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authorandreas128 <Andreas>2017-05-29 21:55:44 +0100
committerandreas128 <Andreas>2017-05-29 21:55:44 +0100
commit59ff35e5b6a81150a87cc0b5a972a91bd64c3ab9 (patch)
tree84996d2f963fc90bef09802fbb74916e14da0f15
parentc8d61fa0a7b36e3c3acec5a4c22ee4b4ab14a700 (diff)
downloadODR-StaticPrecorrection-59ff35e5b6a81150a87cc0b5a972a91bd64c3ab9.tar.gz
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Add subsample_alignment and it's test
-rw-r--r--calc_lag.py44
-rwxr-xr-xgrc/live_analyse_dab_poly.py51
-rw-r--r--src/dab_util.py40
-rw-r--r--src/dab_util_test.py49
-rwxr-xr-xsrc/subsample_align.py13
5 files changed, 145 insertions, 52 deletions
diff --git a/calc_lag.py b/calc_lag.py
new file mode 100644
index 0000000..5f5cf70
--- /dev/null
+++ b/calc_lag.py
@@ -0,0 +1,44 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import re
+import sys
+from tqdm import tqdm
+
+from glob import glob
+from natsort import natsorted
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import src.dab_util as du
+
+
+
+
+tx_paths = natsorted(glob(r'/home/andreas/dab/ODR-StaticPrecorrection/data/received/*_tx_record.iq'))
+rx_paths = natsorted(glob(r'/home/andreas/dab/ODR-StaticPrecorrection/data/received/*_rx_record.iq'))
+
+res = []
+
+for tx_path, rx_path in zip(tx_paths, rx_paths):
+ s1 = du.fromfile(tx_path)
+ s2 = du.fromfile(rx_path)
+
+ res.append({
+ '1':du.lag_upsampling(s2, s1, n_up=1),
+ '2':du.lag_upsampling(s2, s1, n_up=2),
+ '3':du.lag_upsampling(s2, s1, n_up=3),
+ '4':du.lag_upsampling(s2, s1, n_up=4),
+ '8':du.lag_upsampling(s2, s1, n_up=8),
+ '16':du.lag_upsampling(s2, s1, n_up=16),
+ '32':du.lag_upsampling(s2, s1, n_up=32),
+ })
+
+df = pd.DataFrame(res)
+df = df.reindex_axis(sorted(df.columns), axis=1)
+print(df)
+print(df.describe())
+
diff --git a/grc/live_analyse_dab_poly.py b/grc/live_analyse_dab_poly.py
index cc7672b..126c43d 100755
--- a/grc/live_analyse_dab_poly.py
+++ b/grc/live_analyse_dab_poly.py
@@ -3,7 +3,7 @@
##################################################
# GNU Radio Python Flow Graph
# Title: Live Analyse Dab Poly
-# Generated: Mon Apr 10 14:20:48 2017
+# Generated: Tue May 23 20:21:44 2017
##################################################
if __name__ == '__main__':
@@ -60,13 +60,12 @@ class live_analyse_dab_poly(gr.top_block, Qt.QWidget):
##################################################
# Variables
##################################################
- self.samp_rate = samp_rate = 8e6
self.txgain = txgain = 80
self.shift_freq = shift_freq = 1
+ self.samp_rate_2 = samp_rate_2 = 2048000
+ self.samp_rate_1 = samp_rate_1 = 8192000
self.rxgain = rxgain = 10
self.freq = freq = 222e6
- self.f2 = f2 = samp_rate / 3.875
- self.f1 = f1 = samp_rate / 4
self.ampl = ampl = 0.4
self.a_8 = a_8 = 0
self.a_7 = a_7 = 0
@@ -122,7 +121,7 @@ class live_analyse_dab_poly(gr.top_block, Qt.QWidget):
channels=range(1),
),
)
- self.uhd_usrp_source_0.set_samp_rate(samp_rate)
+ self.uhd_usrp_source_0.set_samp_rate(samp_rate_2)
self.uhd_usrp_source_0.set_center_freq(freq, 0)
self.uhd_usrp_source_0.set_gain(rxgain, 0)
self.uhd_usrp_sink_0 = uhd.usrp_sink(
@@ -132,7 +131,7 @@ class live_analyse_dab_poly(gr.top_block, Qt.QWidget):
channels=range(1),
),
)
- self.uhd_usrp_sink_0.set_samp_rate(samp_rate)
+ self.uhd_usrp_sink_0.set_samp_rate(samp_rate_1)
self.uhd_usrp_sink_0.set_center_freq(freq, 0)
self.uhd_usrp_sink_0.set_gain(txgain, 0)
self.uhd_amsg_source_0 = uhd.amsg_source(device_addr="", msgq=uhd_amsg_source_0_msgq_out)
@@ -143,7 +142,7 @@ class live_analyse_dab_poly(gr.top_block, Qt.QWidget):
16000, #size
firdes.WIN_BLACKMAN_hARRIS, #wintype
0, #fc
- samp_rate, #bw
+ samp_rate_2, #bw
"", #name
1 #number of inputs
)
@@ -206,17 +205,6 @@ class live_analyse_dab_poly(gr.top_block, Qt.QWidget):
event.accept()
- def get_samp_rate(self):
- return self.samp_rate
-
- def set_samp_rate(self, samp_rate):
- self.samp_rate = samp_rate
- self.set_f1(self.samp_rate / 4)
- self.set_f2(self.samp_rate / 3.875)
- self.qtgui_freq_sink_x_0_0.set_frequency_range(0, self.samp_rate)
- self.uhd_usrp_sink_0.set_samp_rate(self.samp_rate)
- self.uhd_usrp_source_0.set_samp_rate(self.samp_rate)
-
def get_txgain(self):
return self.txgain
@@ -231,6 +219,21 @@ class live_analyse_dab_poly(gr.top_block, Qt.QWidget):
def set_shift_freq(self, shift_freq):
self.shift_freq = shift_freq
+ def get_samp_rate_2(self):
+ return self.samp_rate_2
+
+ def set_samp_rate_2(self, samp_rate_2):
+ self.samp_rate_2 = samp_rate_2
+ self.qtgui_freq_sink_x_0_0.set_frequency_range(0, self.samp_rate_2)
+ self.uhd_usrp_source_0.set_samp_rate(self.samp_rate_2)
+
+ def get_samp_rate_1(self):
+ return self.samp_rate_1
+
+ def set_samp_rate_1(self, samp_rate_1):
+ self.samp_rate_1 = samp_rate_1
+ self.uhd_usrp_sink_0.set_samp_rate(self.samp_rate_1)
+
def get_rxgain(self):
return self.rxgain
@@ -247,18 +250,6 @@ class live_analyse_dab_poly(gr.top_block, Qt.QWidget):
self.uhd_usrp_sink_0.set_center_freq(self.freq, 0)
self.uhd_usrp_source_0.set_center_freq(self.freq, 0)
- def get_f2(self):
- return self.f2
-
- def set_f2(self, f2):
- self.f2 = f2
-
- def get_f1(self):
- return self.f1
-
- def set_f1(self, f1):
- self.f1 = f1
-
def get_ampl(self):
return self.ampl
diff --git a/src/dab_util.py b/src/dab_util.py
index 617bd9a..3187036 100644
--- a/src/dab_util.py
+++ b/src/dab_util.py
@@ -2,6 +2,7 @@ import numpy as np
import scipy
import matplotlib.pyplot as plt
import src.dabconst as dabconst
+import src.subsample_align as sa
from scipy import signal
c = {}
@@ -76,22 +77,26 @@ def lag_upsampling(sig_orig, sig_rec, n_up):
l_orig = float(l) / n_up
return l_orig
-def fftlag(sig_orig, sig_rec, n_upsampling = 1):
+def subsample_align(sig1, sig2):
"""
- Efficient way to find lag between two signals
- Args:
- sig_orig: The signal that has been sent
- sig_rec: The signal that has been recored
+ Returns an aligned version of sig1 and sig2 by cropping and subsample alignment
"""
- #off = sig_rec.shape[0]
- #fft1 = np.fft.fft(sig_orig, n=sig_orig.shape[0])
- #fft2 = np.fft.fft(np.flipud(sig_rec), n=sig_rec.shape[0])
- #fftc = fft1 * fft2
- #c = np.fft.ifft(fftc)
- c = signal.convolve(sig_orig, np.flipud(sig_rec))
- #c = signal.correlate(sig_orig, sig_rec)
- return c
- return np.argmax(c) - off + 1
+ off_meas = lag_upsampling(sig2, sig1, n_up=1)
+ off = int(abs(off_meas))
+
+ if off_meas > 0:
+ sig1 = sig1[:-off]
+ sig2 = sig2[off:]
+ elif off_meas < 0:
+ sig1 = sig1[off:]
+ sig2 = sig2[:-off]
+
+ if off % 2 == 1:
+ sig1 = sig1[:-1]
+ sig2 = sig2[:-1]
+
+ sig2 = sa.subsample_align(sig2, sig1)
+ return sig1, sig2
def get_amp_ratio(ampl_1, ampl_2, a_out_abs, a_in_abs):
idxs = (a_in_abs > ampl_1) & (a_in_abs < ampl_2)
@@ -108,5 +113,8 @@ def get_transmission_frame_indices(n_frames, offset, rate = 2048000):
indices = [tm1.S_F * i + offset for i in range(n_frames)]
return indices
-def fromfile(filename, offset, length):
- return np.memmap(filename, dtype=np.complex64, mode='r', offset=64/8*offset, shape=length)
+def fromfile(filename, offset=0, length=None):
+ if length is None:
+ return np.memmap(filename, dtype=np.complex64, mode='r', offset=64/8*offset)
+ else:
+ return np.memmap(filename, dtype=np.complex64, mode='r', offset=64/8*offset, shape=length)
diff --git a/src/dab_util_test.py b/src/dab_util_test.py
index be36d53..3f9e941 100644
--- a/src/dab_util_test.py
+++ b/src/dab_util_test.py
@@ -1,5 +1,7 @@
from scipy import signal
import numpy as np
+import pandas as pd
+from tqdm import tqdm
import src.gen_source as gs
import src.dab_util as du
@@ -28,7 +30,54 @@ def test_phase_offset(lag_function, tol):
res.append(np.abs(off-off_meas)<tol)
return np.mean(res)
+
+def test_using_aligned_pair(sample_orig=r'../data/orig_rough_aligned.dat', sample_rec =r'../data/recored_rough_aligned.dat', length = 10240, max_size = 1000000):
+ res = []
+ for i in tqdm(range(100)):
+ start = np.random.randint(50, max_size)
+ r = np.random.randint(-50, 50)
+
+ s1 = du.fromfile(sample_orig, offset=start+r, length=length)
+ s2 = du.fromfile(sample_rec, offset=start, length=length)
+
+ res.append({'offset':r,
+ '1':r - du.lag_upsampling(s2, s1, n_up=1),
+ '2':r - du.lag_upsampling(s2, s1, n_up=2),
+ '3':r - du.lag_upsampling(s2, s1, n_up=3),
+ '4':r - du.lag_upsampling(s2, s1, n_up=4),
+ '8':r - du.lag_upsampling(s2, s1, n_up=8),
+ '16':r - du.lag_upsampling(s2, s1, n_up=16),
+ '32':r - du.lag_upsampling(s2, s1, n_up=32),
+ })
+ df = pd.DataFrame(res)
+ df = df.reindex_axis(sorted(df.columns), axis=1)
+ print(df.describe())
+
+def test_subsample_alignment(sample_orig=r'../data/orig_rough_aligned.dat',
+ sample_rec =r'../data/recored_rough_aligned.dat', length = 10240, max_size = 1000000):
+ res1 = []
+ res2 = []
+ for i in tqdm(range(10)):
+ start = np.random.randint(50, max_size)
+ r = np.random.randint(-50, 50)
+
+ s1 = du.fromfile(sample_orig, offset=start+r, length=length)
+ s2 = du.fromfile(sample_rec, offset=start, length=length)
+
+ res1.append(du.lag_upsampling(s2, s1, 32))
+
+ s1_aligned, s2_aligned = du.subsample_align(s1,s2)
+
+ res2.append(du.lag_upsampling(s2_aligned, s1_aligned, 32))
+
+ print("Before subsample alignment: lag_std = %.2f, lag_abs_mean = %.2f" % (np.std(res1), np.mean(np.abs(res1))))
+ print("After subsample alignment: lag_std = %.2f, lag_abs_mean = %.2f" % (np.std(res2), np.mean(np.abs(res2))))
+
+print("Align using upsampling")
for n_up in [1, 2, 3, 4, 7, 8, 16]:
correct_ratio = test_phase_offset(lambda x,y: du.lag_upsampling(x,y,n_up), tol=1./n_up)
print("%.1f%% of the tested offsets were measured within tolerance %.4f for n_up = %d" % (correct_ratio * 100, 1./n_up, n_up))
+test_using_aligned_pair()
+print("Phase alignment")
+test_subsample_alignment()
diff --git a/src/subsample_align.py b/src/subsample_align.py
index 376058c..1657131 100755
--- a/src/subsample_align.py
+++ b/src/subsample_align.py
@@ -3,6 +3,7 @@ import numpy as np
from scipy import signal, optimize
import sys
import matplotlib.pyplot as plt
+import dab_util as du
def gen_omega(length):
if (length % 2) == 1:
@@ -59,29 +60,29 @@ def subsample_align(sig, ref_sig):
optim_result = optimize.minimize_scalar(correlate_for_delay, bounds=(-1,1), method='bounded', options={'disp': True})
if optim_result.success:
- print("x:")
- print(optim_result.x)
+ #print("x:")
+ #print(optim_result.x)
best_tau = optim_result.x
- print("Found subsample delay = {}".format(best_tau))
+ #print("Found subsample delay = {}".format(best_tau))
# Prepare rotate_vec = fft_sig with rotated phase
rotate_vec = np.exp(1j * best_tau * omega)
rotate_vec[halflen] = np.cos(np.pi * best_tau)
return np.fft.ifft(rotate_vec * fft_sig)
else:
- print("Could not optimize: " + optim_result.message)
+ #print("Could not optimize: " + optim_result.message)
return np.zeros(0, dtype=np.complex64)
if __name__ == "__main__":
- phaseref_filename = "/home/bram/dab/aux/odr-dab-cir/phasereference.2048000.fc64.iq"
+ phaseref_filename = "/home/andreas/dab/ODR-StaticPrecorrection/data/samples/sample_orig_0.iq"
phase_ref = np.fromfile(phaseref_filename, np.complex64)
delay = 15
n_up = 32
- print("Generate signal with delay {}/{} = {}".format(delay, n_up, delay/n_up))
+ print("Generate signal with delay {}/{} = {}".format(delay, n_up, float(delay)/n_up))
phase_ref_up = signal.resample(phase_ref, phase_ref.shape[0] * n_up)
phase_ref_up_late = np.append(np.zeros(delay, dtype=np.complex64), phase_ref_up[:-delay])
phase_ref_late = signal.resample(phase_ref_up_late, phase_ref.shape[0])