# Utils¶

The utils.py file contains many useful functions called by other classes in PyPulse.

## Functions¶

acf(array[, var=False, norm_by_tau=True, lagaxis=False])

Uses numpy’s correlate function to calculate the autocorrelation function of an array $$x$$, defined as

$\frac{1}{N} \sum_{i\le j} x_i x_j$

where

Parameters: array (list/numpy.ndarray) – Data array. var (bool) – Divide by the variance (using numpy.var) of the time series norm_by_tau (bool) – Normalize using the number of bins going into each lag bin’s computation (thus making each bin an average value). Otherwise just divide by the length of the input array. lagaxis (bool) – Return the axis of lags and the autocorrelation function rather than just the autocorrelation function. autocorrelation function, numpy.ndarray
imshow(x[, ax=None, origin='lower', interpolation='nearest', aspect='auto', **kwargs])

Convenience function for calling matplotlib’s imshow().

Parameters: x (list/numpy.ndarray) – 2D data array. ax (axis) – Uses a matplotlib axis to draw to. If None, then just run open a new figure. origin (str) – Explicitly pass origin argument to imshow() interpolation (str) – Explicitly pass interpolation argument to imshow() aspect (str) – Explicitly pass aspect argument to imshow() **kwargs – Additional arguments to pass to imshow() im, the return value of either ax.imshow() or plt.imshow()
normalize(array[, simple=False, minimum=None])

Normalize an array to unit height.

Parameters: array (numpy.ndarray) – Data array simple (bool) – If simple, divide by the maximum of the array. Otherwise, normalize according to $$\mathrm{(array-minimum)}/\mathrm{(maximum-minimum)}$$, where the minimum is the minimum of the array. minimum (float) – Provide the minimum value to normalize in the above equation. array, numpy.ndarray