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 packaging.version import Version 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 dask.dataframe as dd import datashader as ds import xarray as xr from holoviews.operation.datashader import ( AggregationOperation, aggregate, datashade, directly_connect_edges, ds_version, inspect, inspect_points, inspect_polygons, rasterize, regrid, shade, spread, stack, ) except ImportError: raise SkipTest('Datashader not available') try: import cudf import cupy except ImportError: cudf = None try: import spatialpandas except ImportError: spatialpandas = None spatialpandas_skip = skipIf(spatialpandas is None, "SpatialPandas not available") cudf_skip = skipIf(cudf is None, "cuDF 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_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) @cudf_skip def test_aggregate_points_cudf(self): points = Points([(0.2, 0.3), (0.4, 0.7), (0, 0.99)], datatype=['cuDF']) self.assertIsInstance(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)]) self.assertIsInstance(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 ds_version < 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_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 setUp(self): if ds_version <= Version('0.5.0'): raise SkipTest('Regridding operations require datashader>=0.6.0') 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 ds_version <= 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 @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 ds_version < 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 ds_version <= 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, x_range=(0, 1), y_range=(0, 1), width=4, height=4) 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_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