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import numpy as np
from aira.engine.filtering import moving_average_filter
def w_channel_preprocess(
w_channel: np.ndarray, window_size: int, analysis_length: float, sample_rate: float
) -> np.ndarray:
"""_summary_
Parameters
----------
w_channel : np.ndarray
_description_
window_size : int
_description_
analysis_length : float
_description_
sample_rate : float
_description_
Returns
-------
np.ndarray
_description_
"""
w_channel_cropped = np.abs(
analysis_crop_1d(w_channel, analysis_length, sample_rate)
)
w_channel_filtered = moving_average_filter(w_channel_cropped, int(window_size / 2))
w_channel_filtered /= np.max(w_channel_filtered)
return w_channel_filtered
def analysis_crop_1d(
array: np.ndarray,
analysis_length: float,
sample_rate: int,
):
"""_summary_
Parameters
----------
analysis_length : float
_description_
sample_rate : int
_description_
array : np.ndarray
_description_
Returns
-------
_type_
_description_
"""
# Get analysis length max index
analysis_length_idx = int(analysis_length * sample_rate)
# Slice from intensity max to analysis length from intensity max
earliest_peak_index = np.argmax(np.abs(array))
array_cropped = array[
earliest_peak_index : earliest_peak_index + analysis_length_idx
]
return array_cropped
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