import datetime as dt from importlib.util import find_spec from unittest import SkipTest, skipIf import numpy as np import pandas as pd import pytest try: import dask.array as da except ImportError: da = None try: import ibis except ImportError: ibis = None try: import cudf except ImportError: cudf = None from holoviews import ( Area, Contours, Curve, Dataset, GridSpace, Histogram, HoloMap, Image, NdLayout, NdOverlay, Points, Polygons, QuadMesh, renderer, ) from holoviews.core.data.grid import GridInterface from holoviews.element.comparison import ComparisonTestCase from holoviews.operation.element import ( contours, decimate, gradient, histogram, interpolate_curve, operation, threshold, transform, ) mpl = find_spec("matplotlib") da_skip = skipIf(da is None, "dask.array is not available") ibis_skip = skipIf(ibis is None, "ibis is not available") class OperationTests(ComparisonTestCase): """ Tests allowable data formats when constructing the basic Element types. """ def test_operation_element(self): img = Image(np.random.rand(10, 10)) op_img = operation(img, op=lambda x, k: x.clone(x.data*2)) self.assertEqual(op_img, img.clone(img.data*2, group='Operation')) def test_operation_ndlayout(self): ndlayout = NdLayout({i: Image(np.random.rand(10, 10)) for i in range(10)}) op_ndlayout = operation(ndlayout, op=lambda x, k: x.clone(x.data*2)) doubled = ndlayout.clone({k: v.clone(v.data*2, group='Operation') for k, v in ndlayout.items()}) self.assertEqual(op_ndlayout, doubled) def test_operation_grid(self): grid = GridSpace({i: Image(np.random.rand(10, 10)) for i in range(10)}, kdims=['X']) op_grid = operation(grid, op=lambda x, k: x.clone(x.data*2)) doubled = grid.clone({k: v.clone(v.data*2, group='Operation') for k, v in grid.items()}) self.assertEqual(op_grid, doubled) def test_operation_holomap(self): hmap = HoloMap({1: Image(np.random.rand(10, 10))}) op_hmap = operation(hmap, op=lambda x, k: x.clone(x.data*2)) self.assertEqual(op_hmap.last, hmap.last.clone(hmap.last.data*2, group='Operation')) def test_image_transform(self): img = Image(np.random.rand(10, 10)) op_img = transform(img, operator=lambda x: x*2) self.assertEqual(op_img, img.clone(img.data*2, group='Transform')) def test_image_threshold(self): img = Image(np.array([[0, 1, 0], [3, 4, 5.]])) op_img = threshold(img) self.assertEqual(op_img, img.clone(np.array([[0, 1, 0], [1, 1, 1]]), group='Threshold')) def test_image_gradient(self): img = Image(np.array([[0, 1, 0], [3, 4, 5.], [6, 7, 8]])) op_img = gradient(img) self.assertEqual(op_img, img.clone(np.array([[3.162278, 3.162278], [3.162278, 3.162278]]), group='Gradient')) def test_image_contours(self): img = Image(np.array([[0, 1, 0], [0, 1, 0]])) op_contours = contours(img, levels=[0.5]) # Note multiple lines which are nan-separated. contour = Contours([[(-0.166667, 0.25, 0.5), (-0.1666667, -0.25, 0.5), (np.nan, np.nan, 0.5), (0.1666667, -0.25, 0.5), (0.1666667, 0.25, 0.5)]], vdims=img.vdims) self.assertEqual(op_contours, contour) def test_image_contours_empty(self): img = Image(np.array([[0, 1, 0], [0, 1, 0]])) # Contour level outside of data limits op_contours = contours(img, levels=[23.0]) contour = Contours([], vdims=img.vdims) self.assertEqual(op_contours, contour) def test_image_contours_auto_levels(self): z = np.array([[0, 1, 0], [3, 4, 5.], [6, 7, 8]]) img = Image(z) for nlevels in range(3, 20): op_contours = contours(img, levels=nlevels) levels = [item['z'] for item in op_contours.data] assert len(levels) <= nlevels + 2 assert np.min(levels) <= z.min() assert np.max(levels) < z.max() def test_image_contours_no_range(self): img = Image(np.zeros((2, 2))) op_contours = contours(img, levels=2) contour = Contours([], vdims=img.vdims) self.assertEqual(op_contours, contour) def test_image_contours_x_datetime(self): if mpl is None: raise SkipTest("Matplotlib required to test datetime axes") x = np.array(['2023-09-01', '2023-09-03', '2023-09-05'], dtype='datetime64') y = [14, 15] z = np.array([[0, 1, 0], [0, 1, 0]]) img = Image((x, y, z)) op_contours = contours(img, levels=[0.5]) # Note multiple lines which are nan-separated. tz = dt.timezone.utc expected_x = np.array( [dt.datetime(2023, 9, 2, tzinfo=tz), dt.datetime(2023, 9, 2, tzinfo=tz), np.nan, dt.datetime(2023, 9, 4, tzinfo=tz), dt.datetime(2023, 9, 4, tzinfo=tz)], dtype=object) # Separately compare nans and datetimes x = op_contours.dimension_values('x') mask = np.array([True, True, False, True, True]) # Mask ignoring nans np.testing.assert_array_equal(x[mask], expected_x[mask]) np.testing.assert_array_equal(x[~mask].astype(float), expected_x[~mask].astype(float)) np.testing.assert_array_almost_equal(op_contours.dimension_values('y').astype(float), [15, 14, np.nan, 14, 15]) np.testing.assert_array_almost_equal(op_contours.dimension_values('z'), [0.5]*5) def test_image_contours_y_datetime(self): if mpl is None: raise SkipTest("Matplotlib required to test datetime axes") x = [14, 15, 16] y = np.array(['2023-09-01', '2023-09-03'], dtype='datetime64') z = np.array([[0, 1, 0], [0, 1, 0]]) img = Image((x, y, z)) op_contours = contours(img, levels=[0.5]) # Note multiple lines which are nan-separated. np.testing.assert_array_almost_equal(op_contours.dimension_values('x').astype(float), [14.5, 14.5, np.nan, 15.5, 15.5]) tz = dt.timezone.utc expected_y = np.array( [dt.datetime(2023, 9, 3, tzinfo=tz), dt.datetime(2023, 9, 1, tzinfo=tz), np.nan, dt.datetime(2023, 9, 1, tzinfo=tz), dt.datetime(2023, 9, 3, tzinfo=tz)], dtype=object) # Separately compare nans and datetimes y = op_contours.dimension_values('y') mask = np.array([True, True, False, True, True]) # Mask ignoring nans np.testing.assert_array_equal(y[mask], expected_y[mask]) np.testing.assert_array_equal(y[~mask].astype(float), expected_y[~mask].astype(float)) np.testing.assert_array_almost_equal(op_contours.dimension_values('z'), [0.5]*5) def test_image_contours_xy_datetime(self): if mpl is None: raise SkipTest("Matplotlib required to test datetime axes") x = np.array(['2023-09-01', '2023-09-03', '2023-09-05'], dtype='datetime64') y = np.array(['2023-10-07', '2023-10-08'], dtype='datetime64') z = np.array([[0, 1, 0], [0, 1, 0]]) img = Image((x, y, z)) op_contours = contours(img, levels=[0.5]) # Note multiple lines which are nan-separated. tz = dt.timezone.utc expected_x = np.array( [dt.datetime(2023, 9, 2, tzinfo=tz), dt.datetime(2023, 9, 2, tzinfo=tz), np.nan, dt.datetime(2023, 9, 4, tzinfo=tz), dt.datetime(2023, 9, 4, tzinfo=tz)], dtype=object) expected_y = np.array( [dt.datetime(2023, 10, 8, tzinfo=tz), dt.datetime(2023, 10, 7, tzinfo=tz), np.nan, dt.datetime(2023, 10, 7, tzinfo=tz), dt.datetime(2023, 10, 8, tzinfo=tz)], dtype=object) # Separately compare nans and datetimes x = op_contours.dimension_values('x') mask = np.array([True, True, False, True, True]) # Mask ignoring nans np.testing.assert_array_equal(x[mask], expected_x[mask]) np.testing.assert_array_equal(x[~mask].astype(float), expected_x[~mask].astype(float)) y = op_contours.dimension_values('y') np.testing.assert_array_equal(y[mask], expected_y[mask]) np.testing.assert_array_equal(y[~mask].astype(float), expected_y[~mask].astype(float)) np.testing.assert_array_almost_equal(op_contours.dimension_values('z'), [0.5]*5) def test_image_contours_z_datetime(self): if mpl is None: raise SkipTest("Matplotlib required to test datetime axes") z = np.array([['2023-09-10', '2023-09-10'], ['2023-09-10', '2023-09-12']], dtype='datetime64') img = Image(z) op_contours = contours(img, levels=[np.datetime64('2023-09-11')]) np.testing.assert_array_almost_equal(op_contours.dimension_values('x'), [0.25, 0.0]) np.testing.assert_array_almost_equal(op_contours.dimension_values('y'), [0.0, -0.25]) expected_z = np.array([ dt.datetime(2023, 9, 11, 0, 0, tzinfo=dt.timezone.utc), dt.datetime(2023, 9, 11, 0, 0, tzinfo=dt.timezone.utc)], dtype=object) np.testing.assert_array_equal(op_contours.dimension_values('z'), expected_z) def test_qmesh_contours(self): qmesh = QuadMesh(([0, 1, 2], [1, 2, 3], np.array([[0, 1, 0], [3, 4, 5.], [6, 7, 8]]))) op_contours = contours(qmesh, levels=[0.5]) contour = Contours([[(0, 1.166667, 0.5), (0.5, 1., 0.5), (np.nan, np.nan, 0.5), (1.5, 1., 0.5), (2, 1.1, 0.5)]], vdims=qmesh.vdims) self.assertEqual(op_contours, contour) def test_qmesh_curvilinear_contours(self): x = y = np.arange(3) xs, ys = np.meshgrid(x, y) zs = np.array([[0, 1, 0], [3, 4, 5.], [6, 7, 8]]) qmesh = QuadMesh((xs, ys+0.1, zs)) op_contours = contours(qmesh, levels=[0.5]) contour = Contours([[(0, 0.266667, 0.5), (0.5, 0.1, 0.5), (np.nan, np.nan, 0.5), (1.5, 0.1, 0.5), (2, 0.2, 0.5)]], vdims=qmesh.vdims) self.assertEqual(op_contours, contour) def test_qmesh_curvilinear_edges_contours(self): x = y = np.arange(3) xs, ys = np.meshgrid(x, y) xs = GridInterface._infer_interval_breaks(xs) xs = GridInterface._infer_interval_breaks(xs, 1) ys = GridInterface._infer_interval_breaks(ys) ys = GridInterface._infer_interval_breaks(ys, 1) zs = np.array([[0, 1, 0], [3, 4, 5.], [6, 7, 8]]) qmesh = QuadMesh((xs, ys+0.1, zs)) op_contours = contours(qmesh, levels=[0.5]) contour = Contours([[(0, 0.266667, 0.5), (0.5, 0.1, 0.5), (np.nan, np.nan, 0.5), (1.5, 0.1, 0.5), (2, 0.2, 0.5)]], vdims=qmesh.vdims) self.assertEqual(op_contours, contour) def test_image_contours_filled(self): img = Image(np.array([[0, 2, 0], [0, 2, 0]])) # Two polygons (nan-separated) without holes op_contours = contours(img, filled=True, levels=[0.5, 1.5]) data = [[(-0.25, -0.25, 1), (-0.08333333, -0.25, 1), (-0.08333333, 0.25, 1), (-0.25, 0.25, 1), (-0.25, -0.25, 1), (np.nan, np.nan, 1), (0.08333333, -0.25, 1), (0.25, -0.25, 1), (0.25, 0.25, 1), (0.08333333, 0.25, 1), (0.08333333, -0.25, 1)]] polys = Polygons(data, vdims=img.vdims[0].clone(range=(0.5, 1.5))) self.assertEqual(op_contours, polys) def test_image_contours_filled_with_hole(self): img = Image(np.array([[0, 0, 0], [0, 1, 0.], [0, 0, 0]])) # Single polygon with hole op_contours = contours(img, filled=True, levels=[0.25, 0.75]) data = [[(-0.25, 0.0, 0.5), (0.0, -0.25, 0.5), (0.25, 0.0, 0.5), (0.0, 0.25, 0.5), (-0.25, 0.0, 0.5)]] polys = Polygons(data, vdims=img.vdims[0].clone(range=(0.25, 0.75))) self.assertEqual(op_contours, polys) expected_holes = [[[np.array([[0.0, -0.08333333], [-0.08333333, 0.0], [0.0, 0.08333333], [0.08333333, 0.0], [0.0, -0.08333333]])]]] np.testing.assert_array_almost_equal(op_contours.holes(), expected_holes) def test_image_contours_filled_multi_holes(self): img = Image(np.array([[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]])) # Single polygon with two holes op_contours = contours(img, filled=True, levels=[-0.5, 0.5]) data = [[(-0.4, -0.3333333, 0), (-0.2, -0.3333333, 0), (0, -0.3333333, 0), (0.2, -0.3333333, 0), (0.4, -0.3333333, 0), (0.4, 0, 0), (0.4, 0.3333333, 0), (0.2, 0.3333333, 0), (0, 0.3333333, 0), (-0.2, 0.3333333, 0), (-0.4, 0.3333333, 0), (-0.4, 0, 0), (-0.4, -0.3333333, 0)]] polys = Polygons(data, vdims=img.vdims[0].clone(range=(-0.5, 0.5))) self.assertEqual(op_contours, polys) expected_holes = [[[np.array([[-0.2, -0.16666667], [-0.3, 0], [-0.2, 0.16666667], [-0.1, 0], [-0.2, -0.16666667]]), np.array([[0.2, -0.16666667], [0.1, 0], [0.2, 0.16666667], [0.3, 0], [0.2, -0.16666667]])]]] np.testing.assert_array_almost_equal(op_contours.holes(), expected_holes) def test_image_contours_filled_empty(self): img = Image(np.array([[0, 1, 0], [3, 4, 5.], [6, 7, 8]])) # Contour level outside of data limits op_contours = contours(img, filled=True, levels=[20.0, 23.0]) polys = Polygons([], vdims=img.vdims[0].clone(range=(20.0, 23.0))) self.assertEqual(op_contours, polys) def test_image_contours_filled_auto_levels(self): z = np.array([[0, 1, 0], [3, 4, 5], [6, 7, 8]]) img = Image(z) for nlevels in range(3, 20): op_contours = contours(img, filled=True, levels=nlevels) levels = [item['z'] for item in op_contours.data] assert len(levels) <= nlevels + 1 delta = 0.5*(levels[1] - levels[0]) assert np.min(levels) <= z.min() + delta assert np.max(levels) >= z.max() - delta def test_image_contours_filled_x_datetime(self): x = np.array(['2023-09-01', '2023-09-05', '2023-09-09'], dtype='datetime64') y = np.array([6, 7]) z = np.array([[0, 2, 0], [0, 2, 0]]) img = Image((x, y, z)) msg = r'Datetime spatial coordinates are not supported for filled contour calculations.' with pytest.raises(RuntimeError, match=msg): _ = contours(img, filled=True, levels=[0.5, 1.5]) def test_points_histogram(self): points = Points([float(i) for i in range(10)]) op_hist = histogram(points, num_bins=3, normed=True) hist = Histogram(([0, 3, 6, 9], [0.1, 0.1, 0.133333]), vdims=('x_frequency', 'Frequency')) self.assertEqual(op_hist, hist) def test_dataset_histogram_empty_explicit_bins(self): ds = Dataset([np.nan, np.nan], ['x']) op_hist = histogram(ds, bins=[0, 1, 2]) hist = Histogram(([0, 1, 2], [0, 0]), vdims=('x_count', 'Count')) self.assertEqual(op_hist, hist) def test_dataset_histogram_groupby_range_shared(self): x = np.arange(10) y = np.arange(10) + 10 xy = np.concatenate([x, y]) label = ["x"] * 10 + ["y"] * 10 ds = Dataset(pd.DataFrame([xy, label], index=["xy", "label"]).T, vdims=["xy", "label"]) hist = histogram(ds, groupby="label", groupby_range="shared") exp = np.linspace(0, 19, 21) for k, v in hist.items(): np.testing.assert_equal(exp, v.data["xy"]) sel = np.asarray(label) == k assert sel.sum() == 10 assert (v.data["xy_count"][sel] == 1).all() assert (v.data["xy_count"][~sel] == 0).all() def test_dataset_histogram_groupby_range_separated(self): x = np.arange(10) y = np.arange(10) + 10 xy = np.concatenate([x, y]) label = ["x"] * 10 + ["y"] * 10 ds = Dataset(pd.DataFrame([xy, label], index=["xy", "label"]).T, vdims=["xy", "label"]) hist = histogram(ds, groupby="label", groupby_range="separated") for idx, v in enumerate(hist): exp = np.linspace(idx * 10, 10 * idx + 9, 21) np.testing.assert_equal(exp, v.data["xy"]) assert v.data["xy_count"].sum() == 10 def test_dataset_histogram_groupby_datetime(self): x = pd.date_range("2020-01-01", periods=100) y = pd.date_range("2020-01-01", periods=100) xy = np.concatenate([x, y]) label = ["x"] * 100 + ["y"] * 100 ds = Dataset(pd.DataFrame([xy, label], index=["xy", "label"]).T, vdims=["xy", "label"]) hist = histogram(ds, groupby="label") exp = pd.date_range("2020-01-01", '2020-04-09', periods=21) for h in hist: np.testing.assert_equal(exp, h.data["xy"]) assert (h.data["xy_count"] == 5).all() @da_skip def test_dataset_histogram_dask(self): import dask.array as da ds = Dataset((da.from_array(np.array(range(10), dtype='f'), chunks=(3)),), ['x'], datatype=['dask']) op_hist = histogram(ds, num_bins=3, normed=True) hist = Histogram(([0, 3, 6, 9], [0.1, 0.1, 0.133333]), vdims=('x_frequency', 'Frequency')) self.assertIsInstance(op_hist.data['x_frequency'], da.Array) self.assertEqual(op_hist, hist) @da_skip def test_dataset_cumulative_histogram_dask(self): import dask.array as da ds = Dataset((da.from_array(np.array(range(10), dtype='f'), chunks=(3)),), ['x'], datatype=['dask']) op_hist = histogram(ds, num_bins=3, cumulative=True, normed=True) hist = Histogram(([0, 3, 6, 9], [0.3, 0.6, 1]), vdims=('x_frequency', 'Frequency')) self.assertIsInstance(op_hist.data['x_frequency'], da.Array) self.assertEqual(op_hist, hist) @da_skip def test_dataset_weighted_histogram_dask(self): import dask.array as da ds = Dataset((da.from_array(np.array(range(10), dtype='f'), chunks=3), da.from_array([i/10. for i in range(10)], chunks=3)), ['x', 'y'], datatype=['dask']) op_hist = histogram(ds, weight_dimension='y', num_bins=3, normed=True) hist = Histogram(([0, 3, 6, 9], [0.022222, 0.088889, 0.222222]), vdims='y') self.assertIsInstance(op_hist.data['y'], da.Array) self.assertEqual(op_hist, hist) @ibis_skip @pytest.mark.usefixtures('ibis_sqlite_backend') def test_dataset_histogram_ibis(self): df = pd.DataFrame(dict(x=np.arange(10))) t = ibis.memtable(df, name='t') ds = Dataset(t, vdims='x') op_hist = histogram(ds, dimension='x', num_bins=3, normed=True) hist = Histogram(([0, 3, 6, 9], [0.1, 0.1, 0.133333]), vdims=('x_frequency', 'Frequency')) self.assertEqual(op_hist, hist) @ibis_skip @pytest.mark.usefixtures('ibis_sqlite_backend') def test_dataset_cumulative_histogram_ibis(self): df = pd.DataFrame(dict(x=np.arange(10))) t = ibis.memtable(df, name='t') ds = Dataset(t, vdims='x') op_hist = histogram(ds, num_bins=3, cumulative=True, normed=True) hist = Histogram(([0, 3, 6, 9], [0.3, 0.6, 1]), vdims=('x_frequency', 'Frequency')) self.assertEqual(op_hist, hist) @ibis_skip @pytest.mark.usefixtures('ibis_sqlite_backend') def test_dataset_histogram_explicit_bins_ibis(self): df = pd.DataFrame(dict(x=np.arange(10))) t = ibis.memtable(df, name='t') ds = Dataset(t, vdims='x') op_hist = histogram(ds, bins=[0, 1, 3], normed=False) hist = Histogram(([0, 1, 3], [1, 3]), vdims=('x_count', 'Count')) self.assertEqual(op_hist, hist) @pytest.mark.gpu def test_dataset_histogram_cudf(self): df = pd.DataFrame(dict(x=np.arange(10))) t = cudf.from_pandas(df) ds = Dataset(t, vdims='x') op_hist = histogram(ds, dimension='x', num_bins=3, normed=True) hist = Histogram(([0, 3, 6, 9], [0.1, 0.1, 0.133333]), vdims=('x_frequency', 'Frequency')) self.assertEqual(op_hist, hist) @pytest.mark.gpu def test_dataset_cumulative_histogram_cudf(self): df = pd.DataFrame(dict(x=np.arange(10))) t = cudf.from_pandas(df) ds = Dataset(t, vdims='x') op_hist = histogram(ds, num_bins=3, cumulative=True, normed=True) hist = Histogram(([0, 3, 6, 9], [0.3, 0.6, 1]), vdims=('x_frequency', 'Frequency')) self.assertEqual(op_hist, hist) @pytest.mark.gpu def test_dataset_histogram_explicit_bins_cudf(self): df = pd.DataFrame(dict(x=np.arange(10))) t = cudf.from_pandas(df) ds = Dataset(t, vdims='x') op_hist = histogram(ds, bins=[0, 1, 3], normed=False) hist = Histogram(([0, 1, 3], [1, 3]), vdims=('x_count', 'Count')) self.assertEqual(op_hist, hist) def test_points_histogram_bin_range(self): points = Points([float(i) for i in range(10)]) op_hist = histogram(points, num_bins=3, bin_range=(0, 3), normed=True) hist = Histogram(([0.25, 0.25, 0.5], [0., 1., 2., 3.]), vdims=('x_frequency', 'Frequency')) self.assertEqual(op_hist, hist) def test_points_histogram_explicit_bins(self): points = Points([float(i) for i in range(10)]) op_hist = histogram(points, bins=[0, 1, 3], normed=False) hist = Histogram(([0, 1, 3], [1, 3]), vdims=('x_count', 'Count')) self.assertEqual(op_hist, hist) def test_points_histogram_cumulative(self): arr = np.arange(4) points = Points(arr) op_hist = histogram(points, cumulative=True, num_bins=3, normed=False) hist = Histogram(([0, 1, 2, 3], [1, 2, 4]), vdims=('x_count', 'Count')) self.assertEqual(op_hist, hist) def test_points_histogram_not_normed(self): points = Points([float(i) for i in range(10)]) op_hist = histogram(points, num_bins=3, normed=False) hist = Histogram(([0, 3, 6, 9], [3, 3, 4]), vdims=('x_count', 'Count')) self.assertEqual(op_hist, hist) def test_histogram_operation_datetime(self): dates = np.array([dt.datetime(2017, 1, i) for i in range(1, 5)]) op_hist = histogram(Dataset(dates, 'Date'), num_bins=4, normed=True) hist_data = { 'Date': np.array([ '2017-01-01T00:00:00.000000', '2017-01-01T18:00:00.000000', '2017-01-02T12:00:00.000000', '2017-01-03T06:00:00.000000', '2017-01-04T00:00:00.000000'], dtype='datetime64[us]'), 'Date_frequency': np.array([ 3.85802469e-18, 3.85802469e-18, 3.85802469e-18, 3.85802469e-18]) } hist = Histogram(hist_data, kdims='Date', vdims=('Date_frequency', 'Frequency')) self.assertEqual(op_hist, hist) def test_histogram_operation_datetime64(self): dates = np.array([dt.datetime(2017, 1, i) for i in range(1, 5)]).astype('M') op_hist = histogram(Dataset(dates, 'Date'), num_bins=4, normed=True) hist_data = { 'Date': np.array([ '2017-01-01T00:00:00.000000', '2017-01-01T18:00:00.000000', '2017-01-02T12:00:00.000000', '2017-01-03T06:00:00.000000', '2017-01-04T00:00:00.000000'], dtype='datetime64[us]'), 'Date_frequency': np.array([ 3.85802469e-18, 3.85802469e-18, 3.85802469e-18, 3.85802469e-18]) } hist = Histogram(hist_data, kdims='Date', vdims=('Date_frequency', 'Frequency')) self.assertEqual(op_hist, hist) def test_histogram_operation_pd_period(self): dates = pd.date_range('2017-01-01', '2017-01-04', freq='D').to_period('D') op_hist = histogram(Dataset(dates, 'Date'), num_bins=4, normed=True) hist_data = { 'Date': np.array([ '2017-01-01T00:00:00.000000', '2017-01-01T18:00:00.000000', '2017-01-02T12:00:00.000000', '2017-01-03T06:00:00.000000', '2017-01-04T00:00:00.000000'], dtype='datetime64[us]'), 'Date_frequency': np.array([ 3.85802469e-18, 3.85802469e-18, 3.85802469e-18, 3.85802469e-18]) } hist = Histogram(hist_data, kdims='Date', vdims=('Date_frequency', 'Frequency')) self.assertEqual(op_hist, hist) def test_histogram_categorical(self): series = Dataset(pd.Series(['A', 'B', 'C'])) kwargs = {'bin_range': ('A', 'C'), 'normed': False, 'cumulative': False, 'num_bins': 3} with pytest.raises(ValueError): histogram(series, **kwargs) def test_points_histogram_weighted(self): points = Points([float(i) for i in range(10)]) op_hist = histogram(points, num_bins=3, weight_dimension='y', normed=True) hist = Histogram(([0.022222, 0.088889, 0.222222], [0, 3, 6, 9]), vdims=['y']) self.assertEqual(op_hist, hist) def test_points_histogram_mean_weighted(self): points = Points([float(i) for i in range(10)]) op_hist = histogram(points, num_bins=3, weight_dimension='y', mean_weighted=True, normed=True) hist = Histogram(([1., 4., 7.5], [0, 3, 6, 9]), vdims=['y']) self.assertEqual(op_hist, hist) def test_interpolate_curve_pre(self): interpolated = interpolate_curve(Curve([0, 0.5, 1]), interpolation='steps-pre') curve = Curve([(0, 0), (0, 0.5), (1, 0.5), (1, 1), (2, 1)]) self.assertEqual(interpolated, curve) def test_interpolate_curve_pre_with_values(self): interpolated = interpolate_curve(Curve([(0, 0, 'A'), (1, 0.5, 'B'), (2, 1, 'C')], vdims=['y', 'z']), interpolation='steps-pre') curve = Curve([(0, 0, 'A'), (0, 0.5, 'B'), (1, 0.5, 'B'), (1, 1, 'C'), (2, 1, 'C')], vdims=['y', 'z']) self.assertEqual(interpolated, curve) def test_interpolate_datetime_curve_pre(self): dates = np.array([dt.datetime(2017, 1, i) for i in range(1, 5)]).astype('M') values = [0, 1, 2, 3] interpolated = interpolate_curve(Curve((dates, values)), interpolation='steps-pre') dates_interp = np.array([ '2017-01-01T00:00:00', '2017-01-01T00:00:00', '2017-01-02T00:00:00', '2017-01-02T00:00:00', '2017-01-03T00:00:00', '2017-01-03T00:00:00', '2017-01-04T00:00:00' ], dtype='datetime64[ns]') curve = Curve((dates_interp, [0, 1, 1, 2, 2, 3, 3])) self.assertEqual(interpolated, curve) def test_interpolate_curve_mid(self): interpolated = interpolate_curve(Curve([0, 0.5, 1]), interpolation='steps-mid') curve = Curve([(0, 0), (0.5, 0), (0.5, 0.5), (1.5, 0.5), (1.5, 1), (2, 1)]) self.assertEqual(interpolated, curve) def test_interpolate_curve_mid_with_values(self): interpolated = interpolate_curve(Curve([(0, 0, 'A'), (1, 0.5, 'B'), (2, 1, 'C')], vdims=['y', 'z']), interpolation='steps-mid') curve = Curve([(0, 0, 'A'), (0.5, 0, 'A'), (0.5, 0.5, 'B'), (1.5, 0.5, 'B'), (1.5, 1, 'C'), (2, 1, 'C')], vdims=['y', 'z']) self.assertEqual(interpolated, curve) def test_interpolate_datetime_curve_mid(self): dates = np.array([dt.datetime(2017, 1, i) for i in range(1, 5)]).astype('M') values = [0, 1, 2, 3] interpolated = interpolate_curve(Curve((dates, values)), interpolation='steps-mid') dates_interp = np.array([ '2017-01-01T00:00:00', '2017-01-01T12:00:00', '2017-01-01T12:00:00', '2017-01-02T12:00:00', '2017-01-02T12:00:00', '2017-01-03T12:00:00', '2017-01-03T12:00:00', '2017-01-04T00:00:00' ], dtype='datetime64[ns]') curve = Curve((dates_interp, [0, 0, 1, 1, 2, 2, 3, 3])) self.assertEqual(interpolated, curve) def test_interpolate_curve_post(self): interpolated = interpolate_curve(Curve([0, 0.5, 1]), interpolation='steps-post') curve = Curve([(0, 0), (1, 0), (1, 0.5), (2, 0.5), (2, 1)]) self.assertEqual(interpolated, curve) def test_interpolate_curve_post_with_values(self): interpolated = interpolate_curve(Curve([(0, 0, 'A'), (1, 0.5, 'B'), (2, 1, 'C')], vdims=['y', 'z']), interpolation='steps-post') curve = Curve([(0, 0, 'A'), (1, 0, 'A'), (1, 0.5, 'B'), (2, 0.5, 'B'), (2, 1, 'C')], vdims=['y', 'z']) self.assertEqual(interpolated, curve) def test_interpolate_datetime_curve_post(self): dates = np.array([dt.datetime(2017, 1, i) for i in range(1, 5)]).astype('M') values = [0, 1, 2, 3] interpolated = interpolate_curve(Curve((dates, values)), interpolation='steps-post') dates_interp = np.array([ '2017-01-01T00:00:00', '2017-01-02T00:00:00', '2017-01-02T00:00:00', '2017-01-03T00:00:00', '2017-01-03T00:00:00', '2017-01-04T00:00:00', '2017-01-04T00:00:00' ], dtype='datetime64[ns]') curve = Curve((dates_interp, [0, 0, 1, 1, 2, 2, 3])) self.assertEqual(interpolated, curve) def test_stack_area_overlay(self): areas = Area([1, 2, 3]) * Area([1, 2, 3]) stacked = Area.stack(areas) area1 = Area(([0, 1, 2], [1, 2, 3], [0, 0, 0]), vdims=['y', 'Baseline']) area2 = Area(([0, 1, 2], [2, 4, 6], [1, 2, 3]), vdims=['y', 'Baseline']) self.assertEqual(stacked, area1 * area2) def test_stack_area_ndoverlay(self): areas = NdOverlay([(0, Area([1, 2, 3])), (1, Area([1, 2, 3]))]) stacked = Area.stack(areas) area1 = Area(([0, 1, 2], [1, 2, 3], [0, 0, 0]), vdims=['y', 'Baseline']) area2 = Area(([0, 1, 2], [2, 4, 6], [1, 2, 3]), vdims=['y', 'Baseline']) self.assertEqual(stacked, NdOverlay([(0, area1), (1, area2)])) def test_pre_and_postprocess_hooks(self): pre_backup = operation._preprocess_hooks post_backup = operation._postprocess_hooks operation._preprocess_hooks = [lambda op, x: {'label': str(x.id)}] operation._postprocess_hooks = [lambda op, x, **kwargs: x.clone(**kwargs)] curve = Curve([1, 2, 3]) self.assertEqual(operation(curve).label, str(curve.id)) operation._preprocess_hooks = pre_backup operation._postprocess_hooks = post_backup def test_decimate_ordering(self): x = np.linspace(0, 10, 100) y = np.sin(x) curve = Curve((x, y)) decimated = decimate(curve, max_samples=20) renderer("bokeh").get_plot(decimated) index = decimated.data[()].data.index assert np.all(index == np.sort(index))