Add gr_client that uses gr_server to take measurements

2 files changed, 88 insertions, 3 deletions

diff --git a/README b/README
index c04e7f5..767958c 100644
--- a/README
+++ b/README
@@ -1,5 +1,5 @@
-sync-measurement-while-sending.ipynb:
+gr_server.py
 Starts the parallel_measurement gnu radio script and a server to control coefficients, txgain and rxtain.
 
-receive_tcp.ipynb:
-Receives the measurements from sync-measurement-while-sending.ipynb and controls it's parameters.
+gr_client.py
+Receives the measurements from gr_server and controls it's parameters.
diff --git a/gr_client.py b/gr_client.py
new file mode 100644
index 0000000..398e1ce
--- /dev/null
+++ b/gr_client.py
@@ -0,0 +1,85 @@
+"""
+This is a client for gr_server.py
+
+It attaches to the tcp sockets of both the gnu radio server and the control
+server of gr_server
+"""
+
+import time
+import numpy as np
+import src.tcp_sync as ts
+import src.dab_tuning_lib as dt
+import pandas as pd
+import src.SendDictTcp as sdt
+
+use_fft=True
+
+if use_fft: t1 = ts.UhdSyncMsg(port=47009, packet_size=4*16384, packet_type="f"*16384)
+else: t1 = ts.UhdSyncMsg(port=47009, packet_size=4*1, packet_type="f")
+
+sender = sdt.SendDictTcp('127.0.0.1', 1112)
+
+sender.send({"txgain":83})
+sender.send({"rxgain":15})
+sender.send({"a1":0.8})
+sender.send({"a2":0.0})
+sender.send({"a3":0.0})
+sender.send({"a4":0.0})
+sender.send({"a5":0.0})
+sender.send({"a6":0.0})
+sender.send({"a7":0.0})
+sender.send({"a8":0.0})
+t1.has_msg()
+np.mean(t1.get_msgs(10))
+
+def measure_sholders(verbose = False, raw_data=False, std_max=0.025):
+    """
+    Measure the soulders of the received fft spectrum. Repeat measurement if
+    standard deviation larger than std_max
+    """
+    for i in range(20):
+        try:
+            if verbose: print("%d measurement" % i)
+            t1.has_msg()
+            msgs = t1.get_msgs_fft(200)
+
+            def sig_mean(s): return dt.calc_mean_in_freq_range(np.array(s), 8192000, -700000, 700000)
+            def sholder_mean(s): return dt.calc_mean_in_freq_range(np.array(s), 8192000, 900000, 1500000)
+
+            sig = [sig_mean(msg) for msg in msgs]
+            sholders = [sholder_mean(msg) for msg in msgs]
+            std = np.std(sholders)
+            mean = np.mean(sholders)
+            std_perc = std/np.abs(mean)
+            if verbose == 2:
+                print( {"mean":mean, "std":std, "std_perc":std_perc})
+            if std_perc > std_max:
+                if verbose: print("%.4f std/mean" % (std_perc))
+                continue
+            else:
+                if raw_data: return sholders, sig
+                else: return np.mean(sholders), np.mean(sig)
+        except Exception as e:
+            print (e)
+    raise Exception("Variance of measurement to high")
+
+res = []
+for i in range(5):
+    for txgain in range(80, 89):
+        sender.send({"txgain":txgain})
+
+        sender.send({"input_path":"/home/andreas/dab/out_cut.iq"})
+        sh, sig = measure_sholders(verbose=0, std_max=100)
+        res.append({"txgain":txgain, "shoulder":sh, "sig":sig, "dpd":False})
+
+        sender.send({"input_path":"/home/andreas/dab/out_dpd_cut.iq"})
+        sh, sig = measure_sholders(verbose=0, std_max=100)
+        res.append({"txgain":txgain, "shoulder":sh, "sig":sig, "dpd":True})
+
+df = pd.DataFrame(res)
+
+df.to_csv("~/dab/doc/dab_mod_sholder.csv")
+
+sender.send({"txgain":20})
+sender.send({"rxgain":15})
+sender.send({"a1":0.1})