def open_done(self):
self.is_file_open = True
# HACK: force release of Control key.
self.force_key_release()
channels = self.get_view('ChannelView').selected_channels()
if channels:
self.get_view('ChannelView').unselect()
# Create the Controller.
self.controller = Controller(self.loader)
# Create the cache for the channel statistics that need to be
# computed in the background.
# self.statscache = StatsCache(self.loader.ncorrbins)
# Update stats cache in IPython view.
ipython = self.get_view('IPythonView')
# if ipython:
# ipython.set_data(stats=self.statscache)
# Initialize the wizard.
self.wizard = Wizard()
# Update the views.
self.update_channel_view()
self.update_trace_view()
def load():
# Open the mock data.
dir = TEST_FOLDER
xmlfile = os.path.join(dir, 'test.xml')
l = KlustersLoader(filename=xmlfile)
c = Controller(l)
return (l, c)
def open_done(self):
self.is_file_open = True
self.setWindowTitle('KlustaViewa: {0:s}'.format(
os.path.basename(self.loader.filename)
))
register(FileLogger(self.loader.log_filename, name='kwik',
level=logging.INFO))
# Start the selection buffer.
self.buffer = Buffer(self,
# delay_timer=.1, delay_buffer=.2
delay_timer=USERPREF['delay_timer'],
delay_buffer=USERPREF['delay_buffer']
)
self.buffer.start()
self.buffer.accepted.connect(self.buffer_accepted_callback)
# HACK: force release of Control key.
self.force_key_release()
clusters = self.get_view('ClusterView').selected_clusters()
if clusters:
self.get_view('ClusterView').unselect()
# Create the Controller.
self.controller = Controller(self.loader)
# Create the cache for the cluster statistics that need to be
# computed in the background.
self.statscache = StatsCache(SETTINGS.get('correlograms.ncorrbins', NCORRBINS_DEFAULT))
# Update stats cache in IPython view.
ipython = self.get_view('IPythonView')
if ipython:
ipython.set_data(stats=self.statscache)
# Initialize the wizard.
self.wizard = Wizard()
# Update the task graph.
self.taskgraph.set(self)
# self.taskgraph.update_projection_view()
self.taskgraph.update_cluster_view()
self.taskgraph.compute_similarity_matrix()