def _get_passive_spacers(session_path, sync=None, sync_map=None):
"""
load and get spacer information, do corr to find spacer timestamps
returns t_passive_starts, t_starts, t_ends
"""
if sync is None or sync_map is None:
sync, sync_map = ephys_fpga._get_main_probe_sync(session_path,
bin_exists=False)
meta = _load_passive_stim_meta()
# t_end_ephys = passive.ephysCW_end(session_path=session_path)
fttl = ephys_fpga._get_sync_fronts(sync, sync_map["frame2ttl"], tmin=None)
spacer_template = (
np.array(meta["VISUAL_STIM_0"]["ttl_frame_nums"], dtype=np.float32) /
FRAME_FS)
jitter = 3 / FRAME_FS # allow for 3 screen refresh as jitter
t_quiet = meta["VISUAL_STIM_0"]["delay_around"]
spacer_times, _ = _get_spacer_times(spacer_template=spacer_template,
jitter=jitter,
ttl_signal=fttl["times"],
t_quiet=t_quiet)
# Check correct number of spacers found
n_exp_spacer = np.sum(np.array(
meta["STIM_ORDER"]) == 0) # Hardcoded 0 for spacer
if n_exp_spacer != np.size(spacer_times) / 2:
raise ValueError(f"The number of expected spacer ({n_exp_spacer}) "
f"is different than the one found on the raw "
f"trace ({np.size(spacer_times)/2})")
spacer_times = np.r_[spacer_times.flatten(), sync["times"][-1]]
return spacer_times[0], spacer_times[1::2], spacer_times[2::2]
def load_raw_data(self):
"""
Loads the TTLs, raw task data and task settings
:return:
"""
self.log.info(f"Loading raw data from {self.session_path}")
self.type = self.type or raw.get_session_extractor_type(
self.session_path)
self.settings, self.raw_data = raw.load_bpod(self.session_path)
# Fetch the TTLs for the photodiode and audio
if self.type != 'ephys' or self.bpod_only is True: # Extract from Bpod
self.frame_ttls, self.audio_ttls = raw.load_bpod_fronts(
self.session_path, data=self.raw_data)
else: # Extract from FPGA
sync, chmap = _get_main_probe_sync(self.session_path)
def channel_events(name):
"""Fetches the polarities and times for a given channel"""
keys = ('polarities', 'times')
mask = sync['channels'] == chmap[name]
return dict(zip(keys, (sync[k][mask] for k in keys)))
ttls = [
channel_events(ch) for ch in ('frame2ttl', 'audio', 'bpod')
]
self.frame_ttls, self.audio_ttls, self.bpod_ttls = ttls
def _qc_from_path(sess_path, display=True):
WHEEL = False
sess_path = Path(sess_path)
temp_alf_folder = sess_path.joinpath('fpga_test', 'alf')
temp_alf_folder.mkdir(parents=True, exist_ok=True)
raw_trials = raw_data_loaders.load_data(sess_path)
tmax = raw_trials[-1]['behavior_data']['States timestamps']['exit_state'][
0][-1] + 60
sync, chmap = ephys_fpga._get_main_probe_sync(sess_path, bin_exists=False)
_ = ephys_fpga.extract_all(sess_path,
output_path=temp_alf_folder,
save=True)
# check that the output is complete
fpga_trials = ephys_fpga.extract_behaviour_sync(
sync,
output_path=temp_alf_folder,
tmax=tmax,
chmap=chmap,
save=True,
display=display)
# align with the bpod
bpod2fpga = ephys_fpga.align_with_bpod(temp_alf_folder.parent)
alf_trials = alf.io.load_object(temp_alf_folder, 'trials')
shutil.rmtree(temp_alf_folder)
# do the QC
qcs, qct = qc_fpga_task(fpga_trials, alf_trials)
# do the wheel part
if WHEEL:
bpod_wheel = training_wheel.get_wheel_data(sess_path, save=False)
fpga_wheel = ephys_fpga.extract_wheel_sync(sync,
chmap=chmap,
save=False)
if display:
import matplotlib.pyplot as plt
t0 = max(np.min(bpod2fpga(bpod_wheel['re_ts'])),
np.min(fpga_wheel['re_ts']))
dy = np.interp(
t0, fpga_wheel['re_ts'], fpga_wheel['re_pos']) - np.interp(
t0, bpod2fpga(bpod_wheel['re_ts']), bpod_wheel['re_pos'])
fix, axes = plt.subplots(nrows=2, sharex='all', sharey='all')
# axes[0].plot(t, pos), axes[0].title.set_text('Extracted')
axes[0].plot(bpod2fpga(bpod_wheel['re_ts']),
bpod_wheel['re_pos'] + dy)
axes[0].plot(fpga_wheel['re_ts'], fpga_wheel['re_pos'])
axes[0].title.set_text('FPGA')
axes[1].plot(bpod2fpga(bpod_wheel['re_ts']),
bpod_wheel['re_pos'] + dy)
axes[1].title.set_text('Bpod')
return alf.io.dataframe({**fpga_trials, **alf_trials, **qct})
def extract_rfmapping(session_path: str,
sync: dict = None,
sync_map: dict = None,
trfm: np.array = None) -> Tuple[np.array, np.array]:
meta = _load_passive_stim_meta()
mkey = ("VISUAL_STIM_" + {v: k
for k, v in meta["VISUAL_STIMULI"].items()
}["receptive_field_mapping"])
if sync is None or sync_map is None:
sync, sync_map = ephys_fpga._get_main_probe_sync(session_path,
bin_exists=False)
if trfm is None:
passivePeriods_df = extract_passive_periods(session_path,
sync=sync,
sync_map=sync_map)
trfm = passivePeriods_df.RFM.values
fttl = ephys_fpga._get_sync_fronts(sync,
sync_map["frame2ttl"],
tmin=trfm[0],
tmax=trfm[1])
RF_file = Path().joinpath(session_path, "raw_passive_data",
"_iblrig_RFMapStim.raw.bin")
passiveRFM_frames, RF_ttl_trace = _reshape_RF(RF_file=RF_file,
meta_stim=meta[mkey])
rf_id_up, rf_id_dw, RF_n_ttl_expected = _get_id_raisefall_from_analogttl(
RF_ttl_trace)
meta[mkey]["ttl_num"] = RF_n_ttl_expected
rf_times_on_idx = np.where(np.diff(fttl["times"]) < 1)[0]
rf_times_off_idx = rf_times_on_idx + 1
RF_times = fttl["times"][np.sort(
np.concatenate([rf_times_on_idx, rf_times_off_idx]))]
RF_times_1 = RF_times[0::2]
# Interpolate times for RF before outputting dataset
passiveRFM_times = _interpolate_rf_mapping_stimulus(
idxs_up=rf_id_up,
idxs_dn=rf_id_dw,
times=RF_times_1,
Xq=np.arange(passiveRFM_frames.shape[0]),
t_bin=1 / FRAME_FS,
)
return passiveRFM_times # _ibl_passiveRFM.times.npy
def load_ttl_pulses(session_path):
"""
Extract ttl pulses from sync signals
:param session_path: absolute path of a session, i.e. /mnt/data/Subjects/ZM_1887/2019-07-10/001
:type session_path: str
:return: ttl pulse times
:rtype: np.ndarray
"""
from ibllib.io.extractors.ephys_fpga import _get_main_probe_sync
sync, sync_chmap = _get_main_probe_sync(session_path, bin_exists=False)
fr2ttl_ch = sync_chmap['frame2ttl']
# find times of when ttl polarity changes on fr2ttl channel
sync_pol_ = sync['polarities'][sync['channels'] == fr2ttl_ch]
sync_times_ = sync['times'][sync['channels'] == fr2ttl_ch]
return sync_pol_, sync_times_
def extract_task_replay(
session_path: str,
sync: dict = None,
sync_map: dict = None,
treplay: np.array = None) -> Tuple[pd.DataFrame, pd.DataFrame]:
if sync is None or sync_map is None:
sync, sync_map = ephys_fpga._get_main_probe_sync(session_path,
bin_exists=False)
if treplay is None:
passivePeriods_df = extract_passive_periods(session_path,
sync=sync,
sync_map=sync_map)
treplay = passivePeriods_df.taskReplay.values
fttl = ephys_fpga._get_sync_fronts(sync,
sync_map["frame2ttl"],
tmin=treplay[0])
passiveGabor_df = _extract_passiveGabor_df(fttl, session_path)
bpod = ephys_fpga._get_sync_fronts(sync, sync_map["bpod"], tmin=treplay[0])
passiveValve_intervals = _extract_passiveValve_intervals(bpod)
audio = ephys_fpga._get_sync_fronts(sync,
sync_map["audio"],
tmin=treplay[0])
passiveTone_intervals, passiveNoise_intervals = _extract_passiveAudio_intervals(
audio)
passiveStims_df = np.concatenate([
passiveValve_intervals, passiveTone_intervals, passiveNoise_intervals
],
axis=1)
columns = [
"valveOn", "valveOff", "toneOn", "toneOff", "noiseOn", "noiseOff"
]
passiveStims_df = pd.DataFrame(passiveStims_df, columns=columns)
return (
passiveGabor_df,
passiveStims_df,
) # _ibl_passiveGabor.table.csv, _ibl_passiveStims.times_table.csv
def extract_passive_periods(session_path: str,
sync: dict = None,
sync_map: dict = None) -> pd.DataFrame:
if sync is None or sync_map is None:
sync, sync_map = ephys_fpga._get_main_probe_sync(session_path,
bin_exists=False)
t_start_passive, t_starts, t_ends = _get_passive_spacers(session_path,
sync=sync,
sync_map=sync_map)
t_starts_col = np.insert(t_starts, 0, t_start_passive)
t_ends_col = np.insert(t_ends, 0, t_ends[-1])
# tpassive_protocol = [t_start_passive, t_ends[-1]]
# tspontaneous = [t_starts[0], t_ends[0]]
# trfm = [t_starts[1], t_ends[1]]
# treplay = [t_starts[2], t_ends[2]]
passivePeriods_df = pd.DataFrame(
[t_starts_col, t_ends_col],
index=["start", "stop"],
columns=[
"passiveProtocol", "spontaneousActivity", "RFM", "taskReplay"
],
)
return passivePeriods_df # _ibl_passivePeriods.intervalsTable.csv
def validate_ttl_test(ses_path, display=False):
"""
For a mock session on the Ephys Choice world task, check the sync channels for all
device properly connected and perform a synchronization if dual probes to check that
all channels are recorded properly
:param ses_path: session path
:param display: show the probe synchronization plot if several probes
:return: True if tests pass, errors otherwise
"""
def _single_test(assertion, str_ok, str_ko):
if assertion:
_logger.info(str_ok)
return True
else:
_logger.error(str_ko)
return False
EXPECTED_RATES_HZ = {'left_camera': 60, 'right_camera': 150, 'body_camera': 30}
SYNC_RATE_HZ = 1
MIN_TRIALS_NB = 6
ok = True
ses_path = Path(ses_path)
if not ses_path.exists():
return False
rawsync, sync_map = fpga._get_main_probe_sync(ses_path)
last_time = rawsync['times'][-1]
# get upgoing fronts for each
sync = Bunch({})
for k in sync_map:
fronts = fpga._get_sync_fronts(rawsync, sync_map[k])
sync[k] = fronts['times'][fronts['polarities'] == 1]
wheel = fpga.extract_wheel_sync(rawsync, chmap=sync_map, save=False)
frame_rates = {'right_camera': np.round(1 / np.median(np.diff(sync.right_camera))),
'left_camera': np.round(1 / np.median(np.diff(sync.left_camera))),
'body_camera': np.round(1 / np.median(np.diff(sync.body_camera)))}
# check the camera frame rates
for lab in frame_rates:
expect = EXPECTED_RATES_HZ[lab]
ok &= _single_test(assertion=abs((1 - frame_rates[lab] / expect)) < 0.1,
str_ok=f'PASS: {lab} frame rate: {frame_rates[lab]} = {expect} Hz',
str_ko=f'FAILED: {lab} frame rate: {frame_rates[lab]} != {expect} Hz')
# check that the wheel has a minimum rate of activity on both channels
re_test = abs(1 - sync.rotary_encoder_1.size / sync.rotary_encoder_0.size) < 0.1
re_test &= len(wheel['re_pos']) / last_time > 5
ok &= _single_test(assertion=re_test,
str_ok="PASS: Rotary encoder", str_ko="FAILED: Rotary encoder")
# check that the frame 2 ttls has a minimum rate of activity
ok &= _single_test(assertion=len(sync.frame2ttl) / last_time > 0.2,
str_ok="PASS: Frame2TTL", str_ko="FAILED: Frame2TTL")
# the audio has to have at least one event per trial
ok &= _single_test(assertion=len(sync.bpod) > len(sync.audio) > MIN_TRIALS_NB,
str_ok="PASS: audio", str_ko="FAILED: audio")
# the bpod has to have at least twice the amount of min trial pulses
ok &= _single_test(assertion=len(sync.bpod) > MIN_TRIALS_NB * 2,
str_ok="PASS: Bpod", str_ko="FAILED: Bpod")
try:
# note: tried to depend as little as possible on the extraction code but for the valve...
behaviour = fpga.extract_behaviour_sync(rawsync, save=False, chmap=sync_map)
res = behaviour.valve_open.size > 1
except AssertionError:
res = False
# check that the reward valve is actionned at least once
ok &= _single_test(assertion=res,
str_ok="PASS: Valve open", str_ko="FAILED: Valve open not detected")
_logger.info('ALL CHECKS PASSED !')
# the imec sync is for 3B Probes only
if sync.get('imec_sync') is not None:
ok &= _single_test(assertion=np.all(1 - SYNC_RATE_HZ * np.diff(sync.imec_sync) < 0.1),
str_ok="PASS: imec sync", str_ko="FAILED: imec sync")
# second step is to test that we can make the sync. Assertions are whithin the synch code
if sync.get('imec_sync') is not None:
sync_result = sync_probes.version3B(ses_path, display=display)
else:
sync_result = sync_probes.version3A(ses_path, display=display)
ok &= _single_test(assertion=sync_result, str_ok="PASS: synchronisation",
str_ko="FAILED: probe synchronizations threshold exceeded")
if not ok:
raise ValueError('FAILED TTL test')
return ok
def _extract(self,
sync: dict = None,
sync_map: dict = None,
plot: bool = False,
**kwargs) -> tuple:
if sync is None or sync_map is None:
sync, sync_map = ephys_fpga._get_main_probe_sync(self.session_path,
bin_exists=False)
try:
# Passive periods
passivePeriods_df = extract_passive_periods(self.session_path,
sync=sync,
sync_map=sync_map)
trfm = passivePeriods_df.RFM.values
treplay = passivePeriods_df.taskReplay.values
except BaseException as e:
log.error("Failed to extract passive periods", e)
passivePeriods_df = None
trfm = None
treplay = None
try:
# RFMapping
passiveRFM_times = extract_rfmapping(self.session_path,
sync=sync,
sync_map=sync_map,
trfm=trfm)
except BaseException as e:
log.error("Failed to extract RFMapping datasets", e)
passiveRFM_times = None
try:
(
passiveGabor_df,
passiveStims_df,
) = extract_task_replay(self.session_path,
sync=sync,
sync_map=sync_map,
treplay=treplay)
except BaseException as e:
log.error("Failed to extract task replay stimuli", e)
(
passiveGabor_df,
passiveStims_df,
) = (
None,
None,
)
if plot:
f, ax = plt.subplots(1, 1)
f.suptitle("/".join(str(self.session_path).split("/")[-5:]))
plot_sync_channels(sync=sync, sync_map=sync_map, ax=ax)
plot_passive_periods(passivePeriods_df, ax=ax)
plot_rfmapping(passiveRFM_times, ax=ax)
plot_gabor_times(passiveGabor_df, ax=ax)
plot_stims_times(passiveStims_df, ax=ax)
return (
passivePeriods_df, # _ibl_passivePeriods.intervalsTable.csv
passiveRFM_times, # _ibl_passiveRFM.times.npy
passiveGabor_df, # _ibl_passiveGabor.table.csv,
passiveStims_df, # _ibl_passiveStims.table.csv
)
def extract_replay_debug(
session_path: str,
sync: dict = None,
sync_map: dict = None,
treplay: np.array = None,
ax: plt.axes = None,
) -> Tuple[pd.DataFrame, pd.DataFrame]:
# Load sessions sync channels, map
if sync is None or sync_map is None:
sync, sync_map = ephys_fpga._get_main_probe_sync(session_path,
bin_exists=False)
if treplay is None:
passivePeriods_df = extract_passive_periods(session_path,
sync=sync,
sync_map=sync_map)
treplay = passivePeriods_df.taskReplay.values
if ax is None:
f, ax = plt.subplots(1, 1)
f = ax.figure
f.suptitle("/".join(str(session_path).split("/")[-5:]))
plot_sync_channels(sync=sync, sync_map=sync_map, ax=ax)
passivePeriods_df = extract_passive_periods(session_path,
sync=sync,
sync_map=sync_map)
treplay = passivePeriods_df.taskReplay.values
plot_passive_periods(passivePeriods_df, ax=ax)
fttl = ephys_fpga._get_sync_fronts(sync,
sync_map["frame2ttl"],
tmin=treplay[0])
passiveGabor_df = _extract_passiveGabor_df(fttl, session_path)
plot_gabor_times(passiveGabor_df, ax=ax)
bpod = ephys_fpga._get_sync_fronts(sync, sync_map["bpod"], tmin=treplay[0])
passiveValve_intervals = _extract_passiveValve_intervals(bpod)
plot_valve_times(passiveValve_intervals, ax=ax)
audio = ephys_fpga._get_sync_fronts(sync,
sync_map["audio"],
tmin=treplay[0])
passiveTone_intervals, passiveNoise_intervals = _extract_passiveAudio_intervals(
audio)
plot_audio_times(passiveTone_intervals, passiveNoise_intervals, ax=ax)
passiveStims_df = np.concatenate([
passiveValve_intervals, passiveTone_intervals, passiveNoise_intervals
],
axis=1)
columns = [
"valveOn", "valveOff", "toneOn", "toneOff", "noiseOn", "noiseOff"
]
passiveStims_df = pd.DataFrame(passiveStims_df, columns=columns)
return (
passiveGabor_df,
passiveStims_df,
) # _ibl_passiveGabor.table.csv, _ibl_passiveStims.table.csv
session_path = alf.io.get_session_path(local_paths[0])
# load session fixtures
settings = rawio.load_settings(session_path)
ses_nb = settings['SESSION_ORDER'][settings['SESSION_IDX']]
path_fixtures = Path(ephys_fpga.__file__).parent.joinpath('ephys_sessions')
fixture = {'pcs': np.load(path_fixtures.joinpath(f'session_{ses_nb}_passive_pcs.npy')),
'delays': np.load(path_fixtures.joinpath(f'session_{ses_nb}_passive_stimDelays.npy')),
'ids': np.load(path_fixtures.joinpath(f'session_{ses_nb}_passive_stimIDs.npy'))}
# load general metadata
with open(path_fixtures.joinpath('passive_stim_meta.json'), 'r') as f:
meta = json.load(f)
t_end_ephys = passive.ephysCW_end(session_path=session_path)
# load stimulus sequence
sync, sync_map = ephys_fpga._get_main_probe_sync(session_path, bin_exists=False)
fpga_sync = ephys_fpga._get_sync_fronts(sync, sync_map['frame2ttl'])
fttl = ephys_fpga._get_sync_fronts(sync, sync_map['frame2ttl'], tmin=t_end_ephys)
def get_spacers():
"""
load and get spacer information, do corr to find spacer timestamps
returns t_passive_starts, t_starts, t_ends
"""
spacer_template = np.array(meta['VISUAL_STIM_0']['ttl_frame_nums'],
dtype=np.float32) / FRAME_FS
jitter = 3 / FRAME_FS # allow for 3 screen refresh as jitter
t_quiet = meta['VISUAL_STIM_0']['delay_around']
spacer_times, _ = passive.get_spacer_times(spacer_template=spacer_template, jitter=jitter,
ttl_signal=fttl['times'], t_quiet=t_quiet)