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.
Returns: 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()
Returns: 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.
Returns: array, numpy.ndarray