from etl import ETL import pandas as pd import numpy as np from random import gauss DATA_PATH = "/home/erlend/datasets" etl = ETL(DATA_PATH, [128, 256, 512, 1024]) etl.cache = False etl.load("CIMA") infant = etl.cima["001"] noisy_cima = {} noisy_cima["001"] = infant for i in range(100): data = infant["data"].copy() for key, val in data.items(): if key == "frame": continue noise = pd.Series([gauss(0.0, 0.005) for i in range(len(val))]) data[key] = val.add(noise) noisy_cima[f"noise_{i}"] = {"data": data, "label": 0, "fps": 24} etl.cima = noisy_cima etl.preprocess_pooled(20)