def test_layout_title_format(self):
title_str = ('Label: {label}, group: {group}, '
'dims: {dimensions}, type: {type}')
layout = NdLayout(
{
'Element 1': Scatter(
[],
label='ONE',
group='first',
),
'Element 2': Scatter(
[],
label='TWO',
group='second',
)
},
kdims='MYDIM',
label='the_label',
group='the_group',
).opts(opts.NdLayout(title=title_str), opts.Scatter(title=title_str))
# Title of NdLayout
title = bokeh_renderer.get_plot(layout).handles['title']
self.assertIsInstance(title, Div)
text = 'Label: the_label, group: the_group, dims: , type: NdLayout'
self.assertEqual(re.split('>|</', title.text)[1], text)
# Titles of subplots
plot = render(layout)
titles = {title.text for title in list(plot.select({'type': Title}))}
titles_correct = {
'Label: ONE, group: first, dims: MYDIM: Element 1, type: Scatter',
'Label: TWO, group: second, dims: MYDIM: Element 2, type: Scatter',
}
self.assertEqual(titles_correct, titles)
def test_deep_apply_transform_element_type(self):
fn = lambda i: Curve(np.arange(i))
dmap = DynamicMap(fn, kdims=[Dimension('Test', range=(10, 20))])
dmap[10]
mapped = dmap.apply(lambda x: Scatter(x))
area = mapped[11]
self.assertEqual(area, Scatter(fn(11)))
def test_scatter_selection_categorical(self):
scatter = Scatter((['B', 'A', 'C', 'D', 'E'], [3, 2, 1, 3, 4]))
expr, bbox, region = scatter._get_selection_expr_for_stream_value(
bounds=(0, 1, 2, 3), x_selection=['B', 'A', 'C'], y_selection=None
)
self.assertEqual(bbox, {'x': ['B', 'A', 'C'], 'y': (1, 3)})
self.assertEqual(expr.apply(scatter), np.array([True, True, True, False, False]))
self.assertEqual(region, Rectangles([(0, 1, 2, 3)]) * Path([]))
def test_scatter_selection_numeric_index_cols(self):
scatter = Scatter([3, 2, 1, 3, 2])
expr, bbox, region = scatter._get_selection_expr_for_stream_value(
bounds=(1, 0, 3, 2), index_cols=['y']
)
self.assertEqual(bbox, {'x': (1, 3), 'y': (0, 2)})
self.assertEqual(expr.apply(scatter), np.array([False, False, True, False, False]))
self.assertEqual(region, None)
def test_scatter_selection_numeric_inverted(self):
scatter = Scatter([3, 2, 1, 3, 4]).opts(invert_axes=True)
expr, bbox, region = scatter._get_selection_expr_for_stream_value(
bounds=(0, 1, 2, 3))
self.assertEqual(bbox, {'x': (1, 3), 'y': (0, 2)})
self.assertEqual(expr.apply(scatter),
np.array([False, True, True, False, False]))
self.assertEqual(region, Rectangles([(0, 1, 2, 3)]) * Path([]))
def test_scatter_selection_numeric(self):
scatter = Scatter([3, 2, 1, 3, 4])
expr, bbox, region = scatter._get_selection_expr_for_stream_value(
bounds=(1, 0, 3, 2))
self.assertEqual(bbox, {'x': (1, 3), 'y': (0, 2)})
self.assertEqual(expr.apply(scatter),
np.array([False, True, True, False, False]))
self.assertEqual(region, Rectangles([(1, 0, 3, 2)]))
def test_dataset_conversion_groupby_with_index(self):
df = pd.DataFrame({'y': [1, 2, 3], 'x': [0, 0, 1]}, index=[0, 1, 2])
scatters = Dataset(df).to(Scatter, 'index', 'y')
hmap = HoloMap(
{
0: Scatter(([0, 1], [1, 2]), 'index', 'y'),
1: Scatter([(2, 3)], 'index', 'y')
}, 'x')
self.assertEqual(scatters, hmap)
def test_data_link_nan(self):
arr = np.random.rand(3, 5)
arr[0, 0] = np.nan
data = {k: v for k, v in zip(['x', 'y', 'z'], arr)}
a = Scatter(data, 'x', 'z')
b = Scatter(data, 'x', 'y')
DataLink(a, b)
try:
bokeh_renderer.get_plot(a + b)
except:
self.fail()
def test_dynamic_groupby_kdims_and_streams(self):
def plot_function(mydim, data):
return Scatter(data[data[:, 2]==mydim])
buff = Buffer(data=np.empty((0, 3)))
dmap = DynamicMap(plot_function, streams=[buff], kdims='mydim').redim.values(mydim=[0, 1, 2])
ndlayout = dmap.groupby('mydim', container_type=NdLayout)
self.assertIsInstance(ndlayout[0], DynamicMap)
data = np.array([(0, 0, 0), (1, 1, 1), (2, 2, 2)])
buff.send(data)
self.assertEqual(ndlayout[0][()], Scatter([(0, 0)]))
self.assertEqual(ndlayout[1][()], Scatter([(1, 1)]))
self.assertEqual(ndlayout[2][()], Scatter([(2, 2)]))
def test_overlay_framewise_norm(self):
a = {'X': [0, 1, 2], 'Y': [0, 1, 2], 'Z': [0, 50, 100]}
b = {'X': [3, 4, 5], 'Y': [0, 10, 20], 'Z': [50, 50, 150]}
sa = Scatter(a, 'X', ['Y', 'Z']).opts(color='Z', framewise=True)
sb = Scatter(b, 'X', ['Y', 'Z']).opts(color='Z', framewise=True)
plot = bokeh_renderer.get_plot(sa * sb)
sa_plot, sb_plot = plot.subplots.values()
sa_cmapper = sa_plot.handles['color_color_mapper']
sb_cmapper = sb_plot.handles['color_color_mapper']
self.assertEqual(sa_cmapper.low, 0)
self.assertEqual(sb_cmapper.low, 0)
self.assertEqual(sa_cmapper.high, 150)
self.assertEqual(sb_cmapper.high, 150)
def test_dynamic_keywords_and_kwargs(self):
def fn(A='default', x=1, y=2, **kws):
return Scatter([(x, y)], label=A)
xy = streams.PointerXY(x=1, y=2)
dmap = DynamicMap(fn, kdims=['A'], streams=[xy])
self.assertEqual(dmap['Test'], Scatter([(1, 2)], label='Test'))
def test_dynamic_streams_only_keywords(self):
def fn(**kwargs):
return Scatter([(kwargs['x'], kwargs['y'])], label='default')
xy = streams.PointerXY(x=1, y=2)
dmap = DynamicMap(fn, kdims=[], streams=[xy])
self.assertEqual(dmap[:], Scatter([(1, 2)], label='default'))
def test_dynamic_kdims_args_only(self):
def fn(*args):
(A, B) = args
return Scatter([(A, 2)], label=A)
dmap = DynamicMap(fn, kdims=['A', 'B'])
self.assertEqual(dmap['Test', 1], Scatter([(1, 2)], label='Test'))
def test_dynamic_mixed_kwargs(self):
def fn(x, A, y):
return Scatter([(x, y)], label=A)
xy = streams.PointerXY(x=1, y=2)
dmap = DynamicMap(fn, kdims=['A'], streams=[xy])
self.assertEqual(dmap['Test'], Scatter([(1, 2)], label='Test'))
def test_colorbar_label_style_mapping(self):
scatter = Scatter(np.random.rand(100, 3),
vdims=["y", "color"]).options(color='color',
colorbar=True)
plot = mpl_renderer.get_plot(scatter)
cbar_ax = plot.handles['cax']
self.assertEqual(cbar_ax.get_ylabel(), 'color')
def test_scatter_colors(self):
scatter = Scatter([
(0, 1, 'red'), (1, 2, 'green'), (2, 3, 'blue')
], vdims=['y', 'color']).options(color='color')
state = self._get_plot_state(scatter)
self.assertEqual(state['data'][0]['marker']['color'],
np.array(['red', 'green', 'blue']))
def test_active_tools_scroll(self):
curve = Curve([1, 2, 3])
scatter = Scatter([1, 2, 3])
overlay = (scatter * curve).options(active_tools=['wheel_zoom'])
plot = bokeh_renderer.get_plot(overlay)
toolbar = plot.state.toolbar
self.assertIsInstance(toolbar.active_scroll, tools.WheelZoomTool)
def test_active_tools_tap(self):
curve = Curve([1, 2, 3])
scatter = Scatter([1, 2, 3]).options(tools=['tap'])
overlay = (scatter * curve).options(active_tools=['tap'])
plot = bokeh_renderer.get_plot(overlay)
toolbar = plot.state.toolbar
self.assertIsInstance(toolbar.active_tap, tools.TapTool)
def test_active_tools_draw_stream(self):
scatter = Scatter([1, 2, 3]).options(active_tools=['point_draw'])
PointDraw(source=scatter)
plot = bokeh_renderer.get_plot(scatter)
toolbar = plot.state.toolbar
self.assertIsInstance(toolbar.active_tap, tools.PointDrawTool)
self.assertIsInstance(toolbar.active_drag, tools.PointDrawTool)
def test_overlay_gridstyle_applies(self):
grid_style = {'grid_line_color': 'blue', 'grid_line_width': 2}
overlay = (Scatter([(10,10)]).options(gridstyle=grid_style, show_grid=True, size=20)
* Labels([(10, 10, 'A')]))
plot = bokeh_renderer.get_plot(overlay)
self.assertEqual(plot.state.xgrid[0].grid_line_color, 'blue')
self.assertEqual(plot.state.xgrid[0].grid_line_width, 2)
def test_scatter_markers(self):
scatter = Scatter([(0, 1, 'square'), (1, 2, 'circle'),
(2, 3, 'triangle-up')],
vdims=['y', 'marker']).options(marker='marker')
state = self._get_plot_state(scatter)
self.assertEqual(state['data'][0]['marker']['symbol'],
np.array(['square', 'circle', 'triangle-up']))
def test_scatter_selectedpoints(self):
scatter = Scatter([(
0,
1,
), (1, 2), (2, 3)]).options(selectedpoints=[1, 2])
state = self._get_plot_state(scatter)
self.assertEqual(state['data'][0]['selectedpoints'], [1, 2])
def test_get_range_from_element(self):
dim = Dimension('y', soft_range=(0, 3), range=(0, 2))
element = Scatter([1, 2, 3], vdims=dim)
drange, srange, hrange = get_range(element, {}, dim)
self.assertEqual(drange, (1, 3))
self.assertEqual(srange, (0, 3))
self.assertEqual(hrange, (0, 2))
def test_scatter_color_mapped(self):
scatter = Scatter([3, 2, 1]).options(color='x')
state = self._get_plot_state(scatter)
self.assertEqual(state['data'][0]['marker']['color'],
np.array([0, 1, 2]))
self.assertEqual(state['data'][0]['marker']['cmin'], 0)
self.assertEqual(state['data'][0]['marker']['cmax'], 2)
def test_scatter_state(self):
scatter = Scatter([3, 2, 1])
state = self._get_plot_state(scatter)
self.assertEqual(state['data'][0]['type'], 'scatter')
self.assertEqual(state['data'][0]['y'], np.array([3, 2, 1]))
self.assertEqual(state['data'][0]['mode'], 'markers')
self.assertEqual(state['layout']['yaxis']['range'], [1, 3])
def test_categorical_overlay_dimension_values_skip_factor(self):
curve = Curve([('C', 1), ('B', 3)])
scatter = Scatter([('A', 2)])
plot = bokeh_renderer.get_plot(
(curve * scatter).redim.values(x=['A', 'C']))
x_range = plot.handles['x_range']
self.assertEqual(x_range.factors, ['A', 'C'])
def test_scatter_state(self):
# Precompute coordinates
xs = [3000000, 2000000, 1000000]
ys = [-3000000, -2000000, -1000000]
x_range = (-5000000, 4000000)
x_center = sum(x_range) / 2.0
y_range = (-3000000, 2000000)
y_center = sum(y_range) / 2.0
lon_centers, lat_centers = Tiles.easting_northing_to_lon_lat([x_center], [y_center])
lon_center, lat_center = lon_centers[0], lat_centers[0]
lons, lats = Tiles.easting_northing_to_lon_lat(xs, ys)
scatter = Tiles('') * Scatter((xs, ys)).redim.range(x=x_range, y=y_range)
state = self._get_plot_state(scatter)
self.assertEqual(state['data'][1]['type'], 'scattermapbox')
self.assertEqual(state['data'][1]['lon'], lons)
self.assertEqual(state['data'][1]['lat'], lats)
self.assertEqual(state['data'][1]['mode'], 'markers')
self.assertEqual(
state['layout']['mapbox']['center'], {'lat': lat_center, 'lon': lon_center}
)
# There xaxis and yaxis should not be in the layout
self.assertFalse('xaxis' in state['layout'])
self.assertFalse('yaxis' in state['layout'])
def setUp(self):
self.dmap_element = DynamicMap(lambda: Image([]))
self.dmap_overlay = DynamicMap(lambda: Overlay([Curve([]), Points([])]))
self.dmap_ndoverlay = DynamicMap(lambda: NdOverlay({0: Curve([]), 1: Curve([])}))
self.element = Scatter([])
self.el1, self.el2 = Path([]), HLine(0)
self.overlay = Overlay([self.el1, self.el2])
self.ndoverlay = NdOverlay({0: VectorField([]), 1: VectorField([])})
def test_get_range_from_ranges(self):
dim = Dimension('y', soft_range=(0, 3), range=(0, 2))
element = Scatter([1, 2, 3], vdims=dim)
ranges = {'y': {'soft': (-1, 4), 'hard': (-1, 3), 'data': (-0.5, 2.5)}}
drange, srange, hrange = get_range(element, ranges, dim)
self.assertEqual(drange, (-0.5, 2.5))
self.assertEqual(srange, (-1, 4))
self.assertEqual(hrange, (-1, 3))
def test_dynamic_split_args_and_kwargs(self):
# Corresponds to the old style of kdims as posargs and streams
# as kwargs, captured as *args and **kwargs
def fn(*args, **kwargs):
return Scatter([(kwargs['x'], kwargs['y'])], label=args[0])
xy = streams.PointerXY(x=1, y=2)
dmap = DynamicMap(fn, kdims=['A'], streams=[xy])
self.assertEqual(dmap['Test'], Scatter([(1, 2)], label='Test'))