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# coding: utf-8
# In[1]:
get_ipython().magic('matplotlib inline')
import matplotlib.pyplot as plt
import numpy as np
import time
import src.gen_source as gen_source
import src.two_tone_lib as tt
import src.tcp_async as tcp_async
import src.tcp_sync as tcp_sync
import src.dab_util as du
import src.dab_tuning_lib as dt
from live_analyse_py import live_analyse_py
# In[15]:
try:
__IPYTHON__
reload(tcp_async)
reload(tcp_sync)
reload(gen_source)
reload(tt)
reload(du)
reload(dt)
except:
pass
# In[3]:
sync = tcp_sync.UhdSyncMsg(packet_size=4*8192,
packet_type="".join(["f"]*8192))
async = tcp_async.UhdAsyncMsg()
# In[4]:
top = live_analyse_py()
# In[5]:
top.start()
# In[6]:
top.set_txgain(86)
top.set_rxgain(10)
# In[7]:
top.blocks_file_source_0.open("./../dab_normalized_c64.dat", True)
# In[8]:
sync.has_msg()
async.has_msg()
# In[9]:
tt.gen_two_tone(debug = True)
# In[10]:
msgs = sync.get_msgs(1)
msgs = [np.fft.fftshift(msg) for msg in msgs]
# In[18]:
def measure(param):
n_avg = 20
x2, x3, x4, x5, x6, x7, x8 = param
repeat = True
while repeat:
#tt.gen_two_tone(debug = True, predist=tt.predist_poly, par=(x2, x3, x4))
top.dpd_memless_poly_0.set_a1(1)
top.dpd_memless_poly_0.set_a2(x2)
top.dpd_memless_poly_0.set_a3(x3)
top.dpd_memless_poly_0.set_a4(x4)
top.dpd_memless_poly_0.set_a5(x5)
top.dpd_memless_poly_0.set_a6(x6)
top.dpd_memless_poly_0.set_a7(x7)
top.dpd_memless_poly_0.set_a8(x8)
sync.has_msg()
np.array(sync.get_msgs(0.8))
msgs = np.array(sync.get_msgs(n_avg))
scores = np.zeros(n_avg)
msgs = [np.fft.fftshift(msg) for msg in msgs]
if async.has_msg():
print ("repeat due to async message")
continue
a = np.array(msgs)
mean_msg = a.mean(axis = 0)
suffix = "x_2_%.3f_x_3_%.3f_x_4_%.3fx_5_%.3fx_6_%.3fx_7_%.3fx_8_%.3f" % (x2, x3, x4, x5, x6, x7, x8)
#sig_to_noise = tt.analyse_power_spec(mean_msg, debug=True, debug_path="/tmp/out", suffix=suffix)
for i in range(n_avg):
if i == 0:
scores[i] = dt.calc_signal_sholder_ratio(msgs[0], sampling_rate=8000000, debug=True, debug_path="/tmp/out", suffix=suffix)
else:
scores[i] = dt.calc_signal_sholder_ratio(msgs[0], sampling_rate=8000000)
score = np.mean(scores)
print(score, x2, x3, x4, x5, x6, x7, x8)
repeat = False
return score
# In[16]:
def simple_opt(pars, i, d, func):
par = pars[i]
test_pars = []
for x in [-1, 0, 1]:
new_par = list(pars)
new_par[i] = par + x * d
test_pars.append(new_par)
res = [func(par_new) for par_new in test_pars]
sel = np.argmax(res)
best_par = test_pars[sel]
return best_par
#pars = [1,1,1]
#i_rand = np.random.randint(0, len(pars))
#pars = simple_opt(pars, i_rand, 0.01, lambda x:np.sum(x))
#pars
# In[ ]:
top.set_txgain(86)
top.set_rxgain(5)
pars = np.zeros(7)
for i in range(10000):
i_rand = np.random.randint(0, len(pars))
pars = simple_opt(pars, i_rand, 0.005, measure)
# In[ ]:
# In[ ]:
# In[ ]:
# In[15]:
top.set_txgain(85)
params = []
for x2 in np.linspace(-0.1, 0.1, num = 11):
for x3 in np.linspace(-0.1, 0.1, num = 11):
for x4 in np.linspace(-0.1, 0.1, num = 11):
params.append((x2, x3, x4))
t_start = time.time()
for idx, param in enumerate(params):
measure(param)
time_per_element = (time.time() - t_start) / (idx + 1)
print ("Time per Element " + str(time_per_element) +
", total: " + str(time_per_element * len(params)),
", left: " + str(time_per_element * (len(params) - 1 - idx))
)
# In[ ]:
# In[31]:
sync.stop()
async.stop()
top.stop()
top.wait()
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
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