def read_kwa_clusters(self):
# Read KWA JSON string.
# Read the cluster info.
clusters = self.clusters_table.col('cluster')
cluster_groups = self.clusters_table.col('group')
groups = self.groups_table.col('group')
group_names = self.groups_table.col('name')
# Getting the colors from the KWA file, or creating them.
# kwa = load_kwa_json(self.kwa_json)
if self.kwa:
cluster_colors = self.kwa['shanks'][self.shank]['cluster_colors']
group_colors = self.kwa['shanks'][self.shank]['group_colors']
else:
cluster_colors = generate_colors(len(clusters))
group_colors = generate_colors(len(groups))
# Create the cluster_info DataFrame.
self.cluster_info = pd.DataFrame(dict(
color=cluster_colors,
group=cluster_groups,
), index=clusters)
self.cluster_colors = self.cluster_info['color'].astype(np.int32)
self.cluster_groups = self.cluster_info['group'].astype(np.int32)
# Create the group_info DataFrame.
self.group_info = pd.DataFrame(dict(
color=group_colors,
name=group_names,
), index=groups)
self.group_colors = self.group_info['color'].astype(np.int32)
self.group_names = self.group_info['name']
def set_data(self, trace=None, freq=None, channel_height=None, channel_names=None, ignored_channels=None, channel_colors=None):
# default settings
self.max_size = 1000
self.duration_initial = 10.
self.default_channel_height = 0.25
self.channel_height_limits = (0.01, 20.)
self.nticks = 10
# these variables will be overwritten after initialization (used to check if init is complete)
self.slice_ref = (-1, -1) # slice paging
self.paintinitialized = False # to stop first slice from being loaded until correctly-shaped data drawn
self.real_data = True # hides grid and painting if we've made up false data of zeros
self.size = 1
if trace is None:
# make up some data to keep the GPU happy, warm, and feeling loved
trace = np.zeros((self.duration_initial * 2, 32))
freq = 1
# don't worry, we won't tell the GPU that it's not actually rendering any useful data, but we need to keep track
self.real_data = False
if channel_colors is None:
channel_colors = pd.Series(generate_colors(trace.shape[1]))
# load initial variables
self.trace = trace
self.channel_colors = channel_colors
self.ignored_channels = ignored_channels
self.freq = freq
self.totalduration = (self.trace.shape[0] - 1) / self.freq
self.totalsamples, self.nchannels = self.trace.shape
self.channels = np.arange(self.nchannels)
if channel_height is None:
channel_height = self.default_channel_height
else:
self.default_channel_height = channel_height
self.channel_height = channel_height
if channel_names is None:
channel_names = pd.Series(['ch{0:d}'.format(i) for i in xrange(self.nchannels)])
self.channel_names = channel_names
x = np.tile(np.linspace(0., self.totalduration, 2), (self.nchannels, 1))
y = np.zeros_like(x)+ np.linspace(-1, 1, self.nchannels).reshape((-1, 1))
self.position, self.shape = process_coordinates(x=x, y=y)
# activate the grid
if self.real_data == True:
self.interaction_manager.get_processor('viewport').update_viewbox()
self.interaction_manager.activate_grid()
# register the updater threads
self.slice_retriever = inthread(SliceRetriever)(impatient=True)
self.slice_retriever.sliceLoaded.connect(self.slice_loaded)
def default_group_info():
group_info = np.zeros((4, 3), dtype=object)
group_info[:, 0] = np.arange(4)
group_info[:, 1] = generate_colors(group_info.shape[0])
group_info[:, 2] = np.array(["Noise", "MUA", "Good", "Unsorted"], dtype=object)
group_info = pd.DataFrame(
{"color": group_info[:, 1].astype(np.int32), "name": group_info[:, 2]}, index=group_info[:, 0].astype(np.int32)
)
return group_info
def default_group_info():
group_info = np.zeros((4, 3), dtype=object)
group_info[:, 0] = np.arange(4)
group_info[:, 1] = generate_colors(group_info.shape[0])
group_info[:, 2] = np.array(['Noise', 'MUA', 'Good', 'Unsorted'],
dtype=object)
group_info = pd.DataFrame(
{'color': group_info[:, 1].astype(np.int32),
'name': group_info[:, 2]},
index=group_info[:, 0].astype(np.int32))
return group_info
def default_group_info():
group_info = np.zeros((4, 3), dtype=object)
group_info[:, 0] = np.arange(4)
group_info[:, 1] = generate_colors(group_info.shape[0])
group_info[:, 2] = np.array(['Noise', 'MUA', 'Good', 'Unsorted'],
dtype=object)
group_info = pd.DataFrame(
{'color': group_info[:, 1].astype(np.int32),
'name': group_info[:, 2]},
index=group_info[:, 0].astype(np.int32))
return group_info
def default_cluster_info(clusters_unique):
n = len(clusters_unique)
cluster_info = pd.DataFrame(
{"color": generate_colors(n), "group": 3 * np.ones(n)}, dtype=np.int32, index=clusters_unique
)
# Put cluster 0 in group 0 (=noise), cluster 1 in group 1 (=MUA)
if 0 in clusters_unique:
cluster_info["group"][0] = 0
if 1 in clusters_unique:
cluster_info["group"][1] = 1
return cluster_info
def default_cluster_info(clusters_unique):
n = len(clusters_unique)
cluster_info = pd.DataFrame({
'color': generate_colors(n),
'group': 3 * np.ones(n)},
dtype=np.int32,
index=clusters_unique)
# Put cluster 0 in group 0 (=noise), cluster 1 in group 1 (=MUA)
if 0 in clusters_unique:
cluster_info['group'][0] = 0
if 1 in clusters_unique:
cluster_info['group'][1] = 1
return cluster_info
def set_data(self,
trace=None,
freq=None,
channel_height=None,
channel_names=None,
ignored_channels=None,
channel_colors=None):
# default settings
self.max_size = 1000
self.duration_initial = 10.
self.default_channel_height = 0.25
self.channel_height_limits = (0.01, 20.)
self.nticks = 10
# these variables will be overwritten after initialization (used to check if init is complete)
self.slice_ref = (-1, -1) # slice paging
self.paintinitialized = False # to stop first slice from being loaded until correctly-shaped data drawn
self.real_data = True # hides grid and painting if we've made up false data of zeros
self.size = 1
if trace is None:
# make up some data to keep the GPU happy, warm, and feeling loved
trace = np.zeros((self.duration_initial * 2, 32))
freq = 1
# don't worry, we won't tell the GPU that it's not actually rendering any useful data, but we need to keep track
self.real_data = False
if channel_colors is None:
channel_colors = pd.Series(generate_colors(trace.shape[1]))
# load initial variables
self.trace = trace
self.channel_colors = channel_colors
self.ignored_channels = ignored_channels
self.freq = freq
self.totalduration = (self.trace.shape[0] - 1) / self.freq
self.totalsamples, self.nchannels = self.trace.shape
self.channels = np.arange(self.nchannels)
if channel_height is None:
channel_height = self.default_channel_height
else:
self.default_channel_height = channel_height
self.channel_height = channel_height
if channel_names is None:
channel_names = pd.Series(
['ch{0:d}'.format(i) for i in xrange(self.nchannels)])
self.channel_names = channel_names
x = np.tile(np.linspace(0., self.totalduration, 2),
(self.nchannels, 1))
y = np.zeros_like(x) + np.linspace(-1, 1, self.nchannels).reshape(
(-1, 1))
self.position, self.shape = process_coordinates(x=x, y=y)
# activate the grid
if self.real_data == True:
self.interaction_manager.get_processor('viewport').update_viewbox()
self.interaction_manager.activate_grid()
# register the updater threads
self.slice_retriever = inthread(SliceRetriever)(impatient=True)
self.slice_retriever.sliceLoaded.connect(self.slice_loaded)
