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Regional baseline drifts

These functions introduce different baseline drift behaviors, allowing for localized piecewise, polynomial, or middle-peak drifts.

apply_baseline_drift_region

Location: variations/baseline_drift.py

Description

apply_baseline_drift_region introduces localized baseline drift in a specific segment of a waveform.

Notes

  • Drift is applied only within a selected fraction of the signal, defined by start_frac and end_frac.
  • Outside this region, the original waveform remains unchanged.
  • Uses linear interpolation for smooth transition within the drift region.

Parameters

  • wave (numpy.ndarray):
    The input signal.

  • max_drift (float):
    Maximum amplitude of the drift.

  • start_frac (float, optional):
    Fractional position where the drift starts (0.3 = 30% into the signal).
    Default: 0.3.

  • end_frac (float, optional):
    Fractional position where the drift ends (0.7 = 70% into the signal).
    Default: 0.7.

Returns

  • drifted_wave (numpy.ndarray):
    The waveform with localized baseline drift.

Usage Example

import SigVarGen as svg

drifted_wave = svg.apply_baseline_drift_region(wave, max_drift=0.2, start_frac=0.4, end_frac=0.8)

apply_baseline_drift_polynomial

Location: variations/baseline_drift.py

Description

apply_baseline_drift_polynomial introduces a smooth, polynomial drift across an entire signal. The drift gradually increases or decreases, making it ideal for modeling slow environmental changes, power fluctuations, or long-term sensor drift.

Notes

  • Uses polynomial scaling (order=2 by default, quadratic drift).
  • The drift can be reversed, causing it to start at the peak and return to zero.
  • A higher-order polynomial (e.g., order=3) introduces nonlinear drift effects.

Parameters

  • wave (numpy.ndarray):
    The input signal.

  • max_drift (float):
    Maximum amplitude of the drift.

  • reversed (bool, optional):
    If True, the drift starts at its peak and decreases.
    Default: False.

  • order (int, optional):
    Polynomial order of the drift.
    Default: 2 (quadratic).

Returns

  • drifted_wave (numpy.ndarray):
    The waveform with a polynomial baseline drift.

Usage Example

drifted_wave = svg.apply_baseline_drift_polynomial(wave, max_drift=0.3, order=3)

apply_baseline_drift_piecewise

Location: variations/baseline_drift.py

Description

apply_baseline_drift_piecewise introduces stepwise drift changes, where different sections of the waveform have randomly varying baseline shifts.

Notes

  • Divides the waveform into multiple segments, each with an independent drift value.
  • The drift values can be reversed, starting from the maximum and decreasing.
  • The number of segments is controlled by num_pieces, with each piece having a unique baseline level.

Parameters

  • wave (numpy.ndarray):
    The input signal.

  • max_drift (float):
    Maximum amplitude of drift per segment.

  • reversed (bool, optional):
    If True, the drift starts at its highest and gradually decreases.
    Default: False.

  • num_pieces (int, optional):
    Number of segments for the drift.
    Default: 3.

Returns

  • drifted_wave (numpy.ndarray):
    The waveform with piecewise baseline drift.

Usage Example

drifted_wave = svg.apply_baseline_drift_piecewise(wave, max_drift=0.4, num_pieces=4)

apply_baseline_drift_middle_peak

Location: variations/baseline_drift.py

Description

apply_baseline_drift_middle_peak applies a parabolic drift that peaks in the middle of the waveform.

Notes

  • Drift is zero at the start and end, with a peak at the center of the signal.
  • The peak can shift downward or upward, depending on the direction.
  • Works well for short-term drift simulations.

Parameters

  • wave (numpy.ndarray):
    The input signal.

  • max_drift (float):
    Maximum amplitude of the drift.

  • direction (str, optional):

  • 'down': Peak drifts downward (negative shift).

  • 'up': Peak drifts upward (positive shift).
    Default: 'down'.

  • min_drift (float, optional):
    Minimum drift value to ensure drift is non-zero.
    Default: 0.

Returns

  • drifted_wave (numpy.ndarray):
    The waveform with a middle-peak baseline drift.

Usage Example

drifted_wave = svg.apply_baseline_drift_middle_peak(wave, max_drift=0.3, direction='up')