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)
