def test_dataset_reindex_non_constant(self): with DatatypeContext([self.datatype, 'dictionary' , 'dataframe', 'grid'], self.rgb): ds = Dataset(self.rgb) reindexed = ds.reindex(['y'], ['R']) data = Dataset(ds.columns(['y', 'R']), kdims=['y'], vdims=[ds.vdims[0]]) self.assertEqual(reindexed, data)
def test_dataset_reindex_non_constant(self): ds = Dataset(self.rgb) reindexed = ds.reindex(['y'], ['R']) data = Dataset(ds.columns(['y', 'R']), kdims=['y'], vdims=[ds.vdims[0]]) self.assertEqual(reindexed, data)
def test_dataset_reindex_constant(self): with DatatypeContext([self.datatype, 'dictionary', 'dataframe', 'grid'], self.image): selected = Dataset(self.image.select(x=0)) reindexed = selected.reindex(['y']) data = Dataset(selected.columns(['y', 'z']), kdims=['y'], vdims=['z']) self.assertEqual(reindexed, data)
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())
def _table_data(self): if self.format in ('json', 'csv'): io = StringIO() else: io = BytesIO() table = self._select_download.value query = { filt.field: filt.query for filt in self.filters if filt.query is not None and ( filt.table is None or filt.table == table) } data = self.source.get(table, **query) for filt in self.filters: if not isinstance(filt, ParamFilter): continue from holoviews import Dataset if filt.value is not None: ds = Dataset(data) data = ds.select(filt.value).data if self.format == 'csv': data.to_csv(io, **self.kwargs) elif self.format == 'json': data.to_json(io, **self.kwargs) elif self.format == 'xlsx': data.to_excel(io, **self.kwargs) elif self.format == 'parquet': data.to_parquet(io, **self.kwargs) io.seek(0) return io
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_to_holomap_dask(self): if dd is None: raise SkipTest("Dask required to test .to with dask dataframe.") ddf = dd.from_pandas(self.df, npartitions=2) dds = Dataset( ddf, kdims=[ Dimension('a', label="The a Column"), Dimension('b', label="The b Column"), Dimension('c', label="The c Column"), Dimension('d', label="The d Column"), ] ) curve_hmap = dds.to(Curve, 'a', 'b', groupby=['c']) # Check HoloMap element datasets for v in self.df.c.drop_duplicates(): curve = curve_hmap.data[(v,)] self.assertEqual( curve.dataset, self.ds ) # Execute pipeline self.assertEqual(curve.pipeline(curve.dataset), curve)
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_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_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(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_sort_vdim_hm(self): xs_2 = np.array(self.xs_2) dataset = Dataset(np.column_stack([self.xs, -xs_2]), kdims=['x'], vdims=['y']) dataset_sorted = Dataset(np.column_stack([self.xs[::-1], -xs_2[::-1]]), kdims=['x'], vdims=['y']) self.assertEqual(dataset.sort('y'), dataset_sorted)
def test_dataset_sort_reverse_vdim_hm(self): xs_2 = np.array(self.xs_2) dataset = Dataset(np.column_stack([self.xs, -xs_2]), kdims=['x'], vdims=['y']) dataset_sorted = Dataset(np.column_stack([self.xs, -xs_2]), kdims=['x'], vdims=['y']) self.assertEqual(dataset.sort('y', reverse=True), dataset_sorted)
def setUp(self): self.df = pd.DataFrame({ 'a': [1, 1, 3, 3, 2, 2, 0, 0], 'b': [10, 20, 30, 40, 10, 20, 30, 40], 'c': ['A', 'A', 'B', 'B', 'C', 'C', 'D', 'D'], 'd': [-1, -2, -3, -4, -5, -6, -7, -8] }) self.ds = Dataset( self.df, kdims=[ Dimension('a', label="The a Column"), Dimension('b', label="The b Column"), Dimension('c', label="The c Column"), Dimension('d', label="The d Column"), ] ) self.ds2 = Dataset( self.df.iloc[2:], kdims=[ Dimension('a', label="The a Column"), Dimension('b', label="The b Column"), Dimension('c', label="The c Column"), Dimension('d', label="The d Column"), ] )
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)
def get_data(self): """ Queries the Source for the specified table applying any filters and transformations specified on the View. Unlike `get_value` this should be used when multiple return values are expected. Returns ------- DataFrame The queried table after filtering and transformations are applied. """ if self._cache is not None: return self._cache query = { filt.field: filt.query for filt in self.filters if filt.query is not None and ( filt.table is None or filt.table == self.table) } data = self.source.get(self.table, **query) for transform in self.transforms: data = transform.apply(data) if len(data): data = self.source._filter_dataframe(data, **query) for filt in self.filters: if not isinstance(filt, ParamFilter): continue from holoviews import Dataset if filt.value is not None: ds = Dataset(data) data = ds.select(filt.value).data self._cache = data return data
def test_dataset_groupby_drop_dims_with_vdim(self): array = np.random.rand(3, 20, 10) ds = Dataset({'x': range(10), 'y': range(20), 'z': range(3), 'Val': array, 'Val2': array*2}, kdims=['x', 'y', 'z'], vdims=['Val', 'Val2']) with DatatypeContext([self.datatype, 'dictionary' , 'dataframe'], (ds, Dataset)): partial = ds.to(Dataset, kdims=['Val'], vdims=['Val2'], groupby='y') self.assertEqual(partial.last['Val'], array[:, -1, :].T.flatten())
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_dataset_sort_vdim_hm_alias(self): xs_2 = np.array(self.xs_2) dataset = Dataset(np.column_stack([self.xs, -xs_2]), kdims=[('x', 'X-label')], vdims=[('y', 'Y-label')]) dataset_sorted = Dataset(np.column_stack([self.xs[::-1], -xs_2[::-1]]), kdims=[('x', 'X-label')], vdims=[('y', 'Y-label')]) self.assertEqual(dataset.sort('y'), dataset_sorted) self.assertEqual(dataset.sort('Y-label'), dataset_sorted)
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_xarray_dataset_with_scalar_dim_canonicalize(self): import xarray as xr xs = [0, 1] ys = [0.1, 0.2, 0.3] zs = np.array([[[0, 1], [2, 3], [4, 5]]]) xrarr = xr.DataArray(zs, coords={'x': xs, 'y': ys, 't': [1]}, dims=['t', 'y', 'x']) ds = Dataset(xrarr, kdims=['x', 'y'], vdims=['z'], datatype=['xarray']) self.assertEqual(ds.dimension_values(2, flat=False).ndim, 2)
def test_dataset_1D_reduce_hm(self): dataset = Dataset({ 'x': self.xs, 'y': self.y_ints }, kdims=['x'], vdims=['y']) self.assertEqual(dataset.reduce('x', np.mean), 10)
def test_aggregate_ndoverlay(self): ds = Dataset([(0.2, 0.3, 0), (0.4, 0.7, 1), (0, 0.99, 2)], kdims=['x', 'y', 'z']) ndoverlay = ds.to(Points, ['x', 'y'], [], 'z').overlay() expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [2, 0]]), vdims=['Count']) img = aggregate(ndoverlay, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) self.assertEqual(img, expected)
def test_dataset_1D_reduce_hm_alias(self): dataset = Dataset({ 'x': self.xs, 'y': self.y_ints }, kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.assertEqual(dataset.reduce('X', np.mean), 10)
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_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_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_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_constructors_dataset(self): ds = Dataset(self.df) self.assertIs(ds, ds.dataset) # Check pipeline ops = ds.pipeline.operations self.assertEqual(len(ops), 1) self.assertIs(ops[0].output_type, Dataset) self.assertEqual(ds, ds.pipeline(ds.dataset))
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)
def init_column_data(self): self.xs = np.array(range(11)) self.xs_2 = self.xs**2 self.y_ints = self.xs*2 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')])
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_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 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'])
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 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'])
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'])
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_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_dataset_scalar_sort(self): ds = Dataset({'A': 1, 'B': np.arange(10)[::-1]}, kdims=['A', 'B']) self.assertEqual(ds.sort().dimension_values('B'), np.arange(10))
class HomogeneousColumnTests(object): """ Tests for data formats that require all dataset to have the same type (e.g numpy arrays) """ def init_column_data(self): self.xs = np.array(range(11)) self.xs_2 = self.xs**2 self.y_ints = self.xs*2 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')]) # Test the array constructor (homogeneous data) to be supported by # all interfaces. def test_dataset_array_init_hm(self): dataset = Dataset(np.column_stack([self.xs, self.xs_2]), kdims=['x'], vdims=['x2']) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_array_init_hm_tuple_dims(self): dataset = Dataset(np.column_stack([self.xs, self.xs_2]), kdims=[('x', 'X')], vdims=[('x2', 'X2')]) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_dataframe_init_hm(self): "Tests support for homogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") dataset = Dataset(pd.DataFrame({'x':self.xs, 'x2':self.xs_2}), kdims=['x'], vdims=['x2']) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_dataframe_init_hm_alias(self): "Tests support for homogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") dataset = Dataset(pd.DataFrame({'x':self.xs, 'x2':self.xs_2}), kdims=[('x', 'X-label')], vdims=[('x2', 'X2-label')]) self.assertTrue(isinstance(dataset.data, self.data_type)) def test_dataset_empty_list_init(self): dataset = Dataset([], kdims=['x'], vdims=['y']) for d in 'xy': self.assertEqual(dataset.dimension_values(d), np.array([])) def test_dataset_dict_dim_not_found_raises_on_array(self): with self.assertRaises(ValueError): Dataset({'x': np.zeros(5)}, kdims=['Test'], vdims=[]) def test_dataset_dict_dim_not_found_raises_on_scalar(self): with self.assertRaises(ValueError): Dataset({'x': 1}, kdims=['Test'], vdims=[]) # Properties and information def test_dataset_shape(self): self.assertEqual(self.dataset_hm.shape, (11, 2)) def test_dataset_range(self): self.assertEqual(self.dataset_hm.range('y'), (0, 20)) def test_dataset_closest(self): closest = self.dataset_hm.closest([0.51, 1, 9.9]) self.assertEqual(closest, [1., 1., 10.]) # Operations def test_dataset_sort_hm(self): ds = Dataset(([2, 2, 1], [2,1,2], [0.1, 0.2, 0.3]), kdims=['x', 'y'], vdims=['z']).sort() ds_sorted = Dataset(([1, 2, 2], [2, 1, 2], [0.3, 0.2, 0.1]), kdims=['x', 'y'], vdims=['z']) self.assertEqual(ds.sort(), ds_sorted) def test_dataset_sort_reverse_hm(self): ds = Dataset(([2, 1, 2, 1], [2, 2, 1, 1], [0.1, 0.2, 0.3, 0.4]), kdims=['x', 'y'], vdims=['z']) ds_sorted = Dataset(([2, 2, 1, 1], [2, 1, 2, 1], [0.1, 0.3, 0.2, 0.4]), kdims=['x', 'y'], vdims=['z']) self.assertEqual(ds.sort(reverse=True), ds_sorted) def test_dataset_sort_vdim_hm(self): xs_2 = np.array(self.xs_2) dataset = Dataset(np.column_stack([self.xs, -xs_2]), kdims=['x'], vdims=['y']) dataset_sorted = Dataset(np.column_stack([self.xs[::-1], -xs_2[::-1]]), kdims=['x'], vdims=['y']) self.assertEqual(dataset.sort('y'), dataset_sorted) def test_dataset_sort_reverse_vdim_hm(self): xs_2 = np.array(self.xs_2) dataset = Dataset(np.column_stack([self.xs, -xs_2]), kdims=['x'], vdims=['y']) dataset_sorted = Dataset(np.column_stack([self.xs, -xs_2]), kdims=['x'], vdims=['y']) self.assertEqual(dataset.sort('y', reverse=True), dataset_sorted) def test_dataset_sort_vdim_hm_alias(self): xs_2 = np.array(self.xs_2) dataset = Dataset(np.column_stack([self.xs, -xs_2]), kdims=[('x', 'X-label')], vdims=[('y', 'Y-label')]) dataset_sorted = Dataset(np.column_stack([self.xs[::-1], -xs_2[::-1]]), kdims=[('x', 'X-label')], vdims=[('y', 'Y-label')]) self.assertEqual(dataset.sort('y'), dataset_sorted) self.assertEqual(dataset.sort('Y-label'), dataset_sorted) def test_dataset_redim_hm_kdim(self): redimmed = self.dataset_hm.redim(x='Time') self.assertEqual(redimmed.dimension_values('Time'), self.dataset_hm.dimension_values('x')) def test_dataset_redim_hm_kdim_range_aux(self): redimmed = self.dataset_hm.redim.range(x=(-100,3)) self.assertEqual(redimmed.kdims[0].range, (-100,3)) def test_dataset_redim_hm_kdim_soft_range_aux(self): redimmed = self.dataset_hm.redim.soft_range(x=(-100,30)) self.assertEqual(redimmed.kdims[0].soft_range, (-100,30)) def test_dataset_redim_hm_kdim_alias(self): redimmed = self.dataset_hm_alias.redim(x='Time') self.assertEqual(redimmed.dimension_values('Time'), self.dataset_hm_alias.dimension_values('x')) def test_dataset_redim_hm_vdim(self): redimmed = self.dataset_hm.redim(y='Value') self.assertEqual(redimmed.dimension_values('Value'), self.dataset_hm.dimension_values('y')) def test_dataset_redim_hm_vdim_alias(self): redimmed = self.dataset_hm_alias.redim(y=Dimension(('val', 'Value'))) self.assertEqual(redimmed.dimension_values('Value'), self.dataset_hm_alias.dimension_values('y')) def test_dataset_sample_hm(self): samples = self.dataset_hm.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 10, 20])) def test_dataset_sample_hm_alias(self): samples = self.dataset_hm_alias.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 10, 20])) def test_dataset_array_hm(self): self.assertEqual(self.dataset_hm.array(), np.column_stack([self.xs, self.y_ints])) def test_dataset_array_hm_alias(self): self.assertEqual(self.dataset_hm_alias.array(), np.column_stack([self.xs, self.y_ints])) def test_dataset_add_dimensions_value_hm(self): table = self.dataset_hm.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_values_hm(self): table = self.dataset_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_dataset_slice_hm(self): dataset_slice = Dataset({'x':range(5, 9), 'y':[2 * i for i in range(5, 9)]}, kdims=['x'], vdims=['y']) self.assertEqual(self.dataset_hm[5:9], dataset_slice) def test_dataset_slice_hm_alias(self): dataset_slice = Dataset({'x':range(5, 9), 'y':[2 * i for i in range(5, 9)]}, kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.assertEqual(self.dataset_hm_alias[5:9], dataset_slice) def test_dataset_slice_fn_hm(self): dataset_slice = Dataset({'x':range(5, 9), 'y':[2 * i for i in range(5, 9)]}, kdims=['x'], vdims=['y']) self.assertEqual(self.dataset_hm[lambda x: (x >= 5) & (x < 9)], dataset_slice) def test_dataset_1D_reduce_hm(self): dataset = Dataset({'x':self.xs, 'y':self.y_ints}, kdims=['x'], vdims=['y']) self.assertEqual(dataset.reduce('x', np.mean), 10) def test_dataset_1D_reduce_hm_alias(self): dataset = Dataset({'x':self.xs, 'y':self.y_ints}, kdims=[('x', 'X')], vdims=[('y', 'Y')]) self.assertEqual(dataset.reduce('X', np.mean), 10) def test_dataset_2D_reduce_hm(self): dataset = Dataset({'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(dataset.reduce(['x', 'y'], np.mean)), np.array(140)) def test_dataset_2D_aggregate_partial_hm(self): z_ints = [el**2 for el in self.y_ints] dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z':z_ints}, kdims=['x', 'y'], vdims=['z']) self.assertEqual(dataset.aggregate(['x'], np.mean), Dataset({'x':self.xs, 'z':z_ints}, kdims=['x'], vdims=['z'])) # Indexing def test_dataset_index_column_idx_hm(self): self.assertEqual(self.dataset_hm[5], self.y_ints[5]) def test_dataset_index_column_ht(self): self.compare_arrays(self.dataset_hm['y'], self.y_ints) def test_dataset_array_ht(self): self.assertEqual(self.dataset_hm.array(), np.column_stack([self.xs, self.y_ints])) # Tabular indexing def test_dataset_iloc_slice_rows(self): sliced = self.dataset_hm.iloc[1:4] table = Dataset({'x': self.xs[1:4], 'y': self.y_ints[1:4]}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_slice_rows_slice_cols(self): sliced = self.dataset_hm.iloc[1:4, 1:] table = Dataset({'y': self.y_ints[1:4]}, kdims=[], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_slice_rows_list_cols(self): sliced = self.dataset_hm.iloc[1:4, [0, 1]] table = Dataset({'x': self.xs[1:4], 'y': self.y_ints[1:4]}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_slice_rows_index_cols(self): sliced = self.dataset_hm.iloc[1:4, 1] table = Dataset({'y': self.y_ints[1:4]}, kdims=[], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_list_rows(self): sliced = self.dataset_hm.iloc[[0, 2]] table = Dataset({'x': self.xs[[0, 2]], 'y': self.y_ints[[0, 2]]}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_list_rows_list_cols(self): sliced = self.dataset_hm.iloc[[0, 2], [0, 1]] table = Dataset({'x': self.xs[[0, 2]], 'y': self.y_ints[[0, 2]]}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_list_rows_list_cols_by_name(self): sliced = self.dataset_hm.iloc[[0, 2], ['x', 'y']] table = Dataset({'x': self.xs[[0, 2]], 'y': self.y_ints[[0, 2]]}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_list_rows_slice_cols(self): sliced = self.dataset_hm.iloc[[0, 2], slice(0, 2)] table = Dataset({'x': self.xs[[0, 2]], 'y': self.y_ints[[0, 2]]}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_index_rows_index_cols(self): indexed = self.dataset_hm.iloc[1, 1] self.assertEqual(indexed, self.y_ints[1]) def test_dataset_iloc_index_rows_slice_cols(self): indexed = self.dataset_hm.iloc[1, :2] table = Dataset({'x':self.xs[[1]], 'y':self.y_ints[[1]]}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(indexed, table) def test_dataset_iloc_list_cols(self): sliced = self.dataset_hm.iloc[:, [0, 1]] table = Dataset({'x':self.xs, 'y':self.y_ints}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_list_cols_by_name(self): sliced = self.dataset_hm.iloc[:, ['x', 'y']] table = Dataset({'x':self.xs, 'y':self.y_ints}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_ellipsis_list_cols(self): sliced = self.dataset_hm.iloc[..., [0, 1]] table = Dataset({'x':self.xs, 'y':self.y_ints}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_iloc_ellipsis_list_cols_by_name(self): sliced = self.dataset_hm.iloc[..., ['x', 'y']] table = Dataset({'x':self.xs, 'y':self.y_ints}, kdims=['x'], vdims=['y'], datatype=['dictionary']) self.assertEqual(sliced, table) def test_dataset_get_array(self): arr = self.dataset_hm.array() self.assertEqual(arr, np.column_stack([self.xs, self.y_ints])) def test_dataset_get_array_by_dimension(self): arr = self.dataset_hm.array(['x']) self.assertEqual(arr, self.xs[:, np.newaxis]) @pd_skip def test_dataset_get_dframe(self): df = self.dataset_hm.dframe() self.assertEqual(df.x.values, self.xs) self.assertEqual(df.y.values, self.y_ints) @pd_skip def test_dataset_get_dframe_by_dimension(self): df = self.dataset_hm.dframe(['x']) self.assertEqual(df, pd.DataFrame({'x': self.xs}, dtype=df.dtypes[0]))
def test_dataset_scalar_constructor(self): ds = Dataset({'A': 1, 'B': np.arange(10)}, kdims=['A', 'B']) self.assertEqual(ds.dimension_values('A'), np.ones(10))
def test_dataset_scalar_array(self): ds = Dataset({'A': 1, 'B': np.arange(10)}, kdims=['A', 'B']) self.assertEqual(ds.array(), np.column_stack([np.ones(10), np.arange(10)]))
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_dataset_scalar_sample(self): ds = Dataset({'A': 1, 'B': np.arange(10)}, kdims=['A', 'B']) self.assertEqual(ds.sample([(1,)]).dimension_values('B'), np.arange(10))
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'))
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_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_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_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]))
def test_dataset_scalar_empty_select(self): ds = Dataset({'A': 1, 'B': np.arange(10)}, kdims=['A', 'B']) self.assertEqual(ds.select(A=0).dimension_values('B'), np.array([]))
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)