reijii/klippain-shaketune-telegramm
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updated documentation

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This commit is contained in:
Félix Boisselier
2023-12-11 13:23:59 +00:00
parent b32abe2eca
commit 87cb9015fa
7 changed files with 65 additions and 26 deletions
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18
K-ShakeTune/scripts/graph_belts.py
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@@ -212,14 +212,12 @@ def combined_spectrogram(data1, data2):
pdata1, bins1, t1 = compute_spectrogram(data1)
pdata2, bins2, t2 = compute_spectrogram(data2)
# Interpolate the spectrograms and apply logarithmic scaling
# Interpolate the spectrograms
pdata2_interpolated = interpolate_2d(bins1, t1, bins2, t2, pdata2)
pdata1_log = np.log1p(pdata1 / np.max(pdata1))
pdata2_log = np.log1p(pdata2_interpolated / np.max(pdata2_interpolated))
# Combined spectrograms
# Cobine them in two form: a summed diff for the MHI computation and a diverging diff for the spectrogram colors
combined_sum = np.abs(pdata1 - pdata2_interpolated)
combined_divergent = pdata1_log - pdata2_log
combined_divergent = pdata1 - pdata2_interpolated
return combined_sum, combined_divergent, bins1, t1
@@ -388,11 +386,9 @@ def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_f
ax.plot([], [], ' ', label=f'{textual_mhi} (experimental)')
# Draw the differential spectrogram with a specific custom norm to get orange or purple values where there is signal or white near zeros
colors = [KLIPPAIN_COLORS['orange'], 'white', 'white', KLIPPAIN_COLORS['purple']]
n_bins = [0, 0.3, 0.7, 1] # These values where found experimentaly to get a good higlhlighting of the differences only
cm = matplotlib.colors.LinearSegmentedColormap.from_list('klippain_divergent', list(zip(n_bins, colors)))
# norm = matplotlib.colors.Normalize(vmin=np.min(combined_divergent), vmax=np.max(combined_divergent))
norm = matplotlib.colors.SymLogNorm(linthresh=0.01, vmin=np.min(combined_divergent), vmax=np.max(combined_divergent), base=10, clip=False)
colors = [KLIPPAIN_COLORS['dark_orange'], KLIPPAIN_COLORS['orange'], 'white', KLIPPAIN_COLORS['purple'], KLIPPAIN_COLORS['dark_purple']]
cm = matplotlib.colors.LinearSegmentedColormap.from_list('klippain_divergent', list(zip([0, 0.25, 0.5, 0.75, 1], colors)))
norm = matplotlib.colors.TwoSlopeNorm(vmin=np.min(combined_divergent), vcenter=0, vmax=np.max(combined_divergent))
ax.pcolormesh(t, bins, combined_divergent.T, cmap=cm, norm=norm, shading='gouraud')
ax.set_xlabel('Frequency (hz)')