Merge branch 'release/2.25.0'

Author: oliche

MANIFEST.in

@@ -5,10 +5,12 @@ include ibllib/atlas/cosmos.npy
 include ibllib/atlas/swanson.npy
 include ibllib/atlas/mappings.pqt
 include ibllib/io/extractors/extractor_types.json
+include ibllib/io/extractors/task_extractor_map.json
 include brainbox/tests/wheel_test.p
 recursive-include brainbox/tests/fixtures *
 recursive-include ibllib/qc/reference *
 graft ibllib/tests/extractors/data
 graft ibllib/io/extractors/ephys_sessions
+graft ibllib/io/extractors/mesoscope
 graft ibllib/tests/fixtures
 recursive-include oneibl/tests/fixtures *

brainbox/behavior/__init__.py

@@ -0,0 +1 @@
+"""Behaviour analysis functions for the IBL task."""

brainbox/behavior/dlc.py

@@ -12,7 +12,7 @@
 from scipy.stats import zscore
 
 from neurodsp.smooth import smooth_interpolate_savgol
-from brainbox.processing import bincount2D
+from iblutil.numerical import bincount2D
 import brainbox.behavior.wheel as bbox_wheel
 
 logger = logging.getLogger('ibllib')

brainbox/behavior/pyschofit.py

@@ -1,5 +1,5 @@
 """
-The psychofit toolbox contains tools to fit two-alternative psychometric
+(DEPRECATED) The psychofit toolbox contains tools to fit two-alternative psychometric
 data. The fitting is done using maximal likelihood estimation: one
 assumes that the responses of the subject are given by a binomial
 distribution whose mean is given by the psychometric function.
@@ -16,14 +16,28 @@
 For more info, see:
   - Examples:           Examples of use of psychofit toolbox
 Matteo Carandini, 2000-2015
+
+NB: USE THE PSYCHOFIT PIP PACKAGE INSTEAD.
 """
 
 import functools
+import warnings
+import traceback
+import logging
+
 import numpy as np
 import scipy.optimize
 from scipy.special import erf
 
 
+for line in traceback.format_stack():
+    print(line.strip())
+
+msg = 'brainbox.behavior.pyschofit has been deprecated. Install psychofit via pip. See stack above'
+warnings.warn(msg, DeprecationWarning)
+logging.getLogger(__name__).warning(msg)
+
+
 def mle_fit_psycho(data, P_model='weibull', parstart=None, parmin=None, parmax=None, nfits=5):
     """
     Maximumum likelihood fit of psychometric function.

brainbox/behavior/training.py

@@ -13,7 +13,7 @@
 from one.api import ONE
 from one.alf.exceptions import ALFObjectNotFound
 
-import brainbox.behavior.pyschofit as psy
+import psychofit as psy
 
 _logger = logging.getLogger('ibllib')
 
@@ -123,9 +123,9 @@ def get_subject_training_status(subj, date=None, details=True, one=None):
 
 def get_sessions(subj, date=None, one=None):
     """
-    Download and load in training data for a specfied subject. If a date is given it will load data
-    from the three (or as many are available) previous sessions up to the specified date, if not it
-    will load data from the last three training sessions that have data available
+    Download and load in training data for a specified subject. If a date is given it will load
+    data from the three (or as many as are available) previous sessions up to the specified date.
+    If not it will load data from the last three training sessions that have data available.
 
     :param subj: subject nickname (must match the name registered on Alyx)
     :type subj: string
@@ -227,7 +227,7 @@ def get_training_status(trials, task_protocol, ephys_sess_dates, n_delay):
     """
     Compute training status of a subject from three consecutive training datasets
 
-    :param trials: dict containing trials objects from three consective training sessions
+    :param trials: dict containing trials objects from three consecutive training sessions
     :type trials: Bunch
     :param task_protocol: task protocol used for the three training session, can be 'training',
     'biased' or 'ephys'
@@ -385,7 +385,7 @@ def compute_training_info(trials, trials_all):
     """
     Compute all relevant performance metrics for when subject is on trainingChoiceWorld
 
-    :param trials: dict containing trials objects from three consective training sessions,
+    :param trials: dict containing trials objects from three consecutive training sessions,
     keys are session dates
     :type trials: Bunch
     :param trials_all: trials object with data concatenated over three training sessions
@@ -410,7 +410,7 @@ def compute_bias_info(trials, trials_all):
     """
     Compute all relevant performance metrics for when subject is on biasedChoiceWorld
 
-    :param trials: dict containing trials objects from three consective training sessions,
+    :param trials: dict containing trials objects from three consecutive training sessions,
     keys are session dates
     :type trials: Bunch
     :param trials_all: trials object with data concatenated over three training sessions
@@ -667,7 +667,7 @@ def criterion_delay(n_trials, perf_easy):
 
 def plot_psychometric(trials, ax=None, title=None, plot_ci=False, ci_aplha=0.32, **kwargs):
     """
-    Function to plot pyschometric curve plots a la datajoint webpage
+    Function to plot psychometric curve plots a la datajoint webpage
     :param trials:
     :return:
     """
@@ -730,7 +730,7 @@ def plot_psychometric(trials, ax=None, title=None, plot_ci=False, ci_aplha=0.32,
 
 def plot_reaction_time(trials, ax=None, title=None, plot_ci=False, ci_alpha=0.32, **kwargs):
     """
-    Function to plot reaction time against contrast a la datajoint webpage (inversed for some reason??)
+    Function to plot reaction time against contrast a la datajoint webpage (inverted for some reason??)
     :param trials:
     :return:
     """

brainbox/behavior/wheel.py

@@ -1,8 +1,13 @@
 """
-Set of functions to handle wheel data
+Set of functions to handle wheel data.
 """
+import logging
+import warnings
+import traceback
+
 import numpy as np
 from numpy import pi
+from iblutil.numerical import between_sorted
 import scipy.interpolate as interpolate
 import scipy.signal
 from scipy.linalg import hankel
@@ -13,11 +18,11 @@
 __all__ = ['cm_to_deg',
            'cm_to_rad',
            'interpolate_position',
-           'last_movement_onset',
+           'get_movement_onset',
            'movements',
            'samples_to_cm',
            'traces_by_trial',
-           'velocity_smoothed']
+           'velocity_filtered']
 
 # Define some constants
 ENC_RES = 1024 * 4  # Rotary encoder resolution, assumes X4 encoding
@@ -49,6 +54,8 @@ def interpolate_position(re_ts, re_pos, freq=1000, kind='linear', fill_gaps=None
         Timestamps of interpolated positions
     """
     t = np.arange(re_ts[0], re_ts[-1], 1 / freq)  # Evenly resample at frequency
+    if t[-1] > re_ts[-1]:
+        t = t[:-1]  # Occasionally due to precision errors the last sample may be outside of range.
     yinterp = interpolate.interp1d(re_ts, re_pos, kind=kind)(t)
 
     if fill_gaps:
@@ -63,7 +70,7 @@ def interpolate_position(re_ts, re_pos, freq=1000, kind='linear', fill_gaps=None
 
 def velocity(re_ts, re_pos):
     """
-    Compute wheel velocity from non-uniformly sampled wheel data. Returns the velocity
+    (DEPRECATED) Compute wheel velocity from non-uniformly sampled wheel data. Returns the velocity
     at the same samples locations as the position through interpolation.
 
     Parameters
@@ -78,6 +85,13 @@ def velocity(re_ts, re_pos):
     np.ndarray
         numpy array of velocities
     """
+    for line in traceback.format_stack():
+        print(line.strip())
+
+    msg = 'brainbox.behavior.wheel.velocity has been deprecated. Use velocity_filtered instead.'
+    warnings.warn(msg, DeprecationWarning)
+    logging.getLogger(__name__).warning(msg)
+
     dp = np.diff(re_pos)
     dt = np.diff(re_ts)
     # Compute raw velocity
@@ -92,12 +106,23 @@ def velocity(re_ts, re_pos):
 
 def velocity_filtered(pos, fs, corner_frequency=20, order=8):
     """
-    Compute wheel velocity from uniformly sampled wheel data
+    Compute wheel velocity from uniformly sampled wheel data.
+
+    pos: array_like
+        Vector of uniformly sampled wheel positions.
+    fs : float
+        Frequency in Hz of the sampling frequency.
+    corner_frequency : float
+       Corner frequency of low-pass filter.
+    order : int
+        Order of Butterworth filter.
 
-    :param pos: vector of uniformly sampled wheel positions
-    :param fs: scalar, sampling frequency
-    :param corner_frequency: scalar, corner frequency of low-pass filter
-    :param order: scalar, order of Butterworth filter
+    Returns
+    -------
+    vel : np.ndarray
+        Array of velocity values.
+    acc : np.ndarray
+        Array of acceleration values.
     """
     sos = scipy.signal.butter(**{'N': order, 'Wn': corner_frequency / fs * 2, 'btype': 'lowpass'}, output='sos')
     vel = np.insert(np.diff(scipy.signal.sosfiltfilt(sos, pos)), 0, 0) * fs
@@ -107,7 +132,7 @@ def velocity_filtered(pos, fs, corner_frequency=20, order=8):
 
 def velocity_smoothed(pos, freq, smooth_size=0.03):
     """
-    Compute wheel velocity from uniformly sampled wheel data
+    (DEPRECATED) Compute wheel velocity from uniformly sampled wheel data.
 
     Parameters
     ----------
@@ -125,6 +150,13 @@ def velocity_smoothed(pos, freq, smooth_size=0.03):
     acc : np.ndarray
         Array of acceleration values
     """
+    for line in traceback.format_stack():
+        print(line.strip())
+
+    msg = 'brainbox.behavior.wheel.velocity_smoothed has been deprecated. Use velocity_filtered instead.'
+    warnings.warn(msg, DeprecationWarning)
+    logging.getLogger(__name__).warning(msg)
+
     # Define our smoothing window with an area of 1 so the units won't be changed
     std_samps = np.round(smooth_size * freq)  # Standard deviation relative to sampling frequency
     N = std_samps * 6  # Number of points in the Gaussian covering +/-3 standard deviations
@@ -141,15 +173,24 @@ def velocity_smoothed(pos, freq, smooth_size=0.03):
 
 def last_movement_onset(t, vel, event_time):
     """
-    Find the time at which movement started, given an event timestamp that occurred during the
-    movement.  Movement start is defined as the first sample after the velocity has been zero
-    for at least 50ms.  Wheel inputs should be evenly sampled.
+    (DEPRECATED) Find the time at which movement started, given an event timestamp that occurred during the
+    movement.
+
+    Movement start is defined as the first sample after the velocity has been zero for at least 50ms.
+    Wheel inputs should be evenly sampled.
 
     :param t: numpy array of wheel timestamps in seconds
     :param vel: numpy array of wheel velocities
     :param event_time: timestamp anywhere during movement of interest, e.g. peak velocity
     :return: timestamp of movement onset
     """
+    for line in traceback.format_stack():
+        print(line.strip())
+
+    msg = 'brainbox.behavior.wheel.last_movement_onset has been deprecated. Use get_movement_onset instead.'
+    warnings.warn(msg, DeprecationWarning)
+    logging.getLogger(__name__).warning(msg)
+
     # Look back from timestamp
     threshold = 50e-3
     mask = t < event_time
@@ -166,6 +207,42 @@ def last_movement_onset(t, vel, event_time):
     return t
 
 
+def get_movement_onset(intervals, event_times):
+    """
+    Find the time at which movement started, given an event timestamp that occurred during the
+    movement.
+
+    Parameters
+    ----------
+    intervals : numpy.array
+        The wheel movement intervals.
+    event_times : numpy.array
+        Sorted event timestamps anywhere during movement of interest, e.g. peak velocity, feedback
+        time.
+
+    Returns
+    -------
+    numpy.array
+        An array the length of event_time of intervals.
+
+    Examples
+    --------
+    Find the last movement onset before each trial response time
+
+    >>> trials = one.load_object(eid, 'trials')
+    >>> wheelMoves = one.load_object(eid, 'wheelMoves')
+    >>> onsets = last_movement_onset(wheelMoves.intervals, trials.response_times)
+    """
+    if not np.all(np.diff(event_times) > 0):
+        raise ValueError('event_times must be in ascending order.')
+    onsets = np.full(event_times.size, np.nan)
+    for i in np.arange(intervals.shape[0]):
+        onset = between_sorted(event_times, intervals[i, :])
+        if np.any(onset):
+            onsets[onset] = intervals[i, 0]
+    return onsets
+
+
 def movements(t, pos, freq=1000, pos_thresh=8, t_thresh=.2, min_gap=.1, pos_thresh_onset=1.5,
               min_dur=.05, make_plots=False):
     """
@@ -296,7 +373,7 @@ def movements(t, pos, freq=1000, pos_thresh=8, t_thresh=.2, min_gap=.1, pos_thre
     if make_plots:
         fig, axes = plt.subplots(nrows=2, sharex='all')
         indices = np.sort(np.hstack((onset_samps, offset_samps)))  # Points to split trace
-        vel, acc = velocity_smoothed(pos, freq, 0.015)
+        vel, acc = velocity_filtered(pos, freq)
 
         # Plot the wheel position and velocity
         for ax, y in zip(axes, (pos, vel)):
@@ -440,6 +517,6 @@ def to_mask(a, b):
     return [(cuts[n][0, :], cuts[n][1, :]) for n in range(len(cuts))] if separate else cuts
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     import doctest
     doctest.testmod()

brainbox/ephys_plots.py

@@ -3,7 +3,8 @@
 import matplotlib.pyplot as plt
 from brainbox.plot_base import (ImagePlot, ScatterPlot, ProbePlot, LinePlot, plot_line,
                                 plot_image, plot_probe, plot_scatter, arrange_channels2banks)
-from brainbox.processing import bincount2D, compute_cluster_average
+from brainbox.processing import compute_cluster_average
+from iblutil.numerical import bincount2D
 from ibllib.atlas.regions import BrainRegions