import datetime as dt from unittest import SkipTest, skipIf import colorcet as cc import numpy as np import pandas as pd import pytest from numpy import nan from holoviews import ( RGB, Area, Contours, Curve, Dataset, Dimension, DynamicMap, Graph, Image, ImageStack, NdOverlay, Nodes, Overlay, Path, Points, Polygons, QuadMesh, Rectangles, Segments, Spikes, Spread, TriMesh, ) from holoviews.element.comparison import ComparisonTestCase from holoviews.operation import apply_when from holoviews.streams import Tap from holoviews.util import render try: import datashader as ds except ImportError: raise SkipTest('Datashader not available') import dask.dataframe as dd import xarray as xr from holoviews.operation.datashader import ( DATASHADER_VERSION, AggregationOperation, aggregate, datashade, directly_connect_edges, dynspread, inspect, inspect_points, inspect_polygons, rasterize, regrid, shade, spread, stack, ) try: import spatialpandas except ImportError: spatialpandas = None spatialpandas_skip = skipIf(spatialpandas is None, "SpatialPandas not available") import logging numba_logger = logging.getLogger('numba') numba_logger.setLevel(logging.WARNING) AggregationOperation.vdim_prefix = '' @pytest.fixture() def point_data(): num = 100 np.random.seed(1) dists = { cat: pd.DataFrame( { "x": np.random.normal(x, s, num), "y": np.random.normal(y, s, num), "s": s, "val": val, "cat": cat, } ) for x, y, s, val, cat in [ (2, 2, 0.03, 0, "d1"), (2, -2, 0.10, 1, "d2"), (-2, -2, 0.50, 2, "d3"), (-2, 2, 1.00, 3, "d4"), (0, 0, 3.00, 4, "d5"), ] } df = pd.concat(dists, ignore_index=True) return df @pytest.fixture() def point_plot(point_data): return Points(point_data) class DatashaderAggregateTests(ComparisonTestCase): """ Tests for datashader aggregation """ def test_aggregate_points(self): points = Points([(0.2, 0.3), (0.4, 0.7), (0, 0.99)]) img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [2, 0]]), vdims=[Dimension('Count', nodata=0)]) self.assertEqual(img, expected) def test_aggregate_points_empty(self): points = Points([]) img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2, pixel_ratio=1) expected = Image(([0.25, 0.75], [0.25, 0.75], [[0, 0], [0, 0]]), vdims=[Dimension('Count', nodata=0)]) self.assertEqual(img, expected) def test_aggregate_points_empty_with_pixel_ratio(self): points = Points([]) img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2, pixel_ratio=2) expected = Image(( [0.125, 0.375, 0.625, 0.875], [0.125, 0.375, 0.625, 0.875], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]] ), vdims=[Dimension('Count', nodata=0)]) self.assertEqual(img, expected) def test_aggregate_points_count_column(self): points = Points([(0.2, 0.3, np.nan), (0.4, 0.7, 22), (0, 0.99,np.nan)], vdims='z') img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2, aggregator=ds.count('z')) expected = Image(([0.25, 0.75], [0.25, 0.75], [[0, 0], [1, 0]]), vdims=[Dimension('z Count', nodata=0)]) self.assertEqual(img, expected) @pytest.mark.gpu def test_aggregate_points_cudf(self): import cudf import cupy points = Points([(0.2, 0.3), (0.4, 0.7), (0, 0.99)], datatype=['cuDF']) assert isinstance(points.data, cudf.DataFrame) img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [2, 0]]), vdims=[Dimension('Count', nodata=0)]) assert isinstance(img.data.Count.data, cupy.ndarray) self.assertEqual(img, expected) def test_aggregate_zero_range_points(self): p = Points([(0, 0), (1, 1)]) agg = rasterize(p, x_range=(0, 0), y_range=(0, 1), expand=False, dynamic=False, width=2, height=2) img = Image(([], [0.25, 0.75], np.zeros((2, 0))), bounds=(0, 0, 0, 1), xdensity=1, vdims=[Dimension('Count', nodata=0)]) self.assertEqual(agg, img) def test_aggregate_points_target(self): points = Points([(0.2, 0.3), (0.4, 0.7), (0, 0.99)]) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [2, 0]]), vdims=[Dimension('Count', nodata=0)]) img = aggregate(points, dynamic=False, target=expected) self.assertEqual(img, expected) def test_aggregate_points_sampling(self): points = Points([(0.2, 0.3), (0.4, 0.7), (0, 0.99)]) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [2, 0]]), vdims=[Dimension('Count', nodata=0)]) img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), x_sampling=0.5, y_sampling=0.5) self.assertEqual(img, expected) def test_aggregate_points_categorical(self): points = Points([(0.2, 0.3, 'A'), (0.4, 0.7, 'B'), (0, 0.99, 'C')], vdims='z') img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2, aggregator=ds.count_cat('z')) x = np.array([0.25, 0.75]) y = np.array([0.25, 0.75]) a = np.array([[1, 0], [0, 0]]) b = np.array([[0, 1], [0, 0]]) c = np.array([[0, 1], [0, 0]]) xrds = xr.Dataset( coords={"x": x, "y": y}, data_vars={"a": (("x", "y"), a), "b": (("x", "y"), b), "c": (("x", "y"), c)}, ) expected = ImageStack(xrds, kdims=["x", "y"], vdims=["a", "b", "c"]) actual = img.data assert (expected.data.to_array("z").values == actual.T.values).all() def test_aggregate_points_categorical_zero_range(self): points = Points([(0.2, 0.3, 'A'), (0.4, 0.7, 'B'), (0, 0.99, 'C')], vdims='z') img = aggregate(points, dynamic=False, x_range=(0, 0), y_range=(0, 1), aggregator=ds.count_cat('z'), height=2) xs, ys = [], [0.25, 0.75] params = dict(bounds=(0, 0, 0, 1), xdensity=1) expected = NdOverlay({'A': Image((xs, ys, np.zeros((2, 0))), vdims=Dimension('z Count', nodata=0), **params), 'B': Image((xs, ys, np.zeros((2, 0))), vdims=Dimension('z Count', nodata=0), **params), 'C': Image((xs, ys, np.zeros((2, 0))), vdims=Dimension('z Count', nodata=0), **params)}, kdims=['z']) self.assertEqual(img, expected) def test_aggregate_curve(self): curve = Curve([(0.2, 0.3), (0.4, 0.7), (0.8, 0.99)]) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [1, 1]]), vdims=[Dimension('Count', nodata=0)]) img = aggregate(curve, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) self.assertEqual(img, expected) def test_aggregate_curve_datetimes(self): dates = pd.date_range(start="2016-01-01", end="2016-01-03", freq='1D') curve = Curve((dates, [1, 2, 3])) img = aggregate(curve, width=2, height=2, dynamic=False) bounds = (np.datetime64('2016-01-01T00:00:00.000000'), 1.0, np.datetime64('2016-01-03T00:00:00.000000'), 3.0) dates = [np.datetime64('2016-01-01T12:00:00.000000000'), np.datetime64('2016-01-02T12:00:00.000000000')] expected = Image((dates, [1.5, 2.5], [[1, 0], [0, 2]]), datatype=['xarray'], bounds=bounds, vdims=Dimension('Count', nodata=0)) self.assertEqual(img, expected) def test_aggregate_curve_datetimes_dask(self): df = pd.DataFrame( data=np.arange(1000), columns=['a'], index=pd.date_range('2019-01-01', freq='1min', periods=1000), ) ddf = dd.from_pandas(df, npartitions=4) curve = Curve(ddf, kdims=['index'], vdims=['a']) img = aggregate(curve, width=2, height=3, dynamic=False) bounds = (np.datetime64('2019-01-01T00:00:00.000000'), 0.0, np.datetime64('2019-01-01T16:39:00.000000'), 999.0) dates = [np.datetime64('2019-01-01T04:09:45.000000000'), np.datetime64('2019-01-01T12:29:15.000000000')] expected = Image((dates, [166.5, 499.5, 832.5], [[332, 0], [167, 166], [0, 334]]), kdims=['index', 'a'], vdims=Dimension('Count', nodata=0), datatype=['xarray'], bounds=bounds) self.assertEqual(img, expected) def test_aggregate_curve_datetimes_microsecond_timebase(self): dates = pd.date_range(start="2016-01-01", end="2016-01-03", freq='1D') xstart = np.datetime64('2015-12-31T23:59:59.723518000', 'us') xend = np.datetime64('2016-01-03T00:00:00.276482000', 'us') curve = Curve((dates, [1, 2, 3])) img = aggregate(curve, width=2, height=2, x_range=(xstart, xend), dynamic=False) bounds = (np.datetime64('2015-12-31T23:59:59.723518'), 1.0, np.datetime64('2016-01-03T00:00:00.276482'), 3.0) dates = [np.datetime64('2016-01-01T11:59:59.861759000',), np.datetime64('2016-01-02T12:00:00.138241000')] expected = Image((dates, [1.5, 2.5], [[1, 0], [0, 2]]), datatype=['xarray'], bounds=bounds, vdims=Dimension('Count', nodata=0)) self.assertEqual(img, expected) def test_aggregate_ndoverlay_count_cat_datetimes_microsecond_timebase(self): dates = pd.date_range(start="2016-01-01", end="2016-01-03", freq='1D') xstart = np.datetime64('2015-12-31T23:59:59.723518000', 'us') xend = np.datetime64('2016-01-03T00:00:00.276482000', 'us') curve = Curve((dates, [1, 2, 3])) curve2 = Curve((dates, [3, 2, 1])) ndoverlay = NdOverlay({0: curve, 1: curve2}, 'Cat') imgs = aggregate(ndoverlay, aggregator=ds.count_cat('Cat'), width=2, height=2, x_range=(xstart, xend), dynamic=False) bounds = (np.datetime64('2015-12-31T23:59:59.723518'), 1.0, np.datetime64('2016-01-03T00:00:00.276482'), 3.0) dates = [np.datetime64('2016-01-01T11:59:59.861759000',), np.datetime64('2016-01-02T12:00:00.138241000')] expected = Image((dates, [1.5, 2.5], [[1, 0], [0, 2]]), datatype=['xarray'], bounds=bounds, vdims=Dimension('Count', nodata=0)) expected2 = Image((dates, [1.5, 2.5], [[0, 1], [1, 1]]), datatype=['xarray'], bounds=bounds, vdims=Dimension('Count', nodata=0)) self.assertEqual(imgs[0], expected) self.assertEqual(imgs[1], expected2) def test_aggregate_dt_xaxis_constant_yaxis(self): df = pd.DataFrame({'y': np.ones(100)}, index=pd.date_range('1980-01-01', periods=100, freq='1min')) img = rasterize(Curve(df), dynamic=False, width=3) xs = np.array(['1980-01-01T00:16:30.000000', '1980-01-01T00:49:30.000000', '1980-01-01T01:22:30.000000'], dtype='datetime64[us]') ys = np.array([]) bounds = (np.datetime64('1980-01-01T00:00:00.000000'), 1.0, np.datetime64('1980-01-01T01:39:00.000000'), 1.0) expected = Image((xs, ys, np.empty((0, 3))), ['index', 'y'], vdims=Dimension('Count', nodata=0), xdensity=1, ydensity=1, bounds=bounds) self.assertEqual(img, expected) 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=[Dimension('Count', nodata=0)]) img = aggregate(ndoverlay, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) self.assertEqual(img, expected) def test_aggregate_path(self): path = Path([[(0.2, 0.3), (0.4, 0.7)], [(0.4, 0.7), (0.8, 0.99)]]) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [2, 1]]), vdims=[Dimension('Count', nodata=0)]) img = aggregate(path, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) self.assertEqual(img, expected) def test_aggregate_contours_with_vdim(self): contours = Contours([[(0.2, 0.3, 1), (0.4, 0.7, 1)], [(0.4, 0.7, 2), (0.8, 0.99, 2)]], vdims='z') img = rasterize(contours, dynamic=False) self.assertEqual(img.vdims, ['z']) def test_aggregate_contours_without_vdim(self): contours = Contours([[(0.2, 0.3), (0.4, 0.7)], [(0.4, 0.7), (0.8, 0.99)]]) img = rasterize(contours, dynamic=False) self.assertEqual(img.vdims, [Dimension('Any', nodata=0)]) def test_aggregate_dframe_nan_path(self): path = Path([Path([[(0.2, 0.3), (0.4, 0.7)], [(0.4, 0.7), (0.8, 0.99)]]).dframe()]) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [2, 1]]), vdims=[Dimension('Count', nodata=0)]) img = aggregate(path, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) self.assertEqual(img, expected) def test_spikes_aggregate_count(self): spikes = Spikes([1, 2, 3]) agg = rasterize(spikes, width=5, dynamic=False, expand=False) expected = Image(np.array([[1, 0, 1, 0, 1]]), vdims=Dimension('Count', nodata=0), xdensity=2.5, ydensity=1, bounds=(1, 0, 3, 0.5)) self.assertEqual(agg, expected) def test_spikes_aggregate_count_dask(self): spikes = Spikes([1, 2, 3], datatype=['dask']) agg = rasterize(spikes, width=5, dynamic=False, expand=False) expected = Image(np.array([[1, 0, 1, 0, 1]]), vdims=Dimension('Count', nodata=0), xdensity=2.5, ydensity=1, bounds=(1, 0, 3, 0.5)) self.assertEqual(agg, expected) def test_spikes_aggregate_dt_count(self): spikes = Spikes([dt.datetime(2016, 1, 1), dt.datetime(2016, 1, 2), dt.datetime(2016, 1, 3)]) agg = rasterize(spikes, width=5, dynamic=False, expand=False) bounds = (np.datetime64('2016-01-01T00:00:00.000000'), 0, np.datetime64('2016-01-03T00:00:00.000000'), 0.5) expected = Image(np.array([[1, 0, 1, 0, 1]]), vdims=Dimension('Count', nodata=0), bounds=bounds) self.assertEqual(agg, expected) def test_spikes_aggregate_dt_count_dask(self): spikes = Spikes([dt.datetime(2016, 1, 1), dt.datetime(2016, 1, 2), dt.datetime(2016, 1, 3)], datatype=['dask']) agg = rasterize(spikes, width=5, dynamic=False, expand=False) bounds = (np.datetime64('2016-01-01T00:00:00.000000'), 0, np.datetime64('2016-01-03T00:00:00.000000'), 0.5) expected = Image(np.array([[1, 0, 1, 0, 1]]), vdims=Dimension('Count', nodata=0), bounds=bounds) self.assertEqual(agg, expected) def test_spikes_aggregate_spike_length(self): spikes = Spikes([1, 2, 3]) agg = rasterize(spikes, width=5, dynamic=False, expand=False, spike_length=7) expected = Image(np.array([[1, 0, 1, 0, 1]]), vdims=Dimension('Count', nodata=0), xdensity=2.5, ydensity=1, bounds=(1, 0, 3, 7.0)) self.assertEqual(agg, expected) def test_spikes_aggregate_with_height_count(self): spikes = Spikes([(1, 0.2), (2, 0.8), (3, 0.4)], vdims='y') agg = rasterize(spikes, width=5, height=5, y_range=(0, 1), dynamic=False) xs = [1.2, 1.6, 2.0, 2.4, 2.8] ys = [0.1, 0.3, 0.5, 0.7, 0.9] arr = np.array([ [1, 0, 1, 0, 1], [1, 0, 1, 0, 1], [0, 0, 1, 0, 1], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_spikes_aggregate_with_height_count_override(self): spikes = Spikes([(1, 0.2), (2, 0.8), (3, 0.4)], vdims='y') agg = rasterize(spikes, width=5, height=5, y_range=(0, 1), spike_length=0.3, dynamic=False) xs = [1.2, 1.6, 2.0, 2.4, 2.8] ys = [0.1, 0.3, 0.5, 0.7, 0.9] arr = np.array([[1, 0, 1, 0, 1], [1, 0, 1, 0, 1], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_rasterize_regrid_and_spikes_overlay(self): img = Image(([0.5, 1.5], [0.5, 1.5], [[0, 1], [2, 3]])) spikes = Spikes([(0.5, 0.2), (1.5, 0.8), ], vdims='y') expected_regrid = Image(([0.25, 0.75, 1.25, 1.75], [0.25, 0.75, 1.25, 1.75], [[0, 0, 1, 1], [0, 0, 1, 1], [2, 2, 3, 3], [2, 2, 3, 3]])) spikes_arr = np.array([[0, 1, 0, 1], [0, 1, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0]]) expected_spikes = Image(([0.25, 0.75, 1.25, 1.75], [0.25, 0.75, 1.25, 1.75], spikes_arr), vdims=Dimension('Count', nodata=0)) overlay = img * spikes agg = rasterize(overlay, width=4, height=4, x_range=(0, 2), y_range=(0, 2), spike_length=0.5, upsample=True, dynamic=False) self.assertEqual(agg.Image.I, expected_regrid) self.assertEqual(agg.Spikes.I, expected_spikes) def test_spikes_aggregate_with_height_count_dask(self): spikes = Spikes([(1, 0.2), (2, 0.8), (3, 0.4)], vdims='y', datatype=['dask']) agg = rasterize(spikes, width=5, height=5, y_range=(0, 1), dynamic=False) xs = [1.2, 1.6, 2.0, 2.4, 2.8] ys = [0.1, 0.3, 0.5, 0.7, 0.9] arr = np.array([ [1, 0, 1, 0, 1], [1, 0, 1, 0, 1], [0, 0, 1, 0, 1], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_spikes_aggregate_with_negative_height_count(self): spikes = Spikes([(1, -0.2), (2, -0.8), (3, -0.4)], vdims='y', datatype=['dask']) agg = rasterize(spikes, width=5, height=5, y_range=(-1, 0), dynamic=False) xs = [1.2, 1.6, 2.0, 2.4, 2.8] ys = [-0.9, -0.7, -0.5, -0.3, -0.1] arr = np.array([ [0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [0, 0, 1, 0, 1], [1, 0, 1, 0, 1] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_spikes_aggregate_with_positive_and_negative_height_count(self): spikes = Spikes([(1, -0.2), (2, 0.8), (3, -0.4)], vdims='y', datatype=['dask']) agg = rasterize(spikes, width=5, height=5, y_range=(-1, 1), dynamic=False) xs = [1.2, 1.6, 2.0, 2.4, 2.8] ys = [-0.8, -0.4, 0.0, 0.4, 0.8] arr = np.array([ [0, 0, 0, 0, 0], [0, 0, 0, 0, 1], [1, 0, 1, 0, 1], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_rectangles_aggregate_count(self): rects = Rectangles([(0, 0, 1, 2), (1, 1, 3, 2)]) agg = rasterize(rects, width=4, height=4, dynamic=False) xs = [0.375, 1.125, 1.875, 2.625] ys = [0.25, 0.75, 1.25, 1.75] arr = np.array([ [1, 1, 0, 0], [1, 1, 0, 0], [1, 2, 1, 1], [0, 0, 0, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_rectangles_aggregate_count_cat(self): rects = Rectangles([(0, 0, 1, 2, 'A'), (1, 1, 3, 2, 'B')], vdims=['cat']) agg = rasterize(rects, width=4, height=4, aggregator=ds.count_cat('cat'), dynamic=False) xs = [0.375, 1.125, 1.875, 2.625] ys = [0.25, 0.75, 1.25, 1.75] arr1 = np.array([ [1, 1, 0, 0], [1, 1, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0] ]) arr2 = np.array([ [0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 1], [0, 0, 0, 0] ]) expected1 = Image((xs, ys, arr1), vdims=Dimension('cat Count', nodata=0)) expected2 = Image((xs, ys, arr2), vdims=Dimension('cat Count', nodata=0)) expected = NdOverlay({'A': expected1, 'B': expected2}, kdims=['cat']) self.assertEqual(agg, expected) def test_rectangles_aggregate_sum(self): rects = Rectangles([(0, 0, 1, 2, 0.5), (1, 1, 3, 2, 1.5)], vdims=['value']) agg = rasterize(rects, width=4, height=4, aggregator='sum', dynamic=False) xs = [0.375, 1.125, 1.875, 2.625] ys = [0.25, 0.75, 1.25, 1.75] arr = np.array([ [0.5, 0.5, nan, nan], [0.5, 0.5, nan, nan], [0.5, 2. , 1.5, 1.5], [nan, nan, nan, nan] ]) expected = Image((xs, ys, arr), vdims='value') self.assertEqual(agg, expected) def test_rectangles_aggregate_dt_count(self): rects = Rectangles([ (0, dt.datetime(2016, 1, 2), 4, dt.datetime(2016, 1, 3)), (1, dt.datetime(2016, 1, 1), 2, dt.datetime(2016, 1, 5)) ]) agg = rasterize(rects, width=4, height=4, dynamic=False) xs = [0.5, 1.5, 2.5, 3.5] ys = [ np.datetime64('2016-01-01T12:00:00'), np.datetime64('2016-01-02T12:00:00'), np.datetime64('2016-01-03T12:00:00'), np.datetime64('2016-01-04T12:00:00') ] arr = np.array([ [0, 1, 1, 0], [1, 2, 2, 1], [0, 1, 1, 0], [0, 0, 0, 0] ]) bounds = (0.0, np.datetime64('2016-01-01T00:00:00'), 4.0, np.datetime64('2016-01-05T00:00:00')) expected = Image((xs, ys, arr), bounds=bounds, vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_segments_aggregate_count(self): segments = Segments([(0, 1, 4, 1), (1, 0, 1, 4)]) agg = rasterize(segments, width=4, height=4, dynamic=False) xs = [0.5, 1.5, 2.5, 3.5] ys = [0.5, 1.5, 2.5, 3.5] arr = np.array([ [0, 1, 0, 0], [1, 2, 1, 1], [0, 1, 0, 0], [0, 1, 0, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_segments_aggregate_sum(self, instance=False): segments = Segments([(0, 1, 4, 1, 2), (1, 0, 1, 4, 4)], vdims=['value']) if instance: agg = rasterize.instance( width=10, height=10, dynamic=False, aggregator='sum' )(segments, width=4, height=4) else: agg = rasterize( segments, width=4, height=4, dynamic=False, aggregator='sum' ) xs = [0.5, 1.5, 2.5, 3.5] ys = [0.5, 1.5, 2.5, 3.5] na = np.nan arr = np.array([ [na, 4, na, na], [2 , 6, 2 , 2 ], [na, 4, na, na], [na, 4, na, na] ]) expected = Image((xs, ys, arr), vdims='value') self.assertEqual(agg, expected) def test_segments_aggregate_sum_instance(self): self.test_segments_aggregate_sum(instance=True) def test_segments_aggregate_dt_count(self): segments = Segments([ (0, dt.datetime(2016, 1, 2), 4, dt.datetime(2016, 1, 2)), (1, dt.datetime(2016, 1, 1), 1, dt.datetime(2016, 1, 5)) ]) agg = rasterize(segments, width=4, height=4, dynamic=False) xs = [0.5, 1.5, 2.5, 3.5] ys = [ np.datetime64('2016-01-01T12:00:00'), np.datetime64('2016-01-02T12:00:00'), np.datetime64('2016-01-03T12:00:00'), np.datetime64('2016-01-04T12:00:00') ] arr = np.array([ [0, 1, 0, 0], [1, 2, 1, 1], [0, 1, 0, 0], [0, 1, 0, 0] ]) bounds = (0.0, np.datetime64('2016-01-01T00:00:00'), 4.0, np.datetime64('2016-01-05T00:00:00')) expected = Image((xs, ys, arr), bounds=bounds, vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_area_aggregate_simple_count(self): area = Area([1, 2, 1]) agg = rasterize(area, width=4, height=4, y_range=(0, 3), dynamic=False) xs = [0.25, 0.75, 1.25, 1.75] ys = [0.375, 1.125, 1.875, 2.625] arr = np.array([ [1, 1, 1, 1], [1, 1, 1, 1], [0, 1, 1, 0], [0, 0, 0, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_area_aggregate_negative_count(self): area = Area([-1, -2, -3]) agg = rasterize(area, width=4, height=4, y_range=(-3, 0), dynamic=False) xs = [0.25, 0.75, 1.25, 1.75] ys = [-2.625, -1.875, -1.125, -0.375] arr = np.array([ [0, 0, 0, 1], [0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_area_aggregate_crossover_count(self): area = Area([-1, 2, 3]) agg = rasterize(area, width=4, height=4, y_range=(-3, 3), dynamic=False) xs = [0.25, 0.75, 1.25, 1.75] ys = [-2.25, -0.75, 0.75, 2.25] arr = np.array([ [0, 0, 0, 0], [1, 0, 0, 0], [1, 1, 1, 1], [0, 0, 1, 1] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_spread_aggregate_symmetric_count(self): spread = Spread([(0, 1, 0.8), (1, 2, 0.3), (2, 3, 0.8)]) agg = rasterize(spread, width=4, height=4, dynamic=False) xs = [0.25, 0.75, 1.25, 1.75] ys = [0.65, 1.55, 2.45, 3.35] arr = np.array([ [0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_spread_aggregate_assymmetric_count(self): spread = Spread([(0, 1, 0.4, 0.8), (1, 2, 0.8, 0.4), (2, 3, 0.5, 1)], vdims=['y', 'pos', 'neg']) agg = rasterize(spread, width=4, height=4, dynamic=False) xs = [0.25, 0.75, 1.25, 1.75] ys = [0.6125, 1.4375, 2.2625, 3.0875] arr = np.array([ [0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 1, 0], [0, 0, 1, 1] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) def test_rgb_regrid_packed(self): coords = {'x': [1, 2], 'y': [1, 2], 'band': [0, 1, 2]} arr = np.array([ [[255, 10], [ 0, 30]], [[ 1, 0], [ 0, 0]], [[127, 0], [ 0, 68]], ]).T da = xr.DataArray(data=arr, dims=('x', 'y', 'band'), coords=coords) im = RGB(da, ['x', 'y']) agg = rasterize(im, width=3, height=3, dynamic=False, upsample=True) xs = [0.8333333, 1.5, 2.166666] ys = [0.8333333, 1.5, 2.166666] arr = np.array([ [[255, 255, 10], [255, 255, 10], [ 0, 0, 30]], [[ 1, 1, 0], [ 1, 1, 0], [ 0, 0, 0]], [[127, 127, 0], [127, 127, 0], [ 0, 0, 68]], ]).transpose((1, 2, 0)) expected = RGB((xs, ys, arr)) self.assertEqual(agg, expected) @spatialpandas_skip def test_line_rasterize(self): path = Path([[(0, 0), (1, 1), (2, 0)], [(0, 0), (0, 1)]], datatype=['spatialpandas']) agg = rasterize(path, width=4, height=4, dynamic=False) xs = [0.25, 0.75, 1.25, 1.75] ys = [0.125, 0.375, 0.625, 0.875] arr = np.array([ [2, 0, 0, 1], [1, 1, 0, 1], [1, 1, 1, 0], [1, 0, 1, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) @spatialpandas_skip def test_multi_line_rasterize(self): path = Path([{'x': [0, 1, 2, np.nan, 0, 0], 'y': [0, 1, 0, np.nan, 0, 1]}], datatype=['spatialpandas']) agg = rasterize(path, width=4, height=4, dynamic=False) xs = [0.25, 0.75, 1.25, 1.75] ys = [0.125, 0.375, 0.625, 0.875] arr = np.array([ [2, 0, 0, 1], [1, 1, 0, 1], [1, 1, 1, 0], [1, 0, 1, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) @spatialpandas_skip def test_ring_rasterize(self): path = Path([{'x': [0, 1, 2], 'y': [0, 1, 0], 'geom_type': 'Ring'}], datatype=['spatialpandas']) agg = rasterize(path, width=4, height=4, dynamic=False) xs = [0.25, 0.75, 1.25, 1.75] ys = [0.125, 0.375, 0.625, 0.875] arr = np.array([ [1, 1, 1, 1], [0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 1, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) @spatialpandas_skip def test_polygon_rasterize(self): poly = Polygons([ {'x': [0, 1, 2], 'y': [0, 1, 0], 'holes': [[[(1.6, 0.2), (1, 0.8), (0.4, 0.2)]]]} ]) agg = rasterize(poly, width=6, height=6, dynamic=False) xs = [0.166667, 0.5, 0.833333, 1.166667, 1.5, 1.833333] ys = [0.083333, 0.25, 0.416667, 0.583333, 0.75, 0.916667] arr = np.array([ [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) @spatialpandas_skip def test_polygon_rasterize_mean_agg(self): poly = Polygons([ {'x': [0, 1, 2], 'y': [0, 1, 0], 'z': 2.4}, {'x': [0, 0, 1], 'y': [0, 1, 1], 'z': 3.6} ], vdims='z') agg = rasterize(poly, width=4, height=4, dynamic=False, aggregator='mean') xs = [0.25, 0.75, 1.25, 1.75] ys = [0.125, 0.375, 0.625, 0.875] arr = np.array([ [ 2.4, 2.4, 2.4, 2.4], [ 3.6, 2.4, 2.4, np.nan], [ 3.6, 2.4, 2.4, np.nan], [ 3.6, 3.6, np.nan, np.nan]]) expected = Image((xs, ys, arr), vdims='z') self.assertEqual(agg, expected) @spatialpandas_skip def test_multi_poly_rasterize(self): poly = Polygons([{'x': [0, 1, 2, np.nan, 0, 0, 1], 'y': [0, 1, 0, np.nan, 0, 1, 1]}], datatype=['spatialpandas']) agg = rasterize(poly, width=4, height=4, dynamic=False) xs = [0.25, 0.75, 1.25, 1.75] ys = [0.125, 0.375, 0.625, 0.875] arr = np.array([ [1, 1, 1, 1], [1, 1, 1, 0], [1, 1, 1, 0], [1, 1, 0, 0] ]) expected = Image((xs, ys, arr), vdims=Dimension('Count', nodata=0)) self.assertEqual(agg, expected) class DatashaderCatAggregateTests(ComparisonTestCase): def setUp(self): if DATASHADER_VERSION < (0, 11, 0): raise SkipTest('Regridding operations require datashader>=0.11.0') def test_aggregate_points_categorical(self): points = Points([(0.2, 0.3, 'A'), (0.4, 0.7, 'B'), (0, 0.99, 'C')], vdims='z') img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2, aggregator=ds.by('z', ds.count())) x = np.array([0.25, 0.75]) y = np.array([0.25, 0.75]) a = np.array([[1, 0], [0, 0]]) b = np.array([[0, 1], [0, 0]]) c = np.array([[0, 1], [0, 0]]) xrds = xr.Dataset( coords={"x": x, "y": y}, data_vars={"a": (("x", "y"), a), "b": (("x", "y"), b), "c": (("x", "y"), c)}, ) expected = ImageStack(xrds, kdims=["x", "y"], vdims=["a", "b", "c"]) actual = img.data assert (expected.data.to_array("z").values == actual.T.values).all() def test_aggregate_points_categorical_one_category(self): points = Points([(0.2, 0.3, 'A'), (0.4, 0.7, 'A'), (0, 0.99, 'A')], vdims='z') img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2, aggregator=ds.by('z', ds.count())) x = np.array([0.25, 0.75]) y = np.array([0.25, 0.75]) a = np.array([[1, 2], [0, 0]]) xrds = xr.DataArray( a, dims=('x', 'y'), coords={"x": x, "y": y} ) expected = ImageStack(xrds, kdims=["x", "y"], vdims=["a"]) actual = img.data assert (expected.data.to_array("z").values == actual.T.values).all() def test_aggregate_points_categorical_mean(self): points = Points([(0.2, 0.3, 'A', 0.1), (0.4, 0.7, 'B', 0.2), (0, 0.99, 'C', 0.3)], vdims=['cat', 'z']) img = aggregate(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2, aggregator=ds.by('cat', ds.mean('z'))) x = np.array([0.25, 0.75]) y = np.array([0.25, 0.75]) a = np.array([[0.1, np.nan], [np.nan, np.nan]]) b = np.array([[np.nan, 0.2], [np.nan, np.nan]]) c = np.array([[np.nan, 0.3], [np.nan, np.nan]]) xrds = xr.Dataset( coords={"x": x, "y": y}, data_vars={"a": (("x", "y"), a), "b": (("x", "y"), b), "c": (("x", "y"), c)}, ) expected = ImageStack(xrds, kdims=["x", "y"], vdims=["a", "b", "c"]) actual = img.data np.testing.assert_equal(expected.data.to_array("z").values, actual.T.values) class DatashaderShadeTests(ComparisonTestCase): def test_shade_categorical_images_xarray(self): xs, ys = [0.25, 0.75], [0.25, 0.75] data = NdOverlay({'A': Image((xs, ys, np.array([[1, 0], [0, 0]], dtype='u4')), datatype=['xarray'], vdims=Dimension('z Count', nodata=0)), 'B': Image((xs, ys, np.array([[0, 0], [1, 0]], dtype='u4')), datatype=['xarray'], vdims=Dimension('z Count', nodata=0)), 'C': Image((xs, ys, np.array([[0, 0], [1, 0]], dtype='u4')), datatype=['xarray'], vdims=Dimension('z Count', nodata=0))}, kdims=['z']) shaded = shade(data, rescale_discrete_levels=False) r = [[228, 120], [66, 120]] g = [[26, 109], [150, 109]] b = [[28, 95], [129, 95]] a = [[40, 0], [255, 0]] expected = RGB((xs, ys, r, g, b, a), datatype=['grid'], vdims=[*RGB.vdims, Dimension('A', range=(0, 1))]) self.assertEqual(shaded, expected) def test_shade_categorical_images_grid(self): xs, ys = [0.25, 0.75], [0.25, 0.75] data = NdOverlay({'A': Image((xs, ys, np.array([[1, 0], [0, 0]], dtype='u4')), datatype=['grid'], vdims=Dimension('z Count', nodata=0)), 'B': Image((xs, ys, np.array([[0, 0], [1, 0]], dtype='u4')), datatype=['grid'], vdims=Dimension('z Count', nodata=0)), 'C': Image((xs, ys, np.array([[0, 0], [1, 0]], dtype='u4')), datatype=['grid'], vdims=Dimension('z Count', nodata=0))}, kdims=['z']) shaded = shade(data, rescale_discrete_levels=False) r = [[228, 120], [66, 120]] g = [[26, 109], [150, 109]] b = [[28, 95], [129, 95]] a = [[40, 0], [255, 0]] expected = RGB((xs, ys, r, g, b, a), datatype=['grid'], vdims=[*RGB.vdims, Dimension('A', range=(0, 1))]) self.assertEqual(shaded, expected) def test_shade_dt_xaxis_constant_yaxis(self): df = pd.DataFrame({'y': np.ones(100)}, index=pd.date_range('1980-01-01', periods=100, freq='1min')) rgb = shade(rasterize(Curve(df), dynamic=False, width=3)) xs = np.array(['1980-01-01T00:16:30.000000', '1980-01-01T00:49:30.000000', '1980-01-01T01:22:30.000000'], dtype='datetime64[us]') ys = np.array([]) bounds = (np.datetime64('1980-01-01T00:00:00.000000'), 1.0, np.datetime64('1980-01-01T01:39:00.000000'), 1.0) expected = RGB((xs, ys, np.empty((0, 3, 4))), ['index', 'y'], xdensity=1, ydensity=1, bounds=bounds) self.assertEqual(rgb, expected) class DatashaderRegridTests(ComparisonTestCase): """ Tests for datashader aggregation """ def test_regrid_mean(self): img = Image((range(10), range(5), np.arange(10) * np.arange(5)[np.newaxis].T)) regridded = regrid(img, width=2, height=2, dynamic=False) expected = Image(([2., 7.], [0.75, 3.25], [[1, 5], [6, 22]])) self.assertEqual(regridded, expected) def test_regrid_mean_xarray_transposed(self): img = Image((range(10), range(5), np.arange(10) * np.arange(5)[np.newaxis].T), datatype=['xarray']) img.data = img.data.transpose() regridded = regrid(img, width=2, height=2, dynamic=False) expected = Image(([2., 7.], [0.75, 3.25], [[1, 5], [6, 22]])) self.assertEqual(regridded, expected) def test_regrid_rgb_mean(self): arr = (np.arange(10) * np.arange(5)[np.newaxis].T).astype('float64') rgb = RGB((range(10), range(5), arr, arr*2, arr*2)) regridded = regrid(rgb, width=2, height=2, dynamic=False) new_arr = np.array([[1.6, 5.6], [6.4, 22.4]]) expected = RGB(([2., 7.], [0.75, 3.25], new_arr, new_arr*2, new_arr*2), datatype=['xarray']) self.assertEqual(regridded, expected) def test_regrid_max(self): img = Image((range(10), range(5), np.arange(10) * np.arange(5)[np.newaxis].T)) regridded = regrid(img, aggregator='max', width=2, height=2, dynamic=False) expected = Image(([2., 7.], [0.75, 3.25], [[8, 18], [16, 36]])) self.assertEqual(regridded, expected) def test_regrid_upsampling(self): img = Image(([0.5, 1.5], [0.5, 1.5], [[0, 1], [2, 3]])) regridded = regrid(img, width=4, height=4, upsample=True, dynamic=False) expected = Image(([0.25, 0.75, 1.25, 1.75], [0.25, 0.75, 1.25, 1.75], [[0, 0, 1, 1], [0, 0, 1, 1], [2, 2, 3, 3], [2, 2, 3, 3]])) self.assertEqual(regridded, expected) def test_regrid_upsampling_linear(self): img = Image(([0.5, 1.5], [0.5, 1.5], [[0, 1], [2, 3]])) regridded = regrid(img, width=4, height=4, upsample=True, interpolation='linear', dynamic=False) expected = Image(([0.25, 0.75, 1.25, 1.75], [0.25, 0.75, 1.25, 1.75], [[0, 0, 0, 1], [0, 1, 1, 1], [1, 1, 2, 2], [2, 2, 2, 3]])) self.assertEqual(regridded, expected) def test_regrid_disabled_upsampling(self): img = Image(([0.5, 1.5], [0.5, 1.5], [[0, 1], [2, 3]])) regridded = regrid(img, width=3, height=3, dynamic=False, upsample=False) self.assertEqual(regridded, img) def test_regrid_disabled_expand(self): img = Image(([0.5, 1.5], [0.5, 1.5], [[0., 1.], [2., 3.]])) regridded = regrid(img, width=2, height=2, x_range=(-2, 4), y_range=(-2, 4), expand=False, dynamic=False) self.assertEqual(regridded, img) def test_regrid_zero_range(self): ls = np.linspace(0, 10, 200) xx, yy = np.meshgrid(ls, ls) img = Image(np.sin(xx)*np.cos(yy), bounds=(0, 0, 1, 1)) regridded = regrid(img, x_range=(-1, -0.5), y_range=(-1, -0.5), dynamic=False) expected = Image(np.zeros((0, 0)), bounds=(0, 0, 0, 0), xdensity=1, ydensity=1) self.assertEqual(regridded, expected) class DatashaderRasterizeTests(ComparisonTestCase): """ Tests for datashader aggregation """ def setUp(self): if DATASHADER_VERSION <= (0, 6, 4): raise SkipTest('Regridding operations require datashader>=0.7.0') self.simplexes = [(0, 1, 2), (3, 2, 1)] self.vertices = [(0., 0.), (0., 1.), (1., 0), (1, 1)] self.simplexes_vdim = [(0, 1, 2, 0.5), (3, 2, 1, 1.5)] self.vertices_vdim = [(0., 0., 1), (0., 1., 2), (1., 0, 3), (1, 1, 4)] def test_rasterize_trimesh_no_vdims(self): trimesh = TriMesh((self.simplexes, self.vertices)) img = rasterize(trimesh, width=3, height=3, dynamic=False) image = Image(np.array([[True, True, True], [True, True, True], [True, True, True]]), bounds=(0, 0, 1, 1), vdims=Dimension('Any', nodata=0)) self.assertEqual(img, image) def test_rasterize_trimesh_no_vdims_zero_range(self): trimesh = TriMesh((self.simplexes, self.vertices)) img = rasterize(trimesh, height=2, x_range=(0, 0), dynamic=False) image = Image(([], [0.25, 0.75], np.zeros((2, 0))), bounds=(0, 0, 0, 1), xdensity=1, vdims=Dimension('Any', nodata=0)) self.assertEqual(img, image) def test_rasterize_trimesh_with_vdims_as_wireframe(self): trimesh = TriMesh((self.simplexes_vdim, self.vertices), vdims=['z']) img = rasterize(trimesh, width=3, height=3, aggregator='any', interpolation=None, dynamic=False) array = np.array([ [True, True, True], [True, True, True], [True, True, True] ]) image = Image(array, bounds=(0, 0, 1, 1), vdims=Dimension('Any', nodata=0)) self.assertEqual(img, image) def test_rasterize_trimesh(self): trimesh = TriMesh((self.simplexes_vdim, self.vertices), vdims=['z']) img = rasterize(trimesh, width=3, height=3, dynamic=False) array = np.array([ [0.5, 1.5, 1.5], [0.5, 0.5, 1.5], [0.5, 0.5, 0.5] ]) image = Image(array, bounds=(0, 0, 1, 1)) self.assertEqual(img, image) def test_rasterize_pandas_trimesh_implicit_nodes(self): simplex_df = pd.DataFrame(self.simplexes, columns=['v0', 'v1', 'v2']) vertex_df = pd.DataFrame(self.vertices_vdim, columns=['x', 'y', 'z']) trimesh = TriMesh((simplex_df, vertex_df)) img = rasterize(trimesh, width=3, height=3, dynamic=False) array = np.array([ [2.166667, 2.833333, 3.5 ], [1.833333, 2.5, 3.166667], [1.5, 2.166667, 2.833333] ]) image = Image(array, bounds=(0, 0, 1, 1)) self.assertEqual(img, image) def test_rasterize_dask_trimesh_implicit_nodes(self): simplex_df = pd.DataFrame(self.simplexes, columns=['v0', 'v1', 'v2']) vertex_df = pd.DataFrame(self.vertices_vdim, columns=['x', 'y', 'z']) simplex_ddf = dd.from_pandas(simplex_df, npartitions=2) vertex_ddf = dd.from_pandas(vertex_df, npartitions=2) trimesh = TriMesh((simplex_ddf, vertex_ddf)) ri = rasterize.instance() img = ri(trimesh, width=3, height=3, dynamic=False, precompute=True) cache = ri._precomputed self.assertEqual(len(cache), 1) self.assertIn(trimesh._plot_id, cache) self.assertIsInstance(cache[trimesh._plot_id]['mesh'], dd.DataFrame) array = np.array([ [2.166667, 2.833333, 3.5 ], [1.833333, 2.5, 3.166667], [1.5, 2.166667, 2.833333] ]) image = Image(array, bounds=(0, 0, 1, 1)) self.assertEqual(img, image) def test_rasterize_dask_trimesh(self): simplex_df = pd.DataFrame(self.simplexes_vdim, columns=['v0', 'v1', 'v2', 'z']) vertex_df = pd.DataFrame(self.vertices, columns=['x', 'y']) simplex_ddf = dd.from_pandas(simplex_df, npartitions=2) vertex_ddf = dd.from_pandas(vertex_df, npartitions=2) tri_nodes = Nodes(vertex_ddf, ['x', 'y', 'index']) trimesh = TriMesh((simplex_ddf, tri_nodes), vdims=['z']) ri = rasterize.instance() img = ri(trimesh, width=3, height=3, dynamic=False, precompute=True) cache = ri._precomputed self.assertEqual(len(cache), 1) self.assertIn(trimesh._plot_id, cache) self.assertIsInstance(cache[trimesh._plot_id]['mesh'], dd.DataFrame) array = np.array([ [0.5, 1.5, 1.5], [0.5, 0.5, 1.5], [0.5, 0.5, 0.5] ]) image = Image(array, bounds=(0, 0, 1, 1)) self.assertEqual(img, image) def test_rasterize_dask_trimesh_with_node_vdims(self): simplex_df = pd.DataFrame(self.simplexes, columns=['v0', 'v1', 'v2']) vertex_df = pd.DataFrame(self.vertices_vdim, columns=['x', 'y', 'z']) simplex_ddf = dd.from_pandas(simplex_df, npartitions=2) vertex_ddf = dd.from_pandas(vertex_df, npartitions=2) tri_nodes = Nodes(vertex_ddf, ['x', 'y', 'index'], ['z']) trimesh = TriMesh((simplex_ddf, tri_nodes)) ri = rasterize.instance() img = ri(trimesh, width=3, height=3, dynamic=False, precompute=True) cache = ri._precomputed self.assertEqual(len(cache), 1) self.assertIn(trimesh._plot_id, cache) self.assertIsInstance(cache[trimesh._plot_id]['mesh'], dd.DataFrame) array = np.array([ [2.166667, 2.833333, 3.5 ], [1.833333, 2.5, 3.166667], [1.5, 2.166667, 2.833333] ]) image = Image(array, bounds=(0, 0, 1, 1)) self.assertEqual(img, image) def test_rasterize_trimesh_node_vdim_precedence(self): nodes = Points(self.vertices_vdim, vdims=['node_z']) trimesh = TriMesh((self.simplexes_vdim, nodes), vdims=['z']) img = rasterize(trimesh, width=3, height=3, dynamic=False) array = np.array([ [2.166667, 2.833333, 3.5 ], [1.833333, 2.5, 3.166667], [1.5, 2.166667, 2.833333] ]) image = Image(array, bounds=(0, 0, 1, 1), vdims='node_z') self.assertEqual(img, image) def test_rasterize_trimesh_node_explicit_vdim(self): nodes = Points(self.vertices_vdim, vdims=['node_z']) trimesh = TriMesh((self.simplexes_vdim, nodes), vdims=['z']) img = rasterize(trimesh, width=3, height=3, dynamic=False, aggregator=ds.mean('z')) array = np.array([ [0.5, 1.5, 1.5], [0.5, 0.5, 1.5], [0.5, 0.5, 0.5] ]) image = Image(array, bounds=(0, 0, 1, 1)) self.assertEqual(img, image) def test_rasterize_trimesh_zero_range(self): trimesh = TriMesh((self.simplexes_vdim, self.vertices), vdims=['z']) img = rasterize(trimesh, x_range=(0, 0), height=2, dynamic=False) image = Image(([], [0.25, 0.75], np.zeros((2, 0))), bounds=(0, 0, 0, 1), xdensity=1) self.assertEqual(img, image) def test_rasterize_trimesh_vertex_vdims(self): simplices = [(0, 1, 2), (3, 2, 1)] vertices = [(0., 0., 1), (0., 1., 2), (1., 0., 3), (1., 1., 4)] trimesh = TriMesh((simplices, Points(vertices, vdims='z'))) img = rasterize(trimesh, width=3, height=3, dynamic=False) array = np.array([ [2.166667, 2.833333, 3.5 ], [1.833333, 2.5, 3.166667], [1.5, 2.166667, 2.833333] ]) image = Image(array, bounds=(0, 0, 1, 1), vdims='z') self.assertEqual(img, image) def test_rasterize_trimesh_ds_aggregator(self): trimesh = TriMesh((self.simplexes_vdim, self.vertices), vdims=['z']) img = rasterize(trimesh, width=3, height=3, dynamic=False, aggregator=ds.mean('z')) array = np.array([ [0.5, 1.5, 1.5], [0.5, 0.5, 1.5], [0.5, 0.5, 0.5] ]) image = Image(array, bounds=(0, 0, 1, 1)) self.assertEqual(img, image) def test_rasterize_trimesh_string_aggregator(self): trimesh = TriMesh((self.simplexes_vdim, self.vertices), vdims=['z']) img = rasterize(trimesh, width=3, height=3, dynamic=False, aggregator='mean') array = np.array([ [0.5, 1.5, 1.5], [0.5, 0.5, 1.5], [0.5, 0.5, 0.5] ]) image = Image(array, bounds=(0, 0, 1, 1)) self.assertEqual(img, image) def test_rasterize_quadmesh(self): qmesh = QuadMesh(([0, 1], [0, 1], np.array([[0, 1], [2, 3]]))) img = rasterize(qmesh, width=3, height=3, dynamic=False, aggregator=ds.mean('z')) image = Image(np.array([[2, 3, 3], [2, 3, 3], [0, 1, 1]]), bounds=(-.5, -.5, 1.5, 1.5)) self.assertEqual(img, image) def test_rasterize_quadmesh_string_aggregator(self): qmesh = QuadMesh(([0, 1], [0, 1], np.array([[0, 1], [2, 3]]))) img = rasterize(qmesh, width=3, height=3, dynamic=False, aggregator='mean') image = Image(np.array([[2, 3, 3], [2, 3, 3], [0, 1, 1]]), bounds=(-.5, -.5, 1.5, 1.5)) self.assertEqual(img, image) def test_rasterize_points(self): points = Points([(0.2, 0.3), (0.4, 0.7), (0, 0.99)]) img = rasterize(points, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [2, 0]]), vdims=[Dimension('Count', nodata=0)]) self.assertEqual(img, expected) def test_rasterize_curve(self): curve = Curve([(0.2, 0.3), (0.4, 0.7), (0.8, 0.99)]) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [1, 1]]), vdims=[Dimension('Count', nodata=0)]) img = rasterize(curve, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) self.assertEqual(img, expected) def test_rasterize_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=[Dimension('Count', nodata=0)]) img = rasterize(ndoverlay, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) self.assertEqual(img, expected) def test_rasterize_path(self): path = Path([[(0.2, 0.3), (0.4, 0.7)], [(0.4, 0.7), (0.8, 0.99)]]) expected = Image(([0.25, 0.75], [0.25, 0.75], [[1, 0], [2, 1]]), vdims=[Dimension('Count', nodata=0)]) img = rasterize(path, dynamic=False, x_range=(0, 1), y_range=(0, 1), width=2, height=2) self.assertEqual(img, expected) def test_rasterize_image(self): img = Image((range(10), range(5), np.arange(10) * np.arange(5)[np.newaxis].T)) regridded = regrid(img, width=2, height=2, dynamic=False) expected = Image(([2., 7.], [0.75, 3.25], [[1, 5], [6, 22]])) self.assertEqual(regridded, expected) def test_rasterize_image_string_aggregator(self): img = Image((range(10), range(5), np.arange(10) * np.arange(5)[np.newaxis].T)) regridded = regrid(img, width=2, height=2, dynamic=False, aggregator='mean') expected = Image(([2., 7.], [0.75, 3.25], [[1, 5], [6, 22]])) self.assertEqual(regridded, expected) def test_rasterize_image_expand_default(self): # Should use expand=False by default assert not regrid.expand data = np.arange(100.0).reshape(10, 10) c = np.arange(10.0) da = xr.DataArray(data, coords=dict(x=c, y=c)) rast_input = dict(x_range=(-1, 10), y_range=(-1, 10), precompute=True, dynamic=False) img = rasterize(Image(da), **rast_input) output = img.data["z"].to_numpy() np.testing.assert_array_equal(output, data.T) assert not np.isnan(output).any() # Setting expand=True with the {x,y}_ranges will expand the data with nan's img = rasterize(Image(da), expand=True, **rast_input) output = img.data["z"].to_numpy() assert np.isnan(output).any() def test_rasterize_apply_when_instance_with_line_width(self): df = pd.DataFrame( np.random.multivariate_normal( (0, 0), [[0.1, 0.1], [0.1, 1.0]], (100,)) ) df.columns = ["a", "b"] curve = Curve(df, kdims=["a"], vdims=["b"]) # line_width is not a parameter custom_rasterize = rasterize.instance(line_width=2) assert {'line_width': 2} == custom_rasterize._rasterize__instance_kwargs output = apply_when( curve, operation=custom_rasterize, predicate=lambda x: len(x) > 10 ) render(output, "bokeh") assert isinstance(output, DynamicMap) overlay = output.items()[0][1] assert isinstance(overlay, Overlay) assert len(overlay) == 2 def test_rasterize_path_empty_string_as_cat_sep(self): # https://github.com/holoviz/holoviews/issues/6326 df = pd.DataFrame({ 'x': [1, 1, np.nan, 3, 3, np.nan], 'y': [0, 1, np.nan, 0, 1, np.nan], # Empty strings on the sep rows. 'cat': ['a', 'a', '', 'b', 'b', ''], }) path = Path(df, ['x', 'y']) rasterized = rasterize( path, aggregator=ds.count_cat('cat'), dynamic=False, width=4, height=4, pixel_ratio=1, ) expected = xr.DataArray( coords={ "y": [0.125, 0.375, 0.625, 0.875], "x": [1.25, 1.75, 2.25, 2.75], "cat": ["a", "b"], }, data=4 * [[[1, 0], [0, 0], [0, 0], [0, 1]]] ) xr.testing.assert_equal(rasterized.data, expected) @pytest.mark.parametrize("agg_input_fn,index_col", ( [ds.first, [311, 433, 309, 482]], [ds.last, [491, 483, 417, 482]], [ds.min, [311, 433, 309, 482]], [ds.max, [404, 433, 417, 482]], ) ) def test_rasterize_where_agg_no_column(point_plot, agg_input_fn, index_col): agg_fn = ds.where(agg_input_fn("val")) rast_input = dict(dynamic=False, x_range=(-1, 1), y_range=(-1, 1), width=2, height=2) img = rasterize(point_plot, aggregator=agg_fn, **rast_input) assert list(img.data) == ["__index__", "s", "val", "cat"] assert list(img.vdims) == ["val", "s", "cat"] # val first and no index # N=100 in point_data is chosen to have a big enough sample size # so that the index are not the same for the different agg_input_fn np.testing.assert_array_equal(img.data["__index__"].data.flatten(), index_col) img_simple = rasterize(point_plot, aggregator=agg_input_fn("val"), **rast_input) np.testing.assert_array_equal(img_simple["val"], img["val"]) @pytest.mark.parametrize("agg_input_fn", (ds.first, ds.last, ds.min, ds.max)) def test_rasterize_where_agg_with_column(point_plot, agg_input_fn): agg_fn = ds.where(agg_input_fn("val"), "s") rast_input = dict(dynamic=False, x_range=(-1, 1), y_range=(-1, 1), width=2, height=2) img = rasterize(point_plot, aggregator=agg_fn, **rast_input) assert list(img.data) == ["s"] img_no_column = rasterize(point_plot, aggregator=ds.where(agg_input_fn("val")), **rast_input) np.testing.assert_array_equal(img["s"], img_no_column["s"]) def test_rasterize_summerize(point_plot): agg_fn_count, agg_fn_first = ds.count(), ds.first("val") agg_fn = ds.summary(count=agg_fn_count, first=agg_fn_first) rast_input = dict(dynamic=False, x_range=(-1, 1), y_range=(-1, 1), width=2, height=2) img_sum = rasterize(point_plot, aggregator=agg_fn, **rast_input) img_count = rasterize(point_plot, aggregator=agg_fn_count, **rast_input) img_first = rasterize(point_plot, aggregator=agg_fn_first, **rast_input) np.testing.assert_array_equal(img_sum["first"], img_first["val"]) # Count has special handling in AggregationOperation which sets nodata=0 # this is not done for count in summary. np.testing.assert_array_equal(img_sum["count"], np.nan_to_num(img_count["Count"])) @pytest.mark.parametrize("sel_fn", (ds.first, ds.last, ds.min, ds.max)) def test_rasterize_selector(point_plot, sel_fn): rast_input = dict(dynamic=False, x_range=(-1, 1), y_range=(-1, 1), width=2, height=2) img = rasterize(point_plot, selector=sel_fn("val"), **rast_input) # Count is from the aggregator assert list(img.data) == ["Count", "__index__", "s", "val", "cat"] assert list(img.vdims) == ["Count", "s", "val", "cat"] # no index # The output for the selector should be equal to the output for the aggregator using # ds.where img_agg = rasterize(point_plot, aggregator=ds.where(sel_fn("val")), **rast_input) for c in ["s", "val", "cat"]: np.testing.assert_array_equal(img[c], img_agg[c]) # Checking the count is also the same img_count = rasterize(point_plot, **rast_input) np.testing.assert_array_equal(img["Count"], img_count["Count"]) def test_rasterize_with_datetime_column(): n = 4 df = pd.DataFrame({ "x": np.random.uniform(-180, 180, n), "y": np.random.uniform(-90, 90, n), "Timestamp": pd.date_range(start="2023-01-01", periods=n, freq="D"), "Value": np.random.rand(n) * 100, }) point_plot = Points(df) rast_input = dict(dynamic=False, x_range=(-1, 1), y_range=(-1, 1), width=2, height=2) img_agg = rasterize(point_plot, selector=ds.first("Value"), **rast_input) assert img_agg["Timestamp"].dtype == np.dtype("datetime64[ns]") class DatashaderSpreadTests(ComparisonTestCase): def test_spread_rgb_1px(self): arr = np.array([[[0, 0, 0], [0, 1, 1], [0, 1, 1]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]]], dtype=np.uint8).T*255 spreaded = spread(RGB(arr)) arr = np.array([[[0, 0, 1], [0, 0, 1], [0, 0, 1]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[1, 1, 1], [1, 1, 1], [1, 1, 1]]], dtype=np.uint8).T*255 self.assertEqual(spreaded, RGB(arr)) def test_spread_img_1px(self): if DATASHADER_VERSION < (0, 12, 0): raise SkipTest('Datashader does not support DataArray yet') arr = np.array([[0, 0, 0], [0, 0, 0], [1, 1, 1]]).T spreaded = spread(Image(arr)) arr = np.array([[0, 0, 0], [2, 3, 2], [2, 3, 2]]).T self.assertEqual(spreaded, Image(arr)) class DatashaderStackTests(ComparisonTestCase): def setUp(self): self.rgb1_arr = np.array([[[0, 1], [1, 0]], [[1, 0], [0, 1]], [[0, 0], [0, 0]]], dtype=np.uint8).T*255 self.rgb2_arr = np.array([[[0, 0], [0, 0]], [[0, 0], [0, 0]], [[1, 0], [0, 1]]], dtype=np.uint8).T*255 self.rgb1 = RGB(self.rgb1_arr) self.rgb2 = RGB(self.rgb2_arr) def test_stack_add_compositor(self): combined = stack(self.rgb1*self.rgb2, compositor='add') arr = np.array([[[0, 255, 255], [255,0, 0]], [[255, 0, 0], [0, 255, 255]]], dtype=np.uint8) expected = RGB(arr) self.assertEqual(combined, expected) def test_stack_over_compositor(self): combined = stack(self.rgb1*self.rgb2, compositor='over') self.assertEqual(combined, self.rgb2) def test_stack_over_compositor_reverse(self): combined = stack(self.rgb2*self.rgb1, compositor='over') self.assertEqual(combined, self.rgb1) def test_stack_saturate_compositor(self): combined = stack(self.rgb1*self.rgb2, compositor='saturate') self.assertEqual(combined, self.rgb1) def test_stack_saturate_compositor_reverse(self): combined = stack(self.rgb2*self.rgb1, compositor='saturate') self.assertEqual(combined, self.rgb2) class GraphBundlingTests(ComparisonTestCase): def setUp(self): if DATASHADER_VERSION <= (0, 7, 0): raise SkipTest('Regridding operations require datashader>=0.7.0') self.source = np.arange(8) self.target = np.zeros(8) self.graph = Graph(((self.source, self.target),)) def test_directly_connect_paths(self): direct = directly_connect_edges(self.graph)._split_edgepaths self.assertEqual(direct, self.graph.edgepaths) class InspectorTests(ComparisonTestCase): """ Tests for inspector operations """ def setUp(self): points = Points([(0.2, 0.3), (0.4, 0.7), (0, 0.99)]) self.pntsimg = rasterize( points, dynamic=False, height=4, width=4, x_range=(0, 1), y_range=(0, 1) ) date_pts = Points([ (np.datetime64('2024-09-25 11:00'), 0.3), (np.datetime64('2024-09-25 11:01'), 0.7), (np.datetime64('2024-09-25 11:04'), 0.99)]) self.datesimg = rasterize( date_pts, dynamic=False, height=4, width=4, x_range=(np.datetime64('2024-09-25 11:00'), np.datetime64('2024-09-25 11:04')), y_range=(0, 1) ) if spatialpandas is None: return xs1, xs2, ys1, ys2 = [1, 2, 3], [6, 7, 3], [2, 0, 7], [7, 5, 2] holes = [ [[(1.5, 2), (2, 3), (1.6, 1.6)], [(2.1, 4.5), (2.5, 5), (2.3, 3.5)]],] polydata = [{'x': xs1, 'y': ys1, 'holes': holes, 'z': 1}, {'x': xs2, 'y': ys2, 'holes': [[]], 'z': 2}] self.polysrgb = datashade(Polygons(polydata, vdims=['z'], datatype=['spatialpandas']), x_range=(0, 7), y_range=(0, 7), dynamic=False) def tearDown(self): Tap.x, Tap.y = None, None def test_inspect_points_or_polygons(self): if spatialpandas is None: raise SkipTest('Polygon inspect tests require spatialpandas') polys = inspect(self.polysrgb, max_indicators=3, dynamic=False, pixels=1, x=6, y=5) self.assertEqual(polys, Polygons([{'x': [6, 3, 7], 'y': [7, 2, 5], 'z': 2}], vdims='z')) points = inspect(self.pntsimg, max_indicators=3, dynamic=False, pixels=1, x=-0.1, y=-0.1) self.assertEqual(points.dimension_values('x'), np.array([])) self.assertEqual(points.dimension_values('y'), np.array([])) def test_points_inspection_1px_mask(self): points = inspect_points(self.pntsimg, max_indicators=3, dynamic=False, pixels=1, x=-0.1, y=-0.1) self.assertEqual(points.dimension_values('x'), np.array([])) self.assertEqual(points.dimension_values('y'), np.array([])) def test_points_inspection_2px_mask(self): points = inspect_points(self.pntsimg, max_indicators=3, dynamic=False, pixels=2, x=-0.1, y=-0.1) self.assertEqual(points.dimension_values('x'), np.array([0.2])) self.assertEqual(points.dimension_values('y'), np.array([0.3])) def test_points_inspection_4px_mask(self): points = inspect_points(self.pntsimg, max_indicators=3, dynamic=False, pixels=4, x=-0.1, y=-0.1) self.assertEqual(points.dimension_values('x'), np.array([0.2, 0.4])) self.assertEqual(points.dimension_values('y'), np.array([0.3, 0.7])) def test_points_inspection_5px_mask(self): points = inspect_points(self.pntsimg, max_indicators=3, dynamic=False, pixels=5, x=-0.1, y=-0.1) self.assertEqual(points.dimension_values('x'), np.array([0.2, 0.4, 0])) self.assertEqual(points.dimension_values('y'), np.array([0.3, 0.7, 0.99])) def test_inspection_5px_mask_points_df(self): inspector = inspect.instance(max_indicators=3, dynamic=False, pixels=5, x=-0.1, y=-0.1) inspector(self.pntsimg) self.assertEqual(list(inspector.hits['x']),[0.2,0.4,0.0]) self.assertEqual(list(inspector.hits['y']),[0.3,0.7,0.99]) def test_points_inspection_dict_streams(self): Tap.x, Tap.y = 0.4, 0.7 points = inspect_points(self.pntsimg, max_indicators=3, dynamic=True, pixels=1, streams=dict(x=Tap.param.x, y=Tap.param.y)) self.assertEqual(len(points.streams), 1) self.assertEqual(isinstance(points.streams[0], Tap), True) self.assertEqual(points.streams[0].x, 0.4) self.assertEqual(points.streams[0].y, 0.7) def test_points_inspection_dict_streams_instance(self): Tap.x, Tap.y = 0.2, 0.3 inspector = inspect_points.instance(max_indicators=3, dynamic=True, pixels=1, streams=dict(x=Tap.param.x, y=Tap.param.y)) points = inspector(self.pntsimg) self.assertEqual(len(points.streams), 1) self.assertEqual(isinstance(points.streams[0], Tap), True) self.assertEqual(points.streams[0].x, 0.2) self.assertEqual(points.streams[0].y, 0.3) def test_points_with_dates_inspection_1px_mask(self): points = inspect_points(self.datesimg, max_indicators=3, dynamic=False, pixels=1, x=np.datetime64('2024-09-25 11:01'), y=-0.1) self.assertEqual(points.dimension_values('x'), np.array([])) self.assertEqual(points.dimension_values('y'), np.array([])) def test_points_with_dates_inspection_2px_mask(self): points = inspect_points(self.datesimg, max_indicators=3, dynamic=False, pixels=2, x=np.datetime64('2024-09-25 11:01'), y=-0.1) self.assertEqual(points.dimension_values('x'), np.array([np.datetime64('2024-09-25 11:00')])) self.assertEqual(points.dimension_values('y'), np.array([0.3])) def test_polys_inspection_1px_mask_hit(self): if spatialpandas is None: raise SkipTest('Polygon inspect tests require spatialpandas') polys = inspect_polygons(self.polysrgb, max_indicators=3, dynamic=False, pixels=1, x=6, y=5) self.assertEqual(polys, Polygons([{'x': [6, 3, 7], 'y': [7, 2, 5], 'z': 2}], vdims='z')) def test_inspection_1px_mask_poly_df(self): if spatialpandas is None: raise SkipTest('Polygon inspect tests require spatialpandas') inspector = inspect.instance(max_indicators=3, dynamic=False, pixels=1, x=6, y=5) inspector(self.polysrgb) self.assertEqual(len(inspector.hits), 1) data = [[6.0, 7.0, 3.0, 2.0, 7.0, 5.0, 6.0, 7.0]] self.assertEqual(inspector.hits.iloc[0].geometry, spatialpandas.geometry.polygon.Polygon(data)) def test_polys_inspection_1px_mask_miss(self): if spatialpandas is None: raise SkipTest('Polygon inspect tests require spatialpandas') polys = inspect_polygons(self.polysrgb, max_indicators=3, dynamic=False, pixels=1, x=0, y=0) self.assertEqual(polys, Polygons([], vdims='z')) @pytest.mark.parametrize("dtype", [np.uint8, np.uint16, np.uint32]) def test_uint_dtype(dtype): df = pd.DataFrame(np.arange(2, dtype=dtype), columns=["A"]) curve = Curve(df) img = rasterize(curve, dynamic=False, height=10, width=10) assert (np.asarray(img.data["Count"]) == np.eye(10)).all() def test_uint64_dtype(): df = pd.DataFrame(np.arange(2, dtype=np.uint64), columns=["A"]) curve = Curve(df) with pytest.raises(TypeError, match="Dtype of uint64 for column A is not supported."): rasterize(curve, dynamic=False, height=10, width=10) def test_imagestack_datashader_color_key(): d = np.arange(23) df = pd.DataFrame({"x": d, "y": d, "language": list(map(str, d))}) points = Points(df, ["x", "y"], ["language"]) # This will run rasterize which outputs an ImageStack op = datashade( points, aggregator=ds.by("language", ds.count()), color_key=cc.glasbey_light, ) render(op) # should not error out def test_imagestack_datashade_count_cat(): # Test for https://github.com/holoviz/holoviews/issues/6154 df = pd.DataFrame({"x": range(3), "y": range(3), "c": range(3)}) op = datashade(Points(df), aggregator=ds.count_cat("c")) render(op) # should not error out def test_imagestack_dynspread(): df = pd.DataFrame({'x':[-16.8, 7.3], 'y': [-0.42, 13.6], 'language':['Marathi', 'Luganda']}) points = Points(df, ['x','y'], ['language']) op = dynspread(rasterize(points, aggregator=ds.by('language', ds.count()))) render(op) # should not error out def test_datashade_count_cat_no_change_inplace(): # Test for https://github.com/holoviz/holoviews/issues/6324 df = pd.DataFrame({"x": range(3), "y": range(3), "c": list(map(str, range(3)))}) assert df["c"].dtype == "object" op = datashade(Points(df), aggregator=ds.count_cat("c")) render(op) # Should not convert to category dtype assert df["c"].dtype == "object"