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author | Matthias P. Braendli <matthias.braendli@mpb.li> | 2017-08-12 14:37:45 +0200 |
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committer | Matthias P. Braendli <matthias.braendli@mpb.li> | 2017-08-16 19:48:34 +0200 |
commit | 15651ab0020ba594bff02491b3d2b6472f5e4fda (patch) | |
tree | 025cef12694ea5fdfa399d7676bd1a990367f5e3 /dpd/show_spectrum.py | |
parent | 2d70a92c178b4cf7460d09efd93a0e7918781a64 (diff) | |
download | dabmod-15651ab0020ba594bff02491b3d2b6472f5e4fda.tar.gz dabmod-15651ab0020ba594bff02491b3d2b6472f5e4fda.tar.bz2 dabmod-15651ab0020ba594bff02491b3d2b6472f5e4fda.zip |
show_spectrum: dump image and try to get upsampled constellation to work
Diffstat (limited to 'dpd/show_spectrum.py')
-rwxr-xr-x | dpd/show_spectrum.py | 20 |
1 files changed, 16 insertions, 4 deletions
diff --git a/dpd/show_spectrum.py b/dpd/show_spectrum.py index 95dbef9..f23dba2 100755 --- a/dpd/show_spectrum.py +++ b/dpd/show_spectrum.py @@ -169,14 +169,26 @@ def plot_constellation_once(options): num_syms = int(len(frame) / n) print("frame {} has {} symbols".format(len(frame), num_syms)) spectrums = np.array([np.fft.fftshift(np.fft.fft(frame[n*i:n*(i+1)], n)) for i in range(num_syms)]) - #imsave("spectrums.png", np.abs(spectrums)) + + def normalise(x): + """Normalise a real-valued array x to the range [0,1]""" + y = x + np.min(x) + return x / np.max(x) + + imsave("spectrums.png", np.concatenate([ + normalise(np.abs(spectrums)), + normalise(np.angle(spectrums))])) # Only take bins that are supposed to contain energy - #TODO this is only valid for 2048000 sample rate! - spectrums = np.concatenate([spectrums[...,256:1024], spectrums[...,1025:1793]], axis=1) + # i.e. the middle 1536 bins, excluding the bin at n/2 + assert(n % 2 == 0) + n_half = int(n/2) + spectrums = np.concatenate( + [spectrums[...,n_half-768:n_half], + spectrums[...,n_half + 1:n_half + 769]], axis=1) sym_indices = (np.tile(np.arange(num_syms-1).reshape(num_syms-1,1), (1,NbCarriers)) + - np.tile(np.linspace(-0.25, 0.25, NbCarriers), (num_syms-1, 1) ) ) + np.tile(np.linspace(-0.4, 0.4, NbCarriers), (num_syms-1, 1) ) ) sym_indices = sym_indices.reshape(-1) diff_angles = np.mod(np.diff(np.angle(spectrums, deg=1), axis=0), 360) #sym_points = spectrums[:-1].reshape(-1) |