def test_points_histogram_bin_range(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3, bin_range=(0, 3))
hist = Histogram(([0.25, 0.25, 0.5], [0., 1., 2., 3.]),
vdims=('x_frequency', 'Frequency'))
self.assertEqual(op_hist, hist)
def test_points_histogram_cumulative(self):
arr = np.arange(4)
points = Points(arr)
op_hist = histogram(points, cumulative=True, num_bins=3, normed=False)
hist = Histogram(([0, 1, 2, 3], [1, 2, 4]), vdims=('x_count', 'Count'))
self.assertEqual(op_hist, hist)
def test_points_histogram_not_normed(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3, normed=False)
hist = Histogram(([0, 3, 6, 9], [3, 3, 4]),
vdims=('x_count', 'Count'))
self.assertEqual(op_hist, hist)
def test_points_histogram(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3)
hist = Histogram(([0.1, 0.1, 0.133333], [0, 3, 6, 9]),
vdims=('x_frequency', 'Frequency'))
self.assertEqual(op_hist, hist)
def test_points_histogram_explicit_bins(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, bins=[0, 1, 3], normed=False)
hist = Histogram(([0, 1, 3], [1, 3]),
vdims=('x_count', 'Count'))
self.assertEqual(op_hist, hist)
def test_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=['Count'])
self.assertEqual(agg, img)
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=['Count'])
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=['Count'])
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_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)
image = Image(np.array([[2., 3., np.NaN], [1.5, 2.5, np.NaN], [np.NaN, np.NaN, np.NaN]]),
bounds=(0, 0, 1, 1), vdims='z')
self.assertEqual(img, image)
def test_rasterize_trimesh_node_explicit_vdim(self):
simplices = [(0, 1, 2, 0.5), (3, 2, 1, 1.5)]
vertices = [(0., 0., 1), (0., 1., 2), (1., 0, 3), (1, 1, 4)]
trimesh = TriMesh((simplices, Points(vertices, vdims=['node_z'])), vdims=['z'])
img = rasterize(trimesh, width=3, height=3, dynamic=False, aggregator=ds.mean('z'))
image = Image(np.array([[1.5, 1.5, np.NaN], [0.5, 1.5, np.NaN], [np.NaN, np.NaN, np.NaN]]),
bounds=(0, 0, 1, 1))
self.assertEqual(img, image)
def test_points_histogram_mean_weighted(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points,
num_bins=3,
weight_dimension='y',
mean_weighted=True)
hist = Histogram(([1., 4., 7.5], [0, 3, 6, 9]), vdims=['y'])
self.assertEqual(op_hist, hist)
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=['Count'])
self.assertIsInstance(img.data.Count.data, cupy.ndarray)
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'))
xs, ys = [0.25, 0.75], [0.25, 0.75]
expected = NdOverlay({'A': Image((xs, ys, [[1, 0], [0, 0]]), vdims='z Count'),
'B': Image((xs, ys, [[0, 0], [1, 0]]), vdims='z Count'),
'C': Image((xs, ys, [[0, 0], [1, 0]]), vdims='z Count')},
kdims=['z'])
self.assertEqual(img, expected)
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'))
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='z Count', **params),
'B': Image((xs, ys, np.zeros((2, 0))), vdims='z Count', **params),
'C': Image((xs, ys, np.zeros((2, 0))), vdims='z Count', **params)},
kdims=['z'])
self.assertEqual(img, expected)
def test_points_histogram_not_normed(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3, normed=False)
# Make sure that the name and label are as desired
op_freq_dim = op_hist.get_dimension('x_frequency')
self.assertEqual(op_freq_dim.label, 'x Frequency')
# Because the operation labels are now different from the
# default Element label, change back before comparing.
op_hist = op_hist.redim(x_frequency='Frequency')
hist = Histogram(([3, 3, 4], [0, 3, 6, 9]))
self.assertEqual(op_hist, hist)
def test_points_histogram_cumulative(self):
arr = np.arange(4)
points = Points(arr)
op_hist = histogram(points, cumulative=True, num_bins=3, normed=False)
# Make sure that the name and label are as desired
op_freq_dim = op_hist.get_dimension('x_frequency')
self.assertEqual(op_freq_dim.label, 'x Frequency')
# Because the operation labels are now different from the
# default Element label, change back before comparing.
op_hist = op_hist.redim(x_frequency='Frequency')
hist = Histogram(([0, 1, 2, 3], [1, 2, 4]))
self.assertEqual(op_hist, hist)
def test_histogram_operation_kwargs(self):
points = Points([float(j) for i in range(10) for j in [i] * (2 * i)])
op_hist = histogram(
points,
dimension='y',
normed=False,
num_bins=10,
bin_range=[0, 10],
)
hist = Histogram(
([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0
], [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]),
vdims=('y_count', 'Count'),
kdims='y')
# Check histogram
self.assertEqual(op_hist, hist)
# Check operation kwargs for histogram generated with operation
self.assertEqual(
op_hist._operation_kwargs, {
'dimension': 'y',
'normed': False,
'dynamic': False,
'bins':
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
})
# Test that operation_kwargs is preserved through clone
self.assertEqual(
op_hist.clone()._operation_kwargs, {
'dimension': 'y',
'normed': False,
'dynamic': False,
'bins':
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
})
# Check that operation kwargs is None for histogram generated directly
# from the Histogram constructor
self.assertIsNone(hist._operation_kwargs)
def test_points_histogram_weighted(self):
points = Points([float(i) for i in range(10)])
op_hist = histogram(points, num_bins=3, weight_dimension='y')
hist = Histogram(([0.022222, 0.088889, 0.222222], [0, 3, 6, 9]),
vdims=['y'])
self.assertEqual(op_hist, hist)
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=['Count'])
img = aggregate(points, dynamic=False, target=expected)
self.assertEqual(img, expected)
def viz_mesh(verts, tris):
points = Points(verts, vdims=['z'])
return TriMesh((tris, points), crs=ccrs.GOOGLE_MERCATOR)
def test_points_string_signature(self):
points = Points([], ['a', 'b'], 'c')
self.assertEqual(points.kdims, [Dimension('a'), Dimension('b')])
self.assertEqual(points.vdims, [Dimension('c')])
def setUp(self):
"Variations on the constructors in the Elements notebook"
self.points1 = Points([(1, i) for i in range(20)])
self.points2 = Points([(1, i) for i in range(21)])
self.points3 = Points([(1, i*2) for i in range(20)])
