Source code for spikify.encoding.temporal.contrast.step_forward_algorithm

"""
.. raw:: html

    <h2>Step Forward Algorithm</h2>
"""

import numpy as np


[docs] def step_forward(signal: np.ndarray, threshold: float) -> np.ndarray: """ Perform Step-Forward encoding on the input signal. This function takes a continuous signal and converts it into a spike train using a dynamically updated baseline and threshold-based approach. A spike is generated when the signal exceeds or drops below the dynamically adjusted baseline (`Base`) by the specified `Threshold`. Refer to the :ref:`step_forward_algorithm_desc` for a detailed explanation of the Step-Forward encoding algorithm. **Code Example:** .. code-block:: python import numpy as np from spikify.encoding.temporal.contrast import step_forward signal = np.array([0.1, 0.3, 0.4, 0.2, 0.5, 0.6]) threshold = 0.2 encoded_signal = step_forward(signal, threshold) .. doctest:: :hide: >>> import numpy as np >>> from spikify.encoding.temporal.contrast import step_forward >>> signal = np.array([0.1, 0.3, 0.4, 0.2, 0.5, 0.6]) >>> threshold = 0.2 >>> encoded_signal = step_forward(signal, threshold) >>> encoded_signal array([0, 0, 1, 0, 0, 1], dtype=int8) :param signal: The input signal to be encoded. This should be a numpy ndarray. :type signal: numpy.ndarray :param threshold: The threshold value for spike detection. :type threshold: float :return: A 1D numpy array representing the encoded spike train. :rtype: numpy.ndarray :raises ValueError: If the input signal is empty. :raises TypeError: If the signal is not a numpy ndarray. """ if len(signal) == 0: raise ValueError("Signal cannot be empty.") spike = np.zeros_like(signal, dtype=np.int8) # Base value initialized at the start of the signal base = signal[0] for t, value in enumerate(signal): if value > base + threshold: spike[t] = 1 base += threshold elif value < base - threshold: spike[t] = -1 base -= threshold return spike