Биорадар: Часть 2. Обучение ИНС реконструкции ЭКГ

using PyCall
using MAT
include("utils.jl")
np = pyimport("numpy");
nk = pyimport("neurokit2");
function prepareData(path::AbstractString, save_path::AbstractString, order_filter)
    i, q, fs_radar, ecg_data1, ecg_data2, fs_ecg, filename = load_data(path);
    cur_ecg = vec(ecg_data2);
    I_comp, Q_comp, sigma, range_ = EllipseReconstruction(i, q);
    radar_respiration, radar_pulse, radar_heartsound, radar_heartsound_denoise = getVitalSignals(range_, fs_radar, order_filter);   
    acc = np.gradient(np.gradient(radar_pulse)) * fs_radar^2
    radar_pulse_acc = Filter(acc, 4, 20, fs_radar, 6);
    ecg_cleaned = nk.ecg_clean(cur_ecg, sampling_rate=fs_ecg)
    data_dict = Dict("radar"=>radar_pulse_acc, "ecg"=>ecg_cleaned, "fs_radar"=> fs_radar, "fs_ecg"=>fs_ecg)
    save_path = joinpath(save_path, filename)
    matwrite(save_path, data_dict)
end


function loadDataNoPrepare(path::AbstractString, save_path::AbstractString)
    i, q, fs_radar, ecg_data1, ecg_data2, fs_ecg, filename = load_data(path);
    cur_ecg = vec(ecg_data2);

    signal = Complex.(i, q);
    sigma = angle.(signal);
    ecg_cleaned = nk.ecg_clean(cur_ecg, sampling_rate=fs_ecg)

    data_dict = Dict("radar"=>sigma, "ecg"=>ecg_cleaned, "fs_radar"=> fs_radar, "fs_ecg"=>fs_ecg)
    save_path = joinpath(save_path, filename)
    matwrite(save_path, data_dict)
end;