def load_data(epoch_key):
logger.info('Loading position information and linearizing...')
position_info = (get_interpolated_position_info(
epoch_key, ANIMALS).dropna(subset=["linear_position"]))
track_graph = get_track_graph()
logger.info('Loading multiunits...')
tetrode_info = make_tetrode_dataframe(ANIMALS, epoch_key=epoch_key)
is_brain_areas = tetrode_info.area.str.upper().isin(
["CA1", "DCA1", "ICA1"])
tetrode_keys = tetrode_info.loc[is_brain_areas].index
def _time_function(*args, **kwargs):
return position_info.index
multiunits = get_all_multiunit_indicators(tetrode_keys, ANIMALS,
_time_function)
multiunit_spikes = (np.any(~np.isnan(multiunits.values),
axis=1)).astype(np.float)
multiunit_firing_rate = pd.DataFrame(get_multiunit_population_firing_rate(
multiunit_spikes, SAMPLING_FREQUENCY),
index=position_info.index,
columns=['firing_rate'])
return {
'position_info': position_info,
'tetrode_info': tetrode_info,
'multiunits': multiunits,
'multiunit_firing_rate': multiunit_firing_rate,
'track_graph': track_graph,
}
def load_data(epoch_key, brain_areas=None):
if brain_areas is None:
brain_areas = BRAIN_AREAS
time = get_trial_time(epoch_key, ANIMALS)
time = (pd.Series(np.ones_like(time, dtype=np.float),
index=time).resample('2ms').mean().index)
def _time_function(*args, **kwargs):
return time
position_info = (get_interpolated_position_dataframe(
epoch_key, ANIMALS,
_time_function).dropna(subset=['linear_position', 'speed']))
time = position_info.index
tetrode_info = make_tetrode_dataframe(ANIMALS, epoch_key=epoch_key)
is_brain_areas = (
tetrode_info.area.astype(str).str.upper().isin(brain_areas))
tetrode_keys = tetrode_info.loc[is_brain_areas].index
lfps = get_LFPs(tetrode_keys, ANIMALS)
lfps = lfps.resample('2ms').mean().fillna(method='pad').reindex(time)
try:
neuron_info = make_neuron_dataframe(ANIMALS).xs(epoch_key,
drop_level=False)
neuron_info = neuron_info.loc[(neuron_info.numspikes > 100)
& neuron_info.area.isin(brain_areas) &
(neuron_info.type == 'principal')]
spikes = get_all_spike_indicators(neuron_info.index, ANIMALS,
_time_function).reindex(time)
except KeyError:
spikes = None
tetrode_info = tetrode_info.loc[is_brain_areas]
multiunit = (get_all_multiunit_indicators(
tetrode_info.index, ANIMALS,
_time_function).sel(features=_MARKS).reindex({'time': time}))
multiunit_spikes = (np.any(~np.isnan(multiunit.values),
axis=1)).astype(np.float)
multiunit_firing_rate = pd.DataFrame(get_multiunit_population_firing_rate(
multiunit_spikes, SAMPLING_FREQUENCY, smoothing_sigma=0.020),
index=time,
columns=['firing_rate'])
return {
'position_info': position_info,
'spikes': spikes,
'multiunit': multiunit,
'lfps': lfps,
'tetrode_info': tetrode_info,
'multiunit_firing_rate': multiunit_firing_rate,
'sampling_frequency': SAMPLING_FREQUENCY,
}
def get_adhoc_multiunit(speed, tetrode_info, time_function):
time = time_function()
is_brain_areas = (
tetrode_info.area.astype(str).str.upper().isin(BRAIN_AREAS))
tetrode_info = tetrode_info.loc[is_brain_areas]
multiunit = (get_all_multiunit_indicators(
tetrode_info.index, ANIMALS,
time_function).sel(features=_MARKS).reindex({'time': time}))
multiunit_spikes = (np.any(~np.isnan(multiunit.values),
axis=1)).astype(np.float)
multiunit_firing_rate = pd.DataFrame(get_multiunit_population_firing_rate(
multiunit_spikes, SAMPLING_FREQUENCY),
index=time,
columns=['firing_rate'])
multiunit_rate_change = multiunit_firing_rate.transform(
lambda df: df / df.mean())
multiunit_rate_zscore = multiunit_firing_rate.transform(
lambda df: (df - df.mean()) / df.std())
multiunit_high_synchrony_times = multiunit_HSE_detector(
time,
multiunit_spikes,
speed.values,
SAMPLING_FREQUENCY,
minimum_duration=np.timedelta64(15, 'ms'),
zscore_threshold=2.0,
close_event_threshold=np.timedelta64(0, 'ms'))
multiunit_high_synchrony_times.index = (
multiunit_high_synchrony_times.index.rename('replay_number'))
multiunit_high_synchrony_labels = get_labels(
multiunit_high_synchrony_times, time)
is_multiunit_high_synchrony = multiunit_high_synchrony_labels > 0
multiunit_high_synchrony_times = multiunit_high_synchrony_times.assign(
duration=lambda df: (df.end_time - df.start_time).dt.total_seconds())
multiunit_spikes = pd.DataFrame(multiunit_spikes, index=time)
return dict(
multiunit=multiunit,
multiunit_spikes=multiunit_spikes,
multiunit_firing_rate=multiunit_firing_rate,
multiunit_high_synchrony_times=multiunit_high_synchrony_times,
multiunit_high_synchrony_labels=multiunit_high_synchrony_labels,
multiunit_rate_change=multiunit_rate_change,
multiunit_rate_zscore=multiunit_rate_zscore,
is_multiunit_high_synchrony=is_multiunit_high_synchrony)
def get_adhoc_multiunit(position_info, tetrode_keys, time_function,
position_to_linearize):
time = position_info.index
multiunits = get_all_multiunit_indicators(tetrode_keys, ANIMALS,
time_function)
multiunit_spikes = (np.any(~np.isnan(multiunits.values),
axis=1)).astype(np.float)
multiunit_firing_rate = pd.DataFrame(get_multiunit_population_firing_rate(
multiunit_spikes, SAMPLING_FREQUENCY),
index=position_info.index,
columns=['firing_rate'])
multiunit_rate_change = multiunit_firing_rate.transform(
lambda df: df / df.mean())
multiunit_rate_zscore = np.log(multiunit_firing_rate).transform(
lambda df: (df - df.mean()) / df.std())
speed_feature = position_to_linearize[0].split('_')[0]
speed = np.asarray(position_info[f'{speed_feature}_vel'])
multiunit_high_synchrony_times = multiunit_HSE_detector(
time,
multiunit_spikes,
speed,
SAMPLING_FREQUENCY,
minimum_duration=np.timedelta64(15, 'ms'),
zscore_threshold=2.0,
close_event_threshold=np.timedelta64(0, 'ms'))
multiunit_high_synchrony_times.index = (
multiunit_high_synchrony_times.index.rename('replay_number'))
multiunit_high_synchrony_labels = get_labels(
multiunit_high_synchrony_times, time)
is_multiunit_high_synchrony = multiunit_high_synchrony_labels > 0
multiunit_high_synchrony_times = multiunit_high_synchrony_times.assign(
duration=lambda df: (df.end_time - df.start_time).dt.total_seconds())
return dict(
multiunits=multiunits,
multiunit_spikes=multiunit_spikes,
multiunit_firing_rate=multiunit_firing_rate,
multiunit_high_synchrony_times=multiunit_high_synchrony_times,
multiunit_high_synchrony_labels=multiunit_high_synchrony_labels,
multiunit_rate_change=multiunit_rate_change,
multiunit_rate_zscore=multiunit_rate_zscore,
is_multiunit_high_synchrony=is_multiunit_high_synchrony)
def load_data(epoch_key, brain_areas=['CA1', 'CA2', 'CA3']):
time = get_trial_time(epoch_key, ANIMALS)
time = (pd.Series(np.ones_like(time, dtype=np.float), index=time)
.resample('2ms').mean()
.index)
def _time_function(*args, **kwargs):
return time
logger.info('Loading position info...')
position_info = (
get_interpolated_position_dataframe(
epoch_key, ANIMALS, _time_function)
.dropna(subset=['linear_position', 'speed']))
time = position_info.index
tetrode_info = make_tetrode_dataframe(ANIMALS, epoch_key=epoch_key)
is_brain_areas = (
tetrode_info.area.astype(str).str.upper().isin(brain_areas))
tetrode_keys = tetrode_info.loc[is_brain_areas].index
lfps = get_LFPs(tetrode_keys, ANIMALS)
lfps = lfps.resample('2ms').mean().fillna(method='pad').reindex(time)
logger.info('Loading spikes...')
try:
neuron_info = make_neuron_dataframe(ANIMALS).xs(
epoch_key, drop_level=False)
neuron_info = neuron_info.loc[
(neuron_info.numspikes > 100) &
neuron_info.area.isin(brain_areas) &
(neuron_info.type == 'principal')]
spikes = get_all_spike_indicators(
neuron_info.index, ANIMALS, _time_function).reindex(time)
except KeyError:
spikes = None
logger.info('Loading multiunit...')
tetrode_info = tetrode_info.loc[is_brain_areas]
multiunit = (get_all_multiunit_indicators(
tetrode_info.index, ANIMALS, _time_function)
.reindex({'time': time}))
multiunit = multiunit.sel(features=MARKS)
multiunit_spikes = (np.any(~np.isnan(multiunit.values), axis=1)
).astype(np.float)
multiunit_firing_rate = pd.DataFrame(
get_multiunit_population_firing_rate(
multiunit_spikes, SAMPLING_FREQUENCY), index=time,
columns=['firing_rate'])
logger.info('Finding ripple times...')
(ripple_times, ripple_filtered_lfps, ripple_lfps,
ripple_consensus_trace_zscore) = get_ripple_times(epoch_key)
ripple_times = ripple_times.assign(
duration=lambda df: (df.end_time - df.start_time).dt.total_seconds())
return {
'position_info': position_info,
'ripple_times': ripple_times,
'spikes': spikes,
'multiunit': multiunit,
'lfps': lfps,
'tetrode_info': tetrode_info,
'ripple_filtered_lfps': ripple_filtered_lfps,
'ripple_lfps': ripple_lfps,
'ripple_consensus_trace_zscore': ripple_consensus_trace_zscore,
'multiunit_firing_rate': multiunit_firing_rate,
'sampling_frequency': SAMPLING_FREQUENCY,
}
def load_data(epoch_key):
logger.info('Loading position information and linearizing...')
position_info = (get_interpolated_position_info(epoch_key, ANIMALS)
.dropna(subset=["linear_position"]))
track_graph = get_track_graph()
logger.info('Loading multiunits...')
tetrode_info = make_tetrode_dataframe(
ANIMALS, epoch_key=epoch_key)
def n_dead_chans(x):
if isinstance(x, float):
return 1
elif isinstance(x, (list, tuple, np.ndarray)):
return len(x)
# Temporarily remove these tetrodes that have bad marks in peanut, 14, 4
bad_trode = [9, 16, 21]
tetrode_keys = tetrode_info.loc[
(tetrode_info.area.str.upper() == "CA1")
& (tetrode_info.deadchans.apply(lambda x: n_dead_chans(x)) < 4)
& ~tetrode_info.nTrode.isin(tetrode_info.ref.dropna().unique())
& ~tetrode_info.nTrode.isin(bad_trode)
].index
def _time_function(*args, **kwargs):
return position_info.index
multiunits = get_all_multiunit_indicators(
tetrode_keys, ANIMALS, _time_function)
multiunit_spikes = (np.any(~np.isnan(multiunits.values), axis=1)
).astype(np.float)
multiunit_firing_rate = pd.DataFrame(
get_multiunit_population_firing_rate(
multiunit_spikes, SAMPLING_FREQUENCY), index=position_info.index,
columns=['firing_rate'])
logger.info('Loading sorted spikes...')
neuron_info = make_neuron_dataframe(ANIMALS).xs(
epoch_key, drop_level=False)
neuron_info = pd.merge(
neuron_info,
tetrode_info.loc[:, ["area", "suparea"]],
left_index=True,
right_index=True,
)
neuron_info = (neuron_info
.loc[neuron_info.accepted.astype(bool)] # only accepted
.dropna(subset=["area"])) # drop cells that don't have area
spikes = get_all_spike_indicators(
neuron_info.index, ANIMALS, _time_function)
return {
'position_info': position_info,
'tetrode_info': tetrode_info,
'multiunits': multiunits,
'multiunit_firing_rate': multiunit_firing_rate,
'neuron_info': neuron_info,
'spikes': spikes,
'track_graph': track_graph,
}