Moving Average Utility

Smooth one-dimensional time-series data using moving-average methods.

This module provides simple and exponential moving-average functions for smoothing one-dimensional data series commonly produced by ReaxFF simulations, such as energies, bond orders, dipole moments, or polarization signals.

Usage context

Noise reduction: Suppress high-frequency variance in MD trajectories.
Curve conditioning: Smooth field-response or hysteresis signals.
Preprocessing: Prepare series for extrema and trend analysis steps.

Function: `simple_moving_average`

Compute a simple moving average (SMA) of a 1D data series.

The moving average is computed over a fixed-size sliding window and returned as a pandas Series. If the input is already a Series, its index is preserved.

Parameters:

Name	Type	Description	Default
`y`	`array - like`	Input data values to smooth.	required
`window`	`int`	Size of the moving window.	`5`
`center`	`bool`	Whether the window is centered on each data point.	`True`
`min_periods`	`int`	Minimum number of observations required to compute a value.	`1`

Notes

This function uses pandas.Series.rolling under the hood for SMA computation.
Main documentation for pandas.Series.rolling: https://pandas.pydata.org/docs/reference/api/pandas.Series.rolling.html
An example is at: https://www.geeksforgeeks.org/python/pandas-rolling-mean-by-time-interval/

Returns:

Type	Description
`Series`	Smoothed data series using a simple moving average.

Examples:

>>> simple_moving_average(energy, window=10)

Function: `exponential_moving_average`

Compute an exponential moving average (EMA) of a 1D data series.

The exponential moving average applies exponentially decreasing weights to past observations. The smoothing factor may be specified directly via alpha or indirectly via a window size.