Ready4recording

brainbox/behavior/training.py

@@ -157,7 +157,7 @@ def get_subject_training_status(subj, date=None, details=True, one=None):
     if not trials:
         return
     sess_dates = list(trials.keys())
-    status, info = get_training_status(trials, task_protocol, ephys_sess, n_delay)
+    status, info, _ = get_training_status(trials, task_protocol, ephys_sess, n_delay)
 
     if details:
         if np.any(info.get('psych')):
@@ -265,13 +265,13 @@ def get_sessions(subj, date=None, one=None):
     if not np.any(np.array(task_protocol) == 'training'):
         ephys_sess = one.alyx.rest('sessions', 'list', subject=subj,
                                    date_range=[sess_dates[-1], sess_dates[0]],
-                                   django='json__PYBPOD_BOARD__icontains,ephys')
+                                   django='location__name__icontains,ephys')
         if len(ephys_sess) > 0:
             ephys_sess_dates = [sess['start_time'][:10] for sess in ephys_sess]
 
             n_delay = len(one.alyx.rest('sessions', 'list', subject=subj,
                                         date_range=[sess_dates[-1], sess_dates[0]],
-                                        django='json__SESSION_START_DELAY_SEC__gte,900'))
+                                        django='json__SESSION_DELAY_START__gte,900'))
         else:
             ephys_sess_dates = []
             n_delay = 0
@@ -313,23 +313,32 @@ def get_training_status(trials, task_protocol, ephys_sess_dates, n_delay):
 
     info = Bunch()
     trials_all = concatenate_trials(trials)
+    info.session_dates = list(trials.keys())
+    info.protocols = [p for p in task_protocol]
 
     # Case when all sessions are trainingChoiceWorld
     if np.all(np.array(task_protocol) == 'training'):
-        signed_contrast = get_signed_contrast(trials_all)
+        signed_contrast = np.unique(get_signed_contrast(trials_all))
         (info.perf_easy, info.n_trials,
          info.psych, info.rt) = compute_training_info(trials, trials_all)
-        if not np.any(signed_contrast == 0):
-            status = 'in training'
+
+        pass_criteria, criteria = criterion_1b(info.psych, info.n_trials, info.perf_easy, info.rt,
+                                               signed_contrast)
+        if pass_criteria:
+            failed_criteria = Bunch()
+            failed_criteria['NBiased'] = {'val': info.protocols, 'pass': False}
+            failed_criteria['Criteria'] = {'val': 'ready4ephysrig', 'pass': False}
+            status = 'trained 1b'
         else:
-            if criterion_1b(info.psych, info.n_trials, info.perf_easy, info.rt):
-                status = 'trained 1b'
-            elif criterion_1a(info.psych, info.n_trials, info.perf_easy):
+            failed_criteria = criteria
+            pass_criteria, criteria = criterion_1a(info.psych, info.n_trials, info.perf_easy, signed_contrast)
+            if pass_criteria:
                 status = 'trained 1a'
             else:
+                failed_criteria = criteria
                 status = 'in training'
 
-        return status, info
+        return status, info, failed_criteria
 
     # Case when there are < 3 biasedChoiceWorld sessions after reaching trained_1b criterion
     if ~np.all(np.array(task_protocol) == 'training') and \
@@ -338,45 +347,52 @@ def get_training_status(trials, task_protocol, ephys_sess_dates, n_delay):
         (info.perf_easy, info.n_trials,
          info.psych, info.rt) = compute_training_info(trials, trials_all)
 
-        return status, info
+        criteria = Bunch()
+        criteria['NBiased'] = {'val': info.protocols, 'pass': False}
+        criteria['Criteria'] = {'val': 'ready4ephysrig', 'pass': False}
+
+        return status, info, criteria
 
     # Case when there is biasedChoiceWorld or ephysChoiceWorld in last three sessions
     if not np.any(np.array(task_protocol) == 'training'):
 
         (info.perf_easy, info.n_trials,
          info.psych_20, info.psych_80,
          info.rt) = compute_bias_info(trials, trials_all)
-        # We are still on training rig and so all sessions should be biased
-        if len(ephys_sess_dates) == 0:
-            assert np.all(np.array(task_protocol) == 'biased')
-            if criterion_ephys(info.psych_20, info.psych_80, info.n_trials, info.perf_easy,
-                               info.rt):
-                status = 'ready4ephysrig'
-            else:
-                status = 'trained 1b'
 
-        elif len(ephys_sess_dates) < 3:
+        n_ephys = len(ephys_sess_dates)
+        info.n_ephys = n_ephys
+        info.n_delay = n_delay
+
+        # Criterion recording
+        pass_criteria, criteria = criteria_recording(n_ephys, n_delay, info.psych_20, info.psych_80, info.n_trials,
+                                                     info.perf_easy, info.rt)
+        if pass_criteria:
+            # Here the criteria doesn't actually fail but we have no other criteria to meet so we return this
+            failed_criteria = criteria
+            status = 'ready4recording'
+        else:
+            failed_criteria = criteria
             assert all(date in trials for date in ephys_sess_dates)
             perf_ephys_easy = np.array([compute_performance_easy(trials[k]) for k in
                                         ephys_sess_dates])
             n_ephys_trials = np.array([compute_n_trials(trials[k]) for k in ephys_sess_dates])
 
-            if criterion_delay(n_ephys_trials, perf_ephys_easy):
-                status = 'ready4delay'
-            else:
-                status = 'ready4ephysrig'
-
-        elif len(ephys_sess_dates) >= 3:
-            if n_delay > 0 and \
-                    criterion_ephys(info.psych_20, info.psych_80, info.n_trials, info.perf_easy,
-                                    info.rt):
-                status = 'ready4recording'
-            elif criterion_delay(info.n_trials, info.perf_easy):
+            pass_criteria, criteria = criterion_delay(n_ephys, n_ephys_trials, perf_ephys_easy)
+
+            if pass_criteria:
                 status = 'ready4delay'
             else:
-                status = 'ready4ephysrig'
+                failed_criteria = criteria
+                pass_criteria, criteria = criterion_ephys(info.psych_20, info.psych_80, info.n_trials,
+                                                          info.perf_easy, info.rt)
+                if pass_criteria:
+                    status = 'ready4ephysrig'
+                else:
+                    failed_criteria = criteria
+                    status = 'trained 1b'
 
-        return status, info
+        return status, info, failed_criteria
 
 
 def display_status(subj, sess_dates, status, perf_easy=None, n_trials=None, psych=None,
@@ -814,7 +830,7 @@ def compute_reaction_time(trials, stim_on_type='stimOn_times', stim_off_type='re
         return reaction_time, contrasts, n_contrasts,
 
 
-def criterion_1a(psych, n_trials, perf_easy):
+def criterion_1a(psych, n_trials, perf_easy, signed_contrast):
     """
     Returns bool indicating whether criteria for status 'trained_1a' are met.
 
@@ -825,6 +841,7 @@ def criterion_1a(psych, n_trials, perf_easy):
     - Lapse rate on both sides is less than 0.2
     - The total number of trials is greater than 200 for each session
     - Performance on easy contrasts > 80% for all sessions
+    - Zero contrast trials must be present
 
     Parameters
     ----------
@@ -835,24 +852,39 @@ def criterion_1a(psych, n_trials, perf_easy):
         The number for trials for each session.
     perf_easy : numpy.array of float
         The proportion of correct high contrast trials for each session.
+    signed_contrast: numpy.array
+        Unique list of contrasts displayed
 
     Returns
     -------
     bool
         True if the criteria are met for 'trained_1a'.
+    Bunch
+        Bunch containing breakdown of the passing/ failing critieria
 
     Notes
     -----
     The parameter thresholds chosen here were originally determined by averaging the parameter fits
     for a number of sessions determined to be of 'good' performance by an experimenter.
     """
 
-    criterion = (abs(psych[0]) < 16 and psych[1] < 19 and psych[2] < 0.2 and psych[3] < 0.2 and
-                 np.all(n_trials > 200) and np.all(perf_easy > 0.8))
-    return criterion
+    criteria = Bunch()
+    criteria['Zero_contrast'] = {'val': signed_contrast, 'pass': np.any(signed_contrast == 0)}
+    criteria['LapseLow_50'] = {'val': psych[2], 'pass': psych[2] < 0.2}
+    criteria['LapseHigh_50'] = {'val': psych[3], 'pass': psych[3] < 0.2}
+    criteria['Bias'] = {'val': psych[0], 'pass': abs(psych[0]) < 16}
+    criteria['Threshold'] = {'val': psych[1], 'pass': psych[1] < 19}
+    criteria['N_trials'] = {'val': n_trials, 'pass': np.all(n_trials > 200)}
+    criteria['Perf_easy'] = {'val': perf_easy, 'pass': np.all(perf_easy > 0.8)}
+
+    passing = np.all([v['pass'] for k, v in criteria.items()])
 
+    criteria['Criteria'] = {'val': 'trained_1a', 'pass': passing}
 
-def criterion_1b(psych, n_trials, perf_easy, rt):
+    return passing, criteria
+
+
+def criterion_1b(psych, n_trials, perf_easy, rt, signed_contrast):
     """
     Returns bool indicating whether criteria for trained_1b are met.
 
@@ -864,6 +896,7 @@ def criterion_1b(psych, n_trials, perf_easy, rt):
     - The total number of trials is greater than 400 for each session
     - Performance on easy contrasts > 90% for all sessions
     - The median response time across all zero contrast trials is less than 2 seconds
+    - Zero contrast trials must be present
 
     Parameters
     ----------
@@ -876,11 +909,15 @@ def criterion_1b(psych, n_trials, perf_easy, rt):
         The proportion of correct high contrast trials for each session.
     rt : float
         The median response time for zero contrast trials.
+    signed_contrast: numpy.array
+        Unique list of contrasts displayed
 
     Returns
     -------
     bool
         True if the criteria are met for 'trained_1b'.
+    Bunch
+        Bunch containing breakdown of the passing/ failing critieria
 
     Notes
     -----
@@ -890,17 +927,27 @@ def criterion_1b(psych, n_trials, perf_easy, rt):
     regrettably means that the maximum threshold fit for 1b is greater than for 1a, meaning the
     slope of the psychometric curve may be slightly less steep than 1a.
     """
-    criterion = (abs(psych[0]) < 10 and psych[1] < 20 and psych[2] < 0.1 and psych[3] < 0.1 and
-                 np.all(n_trials > 400) and np.all(perf_easy > 0.9) and rt < 2)
-    return criterion
+
+    criteria = Bunch()
+    criteria['Zero_contrast'] = {'val': signed_contrast, 'pass': np.any(signed_contrast == 0)}
+    criteria['LapseLow_50'] = {'val': psych[2], 'pass': psych[2] < 0.1}
+    criteria['LapseHigh_50'] = {'val': psych[3], 'pass': psych[3] < 0.1}
+    criteria['Bias'] = {'val': psych[0], 'pass': abs(psych[0]) < 10}
+    criteria['Threshold'] = {'val': psych[1], 'pass': psych[1] < 20}
+    criteria['N_trials'] = {'val': n_trials, 'pass': np.all(n_trials > 400)}
+    criteria['Perf_tasy'] = {'val': perf_easy, 'pass': np.all(perf_easy > 0.9)}
+    criteria['Reaction_time'] = {'val': rt, 'pass': rt < 2}
+
+    passing = np.all([v['pass'] for k, v in criteria.items()])
+
+    criteria['Criteria'] = {'val': 'trained_1b', 'pass': passing}
+
+    return passing, criteria
 
 
 def criterion_ephys(psych_20, psych_80, n_trials, perf_easy, rt):
     """
-    Returns bool indicating whether criteria for ready4ephysrig or ready4recording are met.
-
-    NB: The difference between these two is whether the sessions were acquired ot a recording rig
-    with a delay before the first trial. Neither of these two things are tested here.
+    Returns bool indicating whether criteria for ready4ephysrig are met.
 
     Criteria
     --------
@@ -929,21 +976,34 @@ def criterion_ephys(psych_20, psych_80, n_trials, perf_easy, rt):
     Returns
     -------
     bool
-        True if subject passes the ready4ephysrig or ready4recording criteria.
+        True if subject passes the ready4ephysrig criteria.
+    Bunch
+        Bunch containing breakdown of the passing/ failing critieria
     """
+    criteria = Bunch()
+    criteria['LapseLow_80'] = {'val': psych_80[2], 'pass': psych_80[2] < 0.1}
+    criteria['LapseHigh_80'] = {'val': psych_80[3], 'pass': psych_80[3] < 0.1}
+    criteria['LapseLow_20'] = {'val': psych_20[2], 'pass': psych_20[2] < 0.1}
+    criteria['LapseHigh_20'] = {'val': psych_20[3], 'pass': psych_20[3] < 0.1}
+    criteria['Bias_shift'] = {'val': psych_80[0] - psych_20[0], 'pass': psych_80[0] - psych_20[0] > 5}
+    criteria['N_trials'] = {'val': n_trials, 'pass': np.all(n_trials > 400)}
+    criteria['Perf_easy'] = {'val': perf_easy, 'pass': np.all(perf_easy > 0.9)}
+    criteria['Reaction_time'] = {'val': rt, 'pass': rt < 2}
 
-    criterion = (np.all(np.r_[psych_20[2:4], psych_80[2:4]] < 0.1) and  # lapse
-                 psych_80[0] - psych_20[0] > 5 and np.all(n_trials > 400) and  # bias shift and n trials
-                 np.all(perf_easy > 0.9) and rt < 2)  # overall performance and response times
-    return criterion
+    passing = np.all([v['pass'] for k, v in criteria.items()])
 
+    criteria['Criteria'] = {'val': 'ready4ephysrig', 'pass': passing}
 
-def criterion_delay(n_trials, perf_easy):
+    return passing, criteria
+
+
+def criterion_delay(n_ephys, n_trials, perf_easy):
     """
     Returns bool indicating whether criteria for 'ready4delay' is met.
 
     Criteria
     --------
+    - At least one session on an ephys rig
     - Total number of trials for any of the sessions is greater than 400
     - Performance on easy contrasts is greater than 90% for any of the sessions
 
@@ -959,9 +1019,69 @@ def criterion_delay(n_trials, perf_easy):
     -------
     bool
         True if subject passes the 'ready4delay' criteria.
+    Bunch
+        Bunch containing breakdown of the passing/ failing critieria
+    """
+
+    criteria = Bunch()
+    criteria['N_ephys'] = {'val': n_ephys, 'pass': n_ephys > 0}
+    criteria['N_trials'] = {'val': n_trials, 'pass': np.any(n_trials > 400)}
+    criteria['Perf_easy'] = {'val': perf_easy, 'pass': np.any(perf_easy > 0.9)}
+
+    passing = np.all([v['pass'] for k, v in criteria.items()])
+
+    criteria['Criteria'] = {'val': 'ready4delay', 'pass': passing}
+
+    return passing, criteria
+
+
+def criteria_recording(n_ephys, delay, psych_20, psych_80, n_trials, perf_easy, rt):
     """
-    criterion = np.any(n_trials > 400) and np.any(perf_easy > 0.9)
-    return criterion
+    Returns bool indicating whether criteria for ready4recording are met.
+
+    Criteria
+    --------
+    - At least 3 ephys sessions
+    - Delay on any session > 0
+    - Lapse on both sides < 0.1 for both bias blocks
+    - Bias shift between blocks > 5
+    - Total number of trials > 400 for all sessions
+    - Performance on easy contrasts > 90% for all sessions
+    - Median response time for zero contrast stimuli < 2 seconds
+
+    Parameters
+    ----------
+    psych_20 : numpy.array
+        The fit psychometric parameters for the blocks where probability of a left stimulus is 0.2.
+        Parameters are bias, threshold, lapse high, lapse low.
+    psych_80 : numpy.array
+        The fit psychometric parameters for the blocks where probability of a left stimulus is 0.8.
+        Parameters are bias, threshold, lapse high, lapse low.
+    n_trials : numpy.array
+        The number of trials for each session (typically three consecutive sessions).
+    perf_easy : numpy.array
+        The proportion of correct high contrast trials for each session (typically three
+        consecutive sessions).
+    rt : float
+        The median response time for zero contrast trials.
+
+    Returns
+    -------
+    bool
+        True if subject passes the ready4recording criteria.
+    Bunch
+        Bunch containing breakdown of the passing/ failing critieria
+    """
+
+    _, criteria = criterion_ephys(psych_20, psych_80, n_trials, perf_easy, rt)
+    criteria['N_ephys'] = {'val': n_ephys, 'pass': n_ephys >= 3}
+    criteria['N_delay'] = {'val': delay, 'pass': delay > 0}
+
+    passing = np.all([v['pass'] for k, v in criteria.items()])
+
+    criteria['Criteria'] = {'val': 'ready4recording', 'pass': passing}
+
+    return passing, criteria
 
 
 def plot_psychometric(trials, ax=None, title=None, plot_ci=False, ci_alpha=0.032, **kwargs):