def draw_network_graph(data_files: Dict[int, File], params: MotionParams, threshold: int = 16000):
"""Draw neuron functional connection for each session, with neurons colored by the last session.
Args:
data_files: {day_id: int, data_file: File}
params: classify_cells need ["quiet_var", "window_size", "event_thres", "pre_time"]
threshold: threshold for motion_corr, single linked cluster distance
"""
last_day = data_files[max(data_files.keys())]
neurons = common_axis([DataFrame.load(x['spike']) for x in data_files.values()])
neuron_rate = last_day.attrs['frame_rate']
final_corr_mat = noise_autocorrelation(load_mat(last_day['response']), neurons[-1], neuron_rate)
categories = classify_cells(motion_corr(last_day, neurons[-1], neuron_rate, threshold, params), 0.001)
layout = corr_graph.get_layout(final_corr_mat, neurons[-1].axes[0])
for (day_id, data_file), neuron in zip(data_files.items(), neurons):
corr_mat = noise_autocorrelation(load_mat(data_file['response']), neuron, neuron_rate)
with Figure(join(img_folder, f"network-day-{day_id:02d}.svg")) as ax:
corr_graph.corr_plot(ax[0], corr_mat, categories, neuron.axes[0], layout=layout)
print('done')
def draw_classify_neurons(data_file: File, neuron_ids: Optional[np.ndarray] = None):
lever = load_mat(data_file['response'])
neuron = DataFrame.load(data_file['spike'])
if neuron_ids is not None:
neuron = neuron[search_ar(neuron_ids, neuron.axes[0]), :]
neuron_rate = data_file.attrs['frame_rate']
corr = motion_corr(lever, neuron, neuron_rate, 16000, motion_params)
good, bad, anti = [corr[x, 0] for x in classify_cells(corr, 0.001)]
with Figure(join(img_folder, "good_unrelated_cmp.svg"), (4, 6)) as ax:
ax[0].bar((0, 1), [good.mean(), np.r_[bad, anti].mean()], yerr=[_sem(good), _sem(np.r_[bad, anti])])
def draw_neuron_corr(data_files: Dict[int, File], params: MotionParams, fov_id: str = None):
neurons = common_axis([DataFrame.load(x['spike']) for x in data_files.values()])
last_day = max(data_files.keys())
lever = load_mat(data_files[last_day]['response'])
neuron_rate = data_files[last_day].attrs['frame_rate']
good, bad, anti = classify_cells(motion_corr(
lever, neurons[-1], neuron_rate, 16000, params), 0.001)
result_list = list()
for (day, data_file), neuron in zip(data_files.items(), neurons):
lever.center_on('motion') # type: ignore
motion_neurons = fold_by(neuron, lever, neuron_rate, True)
result_list.append([reliability(motion_neuron) for motion_neuron in motion_neurons.values])
result = np.array(result_list)
with Figure(join(img_folder, ("neuron_corr.svg" if fov_id is None else f"{fov_id}.svg"))) as ax:
ax[0].plot(list(data_files.keys()), result[:, good])