dsp.py

import numpy as np

def generate_features(implementation_version, draw_graphs, raw_data, axes, sampling_freq, scale_axes):
    # features is a 1D array, reshape so we have a matrix
    raw_data = raw_data.reshape(int(len(raw_data) / len(axes)), len(axes))

    features = []
    graphs = []

    # split out the data from all axes
    for ax in range(0, len(axes)):
        X = []
        for ix in range(0, raw_data.shape[0]):
            X.append(float(raw_data[ix][ax]))

        # X now contains only the current axis
        fx = np.array(X)

        # process the signal here
        fx = fx * scale_axes

        # we need to return a 1D array again, so flatten here again
        for f in fx:
            features.append(f)

    return {
        'features': features,
        'graphs': graphs,
        # if you use FFTs then set the used FFTs here (this helps with memory optimization on MCUs)
        'fft_used': [],
        'output_config': {
            # type can be 'flat', 'image' or 'spectrogram'
            'type': 'flat',
            'shape': {
                # shape should be { width, height, channels } for image, { width, height } for spectrogram
                'width': len(features)
            }
        }
    }