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 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()
def test_wizard_merge():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
target = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
cluster = w.current_candidate()
# Simulate a merge: target and cluster ==> cluster_new.
cluster_new = clusters_unique.max() + 1
clusters[clusters == target] = cluster_new
clusters[clusters == cluster] = cluster_new
log.debug("Merged {0:d} and {1:d} to {2:d}".format(target, cluster,
cluster_new))
similarity_matrix = create_similarity_matrix(nclusters - 1)
indices = [
x for x in xrange(cluster_offset, cluster_offset + nclusters + 1)
if x != cluster and x != target
]
cluster_groups = pd.Series(np.array(np.ones(nclusters - 1) * 3,
dtype=np.int32),
index=np.array(indices))
# Update the wizard.
quality = np.diag(similarity_matrix)
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates(cluster_new)
assert w.current_target() == cluster_new
c = w.current_candidate()
assert c is not None
assert w.previous_candidate() == w.current_candidate()
assert w.next_candidate() == c
for _ in xrange(nclusters):
c = w.next_candidate()
assert c not in (target, cluster)
def test_wizard():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
best_cluster = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
# Check the first target cluster.
assert w.current_target() == best_cluster
# Test impossible previous.
assert w.previous_candidate() == w.current_candidate()
# Check next/previous.
c0 = w.next_candidate()
c1 = w.next_candidate()
assert w.previous_candidate() == c0
assert w.next_candidate() == c1
# Check skip target.
t0 = w.current_target()
w.skip_target()
w.update_candidates()
t1 = w.current_target()
assert t0 != t1
def test_wizard_move():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
target = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
cluster0 = w.current_candidate()
cluster1 = w.next_candidate()
cluster2 = w.next_candidate()
# Simulate a move.
cluster_groups.ix[cluster2] = 1
# Update the wizard.
w.set_data(cluster_groups=cluster_groups)
w.update_candidates(target)
assert w.current_target() == target
assert w.current_candidate() not in (cluster0, cluster1, cluster2)
for _ in xrange(nclusters):
c = w.next_candidate()
assert c != cluster2
def test_wizard_merge():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
target = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
cluster = w.current_candidate()
# Simulate a merge: target and cluster ==> cluster_new.
cluster_new = clusters_unique.max() + 1
clusters[clusters == target] = cluster_new
clusters[clusters == cluster] = cluster_new
log.debug("Merged {0:d} and {1:d} to {2:d}".format(
target, cluster, cluster_new))
similarity_matrix = create_similarity_matrix(nclusters - 1)
indices = [x for x in xrange(cluster_offset, cluster_offset + nclusters + 1)
if x != cluster and x != target]
cluster_groups = pd.Series(np.array(np.ones(nclusters - 1) * 3, dtype=np.int32),
index=np.array(indices))
# Update the wizard.
quality = np.diag(similarity_matrix)
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates(cluster_new)
assert w.current_target() == cluster_new
c = w.current_candidate()
assert c is not None
assert w.previous_candidate() == w.current_candidate()
assert w.next_candidate() == c
for _ in xrange(nclusters):
c = w.next_candidate()
assert c not in (target, cluster)
def test_wizard():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
best_cluster = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
# Check the first target cluster.
assert w.current_target() == best_cluster
# Test impossible previous.
assert w.previous_candidate() == w.current_candidate()
# Check next/previous.
c0 = w.next_candidate()
c1 = w.next_candidate()
assert w.previous_candidate() == c0
assert w.next_candidate() == c1
# Check skip target.
t0 = w.current_target()
w.skip_target()
w.update_candidates()
t1 = w.current_target()
assert t0 != t1
def test_wizard_move():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
target = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
cluster0 = w.current_candidate()
cluster1 = w.next_candidate()
cluster2 = w.next_candidate()
# Simulate a move.
cluster_groups.ix[cluster2] = 1
# Update the wizard.
w.set_data(cluster_groups=cluster_groups)
w.update_candidates(target)
assert w.current_target() == target
assert w.current_candidate() not in (cluster0, cluster1, cluster2)
for _ in xrange(nclusters):
c = w.next_candidate()
assert c != cluster2
def __init__(self, parent=None, dolog=True, filename=None):
super(KwikSkope, self).__init__(parent)
# HACK: display the icon in Windows' taskbar.
if os.name == 'nt':
try:
import ctypes
myappid = 'klustateam.kwikskope'
ctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID(
myappid)
except:
pass
self.dolog = dolog
if self.dolog:
create_file_logger()
log.debug("Using {0:s}.".format(QT_BINDING))
# Main window options.
self.move(50, 50)
self.setWindowTitle('KwikSkope')
# Focus options.
self.setFocusPolicy(QtCore.Qt.WheelFocus)
self.setMouseTracking(True)
# Dock widgets options.
self.setDockNestingEnabled(True)
self.setAnimated(False)
self.setWindowIcon(get_icon('logo'))
# Initialize some variables.
# self.statscache = None
# self.loader = KlustersLoader()
self.loader = HDF5Loader()
self.loader.progressReported.connect(self.open_progress_reported)
self.loader.saveProgressReported.connect(self.save_progress_reported)
self.wizard = Wizard()
self.controller = None
self.spikes_highlighted = []
self.spikes_selected = []
self._wizard = False
self.is_file_open = False
self.need_save = False
self.busy_cursor = QtGui.QCursor(QtCore.Qt.BusyCursor)
self.normal_cursor = QtGui.QCursor(QtCore.Qt.ArrowCursor)
self.is_busy = False
self.override_color = False
self.computing_correlograms = False
self.computing_matrix = False
# Create the main window.
self.create_views()
self.create_file_actions()
self.create_edit_actions()
self.create_view_actions()
self.create_help_actions()
self.create_menu()
self.create_toolbar()
self.create_open_progress_dialog()
self.create_save_progress_dialog()
self.create_threads()
# Update action enabled/disabled property.
self.update_action_enabled()
# Show the main window.
self.set_styles()
self.restore_geometry()
# Automatically load a file upon startup if requested.
if filename:
filename = os.path.realpath(filename)
self.open_task.open(self.loader, filename)
self.show()