def plot_prefreezing_traces(session_index,
neurons=None,
dtype='events',
window=1):
# Load data and get freezing timestamps.
session = ff.load_session(session_index)
freeze_epochs = session.get_freezing_epochs_imaging_framerate()
if dtype == 'traces':
data, _ = ca_traces.load_traces(session_index)
data = zscore(data, axis=1)
elif dtype == 'events':
data, _ = ca_events.load_events(session_index)
data[data > 0] = 1
else:
raise ValueError("Invalid data type.")
if neurons is not None:
data = data[neurons]
titles = neuron_number_title(neurons)
else:
titles = neuron_number_title(range(len(data)))
n_neurons = len(data)
n_freezes = freeze_epochs.shape[0]
freeze_duration = np.ceil(window * 20).astype(int)
prefreezing_traces = np.zeros((n_neurons, n_freezes, freeze_duration))
for n, trace in enumerate(data):
for i, epoch in enumerate(freeze_epochs):
start = epoch[0] - freeze_duration
stop = epoch[0]
prefreezing_traces[n, i, :] = trace[start:stop]
if dtype == 'events':
events = [[(np.where(bout)[0] - freeze_duration) / 20 for bout in cell]
for cell in prefreezing_traces]
f = ScrollPlot(plot_funcs.plot_raster,
events=events,
window=window,
xlabel='Time from start of freezing (s)',
ylabel='Freezing bout #',
titles=titles)
elif dtype == 'traces':
f = ScrollPlot(plot_funcs.plot_heatmap,
heatmap=prefreezing_traces,
xlabel='Time from start of freezing (s)',
ylabel='Freezing bout #',
titles=titles)
else:
raise ValueError("Invalid data type.")
def overlay_events(session_index, neurons):
"""
Plot events on top of traces.
:param
session_index: Number corresponding to a session.
neurons: List of neurons.
:return
f: ScrollPlot class.
"""
# Load events and traces.
event_times, event_values = load_event_times(session_index)
traces, t = ca_traces.load_traces(session_index)
# Plot and scroll.
titles = neuron_number_title(neurons)
f = ScrollPlot(plot_funcs.overlay_events,
t=t,
traces=traces[neurons],
event_times=event_times[neurons],
event_values=event_values[neurons],
xlabel='Time (s)',
ylabel='% DF/F',
titles=titles)
return f
def freezing_trace_heatmap(session_index, neurons='all'):
# Load the position data.
session = ff.load_session(session_index)
# Get freezing epochs.
freeze_epochs = session.get_freezing_epochs_imaging_framerate()
# Load trace data.
traces, t = load_traces(session_index)
if neurons == 'all':
n_neurons = len(traces)
neurons = arange(n_neurons)
else:
n_neurons = len(neurons)
# Get dimensions for heatmap.
n_freezes = freeze_epochs.shape[0]
freeze_durations = np.diff(freeze_epochs)
longest_freeze = freeze_durations.max()
# Preallocate heatmap.
freezing_traces = np.full([n_neurons, n_freezes, longest_freeze], np.nan)
for n in neurons:
for i, epoch in enumerate(freeze_epochs):
freezing_traces[n,i,np.arange(freeze_durations[i])] = \
traces[n,epoch[0]:epoch[1]]
titles = neuron_number_title(neurons)
f = ScrollPlot(plot_funcs.heatmap,
heatmap=freezing_traces,
xlabel='Time from start of freezing (s)',
ylabel='Freezing bout #',
titles=titles)
def plot_traces(self, neurons='all'):
if neurons == 'all':
neurons = np.arange(len(self.traces))
selected_traces = self.aligned_traces
else:
selected_traces = [self.aligned_traces[n] for n in neurons]
titles = neuron_number_title(neurons)
f = ScrollPlot(plot_funcs.plot_multiple_traces,
t=self.ref_time,
traces=selected_traces,
xlabel='Time from shock (s)',
ylabel='Calcium activity (s.d.)',
titles=titles)
return f
def plot_events(session_index, neurons):
"""
Plot events as a scatter plot.
:param
session_index: Number corresponding to a session.
neurons: List of neurons.
:return
f: ScrollPlot class.
"""
# Load events.
event_times, event_values = load_event_times(session_index)
# Plot and scroll through calcium events.
titles = neuron_number_title(neurons)
f = ScrollPlot(plot_funcs.plot_events,
event_times=event_times[neurons],
event_values=event_values[neurons],
xlabel='Time (s)',
ylabel='Event magnitude',
titles=titles)
return f
def plot_traces(session_index, neurons):
"""
Plot the traces of multiple neurons. You can scroll between neurons!
:param
session_index: Number corresponding to a session.
neurons: List of neurons.
:return
"""
# Load the traces.
traces, t = load_traces(session_index)
# Scroll through.
titles = neuron_number_title(neurons)
f = ScrollPlot(plot_funcs.plot_traces,
t=t,
traces=traces[neurons],
xlabel='Time (s)',
ylabel='%DF/F',
titles=titles)
# Gets the ScrollPlot object.
return f