def make(self, key):
# fetch behavior and ephys sample rates
fs_beh = int((acquisition.BehaviorRecording
& key).fetch1('behavior_recording_sample_rate'))
fs_ephys = int((acquisition.EphysRecording
& key).fetch1('ephys_recording_sample_rate'))
# fetch condition time (behavior time base)
t_beh = (pacman_acquisition.Behavior.Condition
& key).fetch1('condition_time')
n_samples = len(t_beh)
# make condition time in ephys time base
t_ephys, _ = pacman_acquisition.ConditionParams.target_force_profile(
key['condition_id'], fs_ephys)
# fetch spike rasters (ephys time base)
spike_raster_keys = (NeuronSpikeRaster & key).fetch(as_dict=True)
# rebin spike raster to behavior time base
time_bin_edges = np.append(
t_beh, t_beh[-1] + (1 + np.arange(2)) / fs_beh) - 1 / (2 * fs_beh)
spike_bins = [np.digitize(t_ephys[spike_raster_key['neuron_spike_raster']], time_bin_edges) - 1 \
for spike_raster_key in spike_raster_keys]
spike_bins = [b[(b >= 0) & (b < len(t_beh))] for b in spike_bins]
for spike_raster_key, spk_bins in zip(spike_raster_keys, spike_bins):
spike_raster_key['neuron_spike_raster'] = np.zeros(n_samples,
dtype=bool)
spike_raster_key['neuron_spike_raster'][spk_bins] = 1
# get filter kernel
filter_key = (processing.Filter &
(pacman_processing.FilterParams & key)).fetch1('KEY')
filter_parts = datajointutils.get_parts(processing.Filter,
context=inspect.currentframe())
filter_rel = next(part for part in filter_parts if part & filter_key)
# filter rebinned spike raster
neuron_rate_keys = spike_raster_keys.copy()
[
neuron_rate_key.update(
filter_params_id=key['filter_params_id'],
neuron_rate=fs_beh * filter_rel().filt(
neuron_rate_key['neuron_spike_raster'], fs_beh))
for neuron_rate_key in neuron_rate_keys
]
# remove spike rasters
[
neuron_rate_key.pop('neuron_spike_raster')
for neuron_rate_key in neuron_rate_keys
]
# insert neuron rates
self.insert(neuron_rate_keys, skip_duplicates=True)
def proj_rank(self):
"""Project rank in all child target tables and joins with master."""
target_children = datajointutils.get_parts(ConditionParams.Target)
target_ranks = [
dj.U('condition_id', 'condition_rank') & (x & self).proj_rank()
for x in target_children
]
ranked_self = reduce(lambda x, y: x + y, target_ranks)
return ranked_self
def proj_label(self, n_sigfigs: int = 4):
"""Project label in all child target tables and joins with master."""
target_children = datajointutils.get_parts(ConditionParams.Target)
target_labels = [
dj.U('condition_id', 'condition_label') &
(x & self).proj_label(n_sigfigs=n_sigfigs) for x in target_children
]
labeled_self = reduce(lambda x, y: x + y, target_labels)
return labeled_self
def make(self, key):
# trial source
trial_source = (pacman_processing.BehaviorTrialAlignment & 'valid_alignment') \
* pacman_processing.FilterParams & key
# convert raw force signal to Newtons
trial_rel = pacman_acquisition.Behavior.Trial & trial_source
force_data = trial_rel.process_force(data_type='raw',
apply_filter=False,
keep_keys=True)
# get filter kernel
filter_key = (processing.Filter &
(pacman_processing.FilterParams & key)).fetch1('KEY')
filter_parts = datajointutils.get_parts(processing.Filter,
context=inspect.currentframe())
filter_rel = next(part for part in filter_parts if part & filter_key)
# filter raw data
fs = (acquisition.BehaviorRecording
& key).fetch1('behavior_recording_sample_rate')
[
frc.update(force_filt_offline=filter_rel().filt(
frc['force_raw_online'], fs)) for frc in force_data
]
# fetch alignment indices and cast as integers
behavior_alignment = (trial_source).fetch('behavior_alignment',
order_by='trial')
behavior_alignment = list(
map(lambda x: x.astype(int), behavior_alignment))
# append key and align raw and filtered forces
[
frc.update(force_raw=frc['force_raw_online'][align_idx],
force_filt=frc['force_filt_offline'][align_idx])
for frc, align_idx in zip(force_data, behavior_alignment)
]
# pop pre-aligned data
for f_key in ['force_raw_online', 'force_filt_offline']:
[frc.pop(f_key) for frc in force_data]
# merge key with force data
key = [dict(key, **frc) for frc in force_data]
# insert aligned forces
self.insert(key)
def make(self, key):
# fetch ephys sample rates
fs_ephys = int((acquisition.EphysRecording & key).fetch1('ephys_recording_sample_rate'))
# fetch condition time (behavior time base)
t_beh = (pacman_acquisition.Behavior.Condition & key).fetch1('condition_time')
# make condition time in ephys time base
t_ephys, _ = pacman_acquisition.ConditionParams.target_force_profile(key['condition_id'], fs_ephys)
# fetch raw emg data
emg_attributes = (Emg & key).fetch(as_dict=True)
# highpass filter and rectify raw emg signals
[emg_attr.update(emg_envelope=abs(processing.Filter.Butterworth().filt(emg_attr['emg_signal'], fs_ephys, order=2, low_cut=40)))
for emg_attr in emg_attributes];
# remove raw emg signal
[emg_attr.pop('emg_signal') for emg_attr in emg_attributes];
# get filter kernel
filter_key = (processing.Filter & (pacman_processing.FilterParams & key)).fetch1('KEY')
filter_parts = datajointutils.get_parts(processing.Filter, context=inspect.currentframe())
filter_rel = next(part for part in filter_parts if part & filter_key)
# smooth rectified emg signals to construct envelope and remove
[emg_attr.update(
filter_params_id=key['filter_params_id'],
emg_envelope=filter_rel().filt(emg_attr['emg_envelope'], fs_ephys)
) for emg_attr in emg_attributes];
# resample emg to behavior time base
[emg_attr.update(
emg_envelope=np.interp(t_beh, t_ephys, emg_attr['emg_envelope'])
) for emg_attr in emg_attributes];
# insert emg envelopes
self.insert(emg_attributes)
