', 12, 'Weight'], 10) def test_columns_index_value2(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) self.assertEquals(columns['F', 12, 'Height'], 0.8) def test_columns_sort_vdim(self): columns = Columns(pd.DataFrame({'x': self.xs, 'y': -self.ys}), kdims=['x'], vdims=['y']) columns_sorted = Columns(pd.DataFrame({'x': self.xs[::-1], 'y': -self.ys[::-1]}), kdims=['x'], vdims=['y']) self.assertEqual(columns.sort('y'), columns_sorted) def test_columns_sort_heterogeneous_string(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) columns_sorted = Columns([self.column_data[i] for i in [2, 0, 1]], kdims=self.kdims, vdims=self.vdims) self.assertEqual(columns.sort(), columns_sorted) def test_columns_add_dimensions_value(self): columns = self.columns.add_dimension('z', 1, 0) self.assertEqual(columns.kdims[1], 'z') self.compare_arrays(columns.dimension_values('z'), np.zeros(len(columns))) def test_columns_add_dimensions_values(self): columns = self.columns.add_dimension('z', 1, range(1,12)) self.assertEqual(columns.kdims[1], 'z') self.compare_arrays(columns.dimension_values('z'), np.array(list(range(1,12)))) def test_columns_getitem_column(self): self.compare_arrays(self.columns['y'], self.ys) def test_columns_collapse(self): collapsed = HoloMap({i: Columns(pd.DataFrame({'x': self.xs, 'y': self.ys*i}), kdims=['x'], vdims=['y']) for i in range(10)}, kdims=['z']).collapse('z', np.mean) self.compare_columns(collapsed, Columns(pd.DataFrame({'x': self.xs, 'y': self.ys*4.5}), kdims=['x'], vdims=['y'])) def test_columns_1d_reduce(self): self.assertEqual(self.columns.reduce('x', np.mean), np.float64(0.5)) def test_columns_2d_reduce(self): columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys, 'z': self.zs}), kdims=['x', 'y'], vdims=['z']) self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)), np.array(0.12828985192891)) def test_columns_2d_partial_reduce(self): columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys, 'z': self.zs}), kdims=['x', 'y'], vdims=['z']) self.assertEqual(columns.reduce(['y'], np.mean), Columns(pd.DataFrame({'x': self.xs, 'z': self.zs}), kdims=['x'], vdims=['z'])) def test_columns_heterogeneous_reduce(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) reduced_data = pd.DataFrame([(10, 15, 0.8), (12, 10, 0.8), (16, 18, 0.6)], columns=columns.dimensions(label=True)[1:]) reduced = Columns(reduced_data, kdims=self.kdims[1:], vdims=self.vdims) self.assertEqual(columns.reduce(['Gender'], np.mean), reduced) def test_columns_heterogeneous_reduce2d(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) reduced_data = pd.DataFrame([d[1:] for d in self.column_data], columns=columns.dimensions(label=True)[1:]) reduced = Columns(pd.DataFrame([(14.333333333333334, 0.73333333333333339)], columns=self.vdims), kdims=[], vdims=self.vdims) self.assertEqual(columns.reduce(function=np.mean), reduced) def test_columns_groupby(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) cols = self.kdims + self.vdims group1 = pd.DataFrame(self.column_data[:2], columns=cols) group2 = pd.DataFrame(self.column_data[2:], columns=cols) grouped = HoloMap({'M': Columns(group1, kdims=['Age'], vdims=self.vdims), 'F': Columns(group2, kdims=['Age'], vdims=self.vdims)}, kdims=['Gender']) self.assertEqual(columns.groupby(['Gender']), grouped) def test_columns_heterogeneous_aggregate(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) aggregated = Columns(pd.DataFrame([('F', 10., 0.8), ('M', 16.5, 0.7)], columns=['Gender']+self.vdims), kdims=self.kdims[:1], vdims=self.vdims) self.compare_columns(columns.aggregate(['Gender'], np.mean), aggregated) def test_columns_2d_partial_reduce(self): columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys, 'z': self.zs}), kdims=['x', 'y'], vdims=['z']) self.assertEqual(columns.aggregate(['x'], np.mean), Columns(pd.DataFrame({'x': self.xs, 'z': self.zs}), kdims=['x'], vdims=['z'])) def test_columns_array(self): self.assertEqual(self.columns.array(), np.column_stack([self.xs, self.ys]))
class ColumnsNdArrayTest(ComparisonTestCase): def setUp(self): self.xs = range(11) self.ys = np.linspace(0, 1, 11) self.zs = np.sin(self.xs) self.columns = Columns((self.xs, self.ys), kdims=['x'], vdims=['y']) def test_columns_shape(self): self.assertEqual(self.columns.shape, (11, 2)) def test_columns_range(self): self.assertEqual(self.columns.range('y'), (0., 1.)) def test_columns_closest(self): closest = self.columns.closest([0.51, 1, 9.9]) self.assertEqual(closest, [1., 1., 10.]) def test_columns_values_construct(self): columns = Columns(self.ys) self.assertTrue(isinstance(columns.data, np.ndarray)) def test_columns_tuple_construct(self): columns = Columns((self.xs, self.ys)) self.assertTrue(isinstance(columns.data, np.ndarray)) def test_columns_array_construct(self): columns = Columns(np.column_stack([self.xs, self.ys])) self.assertTrue(isinstance(columns.data, np.ndarray)) def test_columns_tuple_list_construct(self): columns = Columns(zip(self.xs, self.ys)) self.assertTrue(isinstance(columns.data, np.ndarray)) def test_columns_sort_vdim(self): columns = Columns((self.xs, -self.ys), kdims=['x'], vdims=['y']) columns_sorted = Columns((self.xs[::-1], -self.ys[::-1]), kdims=['x'], vdims=['y']) self.assertEqual(columns.sort('y'), columns_sorted) def test_columns_index(self): self.assertEqual(self.columns[5], self.ys[5]) def test_columns_slice(self): columns_slice = Columns(zip(range(5, 9), np.linspace(0.5,0.8, 4)), kdims=['x'], vdims=['y']) self.assertEqual(self.columns[5:9], columns_slice) def test_columns_closest(self): closest = self.columns.closest([0.51, 1, 9.9]) self.assertEqual(closest, [1., 1., 10.]) def test_columns_getitem_column(self): self.compare_arrays(self.columns['y'], self.ys) def test_columns_sample(self): samples = self.columns.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 0.5, 1])) def test_columns_add_dimensions_value(self): table = Columns((self.xs, self.ys), kdims=['x'], vdims=['y']) table = table.add_dimension('z', 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(len(table))) def test_columns_add_dimensions_values(self): table = Columns((self.xs, self.ys), kdims=['x'], vdims=['y']) table = table.add_dimension('z', 1, range(1,12)) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12)))) def test_columns_collapse(self): collapsed = HoloMap({i: Columns((self.xs, self.ys*i), kdims=['x'], vdims=['y']) for i in range(10)}, kdims=['z']).collapse('z', np.mean) self.compare_columns(collapsed, Columns((self.xs, self.ys*4.5), kdims=['x'], vdims=['y'])) def test_columns_1d_reduce(self): columns = Columns((self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertEqual(columns.reduce('x', np.mean), np.float64(0.5)) def test_columns_2d_reduce(self): columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z']) self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)), np.array(0.12828985192891)) def test_columns_2d_partial_reduce(self): columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z']) self.assertEqual(columns.reduce(['y'], np.mean), Columns((self.xs, self.zs), kdims=['x'], vdims=['z'])) def test_columns_2d_aggregate_partial(self): columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z']) self.assertEqual(columns.aggregate(['x'], np.mean), Columns((self.xs, self.zs), kdims=['x'], vdims=['z'])) def test_columns_array(self): self.assertEqual(self.columns.array(), np.column_stack([self.xs, self.ys]))
class HomogeneousColumnTypes(object): """ Tests for data formats that require all columns to have the same type (e.g numpy arrays) """ def setUp(self): self.restore_datatype = Columns.datatype self.data_instance_type = None def init_data(self): self.xs = range(11) self.xs_2 = [el**2 for el in self.xs] self.y_ints = [i*2 for i in range(11)] self.columns_hm = Columns(np.column_stack([self.xs, self.y_ints]), kdims=['x'], vdims=['y']) def tearDown(self): Columns.datatype = self.restore_datatype # Test the array constructor (homogenous data) to be supported by # all interfaces. def test_columns_array_init_hm(self): "Tests support for arrays (homogeneous)" columns = Columns(np.column_stack([self.xs, self.xs_2]), kdims=['x'], vdims=['x2']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_ndelement_init_hm(self): "Tests support for homogeneous NdElement (backwards compatibility)" columns = Columns(NdElement(zip(self.xs, self.xs_2), kdims=['x'], vdims=['x2'])) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_dataframe_init_hm(self): "Tests support for homogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") columns = Columns(pd.DataFrame({'x':self.xs, 'x2':self.xs_2}), kdims=['x'], vdims=[ 'x2']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) # Properties and information def test_columns_shape(self): self.assertEqual(self.columns_hm.shape, (11, 2)) def test_columns_range(self): self.assertEqual(self.columns_hm.range('y'), (0, 20)) def test_columns_closest(self): closest = self.columns_hm.closest([0.51, 1, 9.9]) self.assertEqual(closest, [1., 1., 10.]) # Operations def test_columns_sort_vdim_hm(self): xs_2 = np.array(self.xs_2) columns = Columns(np.column_stack([self.xs, -xs_2]), kdims=['x'], vdims=['y']) columns_sorted = Columns(np.column_stack([self.xs[::-1], -xs_2[::-1]]), kdims=['x'], vdims=['y']) self.assertEqual(columns.sort('y'), columns_sorted) def test_columns_sample_hm(self): samples = self.columns_hm.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 10, 20])) def test_columns_array_hm(self): self.assertEqual(self.columns_hm.array(), np.column_stack([self.xs, self.y_ints])) def test_columns_add_dimensions_value_hm(self): table = self.columns_hm.add_dimension('z', 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(len(table))) def test_columns_add_dimensions_values_hm(self): table = self.columns_hm.add_dimension('z', 1, range(1,12)) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12)))) def test_columns_slice_hm(self): columns_slice = Columns({'x':range(5, 9), 'y':[2*i for i in range(5, 9)]}, kdims=['x'], vdims=['y']) self.assertEqual(self.columns_hm[5:9], columns_slice) def test_columns_1D_reduce_hm(self): columns = Columns({'x':self.xs, 'y':self.y_ints}, kdims=['x'], vdims=['y']) self.assertEqual(columns.reduce('x', np.mean), 10) def test_columns_2D_reduce_hm(self): columns = Columns({'x':self.xs, 'y':self.y_ints, 'z':[el**2 for el in self.y_ints]}, kdims=['x', 'y'], vdims=['z']) self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)), np.array(140)) def test_columns_2D_aggregate_partial_hm(self): z_ints = [el**2 for el in self.y_ints] columns = Columns({'x':self.xs, 'y':self.y_ints, 'z':z_ints}, kdims=['x', 'y'], vdims=['z']) self.assertEqual(columns.aggregate(['x'], np.mean), Columns({'x':self.xs, 'z':z_ints}, kdims=['x'], vdims=['z'])) # Indexing def test_columns_index_column_idx_hm(self): self.assertEqual(self.columns_hm[5], self.y_ints[5]) def test_columns_index_column_ht(self): self.compare_arrays(self.columns_hm['y'], self.y_ints) def test_columns_array_ht(self): self.assertEqual(self.columns_hm.array(), np.column_stack([self.xs, self.y_ints]))
class ColumnsNdElementTest(ComparisonTestCase): """ Test for the Chart baseclass methods. """ def setUp(self): self.datatype = Columns.datatype Columns.datatype = ['dictionary', 'array'] self.xs = range(11) self.ys = np.linspace(0, 1, 11) self.zs = np.sin(self.xs) self.keys1 = [('M',10), ('M',16), ('F',12)] self.values1 = [(15, 0.8), (18, 0.6), (10, 0.8)] self.kdims = ['Gender', 'Age'] self.vdims = ['Weight', 'Height'] self.columns = Columns(dict(zip(self.xs, self.ys)), kdims=['x'], vdims=['y']) def tearDown(self): Columns.datatype = self.datatype def test_columns_sort_vdim(self): columns = Columns(OrderedDict(zip(self.xs, -self.ys)), kdims=['x'], vdims=['y']) columns_sorted = Columns(OrderedDict(zip(self.xs[::-1], -self.ys[::-1])), kdims=['x'], vdims=['y']) self.assertEqual(columns.sort('y'), columns_sorted) def test_columns_sort_heterogeneous_string(self): columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) keys = [('F',12), ('M',10), ('M',16)] values = [(10, 0.8), (15, 0.8), (18, 0.6)] columns_sorted = Columns(zip(keys, values), kdims=self.kdims, vdims=self.vdims) self.assertEqual(columns.sort(), columns_sorted) def test_columns_shape(self): self.assertEqual(self.columns.shape, (11, 2)) def test_columns_range(self): self.assertEqual(self.columns.range('y'), (0., 1.)) def test_columns_odict_construct(self): columns = Columns(OrderedDict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(columns.data, NdElement)) def test_columns_closest(self): closest = self.columns.closest([0.51, 1, 9.9]) self.assertEqual(closest, [1., 1., 10.]) def test_columns_dict_construct(self): self.assertTrue(isinstance(self.columns.data, NdElement)) def test_columns_ndelement_construct(self): columns = Columns(NdElement(zip(self.xs, self.ys))) self.assertTrue(isinstance(columns.data, NdElement)) def test_columns_items_construct(self): columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) self.assertTrue(isinstance(columns.data, NdElement)) def test_columns_sample(self): samples = self.columns.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 0.5, 1])) def test_columns_index_row_gender(self): table = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) indexed = Columns(OrderedDict([(('F', 12), (10, 0.8))]), kdims=self.kdims, vdims=self.vdims) row = table['F',:] self.assertEquals(row, indexed) def test_columns_index_rows_gender(self): table = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) row = table['M',:] indexed = Columns(OrderedDict([(('M', 10), (15, 0.8)), (('M', 16), (18, 0.6))]), kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_columns_index_row_age(self): table = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) indexed = Columns(OrderedDict([(('F', 12), (10, 0.8))]), kdims=self.kdims, vdims=self.vdims) self.assertEquals(table[:, 12], indexed) def test_columns_index_item_table(self): table = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) indexed = Columns(OrderedDict([(('F', 12), (10, 0.8))]), kdims=self.kdims, vdims=self.vdims) self.assertEquals(table['F', 12], indexed) def test_columns_index_value1(self): table = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) self.assertEquals(table['F', 12, 'Weight'], 10) def test_columns_index_value2(self): table = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) self.assertEquals(table['F', 12, 'Height'], 0.8) def test_columns_getitem_column(self): self.compare_arrays(self.columns['y'], self.ys) def test_columns_add_dimensions_value(self): table = self.columns.add_dimension('z', 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(len(table))) def test_columns_add_dimensions_values(self): table = self.columns.add_dimension('z', 1, range(1,12)) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12)))) def test_columns_collapse(self): collapsed = HoloMap({i: Columns(dict(zip(self.xs, self.ys*i)), kdims=['x'], vdims=['y']) for i in range(10)}, kdims=['z']).collapse('z', np.mean) self.compare_columns(collapsed, Columns(zip(zip(self.xs), self.ys*4.5), kdims=['x'], vdims=['y'])) def test_columns_1d_reduce(self): self.assertEqual(self.columns.reduce('x', np.mean), np.float64(0.5)) def test_columns_2d_reduce(self): columns = Columns(zip(zip(self.xs, self.ys), self.zs), kdims=['x', 'y'], vdims=['z']) self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)), np.array(0.12828985192891)) def test_columns_2d_partial_reduce(self): columns = Columns(zip(zip(self.xs, self.ys), self.zs), kdims=['x', 'y'], vdims=['z']) reduced = Columns(zip(zip(self.xs), self.zs), kdims=['x'], vdims=['z']) self.assertEqual(columns.reduce(['y'], np.mean), reduced) def test_columns_heterogeneous_reduce(self): columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) reduced = Columns(zip([k[1:] for k in self.keys1], self.values1), kdims=self.kdims[1:], vdims=self.vdims) self.assertEqual(columns.reduce(['Gender'], np.mean), reduced) def test_columns_heterogeneous_reduce2d(self): columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) reduced = Columns([((), (14.333333333333334, 0.73333333333333339))], kdims=[], vdims=self.vdims) self.assertEqual(columns.reduce(function=np.mean), reduced) def test_column_heterogeneous_aggregate(self): columns = Columns(zip(self.keys1, self.values1), kdims=self.kdims, vdims=self.vdims) aggregated = Columns(OrderedDict([('M', (16.5, 0.7)), ('F', (10., 0.8))]), kdims=self.kdims[:1], vdims=self.vdims) self.compare_columns(columns.aggregate(['Gender'], np.mean), aggregated) def test_columns_2d_aggregate_partial(self): columns = Columns(zip(zip(self.xs, self.ys), self.zs), kdims=['x', 'y'], vdims=['z']) reduced = Columns(zip(zip(self.xs), self.zs), kdims=['x'], vdims=['z']) self.assertEqual(columns.aggregate(['x'], np.mean), reduced) def test_columns_array(self): self.assertEqual(self.columns.array(), np.column_stack([self.xs, self.ys]))
class HomogeneousColumnTypes(object): """ Tests for data formats that require all columns to have the same type (e.g numpy arrays) """ def setUp(self): self.restore_datatype = Columns.datatype self.data_instance_type = None def init_data(self): self.xs = range(11) self.xs_2 = [el**2 for el in self.xs] self.y_ints = [i * 2 for i in range(11)] self.columns_hm = Columns(np.column_stack([self.xs, self.y_ints]), kdims=['x'], vdims=['y']) def tearDown(self): Columns.datatype = self.restore_datatype # Test the array constructor (homogenous data) to be supported by # all interfaces. def test_columns_array_init_hm(self): "Tests support for arrays (homogeneous)" columns = Columns(np.column_stack([self.xs, self.xs_2]), kdims=['x'], vdims=['x2']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_ndelement_init_hm(self): "Tests support for homogeneous NdElement (backwards compatibility)" columns = Columns( NdElement(zip(self.xs, self.xs_2), kdims=['x'], vdims=['x2'])) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_dataframe_init_hm(self): "Tests support for homogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") columns = Columns(pd.DataFrame({ 'x': self.xs, 'x2': self.xs_2 }), kdims=['x'], vdims=['x2']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) # Properties and information def test_columns_shape(self): self.assertEqual(self.columns_hm.shape, (11, 2)) def test_columns_range(self): self.assertEqual(self.columns_hm.range('y'), (0, 20)) def test_columns_closest(self): closest = self.columns_hm.closest([0.51, 1, 9.9]) self.assertEqual(closest, [1., 1., 10.]) # Operations def test_columns_sort_vdim_hm(self): xs_2 = np.array(self.xs_2) columns = Columns(np.column_stack([self.xs, -xs_2]), kdims=['x'], vdims=['y']) columns_sorted = Columns(np.column_stack([self.xs[::-1], -xs_2[::-1]]), kdims=['x'], vdims=['y']) self.assertEqual(columns.sort('y'), columns_sorted) def test_columns_sample_hm(self): samples = self.columns_hm.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 10, 20])) def test_columns_array_hm(self): self.assertEqual(self.columns_hm.array(), np.column_stack([self.xs, self.y_ints])) def test_columns_add_dimensions_value_hm(self): table = self.columns_hm.add_dimension('z', 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(len(table))) def test_columns_add_dimensions_values_hm(self): table = self.columns_hm.add_dimension('z', 1, range(1, 12)) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1, 12)))) def test_columns_slice_hm(self): columns_slice = Columns( { 'x': range(5, 9), 'y': [2 * i for i in range(5, 9)] }, kdims=['x'], vdims=['y']) self.assertEqual(self.columns_hm[5:9], columns_slice) def test_columns_1D_reduce_hm(self): columns = Columns({ 'x': self.xs, 'y': self.y_ints }, kdims=['x'], vdims=['y']) self.assertEqual(columns.reduce('x', np.mean), 10) def test_columns_2D_reduce_hm(self): columns = Columns( { 'x': self.xs, 'y': self.y_ints, 'z': [el**2 for el in self.y_ints] }, kdims=['x', 'y'], vdims=['z']) self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)), np.array(140)) def test_columns_2D_aggregate_partial_hm(self): z_ints = [el**2 for el in self.y_ints] columns = Columns({ 'x': self.xs, 'y': self.y_ints, 'z': z_ints }, kdims=['x', 'y'], vdims=['z']) self.assertEqual( columns.aggregate(['x'], np.mean), Columns({ 'x': self.xs, 'z': z_ints }, kdims=['x'], vdims=['z'])) # Indexing def test_columns_index_column_idx_hm(self): self.assertEqual(self.columns_hm[5], self.y_ints[5]) def test_columns_index_column_ht(self): self.compare_arrays(self.columns_hm['y'], self.y_ints) def test_columns_array_ht(self): self.assertEqual(self.columns_hm.array(), np.column_stack([self.xs, self.y_ints]))