def test_sample_datetime_yaxis(self):
start = np.datetime64(dt.datetime.today())
end = start+np.timedelta64(1, 's')
ys = date_range(start, end, 10)
image = Image((self.xs, ys, self.array))
curve = image.sample(y=ys[3])
self.assertEqual(curve, Curve((self.xs, self.array[3]), 'x', 'z'))
def _generate_plots(self):
dynamic_plots = []
for kw in [dict(sheet=sheet) for sheet in self.sheets()]:
sheet = kw['sheet']
views = topo.sim.views[sheet.name].Maps
im = Image(sheet.input_generator(), bounds=sheet.bounds)
im.metadata=AttrDict(timestamp=topo.sim.time())
if 'Activity' not in views:
views['Activity'] = NdMapping((topo.sim.time(), im))
views['Activity'].metadata = AttrDict(precedence=sheet.precedence,
row_precedence=sheet.row_precedence,
src_name=sheet.name,
timestamp=topo.sim.time())
else:
views['Activity'][topo.sim.time()] = im
channels = {'Strength': 'Activity','Hue':None,'Confidence':None}
view = topo.sim.views[sheet.name].Maps
view_dict = {'Strength':view,'Hue':view,'Confidence':view}
### JCALERT! it is not good to have to pass '' here... maybe a test in plot would be better
dynamic_plots.append(make_template_plot(channels, view_dict,
sheet.xdensity,sheet.bounds,self.normalize,
name=''))
return self._static_plots[:]+dynamic_plots
def callback(x, y):
return Image(np.array([[0, 1], [2, 3]])) + Image(
np.array([[0, 1], [2, 3]]))
def fn2(x, y):
counter[0] += 1
return Image(np.random.rand(10, 10))
def test_dynamic_operation_init_stream_params(self):
img = Image(sine_array(0, 5))
stream = Stream.define('TestStream', bin_range=None)()
histogram(img, bin_range=(0, 1), streams=[stream], dynamic=True)
self.assertEqual(stream.bin_range, (0, 1))
def test_dynamic_operation_on_hmap(self):
hmap = HoloMap({i: Image(sine_array(0, i)) for i in range(10)})
dmap = Dynamic(hmap, operation=lambda x: x)
self.assertEqual(dmap.kdims[0].name, hmap.kdims[0].name)
self.assertEqual(dmap.kdims[0].values, hmap.keys())
def test_sampled_unbounded_resample(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i'])
self.assertEqual(dmap[{0, 1, 2}].keys(), [0, 1, 2])
self.assertEqual(dmap.unbounded, ['i'])
def test_callable_bounded_clone(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=[Dimension('dim', range=(0, 10))])
self.assertEqual(dmap, dmap.clone())
self.assertEqual(dmap.unbounded, [])
def test_colormapper_clims(self):
img = Image(np.array([[0, 1], [2, 3]])).options(clims=(0, 4))
plot = mpl_renderer.get_plot(img)
artist = plot.handles['artist']
self.assertEqual(artist.get_clim(), (0, 4))
def test_redim_dimension_range_aux(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i']).redim.range(i=(0, 1))
self.assertEqual(dmap.kdims[0].range, (0, 1))
def callback(x, y):
return NdLayout(
{i: Image(np.array([[i, 1], [2, 3]]))
for i in range(1, 3)})
def test_redim_dimension_name(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i']).redim(i='New')
self.assertEqual(dmap.kdims[0].name, 'New')
def test_layout_empty_subplots(self):
layout = Curve(range(10)) + NdOverlay() + HoloMap() + HoloMap(
{1: Image(np.random.rand(10, 10))})
plot = mpl_renderer.get_plot(layout)
self.assertEqual(len(plot.subplots.values()), 2)
def test_colormapper_transparent_nan(self):
img = Image(np.array(
[[0, 1], [2, 3]])).options(clipping_colors={'NaN': 'transparent'})
plot = mpl_renderer.get_plot(img)
cmap = plot.handles['artist'].cmap
self.assertEqual(cmap._rgba_bad, (1.0, 1.0, 1.0, 0))
def test_colormapper_color_levels(self):
img = Image(np.array([[0, 1], [2, 3]])).options(color_levels=5)
plot = mpl_renderer.get_plot(img)
artist = plot.handles['artist']
self.assertEqual(len(artist.cmap.colors), 5)
def test_dimension_values_datetime_ycoords(self):
start = np.datetime64(dt.datetime.today())
end = start+np.timedelta64(1, 's')
ys = date_range(start, end, 10)
image = Image((self.xs, ys, self.array))
self.assertEqual(image.dimension_values(1, expanded=False), ys)
def callback():
return GridSpace({(i, j): Image(np.array([[i, j], [2, 3]]))
for i in range(1, 3) for j in range(1, 3)})
def test_deep_redim_dimension_name_with_spec(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i']).redim(Image, x='X')
self.assertEqual(dmap[0].kdims[0].name, 'X')
def test_colormapper_symmetric(self):
img = Image(np.array([[0, 1], [2, 3]])).options(symmetric=True)
plot = bokeh_renderer.get_plot(img)
cmapper = plot.handles['color_mapper']
self.assertEqual(cmapper.low, -3)
self.assertEqual(cmapper.high, 3)
def test_sampled_unbounded_init(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i'])
self.assertEqual(dmap.unbounded, ['i'])
def test_colormapper_color_levels(self):
img = Image(np.array([[0, 1], [2, 3]])).options(color_levels=5)
plot = bokeh_renderer.get_plot(img)
cmapper = plot.handles['color_mapper']
self.assertEqual(len(cmapper.palette), 5)
def test_dynamic_operation(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i'])
dmap_with_fn = Dynamic(dmap, operation=lambda x: x.clone(x.data * 2))
self.assertEqual(dmap_with_fn[5], Image(sine_array(0, 5) * 2))
def test_colormapper_transparent_nan(self):
img = Image(np.array([[0, 1], [2, 3]])).options(clipping_colors={'NaN': 'transparent'})
plot = bokeh_renderer.get_plot(img)
cmapper = plot.handles['color_mapper']
self.assertEqual(cmapper.nan_color, 'rgba(0, 0, 0, 0)')
def test_dynamic_operation_init_renamed_stream_params(self):
img = Image(sine_array(0, 5))
stream = RangeX(rename={'x_range': 'bin_range'})
histogram(img, bin_range=(0, 1), streams=[stream], dynamic=True)
self.assertEqual(stream.x_range, (0, 1))
def test_colormapper_min_max_colors(self):
img = Image(np.array([[0, 1], [2, 3]])).options(clipping_colors={'min': 'red', 'max': 'blue'})
plot = bokeh_renderer.get_plot(img)
cmapper = plot.handles['color_mapper']
self.assertEqual(cmapper.low_color, 'red')
self.assertEqual(cmapper.high_color, 'blue')
def test_dynamic_element_underlay(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i'])
dynamic_overlay = Image(sine_array(0, 10)) * dmap
overlaid = Image(sine_array(0, 10)) * Image(sine_array(0, 5))
self.assertEqual(dynamic_overlay[5], overlaid)
def test_distribution_from_image(self):
dist = Distribution(Image(np.arange(5) * np.arange(5)[:, np.newaxis]),
'z')
self.assertEqual(dist.range(0), (0, 16))
def test_deep_relabel_label(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i']).relabel(label='Test')
self.assertEqual(dmap[0].label, 'Test')
def test_image_colormapping(self):
img = Image(np.random.rand(10, 10))(plot=dict(logz=True))
self._test_colormapping(img, 2, True)
def callback(x, y):
return Image(np.array([[0, 1], [2, 3]])) + Text(0, 0, 'Test')
def test_layout_title_show_title_false(self):
hmap1 = HoloMap({a: Image(np.random.rand(10, 10)) for a in range(3)})
hmap2 = HoloMap({a: Image(np.random.rand(10, 10)) for a in range(3)})
layout = Layout([hmap1, hmap2])(plot=dict(show_title=False))
plot = bokeh_renderer.get_plot(layout)
self.assertTrue('title' not in plot.handles)
def test_range_datetime_ydim(self):
start = np.datetime64(dt.datetime.today())
end = start+np.timedelta64(1, 's')
ys = date_range(start, end, 10)
image = Image((self.xs, ys, self.array))
self.assertEqual(image.range(1), (start, end))
def test_redim_dimension_unit_aux(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i']).redim.unit(i='m/s')
self.assertEqual(dmap.kdims[0].unit, 'm/s')
def test_image_sample(self):
image = Image(self.array1)
self.assertEqual(image.sample(y=0.25),
Curve(np.array([(-0.333333, 0), (0, 1), (0.333333, 2)]),
kdims=['x'], vdims=['z']))
def test_redim_dimension_type_aux(self):
fn = lambda i: Image(sine_array(0, i))
dmap = DynamicMap(fn, kdims=['i']).redim.type(i=int)
self.assertEqual(dmap.kdims[0].type, int)
