From b123e5c7c7469aa443f93faa56df422c2084116a Mon Sep 17 00:00:00 2001 From: shenshan Date: Wed, 20 Jan 2021 01:20:15 +0000 Subject: [PATCH 01/34] add .dockerignore --- .dockerignore | 1 + 1 file changed, 1 insertion(+) create mode 100644 .dockerignore diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..60baa9cb --- /dev/null +++ b/.dockerignore @@ -0,0 +1 @@ +data/* From feb5693ebcf60237c454b68ecb6282e60cc8df7c Mon Sep 17 00:00:00 2001 From: shenshan Date: Fri, 22 Jan 2021 22:57:19 +0000 Subject: [PATCH 02/34] Change the way to fetch delay from alyx --- ibl_pipeline/analyses/behavior.py | 17 +++-------------- ibl_pipeline/behavior.py | 30 +++++++++++++----------------- 2 files changed, 16 insertions(+), 31 deletions(-) diff --git a/ibl_pipeline/analyses/behavior.py b/ibl_pipeline/analyses/behavior.py index 3105b0cf..1088697d 100755 --- a/ibl_pipeline/analyses/behavior.py +++ b/ibl_pipeline/analyses/behavior.py @@ -109,12 +109,7 @@ def make(self, key): psych_results = {**key, **psych_results} psych_results['prob_left'] = prob_left[ 'trial_stim_prob_left'] - if abs(p_left - 0.8) < 0.001: - psych_results['prob_left_block'] = 80 - elif abs(p_left - 0.2) < 0.001: - psych_results['prob_left_block'] = 20 - elif abs(p_left - 0.5) < 0.001: - psych_results['prob_left_block'] = 50 + psych_results['prob_left_block'] = round(p_left*10)*10 self.insert1(psych_results) @@ -183,12 +178,7 @@ def make(self, key): rt['reaction_time_ci_high'] = utils.compute_reaction_time( trials_sub, compute_ci=True) - if abs(p_left - 0.8) < 0.001: - rt['prob_left_block'] = 80 - elif abs(p_left - 0.2) < 0.001: - rt['prob_left_block'] = 20 - elif abs(p_left - 0.5) < 0.001: - rt['prob_left_block'] = 50 + rt['prob_left_block'] = round(p_left*10)*10 self.insert1(rt) @@ -498,10 +488,9 @@ def make(self, key): bpod_board = (behavior.Settings & sessions_rel).fetch('pybpod_board') ephys_board = [True for i in list(bpod_board) if 'ephys' in i] - task_protocols = (acquisition.Session & sessions_rel).fetch('task_protocol') delays = (behavior.SessionDelay & sessions_rel).fetch('session_delay_in_mins') - if len(ephys_board) == 3 and np.any(delays > 15): + if len(ephys_board) == 3 and np.any(delays >= 15): n_trials = (behavior.TrialSet & sessions_rel).fetch('n_trials') performance_easy = (PsychResults & sessions_rel).fetch( diff --git a/ibl_pipeline/behavior.py b/ibl_pipeline/behavior.py index 9730c8dd..1bf4799c 100755 --- a/ibl_pipeline/behavior.py +++ b/ibl_pipeline/behavior.py @@ -669,25 +669,21 @@ class SessionDelay(dj.Imported): date.strftime('%Y-%m-%d'))) def make(self, key): + eID = (acquisition.Session & key).fetch1('session_uuid') - data = one.load(str(eID), dataset_types=['_iblrig_taskData.raw']) - trial_start, trial_end = (TrialSet.Trial & key & 'trial_id=1').fetch1( - 'trial_start_time', 'trial_end_time') - first_trial_duration = trial_end - trial_start - - if data[0]: - if 'elapsed_time' in data[0][0].keys(): - elapsed_time = data[0][0]['elapsed_time'].split(':') - key['session_delay_in_secs'] = float(elapsed_time[1])*60 + \ - float(elapsed_time[2]) - first_trial_duration - key['session_delay_in_mins'] = key['session_delay_in_secs']/60 - self.insert1(key) - else: - key['error_type'] = 'raw task data not available' - SessionDelayAvailability.insert1(key, allow_direct_insert=True) + json = one.alyx.get(one.get_details(str(eID))['url'])['json'] + + if 'SESSION_START_DELAY_SEC' in json.keys(): + self.insert1(dict( + **key, + session_delay_in_secs=json['SESSION_START_DELAY_SEC'], + session_delay_in_mins=json['SESSION_START_DELAY_SEC'] / 60 + )) else: - key['error_type'] = 'elapsed time not available' - SessionDelayAvailability.insert1(key, allow_direct_insert=True) + key['error_type'] = 'delay not available' + SessionDelayAvailability.insert1( + key, allow_direct_insert=True, + skip_duplicates=True) @schema From d17f9333811d4f9d0c79977a81bff094cbf12b2e Mon Sep 17 00:00:00 2001 From: shenshan Date: Fri, 22 Jan 2021 22:57:52 +0000 Subject: [PATCH 03/34] Update wheel computation --- ibl_pipeline/group_shared/wheel.py | 302 +++++++++++++---------------- 1 file changed, 131 insertions(+), 171 deletions(-) diff --git a/ibl_pipeline/group_shared/wheel.py b/ibl_pipeline/group_shared/wheel.py index 96770ded..593020ac 100755 --- a/ibl_pipeline/group_shared/wheel.py +++ b/ibl_pipeline/group_shared/wheel.py @@ -1,32 +1,22 @@ -import datajoint as dj -from ibl_pipeline import acquisition, behavior -from oneibl.one import ONE -import numpy as np -import brainbox.behavior.wheel as wh import logging from logging.handlers import RotatingFileHandler -import alf.io -import os -log_dir = '/tmp/ibllib/logs' +import datajoint as dj +from ibl_pipeline import acquisition, behavior +import numpy as np -if not os.path.exists(log_dir): - os.makedirs(log_dir) +from oneibl.one import ONE +import brainbox.behavior.wheel as wh +from ibllib.io.extractors.training_wheel import extract_wheel_moves, extract_first_movement_times, infer_wheel_units +from ibllib.io.params import getfile -os.chmod(log_dir, 0o0777) logger = logging.getLogger(__name__) -fh = RotatingFileHandler(log_dir + '/Movements.log', maxBytes=(1048576*5)) +fh = RotatingFileHandler(getfile('dj_wheelMoves.log'), maxBytes=(1048576 * 5)) logger.addHandler(fh) logger.setLevel(logging.DEBUG) -mode = os.environ.get('MODE') - -if mode == 'update': - schema = dj.schema('group_shared_wheel') -else: - schema = dj.schema(dj.config.get('database.prefix', '') + - 'group_shared_wheel') +schema = dj.schema('group_shared_wheel') # group_shared_wheel @schema @@ -38,13 +28,14 @@ class WheelMoveSet(dj.Imported): n_movements: int # total number of movements within the session total_displacement: float # total displacement of the wheel during session in radians total_distance: float # total movement of the wheel in radians + n_direction_changes: int # total number of direction changes within a session """ class Move(dj.Part): # all times are in absolute seconds relative to session definition = """ -> master - move_id: int # movement id + move_id: int # wheel movement id --- movement_onset: float # time of movement onset in seconds from session start movement_offset: float # time of movement offset in seconds from session start @@ -52,15 +43,22 @@ class Move(dj.Part): movement_amplitude: float # the absolute peak amplitude relative to onset position """ - key_source = behavior.CompleteWheelSession + class DirectionChange(dj.Part): + # all times are in absolute seconds relative to session + definition = """ + -> master.Move + change_id: int # direction change id + --- + change_time: float # time of direction change + """ - # key_source = behavior.CompleteWheelSession & \ - # (acquisition.Session & 'task_protocol LIKE "%_iblrig_tasks_ephys%"') + key_source = behavior.CompleteWheelSession - def make(self, key): + def make(self, key, one=None): # Load the wheel for this session move_key = key.copy() - one = ONE() + change_key = move_key.copy() + one = one or ONE() eid, ver = (acquisition.Session & key).fetch1('session_uuid', 'task_protocol') logger.info('WheelMoves for session %s, %s', str(eid), ver) @@ -70,32 +68,13 @@ def make(self, key): all([isinstance(wheel[lab], np.ndarray) for lab in wheel]) and \ all(k in wheel for k in ('timestamps', 'position')) assert all_loaded, 'wheel data missing' - alf.io.check_dimensions(wheel) - if len(wheel['timestamps'].shape) == 1: - assert wheel['timestamps'].size == wheel['position'].size, 'wheel data dimension mismatch' - assert np.all(np.diff(wheel['timestamps']) > 0), 'wheel timestamps not monotonically increasing' - else: - logger.debug('2D timestamps') - # Check the values and units of wheel position - res = np.array([wh.ENC_RES, wh.ENC_RES/2, wh.ENC_RES/4]) - min_change_rad = 2 * np.pi / res - min_change_cm = wh.WHEEL_DIAMETER * np.pi / res - pos_diff = np.abs(np.ediff1d(wheel['position'])) - if pos_diff.min() < min_change_cm.min(): - # Assume values are in radians - units = 'rad' - encoding = np.argmin(np.abs(min_change_rad - pos_diff.min())) - min_change = min_change_rad[encoding] - else: - units = 'cm' - encoding = np.argmin(np.abs(min_change_cm - pos_diff.min())) - min_change = min_change_cm[encoding] - enc_names = {0: '4X', 1: '2X', 2: '1X'} - logger.info('Wheel in %s units using %s encoding', units, enc_names[int(encoding)]) - if '_iblrig_tasks_ephys' in ver: - assert np.allclose(pos_diff, min_change, rtol=1e-05), 'wheel position skips' + + # If times and timestamps present, drop times + if {'times', 'timestamps'}.issubset(wheel): + wheel.pop('times') + wheel_moves = extract_wheel_moves(wheel.timestamps, wheel.position) except ValueError: - logger.exception('Inconsistent wheel data') + logger.exception('Failed to find movements') raise except AssertionError as ex: logger.exception(str(ex)) @@ -104,41 +83,50 @@ def make(self, key): logger.exception(str(ex)) raise - try: - # Convert the pos threshold defaults from samples to correct unit - thresholds = wh.samples_to_cm(np.array([8, 1.5]), resolution=res[encoding]) - if units == 'rad': - thresholds = wh.cm_to_rad(thresholds) - kwargs = {'pos_thresh': thresholds[0], 'pos_thresh_onset': thresholds[1]} - # kwargs = {'make_plots': True, **kwargs} - # Interpolate and get onsets - pos, t = wh.interpolate_position(wheel['timestamps'], wheel['position'], freq=1000) - on, off, amp, peak_vel = wh.movements(t, pos, freq=1000, **kwargs) - assert on.size == off.size, 'onset/offset number mismatch' - assert np.all(np.diff(on) > 0) and np.all(np.diff(off) > 0), 'onsets/offsets not monotonically increasing' - assert np.all((off - on) > 0), 'not all offsets occur after onset' - except ValueError: - logger.exception('Failed to find movements') - raise - except AssertionError as ex: - logger.exception('Wheel integrity check failed: ' + str(ex)) - raise - - key['n_movements'] = on.size # total number of movements within the session - key['total_displacement'] = float(np.diff(pos[[0, -1]])) # total displacement of the wheel during session - key['total_distance'] = float(np.abs(np.diff(pos)).sum()) # total movement of the wheel - if units is 'cm': # convert to radians + # Build list of table entries + keys = ('move_id', 'movement_onset', 'movement_offset', 'max_velocity', 'movement_amplitude') + on_off, amp, vel_t = wheel_moves.values() # Unpack into short vars + moves = [dict(zip(keys, (i, on, off, vel_t[i], amp[i])), **move_key) + for i, (on, off) in enumerate(on_off)] + + # Calculate direction changes + Fs = 1000 + re_ts, re_pos = wheel.timestamps, wheel.position + if len(re_ts.shape) != 1: + logger.info('2D wheel timestamps') + if len(re_pos.shape) > 1: # Ensure 1D array of positions + re_pos = re_pos.flatten() + # Linearly interpolate the times + x = np.arange(re_pos.size) + re_ts = np.interp(x, re_ts[:, 0], re_ts[:, 1]) + + pos, ts = wh.interpolate_position(re_pos, re_ts, freq=Fs) + vel, _ = wh.velocity_smoothed(pos, Fs) + change_mask = np.insert(np.diff(np.sign(vel)) != 0, 0, 0) + + changes = [] + for i, (on, off) in enumerate(on_off.reshape(-1, 2)): + mask = np.logical_and(ts > on, ts < off) + ind = np.logical_and(mask, change_mask) + changes.extend( + dict(change_key, move_id=i, change_id=j, change_time=t) for j, t in enumerate(ts[ind]) + ) + + # Get the units of the position data + units, *_ = infer_wheel_units(wheel.position) + key['n_movements'] = wheel_moves['intervals'].shape[0] # total number of movements within the session + key['total_displacement'] = float(np.diff(wheel.position[[0, -1]])) # total displacement of the wheel during session + key['total_distance'] = float(np.abs(np.diff(wheel.position)).sum()) # total movement of the wheel + key['n_direction_changes'] = sum(change_mask) # total number of direction changes + if units == 'cm': # convert to radians key['total_displacement'] = wh.cm_to_rad(key['total_displacement']) key['total_distance'] = wh.cm_to_rad(key['total_distance']) - amp = wh.cm_to_rad(amp) + wheel_moves['peakAmplitude'] = wh.cm_to_rad(wheel_moves['peakAmplitude']) + # Insert the keys in order self.insert1(key) - - keys = ('move_id', 'movement_onset', 'movement_offset', 'max_velocity', 'movement_amplitude') - moves = [dict(zip(keys, (i, on[i], off[i], amp[i], peak_vel[i]))) for i in np.arange(on.size)] - [x.update(move_key) for x in moves] - self.Move.insert(moves) + self.DirectionChange.insert(changes) @schema @@ -157,122 +145,94 @@ class MovementTimes(dj.Computed): key_source = behavior.CompleteTrialSession & WheelMoveSet - def make(self, key): - THRESH = .1 # peak amp should be at least .1 rad; ~1/3rd of the threshold - eid, ver = (acquisition.Session & key).fetch1('session_uuid', 'task_protocol') # For logging purposes + def make(self, key, one=None): + # Log eid and task version + eid, ver = (acquisition.Session & key).fetch1('session_uuid', 'task_protocol') logger.info('MovementTimes for session %s, %s', str(eid), ver) - query = (WheelMoveSet.Move & key).proj( - 'move_id', - 'movement_onset', - 'movement_offset', - 'movement_amplitude') - wheel_move_data = query.fetch(order_by='move_id') - - query = (behavior.TrialSet.Trial & key).proj( - 'trial_response_choice', - 'trial_response_time', - 'trial_stim_on_time', - 'trial_go_cue_time', - 'trial_feedback_time', - 'trial_start_time') - trial_data = query.fetch(order_by='trial_id') - - if trial_data.size == 0 or wheel_move_data.size == 0: + + # Get required data from wheel moves and trials tables, each as a dict of numpy arrays + fields = ('move_id', 'movement_onset', 'movement_offset', 'movement_amplitude') + wheel_move_data = {k: v.values for k, v in ( + ( + (WheelMoveSet.Move & key) + .proj(*fields) + .fetch(order_by='move_id', format='frame') + .reset_index() + .drop(['subject_uuid', 'session_start_time'], axis=1) + .rename(columns={'movement_amplitude': 'peakAmplitude'}) + .iteritems() + ) + )} + + fields = ('trial_response_choice', 'trial_response_time', 'trial_stim_on_time', + 'trial_go_cue_time', 'trial_feedback_time', 'trial_start_time') + trial_data = {k: v.values for k, v in ( + ( + (behavior.TrialSet.Trial & key) + .proj(*fields) + .fetch(order_by='trial_id', format='frame') + .reset_index() + .drop(['subject_uuid', 'session_start_time'], axis=1) + .iteritems() + ) + )} + + if trial_data['trial_id'].size == 0 or wheel_move_data['move_id'].size == 0: logger.warning('Missing DJ trial or move data') return - all_move_onsets = wheel_move_data['movement_onset'] - peak_amp = wheel_move_data['movement_amplitude'] - flinch = abs(peak_amp) < THRESH - go_trial = trial_data['trial_response_choice'] != 'No Go' - feedback_times = trial_data['trial_feedback_time'] - cue_times = trial_data['trial_go_cue_time'] - - # Check integrity of feedback and start times - try: - # Log presence of nans in feedback times (common) - nan_trial = np.isnan(feedback_times) - if nan_trial.any(): - n_feedback_nans = np.count_nonzero(nan_trial) - logger.warning('%i feedback_times nan', np.count_nonzero(nan_trial)) - response_times = trial_data['trial_response_time'] - if n_feedback_nans > np.count_nonzero(np.isnan(response_times)): - logger.warning('using response times instead of feedback times') - feedback_times = response_times - nan_trial = np.isnan(feedback_times) - - # Assert all feedback times are monotonically increasing - assert np.all(np.diff(feedback_times[~nan_trial]) > 0), 'feedback times not monotonically increasing' - # Log presence of nans in go cue times times (common) - if np.isnan(cue_times).any(): - # If all nan, use stim on - if np.isnan(cue_times).all(): - logger.warning('trial_go_cue_time is all nan, using trial_stim_on_time') - cue_times = trial_data['trial_stim_on_time'] - if np.isnan(cue_times).any(): - n_nan = 'all' if np.isnan(cue_times).all() else str(np.count_nonzero(np.isnan(cue_times))) - logger.warning('trial_stim_on_time nan for %s trials', n_nan) - else: - logger.warning('trial_go_cue_time is nan for %i trials', np.count_nonzero(np.isnan(cue_times))) - # Assert all cue times are montonically increasing - assert np.all(np.diff(cue_times[~np.isnan(cue_times)]) > 0), 'cue times not monotonically increasing' - # Assert all start times occur before feedback times - # assert np.all((feedback_times[~nan_trial] - start_times) > 0), 'feedback occurs before start time' - except AssertionError as ex: - logger.exception('Movement integrity check failed: ' + str(ex)) - raise - # Get minimum quiescent period for session try: - one = ONE() + one = one or ONE() task_params = one.load_object(str(eid), '_iblrig_taskSettings.raw') min_qt = task_params['raw']['QUIESCENT_PERIOD'] - if len(min_qt) > len(cue_times): - min_qt = np.array(min_qt[0:cue_times.size]) except BaseException: logger.warning('failed to load min quiescent time') - min_qt = 0.2 + min_qt = None + + # Many of the timestamps are missing for sessions, therefore will patch together the approximate + # closed-loop periods by taking the minimum of go_cue and stim_on, response and feedback. + start = np.nanmin(np.c_[trial_data['trial_stim_on_time'], trial_data['trial_go_cue_time']], axis=1) + end = np.nanmin(np.c_[trial_data['trial_response_time'], trial_data['trial_feedback_time']], axis=1) + assert np.all(start < end) and np.all(np.diff(start) > 0), 'timestamps not increasing' + go_trial = trial_data['trial_response_choice'] != 'No Go' + + # Check we have times for at least some trials + nan_trial = np.isnan(np.c_[start, end]).any(axis=1) + assert ~nan_trial.all(), 'no reliable trials times for session' + + # Rename data for the firstMovement_times extractor function + wheel_move_data['intervals'] = np.c_[ + wheel_move_data['movement_onset'], wheel_move_data['movement_offset'] + ] + trial_data = {'goCue_times': start, 'feedback_times': end, 'trial_id': trial_data['trial_id']} # Find first significant movement for each trial. To be counted, the movement must # occur between go cue / stim on and before feedback / response time. The movement # onset is sometimes just before the cue (occurring in the gap between quiescence end and # cue start, or during the quiescence period but sub-threshold). The movement is # sufficiently large if it is greater than or equal to THRESH + onsets, final_movement, ids = extract_first_movement_times(wheel_move_data, trial_data, min_qt) + move_ids = np.full_like(onsets, np.nan) + move_ids[~np.isnan(onsets)] = ids - # Initialize as nans - onsets = np.full(trial_data['trial_id'].shape, np.nan) - ids = np.full(trial_data['trial_id'].shape, int(-1)) - final_movement = np.zeros(trial_data['trial_id'].shape, bool) - # Iterate over trials, extracting onsets approx. within closed-loop period - for i, (t1, t2) in enumerate(zip(cue_times - min_qt, feedback_times)): - if ~np.isnan(t2 - t1): # If both timestamps defined - mask = (all_move_onsets > t1) & (all_move_onsets < t2) - if np.any(mask): # If any onsets for this trial - trial_onset_ids, = np.where(mask) - if np.any(~flinch[mask]): # If any trial moves were sufficiently large - ids[i] = trial_onset_ids[~flinch[mask]][0] # Find first large move id - onsets[i] = all_move_onsets[ids[i]] # Save first large move onset - final_movement[i] = ids[i] == trial_onset_ids[-1] # Final move of trial - else: # Check if trial was no-go - if ~go_trial[i]: # Report if not no-go - logger.warning('failed to find any onsets for trial id %i', i+1) - else: # Log missing timestamps - logger.warning('no reliable times for trial id %i', i + 1) + # Check if any movements failed to be detected + n_nan = np.count_nonzero(np.isnan(onsets[go_trial])) + if n_nan > 0: + logger.warning('failed to detect movement on %i go trials', n_nan) # Create matrix of values for insertion into table movement_data = np.c_[ trial_data['trial_id'], # trial_id - ids, # wheel_move_id - onsets - cue_times, # reaction_time + move_ids, # wheel_move_id + onsets - start, # reaction_time final_movement, # final_movement - feedback_times - onsets, # movement_time - feedback_times - cue_times, # response_time + end - onsets, # movement_time + end - start, # response_time onsets # movement_onset ] data = [] for row in movement_data: - if np.isnan(row).any(): # don't insert; leave as null - logger.warning('nan found for trial %i', row[0]) - else: # insert row + if ~np.isnan(row).any(): # insert row data.append(tuple([*key.values(), *list(row)])) self.insert(data) From bcb10c0fced36d8ba7c27141aac4b100ca893903 Mon Sep 17 00:00:00 2001 From: shenshan Date: Fri, 22 Jan 2021 23:00:26 +0000 Subject: [PATCH 04/34] Add complete wheel computation to daily populate --- ibl_pipeline/process/populate_behavior.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ibl_pipeline/process/populate_behavior.py b/ibl_pipeline/process/populate_behavior.py index f1a08f67..344c27fe 100755 --- a/ibl_pipeline/process/populate_behavior.py +++ b/ibl_pipeline/process/populate_behavior.py @@ -13,6 +13,7 @@ mode = environ.get('MODE') BEHAVIOR_TABLES = [ + behavior.CompleteWheelSession, behavior.CompleteWheelMoveSession, behavior.CompleteTrialSession, behavior.TrialSet, From ca2ec20b9b32a1ea9aa1cec62e57d1ba816d1c5c Mon Sep 17 00:00:00 2001 From: shenshan Date: Fri, 22 Jan 2021 23:01:03 +0000 Subject: [PATCH 05/34] Fix a bug in ephys plotting --- ibl_pipeline/plotting/plotting_utils_ephys.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ibl_pipeline/plotting/plotting_utils_ephys.py b/ibl_pipeline/plotting/plotting_utils_ephys.py index a7628467..0f5d360a 100755 --- a/ibl_pipeline/plotting/plotting_utils_ephys.py +++ b/ibl_pipeline/plotting/plotting_utils_ephys.py @@ -637,9 +637,9 @@ def driftmap( ax = plt.Axes(fig, [0., 0., 1., 1.]) ax.scatter(x, y, color=colorvec, edgecolors='none') - x_edge = (max(x) - min(x)) * 0.05 - x_lim = [min(x) - x_edge, max(x) + x_edge] - y_lim = [min(y) - 50, max(y) + 100] + x_edge = (np.nanmax(x) - np.nanmin(x)) * 0.05 + x_lim = [np.nanmin(x) - x_edge, np.nanmax(x) + x_edge] + y_lim = [np.nanmin(y) - 50, np.nanmax(y) + 100] ax.set_xlim(x_lim[0], x_lim[1]) ax.set_ylim(y_lim[0], y_lim[1]) From 6df3642c103ed55b475481b192be36776e38da61 Mon Sep 17 00:00:00 2001 From: shenshan Date: Fri, 22 Jan 2021 23:24:05 +0000 Subject: [PATCH 06/34] Add enum option to DelayAvailability --- ibl_pipeline/behavior.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ibl_pipeline/behavior.py b/ibl_pipeline/behavior.py index 1bf4799c..44a5d470 100755 --- a/ibl_pipeline/behavior.py +++ b/ibl_pipeline/behavior.py @@ -649,7 +649,7 @@ class SessionDelayAvailability(dj.Imported): definition = """ -> acquisition.Session --- - error_type: enum("elapsed time not available", "raw task data not available") + error_type: enum("elapsed time not available", "raw task data not available", "delay not available") """ From 3c601dcbc561287e0bb6808ad3b544bab83590e2 Mon Sep 17 00:00:00 2001 From: shenshan Date: Sat, 23 Jan 2021 01:51:09 +0000 Subject: [PATCH 07/34] Add first day on ephys rig to cumulative plots --- .../plotting/plotting_utils_behavior.py | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/ibl_pipeline/plotting/plotting_utils_behavior.py b/ibl_pipeline/plotting/plotting_utils_behavior.py index abc20304..d73ccf1c 100755 --- a/ibl_pipeline/plotting/plotting_utils_behavior.py +++ b/ibl_pipeline/plotting/plotting_utils_behavior.py @@ -146,6 +146,8 @@ def get_status(subj): first_trained_1b = subj.aggr( behavior.SessionTrainingStatus & 'training_status = "trained_1b"', first_session='DATE(min(session_start_time))') + first_ephysrig = subj.aggr(behavior_ingest.Settings & 'pybpod_board like "%ephys%"', + first_session='DATE(min(session_start_time))') first_ready4ephysrig = subj.aggr( behavior.SessionTrainingStatus & 'training_status = "ready4ephysrig"', first_session='DATE(min(session_start_time))') @@ -187,6 +189,14 @@ def get_status(subj): else: result.update(is_ready4ephysrig=False) + if len(first_ephysrig): + first_ephysrig_date = first_ephysrig.fetch1( + 'first_session').strftime('%Y-%m-%d') + result.update(is_on_ephysrig=True, + first_ephysrig_date=first_ephysrig_date) + else: + result.update(is_on_ephysrig=False) + if len(first_ready4delay): first_ready4delay_date = first_ready4delay.fetch1( 'first_session').strftime('%Y-%m-%d') @@ -557,6 +567,22 @@ def create_status_plot(data, yrange, status, xaxis='x1', yaxis='y1', ) ) + if status['is_on_ephysrig'] and (not public): + data.append( + go.Scatter( + x=[status['first_ephysrig_date'], + status['first_ephysrig_date']], + y=yrange, + mode="lines", + marker=dict(color='rgba(116, 15, 170, 1)'), + name='first day on ephysrig', + xaxis=xaxis, + yaxis=yaxis, + showlegend=show_legend_external, + hoverinfo='x' + ) + ) + if status['is_ready4delay'] and (not public): data.append( go.Scatter( From ce895f95b2625337b468117e11d54715145cc0fe Mon Sep 17 00:00:00 2001 From: shenshan Date: Thu, 11 Feb 2021 01:13:04 +0000 Subject: [PATCH 08/34] Update notebooks for behavior paper --- .../Replication of Figure 2a and b.ipynb | 269 ++++++++ .../202102_behavior_paper/one_example.ipynb | 210 ++++++ .../paper_behavior_functions.py | 612 ++++++++++++++++++ 3 files changed, 1091 insertions(+) create mode 100644 notebooks/notebooks_tutorial/202102_behavior_paper/Replication of Figure 2a and b.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_behavior_paper/one_example.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_behavior_paper/paper_behavior_functions.py diff --git a/notebooks/notebooks_tutorial/202102_behavior_paper/Replication of Figure 2a and b.ipynb b/notebooks/notebooks_tutorial/202102_behavior_paper/Replication of Figure 2a and b.ipynb new file mode 100644 index 00000000..b0028e97 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_behavior_paper/Replication of Figure 2a and b.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Learning curves for all labs\n", + "@author: Anne Urai, Miles Wells\n", + "15 January 2020\n", + "\"\"\"\n", + "import os\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "from scipy.signal import medfilt\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as ticker\n", + "\n", + "from paper_behavior_functions import (query_subjects, figpath, load_csv, group_colors,\n", + " institution_map, seaborn_style, EXAMPLE_MOUSE,\n", + " FIGURE_HEIGHT, FIGURE_WIDTH, QUERY)\n", + "from ibl_pipeline.analyses import behavior as behavioral_analyses\n", + "\n", + "# INITIALIZE A FEW THINGS\n", + "seaborn_style()\n", + "figpath = figpath()\n", + "pal = group_colors()\n", + "institution_map, col_names = institution_map()\n", + "col_names = col_names[:-1]\n", + "\n", + "# %% ============================== #\n", + "# GET DATA FROM TRAINED ANIMALS\n", + "# ================================= #\n", + "\n", + "if QUERY is True:\n", + " use_subjects = query_subjects()\n", + " b = (behavioral_analyses.BehavioralSummaryByDate * use_subjects)\n", + " behav = b.fetch(order_by='institution_short, subject_nickname, training_day',\n", + " format='frame').reset_index()\n", + " behav['institution_code'] = behav.institution_short.map(institution_map)\n", + "else:\n", + " behav = load_csv('Fig2ab.csv')\n", + "\n", + "# exclude sessions with fewer than 100 trials\n", + "behav = behav[behav['n_trials_date'] > 100]\n", + "\n", + "# convolve performance over 3 days\n", + "for i, nickname in enumerate(behav['subject_nickname'].unique()):\n", + " perf = behav.loc[behav['subject_nickname'] == nickname, 'performance_easy'].values\n", + " perf_conv = np.convolve(perf, np.ones((3,))/3, mode='valid')\n", + " # perf_conv = np.append(perf_conv, [np.nan, np.nan])\n", + " perf_conv = medfilt(perf, kernel_size=3)\n", + " behav.loc[behav['subject_nickname'] == nickname, 'performance_easy'] = perf_conv\n", + "\n", + "# how many mice are there for each lab?\n", + "N = behav.groupby(['institution_code'])['subject_nickname'].nunique().to_dict()\n", + "behav['n_mice'] = behav.institution_code.map(N)\n", + "behav['institution_name'] = behav.institution_code + '\\n ' + behav.n_mice.apply(str) + ' mice'\n", + "\n", + "# make sure each mouse starts at 0\n", + "for index, group in behav.groupby(['lab_name', 'subject_nickname']):\n", + " behav.loc[group.index, 'training_day'] = group['training_day'] - group['training_day'].min()\n", + "\n", + "# create another column only after the mouse is trained\n", + "behav2 = pd.DataFrame([])\n", + "for index, group in behav.groupby(['institution_code', 'subject_nickname']):\n", + " group['performance_easy_trained'] = group.performance_easy\n", + " group.loc[pd.to_datetime(group['session_date']) < pd.to_datetime(group['date_trained']),\n", + " 'performance_easy_trained'] = np.nan\n", + " # add this\n", + " behav2 = behav2.append(group)\n", + "\n", + "behav = behav2\n", + "behav['performance_easy'] = behav.performance_easy * 100\n", + "behav['performance_easy_trained'] = behav.performance_easy_trained * 100\n", + "\n", + "# Create column for cumulative trials per mouse\n", + "behav.n_trials_date = behav.n_trials_date.astype(int)\n", + "behav['cum_trials'] = (\n", + " (behav\n", + " .groupby(by=['subject_uuid'])\n", + " .cumsum()\n", + " .n_trials_date)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %% ============================== #\n", + "# LEARNING CURVES\n", + "# ================================= #\n", + "\n", + "# plot one curve for each animal, one panel per lab\n", + "fig = sns.FacetGrid(behav,\n", + " col=\"institution_code\", col_wrap=7, col_order=col_names,\n", + " sharex=True, sharey=True, hue=\"subject_uuid\", xlim=[-1, 40],\n", + " height=FIGURE_HEIGHT, aspect=(FIGURE_WIDTH/7)/FIGURE_HEIGHT)\n", + "fig.map(sns.lineplot, \"training_day\",\n", + " \"performance_easy\", color='grey', alpha=0.3)\n", + "fig.map(sns.lineplot, \"training_day\",\n", + " \"performance_easy_trained\", color='black', alpha=0.3)\n", + "fig.set_titles(\"{col_name}\")\n", + "fig.set(xticks=[0, 20, 40])\n", + "\n", + "# overlay the example mouse\n", + "sns.lineplot(ax=fig.axes[0], x='training_day', y='performance_easy', color='black',\n", + " data=behav[behav['subject_nickname'].str.contains(EXAMPLE_MOUSE)], legend=False)\n", + "\n", + "for axidx, ax in enumerate(fig.axes.flat):\n", + " # add the lab mean to each panel\n", + " sns.lineplot(data=behav.loc[behav.institution_name == behav.institution_name.unique()[axidx], :],\n", + " x='training_day', y='performance_easy',\n", + " color=pal[axidx], ci=None, ax=ax, legend=False, linewidth=2)\n", + " ax.set_title(behav.institution_name.unique()[\n", + " axidx], color=pal[axidx], fontweight='bold')\n", + "\n", + "fig.set_axis_labels('Training day', 'Performance (%)\\n on easy trials')\n", + "fig.despine(trim=True)\n", + "plt.tight_layout(w_pad=-2.2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot all labs\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(FIGURE_WIDTH/3, FIGURE_HEIGHT))\n", + "sns.lineplot(x='training_day', y='performance_easy', hue='institution_code', palette=pal,\n", + " ax=ax1, legend=False, data=behav, ci=None)\n", + "ax1.set_title('All labs: %d mice'%behav['subject_nickname'].nunique())\n", + "ax1.set(xlabel='Training day',\n", + " ylabel='Performance (%)\\non easy trials', xlim=[-1, 60], ylim=[15, 100])\n", + "ax1.set(xticks=[0, 10, 20, 30, 40, 50, 60])\n", + "\n", + "sns.despine(trim=True)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "###############\n", + "# plot one curve for each animal, one panel per lab\n", + "fig = sns.FacetGrid(behav,\n", + " col=\"institution_code\", col_wrap=7, col_order=col_names,\n", + " sharex=True, sharey=True, hue=\"subject_uuid\", xlim=[-1, 3e4],\n", + " height=FIGURE_HEIGHT, aspect=(FIGURE_WIDTH / 7) / FIGURE_HEIGHT)\n", + "fig.map(sns.lineplot, \"cum_trials\",\n", + " \"performance_easy\", color='grey', alpha=0.3)\n", + "fig.map(sns.lineplot, \"cum_trials\",\n", + " \"performance_easy_trained\", color='black', alpha=0.3)\n", + "fig.set_titles(\"{col_name}\")\n", + "format_fcn = ticker.FuncFormatter(lambda x, pos: '{:,.0f}'.format(x / 1e3) + 'K')\n", + "\n", + "# overlay the example mouse\n", + "sns.lineplot(ax=fig.axes[0], x='cum_trials', y='performance_easy', color='black',\n", + " data=behav[behav['subject_nickname'].str.contains(EXAMPLE_MOUSE)], legend=False)\n", + "\n", + "for axidx, ax in enumerate(fig.axes.flat):\n", + " # add the lab mean to each panel\n", + " d = (behav.loc[behav.institution_name == behav.institution_name.unique()[axidx], :])\\\n", + " .groupby('training_day').mean() # Binning by day\n", + " sns.lineplot(data=d, x='cum_trials', y='performance_easy',\n", + " color=pal[axidx], ci=None, ax=ax, legend=False, linewidth=2)\n", + " ax.set_title(behav.institution_name.unique()[\n", + " axidx], color=pal[axidx], fontweight='bold')\n", + " fig.set(xticks=[0, 10000, 20000, 30000])\n", + " ax.xaxis.set_major_formatter(format_fcn)\n", + "\n", + "fig.set_axis_labels('Trial', 'Performance (%)\\n on easy trials')\n", + "fig.despine(trim=True)\n", + "plt.tight_layout(w_pad=-2.2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot all labs\n", + "d = behav.groupby(['institution_code', 'training_day']).mean() # Binned by day\n", + "\n", + "fig, ax1 = plt.subplots(1, 1, figsize=(FIGURE_WIDTH/3, FIGURE_HEIGHT))\n", + "sns.lineplot(x='cum_trials', y='performance_easy', hue='institution_code', palette=pal,\n", + " ax=ax1, legend=False, data=d, ci=None)\n", + "ax1.set_title('All labs: %d mice' % behav['subject_nickname'].nunique())\n", + "ax1.set(xlabel='Trial',\n", + " ylabel='Performance (%)\\non easy trials', xlim=[-1, 30000], ylim=[15, 100])\n", + "ax1.xaxis.set_major_formatter(format_fcn)\n", + "\n", + "sns.despine(trim=True)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ================================= #\n", + "# print some stats\n", + "# ================================= #\n", + "behav_summary_std = behav.groupby(['training_day'])[\n", + " 'performance_easy'].std().reset_index()\n", + "behav_summary = behav.groupby(['training_day'])[\n", + " 'performance_easy'].mean().reset_index()\n", + "print('number of days to reach 80% accuracy on easy trials: ')\n", + "print(behav_summary.loc[behav_summary.performance_easy >\n", + " 80, 'training_day'].min())\n", + "\n", + "\n", + "# ================================= #\n", + "# print some stats\n", + "# ================================= #\n", + "behav_summary_std = behav.groupby(['training_day'])[\n", + " ['performance_easy', 'cum_trials']].std().reset_index()\n", + "behav_summary = behav.groupby(['training_day'])[\n", + " ['performance_easy', 'cum_trials']].mean().reset_index()\n", + "print('number of trials to reach 80% accuracy on easy trials: ')\n", + "print(behav_summary.loc[behav_summary.performance_easy >\n", + " 80, 'cum_trials'].round().min())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_tutorial/202102_behavior_paper/one_example.ipynb b/notebooks/notebooks_tutorial/202102_behavior_paper/one_example.ipynb new file mode 100644 index 00000000..5abb27f7 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_behavior_paper/one_example.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A standardized and reproducible method to measure decision-making in mice: Data\n", + "\n", + "This Jupyter notebooks shows how to access the data via the [Open Neurophysiology Environment (ONE) interface in Python](https://ibllib.readthedocs.io/en/latest/02_tutorial_python.html). The particular ONE implementation used in this notebook (\"ONE light\") only requires a local copy of the data, organized in a hierarchy of appropriately named subfolders and file names." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by importing the ONE light implementation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn\n", + "\n", + "from oneibl.onelight import ONE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, this ONE implementation looks for data in the current directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%cd one_data/ibl-behavioral-data-Dec2019" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create the main ONE() instance that we will use to access the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "one = ONE()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We search all sessions that have a given dataset type pattern." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eids = one.search(['_ibl_trials.*'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We select the tenth session." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eid = eids[10]\n", + "print(f\"Loading session {eid}.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We list all dataset types available in that session." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dset_types = one.list(eid)\n", + "print(f\"Available dataset types: {', '.join(dset_types)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We load a single dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Loading {dset_types[0]}\")\n", + "choice = one.load_dataset(eid, dset_types[0])\n", + "print(choice.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We load an entire object (a kind of Python dictionary)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Loading the _ibl_trials object.\")\n", + "trials = one.load_object(eid, \"_ibl_trials\")\n", + "for key, value in trials.items():\n", + " print(key, value.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We make a very basic plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "out = []\n", + "for sgn, contrast in ((-1, trials.contrastRight), (+1, trials.contrastLeft)):\n", + " for c in np.unique(contrast)[::sgn]:\n", + " if not np.isnan(c) and (c != 0 or sgn == +1):\n", + " out.append((sgn * c, (trials.choice[contrast == c] == +1).mean()))\n", + "out = np.array(out) * 100\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(out[:, 0], out[:, 1], 'o-', lw=4, ms=10)\n", + "plt.xlabel(\"Signed contrast (%)\")\n", + "plt.ylabel(\"Rightward choice (%)\")\n", + "plt.ylim(0, 100)\n", + "plt.title(\"Psychometric curve for %s\" % eid);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_tutorial/202102_behavior_paper/paper_behavior_functions.py b/notebooks/notebooks_tutorial/202102_behavior_paper/paper_behavior_functions.py new file mode 100644 index 00000000..9e6c163e --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_behavior_paper/paper_behavior_functions.py @@ -0,0 +1,612 @@ +# -*- coding: utf-8 -*- +""" +General functions and queries for the analysis of behavioral data from the IBL task +Guido Meijer, Anne Urai, Alejandro Pan Vazquez & Miles Wells +16 Jan 2020 +""" +import warnings +import os +from io import BytesIO +from zipfile import ZipFile +from urllib.request import urlopen + +import seaborn as sns +import matplotlib +import numpy as np +import datajoint as dj +import pandas as pd +import matplotlib.pyplot as plt + +import brainbox.behavior.pyschofit as psy + +# Supress seaborn future warnings +warnings.simplefilter(action='ignore', category=FutureWarning) + +# Some constants +URL = 'http://ibl.flatironinstitute.org/public/behavior_paper_data.zip' +QUERY = True # Whether to query data through DataJoint (True) or use downloaded csv files (False) +EXAMPLE_MOUSE = 'KS014' # Mouse nickname used as an example +CUTOFF_DATE = '2020-03-23' # Date after which sessions are excluded, previously 30th Nov +STABLE_HW_DATE = '2019-06-10' # Date after which hardware was deemed stable + +# LAYOUT +FIGURE_HEIGHT = 2 # inch +FIGURE_WIDTH = 8 # inch + +# EXCLUDED SESSIONS +EXCLUDED_SESSIONS = ['a9fb578a-9d7d-42b4-8dbc-3b419ce9f424'] # Session UUID + + +def group_colors(): + return sns.color_palette("Dark2", 7) + + +def institution_map(): + institution_map = {'UCL': 'Lab 1', 'CCU': 'Lab 2', 'CSHL': 'Lab 3', 'NYU': 'Lab 4', + 'Princeton': 'Lab 5', 'SWC': 'Lab 6', 'Berkeley': 'Lab 7'} + col_names = ['Lab 1', 'Lab 2', 'Lab 3', 'Lab 4', 'Lab 5', 'Lab 6', 'Lab 7', 'All labs'] + + return institution_map, col_names + + +def seaborn_style(): + """ + Set seaborn style for plotting figures + """ + sns.set(style="ticks", context="paper", + font="Arial", + rc={"font.size": 9, + "axes.titlesize": 9, + "axes.labelsize": 9, + "lines.linewidth": 1, + "xtick.labelsize": 7, + "ytick.labelsize": 7, + "savefig.transparent": True, + "xtick.major.size": 2.5, + "ytick.major.size": 2.5, + "xtick.minor.size": 2, + "ytick.minor.size": 2, + }) + matplotlib.rcParams['pdf.fonttype'] = 42 + matplotlib.rcParams['ps.fonttype'] = 42 + + +def figpath(): + # Retrieve absolute path of paper-behavior dir + repo_dir = os.path.dirname(os.path.realpath(__file__)) + # Make figure directory + fig_dir = os.path.join(repo_dir, 'exported_figs') + # If doesn't already exist, create + if not os.path.exists(fig_dir): + os.mkdir(fig_dir) + return fig_dir + + +def datapath(): + """ + Return the location of data directory + """ + # Retrieve absolute path of paper-behavior dir + repo_dir = os.path.dirname(os.path.realpath(__file__)) + # Make figure directory + data_dir = os.path.join(repo_dir, 'data') + # If doesn't already exist, create + if not os.path.exists(data_dir): + os.mkdir(data_dir) + return data_dir + + +def load_csv(*args, **kwargs): + """Loads CSV and pickle data either locally or remotely + If the input file is not found in the data directory the file is downloaded from a remote + http server and returned as a pandas DataFrame. + """ + repo_dir = os.path.dirname(os.path.realpath(__file__)) + local = os.path.join(repo_dir, 'data', *args) + if not os.path.exists(local): + resp = urlopen(URL) + zipfile = ZipFile(BytesIO(resp.read())) + files = zipfile.namelist() + if not any(x.endswith(args[-1]) for x in files): + raise FileNotFoundError(f'{args[-1]} not found in {URL}') + local = zipfile.extract('/'.join(('data', *args)), repo_dir) + loader = pd.read_pickle if local.endswith('.pkl') else pd.read_csv + return loader(local, **kwargs) + + +def query_subjects(as_dataframe=False, from_list=False, criterion='trained'): + """ + Query all mice for analysis of behavioral data + Parameters + ---------- + as_dataframe: boolean if true returns a pandas dataframe (default is False) + from_list: loads files from list uuids (array of uuids objects) + criterion: what criterion by the 30th of November - trained (a and b), biased, ephys + (includes ready4ephysrig, ready4delay and ready4recording). If None, + all mice that completed a training session are returned, with date_trained + being the date of their first training session. + """ + from ibl_pipeline import subject, acquisition, reference + from ibl_pipeline.analyses import behavior as behavior_analysis + + # Query all subjects with project ibl_neuropixel_brainwide_01 and get the date at which + # they reached a given training status + all_subjects = (subject.Subject * subject.SubjectLab * reference.Lab * subject.SubjectProject + & 'subject_project = "ibl_neuropixel_brainwide_01"') + sessions = acquisition.Session * behavior_analysis.SessionTrainingStatus() + fields = ('subject_nickname', 'sex', 'subject_birth_date', 'institution_short') + + if criterion is None: + # Find first session of all mice; date_trained = date of first training session + subj_query = all_subjects.aggr( + sessions, *fields, date_trained='min(date(session_start_time))') + else: # date_trained = date of first session when criterion was reached + if criterion == 'trained': + restriction = 'training_status="trained_1a" OR training_status="trained_1b"' + elif criterion == 'biased': + restriction = 'task_protocol LIKE "%biased%"' + elif criterion == 'ephys': + restriction = 'training_status LIKE "ready%"' + else: + raise ValueError('criterion must be "trained", "biased" or "ephys"') + subj_query = all_subjects.aggr( + sessions & restriction, *fields, date_trained='min(date(session_start_time))') + + if from_list is True: + data_path = os.path.join(datapath(), 'uuids_trained.npy') + ids = np.load(data_path, allow_pickle=True) + subj_query = subj_query & [{'subject_uuid': u_id} for u_id in ids] + + # Select subjects that reached criterion before cutoff date + subjects = (subj_query & 'date_trained <= "%s"' % CUTOFF_DATE) + if as_dataframe is True: + subjects = subjects.fetch(format='frame') + subjects = subjects.sort_values(by=['lab_name']).reset_index() + + return subjects + + +def query_sessions(task='all', stable=False, as_dataframe=False, + force_cutoff=False, criterion='biased'): + """ + Query all sessions for analysis of behavioral data + Parameters + ---------- + task: string indicating sessions of which task to return, can be trianing or biased + default is all + stable: boolean if True only return sessions with stable hardware, which means + sessions after particular date (default is False) + as_dataframe: boolean if True returns a pandas dataframe (default is False) + force_cutoff: whether the animal had to reach the criterion by the 30th of Nov. Only + applies to biased and ready for ephys criterion + criterion: what criterion by the 30th of November - trained (includes + a and b), biased, ready (includes ready4ephysrig, ready4delay and + ready4recording) + """ + + from ibl_pipeline import acquisition + + # Query sessions + if force_cutoff is True: + use_subjects = query_subjects(criterion=criterion).proj('subject_uuid') + else: + use_subjects = query_subjects().proj('subject_uuid') + + # Query all sessions or only training or biased if required + if task == 'all': + sessions = acquisition.Session * use_subjects & 'task_protocol NOT LIKE "%habituation%"' + elif task == 'training': + sessions = acquisition.Session * use_subjects & 'task_protocol LIKE "%training%"' + elif task == 'biased': + sessions = acquisition.Session * use_subjects & 'task_protocol LIKE "%biased%"' + elif task == 'ephys': + sessions = acquisition.Session * use_subjects & 'task_protocol LIKE "%ephys%"' + else: + raise ValueError('task must be "all", "training", "biased" or "ephys"') + + # Only use sessions up until the end of December + sessions = sessions & 'date(session_start_time) <= "%s"' % CUTOFF_DATE + + # Exclude weird sessions + sessions = sessions & dj.Not([{'session_uuid': u_id} for u_id in EXCLUDED_SESSIONS]) + + # If required only output sessions with stable hardware + if stable is True: + sessions = sessions & 'date(session_start_time) > "%s"' % STABLE_HW_DATE + + # Transform into pandas Dataframe if requested + if as_dataframe is True: + sessions = sessions.fetch( + order_by='institution_short, subject_nickname, session_start_time', format='frame') + sessions = sessions.reset_index() + + return sessions + + +def query_sessions_around_criterion(criterion='trained', days_from_criterion=(2, 0), + as_dataframe=False, force_cutoff=False): + """ + Query all sessions for analysis of behavioral data + Parameters + ---------- + criterion: string indicating which criterion to use: trained, biased or ephys + days_from_criterion: two-element array which indicates which training days around the day + the mouse reached criterium to return, e.g. [3, 2] returns three days + before criterium reached up until 2 days after (default: [2, 0]) + as_dataframe: return sessions as a pandas dataframe + force_cutoff: whether the animal had to reach the criterion by the 30th of Nov. Only + applies to biased and ready for ephys criterion + Returns + --------- + sessions: The sessions around the criterion day, works in conjunction with + any table that has session_start_time as primary key (such as + behavior.TrialSet.Trial) + days: The training days around the criterion day. Can be used in conjunction + with tables that have session_date as primary key (such as + behavior_analysis.BehavioralSummaryByDate) + """ + + from ibl_pipeline import subject, acquisition + from ibl_pipeline.analyses import behavior as behavior_analysis + + # Query all included subjects + if force_cutoff is True: + use_subjects = query_subjects(criterion=criterion).proj('subject_uuid') + else: + use_subjects = query_subjects().proj('subject_uuid') + + # Query per subject the date at which the criterion is reached + sessions = acquisition.Session * behavior_analysis.SessionTrainingStatus + if criterion == 'trained': + restriction = 'training_status="trained_1a" OR training_status="trained_1b"' + elif criterion == 'biased': + restriction = 'task_protocol LIKE "%biased%" AND training_status="trained_1b"' + elif criterion == 'ephys': + restriction = 'training_status LIKE "ready%"' + else: + raise ValueError('criterion must be "trained", "biased" or "ephys"') + + subj_crit = (subject.Subject * use_subjects).aggr( + sessions & restriction, 'subject_nickname', date_criterion='min(date(session_start_time))') + + # Query the training day at which criterion is reached + subj_crit_day = (dj.U('subject_uuid', 'day_of_crit') + & (behavior_analysis.BehavioralSummaryByDate * subj_crit + & 'session_date=date_criterion').proj(day_of_crit='training_day')) + + # Query days around the day at which criterion is reached + days = (behavior_analysis.BehavioralSummaryByDate * subject.Subject * subj_crit_day + & ('training_day - day_of_crit between %d and %d' + % (-days_from_criterion[0], days_from_criterion[1]))).proj( + 'subject_uuid', 'subject_nickname', 'session_date') + + # Use dates to query sessions + ses_query = acquisition.Session.aggr( + days, from_date='min(session_date)', to_date='max(session_date)') + + sessions = (acquisition.Session * ses_query & 'date(session_start_time) >= from_date' + & 'date(session_start_time) <= to_date') + + # Exclude weird sessions + sessions = sessions & dj.Not([{'session_uuid': u_id} for u_id in EXCLUDED_SESSIONS]) + + # Transform to pandas dataframe if necessary + if as_dataframe is True: + sessions = sessions.fetch(format='frame').reset_index() + days = days.fetch(format='frame').reset_index() + + return sessions, days + + +def query_session_around_performance(perform_thres=0.8, stage='training'): + ''' + Parameters + ---------- + perform_thres : float, optional + DESCRIPTION. Performance threshold that need to be met in all 3 + session. The default is 0.8. + stage: string, optional. + DESCRIPTION. Stage of trial too pull from datajoint to calculate + performance. The default is training. Other options e.g 'biased' + Returns + ------- + selection : dataframe + DESCRIPTION. Dataframe with all trials from mice reaching + performance criterion + ''' + from ibl_pipeline import behavior, subject, reference + use_sessions = query_sessions(task='all', stable=False, as_dataframe=False, + force_cutoff=True, criterion=None) + behav = dj2pandas( + ((use_sessions & 'task_protocol LIKE "%' + stage + '%"') # only get training sessions + * subject.Subject * subject.SubjectLab * reference.Lab * behavior.TrialSet.Trial) + + # Query only the fields we require, reducing the size of the fetch + .proj('institution_short', 'subject_nickname', 'task_protocol', 'session_uuid', + 'trial_stim_contrast_left', 'trial_stim_contrast_right', 'trial_response_choice', + 'task_protocol', 'trial_stim_prob_left', 'trial_feedback_type', + 'trial_response_time', 'trial_stim_on_time', 'session_end_time', 'time_zone') + + # Fetch as a pandas DataFrame, ordered by institute + .fetch(order_by='institution_short, subject_nickname, session_start_time, trial_id', + format='frame') + .reset_index() + ) + behav_ses = behav.groupby(['subject_nickname', + 'session_start_time']).mean()['correct_easy'].reset_index() + behav_ses['above_criterion'] = behav_ses['correct_easy']>perform_thres + # Check rolling sum of sessions above 0.8, must be 3 + behav_ses['met_session_criterion'] = \ + behav_ses.groupby(['subject_nickname'] + )['above_criterion'].rolling(3).sum().to_numpy() + # Select trials from sessions where criterion was first met + selection = pd.DataFrame() + for mouse in behav_ses['subject_nickname'].unique(): + mouse_ses = behav_ses[behav_ses['subject_nickname']==mouse] + if any(mouse_ses['met_session_criterion']==3): + mouse_ses_select = mouse_ses.iloc[np.where( + mouse_ses['met_session_criterion']==3)[0][0]-2:\ + np.where(mouse_ses['met_session_criterion']==3)[0][0]+1,:] + trial_select = behav.loc[(behav['subject_nickname']==mouse) & + (behav['session_start_time'].isin( + mouse_ses_select['session_start_time']))] + selection = pd.concat([selection,trial_select]) + return selection + +# ================================================================== # +# DEFINE PSYCHFUNCFIT TO WORK WITH FACETGRID IN SEABORN +# ================================================================== # + + +def fit_psychfunc(df): + choicedat = df.groupby('signed_contrast').agg( + {'choice': 'count', 'choice2': 'mean'}).reset_index() + if len(choicedat) >= 4: # need some minimum number of unique x-values + pars, L = psy.mle_fit_psycho(choicedat.values.transpose(), P_model='erf_psycho_2gammas', + parstart=np.array( + [0, 20., 0.05, 0.05]), + parmin=np.array( + [choicedat['signed_contrast'].min(), 5, 0., 0.]), + parmax=np.array([choicedat['signed_contrast'].max(), 40., 1, 1])) + else: + pars = [np.nan, np.nan, np.nan, np.nan] + + df2 = {'bias': pars[0], 'threshold': pars[1], + 'lapselow': pars[2], 'lapsehigh': pars[3]} + df2 = pd.DataFrame(df2, index=[0]) + + df2['ntrials'] = df['choice'].count() + + return df2 + + +def plot_psychometric(x, y, subj, **kwargs): + # summary stats - average psychfunc over observers + df = pd.DataFrame({'signed_contrast': x, 'choice': y, + 'choice2': y, 'subject_nickname': subj}) + df2 = df.groupby(['signed_contrast', 'subject_nickname']).agg( + {'choice2': 'count', 'choice': 'mean'}).reset_index() + df2.rename(columns={"choice2": "ntrials", + "choice": "fraction"}, inplace=True) + df2 = df2.groupby(['signed_contrast']).mean().reset_index() + df2 = df2[['signed_contrast', 'ntrials', 'fraction']] + + # only 'break' the x-axis and remove 50% contrast when 0% is present + # print(df2.signed_contrast.unique()) + if 0. in df2.signed_contrast.values: + brokenXaxis = True + else: + brokenXaxis = False + + # fit psychfunc + pars, L = psy.mle_fit_psycho(df2.transpose().values, # extract the data from the df + P_model='erf_psycho_2gammas', + parstart=np.array( + [0, 20., 0.05, 0.05]), + parmin=np.array( + [df2['signed_contrast'].min(), 5, 0., 0.]), + parmax=np.array([df2['signed_contrast'].max(), 40., 1, 1])) + + if brokenXaxis: + # plot psychfunc + g = sns.lineplot(np.arange(-27, 27), + psy.erf_psycho_2gammas(pars, np.arange(-27, 27)), **kwargs) + + # plot psychfunc: -100, +100 + sns.lineplot(np.arange(-36, -31), + psy.erf_psycho_2gammas(pars, np.arange(-103, -98)), **kwargs) + sns.lineplot(np.arange(31, 36), + psy.erf_psycho_2gammas(pars, np.arange(98, 103)), **kwargs) + + # if there are any points at -50, 50 left, remove those + if 50 in df.signed_contrast.values or -50 in df.signed_contrast.values: + df.drop(df[(df['signed_contrast'] == -50.) | (df['signed_contrast'] == 50)].index, + inplace=True) + + # now break the x-axis + df['signed_contrast'] = df['signed_contrast'].replace(-100, -35) + df['signed_contrast'] = df['signed_contrast'].replace(100, 35) + + else: + # plot psychfunc + g = sns.lineplot(np.arange(-103, 103), + psy.erf_psycho_2gammas(pars, np.arange(-103, 103)), **kwargs) + + df3 = df.groupby(['signed_contrast', 'subject_nickname']).agg( + {'choice2': 'count', 'choice': 'mean'}).reset_index() + + # plot datapoints with errorbars on top + if df['subject_nickname'].nunique() > 1: + # put the kwargs into a merged dict, so that overriding does not cause an error + sns.lineplot(df3['signed_contrast'], df3['choice'], + **{**{'err_style':"bars", + 'linewidth':0, 'linestyle':'None', 'mew':0.5, + 'marker':'o', 'ci':68}, **kwargs}) + + if brokenXaxis: + g.set_xticks([-35, -25, -12.5, 0, 12.5, 25, 35]) + g.set_xticklabels(['-100', '-25', '-12.5', '0', '12.5', '25', '100'], + size='small', rotation=60) + g.set_xlim([-40, 40]) + break_xaxis(y=-0.004) + + else: + g.set_xticks([-100, -50, 0, 50, 100]) + g.set_xticklabels(['-100', '-50', '0', '50', '100'], + size='small', rotation=60) + g.set_xlim([-110, 110]) + + g.set_ylim([0, 1.02]) + g.set_yticks([0, 0.25, 0.5, 0.75, 1]) + g.set_yticklabels(['0', '25', '50', '75', '100']) + + +def plot_chronometric(x, y, subj, **kwargs): + df = pd.DataFrame( + {'signed_contrast': x, 'rt': y, 'subject_nickname': subj}) + df.dropna(inplace=True) # ignore NaN RTs + df2 = df.groupby(['signed_contrast', 'subject_nickname'] + ).agg({'rt': 'median'}).reset_index() + # df2 = df2.groupby(['signed_contrast']).mean().reset_index() + df2 = df2[['signed_contrast', 'rt', 'subject_nickname']] + + # if 100 in df.signed_contrast.values and not 50 in + # df.signed_contrast.values: + df2['signed_contrast'] = df2['signed_contrast'].replace(-100, -35) + df2['signed_contrast'] = df2['signed_contrast'].replace(100, 35) + df2 = df2.loc[np.abs(df2.signed_contrast) != 50, :] # remove those + + ax = sns.lineplot(x='signed_contrast', y='rt', err_style="bars", mew=0.5, + ci=68, data=df2, **kwargs) + + # all the points + if df['subject_nickname'].nunique() > 1: + sns.lineplot( + x='signed_contrast', + y='rt', + err_style="bars", + mew=0.5, + linewidth=0, + marker='o', + ci=68, + data=df2, + **kwargs) + + ax.set_xticks([-35, -25, -12.5, 0, 12.5, 25, 35]) + ax.set_xticklabels(['-100', '-25', '-12.5', '0', '12.5', '25', '100'], + size='small', rotation=45) + ax.set_xlim([-40, 40]) + + if df['signed_contrast'].min() >= 0: + ax.set_xlim([-5, 40]) + ax.set_xticks([0, 6, 12.5, 25, 35]) + ax.set_xticklabels(['0', '6.25', '12.5', '25', '100'], + size='small', rotation=45) + + +def break_xaxis(y=-0.004, **kwargs): + + # axisgate: show axis discontinuities with a quick hack + # https://twitter.com/StevenDakin/status/1313744930246811653?s=19 + # first, white square for discontinuous axis + plt.text(-30, y, '-', fontsize=14, fontweight='bold', + horizontalalignment='center', verticalalignment='center', + color='w') + plt.text(30, y, '-', fontsize=14, fontweight='bold', + horizontalalignment='center', verticalalignment='center', + color='w') + + # put little dashes to cut axes + plt.text(-30, y, '/ /', horizontalalignment='center', + verticalalignment='center', fontsize=6, fontweight='bold') + plt.text(30, y, '/ /', horizontalalignment='center', + verticalalignment='center', fontsize=6, fontweight='bold') + + +def add_n(x, y, sj, **kwargs): + + df = pd.DataFrame({'signed_contrast': x, 'choice': y, + 'choice2': y, 'subject_nickname': sj}) + + # ADD TEXT ABOUT NUMBER OF ANIMALS AND TRIALS + plt.text( + 15, + 0.2, + '%d mice, %d trials' % + (df.subject_nickname.nunique(), + df.choice.count()), + fontweight='normal', + fontsize=6, + color='k') + + +def dj2pandas(behav): + + # make sure all contrasts are positive + behav['trial_stim_contrast_right'] = np.abs( + behav['trial_stim_contrast_right']) + behav['trial_stim_contrast_left'] = np.abs( + behav['trial_stim_contrast_left']) + + behav['signed_contrast'] = ( + behav['trial_stim_contrast_right'] - behav['trial_stim_contrast_left']) * 100 + # behav['signed_contrast'] = behav.signed_contrast.astype(int) + + behav['trial'] = behav.trial_id # for psychfuncfit + val_map = {'CCW': 1, 'No Go': 0, 'CW': -1} + behav['choice'] = behav['trial_response_choice'].map(val_map) + behav['correct'] = np.where( + np.sign(behav['signed_contrast']) == behav['choice'], 1, 0) + behav.loc[behav['signed_contrast'] == 0, 'correct'] = np.NaN + + behav['choice_right'] = behav.choice.replace( + [-1, 0, 1], [0, np.nan, 1]) # code as 0, 100 for percentages + behav['choice2'] = behav.choice_right # for psychfuncfit + behav['correct_easy'] = behav.correct + behav.loc[np.abs(behav['signed_contrast']) < 50, 'correct_easy'] = np.NaN + behav.rename( + columns={'trial_stim_prob_left': 'probabilityLeft'}, inplace=True) + behav['probabilityLeft'] = behav['probabilityLeft'] * 100 + behav['probabilityLeft'] = behav.probabilityLeft.astype(int) + + # compute rt + if 'trial_response_time' in behav.columns: + behav['rt'] = behav['trial_response_time'] - \ + behav['trial_stim_on_time'] + # ignore a bunch of things for missed trials + # don't count RT if there was no response + behav.loc[behav.choice == 0, 'rt'] = np.nan + # don't count RT if there was no response + behav.loc[behav.choice == 0, 'trial_feedback_type'] = np.nan + + # CODE FOR HISTORY + behav['previous_choice'] = behav.choice.shift(1) + behav.loc[behav.previous_choice == 0, 'previous_choice'] = np.nan + behav['previous_outcome'] = behav.trial_feedback_type.shift(1) + behav.loc[behav.previous_outcome == 0, 'previous_outcome'] = np.nan + behav['previous_contrast'] = np.abs(behav.signed_contrast.shift(1)) + behav['previous_choice_name'] = behav['previous_choice'].map( + {-1: 'left', 1: 'right'}) + behav['previous_outcome_name'] = behav['previous_outcome'].map( + {-1: 'post_error', 1: 'post_correct'}) + behav['repeat'] = (behav.choice == behav.previous_choice) + + # # to more easily retrieve specific training days + # behav['days'] = (behav['session_start_time'] - + # behav['session_start_time'].min()).dt.days + + return behav + + +def num_star(pvalue): + if pvalue < 0.05: + stars = '* p < 0.05' + elif pvalue < 0.01: + stars = '** p < 0.01' + elif pvalue < 0.001: + stars = '*** p < 0.001' + elif pvalue < 0.0001: + stars = '**** p < 0.0001' + else: + stars = '' + return stars \ No newline at end of file From c8168a80fd2a9044e8dd1817682ef4ca8180cb45 Mon Sep 17 00:00:00 2001 From: shenshan Date: Tue, 16 Feb 2021 15:49:09 +0000 Subject: [PATCH 09/34] Fix the transaction problem for big diff ingestion --- .dockerignore | 1 + ibl_pipeline/ingest/__init__.py | 41 ++-- ibl_pipeline/ingest/qc.py | 178 +++++++++++++++++- ibl_pipeline/process/__init__.py | 25 ++- ibl_pipeline/process/autoprocess.py | 6 +- ibl_pipeline/process/create_ingest_task.py | 4 +- ibl_pipeline/process/delete_update_entries.py | 26 ++- ibl_pipeline/process/process_histology.py | 2 +- ibl_pipeline/qc.py | 84 ++++++--- ibl_pipeline/utils/__init__.py | 3 +- 10 files changed, 310 insertions(+), 60 deletions(-) diff --git a/.dockerignore b/.dockerignore index 60baa9cb..f626ff96 100755 --- a/.dockerignore +++ b/.dockerignore @@ -1 +1,2 @@ data/* +snapshots/* diff --git a/ibl_pipeline/ingest/__init__.py b/ibl_pipeline/ingest/__init__.py index b5954abc..25fffd5b 100755 --- a/ibl_pipeline/ingest/__init__.py +++ b/ibl_pipeline/ingest/__init__.py @@ -85,6 +85,7 @@ def __init__(self, rel): self._rel = rel self._queue = [] self._delete_queue = [] + self.fetched_results = [] def insert1(self, r): self._queue.append(r) @@ -92,9 +93,6 @@ def insert1(self, r): def insert(self, recs): self._queue += recs - def delete1(self, r): - self._delete_queue.append(r) - def flush(self, replace=False, skip_duplicates=False, ignore_extra_fields=False, allow_direct_insert=False, chunksz=1): ''' @@ -125,28 +123,47 @@ def flush(self, replace=False, skip_duplicates=False, def flush_delete(self, chunksz=1, quick=True): ''' - flush the buffer - XXX: ignore_extra_fields na, requires .insert() support + flush the delete ''' - qlen = len(self._delete_queue) + qlen = len(self._queue) if qlen > 0 and qlen % chunksz == 0: try: if quick: - (self._rel & self._delete_queue).delete_quick() + (self._rel & self._queue).delete_quick() else: - (self._rel & self._delete_queue).delete() + (self._rel & self._queue).delete() except Exception as e: print('error in flush delete: {}, trying deletion one by one'.format(e)) - for t in self._delete_queue: + for t in self._queue: try: if quick: - (self._rel & self._delete_queue).delete_quick() + (self._rel & t).delete_quick() else: - (self._rel & self._delete_queue).delete() + (self._rel & t).delete() except Exception as e: print('error in flush delete: {}'.format(e)) - self._delete_queue.clear() + self._queue.clear() + return qlen + else: + return 0 + + def flush_fetch(self, field, chunksz=1): + ''' + flush the fetch + ''' + qlen = len(self._queue) + if qlen > 0 and qlen % chunksz == 0: + try: + self.fetched_results += (self._rel & self._queue).fetch(field) + except Exception as e: + print('error in flush fetch: {}, trying fetch one by one'.format(e)) + for t in self._queue: + try: + self.fetched_results.append((self._rel & self.__queue).fetch1(field)) + except Exception as e: + print('error in flush fetch: {}'.format(e)) + self._queue.clear() return qlen else: return 0 diff --git a/ibl_pipeline/ingest/qc.py b/ibl_pipeline/ingest/qc.py index 36c5f99f..ddc67835 100644 --- a/ibl_pipeline/ingest/qc.py +++ b/ibl_pipeline/ingest/qc.py @@ -1,14 +1,83 @@ import datajoint as dj import json -from . import alyxraw, reference, subject, action, acquisition +from . import alyxraw, reference, subject, action, acquisition, ephys from .. import acquisition as acquisition_real +from .. import ephys as ephys_real from .. import qc from . import get_raw_field as grf schema = dj.schema(dj.config.get('database.prefix', '') + 'ibl_ingest_qc') +json_replace_map = { + "\'": "\"", + 'None': '\"None\"', + 'True': 'true', + 'False': 'false' +} + + +# This function automatically get qc types from alyx +def get_extended_qc_fields_from_alyx(level='session'): + if level == 'session': + key_source = dj.U('uuid') & \ + (alyxraw.AlyxRaw.Field & + (alyxraw.AlyxRaw & 'model="actions.session"') & + 'fname="extended_qc"' & + 'fvalue!="None"') + + fname = 'extended_qc' + + elif level == 'probe': + + key_source = dj.U('uuid') & \ + (alyxraw.AlyxRaw.Field & + (alyxraw.AlyxRaw & 'model="experiments.probeinsertion"') & + 'fname="json"' & + 'fvalue like "%extended_qc%"') + fname = 'json' + else: + raise ValueError('Incorrect level argument, has to be "session" or "probe"') + + eqc_fields = [] + + for key in tqdm(key_source): + qc_extended = grf(key, fname) + + try: + qc_extended = json.loads(qc_extended) + except json.decoder.JSONDecodeError: + for k, v in json_replace_map.items(): + qc_extended = qc_extended.replace(k, v) + qc_extended = json.loads(qc_extended) + + if qc_extended != 'None': + if level == 'probe' and 'extended_qc' in qc_extended: + qc_extended = qc_extended['extended_qc'] + + eqc_fields += list(qc_extended.keys()) + + return set(eqc_fields) + + +@schema +class QCChoice(dj.Lookup): + definition = """ + # Available flags to quantify the quality of a session or a specific aspect of a session, lookup table got referred in SessionQC and SessionExtendedQC + qc : tinyint unsigned + --- + qc_label : varchar(32) + """ + + contents = [ + (0, 'NOT_SET'), + (10, 'PASS'), + (30, 'WARNING'), + (40, 'FAIL'), + (50, 'CRITICAL'), + ] + @schema class SessionQC(dj.Manual): @@ -28,18 +97,19 @@ class SessionExtendedQC(dj.Manual): session_start_time : datetime qc_type : varchar(16) --- - extended_qc : tinyint unsigned + session_extended_qc : tinyint unsigned session_extended_qc_ts=CURRENT_TIMESTAMP: timestamp """ class Field(dj.Part): definition = """ -> master - qc_fname : varchar(32) + session_qc_fname : varchar(32) --- - qc_fvalue_float=null : float - qc_fvalue_str=null : varchar(32) - qc_fvalue_blob=null : blob + session_qc_fvalue_bool=null : bool + session_qc_fvalue_float=null : float + session_qc_fvalue_str=null : varchar(32) + session_qc_fvalue_blob=null : blob """ @@ -83,7 +153,7 @@ def make(self, key): ) for qc_type in qc_types: - try: + if qc_type in qc_extended: session_qc_type = qc_extended[qc_type] qc_choice = qc_choices[ qc_choices['qc_label'] == session_qc_type].index[0] @@ -110,5 +180,95 @@ def make(self, key): SessionExtendedQC.Field.insert1( {**qc_field, qc_fvalue_name: v}) - except Exception: - pass + + +@schema +class ProbeInsertionQC(dj.Manual): + definition = """ + subject_uuid : uuid + session_start_time : datetime + probe_idx : int + --- + insertion_qc : tinyint + """ + + +@schema +class ProbeInsertionExtendedQC(dj.Manual): + definition = """ + subject_uuid : uuid + session_start_time : datetime + probe_idx : int + qc_type : varchar(16) + --- + insertion_extended_qc : tinyint + insertion_extended_qc_ts=CURRENT_TIMESTAMP: timestamp + """ + + class Field(dj.Part): + definition = """ + insertion_qc_fname : varchar(64) + --- + insertion_qc_fvalue_float=null : float + insertion_qc_fvalue_bool=null : bool + insertion_qc_fvalue_str=null : varchar(32) + insertion_qc_fvalue_blob=null : blob + """ + + +@schema +class ProbeInsertionQCIngest(dj.Computed): + definition = """ + -> alyxraw.AlyxRaw.proj(probe_insertion_uuid='uuid') + """ + key_source = dj.U('probe_insertion_uuid') & \ + (alyxraw.AlyxRaw.Field & + (alyxraw.AlyxRaw & 'model="experiments.probeinsertion"') & + 'fname="json"' & + 'fvalue like "%qc%"').proj(probe_insertion_uuid='uuid') + + def make(self, key): + + self.insert1(key) + + key['uuid'] = key['probe_insertion_uuid'] + json_field = grf(key, 'json') + + try: + json_field = json.loads(json_field) + except json.decoder.JSONDecodeError: + # fix the json field before decording. + for k, v in json_replace_map.items(): + json_field = json_field.replace(k, v) + json_field = json.loads(json_field) + + if len(ephys_real.ProbeInsertion & key) == 1: + probe_insertion_key = (ephys_real.ProbeInsertion & key).fetch1('KEY') + else: + probe_insertion_key = (ephys.ProbeInsertion & key).fetch1('KEY') + + if 'qc' in json_field: + qc = (QCChoice & {'qc_label': json_field['qc']}).fetch1('qc') + + ProbeInsertionQC.insert1( + dict(**probe_insertion_key, insertion_qc=qc)) + + if 'extended_qc' in json_field: + extended_qc = json_field['extended_qc'] + + for qc_type in qc_types: + + for k, v in json_field['extended_qc'].items(): + if type(v) == float: + qc_fvalue_name = 'insertion_qc_fvalue_float' + elif v == "None": + pass + elif type(v) == str: + qc_fvalue_name = 'insertion_qc_fvalue_str' + else: + qc_fvalue_name = 'insertion_qc_fvalue_blob' + + ProbeInsertionExtendedQC.insert1( + {**probe_insertion_key, + 'insertion_qc_fname': k, + qc_fvalue_name: v}) diff --git a/ibl_pipeline/process/__init__.py b/ibl_pipeline/process/__init__.py index ecb4b2aa..884bdf70 100755 --- a/ibl_pipeline/process/__init__.py +++ b/ibl_pipeline/process/__init__.py @@ -1,6 +1,7 @@ -from ibl_pipeline.ingest import alyxraw +from ibl_pipeline.ingest import alyxraw, InsertBuffer from ibl_pipeline.utils import is_valid_uuid import datetime +from tqdm import tqdm def get_timezone(t=datetime.datetime.now().time()): @@ -26,9 +27,25 @@ def get_important_pks(pks, return_original_dict=False): pks = [pk for pk in pks if is_valid_uuid(pk)] pks_dict = [{'uuid': pk} for pk in pks] - pks_unimportant = [ - str(pk['uuid']) - for pk in (alyxraw.AlyxRaw & 'model in ("data.filerecord", "jobs.task")' & pks_dict).fetch('KEY')] + + models_ignored = '"data.dataset", "data.filerecord", "jobs.task", "actions.wateradministration", "experiments.trajectoryestimate", "experiments.channel"' + + if len(pks) < 1000: + pks_unimportant = [ + str(pk['uuid']) + for pk in (alyxraw.AlyxRaw & + f'model in ({models_ignored})' & + pks_dict).fetch('KEY')] + else: + buffer = InsertBuffer( + alyxraw.AlyxRaw & f'model in ({models_ignored})') + for pk in tqdm(pks_dict): + buffer.insert1(pk) + buffer.flush_fetch('KEY', chunksz=200) + + buffer.flush_fetch('KEY') + pks_unimportant = [str(pk['uuid']) for pk in buffer.fetched_results] + pks_important = list(set(pks) - set(pks_unimportant)) if return_original_dict: diff --git a/ibl_pipeline/process/autoprocess.py b/ibl_pipeline/process/autoprocess.py index a2219f93..bba9ff74 100755 --- a/ibl_pipeline/process/autoprocess.py +++ b/ibl_pipeline/process/autoprocess.py @@ -198,6 +198,6 @@ def process_updates(pks, current_dump='/data/alyxfull.json'): if __name__ == '__main__': - process_new(previous_dump='/data/alyxfull.json.last', - latest_dump='/data/alyxfull_20201128_0400.json', - job_date='2020-11-28', timezone='European') + process_new(previous_dump='/data/alyxfull_20210212_0400.json', + latest_dump='/data/alyxfull.json', + job_date='2021-02-15', timezone='European') diff --git a/ibl_pipeline/process/create_ingest_task.py b/ibl_pipeline/process/create_ingest_task.py index 8656859b..d8a0b08f 100644 --- a/ibl_pipeline/process/create_ingest_task.py +++ b/ibl_pipeline/process/create_ingest_task.py @@ -21,8 +21,8 @@ def get_modified_pks(data0, data1): def get_created_deleted_pks(data0, data1): - old_pks = {_['pk'] for _ in data0} - new_pks = {_['pk'] for _ in data1} + old_pks = {_['pk'] for _ in data0 if not isinstance(_['pk'], int)} + new_pks = {_['pk'] for _ in data1 if not isinstance(_['pk'], int)} return [pk for pk in sorted(new_pks - old_pks) if is_valid_uuid(pk)], \ [pk for pk in sorted(old_pks - new_pks) if is_valid_uuid(pk)] diff --git a/ibl_pipeline/process/delete_update_entries.py b/ibl_pipeline/process/delete_update_entries.py index 20b0dbd3..6ee1209a 100755 --- a/ibl_pipeline/process/delete_update_entries.py +++ b/ibl_pipeline/process/delete_update_entries.py @@ -40,11 +40,27 @@ def delete_entries_from_alyxraw(pks_to_be_deleted=[], modified_pks_important=[]) if modified_pks_important: pk_list = [{'uuid': pk} for pk in modified_pks_important - if is_valid_uuid(pk)] - (alyxraw.AlyxRaw & 'model != "actions.session"' & - pk_list).delete() - (alyxraw.AlyxRaw.Field & pk_list & 'fname!="start_time"' & - (alyxraw.AlyxRaw & 'model="actions.session"')).delete_quick() + if is_valid_uuid(pk)] + if len(pk_list) > 1000: + + print('Long pk list, deleting from alyxraw.AlyxRaw ...') + alyxraw_buffer = InsertBuffer(alyxraw.AlyxRaw & 'model != "actions.session"') + for pk in tqdm(pk_list): + alyxraw_buffer.insert1(pk) + alyxraw_buffer.flush_delete(chunksz=50, quick=False) + + alyxraw_buffer.flush_delete(quick=False) + + # delete session fields without deleting the primary keys. + print('Long pk list, deleting from alyxraw.AlyxRaw.Field ...') + alyxraw_field_buffer = InsertBuffer( + alyxraw.AlyxRaw.Field & 'fname!="start_time"' & + (alyxraw.AlyxRaw & 'model="actions.session"')) + + for pk in tqdm(pk_list): + alyxraw_field_buffer.insert1(pk) + alyxraw_field_buffer.flush_delete(chunksz=50, quick=True) + alyxraw_field_buffer.flush_delete(quick=True) def delete_entries_from_membership(pks_to_be_deleted): diff --git a/ibl_pipeline/process/process_histology.py b/ibl_pipeline/process/process_histology.py index 00354503..5c6bd969 100644 --- a/ibl_pipeline/process/process_histology.py +++ b/ibl_pipeline/process/process_histology.py @@ -123,7 +123,7 @@ def delete_histology_alyx_shadow(verbose=False): keys = [{uuid_name: k['uuid']} for k in traj_keys] table = InsertBuffer(t) for k in tqdm(keys, position=0): - table.delete1(k) + table.insert1(k) if table.flush_delete(chunksz=1000, quick=True) and verbose: print(f'Deleted 1000 entries from {t.__name__}') table.flush_delete(quick=True) diff --git a/ibl_pipeline/qc.py b/ibl_pipeline/qc.py index f6bab356..c7910f0e 100644 --- a/ibl_pipeline/qc.py +++ b/ibl_pipeline/qc.py @@ -1,5 +1,5 @@ import datajoint as dj -from . import acquisition +from . import acquisition, ephys import os @@ -29,33 +29,38 @@ class QCChoice(dj.Lookup): @schema -class SessionQC(dj.Manual): +class QCType(dj.Lookup): definition = """ - # QCChoice for each session, ingested from alyx field qc in the table actions.session - -> acquisition.Session + # Aspect of a session for quality check. e.g. task, behavior, experimenter… + qc_type : varchar(16) --- - -> QCChoice - sessionqc_ts=CURRENT_TIMESTAMP: timestamp + qc_ + qc_type_description='' : varchar(1000) """ + # contents = [ + # ['experimenter', 'Manual labeling of a session by user'], + # ['task', 'Quality check when running the task'], + # ['behavior', 'Behavior criteria'], + # ['videoBody', 'Quality check for video recording of body camera'], + # ['videoLeft', 'Quality check for video recording of left camera'], + # ['videoRight', 'Quality check for video recording of right camera'], + # ['dlc', 'Deep lab cut on behavioral video data'], + # ['tracing_exists', 'Histology tracing'], + # ['alignment_resolved', 'Ephys alignment with histology'] + # ] + @schema -class QCType(dj.Lookup): +class SessionQC(dj.Manual): definition = """ - # Aspect of a session for quality check. e.g. task, behavior, experimenter… - qc_type : varchar(16) + # QCChoice for each session, ingested from alyx field qc in the table actions.session + -> acquisition.Session --- - qc_type_description='' : varchar(1000) + -> QCChoice.proj(session_qc='qc') + sessionqc_ts=CURRENT_TIMESTAMP: timestamp """ - contents = [ - ['experimenter', 'Manual labeling of a session by user'], - ['task', 'Quality check when running the task'], - ['behavior', 'Behavior criteria'], - ['video', 'Quality check for video recording'], - ['dlc', ''] - ] - @schema class SessionExtendedQC(dj.Manual): @@ -64,7 +69,7 @@ class SessionExtendedQC(dj.Manual): -> acquisition.Session -> QCType --- - -> QCChoice.proj(extended_qc='qc') + -> QCChoice.proj(session_extended_qc='qc') session_extended_qc_ts=CURRENT_TIMESTAMP: timestamp """ @@ -72,9 +77,42 @@ class Field(dj.Part): definition = """ # Part table of SessionExtendedQC. For each entry of SessionExtendedQC, there may be multiple fields describing each value (e.g. 0.99) of a qc aspect (e.g. _task_stimOn_delays) that belongs to a QCType (e.g. task). -> master - qc_fname : varchar(32) + session_qc_fname : varchar(32) + --- + session_qc_fvalue_bool=null : bool + session_qc_fvalue_float=null : float + session_qc_fvalue_str=null : varchar(32) + session_qc_fvalue_blob=null : blob + """ + + +@schema +class ProbeInsertionQC(dj.Manual): + definition = """ + -> ephys.ProbeInsertion + --- + -> QCChoice.proj(insertion_qc='qc') + """ + + +@schema +class ProbeInsertionExtendedQC(dj.Manual): + definition = """ + -> ephys.ProbeInsertion + -> QCType + --- + -> QCChoice.proj(insertion_extended_qc='qc') + insertion_extended_qc_ts=CURRENT_TIMESTAMP: timestamp + """ + + class Field(dj.Part): + definition = """ + # Part table of SessionExtendedQC. For each entry of ProbeInsertionExtendedQC. + -> master + insertion_qc_fname : varchar(32) --- - qc_fvalue_float=null : float - qc_fvalue_str=null : varchar(32) - qc_fvalue_blob=null : blob + insertion_qc_fvalue_bool=null : tinyint + insertion_qc_fvalue_float=null : float + insertion_qc_fvalue_str=null : varchar(32) + insertion_qc_fvalue_blob=null : blob """ diff --git a/ibl_pipeline/utils/__init__.py b/ibl_pipeline/utils/__init__.py index d98bf5c4..b95ead47 100755 --- a/ibl_pipeline/utils/__init__.py +++ b/ibl_pipeline/utils/__init__.py @@ -1,9 +1,10 @@ from uuid import UUID + def is_valid_uuid(uuid): try: UUID(uuid) return True - except ValueError: + except (ValueError, AttributeError): return False From 46f81543bbfadb4fac941274718e4749459e2bf1 Mon Sep 17 00:00:00 2001 From: shenshan Date: Tue, 16 Feb 2021 15:56:37 +0000 Subject: [PATCH 10/34] remove histology ingestion from ephys ingestion --- ibl_pipeline/ephys.py | 12 +- ibl_pipeline/histology.py | 287 +++++++++++++++++++++----------------- scripts/ingest_ephys.py | 2 - 3 files changed, 169 insertions(+), 132 deletions(-) diff --git a/ibl_pipeline/ephys.py b/ibl_pipeline/ephys.py index 242c2ff0..2d9ee85e 100755 --- a/ibl_pipeline/ephys.py +++ b/ibl_pipeline/ephys.py @@ -2,7 +2,6 @@ import numpy as np from os import path, environ from . import acquisition, reference, behavior, data -from .ingest import ephys as ephys_ingest from tqdm import tqdm import numpy as np import pandas as pd @@ -224,10 +223,13 @@ def make(self, key): probe_name = (ProbeInsertion & key).fetch1('probe_label') - clusters = alf.io.load_object( - ses_path.joinpath('alf', probe_name), 'clusters') - spikes = alf.io.load_object( - ses_path.joinpath('alf', probe_name), 'spikes') + try: + clusters = alf.io.load_object( + ses_path.joinpath('alf', probe_name), 'clusters') + spikes = alf.io.load_object( + ses_path.joinpath('alf', probe_name), 'spikes') + except: + return time_fnames = [k for k in spikes.keys() if 'times' in k] diff --git a/ibl_pipeline/histology.py b/ibl_pipeline/histology.py index 473172c4..1b8722d0 100755 --- a/ibl_pipeline/histology.py +++ b/ibl_pipeline/histology.py @@ -1,17 +1,26 @@ import datajoint as dj -from . import reference, acquisition, ephys +from . import reference, acquisition, data, ephys from .ingest import histology as histology_ingest from os import path, environ import numpy as np from .utils import atlas import pdb +from tqdm import tqdm try: from ibllib.pipes.ephys_alignment import EphysAlignment except Exception as e: Warning('Need to install the WIPhistologymayo branch for ibllib') +try: + from oneibl.one import ONE + import alf.io + one = ONE(silent=True) +except ImportError: + warnings.warn('ONE not installed, cannot use populate') + pass + mode = environ.get('MODE') if mode == 'update': @@ -20,52 +29,40 @@ schema = dj.schema(dj.config.get('database.prefix', '') + 'ibl_histology') -@schema -class InsertionDataSource(dj.Lookup): - definition = """ - # Method to estimate the probe trajectory, including Ephys aligned histology track, Histology track, Micro-manipulator, and Planned - insertion_data_source: varchar(128) # type of trajectory - --- - provenance: int # provenance code - """ - contents = [ - ('Ephys aligned histology track', 70), - ('Histology track', 50), - ('Micro-manipulator', 30), - ('Planned', 10), - ] - - @schema class ProbeTrajectory(dj.Imported): definition = """ - # Probe trajectory estimated with each method, ingested from Alyx table experiments.probetrajectory + # Probe Trajectory resolved by 3 users, ingested from ALF dataset probes.trajectory -> ephys.ProbeInsertion - -> InsertionDataSource --- -> [nullable] reference.CoordinateSystem - probe_trajectory_uuid: uuid x: float # (um) medio-lateral coordinate relative to Bregma, left negative y: float # (um) antero-posterior coordinate relative to Bregma, back negative z: float # (um) dorso-ventral coordinate relative to Bregma, ventral negative phi: float # (degrees)[-180 180] azimuth theta: float # (degrees)[0 180] polar angle depth: float # (um) insertion depth - roll=null: float # (degrees) roll angle of the probe - trajectory_ts: datetime + beta=null: float # (degrees) roll angle of the probe + trajectory_ts=CURRENT_TIMESTAMP: timestamp """ - keys = histology_ingest.ProbeTrajectory.fetch( - 'subject_uuid', 'session_start_time', 'probe_idx', - 'insertion_data_source', as_dict=True) - key_source = ephys.ProbeInsertion * InsertionDataSource & keys + key_source = ephys.ProbeInsertion & (data.FileRecord & 'dataset_name like "%probes.trajectory%"') def make(self, key): - trajs = (histology_ingest.ProbeTrajectory & key).fetch(as_dict=True) - for traj in trajs: - if not traj['coordinate_system_name']: - traj.pop('coordinate_system_name') - self.insert1(traj, skip_duplicates=True) + eID = str((acquisition.Session & key).fetch1('session_uuid')) + probes_trajectories = one.load(eID, dataset_types=['probes.trajectory']) + + probe_label = (ephys.ProbeInsertion & key).fetch1('probe_label') + + if not probe_label: + probe_label = 'probe0' + str(key['probe_idx']) + + for probe_trajectory in probes_trajectories: + if type(probe_trajectory) == list: + probe_trajectory = probe_trajectory[0] + if probe_trajectory['label'] == probe_label: + probe_trajectory.pop('label') + self.insert1(dict(**key, **probe_trajectory)) @schema @@ -73,110 +70,160 @@ class ChannelBrainLocation(dj.Imported): definition = """ # Brain coordinates and region assignment of each channel, ingested from Alyx table experiments.channel -> ProbeTrajectory - channel_brain_location_uuid : uuid + channel_idx : int --- - channel_axial : decimal(6, 1) - channel_lateral : decimal(6, 1) - channel_x : decimal(6, 1) - channel_y : decimal(6, 1) - channel_z : decimal(6, 1) + channel_ml : decimal(6, 1) # (um) medio-lateral coordinate relative to Bregma, left negative + channel_ap : decimal(6, 1) # (um) antero-posterior coordinate relative to Bregma, back negative + channel_dv : decimal(6, 1) # (um) dorso-ventral coordinate relative to Bregma, ventral negative -> reference.BrainRegion """ - - -@schema -class ClusterBrainRegion(dj.Computed): - definition = """ - # Brain region assignment to each cluster - -> ephys.DefaultCluster - -> InsertionDataSource - --- - -> reference.BrainRegion - """ - key_source = ephys.DefaultCluster * InsertionDataSource & \ - ProbeTrajectory & ephys.ChannelGroup & ChannelBrainLocation + key_source = ProbeTrajectory & \ + (data.FileRecord & 'dataset_name like "%channels.brainLocationIds%"') & \ + (data.FileRecord & 'dataset_name like "%channels.mlapdv%"') def make(self, key): - channel_raw_inds, channel_local_coordinates = \ - (ephys.ChannelGroup & key).fetch1( - 'channel_raw_inds', 'channel_local_coordinates') - channel = (ephys.DefaultCluster & key).fetch1('cluster_channel') - if channel in channel_raw_inds: - channel_coords = np.squeeze( - channel_local_coordinates[channel_raw_inds == channel]) - else: - return - q = ChannelBrainLocation & key & \ - dict(channel_lateral=channel_coords[0], - channel_axial=channel_coords[1]) + eID = str((acquisition.Session & key).fetch1('session_uuid')) + dtypes = [ + 'channels.brainLocationIds_ccf_2017', + 'channels.mlapdv' + ] - if len(q) == 1: - key['ontology'], key['acronym'] = q.fetch1( - 'ontology', 'acronym') - - self.insert1(key) - elif len(q) > 1: - ontology, acronym = q.fetch('ontology', 'acronym') - if len(set(acronym)) == 1: - key['ontology'] = 'CCF 2017' - key['acronym'] = acronym[0] - self.insert1(key) - else: - print('Conflict regions') - else: - return + files = one.load(eID, dataset_types=dtypes, download_only=True, + clobber=True) + ses_path = alf.io.get_session_path(files[0]) + probe_label = (ephys.ProbeInsertion & key).fetch1('probe_label') + if not probe_label: + probe_label = 'probe0' + key['probe_idx'] -@schema -class SessionBrainRegion(dj.Computed): - definition = """ - # Brain regions assignment to each session - # including the regions of finest granularity and their upper-level areas. - -> acquisition.Session - -> reference.BrainRegion - """ - key_source = acquisition.Session & ClusterBrainRegion + channels = alf.io.load_object( + ses_path.joinpath('alf', probe_label), 'channels') - def make(self, key): - regions = (dj.U('acronym') & (ClusterBrainRegion & key)).fetch('acronym') + channel_entries = [] + for ichannel, (brain_loc_id, loc) in tqdm( + enumerate(zip(channels['brainLocationIds_ccf_2017'], + channels['mlapdv']))): + brain_region_key = (reference.BrainRegion & + {'brain_region_pk': brain_loc_id}).fetch1('KEY') - associated_regions = [ - atlas.BrainAtlas.get_parents(acronym) - for acronym in regions] + list(regions) + channel_entries.append( + dict( + channel_idx=ichannel, + **key, **brain_region_key, + channel_ml=loc[0], + channel_ap=loc[1], + channel_dv=loc[2] + ) + ) - self.insert([dict(**key, ontology='CCF 2017', acronym=region) - for region in np.unique(np.hstack(associated_regions))]) + self.insert(channel_entries) @schema -class DepthBrainRegion(dj.Computed): +class ClusterBrainRegion(dj.Imported): definition = """ - # For each ProbeTrajectory, assign depth boundaries relative to the probe tip to each brain region covered by the trajectory + # Brain region assignment to each cluster + -> ephys.DefaultCluster -> ProbeTrajectory --- - region_boundaries : blob - region_label : blob - region_color : blob - region_id : blob + cluster_ml : decimal(6, 1) # (um) medio-lateral coordinate relative to Bregma, left negative + cluster_ap : decimal(6, 1) # (um) antero-posterior coordinate relative to Bregma, back negative + cluster_dv : decimal(6, 1) # (um) dorso-ventral coordinate relative to Bregma, ventral negative + -> reference.BrainRegion """ - key_source = ProbeTrajectory & ChannelBrainLocation + key_source = ProbeTrajectory & \ + (data.FileRecord & 'dataset_name like "%channels.brainLocationIds%"') & \ + (data.FileRecord & 'dataset_name like "%channels.mlapdv%"') def make(self, key): - x, y, z, axial = (ChannelBrainLocation & key).fetch( - 'channel_x', 'channel_y', 'channel_z', 'channel_axial', - order_by='channel_axial') - xyz_channels = np.c_[x, y, z] - key['region_boundaries'], key['region_label'], \ - key['region_color'], key['region_id'] = \ - EphysAlignment.get_histology_regions( - xyz_channels.astype('float')/1e6, axial.astype('float')) - - self.insert1(key) - - -# ================= The following tables will replace the above ones eventually ==================== + eID = str((acquisition.Session & key).fetch1('session_uuid')) + dtypes = [ + 'clusters.brainLocationIds_ccf_2017', + 'clusters.mlapdv' + ] + + files = one.load(eID, dataset_types=dtypes, download_only=True, + clobber=True) + ses_path = alf.io.get_session_path(files[0]) + + probe_label = (ephys.ProbeInsertion & key).fetch1('probe_label') + if not probe_label: + probe_label = 'probe0' + key['probe_idx'] + + clusters = alf.io.load_object( + ses_path.joinpath('alf', probe_label), 'channels') + + cluster_entries = [] + for icluster, (brain_loc_id, loc) in tqdm( + enumerate(zip(clusters['brainLocationIds_ccf_2017'], + clusters['mlapdv']))): + brain_region_key = (reference.BrainRegion & + {'brain_region_pk': brain_loc_id}).fetch1('KEY') + + cluster_entries.append( + dict( + cluster_id=icluster, + **key, **brain_region_key, + cluster_ml=loc[0], + cluster_ap=loc[1], + cluster_dv=loc[2] + ) + ) + + self.insert(cluster_entries) + + +# @schema +# class SessionBrainRegion(dj.Computed): +# definition = """ +# # Brain regions assignment to each session +# # including the regions of finest granularity and their upper-level areas. +# -> acquisition.Session +# -> reference.BrainRegion +# """ +# key_source = acquisition.Session & ClusterBrainRegion + +# def make(self, key): +# regions = (dj.U('acronym') & (ClusterBrainRegion & key)).fetch('acronym') + +# associated_regions = [ +# atlas.BrainAtlas.get_parents(acronym) +# for acronym in regions] + list(regions) + +# self.insert([dict(**key, ontology='CCF 2017', acronym=region) +# for region in np.unique(np.hstack(associated_regions))]) + + +# @schema +# class DepthBrainRegion(dj.Computed): +# definition = """ +# # For each ProbeTrajectory, assign depth boundaries relative to the probe tip to each brain region covered by the trajectory +# -> ProbeTrajectory +# --- +# region_boundaries : blob +# region_label : blob +# region_color : blob +# region_id : blob +# """ +# key_source = ProbeTrajectory & ChannelBrainLocation + +# def make(self, key): + +# x, y, z, axial = (ChannelBrainLocation & key).fetch( +# 'channel_x', 'channel_y', 'channel_z', 'channel_axial', +# order_by='channel_axial') +# xyz_channels = np.c_[x, y, z] +# key['region_boundaries'], key['region_label'], \ +# key['region_color'], key['region_id'] = \ +# EphysAlignment.get_histology_regions( +# xyz_channels.astype('float')/1e6, axial.astype('float')) + +# self.insert1(key) + + +# ================= The following tables will replace the above ones eventually =================== @schema class Provenance(dj.Lookup): @@ -212,18 +259,7 @@ class ProbeTrajectoryTemp(dj.Imported): roll=null: float # (degrees) roll angle of the probe trajectory_ts: datetime """ - keys = histology_ingest.ProbeTrajectory.fetch( - 'subject_uuid', 'session_start_time', 'probe_idx', - 'insertion_data_source', as_dict=True) - key_source = ephys.ProbeInsertion * InsertionDataSource & keys - - def make(self, key): - - trajs = (histology_ingest.ProbeTrajectory & key).fetch(as_dict=True) - for traj in trajs: - if not traj['coordinate_system_name']: - traj.pop('coordinate_system_name') - self.insert1(traj, skip_duplicates=True) + # this table rely on copying from the shadow table in ibl_pipeline.ingest.histology @schema @@ -240,6 +276,7 @@ class ChannelBrainLocationTemp(dj.Imported): channel_z : decimal(6, 1) -> reference.BrainRegion """ + # this table rely on copying from the shadow table in ibl_pipeline.ingest.histology @schema diff --git a/scripts/ingest_ephys.py b/scripts/ingest_ephys.py index e6e49ea6..309fad8c 100755 --- a/scripts/ingest_ephys.py +++ b/scripts/ingest_ephys.py @@ -22,8 +22,6 @@ ephys.GoodCluster, ephys_analyses.DepthPeth, ephys_analyses.NormedDepthPeth, - histology.ClusterBrainRegion, - histology.SessionBrainRegion, ephys_plotting.DepthRaster, ephys_plotting.DepthPeth, ephys_plotting.Raster, From 7abec78c130ded190f5b73b996e8de1563c6f529 Mon Sep 17 00:00:00 2001 From: shenshan Date: Tue, 16 Feb 2021 17:11:47 +0000 Subject: [PATCH 11/34] update the notebooks --- docker-compose-brain.yml | 19 + ibl_pipeline/analyses/behavior.py | 1 + .../Check all brainwidemap sessions.ipynb | 334 ++++ notebooks/notebooks_qc/behavior_populate.txt | 1593 +++++++++++++++++ .../notebooks_qc/brainwidemap_sessions.txt | 763 ++++++++ 5 files changed, 2710 insertions(+) create mode 100644 docker-compose-brain.yml create mode 100644 notebooks/notebooks_qc/Check all brainwidemap sessions.ipynb create mode 100644 notebooks/notebooks_qc/behavior_populate.txt create mode 100644 notebooks/notebooks_qc/brainwidemap_sessions.txt diff --git a/docker-compose-brain.yml b/docker-compose-brain.yml new file mode 100644 index 00000000..439e636f --- /dev/null +++ b/docker-compose-brain.yml @@ -0,0 +1,19 @@ +version: '3' +services: + datajoint: + build: . + env_file: .env + volumes: + - ./notebooks:/home/dja + - ./images:/images + - .:/src/IBL-pipeline + - ./data:/data + - ./raster:/raster + - ./root/.one_params:/home/dja/.one_params + user: 1000:anaconda + ports: + - "8888:8888" + networks: + - ibl +networks: + ibl: diff --git a/ibl_pipeline/analyses/behavior.py b/ibl_pipeline/analyses/behavior.py index 1088697d..0d73d080 100755 --- a/ibl_pipeline/analyses/behavior.py +++ b/ibl_pipeline/analyses/behavior.py @@ -522,6 +522,7 @@ def make(self, key): return # also compute the median reaction time + # to put into medRT = compute_reaction_time(trials) # psych_unbiased = utils.compute_psych_pars(trials_unbiased) diff --git a/notebooks/notebooks_qc/Check all brainwidemap sessions.ipynb b/notebooks/notebooks_qc/Check all brainwidemap sessions.ipynb new file mode 100644 index 00000000..52cc127d --- /dev/null +++ b/notebooks/notebooks_qc/Check all brainwidemap sessions.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ibl_pipeline import ephys, acquisition, subject, data\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load eids of all brainwidemap_sessions\n", + "with open('notebooks_qc/brainwidemap_sessions.txt', 'r') as file:\n", + " eids_raw = file.readlines()\n", + "\n", + "eids = {eid.replace('\\n', '') for eid in eids_raw}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(eids)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check whether required datasets are missing\n", + "required_datasets = ephys.CompleteClusterSession.required_datasets\n", + "required_datasets\n", + "\n", + "# sessions with complete datasets, but DefaultCluster data are missing\n", + "missing_sessions_with_complete_datasets = []\n", + "missing_sessions_with_incomplete_datasets = dict()\n", + "for eid in tqdm(eids):\n", + " key = acquisition.Session & {'session_uuid': eid}\n", + " if not ephys.DefaultCluster & key:\n", + " missing_datasets = [\n", + " dataset for dataset in required_datasets \n", + " if not data.FileRecord & key & {'dataset_name': dataset} & 'repo_name LIKE \"flatiron_%\"' & {'exists': 1}]\n", + " if missing_datasets:\n", + " missing_sessions_with_incomplete_datasets[eid] = missing_datasets\n", + " else:\n", + " missing_sessions_with_complete_datasets.append(eid)\n", + "\n", + "print(f'Number of sessions with complete datasets but missing cluster data: \\\n", + " {len(missing_sessions_with_complete_datasets)}')\n", + "\n", + "print(f'Number of sessions with incomplete datasets: \\\n", + " {len(missing_sessions_with_incomplete_datasets)}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "np.save('missing_sessions', missing_sessions_with_complete_datasets, missing_sessions_with_incomplete_datasets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "eids = np.load('missing_sessions.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.DefaultCluster.populate(\n", + " acquisition.Session & [{'session_uuid': eid} for eid in eids], display_progress=True, suppress_errors=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check with ONE whether these missing datasets are really missing\n", + "from oneibl.one import ONE\n", + "one = ONE()\n", + "\n", + "for eid in tqdm(missing_sessions_with_incomplete_datasets.keys()):\n", + " datasets = one.alyx.rest('datasets', 'list', session=eid)\n", + " for d in datasets:\n", + " if d['name'] in missing_sessions_with_incomplete_datasets[eid]:\n", + " print(f'File {d[\"name\"]} exists for session {eid}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import alf.io\n", + "\n", + "# check data length consistency of the 17 sessions\n", + "\n", + "ephys_dtypes = [\n", + " 'clusters.amps',\n", + " 'clusters.channels',\n", + " 'clusters.depths',\n", + " 'clusters.metrics',\n", + " 'clusters.peakToTrough',\n", + " 'clusters.uuids',\n", + " 'clusters.waveforms',\n", + " 'clusters.waveformsChannels',\n", + " 'spikes.amps',\n", + " 'spikes.clusters',\n", + " 'spikes.depths',\n", + " 'spikes.samples',\n", + " 'spikes.templates'\n", + "]\n", + "for eid in missing_sessions_with_complete_datasets[0:1]:\n", + " \n", + " session_key = acquisition.Session & {'session_uuid': eid}\n", + " probe_keys = (ephys.ProbeInsertion & session_key).fetch('KEY')\n", + " \n", + " for key in probe_keys:\n", + " # load relevant data\n", + " spikes_times_dtype_name = (\n", + " data.FileRecord & key &\n", + " 'dataset_name like \"%spikes.times%.npy\"').fetch1(\n", + " 'dataset_name').split('.npy')[0]\n", + " dtypes = ephys_dtypes + [spikes_times_dtype_name]\n", + " \n", + " files = one.load(eid, dataset_types=dtypes, download_only=True,\n", + " clobber=True)\n", + " ses_path = alf.io.get_session_path(files[0])\n", + "\n", + " probe_name = (ephys.ProbeInsertion & key).fetch1('probe_label')\n", + "\n", + " clusters = alf.io.load_object(\n", + " ses_path.joinpath('alf', probe_name), 'clusters')\n", + " spikes = alf.io.load_object(\n", + " ses_path.joinpath('alf', probe_name), 'spikes')\n", + "\n", + " time_fnames = [k for k in spikes.keys() if 'times' in k]\n", + "\n", + " \n", + " # check clusters.* data, report if length does not match the length of clusters.uuids\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check what's happening with the 17 sessions\n", + "ephys.DefaultCluster.populate(acquisition.Session & [{'session_uuid': eid} for eid in missing_sessions_with_complete_datasets], display_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alf.io.load_object(ses_path.joinpath('alf', probe_name), object='spikes')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "dj.U('session_uuid') & (acquisition.Session & ephys.DefaultCluster)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eid = 'f354dc45-caef-4e3e-bd42-2c19a5425114'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session_key = acquisition.Session & {'session_uuid': eid}\n", + "probe_keys = (ephys.ProbeInsertion & session_key).fetch('KEY')\n", + "\n", + "for key in probe_keys:\n", + " # load relevant data\n", + " spikes_times_dtype_name = (\n", + " data.FileRecord & key &\n", + " 'dataset_name like \"%spikes.times.npy\"').fetch1(\n", + " 'dataset_name').split('.npy')[0]\n", + " dtypes = ephys_dtypes + [spikes_times_dtype_name]\n", + "\n", + " files = one.load(eid, dataset_types=dtypes, download_only=True,\n", + " clobber=True)\n", + " ses_path = alf.io.get_session_path(files[0])\n", + "\n", + " probe_name = (ephys.ProbeInsertion & key).fetch1('probe_label')\n", + "\n", + " clusters = alf.io.load_object(\n", + " ses_path.joinpath('alf', probe_name), 'clusters')\n", + " spikes = alf.io.load_object(\n", + " ses_path.joinpath('alf', probe_name), 'spikes')\n", + "\n", + " time_fnames = [k for k in spikes.keys() if 'times' in k]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clusters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ibl_pipeline import subject, acquisition, ephys, behavior, data\n", + "from ibl_pipeline.plotting import ephys as ephys_plotting\n", + "from ibl_pipeline.group_shared import wheel\n", + "import datajoint as dj\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "keys = (acquisition.Session & ephys.DefaultCluster).fetch('KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys_plotting.Raster.populate(display_progress=True, suppress_errors=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn().connect()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.kill()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_qc/behavior_populate.txt 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00:00:00 2001 From: shenshan Date: Wed, 17 Feb 2021 21:43:59 +0000 Subject: [PATCH 12/34] Remove the ingest histology non-temp tables. Ingestions of these tables are directly from FlatIron not alyx --- ibl_pipeline/ingest/histology.py | 146 +------------------------------ 1 file changed, 1 insertion(+), 145 deletions(-) diff --git a/ibl_pipeline/ingest/histology.py b/ibl_pipeline/ingest/histology.py index 15b545d0..996686c3 100755 --- a/ibl_pipeline/ingest/histology.py +++ b/ibl_pipeline/ingest/histology.py @@ -15,151 +15,7 @@ 'ibl_ingest_histology') -@schema -class InsertionDataSource(dj.Lookup): - definition = """ - insertion_data_source: varchar(128) # type of trajectory - --- - provenance: int # provenance code - """ - contents = [ - ('Ephys aligned histology track', 70), - ('Histology track', 50), - ('Micro-manipulator', 30), - ('Planned', 10), - ] - - -@schema -class ProbeTrajectory(dj.Imported): - definition = """ - (probe_trajectory_uuid) -> alyxraw.AlyxRaw - --- - subject_uuid: uuid - session_start_time: datetime - probe_idx: int - x: float - y: float - z: float - depth: float - theta: float - phi: float - roll=null: float - insertion_data_source: varchar(128) - coordinate_system_name=null: varchar(32) - trajectory_ts: datetime - """ - - key_source = (alyxraw.AlyxRaw - alyxraw.ProblematicData & 'model="experiments.trajectoryestimate"').proj( - probe_trajectory_uuid='uuid') - - def make(self, key): - key_traj = key.copy() - key['uuid'] = key_traj['probe_trajectory_uuid'] - - probe_insertion_uuid = grf(key, 'probe_insertion') - subject_uuid, session_start_time, probe_idx = \ - (ephys.ProbeInsertion & - dict(probe_insertion_uuid=probe_insertion_uuid)).fetch1( - 'subject_uuid', 'session_start_time', 'probe_idx') - - provenance = grf(key, 'provenance') - insertion_data_source = \ - (InsertionDataSource & - dict(provenance=provenance)).fetch1('insertion_data_source') - - key_traj.update( - x=grf(key, 'x'), - y=grf(key, 'y'), - z=grf(key, 'z'), - depth=grf(key, 'depth'), - theta=grf(key, 'theta'), - phi=grf(key, 'phi'), - subject_uuid=subject_uuid, - session_start_time=session_start_time, - probe_idx=probe_idx, - insertion_data_source=insertion_data_source, - trajectory_ts=grf(key, 'datetime') - ) - - roll = grf(key, 'roll') - if roll != 'None': - key_traj.update(roll=roll) - - coord_uuid = grf(key, 'coordinate_system') - if coord_uuid != 'None': - key['coordinate_system_uuid'] = \ - (reference.CoordinateSystem & - {'coordinate_system_uuid': coord_uuid}).fetch1( - 'coordinate_system_name') - # try: - self.insert1(key_traj) - # except ValueError: - # alyxraw.ProblematicData.insert1( - # {'uuid': key_traj['probe_trajectory_uuid']} - # ) - - -@schema -class ChannelBrainLocation(dj.Imported): - definition = """ - (channel_brain_location_uuid) -> alyxraw.AlyxRaw - --- - subject_uuid : uuid - session_start_time : datetime - probe_idx : tinyint - channel_axial : decimal(6, 1) - channel_lateral : decimal(6, 1) - channel_x : decimal(6, 1) - channel_y : decimal(6, 1) - channel_z : decimal(6, 1) - insertion_data_source : varchar(128) - ontology : varchar(32) - acronym : varchar(32) - """ - key_source = (alyxraw.AlyxRaw & 'model="experiments.channel"').proj( - channel_brain_location_uuid='uuid') - - def make(self, key): - key_brain_loc = key.copy() - key['uuid'] = key_brain_loc['channel_brain_location_uuid'] - - probe_trajectory_uuid = grf(key, 'trajectory_estimate') - try: - subject_uuid, session_start_time, probe_idx, insertion_data_source = \ - (ProbeTrajectory & dict( - probe_trajectory_uuid=probe_trajectory_uuid)).fetch1( - 'subject_uuid', 'session_start_time', 'probe_idx', - 'insertion_data_source') - except Exception: - print(probe_trajectory_uuid) - return - - brain_region_pk = grf(key, 'brain_region') - ontology, acronym = (reference.BrainRegion & - dict(brain_region_pk=brain_region_pk)).fetch1( - 'ontology', 'acronym') - - key_brain_loc.update( - channel_x=grf(key, 'x'), - channel_y=grf(key, 'y'), - channel_z=grf(key, 'z'), - channel_axial=grf(key, 'axial'), - channel_lateral=grf(key, 'lateral'), - subject_uuid=subject_uuid, - session_start_time=session_start_time, - probe_idx=probe_idx, - insertion_data_source=insertion_data_source, - ontology=ontology, - acronym=acronym - ) - - self.insert1(key_brain_loc) - - -# These tables will replace the above ones eventually - - +# Temporary probe trajectories and channel brain location based on methods @schema class Provenance(dj.Lookup): definition = """ From 31594fa3538e498883115e3ab0aabb1208d7ef8f Mon Sep 17 00:00:00 2001 From: shenshan Date: Wed, 17 Feb 2021 22:16:49 +0000 Subject: [PATCH 13/34] Change InsertBuffer to QueryBuffer, insert1 -> add_to_queue1, flush -> flush_insert --- ibl_pipeline/ingest/__init__.py | 16 ++++++------ ibl_pipeline/process/__init__.py | 6 ++--- ibl_pipeline/process/autoprocess.py | 6 ++--- ibl_pipeline/process/delete_update_entries.py | 10 +++---- ibl_pipeline/process/ingest_alyx_raw.py | 26 +++++++++---------- ibl_pipeline/process/ingest_membership.py | 19 +++++++++++--- ibl_pipeline/process/ingest_real.py | 6 ++--- ibl_pipeline/process/ingest_shadow.py | 18 ++++++------- ibl_pipeline/process/process_histology.py | 10 +++---- scripts/ingest_alyx_raw.py | 24 ++++++++--------- scripts/ingest_brainregion.py | 10 +++---- scripts/ingest_data_tables.py | 18 ++++++------- scripts/ingest_ephys.sh | 1 + scripts/public/ingest_alyx_raw.py | 24 ++++++++--------- .../public/ingest_alyx_shadow_membership.py | 26 +++++++++---------- scripts/public/ingest_data_tables.py | 18 ++++++------- scripts/test_pipeline/ingest_alyx_raw.py | 24 ++++++++--------- scripts/updates/ingest_alyx_raw.py | 24 ++++++++--------- 18 files changed, 149 insertions(+), 137 deletions(-) create mode 100755 scripts/ingest_ephys.sh diff --git a/ibl_pipeline/ingest/__init__.py b/ibl_pipeline/ingest/__init__.py index 25fffd5b..0905a241 100755 --- a/ibl_pipeline/ingest/__init__.py +++ b/ibl_pipeline/ingest/__init__.py @@ -76,9 +76,9 @@ def get_raw_field(key, field, multiple_entries=False, model=None): if not multiple_entries and len(query) else query.fetch('fvalue') -class InsertBuffer(object): +class QueryBuffer(object): ''' - InsertBuffer: a utility class to help managed chunked inserts + QueryBuffer: a utility class to help managed chunked inserts Currently requires records do not have prerequisites. ''' def __init__(self, rel): @@ -87,13 +87,13 @@ def __init__(self, rel): self._delete_queue = [] self.fetched_results = [] - def insert1(self, r): + def add_to_queue1(self, r): self._queue.append(r) - def insert(self, recs): + def add_to_queue(self, recs): self._queue += recs - def flush(self, replace=False, skip_duplicates=False, + def flush_insert(self, replace=False, skip_duplicates=False, ignore_extra_fields=False, allow_direct_insert=False, chunksz=1): ''' flush the buffer @@ -172,16 +172,16 @@ def flush_fetch(self, field, chunksz=1): def populate_batch(t, chunksz=1000, verbose=True): keys = (t.key_source - t.proj()).fetch('KEY') - table = InsertBuffer(t) + table = QueryBuffer(t) for key in tqdm(keys, position=0): entry = t.create_entry(key) if entry: table.insert1(entry) - if table.flush( + if table.flush_insert( skip_duplicates=True, allow_direct_insert=True, chunksz=chunksz) and verbose: print(f'Inserted {chunksz} {t.__name__} tuples.') - if table.flush(skip_duplicates=True, allow_direct_insert=True) and verbose: + if table.flush_insert(skip_duplicates=True, allow_direct_insert=True) and verbose: print(f'Inserted all remaining {t.__name__} tuples.') diff --git a/ibl_pipeline/process/__init__.py b/ibl_pipeline/process/__init__.py index 884bdf70..7e654b47 100755 --- a/ibl_pipeline/process/__init__.py +++ b/ibl_pipeline/process/__init__.py @@ -1,4 +1,4 @@ -from ibl_pipeline.ingest import alyxraw, InsertBuffer +from ibl_pipeline.ingest import alyxraw, QueryBuffer from ibl_pipeline.utils import is_valid_uuid import datetime from tqdm import tqdm @@ -37,10 +37,10 @@ def get_important_pks(pks, return_original_dict=False): f'model in ({models_ignored})' & pks_dict).fetch('KEY')] else: - buffer = InsertBuffer( + buffer = QueryBuffer( alyxraw.AlyxRaw & f'model in ({models_ignored})') for pk in tqdm(pks_dict): - buffer.insert1(pk) + buffer.add_to_queue1(pk) buffer.flush_fetch('KEY', chunksz=200) buffer.flush_fetch('KEY') diff --git a/ibl_pipeline/process/autoprocess.py b/ibl_pipeline/process/autoprocess.py index bba9ff74..a3940b3e 100755 --- a/ibl_pipeline/process/autoprocess.py +++ b/ibl_pipeline/process/autoprocess.py @@ -139,12 +139,12 @@ def process_public(): public=True) # delete non-releasing tables - from ibl_pipeline.ingest import InsertBuffer + from ibl_pipeline.ingest import QueryBuffer - table = InsertBuffer(acquisition.Session) + table = QueryBuffer(acquisition.Session) for key in tqdm( (acquisition.Session - public.PublicSession - behavior.TrialSet).fetch('KEY')): - table.delete1(key) + table.add_to_queque1(key) if table.flush_delete(chunksz=100): print('Deleted 100 sessions') diff --git a/ibl_pipeline/process/delete_update_entries.py b/ibl_pipeline/process/delete_update_entries.py index 6ee1209a..71ad22eb 100755 --- a/ibl_pipeline/process/delete_update_entries.py +++ b/ibl_pipeline/process/delete_update_entries.py @@ -5,7 +5,7 @@ from ibl_pipeline.process.ingest_membership import membership_tables from ibl_pipeline.common import * from ibl_pipeline.ingest.common import * -from ibl_pipeline.ingest import job, InsertBuffer +from ibl_pipeline.ingest import job, QueryBuffer from ibl_pipeline.ingest import ingest_utils from ibl_pipeline import update from uuid import UUID @@ -44,21 +44,21 @@ def delete_entries_from_alyxraw(pks_to_be_deleted=[], modified_pks_important=[]) if len(pk_list) > 1000: print('Long pk list, deleting from alyxraw.AlyxRaw ...') - alyxraw_buffer = InsertBuffer(alyxraw.AlyxRaw & 'model != "actions.session"') + alyxraw_buffer = QueryBuffer(alyxraw.AlyxRaw & 'model != "actions.session"') for pk in tqdm(pk_list): - alyxraw_buffer.insert1(pk) + alyxraw_buffer.add_to_queue1(pk) alyxraw_buffer.flush_delete(chunksz=50, quick=False) alyxraw_buffer.flush_delete(quick=False) # delete session fields without deleting the primary keys. print('Long pk list, deleting from alyxraw.AlyxRaw.Field ...') - alyxraw_field_buffer = InsertBuffer( + alyxraw_field_buffer = QueryBuffer( alyxraw.AlyxRaw.Field & 'fname!="start_time"' & (alyxraw.AlyxRaw & 'model="actions.session"')) for pk in tqdm(pk_list): - alyxraw_field_buffer.insert1(pk) + alyxraw_field_buffer.add_to_queue1(pk) alyxraw_field_buffer.flush_delete(chunksz=50, quick=True) alyxraw_field_buffer.flush_delete(quick=True) diff --git a/ibl_pipeline/process/ingest_alyx_raw.py b/ibl_pipeline/process/ingest_alyx_raw.py index 3df87ad9..eb18ab4e 100755 --- a/ibl_pipeline/process/ingest_alyx_raw.py +++ b/ibl_pipeline/process/ingest_alyx_raw.py @@ -8,7 +8,7 @@ import math import collections import os.path as path -from ibl_pipeline.ingest import alyxraw, InsertBuffer +from ibl_pipeline.ingest import alyxraw, QueryBuffer import sys import uuid import re @@ -68,7 +68,7 @@ def insert_to_alyxraw( # use insert buffer to speed up the insertion process if alyx_type in ('all', 'main'): - ib_main = InsertBuffer(alyxraw_module.AlyxRaw) + ib_main = QueryBuffer(alyxraw_module.AlyxRaw) # insert into AlyxRaw table for key in tqdm(keys, position=0): try: @@ -77,16 +77,16 @@ def insert_to_alyxraw( print('Error for key: {}'.format(key)) continue - ib_main.insert1(dict(uuid=pk, model=key['model'])) - if ib_main.flush(skip_duplicates=True, chunksz=10000): + ib_main.add_to_queue1(dict(uuid=pk, model=key['model'])) + if ib_main.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw tuples.') - if ib_main.flush(skip_duplicates=True): + if ib_main.flush_insert(skip_duplicates=True): logger.debug('Inserted remaining raw tuples') - ib_main = InsertBuffer(alyxraw_module.AlyxRaw) + ib_main = QueryBuffer(alyxraw_module.AlyxRaw) if alyx_type in ('all', 'part'): - ib_part = InsertBuffer(alyxraw_module.AlyxRaw.Field) + ib_part = QueryBuffer(alyxraw_module.AlyxRaw.Field) # insert into the part table AlyxRaw.Field for ikey, key in tqdm(enumerate(keys), position=0): try: @@ -105,7 +105,7 @@ def insert_to_alyxraw( key_field['value_idx'] = 0 key_field['fvalue'] = json.dumps(field_value) if len(key_field['fvalue']) < 10000: - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) else: continue if field_name == 'narrative' and field_value is not None: @@ -127,27 +127,27 @@ def insert_to_alyxraw( (isinstance(field_value, float) and math.isnan(field_value)): key_field['value_idx'] = 0 key_field['fvalue'] = 'None' - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) elif type(field_value) is list and \ (type(field_value[0]) is dict or type(field_value[0]) is str): for value_idx, value in enumerate(field_value): key_field['value_idx'] = value_idx key_field['fvalue'] = str(value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) else: key_field['value_idx'] = 0 key_field['fvalue'] = str(field_value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) - if ib_part.flush(skip_duplicates=True, chunksz=10000): + if ib_part.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw field tuples') except Exception: print('Problematic entry:{}'.format(ikey)) raise - if ib_part.flush(skip_duplicates=True): + if ib_part.flush_insert(skip_duplicates=True): logger.debug('Inserted all remaining raw field tuples') diff --git a/ibl_pipeline/process/ingest_membership.py b/ibl_pipeline/process/ingest_membership.py index 75e9c21b..cd57f8f8 100755 --- a/ibl_pipeline/process/ingest_membership.py +++ b/ibl_pipeline/process/ingest_membership.py @@ -6,7 +6,8 @@ import datajoint as dj import json import uuid -from ibl_pipeline.ingest import alyxraw, reference, subject, action, acquisition, data +from tqdm import tqdm +from ibl_pipeline.ingest import alyxraw, reference, subject, action, acquisition, data, QueryBuffer from ibl_pipeline.ingest import get_raw_field as grf from ibl_pipeline.utils import is_valid_uuid @@ -194,14 +195,24 @@ def ingest_membership_table(dj_current_table, else: restr = {} - alyxraw_to_insert = (alyxraw.AlyxRaw & restr & - {'model': alyx_parent_model}).fetch('KEY') + if len(restr) > 1000: + print('More than 1000 entries to insert, using buffer...') + buffer = QueryBuffer(alyxraw.AlyxRaw & {'model': alyx_parent_model}) + for r in tqdm(restr): + buffer.add_to_queue1(r) + buffer.flush_fetch('KEY', chunksz=200) + buffer.flush_fetch('KEY') + alyxraw_to_insert = buffer.fetched_results + + else: + alyxraw_to_insert = (alyxraw.AlyxRaw & restr & + {'model': alyx_parent_model}).fetch('KEY') if not alyxraw_to_insert: return alyx_field_entries = alyxraw.AlyxRaw.Field & alyxraw_to_insert & \ - {'fname': alyx_field} & 'fvalue!="None"' + {'fname': alyx_field} & 'fvalue!="None"' keys = (alyxraw.AlyxRaw & alyx_field_entries).proj(**{dj_parent_uuid_name: 'uuid'}) diff --git a/ibl_pipeline/process/ingest_real.py b/ibl_pipeline/process/ingest_real.py index bca79e05..412ddada 100755 --- a/ibl_pipeline/process/ingest_real.py +++ b/ibl_pipeline/process/ingest_real.py @@ -164,9 +164,9 @@ def main(excluded_tables=[], public=False): print('ProbeTrajectory') histology.ProbeTrajectory.populate(suppress_errors=True, display_progress=True) - print('ChannelBrainLocation') - copy_table(histology, histology_ingest, 'ChannelBrainLocation', - allow_direct_insert=True) + # print('ChannelBrainLocation') + # copy_table(histology, histology_ingest, 'ChannelBrainLocation', + # allow_direct_insert=True) if __name__ == '__main__': diff --git a/ibl_pipeline/process/ingest_shadow.py b/ibl_pipeline/process/ingest_shadow.py index 3ad1691a..e95f5f23 100755 --- a/ibl_pipeline/process/ingest_shadow.py +++ b/ibl_pipeline/process/ingest_shadow.py @@ -1,6 +1,6 @@ import datajoint as dj from ibl_pipeline.ingest import \ - (alyxraw, InsertBuffer, + (alyxraw, QueryBuffer, reference, subject, action, acquisition, data) from os import environ @@ -104,7 +104,7 @@ def main(excluded_tables=[], modified_pks=None): key_source = (alyxraw.AlyxRaw & 'model="data.dataset"').proj( dataset_uuid="uuid") - data.DataSet - data_set = InsertBuffer(data.DataSet) + data_set = QueryBuffer(data.DataSet) for key in tqdm(key_source.fetch('KEY'), position=0): key_ds = key.copy() @@ -173,14 +173,14 @@ def main(excluded_tables=[], modified_pks=None): else: key_ds['file_size'] = None - data_set.insert1(key_ds) + data_set.add_to_queue1(key_ds) - if data_set.flush( + if data_set.flush_insert( skip_duplicates=True, allow_direct_insert=True, chunksz=100): print('Inserted 100 dataset tuples') - if data_set.flush(skip_duplicates=True, allow_direct_insert=True): + if data_set.flush_insert(skip_duplicates=True, allow_direct_insert=True): print('Inserted all remaining dataset tuples') if 'FileRecord' not in excluded_tables: @@ -195,7 +195,7 @@ def main(excluded_tables=[], modified_pks=None): key_source = (alyxraw.AlyxRaw & record_exists & records_flatiron).proj( record_uuid='uuid') - data.FileRecord - file_record = InsertBuffer(data.FileRecord) + file_record = QueryBuffer(data.FileRecord) for key in tqdm(key_source.fetch('KEY'), position=0): key_fr = key.copy() @@ -220,13 +220,13 @@ def main(excluded_tables=[], modified_pks=None): key_fr['relative_path'] = grf(key, 'relative_path') - file_record.insert1(key_fr) + file_record.add_to_queue1(key_fr) - if file_record.flush( + if file_record.flush_insert( skip_duplicates=True, allow_direct_insert=True, chunksz=1000): print('Inserted 1000 raw field tuples') - if file_record.flush(skip_duplicates=True, allow_direct_insert=True): + if file_record.flush_insert(skip_duplicates=True, allow_direct_insert=True): print('Inserted all remaining file record tuples') diff --git a/ibl_pipeline/process/process_histology.py b/ibl_pipeline/process/process_histology.py index 5c6bd969..167e1d0a 100644 --- a/ibl_pipeline/process/process_histology.py +++ b/ibl_pipeline/process/process_histology.py @@ -1,6 +1,6 @@ from ibl_pipeline.process import ingest_alyx_raw, ingest_real from ibl_pipeline.ingest.common import * -from ibl_pipeline.ingest import populate_batch, InsertBuffer +from ibl_pipeline.ingest import populate_batch, QueryBuffer from ibl_pipeline.common import * from ibl_pipeline.process import update_utils from tqdm import tqdm @@ -99,10 +99,10 @@ def delete_histology_alyx_shadow(verbose=False): print(f'Deleting from table {t.__name__}') uuid_name = t.heading.primary_key[0] keys = [{uuid_name: k['uuid']} for k in tqdm(channel_loc_keys)] - table = InsertBuffer(t) + table = QueryBuffer(t) for k in tqdm(keys, position=0): - table.delete1(k) + table.add_to_queue1(k) if table.flush_delete(chunksz=1000, quick=True) and verbose: print(f'Deleted 1000 entries from {t.__name__}') @@ -121,9 +121,9 @@ def delete_histology_alyx_shadow(verbose=False): for t in TRAJ_TABLES: uuid_name = t.heading.primary_key[0] keys = [{uuid_name: k['uuid']} for k in traj_keys] - table = InsertBuffer(t) + table = QueryBuffer(t) for k in tqdm(keys, position=0): - table.insert1(k) + table.add_to_queue1(k) if table.flush_delete(chunksz=1000, quick=True) and verbose: print(f'Deleted 1000 entries from {t.__name__}') table.flush_delete(quick=True) diff --git a/scripts/ingest_alyx_raw.py b/scripts/ingest_alyx_raw.py index 2c250797..9e27a005 100755 --- a/scripts/ingest_alyx_raw.py +++ b/scripts/ingest_alyx_raw.py @@ -8,7 +8,7 @@ import math import collections import os.path as path -from ibl_pipeline.ingest import alyxraw, InsertBuffer +from ibl_pipeline.ingest import alyxraw, QueryBuffer import sys import uuid import re @@ -48,8 +48,8 @@ def get_alyx_entries(filename=None, models=None): def insert_to_alyxraw(keys): # use insert buffer to speed up the insertion process - ib_main = InsertBuffer(alyxraw.AlyxRaw) - ib_part = InsertBuffer(alyxraw.AlyxRaw.Field) + ib_main = QueryBuffer(alyxraw.AlyxRaw) + ib_part = QueryBuffer(alyxraw.AlyxRaw.Field) # insert into AlyxRaw table for key in tqdm(keys, position=0): @@ -59,12 +59,12 @@ def insert_to_alyxraw(keys): print('Error for key: {}'.format(key)) continue - ib_main.insert1(dict(uuid=pk, model=key['model'])) - if ib_main.flush(skip_duplicates=True, chunksz=10000): + ib_main.add_to_queue1(dict(uuid=pk, model=key['model'])) + if ib_main.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw tuples.') # print('Inserted 10000 raw tuples.') - if ib_main.flush(skip_duplicates=True): + if ib_main.flush_insert(skip_duplicates=True): logger.debug('Inserted remaining raw tuples') # print('Inserted remaining raw tuples') @@ -86,7 +86,7 @@ def insert_to_alyxraw(keys): key_field['value_idx'] = 0 key_field['fvalue'] = json.dumps(field_value) if len(key_field['fvalue']) < 10000: - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) else: continue if field_name == 'narrative' and field_value is not None: @@ -108,27 +108,27 @@ def insert_to_alyxraw(keys): (isinstance(field_value, float) and math.isnan(field_value)): key_field['value_idx'] = 0 key_field['fvalue'] = 'None' - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) elif type(field_value) is list and \ (type(field_value[0]) is dict or type(field_value[0]) is str): for value_idx, value in enumerate(field_value): key_field['value_idx'] = value_idx key_field['fvalue'] = str(value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) else: key_field['value_idx'] = 0 key_field['fvalue'] = str(field_value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) - if ib_part.flush(skip_duplicates=True, chunksz=10000): + if ib_part.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw field tuples') # print('Inserted 10000 raw field tuples') except Exception as e: print('Problematic entry:{}'.format(ikey)) raise - if ib_part.flush(skip_duplicates=True): + if ib_part.flush_insert(skip_duplicates=True): logger.debug('Inserted all remaining raw field tuples') # print('Inserted all remaining raw field tuples') diff --git a/scripts/ingest_brainregion.py b/scripts/ingest_brainregion.py index 88b86409..1eceb891 100755 --- a/scripts/ingest_brainregion.py +++ b/scripts/ingest_brainregion.py @@ -2,7 +2,7 @@ import json import os.path as path import sys -from ibl_pipeline.ingest import InsertBuffer +from ibl_pipeline.ingest import QueryBuffer from ibl_pipeline.ingest import reference as reference_ingest from ibl_pipeline import reference from ingest_alyx_raw import get_alyx_entries @@ -17,7 +17,7 @@ def ingest_all(): - ib_brainregion = InsertBuffer(reference_ingest.BrainRegion) + ib_brainregion = QueryBuffer(reference_ingest.BrainRegion) for key in tqdm(keys, position=0): fields = key['fields'] @@ -28,17 +28,17 @@ def ingest_all(): else: graph_order = int(graph_order) - ib_brainregion.insert1( + ib_brainregion.add_to_queue1( dict(brain_region_pk=key['pk'], acronym=fields['acronym'], brain_region_name=fields['name'], parent=fields['parent'], brain_region_level=fields['level'], graph_order=graph_order)) - if ib_brainregion.flush(skip_duplicates=True, chunksz=1000): + if ib_brainregion.flush_insert(skip_duplicates=True, chunksz=1000): print('Inserted 1000 raw tuples.') - if ib_brainregion.flush(skip_duplicates=True): + if ib_brainregion.flush_insert(skip_duplicates=True): print('Inserted all remaining raw field tuples') diff --git a/scripts/ingest_data_tables.py b/scripts/ingest_data_tables.py index c89d6666..f43e6ef5 100755 --- a/scripts/ingest_data_tables.py +++ b/scripts/ingest_data_tables.py @@ -1,5 +1,5 @@ -from ibl_pipeline.ingest import alyxraw, data, reference, acquisition, InsertBuffer +from ibl_pipeline.ingest import alyxraw, data, reference, acquisition, QueryBuffer from ibl_pipeline.ingest import get_raw_field as grf import uuid from tqdm import tqdm @@ -10,7 +10,7 @@ key_source = (alyxraw.AlyxRaw & 'model="data.dataset"').proj( dataset_uuid="uuid") - data.DataSet - data_set = InsertBuffer(data.DataSet) + data_set = QueryBuffer(data.DataSet) for key in tqdm(key_source.fetch('KEY'), position=0): key_ds = key.copy() @@ -79,14 +79,14 @@ else: key_ds['file_size'] = None - data_set.insert1(key_ds) + data_set.add_to_queue1(key_ds) - if data_set.flush( + if data_set.flush_insert( skip_duplicates=True, allow_direct_insert=True, chunksz=100): print('Inserted 100 dataset tuples') - if data_set.flush(skip_duplicates=True, allow_direct_insert=True): + if data_set.flush_insert(skip_duplicates=True, allow_direct_insert=True): print('Inserted all remaining dataset tuples') @@ -101,7 +101,7 @@ key_source = (alyxraw.AlyxRaw & record_exists & records_flatiron).proj( record_uuid='uuid') - data.FileRecord - file_record = InsertBuffer(data.FileRecord) + file_record = QueryBuffer(data.FileRecord) for key in tqdm(key_source.fetch('KEY'), position=0): key_fr = key.copy() @@ -126,11 +126,11 @@ key_fr['relative_path'] = grf(key, 'relative_path') - file_record.insert1(key_fr) + file_record.add_to_queue1(key_fr) - if file_record.flush( + if file_record.flush_insert( skip_duplicates=True, allow_direct_insert=True, chunksz=1000): print('Inserted 1000 raw field tuples') - if file_record.flush(skip_duplicates=True, allow_direct_insert=True): + if file_record.flush_insert(skip_duplicates=True, allow_direct_insert=True): print('Inserted all remaining file record tuples') diff --git a/scripts/ingest_ephys.sh b/scripts/ingest_ephys.sh new file mode 100755 index 00000000..fb3842d7 --- /dev/null +++ b/scripts/ingest_ephys.sh @@ -0,0 +1 @@ +python ingest_ephys.py diff --git a/scripts/public/ingest_alyx_raw.py b/scripts/public/ingest_alyx_raw.py index 8a8269ec..a6857054 100644 --- a/scripts/public/ingest_alyx_raw.py +++ b/scripts/public/ingest_alyx_raw.py @@ -8,7 +8,7 @@ import math import collections import os.path as path -from ibl_pipeline.ingest import alyxraw, InsertBuffer +from ibl_pipeline.ingest import alyxraw, QueryBuffer import sys import uuid @@ -32,16 +32,16 @@ ['auth.group', 'sessions.session', 'authtoken.token']] # use insert buffer to speed up the insersion process -ib_main = InsertBuffer(alyxraw.AlyxRaw) -ib_part = InsertBuffer(alyxraw.AlyxRaw.Field) +ib_main = QueryBuffer(alyxraw.AlyxRaw) +ib_part = QueryBuffer(alyxraw.AlyxRaw.Field) # insert into AlyxRaw table for key in keys: - ib_main.insert1(dict(uuid=uuid.UUID(key['pk']), model=key['model'])) - if ib_main.flush(skip_duplicates=True, chunksz=10000): + ib_main.add_to_queue1(dict(uuid=uuid.UUID(key['pk']), model=key['model'])) + if ib_main.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw tuples.') -if ib_main.flush(skip_duplicates=True): +if ib_main.flush_insert(skip_duplicates=True): logger.debug('Inserted remaining raw tuples') # insert into the part table AlyxRaw.Field @@ -56,20 +56,20 @@ else: key_field['value_idx'] = 0 key_field['fvalue'] = json.dumps(field_value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) elif field_value is None or field_value == '' or field_value == [] or \ (isinstance(field_value, float) and math.isnan(field_value)): key_field['value_idx'] = 0 key_field['fvalue'] = 'None' - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) elif type(field_value) is list and \ (type(field_value[0]) is dict or type(field_value[0]) is str): for value_idx, value in enumerate(field_value): key_field['value_idx'] = value_idx key_field['fvalue'] = str(value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) # elif isinstance(field_value, collections.Sequence) and \ # isinstance(field_value, (collections.Mapping, str)): # ib_part.insert(dict(key_field, value_idx=value_idx, @@ -78,10 +78,10 @@ else: key_field['value_idx'] = 0 key_field['fvalue'] = str(field_value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) - if ib_part.flush(skip_duplicates=True, chunksz=10000): + if ib_part.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw field tuples') -if ib_part.flush(skip_duplicates=True): +if ib_part.flush_insert(skip_duplicates=True): logger.debug('Inserted all remaining raw field tuples') diff --git a/scripts/public/ingest_alyx_shadow_membership.py b/scripts/public/ingest_alyx_shadow_membership.py index dbfde65f..001ce7b4 100644 --- a/scripts/public/ingest_alyx_shadow_membership.py +++ b/scripts/public/ingest_alyx_shadow_membership.py @@ -7,7 +7,7 @@ import uuid from ibl_pipeline.ingest import ( alyxraw, reference, subject, action, - acquisition, data, InsertBuffer) + acquisition, data, QueryBuffer) from ibl_pipeline.ingest import get_raw_field as grf from tqdm import tqdm @@ -173,7 +173,7 @@ keys = (alyxraw.AlyxRaw & sessions_with_users).proj( session_uuid='uuid') -session_user = InsertBuffer(acquisition.SessionUser) +session_user = QueryBuffer(acquisition.SessionUser) for key in tqdm(keys, position=0): @@ -198,12 +198,12 @@ 'user_name') acquisition.SessionUser.insert1(key_su, skip_duplicates=True) - session_user.insert1(key_su) - if session_user.flush( + session_user.add_to_queue1(key_su) + if session_user.flush_insert( skip_duplicates=True, chunksz=1000): print('Inserted 1000 session user tuples') -if session_user.flush(skip_duplicates=True): +if session_user.flush_insert(skip_duplicates=True): print('Inserted all remaining session user tuples') @@ -215,7 +215,7 @@ keys = (alyxraw.AlyxRaw & sessions_with_procedures).proj( session_uuid='uuid') -session_procedure = InsertBuffer(acquisition.SessionProcedure) +session_procedure = QueryBuffer(acquisition.SessionProcedure) for key in tqdm(keys, position=0): key['uuid'] = key['session_uuid'] @@ -236,12 +236,12 @@ (action.ProcedureType & dict(procedure_type_uuid=uuid.UUID(procedure))).fetch1( 'procedure_type_name') - session_procedure.insert1(key_sp) - if session_procedure.flush( + session_procedure.add_to_queue1(key_sp) + if session_procedure.flush_insert( skip_duplicates=True, chunksz=1000): print('Inserted 1000 session procedure tuples') -if session_procedure.flush(skip_duplicates=True): +if session_procedure.flush_insert(skip_duplicates=True): print('Inserted all remaining session procedure tuples') # acquisition.SessionProject @@ -252,7 +252,7 @@ keys = (alyxraw.AlyxRaw & sessions_with_projects).proj( session_uuid='uuid') -session_project = InsertBuffer(acquisition.SessionProject) +session_project = QueryBuffer(acquisition.SessionProject) for key in tqdm(keys, position=0): key['uuid'] = key['session_uuid'] @@ -273,13 +273,13 @@ dict(project_uuid=uuid.UUID(project))).fetch1( 'project_name') - session_project.insert1(key_sp) + session_project.add_to_queue1(key_sp) - if session_project.flush( + if session_project.flush_insert( skip_duplicates=True, chunksz=1000): print('Inserted 1000 session procedure tuples') -if session_project.flush(skip_duplicates=True): +if session_project.flush_insert(skip_duplicates=True): print('Inserted all remaining session procedure tuples') diff --git a/scripts/public/ingest_data_tables.py b/scripts/public/ingest_data_tables.py index bb073945..03f29339 100644 --- a/scripts/public/ingest_data_tables.py +++ b/scripts/public/ingest_data_tables.py @@ -1,5 +1,5 @@ -from ibl_pipeline.ingest import alyxraw, data, reference, acquisition, InsertBuffer +from ibl_pipeline.ingest import alyxraw, data, reference, acquisition, QueryBuffer from ibl_pipeline.ingest import get_raw_field as grf import uuid from tqdm import tqdm @@ -8,7 +8,7 @@ key_source = (alyxraw.AlyxRaw & 'model="data.dataset"').proj( dataset_uuid="uuid") - data.DataSet -data_set = InsertBuffer(data.DataSet) +data_set = QueryBuffer(data.DataSet) for key in tqdm(key_source.fetch('KEY'), position=0): key_ds = key.copy() @@ -77,14 +77,14 @@ else: key_ds['file_size'] = None - data_set.insert1(key_ds) + data_set.add_to_queue1(key_ds) - if data_set.flush( + if data_set.flush_insert( skip_duplicates=True, allow_direct_insert=True, chunksz=100): print('Inserted 100 dataset tuples') -if data_set.flush(skip_duplicates=True, allow_direct_insert=True): +if data_set.flush_insert(skip_duplicates=True, allow_direct_insert=True): print('Inserted all remaining dataset tuples') @@ -99,7 +99,7 @@ key_source = (alyxraw.AlyxRaw & record_exists & records_flatiron).proj( record_uuid='uuid') - data.FileRecord -file_record = InsertBuffer(data.FileRecord) +file_record = QueryBuffer(data.FileRecord) for key in tqdm(key_source.fetch('KEY'), position=0): key_fr = key.copy() @@ -124,11 +124,11 @@ key_fr['relative_path'] = grf(key, 'relative_path') - file_record.insert1(key_fr) + file_record.add_to_queue1(key_fr) - if file_record.flush( + if file_record.flush_insert( skip_duplicates=True, allow_direct_insert=True, chunksz=1000): print('Inserted 1000 raw field tuples') -if file_record.flush(skip_duplicates=True, allow_direct_insert=True): +if file_record.flush_insert(skip_duplicates=True, allow_direct_insert=True): print('Inserted all remaining file record tuples') diff --git a/scripts/test_pipeline/ingest_alyx_raw.py b/scripts/test_pipeline/ingest_alyx_raw.py index 8a8269ec..a6857054 100644 --- a/scripts/test_pipeline/ingest_alyx_raw.py +++ b/scripts/test_pipeline/ingest_alyx_raw.py @@ -8,7 +8,7 @@ import math import collections import os.path as path -from ibl_pipeline.ingest import alyxraw, InsertBuffer +from ibl_pipeline.ingest import alyxraw, QueryBuffer import sys import uuid @@ -32,16 +32,16 @@ ['auth.group', 'sessions.session', 'authtoken.token']] # use insert buffer to speed up the insersion process -ib_main = InsertBuffer(alyxraw.AlyxRaw) -ib_part = InsertBuffer(alyxraw.AlyxRaw.Field) +ib_main = QueryBuffer(alyxraw.AlyxRaw) +ib_part = QueryBuffer(alyxraw.AlyxRaw.Field) # insert into AlyxRaw table for key in keys: - ib_main.insert1(dict(uuid=uuid.UUID(key['pk']), model=key['model'])) - if ib_main.flush(skip_duplicates=True, chunksz=10000): + ib_main.add_to_queue1(dict(uuid=uuid.UUID(key['pk']), model=key['model'])) + if ib_main.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw tuples.') -if ib_main.flush(skip_duplicates=True): +if ib_main.flush_insert(skip_duplicates=True): logger.debug('Inserted remaining raw tuples') # insert into the part table AlyxRaw.Field @@ -56,20 +56,20 @@ else: key_field['value_idx'] = 0 key_field['fvalue'] = json.dumps(field_value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) elif field_value is None or field_value == '' or field_value == [] or \ (isinstance(field_value, float) and math.isnan(field_value)): key_field['value_idx'] = 0 key_field['fvalue'] = 'None' - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) elif type(field_value) is list and \ (type(field_value[0]) is dict or type(field_value[0]) is str): for value_idx, value in enumerate(field_value): key_field['value_idx'] = value_idx key_field['fvalue'] = str(value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) # elif isinstance(field_value, collections.Sequence) and \ # isinstance(field_value, (collections.Mapping, str)): # ib_part.insert(dict(key_field, value_idx=value_idx, @@ -78,10 +78,10 @@ else: key_field['value_idx'] = 0 key_field['fvalue'] = str(field_value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) - if ib_part.flush(skip_duplicates=True, chunksz=10000): + if ib_part.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw field tuples') -if ib_part.flush(skip_duplicates=True): +if ib_part.flush_insert(skip_duplicates=True): logger.debug('Inserted all remaining raw field tuples') diff --git a/scripts/updates/ingest_alyx_raw.py b/scripts/updates/ingest_alyx_raw.py index 1de74aea..ce4f8e23 100755 --- a/scripts/updates/ingest_alyx_raw.py +++ b/scripts/updates/ingest_alyx_raw.py @@ -8,7 +8,7 @@ import math import collections import os.path as path -from ibl_pipeline.ingest import alyxraw, InsertBuffer +from ibl_pipeline.ingest import alyxraw, QueryBuffer import sys import uuid @@ -32,16 +32,16 @@ ['auth.group', 'sessions.session', 'authtoken.token']] # use insert buffer to speed up the insersion process -ib_main = InsertBuffer(alyxraw.AlyxRaw) -ib_part = InsertBuffer(alyxraw.AlyxRaw.Field) +ib_main = QueryBuffer(alyxraw.AlyxRaw) +ib_part = QueryBuffer(alyxraw.AlyxRaw.Field) # insert into AlyxRaw table for key in keys: - ib_main.insert1(dict(uuid=uuid.UUID(key['pk']), model=key['model'])) - if ib_main.flush(skip_duplicates=True, chunksz=10000): + ib_main.add_to_queue1(dict(uuid=uuid.UUID(key['pk']), model=key['model'])) + if ib_main.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw tuples.') -if ib_main.flush(skip_duplicates=True): +if ib_main.flush_insert(skip_duplicates=True): logger.debug('Inserted remaining raw tuples') # insert into the part table AlyxRaw.Field @@ -56,7 +56,7 @@ key_field['value_idx'] = 0 key_field['fvalue'] = json.dumps(field_value) if len(key_field['fvalue']) < 10000: - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) else: continue if field_name == 'narrative' and field_value is not None: @@ -78,14 +78,14 @@ (isinstance(field_value, float) and math.isnan(field_value)): key_field['value_idx'] = 0 key_field['fvalue'] = 'None' - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) elif type(field_value) is list and \ (type(field_value[0]) is dict or type(field_value[0]) is str): for value_idx, value in enumerate(field_value): key_field['value_idx'] = value_idx key_field['fvalue'] = str(value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) # elif isinstance(field_value, collections.Sequence) and \ # isinstance(field_value, (collections.Mapping, str)): # ib_part.insert(dict(key_field, value_idx=value_idx, @@ -94,13 +94,13 @@ else: key_field['value_idx'] = 0 key_field['fvalue'] = str(field_value) - ib_part.insert1(key_field) + ib_part.add_to_queue1(key_field) - if ib_part.flush(skip_duplicates=True, chunksz=10000): + if ib_part.flush_insert(skip_duplicates=True, chunksz=10000): logger.debug('Inserted 10000 raw field tuples') except Exception as e: print('Problematic entry:{}'.format(ikey)) raise -if ib_part.flush(skip_duplicates=True): +if ib_part.flush_insert(skip_duplicates=True): logger.debug('Inserted all remaining raw field tuples') From cf196dcbc121d27d1abd6402a9701a2240abd5a2 Mon Sep 17 00:00:00 2001 From: shenshan Date: Wed, 17 Feb 2021 23:32:08 +0000 Subject: [PATCH 14/34] remove the ingest tables ChannelBrainLocation and ProbeTrajectory --- ibl_pipeline/process/process_histology.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/ibl_pipeline/process/process_histology.py b/ibl_pipeline/process/process_histology.py index 167e1d0a..0fa894a1 100644 --- a/ibl_pipeline/process/process_histology.py +++ b/ibl_pipeline/process/process_histology.py @@ -89,7 +89,6 @@ def delete_histology_alyx_shadow(verbose=False): CHANNEL_TABLES = [ histology_ingest.ChannelBrainLocationTemp, - histology_ingest.ChannelBrainLocation, alyxraw.AlyxRaw.Field, alyxraw.AlyxRaw ] @@ -113,7 +112,6 @@ def delete_histology_alyx_shadow(verbose=False): TRAJ_TABLES = [ histology_ingest.ProbeTrajectoryTemp, - histology_ingest.ProbeTrajectory, alyxraw.AlyxRaw.Field, alyxraw.AlyxRaw ] From 0c9235b94830c58a1f5cdd1655eefd951d749d5c Mon Sep 17 00:00:00 2001 From: shenshan Date: Wed, 17 Feb 2021 23:33:48 +0000 Subject: [PATCH 15/34] One more case of insert1 -> add_to_queue1 --- ibl_pipeline/ingest/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ibl_pipeline/ingest/__init__.py b/ibl_pipeline/ingest/__init__.py index 0905a241..baee0d2d 100755 --- a/ibl_pipeline/ingest/__init__.py +++ b/ibl_pipeline/ingest/__init__.py @@ -176,7 +176,7 @@ def populate_batch(t, chunksz=1000, verbose=True): for key in tqdm(keys, position=0): entry = t.create_entry(key) if entry: - table.insert1(entry) + table.add_to_queue1(entry) if table.flush_insert( skip_duplicates=True, From e7abc838cce698a5fa5519d7a8c1ff19c8f186eb Mon Sep 17 00:00:00 2001 From: shenshan Date: Thu, 18 Feb 2021 00:29:32 +0000 Subject: [PATCH 16/34] update qc field names for SessionQC tables. --- ibl_pipeline/ingest/qc.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/ibl_pipeline/ingest/qc.py b/ibl_pipeline/ingest/qc.py index ddc67835..0b009a78 100644 --- a/ibl_pipeline/ingest/qc.py +++ b/ibl_pipeline/ingest/qc.py @@ -6,6 +6,7 @@ from .. import ephys as ephys_real from .. import qc from . import get_raw_field as grf +from tqdm import tqdm schema = dj.schema(dj.config.get('database.prefix', '') + 'ibl_ingest_qc') @@ -85,8 +86,8 @@ class SessionQC(dj.Manual): subject_uuid : uuid session_start_time : datetime --- - qc : tinyint unsigned - sessionqc_ts=CURRENT_TIMESTAMP: timestamp + session_qc : tinyint unsigned + session_qc_ts=CURRENT_TIMESTAMP: timestamp """ @@ -149,7 +150,7 @@ def make(self, key): session_key = (acquisition.Session & key).fetch1('KEY') SessionQC.insert1( - dict(**session_key, qc=int(qc)) + dict(**session_key, session_qc=int(qc)) ) for qc_type in qc_types: @@ -169,13 +170,13 @@ def make(self, key): qc_type=qc_type, qc_fname=k) if type(v) == float: - qc_fvalue_name = 'qc_fvalue_float' + qc_fvalue_name = 'session_qc_fvalue_float' elif v == "None": pass elif type(v) == str: - qc_fvalue_name = 'qc_fvalue_varchar' + qc_fvalue_name = 'session_qc_fvalue_varchar' else: - qc_fvalue_name = 'qc_fvalue_blob' + qc_fvalue_name = 'session_qc_fvalue_blob' SessionExtendedQC.Field.insert1( {**qc_field, @@ -270,5 +271,5 @@ def make(self, key): ProbeInsertionExtendedQC.insert1( {**probe_insertion_key, - 'insertion_qc_fname': k, - qc_fvalue_name: v}) + 'insertion_qc_fname': k, + qc_fvalue_name: v}) From cb31319cc4465f61ac562b094e20ee3c364a6905 Mon Sep 17 00:00:00 2001 From: shenshan Date: Thu, 18 Feb 2021 00:45:21 +0000 Subject: [PATCH 17/34] Update session qc field name --- ibl_pipeline/ingest/qc.py | 10 +++++----- ibl_pipeline/qc.py | 8 ++++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/ibl_pipeline/ingest/qc.py b/ibl_pipeline/ingest/qc.py index 0b009a78..47b246b9 100644 --- a/ibl_pipeline/ingest/qc.py +++ b/ibl_pipeline/ingest/qc.py @@ -105,11 +105,11 @@ class SessionExtendedQC(dj.Manual): class Field(dj.Part): definition = """ -> master - session_qc_fname : varchar(32) + session_qc_fname : varchar(64) --- session_qc_fvalue_bool=null : bool session_qc_fvalue_float=null : float - session_qc_fvalue_str=null : varchar(32) + session_qc_fvalue_str=null : varchar(64) session_qc_fvalue_blob=null : blob """ @@ -161,18 +161,18 @@ def make(self, key): SessionExtendedQC.insert1( dict(**session_key, qc_type=qc_type, - extended_qc=qc_choice) + session_extended_qc=qc_choice) ) for k, v in qc_extended.items(): if f'_{qc_type}' in k: qc_field = dict( **session_key, qc_type=qc_type, - qc_fname=k) + session_qc_fname=k) if type(v) == float: qc_fvalue_name = 'session_qc_fvalue_float' elif v == "None": - pass + continue elif type(v) == str: qc_fvalue_name = 'session_qc_fvalue_varchar' else: diff --git a/ibl_pipeline/qc.py b/ibl_pipeline/qc.py index c7910f0e..781beb0f 100644 --- a/ibl_pipeline/qc.py +++ b/ibl_pipeline/qc.py @@ -77,11 +77,11 @@ class Field(dj.Part): definition = """ # Part table of SessionExtendedQC. For each entry of SessionExtendedQC, there may be multiple fields describing each value (e.g. 0.99) of a qc aspect (e.g. _task_stimOn_delays) that belongs to a QCType (e.g. task). -> master - session_qc_fname : varchar(32) + session_qc_fname : varchar(64) --- session_qc_fvalue_bool=null : bool session_qc_fvalue_float=null : float - session_qc_fvalue_str=null : varchar(32) + session_qc_fvalue_str=null : varchar(64) session_qc_fvalue_blob=null : blob """ @@ -109,10 +109,10 @@ class Field(dj.Part): definition = """ # Part table of SessionExtendedQC. For each entry of ProbeInsertionExtendedQC. -> master - insertion_qc_fname : varchar(32) + insertion_qc_fname : varchar(64) --- insertion_qc_fvalue_bool=null : tinyint insertion_qc_fvalue_float=null : float - insertion_qc_fvalue_str=null : varchar(32) + insertion_qc_fvalue_str=null : varchar(64) insertion_qc_fvalue_blob=null : blob """ From 7de56b3b245b2b6529b11b3fe61ae7505478be8b Mon Sep 17 00:00:00 2001 From: shenshan Date: Thu, 18 Feb 2021 06:50:53 +0000 Subject: [PATCH 18/34] add workshop materials --- .../0-Get DataJoint Ready.ipynb | 228 +++++ ...ore IBL data pipeline with DataJoint.ipynb | 780 ++++++++++++++++++ ...a with IBL pipeline and save results.ipynb | 725 ++++++++++++++++ 3 files changed, 1733 insertions(+) create mode 100644 notebooks/notebooks_tutorial/202102_workshop/0-Get DataJoint Ready.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_workshop/1-Explore IBL data pipeline with DataJoint.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_workshop/2-Analyze data with IBL pipeline and save results.ipynb diff --git a/notebooks/notebooks_tutorial/202102_workshop/0-Get DataJoint Ready.ipynb b/notebooks/notebooks_tutorial/202102_workshop/0-Get DataJoint Ready.ipynb new file mode 100644 index 00000000..29e181f3 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/0-Get DataJoint Ready.ipynb @@ -0,0 +1,228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuring DataJoint\n", + "\n", + "Before you can start using anything with DataJoint, you need to configure DataJoint. Installing `ibl-pipeline` packaged automatically install the latest DataJoint with it. In order for DataJoint to work, you need to tell it informatoin about the database connection, namely the database hostname." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start by importing the package. Convention is to import it as `dj`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that `database.host` is already set to `datajoint.internationalbrainlab.org`. If not, we can manually set it by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config['database.host'] = 'datajoint.internationalbrainlab.org'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are pointing to the right place, let's try connecting. You can explicitly trigger a connection with `dj.conn()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you verify that the connection is working, you'd want to save the configuration so that you don't have to keep on changing the `database.host` everytime you work with DataJoint. Simply run the following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config.save_local()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that this created `dj_local_conf.json` in the local directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load dj_local_conf.json\n", + "{\n", + " \"database.host\": \"datajoint.internationalbrainlab.org\",\n", + " \"database.password\": null,\n", + " \"database.user\": null,\n", + " \"database.port\": 3306,\n", + " \"database.reconnect\": true,\n", + " \"connection.init_function\": null,\n", + " \"connection.charset\": \"\",\n", + " \"loglevel\": \"INFO\",\n", + " \"safemode\": true,\n", + " \"fetch_format\": \"array\",\n", + " \"display.limit\": 12,\n", + " \"display.width\": 14,\n", + " \"display.show_tuple_count\": true\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inside the dj_local_conf.json, you will find saved dj.config information, and notice that database.host is set to the right value." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bonus: saving username and password into local config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although now you don't have to keep on specifying `database.host` inside `dj.config`, everytime DataJoint tries to connect to the database, it'll prompt you for your username and password. Although this maybe fine when working interactively, it can be rather limiting when you want a script to run without interaction. To get around this, you can also save your username and password into the configuration as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from getpass import getpass # use this to type password in without showing it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config['database.user'] = 'shan'\n", + "dj.config['database.password'] = getpass('Type password:')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test the connection with it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now save the username and password into the local config:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config.save_local()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/notebooks_tutorial/202102_workshop/1-Explore IBL data pipeline with DataJoint.ipynb b/notebooks/notebooks_tutorial/202102_workshop/1-Explore IBL data pipeline with DataJoint.ipynb new file mode 100644 index 00000000..5706a227 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/1-Explore IBL data pipeline with DataJoint.ipynb @@ -0,0 +1,780 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explore IBL data Pipeline\n", + "\n", + "Here we introduce some useful DataJoint tools to explore the IBL data pipeline\n", + "\n", + "First thing first, let's **import DataJoint and the IBL pipeline package**." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting dbadmin@datajoint-rds.cyuksi65nrdq.us-east-1.rds.amazonaws.com:3306\n", + "Connected to https://alyx.internationalbrainlab.org as vathes\n", + "Connected to https://alyx.internationalbrainlab.org as vathes\n", + "Connected to https://alyx.internationalbrainlab.org as vathes\n" + ] + } + ], + "source": [ + "import datajoint as dj\n", + "from ibl_pipeline import reference, subject, action, acquisition, data, behavior, ephys, histology\n", + "from ibl_pipeline.analyses import behavior as behavior_analyses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Browse schemas you have access to - `dj.list_schemas()`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['datajoint_monitoring',\n", + " 'group_shared_anneurai_analyses',\n", + " 'group_shared_anneurai_psytrack',\n", + " 'group_shared_end_criteria',\n", + " 'group_shared_ephys',\n", + " 'group_shared_sfndata',\n", + " 'group_shared_testing',\n", + " 'group_shared_wheel',\n", + " 'group_shared_wheel_moves',\n", + " 'ibl_acquisition',\n", + " 'ibl_action',\n", + " 'ibl_alyxraw',\n", + " 'ibl_analyses_behavior',\n", + " 'ibl_analyses_ephys',\n", + " 'ibl_behavior',\n", + " 'ibl_data',\n", + " 'ibl_debug',\n", + " 'ibl_dj_acquisition',\n", + " 'ibl_dj_action',\n", + " 'ibl_dj_behavior',\n", + " 'ibl_dj_data',\n", + " 'ibl_dj_reference',\n", + " 'ibl_dj_subject',\n", + " 'ibl_ephys',\n", + " 'ibl_histology',\n", + " 'ibl_ingest_acquisition',\n", + " 'ibl_ingest_action',\n", + " 'ibl_ingest_data',\n", + " 'ibl_ingest_ephys',\n", + " 'ibl_ingest_histology',\n", + " 'ibl_ingest_job',\n", + " 'ibl_ingest_qc',\n", + " 'ibl_ingest_reference',\n", + " 'ibl_ingest_subject',\n", + " 'ibl_jaib1_tutorial',\n", + " 'ibl_patch',\n", + " 'ibl_plotting_behavior',\n", + " 'ibl_plotting_ephys',\n", + " 'ibl_public',\n", + " 'ibl_qc',\n", + " 'ibl_reference',\n", + " 'ibl_storage',\n", + " 'ibl_subject',\n", + " 'ibl_update',\n", + " 'innodb',\n", + " 'kenneth_test',\n", + " 'mysql',\n", + " 'performance_schema',\n", + " 'sys',\n", + " 'test',\n", + " 'test_group_shared_ephys',\n", + " 'test_group_shared_wheel',\n", + " 'test_ibl_acquisition',\n", + " 'test_ibl_action',\n", + " 'test_ibl_alyxraw',\n", + " 'test_ibl_analyses_behavior',\n", + " 'test_ibl_analyses_ephys',\n", + " 'test_ibl_behavior',\n", + " 'test_ibl_data',\n", + " 'test_ibl_ephys',\n", + " 'test_ibl_histology',\n", + " 'test_ibl_ingest_acquisition',\n", + " 'test_ibl_ingest_action',\n", + " 'test_ibl_ingest_data',\n", + " 'test_ibl_ingest_ephys',\n", + " 'test_ibl_ingest_histology',\n", + " 'test_ibl_ingest_job',\n", + " 'test_ibl_ingest_reference',\n", + " 'test_ibl_ingest_subject',\n", + " 'test_ibl_plotting_behavior',\n", + " 'test_ibl_plotting_ephys',\n", + " 'test_ibl_reference',\n", + " 'test_ibl_storage',\n", + " 'test_ibl_subject',\n", + " 'test_ibl_update',\n", + " 'test_mcg_ibl_ibl_acquisition',\n", + " 'test_mcg_ibl_ibl_action',\n", + " 'test_mcg_ibl_ibl_reference',\n", + " 'test_mcg_ibl_ibl_subject',\n", + " 'tmp',\n", + " 'update_ibl_alyxraw',\n", + " 'user_alejandro.pan_tutorial',\n", + " 'user_anneurai_aging',\n", + " 'user_anneurai_analyses',\n", + " 'user_anneurai_behavior',\n", + " 'user_anneurai_sessiongroup',\n", + " 'user_anneurai_tutorial',\n", + " 'user_chris_exttest',\n", + " 'user_chris_tutorial',\n", + " 'user_cskrasniak_tutorial',\n", + " 'user_eejd_tutorial',\n", + " 'user_ekb2154_tutorial',\n", + " 'user_eywalker_tutorial',\n", + " 'user_gaellechapuis_tutorial',\n", + " 'user_gercek_tutorial',\n", + " 'user_guido.meijer_tutorial',\n", + " 'user_guidomeijer_tutorial',\n", + " 'user_hernandomv_tutorial',\n", + " 'user_ineslaranjeira_tutorial',\n", + " 'user_jaib1_tutorial',\n", + " 'user_jeanpaulnc_tutorial',\n", + " 'user_jeanpaulnc_tutorial1',\n", + " 'user_karolinasocha_tutorial',\n", + " 'user_kushbanga_tutorial',\n", + " 'user_lacerbi_tutorial',\n", + " 'user_mayofaulkner_tutorial',\n", + " 'user_mileswells_end_criteria',\n", + " 'user_mileswells_test',\n", + " 'user_mileswells_trials_qc',\n", + " 'user_mileswells_wheel',\n", + " 'user_mileswells_wheel_new',\n", + " 'user_mw3323_tutorial',\n", + " 'user_nicco_tutorial',\n", + " 'user_njmiska_tutorial',\n", + " 'user_shan_test',\n", + " 'user_shan_tutorial',\n", + " 'user_tsukasar_test']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Major schemas: \n", + "\n", + "Meta data from **Alyx**: `ibl_reference`, `ibl_subject`, `ibl_action`, `ibl_acquisition`, `ibl_data`, `ibl_qc`\n", + "Imported data from **FlatIron**: `ibl_behavior`, `ibl_ephys`, `ibl_histology` \n", + "Computed analzyed results: `ibl_analyses_behavior` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Browse tables in a schema - `dj.Diagram`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Table tiers**: \n", + "Manual table: green box \n", + "Lookup table: gray box \n", + "Imported table: blue oval \n", + "Computed table: red circle \n", + "Part table: plain text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Dependencies**: \n", + "One-to-one primary: thick solid line \n", + "One-to-many primary: thin solid line \n", + "Secondary foreign key reference: dashed line\n", + "Renamed secondary foreign key references: orange dot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show tables in the whole schema" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(reference)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show diagram of arbitruary parts of the database" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(subject)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A combination of arbitruary tables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(subject.Subject) + dj.Diagram(subject.Line) + dj.Diagram(subject.SubjectLab) + dj.Diagram(subject.SubjectProject)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tables could be from different schemas:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(subject.Subject) + dj.Diagram(acquisition.Session)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Getting the detailed definition of a table - `table.describe()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Browsing of data - queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Query all subjects" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Restriction `&`: filtering data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query one subject" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# restrict by string\n", + "subject.Subject & 'subject_nickname=\"ibl_witten_10\"'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# restrict by dictionary\n", + "from uuid import UUID\n", + "subject.Subject & {'subject_uuid': UUID('00c60db3-74c3-4ee2-9df9-2c84acf84e92')}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & {'sex': 'm'}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query subjects born after a date" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & 'subject_birth_date > \"2019-01-01\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: subjects within a range of dates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & 'subject_birth_date between \"2019-01-01\" and \"2019-04-01\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query subjects on multiple attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & 'subject_birth_date > \"2019-01-01\"' & 'sex=\"M\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query subjects restricted by other tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subjects with Ephys recording\n", + "subject.Subject & ephys.ProbeInsertion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subjects without Ephys recording\n", + "subject.Subject - ephys.ProbeInsertion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Join `*`: gather information from different tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject * acquisition.Session" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Projection `.proj()`: focus on attributes of interest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.proj()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.proj('subject_birth_date', 'sex')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### rename attribute with ***proj()***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.proj('sex', dob='subject_birth_date')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### perform simple computations with ***proj***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 1: Get date of a session:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sessions_with_date = acquisition.Session.proj(session_date='date(session_start_time)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sessions_with_date" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 2: Age of the animal when performing each session?**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First get the date of birth and the session date into the same query\n", + "q = subject.Subject * acquisition.Session" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Then compute the age\n", + "q_with_age = q.proj(age='datediff(session_start_time, subject_birth_date)')\n", + "q_with_age" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aggregation `.aggr()`: simple computation of one table against another table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example: how many sessions does each subject do so far?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.aggr(acquisition.Session, 'subject_nickname', n='count(*)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Fetching data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fetch all fields: `fetch()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch all data from a table\n", + "subjs = subject.Subject.fetch()\n", + "subjs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subjs['subject_uuid']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subjs['subject_birth_date']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch as a list of dictionaries\n", + "subjs_dict = subject.Subject.fetch(as_dict=True)\n", + "subjs_dict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch as pandas dataframe\n", + "subjs_df = subject.Subject.fetch(format='frame')\n", + "subjs_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch the primary key\n", + "pk = subject.Subject.fetch('KEY')\n", + "pk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch specific attributes\n", + "dob, sex = subject.Subject.fetch('subject_birth_date', 'sex')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "info = subject.Subject.fetch('subject_birth_date', 'sex', as_dict=True)\n", + "info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## fetch data only from one entry: `fetch1`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ibl_witten_10 = (subject.Subject & {'subject_nickname': 'ibl_witten_10'}).fetch1('KEY') # \"fetch1()\" because we know there's only one" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ibl_witten_10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IBL_10 = (subject.Subject & {'subject_nickname': 'IBL_10'}).fetch1()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IBL_10" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ibl_dj = dj.create_virtual_module('ibl_dj', 'ibl')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_tutorial/202102_workshop/2-Analyze data with IBL pipeline and save results.ipynb b/notebooks/notebooks_tutorial/202102_workshop/2-Analyze data with IBL pipeline and save results.ipynb new file mode 100644 index 00000000..f06e6eca --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/2-Analyze data with IBL pipeline and save results.ipynb @@ -0,0 +1,725 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Here we would like to perform some analysis with the IBL pipeline.\n", + "\n", + "First thing first, let's **import the IBL pipeline package**, and a few other useful packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ibl_pipeline import subject, acquisition, action, behavior, reference, ephys\n", + "import datajoint as dj\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from uuid import UUID\n", + "import datetime\n", + "dj.config['safemode'] = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyzing existing data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A simple example: compute the firing rate of each cluster across one session**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a quick look of the ephys schema:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(ephys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many ephys sessions do we have?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acquisition.Session & ephys.ProbeInsertion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many ephys sessions with clustering results?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In IBL pipeline, we saved our clustering results in the tabble DefaultCluster. This is the up-to-date version of the clustering results. Entries in this table will reflect manual curation results as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acquisition.Session & ephys.DefaultCluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's then pick one ephys probe insertion from cortexlab to focus on" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# limit=1 makes fetch return the first entry in the query\n", + "probe_insertion = (ephys.ProbeInsertion & (acquisition.Session & 'session_lab=\"cortexlab\"')).fetch('KEY', limit=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "probe_insertion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An overview of clusters for this probe insertion:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.DefaultCluster & probe_insertion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pick one cluster:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cluster = ephys.DefaultCluster & probe_insertion & 'cluster_id=0'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What do we need to compute the firing rate of each cluster? \n", + "1. Total spike number \n", + "2. Recording time length" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Total spike number\n", + "spk_times = cluster.fetch1('cluster_spikes_times')\n", + "total_spk_num = len(spk_times)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Session duration\n", + "session_duration = (acquisition.Session & cluster).proj(\n", + " session_duration='session_end_time - session_start_time').fetch1('session_duration')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compute firing rate \n", + "fr = total_spk_num/session_duration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cool! We got the firing rate! \n", + "The next question is, how do we save it in the database? \n", + "Put the entry in a table!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create your own schema and tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first thing we would like to do is to create a schema with `dj.schema`. \n", + "**Note**: the schema name you create has to either start with `user_{your user name}`, which is only accessible by you, or start with `group_share_`, which is accessible by the entire group. Here we use our user_name \n", + "**Note 2**: if your user_name contains a `.`, such as `miles.wells`, please delete it (`mileswells`) when creating the table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schema = dj.schema('user_shan_tutorial')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check if the new schema is there:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a **manual** table to save the firing rate result. \n", + "A class created with DataJoint correponds to a table in the database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@schema\n", + "class FiringRateManual(dj.Manual):\n", + " definition = \"\"\"\n", + " -> ephys.DefaultCluster # Each cluster has a firing rate\n", + " ---\n", + " firing_rate: float # Hz\n", + " \"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the brand-new table we just created." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateManual()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes, sure, it's emtpy. We haven't inserted anything into it. \n", + "Now let's insert the firing rate we just computed into this empty table. \n", + "We need to insert the entry with all fields defined in the table, usually in a format of dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# firing rate entry needs to inherit all primary keys from ephys.Cluster\n", + "cluster_key = cluster.fetch1('KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cluster_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "firing_rate_entry = dict(\n", + " **cluster_key,\n", + " firing_rate=fr,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "firing_rate_entry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now insert it!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateManual.insert1(firing_rate_entry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the table again to see what happened:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateManual()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cool the entry is there!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we can of course write a for loop to compute all fr and insert them one by one, but that's too slow. We can compute the results and insert them all at once!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# loop through the first 30 clusters and insert one by one, and compute time\n", + "import time\n", + "start_time = time.time()\n", + "\n", + "for icluster in (ephys.DefaultCluster & 'cluster_id between 1 and 30' & probe_insertion).fetch('KEY'):\n", + " spk_times = (ephys.DefaultCluster & icluster).fetch1('cluster_spikes_times')\n", + " fr_entry = dict(**icluster,\n", + " firing_rate=len(spk_times)/session_duration)\n", + " FiringRateManual.insert1(fr_entry)\n", + " \n", + "print(\"--- %s seconds ---\" % (time.time() - start_time))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# loop through the next 30 clusters and insert all at once as a list of dictionaries!\n", + "start_time = time.time()\n", + "\n", + "fr_entries = []\n", + "for icluster in (ephys.DefaultCluster & 'cluster_id between 31 and 60' & probe_insertion).fetch('KEY'):\n", + " spk_times = (ephys.DefaultCluster & icluster).fetch1('cluster_spikes_times')\n", + " fr_entry = dict(**icluster,\n", + " firing_rate=len(spk_times)/session_duration)\n", + " fr_entries.append(fr_entry)\n", + " \n", + "FiringRateManual.insert(fr_entries)\n", + "print(\"--- %s seconds ---\" % (time.time() - start_time))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this way, we will need to remember which clusters has been computed and inserted. If we insert the same entry twice, there will be an error. For example, let's rerun the above cell. We can overcome that problem by add the argument `skip_duplicates=True` inside `.insert()` or `.insert1()`, but it is not a very elegant solution. \n", + "The best approach here is to use a **Computed** table, it has the exact definition as the previous manual table, but with a magic **make** function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@schema\n", + "class FiringRateComputed(dj.Computed):\n", + " definition = \"\"\"\n", + " -> ephys.DefaultCluster # Each cluster has a firing rate\n", + " ---\n", + " firing_rate: float # Hz\n", + " \"\"\"\n", + " def make(self, key):\n", + " session_duration = (acquisition.Session & key).proj(\n", + " session_duration='session_end_time - session_start_time').fetch1('session_duration')\n", + " \n", + " spk_times = (ephys.DefaultCluster & key).fetch1('cluster_spikes_times')\n", + " firing_rate_entry = dict(**key, firing_rate=len(spk_times)/session_duration)\n", + " self.insert1(firing_rate_entry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can `populate` the table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputed().drop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "probe_insertion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Here the first argument provides some restrictions on the entries to populate, \n", + "# display_progress=True shows the progress,\n", + "# suppress_errors=True keeps the jobs running even if there are errors \n", + "FiringRateComputed.populate(probe_insertion, display_progress=True, suppress_errors=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputed()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**What does `populate` do?** \n", + "\n", + "It does two major things: \n", + "1. From the table definition, get the keys that needs to computed, which we called `key_source`. By default, it would be the join result of the primary dependent tables minus the once has been computed. \n", + "2. Call `make` function defined in the class, and compute one by one, with each individual key from the `key_source`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we still have to insert one by one, which is a bit slow. How do we do the trick of insert all firing rate of clusters in one session together?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can change the `key_source` by redefining it to a larger scale" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@schema\n", + "class FiringRateComputedFromInsertion(dj.Computed):\n", + " definition = \"\"\"\n", + " -> ephys.DefaultCluster # Each cluster has a firing rate\n", + " ---\n", + " firing_rate: float # Hz\n", + " \"\"\"\n", + " key_source = ephys.ProbeInsertion & ephys.DefaultCluster # populate for each ephys data set where clustering is available.\n", + " \n", + " def make(self, key): # the key here is now the primary key of ephys.Ephys, instead of ephys.Cluster\n", + " session_duration = (acquisition.Session & key).proj(\n", + " session_duration='session_end_time - session_start_time').fetch1('session_duration')\n", + " \n", + " fr_entries = []\n", + " for icluster in (ephys.DefaultCluster & key).fetch('KEY'):\n", + " try:\n", + " spk_times = (ephys.DefaultCluster & icluster).fetch1('cluster_spikes_times')\n", + " fr_entry = dict(**icluster,\n", + " firing_rate=len(spk_times)/session_duration)\n", + " fr_entries.append(fr_entry)\n", + " except Exception as e:\n", + " print(e)\n", + " continue\n", + " \n", + " self.insert(fr_entries)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputedFromInsertion.populate(probe_insertion, display_progress=True, suppress_errors=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compared to inserting each cluster one by one, inserting on the probe insertion level accelerate the processing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Delete entries and drop a table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(FiringRateManual & 'cluster_id=0').delete() # any restrictor would work here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateManual.drop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputed.drop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputedFromInsertion.drop()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus: How to work with data where you don't have the code to generate the class?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `dj.create_virtual_module`, you'll be able to reconstruct the virtual module to access the tables in a schema." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# the first argument is the `__name__` of this module, the second argument is the schema name.\n", + "anne_analyses = dj.create_virtual_module('analyses', 'group_shared_anneurai_analyses')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you could check the Diagram of this module" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(anne_analyses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or do any query you want:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "anne_analyses.Age()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2c3cc0aeb28f96d73ec58f5562b189bf03e6a61e Mon Sep 17 00:00:00 2001 From: shenshan Date: Thu, 18 Feb 2021 08:22:38 +0000 Subject: [PATCH 19/34] Notebooks for workshop --- .../00-Get DataJoint Ready.ipynb | 228 +++++ ...ore IBL data pipeline with DataJoint.ipynb | 780 ++++++++++++++++++ .../02-Plot Psychometric curve.ipynb | 412 +++++++++ .../03-ephys and histology pipelines.ipynb | 630 ++++++++++++++ .../202102_workshop/04-QC tables.ipynb | 183 ++++ ...a with IBL pipeline and save results.ipynb | 725 ++++++++++++++++ 6 files changed, 2958 insertions(+) create mode 100644 notebooks/notebooks_tutorial/202102_workshop/00-Get DataJoint Ready.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_workshop/02-Plot Psychometric curve.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_workshop/03-ephys and histology pipelines.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_workshop/04-QC tables.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_workshop/05-Analyze data with IBL pipeline and save results.ipynb diff --git a/notebooks/notebooks_tutorial/202102_workshop/00-Get DataJoint Ready.ipynb b/notebooks/notebooks_tutorial/202102_workshop/00-Get DataJoint Ready.ipynb new file mode 100644 index 00000000..29e181f3 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/00-Get DataJoint Ready.ipynb @@ -0,0 +1,228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuring DataJoint\n", + "\n", + "Before you can start using anything with DataJoint, you need to configure DataJoint. Installing `ibl-pipeline` packaged automatically install the latest DataJoint with it. In order for DataJoint to work, you need to tell it informatoin about the database connection, namely the database hostname." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start by importing the package. Convention is to import it as `dj`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that `database.host` is already set to `datajoint.internationalbrainlab.org`. If not, we can manually set it by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config['database.host'] = 'datajoint.internationalbrainlab.org'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are pointing to the right place, let's try connecting. You can explicitly trigger a connection with `dj.conn()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you verify that the connection is working, you'd want to save the configuration so that you don't have to keep on changing the `database.host` everytime you work with DataJoint. Simply run the following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config.save_local()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that this created `dj_local_conf.json` in the local directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load dj_local_conf.json\n", + "{\n", + " \"database.host\": \"datajoint.internationalbrainlab.org\",\n", + " \"database.password\": null,\n", + " \"database.user\": null,\n", + " \"database.port\": 3306,\n", + " \"database.reconnect\": true,\n", + " \"connection.init_function\": null,\n", + " \"connection.charset\": \"\",\n", + " \"loglevel\": \"INFO\",\n", + " \"safemode\": true,\n", + " \"fetch_format\": \"array\",\n", + " \"display.limit\": 12,\n", + " \"display.width\": 14,\n", + " \"display.show_tuple_count\": true\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inside the dj_local_conf.json, you will find saved dj.config information, and notice that database.host is set to the right value." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bonus: saving username and password into local config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although now you don't have to keep on specifying `database.host` inside `dj.config`, everytime DataJoint tries to connect to the database, it'll prompt you for your username and password. Although this maybe fine when working interactively, it can be rather limiting when you want a script to run without interaction. To get around this, you can also save your username and password into the configuration as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from getpass import getpass # use this to type password in without showing it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config['database.user'] = 'shan'\n", + "dj.config['database.password'] = getpass('Type password:')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test the connection with it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now save the username and password into the local config:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config.save_local()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb b/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb new file mode 100644 index 00000000..5706a227 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb @@ -0,0 +1,780 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explore IBL data Pipeline\n", + "\n", + "Here we introduce some useful DataJoint tools to explore the IBL data pipeline\n", + "\n", + "First thing first, let's **import DataJoint and the IBL pipeline package**." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting dbadmin@datajoint-rds.cyuksi65nrdq.us-east-1.rds.amazonaws.com:3306\n", + "Connected to https://alyx.internationalbrainlab.org as vathes\n", + "Connected to https://alyx.internationalbrainlab.org as vathes\n", + "Connected to https://alyx.internationalbrainlab.org as vathes\n" + ] + } + ], + "source": [ + "import datajoint as dj\n", + "from ibl_pipeline import reference, subject, action, acquisition, data, behavior, ephys, histology\n", + "from ibl_pipeline.analyses import behavior as behavior_analyses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Browse schemas you have access to - `dj.list_schemas()`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['datajoint_monitoring',\n", + " 'group_shared_anneurai_analyses',\n", + " 'group_shared_anneurai_psytrack',\n", + " 'group_shared_end_criteria',\n", + " 'group_shared_ephys',\n", + " 'group_shared_sfndata',\n", + " 'group_shared_testing',\n", + " 'group_shared_wheel',\n", + " 'group_shared_wheel_moves',\n", + " 'ibl_acquisition',\n", + " 'ibl_action',\n", + " 'ibl_alyxraw',\n", + " 'ibl_analyses_behavior',\n", + " 'ibl_analyses_ephys',\n", + " 'ibl_behavior',\n", + " 'ibl_data',\n", + " 'ibl_debug',\n", + " 'ibl_dj_acquisition',\n", + " 'ibl_dj_action',\n", + " 'ibl_dj_behavior',\n", + " 'ibl_dj_data',\n", + " 'ibl_dj_reference',\n", + " 'ibl_dj_subject',\n", + " 'ibl_ephys',\n", + " 'ibl_histology',\n", + " 'ibl_ingest_acquisition',\n", + " 'ibl_ingest_action',\n", + " 'ibl_ingest_data',\n", + " 'ibl_ingest_ephys',\n", + " 'ibl_ingest_histology',\n", + " 'ibl_ingest_job',\n", + " 'ibl_ingest_qc',\n", + " 'ibl_ingest_reference',\n", + " 'ibl_ingest_subject',\n", + " 'ibl_jaib1_tutorial',\n", + " 'ibl_patch',\n", + " 'ibl_plotting_behavior',\n", + " 'ibl_plotting_ephys',\n", + " 'ibl_public',\n", + " 'ibl_qc',\n", + " 'ibl_reference',\n", + " 'ibl_storage',\n", + " 'ibl_subject',\n", + " 'ibl_update',\n", + " 'innodb',\n", + " 'kenneth_test',\n", + " 'mysql',\n", + " 'performance_schema',\n", + " 'sys',\n", + " 'test',\n", + " 'test_group_shared_ephys',\n", + " 'test_group_shared_wheel',\n", + " 'test_ibl_acquisition',\n", + " 'test_ibl_action',\n", + " 'test_ibl_alyxraw',\n", + " 'test_ibl_analyses_behavior',\n", + " 'test_ibl_analyses_ephys',\n", + " 'test_ibl_behavior',\n", + " 'test_ibl_data',\n", + " 'test_ibl_ephys',\n", + " 'test_ibl_histology',\n", + " 'test_ibl_ingest_acquisition',\n", + " 'test_ibl_ingest_action',\n", + " 'test_ibl_ingest_data',\n", + " 'test_ibl_ingest_ephys',\n", + " 'test_ibl_ingest_histology',\n", + " 'test_ibl_ingest_job',\n", + " 'test_ibl_ingest_reference',\n", + " 'test_ibl_ingest_subject',\n", + " 'test_ibl_plotting_behavior',\n", + " 'test_ibl_plotting_ephys',\n", + " 'test_ibl_reference',\n", + " 'test_ibl_storage',\n", + " 'test_ibl_subject',\n", + " 'test_ibl_update',\n", + " 'test_mcg_ibl_ibl_acquisition',\n", + " 'test_mcg_ibl_ibl_action',\n", + " 'test_mcg_ibl_ibl_reference',\n", + " 'test_mcg_ibl_ibl_subject',\n", + " 'tmp',\n", + " 'update_ibl_alyxraw',\n", + " 'user_alejandro.pan_tutorial',\n", + " 'user_anneurai_aging',\n", + " 'user_anneurai_analyses',\n", + " 'user_anneurai_behavior',\n", + " 'user_anneurai_sessiongroup',\n", + " 'user_anneurai_tutorial',\n", + " 'user_chris_exttest',\n", + " 'user_chris_tutorial',\n", + " 'user_cskrasniak_tutorial',\n", + " 'user_eejd_tutorial',\n", + " 'user_ekb2154_tutorial',\n", + " 'user_eywalker_tutorial',\n", + " 'user_gaellechapuis_tutorial',\n", + " 'user_gercek_tutorial',\n", + " 'user_guido.meijer_tutorial',\n", + " 'user_guidomeijer_tutorial',\n", + " 'user_hernandomv_tutorial',\n", + " 'user_ineslaranjeira_tutorial',\n", + " 'user_jaib1_tutorial',\n", + " 'user_jeanpaulnc_tutorial',\n", + " 'user_jeanpaulnc_tutorial1',\n", + " 'user_karolinasocha_tutorial',\n", + " 'user_kushbanga_tutorial',\n", + " 'user_lacerbi_tutorial',\n", + " 'user_mayofaulkner_tutorial',\n", + " 'user_mileswells_end_criteria',\n", + " 'user_mileswells_test',\n", + " 'user_mileswells_trials_qc',\n", + " 'user_mileswells_wheel',\n", + " 'user_mileswells_wheel_new',\n", + " 'user_mw3323_tutorial',\n", + " 'user_nicco_tutorial',\n", + " 'user_njmiska_tutorial',\n", + " 'user_shan_test',\n", + " 'user_shan_tutorial',\n", + " 'user_tsukasar_test']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Major schemas: \n", + "\n", + "Meta data from **Alyx**: `ibl_reference`, `ibl_subject`, `ibl_action`, `ibl_acquisition`, `ibl_data`, `ibl_qc`\n", + "Imported data from **FlatIron**: `ibl_behavior`, `ibl_ephys`, `ibl_histology` \n", + "Computed analzyed results: `ibl_analyses_behavior` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Browse tables in a schema - `dj.Diagram`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Table tiers**: \n", + "Manual table: green box \n", + "Lookup table: gray box \n", + "Imported table: blue oval \n", + "Computed table: red circle \n", + "Part table: plain text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Dependencies**: \n", + "One-to-one primary: thick solid line \n", + "One-to-many primary: thin solid line \n", + "Secondary foreign key reference: dashed line\n", + "Renamed secondary foreign key references: orange dot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show tables in the whole schema" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(reference)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show diagram of arbitruary parts of the database" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(subject)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A combination of arbitruary tables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(subject.Subject) + dj.Diagram(subject.Line) + dj.Diagram(subject.SubjectLab) + dj.Diagram(subject.SubjectProject)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tables could be from different schemas:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(subject.Subject) + dj.Diagram(acquisition.Session)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Getting the detailed definition of a table - `table.describe()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Browsing of data - queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Query all subjects" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Restriction `&`: filtering data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query one subject" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# restrict by string\n", + "subject.Subject & 'subject_nickname=\"ibl_witten_10\"'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# restrict by dictionary\n", + "from uuid import UUID\n", + "subject.Subject & {'subject_uuid': UUID('00c60db3-74c3-4ee2-9df9-2c84acf84e92')}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & {'sex': 'm'}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query subjects born after a date" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & 'subject_birth_date > \"2019-01-01\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: subjects within a range of dates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & 'subject_birth_date between \"2019-01-01\" and \"2019-04-01\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query subjects on multiple attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & 'subject_birth_date > \"2019-01-01\"' & 'sex=\"M\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query subjects restricted by other tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subjects with Ephys recording\n", + "subject.Subject & ephys.ProbeInsertion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subjects without Ephys recording\n", + "subject.Subject - ephys.ProbeInsertion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Join `*`: gather information from different tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject * acquisition.Session" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Projection `.proj()`: focus on attributes of interest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.proj()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.proj('subject_birth_date', 'sex')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### rename attribute with ***proj()***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.proj('sex', dob='subject_birth_date')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### perform simple computations with ***proj***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 1: Get date of a session:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sessions_with_date = acquisition.Session.proj(session_date='date(session_start_time)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sessions_with_date" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 2: Age of the animal when performing each session?**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First get the date of birth and the session date into the same query\n", + "q = subject.Subject * acquisition.Session" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Then compute the age\n", + "q_with_age = q.proj(age='datediff(session_start_time, subject_birth_date)')\n", + "q_with_age" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aggregation `.aggr()`: simple computation of one table against another table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example: how many sessions does each subject do so far?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.aggr(acquisition.Session, 'subject_nickname', n='count(*)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Fetching data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fetch all fields: `fetch()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch all data from a table\n", + "subjs = subject.Subject.fetch()\n", + "subjs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subjs['subject_uuid']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subjs['subject_birth_date']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch as a list of dictionaries\n", + "subjs_dict = subject.Subject.fetch(as_dict=True)\n", + "subjs_dict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch as pandas dataframe\n", + "subjs_df = subject.Subject.fetch(format='frame')\n", + "subjs_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch the primary key\n", + "pk = subject.Subject.fetch('KEY')\n", + "pk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch specific attributes\n", + "dob, sex = subject.Subject.fetch('subject_birth_date', 'sex')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "info = subject.Subject.fetch('subject_birth_date', 'sex', as_dict=True)\n", + "info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## fetch data only from one entry: `fetch1`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ibl_witten_10 = (subject.Subject & {'subject_nickname': 'ibl_witten_10'}).fetch1('KEY') # \"fetch1()\" because we know there's only one" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ibl_witten_10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IBL_10 = (subject.Subject & {'subject_nickname': 'IBL_10'}).fetch1()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IBL_10" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ibl_dj = dj.create_virtual_module('ibl_dj', 'ibl')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_tutorial/202102_workshop/02-Plot Psychometric curve.ipynb b/notebooks/notebooks_tutorial/202102_workshop/02-Plot Psychometric curve.ipynb new file mode 100644 index 00000000..90b8e8fb --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/02-Plot Psychometric curve.ipynb @@ -0,0 +1,412 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook walks you through the main schemas and tables of the IBL behavior pipeline, and show you how to plot psychometric curves with data from the database:\n", + "\n", + "1. Fetch trial data and compute psychometric function yourself\n", + "2. Fetch the pre-computed fit results and original data and plot directly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Behavior tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a list of major tables in schema `ibl_behavior`: \n", + "\n", + "* TrialSet: Trial collection of a behavioral session.\n", + "* TrialSet.Trial: data of each individual trial in a TrialSet. Contains all the information in the ALF files `trials.*`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Behavior analyses tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a list of major tables in schema `ibl_analyses_behavior`: \n", + "\n", + "* PsychResults: fit parameters of the entire TrialSet (Session). \n", + "* PsychResultsBlock: fit parameters of each block with different probability left.\n", + "* ReactionTime: median reaction time for the entire TrialSet (Session)\n", + "* ReactionTimeContrastBlock: median reaction time as a function of contrast, for each block with different probability left.\n", + "* BehavioralSummaryByDate: results achieved with all trials of the day\n", + ">* PsychResults: fit parameters of each probability left. \n", + ">* ReactionTimeContrast: For each probability left, reaction time of each contrast.\n", + ">* ReactionTimeByDate: median reaction time of the date.\n", + "* TrainingStatus: look up table for available status, 'untrainable', 'unbiasable', 'in_training', 'trained_1a', 'trained_1b', 'ready4ephysrig', 'ready4delay', 'ready4recording' \n", + "* SessionTrainingStatus: training status of each session\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot Psychometric curves" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we first import some modules from the ibl pipeline and some other modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# behavior data\n", + "from ibl_pipeline import behavior\n", + "\n", + "# analyzed result of behavioral data\n", + "from ibl_pipeline.analyses import behavior as behavior_analyses\n", + "\n", + "# meta information of the subject and session\n", + "from ibl_pipeline import subject, acquisition\n", + "\n", + "# Function to perform the model fits of the psychometric function\n", + "from ibl_pipeline.utils import psychofit as psy\n", + "\n", + "# some regular modules\n", + "import numpy as np\n", + "import datetime\n", + "import seaborn as sns \n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fetch data from behavior.TrialSet.Trial table and compute psych curve yourself" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a quick look at the table `behavior.TrialSet`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The TrialSet table stores some summary statistics in one session of behavioral experiment. To also show the information of the subject, we could join the table with subject related tables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet * subject.Subject * subject.SubjectLab * subject.SubjectProject" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could restrict to one session by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "q = behavior.TrialSet * subject.Subject * subject.SubjectLab * subject.SubjectProject & \\\n", + " 'subject_nickname=\"CSHL_015\"' & {'session_start_time': datetime.datetime(2019, 9, 16, 13, 44, 46)}\n", + "q" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The trial-by-trial information are shown in a **part table** `behavior.TrialSet.Trial`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could check the documentation of each of the column with `describe()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet.Trial.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To fetch some part of data, we could do use the fetch method: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example we would like to see the stimulus contrasts, and the animal choices in one session (that we already queried and saved the results as q), we could do:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we could fetch the fields as a list of dictionary, only for the trials with a choice\n", + "data = (behavior.TrialSet.Trial & q & 'trial_response_choice !=\"No Go\"').fetch(\n", + " 'trial_stim_contrast_left', 'trial_stim_contrast_right', 'trial_response_choice', as_dict=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We could then convert the data to a dataframe for further analyses\n", + "import pandas as pd\n", + "df = pd.DataFrame(data)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can do some computation here to explore the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first compute the signed contrast, so that the contrasts on the right are positive" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['signed_contrast'] = df['trial_stim_contrast_right'] - df['trial_stim_contrast_left']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['report_right'] = df['trial_response_choice'] == \"CCW\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "report_right = df.groupby(['signed_contrast'], as_index=False).mean()\n", + "report_right" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then let's plot the psychometric curve: prob_report_right vs signed_contrast:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(report_right['signed_contrast'], report_right['report_right'], 'o')\n", + "plt.xlabel('Signed Contrast')\n", + "plt.ylabel('Probability reporting right')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Psychometric curve with pre-computed tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we could plot the psychometric curves with results directly fetched from the pre-computed tables. The fit results of the psychometric curve are saved in the table `behavior_analyses.PsychResultsBlock`, we can browse entries in the table for a particular subject, the prob_left_block marks which prior of probability left have been used in the block" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q = behavior_analyses.PsychResultsBlock & (subject.Subject & 'subject_nickname=\"CSHL_015\"')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the results of sessions collected after 2019-09-15, by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q & 'session_start_time > \"2019-09-15\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the psychometric curve of the last session on 9/17, containing three blocks with `prob_left` 0.2, 0.5 and 0.8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "psych_results = q & {'session_start_time': datetime.datetime(2019, 9, 16, 13, 44, 46)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's fetch the fit parameters and behavioral statistics from the table as a list of dictionaries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dict_results = psych_results.fetch(\n", + " 'signed_contrasts', 'prob_choose_right', 'n_trials_stim', 'n_trials_stim_right',\n", + " 'threshold', 'bias', 'lapse_low', 'lapse_high', as_dict=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next let's loop through the blocks and plot the psychometric curves:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "colors = [[1., 0., 0.], [0., 0., 0.], [0., 0., 1.]]\n", + "for result, color in zip(dict_results, colors):\n", + " pars = [result['bias'], result['threshold'], result['lapse_low'], result['lapse_high']]\n", + " contrasts = result['signed_contrasts'] * 100\n", + " contrasts_fit = np.arange(-100, 100)\n", + " prob_right_fit = psy.erf_psycho_2gammas(pars, contrasts_fit) *100\n", + " sns.lineplot(contrasts_fit, prob_right_fit, color=color)\n", + " sns.lineplot(x=contrasts, y=result['prob_choose_right']*100, err_style=\"bars\", linewidth=0, linestyle='None', mew=0.5,\n", + " marker='.', ci=68, color=color)\n", + "\n", + "plt.gca().set_xlabel('Signed Contrast (%)')\n", + "plt.gca().set_ylabel('Rightward Choice (%)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_tutorial/202102_workshop/03-ephys and histology pipelines.ipynb b/notebooks/notebooks_tutorial/202102_workshop/03-ephys and histology pipelines.ipynb new file mode 100644 index 00000000..3394fe33 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/03-ephys and histology pipelines.ipynb @@ -0,0 +1,630 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook will walk you through the important tables in ephys and histology schemas and introduce some useful queries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import datajoint and modules from ibl_pipeline\n", + "import datajoint as dj\n", + "from ibl_pipeline import reference, subject, acquisition, behavior\n", + "from ibl_pipeline.analyses import behavior as behavior_analyses\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ephys and histology tables are still in active development. We therefore recommend accessing them with `dj.create_virtual_module()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys = dj.create_virtual_module('ephys', 'ibl_ephys')\n", + "histology = dj.create_virtual_module('histology', 'ibl_histology')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ephys tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(ephys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the diagram, tables not represented as a class are the ones that were the leftovers during development. We will clean these tables up once in a while and you can ignore them for the moment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a list of important tables:\n", + " \n", + ">* ProbeModel: model of a probe, ingested from the alyx table experiments.probemodel \n", + ">* ProbeInsertion: ingested from the alyx table experiments.probeinsertion \n", + ">* ChannelGroup: raw index and local coordinates of each channel group\n", + ">* DefaultCluster: Cluster properties achieved from the default clustering method.\n", + ">* DefaultCluster.Metrics: metrics exported from the spike sorting softwares.\n", + ">* DefaultCluster.Metric: same contents as Metrics, each metric is a separate entry (metric_name, metric_value), to support filtering on each of the metrics. \n", + ">* DefaultCluster.Ks2Label: label given by kilosort2, ‘good’ or ‘mua’\n", + ">* GoodClusterCriterion: Criterion to identify whether a cluster is good.\n", + ">* GoodCluster: whether a cluster is good based on the criterion defined in GoodClusterCriterion\n", + ">* Event: Different behavioral events, including 'go cue', 'stim on', 'response', 'feedback', and 'movement'\n", + ">* AlignedTrialSpikes: spike times of each trial aligned to different events\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Detailed table definitions could be easily checked with the method `describe()`, for example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.DefaultCluster.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`blob@ephys` refers to a blob field in external storage, but as a user, you will feel that the field is similar as an internal blob field. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Preview the contents of the table:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.DefaultCluster()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Histology tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(histology) + ephys.DefaultCluster + ephys.ProbeInsertion + acquisition.Session" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a list of important histology tables:\n", + "\n", + ">* ProbeTrajectory: the final probe trajectory with multiple users' approval, ingested from FlatIron data.\n", + ">* ChannelBrainLocation: the final brain location assignment of each channel, ingested from FlatIron data.\n", + ">* ClusterBrainRegion: the final brain region assignment of each cluster, based on ChannelBrainLocation data on Flatiron\n", + "\n", + "\n", + "Before the final trajectory is resolved, intermediate histological results are saved in the following tables:\n", + "\n", + ">* Provenance: method to estimate the probe trajectory, including Ephys aligned histology track, Histology track, Micro-manipulator, and Planned \n", + ">* ProbeTrajectoryTemp: probe trajectory estimated with each method, ingested from Alyx table experiments.probetrajectory \n", + ">* ChannelBrainLocationTemp: brain coordinates and region assignment of each channel, ingested from Alyx table experiments.channel \n", + ">* ClusterBrainRegionTemp: Brain region assignment to each cluster \n", + ">* ProbeBrainRegionTemp: Brain regions assignment to each probe, including the regions of finest granularity and their upper-level areas. \n", + ">* DepthBrainRegionTemp: For each ProbeTrajectoryTemp, assign depth boundaries relative to the probe tip to each brain region covered by the trajectory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Useful queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Select clusters from a particular session" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# which sessions have cluster data?\n", + "acquisition.Session & ephys.DefaultCluster" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch the key of one of them\n", + "key = (acquisition.Session & ephys.DefaultCluster).fetch('KEY', limit=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get all clusters of one session\n", + "ephys.DefaultCluster & key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch spike times, takes a little while\n", + "clusters_spikes_times = (ephys.DefaultCluster & key).fetch('cluster_spikes_times')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter clusters with particular metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are three part tables of `ephys.DefaultCluster` that stores information of Metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.DefaultCluster.Metrics.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since metrics is longblob and we cannot query on the metrics. \n", + "Therefore, we created another table ephys.DefaultCluster.Metric to store the values of individual field of the metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.DefaultCluster.Metric()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check what are the available metric names, we could use `dj.U()`. \n", + "\n", + "dj.U('field_name') is a uniform set of all possible values of a `field_name`, which is very useful to get how many unique values of a field in a table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.U('metric_name') & ephys.DefaultCluster.Metric()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check [this document](https://docs.google.com/document/d/1ba_krsfm4epiAd0zbQ8hdvDN908P9VZOpTxkkH3P_ZY/edit) for the meaning of each metric." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's filter on some fields. For example, only keep clusters with firing_rate > 1 spks/sec:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.DefaultCluster & (ephys.DefaultCluster.Metric & 'metric_name=\"firing_rate\"' & 'metric_value>1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another example, presence_ratio > 0.5:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.DefaultCluster & (ephys.DefaultCluster.Metric & 'metric_name=\"presence_ratio\"' & 'metric_value > 0.5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the distributions with certain metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's visualize some metrics of a subset of clusters, for example, all clusters from cortexlab" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clusters_cortexlab = ephys.DefaultCluster & (acquisition.Session & 'session_lab=\"cortexlab\"')\n", + "clusters_cortexlab" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch the values of a metric for all these clusters, for example, the \"missed_spikes_est\"\n", + "values = (ephys.DefaultCluster.Metric & 'metric_name=\"missed_spikes_est\"').fetch('metric_value')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot histogram\n", + "import matplotlib.pyplot as plt\n", + "plt.hist(values, bins=100);\n", + "plt.xlabel('Missed spikes estimate');\n", + "plt.ylabel('Cluster counts');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter ephys data based on behavioral performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's address a common question of how to fetch ephys data from sessions with good performance and recorded from a particular brain region" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get sessions with performance > 80% on easy trials\n", + "good_performance_clusters = ephys.DefaultCluster & (behavior_analyses.PsychResults & 'performance_easy > 0.8')\n", + "good_performance_clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we further need to filter with brain regions, we'll need to bring in several histology related tables.\n", + "The assignment of the brain region of each cluster is in the table `histology.ClusterBrainRegionTemp`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "histology.ClusterBrainRegionTemp.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The brain region assignment depends on the Provenance:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "histology.Provenance()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The brain region assignment is usually only meaningful with the \"Ephys aligned histology track\", which has the highest provenance of 70." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "histology.ClusterBrainRegionTemp & 'provenance=70'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's figure out the acronym of the region of your interest, for example we would like to fetch from \"frontopolar cortex\", we could do a vague search in the table `reference.BrainRegion` with the `like` keyword" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fronto_pole = reference.BrainRegion() & 'brain_region_name like \"%front%\"'\n", + "fronto_pole" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Restrict the ClusterBrainRegion with these region entries and good performance clusters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "histology.ClusterBrainRegionTemp & 'provenance = 70' & fronto_pole & good_performance_clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute firing rate during a time period aligned to behavior event" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's introduce another table `ephys.AlignedTrialSpikes` that might be helpful for your exploration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.AlignedTrialSpikes.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Spike times of each cluster where cut into different trials, aligned to one of the following events:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.U('event') & ephys.AlignedTrialSpikes()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The table has many many entries, so we **do not** recommend loading the whole table. Some restrictions are necessary." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compute the firing rate of one cluster in a time window of 0-100ms relative to the stim on time of a trial:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn().connect()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First, pick a cluster, for example, a recent ephys session from cortexlab\n", + "clusters_recent_sessions = ephys.DefaultCluster & \\\n", + " (acquisition.Session & 'session_lab=\"angelakilab\"' & 'session_start_time > \"2020-08-01\"')\n", + "clusters_recent_sessions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Then get the key of the first cluster listed above\n", + "cluster = clusters_recent_sessions.fetch('KEY', limit=1)\n", + "cluster" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch all `trial_spike_times` aligned to stim on events, compute trial number\n", + "trials_spike_times = (ephys.AlignedTrialSpikes & cluster & 'event=\"stim on\"').fetch('trial_spike_times')\n", + "n_trials = len(trials_spike_times)\n", + "n_trials" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# stack spikes and only count the ones that are between 0 and 100. Remember that these spike times are pre-aligned\n", + "trials_spike_times_all = np.hstack(trials_spike_times)\n", + "spike_times_window = trials_spike_times_all[np.logical_and(trials_spike_times_all>0, trials_spike_times_all<0.1)]\n", + "spike_times_window" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# count the total spike numbers, divided by number of trials and the time window.\n", + "firing_rate = len(spike_times_window)/0.1/n_trials\n", + "firing_rate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we listed a few query examples related to ephys and histology schemas that might be helpful for your research. For a full fledged introduction of major types of queries and fetches, please refer to [this notebook](01-Tools%20to%20explore%20IBL%20data%20pipeline%20with%20DataJoint.ipynb).\n", + "\n", + "Note that ephys and histology are still under active development, so it is very likely that there are bugs in the data and pipeline. This tutorial hopefully will help the users to access the current datasets easier and could contribute to the detection and fixation of bugs." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_tutorial/202102_workshop/04-QC tables.ipynb b/notebooks/notebooks_tutorial/202102_workshop/04-QC tables.ipynb new file mode 100644 index 00000000..b918026d --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/04-QC tables.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook will walk you through the important tables in the qc schema and how to use them to filter the sessions you need" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import datajoint and modules from ibl_pipeline\n", + "import datajoint as dj\n", + "from ibl_pipeline import reference, subject, acquisition, behavior\n", + "from ibl_pipeline.analyses import behavior as behavior_analyses\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "QC tables are still in active development. We therefore recommend accessing them with `dj.create_virtual_module()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qc = dj.create_virtual_module('qc', 'ibl_qc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# QC tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(qc) + acquisition.Session" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This diagram shows the QC related tables, and here is the description of the tables." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + ">* QCChoice: Available flags to quantify the quality of a session or a specific aspect of a session, lookup table got referred in SessionQC and SessionExtendedQC, 50 for CRITICAL, 40 for FAIL, 30 for WARNING, 10 for PASS, and 0 for NOT SET \n", + ">* SessionQC: QCChoice for each session, ingested from alyx field `qc` in the table `actions.session` \n", + ">* QCType: Aspect of a session for quality check. e.g. task, behavior, experimenter… \n", + ">* SessionExtendedQC: QCChoice (e.g. FAIL) for a QCType (e.g. task) for each session, structured data about SessionQC \n", + ">* SessionExtendedQC.Field: Part table of SessionExtendedQC. For each entry of SessionExtendedQC, there may be multiple fields describing each value (e.g. 0.99) of a qc aspect (e.g. _task_stimOn_delays) that belongs to a QCType (e.g. task).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Detailed table definitions could be easily checked with the method `describe()`, for example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qc.QCChoice.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Preview the contents of the table:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qc.QCChoice()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Useful queries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sessions better than CRITICAL?\n", + "acquisition.Session & (qc.SessionQC & 'qc < 50')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sessions better than critical and also good enough for brainwide map?\n", + "\n", + "acquisition.Session & (qc.SessionQC & 'qc < 50') & \\\n", + "(behavior_analyses.SessionTrainingStatus & 'good_enough_for_brainwide_map')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sessions better than critical for task criteria?\n", + "\n", + "acquisition.Session & (qc.SessionExtendedQC & 'qc_type=\"task\"' & 'extended_qc < 40')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we listed a few query examples related to the qc schema that might be helpful for your research. For a full fledged introduction of major types of queries and fetches, please refer to [this notebook](01-Tools%20to%20explore%20IBL%20data%20pipeline%20with%20DataJoint.ipynb)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_tutorial/202102_workshop/05-Analyze data with IBL pipeline and save results.ipynb b/notebooks/notebooks_tutorial/202102_workshop/05-Analyze data with IBL pipeline and save results.ipynb new file mode 100644 index 00000000..5ae28893 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/05-Analyze data with IBL pipeline and save results.ipynb @@ -0,0 +1,725 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Here we would like to perform some analysis with the IBL pipeline.\n", + "\n", + "First thing first, let's **import the IBL pipeline package**, and a few other useful packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ibl_pipeline import subject, acquisition, action, behavior, reference, ephys\n", + "import datajoint as dj\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from uuid import UUID\n", + "import datetime\n", + "dj.config['safemode'] = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyzing existing data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A simple example: compute the firing rate of each cluster across one session**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a quick look of the ephys schema:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(ephys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many ephys sessions do we have?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acquisition.Session & ephys.ProbeInsertion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many ephys sessions with clustering results?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In IBL pipeline, we saved our clustering results in the tabble DefaultCluster. This is the up-to-date version of the clustering results. Entries in this table will reflect manual curation results as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acquisition.Session & ephys.DefaultCluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's then pick one ephys probe insertion from cortexlab to focus on" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# limit=1 makes fetch return the first entry in the query\n", + "probe_insertion = (ephys.ProbeInsertion & (acquisition.Session & 'session_lab=\"cortexlab\"')).fetch('KEY', limit=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "probe_insertion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An overview of clusters for this probe insertion:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ephys.DefaultCluster & probe_insertion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pick one cluster:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cluster = ephys.DefaultCluster & probe_insertion & 'cluster_id=0'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What do we need to compute the firing rate of each cluster? \n", + "1. Total spike number \n", + "2. Recording time length" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Total spike number\n", + "spk_times = cluster.fetch1('cluster_spikes_times')\n", + "total_spk_num = len(spk_times)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Session duration\n", + "session_duration = (acquisition.Session & cluster).proj(\n", + " session_duration='session_end_time - session_start_time').fetch1('session_duration')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compute firing rate \n", + "fr = total_spk_num/session_duration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cool! We got the firing rate! \n", + "The next question is, how do we save it in the database? \n", + "Put the entry in a table!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create your own schema and tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first thing we would like to do is to create a schema with `dj.schema`. \n", + "**Note**: the schema name you create has to either start with `user_{your user name}`, which is only accessible by you, or start with `group_share_`, which is accessible by the entire group. Here we use our user_name \n", + "**Note 2**: if your user_name contains a `.`, such as `miles.wells`, please delete it (`mileswells`) when creating the table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schema = dj.schema('user_shan_tutorial')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check if the new schema is there:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a **manual** table to save the firing rate result. \n", + "A class created with DataJoint correponds to a table in the database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@schema\n", + "class FiringRateManual(dj.Manual):\n", + " definition = \"\"\"\n", + " -> ephys.DefaultCluster # Each cluster has a firing rate\n", + " ---\n", + " firing_rate: float # Hz\n", + " \"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the brand-new table we just created." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateManual()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes, sure, it's emtpy. We haven't inserted anything into it. \n", + "Now let's insert the firing rate we just computed into this empty table. \n", + "We need to insert the entry with all fields defined in the table, usually in a format of dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# firing rate entry needs to inherit all primary keys from ephys.Cluster\n", + "cluster_key = cluster.fetch1('KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cluster_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "firing_rate_entry = dict(\n", + " **cluster_key,\n", + " firing_rate=fr,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "firing_rate_entry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now insert it!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateManual.insert1(firing_rate_entry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the table again to see what happened:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateManual()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cool the entry is there!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we can of course write a for loop to compute all fr and insert them one by one, but that's too slow. We can compute the results and insert them all at once!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# loop through the first 30 clusters and insert one by one, and compute time\n", + "import time\n", + "start_time = time.time()\n", + "\n", + "for icluster in (ephys.DefaultCluster & 'cluster_id between 1 and 30' & probe_insertion).fetch('KEY'):\n", + " spk_times = (ephys.DefaultCluster & icluster).fetch1('cluster_spikes_times')\n", + " fr_entry = dict(**icluster,\n", + " firing_rate=len(spk_times)/session_duration)\n", + " FiringRateManual.insert1(fr_entry)\n", + " \n", + "print(\"--- %s seconds ---\" % (time.time() - start_time))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# loop through the next 30 clusters and insert all at once as a list of dictionaries!\n", + "start_time = time.time()\n", + "\n", + "fr_entries = []\n", + "for icluster in (ephys.DefaultCluster & 'cluster_id between 31 and 60' & probe_insertion).fetch('KEY'):\n", + " spk_times = (ephys.DefaultCluster & icluster).fetch1('cluster_spikes_times')\n", + " fr_entry = dict(**icluster,\n", + " firing_rate=len(spk_times)/session_duration)\n", + " fr_entries.append(fr_entry)\n", + " \n", + "FiringRateManual.insert(fr_entries)\n", + "print(\"--- %s seconds ---\" % (time.time() - start_time))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this way, we will need to remember which clusters has been computed and inserted. If we insert the same entry twice, there will be an error. For example, let's rerun the above cell. We can overcome that problem by add the argument `skip_duplicates=True` inside `.insert()` or `.insert1()`, but it is not a very elegant solution. \n", + "The best approach here is to use a **Computed** table, it has the exact definition as the previous manual table, but with a magic **make** function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@schema\n", + "class FiringRateComputed(dj.Computed):\n", + " definition = \"\"\"\n", + " -> ephys.DefaultCluster # Each cluster has a firing rate\n", + " ---\n", + " firing_rate: float # Hz\n", + " \"\"\"\n", + " def make(self, key):\n", + " session_duration = (acquisition.Session & key).proj(\n", + " session_duration='session_end_time - session_start_time').fetch1('session_duration')\n", + " \n", + " spk_times = (ephys.DefaultCluster & key).fetch1('cluster_spikes_times')\n", + " firing_rate_entry = dict(**key, firing_rate=len(spk_times)/session_duration)\n", + " self.insert1(firing_rate_entry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can `populate` the table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputed().drop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "probe_insertion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Here the first argument provides some restrictions on the entries to populate, \n", + "# display_progress=True shows the progress,\n", + "# suppress_errors=True keeps the jobs running even if there are errors \n", + "FiringRateComputed.populate(probe_insertion, display_progress=True, suppress_errors=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputed()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**What does `populate` do?** \n", + "\n", + "It does two major things: \n", + "1. From the table definition, get the keys that needs to computed, which we called `key_source`. By default, it would be the join result of the primary dependent tables minus the once has been computed. \n", + "2. Call `make` function defined in the class, and compute one by one, with each individual key from the `key_source`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we still have to insert one by one, which is a bit slow. How do we do the trick of insert all firing rate of clusters in one session together?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can change the `key_source` by redefining it to a larger scale" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@schema\n", + "class FiringRateComputedFromInsertion(dj.Computed):\n", + " definition = \"\"\"\n", + " -> ephys.DefaultCluster # Each cluster has a firing rate\n", + " ---\n", + " firing_rate: float # Hz\n", + " \"\"\"\n", + " key_source = ephys.ProbeInsertion & ephys.DefaultCluster # populate for each ephys data set where clustering is available.\n", + " \n", + " def make(self, key): # the key here is now the primary key of ephys.Ephys, instead of ephys.Cluster\n", + " session_duration = (acquisition.Session & key).proj(\n", + " session_duration='session_end_time - session_start_time').fetch1('session_duration')\n", + " \n", + " fr_entries = []\n", + " for icluster in (ephys.DefaultCluster & key).fetch('KEY'):\n", + " try:\n", + " spk_times = (ephys.DefaultCluster & icluster).fetch1('cluster_spikes_times')\n", + " fr_entry = dict(**icluster,\n", + " firing_rate=len(spk_times)/session_duration)\n", + " fr_entries.append(fr_entry)\n", + " except Exception as e:\n", + " print(e)\n", + " continue\n", + " \n", + " self.insert(fr_entries)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputedFromInsertion.populate(probe_insertion, display_progress=True, suppress_errors=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compared to inserting each cluster one by one, inserting on the probe insertion level accelerate the processing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Delete entries and drop a table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(FiringRateManual & 'cluster_id=0').delete() # any restrictor would work here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateManual.drop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputed.drop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "FiringRateComputedFromInsertion.drop()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus: How to work with data where you don't have the code to generate the class?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `dj.create_virtual_module`, you'll be able to reconstruct the virtual module to access the tables in a schema." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# the first argument is the `__name__` of this module, the second argument is the schema name.\n", + "anne_analyses = dj.create_virtual_module('analyses', 'group_shared_anneurai_analyses')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you could check the Diagram of this module" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(anne_analyses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or do any query you want:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "anne_analyses.Age()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From dc92b6d64d4521afcb9e603a66f300f6a397b81a Mon Sep 17 00:00:00 2001 From: shenshan Date: Thu, 18 Feb 2021 09:00:32 +0000 Subject: [PATCH 20/34] remove redundant notebooks --- .../0-Get DataJoint Ready.ipynb | 228 ----- ...ore IBL data pipeline with DataJoint.ipynb | 780 ------------------ ...a with IBL pipeline and save results.ipynb | 725 ---------------- 3 files changed, 1733 deletions(-) delete mode 100644 notebooks/notebooks_tutorial/202102_workshop/0-Get DataJoint Ready.ipynb delete mode 100644 notebooks/notebooks_tutorial/202102_workshop/1-Explore IBL data pipeline with DataJoint.ipynb delete mode 100644 notebooks/notebooks_tutorial/202102_workshop/2-Analyze data with IBL pipeline and save results.ipynb diff --git a/notebooks/notebooks_tutorial/202102_workshop/0-Get DataJoint Ready.ipynb b/notebooks/notebooks_tutorial/202102_workshop/0-Get DataJoint Ready.ipynb deleted file mode 100644 index 29e181f3..00000000 --- a/notebooks/notebooks_tutorial/202102_workshop/0-Get DataJoint Ready.ipynb +++ /dev/null @@ -1,228 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Configuring DataJoint\n", - "\n", - "Before you can start using anything with DataJoint, you need to configure DataJoint. Installing `ibl-pipeline` packaged automatically install the latest DataJoint with it. In order for DataJoint to work, you need to tell it informatoin about the database connection, namely the database hostname." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Start by importing the package. Convention is to import it as `dj`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that `database.host` is already set to `datajoint.internationalbrainlab.org`. If not, we can manually set it by:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config['database.host'] = 'datajoint.internationalbrainlab.org'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are pointing to the right place, let's try connecting. You can explicitly trigger a connection with `dj.conn()`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.conn()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you verify that the connection is working, you'd want to save the configuration so that you don't have to keep on changing the `database.host` everytime you work with DataJoint. Simply run the following:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config.save_local()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that this created `dj_local_conf.json` in the local directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load dj_local_conf.json\n", - "{\n", - " \"database.host\": \"datajoint.internationalbrainlab.org\",\n", - " \"database.password\": null,\n", - " \"database.user\": null,\n", - " \"database.port\": 3306,\n", - " \"database.reconnect\": true,\n", - " \"connection.init_function\": null,\n", - " \"connection.charset\": \"\",\n", - " \"loglevel\": \"INFO\",\n", - " \"safemode\": true,\n", - " \"fetch_format\": \"array\",\n", - " \"display.limit\": 12,\n", - " \"display.width\": 14,\n", - " \"display.show_tuple_count\": true\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Inside the dj_local_conf.json, you will find saved dj.config information, and notice that database.host is set to the right value." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bonus: saving username and password into local config" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although now you don't have to keep on specifying `database.host` inside `dj.config`, everytime DataJoint tries to connect to the database, it'll prompt you for your username and password. Although this maybe fine when working interactively, it can be rather limiting when you want a script to run without interaction. To get around this, you can also save your username and password into the configuration as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from getpass import getpass # use this to type password in without showing it" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config['database.user'] = 'shan'\n", - "dj.config['database.password'] = getpass('Type password:')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Test the connection with it" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.conn()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now save the username and password into the local config:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config.save_local()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/notebooks_tutorial/202102_workshop/1-Explore IBL data pipeline with DataJoint.ipynb b/notebooks/notebooks_tutorial/202102_workshop/1-Explore IBL data pipeline with DataJoint.ipynb deleted file mode 100644 index 5706a227..00000000 --- a/notebooks/notebooks_tutorial/202102_workshop/1-Explore IBL data pipeline with DataJoint.ipynb +++ /dev/null @@ -1,780 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Explore IBL data Pipeline\n", - "\n", - "Here we introduce some useful DataJoint tools to explore the IBL data pipeline\n", - "\n", - "First thing first, let's **import DataJoint and the IBL pipeline package**." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting dbadmin@datajoint-rds.cyuksi65nrdq.us-east-1.rds.amazonaws.com:3306\n", - "Connected to https://alyx.internationalbrainlab.org as vathes\n", - "Connected to https://alyx.internationalbrainlab.org as vathes\n", - "Connected to https://alyx.internationalbrainlab.org as vathes\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "from ibl_pipeline import reference, subject, action, acquisition, data, behavior, ephys, histology\n", - "from ibl_pipeline.analyses import behavior as behavior_analyses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Browse schemas you have access to - `dj.list_schemas()`" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['datajoint_monitoring',\n", - " 'group_shared_anneurai_analyses',\n", - " 'group_shared_anneurai_psytrack',\n", - " 'group_shared_end_criteria',\n", - " 'group_shared_ephys',\n", - " 'group_shared_sfndata',\n", - " 'group_shared_testing',\n", - " 'group_shared_wheel',\n", - " 'group_shared_wheel_moves',\n", - " 'ibl_acquisition',\n", - " 'ibl_action',\n", - " 'ibl_alyxraw',\n", - " 'ibl_analyses_behavior',\n", - " 'ibl_analyses_ephys',\n", - " 'ibl_behavior',\n", - " 'ibl_data',\n", - " 'ibl_debug',\n", - " 'ibl_dj_acquisition',\n", - " 'ibl_dj_action',\n", - " 'ibl_dj_behavior',\n", - " 'ibl_dj_data',\n", - " 'ibl_dj_reference',\n", - " 'ibl_dj_subject',\n", - " 'ibl_ephys',\n", - " 'ibl_histology',\n", - " 'ibl_ingest_acquisition',\n", - " 'ibl_ingest_action',\n", - " 'ibl_ingest_data',\n", - " 'ibl_ingest_ephys',\n", - " 'ibl_ingest_histology',\n", - " 'ibl_ingest_job',\n", - " 'ibl_ingest_qc',\n", - " 'ibl_ingest_reference',\n", - " 'ibl_ingest_subject',\n", - " 'ibl_jaib1_tutorial',\n", - " 'ibl_patch',\n", - " 'ibl_plotting_behavior',\n", - " 'ibl_plotting_ephys',\n", - " 'ibl_public',\n", - " 'ibl_qc',\n", - " 'ibl_reference',\n", - " 'ibl_storage',\n", - " 'ibl_subject',\n", - " 'ibl_update',\n", - " 'innodb',\n", - " 'kenneth_test',\n", - " 'mysql',\n", - " 'performance_schema',\n", - " 'sys',\n", - " 'test',\n", - " 'test_group_shared_ephys',\n", - " 'test_group_shared_wheel',\n", - " 'test_ibl_acquisition',\n", - " 'test_ibl_action',\n", - " 'test_ibl_alyxraw',\n", - " 'test_ibl_analyses_behavior',\n", - " 'test_ibl_analyses_ephys',\n", - " 'test_ibl_behavior',\n", - " 'test_ibl_data',\n", - " 'test_ibl_ephys',\n", - " 'test_ibl_histology',\n", - " 'test_ibl_ingest_acquisition',\n", - " 'test_ibl_ingest_action',\n", - " 'test_ibl_ingest_data',\n", - " 'test_ibl_ingest_ephys',\n", - " 'test_ibl_ingest_histology',\n", - " 'test_ibl_ingest_job',\n", - " 'test_ibl_ingest_reference',\n", - " 'test_ibl_ingest_subject',\n", - " 'test_ibl_plotting_behavior',\n", - " 'test_ibl_plotting_ephys',\n", - " 'test_ibl_reference',\n", - " 'test_ibl_storage',\n", - " 'test_ibl_subject',\n", - " 'test_ibl_update',\n", - " 'test_mcg_ibl_ibl_acquisition',\n", - " 'test_mcg_ibl_ibl_action',\n", - " 'test_mcg_ibl_ibl_reference',\n", - " 'test_mcg_ibl_ibl_subject',\n", - " 'tmp',\n", - " 'update_ibl_alyxraw',\n", - " 'user_alejandro.pan_tutorial',\n", - " 'user_anneurai_aging',\n", - " 'user_anneurai_analyses',\n", - " 'user_anneurai_behavior',\n", - " 'user_anneurai_sessiongroup',\n", - " 'user_anneurai_tutorial',\n", - " 'user_chris_exttest',\n", - " 'user_chris_tutorial',\n", - " 'user_cskrasniak_tutorial',\n", - " 'user_eejd_tutorial',\n", - " 'user_ekb2154_tutorial',\n", - " 'user_eywalker_tutorial',\n", - " 'user_gaellechapuis_tutorial',\n", - " 'user_gercek_tutorial',\n", - " 'user_guido.meijer_tutorial',\n", - " 'user_guidomeijer_tutorial',\n", - " 'user_hernandomv_tutorial',\n", - " 'user_ineslaranjeira_tutorial',\n", - " 'user_jaib1_tutorial',\n", - " 'user_jeanpaulnc_tutorial',\n", - " 'user_jeanpaulnc_tutorial1',\n", - " 'user_karolinasocha_tutorial',\n", - " 'user_kushbanga_tutorial',\n", - " 'user_lacerbi_tutorial',\n", - " 'user_mayofaulkner_tutorial',\n", - " 'user_mileswells_end_criteria',\n", - " 'user_mileswells_test',\n", - " 'user_mileswells_trials_qc',\n", - " 'user_mileswells_wheel',\n", - " 'user_mileswells_wheel_new',\n", - " 'user_mw3323_tutorial',\n", - " 'user_nicco_tutorial',\n", - " 'user_njmiska_tutorial',\n", - " 'user_shan_test',\n", - " 'user_shan_tutorial',\n", - " 'user_tsukasar_test']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.list_schemas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Major schemas: \n", - "\n", - "Meta data from **Alyx**: `ibl_reference`, `ibl_subject`, `ibl_action`, `ibl_acquisition`, `ibl_data`, `ibl_qc`\n", - "Imported data from **FlatIron**: `ibl_behavior`, `ibl_ephys`, `ibl_histology` \n", - "Computed analzyed results: `ibl_analyses_behavior` " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Browse tables in a schema - `dj.Diagram`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Table tiers**: \n", - "Manual table: green box \n", - "Lookup table: gray box \n", - "Imported table: blue oval \n", - "Computed table: red circle \n", - "Part table: plain text" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Dependencies**: \n", - "One-to-one primary: thick solid line \n", - "One-to-many primary: thin solid line \n", - "Secondary foreign key reference: dashed line\n", - "Renamed secondary foreign key references: orange dot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Show tables in the whole schema" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(reference)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Show diagram of arbitruary parts of the database" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(subject)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A combination of arbitruary tables:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(subject.Subject) + dj.Diagram(subject.Line) + dj.Diagram(subject.SubjectLab) + dj.Diagram(subject.SubjectProject)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The tables could be from different schemas:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(subject.Subject) + dj.Diagram(acquisition.Session)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Getting the detailed definition of a table - `table.describe()`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject.describe();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Browsing of data - queries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Query all subjects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Restriction `&`: filtering data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Restriction: Query one subject" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# restrict by string\n", - "subject.Subject & 'subject_nickname=\"ibl_witten_10\"'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# restrict by dictionary\n", - "from uuid import UUID\n", - "subject.Subject & {'subject_uuid': UUID('00c60db3-74c3-4ee2-9df9-2c84acf84e92')}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject & {'sex': 'm'}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Restriction: Query subjects born after a date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject & 'subject_birth_date > \"2019-01-01\"'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Restriction: subjects within a range of dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject & 'subject_birth_date between \"2019-01-01\" and \"2019-04-01\"'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Restriction: Query subjects on multiple attributes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject & 'subject_birth_date > \"2019-01-01\"' & 'sex=\"M\"'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Restriction: Query subjects restricted by other tables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# subjects with Ephys recording\n", - "subject.Subject & ephys.ProbeInsertion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# subjects without Ephys recording\n", - "subject.Subject - ephys.ProbeInsertion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Join `*`: gather information from different tables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject * acquisition.Session" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Projection `.proj()`: focus on attributes of interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject.proj()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject.proj('subject_birth_date', 'sex')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### rename attribute with ***proj()***" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject.proj('sex', dob='subject_birth_date')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### perform simple computations with ***proj***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 1: Get date of a session:**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sessions_with_date = acquisition.Session.proj(session_date='date(session_start_time)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sessions_with_date" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 2: Age of the animal when performing each session?**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# First get the date of birth and the session date into the same query\n", - "q = subject.Subject * acquisition.Session" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "q" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Then compute the age\n", - "q_with_age = q.proj(age='datediff(session_start_time, subject_birth_date)')\n", - "q_with_age" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aggregation `.aggr()`: simple computation of one table against another table" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Example: how many sessions does each subject do so far?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject.aggr(acquisition.Session, 'subject_nickname', n='count(*)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. Fetching data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fetch all fields: `fetch()`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fetch all data from a table\n", - "subjs = subject.Subject.fetch()\n", - "subjs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subjs['subject_uuid']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subjs['subject_birth_date']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fetch as a list of dictionaries\n", - "subjs_dict = subject.Subject.fetch(as_dict=True)\n", - "subjs_dict" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fetch as pandas dataframe\n", - "subjs_df = subject.Subject.fetch(format='frame')\n", - "subjs_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fetch the primary key\n", - "pk = subject.Subject.fetch('KEY')\n", - "pk" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fetch specific attributes\n", - "dob, sex = subject.Subject.fetch('subject_birth_date', 'sex')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "info = subject.Subject.fetch('subject_birth_date', 'sex', as_dict=True)\n", - "info" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## fetch data only from one entry: `fetch1`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ibl_witten_10 = (subject.Subject & {'subject_nickname': 'ibl_witten_10'}).fetch1('KEY') # \"fetch1()\" because we know there's only one" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ibl_witten_10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "IBL_10 = (subject.Subject & {'subject_nickname': 'IBL_10'}).fetch1()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "IBL_10" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ibl_dj = dj.create_virtual_module('ibl_dj', 'ibl')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/notebooks_tutorial/202102_workshop/2-Analyze data with IBL pipeline and save results.ipynb b/notebooks/notebooks_tutorial/202102_workshop/2-Analyze data with IBL pipeline and save results.ipynb deleted file mode 100644 index f06e6eca..00000000 --- a/notebooks/notebooks_tutorial/202102_workshop/2-Analyze data with IBL pipeline and save results.ipynb +++ /dev/null @@ -1,725 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Here we would like to perform some analysis with the IBL pipeline.\n", - "\n", - "First thing first, let's **import the IBL pipeline package**, and a few other useful packages." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ibl_pipeline import subject, acquisition, action, behavior, reference, ephys\n", - "import datajoint as dj\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from uuid import UUID\n", - "import datetime\n", - "dj.config['safemode'] = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyzing existing data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**A simple example: compute the firing rate of each cluster across one session**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a quick look of the ephys schema:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(ephys)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How many ephys sessions do we have?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "acquisition.Session & ephys.ProbeInsertion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How many ephys sessions with clustering results?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In IBL pipeline, we saved our clustering results in the tabble DefaultCluster. This is the up-to-date version of the clustering results. Entries in this table will reflect manual curation results as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "acquisition.Session & ephys.DefaultCluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's then pick one ephys probe insertion from cortexlab to focus on" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# limit=1 makes fetch return the first entry in the query\n", - "probe_insertion = (ephys.ProbeInsertion & (acquisition.Session & 'session_lab=\"cortexlab\"')).fetch('KEY', limit=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "probe_insertion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An overview of clusters for this probe insertion:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ephys.DefaultCluster & probe_insertion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pick one cluster:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cluster = ephys.DefaultCluster & probe_insertion & 'cluster_id=0'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What do we need to compute the firing rate of each cluster? \n", - "1. Total spike number \n", - "2. Recording time length" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total spike number\n", - "spk_times = cluster.fetch1('cluster_spikes_times')\n", - "total_spk_num = len(spk_times)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Session duration\n", - "session_duration = (acquisition.Session & cluster).proj(\n", - " session_duration='session_end_time - session_start_time').fetch1('session_duration')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# compute firing rate \n", - "fr = total_spk_num/session_duration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cool! We got the firing rate! \n", - "The next question is, how do we save it in the database? \n", - "Put the entry in a table!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create your own schema and tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first thing we would like to do is to create a schema with `dj.schema`. \n", - "**Note**: the schema name you create has to either start with `user_{your user name}`, which is only accessible by you, or start with `group_share_`, which is accessible by the entire group. Here we use our user_name \n", - "**Note 2**: if your user_name contains a `.`, such as `miles.wells`, please delete it (`mileswells`) when creating the table." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "schema = dj.schema('user_shan_tutorial')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check if the new schema is there:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.list_schemas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's define a **manual** table to save the firing rate result. \n", - "A class created with DataJoint correponds to a table in the database." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class FiringRateManual(dj.Manual):\n", - " definition = \"\"\"\n", - " -> ephys.DefaultCluster # Each cluster has a firing rate\n", - " ---\n", - " firing_rate: float # Hz\n", - " \"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the brand-new table we just created." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FiringRateManual()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Yes, sure, it's emtpy. We haven't inserted anything into it. \n", - "Now let's insert the firing rate we just computed into this empty table. \n", - "We need to insert the entry with all fields defined in the table, usually in a format of dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# firing rate entry needs to inherit all primary keys from ephys.Cluster\n", - "cluster_key = cluster.fetch1('KEY')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cluster_key" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "firing_rate_entry = dict(\n", - " **cluster_key,\n", - " firing_rate=fr,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "firing_rate_entry" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now insert it!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FiringRateManual.insert1(firing_rate_entry)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the table again to see what happened:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FiringRateManual()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cool the entry is there!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So we can of course write a for loop to compute all fr and insert them one by one, but that's too slow. We can compute the results and insert them all at once!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# loop through the first 30 clusters and insert one by one, and compute time\n", - "import time\n", - "start_time = time.time()\n", - "\n", - "for icluster in (ephys.DefaultCluster & 'cluster_id between 1 and 30' & probe_insertion).fetch('KEY'):\n", - " spk_times = (ephys.DefaultCluster & icluster).fetch1('cluster_spikes_times')\n", - " fr_entry = dict(**icluster,\n", - " firing_rate=len(spk_times)/session_duration)\n", - " FiringRateManual.insert1(fr_entry)\n", - " \n", - "print(\"--- %s seconds ---\" % (time.time() - start_time))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# loop through the next 30 clusters and insert all at once as a list of dictionaries!\n", - "start_time = time.time()\n", - "\n", - "fr_entries = []\n", - "for icluster in (ephys.DefaultCluster & 'cluster_id between 31 and 60' & probe_insertion).fetch('KEY'):\n", - " spk_times = (ephys.DefaultCluster & icluster).fetch1('cluster_spikes_times')\n", - " fr_entry = dict(**icluster,\n", - " firing_rate=len(spk_times)/session_duration)\n", - " fr_entries.append(fr_entry)\n", - " \n", - "FiringRateManual.insert(fr_entries)\n", - "print(\"--- %s seconds ---\" % (time.time() - start_time))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this way, we will need to remember which clusters has been computed and inserted. If we insert the same entry twice, there will be an error. For example, let's rerun the above cell. We can overcome that problem by add the argument `skip_duplicates=True` inside `.insert()` or `.insert1()`, but it is not a very elegant solution. \n", - "The best approach here is to use a **Computed** table, it has the exact definition as the previous manual table, but with a magic **make** function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class FiringRateComputed(dj.Computed):\n", - " definition = \"\"\"\n", - " -> ephys.DefaultCluster # Each cluster has a firing rate\n", - " ---\n", - " firing_rate: float # Hz\n", - " \"\"\"\n", - " def make(self, key):\n", - " session_duration = (acquisition.Session & key).proj(\n", - " session_duration='session_end_time - session_start_time').fetch1('session_duration')\n", - " \n", - " spk_times = (ephys.DefaultCluster & key).fetch1('cluster_spikes_times')\n", - " firing_rate_entry = dict(**key, firing_rate=len(spk_times)/session_duration)\n", - " self.insert1(firing_rate_entry)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we can `populate` the table." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FiringRateComputed().drop()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "probe_insertion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Here the first argument provides some restrictions on the entries to populate, \n", - "# display_progress=True shows the progress,\n", - "# suppress_errors=True keeps the jobs running even if there are errors \n", - "FiringRateComputed.populate(probe_insertion, display_progress=True, suppress_errors=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FiringRateComputed()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**What does `populate` do?** \n", - "\n", - "It does two major things: \n", - "1. From the table definition, get the keys that needs to computed, which we called `key_source`. By default, it would be the join result of the primary dependent tables minus the once has been computed. \n", - "2. Call `make` function defined in the class, and compute one by one, with each individual key from the `key_source`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we still have to insert one by one, which is a bit slow. How do we do the trick of insert all firing rate of clusters in one session together?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can change the `key_source` by redefining it to a larger scale" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class FiringRateComputedFromInsertion(dj.Computed):\n", - " definition = \"\"\"\n", - " -> ephys.DefaultCluster # Each cluster has a firing rate\n", - " ---\n", - " firing_rate: float # Hz\n", - " \"\"\"\n", - " key_source = ephys.ProbeInsertion & ephys.DefaultCluster # populate for each ephys data set where clustering is available.\n", - " \n", - " def make(self, key): # the key here is now the primary key of ephys.Ephys, instead of ephys.Cluster\n", - " session_duration = (acquisition.Session & key).proj(\n", - " session_duration='session_end_time - session_start_time').fetch1('session_duration')\n", - " \n", - " fr_entries = []\n", - " for icluster in (ephys.DefaultCluster & key).fetch('KEY'):\n", - " try:\n", - " spk_times = (ephys.DefaultCluster & icluster).fetch1('cluster_spikes_times')\n", - " fr_entry = dict(**icluster,\n", - " firing_rate=len(spk_times)/session_duration)\n", - " fr_entries.append(fr_entry)\n", - " except Exception as e:\n", - " print(e)\n", - " continue\n", - " \n", - " self.insert(fr_entries)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FiringRateComputedFromInsertion.populate(probe_insertion, display_progress=True, suppress_errors=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compared to inserting each cluster one by one, inserting on the probe insertion level accelerate the processing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete entries and drop a table" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(FiringRateManual & 'cluster_id=0').delete() # any restrictor would work here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FiringRateManual.drop()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FiringRateComputed.drop()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FiringRateComputedFromInsertion.drop()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bonus: How to work with data where you don't have the code to generate the class?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.list_schemas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using `dj.create_virtual_module`, you'll be able to reconstruct the virtual module to access the tables in a schema." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# the first argument is the `__name__` of this module, the second argument is the schema name.\n", - "anne_analyses = dj.create_virtual_module('analyses', 'group_shared_anneurai_analyses')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now you could check the Diagram of this module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(anne_analyses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or do any query you want:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "anne_analyses.Age()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From bab7bc118a700df1bdd085f024f7054e8bb756e7 Mon Sep 17 00:00:00 2001 From: shenshan Date: Thu, 18 Feb 2021 09:04:10 +0000 Subject: [PATCH 21/34] minor fix --- .../03-ephys and histology pipelines.ipynb | 20 +------------------ 1 file changed, 1 insertion(+), 19 deletions(-) diff --git a/notebooks/notebooks_tutorial/202102_workshop/03-ephys and histology pipelines.ipynb b/notebooks/notebooks_tutorial/202102_workshop/03-ephys and histology pipelines.ipynb index 3394fe33..96ccc406 100644 --- a/notebooks/notebooks_tutorial/202102_workshop/03-ephys and histology pipelines.ipynb +++ b/notebooks/notebooks_tutorial/202102_workshop/03-ephys and histology pipelines.ipynb @@ -519,25 +519,7 @@ "metadata": {}, "outputs": [], "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.conn().connect()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# First, pick a cluster, for example, a recent ephys session from cortexlab\n", + "# First, pick a cluster, for example, a recent ephys session from angelakilab\n", "clusters_recent_sessions = ephys.DefaultCluster & \\\n", " (acquisition.Session & 'session_lab=\"angelakilab\"' & 'session_start_time > \"2020-08-01\"')\n", "clusters_recent_sessions" From 9324c1523c0d170eb15b7b7a786ff33576f7681b Mon Sep 17 00:00:00 2001 From: shenshan Date: Fri, 19 Feb 2021 23:18:16 +0000 Subject: [PATCH 22/34] remove the alyx import for real histology tables --- ibl_pipeline/process/ingest_real.py | 7 ------- ibl_pipeline/process/ingest_shadow.py | 4 +--- 2 files changed, 1 insertion(+), 10 deletions(-) diff --git a/ibl_pipeline/process/ingest_real.py b/ibl_pipeline/process/ingest_real.py index 412ddada..71dd6b04 100755 --- a/ibl_pipeline/process/ingest_real.py +++ b/ibl_pipeline/process/ingest_real.py @@ -160,13 +160,6 @@ def main(excluded_tables=[], public=False): print(table) copy_table(ephys, ephys_ingest, table, allow_direct_insert=True) - # histology tables - print('ProbeTrajectory') - histology.ProbeTrajectory.populate(suppress_errors=True, display_progress=True) - - # print('ChannelBrainLocation') - # copy_table(histology, histology_ingest, 'ChannelBrainLocation', - # allow_direct_insert=True) if __name__ == '__main__': diff --git a/ibl_pipeline/process/ingest_shadow.py b/ibl_pipeline/process/ingest_shadow.py index e95f5f23..9acc0f6c 100755 --- a/ibl_pipeline/process/ingest_shadow.py +++ b/ibl_pipeline/process/ingest_shadow.py @@ -63,9 +63,7 @@ if mode != 'public': SHADOW_TABLES = SHADOW_TABLES + [ ephys.ProbeModel, - ephys.ProbeInsertion, - histology.ProbeTrajectory, - # histology.ChannelBrainLocation + ephys.ProbeInsertion ] From c01f1ea0ce9797a6e9bdf614b99739ff34ff8d8f Mon Sep 17 00:00:00 2001 From: shenshan Date: Sat, 20 Feb 2021 00:56:00 +0000 Subject: [PATCH 23/34] Update workshop notebooks --- .../00-Get DataJoint Ready.ipynb | 16 +- ...ore IBL data pipeline with DataJoint.ipynb | 302 +++++++------ .../02-behavior pipeline.ipynb | 414 ++++++++++++++++++ ...a with IBL pipeline and save results.ipynb | 72 --- 4 files changed, 579 insertions(+), 225 deletions(-) create mode 100644 notebooks/notebooks_tutorial/202102_workshop/02-behavior pipeline.ipynb diff --git a/notebooks/notebooks_tutorial/202102_workshop/00-Get DataJoint Ready.ipynb b/notebooks/notebooks_tutorial/202102_workshop/00-Get DataJoint Ready.ipynb index 29e181f3..168b50f5 100644 --- a/notebooks/notebooks_tutorial/202102_workshop/00-Get DataJoint Ready.ipynb +++ b/notebooks/notebooks_tutorial/202102_workshop/00-Get DataJoint Ready.ipynb @@ -202,6 +202,20 @@ "source": [ "dj.config.save_local()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To reset your password, you could do `dj.set_password()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -220,7 +234,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb b/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb index 5706a227..b7a9e664 100644 --- a/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb +++ b/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb @@ -13,20 +13,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting dbadmin@datajoint-rds.cyuksi65nrdq.us-east-1.rds.amazonaws.com:3306\n", - "Connected to https://alyx.internationalbrainlab.org as vathes\n", - "Connected to https://alyx.internationalbrainlab.org as vathes\n", - "Connected to https://alyx.internationalbrainlab.org as vathes\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import datajoint as dj\n", "from ibl_pipeline import reference, subject, action, acquisition, data, behavior, ephys, histology\n", @@ -42,136 +31,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['datajoint_monitoring',\n", - " 'group_shared_anneurai_analyses',\n", - " 'group_shared_anneurai_psytrack',\n", - " 'group_shared_end_criteria',\n", - " 'group_shared_ephys',\n", - " 'group_shared_sfndata',\n", - " 'group_shared_testing',\n", - " 'group_shared_wheel',\n", - " 'group_shared_wheel_moves',\n", - " 'ibl_acquisition',\n", - " 'ibl_action',\n", - " 'ibl_alyxraw',\n", - " 'ibl_analyses_behavior',\n", - " 'ibl_analyses_ephys',\n", - " 'ibl_behavior',\n", - " 'ibl_data',\n", - " 'ibl_debug',\n", - " 'ibl_dj_acquisition',\n", - " 'ibl_dj_action',\n", - " 'ibl_dj_behavior',\n", - " 'ibl_dj_data',\n", - " 'ibl_dj_reference',\n", - " 'ibl_dj_subject',\n", - " 'ibl_ephys',\n", - " 'ibl_histology',\n", - " 'ibl_ingest_acquisition',\n", - " 'ibl_ingest_action',\n", - " 'ibl_ingest_data',\n", - " 'ibl_ingest_ephys',\n", - " 'ibl_ingest_histology',\n", - " 'ibl_ingest_job',\n", - " 'ibl_ingest_qc',\n", - " 'ibl_ingest_reference',\n", - " 'ibl_ingest_subject',\n", - " 'ibl_jaib1_tutorial',\n", - " 'ibl_patch',\n", - " 'ibl_plotting_behavior',\n", - " 'ibl_plotting_ephys',\n", - " 'ibl_public',\n", - " 'ibl_qc',\n", - " 'ibl_reference',\n", - " 'ibl_storage',\n", - " 'ibl_subject',\n", - " 'ibl_update',\n", - " 'innodb',\n", - " 'kenneth_test',\n", - " 'mysql',\n", - " 'performance_schema',\n", - " 'sys',\n", - " 'test',\n", - " 'test_group_shared_ephys',\n", - " 'test_group_shared_wheel',\n", - " 'test_ibl_acquisition',\n", - " 'test_ibl_action',\n", - " 'test_ibl_alyxraw',\n", - " 'test_ibl_analyses_behavior',\n", - " 'test_ibl_analyses_ephys',\n", - " 'test_ibl_behavior',\n", - " 'test_ibl_data',\n", - " 'test_ibl_ephys',\n", - " 'test_ibl_histology',\n", - " 'test_ibl_ingest_acquisition',\n", - " 'test_ibl_ingest_action',\n", - " 'test_ibl_ingest_data',\n", - " 'test_ibl_ingest_ephys',\n", - " 'test_ibl_ingest_histology',\n", - " 'test_ibl_ingest_job',\n", - " 'test_ibl_ingest_reference',\n", - " 'test_ibl_ingest_subject',\n", - " 'test_ibl_plotting_behavior',\n", - " 'test_ibl_plotting_ephys',\n", - " 'test_ibl_reference',\n", - " 'test_ibl_storage',\n", - " 'test_ibl_subject',\n", - " 'test_ibl_update',\n", - " 'test_mcg_ibl_ibl_acquisition',\n", - " 'test_mcg_ibl_ibl_action',\n", - " 'test_mcg_ibl_ibl_reference',\n", - " 'test_mcg_ibl_ibl_subject',\n", - " 'tmp',\n", - " 'update_ibl_alyxraw',\n", - " 'user_alejandro.pan_tutorial',\n", - " 'user_anneurai_aging',\n", - " 'user_anneurai_analyses',\n", - " 'user_anneurai_behavior',\n", - " 'user_anneurai_sessiongroup',\n", - " 'user_anneurai_tutorial',\n", - " 'user_chris_exttest',\n", - " 'user_chris_tutorial',\n", - " 'user_cskrasniak_tutorial',\n", - " 'user_eejd_tutorial',\n", - " 'user_ekb2154_tutorial',\n", - " 'user_eywalker_tutorial',\n", - " 'user_gaellechapuis_tutorial',\n", - " 'user_gercek_tutorial',\n", - " 'user_guido.meijer_tutorial',\n", - " 'user_guidomeijer_tutorial',\n", - " 'user_hernandomv_tutorial',\n", - " 'user_ineslaranjeira_tutorial',\n", - " 'user_jaib1_tutorial',\n", - " 'user_jeanpaulnc_tutorial',\n", - " 'user_jeanpaulnc_tutorial1',\n", - " 'user_karolinasocha_tutorial',\n", - " 'user_kushbanga_tutorial',\n", - " 'user_lacerbi_tutorial',\n", - " 'user_mayofaulkner_tutorial',\n", - " 'user_mileswells_end_criteria',\n", - " 'user_mileswells_test',\n", - " 'user_mileswells_trials_qc',\n", - " 'user_mileswells_wheel',\n", - " 'user_mileswells_wheel_new',\n", - " 'user_mw3323_tutorial',\n", - " 'user_nicco_tutorial',\n", - " 'user_njmiska_tutorial',\n", - " 'user_shan_test',\n", - " 'user_shan_tutorial',\n", - " 'user_tsukasar_test']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dj.list_schemas()" ] @@ -180,12 +42,84 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Schema types\n", "\n", - "## Major schemas: \n", + "`ibl_*`: common schemas that regular users just have the read access to it.\n", + "`group_shared_*`: schemas created by users, every user has read and write access to it. This is a place for people to share their research with other people in the collaboration.\n", + "`user_{your_user_name}_*`: schemas created by a particular user. Only that user and admin has read and write access to that schema. This is your private space for your own analyses. If you would like to share a particular schema to other people, please contact the admin (Shan) to grant users privilege to access that schema." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Major schemas: \n", "\n", - "Meta data from **Alyx**: `ibl_reference`, `ibl_subject`, `ibl_action`, `ibl_acquisition`, `ibl_data`, `ibl_qc`\n", - "Imported data from **FlatIron**: `ibl_behavior`, `ibl_ephys`, `ibl_histology` \n", - "Computed analzyed results: `ibl_analyses_behavior` " + "`ibl_reference`: lab, user, project and the general information such as brain regions.\n", + "`ibl_subject`: colony management system of subjects.\n", + "`ibl_action`: mainly the watering information of the animals.\n", + "`ibl_acquisition`: session information.\n", + "`ibl_data`: data repository, dataset and file uploading records.\n", + "`ibl_behavior`: behavior trial information.\n", + "`ibl_analyses_behavior`: pre-computed behavioral summary.\n", + "`ibl_ephys`: ephys data, e.g. clusters.\n", + "`ibl_histology`: histological data, probe trajectory and brain region assignment on each cluster.\n", + "`ibl_qc`: quality check results on session level and probe level." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: each module we imported from `ibl_pipeline` corresponds to a schema in the database. This is because there is a schema object in each of these modules. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.schema" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This object talks to the schema `ibl_subject` in the database." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inside the module, there are many classes that corresponds to individual tables in this schema, for example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Underlying, it connects to the table in the schema inside the database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.full_table_name" ] }, { @@ -737,13 +671,77 @@ "IBL_10" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6. How to work with data where you don't have the code to generate the class?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `dj.create_virtual_module`, you'll be able to reconstruct the virtual module to access the tables in a schema." + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import datajoint as dj" + "# the first argument is the `__name__` of this module, the second argument is the schema name.\n", + "anne_analyses = dj.create_virtual_module('analyses', 'group_shared_anneurai_analyses')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you could check the Diagram of this module" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(anne_analyses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or do any query you want:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "anne_analyses.Age()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another choice is to use `spawn_missing_classes`" ] }, { @@ -752,7 +750,7 @@ "metadata": {}, "outputs": [], "source": [ - "ibl_dj = dj.create_virtual_module('ibl_dj', 'ibl')" + "schema = dj.schema()" ] } ], @@ -772,7 +770,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/notebooks/notebooks_tutorial/202102_workshop/02-behavior pipeline.ipynb b/notebooks/notebooks_tutorial/202102_workshop/02-behavior pipeline.ipynb new file mode 100644 index 00000000..bbe86f75 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_workshop/02-behavior pipeline.ipynb @@ -0,0 +1,414 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook walks you through the main schemas and tables of the IBL behavior pipeline, and show you how to plot psychometric curves with data from the database:\n", + "\n", + "1. Fetch trial data and compute psychometric function yourself\n", + "2. Fetch the pre-computed fit results and original data and plot directly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Behavior tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a list of major tables in schema `ibl_behavior`: \n", + "\n", + "* TrialSet: Trial collection of a behavioral session.\n", + "* TrialSet.Trial: data of each individual trial in a TrialSet. Contains all the information in the ALF files `trials.*`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Behavior analyses tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a list of major tables in schema `ibl_analyses_behavior`: \n", + "\n", + "* PsychResults: fit parameters of the entire TrialSet (Session). \n", + "* PsychResultsBlock: fit parameters of each block with different probability left.\n", + "* ReactionTime: median reaction time for the entire TrialSet (Session)\n", + "* ReactionTimeContrastBlock: median reaction time as a function of contrast, for each block with different probability left.\n", + "* BehavioralSummaryByDate: results achieved with all trials of the day\n", + ">* PsychResults: fit parameters of each probability left. \n", + ">* ReactionTimeContrast: For each probability left, reaction time of each contrast.\n", + ">* ReactionTimeByDate: median reaction time of the date.\n", + "* TrainingStatus: look up table for available status, 'untrainable', 'unbiasable', 'in_training', 'trained_1a', 'trained_1b', 'ready4ephysrig', 'ready4delay', 'ready4recording' \n", + "* SessionTrainingStatus: training status of each session\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot Psychometric curves" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we first import some modules from the ibl pipeline and some other modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# behavior data\n", + "from ibl_pipeline import behavior\n", + "\n", + "# analyzed result of behavioral data\n", + "from ibl_pipeline.analyses import behavior as behavior_analyses\n", + "\n", + "# meta information of the subject and session\n", + "from ibl_pipeline import subject, acquisition\n", + "\n", + "# Function to perform the model fits of the psychometric function\n", + "from ibl_pipeline.utils import psychofit as psy\n", + "\n", + "# some regular modules\n", + "import numpy as np\n", + "import datetime\n", + "import seaborn as sns \n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fetch data from behavior.TrialSet.Trial table and compute psych curve yourself" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a quick look at the table `behavior.TrialSet`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The TrialSet table stores some summary statistics in one session of behavioral experiment. To also show the information of the subject, we could join the table with subject related tables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet * subject.Subject * subject.SubjectLab * subject.SubjectProject" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could restrict to one session by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "q = behavior.TrialSet * subject.Subject * subject.SubjectLab * subject.SubjectProject & \\\n", + " 'subject_nickname=\"CSHL_015\"' & {'session_start_time': datetime.datetime(2019, 9, 16, 13, 44, 46)}\n", + "q" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The trial-by-trial information are shown in a **part table** `behavior.TrialSet.Trial`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could check the documentation of each of the column with `describe()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet.Trial.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To fetch some part of data, we could do use the fetch method: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example we would like to see the stimulus contrasts, and the animal choices in one session (that we already queried and saved the results as q), we could do:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we could fetch the fields as a list of dictionary, only for the trials with a choice\n", + "data = (behavior.TrialSet.Trial & q & 'trial_response_choice !=\"No Go\"').fetch(\n", + " 'trial_stim_contrast_left', 'trial_stim_contrast_right', 'trial_response_choice', as_dict=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We could then convert the data to a dataframe for further analyses\n", + "import pandas as pd\n", + "df = pd.DataFrame(data)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can do some computation here to explore the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first compute the signed contrast, so that the contrasts on the right are positive" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['signed_contrast'] = df['trial_stim_contrast_right'] - df['trial_stim_contrast_left']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['report_right'] = df['trial_response_choice'] == \"CCW\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "report_right = df.groupby(['signed_contrast'], as_index=False).mean()\n", + "report_right" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then let's plot the psychometric curve: prob_report_right vs signed_contrast:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(report_right['signed_contrast'], report_right['report_right'], 'o')\n", + "plt.xlabel('Signed Contrast')\n", + "plt.ylabel('Probability reporting right')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Psychometric curve with pre-computed tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we could plot the psychometric curves with results directly fetched from the pre-computed tables. The fit results of the psychometric curve are saved in the table `behavior_analyses.PsychResultsBlock`, we can browse entries in the table for a particular subject, the prob_left_block marks which prior of probability left have been used in the block" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q = behavior_analyses.PsychResultsBlock & (subject.Subject & 'subject_nickname=\"CSHL_015\"')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the results of sessions collected after 2019-09-15, by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q & 'session_start_time > \"2019-09-15\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the psychometric curve of the last session on 9/17, containing three blocks with `prob_left` 0.2, 0.5 and 0.8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "psych_results = q & {'session_start_time': datetime.datetime(2019, 9, 16, 13, 44, 46)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's fetch the fit parameters and behavioral statistics from the table as a list of dictionaries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dict_results = psych_results.fetch(\n", + " 'signed_contrasts', 'prob_choose_right', 'n_trials_stim', 'n_trials_stim_right',\n", + " 'threshold', 'bias', 'lapse_low', 'lapse_high', as_dict=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next let's loop through the blocks and plot the psychometric curves:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "colors = [[1., 0., 0.], [0., 0., 0.], [0., 0., 1.]]\n", + "\n", + "fig, ax = plt.subplots()\n", + "for result, color in zip(dict_results, colors):\n", + " pars = [result['bias'], result['threshold'], result['lapse_low'], result['lapse_high']]\n", + " contrasts = result['signed_contrasts'] * 100\n", + " contrasts_fit = np.arange(-100, 100)\n", + " prob_right_fit = psy.erf_psycho_2gammas(pars, contrasts_fit) *100\n", + " sns.lineplot(contrasts_fit, prob_right_fit, color=color, ax=ax)\n", + " sns.lineplot(x=contrasts, y=result['prob_choose_right']*100, err_style=\"bars\", linewidth=0, linestyle='None', mew=0.5,\n", + " marker='.', ci=68, color=color, ax=ax)\n", + "\n", + "plt.gca().set_xlabel('Signed Contrast (%)')\n", + "plt.gca().set_ylabel('Rightward Choice (%)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_tutorial/202102_workshop/05-Analyze data with IBL pipeline and save results.ipynb b/notebooks/notebooks_tutorial/202102_workshop/05-Analyze data with IBL pipeline and save results.ipynb index 5ae28893..29e8ea6b 100644 --- a/notebooks/notebooks_tutorial/202102_workshop/05-Analyze data with IBL pipeline and save results.ipynb +++ b/notebooks/notebooks_tutorial/202102_workshop/05-Analyze data with IBL pipeline and save results.ipynb @@ -627,78 +627,6 @@ "source": [ "FiringRateComputedFromInsertion.drop()" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bonus: How to work with data where you don't have the code to generate the class?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.list_schemas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using `dj.create_virtual_module`, you'll be able to reconstruct the virtual module to access the tables in a schema." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# the first argument is the `__name__` of this module, the second argument is the schema name.\n", - "anne_analyses = dj.create_virtual_module('analyses', 'group_shared_anneurai_analyses')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now you could check the Diagram of this module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(anne_analyses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or do any query you want:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "anne_analyses.Age()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 1f2ea3bfe5193f1acadb8e4f0d2a8f9018786085 Mon Sep 17 00:00:00 2001 From: shenshan Date: Sat, 20 Feb 2021 01:17:32 +0000 Subject: [PATCH 24/34] add back the explore IBL pipeline notebooks for public jupyterhub --- ...1-Explore IBL behavior data pipeline.ipynb | 862 ++++++++++++++++++ .../02-Plot Psychometric curve.ipynb | 166 ++++ .../03-Access the database locally.ipynb | 82 ++ 3 files changed, 1110 insertions(+) create mode 100644 notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/01-Explore IBL behavior data pipeline.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/02-Plot Psychometric curve.ipynb create mode 100644 notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/03-Access the database locally.ipynb diff --git a/notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/01-Explore IBL behavior data pipeline.ipynb b/notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/01-Explore IBL behavior data pipeline.ipynb new file mode 100644 index 00000000..04df0203 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/01-Explore IBL behavior data pipeline.ipynb @@ -0,0 +1,862 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explore IBL data Pipeline\n", + "\n", + "This notebook gives an brief introduction on the IBL pipeline, and provide some guidance on how to explore the data pipeline with DataJoint tools\n", + "\n", + "*DataJoint* is a general framework in Python (or Matlab) that allow users to interact with MySQL database with Python (or Matlab). To learn more, visit the website https://datajoint.io/, DataJoint Neuro is the team that develop DataJoint and help researchers to adopt DataJoint and design pipeline. To check out more, please visit https://djneuro.io/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using DataJoint framework, we have been processing data daily within IBL project, and provides a website that display the daily training results at https://data.internationalbrainlab.org" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we will go through some useful tools to navigate with the data itself. \n", + "\n", + "First thing first, let's **import DataJoint and the IBL pipeline package**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "from ibl_pipeline import reference, subject, action, acquisition, data, behavior\n", + "from ibl_pipeline.analyses import behavior as behavior_analyses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Browse schemas you have access to - `dj.list_schemas()`\n", + "\n", + "Schemas are groups of tables. Tables in one schema are intuitively related.\n", + "\n", + "The IBL database current have the following schemas, which could be listed with a DataJoint method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Major schemas: \n", + "\n", + "1. Meta data from **Alyx**: \n", + " 1) `ibl_reference`: general information on labs, users and projects \n", + " 2) `ibl_subject`: subject related housing, genetic, information \n", + " 3) `ibl_action`: surgery information \n", + " 4) `ibl_acquisition`: session information \n", + " 5) `ibl_data`: data managing information \n", + " \n", + "2. Imported data from **FlatIron**: `ibl_behavior`, trial information of the task.\n", + "3. Computed analyzed results of the behavioral data: `ibl_analyses_behavior` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Browse tables in a schema - `dj.Diagram` or `dj.Di` \n", + "\n", + "`dj.Diagram` shows the tables and the dependencies among them. There are four types of DataJoint tables:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Table tiers**: \n", + "\n", + "_Manual table_: green box, entries in a manual table were inserted manually and cannot recovered if deleted. A typical example of a manual table is the subject table that saves information of animal subjects \n", + "\n", + "**Lookup table**: gray box, lookup tables save general facts that are rarely changed, such as possible genetic lines of an animals, or parameter information of a model \n", + "\n", + "**Imported table**: blue oval, entries in an imported table are imported from the external data, usually with code within the DataJoint table. If deleted, the entries are recoverable if external data still exists. Examples of imported tables are tables for experimental recordings. \n", + "\n", + "**Computed table**: red circle, entries in an imported table are computed from data in the database. If deleted, the entries are easily recoverable. Examples of computed tables are tables for analyzed results.\n", + "\n", + "**Part table**: plain text, tightly link to its master table, usually imported through the master table." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Dependencies**: \n", + "**One-to-one primary**: thick solid line, tables with one-to-one primary dependency have the exact same definition of the primary key. \n", + "\n", + "**One-to-many primary**: thin solid line, the child table inherits the primary key definition from its parent, but has additional field as part of the primary key as well.\n", + "\n", + "**Secondary foreign key reference**: dashed line, a secondary attribute references to a \n", + "Renamed secondary foreign key references: orange dot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show tables in the whole schema" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "from ibl_pipeline import reference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(reference)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show diagram of arbitruary parts of the database" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A combination of arbitruary tables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(subject.Subject) + dj.Diagram(subject.Line) + dj.Diagram(subject.SubjectLab) + dj.Diagram(subject.SubjectProject)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tables could be from different schemas:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(subject.Subject) + dj.Diagram(acquisition.Session)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Getting the detailed definition of a table - `table.describe()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Browsing of data - queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Query all subjects" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Restriction `&`: filtering data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query one subject" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# restrict by string\n", + "subject.Subject & 'subject_nickname=\"IBL-T1\"'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# restrict by dictionary\n", + "from uuid import UUID\n", + "subject.Subject & {'subject_uuid': UUID('00c60db3-74c3-4ee2-9df9-2c84acf84e92')}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & {'sex': 'm'}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query subjects born after a date" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & 'subject_birth_date > \"2019-01-01\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: subjects within a range of dates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & 'subject_birth_date between \"2019-01-01\" and \"2019-04-01\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query subjects on multiple attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & 'subject_birth_date > \"2019-01-01\"' & 'sex=\"M\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restriction: Query dead subjects" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subjects that are dead\n", + "subject.Subject & subject.Death" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subjects that are alive\n", + "subject.Subject - subject.Death" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Join `*`: gather information from different tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject * acquisition.Session" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Projection `.proj()`: focus on attributes of interest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.proj()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.proj('subject_birth_date', 'sex')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### rename attribute with ***proj()***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.proj('sex', dob='subject_birth_date')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### perform simple computations with ***proj***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 1: Get date of a session:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sessions_with_date = acquisition.Session.proj(session_date='date(session_start_time)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sessions_with_date" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 2: Age of the animal when performing each session?**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First get the date of birth and the session date into the same query\n", + "q = subject.Subject * acquisition.Session" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Then compute the age\n", + "q_with_age = q.proj(age='datediff(session_start_time, subject_birth_date)')\n", + "q_with_age" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aggregation `.aggr()`: simple computation of one table against another table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example: how many sessions does each subject do so far?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.aggr(acquisition.Session, 'subject_nickname', n='count(*)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Fetching data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fetch all fields: `fetch()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch all data from a table\n", + "subjs = subject.Subject.fetch()\n", + "subjs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subjs['subject_uuid']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subjs['subject_birth_date']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch as a list of dictionaries\n", + "subjs_dict = subject.Subject.fetch(as_dict=True)\n", + "subjs_dict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch as pandas dataframe\n", + "subjs_df = subject.Subject.fetch(format='frame')\n", + "subjs_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch the primary key\n", + "pk = subject.Subject.fetch('KEY')\n", + "pk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fetch specific attributes\n", + "dob, sex = subject.Subject.fetch('subject_birth_date', 'sex')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "info = subject.Subject.fetch('subject_birth_date', 'sex', as_dict=True)\n", + "info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## fetch data only from one entry: `fetch1`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "CSHL_015 = (subject.Subject & {'subject_nickname': 'CSHL_015'}).fetch1('KEY') # \"fetch1()\" because we know there's only one" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "CSHL_015" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IBL_T1 = (subject.Subject & {'subject_nickname': 'IBL-T1'}).fetch1()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IBL_T1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Behavioral trial information" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most important information in the data pipeline so far is the behavioral trials for the 2 alternative force choice task. We would go into a bit more details in the relevant tables." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `behavior` schema contains tables for the behavior information. The most important table is `behavior.TrialSet`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The TrialSet table stores some summary statistics in one session of behavioral experiment. To also show the information of the subject, we could join the table with subject related tables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet * subject.Subject * subject.SubjectLab * subject.SubjectProject" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could restrict to one session by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "q = behavior.TrialSet * subject.Subject * subject.SubjectLab * subject.SubjectProject & \\\n", + " 'subject_nickname=\"CSHL_015\"' & {'session_start_time': datetime.datetime(2019, 9, 16, 13, 44, 46)}\n", + "q" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The trial-by-trial information are shown in a **part table** `behavior.TrialSet.Trial`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet.Trial()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could check the documentation of each of the column with `describe()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "behavior.TrialSet.Trial.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To fetch some part of data, we could do use the fetch method: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example we would like to see the stimulus contrasts, and the animal choices in one session (that we already queried and saved the results as q), we could do:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we could fetch the fields as a list of dictionary, only for the trials with a choice\n", + "data = (behavior.TrialSet.Trial & q & 'trial_response_choice !=\"No Go\"').fetch(\n", + " 'trial_stim_contrast_left', 'trial_stim_contrast_right', 'trial_response_choice', as_dict=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We could then convert the data to a dataframe for further analyses\n", + "import pandas as pd\n", + "df = pd.DataFrame(data)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can do some computation here to explore the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first compute the signed contrast, so that the contrasts on the right are positive" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['signed_contrast'] = df['trial_stim_contrast_right'] - df['trial_stim_contrast_left']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['report_right'] = df['trial_response_choice'] == \"CCW\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "report_right = df.groupby(['signed_contrast'], as_index=False).mean()\n", + "report_right" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then let's plot the psychometric curve: prob_report_right vs signed_contrast:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib notebook\n", + "plt.plot(report_right['signed_contrast'], report_right['report_right'], 'o')\n", + "plt.xlabel('Signed Contrast')\n", + "plt.ylabel('Probability reporting right')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some basic analyses such as psychometric curves have been pre-processed and saved in the downstream tables, such as `behavior_analyses.PsychResults`. In the next notebook, we would like to step through how to fetch data from that table and recreate the psychometric curves for a particular session." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/02-Plot Psychometric curve.ipynb b/notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/02-Plot Psychometric curve.ipynb new file mode 100644 index 00000000..85bc0607 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/02-Plot Psychometric curve.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook shows how to plot the psychometric curve of the data in a session from the IBL DataJoint database" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we first import some modules from the ibl pipeline and some other modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# behavior data\n", + "from ibl_pipeline import behavior\n", + "\n", + "# analyzed result of behavioral data\n", + "from ibl_pipeline.analyses import behavior as behavior_analyses\n", + "\n", + "# meta information of the subject and session\n", + "from ibl_pipeline import subject, acquisition\n", + "\n", + "# Function to perform the model fits of the psychometric function\n", + "from ibl_pipeline.utils import psychofit as psy\n", + "\n", + "# some regular modules\n", + "import numpy as np\n", + "import datetime\n", + "import seaborn as sns \n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fit results of the psychometric curve are saved in the table `behavior_analyses.PsychResultsBlock`, we can browse entries in the table for a particular subject, the prob_left_block marks which prior of probability left have been used in the block" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q = behavior_analyses.PsychResultsBlock & (subject.Subject & 'subject_nickname=\"CSHL_015\"')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the results of sessions collected after 2019-09-15, by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q & 'session_start_time > \"2019-09-15\"'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the psychometric curve of the last session on 9/17, containing three blocks with `prob_left` 0.2, 0.5 and 0.8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "psych_results = q & {'session_start_time': datetime.datetime(2019, 9, 16, 13, 44, 46)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's fetch the fit parameters and behavioral statistics from the table as a list of dictionaries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dict_results = psych_results.fetch(\n", + " 'signed_contrasts', 'prob_choose_right', 'n_trials_stim', 'n_trials_stim_right',\n", + " 'threshold', 'bias', 'lapse_low', 'lapse_high', as_dict=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next let's loop through the blocks and plot the psychometric curves:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colors = [[1., 0., 0.], [0., 0., 0.], [0., 0., 1.]]\n", + "for result, color in zip(dict_results, colors):\n", + " pars = [result['bias'], result['threshold'], result['lapse_low'], result['lapse_high']]\n", + " contrasts = result['signed_contrasts'] * 100\n", + " contrasts_fit = np.arange(-100, 100)\n", + " prob_right_fit = psy.erf_psycho_2gammas(pars, contrasts_fit) *100\n", + " sns.lineplot(contrasts_fit, prob_right_fit, color=color)\n", + " sns.lineplot(x=contrasts, y=result['prob_choose_right']*100, err_style=\"bars\", linewidth=0, linestyle='None', mew=0.5,\n", + " marker='.', ci=68, color=color)\n", + "\n", + "plt.gca().set_xlabel('Signed Contrast (%)')\n", + "plt.gca().set_ylabel('Rightward Choice (%)')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/03-Access the database locally.ipynb b/notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/03-Access the database locally.ipynb new file mode 100644 index 00000000..cb712956 --- /dev/null +++ b/notebooks/notebooks_tutorial/202102_behavior_paper/Explore IBL pipeline/03-Access the database locally.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook shows how to connect to the IBL public database on your local machine.\n", + "\n", + "The database contains the data in this publication: [A standardized and reproducible method to measure decision-making in mice](https://doi.org/10.1101/2020.01.17.909838)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The host of the database, username and password are saved in the global variable `dj.config`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In your local directory, set the dj.config as above, then you will be able connect to the IBL public database, which could be checked by `dj.conn()`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rest of the instructions to replicate the figures in the publication could be found [here](https://github.com/int-brain-lab/paper-behavior/blob/master/README.md)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5ab9a1797e6d551aba8bf2a70f1a761c1f43c9e0 Mon Sep 17 00:00:00 2001 From: shenshan Date: Sat, 20 Feb 2021 01:18:50 +0000 Subject: [PATCH 25/34] Add histology tables to the populate queue --- ibl_pipeline/process/process_histology.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ibl_pipeline/process/process_histology.py b/ibl_pipeline/process/process_histology.py index 0fa894a1..446989ec 100644 --- a/ibl_pipeline/process/process_histology.py +++ b/ibl_pipeline/process/process_histology.py @@ -53,6 +53,10 @@ HISTOLOGY_TABLES_FOR_POPULATE = [ histology.ClusterBrainRegionTemp, histology.ProbeBrainRegionTemp, + histology.DepthBrainRegionTemp, + histology.ProbeTrajectory, + histology.ChannelBrainLocation, + histology.ClusterBrainRegion ] From 2a68a1b2fbc680f1ade8c8aebdca09a55d7b5f5d Mon Sep 17 00:00:00 2001 From: shenshan Date: Sat, 20 Feb 2021 07:09:15 +0000 Subject: [PATCH 26/34] Remove the old notebook --- .../02-Plot Psychometric curve.ipynb | 412 ------------------ 1 file changed, 412 deletions(-) delete mode 100644 notebooks/notebooks_tutorial/202102_workshop/02-Plot Psychometric curve.ipynb diff --git a/notebooks/notebooks_tutorial/202102_workshop/02-Plot Psychometric curve.ipynb b/notebooks/notebooks_tutorial/202102_workshop/02-Plot Psychometric curve.ipynb deleted file mode 100644 index 90b8e8fb..00000000 --- a/notebooks/notebooks_tutorial/202102_workshop/02-Plot Psychometric curve.ipynb +++ /dev/null @@ -1,412 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook walks you through the main schemas and tables of the IBL behavior pipeline, and show you how to plot psychometric curves with data from the database:\n", - "\n", - "1. Fetch trial data and compute psychometric function yourself\n", - "2. Fetch the pre-computed fit results and original data and plot directly." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Behavior tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is a list of major tables in schema `ibl_behavior`: \n", - "\n", - "* TrialSet: Trial collection of a behavioral session.\n", - "* TrialSet.Trial: data of each individual trial in a TrialSet. Contains all the information in the ALF files `trials.*`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Behavior analyses tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is a list of major tables in schema `ibl_analyses_behavior`: \n", - "\n", - "* PsychResults: fit parameters of the entire TrialSet (Session). \n", - "* PsychResultsBlock: fit parameters of each block with different probability left.\n", - "* ReactionTime: median reaction time for the entire TrialSet (Session)\n", - "* ReactionTimeContrastBlock: median reaction time as a function of contrast, for each block with different probability left.\n", - "* BehavioralSummaryByDate: results achieved with all trials of the day\n", - ">* PsychResults: fit parameters of each probability left. \n", - ">* ReactionTimeContrast: For each probability left, reaction time of each contrast.\n", - ">* ReactionTimeByDate: median reaction time of the date.\n", - "* TrainingStatus: look up table for available status, 'untrainable', 'unbiasable', 'in_training', 'trained_1a', 'trained_1b', 'ready4ephysrig', 'ready4delay', 'ready4recording' \n", - "* SessionTrainingStatus: training status of each session\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Psychometric curves" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we first import some modules from the ibl pipeline and some other modules" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# behavior data\n", - "from ibl_pipeline import behavior\n", - "\n", - "# analyzed result of behavioral data\n", - "from ibl_pipeline.analyses import behavior as behavior_analyses\n", - "\n", - "# meta information of the subject and session\n", - "from ibl_pipeline import subject, acquisition\n", - "\n", - "# Function to perform the model fits of the psychometric function\n", - "from ibl_pipeline.utils import psychofit as psy\n", - "\n", - "# some regular modules\n", - "import numpy as np\n", - "import datetime\n", - "import seaborn as sns \n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fetch data from behavior.TrialSet.Trial table and compute psych curve yourself" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a quick look at the table `behavior.TrialSet`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "behavior.TrialSet()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The TrialSet table stores some summary statistics in one session of behavioral experiment. To also show the information of the subject, we could join the table with subject related tables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "behavior.TrialSet * subject.Subject * subject.SubjectLab * subject.SubjectProject" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could restrict to one session by:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "q = behavior.TrialSet * subject.Subject * subject.SubjectLab * subject.SubjectProject & \\\n", - " 'subject_nickname=\"CSHL_015\"' & {'session_start_time': datetime.datetime(2019, 9, 16, 13, 44, 46)}\n", - "q" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The trial-by-trial information are shown in a **part table** `behavior.TrialSet.Trial`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could check the documentation of each of the column with `describe()`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "behavior.TrialSet.Trial.describe();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To fetch some part of data, we could do use the fetch method: " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For example we would like to see the stimulus contrasts, and the animal choices in one session (that we already queried and saved the results as q), we could do:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# we could fetch the fields as a list of dictionary, only for the trials with a choice\n", - "data = (behavior.TrialSet.Trial & q & 'trial_response_choice !=\"No Go\"').fetch(\n", - " 'trial_stim_contrast_left', 'trial_stim_contrast_right', 'trial_response_choice', as_dict=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We could then convert the data to a dataframe for further analyses\n", - "import pandas as pd\n", - "df = pd.DataFrame(data)\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we can do some computation here to explore the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We first compute the signed contrast, so that the contrasts on the right are positive" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['signed_contrast'] = df['trial_stim_contrast_right'] - df['trial_stim_contrast_left']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['report_right'] = df['trial_response_choice'] == \"CCW\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "report_right = df.groupby(['signed_contrast'], as_index=False).mean()\n", - "report_right" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then let's plot the psychometric curve: prob_report_right vs signed_contrast:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(report_right['signed_contrast'], report_right['report_right'], 'o')\n", - "plt.xlabel('Signed Contrast')\n", - "plt.ylabel('Probability reporting right')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot Psychometric curve with pre-computed tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we could plot the psychometric curves with results directly fetched from the pre-computed tables. The fit results of the psychometric curve are saved in the table `behavior_analyses.PsychResultsBlock`, we can browse entries in the table for a particular subject, the prob_left_block marks which prior of probability left have been used in the block" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "q = behavior_analyses.PsychResultsBlock & (subject.Subject & 'subject_nickname=\"CSHL_015\"')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the results of sessions collected after 2019-09-15, by:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "q & 'session_start_time > \"2019-09-15\"'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot the psychometric curve of the last session on 9/17, containing three blocks with `prob_left` 0.2, 0.5 and 0.8" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "psych_results = q & {'session_start_time': datetime.datetime(2019, 9, 16, 13, 44, 46)}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's fetch the fit parameters and behavioral statistics from the table as a list of dictionaries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dict_results = psych_results.fetch(\n", - " 'signed_contrasts', 'prob_choose_right', 'n_trials_stim', 'n_trials_stim_right',\n", - " 'threshold', 'bias', 'lapse_low', 'lapse_high', as_dict=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next let's loop through the blocks and plot the psychometric curves:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "colors = [[1., 0., 0.], [0., 0., 0.], [0., 0., 1.]]\n", - "for result, color in zip(dict_results, colors):\n", - " pars = [result['bias'], result['threshold'], result['lapse_low'], result['lapse_high']]\n", - " contrasts = result['signed_contrasts'] * 100\n", - " contrasts_fit = np.arange(-100, 100)\n", - " prob_right_fit = psy.erf_psycho_2gammas(pars, contrasts_fit) *100\n", - " sns.lineplot(contrasts_fit, prob_right_fit, color=color)\n", - " sns.lineplot(x=contrasts, y=result['prob_choose_right']*100, err_style=\"bars\", linewidth=0, linestyle='None', mew=0.5,\n", - " marker='.', ci=68, color=color)\n", - "\n", - "plt.gca().set_xlabel('Signed Contrast (%)')\n", - "plt.gca().set_ylabel('Rightward Choice (%)')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 9b2c59bfafe3ff43c6175569cf6b5928aca31e12 Mon Sep 17 00:00:00 2001 From: shenshan Date: Sat, 20 Feb 2021 07:16:26 +0000 Subject: [PATCH 27/34] Fix newline problem --- ...ore IBL data pipeline with DataJoint.ipynb | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb b/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb index b7a9e664..2ecc0506 100644 --- a/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb +++ b/notebooks/notebooks_tutorial/202102_workshop/01-Tools to explore IBL data pipeline with DataJoint.ipynb @@ -44,9 +44,9 @@ "source": [ "## Schema types\n", "\n", - "`ibl_*`: common schemas that regular users just have the read access to it.\n", - "`group_shared_*`: schemas created by users, every user has read and write access to it. This is a place for people to share their research with other people in the collaboration.\n", - "`user_{your_user_name}_*`: schemas created by a particular user. Only that user and admin has read and write access to that schema. This is your private space for your own analyses. If you would like to share a particular schema to other people, please contact the admin (Shan) to grant users privilege to access that schema." + "`ibl_*`: common schemas that regular users just have the read access to it. \n", + "`group_shared_*`: schemas created by users, every user has read and write access to it. This is a place for people to share their research with other people in the collaboration. \n", + "`user_{your_user_name}_*`: schemas created by a particular user. Only that user and admin has read and write access to that schema. This is your private space for your own analyses. If you would like to share a particular schema to other people, please contact the admin (Shan) to grant users privilege to access that schema. " ] }, { @@ -55,16 +55,16 @@ "source": [ "## Major schemas: \n", "\n", - "`ibl_reference`: lab, user, project and the general information such as brain regions.\n", - "`ibl_subject`: colony management system of subjects.\n", - "`ibl_action`: mainly the watering information of the animals.\n", - "`ibl_acquisition`: session information.\n", - "`ibl_data`: data repository, dataset and file uploading records.\n", - "`ibl_behavior`: behavior trial information.\n", - "`ibl_analyses_behavior`: pre-computed behavioral summary.\n", - "`ibl_ephys`: ephys data, e.g. clusters.\n", - "`ibl_histology`: histological data, probe trajectory and brain region assignment on each cluster.\n", - "`ibl_qc`: quality check results on session level and probe level." + "`ibl_reference`: lab, user, project and the general information such as brain regions. \n", + "`ibl_subject`: colony management system of subjects. \n", + "`ibl_action`: mainly the watering information of the animals. \n", + "`ibl_acquisition`: session information. \n", + "`ibl_data`: data repository, dataset and file uploading records. \n", + "`ibl_behavior`: behavior trial information. \n", + "`ibl_analyses_behavior`: pre-computed behavioral summary. \n", + "`ibl_ephys`: ephys data, e.g. clusters. \n", + "`ibl_histology`: histological data, probe trajectory and brain region assignment on each cluster. \n", + "`ibl_qc`: quality check results on session level and probe level. " ] }, { From 83296e0fb021a4aed6464440af527aab247c56cf Mon Sep 17 00:00:00 2001 From: shenshan Date: Wed, 24 Feb 2021 22:47:39 +0000 Subject: [PATCH 28/34] update on ephys, histology and qc --- .gitignore | 4991 ++++++++++++++++++++++++++++++++++++- ibl_pipeline/ephys.py | 2 +- ibl_pipeline/histology.py | 4 +- ibl_pipeline/qc.py | 24 +- 4 files changed, 5008 insertions(+), 13 deletions(-) diff --git a/.gitignore b/.gitignore index a2c1cd7e..17fdaee3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,5 @@ - # some unused files +notebooks/notebooks_tutorial/202102_behavior_paper/one_data/* rasters/* notebooks/.* notebooks/notebooks_plotting/* @@ -7,7 +7,7 @@ scripts/update_entries.py scripts/compare_tables.py scripts/delete_shadow_tables.py scripts/report_missing_trialset.py -scripts/update_entries.py +scripts/update_entries.py # IDEA configs .idea/ @@ -143,3 +143,4990 @@ data/* # others notebooks/notebooks_dev/* clear_things_up.txt 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a/ibl_pipeline/ephys.py b/ibl_pipeline/ephys.py index 2d9ee85e..e8034b1d 100755 --- a/ibl_pipeline/ephys.py +++ b/ibl_pipeline/ephys.py @@ -54,7 +54,7 @@ class CompleteClusterSession(dj.Computed): 'clusters.amps.npy', 'clusters.channels.npy', 'clusters.depths.npy', - 'clusters.metrics.csv', + 'clusters.metrics.pqt', 'clusters.peakToTrough.npy', 'clusters.uuids.csv', 'clusters.waveforms.npy', diff --git a/ibl_pipeline/histology.py b/ibl_pipeline/histology.py index 1b8722d0..0fa24220 100755 --- a/ibl_pipeline/histology.py +++ b/ibl_pipeline/histology.py @@ -133,8 +133,8 @@ class ClusterBrainRegion(dj.Imported): -> reference.BrainRegion """ key_source = ProbeTrajectory & \ - (data.FileRecord & 'dataset_name like "%channels.brainLocationIds%"') & \ - (data.FileRecord & 'dataset_name like "%channels.mlapdv%"') + (data.FileRecord & 'dataset_name like "%clusters.brainLocationIds%"') & \ + (data.FileRecord & 'dataset_name like "%clusters.mlapdv%"') def make(self, key): diff --git a/ibl_pipeline/qc.py b/ibl_pipeline/qc.py index 781beb0f..65f5d1e3 100644 --- a/ibl_pipeline/qc.py +++ b/ibl_pipeline/qc.py @@ -28,24 +28,32 @@ class QCChoice(dj.Lookup): ] +@schema +class QCLevel(dj.Lookup): + definition = """ + qc_level : varchar(32) + """ + contents = zip(['session', 'probe_insertion']) + + @schema class QCType(dj.Lookup): definition = """ # Aspect of a session for quality check. e.g. task, behavior, experimenter… qc_type : varchar(16) --- - qc_ + -> QCLevel qc_type_description='' : varchar(1000) """ # contents = [ - # ['experimenter', 'Manual labeling of a session by user'], - # ['task', 'Quality check when running the task'], - # ['behavior', 'Behavior criteria'], - # ['videoBody', 'Quality check for video recording of body camera'], - # ['videoLeft', 'Quality check for video recording of left camera'], - # ['videoRight', 'Quality check for video recording of right camera'], - # ['dlc', 'Deep lab cut on behavioral video data'], + # ['experimenter', 'session', 'Manual labeling of a session by user'], + # ['task', 'session', 'Quality check when running the task'], + # ['behavior', 'session', 'Behavior criteria'], + # ['videoBody', 'session', 'Quality check for video recording of body camera'], + # ['videoLeft', 'session', 'Quality check for video recording of left camera'], + # ['videoRight', 'session', 'Quality check for video recording of right camera'], + # ['dlc', 'session', 'Deep lab cut on behavioral video data'], # ['tracing_exists', 'Histology tracing'], # ['alignment_resolved', 'Ephys alignment with histology'] # ] From 631425b1084acfb291eef1ec9dfc4cf7c2f3aa8a Mon Sep 17 00:00:00 2001 From: shenshan Date: Wed, 24 Feb 2021 22:55:21 +0000 Subject: [PATCH 29/34] remove redundancy in .gitignore --- .gitignore | 4987 ---------------------------------------------------- 1 file changed, 4987 deletions(-) diff --git a/.gitignore b/.gitignore index 17fdaee3..d18e3185 100644 --- a/.gitignore +++ b/.gitignore @@ -143,4990 +143,3 @@ data/* # 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-notebooks/notebooks_tutorial/202102_behavior_paper/one_data/ibl-behavioral-data-Dec2019/angelakilab/Subjects/IBL-T4/2019-08-09/001/alf/_ibl_trials.intervals.npy From 2a82e14c6c3d445cc4d8c3508d60402a57aaacb5 Mon Sep 17 00:00:00 2001 From: shenshan Date: Thu, 25 Feb 2021 19:04:45 +0000 Subject: [PATCH 30/34] Dockerfile and compose file to build the conda evironment for iblenv --- Dockerfile.brain | 32 +++++++++++++++++++++++++++ docker-compose-brain.yml | 13 ++++++----- iblenv.yaml | 47 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 87 insertions(+), 5 deletions(-) create mode 100644 Dockerfile.brain create mode 100644 iblenv.yaml diff --git a/Dockerfile.brain b/Dockerfile.brain new file mode 100644 index 00000000..433a98a3 --- /dev/null +++ b/Dockerfile.brain @@ -0,0 +1,32 @@ +FROM datajoint/djlab:py3.8-debian + +RUN pip install --upgrade pip +RUN pip install --upgrade datajoint + +COPY --chown=dja:anaconda ./iblenv.yaml /tmp/iblenv.yaml +USER root +RUN . /root/.bashrc +RUN conda init bash +RUN conda install conda-build +RUN conda update -n base -c defaults conda +RUN conda update --all +RUN conda config --set channel_priority false +RUN conda env create --file /tmp/iblenv.yaml +RUN conda activate iblenv +RUN pip install importlib_resource imageio +RUN pip install --no-dependencies git+https://github.com/int-brain-lab/ibllib +RUN pip install --no-dependencies git+https://github.com/int-brain-lab/iblapps +RUN pip install --no-dependencies git+https://github.com/cortex-lab/phylib + +USER dja:anaconda +ADD . /src/IBL-pipeline + +USER root +RUN pip install -e --no-dependencies /src/IBL-pipeline +RUN conda install -c conda-forge opencv -y +COPY --chown=dja:anaconda ./apt_requirements.txt /tmp/apt_requirements.txt +RUN apt update +USER dja:anaconda +RUN \ + /entrypoint.sh echo "Requirements updated..." && \ + rm "${APT_REQUIREMENTS}" diff --git a/docker-compose-brain.yml b/docker-compose-brain.yml index 439e636f..c1dd536b 100644 --- a/docker-compose-brain.yml +++ b/docker-compose-brain.yml @@ -1,7 +1,10 @@ version: '3' services: - datajoint: - build: . + datajoint_brain: + build: + context: . + dockerfile: Dockerfile.brain + container_name: ibl_datajoint_brain env_file: .env volumes: - ./notebooks:/home/dja @@ -12,8 +15,8 @@ services: - ./root/.one_params:/home/dja/.one_params user: 1000:anaconda ports: - - "8888:8888" + - "9003:8888" networks: - - ibl + - ibl_brain networks: - ibl: + ibl_brain: diff --git a/iblenv.yaml b/iblenv.yaml new file mode 100644 index 00000000..c3745e55 --- /dev/null +++ b/iblenv.yaml @@ -0,0 +1,47 @@ +name: iblenv +channels: + - defaults + - conda-forge +dependencies: + - apptools >=4.5.0 + - click + - colorcet + - colorlog + - cython + - dataclasses + - flake8 + - globus-sdk + - h5py + - jupyter + - jupyterlab + - matplotlib + - mayavi + - nb_conda + - nb_conda_kernels + - numba + - numpy + - pandas + - pillow + - pip + - plotly + - pyarrow + - pynrrd + - pyopengl + - pyqt >= 5.12 + - pyqtgraph + - pytest + - requests + - scikits-bootstrap + - scikit-learn + - scipy >=1.4.1 + - seaborn + - statsmodels + - tqdm + - pip: + - --pre + - graphviz + - mtscomp + - SimpleITK + - soundfile + - boto3 + - colorlover From 94f6017cda3e0dd78176fc377234e3b0c2ae30c3 Mon Sep 17 00:00:00 2001 From: shenshan Date: Thu, 25 Feb 2021 19:10:45 +0000 Subject: [PATCH 31/34] Add spinning brain script --- scripts/test_spinning_brain.py | 72 ++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 scripts/test_spinning_brain.py diff --git a/scripts/test_spinning_brain.py b/scripts/test_spinning_brain.py new file mode 100644 index 00000000..6aefb407 --- /dev/null +++ b/scripts/test_spinning_brain.py @@ -0,0 +1,72 @@ +import subprocess +subprocess.Popen(["Xvfb",":1","-screen", "0", "1280x1024x24", "-auth", "localhost"]) + +""" +Generates 3D rendering of all probe trajectories for a single subject. +The trajectory plotted are: +'Micro-manipulator': Green +'Histology track': Red +'Planned': Blue +""" +# Author: Olivier +# environment installation guide https://github.com/int-brain-lab/iblenv +# run "%qui qt" magic command from Ipython prompt for interactive mode +from mayavi import mlab +from atlaselectrophysiology import rendering +import ibllib.atlas as atlas +from oneibl.one import ONE +import imageio + +# GIF export params +file_name = 'animation.gif' +fps = 20 + +# Code to generate figure +one = ONE(base_url="https://alyx.internationalbrainlab.org") + +fig = rendering.figure() + +subject = 'KS003' +trajs = one.alyx.rest('trajectories', 'list', subject=subject) + +ba_allen = atlas.AllenAtlas(25) +ba_needles = atlas.NeedlesAtlas(25) + +plt_trj = [] + +for index, trj in enumerate(trajs): + if trj['coordinate_system'] == 'IBL-Allen': + brain_atlas = ba_allen + elif trj['coordinate_system'] == 'Needles-Allen': + brain_atlas = ba_needles + ins = atlas.Insertion.from_dict(trj, brain_atlas=brain_atlas) + ins = atlas.Insertion.from_dict(trj, brain_atlas=ba_allen) + + mlapdv = brain_atlas.xyz2ccf(ins.xyz) + if trj['provenance'] == 'Micro-manipulator': + color = (0., 1., 0.) # Green + elif trj['provenance'] == 'Histology track': + color = (1., 0., 0.) # Red + elif trj['provenance'] == 'Planned': + color = (0., 0., 1.) # Blue + + lab = f"{trj['session']['subject']}/{trj['session']['start_time'][:10]}" + f"{str(trj['session']['number']).zfill(3)}" + plt = mlab.plot3d(mlapdv[:, 1], mlapdv[:, 2], mlapdv[:, 0], + line_width=3, color=color, tube_radius=20) + # setup the labels at the top of the trajectories + mlab.text3d(mlapdv[0, 1], mlapdv[0, 2], mlapdv[0, 0] - 500, lab, + line_width=4, color=tuple(color), figure=fig, scale=150) + plt_trj.append(plt) + + +frames = [] + +for i in range(fps): + mlab.view(azimuth=0, elevation=0 - i * (360 / fps)) + mlab.roll(180) + mlab.test_plot3d() + f = mlab.gcf() + f.scene._lift() + frames.append(mlab.screenshot(mode='rgb', antialiased=True)) + +imageio.mimsave(file_name, frames, duration=(1 / fps)) From e2aa47c81c9501b77bd1cff64d0cec90a95125a1 Mon Sep 17 00:00:00 2001 From: shenshan Date: Fri, 26 Feb 2021 06:40:38 +0000 Subject: [PATCH 32/34] Update Session qc ingestion procedure, directly ingest into the real tables. --- ibl_pipeline/ingest/qc.py | 156 +++++++++-------------------- ibl_pipeline/process/process_qc.py | 17 +--- ibl_pipeline/qc.py | 24 ++--- 3 files changed, 60 insertions(+), 137 deletions(-) diff --git a/ibl_pipeline/ingest/qc.py b/ibl_pipeline/ingest/qc.py index 47b246b9..6b8a053c 100644 --- a/ibl_pipeline/ingest/qc.py +++ b/ibl_pipeline/ingest/qc.py @@ -4,7 +4,7 @@ from . import alyxraw, reference, subject, action, acquisition, ephys from .. import acquisition as acquisition_real from .. import ephys as ephys_real -from .. import qc +from .. import qc as qc_real from . import get_raw_field as grf from tqdm import tqdm @@ -62,60 +62,7 @@ def get_extended_qc_fields_from_alyx(level='session'): return set(eqc_fields) -@schema -class QCChoice(dj.Lookup): - definition = """ - # Available flags to quantify the quality of a session or a specific aspect of a session, lookup table got referred in SessionQC and SessionExtendedQC - qc : tinyint unsigned - --- - qc_label : varchar(32) - """ - - contents = [ - (0, 'NOT_SET'), - (10, 'PASS'), - (30, 'WARNING'), - (40, 'FAIL'), - (50, 'CRITICAL'), - ] - - -@schema -class SessionQC(dj.Manual): - definition = """ - subject_uuid : uuid - session_start_time : datetime - --- - session_qc : tinyint unsigned - session_qc_ts=CURRENT_TIMESTAMP: timestamp - """ - - -@schema -class SessionExtendedQC(dj.Manual): - definition = """ - subject_uuid : uuid - session_start_time : datetime - qc_type : varchar(16) - --- - session_extended_qc : tinyint unsigned - session_extended_qc_ts=CURRENT_TIMESTAMP: timestamp - """ - - class Field(dj.Part): - definition = """ - -> master - session_qc_fname : varchar(64) - --- - session_qc_fvalue_bool=null : bool - session_qc_fvalue_float=null : float - session_qc_fvalue_str=null : varchar(64) - session_qc_fvalue_blob=null : blob - """ - - -qc_types = qc.QCType.fetch('qc_type') -qc_choices = qc.QCChoice.fetch(format='frame') +qc_choices = qc_real.QCChoice.fetch(format='frame') @schema @@ -134,36 +81,54 @@ def make(self, key): self.insert1(key) key['uuid'] = key['session_uuid'] - qc = grf(key, 'qc') - qc_extended = grf(key, 'extended_qc') + qc_alyx = grf(key, 'qc') + qc_extended_alyx = grf(key, 'extended_qc') try: - qc_extended = json.loads(qc_extended) + qc_extended_alyx = json.loads(qc_extended_alyx) except json.decoder.JSONDecodeError: - qc_extended = qc_extended.replace("\'", "\"") - qc_extended = qc_extended.replace('None', "\"None\"") - qc_extended = json.loads(qc_extended) + # fix the json field before decoding. + for k, v in json_replace_map.items(): + qc_extended_alyx = qc_extended_alyx.replace(k, v) + qc_extended_alyx = json.loads(qc_extended_alyx) - if len(acquisition.Session & key) == 1: + if len(acquisition_real.Session & key) == 1: session_key = (acquisition_real.Session & key).fetch1('KEY') else: - session_key = (acquisition.Session & key).fetch1('KEY') + return - SessionQC.insert1( - dict(**session_key, session_qc=int(qc)) + qc_real.SessionQC.insert1( + dict(**session_key, session_qc=int(qc_alyx)), + skip_duplicates=True ) + qc_types = (qc_real.QCType & 'qc_level="session"').fetch('qc_type') + # loop through all qc types on the session level and check whether it's in the current entry for qc_type in qc_types: - if qc_type in qc_extended: - session_qc_type = qc_extended[qc_type] - qc_choice = qc_choices[ - qc_choices['qc_label'] == session_qc_type].index[0] - SessionExtendedQC.insert1( + if qc_type in qc_extended_alyx: + + # get the entry for SessionExtendedQC + extended_qc_label = qc_extended_alyx[qc_type] + + # for behavior, the field is 0 for "NOT SET", 1 for PASS + if extended_qc_label == 0: + continue + elif extended_qc_label == 1 and qc_type == 'behavior': + extended_qc = 10 + else: + extended_qc = qc_choices[ + qc_choices['qc_label'] == extended_qc_label].index[0] + qc_real.SessionExtendedQC.insert1( dict(**session_key, qc_type=qc_type, - session_extended_qc=qc_choice) + session_extended_qc=extended_qc), + skip_duplicates=True ) - for k, v in qc_extended.items(): + + # get the entries for the part table SessionExtendedQC.Field + for k, v in qc_extended_alyx.items(): + + # for the session qc field, it has the format of '_{qc_type}', e.g. '_task_trial_length' if f'_{qc_type}' in k: qc_field = dict( **session_key, @@ -178,43 +143,10 @@ def make(self, key): else: qc_fvalue_name = 'session_qc_fvalue_blob' - SessionExtendedQC.Field.insert1( + qc_real.SessionExtendedQC.Field.insert1( {**qc_field, - qc_fvalue_name: v}) - - -@schema -class ProbeInsertionQC(dj.Manual): - definition = """ - subject_uuid : uuid - session_start_time : datetime - probe_idx : int - --- - insertion_qc : tinyint - """ - - -@schema -class ProbeInsertionExtendedQC(dj.Manual): - definition = """ - subject_uuid : uuid - session_start_time : datetime - probe_idx : int - qc_type : varchar(16) - --- - insertion_extended_qc : tinyint - insertion_extended_qc_ts=CURRENT_TIMESTAMP: timestamp - """ - - class Field(dj.Part): - definition = """ - insertion_qc_fname : varchar(64) - --- - insertion_qc_fvalue_float=null : float - insertion_qc_fvalue_bool=null : bool - insertion_qc_fvalue_str=null : varchar(32) - insertion_qc_fvalue_blob=null : blob - """ + qc_fvalue_name: v}, + skip_duplicates=True) @schema @@ -238,7 +170,7 @@ def make(self, key): try: json_field = json.loads(json_field) except json.decoder.JSONDecodeError: - # fix the json field before decording. + # fix the json field before decoding. for k, v in json_replace_map.items(): json_field = json_field.replace(k, v) json_field = json.loads(json_field) @@ -246,14 +178,16 @@ def make(self, key): if len(ephys_real.ProbeInsertion & key) == 1: probe_insertion_key = (ephys_real.ProbeInsertion & key).fetch1('KEY') else: - probe_insertion_key = (ephys.ProbeInsertion & key).fetch1('KEY') + return if 'qc' in json_field: - qc = (QCChoice & {'qc_label': json_field['qc']}).fetch1('qc') + qc = (qc_real.QCChoice & {'qc_label': json_field['qc']}).fetch1('qc') ProbeInsertionQC.insert1( dict(**probe_insertion_key, insertion_qc=qc)) + qc_types = (qc_real.QCType & 'qc_level="probe_insertion"').fetch('qc_type') + if 'extended_qc' in json_field: extended_qc = json_field['extended_qc'] diff --git a/ibl_pipeline/process/process_qc.py b/ibl_pipeline/process/process_qc.py index f59fb304..46dd048d 100644 --- a/ibl_pipeline/process/process_qc.py +++ b/ibl_pipeline/process/process_qc.py @@ -56,20 +56,12 @@ def process_alyxraw_qc( ) -def ingest_shadow_tables(): +def ingest_tables(): qc_ingest.SessionQCIngest.populate( display_progress=True, suppress_errors=True) -def ingest_real_tables(): - - QC_TABLES = ['SessionQC', 'SessionExtendedQC', 'SessionExtendedQC.Field'] - - for t in QC_TABLES: - copy_table(qc, qc_ingest, t) - - def main(fpath='/data/alyxfull.json'): logger.log(25, 'Insert to update alyxraw...') @@ -83,11 +75,8 @@ def main(fpath='/data/alyxfull.json'): logger.log(25, 'Ingesting Alyxraw for QC...') process_alyxraw_qc() - logger.log(25, 'Ingesting QC shadow tables...') - ingest_shadow_tables() - - logger.log(25, 'Copying real tables...') - ingest_real_tables() + logger.log(25, 'Ingesting QC tables...') + ingest_tables() if __name__ == '__main__': diff --git a/ibl_pipeline/qc.py b/ibl_pipeline/qc.py index 65f5d1e3..84c496af 100644 --- a/ibl_pipeline/qc.py +++ b/ibl_pipeline/qc.py @@ -40,23 +40,23 @@ class QCLevel(dj.Lookup): class QCType(dj.Lookup): definition = """ # Aspect of a session for quality check. e.g. task, behavior, experimenter… - qc_type : varchar(16) + qc_type : varchar(32) --- -> QCLevel qc_type_description='' : varchar(1000) """ - # contents = [ - # ['experimenter', 'session', 'Manual labeling of a session by user'], - # ['task', 'session', 'Quality check when running the task'], - # ['behavior', 'session', 'Behavior criteria'], - # ['videoBody', 'session', 'Quality check for video recording of body camera'], - # ['videoLeft', 'session', 'Quality check for video recording of left camera'], - # ['videoRight', 'session', 'Quality check for video recording of right camera'], - # ['dlc', 'session', 'Deep lab cut on behavioral video data'], - # ['tracing_exists', 'Histology tracing'], - # ['alignment_resolved', 'Ephys alignment with histology'] - # ] + contents = [ + ['experimenter', 'session', 'Manual labeling of a session by user'], + ['task', 'session', 'Quality check when running the task'], + ['behavior', 'session', 'Behavior criteria'], + ['videoBody', 'session', 'Quality check for video recording of body camera'], + ['videoLeft', 'session', 'Quality check for video recording of left camera'], + ['videoRight', 'session', 'Quality check for video recording of right camera'], + ['dlc', 'session', 'Deep lab cut processing on behavioral video data'], + ['tracing_exists', 'probe_insertion', 'Histology tracing'], + ['alignment_resolved', 'probe_insertion', 'Ephys alignment with histology'] + ] @schema From 5915df5613cc97fd9c012f7824d118b3a13a3aa7 Mon Sep 17 00:00:00 2001 From: shenshan Date: Fri, 26 Feb 2021 07:59:17 +0000 Subject: [PATCH 33/34] process probe insertion qc --- ibl_pipeline/ingest/qc.py | 76 ++++++++++++++++++++++-------- ibl_pipeline/process/process_qc.py | 13 +++++ 2 files changed, 69 insertions(+), 20 deletions(-) diff --git a/ibl_pipeline/ingest/qc.py b/ibl_pipeline/ingest/qc.py index 6b8a053c..71383f68 100644 --- a/ibl_pipeline/ingest/qc.py +++ b/ibl_pipeline/ingest/qc.py @@ -153,6 +153,10 @@ def make(self, key): class ProbeInsertionQCIngest(dj.Computed): definition = """ -> alyxraw.AlyxRaw.proj(probe_insertion_uuid='uuid') + --- + subject_uuid : uuid + session_start_time : datetime + probe_idx : tinyint """ key_source = dj.U('probe_insertion_uuid') & \ (alyxraw.AlyxRaw.Field & @@ -162,8 +166,6 @@ class ProbeInsertionQCIngest(dj.Computed): def make(self, key): - self.insert1(key) - key['uuid'] = key['probe_insertion_uuid'] json_field = grf(key, 'json') @@ -177,33 +179,67 @@ def make(self, key): if len(ephys_real.ProbeInsertion & key) == 1: probe_insertion_key = (ephys_real.ProbeInsertion & key).fetch1('KEY') + self.insert1(dict(**probe_insertion_key, probe_insertion_uuid=key['uuid'])) else: return if 'qc' in json_field: qc = (qc_real.QCChoice & {'qc_label': json_field['qc']}).fetch1('qc') - - ProbeInsertionQC.insert1( - dict(**probe_insertion_key, insertion_qc=qc)) + # skip ingestion if qc is "NOT SET" + # turn on skip_duplicates because there are chances to delete this table and reingest everything + if qc != 0: + qc_real.ProbeInsertionQC.insert1( + dict(**probe_insertion_key, insertion_qc=qc), + skip_duplicates=True) qc_types = (qc_real.QCType & 'qc_level="probe_insertion"').fetch('qc_type') + # example extended_qc field in alyx: + # "extended_qc": { + # "experimenter": "PASS", + # "tracing_exists": true, + # "alignment_count": 1, + # "alignment_stored": "2020-09-28T12:00:12_guido", + # "alignment_resolved": false + # } + if 'extended_qc' in json_field: - extended_qc = json_field['extended_qc'] + extended_qc_alyx = json_field['extended_qc'] for qc_type in qc_types: - for k, v in json_field['extended_qc'].items(): - if type(v) == float: - qc_fvalue_name = 'insertion_qc_fvalue_float' - elif v == "None": - pass - elif type(v) == str: - qc_fvalue_name = 'insertion_qc_fvalue_str' - else: - qc_fvalue_name = 'insertion_qc_fvalue_blob' - - ProbeInsertionExtendedQC.insert1( - {**probe_insertion_key, - 'insertion_qc_fname': k, - qc_fvalue_name: v}) + if qc_type in extended_qc_alyx: + # Only ingest when tracing tracing exists + if qc_type == 'tracing_exists' and extended_qc_alyx[qc_type]: + # if tracing exists, then set insertion_extended_qc to 10 (PASS) + qc_real.ProbeInsertionExtendedQC.insert1( + dict(**probe_insertion_key, qc_type=qc_type, + insertion_extended_qc=10), + skip_duplicates=True + ) + # Only ingest when alignment is resolved + if qc_type == 'alignment_resolved' and extended_qc_alyx[qc_type]: + + qc_real.ProbeInsertionExtendedQC.insert1( + dict(**probe_insertion_key, qc_type=qc_type, + insertion_extended_qc=10), + skip_duplicates=True + ) + + # check for alignment field + for k, v in json_field['extended_qc'].items(): + if 'alignment' in k and k != 'alignment_resolved': + if type(v) == float: + qc_fvalue_name = 'insertion_qc_fvalue_float' + elif v == "None": + pass + elif type(v) == str: + qc_fvalue_name = 'insertion_qc_fvalue_str' + else: + qc_fvalue_name = 'insertion_qc_fvalue_blob' + + qc_real.ProbeInsertionExtendedQC.Field.insert1( + {**probe_insertion_key, + 'qc_type': qc_type, + 'insertion_qc_fname': k, + qc_fvalue_name: v}, skip_duplicates=True) diff --git a/ibl_pipeline/process/process_qc.py b/ibl_pipeline/process/process_qc.py index 46dd048d..9fd95403 100644 --- a/ibl_pipeline/process/process_qc.py +++ b/ibl_pipeline/process/process_qc.py @@ -60,6 +60,16 @@ def ingest_tables(): qc_ingest.SessionQCIngest.populate( display_progress=True, suppress_errors=True) + qc_ingest.ProbeInsertionQCIngest.populate( + display_progress=True, suppress_errors=True) + + +def cleanup_qc_ingest(): + ''' + clean up the ProbeInsertionQC table to trigger ingestion if there is no alignment resolved entry + ''' + + (qc_ingest.ProbeInsertionQCIngest - (qc.ProbeInsertionExtendedQC & 'qc_type="alignment_resolved"')).delete() def main(fpath='/data/alyxfull.json'): @@ -78,6 +88,9 @@ def main(fpath='/data/alyxfull.json'): logger.log(25, 'Ingesting QC tables...') ingest_tables() + logger.log(25, 'Cleaning up ProbeInsertionQCIngest table...') + cleanup_qc_ingest() + if __name__ == '__main__': From b474bcc9282a487cdecbe6da93ca227502b1111e Mon Sep 17 00:00:00 2001 From: shenshan Date: Fri, 26 Feb 2021 08:25:12 +0000 Subject: [PATCH 34/34] Update the histology probe trajectory ingestion --- ibl_pipeline/histology.py | 336 ++++++++++++++++++-------------------- 1 file changed, 160 insertions(+), 176 deletions(-) diff --git a/ibl_pipeline/histology.py b/ibl_pipeline/histology.py index 0fa24220..adba42f5 100755 --- a/ibl_pipeline/histology.py +++ b/ibl_pipeline/histology.py @@ -1,17 +1,10 @@ import datajoint as dj -from . import reference, acquisition, data, ephys -from .ingest import histology as histology_ingest - -from os import path, environ +from . import reference, acquisition, data, ephys, qc import numpy as np from .utils import atlas -import pdb from tqdm import tqdm - -try: - from ibllib.pipes.ephys_alignment import EphysAlignment -except Exception as e: - Warning('Need to install the WIPhistologymayo branch for ibllib') +from ibllib.pipes.ephys_alignment import EphysAlignment +import warnings try: from oneibl.one import ONE @@ -29,40 +22,181 @@ schema = dj.schema(dj.config.get('database.prefix', '') + 'ibl_histology') +# ================= The temporary tables before the probe trajectories are finally resolved =================== + @schema -class ProbeTrajectory(dj.Imported): +class Provenance(dj.Lookup): definition = """ - # Probe Trajectory resolved by 3 users, ingested from ALF dataset probes.trajectory + # Method to estimate the probe trajectory, including Ephys aligned histology track, Histology track, Micro-manipulator, and Planned + provenance : tinyint unsigned # provenance code + --- + provenance_description : varchar(128) # type of trajectory + """ + contents = [ + (70, 'Ephys aligned histology track'), + (50, 'Histology track'), + (30, 'Micro-manipulator'), + (10, 'Planned'), + ] + + +@schema +class ProbeTrajectoryTemp(dj.Imported): + definition = """ + # Probe trajectory estimated with each method, ingested from Alyx table experiments.trajectoryestimate -> ephys.ProbeInsertion + -> Provenance --- -> [nullable] reference.CoordinateSystem + probe_trajectory_uuid: uuid x: float # (um) medio-lateral coordinate relative to Bregma, left negative y: float # (um) antero-posterior coordinate relative to Bregma, back negative z: float # (um) dorso-ventral coordinate relative to Bregma, ventral negative phi: float # (degrees)[-180 180] azimuth theta: float # (degrees)[0 180] polar angle depth: float # (um) insertion depth - beta=null: float # (degrees) roll angle of the probe - trajectory_ts=CURRENT_TIMESTAMP: timestamp + roll=null: float # (degrees) roll angle of the probe + trajectory_ts: datetime """ - key_source = ephys.ProbeInsertion & (data.FileRecord & 'dataset_name like "%probes.trajectory%"') + # this table rely on copying from the shadow table in ibl_pipeline.ingest.histology + + +@schema +class ChannelBrainLocationTemp(dj.Imported): + definition = """ + # Brain coordinates and region assignment of each channel, ingested from Alyx table experiments.channel + -> ProbeTrajectoryTemp + channel_brain_location_uuid : uuid + --- + channel_axial : decimal(6, 1) + channel_lateral : decimal(6, 1) + channel_x : decimal(6, 1) + channel_y : decimal(6, 1) + channel_z : decimal(6, 1) + -> reference.BrainRegion + """ + # this table rely on copying from the shadow table in ibl_pipeline.ingest.histology + + +@schema +class DepthBrainRegionTemp(dj.Computed): + definition = """ + # For each ProbeTrajectory, assign depth boundaries relative to the probe tip to each brain region covered by the trajectory + -> ProbeTrajectoryTemp + --- + region_boundaries : blob + region_label : blob + region_color : blob + region_id : blob + """ + key_source = ProbeTrajectoryTemp & ChannelBrainLocationTemp def make(self, key): - eID = str((acquisition.Session & key).fetch1('session_uuid')) - probes_trajectories = one.load(eID, dataset_types=['probes.trajectory']) + x, y, z, axial = (ChannelBrainLocationTemp & key).fetch( + 'channel_x', 'channel_y', 'channel_z', 'channel_axial', + order_by='channel_axial') + xyz_channels = np.c_[x, y, z] + key['region_boundaries'], key['region_label'], \ + key['region_color'], key['region_id'] = \ + EphysAlignment.get_histology_regions( + xyz_channels.astype('float')/1e6, axial.astype('float')) - probe_label = (ephys.ProbeInsertion & key).fetch1('probe_label') + self.insert1(key) - if not probe_label: - probe_label = 'probe0' + str(key['probe_idx']) - for probe_trajectory in probes_trajectories: - if type(probe_trajectory) == list: - probe_trajectory = probe_trajectory[0] - if probe_trajectory['label'] == probe_label: - probe_trajectory.pop('label') - self.insert1(dict(**key, **probe_trajectory)) +@schema +class ClusterBrainRegionTemp(dj.Computed): + definition = """ + # Brain region assignment to each cluster + -> ephys.DefaultCluster + -> ProbeTrajectoryTemp + -> ephys.ChannelGroup + --- + -> reference.BrainRegion + """ + key_source = ephys.DefaultCluster * Provenance & \ + ProbeTrajectoryTemp & ephys.ChannelGroup & ChannelBrainLocationTemp + + def make(self, key): + # pdb.set_trace() + channel_raw_inds, channel_local_coordinates = \ + (ephys.ChannelGroup & key).fetch1( + 'channel_raw_inds', 'channel_local_coordinates') + channel = (ephys.DefaultCluster & key).fetch1('cluster_channel') + if channel in channel_raw_inds: + channel_coords = np.squeeze( + channel_local_coordinates[channel_raw_inds == channel]) + else: + return + + q = ChannelBrainLocationTemp & key & \ + dict(channel_lateral=channel_coords[0], + channel_axial=channel_coords[1]) + + if len(q) == 1: + key['ontology'], key['acronym'] = q.fetch1( + 'ontology', 'acronym') + + self.insert1(key) + elif len(q) > 1: + ontology, acronym = q.fetch('ontology', 'acronym') + if len(np.unique(acronym)) == 1: + key['ontology'] = 'CCF 2017' + key['acronym'] = acronym[0] + self.insert1(key) + else: + print('Conflict regions') + else: + return + + +@schema +class ProbeBrainRegionTemp(dj.Computed): + definition = """ + # Brain regions assignment to each probe insertion, including the regions of finest granularity and their upper-level areas. + -> ProbeTrajectoryTemp + -> reference.BrainRegion + """ + key_source = ProbeTrajectoryTemp & ClusterBrainRegionTemp + + def make(self, key): + + regions = (dj.U('acronym') & (ClusterBrainRegionTemp & key)).fetch('acronym') + + associated_regions = [ + atlas.BrainAtlas.get_parents(acronym) + for acronym in regions] + list(regions) + + self.insert([dict(**key, ontology='CCF 2017', acronym=region) + for region in np.unique(np.hstack(associated_regions))]) + + +@schema +class ProbeTrajectory(dj.Imported): + definition = """ + # Probe Trajectory resolved by 3 users, ingested from ALF dataset probes.trajectory + -> ephys.ProbeInsertion + --- + -> [nullable] reference.CoordinateSystem + probe_trajectory_uuid: uuid + x: float # (um) medio-lateral coordinate relative to Bregma, left negative + y: float # (um) antero-posterior coordinate relative to Bregma, back negative + z: float # (um) dorso-ventral coordinate relative to Bregma, ventral negative + phi: float # (degrees)[-180 180] azimuth + theta: float # (degrees)[0 180] polar angle + depth: float # (um) insertion depth + roll=null: float # (degrees) roll angle of the probe + trajectory_ts=CURRENT_TIMESTAMP: timestamp + """ + key_source = ephys.ProbeInsertion & \ + (qc.ProbeInsertionExtendedQC & 'qc_type="alignment_resolved"') & \ + (ProbeTrajectoryTemp & 'provenance=70') + + def make(self, key): + probe_trajectory = (ProbeTrajectoryTemp & 'provenance=70' & key).fetch1() + probe_trajectory.pop('provenance') + self.insert1(probe_trajectory) @schema @@ -221,153 +355,3 @@ def make(self, key): # xyz_channels.astype('float')/1e6, axial.astype('float')) # self.insert1(key) - - -# ================= The following tables will replace the above ones eventually =================== - -@schema -class Provenance(dj.Lookup): - definition = """ - # Method to estimate the probe trajectory, including Ephys aligned histology track, Histology track, Micro-manipulator, and Planned - provenance : tinyint unsigned # provenance code - --- - provenance_description : varchar(128) # type of trajectory - """ - contents = [ - (70, 'Ephys aligned histology track'), - (50, 'Histology track'), - (30, 'Micro-manipulator'), - (10, 'Planned'), - ] - - -@schema -class ProbeTrajectoryTemp(dj.Imported): - definition = """ - # Probe trajectory estimated with each method, ingested from Alyx table experiments.trajectoryestimate - -> ephys.ProbeInsertion - -> Provenance - --- - -> [nullable] reference.CoordinateSystem - probe_trajectory_uuid: uuid - x: float # (um) medio-lateral coordinate relative to Bregma, left negative - y: float # (um) antero-posterior coordinate relative to Bregma, back negative - z: float # (um) dorso-ventral coordinate relative to Bregma, ventral negative - phi: float # (degrees)[-180 180] azimuth - theta: float # (degrees)[0 180] polar angle - depth: float # (um) insertion depth - roll=null: float # (degrees) roll angle of the probe - trajectory_ts: datetime - """ - # this table rely on copying from the shadow table in ibl_pipeline.ingest.histology - - -@schema -class ChannelBrainLocationTemp(dj.Imported): - definition = """ - # Brain coordinates and region assignment of each channel, ingested from Alyx table experiments.channel - -> ProbeTrajectoryTemp - channel_brain_location_uuid : uuid - --- - channel_axial : decimal(6, 1) - channel_lateral : decimal(6, 1) - channel_x : decimal(6, 1) - channel_y : decimal(6, 1) - channel_z : decimal(6, 1) - -> reference.BrainRegion - """ - # this table rely on copying from the shadow table in ibl_pipeline.ingest.histology - - -@schema -class DepthBrainRegionTemp(dj.Computed): - definition = """ - # For each ProbeTrajectory, assign depth boundaries relative to the probe tip to each brain region covered by the trajectory - -> ProbeTrajectoryTemp - --- - region_boundaries : blob - region_label : blob - region_color : blob - region_id : blob - """ - key_source = ProbeTrajectoryTemp & ChannelBrainLocationTemp - - def make(self, key): - - x, y, z, axial = (ChannelBrainLocationTemp & key).fetch( - 'channel_x', 'channel_y', 'channel_z', 'channel_axial', - order_by='channel_axial') - xyz_channels = np.c_[x, y, z] - key['region_boundaries'], key['region_label'], \ - key['region_color'], key['region_id'] = \ - EphysAlignment.get_histology_regions( - xyz_channels.astype('float')/1e6, axial.astype('float')) - - self.insert1(key) - - -@schema -class ClusterBrainRegionTemp(dj.Computed): - definition = """ - # Brain region assignment to each cluster - -> ephys.DefaultCluster - -> ProbeTrajectoryTemp - -> ephys.ChannelGroup - --- - -> reference.BrainRegion - """ - key_source = ephys.DefaultCluster * Provenance & \ - ProbeTrajectoryTemp & ephys.ChannelGroup & ChannelBrainLocationTemp - - def make(self, key): - # pdb.set_trace() - channel_raw_inds, channel_local_coordinates = \ - (ephys.ChannelGroup & key).fetch1( - 'channel_raw_inds', 'channel_local_coordinates') - channel = (ephys.DefaultCluster & key).fetch1('cluster_channel') - if channel in channel_raw_inds: - channel_coords = np.squeeze( - channel_local_coordinates[channel_raw_inds == channel]) - else: - return - - q = ChannelBrainLocationTemp & key & \ - dict(channel_lateral=channel_coords[0], - channel_axial=channel_coords[1]) - - if len(q) == 1: - key['ontology'], key['acronym'] = q.fetch1( - 'ontology', 'acronym') - - self.insert1(key) - elif len(q) > 1: - ontology, acronym = q.fetch('ontology', 'acronym') - if len(np.unique(acronym)) == 1: - key['ontology'] = 'CCF 2017' - key['acronym'] = acronym[0] - self.insert1(key) - else: - print('Conflict regions') - else: - return - - -@schema -class ProbeBrainRegionTemp(dj.Computed): - definition = """ - # Brain regions assignment to each probe insertion, including the regions of finest granularity and their upper-level areas. - -> ProbeTrajectoryTemp - -> reference.BrainRegion - """ - key_source = ProbeTrajectoryTemp & ClusterBrainRegionTemp - - def make(self, key): - - regions = (dj.U('acronym') & (ClusterBrainRegionTemp & key)).fetch('acronym') - - associated_regions = [ - atlas.BrainAtlas.get_parents(acronym) - for acronym in regions] + list(regions) - - self.insert([dict(**key, ontology='CCF 2017', acronym=region) - for region in np.unique(np.hstack(associated_regions))])