Signal Functions

signal_processing.signal_function.check_peaks_in_data(accel_data, height_range=2.8)

Checks if there are peaks in the acceleration data based on the specified height range.

Parameters:

  • accel_data (np.ndarray) – Array of acceleration data.

  • height_range (float or list, optional) – Can be a single value representing the minimum peak height, or a list with two values representing the minimum and maximum peak heights. Default is 2.8.

Returns:

A list containing: - A boolean indicating if peaks were found. - If peaks were found, a list of peak indices and a dictionary with peak information.

Return type:

list

Notes

The function uses the find_peaks method from scipy.signal to identify peaks based on the height_range.

signal_processing.signal_function.find_deriv_change(x, y, cutoff, greater=True)

Finds the locations where the derivative of the data reaches a specified threshold.

Parameters:

  • x (np.ndarray) – Array of independent data.

  • y (np.ndarray) – Array of dependent data.

  • cutoff (float) – The threshold value for the derivative.

  • greater (bool, optional) – If True, finds locations where the derivative is greater than the cutoff. If False, finds locations where the derivative is less than the cutoff. Default is True.

Returns:

Indices where the derivative meets the specified condition.

Return type:

tuple

Notes

The function calculates the derivative of the data using np.gradient, which employs second-order central differencing for the i nterior and first-order differencing for the boundaries.

signal_processing.signal_function.find_drops(accel_data, impact_time_tol=0.015, sample_freq=120000, min_peak_height=2.8)

Detects the number of drops in the acceleration data by identifying peaks.

Parameters:

  • accel_data (np.ndarray) – Array of acceleration data.

  • impact_time_tol (float, optional) – The assumed minimum time between drops, in seconds. Default is 0.015.

  • sample_freq (int, optional) – Number of samples collected per minute. Default is 120,000.

  • min_peak_height (float, optional) – Minimum acceleration measurement to be considered a drop, measured in g’s. Default is 2.8.

Returns:

A tuple containing: - drop_indexs (np.ndarray): Indices of detected drops in the acceleration data. - drop_info (dict): Information about the detected drops, including peak heights. - num_drops (int): The number of detected drops.

Return type:

tuple

Notes

The function uses the find_peaks method from scipy.signal to identify peaks in the acceleration data. The impact window is calculated based on the impact_time_tol and sample_freq.

signal_processing.signal_function.moving_average(data, window_size)

Calculates the moving average of the data using a specified window size.

Parameters:

  • data (array-like) – The array for which the moving average is to be calculated.

  • window_size (int) – The width of the window in indices of the array.

Returns:

The moving average of the input data.

Return type:

np.ndarray

Notes

The function uses cumulative sums to compute the moving average efficiently.