import snakerf as srf
import matplotlib.pyplot as plt
import numpy as np
from math import inf, pi, log2
from scipy import signal
# see https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.periodogram.html#scipy.signal.periodogram
m = 5
data = '{0:0{1:d}b}'.format(srf.gold_codes(m)[2], 2**m - 1)
print(data)
n = 1
f = 5e6
f_bit = 9001
T_bit = 1 / f_bit
t_max = len(data) * T_bit / n - T_bit / 100
fs = 10e6
ns = 100000
# t_max = ns/fs
V1 = srf.Signal(ns, t_max)
# V1.make_tone(10000, 0)
V1.update_Vt(srf.V_msk(V1.ts, 25000, f_bit, data, 0))
V2 = srf.Signal(ns, t_max)
V2.make_tone(100000, 0.1)
mx = srf.Mixer()
V3 = mx.mix(V1, V2)
import snakerf as srf
import matplotlib.pyplot as plt
import numpy as np
from math import inf, pi, log2, ceil
# f = np.logspace(5,9,1000)
# w = srf.f2w(f)
fc = 10002
f_sym = 1000
m = 11
random_data = '{0:0{1:d}b}'.format(srf.gold_codes(m)[9], 2**m - 1) + '0'
P_dBm = 0
n = 6
test_bits = 2000
f_sim = 2e5
t_sim = test_bits / (f_sym * n)
v1 = srf.Signal(f_sim * t_sim, t_sim)
v1.update_Vt(srf.V_qam(v1.ts, fc, f_sym, random_data, P_dBm, n = n)) # + srf.Vt_thermal_noise(v1.ts, v1.fs)
v1.add_noise()
# t_sym_sample = np.arange(0, t_sim, 1/f_sym)
# v_qam_sample = np.array(np.interp(t_sym_sample, v1.ts, v1.Vt))
# f, (ax1, ax2, ax3, ax4) = plt.subplots(4,1)#,sharex = True)
f = plt.figure(figsize = (12,6))
gs = f.add_gridspec(3, 6)
ax1 = f.add_subplot(gs[0,:3])
ax2 = f.add_subplot(gs[1,:3])
ax3 = f.add_subplot(gs[2,:3])