diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/dab_util.py | 39 | ||||
| -rw-r--r-- | src/dab_util_test.py | 36 | ||||
| -rw-r--r-- | src/gen_source.py | 6 |
3 files changed, 71 insertions, 10 deletions
diff --git a/src/dab_util.py b/src/dab_util.py index 2b23812..843f8a5 100644 --- a/src/dab_util.py +++ b/src/dab_util.py @@ -3,6 +3,7 @@ import scipy import matplotlib.pyplot as plt import fftconvolve import src.dabconst as dabconst +from scipy import signal c = {} c["bw"]=1536000 @@ -47,27 +48,45 @@ def crop_signal(signal, n_window = 1000, n_zeros = 120000, debug = False): signal = signal[max(0,idx_start - n_zeros): min(idx_end + n_zeros, signal.shape[0] -1)] return signal -#def fftlag(signal_original, signal_rec): +#def fftlag(sig_orig, sig_rec): # """ # Efficient way to find lag between two signals # Args: -# signal_original: The signal that has been sent -# signal_rec: The signal that has been recored +# sig_orig: The signal that has been sent +# sig_rec: The signal that has been recored # """ -# c = np.flipud(scipy.signal.fftconvolve(signal_original,np.flipud(signal_rec))) +# c = np.flipud(scipy.signal.fftconvolve(sig_orig,np.flipud(sig_rec))) # #plt.plot(c) -# return np.argmax(c) - signal_original.shape[0] + 1 +# return np.argmax(c) - sig_orig.shape[0] + 1 -def fftlag(signal_original, signal_rec, n_upsampling = 1): +def lag(sig_orig, sig_rec): + """ + Find lag between two signals + Args: + sig_orig: The signal that has been sent + sig_rec: The signal that has been recored + """ + off = sig_rec.shape[0] + c = signal.correlate(sig_orig, sig_rec) + return np.argmax(c) - off + 1 + +def lag_upsampling(sig_orig, sig_rec, n_up): + sig_orig_up = signal.resample(sig_orig, sig_orig.shape[0] * n_up) + sig_rec_up = signal.resample(sig_rec, sig_rec.shape[0] * n_up) + l = lag(sig_orig_up, sig_rec_up) + l_orig = float(l) / n_up + return l_orig + +def fftlag(sig_orig, sig_rec, n_upsampling = 1): """ Efficient way to find lag between two signals Args: - signal_original: The signal that has been sent - signal_rec: The signal that has been recored + sig_orig: The signal that has been sent + sig_rec: The signal that has been recored """ - c = np.flipud(fftconvolve.fftconvolve(signal_original,np.flipud(signal_rec), n_upsampling)) + c = np.flipud(fftconvolve.fftconvolve(sig_orig,np.flipud(sig_rec), n_upsampling)) #plt.plot(c) - return (np.argmax(c) - signal_original.shape[0] + 1) + return (np.argmax(c) - sig_orig.shape[0] + 1) 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) diff --git a/src/dab_util_test.py b/src/dab_util_test.py new file mode 100644 index 0000000..83813a9 --- /dev/null +++ b/src/dab_util_test.py @@ -0,0 +1,36 @@ +from scipy import signal +import numpy as np +import src.gen_source as gs +reload(gs) +import src.dab_util as du +reload(du) + +def gen_test_signals(oversampling=4, sample_offset_float=0): + off = int(sample_offset_float) + phi_samples = sample_offset_float - off + phi = phi_samples*360/oversampling + + s1 = np.zeros((1024)) + s1[256:768] = gs.gen_sin(512, oversampling, 0) + s2 = np.zeros((1024)) + s2[256+off:768+off] = gs.gen_sin(512, oversampling, phi) + + return s1, s2 + +def test_phase_offset(lag_function, tol): + def r(): + return np.random.rand(1)*100-50 + res = [] + for i in range(100): + off = r() + s1, s2 = gen_test_signals( + oversampling=4, sample_offset_float=off) + + off_meas = lag_function(s2, s1) + res.append(np.abs(off-off_meas)<tol) + return np.mean(res) + +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)) + diff --git a/src/gen_source.py b/src/gen_source.py index 7620bc3..c8509ed 100644 --- a/src/gen_source.py +++ b/src/gen_source.py @@ -97,3 +97,9 @@ def gen_file_i(frequency_0, frequency_1, x1 = 0, x2 = 0, x3 = 0, x4 = 0, samp_ra assert(np.isclose(a_load, two_tone).all()), "Inconsistent stored file" return path + +def gen_sin(samples, oversampling, phi): + t = np.arange(samples, dtype=np.float) + sig = np.sin(((2*np.pi)/oversampling) * t - np.pi*phi/180.) + return sig + |