EwRms#

Description#

EwRms calculates the exponentially weighted moving root mean square (RMS), highlighting fluctuations in signal magnitude over time with an emphasis on recent values.

Parameters#

One of the following decay parameters is required to calculate alpha, where a higher alpha value gives recent points more influence:

  • com: Center of mass. alpha = 1 / (1 + com)

  • span: Span. alpha = 2 / (span + 1)

  • halflife: Half-life. alpha = 1 - exp(-log(2) / halflife)

  • alpha: Directly specifies the smoothing factor, where 0 < alpha < 1

NaN handling: NaN values are ignored in the mean calculation.

Usage Example and Plot#

NaN handling#

Policy: ignore. A NaN in any input at index t causes the function to skip that step: output at t is NaN and internal state is unchanged. Subsequent finite samples are processed as if step t had not occurred.

Examples#

Description#

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

data = np.cumsum(np.random.normal(size=300))
ewrms_data = EwRms(span=20)(data)

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

fig.add_trace(go.Scatter(y=data, mode='lines', name='Original Data'), row=1, col=1)
fig.add_trace(go.Scatter(y=ewrms_data, mode='lines', name='EwRms', line=dict(color='red')), row=2, col=1)

fig.update_layout(
    title="Exponentially Weighted Moving RMS",
    xaxis_title="Index",
    yaxis=dict(title="Original Data"),
    yaxis2=dict(title="EwRms"),
    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()