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Fix extract lfp features #752

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Merged
leoscholl merged 2 commits into from
Jun 4, 2026
Merged

Fix extract lfp features #752

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2b5ab0c
fix extract_lfp_features
leoscholl Jun 3, 2026
2f5ecd4
use alternate timestamps
leoscholl Jun 3, 2026
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23 changes: 18 additions & 5 deletions aopy/data/bmi3d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -429,6 +429,7 @@ def load_emg_data(data_dir, emg_filename):
emg_metadata['data_source'] = os.path.join(data_dir, emg_filename)
emg_data_reshape = emg_data.view((dtype, (len(emg_data.dtype),)))
emg_data = emg_data_reshape[:,:nch]
emg_metadata['n_samples'] = emg_data.shape[0]
return emg_data, emg_metadata

def load_emg_analog(data_dir, emg_filename):
Expand All @@ -455,6 +456,7 @@ def load_emg_analog(data_dir, emg_filename):
emg_metadata['data_source'] = os.path.join(data_dir, emg_filename)
emg_data_reshape = emg_data.view((dtype, (len(emg_data.dtype),)))
analog_data = emg_data_reshape[:,-24:-8] # AUX channels
emg_metadata['n_samples'] = analog_data.shape[0]
return analog_data, emg_metadata

def load_emg_digital(data_dir, emg_filename):
Expand All @@ -477,6 +479,7 @@ def load_emg_digital(data_dir, emg_filename):

emg_metadata['n_channels'] = 16
emg_metadata['data_source'] = os.path.join(data_dir, emg_filename)
emg_metadata['n_samples'] = digital_data.shape[0]
return digital_data, emg_metadata

def load_ecube_analog(path, data_dir, channels=None):
Expand Down Expand Up @@ -1405,7 +1408,7 @@ def get_spike_data_aligned(preproc_dir, subject, te_id, date, trigger_times, tim
@lru_cache(maxsize=1)
def _extract_lfp_features(preproc_dir, subject, te_id, date, decoder, samplerate=None, channels=None,
start_time=None, end_time=None, latency=0.02, datatype='lfp', preproc=None,
decode=False, **kwargs):
decode=False, verbose=True, **kwargs):
'''
Extracts features from a BMI3D experiment using data aligned to the timestamps of the experiment.
Using this function, you can replicate closely the features that would have been extracted from
Expand Down Expand Up @@ -1435,6 +1438,8 @@ def _extract_lfp_features(preproc_dir, subject, te_id, date, decoder, samplerate
a smoothing function.
decode (bool, optional): whether to run the features through the decoder before resampling. Only
works if `channels` is None or `len(channels) == len(decoder.channels)`. Default False.
verbose (bool, optional): whether to display a tqdm progress bar while extracting features.
Default True.
kwargs: additional keyword arguments to pass to sample_timestamped_data

Returns:
Expand All @@ -1461,7 +1466,10 @@ def _extract_lfp_features(preproc_dir, subject, te_id, date, decoder, samplerate
# Find times to extract
exp_data, exp_metadata = base.load_preproc_exp_data(preproc_dir, subject, te_id, date)
step = int(decoder.call_rate * decoder.binlen)
ts = exp_data['clock']['timestamp_sync'][::step]
try:
ts = exp_data['clock']['timestamp_sync'][::step]
except:
ts = exp_data['clock']['timestamp_bmi3d'][::step]
ts = ts[ts > start_time]
if end_time is not None:
ts = ts[ts < end_time]
Expand Down Expand Up @@ -1489,7 +1497,10 @@ def _extract_lfp_features(preproc_dir, subject, te_id, date, decoder, samplerate
if hasattr(f_extractor, 'bands'):
n_freq = len(f_extractor.bands)
cycle_data = np.zeros((len(ts), n_freq, n_ch))
for i, t in enumerate(ts):
iterator = enumerate(ts)
if verbose:
iterator = tqdm(iterator, total=len(ts), desc='Extracting LFP features')
for i, t in iterator:
sample_num = int((t-ts_start_time-latency) * ts_samplerate)
cont_samples = ts_data[max(0,sample_num-n_pts):min(ts_data.shape[0], sample_num)]
if cont_samples.shape[0] < n_pts:
Expand All @@ -1515,7 +1526,7 @@ def _extract_lfp_features(preproc_dir, subject, te_id, date, decoder, samplerate

def extract_lfp_features(preproc_dir, subject, te_id, date, decoder, samplerate=None, channels=None,
start_time=None, end_time=None, latency=0.02, datatype='lfp', preproc=None,
**kwargs):
verbose=True, **kwargs):
'''
Extracts features from a BMI3D experiment using data aligned to the timestamps of the experiment.
Using this function, you can replicate closely the features that would have been extracted from
Expand All @@ -1539,6 +1550,8 @@ def extract_lfp_features(preproc_dir, subject, te_id, date, decoder, samplerate=
by decimation.
preproc (fn, optional): function mapping (state, fs) data to (state_new, fs_new). For example,
a smoothing function.
verbose (bool, optional): whether to display a tqdm progress bar while extracting features.
Default True.
kwargs: additional keyword arguments to pass to sample_timestamped_data

Returns:
Expand Down Expand Up @@ -1597,7 +1610,7 @@ def extract_lfp_features(preproc_dir, subject, te_id, date, decoder, samplerate=
'''
return _extract_lfp_features(preproc_dir, subject, te_id, date, decoder, samplerate=samplerate,
channels=channels, start_time=start_time, end_time=end_time, latency=latency,
datatype=datatype, preproc=preproc, **kwargs)
datatype=datatype, preproc=preproc, verbose=verbose, **kwargs)

def get_target_locations(preproc_dir, subject, te_id, date, target_indices):
'''
Expand Down
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