def plot_cno_saline():
data: pd.DataFrame = pd.read_csv(
analysis_folder.joinpath("decoder_cno.csv"),
usecols=[1, 2, 3],
index_col=[2, 1]).sort_index() # type: ignore
group_strs = ('saline', 'cno')
paired_data = [data.loc[treat, 'mutual_info'] for treat in group_strs]
p_value = wilcoxon(*paired_data).pvalue
res = ["median: "] + str(data.groupby("treat").median()).split('\n')[2:]
print_stats("saline vs. cno in Gq", res + [f"paired wilcox: p={p_value}"])
with Figure(fig_folder.joinpath("decoder-pair-cno.svg"),
figsize=(6, 9)) as axes:
boxplots = plots.boxplot(axes[0],
paired_data,
whis=(10., 90.),
zorder=1,
showfliers=False,
colors=colors[2:4],
widths=0.65)
plots.dots(axes[0], paired_data, zorder=3, s=24)
axes[0].set_xticklabels(["Gq Saline", "Gq CNO"])
plots.annotate_boxplot(axes[0], boxplots, 24, 1.2, [((0, 1), p_value)])
[
axes[0].plot([1, 2], x, color='gray')
for x in np.array(paired_data).T
]
def draw_stacked_bar(cluster_file: ClusterFile):
days = ('5', '10', '13', '14')
res = [[len(cluster_file[day].get(str(cluster_id), []))
for cluster_id in range(1, 15)] for day in days]
with Figure() as (ax,):
stacked_bar(ax, res, COLORS)
ax.set_xticks(range(len(days)), days)
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_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 main():
trial_neurons = get_result([x.name for x in mice][0:1], [res_trial_neuron],
'trial-neuron-2s-run')
values = trial_neurons[0][0].values
with Figure(fig_folder.joinpath("classifier", "example-neurons.svg"),
show=True) as axes:
for id_neuron, neuron in enumerate(values[:20, 0:4, :]):
for id_trial, trial in enumerate(neuron):
axes[0].plot(range(id_trial * 11, id_trial * 11 + 10),
trial / trial.max() * 5 + id_neuron * 6,
color='red')
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')