time_tools.py 文件源码

def interp_helper(all_data, trend_data, time_from):
    'performs lf spline + hf fft interpolation of radial distance'
    all_times, all_values = zip(*all_data)
    trend_times, trend_values = zip(*trend_data)

    split_time = int(time_to_index(time_from, all_times[0]))

    trend_indices = array([time_to_index(item, all_times[0]) for item in trend_times])
    spline = splrep(trend_indices, array(trend_values))

    all_indices = array([time_to_index(item, all_times[0]) for item in all_times])
    trend = splev(all_indices, spline)
    detrended = array(all_values) - trend
    trend_add = splev(arange(split_time, all_indices[-1]+1), spline)

    dense_samples = detrended[:split_time]
    sparse_samples = detrended[split_time:]
    sparse_indices = (all_indices[split_time:]-split_time).astype(int)
    amp = log(absolute(rfft(dense_samples)))
    dense_freq = rfftfreq(dense_samples.size, 5)
    periods = (3000.0, 300.0)
    ind_from = int(round(1/(periods[0]*dense_freq[1])))
    ind_to = int(round(1/(periods[1]*dense_freq[1])))
    slope, _ = polyfit(log(dense_freq[ind_from:ind_to]), amp[ind_from:ind_to], 1)

    params = {
        't_max': periods[0],
        'slope': slope,
        'n_harm': 9,
        'scale': [20, 4, 2*pi]
    }
    series_func, residual_func = make_residual_func(sparse_samples, sparse_indices, **params)

    x0 = array([0.5]*(params["n_harm"]+2))
    bounds = [(0, 1)]*(params["n_harm"]+2)
    result = minimize(residual_func, x0, method="L-BFGS-B", bounds=bounds, options={'eps':1e-2})
    interp_values = [trend + high_freq for trend, high_freq in
                     zip(trend_add, series_func(result.x)[:sparse_indices[-1]+1])]
    #make_qc_plot(arange(sparse_indices[-1]+1), interp_values,
    #             sparse_indices, array(all_values[split_time:]))
    interp_times = [index_to_time(ind, time_from) for ind in range(sparse_indices[-1]+1)]
    return list(zip(interp_times, interp_values))