elephant.statistics.cv2¶
- elephant.statistics.cv2(time_intervals, with_nan=False)[source]¶
Calculate the measure of Cv2 for a sequence of time intervals between events (Holt et al., 1996).
Given a vector \(I\) containing a sequence of intervals, the Cv2 is defined as:
\[Cv2 := \frac{1}{N} \sum_{i=1}^{N-1} \frac{2|I_{i+1}-I_i|} {|I_{i+1}+I_i|}\]The Cv2 is typically computed as a substitute for the classical coefficient of variation (Cv) for sequences of events which include some (relatively slow) rate fluctuation. As with the Cv, Cv2=1 for a sequence of intervals generated by a Poisson process.
- Parameters:
- time_intervalspq.Quantity or np.ndarray or list
Vector of consecutive time intervals.
- with_nanbool, optional
If True, cv2 of a spike train with less than two spikes results in a np.NaN value and a warning is raised. If False, ValueError exception is raised with a spike train with less than two spikes. Default: True
- Returns:
- float
The Cv2 of the inter-spike interval of the input sequence.
- Raises:
- ValueError
If an empty list is specified, or if the sequence has less than two entries and with_nan is False.
If a matrix is passed to the function. Only vector inputs are supported.
- Warns:
- UserWarning
If with_nan is True and cv2 is calculated for a sequence with less than two entries, generating a np.NaN.
Examples
>>> from elephant import statistics >>> statistics.cv2([0.3, 4.5, 6.7, 9.3]) 0.8226190476190478