def main():
# define parser to pass options from command line
parser = argparse.ArgumentParser()
parser.add_argument('--mp', required=True, default=0)
parser.add_argument('--nb_samples', required=True, default=1)
parser.add_argument('--cn0', required=True, default=1)
parser.add_argument('--discr', required=True, default=1)
args = parser.parse_args()
multipath_option = False if args.mp == '0' else True
print('check multipath option before sampling: ', args.mp, multipath_option)
# coherent integration period
Tint = 20e-3
# Main for data generation
discr_size_fd = int(args.discr)
scale_code = int(args.discr)
# multipath intervals
delta_tau_interv = [0, 3/2]
delta_dopp_max = min(3/Tint, 800)
delta_dopp_interv = [-delta_dopp_max, delta_dopp_max]
delta_phase = 0
alpha_att_interv = [0.5, 0.9]
cn0_log = int(args.cn0)
print('CHECK CNO ratio: ', cn0_log)
# doppler interval
dopp_max = min(5.5/Tint, 800+2.5/Tint)
dopp_interval = [-dopp_max, dopp_max]
# code interval
tau_interval = [-3/2, 5/2]
print('MODS IN INTERVALS CHECKED')
noise_i_path = r'corr_noise_generator/outputs/i_channel/*.csv'
noise_q_path = r'corr_noise_generator/outputs/q_channel/*.csv'
save_path = r'synth_data/discr_{}/'.format(discr_size_fd)
data_sampler = DataSampler(
discr_size_fd=discr_size_fd,
scale_code=scale_code,
Tint=Tint,
multipath_option=multipath_option,
delta_tau_interv=delta_tau_interv,
delta_dopp_interv=delta_dopp_interv,
delta_phase=delta_phase,
alpha_att_interv=alpha_att_interv,
tau=tau_interval,
dopp=dopp_interval,
cn0_log=cn0_log
)
data_sampler.read_noise(noise_i_path, noise_q_path, matrix_shape=(discr_size_fd, scale_code), nb_samples=int(args.nb_samples))
data_sampler.generate_corr(nb_samples=int(args.nb_samples))
data_sampler.sum_matr(save_csv=True, save_path=save_path)
#visualize_plt(samples[0]['table'][...,1])
#visualize_plt(module)
#%% check fake noise factor in data sampler
noise_i_path = r'corr_noise_generator/outputs/i_channel/*.csv'
noise_q_path = r'corr_noise_generator/outputs/q_channel/*.csv'
for cn0_log in [20, 30, 40, 50, 60]:
print('----', cn0_log)
data_sampler = DataSampler(
discr_size_fd=discr_size_fd,
scale_code=scale_code,
Tint=Tint,
multipath_option=False,
delta_tau_interv=delta_tau_interv,
delta_dopp_interv=delta_dopp_interv,
delta_phase=delta_phase,
alpha_att_interv=alpha_att_interv,
tau=tau_interval,
dopp=dopp_interval,
cn0_log=cn0_log
)
nb_samples = 1
data_sampler.read_noise(noise_i_path, noise_q_path, matrix_shape=(discr_size_fd, scale_code), nb_samples=nb_samples)
data_sampler.generate_corr(nb_samples=nb_samples)
matr_i, matr_q = data_sampler.sum_matr(save_csv=False)
plt.imshow(matr_i[0,...])