1 files changed, 225 insertions, 0 deletions
diff --git a/dab_tuner.py b/dab_tuner.py
new file mode 100644
index 0000000..d2d6df4
--- /dev/null
+++ b/dab_tuner.py
@@ -0,0 +1,225 @@
+
+# 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[ ]:
+
+
+