def update_response(self, electrodes: ElectrodeTable,
pre_alignment_window: Iterable[float],
post_alignment_window: Iterable[float],
event_name: str):
"""
Visualize the electrode grid with on-click selection of channels in one-to-one relationship to units.
Parameters
----------
electrodes: ElectrodeTable
Electrodes Table of an NWBFile.
pre_alignment_window : Iterable[float]
Array-like of the form [start, end] flipped with respect to the value of the alignment time,
in units seconds. E.g., pre_alignment_window = [0.1, 0.5] with alignment_times = [1.2, 2.3, 3.4]
will calculate spiking activity over the windows [[0.7, 1.1], [1.8, 2.2], [2.9, 3.3]] respectively.
post_alignment_window : Iterable[float]
Array-like of the form [start, end] with respect to the value of the alignment time, in units seconds.
E.g., post_alignment_window = [0.1, 0.5] with alignment_times = [1.2, 2.3, 3.4]
will calculate spiking activity over the windows [[1.3, 1.7], [2.4, 2.8], [3.5, 3.9]] respectively.
event_name : str
Name of event from trials table to align to.
"""
unit_ids = electrodes.get_ancestor("NWBFile").units.id.data[:]
n_channels = len(self.scatter.marker.size)
nwbfile = electrodes.get_ancestor("NWBFile")
responses = calculate_all_responses(
units=nwbfile.units,
trials=nwbfile.trials,
pre_alignment_window=pre_alignment_window,
post_alignment_window=post_alignment_window,
event_name=event_name)
channel_response = np.array([np.nan] * n_channels)
channel_response[unit_ids] = responses
self.scatter.marker.cmax = max(responses[~np.isnan(responses)],
default=0)
self.scatter.marker.cmin = min(responses[~np.isnan(responses)],
default=0)
self.scatter.marker.color = channel_response
self.scatter.text = [
f"Channel ID: {channel_id} <br> Responsitivity: {round(response, 2)}"
for channel_id, response in zip(electrodes.id.data[:],
channel_response)
]
def __init__(self, electrodes: ElectrodeTable,
pre_alignment_window: Iterable[float],
post_alignment_window: Iterable[float], event_name: str):
"""
Visualize the electrode grid with on-click selection of channels in one-to-one relationship to units.
Parameters
----------
electrodes: ElectrodeTable
Electrodes Table of an NWBFile.
pre_alignment_window : Iterable[float]
Array-like of the form [start, end] flipped with respect to the value of the alignment time,
in units seconds. E.g., pre_alignment_window = [0.1, 0.5] with alignment_times = [1.2, 2.3, 3.4]
will calculate spiking activity over the windows [[0.7, 1.1], [1.8, 2.2], [2.9, 3.3]] respectively.
post_alignment_window : Iterable[float]
Array-like of the form [start, end] with respect to the value of the alignment time, in units seconds.
E.g., post_alignment_window = [0.1, 0.5] with alignment_times = [1.2, 2.3, 3.4]
will calculate spiking activity over the windows [[1.3, 1.7], [2.4, 2.8], [3.5, 3.9]] respectively.
event_name : str
Name of event from trials table to align to.
"""
super().__init__()
x = electrodes["rel_x"].data[:]
y = electrodes["rel_y"].data[:]
n_channels = len(x)
nwbfile = electrodes.get_ancestor("NWBFile")
unit_ids = nwbfile.units.id.data[:]
responses = calculate_all_responses(
units=nwbfile.units,
trials=nwbfile.trials,
pre_alignment_window=pre_alignment_window,
post_alignment_window=post_alignment_window,
event_name=event_name)
valid_unit_responses = responses[~np.isnan(responses)]
channel_response = np.array([np.nan] * n_channels)
channel_response[unit_ids] = responses
self.fig = go.FigureWidget([
go.Scatter(
x=x,
y=y,
mode="markers",
text=[
f"Channel ID: {channel_id} <br> Responsitivity: {round(response, 2)}"
for channel_id, response in zip(electrodes.id.data[:],
channel_response)
],
marker=dict(colorbar=dict(title="Responsitivity"),
cmax=max(valid_unit_responses, default=0),
cmin=min(valid_unit_responses, default=0),
color=channel_response,
colorscale="Viridis"),
)
])
self.scatter = self.fig.data[0]
s = np.array([ELECTRODE_SIZE] * n_channels)
s[unit_ids] = DETECTED_SIZE
s[unit_ids[0]] = SELECTED_SIZE
self.scatter.marker.size = s
self.scatter.on_click(self.update_point)
self.fig.layout.hovermode = "closest"
self.fig.update_layout(title=dict(text="electrode grid",
xanchor="center",
yanchor="top",
y=0.9,
x=0.5,
font=dict(family="monospace",
size=14)),
autosize=False,
width=420,
height=640,
xaxis=dict(showline=False,
showticklabels=False,
ticks=""),
yaxis=dict(showline=False,
showticklabels=False,
ticks=""))
self.children = [self.fig]