def animate(self, duration, repeat=False, figure=None):
""" Animate the scatter collection, taking *duration* seconds to do so.
LinkedPanels works across figures, but matplotlib's animation tools are
figure-centric, so a figure is chosen arbitrarily from the set of
figures associated with the axes in self.panels.panels. The figure
can be specified by the kwarg *figure* if desired.
"""
# LinkedPanels is figure-agnostic (works across figures), so get all figures
# from panels
fig_set = set()
for k in self.panels.panels:
fig_k = self.panels.panels[k].figure
fig_set.add(fig_k)
if figure is None:
figure = tuple(fig_set)[0]
pipe_anim = PipelineAnimation(
duration, self.artist_outlets,
variable='time',
limits=self.panels.bounds.time,
branchpoint_data_source=self.branchpoint)
# Send a refresh of the data down the pipe to load the animation's
# cache of the current data display.
get_exchange('SD_reflow_start').send("Pre-animation data reflow")
# skip the reflow done message, since nothing should have changed about the data
# or bounds from the previous reflow. Prevents an extra draw of the figure.
# get_exchange('SD_reflow_done').send("Pre-animation data reflow done")
the_animator = FixedDurationAnimation(figure, duration, pipe_anim, interval=50, repeat=repeat)
return the_animator
def on_axes_changed(self, axes):
""" Examine axes to see if axis limits really changed, and if so trigger a message. """
# Expect that the axes where the event was generated are this instance's axes
if axes != self.axes:
return
get_exchange('MPL_interaction_complete').send(self)
def __init__(self, panels, color_field='time', default_color_bounds=None, s=4, antialiased=False, **kwargs):
""" *panels* is a LinkedPanels instance. extra kwargs are passed to the call to scatter"""
if default_color_bounds is None:
default_color_bounds = Bounds()
self.default_color_bounds = default_color_bounds
self.mappable_updaters = set()
self.color_field = color_field
self.panels=panels
self.artist_outlet_controllers = set()
bounds_updated_xchg = get_exchange('SD_bounds_updated')
artist_outlets = []
empty = [0,]
for ax in panels.ax_specs:
# create a new scatter artist
art = ax.scatter(empty, empty, c=empty, s=s, edgecolors='none', antialiased=antialiased, **kwargs)
# Need to update the color mapping using the specified color field. It needs to know that the
# bounds have been updated in order to adjust the color limits.
up = MappableRangeUpdater(art, color_field=color_field, default_bounds=default_color_bounds)
bounds_updated_xchg.attach(up)
self.mappable_updaters.add(up)
# Need to update the actual scatter coordinate data on each scatter artist
outlet = ScatterArtistOutlet(art, coord_names=panels.ax_specs[ax], color_field=color_field)
self.artist_outlet_controllers.add(outlet)
self.mappable_updaters.add(outlet)
artist_outlets.append(outlet.update())
self.artist_outlets=artist_outlets
self.branchpoint = Branchpoint(artist_outlets)
def __init__(self, h5filename, table_path=None, target=None, mode='r'):
self.target = target
self.h5file = tables.openFile(h5filename, mode=mode)
self.table = self.h5file.getNode(table_path)
self.data = self.table[:]
get_exchange('SD_reflow_start').attach(self)
flash_table_path = table_path.replace('events', 'flashes')
try:
self.flash_table = self.h5file.getNode(flash_table_path)
self.flash_data = self.flash_table[:]
except tables.NoSuchNodeError:
self.flash_table = None
print "Did not find flash data at {0}".format(flash_table_path)
def scatter_dataset_on_panels(panels, color_field=None):
bounds_updated_xchg = get_exchange('SD_bounds_updated')
all_outlets = []
empty = [
0,
]
for ax in panels.ax_specs:
# create a new scatter artist
art = ax.scatter(empty,
empty,
c=empty,
s=4,
marker='s',
edgecolors='none')
# Need to update the color mapping using the specified color field. It needs to know that the
# bounds have been updated in order to adjust the color limits.
up = MappableRangeUpdater(art, color_field=color_field)
bounds_updated_xchg.attach(up)
# Need to update the actual scatter coordinate data on each scatter artist
outlet = ScatterArtistOutlet(art,
coord_names=panels.ax_specs[ax],
color_field=color_field)
all_outlets.append(outlet.update())
brancher = Branchpoint(all_outlets)
return brancher
def __init__(self, h5filename, table_path=None, target=None, mode='r'):
self.target = target
self.h5file = tables.openFile(h5filename, mode=mode)
self.table = self.h5file.getNode(table_path)
self.data = self.table[:]
get_exchange('SD_reflow_start').attach(self)
flash_table_path = table_path.replace('events', 'flashes')
try:
self.flash_table = self.h5file.getNode(flash_table_path)
self.flash_data = self.flash_table[:]
except tables.NoSuchNodeError:
self.flash_table = None
print "Did not find flash data at {0}".format(flash_table_path)
def __init__(self, target=None, initial_height=0.0, alt_name='alt', alt_factor=1000.0):
self.bounds_updated_xchg = get_exchange('SD_bounds_updated')
self.bounds_updated_xchg.attach(self)
self.alt_factor = alt_factor
self.alt_name = alt_name
self.z = initial_height
self.target=target
def __init__(self, data, target=None):
self.target = target
self.data = data
self.reflow_start_xchg = get_exchange('SD_reflow_start')
# Need to find a way to detach when "done" with dataset. __del__ doesn't work
# because of some gc issues. context manager doesn't exactly work, since the
# dataset objects are not one-shot tasks; they live for the whole lifecycle.
# Anyway, ignoring for now.
self.reflow_start_xchg.attach(self)
def redraw(panels):
""" this function forces a manual redraw / re-flow of the data to the plot.
"""
get_exchange('SD_bounds_updated').send(panels.bounds)
get_exchange('SD_reflow_start').send('Manual redraw')
get_exchange('SD_reflow_done').send('Manual redraw complete')
def redraw(panels):
""" this function forces a manual redraw / re-flow of the data to the plot.
"""
get_exchange("SD_bounds_updated").send(panels.bounds)
get_exchange("SD_reflow_start").send("Manual redraw")
get_exchange("SD_reflow_done").send("Manual redraw complete")
def __init__(self, target=None, host=None, basedate=None):
self.target = target
get_exchange('SD_reflow_start').attach(self)
self._t_offset = 0.0
if basedate is not None:
# corrects for the meaning of time in the LMA analysis code
self._t_offset += (basedate - datetime(1970, 1, 1)).total_seconds()
self._dataq = deque([])
self.livesource = LiveLMAController()
# New sources are sent as messages to self.show
self.livesource.views.append(self)
self._websocket_client = WebsocketClient(host=host)
# client.connect(on_message=liveDataController.on_message)
sock_thr = threading.Thread(target=self._websocket_client.connect,
kwargs={'on_message':self.livesource.on_message})
sock_thr.daemon=True
sock_thr.start()
def __init__(self,
panels,
color_field='time',
default_color_bounds=None,
s=4,
antialiased=False,
**kwargs):
""" *panels* is a LinkedPanels instance. extra kwargs are passed to the call to scatter"""
if default_color_bounds is None:
default_color_bounds = Bounds()
self.default_color_bounds = default_color_bounds
self.mappable_updaters = set()
self.color_field = color_field
self.panels = panels
self.artist_outlet_controllers = set()
bounds_updated_xchg = get_exchange('SD_bounds_updated')
artist_outlets = []
empty = [
0,
]
for ax in panels.ax_specs:
# create a new scatter artist
art = ax.scatter(empty,
empty,
c=empty,
s=s,
edgecolors='none',
antialiased=antialiased,
**kwargs)
# Need to update the color mapping using the specified color field. It needs to know that the
# bounds have been updated in order to adjust the color limits.
up = MappableRangeUpdater(art,
color_field=color_field,
default_bounds=default_color_bounds)
bounds_updated_xchg.attach(up)
self.mappable_updaters.add(up)
# Need to update the actual scatter coordinate data on each scatter artist
outlet = ScatterArtistOutlet(art,
coord_names=panels.ax_specs[ax],
color_field=color_field)
self.artist_outlet_controllers.add(outlet)
self.mappable_updaters.add(outlet)
artist_outlets.append(outlet.update())
self.artist_outlets = artist_outlets
self.branchpoint = Branchpoint(artist_outlets)
def animate(self, duration, repeat=False, figure=None):
""" Animate the scatter collection, taking *duration* seconds to do so.
LinkedPanels works across figures, but matplotlib's animation tools are
figure-centric, so a figure is chosen arbitrarily from the set of
figures associated with the axes in self.panels.panels. The figure
can be specified by the kwarg *figure* if desired.
"""
# LinkedPanels is figure-agnostic (works across figures), so get all figures
# from panels
fig_set = set()
for k in self.panels.panels:
fig_k = self.panels.panels[k].figure
fig_set.add(fig_k)
if figure is None:
figure = tuple(fig_set)[0]
pipe_anim = PipelineAnimation(duration,
self.artist_outlets,
variable='time',
limits=self.panels.bounds.time,
branchpoint_data_source=self.branchpoint)
# Send a refresh of the data down the pipe to load the animation's
# cache of the current data display.
get_exchange('SD_reflow_start').send("Pre-animation data reflow")
# skip the reflow done message, since nothing should have changed about the data
# or bounds from the previous reflow. Prevents an extra draw of the figure.
# get_exchange('SD_reflow_done').send("Pre-animation data reflow done")
the_animator = FixedDurationAnimation(figure,
duration,
pipe_anim,
interval=50,
repeat=repeat)
return the_animator
def scatter_dataset_on_panels(panels, color_field=None):
bounds_updated_xchg = get_exchange('SD_bounds_updated')
all_outlets = []
empty = [0,]
for ax in panels.ax_specs:
# create a new scatter artist
art = ax.scatter(empty, empty, c=empty, s=4, marker='s', edgecolors='none')
# Need to update the color mapping using the specified color field. It needs to know that the
# bounds have been updated in order to adjust the color limits.
up = MappableRangeUpdater(art, color_field=color_field)
bounds_updated_xchg.attach(up)
# Need to update the actual scatter coordinate data on each scatter artist
outlet = ScatterArtistOutlet(art, coord_names=panels.ax_specs[ax], color_field=color_field)
all_outlets.append(outlet.update())
brancher = Branchpoint(all_outlets)
return brancher
def __init__(self, *args, **kwargs):
""" Register to receive lasso events.
cache_segment is typically set to receive the results of all data
selection operations, as though it were a plot, so that the
current plot state can be subset. The instantiator of this class
should set the target of a data emitter to
payload_lasso.cache_segment.cache_segment(), The target of the
cache segment is a lasso_filter, followed by the addition of the
payload, and finally, the target kwarg passed when this class is
instantiated.
On receiving a new lasso, trigger a resend of the cached data
to the dataset modifier.
"""
self.target = kwargs.pop('target', None)
self._payload = None
self.lasso_filter = LassoFilter(target=self.add_payload_value(target=self.target))
self.lasso_xchg = get_exchange('B4D_panel_lasso_drawn')
self.lasso_xchg.attach(self)
self.cache_segment = CachedTriggerableSegment(target=self.lasso_filter.filter())
def __init__(self, *args, **kwargs):
""" Register to receive lasso events.
cache_segment is typically set to receive the results of all data
selection operations, as though it were a plot, so that the
current plot state can be subset. The instantiator of this class
should set the target of a data emitter to
payload_lasso.cache_segment.cache_segment(), The target of the
cache segment is a lasso_filter, followed by the addition of the
payload, and finally, the target kwarg passed when this class is
instantiated.
On receiving a new lasso, trigger a resend of the cached data
to the dataset modifier.
"""
self.target = kwargs.pop('target', None)
self._payload = None
self.lasso_filter = LassoFilter(target=self.add_payload_value(
target=self.target))
self.lasso_xchg = get_exchange('B4D_panel_lasso_drawn')
self.lasso_xchg.attach(self)
self.cache_segment = CachedTriggerableSegment(
target=self.lasso_filter.filter())
def __init__(self, h5dataset, target=None):
self.target = target
self.h5dataset = h5dataset
get_exchange('SD_reflow_start').attach(self)
def _setup_events(self):
self.interaction_xchg = get_exchange('MPL_interaction_complete')
self.interaction_xchg.attach(self)
self.bounds_updated_xchg = get_exchange('SD_bounds_updated')
self.reflow_start_xchg = get_exchange('SD_reflow_start')
self.reflow_done_xchg = get_exchange('SD_reflow_done')
def __init__(self, figure):
self.figure = figure
# Tell the figure to update (draw) when the bounds change.
get_exchange('SD_reflow_done').attach(self)
def _setup_events(self):
self.interaction_xchg = get_exchange('MPL_interaction_complete')
self.interaction_xchg.attach(self)
self.bounds_updated_xchg = get_exchange('SD_bounds_updated')
self.reflow_start_xchg = get_exchange('SD_reflow_start')
self.reflow_done_xchg = get_exchange('SD_reflow_done')
def __init__(self, figure):
self.figure=figure
# Tell the figure to update (draw) when the bounds change.
get_exchange('SD_reflow_done').attach(self)
def __init__(self, h5dataset, target=None):
self.target=target
self.h5dataset = h5dataset
get_exchange('SD_reflow_start').attach(self)
def _lasso_callback(self, ax, lasso_line, verts):
self.figure.canvas.widgetlock.release(self._active_lasso)
self._active_lasso = None
xchg = get_exchange("B4D_panel_lasso_drawn")
xchg.send((self, ax, lasso_line, verts))
def _lasso_callback(self, ax, lasso_line, verts):
self.figure.canvas.widgetlock.release(self._active_lasso)
self._active_lasso=None
xchg = get_exchange('B4D_panel_lasso_drawn')
xchg.send((self, ax, lasso_line, verts))
