`RollingRms`

Description

The RollingRms class calculates the root mean square (RMS) of values within a specified moving window. This provides a measure of the magnitude of variation in the data, capturing both positive and negative values as positive magnitudes.

Initial values: The constructor requires a positive integer window_size parameter to define the rolling window.
NaN handling: NaN values are not handled natively and should be preprocessed if necessary.

Usage Example and Plot

import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from screamer import RollingRms

data = np.cumsum(np.random.normal(size=300))

fig = make_subplots(
    rows=2, cols=1,
    shared_xaxes=True,
    row_heights=[2/3, 1/3],
    vertical_spacing=0.1
)

fig.add_trace(go.Scatter(y=data, mode='lines', name='Input Data'), row=1, col=1)
fig.add_trace(go.Scatter(y=RollingRms(30)(data), mode='lines', name='Rolling RMS', line=dict(color='teal')), row=2, col=1)

fig.update_layout(
    title="Rolling RMS with Window Size 30",
    xaxis_title="Index",
    yaxis=dict(title="Input Data"),
    yaxis2=dict(title="Rolling RMS"),
    margin=dict(l=20, r=20, t=80, b=20),
    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
)

fig.show()

Implementation Details

Algorithm

RollingRms implements cyclic buffers to accumulate windowed statistics.

Complexity

Time Complexity: O(log(1)) per new element due to the insertion and deletion operations in the heaps.
Space Complexity: O(window_size), as only elements within the current window are stored.