def test_dataset_groupby_path(self): ds = Dataset([(0, 0, 1), (0, 1, 2), (1, 2, 3), (1, 3, 4)], ['group', 'x', 'y']) subpaths = ds.groupby('group', group_type=Path) self.assertEqual(len(subpaths), 2) self.assertEqual(subpaths[0], Path([(0, 1), (1, 2)])) self.assertEqual(subpaths[1], Path([(2, 3), (3, 4)]))
def test_dataset_groupby_dynamic_alias(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=[('x', 'X'), ('y', 'Y')], vdims=[('z', 'Z')]) grouped = dataset.groupby('X', dynamic=True) first = Dataset({'y': self.y_ints, 'z': array[:, 0]}, kdims=[('y', 'Y')], vdims=[('z', 'Z')]) self.assertEqual(grouped[0], first)
def test_dataset_groupby_dynamic(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=['x', 'y'], vdims=['z']) grouped = dataset.groupby('x', dynamic=True) first = Dataset({'y': self.y_ints, 'z': array[:, 0]}, kdims=['y'], vdims=['z']) self.assertEqual(grouped[0], first)
def test_dataset_groupby_multiple_dims(self): dataset = Dataset((range(8), range(8), range(8), range(8), np.random.rand(8, 8, 8, 8)), kdims=['a', 'b', 'c', 'd'], vdims=['Value']) grouped = dataset.groupby(['c', 'd']) keys = list(product(range(8), range(8))) self.assertEqual(list(grouped.keys()), keys) for c, d in keys: self.assertEqual(grouped[c, d], dataset.select(c=c, d=d).reindex(['a', 'b']))
def test_dataset_groupby_dynamic_alias(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=[('x', 'X'), ('y', 'Y')], vdims=[('z', 'Z')]) with DatatypeContext([self.datatype, 'dictionary' , 'dataframe'], dataset): grouped = dataset.groupby('X', dynamic=True) first = Dataset({'y': self.y_ints, 'z': array[:, 0]}, kdims=[('y', 'Y')], vdims=[('z', 'Z')]) self.assertEqual(grouped[0], first)
class GridDatasetTest(HomogeneousColumnTypes, ComparisonTestCase): """ Test of the Grid array interface """ datatype = 'grid' def setUp(self): self.restore_datatype = Dataset.datatype Dataset.datatype = ['grid'] self.data_instance_type = dict self.init_data() 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.dataset_hm = Dataset((self.xs, self.y_ints), kdims=['x'], vdims=['y']) self.dataset_hm_alias = Dataset((self.xs, self.y_ints), kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.grid_xs = [0, 1] self.grid_ys = [0.1, 0.2, 0.3] self.grid_zs = [[0, 1], [2, 3], [4, 5]] self.dataset_grid = Dataset((self.grid_xs, self.grid_ys, self.grid_zs), kdims=['x', 'y'], vdims=['z']) self.dataset_grid_alias = Dataset((self.grid_xs, self.grid_ys, self.grid_zs), kdims=[('x', 'X'), ('y', 'Y')], vdims=[('z', 'Z')]) self.dataset_grid_inv = Dataset((self.grid_xs[::-1], self.grid_ys[::-1], self.grid_zs), kdims=['x', 'y'], vdims=['z']) def test_canonical_vdim(self): x = np.array([ 0. , 0.75, 1.5 ]) y = np.array([ 1.5 , 0.75, 0. ]) z = np.array([[ 0.06925999, 0.05800389, 0.05620127], [ 0.06240918, 0.05800931, 0.04969735], [ 0.05376789, 0.04669417, 0.03880118]]) dataset = Dataset((x, y, z), kdims=['x', 'y'], vdims=['z']) canonical = np.array([[ 0.05376789, 0.04669417, 0.03880118], [ 0.06240918, 0.05800931, 0.04969735], [ 0.06925999, 0.05800389, 0.05620127]]) self.assertEqual(dataset.dimension_values('z', flat=False), canonical) def test_dataset_dim_vals_grid_kdims_xs(self): self.assertEqual(self.dataset_grid.dimension_values(0, expanded=False), np.array([0, 1])) def test_dataset_dim_vals_grid_kdims_xs_alias(self): self.assertEqual(self.dataset_grid_alias.dimension_values('x', expanded=False), np.array([0, 1])) self.assertEqual(self.dataset_grid_alias.dimension_values('X', expanded=False), np.array([0, 1])) def test_dataset_dim_vals_grid_kdims_xs_inv(self): self.assertEqual(self.dataset_grid_inv.dimension_values(0, expanded=False), np.array([0, 1])) def test_dataset_dim_vals_grid_kdims_expanded_xs_flat(self): expanded_xs = np.array([0, 0, 0, 1, 1, 1]) self.assertEqual(self.dataset_grid.dimension_values(0), expanded_xs) def test_dataset_dim_vals_grid_kdims_expanded_xs_flat_inv(self): expanded_xs = np.array([0, 0, 0, 1, 1, 1]) self.assertEqual(self.dataset_grid_inv.dimension_values(0), expanded_xs) def test_dataset_dim_vals_grid_kdims_expanded_xs(self): expanded_xs = np.array([[0, 0, 0], [1, 1, 1]]) self.assertEqual(self.dataset_grid.dimension_values(0, flat=False), expanded_xs) def test_dataset_dim_vals_grid_kdims_expanded_xs_inv(self): expanded_xs = np.array([[0, 0, 0], [1, 1, 1]]) self.assertEqual(self.dataset_grid_inv.dimension_values(0, flat=False), expanded_xs) def test_dataset_dim_vals_grid_kdims_ys(self): self.assertEqual(self.dataset_grid.dimension_values(1, expanded=False), np.array([0.1, 0.2, 0.3])) def test_dataset_dim_vals_grid_kdims_ys_inv(self): self.assertEqual(self.dataset_grid_inv.dimension_values(1, expanded=False), np.array([0.1, 0.2, 0.3])) def test_dataset_dim_vals_grid_kdims_expanded_ys_flat(self): expanded_ys = np.array([0.1, 0.2, 0.3, 0.1, 0.2, 0.3]) self.assertEqual(self.dataset_grid.dimension_values(1), expanded_ys) def test_dataset_dim_vals_grid_kdims_expanded_ys_flat_inv(self): expanded_ys = np.array([0.1, 0.2, 0.3, 0.1, 0.2, 0.3]) self.assertEqual(self.dataset_grid_inv.dimension_values(1), expanded_ys) def test_dataset_dim_vals_grid_kdims_expanded_ys(self): expanded_ys = np.array([[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]]) self.assertEqual(self.dataset_grid.dimension_values(1, flat=False), expanded_ys) def test_dataset_dim_vals_grid_kdims_expanded_ys_inv(self): expanded_ys = np.array([[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]]) self.assertEqual(self.dataset_grid_inv.dimension_values(1, flat=False), expanded_ys) def test_dataset_dim_vals_grid_vdims_zs_flat(self): expanded_zs = np.array([0, 2, 4, 1, 3, 5]) self.assertEqual(self.dataset_grid.dimension_values(2), expanded_zs) def test_dataset_dim_vals_grid_vdims_zs_flat_inv(self): expanded_zs = np.array([5, 3, 1, 4, 2, 0]) self.assertEqual(self.dataset_grid_inv.dimension_values(2), expanded_zs) def test_dataset_dim_vals_grid_vdims_zs(self): expanded_zs = np.array([[0, 1], [2, 3], [4, 5]]) self.assertEqual(self.dataset_grid.dimension_values(2, flat=False), expanded_zs) def test_dataset_dim_vals_grid_vdims_zs_inv(self): expanded_zs = np.array([[5, 4], [3, 2], [1, 0]]) self.assertEqual(self.dataset_grid_inv.dimension_values(2, flat=False), expanded_zs) def test_dataset_array_init_hm(self): "Tests support for arrays (homogeneous)" exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset(np.column_stack([self.xs, self.xs_2]), kdims=['x'], vdims=['x2']) def test_dataset_dataframe_init_hm(self): "Tests support for homogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset(pd.DataFrame({'x':self.xs, 'x2':self.xs_2}), kdims=['x'], vdims=['x2']) def test_dataset_dataframe_init_hm_alias(self): "Tests support for homogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset(pd.DataFrame({'x':self.xs, 'x2':self.xs_2}), kdims=['x'], vdims=['x2']) def test_dataset_ndelement_init_hm(self): "Tests support for homogeneous NdElement (backwards compatibility)" exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset(NdElement(zip(self.xs, self.xs_2), kdims=['x'], vdims=['x2'])) def test_dataset_2D_aggregate_partial_hm(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=['x', 'y'], vdims=['z']) self.assertEqual(dataset.aggregate(['x'], np.mean), Dataset({'x':self.xs, 'z': np.mean(array, axis=0)}, kdims=['x'], vdims=['z'])) def test_dataset_2D_aggregate_partial_hm_alias(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=[('x', 'X'), ('y', 'Y')], vdims=[('z', 'Z')]) self.assertEqual(dataset.aggregate(['X'], np.mean), Dataset({'x':self.xs, 'z': np.mean(array, axis=0)}, kdims=[('x', 'X')], vdims=[('z', 'Z')])) def test_dataset_2D_reduce_hm(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=['x', 'y'], vdims=['z']) self.assertEqual(np.array(dataset.reduce(['x', 'y'], np.mean)), np.mean(array)) def test_dataset_2D_reduce_hm_alias(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=[('x', 'X'), ('y', 'Y')], vdims=[('z', 'Z')]) self.assertEqual(np.array(dataset.reduce(['x', 'y'], np.mean)), np.mean(array)) self.assertEqual(np.array(dataset.reduce(['X', 'Y'], np.mean)), np.mean(array)) def test_dataset_add_dimensions_value_hm(self): with self.assertRaisesRegexp(Exception, 'Cannot add key dimension to a dense representation.'): self.dataset_hm.add_dimension('z', 1, 0) def test_dataset_add_dimensions_values_hm(self): table = self.dataset_hm.add_dimension('z', 1, range(1,12), vdim=True) self.assertEqual(table.vdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12)))) def test_dataset_add_dimensions_values_hm_alias(self): table = self.dataset_hm.add_dimension(('z', 'Z'), 1, range(1,12), vdim=True) self.assertEqual(table.vdims[1], 'Z') self.compare_arrays(table.dimension_values('Z'), np.array(list(range(1,12)))) def test_dataset_sort_vdim_hm(self): exception = ('Compressed format cannot be sorted, either instantiate ' 'in the desired order or use the expanded format.') with self.assertRaisesRegexp(Exception, exception): self.dataset_hm.sort('y') def test_dataset_sort_vdim_hm_alias(self): exception = ('Compressed format cannot be sorted, either instantiate ' 'in the desired order or use the expanded format.') with self.assertRaisesRegexp(Exception, exception): self.dataset_hm.sort('y') def test_dataset_groupby(self): self.assertEqual(self.dataset_hm.groupby('x').keys(), list(self.xs)) def test_dataset_groupby_dynamic(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=['x', 'y'], vdims=['z']) grouped = dataset.groupby('x', dynamic=True) first = Dataset({'y': self.y_ints, 'z': array[:, 0]}, kdims=['y'], vdims=['z']) self.assertEqual(grouped[0], first) def test_dataset_groupby_dynamic_alias(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=[('x', 'X'), ('y', 'Y')], vdims=[('z', 'Z')]) grouped = dataset.groupby('X', dynamic=True) first = Dataset({'y': self.y_ints, 'z': array[:, 0]}, kdims=[('y', 'Y')], vdims=[('z', 'Z')]) self.assertEqual(grouped[0], first) def test_dataset_groupby_multiple_dims(self): dataset = Dataset((range(8), range(8), range(8), range(8), np.random.rand(8, 8, 8, 8)), kdims=['a', 'b', 'c', 'd'], vdims=['Value']) grouped = dataset.groupby(['c', 'd']) keys = list(product(range(8), range(8))) self.assertEqual(list(grouped.keys()), keys) for c, d in keys: self.assertEqual(grouped[c, d], dataset.select(c=c, d=d).reindex(['a', 'b'])) def test_dataset_groupby_drop_dims(self): array = np.random.rand(3, 20, 10) ds = Dataset({'x': range(10), 'y': range(20), 'z': range(3), 'Val': array}, kdims=['x', 'y', 'z'], vdims=['Val']) with DatatypeContext([self.datatype, 'columns', 'dataframe']): partial = ds.to(Dataset, kdims=['x'], vdims=['Val'], groupby='y') self.assertEqual(partial.last['Val'], array[:, -1, :].T.flatten()) def test_dataset_groupby_drop_dims_dynamic(self): array = np.random.rand(3, 20, 10) ds = Dataset({'x': range(10), 'y': range(20), 'z': range(3), 'Val': array}, kdims=['x', 'y', 'z'], vdims=['Val']) with DatatypeContext([self.datatype, 'columns', 'dataframe']): partial = ds.to(Dataset, kdims=['x'], vdims=['Val'], groupby='y', dynamic=True) self.assertEqual(partial[19]['Val'], array[:, -1, :].T.flatten())
class HeterogeneousColumnTypes(HomogeneousColumnTypes): """ Tests for data formats that all dataset to have varied types """ def init_data(self): self.kdims = ['Gender', 'Age'] self.vdims = ['Weight', 'Height'] self.gender, self.age = ['M','M','F'], [10,16,12] self.weight, self.height = [15,18,10], [0.8,0.6,0.8] self.table = Dataset({'Gender':self.gender, 'Age':self.age, 'Weight':self.weight, 'Height':self.height}, kdims=self.kdims, vdims=self.vdims) self.alias_kdims = [('gender', 'Gender'), ('age', 'Age')] self.alias_vdims = [('weight', 'Weight'), ('height', 'Height')] self.alias_table = Dataset({'gender':self.gender, 'age':self.age, 'weight':self.weight, 'height':self.height}, kdims=self.alias_kdims, vdims=self.alias_vdims) super(HeterogeneousColumnTypes, self).init_data() self.ys = np.linspace(0, 1, 11) self.zs = np.sin(self.xs) self.dataset_ht = Dataset({'x':self.xs, 'y':self.ys}, kdims=['x'], vdims=['y']) # Test the constructor to be supported by all interfaces supporting # heterogeneous column types. def test_dataset_ndelement_init_ht(self): "Tests support for heterogeneous NdElement (backwards compatibility)" dataset = Dataset(NdElement(zip(self.xs, self.ys), kdims=['x'], vdims=['y'])) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_dataframe_init_ht(self): "Tests support for heterogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") dataset = Dataset(pd.DataFrame({'x':self.xs, 'y':self.ys}), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_dataframe_init_ht_alias(self): "Tests support for heterogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") dataset = Dataset(pd.DataFrame({'x':self.xs, 'y':self.ys}), kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) # Test literal formats def test_dataset_expanded_dimvals_ht(self): self.assertEqual(self.table.dimension_values('Gender', expanded=False), np.array(['M', 'F'])) def test_dataset_implicit_indexing_init(self): dataset = Dataset(self.ys, kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_tuple_init(self): dataset = Dataset((self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_tuple_init_alias(self): dataset = Dataset((self.xs, self.ys), kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_simple_zip_init(self): dataset = Dataset(zip(self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_simple_zip_init_alias(self): dataset = Dataset(zip(self.xs, self.ys), kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_zip_init(self): dataset = Dataset(zip(self.gender, self.age, self.weight, self.height), kdims=self.kdims, vdims=self.vdims) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_zip_init_alias(self): dataset = self.alias_table.clone(zip(self.gender, self.age, self.weight, self.height)) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_odict_init(self): dataset = Dataset(OrderedDict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_odict_init_alias(self): dataset = Dataset(OrderedDict(zip(self.xs, self.ys)), kdims=[('a', 'A')], vdims=[('b', 'B')]) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_dict_init(self): dataset = Dataset(dict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) # Operations def test_dataset_redim_with_alias_dframe(self): test_df = pd.DataFrame({'x': range(10), 'y': range(0,20,2)}) dataset = Dataset(test_df, kdims=[('x', 'X-label')], vdims=['y']) redim_df = pd.DataFrame({'X': range(10), 'y': range(0,20,2)}) dataset_redim = Dataset(redim_df, kdims=['X'], vdims=['y']) self.assertEqual(dataset.redim(**{'X-label':'X'}), dataset_redim) self.assertEqual(dataset.redim(**{'x':'X'}), dataset_redim) def test_dataset_sort_vdim_ht(self): dataset = Dataset({'x':self.xs, 'y':-self.ys}, kdims=['x'], vdims=['y']) dataset_sorted = Dataset({'x': self.xs[::-1], 'y':-self.ys[::-1]}, kdims=['x'], vdims=['y']) self.assertEqual(dataset.sort('y'), dataset_sorted) def test_dataset_sort_string_ht(self): dataset_sorted = Dataset({'Gender':['F', 'M', 'M'], 'Age':[12, 10, 16], 'Weight':[10,15,18], 'Height':[0.8,0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEqual(self.table.sort(), dataset_sorted) def test_dataset_sample_ht(self): samples = self.dataset_ht.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 0.5, 1])) def test_dataset_reduce_ht(self): reduced = Dataset({'Age':self.age, 'Weight':self.weight, 'Height':self.height}, kdims=self.kdims[1:], vdims=self.vdims) self.assertEqual(self.table.reduce(['Gender'], np.mean), reduced) def test_dataset_1D_reduce_ht(self): self.assertEqual(self.dataset_ht.reduce('x', np.mean), np.float64(0.5)) def test_dataset_2D_reduce_ht(self): reduced = Dataset({'Weight':[14.333333333333334], 'Height':[0.73333333333333339]}, kdims=[], vdims=self.vdims) self.assertEqual(self.table.reduce(function=np.mean), reduced) def test_dataset_2D_partial_reduce_ht(self): dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs}, kdims=['x', 'y'], vdims=['z']) reduced = Dataset({'x':self.xs, 'z':self.zs}, kdims=['x'], vdims=['z']) self.assertEqual(dataset.reduce(['y'], np.mean), reduced) def test_dataset_aggregate_ht(self): aggregated = Dataset({'Gender':['M', 'F'], 'Weight':[16.5, 10], 'Height':[0.7, 0.8]}, kdims=self.kdims[:1], vdims=self.vdims) self.compare_dataset(self.table.aggregate(['Gender'], np.mean), aggregated) def test_dataset_aggregate_ht_alias(self): aggregated = Dataset({'gender':['M', 'F'], 'weight':[16.5, 10], 'height':[0.7, 0.8]}, kdims=self.alias_kdims[:1], vdims=self.alias_vdims) self.compare_dataset(self.alias_table.aggregate('Gender', np.mean), aggregated) def test_dataset_2D_aggregate_partial_ht(self): dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs}, kdims=['x', 'y'], vdims=['z']) reduced = Dataset({'x':self.xs, 'z':self.zs}, kdims=['x'], vdims=['z']) self.assertEqual(dataset.aggregate(['x'], np.mean), reduced) def test_dataset_groupby(self): group1 = {'Age':[10,16], 'Weight':[15,18], 'Height':[0.8,0.6]} group2 = {'Age':[12], 'Weight':[10], 'Height':[0.8]} grouped = HoloMap([('M', Dataset(group1, kdims=['Age'], vdims=self.vdims)), ('F', Dataset(group2, kdims=['Age'], vdims=self.vdims))], kdims=['Gender']) self.assertEqual(self.table.groupby(['Gender']), grouped) def test_dataset_groupby_alias(self): group1 = {'age':[10,16], 'weight':[15,18], 'height':[0.8,0.6]} group2 = {'age':[12], 'weight':[10], 'height':[0.8]} grouped = HoloMap([('M', Dataset(group1, kdims=[('age', 'Age')], vdims=self.alias_vdims)), ('F', Dataset(group2, kdims=[('age', 'Age')], vdims=self.alias_vdims))], kdims=[('gender', 'Gender')]) self.assertEqual(self.alias_table.groupby('Gender'), grouped) def test_dataset_groupby_dynamic(self): grouped_dataset = self.table.groupby('Gender', dynamic=True) self.assertEqual(grouped_dataset['M'], self.table.select(Gender='M').reindex(['Age'])) self.assertEqual(grouped_dataset['F'], self.table.select(Gender='F').reindex(['Age'])) def test_dataset_groupby_dynamic_alias(self): grouped_dataset = self.alias_table.groupby('Gender', dynamic=True) self.assertEqual(grouped_dataset['M'], self.alias_table.select(gender='M').reindex(['Age'])) self.assertEqual(grouped_dataset['F'], self.alias_table.select(gender='F').reindex(['Age'])) def test_dataset_add_dimensions_value_ht(self): table = self.dataset_ht.add_dimension('z', 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(table.shape[0])) def test_dataset_add_dimensions_value_ht_alias(self): table = self.dataset_ht.add_dimension(('z', 'Z'), 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(table.shape[0])) def test_dataset_add_dimensions_values_ht(self): table = self.dataset_ht.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)))) # Indexing def test_dataset_index_row_gender_female(self): indexed = Dataset({'Gender':['F'], 'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) row = self.table['F',:] self.assertEquals(row, indexed) def test_dataset_index_rows_gender_male(self): row = self.table['M',:] indexed = Dataset({'Gender':['M', 'M'], 'Age':[10, 16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_dataset_select_rows_gender_male(self): row = self.table.select(Gender='M') indexed = Dataset({'Gender':['M', 'M'], 'Age':[10, 16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_dataset_select_rows_gender_male_alias(self): row = self.alias_table.select(Gender='M') alias_row = self.alias_table.select(gender='M') indexed = Dataset({'gender':['M', 'M'], 'age':[10, 16], 'weight':[15,18], 'height':[0.8,0.6]}, kdims=self.alias_kdims, vdims=self.alias_vdims) self.assertEquals(row, indexed) self.assertEquals(alias_row, indexed) def test_dataset_index_row_age(self): indexed = Dataset({'Gender':['F'], 'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table[:, 12], indexed) def test_dataset_index_item_table(self): indexed = Dataset({'Gender':['F'], 'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table['F', 12], indexed) def test_dataset_index_value1(self): self.assertEquals(self.table['F', 12, 'Weight'], 10) def test_dataset_index_value2(self): self.assertEquals(self.table['F', 12, 'Height'], 0.8) def test_dataset_index_column_ht(self): self.compare_arrays(self.dataset_ht['y'], self.ys) def test_dataset_boolean_index(self): row = self.table[np.array([True, True, False])] indexed = Dataset({'Gender':['M', 'M'], 'Age':[10, 16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_dataset_value_dim_index(self): row = self.table[:, :, 'Weight'] indexed = Dataset({'Gender':['M', 'M', 'F'], 'Age':[10, 16, 12], 'Weight':[15,18, 10]}, kdims=self.kdims, vdims=self.vdims[:1]) self.assertEquals(row, indexed) def test_dataset_value_dim_scalar_index(self): row = self.table['M', 10, 'Weight'] self.assertEquals(row, 15) # Casting def test_dataset_array_ht(self): self.assertEqual(self.dataset_ht.array(), np.column_stack([self.xs, self.ys]))
def test_dataset_scalar_groupby(self): ds = Dataset({'A': 1, 'B': np.arange(10)}, kdims=['A', 'B']) groups = ds.groupby('A') self.assertEqual(groups, HoloMap({1: Dataset({'B': np.arange(10)}, 'B')}, 'A'))
class HeterogeneousColumnTests(HomogeneousColumnTests): """ Tests for data formats that allow dataset to have varied types """ def init_column_data(self): self.kdims = ['Gender', 'Age'] self.vdims = ['Weight', 'Height'] self.gender, self.age = np.array(['M','M','F']), np.array([10,16,12]) self.weight, self.height = np.array([15,18,10]), np.array([0.8,0.6,0.8]) self.table = Dataset({'Gender':self.gender, 'Age':self.age, 'Weight':self.weight, 'Height':self.height}, kdims=self.kdims, vdims=self.vdims) self.alias_kdims = [('gender', 'Gender'), ('age', 'Age')] self.alias_vdims = [('weight', 'Weight'), ('height', 'Height')] self.alias_table = Dataset({'gender':self.gender, 'age':self.age, 'weight':self.weight, 'height':self.height}, kdims=self.alias_kdims, vdims=self.alias_vdims) super(HeterogeneousColumnTests, self).init_column_data() self.ys = np.linspace(0, 1, 11) self.zs = np.sin(self.xs) self.dataset_ht = Dataset({'x':self.xs, 'y':self.ys}, kdims=['x'], vdims=['y']) # Test the constructor to be supported by all interfaces supporting # heterogeneous column types. @pd_skip def test_dataset_dataframe_init_ht(self): "Tests support for heterogeneous DataFrames" dataset = Dataset(pd.DataFrame({'x':self.xs, 'y':self.ys}), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_type)) @pd_skip def test_dataset_dataframe_init_ht_alias(self): "Tests support for heterogeneous DataFrames" dataset = Dataset(pd.DataFrame({'x':self.xs, 'y':self.ys}), kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.assertTrue(isinstance(dataset.data, self.data_type)) # Test literal formats def test_dataset_expanded_dimvals_ht(self): self.assertEqual(self.table.dimension_values('Gender', expanded=False), np.array(['M', 'F'])) def test_dataset_implicit_indexing_init(self): dataset = Scatter(self.ys, kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_tuple_init(self): dataset = Dataset((self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_tuple_init_alias(self): dataset = Dataset((self.xs, self.ys), kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_simple_zip_init(self): dataset = Dataset(zip(self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_simple_zip_init_alias(self): dataset = Dataset(zip(self.xs, self.ys), kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_zip_init(self): dataset = Dataset(zip(self.gender, self.age, self.weight, self.height), kdims=self.kdims, vdims=self.vdims) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_zip_init_alias(self): dataset = self.alias_table.clone(zip(self.gender, self.age, self.weight, self.height)) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_odict_init(self): dataset = Dataset(OrderedDict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_odict_init_alias(self): dataset = Dataset(OrderedDict(zip(self.xs, self.ys)), kdims=[('a', 'A')], vdims=[('b', 'B')]) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_dict_init(self): dataset = Dataset(dict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_range_with_dimension_range(self): dt64 = np.array([np.datetime64(datetime.datetime(2017, 1, i)) for i in range(1, 4)]) ds = Dataset(dt64, [Dimension('Date', range=(dt64[0], dt64[-1]))]) self.assertEqual(ds.range('Date'), (dt64[0], dt64[-1])) # Operations @pd_skip def test_dataset_redim_with_alias_dframe(self): test_df = pd.DataFrame({'x': range(10), 'y': range(0,20,2)}) dataset = Dataset(test_df, kdims=[('x', 'X-label')], vdims=['y']) redim_df = pd.DataFrame({'X': range(10), 'y': range(0,20,2)}) dataset_redim = Dataset(redim_df, kdims=['X'], vdims=['y']) self.assertEqual(dataset.redim(**{'X-label':'X'}), dataset_redim) self.assertEqual(dataset.redim(**{'x':'X'}), dataset_redim) def test_dataset_mixed_type_range(self): ds = Dataset((['A', 'B', 'C', None],), 'A') self.assertEqual(ds.range(0), ('A', 'C')) def test_dataset_sort_vdim_ht(self): dataset = Dataset({'x':self.xs, 'y':-self.ys}, kdims=['x'], vdims=['y']) dataset_sorted = Dataset({'x': self.xs[::-1], 'y':-self.ys[::-1]}, kdims=['x'], vdims=['y']) self.assertEqual(dataset.sort('y'), dataset_sorted) def test_dataset_sort_string_ht(self): dataset_sorted = Dataset({'Gender':['F', 'M', 'M'], 'Age':[12, 10, 16], 'Weight':[10,15,18], 'Height':[0.8,0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEqual(self.table.sort(), dataset_sorted) def test_dataset_sample_ht(self): samples = self.dataset_ht.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 0.5, 1])) def test_dataset_reduce_ht(self): reduced = Dataset({'Age':self.age, 'Weight':self.weight, 'Height':self.height}, kdims=self.kdims[1:], vdims=self.vdims) self.assertEqual(self.table.reduce(['Gender'], np.mean), reduced) def test_dataset_1D_reduce_ht(self): self.assertEqual(self.dataset_ht.reduce('x', np.mean), np.float64(0.5)) def test_dataset_2D_reduce_ht(self): reduced = Dataset({'Weight':[14.333333333333334], 'Height':[0.73333333333333339]}, kdims=[], vdims=self.vdims) self.assertEqual(self.table.reduce(function=np.mean), reduced) def test_dataset_2D_partial_reduce_ht(self): dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs}, kdims=['x', 'y'], vdims=['z']) reduced = Dataset({'x':self.xs, 'z':self.zs}, kdims=['x'], vdims=['z']) self.assertEqual(dataset.reduce(['y'], np.mean), reduced) def test_dataset_2D_aggregate_spread_fn_with_duplicates(self): dataset = Dataset({'x': np.array([0, 0, 1, 1]), 'y': np.array([0, 1, 2, 3]), 'z': np.array([1, 2, 3, 4])}, kdims=['x', 'y'], vdims=['z']) agg = dataset.aggregate('x', function=np.mean, spreadfn=np.var) self.assertEqual(agg, Dataset({'x': np.array([0, 1]), 'z': np.array([1.5, 3.5]), 'z_var': np.array([0.25, 0.25])}, kdims=['x'], vdims=['z', 'z_var'])) def test_dataset_aggregate_ht(self): aggregated = Dataset({'Gender':['M', 'F'], 'Weight':[16.5, 10], 'Height':[0.7, 0.8]}, kdims=self.kdims[:1], vdims=self.vdims) self.compare_dataset(self.table.aggregate(['Gender'], np.mean), aggregated) def test_dataset_aggregate_string_types(self): ds = Dataset({'Gender':['M', 'M'], 'Weight':[20, 10], 'Name':['Peter', 'Matt']}, kdims='Gender', vdims=['Weight', 'Name']) aggregated = Dataset({'Gender': ['M'], 'Weight': [15]}, kdims='Gender', vdims=['Weight']) self.compare_dataset(ds.aggregate(['Gender'], np.mean), aggregated) def test_dataset_aggregate_string_types_size(self): ds = Dataset({'Gender':['M', 'M'], 'Weight':[20, 10], 'Name':['Peter', 'Matt']}, kdims='Gender', vdims=['Weight', 'Name']) aggregated = Dataset({'Gender': ['M'], 'Weight': [2], 'Name': [2]}, kdims='Gender', vdims=['Weight', 'Name']) self.compare_dataset(ds.aggregate(['Gender'], np.size), aggregated) def test_dataset_aggregate_ht_alias(self): aggregated = Dataset({'gender':['M', 'F'], 'weight':[16.5, 10], 'height':[0.7, 0.8]}, kdims=self.alias_kdims[:1], vdims=self.alias_vdims) self.compare_dataset(self.alias_table.aggregate('Gender', np.mean), aggregated) def test_dataset_2D_aggregate_partial_ht(self): dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs}, kdims=['x', 'y'], vdims=['z']) reduced = Dataset({'x':self.xs, 'z':self.zs}, kdims=['x'], vdims=['z']) self.assertEqual(dataset.aggregate(['x'], np.mean), reduced) def test_dataset_empty_aggregate(self): dataset = Dataset([], kdims=self.kdims, vdims=self.vdims) aggregated = Dataset([], kdims=self.kdims[:1], vdims=self.vdims) self.compare_dataset(dataset.aggregate(['Gender'], np.mean), aggregated) def test_dataset_empty_aggregate_with_spreadfn(self): dataset = Dataset([], kdims=self.kdims, vdims=self.vdims) aggregated = Dataset([], kdims=self.kdims[:1], vdims=[d for vd in self.vdims for d in [vd, vd+'_std']]) self.compare_dataset(dataset.aggregate(['Gender'], np.mean, np.std), aggregated) def test_dataset_groupby(self): group1 = {'Age':[10,16], 'Weight':[15,18], 'Height':[0.8,0.6]} group2 = {'Age':[12], 'Weight':[10], 'Height':[0.8]} grouped = HoloMap([('M', Dataset(group1, kdims=['Age'], vdims=self.vdims)), ('F', Dataset(group2, kdims=['Age'], vdims=self.vdims))], kdims=['Gender'], sort=False) print(grouped.keys()) self.assertEqual(self.table.groupby(['Gender']), grouped) def test_dataset_groupby_alias(self): group1 = {'age':[10,16], 'weight':[15,18], 'height':[0.8,0.6]} group2 = {'age':[12], 'weight':[10], 'height':[0.8]} grouped = HoloMap([('M', Dataset(group1, kdims=[('age', 'Age')], vdims=self.alias_vdims)), ('F', Dataset(group2, kdims=[('age', 'Age')], vdims=self.alias_vdims))], kdims=[('gender', 'Gender')], sort=False) self.assertEqual(self.alias_table.groupby('Gender'), grouped) def test_dataset_groupby_second_dim(self): group1 = {'Gender':['M'], 'Weight':[15], 'Height':[0.8]} group2 = {'Gender':['M'], 'Weight':[18], 'Height':[0.6]} group3 = {'Gender':['F'], 'Weight':[10], 'Height':[0.8]} grouped = HoloMap([(10, Dataset(group1, kdims=['Gender'], vdims=self.vdims)), (16, Dataset(group2, kdims=['Gender'], vdims=self.vdims)), (12, Dataset(group3, kdims=['Gender'], vdims=self.vdims))], kdims=['Age'], sort=False) self.assertEqual(self.table.groupby(['Age']), grouped) def test_dataset_groupby_dynamic(self): grouped_dataset = self.table.groupby('Gender', dynamic=True) self.assertEqual(grouped_dataset['M'], self.table.select(Gender='M').reindex(['Age'])) self.assertEqual(grouped_dataset['F'], self.table.select(Gender='F').reindex(['Age'])) def test_dataset_groupby_dynamic_alias(self): grouped_dataset = self.alias_table.groupby('Gender', dynamic=True) self.assertEqual(grouped_dataset['M'], self.alias_table.select(gender='M').reindex(['Age'])) self.assertEqual(grouped_dataset['F'], self.alias_table.select(gender='F').reindex(['Age'])) def test_dataset_add_dimensions_value_ht(self): table = self.dataset_ht.add_dimension('z', 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(table.shape[0])) def test_dataset_add_dimensions_value_ht_alias(self): table = self.dataset_ht.add_dimension(('z', 'Z'), 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(table.shape[0])) def test_dataset_add_dimensions_values_ht(self): table = self.dataset_ht.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_redim_with_extra_dimension(self): dataset = self.dataset_ht.add_dimension('Temp', 0, 0).clone(kdims=['x', 'y'], vdims=[]) redimmed = dataset.redim(x='Time') self.assertEqual(redimmed.dimension_values('Time'), self.dataset_ht.dimension_values('x')) # Indexing def test_dataset_index_row_gender_female(self): indexed = Dataset({'Gender':['F'], 'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) row = self.table['F',:] self.assertEquals(row, indexed) def test_dataset_index_rows_gender_male(self): row = self.table['M',:] indexed = Dataset({'Gender':['M', 'M'], 'Age':[10, 16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_dataset_select_rows_gender_male(self): row = self.table.select(Gender='M') indexed = Dataset({'Gender':['M', 'M'], 'Age':[10, 16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_dataset_select_rows_gender_male_alias(self): row = self.alias_table.select(Gender='M') alias_row = self.alias_table.select(gender='M') indexed = Dataset({'gender':['M', 'M'], 'age':[10, 16], 'weight':[15,18], 'height':[0.8,0.6]}, kdims=self.alias_kdims, vdims=self.alias_vdims) self.assertEquals(row, indexed) self.assertEquals(alias_row, indexed) def test_dataset_index_row_age(self): indexed = Dataset({'Gender':['F'], 'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table[:, 12], indexed) def test_dataset_index_item_table(self): indexed = Dataset({'Gender':['F'], 'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table['F', 12], indexed) def test_dataset_index_value1(self): self.assertEquals(self.table['F', 12, 'Weight'], 10) def test_dataset_index_value2(self): self.assertEquals(self.table['F', 12, 'Height'], 0.8) def test_dataset_index_column_ht(self): self.compare_arrays(self.dataset_ht['y'], self.ys) def test_dataset_boolean_index(self): row = self.table[np.array([True, True, False])] indexed = Dataset({'Gender':['M', 'M'], 'Age':[10, 16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_dataset_value_dim_index(self): row = self.table[:, :, 'Weight'] indexed = Dataset({'Gender':['M', 'M', 'F'], 'Age':[10, 16, 12], 'Weight':[15,18, 10]}, kdims=self.kdims, vdims=self.vdims[:1]) self.assertEquals(row, indexed) def test_dataset_value_dim_scalar_index(self): row = self.table['M', 10, 'Weight'] self.assertEquals(row, 15) # Tabular indexing def test_dataset_iloc_slice_rows(self): sliced = self.table.iloc[1:2] table = Dataset({'Gender':self.gender[1:2], 'Age':self.age[1:2], 'Weight':self.weight[1:2], 'Height':self.height[1:2]}, kdims=self.kdims, vdims=self.vdims) self.assertEqual(sliced, table) def test_dataset_iloc_slice_rows_slice_cols(self): sliced = self.table.iloc[1:2, 1:3] table = Dataset({'Age':self.age[1:2], 'Weight':self.weight[1:2]}, kdims=self.kdims[1:], vdims=self.vdims[:1]) self.assertEqual(sliced, table) def test_dataset_iloc_slice_rows_list_cols(self): sliced = self.table.iloc[1:2, [1, 3]] table = Dataset({'Age':self.age[1:2], 'Height':self.height[1:2]}, kdims=self.kdims[1:], vdims=self.vdims[1:]) self.assertEqual(sliced, table) def test_dataset_iloc_slice_rows_index_cols(self): sliced = self.table.iloc[1:2, 2] table = Dataset({'Weight':self.weight[1:2]}, kdims=[], vdims=self.vdims[:1]) self.assertEqual(sliced, table) def test_dataset_iloc_list_rows(self): sliced = self.table.iloc[[0, 2]] table = Dataset({'Gender':self.gender[[0, 2]], 'Age':self.age[[0, 2]], 'Weight':self.weight[[0, 2]], 'Height':self.height[[0, 2]]}, kdims=self.kdims, vdims=self.vdims) self.assertEqual(sliced, table) def test_dataset_iloc_list_rows_list_cols(self): sliced = self.table.iloc[[0, 2], [0, 2]] table = Dataset({'Gender':self.gender[[0, 2]], 'Weight':self.weight[[0, 2]]}, kdims=self.kdims[:1], vdims=self.vdims[:1]) self.assertEqual(sliced, table) def test_dataset_iloc_list_rows_list_cols_by_name(self): sliced = self.table.iloc[[0, 2], ['Gender', 'Weight']] table = Dataset({'Gender':self.gender[[0, 2]], 'Weight':self.weight[[0, 2]]}, kdims=self.kdims[:1], vdims=self.vdims[:1]) self.assertEqual(sliced, table) def test_dataset_iloc_list_rows_slice_cols(self): sliced = self.table.iloc[[0, 2], slice(1, 3)] table = Dataset({'Age':self.age[[0, 2]], 'Weight':self.weight[[0, 2]]}, kdims=self.kdims[1:], vdims=self.vdims[:1]) self.assertEqual(sliced, table) def test_dataset_iloc_index_rows_index_cols(self): indexed = self.table.iloc[1, 1] self.assertEqual(indexed, self.age[1]) def test_dataset_iloc_index_rows_slice_cols(self): indexed = self.table.iloc[1, 1:3] table = Dataset({'Age':self.age[[1]], 'Weight':self.weight[[1]]}, kdims=self.kdims[1:], vdims=self.vdims[:1]) self.assertEqual(indexed, table) def test_dataset_iloc_list_cols(self): sliced = self.table.iloc[:, [0, 2]] table = Dataset({'Gender':self.gender, 'Weight':self.weight}, kdims=self.kdims[:1], vdims=self.vdims[:1]) self.assertEqual(sliced, table) def test_dataset_iloc_list_cols_by_name(self): sliced = self.table.iloc[:, ['Gender', 'Weight']] table = Dataset({'Gender':self.gender, 'Weight':self.weight}, kdims=self.kdims[:1], vdims=self.vdims[:1]) self.assertEqual(sliced, table) def test_dataset_iloc_ellipsis_list_cols(self): sliced = self.table.iloc[..., [0, 2]] table = Dataset({'Gender':self.gender, 'Weight':self.weight}, kdims=self.kdims[:1], vdims=self.vdims[:1]) self.assertEqual(sliced, table) def test_dataset_iloc_ellipsis_list_cols_by_name(self): sliced = self.table.iloc[..., ['Gender', 'Weight']] table = Dataset({'Gender':self.gender, 'Weight':self.weight}, kdims=self.kdims[:1], vdims=self.vdims[:1]) self.assertEqual(sliced, table) # Casting def test_dataset_array_ht(self): self.assertEqual(self.dataset_ht.array(), np.column_stack([self.xs, self.ys]))
def test_grid_3d_groupby_concat_roundtrip(self): array = np.random.rand(4, 5, 3, 2) orig = Dataset((range(2), range(3), range(5), range(4), array), ['A', 'B', 'x', 'y'], 'z') self.assertEqual(concat(orig.groupby(['A', 'B'])), orig)
def test_dataset_dynamic_groupby_with_transposed_dimensions(self): dat = np.zeros((3,5,7)) dataset = Dataset((range(7), range(5), range(3), dat), ['z','x','y'], 'value') grouped = dataset.groupby('z', kdims=['y', 'x'], dynamic=True) self.assertEqual(grouped[2].dimension_values(2, flat=False), dat[:, :, -1].T)
class GridDatasetTest(HomogeneousColumnTypes, ComparisonTestCase): """ Test of the NdDataset interface (mostly for backwards compatibility) """ def setUp(self): self.restore_datatype = Dataset.datatype Dataset.datatype = ['grid'] self.data_instance_type = dict self.init_data() 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.dataset_hm = Dataset((self.xs, self.y_ints), kdims=['x'], vdims=['y']) def test_dataset_array_init_hm(self): "Tests support for arrays (homogeneous)" exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset(np.column_stack([self.xs, self.xs_2]), kdims=['x'], vdims=['x2']) def test_dataset_dataframe_init_hm(self): "Tests support for homogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset(pd.DataFrame({'x':self.xs, 'x2':self.xs_2}), kdims=['x'], vdims=['x2']) def test_dataset_ndelement_init_hm(self): "Tests support for homogeneous NdElement (backwards compatibility)" exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset(NdElement(zip(self.xs, self.xs_2), kdims=['x'], vdims=['x2'])) def test_dataset_2D_aggregate_partial_hm(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=['x', 'y'], vdims=['z']) self.assertEqual(dataset.aggregate(['x'], np.mean), Dataset({'x':self.xs, 'z': np.mean(array, axis=0)}, kdims=['x'], vdims=['z'])) def test_dataset_2D_reduce_hm(self): array = np.random.rand(11, 11) dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=['x', 'y'], vdims=['z']) self.assertEqual(np.array(dataset.reduce(['x', 'y'], np.mean)), np.mean(array)) def test_dataset_add_dimensions_value_hm(self): with self.assertRaisesRegexp(Exception, 'Cannot add key dimension to a dense representation.'): self.dataset_hm.add_dimension('z', 1, 0) def test_dataset_add_dimensions_values_hm(self): table = self.dataset_hm.add_dimension('z', 1, range(1,12), vdim=True) self.assertEqual(table.vdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12)))) def test_dataset_sort_vdim_hm(self): exception = ('Compressed format cannot be sorted, either instantiate ' 'in the desired order or use the expanded format.') with self.assertRaisesRegexp(Exception, exception): self.dataset_hm.sort('y') def test_dataset_groupby(self): self.assertEqual(self.dataset_hm.groupby('x').keys(), list(self.xs))
class HeterogeneousColumnTypes(HomogeneousColumnTypes): """ Tests for data formats that all dataset to have varied types """ def init_data(self): self.kdims = ['Gender', 'Age'] self.vdims = ['Weight', 'Height'] self.gender, self.age = ['M','M','F'], [10,16,12] self.weight, self.height = [15,18,10], [0.8,0.6,0.8] self.table = Dataset({'Gender':self.gender, 'Age':self.age, 'Weight':self.weight, 'Height':self.height}, kdims=self.kdims, vdims=self.vdims) super(HeterogeneousColumnTypes, self).init_data() self.ys = np.linspace(0, 1, 11) self.zs = np.sin(self.xs) self.dataset_ht = Dataset({'x':self.xs, 'y':self.ys}, kdims=['x'], vdims=['y']) # Test the constructor to be supported by all interfaces supporting # heterogeneous column types. def test_dataset_ndelement_init_ht(self): "Tests support for heterogeneous NdElement (backwards compatibility)" dataset = Dataset(NdElement(zip(self.xs, self.ys), kdims=['x'], vdims=['y'])) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_dataframe_init_ht(self): "Tests support for heterogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") dataset = Dataset(pd.DataFrame({'x':self.xs, 'y':self.ys}), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) # Test literal formats def test_dataset_expanded_dimvals_ht(self): self.assertEqual(self.table.dimension_values('Gender', expanded=False), np.array(['M', 'F'])) def test_dataset_implicit_indexing_init(self): dataset = Dataset(self.ys, kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_tuple_init(self): dataset = Dataset((self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_simple_zip_init(self): dataset = Dataset(zip(self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_zip_init(self): dataset = Dataset(zip(self.gender, self.age, self.weight, self.height), kdims=self.kdims, vdims=self.vdims) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_odict_init(self): dataset = Dataset(OrderedDict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) def test_dataset_dict_init(self): dataset = Dataset(dict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(dataset.data, self.data_instance_type)) # Operations def test_dataset_sort_vdim_ht(self): dataset = Dataset({'x':self.xs, 'y':-self.ys}, kdims=['x'], vdims=['y']) dataset_sorted = Dataset({'x': self.xs[::-1], 'y':-self.ys[::-1]}, kdims=['x'], vdims=['y']) self.assertEqual(dataset.sort('y'), dataset_sorted) def test_dataset_sort_string_ht(self): dataset_sorted = Dataset({'Gender':['F', 'M', 'M'], 'Age':[12, 10, 16], 'Weight':[10,15,18], 'Height':[0.8,0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEqual(self.table.sort(), dataset_sorted) def test_dataset_sample_ht(self): samples = self.dataset_ht.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 0.5, 1])) def test_dataset_reduce_ht(self): reduced = Dataset({'Age':self.age, 'Weight':self.weight, 'Height':self.height}, kdims=self.kdims[1:], vdims=self.vdims) self.assertEqual(self.table.reduce(['Gender'], np.mean), reduced) def test_dataset_1D_reduce_ht(self): self.assertEqual(self.dataset_ht.reduce('x', np.mean), np.float64(0.5)) def test_dataset_2D_reduce_ht(self): reduced = Dataset({'Weight':[14.333333333333334], 'Height':[0.73333333333333339]}, kdims=[], vdims=self.vdims) self.assertEqual(self.table.reduce(function=np.mean), reduced) def test_dataset_2D_partial_reduce_ht(self): dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs}, kdims=['x', 'y'], vdims=['z']) reduced = Dataset({'x':self.xs, 'z':self.zs}, kdims=['x'], vdims=['z']) self.assertEqual(dataset.reduce(['y'], np.mean), reduced) def test_column_aggregate_ht(self): aggregated = Dataset({'Gender':['M', 'F'], 'Weight':[16.5, 10], 'Height':[0.7, 0.8]}, kdims=self.kdims[:1], vdims=self.vdims) self.compare_dataset(self.table.aggregate(['Gender'], np.mean), aggregated) def test_dataset_2D_aggregate_partial_ht(self): dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs}, kdims=['x', 'y'], vdims=['z']) reduced = Dataset({'x':self.xs, 'z':self.zs}, kdims=['x'], vdims=['z']) self.assertEqual(dataset.aggregate(['x'], np.mean), reduced) def test_dataset_groupby(self): group1 = {'Age':[10,16], 'Weight':[15,18], 'Height':[0.8,0.6]} group2 = {'Age':[12], 'Weight':[10], 'Height':[0.8]} grouped = HoloMap([('M', Dataset(group1, kdims=['Age'], vdims=self.vdims)), ('F', Dataset(group2, kdims=['Age'], vdims=self.vdims))], kdims=['Gender']) self.assertEqual(self.table.groupby(['Gender']), grouped) def test_dataset_add_dimensions_value_ht(self): table = self.dataset_ht.add_dimension('z', 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(len(table))) def test_dataset_add_dimensions_values_ht(self): table = self.dataset_ht.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)))) # Indexing def test_dataset_index_row_gender_female(self): indexed = Dataset({'Gender':['F'], 'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) row = self.table['F',:] self.assertEquals(row, indexed) def test_dataset_index_rows_gender_male(self): row = self.table['M',:] indexed = Dataset({'Gender':['M', 'M'], 'Age':[10, 16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_dataset_index_row_age(self): indexed = Dataset({'Gender':['F'], 'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table[:, 12], indexed) def test_dataset_index_item_table(self): indexed = Dataset({'Gender':['F'], 'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table['F', 12], indexed) def test_dataset_index_value1(self): self.assertEquals(self.table['F', 12, 'Weight'], 10) def test_dataset_index_value2(self): self.assertEquals(self.table['F', 12, 'Height'], 0.8) def test_dataset_index_column_ht(self): self.compare_arrays(self.dataset_ht['y'], self.ys) def test_dataset_boolean_index(self): row = self.table[np.array([True, True, False])] indexed = Dataset({'Gender':['M', 'M'], 'Age':[10, 16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_dataset_value_dim_index(self): row = self.table[:, :, 'Weight'] indexed = Dataset({'Gender':['M', 'M', 'F'], 'Age':[10, 16, 12], 'Weight':[15,18, 10]}, kdims=self.kdims, vdims=self.vdims[:1]) self.assertEquals(row, indexed) def test_dataset_value_dim_scalar_index(self): row = self.table['M', 10, 'Weight'] self.assertEquals(row, 15) # Casting def test_dataset_array_ht(self): self.assertEqual(self.dataset_ht.array(), np.column_stack([self.xs, self.ys]))
class GridDatasetTest(HomogeneousColumnTypes, ComparisonTestCase): """ Test of the NdDataset interface (mostly for backwards compatibility) """ def setUp(self): self.restore_datatype = Dataset.datatype Dataset.datatype = ['grid'] self.data_instance_type = dict self.init_data() 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.dataset_hm = Dataset((self.xs, self.y_ints), kdims=['x'], vdims=['y']) def test_dataset_array_init_hm(self): "Tests support for arrays (homogeneous)" exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset(np.column_stack([self.xs, self.xs_2]), kdims=['x'], vdims=['x2']) def test_dataset_dataframe_init_hm(self): "Tests support for homogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset(pd.DataFrame({ 'x': self.xs, 'x2': self.xs_2 }), kdims=['x'], vdims=['x2']) def test_dataset_ndelement_init_hm(self): "Tests support for homogeneous NdElement (backwards compatibility)" exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Dataset( NdElement(zip(self.xs, self.xs_2), kdims=['x'], vdims=['x2'])) def test_dataset_2D_aggregate_partial_hm(self): array = np.random.rand(11, 11) dataset = Dataset({ 'x': self.xs, 'y': self.y_ints, 'z': array }, kdims=['x', 'y'], vdims=['z']) self.assertEqual( dataset.aggregate(['x'], np.mean), Dataset({ 'x': self.xs, 'z': np.mean(array, axis=1) }, kdims=['x'], vdims=['z'])) def test_dataset_2D_reduce_hm(self): array = np.random.rand(11, 11) dataset = Dataset({ 'x': self.xs, 'y': self.y_ints, 'z': array }, kdims=['x', 'y'], vdims=['z']) self.assertEqual(np.array(dataset.reduce(['x', 'y'], np.mean)), np.mean(array)) def test_dataset_add_dimensions_value_hm(self): with self.assertRaisesRegexp( Exception, 'Cannot add key dimension to a dense representation.'): self.dataset_hm.add_dimension('z', 1, 0) def test_dataset_add_dimensions_values_hm(self): table = self.dataset_hm.add_dimension('z', 1, range(1, 12), vdim=True) self.assertEqual(table.vdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1, 12)))) def test_dataset_sort_vdim_hm(self): exception = ('Compressed format cannot be sorted, either instantiate ' 'in the desired order or use the expanded format.') with self.assertRaisesRegexp(Exception, exception): self.dataset_hm.sort('y') def test_dataset_groupby(self): self.assertEqual(self.dataset_hm.groupby('x').keys(), list(self.xs))