braintools.input.chirp

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braintools.input.chirp#

braintools.input.chirp(amplitude, f_start, f_end, duration, t_start=None, t_end=None, method='linear', bias=False)#

Generate chirp (frequency sweep) current input.

Creates a sinusoidal signal with time-varying frequency that sweeps from a starting to ending frequency. Useful for frequency response analysis, resonance detection, and spectral characterization.

Parameters:

  • amplitude (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Peak amplitude of the chirp signal. Supports current units.

  • f_start (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Starting frequency. Must be in Hz units.

  • f_end (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Ending frequency. Must be in Hz units.

  • duration (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Total duration of the signal. Supports time units.

  • t_start (Array | ndarray | bool | number | bool | int | float | complex | Quantity | None) – Time when chirp starts. Before this, output is 0. Default is 0.

  • t_end (Array | ndarray | bool | number | bool | int | float | complex | Quantity | None) – Time when chirp ends. After this, output is 0. Default is duration.

  • method (str) – Sweep method: ‘linear’ or ‘logarithmic’. Default is ‘linear’.

  • bias (bool) – If True, adds DC offset equal to amplitude (non-negative output). If False, oscillates around 0.

Returns:

current – The chirp signal current array with shape (n_timesteps,).

Return type:

ndarray or Quantity

Raises:

  • AssertionError – If frequencies are not in Hz units.

  • ValueError – If method is not ‘linear’ or ‘logarithmic’.

Examples

>>> import brainunit as u
>>> import brainstate
>>> brainstate.environ.set(dt=0.1 * u.ms)

Linear frequency sweep from 1 to 50 Hz

>>> current = chirp(
...     amplitude=5 * u.pA,
...     f_start=1 * u.Hz,
...     f_end=50 * u.Hz,
...     duration=2000 * u.ms,
...     method='linear'
... )

Logarithmic sweep for spectral analysis

>>> current = chirp(
...     amplitude=2 * u.nA,
...     f_start=0.1 * u.Hz,
...     f_end=100 * u.Hz,
...     duration=5000 * u.ms,
...     method='logarithmic'
... )

Chirp with positive bias

>>> current = chirp(
...     amplitude=10 * u.pA,
...     f_start=5 * u.Hz,
...     f_end=20 * u.Hz,
...     duration=1000 * u.ms,
...     bias=True  # Always positive
... )

Windowed chirp for specific testing

>>> current = chirp(
...     amplitude=8 * u.pA,
...     f_start=2 * u.Hz,
...     f_end=40 * u.Hz,
...     duration=3000 * u.ms,
...     t_start=500 * u.ms,
...     t_end=2500 * u.ms
... )

Reverse chirp (high to low frequency)

>>> current = chirp(
...     amplitude=3 * u.nA,
...     f_start=100 * u.Hz,
...     f_end=1 * u.Hz,
...     duration=2000 * u.ms
... )

Testing resonance in theta-gamma range

>>> current = chirp(
...     amplitude=1 * u.nA,
...     f_start=4 * u.Hz,   # Theta start
...     f_end=80 * u.Hz,    # Gamma end
...     duration=10000 * u.ms,
...     method='logarithmic'
... )

Notes

  • Linear chirp: frequency changes linearly with time

  • Logarithmic chirp: frequency changes exponentially with time

  • Useful for finding resonant frequencies and transfer functions

  • Phase is continuous throughout the sweep

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