import numpy as np
import matplotlib.pyplot as plt
import src.dab_util as du


def calc_signal_sholder_ratio(fft, sampling_rate, debug = False, debug_path="", suffix=""):
    fft_size = fft.shape[0]
    n_sig = (du.freq_to_fft_sample(-du.c["bw"]/2., fft_size, sampling_rate),
             du.freq_to_fft_sample( du.c["bw"]/2., fft_size, sampling_rate))
    sig = np.mean(fft[n_sig[0]:n_sig[1]])

    n_noise = (du.freq_to_fft_sample(-3000000., fft_size, sampling_rate),
               du.freq_to_fft_sample(-2500000, fft_size, sampling_rate))
    noise = np.mean(fft[n_noise[0]:n_noise[1]])

    n_sholder = (du.freq_to_fft_sample(-1500000, fft_size, sampling_rate),
               du.freq_to_fft_sample(-du.c["bw"]/2, fft_size, sampling_rate))
    sholder = np.mean(fft[n_sholder[0]:n_sholder[1]])


    if debug == True:
        print(n_sig, n_sholder, n_noise)
        plt.plot(fft)
        plt.plot((n_sig[0], n_sig[1]), (sig, sig), linewidth=5, color='g')
        plt.plot((n_noise[0], n_noise[1]), (noise, noise), linewidth=5, color='r')
        plt.plot((n_sholder[0], n_sholder[1]), (sholder, sholder), linewidth=5, color='y')
        if debug_path: plt.savefig(debug_path + "/" + str(loss) + suffix + ".png")
        plt.show()
        plt.clf()

    return sholder

def calc_signal_sholder_peak_ratio(fft, sampling_rate, debug = False, debug_path="", suffix=""):
    fft_size = fft.shape[0]
    n_sig = (du.freq_to_fft_sample(-du.c["bw"]/2., fft_size, sampling_rate),
             du.freq_to_fft_sample( du.c["bw"]/2., fft_size, sampling_rate))
    sig = np.mean(fft[n_sig[0]:n_sig[1]])

    n_noise = (du.freq_to_fft_sample(-3000000., fft_size, sampling_rate),
               du.freq_to_fft_sample(-2500000, fft_size, sampling_rate))
    noise = np.mean(fft[n_noise[0]:n_noise[1]])

    n_sholder = (du.freq_to_fft_sample(-1500000, fft_size, sampling_rate),
               du.freq_to_fft_sample(-du.c["bw"]/2, fft_size, sampling_rate))
    sholder = np.mean(fft[n_sholder[0]:n_sholder[1]])

    loss = sholder/sig


    if debug == True:
        print(n_sig, n_sholder, n_noise)
        plt.plot(fft)
        plt.plot((n_sig[0], n_sig[1]), (sig, sig), linewidth=5, color='g')
        plt.plot((n_noise[0], n_noise[1]), (noise, noise), linewidth=5, color='r')
        plt.plot((n_sholder[0], n_sholder[1]), (sholder, sholder), linewidth=5, color='y')
        if debug_path: plt.savefig(debug_path + "/" + str(loss) + suffix + ".png")
        plt.show()
        plt.clf()

    return loss

def calc_max_in_freq_range(fft, sampling_rate, f_start, f_end, debug = False, debug_path="", suffix=""):
    fft_size = fft.shape[0]
    n_sig = (du.freq_to_fft_sample(f_start, fft_size, sampling_rate),
             du.freq_to_fft_sample(f_end, fft_size, sampling_rate))
    sig = np.max(fft[n_sig[0]:n_sig[1]])

    if debug == True:
        print(n_sig)
        plt.plot(fft)
        plt.plot((n_sig[0], n_sig[1]), (sig, sig), linewidth=5, color='g')
        if debug_path: plt.savefig(debug_path + "/" + str(loss) + suffix + ".png")
        plt.show()
        plt.clf()

    return sig

def calc_mean_in_freq_range(fft, sampling_rate, f_start, f_end, debug = False, debug_path="", suffix=""):
    fft_size = fft.shape[0]
    n_sig = (du.freq_to_fft_sample(f_start, fft_size, sampling_rate),
             du.freq_to_fft_sample(f_end, fft_size, sampling_rate))
    sig = np.mean(fft[n_sig[0]:n_sig[1]])

    if debug == True:
        print(n_sig)
        plt.plot(fft)
        plt.plot((n_sig[0], n_sig[1]), (sig, sig), linewidth=5, color='g')
        if debug_path: plt.savefig(debug_path + "/" + str(loss) + suffix + ".png")
        plt.show()
        plt.clf()

    return sig