import sys
sys.path.append("../../sim")
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt
from siggens import train_pulse as gen
from utils import freqaxis_shape as ut
##################### Parameters ######################
f_sampl = 50e3 # sampling frequency in kHz
T_int = 1.8 # entire signal length in ms
##################### Simulation TAU = 1 ######################
t = np.arange(0,T_int,1/f_sampl) # time axis
f = ut.freq_fr_time (t) # frequency axis
tc = ut.corr_fr_time (t) # correlation time axis
cd = np.array([1,1,1,0,1,1,1,1,0,1,0,0,1,0,1]) # code
tau = 1 # time acceleration factor
Tstr = 10e-3 # transmitted symbol interval
Ts = Tstr/tau # Nyquist's symbol interval
td = 0.01 # initial delay of the sequence (time offset)
# Time Domain
a1 = gen.rcos_tr(t,Tstr,td + Tstr/2,cd,Ts,1.0)
a2 = gen.rcos_tr(t,Tstr,td + Tstr/2,cd,Ts,0.5)
a3 = gen.rcos_tr(t,Tstr,td + Tstr/2,cd,Ts,0.0)
c = gen.rect_tr(t,Tstr,0,td,cd)
def main(setup_data):
stp.logger_setup(setup_data["setup"])
logger = logging.getLogger(__name__)
logger.info("Main function started.")
logger.debug("Coder analysis configuration file %s", setup_data["cnf"])
logger.debug("Setup configuration file %s", setup_data["setup"])
logger.debug("Plotting configuration file %s", setup_data["plt"])
logger.debug("Logger output file %s", setup_data["log"])
logger.debug("Python files %s", setup_data["srcpy"])
logger.debug("SSRG state output file %s", setup_data["data_state"])
logger.debug("Coder PRN output file %s", setup_data["data_code"])
##################### code generator ###############################
analysis_setup = stp.analysis_cnf_file_parser(setup_data["cnf"])
logger.debug("%s file read to setup analysis", setup_data["cnf"])
ssrg_init = analysis_setup["ssrg_init"]
logger.debug("initial state of the ssrg, ssrg_init = %s ", ssrg_init)
ssrg_fb = analysis_setup["ssrg_fb"]
logger.debug("feedback vector of the ssrg, ssrg_fb = %s ", ssrg_fb)
srm = prn.build_srm(ssrg_fb)
logger.debug("srm matrix created, srm = %s ", srm)
logger.debug("Number of bits in one period of the code N = %s bits ",
analysis_setup["code_period"])
logger.debug("Number of periods being generated %s ",
analysis_setup["n_o_periods"])
n_of_bits = analysis_setup["code_period"] * analysis_setup["n_o_periods"]
logger.debug("code generator setup - number of generated bits %s ",
n_of_bits)
x = ssrg_init.T
code = np.zeros(1)
for i1 in range(1, n_of_bits):
x = prn.proceed_ssrg_onestep(x, srm)
csvi.write_csv(x, setup_data["data_state"], i1)
csvi.write_csv(x[-1], setup_data["data_code"], i1)
code = np.append(code, x[-1])
logger.debug("coder run - binary sequence generated, number of bits %s ",
code.size)
#################### time related simulation ######################
f_sampl = analysis_setup["chip_rate"] * analysis_setup[
"oversampling_factor"]
logger.debug("sampling rate is %s kHz", f_sampl)
logger.debug("sampling period is %s ms", 1 / f_sampl)
logger.debug("chiprate is %s kHz", analysis_setup["chip_rate"])
Ts = 1 / analysis_setup[
"chip_rate"] # transmitted symbol interval or a chip length
logger.debug("chip length is %s ms", Ts)
logger.debug("code period is %s ms",
analysis_setup["code_period"] / analysis_setup["chip_rate"])
T_int = analysis_setup["n_o_samples"] / f_sampl
logger.debug("time axis length is %s ms", T_int)
t = np.arange(0, T_int, 1 / f_sampl) # time axis
logger.debug("time axis created, length %s samples", t.size)
f = ut.freq_fr_time(t) # frequency axis
logger.debug("frequency axis for spectral analysis, length %s", f.size)
tc = ut.corr_fr_time(t) # correlation time axis
logger.debug("time axis correlation created, length %s samples", tc.size)
tc_h = ut.corr_fr_halftime(t) # correlation time axis
logger.debug("half time axis correlation created, length %s samples",
tc_h.size)
tau = analysis_setup[
"time_accelerating_factor"] # time acceleration factor
logger.debug("time acceleration factor is %s", tau)
Tstr = Ts * tau # Nyquist's symbol interval
logger.debug("Transmitted (accelerated) chip length is %s ms", Tstr)
td = analysis_setup[
"time_offset"] # initial delay of the sequence (time offset)
logger.debug("Time offset (delay) of transmitted baseband signal is %s ms",
td)
# Time Domain Signals
# a1 = gen.rcos_tr(t, Tstr, td + Tstr / 2, x, Ts, 1.0)
# a2 = gen.rcos_tr(t, Tstr, td + Tstr / 2, x, Ts, 0.5)
# a3 = gen.rcos_tr(t, Tstr, td + Tstr / 2, x, Ts, 0.0)
c = gen.rect_tr(t, Tstr, 0, td, code)
logger.debug(
"Oversampled signal with rectangular pulse shape created, number of samples %s",
c.size)
# Correlate processor
c_con = np.concatenate((c, c))
A1_c = ncorr.corr_fd(
c,
c,
)
# A1_c = signal.correlate(c, c_con, 'full', 'fft')
# A1_c = signal.convolve(c, c, 'full')
# A1_c = signal.fftconvolve(c, c, 'full')
# A1_c = np.real(np.fft.ifft( np.fft.fft(c)*np.fft.fft(c) ))
logger.debug("Autocorrelation function calculated, number of samples %s",
A1_c.size)
##################### Plots ###########################
plotting_setup = stp.plotting_cnf_file_parser(setup_data["plt"])
logger.debug("%s file read to setup plotting results", setup_data["plt"])
if plotting_setup["plotting"]:
# Time domain
f1 = plt.figure(1, figsize=(10, 7), dpi=300)
f1ax1 = f1.add_subplot(211)
texts = {
"y_legend": "$h_{rect}(t), \\beta = 1.0$",
"title": "Pulse-shaped time-domain baseband signal",
"y_label": "$prn(t)$",
"x_label": "time [ms]"
}
# figure_axes = [-0.25, 0.25, -100, 253000]
aplt.timedomain_plot(f1ax1, t, c, texts=texts)
# Autocorrelated
# f2 = plt.figure(2, figsize=(10, 7), dpi=300)
# f2ax1 = f2.add_subplot(212)
f1ax2 = f1.add_subplot(212)
texts = {
# "y_legend": "$h_{rect}(t), \\beta = 1.0$",
# "title":"Pulse-shaped Autocorrelated",
"y_label": "$C_{xx}(\\tau)$",
"x_label": "time [ms]"
}
# figure_axes = [-0.25, 0.25, -100, 253000]
tc_con = np.concatenate((tc, t))
aplt.timedomain_plot(f1ax2, f, A1_c, texts=texts)
if plotting_setup["show_plots"]:
# f1.show()
# f2.show()
plt.show()
if plotting_setup["save_plots"]:
ssrg_time = setup_data[
"data_path"] + 'ssrgout_timedomain.' + plotting_setup[
"plot_saving_format"]
ssrg_corr = setup_data[
"data_path"] + 'ssrgout_autocorr.' + plotting_setup[
"plot_saving_format"]
f1.savefig(ssrg_time, format=plotting_setup["plot_saving_format"])
