def test_gridmatrix_overlaid_batched(self):
ds = Dataset((['A']*5+['B']*5, np.random.rand(10), np.random.rand(10)),
kdims=['a', 'b', 'c'])
gmatrix = gridmatrix(ds.groupby('a', container_type=NdOverlay))
plot = bokeh_renderer.get_plot(gmatrix)
sp1 = plot.subplots[('b', 'c')]
self.assertEqual(sp1.state.xaxis[0].visible, False)
self.assertEqual(sp1.state.yaxis[0].visible, True)
sp2 = plot.subplots[('b', 'b')]
self.assertEqual(sp2.state.xaxis[0].visible, True)
self.assertEqual(sp2.state.yaxis[0].visible, True)
sp3 = plot.subplots[('c', 'b')]
self.assertEqual(sp3.state.xaxis[0].visible, True)
self.assertEqual(sp3.state.yaxis[0].visible, False)
sp4 = plot.subplots[('c', 'c')]
self.assertEqual(sp4.state.xaxis[0].visible, False)
self.assertEqual(sp4.state.yaxis[0].visible, False)
def test_grid_shared_source_synced_update(self):
hmap = HoloMap({
i: Dataset({chr(65 + j): np.random.rand(i + 2)
for j in range(4)},
kdims=['A', 'B', 'C', 'D'])
for i in range(3)
})
# Create two holomaps of points sharing the same data source
hmap1 = hmap.map(lambda x: Points(x.clone(kdims=['A', 'B'])), Dataset)
hmap2 = hmap.map(lambda x: Points(x.clone(kdims=['D', 'C'])), Dataset)
# Pop key (1,) for one of the HoloMaps and make GridSpace
hmap2.pop(1)
grid = GridSpace({
0: hmap1,
2: hmap2
}, kdims=['X']).opts(plot=dict(shared_datasource=True))
# Get plot
plot = bokeh_renderer.get_plot(grid)
# Check plot created shared data source and recorded expected columns
sources = plot.handles.get('shared_sources', [])
source_cols = plot.handles.get('source_cols', {})
self.assertEqual(len(sources), 1)
source = sources[0]
data = source.data
cols = source_cols[id(source)]
self.assertEqual(set(cols), {'A', 'B', 'C', 'D'})
# Ensure the source contains the expected columns
self.assertEqual(set(data.keys()), {'A', 'B', 'C', 'D'})
# Update to key (1,) and check the source contains data
# corresponding to hmap1 and filled in NaNs for hmap2,
# which was popped above
plot.update((1, ))
self.assertEqual(data['A'], hmap1[1].dimension_values(0))
self.assertEqual(data['B'], hmap1[1].dimension_values(1))
self.assertEqual(data['C'],
np.full_like(hmap1[1].dimension_values(0), np.NaN))
self.assertEqual(data['D'],
np.full_like(hmap1[1].dimension_values(0), np.NaN))
def test_nongeographic_conversion(self):
converted = Dataset(self.cube).to.curve(['longitude'])
self.assertTrue(isinstance(converted, HoloMap))
self.assertEqual(converted.kdims, ['latitude'])
self.assertTrue(isinstance(converted.last, Curve))
def test_geographic_conversion(self):
self.assertEqual(Dataset(self.cube).to.image(), Image(self.cube))
def test_is_geographic_2d(self):
self.assertTrue(
is_geographic(Dataset(self.cube), ['longitude', 'latitude']))
def test_is_geographic_1d(self):
self.assertFalse(is_geographic(Dataset(self.cube), ['longitude']))