def draw_noise(data_files: Dict[int, File], neuron_id: int, params: MotionParams):
last_day = max(data_files.keys())
lever = load_mat(data_files[last_day]['response'])
neuron_rate = data_files[last_day].attrs['frame_rate']
neurons = common_axis([DataFrame.load(x['spike']) for x in data_files.values()])
good, bad, anti = classify_cells(motion_corr(
lever, neurons[-1], neuron_rate, 16000, params), 0.001)
amp = list()
corrs: Dict[str, List[List[float]]] = {'good': [], 'unrelated': [], 'between': []}
for (day_id, data_file), neuron in zip(data_files.items(), neurons):
if day_id == last_day:
continue
lever = load_mat(data_file['response'])
corrs['good'].append(_take_triu(noise_autocorrelation(lever, neuron[good], neuron_rate)))
corrs['unrelated'].append(_take_triu(noise_autocorrelation(lever, neuron[bad | anti], neuron_rate)))
corrs['between'].append(_take_triu(noise_correlation(lever, neuron[good], neuron[bad | anti], neuron_rate)))
lever.center_on("motion", **params)
neuron_trials = fold_by(neuron, lever, neuron_rate, True)
amp.append(neuron_trials.values[np.argwhere(neuron.axes[0] == neuron_id)[0, 0], :, :].max(axis=1))
with Figure(join(project_folder, 'report', 'img', f'noise_corr_{neuron_id}.svg')) as (ax,):
day_ids = [x for x in data_files.keys() if x != last_day]
for idx, (group_str, group) in enumerate(corrs.items()):
ax.errorbar(day_ids, [np.mean(x) for x in group],
yerr=[_sem(x) for x in group], color=COLORS[idx], label=group_str)
ax2 = ax.twinx()
ax2.errorbar(day_ids, [np.mean(x) for x in amp], [_sem(x) for x in amp], color=COLORS[-1])
ax.set_title(str(neuron_id))
ax.legend()
def draw_hierarchy(data_files: Dict[int, File]):
neurons = common_axis([DataFrame.load(x['spike']) for x in files.values()])
for (day_id, data_file), neuron in zip(files.items(), neurons):
lever = load_mat(data_file['response'])
corr_mat = noise_autocorrelation(lever, neuron, data_file.attrs['frame_rate'])
with Figure() as (ax,):
ax.set_title(f"day-{day_id:02d}")
fancy_dendrogram(linkage(corr_mat, 'average'), ax=ax)
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])
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')
ax2.errorbar(day_ids, [np.mean(x) for x in amp], [_sem(x) for x in amp], color=COLORS[-1])
ax.set_title(str(neuron_id))
ax.legend()
# Cell: Mesuare the inter-cell correlation between trials of typical pushes for single neurons on different days
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])
## actual running
common_id = common_axis([DataFrame.load(x['spike']) for x in files.values()])[-1].axes[0]
draw_classify_neurons(files[14], common_id)
draw_hierarchy(files)
draw_stacked_bar(toml.load(join(res_folder, 'cluster.toml'))) # type: ignore
neuron_ids = toml.load(join(res_folder, "0304-neurons.toml"))['neuron_id']
draw_noise(files, 27, motion_params)
draw_neuron_corr(files, motion_params)
##