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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
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