def test_stream_cleanup(self):
stream = Stream.define(str('Test'), test=1)()
dmap = DynamicMap(lambda test: Curve([]), streams=[stream])
plot = bokeh_renderer.get_plot(dmap)
self.assertTrue(bool(stream._subscribers))
plot.cleanup()
self.assertFalse(bool(stream._subscribers))
def test_stream_cleanup(self):
stream = Stream.define(str('Test'), test=1)()
dmap = DynamicMap(lambda test: Curve([]), streams=[stream])
plot = mpl_renderer.get_plot(dmap)
self.assertTrue(bool(stream._subscribers))
plot.cleanup()
self.assertFalse(bool(stream._subscribers))
def test_server_dynamicmap_with_stream_dims(self):
stream = Stream.define('Custom', y=2)()
dmap = DynamicMap(lambda x, y: Curve([x, 1, y]),
kdims=['x', 'y'],
streams=[stream]).redim.values(x=[1, 2, 3])
obj, _ = bokeh_renderer._validate(dmap, None)
server, session = self._launcher(obj, port=6006)
[(doc, _)] = obj._documents.items()
orig_cds = session.document.roots[0].select_one(
{'type': ColumnDataSource})
self.assertEqual(orig_cds.data['y'][2], 2)
def run():
stream.event(y=3)
doc.add_next_tick_callback(run)
time.sleep(1)
cds = self.session.document.roots[0].select_one(
{'type': ColumnDataSource})
self.assertEqual(cds.data['y'][2], 3)
self.assertEqual(orig_cds.data['y'][0], 1)
slider = obj.layout.select(DiscreteSlider)[0]
def run():
slider.value = 3
doc.add_next_tick_callback(run)
time.sleep(1)
cds = self.session.document.roots[0].select_one(
{'type': ColumnDataSource})
self.assertEqual(cds.data['y'][0], 3)
server.stop()
def test_deep_map_apply_element_function_with_stream_kwarg(self):
stream = Stream.define('Test', label='New label')()
fn = lambda i: Curve(np.arange(i))
dmap = DynamicMap(fn, kdims=[Dimension('Test', range=(10, 20))])
mapped = dmap.apply(lambda x, label: x.relabel(label),
streams=[stream])
self.assertEqual(mapped[10], fn(10).relabel('New label'))
def test_periodic_param_fn_blocking(self):
def callback(x): return Curve([1,2,3])
xval = Stream.define('x',x=0)()
dmap = DynamicMap(callback, streams=[xval])
# Add stream subscriber mocking plot
xval.add_subscriber(lambda **kwargs: dmap[()])
dmap.periodic(0.01, 100, param_fn=lambda i: {'x':i})
self.assertEqual(xval.x, 100)
def __init__(self, groups, **params):
super(AttributeAnnotator, self).__init__(**params)
self.param.group.objects = groups
self.param.group.default = groups[0]
# self.point_sel_stream = Selection1D(source=self.points)
self.poly_sel_stream = Selection1D(source=self.polys)
self._group_data = {g: [] for g in groups}
self.table_stream = Stream.define('TableUpdate')(transient=True)
def test_table_update_selected(self):
stream = Stream.define('Selected', selected=[])()
table = Table([(0, 0), (1, 1), (2, 2)], ['x', 'y']).apply.opts(selected=stream.param.selected)
plot = bokeh_renderer.get_plot(table)
cds = plot.handles['cds']
self.assertEqual(cds.selected.indices, [])
stream.event(selected=[0, 2])
self.assertEqual(cds.selected.indices, [0, 2])
def test_periodic_param_fn_blocking(self):
def callback(x): return Curve([1,2,3])
xval = Stream.define('x',x=0)()
dmap = DynamicMap(callback, streams=[xval])
# Add stream subscriber mocking plot
xval.add_subscriber(lambda **kwargs: dmap[()])
dmap.periodic(0.01, 100, param_fn=lambda i: {'x':i})
self.assertEqual(xval.x, 100)
def test_dynamic_operation_init_stream_params(self):
img = Image(sine_array(0, 5))
stream = Stream.define('TestStream', bin_range=None)()
dmap_with_fn = histogram(img,
bin_range=(0, 1),
streams=[stream],
dynamic=True)
self.assertEqual(stream.bin_range, (0, 1))
def test_batch_subscribers(self):
subscriber1 = TestSubscriber()
subscriber2 = TestSubscriber()
positionX = PositionX(subscribers=[subscriber1, subscriber2])
positionY = PositionY(subscribers=[subscriber1, subscriber2])
positionX.update(trigger=False, x=50)
positionY.update(trigger=False, y=100)
Stream.trigger([positionX, positionY])
self.assertEqual(subscriber1.kwargs, dict(x=50, y=100))
self.assertEqual(subscriber1.call_count, 1)
self.assertEqual(subscriber2.kwargs, dict(x=50, y=100))
self.assertEqual(subscriber2.call_count, 1)
def test_dynamicmap_ndoverlay_shrink_number_of_items(self):
selected = Stream.define('selected', items=3)()
def callback(items):
return NdOverlay({j: Overlay([Curve([1, 2, j])]) for j in range(items)})
dmap = DynamicMap(callback, streams=[selected])
plot = bokeh_renderer.get_plot(dmap)
selected.event(items=2)
self.assertEqual(len([r for r in plot.state.renderers if r.visible]), 2)
def __init__(self, groups, **params):
super(PointWidgetAnnotator, self).__init__(**params)
group_param = self.params('group')
group_param.objects = groups
group_param.default = groups[0]
self.point_sel_stream = Selection1D(source=self.points)
self._group_data = {g: [] for g in groups}
self.table_stream = Stream.define('TableUpdate')(transient=True)
def test_points_update_selected(self):
stream = Stream.define('Selected', selected=[])()
points = Points([(0, 0), (1, 1), (2, 2)]).apply.opts(selected=stream.param.selected)
plot = bokeh_renderer.get_plot(points)
cds = plot.handles['cds']
self.assertEqual(cds.selected.indices, [])
stream.event(selected=[0, 2])
self.assertEqual(cds.selected.indices, [0, 2])
def test_dynamicmap_ndoverlay_shrink_number_of_items(self):
selected = Stream.define('selected', items=3)()
def callback(items):
return NdOverlay({j: Overlay([Curve([1, 2, j])]) for j in range(items)})
dmap = DynamicMap(callback, streams=[selected])
plot = bokeh_renderer.get_plot(dmap)
selected.event(items=2)
self.assertEqual(len([r for r in plot.state.renderers if r.visible]), 2)
def test_layout_dimensioned_stream_title_update(self):
stream = Stream.define('Test', test=0)()
dmap = DynamicMap(lambda test: Curve([]), kdims=['test'], streams=[stream])
layout = dmap + Curve([])
plot = mpl_renderer.get_plot(layout)
self.assertIn('test: 0', plot.handles['title'].get_text())
stream.event(test=1)
self.assertIn('test: 1', plot.handles['title'].get_text())
plot.cleanup()
self.assertEqual(stream._subscribers, [])
def test_grid_dimensioned_stream_title_update(self):
stream = Stream.define('Test', test=0)()
dmap = DynamicMap(lambda test: Curve([]), kdims=['test'], streams=[stream])
grid = GridMatrix({0: dmap, 1: Curve([])}, 'X')
plot = bokeh_renderer.get_plot(grid)
self.assertIn('test: 0', plot.handles['title'].text)
stream.event(test=1)
self.assertIn('test: 1', plot.handles['title'].text)
plot.cleanup()
self.assertEqual(stream._subscribers, [])
def test_layout_dimensioned_stream_title_update(self):
stream = Stream.define('Test', test=0)()
dmap = DynamicMap(lambda test: Curve([]), kdims=['test'], streams=[stream])
layout = dmap + Curve([])
plot = mpl_renderer.get_plot(layout)
self.assertIn('test: 0', plot.handles['title'].get_text())
stream.event(test=1)
self.assertIn('test: 1', plot.handles['title'].get_text())
plot.cleanup()
self.assertEqual(stream._subscribers, [])
def test_periodic_param_fn_blocking_timeout(self):
def callback(x):
return Curve([1,2,3])
xval = Stream.define('x',x=0)()
dmap = DynamicMap(callback, streams=[xval])
# Add stream subscriber mocking plot
xval.add_subscriber(lambda **kwargs: dmap[()])
start = time.time()
dmap.periodic(0.5, 100, param_fn=lambda i: {'x':i}, timeout=3)
end = time.time()
self.assertEqual((end - start) < 5, True)
def test_dimensioned_streams_with_dynamic_map_overlay_clone(self):
time = Stream.define('Time', time=-3.0)()
def crosshair(time):
return VLine(time) * HLine(time)
crosshair = DynamicMap(crosshair, kdims='time', streams=[time])
path = Path([])
t = crosshair * path
html, _ = bokeh_renderer(t)
self.assertIn('Bokeh Application', html)
def test_periodic_param_fn_blocking_timeout(self):
def callback(x):
return Curve([1,2,3])
xval = Stream.define('x',x=0)()
dmap = DynamicMap(callback, streams=[xval])
# Add stream subscriber mocking plot
xval.add_subscriber(lambda **kwargs: dmap[()])
start = time.time()
dmap.periodic(0.5, 100, param_fn=lambda i: {'x':i}, timeout=3)
end = time.time()
self.assertEqual((end - start) < 5, True)
def test_grid_dimensioned_stream_title_update(self):
stream = Stream.define('Test', test=0)()
dmap = DynamicMap(lambda test: Curve([]),
kdims=['test'],
streams=[stream])
grid = GridMatrix({0: dmap, 1: Curve([])}, 'X')
plot = bokeh_renderer.get_plot(grid)
self.assertIn('test: 0', plot.handles['title'].text)
stream.event(test=1)
self.assertIn('test: 1', plot.handles['title'].text)
plot.cleanup()
self.assertEqual(stream._subscribers, [])
def test_render_dynamicmap_with_stream(self):
stream = Stream.define(str('Custom'), y=2)()
dmap = DynamicMap(lambda y: Curve([1, 2, y]), kdims=['y'], streams=[stream])
obj, _ = self.renderer._validate(dmap, None)
self.renderer.components(obj)
[(plot, pane)] = obj._plots.values()
y = plot.handles['fig']['data'][0]['y']
self.assertEqual(y[2], 2)
stream.event(y=3)
y = plot.handles['fig']['data'][0]['y']
self.assertEqual(y[2], 3)
def __init__(self, mapdata):
'''initialize a SubwayMap object
Args:
mapdata (SubwayMapData): container-class for stations and lines dataframes with implemented observer pattern.
This is necessary for data binding of the view to the viewmodel.
'''
Stream.__init__(self)
#create an initial map
stations, lines = mapdata.stationsdf, mapdata.linesdf
self.pipe = Pipe(data=[])
self.subway_map = gv.Path(lines, vdims=['color']).opts(
projection=crs.LambertConformal(),
height=800,
width=800,
color='color') * gv.DynamicMap(self.callback, streams=[self.pipe])
self.pipe.send(stations)
#bind changes in the stationsdf to pipe.send
mapdata.bind_to_stationsdf(self.pipe.send)
self.mapdata = mapdata
def boxes_exploration_interactive(data_path):
'''Returns an interactive plot with the agonia boxes with a confidence value above
a Score selected by the user with a slider. The user can select a box by clicking on it
and mean box Fluorescence and the Caiman DF/F of such box will be ploted.'''
data_name, median_projection, fnames, fname_new, results_caiman_path, boxes_path = get_files_names(
data_path)
cnm = cnmf.load_CNMF(results_caiman_path)
img = hv.Image(median_projection,
bounds=(0, 0, median_projection.shape[1],
median_projection.shape[0])).options(cmap='gray')
with open(boxes_path, 'rb') as f:
boxes = pickle.load(f)
f.close()
centers = np.empty((cnm.estimates.A.shape[1], 2))
for i, factor in enumerate(cnm.estimates.A.T):
centers[i] = center_of_mass(factor.toarray().reshape(
cnm.estimates.dims, order='F'))
#scatter = hv.Scatter((centers[:,1], median_projection.shape[0] - centers[:,0]))
kdims = [hv.Dimension('Score', values=np.arange(0.05, 1, 0.05))]
tap = streams.SingleTap(transient=True, source=img)
Experiment = Stream.define('Experiment', data_path=data_path)
Centers = Stream.define('Centers', centers=centers)
CaImAn_detection = Stream.define('CaImAn_detection', cnm=cnm)
dmap = hv.DynamicMap(plot_AGonia_boxes_interactive,
kdims=kdims,
streams=[Experiment(), tap])
dmap1 = hv.DynamicMap(plot_boxes_traces,
kdims=kdims,
streams=[Experiment(), tap])
dmap2 = hv.DynamicMap(
plot_seeded_traces,
kdims=kdims,
streams=[CaImAn_detection(),
Experiment(), tap,
Centers()])
return ((img * dmap).opts(width=500, height=500) +
dmap1.opts(width=500, height=250) +
dmap2.opts(width=500, height=250)).opts(
opts.Curve(framewise=True)).cols(1)
def test_periodic_param_fn_non_blocking(self):
def callback(x): return Curve([1,2,3])
xval = Stream.define('x',x=0)()
dmap = DynamicMap(callback, streams=[xval])
# Add stream subscriber mocking plot
xval.add_subscriber(lambda **kwargs: dmap[()])
self.assertNotEqual(xval.x, 100)
dmap.periodic(0.0001, 100, param_fn=lambda i: {'x': i}, block=False)
time.sleep(2)
if not dmap.periodic.instance.completed:
raise RuntimeError('Periodic callback timed out.')
dmap.periodic.stop()
self.assertEqual(xval.x, 100)
def test_periodic_param_fn_non_blocking(self):
def callback(x): return Curve([1,2,3])
xval = Stream.define('x',x=0)()
dmap = DynamicMap(callback, streams=[xval])
# Add stream subscriber mocking plot
xval.add_subscriber(lambda **kwargs: dmap[()])
self.assertNotEqual(xval.x, 100)
dmap.periodic(0.0001, 100, param_fn=lambda i: {'x': i}, block=False)
time.sleep(2)
if not dmap.periodic.instance.completed:
raise RuntimeError('Periodic callback timed out.')
dmap.periodic.stop()
self.assertEqual(xval.x, 100)
def test_periodic_counter_blocking(self):
class Counter(object):
def __init__(self):
self.count = 0
def __call__(self):
self.count += 1
return Curve([1,2,3])
next_stream = Stream.define('Next')()
counter = Counter()
dmap = DynamicMap(counter, streams=[next_stream])
# Add stream subscriber mocking plot
next_stream.add_subscriber(lambda **kwargs: dmap[()])
dmap.periodic(0.01, 100)
self.assertEqual(counter.count, 100)
def test_periodic_counter_blocking(self):
class Counter(object):
def __init__(self):
self.count = 0
def __call__(self):
self.count += 1
return Curve([1,2,3])
next_stream = Stream.define('Next')()
counter = Counter()
dmap = DynamicMap(counter, streams=[next_stream])
# Add stream subscriber mocking plot
next_stream.add_subscriber(lambda **kwargs: dmap[()])
dmap.periodic(0.01, 100)
self.assertEqual(counter.count, 100)
def test_periodic_param_fn_non_blocking(self):
def callback(x): return Curve([1,2,3])
xval = Stream.define('x',x=0)()
dmap = DynamicMap(callback, streams=[xval])
# Add stream subscriber mocking plot
xval.add_subscriber(lambda **kwargs: dmap[()])
dmap.periodic(0.0001, 1000, param_fn=lambda i: {'x':i}, block=False)
self.assertNotEqual(xval.x, 1000)
for i in range(1000):
time.sleep(0.01)
if dmap.periodic.instance.completed:
break
dmap.periodic.stop()
self.assertEqual(xval.x, 1000)
def test_dimensioned_streams_with_dynamic_callback_returns_layout(self):
stream = Stream.define('aname', aname='a')()
def cb(aname):
x = np.linspace(0, 1, 10)
y = np.random.randn(10)
curve = Curve((x, y), group=aname)
hist = Histogram(y)
return (curve + hist).opts(shared_axes=False)
m = DynamicMap(cb, kdims=['aname'], streams=[stream])
p = bokeh_renderer.get_plot(m)
T = 'XYZT'
stream.event(aname=T)
self.assertIn('aname: ' + T, p.handles['title'].text, p.handles['title'].text)
p.cleanup()
self.assertEqual(stream._subscribers, [])
def test_server_dynamicmap_with_stream(self):
stream = Stream.define('Custom', y=2)()
dmap = DynamicMap(lambda y: Curve([1, 2, y]), kdims=['y'], streams=[stream])
obj, _ = bokeh_renderer._validate(dmap, None)
session = self._threaded_launcher(obj)
[(doc, _)] = obj.layout._documents.items()
cds = session.document.roots[0].select_one({'type': ColumnDataSource})
self.assertEqual(cds.data['y'][2], 2)
def run():
stream.event(y=3)
doc.add_next_tick_callback(run)
time.sleep(1)
cds = self.session.document.roots[0].select_one({'type': ColumnDataSource})
self.assertEqual(cds.data['y'][2], 3)
def test_periodic_param_fn_non_blocking(self):
def callback(x): return Curve([1,2,3])
xval = Stream.define('x',x=0)()
dmap = DynamicMap(callback, streams=[xval])
# Add stream subscriber mocking plot
xval.add_subscriber(lambda **kwargs: dmap[()])
dmap.periodic(0.0001, 1000, param_fn=lambda i: {'x':i}, block=False)
self.assertNotEqual(xval.x, 1000)
for i in range(1000):
time.sleep(0.01)
if dmap.periodic.instance.completed:
break
dmap.periodic.stop()
self.assertEqual(xval.x, 1000)
def test_update_dynamic_map_with_stream(self):
ys = np.arange(10)
# Build stream
Scale = Stream.define('Scale', scale=1.0)
scale_stream = Scale()
# Build DynamicMap
def build_scatter(scale):
return hv.Scatter(ys * scale)
dmap = hv.DynamicMap(build_scatter, streams=[scale_stream])
# Create HoloViews Pane using panel so that we can access the plotly pane
# used to display the plotly figure
dmap_pane = pn.pane.HoloViews(dmap, backend='plotly')
# Call get_root to force instantiation of internal plots/models
doc = Document()
comm = Comm()
dmap_pane.get_root(doc, comm)
# Get reference to the plotly pane
_, plotly_pane = next(iter(dmap_pane._plots.values()))
# Check initial data
data = plotly_pane.object['data']
self.assertEqual(len(data), 1)
self.assertEqual(data[0]['type'], 'scatter')
np.testing.assert_equal(data[0]['y'], ys)
# Watch object for changes
fn = Mock()
plotly_pane.param.watch(fn, 'object')
# Update stream
scale_stream.event(scale=2.0)
# Check that figure object was updated
data = plotly_pane.object['data']
np.testing.assert_equal(data[0]['y'], ys * 2.0)
# Check that object callback was triggered
fn.assert_called_once()
args, kwargs = fn.call_args_list[0]
event = args[0]
self.assertIs(event.obj, plotly_pane)
self.assertIs(event.new, plotly_pane.object)
def test_static_source_optimization(self):
global data
data = np.ones((5, 5))
img = Image(data)
def get_img(test):
global data
data *= test
return img
stream = Stream.define(str('Test'), test=1)()
dmap = DynamicMap(get_img, streams=[stream])
plot = bokeh_renderer.get_plot(dmap, doc=Document())
source = plot.handles['source']
self.assertEqual(source.data['image'][0].mean(), 1)
stream.event(test=2)
self.assertTrue(plot.static_source)
self.assertEqual(source.data['image'][0].mean(), 2)
self.assertNotIn(source, plot.current_handles)
def test_static_source_optimization(self):
global data
data = np.ones((5, 5))
img = Image(data)
def get_img(test):
global data
data *= test
return img
stream = Stream.define(str('Test'), test=1)()
dmap = DynamicMap(get_img, streams=[stream])
plot = bokeh_renderer.get_plot(dmap, doc=Document())
source = plot.handles['source']
self.assertEqual(source.data['image'][0].mean(), 1)
stream.event(test=2)
self.assertTrue(plot.static_source)
self.assertEqual(source.data['image'][0].mean(), 2)
self.assertNotIn(source, plot.current_handles)
def test_render_dynamicmap_with_stream_dims(self):
stream = Stream.define(str('Custom'), y=2)()
dmap = DynamicMap(lambda x, y: Curve([x, 1, y]),
kdims=['x', 'y'],
streams=[stream]).redim.values(x=[1, 2, 3])
obj, _ = self.renderer._validate(dmap, None)
self.renderer.components(obj)
[(plot, pane)] = obj._plots.values()
cds = plot.handles['cds']
self.assertEqual(cds.data['y'][2], 2)
stream.event(y=3)
self.assertEqual(cds.data['y'][2], 3)
self.assertEqual(cds.data['y'][0], 1)
slider = obj.layout.select(DiscreteSlider)[0]
slider.value = 3
self.assertEqual(cds.data['y'][0], 3)
def boxes_exploration(data_path):
'''Returns an interactive plot with the agonia boxes with a confidence value above
a Score selected by the user with a slider. The user can see the mean-trace of a
specific box and scroll through the boxes with a slider'''
data_name, median_projection, boxes_path = get_files_names(data_path)
with open(boxes_path, 'rb') as f:
boxes = pickle.load(f)
f.close()
kdims = [
hv.Dimension('Score', values=np.arange(0.05, 1, 0.05)),
hv.Dimension('box_idx', values=np.arange(0, len(boxes), 1))
]
Experiment = Stream.define('Experiment', data_path=data_path)
dmap = hv.DynamicMap(plot_AGonia_boxes,
kdims=kdims,
streams=[Experiment()])
return dmap
def test_layout_framewise_nonmatching_norm_update(self):
img1 = Image(np.mgrid[0:5, 0:5][0], vdims='z').opts(framewise=True)
stream = Stream.define('zscale', value=1)()
transform = dim('z2') * stream.param.value
img2 = Image(
np.mgrid[0:5, 0:5][0],
vdims='z2').apply.transform(z2=transform).opts(framewise=True)
plot = bokeh_renderer.get_plot(img1 + img2)
img1_plot = plot.subplots[(0, 0)].subplots['main']
img2_plot = plot.subplots[(0, 1)].subplots['main']
img1_cmapper = img1_plot.handles['color_mapper']
img2_cmapper = img2_plot.handles['color_mapper']
self.assertEqual(img1_cmapper.low, 0)
self.assertEqual(img2_cmapper.low, 0)
self.assertEqual(img1_cmapper.high, 4)
self.assertEqual(img2_cmapper.high, 4)
stream.update(value=10)
self.assertEqual(img1_cmapper.high, 4)
self.assertEqual(img2_cmapper.high, 40)
stream.update(value=2)
self.assertEqual(img1_cmapper.high, 4)
self.assertEqual(img2_cmapper.high, 8)
def test_deep_map_apply_element_function_with_stream_kwarg(self):
stream = Stream.define('Test', label='New label')()
fn = lambda i: Curve(np.arange(i))
dmap = DynamicMap(fn, kdims=[Dimension('Test', range=(10, 20))])
mapped = dmap.apply(lambda x, label: x.relabel(label), streams=[stream])
self.assertEqual(mapped[10], fn(10).relabel('New label'))
def clear_selections(arg=None):
geo_bounds.update(bounds=None)
elev_bounds.update(bounds=None)
temp_bounds.update(boundsx=None)
prcp_bounds.update(boundsx=None)
Stream.trigger(selections)
import param
import numpy as np
from holoviews import Dimension, NdLayout, GridSpace, Layout, NdOverlay
from holoviews.core.spaces import DynamicMap, HoloMap, Callable
from holoviews.core.options import Store
from holoviews.element import Image, Scatter, Curve, Text, Points
from holoviews.operation import histogram
from holoviews.plotting.util import initialize_dynamic
from holoviews.streams import Stream, LinkedStream, PointerXY, PointerX, PointerY, RangeX, Buffer
from holoviews.util import Dynamic
from holoviews.element.comparison import ComparisonTestCase
from ..utils import LoggingComparisonTestCase
from .testdimensioned import CustomBackendTestCase, TestObj
XY = Stream.define('XY', x=0, y=0)
X = Stream.define('X', x=0)
Y = Stream.define('Y', y=0)
frequencies = np.linspace(0.5, 2.0, 5)
phases = np.linspace(0, np.pi * 2, 5)
x, y = np.mgrid[-5:6, -5:6] * 0.1
def sine_array(phase, freq):
return np.sin(phase + (freq * x**2 + freq * y**2))
class TestParameters(param.Parameterized):
example = param.Number(default=1)
import param
import numpy as np
from holoviews import Dimension, NdLayout, GridSpace, Layout, NdOverlay
from holoviews.core.spaces import DynamicMap, HoloMap, Callable
from holoviews.core.options import Store
from holoviews.element import Image, Scatter, Curve, Text, Points
from holoviews.operation import histogram
from holoviews.plotting.util import initialize_dynamic
from holoviews.streams import Stream, LinkedStream, PointerXY, PointerX, PointerY, RangeX, Buffer
from holoviews.util import Dynamic
from holoviews.element.comparison import ComparisonTestCase
from .testdimensioned import CustomBackendTestCase, TestObj
XY = Stream.define('XY', x=0,y=0)
frequencies = np.linspace(0.5,2.0,5)
phases = np.linspace(0, np.pi*2, 5)
x,y = np.mgrid[-5:6, -5:6] * 0.1
def sine_array(phase, freq):
return np.sin(phase + (freq*x**2+freq*y**2))
class DynamicMapConstructor(ComparisonTestCase):
def test_simple_constructor_kdims(self):
DynamicMap(lambda x: x, kdims=['test'])
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))
