Source code for brainbox.behavior.wheel

Set of functions to handle wheel data
import numpy as np
from numpy import pi
import scipy.interpolate as interpolate
from scipy.signal import convolve, gaussian
from scipy.linalg import hankel
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
# from import WHEEL_TICKS  # FIXME Circular dependencies

__all__ = ['cm_to_deg',

# Define some constants
ENC_RES = 1024 * 4  # Rotary encoder resolution, assumes X4 encoding
WHEEL_DIAMETER = 3.1 * 2  # Wheel diameter in cm

[docs]def interpolate_position(re_ts, re_pos, freq=1000, kind='linear', fill_gaps=None): """ Return linearly interpolated wheel position. Parameters ---------- re_ts : array_like Array of timestamps re_pos: array_like Array of unwrapped wheel positions freq : float frequency in Hz of the interpolation kind : {'linear', 'cubic'} Type of interpolation. Defaults to linear interpolation. fill_gaps : float Minimum gap length to fill. For gaps over this time (seconds), forward fill values before interpolation Returns ------- yinterp : array Interpolated position t : array Timestamps of interpolated positions """ t = np.arange(re_ts[0], re_ts[-1], 1 / freq) # Evenly resample at frequency yinterp = interpolate.interp1d(re_ts, re_pos, kind=kind)(t) if fill_gaps: # Find large gaps and forward fill @fixme This is inefficient gaps, = np.where(np.diff(re_ts) >= fill_gaps) for i in gaps: yinterp[(t >= re_ts[i]) & (t < re_ts[i + 1])] = re_pos[i] return yinterp, t
def velocity(re_ts, re_pos): """ Compute wheel velocity from non-uniformly sampled wheel data. Returns the velocity at the same samples locations as the position through interpolation. Parameters ---------- re_ts : array_like Array of timestamps re_pos: array_like Array of unwrapped wheel positions Returns ------- np.ndarray numpy array of velocities """ dp = np.diff(re_pos) dt = np.diff(re_ts) # Compute raw velocity vel = dp / dt # Compute velocity time scale tv = re_ts[:-1] + dt / 2 # interpolate over original time scale if tv.size > 1: ifcn = interpolate.interp1d(tv, vel, fill_value="extrapolate") return ifcn(re_ts)
[docs]def velocity_smoothed(pos, freq, smooth_size=0.03): """ Compute wheel velocity from uniformly sampled wheel data Parameters ---------- pos : array_like Array of wheel positions smooth_size : float Size of Gaussian smoothing window in seconds freq : float Sampling frequency of the data Returns ------- vel : np.ndarray Array of velocity values acc : np.ndarray Array of acceleration values """ # 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 gauss_std = (N - 1) / 6 win = gaussian(N, gauss_std) win = win / win.sum() # Normalize amplitude # Convolve and multiply by sampling frequency to restore original units vel = np.insert(convolve(np.diff(pos), win, mode='same'), 0, 0) * freq acc = np.insert(convolve(np.diff(vel), win, mode='same'), 0, 0) * freq return vel, acc
[docs]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. :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 """ # Look back from timestamp threshold = 50e-3 mask = t < event_time times = t[mask] vel = vel[mask] t = None # Initialize for i, t in enumerate(times[::-1]): i = times.size - i idx = np.min(np.where((t - times) < threshold)) if np.max(np.abs(vel[idx:i])) < 0.5: break # Return timestamp return t
[docs]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): """ Detect wheel movements. Parameters ---------- t : array_like An array of evenly sampled wheel timestamps in absolute seconds pos : array_like An array of evenly sampled wheel positions freq : int The sampling rate of the wheel data pos_thresh : float The minimum required movement during the t_thresh window to be considered part of a movement t_thresh : float The time window over which to check whether the pos_thresh has been crossed min_gap : float The minimum time between one movement's offset and another movement's onset in order to be considered separate. Movements with a gap smaller than this are 'stictched together' pos_thresh_onset : float A lower threshold for finding precise onset times. The first position of each movement transition that is this much bigger than the starting position is considered the onset min_dur : float The minimum duration of a valid movement. Detected movements shorter than this are ignored make_plots : boolean Plot trace of position and velocity, showing detected onsets and offsets Returns ------- onsets : np.ndarray Timestamps of detected movement onsets offsets : np.ndarray Timestamps of detected movement offsets peak_amps : np.ndarray The absolute maximum amplitude of each detected movement, relative to onset position peak_vel_times : np.ndarray Timestamps of peak velocity for each detected movement """ # Wheel position must be evenly sampled. dt = np.diff(t) assert np.all(np.abs(dt - dt.mean()) < 1e-10), 'Values not evenly sampled' # Convert the time threshold into number of samples given the sampling frequency t_thresh_samps = int(np.round(t_thresh * freq)) max_disp = np.empty(t.size, dtype=float) # initialize array of total wheel displacement # Calculate a Hankel matrix of size t_thresh_samps in batches. This is effectively a # sliding window within which we look for changes in position greater than pos_thresh BATCH_SIZE = 10000 # do this in batches in order to keep memory usage reasonable c = 0 # index of 'window' position while True: i2proc = np.arange(BATCH_SIZE) + c i2proc = i2proc[i2proc < t.size] w2e = hankel(pos[i2proc], np.full(t_thresh_samps, np.nan)) # Below is the total change in position for each window max_disp[i2proc] = np.nanmax(w2e, axis=1) - np.nanmin(w2e, axis=1) c += BATCH_SIZE - t_thresh_samps if i2proc[-1] == t.size - 1: break moving = max_disp > pos_thresh # for each window is the change in position greater than # our threshold? moving = np.insert(moving, 0, False) # First sample should always be not moving to ensure # we have an onset moving[-1] = False # Likewise, ensure we always end on an offset onset_samps = np.where(~moving[:-1] & moving[1:])[0] offset_samps = np.where(moving[:-1] & ~moving[1:])[0] too_short = np.where((onset_samps[1:] - offset_samps[:-1]) / freq < min_gap)[0] for p in too_short: moving[offset_samps[p]:onset_samps[p + 1] + 1] = True onset_samps = np.where(~moving[:-1] & moving[1:])[0] onsets_disp_arr = np.empty((onset_samps.size, t_thresh_samps)) c = 0 cwt = 0 while onset_samps.size != 0: i2proc = np.arange(BATCH_SIZE) + c icomm = np.intersect1d(i2proc[:-t_thresh_samps - 1], onset_samps, assume_unique=True) itpltz = np.intersect1d(i2proc[:-t_thresh_samps - 1], onset_samps, return_indices=True, assume_unique=True)[1] i2proc = i2proc[i2proc < t.size] if icomm.size > 0: w2e = hankel(pos[i2proc], np.full(t_thresh_samps, np.nan)) w2e = np.abs((w2e.T - w2e[:, 0]).T) onsets_disp_arr[cwt + np.arange(icomm.size), :] = w2e[itpltz, :] cwt += icomm.size c += BATCH_SIZE - t_thresh_samps if i2proc[-1] >= onset_samps[-1]: break has_onset = onsets_disp_arr > pos_thresh_onset A = np.argmin(np.fliplr(has_onset).T, axis=0) onset_lags = t_thresh_samps - A onset_samps = onset_samps + onset_lags - 1 onsets = t[onset_samps] offset_samps = np.where(moving[:-1] & ~moving[1:])[0] offsets = t[offset_samps] durations = offsets - onsets too_short = durations < min_dur onset_samps = onset_samps[~too_short] onsets = onsets[~too_short] offset_samps = offset_samps[~too_short] offsets = offsets[~too_short] moveGaps = onsets[1:] - offsets[:-1] gap_too_small = moveGaps < min_gap if onsets.size > 0: onsets = onsets[np.insert(~gap_too_small, 0, True)] # always keep first onset onset_samps = onset_samps[np.insert(~gap_too_small, 0, True)] offsets = offsets[np.append(~gap_too_small, True)] # always keep last offset offset_samps = offset_samps[np.append(~gap_too_small, True)] # Calculate the peak amplitudes - # the maximum absolute value of the difference from the onset position peaks = (pos[m + np.abs(pos[m:n] - pos[m]).argmax()] - pos[m] for m, n in zip(onset_samps, offset_samps)) peak_amps = np.fromiter(peaks, dtype=float, count=onsets.size) N = 10 # Number of points in the Gaussian STDEV = 1.8 # Equivalent to a width factor (alpha value) of 2.5 gauss = gaussian(N, STDEV) # A 10-point Gaussian window of a given s.d. vel = convolve(np.diff(np.insert(pos, 0, 0)), gauss, mode='same') # For each movement period, find the timestamp where the absolute velocity was greatest peaks = (t[m + np.abs(vel[m:n]).argmax()] for m, n in zip(onset_samps, offset_samps)) peak_vel_times = np.fromiter(peaks, dtype=float, count=onsets.size) 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) # Plot the wheel position and velocity for ax, y in zip(axes, (pos, vel)): ax.plot(onsets, y[onset_samps], 'go') ax.plot(offsets, y[offset_samps], 'bo') t_split = np.split(np.vstack((t, y)).T, indices, axis=0) ax.add_collection(LineCollection(t_split[1::2], colors='r')) # Moving ax.add_collection(LineCollection(t_split[0::2], colors='k')) # Not moving axes[1].autoscale() # rescale after adding line collections axes[0].autoscale() axes[0].set_ylabel('position') axes[1].set_ylabel('velocity') axes[1].set_xlabel('time') axes[0].legend(['onsets', 'offsets', 'in movement']) return onsets, offsets, peak_amps, peak_vel_times
[docs]def cm_to_deg(positions, wheel_diameter=WHEEL_DIAMETER): """ Convert wheel position to degrees turned. This may be useful for e.g. calculating velocity in revolutions per second :param positions: array of wheel positions in cm :param wheel_diameter: the diameter of the wheel in cm :return: array of wheel positions in degrees turned # Example: Convert linear cm to degrees >>> cm_to_deg(3.142 * WHEEL_DIAMETER) 360.04667846020925 # Example: Get positions in deg from cm for 5cm diameter wheel >>> import numpy as np >>> cm_to_deg(np.array([0.0270526 , 0.04057891, 0.05410521, 0.06763151]), wheel_diameter=5) array([0.61999992, 0.93000011, 1.24000007, 1.55000003]) """ return positions / (wheel_diameter * pi) * 360
[docs]def cm_to_rad(positions, wheel_diameter=WHEEL_DIAMETER): """ Convert wheel position to radians. This may be useful for e.g. calculating angular velocity. :param positions: array of wheel positions in cm :param wheel_diameter: the diameter of the wheel in cm :return: array of wheel angle in radians # Example: Convert linear cm to radians >>> cm_to_rad(1) 0.3225806451612903 # Example: Get positions in rad from cm for 5cm diameter wheel >>> import numpy as np >>> cm_to_rad(np.array([0.0270526 , 0.04057891, 0.05410521, 0.06763151]), wheel_diameter=5) array([0.01082104, 0.01623156, 0.02164208, 0.0270526 ]) """ return positions * (2 / wheel_diameter)
[docs]def samples_to_cm(positions, wheel_diameter=WHEEL_DIAMETER, resolution=ENC_RES): """ Convert wheel position samples to cm linear displacement. This may be useful for inter-converting threshold units :param positions: array of wheel positions in sample counts :param wheel_diameter: the diameter of the wheel in cm :param resolution: resolution of the rotary encoder :return: array of wheel angle in radians # Example: Get resolution in linear cm >>> samples_to_cm(1) 0.004755340442445488 # Example: Get positions in linear cm for 4X, 360 ppr encoder >>> import numpy as np >>> samples_to_cm(np.array([2, 3, 4, 5, 6, 7, 6, 5, 4]), resolution=360*4) array([0.0270526 , 0.04057891, 0.05410521, 0.06763151, 0.08115781, 0.09468411, 0.08115781, 0.06763151, 0.05410521]) """ return positions / resolution * pi * wheel_diameter
def direction_changes(t, vel, intervals): """ Find the direction changes for the given movement intervals. Parameters ---------- t : array_like An array of evenly sampled wheel timestamps in absolute seconds vel : array_like An array of evenly sampled wheel positions intervals : array_like An n-by-2 array of wheel movement intervals Returns ---------- times : iterable A list of numpy arrays of direction change timestamps, one array per interval indices : iterable A list of numpy arrays containing indices of direction changes; the size of times """ indices = [] times = [] chg = np.insert(np.diff(np.sign(vel)) != 0, 0, 0) for on, off in intervals.reshape(-1, 2): mask = np.logical_and(t > on, t < off) ind, = np.where(np.logical_and(mask, chg)) times.append(t[ind]) indices.append(ind) return times, indices
[docs]def traces_by_trial(t, *args, start=None, end=None, separate=True): """ Returns list of tuples of positions and velocity for samples between stimulus onset and feedback. :param t: numpy array of timestamps :param args: optional numpy arrays of the same length as timestamps, such as positions, velocities or accelerations :param start: start timestamp or array thereof :param end: end timestamp or array thereof :param separate: when True, the output is returned as tuples list of the form [(t, args[0], args[1]), ...], when False, the output is a list of n-by-m ndarrays where n = number of positional args and m = len(t) :return: list of sliced arrays where length == len(start) """ if start is None: start = t[0] if end is None: end = t[-1] traces = np.stack((t, *args)) assert len(start) == len(end), 'number of start timestamps must equal end timestamps' def to_mask(a, b): return np.logical_and(t > a, t < b) cuts = [traces[:, to_mask(s, e)] for s, e in zip(start, end)] return [(cuts[n][0, :], cuts[n][1, :]) for n in range(len(cuts))] if separate else cuts
if __name__ == "__main__": import doctest doctest.testmod()