def __init__(self, data: DataFrame, stimulus: dict,
sample_rate: int) -> None:
self.initialized = False # type: bool
self.converted = False
DataFrame.__init__(self, data.values, data.axes)
Stimulus.__init__(self, stimulus)
self.sample_rate = sample_rate
def test_enumerate():
array1 = DataFrame(np.arange(24).reshape(2, 3, 4), [np.arange(2), np.arange(3), np.arange(4)])
result1 = list(array1.enumerate())
correct1 = [[[0, 0, 0], 0], [[0, 0, 1], 1], [[0, 0, 2], 2], [[0, 0, 3], 3],
[[0, 1, 0], 4], [[0, 1, 1], 5], [[0, 1, 2], 6], [[0, 1, 3], 7],
[[0, 2, 0], 8], [[0, 2, 1], 9], [[0, 2, 2], 10], [[0, 2, 3], 11],
[[1, 0, 0], 12], [[1, 0, 1], 13], [[1, 0, 2], 14], [[1, 0, 3], 15],
[[1, 1, 0], 16], [[1, 1, 1], 17], [[1, 1, 2], 18], [[1, 1, 3], 19],
[[1, 2, 0], 20], [[1, 2, 1], 21], [[1, 2, 2], 22], [[1, 2, 3], 23]]
assert(np.array_equal(result1, correct1))
def test_group_mean():
data = DataFrame(np.arange(81).reshape((3, 3, 3, 3)),
[np.arange(3), np.arange(3), np.arange(1, 4), np.arange(2, 5)])
group1 = [np.array(['a', 'b', 'a']), np.array(['c', 'd', 'c'])]
correct1 = np.array([[[[12, 13, 14], [12, 13, 14]],
[[12, 13, 14], [12, 13, 14]]],
[[[39, 40, 41], [39, 40, 41]],
[[39, 40, 41], [39, 40, 41]]],
[[[66, 67, 68], [66, 67, 68]],
[[66, 67, 68], [66, 67, 68]]]])
axes1 = [np.arange(3), np.array(['a', 'b']), np.array(['c', 'd']), np.arange(2, 5)]
assert data.group_by(group1, lambda x: np.mean(x, axis=1)) == DataFrame(correct1, axes1)
group2 = [np.array(['a', 'b', 'a'])]
correct2 = np.array([[[[9, 10, 11], [12, 13, 14], [15, 16, 17]],
[[9, 10, 11], [12, 13, 14], [15, 16, 17]]],
[[[36, 37, 38], [39, 40, 41], [42, 43, 44]],
[[36, 37, 38], [39, 40, 41], [42, 43, 44]]],
[[[63, 64, 65], [66, 67, 68], [69, 70, 71]],
[[63, 64, 65], [66, 67, 68], [69, 70, 71]]]])
axes2 = [np.arange(3), np.array(['a', 'b']), np.arange(1, 4), np.arange(2, 5)]
result2 = data.group_by(group2, lambda x: np.mean(x, axis=1))
assert result2 == DataFrame(correct2, axes2)
correct2_5 = np.array([[[12, 13, 14], [12, 13, 14]],
[[39, 40, 41], [39, 40, 41]],
[[66, 67, 68], [66, 67, 68]]])
axes2_5 = [np.arange(3), np.array(['a', 'b']), np.arange(2, 5)]
result2_5 = data.group_by(group2, lambda x: np.mean(x, axis=(1, 2)))
assert result2_5 == DataFrame(correct2_5, axes2_5)
def full_trace(record_file: File, ax: Axes):
activity = DataFrame.load(record_file["spike"])
lever = load_mat(record_file['response'])
new_lever = resample(lever.values[0], lever.sample_rate, record_file.attrs['frame_rate'])
for x in range(activity.shape[0]):
ax.plot(_scale(activity.values[x, :] / 5))
ax.plot(_scale(new_lever) / 2 - 3, color=COLORS[0])
def visualize_prediction(data_file: File, params: MotionParams):
"""Show 3 PCs of neuron activity and the plane in linear SVC classifier."""
lever = get_trials(data_file, params)
trials = fold_by(DataFrame.load(data_file['spike']), lever,
data_file.attrs['frame_rate'], True)
k_cluster = k_means(lever.values, 2)[1].astype(np.bool)
main_cluster, null_cluster = np.flatnonzero(
k_cluster)[:20], np.flatnonzero(~k_cluster)[:10]
all_neurons = np.hstack([
trials.values.take(main_cluster, 1),
trials.values.take(null_cluster, 1)
])
all_results = np.array([0] * 20 + [1] * 10)
training_X = all_neurons.swapaxes(0, 1).reshape(all_neurons.shape[1], -1)
pca_weigths = PCA(20).fit_transform(training_X)
classifier = SVC(kernel='linear')
classifier.fit(pca_weigths, all_results)
classifier.score(pca_weigths, all_results)
coef = classifier.coef_[0]
intercept = classifier.intercept_
xx, yy = np.meshgrid(
np.linspace(pca_weigths[:, 0].min(), pca_weigths[:, 0].max(), 20),
np.linspace(pca_weigths[:, 1].min(), pca_weigths[:, 1].max(), 20))
z = (-intercept - xx * coef[0] - yy * coef[1]) / coef[2]
with Figure(projection='3d') as ax:
ax[0].scatter(*pca_weigths[:, 6:9].T,
color=np.asarray(COLORS)[all_results],
s=50)
ax[0].plot_surface(xx, yy, z, alpha=0.4, color=COLORS[-1])
ax[0].set_zlim(pca_weigths[:, 2].min(), pca_weigths[:, 2].max())
def svr_parameters(data_file: File, info: Dict[str, str]):
lever = load_mat(data_file['response'])
values = devibrate(lever.values[0], sample_rate=lever.sample_rate)
y = InterpolatedUnivariateSpline(lever.axes[0],
values)(data_file['spike']['y'])[1:]
X = data_file['spike']['data'][:, 1:]
gammas = np.linspace(-8, -5, 12, endpoint=False)
Cs = np.linspace(3, 15, 12, endpoint=False)
def pred(gamma, C):
hat = cross_predict(X,
y,
svr.predictor_factory(y,
gamma=10**gamma,
C=C,
epsilon=1E-3),
section_mi=False)
return mutual_info(y, hat)
res = map_table(pred, gammas, Cs)
save_path = join(res_folder,
f"svr_params_test_{info['id']}_{info['session']}.npz")
np.savez_compressed(save_path, values=np.asarray(res), axes=[gammas, Cs])
res_df = DataFrame(np.asarray(res), [gammas, Cs])
with Figure() as (ax, ):
labeled_heatmap(ax, res_df.values, res_df.axes[1], res_df.axes[0])
print('done')
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 load_plain(file_path: Union[str, Path]) -> SparseRec:
raw_log = unpack(file_path)
mode_switches = flatten_events(raw_log['event'],
('passive', 'wait_push', 'push', 'pull'))
trial_switches = flatten_events(raw_log['event'],
('trial', 'reward', 'intertrial'))
trials = events2trials(trial_switches, mode_switches)
sample_rate = 1000
config = {
**raw_log["design"],
**raw_log["hardware"], "blank_time":
raw_log["design"]["program"]["violation"] / 1E6,
"stim_time":
raw_log["design"]["program"]["trial"] / 1E6
}
stimulus = {
'config': config,
"timestamps": trials["trial"],
"rewardstamps": trials["reward"],
"sequence": {
"trials": trials
}
}
trace = DataFrame(
32768 - raw_log['lever']['value'][np.newaxis, :],
[np.array(["right-lever"]), raw_log['lever']['timestamp']])
return SparseRec(trace, stimulus, sample_rate)
def test_find_deviate(lever_file):
raw_movement = lever_file['response']['mvmtdata'].ravel()
calibration_factor = lever_file['params']['lev_cal']
trace = raw_movement / calibration_factor
recording = DataFrame(trace, [np.arange(len(trace))])
find_deviate(recording)
assert (False)
def make_sample_neurons(spike_framerate: Tuple[Dict[str, np.ndarray], float],
log: SparseRec, params: Dict[str, float]) -> SparseRec:
lever = log.center_on("motion", **params).fold_trials()
lever.values = np.squeeze(lever.values, 0)
lever.axes = lever.axes[1:]
filtered = devibrate_rec(lever, params)
spikes, frame_rate = spike_framerate
return fold_by(DataFrame.load(spikes), filtered, frame_rate, True)
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 create_like(self: T,
values: np.ndarray,
axes: Optional[List[np.ndarray]] = None) -> T:
new_obj = self.__class__(
DataFrame(values, (axes if axes is not None else self.axes)),
deepcopy(self.stimulus), self.sample_rate)
new_obj.set_trials(self.trial_anchors, self.pre_time, self.post_time)
new_obj.initialized = self.initialized
return new_obj
def neuron_lever_unsampled(data_file: File, params: Dict[str, float]) -> Tuple[np.ndarray, np.ndarray]:
"""
Returns:
neural_activity, lever
"""
lever = get_trials(data_file)
neurons = fold_by(DataFrame.load(data_file["spike"]), lever, data_file.attrs['frame_rate'], True)
neurons, lever = filter_empty_trial_sets(neurons.values, lever.values[0])
mask, filtered = devibrate_trials(lever, params["pre_time"])
return neurons[:, mask, :], lever[mask, :]
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 all_traces(record_file: File, ax: Axes):
"""plot full traces of all neurons and trial onsets"""
lever_trajectory = load_mat(record_file["response"])
calcium_trace = _scale(DataFrame.load(record_file["measurement"]).values)
time = np.linspace(0, lever_trajectory.shape[1] / lever_trajectory.sample_rate, lever_trajectory.shape[1])
ax.plot(time, _scale(lever_trajectory.values[0]) - 5, COLORS[1])
for idx, row in enumerate(calcium_trace):
ax.plot(time, row + idx * 5)
for point in lever_trajectory.timestamps / lever_trajectory.sample_rate: # trial onsets
ax.axvline(x=point, color=COLORS[2])
def draw_rasterplot(day: int, data_file: File, neuron_id: int, params: MotionParams):
lever = load_mat(data_file['response'])
lever.center_on('motion', **params)
neurons = DataFrame.load(data_file['spike'])
neuron_rate = data_file.attrs['frame_rate']
traces = fold_by(neurons, lever, neuron_rate, True)[np.flatnonzero(neurons.axes[0] == neuron_id)[0], :, :]
mask = np.all(traces.values > 0, axis=1)
onset = int(round(params['pre_time'] * neuron_rate))
with Figure(join(img_folder, 'neuron-trace', f"raster-day-{day}.svg"), (2, 4)) as (ax,):
labeled_heatmap(ax, traces[mask, :] - traces[mask, 0: onset].mean(axis=1, keepdims=True), cmap="coolwarm")
ax.set_title(f"day-{day}")
def make_related_neurons(
trial_log: SparseRec, spike_framerate: Tuple[Dict[str, np.ndarray],
float]) -> np.ndarray:
"""Calcualte the pearson r between neuron activity and trajectory. Returns an array of p values for each neuron."""
spike, frame_rate = spike_framerate
trial_spikes = fold_by(DataFrame.load(spike), trial_log, frame_rate, False)
trajectory = scale(trial_log.values).ravel()
p_values = [
pearsonr(neuron, trajectory)[1]
for neuron in scale(trial_spikes.values).reshape(
trial_spikes.shape[0], -1)
]
return np.array(p_values)
def load_mat(file_name: Union[str, Dict[str, dict]]) -> SparseRec:
if isinstance(file_name, str):
data_dict = cell2dict(loadmat(file_name)['data'])
else:
data_dict = file_name['data']
samples_rate: float = data_dict['card']['ai_fs'] # type: ignore
stimulus = _convert_psychsr_lever(data_dict) # type: ignore
trace = _calculate_full_trace(data_dict).reshape(1, -1) # type: ignore
axes = [
np.array(["right-lever"]),
np.arange(trace.shape[1]) / samples_rate
]
return SparseRec(DataFrame(trace, axes), stimulus, samples_rate)
def draw_neuron(day: int, data_file: File, neuron_id: int, params: MotionParams):
"""Draw one neuron in trial for one session, with bootstrapped spread as shadow."""
lever = load_mat(data_file['response'])
lever.center_on("motion", **params)
neuron = DataFrame.load(data_file['spike'])
traces = fold_by(neuron, lever, data_file.attrs['frame_rate'])
traces = traces[np.flatnonzero(traces.axes[0] == neuron_id)[0], :, :]
mask = np.all(traces.values > 0, axis=1)
pre_value = traces.values[mask, 0: int(round(params['pre_time'] * lever.sample_rate))].mean(axis=1, keepdims=True)
trace_values = traces.values[mask, :] - pre_value
with Figure(join(img_folder, "neuron-trace", f"day-{day:02d}.svg"), (1, 4)) as (ax,):
tsplot(ax, trace_values, time=traces.axes[2], color=COLORS[4])
ax.set_title(f"day_{day:02d}")
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 align_XY(spike_sample_rate: Tuple[DataFrame, int],
filterd_log: SparseRec) -> Tuple[DataFrame, SparseRec]:
"""
Returns:
X: spikes scaled
y: lever trajectory resampled to the sample rate of spikes
"""
spike, sample_rate = spike_sample_rate
resampled_trace = InterpolatedUnivariateSpline(
filterd_log.axes[1], filterd_log.values[0])(spike['y'])
y = filterd_log.create_like(scale_features(resampled_trace),
[spike['y']]) # type: ignore
y.sample_rate = sample_rate
spike_df = DataFrame(scale_features(spike['data'], axes=1),
[spike['x'], spike['y']]) # type: ignore
return spike_df, y
def show_correspondance(ax: Axes, record_file: File,
motion_params: Dict[str, float]):
def scale(x):
x -= x.mean()
x /= x.std()
return x
lever = load_mat(record_file['response'])
lever.center_on("motion", **motion_params)
activity = DataFrame.load(record_file["measurement"])
neuron_rate = record_file.attrs['frame_rate']
trials = filter_empty_trials(ts.fold_by(activity, lever, neuron_rate,
True))
slow_lever = ts.resample(lever.fold_trials().values, lever.sample_rate,
neuron_rate, -1)[:, 0:trials.shape[1], :]
ax.plot(scale(trials.values[0].reshape(-1)), 'green')
ax.plot(scale(slow_lever.reshape(-1)), 'red')
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 example_traces(ax: Axes, record_file: File, start: float, end: float, cells: Set[int]):
"""Visualize calcium trace of cells and the lever trajectory"""
lever_trajectory = load_mat(record_file["response"])
calcium_trace = DataFrame.load(record_file["measurement"])
neuron_rate = record_file.attrs['frame_rate']
l_start, l_end = np.rint(np.multiply([start, end], lever_trajectory.sample_rate)).astype(np.int_)
c_start, c_end = np.rint(np.multiply([start, end], neuron_rate)).astype(np.int_)
ax.plot(np.linspace(0, l_end - l_start, l_end - l_start), # lever trajectory
_scale(lever_trajectory.values[0][l_start: l_end]), COLORS[1])
time = np.linspace(0, calcium_trace.shape[1] / neuron_rate, lever_trajectory.shape[1])
spacing = iter(range(0, 500, 2))
for idx, row in enumerate(calcium_trace.values):
if idx in cells:
ax.plot(time[c_start: c_end] - l_start, _scale(row[c_start: c_end]) + next(spacing))
stim_onsets = lever_trajectory.timestamps[
(lever_trajectory.timestamps > l_start) & (lever_trajectory.timestamps < l_end)]\
/ lever_trajectory.sample_rate - l_start
for x in stim_onsets:
ax.axvline(x=x, color=COLORS[2])
def run_amp_power(data_file: File) -> Tuple[float, float, float, float]:
"""Try to decode the max lever trajectory amplitude of each trial.
Returns:
pre_amp_power: mutual info between predicted (from neuron activity before motor onset)
and real amplitude of trials in one session
post_amp_power: mutual info between predicted (from neuron activity before motor onset)
and real amplitude of trials in one session
"""
VALIDATE_FOLD = 10
lever = get_trials(data_file, motion_params)
neuron = DataFrame.load(data_file['spike'])
resampled_onsets = np.rint(lever.trial_anchors *
(5 / 256)).astype(np.int_) - 3
folded = np.stack(
[take_segment(trace, resampled_onsets, 6) for trace in neuron.values])
mask, filtered = devibrate_trials(lever.values, motion_params['pre_time'])
mask &= np.any(folded > 0, axis=(0, 2))
amp = filtered[mask, 25:64].max(axis=1) - filtered[mask, 0:15].mean(axis=1)
speed = np.diff(filtered[mask, 5:50], axis=1).max(axis=1)
svr_rbf = SVR('rbf', 3, 1E-7, cache_size=1000)
X = folded[:, mask, 0:3].swapaxes(0, 1).reshape(mask.sum(), -1)
pre_amp_hat = cross_predict(
X.T, amp, lambda x, y, y_t: svr_rbf.fit(x.T, y).predict(y_t.T),
VALIDATE_FOLD, False)
pre_v_hat = cross_predict(
X.T, speed, lambda x, y, y_t: svr_rbf.fit(x.T, y).predict(y_t.T),
VALIDATE_FOLD, False)
X = folded[:, mask, 3:].swapaxes(0, 1).reshape(mask.sum(), -1)
post_amp_hat = cross_predict(
X.T, amp, lambda x, y, y_t: svr_rbf.fit(x.T, y).predict(y_t.T),
VALIDATE_FOLD, False)
post_v_hat = cross_predict(
X.T, speed, lambda x, y, y_t: svr_rbf.fit(x.T, y).predict(y_t.T),
VALIDATE_FOLD, False)
return (mutual_info(pre_amp_hat, amp), mutual_info(post_amp_hat, amp),
mutual_info(pre_v_hat, speed), mutual_info(post_v_hat, speed))
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)
##
def make_trial_neuron(
trial_log: SparseRec, spike_framerate: Tuple[Dict[str, np.ndarray],
float]) -> DataFrame:
spikes, frame_rate = spike_framerate
# trial_neurons should be [neuron, trial, time_points]
return fold_by(DataFrame.load(spikes), trial_log, frame_rate, True)