Compute average firing rate of a spike train over trials

firingrate.py

def da_firing_rate(df, dt, duration, gaussian_std=None, center_time=True):
    '''computes Dayan and Abbot average firing rate or <r>
    df: pandas dataframe with a "start" column in seconds and a
        "trial" column. Starts are relative to the start of a trial
    dt: timestep for computing histogram
    duration: length of a trial in seconds
    gaussian_std: if None, no smoothing is applied. Units: seconds.
    center_time: if True, the returned time vector is the same length as
                the firing rate. Otherwise, the time vector is the bin edges,
                which are useful for histogram style plots.
    returns (fr, t) where fr is the firing rate and t is a time vector
    '''
    t = np.arange(0, duration, dt)
    T = len(unit_data.trial.drop_duplicates())
    n, bin_edges = np.histogram(unit_data.start, t)
    firing_rate = n * (1/T) * (1/dt)
    if gaussian_std:
        std_samp = gaussian_std * 1/dt
        window = gaussian(int(std_samp*10), std_samp)
        window /= sum(window)
        firing_rate = fftconvolve(firing_rate, window, 'same')
    if center_time:
        t = t[:-1]+dt/2
    return firing_rate, t